Comparison of resumption of ovulation after cessation of oral contraceptives and medroxyprogesterone acetate in women with polycystic ovary syndrome

OBJECTIVE

Both oral contraceptive pills (OCPs) and cyclic medroxyprogesterone acetate (MPA) are widely used to control menstrual abnormalities in women with polycystic ovary syndrome (PCOS). We aimed to evaluate the chance of ovulation resumption after cessation of OCPs and MPA in women with PCOS.

METHODS

A retrospective study was conducted of women with PCOS who were treated with OCPs or cyclic MPA from September 2015 to March 2019. After cessation of medication, ovulation was assessed using basal body temperature and/or measurement of serum progesterone. The odds ratio for ovulation resumption was assessed with multivariable logistic regression. Additionally, doubly robust analysis was performed with inverse-probability-weighted analysis and regression adjustment based on the covariate balancing propensity score to adjust for the effect of covariates on the treatment assignment.

RESULTS

Among 272 women with PCOS, 136 were prescribed OCPs and 136 were prescribed cyclic MPA. Ovulation resumed in 18.4% of women (n = 25) after cessation of MPA and in 24.3% of women (n = 33) after cessation of OCPs. The odds of ovulation resumption in MPA users were comparable with those in OCP users (adjusted odds ratio (aOR) 1.00, 95% confidence interval (CI) 0.89-1.12). After multiple imputation due to missing values, the results did not change substantially (aOR 0.99, 95% CI 0.89-1.10).

CONCLUSIONS

Among women with PCOS, MPA users have a similar chance of ovulation resumption as OCP users after cessation of medication. Cyclic MPA can be a good alternative to OCPs in women for whom OCPs are contraindicated or who decline to take OCPs.

The molecular subtypes of autoimmune diseases

Autoimmune diseases (ADs) are characterized by their complexity and a wide range of clinical differences. Despite patients presenting with similar symptoms and disease patterns, their reactions to treatments may vary. The current approach of personalized medicine, which relies on molecular data, is seen as an effective method to address the variability in these diseases. This review examined the pathologic classification of ADs, such as multiple sclerosis and lupus nephritis, over time. Acknowledging the limitations inherent in pathologic classification, the focus shifted to molecular classification to achieve a deeper insight into disease heterogeneity. The study outlined the established methods and findings from the molecular classification of ADs, categorizing systemic lupus erythematosus (SLE) into four subtypes, inflammatory bowel disease (IBD) into two, rheumatoid arthritis (RA) into three, and multiple sclerosis (MS) into a single subtype. It was observed that the high inflammation subtype of IBD, the RA inflammation subtype, and the MS "inflammation & EGF" subtype share similarities. These subtypes all display a consistent pattern of inflammation that is primarily driven by the activation of the JAK-STAT pathway, with the effective drugs being those that target this signaling pathway. Additionally, by identifying markers that are uniquely associated with the various subtypes within the same disease, the study was able to describe the differences between subtypes in detail. The findings are expected to contribute to the development of personalized treatment plans for patients and establish a strong basis for tailored approaches to treating autoimmune diseases.

Downregulation of ABLIM3 confers to the metastasis of neuroblastoma via regulating the cell adhesion molecules pathway

Neuroblastoma (NB) is the most prevalent extracranial solid tumor in pediatric patients, and its treatment failure often associated with metastasis. In this study, LASSO, SVM-RFE, and random forest tree algorithms, was used to identify the pivotal gene involved in NB metastasis. NB cell lines (SK-N-AS and SK-N-BE2), in conjunction with NB tissue were used for further study. ABLIM3 was identified as the hub gene and can be an independent prognostic factor for patients with NB. The immunohistochemical analysis revealed that ABLIM3 is negatively correlated with the metastasis of NB. Patients with low expression of ABLIM3 had a poor prognosis. High ABLIM3 expression correlated with APC co-stimulation and Type1 IFN response, and TIDE analysis indicated that patients with low ABLIM3 expression exhibited enhanced responses to immunotherapy. Downregulation of ABLIM3 by shRNA transfection increased the migration and invasion ability of NB cells. Gene Set Enrichment Analysis (GSEA) revealed that genes associated with ABLIM3 were primarily enriched in the cell adhesion molecules (CAMs) pathway. RT-qPCR and western blot analyses demonstrated that downregulation of ABLIM3 led to decreased expression of ITGA3, ITGA8, and KRT19, the key components of CAMs. This study indicated that ABLIM3 can be an independent prognostic factor for NB patients, and CAMs may mediate the effect of ABLIM3 on the metastasis of NB, suggesting that ABLIM3 is a potential therapeutic target for NB metastasis, which provides a novel strategy for future research and treatment strategies for NB patients.

Associations of nirmatrelvir-ritonavir treatment with death and clinical improvement in hospitalized patients with COVID-19 during the Omicron wave in Beijing, China: a multicentre, retrospective cohort study

BACKGROUND

The effectiveness of nirmatrelvir-ritonavir has mainly been shown in non-hospitalized patients with mild-to-moderate coronavirus disease 2019 (COVID-19). The real-world effectiveness of nirmatrelvir-ritonavir urgently needs to be determined using representative in-hospital patients with COVID-19 during the Omicron wave of the pandemic.

METHODS

We performed a multicentre, retrospective study in five Chinese PLA General Hospital medical centers in Beijing, China. Patients hospitalized with COVID-19 from 10 December 2022 to 20 February 2023 were eligible for inclusion. A 1:1 propensity score matching was performed between the nirmatrelvir-ritonavir group and the control group.

RESULTS

1010 recipients of nirmatrelvir-ritonavir and 1010 matched controls were finally analyzed after matching. Compared with matched controls, the nirmatrelvir-ritonavir group had a lower incidence rate of all-cause death (4.6/1000 vs. 6.3/1000 person-days, p = 0.013) and a higher incidence rate of clinical improvement (47.6/1000 vs. 45.8/1000 person-days, p = 0.012). Nirmatrelvir-ritonavir was associated with a 22% lower all-cause mortality and a 14% higher incidence of clinical improvement. Initiation of nirmatrelvir-ritonavir within 5 days after symptom onset was associated with a 50% lower mortality and a 26% higher clinical improvement rate. By contrast, no significant associations were identified among patients receiving nirmatrelvir-ritonavir treatment more than 5 days after symptom onset. Nirmatrelvir-ritonavir was also associated with a 50% increase in survival days and a 12% decrease in days to clinical improvement.

CONCLUSION

Among hospitalized patients with COVID-19 during the Omicron wave in Beijing, China, the early initiation of nirmatrelvir-ritonavir was associated with clinical benefits of lowering mortality and improving clinical recovery.

Deterministic epidemic models overestimate the basic reproduction number of observed outbreaks

The basic reproduction number, R0, is a well-known quantifier of epidemic spread. However, a class of existing methods for estimating R0 from incidence data early in the epidemic can lead to an over-estimation of this quantity. In particular, when fitting deterministic models to estimate the rate of spread, we do not account for the stochastic nature of epidemics and that, given the same system, some outbreaks may lead to epidemics and some may not. Typically, an observed epidemic that we wish to control is a major outbreak. This amounts to implicit selection for major outbreaks which leads to the over-estimation problem. We formally characterised the split between major and minor outbreaks by using Otsu's method which provides us with a working definition. We show that by conditioning a 'deterministic' model on major outbreaks, we can more reliably estimate the basic reproduction number from an observed epidemic trajectory.

Variable Selection in Bayesian Multiple Instance Regression using Shotgun Stochastic Search

In multiple instance learning (MIL), a bag represents a sample that has a set of instances, each of which is described by a vector of explanatory variables, but the entire bag only has one label/response. Though many methods for MIL have been developed to date, few have paid attention to interpretability of models and results. The proposed Bayesian regression model stands on two levels of hierarchy, which transparently show how explanatory variables explain and instances contribute to bag responses. Moreover, two selection problems are simultaneously addressed; the instance selection to find out the instances in each bag responsible for the bag response, and the variable selection to search for the important covariates. To explore a joint discrete space of indicator variables created for selection of both explanatory variables and instances, the shotgun stochastic search algorithm is modified to fit in the MIL context. Also, the proposed model offers a natural and rigorous way to quantify uncertainty in coefficient estimation and outcome prediction, which many modern MIL applications call for. The simulation study shows the proposed regression model can select variables and instances with high performance (AUC greater than 0.86), thus predicting responses well. The proposed method is applied to the musk data for prediction of binding strengths (labels) between molecules (bags) with different conformations (instances) and target receptors. It outperforms all existing methods, and can identify variables relevant in modeling responses.

