Agent-based modeling in cancer biomedicine: applications and tools for calibration and validation

Computational models are not just appealing because they can simulate and predict the development of biological phenomena across multiple spatial and temporal scales, but also because they can integrate information from well-established in vitro and in vivo models and test new hypotheses in cancer biomedicine. Agent-based models and simulations are especially interesting candidates among computational modeling procedures in cancer research due to the capability to, for instance, recapitulate the dynamics of neoplasia and tumor - host interactions. Yet, the absence of methods to validate the consistency of the results across scales can hinder adoption by turning fine-tuned models into black boxes. This review compiles relevant literature that explores strategies to leverage high-fidelity simulations of multi-scale, or multi-level, cancer models with a focus on verification approached as simulation calibration. We consolidate our review with an outline of modern approaches for agent-based models' validation and provide an ambitious outlook toward rigorous and reliable calibration.

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.

Knowledge-guided learning methods for integrative analysis of multi-omics data

Integrative analysis of multi-omics data has the potential to yield valuable and comprehensive insights into the molecular mechanisms underlying complex diseases such as cancer and Alzheimer's disease. However, a number of analytical challenges complicate multi-omics data integration. For instance, -omics data are usually high-dimensional, and sample sizes in multi-omics studies tend to be modest. Furthermore, when genes in an important pathway have relatively weak signal, it can be difficult to detect them individually. There is a growing body of literature on knowledge-guided learning methods that can address these challenges by incorporating biological knowledge such as functional genomics and functional proteomics into multi-omics data analysis. These methods have been shown to outperform their counterparts that do not utilize biological knowledge in tasks including prediction, feature selection, clustering, and dimension reduction. In this review, we survey recently developed methods and applications of knowledge-guided multi-omics data integration methods and discuss future research directions.

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.

MIMOSA: a resource consisting of improved methylome prediction models increases power to identify DNA methylation-phenotype associations

Although DNA methylation (DNAm) has been implicated in the pathogenesis of numerous complex diseases, from cancer to cardiovascular disease to autoimmune disease, the exact methylation sites that play key roles in these processes remain elusive. One strategy to identify putative causal CpG sites and enhance disease etiology understanding is to conduct methylome-wide association studies (MWASs), in which predicted DNA methylation that is associated with complex diseases can be identified. However, current MWAS models are primarily trained using the data from single studies, thereby limiting the methylation prediction accuracy and the power of subsequent association studies. Here, we introduce a new resource, MWAS Imputing Methylome Obliging Summary-level mQTLs and Associated LD matrices (MIMOSA), a set of models that substantially improve the prediction accuracy of DNA methylation and subsequent MWAS power through the use of a large summary-level mQTL dataset provided by the Genetics of DNA Methylation Consortium (GoDMC). Through the analyses of GWAS (genome-wide association study) summary statistics for 28 complex traits and diseases, we demonstrate that MIMOSA considerably increases the accuracy of DNA methylation prediction in whole blood, crafts fruitful prediction models for low heritability CpG sites, and determines markedly more CpG site-phenotype associations than preceding methods. Finally, we use MIMOSA to conduct a case study on high cholesterol, pinpointing 146 putatively causal CpG sites.

An R-based integrated method for producing river bathymetry and cross-sections from recreational-grade sonar sensor data

Water bodies' bathymetry is a crucial information for understanding and sustainably managing water resources. Bathymetric surveys can be expensive due to sonar equipment cost, but low-cost alternatives options exist. We present a methodology that standardize the bathymetric data collection and processing of recreational-grade sonar data. The sonar data postprocessing if fully implemented in R, with ready to use functions able to produce bathymetric maps or extract river cross sections' metrics with minimal computing efforts. The method robustly produces a variety of outputs; the performance of the equipment adopted and of the interpolation technique allow for high accuracy and low-cost bathymetric reconstruction.•The method implemented allow for a robust and consistent processing of recreational-grade sonar water depth measures.•Through R-based functions the data are postprocessed to obtain bathymetry maps also for complex shape waterbodies.•Further metrics of rivers/channel cross sections can be extracted.

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.

Development and validation of machine learning models to predict perioperative transfusion risk for hip fractures in the elderly

BACKGROUND

Patients with hip fractures frequently need to receive perioperative transfusions of concentrated red blood cells due to preoperative anemia or surgical blood loss. However, the use of perioperative blood products increases the risk of adverse events, and the shortage of blood products is prompting us to minimize blood transfusion. Our study aimed to construct a machine learning algorithm predictive model to identify patients at high risk for perioperative transfusion early in hospital admission and to manage their patient blood to reduce transfusion requirements.

METHODS

This study collected patients hospitalized for hip fractures at a university hospital from May 2016 to November 2022. All patients included in the analysis were randomly divided into a training set and validation set according to 70:30. Eight machine learning algorithms, CART, GBM, KNN, LR, NNet, RF, SVM, and XGBoost, were used to construct the prediction models. The models were evaluated for discrimination, calibration, and clinical utility, and the best prediction model was selected.

RESULTS

A total of 805 patients were included in the study, of whom 306 received transfusions during the perioperative period. We screened eight features used to construct the prediction model: age, fracture time, fracture type, hemoglobin, albumin, creatinine, calcium ion, and activated partial thromboplastin time. After evaluating and comparing the performance of each of the eight models, the model constructed by the XGBoost algorithm had the best performance, with MCC values of 0.828 and 0.939 in the training and validation sets, respectively. In addition, it had good calibration and clinical utility in both the training and validation sets.

CONCLUSION

The model constructed by the XGBoost algorithm has the best performance, using this model to identify patients at high risk for transfusion early in their admission and promptly incorporating them into a patient blood management plan can help reduce the risk of transfusion.

Mapping agricultural tile drainage in the US Midwest using explainable random forest machine learning and satellite imagery

There has been an increase in tile drained area across the US Midwest and other regions worldwide due to agricultural expansion, intensification, and climate variability. Despite this growth, spatially explicit tile drainage maps remain scarce, which limits the accuracy of hydrologic modeling and implementation of nutrient reduction strategies. Here, we developed a machine-learning model to provide a Spatially Explicit Estimate of Tile Drainage (SEETileDrain) across the US Midwest in 2017 at a 30-m resolution. This model used 31 satellite-derived and environmental features after removing less important and highly correlated features. It was trained with 60,938 tile and non-tile ground truth points within the Google Earth Engine cloud-computing platform. We also used multiple feature importance metrics and Accumulated Local Effects to interpret the machine learning model. The results show that our model achieved good accuracy, with 96 % of points classified correctly and an F1 score of 0.90. When tile drainage area is aggregated to the county scale, it agreed well (r2 = 0.69) with the reported area from the Ag Census. We found that Land Surface Temperature (LST) along with climate- and soil-related features were the most important factors for classification. The top-ranked feature is the median summer nighttime LST, followed by median summer soil moisture percent. This study demonstrates the potential of applying satellite remote sensing to map spatially explicit agricultural tile drainage across large regions. The results should be useful for land use change monitoring and hydrologic and nutrient models, including those designed to achieve cost-effective agricultural water and nutrient management strategies. The algorithms developed here should also be applicable for other remote sensing mapping applications.