Development of a processing factor prediction model for pesticides in processed tomato foods using elastic net regularization

A novel regularized elastic net regression model was developed to predict processing factor (PF) for pesticide residues, which represents a change in the residue levels during food processing. The PF values for tomato juice, wet pomace and dry pomace in the evaluations and reports published by the Joint FAO/WHO Meeting on Pesticide Residues significantly correlated with the physicochemical properties of pesticides, and subsequently the correlation was observed in the present tomato processing study. The elastic net regression model predicted the PF values using the physicochemical properties as predictor variables for both training and test data within a 2-fold range for 80-100% of the pesticides tested in the tomato processing study while overcoming multicollinearity. These results suggest that the PF values are predictable at a certain degree of accuracy from the unique sets of physicochemical properties of pesticides using the developed model based on a processing study with representative pesticides.

Estimation of multiple networks with common structures in heterogeneous subgroups

Network estimation has been a critical component of high-dimensional data analysis and can provide an understanding of the underlying complex dependence structures. Among the existing studies, Gaussian graphical models have been highly popular. However, they still have limitations due to the homogeneous distribution assumption and the fact that they are only applicable to small-scale data. For example, cancers have various levels of unknown heterogeneity, and biological networks, which include thousands of molecular components, often differ across subgroups while also sharing some commonalities. In this article, we propose a new joint estimation approach for multiple networks with unknown sample heterogeneity, by decomposing the Gaussian graphical model (GGM) into a collection of sparse regression problems. A reparameterization technique and a composite minimax concave penalty are introduced to effectively accommodate the specific and common information across the networks of multiple subgroups, making the proposed estimator significantly advancing from the existing heterogeneity network analysis based on the regularized likelihood of GGM directly and enjoying scale-invariant, tuning-insensitive, and optimization convexity properties. The proposed analysis can be effectively realized using parallel computing. The estimation and selection consistency properties are rigorously established. The proposed approach allows the theoretical studies to focus on independent network estimation only and has the significant advantage of being both theoretically and computationally applicable to large-scale data. Extensive numerical experiments with simulated data and the TCGA breast cancer data demonstrate the prominent performance of the proposed approach in both subgroup and network identifications.

Risky alcohol use during youth: Impact on emotion, cognitive networks, and resting-state EEG activity

The identification of the risk factors of alcohol consumption in youths is crucial for early interventions focused on reducing harmful alcohol use. In our study, 82 college students (40 healthy control (CO group) and 42 with risky alcohol use (RAU group) determined by AUDIT questionnaire) between the ages of 18 and 25 years underwent a comprehensive neuropsychological assessment covering emotional and cognitive functioning. Their resting-state activity was also recorded with an EEG for 10 min with their eyes open (EO) and 10 min with their eyes closed (EC) and analyzed using the Fitting Oscillations & One-Over-F (FOOOF) paradigm. After adjusting for sex, those in the RAU group had higher emotional dysregulation and impulsivity traits. The RAU girls presented more emotional regulation problems, such as dysregulation and negative urgency compared with the RAU boys. The RAU youths had significantly worse functioning in several cognitive domains, such as sustained attention, verbal memory, and executive functions. Cognitive network analysis revealed a different pattern of connections in each group showing that in the RAU group, the verbal memory domain had the highest connection with other cognitive functions. The EEG analyses did not reveal any significant differences between the CO and the RAU groups. However, we observed only in the EO condition that boys the from the RAU group displayed a higher theta/beta ratio than the RAU girls, whereas these differences were not observed within the CO group. Our findings highlight the need to explore more deeply the emotional, cognitive and brain changes underlying the RAU in young people.

Association of substance-use disorder with outcomes of major elective abdominal operations: A contemporary national analysis

Background

Affecting >20million people in the U.S., including 4 % of all hospitalized patients, substance use disorder (SUD) represents a growing public health crisis. Evaluating a national cohort, we aimed to characterize the association of concurrent SUD with perioperative outcomes and resource utilization following elective abdominal operations.

Methods

All adult hospitalizations entailing elective colectomy, gastrectomy, esophagectomy, hepatectomy, and pancreatectomy were tabulated from the 2016-2020 National Inpatient Sample. Patients with concurrent substance use disorder, comprising alcohol, opioid, marijuana, sedative, cocaine, inhalant, hallucinogen, or other psychoactive/stimulant use, were considered the SUD cohort (others: nSUD). Multivariable regression models were constructed to evaluate the independent association between SUD and key outcomes.

Results

Of ∼1,088,145 patients, 32,865 (3.0 %) comprised the SUD cohort. On average, SUD patients were younger, more commonly male, of lowest quartile income, and of Black race. SUD patients less frequently underwent colectomy, but more often pancreatectomy, relative to nSUD.Following risk adjustment and with nSUD as reference, SUD demonstrated similar likelihood of in-hospital mortality, but remained associated with increased odds of any perioperative complication (Adjusted Odds Ratio [AOR] 1.17, CI 1.09-1.25). Further, SUD was linked with incremental increases in adjusted length of stay (β + 0.90 days, CI +0.68-1.12) and costs (β + $3630, CI +2650-4610), as well as greater likelihood of non-home discharge (AOR 1.54, CI 1.40-1.70).

Conclusions

Concurrent substance use disorder was associated with increased complications, resource utilization, and non-home discharge following major elective abdominal operations. Novel interventions are warranted to address increased risk among this vulnerable population and address significant disparities in postoperative outcomes.

A fully Bayesian approach for comprehensive mapping of magnitude and phase brain activation in complex-valued fMRI data

Functional magnetic resonance imaging (fMRI) plays a crucial role in neuroimaging, enabling the exploration of brain activity through complex-valued signals. These signals, composed of magnitude and phase, offer a rich source of information for understanding brain functions. Traditional fMRI analyses have largely focused on magnitude information, often overlooking the potential insights offered by phase data. In this paper, we propose a novel fully Bayesian model designed for analyzing single-subject complex-valued fMRI (cv-fMRI) data. Our model, which we refer to as the CV-M&P model, is distinctive in its comprehensive utilization of both magnitude and phase information in fMRI signals, allowing for independent prediction of different types of activation maps. We incorporate Gaussian Markov random fields (GMRFs) to capture spatial correlations within the data, and employ image partitioning and parallel computation to enhance computational efficiency. Our model is rigorously tested through simulation studies, and then applied to a real dataset from a unilateral finger-tapping experiment. The results demonstrate the model's effectiveness in accurately identifying brain regions activated in response to specific tasks, distinguishing between magnitude and phase activation.

On the origin of optical rotation changes during the κ-carrageenan disorder-to-order transition

It is well established that solutions of both polymeric and oligomeric κ-carrageenan exhibit a clear change in optical rotation (OR), in concert with gel-formation for polymeric samples, as the solution is cooled in the presence of certain ions. The canonical interpretation - that this OR change reflects a 'coil-to-helix transition' in single chains - has seemed unambiguous; the solution- or 'disordered'-state structure has ubiquitously been assumed to be a 'random coil', and the helical nature of carrageenan in the solid-state was settled in the 1970s. However, recent work has found that κ-carrageenan contains substantial helical secondary structure elements in the disordered-state, raising doubts over the validity of this interpretation. To investigate the origins of the OR, density-functional theory calculations were conducted using atomic models of κ-carrageenan oligomers. Changes were found to occur in the predicted OR owing purely to dimerization of chains, and - together with the additional effects of slight changes in conformation that occur when separated helical chains form double-helices - the predicted OR changes are qualitatively consistent with experimental results. These findings contribute to a growing body of evidence that the carrageenan 'disorder-to-order' transition is a cooperative process, and have further implications for the interpretation of OR changes demonstrated by macromolecules in general.

Disease X epidemic control using a stochastic model and a deterministic approximation: Performance comparison with and without parameter uncertainties

BACKGROUND

The spread of infectious diseases can be modeled using deterministic or stochastic models. A deterministic approximation of a stochastic model can be appropriate under some conditions, but is unable to capture the discrete nature of populations. We look into the choice of a model from the perspective of decision making.

METHOD

We consider an emerging disease (Disease X) in a closed population modeled by a stochastic SIR model or its deterministic approximation. The objective of the decision maker is to minimize the cumulative number of symptomatic infected-days over the course of the epidemic by picking a vaccination policy. We consider four decision making scenarios: based on the stochastic model or the deterministic model, and with or without parameter uncertainty. We also consider different sample sizes for uncertain parameter draws and stochastic model runs. We estimate the average performance of decision making in each scenario and for each sample size.

RESULTS

The model used for decision making has an influence on the picked policies. The best achievable performance is obtained with the stochastic model, knowing parameter values, and for a large sample size. For small sample sizes, the deterministic model can outperform the stochastic model due to stochastic effects. Resolving uncertainties may bring more benefit than switching to the stochastic model in our example.

CONCLUSION

This article illustrates the interplay between the choice of a type of model, parameter uncertainties, and sample sizes. It points to issues to be considered when optimizing a stochastic model.

Browning from headwaters to coastal areas in the boreal region: Trends and drivers

Browning of freshwaters, mainly caused by increased terrestrial organic carbon loading, has been widely studied during the last decades. However, there are still uncertainties regarding both the extent of browning in different aquatic ecosystems and the actual importance of different driving forces and mechanisms. To refine understanding of the extent and causes of browning and its temporal variation, we gathered a comprehensive dataset including 746 Finnish water quality monitoring stations representing various waterbody types: streams, rivers, lakes, and coastal waters. Monotonic trend analyses revealed that TOC concentrations increased in all waterbody types during the study period from 1990 to 2020, whereas non-linear trends indicated that upward trends in TOC concentrations have substantially decreased since the mid-2000s. However, despite the upward trends levelling off, non-linear analyses also indicated decreases in TOC concentrations at only a few stations. As a result, the TOC contents of the majority of Finnish waterbody types in 2020 were at a higher level than in 1990. To examine the driving forces of increasing TOC concentrations, we selected 100 riverine catchments and linked the detected trends to 24 different drivers, including both hydrometeorological and catchment characteristics. The increased TOC concentrations in surface waters could be connected to diverse human impacts: hydrometeorological variables impacted by climate change, decreased acidic deposition, and land use in terms of peatland drainage. The importance of increased temperatures was emphasized, and its role as a driver of increased leaching of organic carbon in the forthcoming years is expected to grow with climate change.

Cell Tree Rings: the structure of somatic evolution as a human aging timer

Biological age is typically estimated using biomarkers whose states have been observed to correlate with chronological age. A persistent limitation of such aging clocks is that it is difficult to establish how the biomarker states are related to the mechanisms of aging. Somatic mutations could potentially form the basis for a more fundamental aging clock since the mutations are both markers and drivers of aging and have a natural timescale. Cell lineage trees inferred from these mutations reflect the somatic evolutionary process, and thus, it has been conjectured, the aging status of the body. Such a timer has been impractical thus far, however, because detection of somatic variants in single cells presents a significant technological challenge. Here, we show that somatic mutations detected using single-cell RNA sequencing (scRNA-seq) from thousands of cells can be used to construct a cell lineage tree whose structure correlates with chronological age. De novo single-nucleotide variants (SNVs) are detected in human peripheral blood mononuclear cells using a modified protocol. A default model based on penalized multiple regression of chronological age on 31 metrics characterizing the phylogenetic tree gives a Pearson correlation of 0.81 and a median absolute error of ~4 years between predicted and chronological ages. Testing of the model on a public scRNA-seq dataset yields a Pearson correlation of 0.85. In addition, cell tree age predictions are found to be better predictors of certain clinical biomarkers than chronological age alone, for instance glucose, albumin levels, and leukocyte count. The geometry of the cell lineage tree records the structure of somatic evolution in the individual and represents a new modality of aging timer. In addition to providing a numerical estimate of "cell tree age," it unveils a temporal history of the aging process, revealing how clonal structure evolves over life span. Cell Tree Rings complements existing aging clocks and may help reduce the current uncertainty in the assessment of geroprotective trials.

A framework to evaluate the impact of a hazard chain and geographical covariates on spatial extreme water levels: A case study in the Pearl River Delta

The interactions and collective impacts of different types of hazards within a compound hazard system, along with the influence of geographical covariates on flooding are presently unclear. Understanding these relationships is crucial for comprehending the formation and dynamic processes of the hazard chain and improving the ability to identify flood warning signals in complex hazard scenarios. In this study, we presented a multivariate spatial extreme value hierarchical (MSEVH) framework to assess the spatial extreme water levels (EWL) at different return levels under the influence of a hazard chain and geographical covariates. The Pearl River Delta (PRD) was selected as a research example to assess the effectiveness of the MSEVH framework. Firstly, we identified a hazard chain (extreme streamflow from the Xijiang River (XR) - extreme streamflow from the Beijiang River (BR) - extreme sea level) and three geographical covariates influencing EWL in the PRD. Then, we compared four hazard scenarios in the MSEVH framework to evaluate the spatial EWL at different return levels under the influence of the hazard chain in the PRD. The final step involves assessing spatial EWL with the effect of the hazard chain and geographical covariates. The results indicate that when extreme streamflow from XR and BR occurs concurrently, the extreme streamflow from BR weakens the influence of extreme streamflow from XR on EWL in the PRD. However, it cannot fully offset the overall impact of extreme streamflow from XR on EWL. In addition, when extreme streamflow from XR, extreme streamflow from BR, and extreme sea level occur simultaneously, the extreme sea level enhances the influence of concurrent extreme streamflow from XR and BR on EWL in the PRD. The proposed MSEVH is not only applicable to the PRD but also shows promising potential for evaluating extreme hydrometeorological variables under the influence of other hazard chains.

Penalized weighted smoothed quantile regression for high-dimensional longitudinal data

Quantile regression, known as a robust alternative to linear regression, has been widely used in statistical modeling and inference. In this paper, we propose a penalized weighted convolution-type smoothed method for variable selection and robust parameter estimation of the quantile regression with high dimensional longitudinal data. The proposed method utilizes a twice-differentiable and smoothed loss function instead of the check function in quantile regression without penalty, and can select the important covariates consistently using the efficient gradient-based iterative algorithms when the dimension of covariates is larger than the sample size. Moreover, the proposed method can circumvent the influence of outliers in the response variable and/or the covariates. To incorporate the correlation within each subject and enhance the accuracy of the parameter estimation, a two-step weighted estimation method is also established. Furthermore, we prove the oracle properties of the proposed method under some regularity conditions. Finally, the performance of the proposed method is demonstrated by simulation studies and two real examples.

RMST-based multiple contrast tests in general factorial designs

Several methods in survival analysis are based on the proportional hazards assumption. However, this assumption is very restrictive and often not justifiable in practice. Therefore, effect estimands that do not rely on the proportional hazards assumption are highly desirable in practical applications. One popular example for this is the restricted mean survival time (RMST). It is defined as the area under the survival curve up to a prespecified time point and, thus, summarizes the survival curve into a meaningful estimand. For two-sample comparisons based on the RMST, previous research found the inflation of the type I error of the asymptotic test for small samples and, therefore, a two-sample permutation test has already been developed. The first goal of the present paper is to further extend the permutation test for general factorial designs and general contrast hypotheses by considering a Wald-type test statistic and its asymptotic behavior. Additionally, a groupwise bootstrap approach is considered. Moreover, when a global test detects a significant difference by comparing the RMSTs of more than two groups, it is of interest which specific RMST differences cause the result. However, global tests do not provide this information. Therefore, multiple tests for the RMST are developed in a second step to infer several null hypotheses simultaneously. Hereby, the asymptotically exact dependence structure between the local test statistics is incorporated to gain more power. Finally, the small sample performance of the proposed global and multiple testing procedures is analyzed in simulations and illustrated in a real data example.

A Bayesian nonparametric meta-analysis model for estimating the reference interval

A reference interval represents the normative range for measurements from a healthy population. It plays an important role in laboratory testing, as well as in differentiating healthy from diseased patients. The reference interval based on a single study might not be applicable to a broader population. Meta-analysis can provide a more generalizable reference interval based on the combined population by synthesizing results from multiple studies. However, the assumptions of normally distributed underlying study-specific means and equal within-study variances, which are commonly used in existing methods, are strong and may not hold in practice. We propose a Bayesian nonparametric model with more flexible assumptions to extend random effects meta-analysis for estimating reference intervals. We illustrate through simulation studies and two real data examples the performance of our proposed approach when the assumptions of normally distributed study means and equal within-study variances do not hold.

Shifts in plant functional groups along an aridity gradient in a tropical dry forest

Increasing aridity associated with climate change may lead to the crossing of critical ecosystem thresholds in drylands, compromising ecosystem services for millions of people. In this context, finding tools to detect at early stages the effects of increasing aridity on ecosystems is extremely urgent to avoid irreversible damage. Here, we assess shifts in plant community functional structure along a spatial aridity gradient in tropical dryland (Brazilian Caatinga), to select the most appropriate plant functional groups as ecological indicators likely useful to predict temporal ecosystem trajectories in response to aridity. We identified seven plant functional groups based on 13 functional traits associated with plant establishment, defense, regeneration, and dispersal, whose relative abundances changed, linearly and non-linearly, with increasing aridity, showing either increasing or decreasing trends. Of particular importance is the increase in abundance of plants with high chemical defense and Crassulacean Acid Metabolism (CAM) photosynthetic pathway, with increasing aridity. We propose the use of these functional groups as early warning indicators to detect aridity impacts on these dryland ecosystems and shifts in ecosystem functioning. This information can also be used in the elaboration of mitigation and ecological restoration measures to prevent and revert current and future climate change impacts on tropical dry forests.