Little change in plastic loads in South Atlantic seabirds since the 1980s

Despite growing concern about the large amounts of waste plastic in marine ecosystems, evidence of an increase in the amount of floating plastic at sea has been mixed. Both at-sea surveys and ingested plastic loads in seabirds show inconsistent evidence of significant increases in the amount of plastic since the 1980s. We use 3727 brown skua Catharacta antarctica regurgitations, each containing the remains of a single seabird, to monitor changes in plastic loads in four seabird taxa breeding at Inaccessible Island, Tristan da Cunha in nine years from 1987 to 2018. Frequency of occurrence in plastic ingestion and types were compared across four near-decadal time periods (1987-1989; 1999-2004; 2009-2014 and 2018) while loads were compared among years. The number and proportions of industrial pellets among ingested plastic decreased consistently over the study period in all four taxa, suggesting that industry initiatives to reduce pellet leakage have reduced the numbers of pellets at sea. Despite global plastic production increasing more than four-fold over the study period, there was no consistent increase in the total amount of ingested plastic in any species. Plastic loads in great shearwaters Ardenna gravis, which spend the austral winter in the North Atlantic Ocean, increased in 2018, but the proportion of shearwaters containing plastic decreased. We conclude that the density of plastic floating at sea has not increased in line with global production over the last 30 years.

A novel coupling interpretable machine learning framework for water quality prediction and environmental effect understanding in different flow discharge regulations of hydro-projects

Machine learning models (MLMs) have been increasingly used to forecast water pollution. However, the "black box" characteristic for understanding mechanism processes still limits the applicability of MLMs for water quality management in hydro-projects under complex and frequently artificial regulation. This study proposes an interpretable machine learning framework for water quality prediction coupled with a hydrodynamic (flow discharge) scenario-based Random Forest (RF) model with multiple model-agnostic techniques and quantifies global, local, and joint interpretations (i.e., partial dependence, individual conditional expectation, and accumulated local effects) of environmental factor implications. The framework was applied and verified to predict the permanganate index (CODMn) under different flow discharge regulation scenarios in the Middle Route of the South-to-North Water Diversion Project of China (MRSNWDPC). A total of 4664 sampling cases data matrices, including water quality, meteorological, and hydrological indicators from eight national stations along the main canal of the MRSNWDPC, were collected from May 2019 to December 2020. The results showed that the RF models were effective in forecasting CODMn in all flow discharge scenarios, with a mean square error, coefficient of determination, and mean absolute error of 0.006-0.026, 0.481-0.792, and 0.069-0.104, respectively, in the testing dataset. A global interpretation indicated that dissolved oxygen, flow discharge, and surface pressure are the three most important variables of CODMn. Local and joint interpretations indicated that the RF-based prediction model provides a basic understanding of the physical mechanisms of environmental systems. The proposed framework can effectively learn the fundamental environmental implications of water quality variations and provide reliable prediction performance, highlighting the importance of model interpretability for trustworthy machine learning applications in water management projects. This study provides scientific references for applying advanced data-driven MLMs to water quality forecasting and a reliable methodological framework for water quality management and similar hydro-projects.

Relationships between radiation, wildfire and the soil microbial communities in the Chornobyl Exclusion Zone

There is considerable uncertainty regarding radiation's effects on biodiversity in natural complex ecosystems typically subjected to multiple environmental disturbances and stresses. In this study we characterised the relationships between soil microbial communities and estimated total absorbed dose rates to bacteria, grassy vegetation and trees in the Red Forest region of the Chornobyl Exclusion Zone. Samples were taken from sites of contrasting ecological histories and along burn and no burn areas following a wildfire. Estimated total absorbed dose rates to bacteria reached levels one order of magnitude higher than those known to affect bacteria in laboratory studies. Sites with harsher ecological conditions, notably acidic pH and low soil moisture, tended to have higher radiation contamination levels. No relationship between the effects of fire and radiation were observed. Microbial groups that correlated with high radiation sites were mostly classified to taxa associated with high environmental stress habitats or stress resistance traits. Distance-based linear models and co-occurrence analysis revealed that the effects of radiation on the soil microbiome were minimal. Hence, the association between high radiation sites and specific microbial groups is more likely a result of the harsher ecological conditions in these sites, rather than due to radiation itself. In this study, we provide a starting point for understanding the relationship between soil microbial communities and estimated total absorbed radiation dose rates to different components of an ecosystem highly contaminated with radiation. Our results suggest that soil microbiomes adapted to natural soil conditions are more likely to be resistant to ionising radiation than expected from laboratory studies, which demonstrates the importance of assessing the impact of ionising radiation on soil microbial communities under field conditions.

The hospitalisation risk of chronic circulatory and respiratory diseases associated with coal mining in the general population in Queensland, Australia

Queensland is the main coal mining state in Australia where populations in coal mining areas have been historically exposed to coal mining emissions. Although a higher risk of chronic circulatory and respiratory diseases has been associated with coal mining globally, few studies have investigated these associations in the Queensland general population. This study estimates the association of coal production with hospitalisations for chronic circulatory and respiratory diseases in Queensland considering spatial and temporal variations during 1997-2014. An ecological analysis used a Bayesian hierarchical spatiotemporal model to estimate the association of coal production with standardised rates of each, chronic circulatory and respiratory diseases, adjusting for sociodemographic factors and considering the spatial structure of Queensland's statistical areas (SA2) in the 18-year period. Two specifications; with and without a space-time interaction effect were compared using the integrated nested Laplace approximation -INLA approach. The posterior mean of the best fit model was used to map the spatial, temporal and spatiotemporal trends of risk. The analysis considered 2,831,121 hospitalisation records. Coal mining was associated with a 4 % (2.4-5.5) higher risk of hospitalisation for chronic respiratory diseases in the model with a space-time interaction effect which had the best fit. An emerging higher risk of either chronic circulatory and respiratory diseases was identified in eastern areas and some coal-mining areas in central and southeast Queensland. There were important disparities in the spatiotemporal trend of risk between coal -and non-coal mining areas for each, chronic circulatory and respiratory diseases. Coal mining is associated with an increased risk of chronic respiratory diseases in the Queensland general population. Bayesian spatiotemporal analyses are robust methods to identify environmental determinants of morbidity in exposed populations. This methodology helps identifying at-risk populations which can be useful to support decision-making in health. Future research is required to investigate the causality links between coal mining and these diseases.

Long-term grassland diversity-productivity relationship regulated by management regimes in northern China

Grasslands are the most extensively distributed terrestrial ecosystems on Earth, providing a range of ecosystem services that are vital for sustaining human life and critical for sustainable development at the global scale. However, the relationship between the two most important attributes of grassland, plant diversity, and productivity, remains controversial even after many years of research. Here, we develop an analysis of covariance (ANCOVA) model based on decadal-scale experimental data from a degraded meadow steppe in northeastern Inner Mongolia, China to quantify the response of aboveground biomass (AGB) to plant species diversity under varying management regimes. We report that AGB responds negatively to the plant diversity in fallow grasslands and positively in grazing grasslands, transiting from negative to positive in mowing grasslands as mowing became more frequent. We show that the changing diversity-productivity relationships are driven by changes in species composition of the plant community, given the significant productivity gap between rare and non-rare species. This highlights the role of management in regulating the diversity-productivity relationships in grasslands. These results not only provide provocative insights into the relationships between plant diversity and productivity but also support more sustainable use and management of grassland resources.

Potential association between PSCA rs2976395 functional variant and pancreatic cancer risk

Correlated regions of systemic interindividual variation (CoRSIV) represent a small proportion of the human genome showing DNA methylation patterns that are the same in all human tissues, are different among individuals, and are partially regulated by genetic variants in cis. In this study we aimed at investigating single-nucleotide polymorphisms (SNPs) within CoRSIVs and their involvement with pancreatic ductal adenocarcinoma (PDAC) risk. We analyzed 29,099 CoRSIV-SNPs and 133,615 CoRSIV-mQTLs in 14,394 cases and 247,022 controls of European and Asian descent. We observed that the A allele of the rs2976395 SNP was associated with increased PDAC risk in Europeans (p = 2.81 × 10-5). This SNP lies in the prostate stem cell antigen gene and is in perfect linkage disequilibrium with a variant (rs2294008) that has been reported to be associated with risk of many other cancer types. The A allele is associated with the DNA methylation level of the gene according to the PanCan-meQTL database and with overexpression according to QTLbase. The expression of the gene has been observed to be deregulated in many tumors of the gastrointestinal tract including pancreatic cancer; however, functional studies are needed to elucidate the function relevance of the association.