Greenhouse gas production from an intermittently dosed cold-climate wastewater treatment wetland

This study explores the greenhouse gas (GHG) fluxes of nitrous oxide (N2O), methane (CH4) and carbon dioxide (CO2) from a two-stage, cold-climate vertical-flow treatment wetland (TW) treating ski area wastewater at 3 °C average water temperature. The system is designed like a modified Ludzack-Ettinger process with the first stage a partially saturated, denitrifying TW followed by an unsaturated nitrifying TW and recycle of nitrified effluent. An intermittent wastewater dosing scheme was established for both stages, with alternating carbon-rich wastewater and nitrate-rich recycle to the first stage. The system has demonstrated effective chemical oxygen demand (COD) and total inorganic nitrogen (TIN) removal in high-strength wastewater over seven years of winter operation. Following two closed-loop, intensive GHG winter sampling campaigns at the TW, the magnitude of N2O flux was 2.2 times higher for denitrification than nitrification. CH4 and N2O emissions were strongly correlated with hydraulic loading, whereas CO2 was correlated with surface temperature. GHG fluxes from each stage were related to both microbial activity and off-gassing of dissolved species during wastewater dosing, thus the time of sampling relative to dosing strongly influenced observed fluxes. These results suggest that estimates of GHG fluxes from TWs may be biased if mass transfer and mechanisms of wastewater application are not considered. Emission factors for N2O and CH4 were 0.27 % as kg-N2O-N/kg-TINremoved and 0.04 % kg-CH4-C/kg-CODremoved, respectively. The system had observed seasonal emissions of 600.5 kg CO2 equivalent of GHGs estimated over 130-days of operation. These results indicate a need for wastewater treatment processes to mitigate GHGs.

Detecting changes in the transmission rate of a stochastic epidemic model

Throughout the course of an epidemic, the rate at which disease spreads varies with behavioral changes, the emergence of new disease variants, and the introduction of mitigation policies. Estimating such changes in transmission rates can help us better model and predict the dynamics of an epidemic, and provide insight into the efficacy of control and intervention strategies. We present a method for likelihood-based estimation of parameters in the stochastic susceptible-infected-removed model under a time-inhomogeneous transmission rate comprised of piecewise constant components. In doing so, our method simultaneously learns change points in the transmission rate via a Markov chain Monte Carlo algorithm. The method targets the exact model posterior in a difficult missing data setting given only partially observed case counts over time. We validate performance on simulated data before applying our approach to data from an Ebola outbreak in Western Africa and COVID-19 outbreak on a university campus.

Depression and fatigue six months post-COVID-19 disease are associated with overlapping symptom constellations: A prospective, multi-center, population-based cohort study

BACKGROUND

Depression and fatigue are commonly observed sequelae following viral diseases such as COVID-19. Identifying symptom constellations that differentially classify post-COVID depression and fatigue may be helpful to individualize treatment strategies. Here, we investigated whether self-reported post-COVID depression and post-COVID fatigue are associated with the same or different symptom constellations.

METHODS

To address this question, we used data from COVIDOM, a population-based cohort study conducted as part of the NAPKON-POP platform. Data were collected in three different German regions (Kiel, Berlin, Würzburg). We analyzed data from >2000 individuals at least six months past a PCR-confirmed COVID-19 disease, using elastic net regression and cluster analysis. The regression model was developed in the Kiel data set, and externally validated using data sets from Berlin and Würzburg.

RESULTS

Our results revealed that post-COVID depression and fatigue are associated with overlapping symptom constellations consisting of difficulties with daily activities, perceived health-related quality of life, chronic exhaustion, unrestful sleep, and impaired concentration. Confirming the overlap in symptom constellations, a follow-up cluster analysis could categorize individuals as scoring high or low on depression and fatigue but could not differentiate between both dimensions.

LIMITATIONS

The data presented are cross-sectional, consisting primarily of self-reported questionnaire or medical records rather than biometric data.

CONCLUSIONS

In summary, our results suggest a strong link between post-COVID depression and fatigue, highlighting the need for integrative treatment approaches.

Lifestyle modification interventions for adults with intellectual disabilities: systematic review and meta-analysis at intervention and component levels

BACKGROUND

Adults with intellectual disabilities (IDs) are susceptible to multiple health risk behaviours such as alcohol consumption, smoking, low physical activity, sedentary behaviour and poor diet. Lifestyle modification interventions can prevent or reduce negative health consequences caused by these behaviours. We aim to determine the effectiveness of lifestyle modification interventions and their components in targeting health risk behaviours in adults with IDs.

METHODS

A systematic review and meta-analysis were conducted. Electronic databases, clinical trial registries, grey literature and citations of systematic reviews and included studies were searched in January 2021 (updated February 2022). Randomised controlled trials and non-randomised controlled trials targeting alcohol consumption, smoking, low physical activity, sedentary behaviours and poor diet in adults (aged ≥ 18 years) with ID were included. Meta-analysis was conducted at the intervention level (pairwise and network meta-analysis) and the component-level (component network meta-analysis). Studies were coded using Michie's 19-item theory coding scheme and 94-item behaviour change taxonomies. Risk of bias was assessed using the Cochrane Risk of Bias (ROB) Version 2 and Risk of Bias in Non-randomised Studies of Interventions (ROBINS-I). The study involved a patient and public involvement (PPI) group, including people with lived experience, who contributed extensively by shaping the methodology, providing valuable insights in interpreting results and organising of dissemination events.

RESULTS

Our literature search identified 12 180 articles, of which 80 studies with 4805 participants were included in the review. The complexity of lifestyle modification intervention was dismantled by identifying six core components that influenced outcomes. Interventions targeting single or multiple health risk behaviours could have a single or combination of multiple core-components. Interventions (2 RCTS; 4 non-RCTs; 228 participants) targeting alcohol consumption and smoking behaviour were effective but based on limited evidence. Similarly, interventions targeting low physical activity only (16 RCTs; 17 non-RCTs; 1413 participants) or multiple behaviours (low physical activity only, sedentary behaviours and poor diet) (17 RCTs; 24 non-RCTs; 3164 participants) yielded mixed effectiveness in outcomes. Most interventions targeting low physical activity only or multiple behaviours generated positive effects on various outcomes while some interventions led to no change or worsened outcomes, which could be attributed to the presence of a single core-component or a combination of similar core components in interventions. The intervention-level meta-analysis for weight management outcomes showed that none of the interventions were associated with a statistically significant change in outcomes when compared with treatment-as-usual and each other. Interventions with core-components combination of energy deficit diet, aerobic exercise and behaviour change techniques showed the highest weight loss [mean difference (MD) = -3.61, 95% credible interval (CrI) -9.68 to 1.95] and those with core-components combination dietary advice and aerobic exercise showed a weight gain (MD 0.94, 95% CrI -3.93 to 4.91). Similar findings were found with the component network meta-analysis for which additional components were identified. Most studies had a high and moderate risk of bias. Various theories and behaviour change techniques were used in intervention development and adaptation.

CONCLUSION

Our systematic review is the first to comprehensively explore lifestyle modification interventions targeting a range of single and multiple health risk behaviours in adults with ID, co-produced with people with lived experience. It has practical implications for future research as it highlights the importance of mixed-methods research in understanding lifestyle modification interventions and the need for population-specific improvements in the field (e.g., tailored interventions, development of evaluation instruments or tools, use of rigorous research methodologies and comprehensive reporting frameworks). Wide dissemination of related knowledge and the involvement of PPI groups, including people with lived experience, will help future researchers design interventions that consider the unique needs, desires and abilities of people with ID.