Combined effects of herbicides and insecticides reduce biomass of sensitive aquatic invertebrates

The structure and biomass of aquatic invertebrate communities play a crucial role in the matter dynamics of streams. However, biomass is rarely quantified in ecological assessments of streams, and little is known about the environmental and anthropogenic factors that influence it. In this study, we aimed to identify environmental factors that are associated with invertebrate structure and biomass through a monitoring of 25 streams across Germany. We identified invertebrates, assigned them to taxonomic and trait-based groups, and quantified biomass using image-based analysis. We found that insecticide pressure generally reduced the abundance of insecticide-vulnerable populations (R2 = 0.43 applying SPEARpesticides indicator), but not invertebrate biomass. In contrast, herbicide pressure reduced the biomass of several biomass aggregations. Especially, insecticide-sensitive populations, that were directly (algae feeder, R2 = 0.39) or indirectly (predators, R2 = 0.29) dependent on algae, were affected. This indicated a combined effect of possible food shortage due to herbicides and direct insecticide pressure. Specifically, all streams with increased herbicide pressure showed a reduced overall biomass share of Trichoptera from 43 % to 3 % and those of Ephemeroptera from 20 % to 3 % compared to streams grouped by low herbicide pressure. In contrast, insecticide-insensitive Gastropoda increased from 10 % to 45 %, and non-vulnerable leaf-shredding Crustacea increased from 10 % to 22 %. In summary, our results indicate that at the community level, the direct effects of insecticides and the indirect, food-mediated effects of herbicides exert a combined effect on the biomass of sensitive insect groups, thus disrupting food chains at ecosystem level.

Freezing-induced acidification of sea ice brine

The acidity of sea ice and snow plays a key role in the chemistry of the cryosphere; an important example lies in the photochemical catalytic release of reactive bromine in polar regions, facilitated at pHs below 6.5. We apply in-situ acid-base indicators to probe the microscopic acidity of the brine within the ice matrix in artificial sea water at a range of concentrations (0.35-70 PPT) and initial pHs (6-9). The results are supported by analogous measurements of the most abundant salts in seawater: NaCl, Na2SO4, and CaCO3. In the research herein, the acidity is expressed in terms of the Hammett acidity function, H2-. The obtained results show a pronounced acidity increase in sea water after freezing at -15 °C and during the subsequent cooling down to -50 °C. Importantly, we did not observe any significant hysteresis; the values of acidity upon warming markedly resembled those at the corresponding temperatures at cooling. The acidity increase is attributed to the minerals' crystallization, which is accompanied by a loss of the buffering capacity. Our observations show that lower salinity sea water samples (≤ 3.5 PPT) reach pH values below 6.5 at the temperature of -15 °C, whereas higher salinity ices attain such values only at -30 °C. The ensuing implications for polar chemistry and the relevance to the field measurements are discussed.

Plasma metals, genetic risk, and rapid kidney function decline among type 2 diabetes

BACKGROUND

Rapid kidney function decline (RKFD) is a main clinical feature of early chronic kidney disease (CKD) in type 2 diabetes (T2D). Environmental and genetic factors influencing RKFD remain inadequately elucidated.

OBJECTIVES

This study aimed to examine the associations of metals with RKFD among T2D and to further investigate the effect of metal mixtures on RKFD with the modifying effect of genetic susceptibility.

METHODS

This study included 2209 people with T2D (1942 had genotyping data) free of CKD at baseline from the Dongfeng-Tongji cohort. We used inductively coupled plasma-mass spectrometry (ICP-MS) to measure 23 metals in baseline plasma. Using elastic net (ENET), multivariate logistic regression, and Bayesian kernel machine regression (BKMR) model, we examined independent associations of multiple metals with RKFD. We calculated the environmental risk score (ERS) to assess the effects of metal mixtures on RKFD and the genetic risk score (GRS) to assess genetic susceptibility. RKFD was defined as estimated glomerular filtration rate (eGFR) loss > 3 mL/min/1.73 m2/year.

RESULTS

During a median of 9.8 years follow-up, 262 participants developed RKFD. Aluminum, vanadium, zinc, selenium, rubidium, tin, barium, and tungsten were screened from ENET. In multivariate logistic models, vanadium, selenium, and tungsten were negatively associated with RKFD, while zinc, tin, and rubidium were positively associated. The BKMR showed a nonlinear association of vanadium and rubidium with RKFD and interactions between metals (barium‑vanadium, barium‑rubidium). The ERS was positive associated with RKFD (per SD increase in ERS, OR = 1.94, 95% CI: 1.66, 2.27). No significant interaction between ERS and GRS was observed on RKFD, however, participants in the highest ERS and GRS group had the highest RKFD risk.

CONCLUSION

Vanadium and rubidium were associated with RKFD in T2D. Metal mixtures was associated with an increased risk of RKFD in T2D, particularly in those at high genetic risk.

Understanding the hydrological performance of green and grey roofs during winter in cold climate regions

Green and grey roofs have emerged as promising and sustainable measures for effectively managing stormwater in urban catchments. However, there is a gap in the literature in understanding and modelling the hydrological performance of these roofs during winter and snow-covered periods in cold climate regions. The present study attempted to address this gap by validating the use of a snow module in simulating the dynamics of snow accumulation and melting of green and grey roofs. Then, the validated model was used to identify and separate the different events that occur in winter (melt only, rainfall only, rain-on-snow) to assess the hydrological performance of six different configurations of green and grey roofs in Trondheim, Norway. The snow module accurately simulated snow accumulation and melting of green and grey roofs. The results showed that rain-on-snow events in winter have longer duration compared to other events including rainfall events in summer. Consequently, rain-on-snow events yield a higher amount of inflow to the roofs compared to rainfall events in summer, despite summer events having higher intensities. The retention and detention performances of green and grey roofs were found to be lowest for rain-on-snow events compared to other types of events, but still yielding significantly lower peak runoffs when compared to standard black roofs. The decrease in retention and detention performances in winter were attributed to the long duration of events, accumulation effect of snow, freezing of roof surface layers, and reduction of evapotranspiration. The study highlights the importance of considering winter conditions in the design of green and grey roofs in cold climates to enhance stormwater management.

Everything, altogether, all at once: Addressing data challenges when measuring speech intelligibility through entropy scores

When investigating unobservable, complex traits, data collection and aggregation processes can introduce distinctive features to the data such as boundedness, measurement error, clustering, outliers, and heteroscedasticity. Failure to collectively address these features can result in statistical challenges that prevent the investigation of hypotheses regarding these traits. This study aimed to demonstrate the efficacy of the Bayesian beta-proportion generalized linear latent and mixed model (beta-proportion GLLAMM) (Rabe-Hesketh et al., Psychometrika, 69(2), 167-90, 2004a, Journal of Econometrics, 128(2), 301-23, 2004c, 2004b; Skrondal and Rabe-Hesketh 2004) in handling data features when exploring research hypotheses concerning speech intelligibility. To achieve this objective, the study reexamined data from transcriptions of spontaneous speech samples initially collected by Boonen et al. (Journal of Child Language, 50(1), 78-103, 2023). The data were aggregated into entropy scores. The research compared the prediction accuracy of the beta-proportion GLLAMM with the normal linear mixed model (LMM) (Holmes et al., 2019) and investigated its capacity to estimate a latent intelligibility from entropy scores. The study also illustrated how hypotheses concerning the impact of speaker-related factors on intelligibility can be explored with the proposed model. The beta-proportion GLLAMM was not free of challenges; its implementation required formulating assumptions about the data-generating process and knowledge of probabilistic programming languages, both central to Bayesian methods. Nevertheless, results indicated the superiority of the model in predicting empirical phenomena over the normal LMM, and its ability to quantify a latent potential intelligibility. Additionally, the proposed model facilitated the exploration of hypotheses concerning speaker-related factors and intelligibility. Ultimately, this research has implications for researchers and data analysts interested in quantitatively measuring intricate, unobservable constructs while accurately predicting the empirical phenomena.