Repeated Divergence in Opsin Gene Expression Mirrors Photic Habitat Changes in Rapidly Evolving Crater Lake Cichlid Fishes

AbstractSelection pressures differ along environmental gradients, and traits tightly linked to fitness (e.g., the visual system) are expected to track such variation. Along gradients, adaptation to local conditions might be due to heritable and nonheritable environmentally induced variation. Disentangling these sources of phenotypic variation requires studying closely related populations in nature and in the laboratory. The Nicaraguan lakes represent an environmental gradient in photic conditions from clear crater lakes to very turbid great lakes. From two old, turbid great lakes, Midas cichlid fish (Amphilophus cf. citrinellus) independently colonized seven isolated crater lakes of varying light conditions, resulting in a small adaptive radiation. We estimated variation in visual sensitivities along this photic gradient by measuring cone opsin gene expression among lake populations. Visual sensitivities observed in all seven derived crater lake populations shifted predictably in direction and magnitude, repeatedly mirroring changes in photic conditions. Comparing wild-caught and laboratory-reared fish revealed that 48% of this phenotypic variation is genetically determined and evolved rapidly. Decreasing intrapopulation variation as environments become spectrally narrower suggests that different selective landscapes operate along the gradient. We conclude that the power to predict phenotypic evolution along gradients depends on both the magnitude of environmental change and the selective landscape shape.

Characterizing advanced heart failure risk and hemodynamic phenotypes using interpretable machine learning

BACKGROUND

Although previous risk models exist for advanced heart failure with reduced ejection fraction (HFrEF), few integrate invasive hemodynamics or support missing data. This study developed and validated a heart failure (HF) hemodynamic risk and phenotyping score for HFrEF, using Machine Learning (ML).

METHODS

Prior to modeling, patients in training and validation HF cohorts were assigned to 1 of 5 risk categories based on the composite endpoint of death, left ventricular assist device (LVAD) implantation or transplantation (DeLvTx), and rehospitalization in 6 months of follow-up using unsupervised clustering. The goal of our novel interpretable ML modeling approach, which is robust to missing data, was to predict this risk category (1, 2, 3, 4, or 5) using either invasive hemodynamics alone or a rich and inclusive feature set that included noninvasive hemodynamics (all features). The models were trained using the ESCAPE trial and validated using 4 advanced HF patient cohorts collected from previous trials, then compared with traditional ML models. Prediction accuracy for each of these 5 categories was determined separately for each risk category to generate 5 areas under the curve (AUCs, or C-statistics) for belonging to risk category 1, 2, 3, 4, or 5, respectively.

RESULTS

Across all outcomes, our models performed well for predicting the risk category for each patient. Accuracies of 5 separate models predicting a patient's risk category ranged from 0.896 +/- 0.074 to 0.969 +/- 0.081 for the invasive hemodynamics feature set and 0.858 +/- 0.067 to 0.997 +/- 0.070 for the all features feature set.

CONCLUSION

Novel interpretable ML models predicted risk categories with a high degree of accuracy. This approach offers a new paradigm for risk stratification that differs from prediction of a binary outcome. Prospective clinical evaluation of this approach is indicated to determine utility for selecting the best treatment approach for patients based on risk and prognosis.

Change in absolute neutrophil count after COVID-19 infection in patients using clozapine versus other antipsychotics

It was reported that patients who contracted COVID-19 while taking clozapine exhibited a distinct hematological response. However, the absence of control groups made it difficult to attribute it to clozapine. The changes in absolute neutrophil counts (ANCs) during the 4 weeks after COVID-19 infection were compared between the two groups of patients with severe mental illnesses (SMIs) (49 patients using clozapine and 54 using other antipsychotics) using generalized additive modeling. Although the pattern of a transient drop in ANC followed by gradual recovery could be demonstrated in both groups, it was more pronounced in the clozapine group ( P  = 0.00025). Nevertheless, overall ANC remained at a higher level in the clozapine group. The results suggested potential interaction between clozapine and COVID-19 at the level of hematological dynamics. However, it did not necessarily indicate that such interaction is inevitably harmful or dangerous. It was more of a concern that some patients using other antipsychotics exhibited decreased ANC, which did not easily recover. Traditionally, clinicians have been concerned about the worsening of hematological side effects in clozapine patients after COVID-19 infection. However, the obtained result highlighted the necessity of hematological monitoring in patients using any type of antipsychotics for SMIs.

Characterizing cognitive subtypes in schizophrenia using cortical curvature

Cognitive deficits are a core symptom of schizophrenia, but research on their neural underpinnings has been challenged by the heterogeneity in deficits' severity among patients. Here, we address this issue by combining logistic regression and random forest to classify two neuropsychological profiles of patients with high (HighCog) and low (LowCog) cognitive performance in two independent samples. We based our analysis on the cortical features grey matter volume (VOL), cortical thickness (CT), and mean curvature (MC) of N = 57 patients (discovery sample) and validated the classification in an independent sample (N = 52). We investigated which cortical feature would yield the best classification results and expected that the 10 most important features would include frontal and temporal brain regions. The model based on MC had the best performance with area under the curve (AUC) values of 76% and 73%, and identified fronto-temporal and occipital brain regions as the most important features for the classification. Moreover, subsequent comparison analyses could reveal significant differences in MC of single brain regions between the two cognitive profiles. The present study suggests MC as a promising neuroanatomical parameter for characterizing schizophrenia cognitive subtypes.

Night-to-night variability of objective sleep outcomes in youth Middle Eastern football players

OBJECTIVE

To describe components of night-to-night variation in objective measures of sleep.

METHODS

We conducted a secondary data analysis of consecutive and chronologically ordered actigraphy-based measurements for time in bed (min), time asleep (min), and wake-after-sleep onset (min). This investigation examined 575 individual night-based measures available for a sub-sample of fifty-two, male youth Middle Eastern football players tracked over a 14-day surveillance period (chronological age range: 12.1 to 16 years). Distinct multivariable-adjusted generalized additive models included each objective sleep outcome measure as dependent variable and disaggregated components of variation for night measurement-by-sleep period interaction, week part (weekday or weekend), and study participant random effects from within-subject night-to-night sleep variation.

RESULTS

The within-subject standard deviation (SD) of ±98 min (95% confidence interval [CI], 92 to 104 min) for time in bed, ±87 min (95%CI, 82 to 93 min) for time asleep, and ±23 min (95%CI, 22 to 25 min) for wake-after-sleep-onset overwhelmed other sources of variability and accounted for ∼44% to 53% of the overall night-to-night variation. The night measurement-by-fragmented sleep period interaction SD was ±83 min (95%CI, 44 to 156 min) for time in bed, ±67 min (95%CI, 34 to 131 min) for time asleep, and ±15 min (95%CI, 7 to 32 min) for wake-after-sleep-onset that accounted for ∼22% to 32% of each sleep outcome measure overall variability.

CONCLUSIONS

Substantial random night-to-night within-subject variability poses additional challenges for strategies aiming to mitigate problems of insufficient and inconsistent sleep that are detrimental to school learning and youth athlete development processes.

Machine learning combined with single-cell analysis reveals predictive capacity and immunotherapy response of T cell exhaustion-associated lncRNAs in uterine corpus endometrial carcinoma

BACKGROUND

The exhaustion of T-cells is a primary factor contributing to immune dysfunction in cancer. Long non-coding RNAs (lncRNAs) play a significant role in the advancement, survival, and treatment of Uterine Corpus Endometrial Carcinoma (UCEC). Nevertheless, there has been no investigation into the involvement of lncRNAs associated with T-cell exhaustion (TEXLs) in UCEC. The goal of this work is to establish predictive models for TEXLs in UCEC and study their related immune features.

METHODS

Using transcriptome and single-cell sequencing data from The Cancer Genome Atlas and Gene Expression Omnibus databases, we employed co-expression analysis and univariate Cox regression to identify prognostic-associated TEXLs (pTEXLs). The prognostic model was developed using the Least Absolute Contraction and Selection Operator. The immunotherapy characteristics of the prognostic model risk score were studied. Then molecular subgroups were identified through non-negative Matrix Factorization based on pTEXLs. The identification of co-expressed genes was done using a weighted correlation network analysis. Subsequently, a diagnostic model for UCEC was created. In-depth investigations, both in vitro and in vivo, were carried out to elucidate the molecular mechanism of the key gene within the diagnostic model.

RESULTS

Receiver operating characteristic curve, calibration curve, and decision curve analysis proved the validity of the predictive models established according to pTEXLs. The subgroup with lower risk scores in the prognostic model has better responses to blocking immune checkpoint therapy. Single-cell analysis suggests that the expression level of MIEN1 is relatively high in immune cells among diagnostic genes. Furthermore, the targeted suppression of MIEN1 via sh-MIEN1 diminishes the proliferative, migratory, and invasive capacities of UCEC cells, potentially associated with CD8+ T cell exhaustion.

CONCLUSIONS

The association between TEXLs and UCEC was methodically elucidated by our investigation. A stable pTEXLs risk prediction model and a diagnosis model for UCEC were also established.

Structured Pruning for Deep Convolutional Neural Networks: A Survey

The remarkable performance of deep Convolutional neural networks (CNNs) is generally attributed to their deeper and wider architectures, which can come with significant computational costs. Pruning neural networks has thus gained interest since it effectively lowers storage and computational costs. In contrast to weight pruning, which results in unstructured models, structured pruning provides the benefit of realistic acceleration by producing models that are friendly to hardware implementation. The special requirements of structured pruning have led to the discovery of numerous new challenges and the development of innovative solutions. This article surveys the recent progress towards structured pruning of deep CNNs. We summarize and compare the state-of-the-art structured pruning techniques with respect to filter ranking methods, regularization methods, dynamic execution, neural architecture search, the lottery ticket hypothesis, and the applications of pruning. While discussing structured pruning algorithms, we briefly introduce the unstructured pruning counterpart to emphasize their differences. Furthermore, we provide insights into potential research opportunities in the field of structured pruning. A curated list of neural network pruning papers can be found at: https://github.com/he-y/Awesome-Pruning. A dedicated website offering a more interactive comparison of structured pruning methods can be found at: https://huggingface.co/spaces/he-yang/Structured-Pruning-Survey.

Characterization and Determinants of Long-Term Immune Recovery Under Suppressive Antiretroviral Therapy

OBJECTIVE

We developed a robust characterization of immune recovery trajectories in people living with HIV on antiretroviral treatment (ART) and relate our findings to epidemiological risk factors and bacterial pneumonia.

METHODS

Using data from the Swiss HIV Cohort Study and the Zurich Primary HIV Infection Cohort Study (n = 5907), we analyzed the long-term trajectories of CD4 cell and CD8 cell counts and their ratio in people living with HIV on ART for at least 8 years by fitting nonlinear mixed-effects models. The determinants of long-term immune recovery were investigated using generalized additive models. In addition, prediction accuracy of the modeled trajectories and their impact on the fit of a model for bacterial pneumonia was assessed.

RESULTS

Overall, our population showed good immune recovery (median plateau [interquartile range]-CD4: 718 [555-900] cells/μL, CD8: 709 [547-893] cells/μL, CD4/CD8: 1.01 [0.76-1.37]). The following factors were predictive of recovery: age, sex, nadir/zenith value, pre-ART HIV-1 viral load, hepatitis C, ethnicity, acquisition risk, and timing of ART initiation. The fitted models proved to be an accurate and efficient way of predicting future CD4 and CD8 cell recovery dynamics: Compared with carrying forward the last observation, mean squared errors of the fitted values were lower by 1.3%-18.3% across outcomes. When modeling future episodes of bacterial pneumonia, using predictors derived from the recovery dynamics improved most model fits.

CONCLUSION

We described and validated a method to characterize individual immune recovery trajectories of people living with HIV on suppressive ART. These trajectories accurately predict long-term immune recovery and the occurrence of bacterial pneumonia.

A simple and robust parametric shared frailty model for recurrent events with the competing risk of death: An application to the Carvedilol Prospective Randomized Cumulative Survival trial

Many non-fatal events can be considered recurrent in that they can occur repeatedly over time, and some researchers may be interested in the trajectory and relative risk of non-fatal events. With the competing risk of death, the treatment effect on the mean number of recurrent events is non-identifiable since the observed mean is a function of both the recurrent event and terminal event processes. In this paper, we assume independence between the non-fatal and the terminal event process, conditional on the shared frailty, to fit a parametric model that recovers the trajectory of, and identifies the effect of treatment on, the non-fatal event process in the presence of the competing risk of death. Simulation studies are conducted to verify the reliability of our estimators. We illustrate the method and perform model diagnostics using the Carvedilol Prospective Randomized Cumulative Survival trial which involves heart-failure events.

Decoding Autism: Uncovering patterns in brain connectivity through sparsity analysis with rs-fMRI data

BACKGROUND

In the realm of neuro-disorders, precise diagnosis and treatment rely heavily on objective imaging-based biomarker identification. This study employs a sparsity approach on resting-state fMRI to discern relevant brain region connectivity for predicting Autism.

NEW METHOD

The proposed methodology involves four key steps: (1) Utilizing three probabilistic brain atlases to extract functionally homogeneous brain regions from fMRI data. (2) Employing a hybrid approach of Graphical Lasso and Akaike Information Criteria to optimize sparse inverse covariance matrices for representing the brain functional connectivity. (3) Employing statistical techniques to scrutinize functional brain structures in Autism and Control subjects. (4) Implementing both autoencoder-based feature extraction and entire feature-based approach coupled with AI-based learning classifiers to predict Autism.

RESULTS

The ensemble classifier with the extracted feature set achieves a classification accuracy of 84.7% ± 0.3% using the MSDL atlas. Meanwhile, the 1D-CNN model, employing all features, exhibits superior classification accuracy of 88.6% ± 1.7% with the Smith 2009 (rsn70) atlas.

COMPARISON WITH EXISTING METHOD (S)

The proposed methodology outperforms the conventional correlation-based functional connectivity approach with a notably high prediction accuracy of more than 88%, whereas considering all direct and noisy indirect region-based functional connectivity, the traditional methods bound the prediction accuracy within 70% to 79%.

CONCLUSIONS

This study underscores the potential of sparsity-based FC analysis using rs-fMRI data as a prognostic biomarker for detecting Autism.

Joint modeling of longitudinal CD4 count data and time to first occurrence of composite outcome

In this study, we jointly modeled longitudinal CD4 count data and survival outcome (time-to-first occurrence of composite outcome of death, cardiac tamponade or constriction) in other to investigate the effects of Mycobacterium indicus pranii immunotherapy and the CD4 count measurements on the hazard of the composite outcome among patients with HIV and tuberculous (TB) pericarditis. In this joint modeling framework, the models for longitudinal and the survival data are linked by an association structure. The association structure represents the hazard of the event for 1-unit increase in the longitudinal measurement. Models fitting and parameter estimation were carried out using R version 4.2.3. The association structure that represents the strength of the association between the hazard for an event at time point j and the area under the longitudinal trajectory up to the same time j provides the best fit. We found that 1-unit increase in CD4 count results in 2 % significant reduction in the hazard of the composite outcome. Among HIV and TB pericarditis individuals, the hazard of the composite outcome does not differ between of M.indicus pranii versus placebo. Application of joint models to investigate the effect of M.indicus pranii on the hazard of the composite outcome is limited. Hence, this study provides information on the effect of M.indicus pranii on the hazard of the composite outcome among HIV and TB pericarditis patients.

Host and rumen microbiome contributions to feed efficiency traits in Holstein cows

It is now widely accepted that dairy cow performance is influenced by both the host genome and rumen microbiome composition. The contributions of the genome and the microbiome to the phenotypes of interest are quantified by heritability (h2) and microbiability (m2), respectively. However, if the genome and microbiome are included in the model, then the h2 reflects only the contribution of the direct genetic effects quantified as direct heritability (hd2), and the holobiont effect reflects the joint action of the genome and the microbiome, quantified as the holobiability (ho2). The objectives of this study were to estimate h2, hd2,m2, and ho2 for dry matter intake, milk energy, and residual feed intake; and to evaluate the predictive ability of different models, including genome, microbiome, and their interaction. Data consisted of feed efficiency records, SNP genotype data, and 16S rRNA rumen microbial abundances from 448 mid-lactation Holstein cows from 2 research farms. Three kernel models were fit to each trait: one with only the genomic effect (model G), one with the genomic and microbiome effects (model GM), and one with the genomic, microbiome, and interaction effects (model GMO). The model GMO, or holobiont model, showed the best goodness-of-fit. The hd2 estimates were always 10% to 15% lower than h2 estimates for all traits, suggesting a mediated genetic effect through the rumen microbiome, and m2 estimates were moderate for all traits, and up to 26% for milk energy. The ho2 was greater than the sum of hd2 and m2, suggesting that the genome-by-microbiome interaction had a sizable effect on feed efficiency. Kernel models fitting the rumen microbiome (i.e., models GM and GMO) showed larger predictive correlations and smaller prediction bias than the model G. These findings reveal a moderate contribution of the rumen microbiome to feed efficiency traits in lactating Holstein cows and strongly suggest that the rumen microbiome mediates part of the host genetic effect.

Adjusting Incidence Estimates with Laboratory Test Performances: A Pragmatic Maximum Likelihood Estimation-Based Approach

Understanding the incidence of disease is often crucial for public policy decision-making, as observed during the COVID-19 pandemic. Estimating incidence is challenging, however, when the definition of incidence relies on tests that imperfectly measure disease, as in the case when assays with variable performance are used to detect the SARS-CoV-2 virus. To our knowledge, there are no pragmatic methods to address the bias introduced by the performance of labs in testing for the virus. In the setting of a longitudinal study, we developed a maximum likelihood estimation-based approach to estimate laboratory performance-adjusted incidence using the expectation-maximization algorithm. We constructed confidence intervals (CIs) using both bootstrapped-based and large-sample interval estimator approaches. We evaluated our methods through extensive simulation and applied them to a real-world study (TrackCOVID), where the primary goal was to determine the incidence of and risk factors for SARS-CoV-2 infection in the San Francisco Bay Area from July 2020 to March 2021. Our simulations demonstrated that our method converged rapidly with accurate estimates under a variety of scenarios. Bootstrapped-based CIs were comparable to the large-sample estimator CIs with a reasonable number of incident cases, shown via a simulation scenario based on the real TrackCOVID study. In more extreme simulated scenarios, the coverage of large-sample interval estimation outperformed the bootstrapped-based approach. Results from the application to the TrackCOVID study suggested that assuming perfect laboratory test performance can lead to an inaccurate inference of the incidence. Our flexible, pragmatic method can be extended to a variety of disease and study settings.

Personality traits and cognitive reserve-High openness benefits cognition in the presence of age-related brain changes

Cognitive reserve explains differential susceptibility of cognitive performance to neuropathology. We investigated whether certain personality traits underlie cognitive reserve and are accordingly associated with better cognition and less cognitive decline in the presence of age-related brain changes. We included healthy adults aged 19-80 years for cross-sectional (N=399) and longitudinal (N=273, mean follow-up time=5 years, SD=0.7 years) analyses. Assessment of the BIG5 personality traits openness, conscientiousness, extraversion, agreeableness, and neuroticism was questionnaire-based. Each cognitive domain (perceptual speed, memory, fluid reasoning, vocabulary) was measured with up to six tasks. Cognitive domain-specific brain status variables were obtained by combining 77 structural brain measures into single scores using elastic net regularization. These brain status variables explained up to 43.1% of the variance in cognitive performance. We found that higher openness was associated with higher fluid reasoning and better vocabulary after controlling for brain status, age, and sex. Further, lower brain status was associated with a greater decline in perceptual speed only in individuals with low openness. We conclude that high openness benefits cognitive reserve.

Long-term trends in migrating Brassicogethes aeneus in the UK

BACKGROUND

The pollen beetle (Brassicogethes aeneus) causes significant yield loss in oilseed rape (Brassica napus). Predicting population changes remains a scientific challenge, especially since its phenology and abundance varies dramatically over space and time. We used generalized additive models to investigate the long-term trends in pollen beetle annual, seasonal and monthly counts from Rothamsted 12.2 m suction-traps. We hypothesised that the beetle's abundance is positively related to the area of oilseed rape at a national and regional level. We used random forest models to investigate the inter-generational relationship within years.

RESULTS

Although Brassicogethes aeneus annual counts and area of oilseed rape grown in the UK both increased by 162% and 113%, respectively, over the time period studied, they were not significantly related. The size of the immigrating pollen beetle population (up to 1 June) can be explained both by the size of the population in the previous summer and prevailing winter temperatures, indicating a positive feedback mechanism.

CONCLUSION

Currently, pollen beetle numbers continue to increase in the UK, meaning that control issues may persist. However the relationship between counts in spring, during the susceptible phase of the crop, and counts in the previous summer indicates that it may be possible to forecast the counts of the spring migration of Brassicogethes aeneus a few months in advance using suction-trap samples, which could aid decisions on control options. © 2023 The Authors. Pest Management Science published by John Wiley & Sons Ltd on behalf of Society of Chemical Industry.

A matching-based machine learning approach to estimating optimal dynamic treatment regimes with time-to-event outcomes

Observational data (e.g. electronic health records) has become increasingly important in evidence-based research on dynamic treatment regimes, which tailor treatments over time to patients based on their characteristics and evolving clinical history. It is of great interest for clinicians and statisticians to identify an optimal dynamic treatment regime that can produce the best expected clinical outcome for each individual and thus maximize the treatment benefit over the population. Observational data impose various challenges for using statistical tools to estimate optimal dynamic treatment regimes. Notably, the task becomes more sophisticated when the clinical outcome of primary interest is time-to-event. Here, we propose a matching-based machine learning method to identify the optimal dynamic treatment regime with time-to-event outcomes subject to right-censoring using electronic health record data. In contrast to the established inverse probability weighting-based dynamic treatment regime methods, our proposed approach provides better protection against model misspecification and extreme weights in the context of treatment sequences, effectively addressing a prevalent challenge in the longitudinal analysis of electronic health record data. In simulations, the proposed method demonstrates robust performance across a range of scenarios. In addition, we illustrate the method with an application to estimate optimal dynamic treatment regimes for patients with advanced non-small cell lung cancer using a real-world, nationwide electronic health record database from Flatiron Health.

Knockoffs-SPR: Clean Sample Selection in Learning With Noisy Labels

A noisy training set usually leads to the degradation of the generalization and robustness of neural networks. In this article, we propose a novel theoretically guaranteed clean sample selection framework for learning with noisy labels. Specifically, we first present a Scalable Penalized Regression (SPR) method, to model the linear relation between network features and one-hot labels. In SPR, the clean data are identified by the zero mean-shift parameters solved in the regression model. We theoretically show that SPR can recover clean data under some conditions. Under general scenarios, the conditions may be no longer satisfied; and some noisy data are falsely selected as clean data. To solve this problem, we propose a data-adaptive method for Scalable Penalized Regression with Knockoff filters (Knockoffs-SPR), which is provable to control the False-Selection-Rate (FSR) in the selected clean data. To improve the efficiency, we further present a split algorithm that divides the whole training set into small pieces that can be solved in parallel to make the framework scalable to large datasets. While Knockoffs-SPR can be regarded as a sample selection module for a standard supervised training pipeline, we further combine it with a semi-supervised algorithm to exploit the support of noisy data as unlabeled data. Experimental results on several benchmark datasets and real-world noisy datasets show the effectiveness of our framework and validate the theoretical results of Knockoffs-SPR.

Mixtures of t $$ t $$ factor analysers with censored responses and external covariates: An application to educational data from Peru

Analysing data from educational tests allows governments to make decisions for improving the quality of life of individuals in a society. One of the key responsibilities of statisticians is to develop models that provide decision-makers with pertinent information about the latent process that educational tests seek to represent. Mixtures oft $$ t $$ factor analysers (MtFA) have emerged as a powerful device for model-based clustering and classification of high-dimensional data containing one or several groups of observations with fatter tails or anomalous outliers. This paper considers an extension of MtFA for robust clustering of censored data, referred to as the MtFAC model, by incorporating external covariates. The enhanced flexibility of including covariates in MtFAC enables cluster-specific multivariate regression analysis of dependent variables with censored responses arising from upper and/or lower detection limits of experimental equipment. An alternating expectation conditional maximization (AECM) algorithm is developed for maximum likelihood estimation of the proposed model. Two simulation experiments are conducted to examine the effectiveness of the techniques presented. Furthermore, the proposed methodology is applied to Peruvian data from the 2007 Early Grade Reading Assessment, and the results obtained from the analysis provide new insights regarding the reading skills of Peruvian students.

Understanding the response of the Western Mediterranean cephalopods to environment and fishing in a context of alleged winners of change

Increasing impacts of both fisheries and climate change have resulted in shifts in the structure and functioning of marine communities. One recurrent observation is the rise of cephalopods as fish recede. This is generally attributed to the removal of main predators and competitors by fishing, while mechanistic evidence is still lacking. In addition, climate change may influence cephalopods due to their high environmental sensitivity. We aim to unveil the effects of different anthropogenic and environmental drivers at different scales focusing on the cephalopod community of the Western Mediterranean Sea. We investigate several ecological indicators offering a wide range of information about their ecology, and statistically relating them with environmental, biotic and fisheries drivers. Our results highlight non-linear changes of indicators along with spatial differences in their responses. Overall, the environment drivers have greater effects than biotic and local human impacts with contrasting effects of temperature across the geographic gradient. We conclude that cephalopods may be impacted by climate change in the future while not necessary through positive warming influence, which should make us cautious when referring to them as generalized winners of current changes.

HLA-DRB1 is associated with cefaclor-induced immediate hypersensitivity

Background

Drug-induced hypersensitivity such as anaphylaxis is an important cause of drug-related morbidity and mortality. Cefaclor is a leading cause of drug induced type I hypersensitivity in Korea, but little is yet known about genetic biomarkers to predict this hypersensitivity reaction. We aimed to evaluate the possible involvement of genes in cefaclor induced type I hypersensitivity.

Methods

Whole exome sequencing (WES) and HLA genotyping were performed in 43 patients with cefaclor induced type I hypersensitivity. In addition, homology modeling was performed to identify the binding forms of cefaclor to HLA site.

Results

Anaphylaxis was the most common phenotype of cefaclor hypersensitivity (90.69%). WES results show that rs62242177 and rs62242178 located in LIMD1 region were genome-wide significant at the 5 × 10-8 significance level. Cefaclor induced type I hypersensitivity was significantly associated with HLA-DRB1∗04:03 (OR 4.61 [95% CI 1.51-14.09], P < 0.002) and HLA-DRB1∗14:54 (OR 3.86 [95% CI 1.09-13.67], P < 0.002).

Conclusion

LIMD1, HLA-DRB1∗04:03 and HLA-DRB1∗14:54 may affect susceptibility to cefaclor induced type I hypersensitivity. Further confirmative studies with a larger patient population should be performed to ascertain the role of HLA-DRB1 and LIMD1 in the development of cefaclor induced hypersensitivity.

Statistical and Machine Learning Analysis in Brain-Imaging Genetics: A Review of Methods

Brain-imaging-genetic analysis is an emerging field of research that aims at aggregating data from neuroimaging modalities, which characterize brain structure or function, and genetic data, which capture the structure and function of the genome, to explain or predict normal (or abnormal) brain performance. Brain-imaging-genetic studies offer great potential for understanding complex brain-related diseases/disorders of genetic etiology. Still, a combined brain-wide genome-wide analysis is difficult to perform as typical datasets fuse multiple modalities, each with high dimensionality, unique correlational landscapes, and often low statistical signal-to-noise ratios. In this review, we outline the progress in brain-imaging-genetic methodologies starting from early massive univariate to current deep learning approaches, highlighting each approach's strengths and weaknesses and elongating it with the field's development. We conclude by discussing selected remaining challenges and prospects for the field.

Behavioural disorders after prenatal exposure to anaesthesia for maternal surgery

BACKGROUND

The association between prenatal exposure to general anaesthesia for maternal surgery during pregnancy and subsequent risk of disruptive or internalising behavioural disorder diagnosis in the child has not been well-defined.

METHODS

A nationwide sample of pregnant women linked to their liveborn infants was evaluated using the Medicaid Analytic eXtract (MAX, 1999-2013). Multivariate matching was used to match each child prenatally exposed to general anaesthesia owing to maternal appendectomy or cholecystectomy during pregnancy with five unexposed children. The primary outcome was diagnosis of a disruptive or internalising behavioural disorder in children. Secondary outcomes included diagnoses for a range of other neuropsychiatric disorders.

RESULTS

We matched 34,271 prenatally exposed children with 171,355 unexposed children in the database. Prenatally exposed children were more likely than unexposed children to receive a diagnosis of a disruptive or internalising behavioural disorder (hazard ratio [HR], 1.31; 95% confidence interval [CI], 1.23-1.40). For secondary outcomes, increased hazards of disruptive (HR, 1.32; 95% CI, 1.24-1.41) and internalising (HR, 1.36; 95% CI, 1.20-1.53) behavioural disorders were identified, and also increased hazards of attention-deficit/hyperactivity disorder (HR, 1.32; 95% CI, 1.22-1.43), behavioural disorders (HR, 1.28; 95% CI, 1.14-1.42), developmental speech or language disorders (HR, 1.16; 95% CI, 1.05-1.28), and autism (HR, 1.31; 95% CI, 1.05-1.64).

CONCLUSIONS

Prenatal exposure to general anaesthesia is associated with a 31% increased risk for a subsequent diagnosis of a disruptive or internalising behavioural disorder in children. Caution is advised when making any clinical decisions regarding care of pregnant women, as avoidance of necessary surgery during pregnancy can have detrimental effects on mothers and their children.

Competitive performance as a discriminator of doping status in elite athletes

As the aim of any doping regime is to improve sporting performance, it has been suggested that analysis of athlete competitive results might be informative in identifying those at greater risk of doping. This research study aimed to investigate the utility of a statistical performance model to discriminate between athletes who have a previous anti-doping rule violation (ADRV) and those who do not. We analysed performances of male and female 100 and 800 m runners obtained from the World Athletics database using a Bayesian spline model. Measures of unusual improvement in performance were quantified by comparing the yearly change in athlete's performance (delta excess performance) to quantiles of performance in their age-matched peers from the database population. The discriminative ability of these measures was investigated using the area under the ROC curve (AUC) with the 55%, 75% and 90% quantiles of the population performance. The highest AUC values across age were identified for the model with a 75% quantile (AUC = 0.78-0.80). The results of this study demonstrate that delta excess performance was able to discriminate between athletes with and without ADRVs and therefore could be used to assist in the risk stratification of athletes for anti-doping purposes.

Gross Hematuria Does not Affect the Selection of Nephrectomy Types for Clinical Stage 1 Clear Cell Renal Cell Carcinoma: A Multicenter, Retrospective Cohort Study

PURPOSE

This study aimed to discuss the correlation between gross hematuria and postoperative upstaging (from T1 to T3a) in patients with cT1 clear cell renal cell carcinoma (ccRCC) and to compare oncologic outcomes of partial nephrectomy (PN) and radical nephrectomy (RN) in patients with gross hematuria.

METHODS

A total of 2145 patients who met the criteria were enrolled in the study (including 363 patients with gross hematuria). The least absolute selection and shrinkage operator logistic regression was used to evaluate the risk factor of postoperative pathological upstaging. The propensity score matching (PSM) and stable inverse probability of treatment weighting (IPTW) analysis were used to balance the confounding factors. The Kaplan-Meier analysis and multivariate Cox proportional risk regression model were used to assess the prognosis.

RESULTS

Gross hematuria was a risk factor of postoperative pathological upstaging (odds ratio [OR] = 3.96; 95% confidence interval [CI] 2.44-6.42; P < 0.001). After PSM and stable IPTW adjustment, the characteristics were similar in corresponding patients in the PN and RN groups. In the PSM cohort, PN did not have a statistically significant impact on recurrence-free survival (hazard ratio [HR] = 1.48; 95% CI 0.25-8.88; P = 0.67), metastasis-free survival (HR = 1.24; 95% CI 0.33-4.66; P = 0.75), and overall survival (HR = 1.46; 95% CI 0.31-6.73; P = 0.63) compared with RN. The results were confirmed in sensitivity analyses.

CONCLUSIONS

Although gross hematuria was associated with postoperative pathological upstaging in patients with cT1 ccRCC, PN should still be the preferred treatment for such patients.

Quantile regression shrinkage and selection via the Lqsso

Quantile regression has recently received a considerable attention due to its remarkable development in enriching the variety of regression models. Many efforts have been made to blend different penalty and loss function to extend or develop novel regression models that are unique from different perspectives. Bearing in mind that the lasso quantile regression model ignores the randomness of the realizations in the penalty part, we propose a new penalty for the quantile regression models. Similar to the adaptive lasso quantile regression model, the proposed model simultaneously does estimation and variable selection tasks. We call the new model 'lqsso-QR', standing for the least quantile shrinkage and selection operator quantile regression. In this article, we present a sufficient and necessary condition for the variable selection of the lasso quantile regression to enjoy the consistent property. We show that the lqsso-QR follows oracle properties under some mild conditions. From computational perspective, we apply an efficient algorithm, originally developed for the lasso quantile regression. Using simulation studies, we elaborate on the superiority of the proposed model compared with other lasso-type penalties, especially regarding relative prediction error. Also, an application of our method to a real-life data; the rat eye data, is reported.

Martingale-residual-based greedy model averaging for high-dimensional current status data

Current status data are a type of failure time data that arise when the failure time of study subject cannot be determined precisely but is known only to occur before or after a random monitoring time. Variable selection methods for the failure time data have been discussed extensively in the literature. However, the statistical inference of the model selected based on the variable selection method ignores the uncertainty caused by model selection. To enhance the prediction accuracy for risk quantities such as survival probability, we propose two optimal model averaging methods under semiparametric additive hazards models. Specifically, based on martingale residuals processes, a delete-one cross-validation (CV) process is defined, and two new CV functional criteria are derived for choosing model weights. Furthermore, we present a greedy algorithm for the implementation of the techniques, and the asymptotic optimality of the proposed model averaging approaches is established, along with the convergence of the greedy averaging algorithms. A series of simulation experiments demonstrate the effectiveness and superiority of the proposed methods. Finally, a real-data example is provided as an illustration.