Methods for online calibration of Q-matrix and item parameters for polytomous responses in cognitive diagnostic computerized adaptive testing

The ability to rapidly provide examinees with detailed and effective diagnostic information is a critical topic in psychology. Knowing what diagnostic criteria the examinees have met enables the practitioner to seek the solution to help them in a timely manner, and this can be achieved by cognitive diagnostic computerized adaptive testing (CD-CAT). However, the pervasive challenge of replenishing items in the CD-CAT item bank limits its practical application. Online calibration is a means to address item replenishment, but in CD-CAT, most existing online calibration methods that jointly calibrate the Q-matrix and item parameters of the new items are developed only for dichotomous responses and are time-consuming. Notably, previous studies pay no attention to polytomously scored items that are frequently observed in testing, even though they can offer additional evidence for the examinees' diagnosis. To fill this gap, we propose a SCAD-based method (SCAD-EM) to calibrate the Q-matrix and item parameters of the new items with polytomous response data in order to promote the application of CD-CAT in practice. The performance of the SCAD-EM was investigated in two comprehensive simulation studies and compared against the revised single-item estimation method (SIE-BIC). Results indicated that the SCAD-EM produces a higher calibration accuracy for the category-level Q-matrix and is computationally more efficient across all conditions, but it produces a lower calibration accuracy for the item-level Q-matrix. An empirical study further demonstrated the utility of the SCAD-EM and the SIE-BIC methods in calibrating new items with a real dataset. The advantages of the proposed method, its limitations, and possible future research directions are offered at the end.

Sequential safe static and dynamic screening rule for accelerating support tensor machine

Support tensor machine (STM), as a higher-order extension of support vector machine, is adept at effectively addressing tensorial data classification problems, which maintains the inherent structure in tensors and mitigates the curse of dimensionality. However, it needs to resort to the alternating projection iterative technique, which is very time-consuming. To overcome this shortcoming, we propose an efficient sequential safe static and dynamic screening rule (SS-SDSR) for accelerating STM in this paper. Its main idea is to reduce every projection iterative sub-model by identifying and deleting the redundant variables before and during the training process without sacrificing accuracy. Its construction mainly consists of two parts: (1) The static screening rule and dynamic screening rule are first built based on the variational inequality and duality gap, respectively. (2) The sequential screening process is achieved by using the static screening rule with the different adjacent parameters and applying the dynamic screening rule under the same parameter. In the experiment, on the one hand, to verify the influence of different parameter intervals, screening frequencies, and forms of data on the effectiveness of our method, three experiments on artificial datasets are conducted, which indicate that our method is effective for any forms of data when the parameter interval is small and the screening frequency is appropriate. On the other hand, to demonstrate the feasibility and validity of our SS-SDSR, numerical experiments on eleven vector-based datasets, and six tensor-based datasets are conducted and compared with the other five algorithms. Experimental results illustrate the effectiveness and safety of our SS-SDSR.

Druggable genome-wide Mendelian randomization for identifying the role of integrated stress response in therapeutic targets of bipolar disorder

For bipolar disorder (BD), the inconsistency of treatment guidelines and the long phases of pharmacological adjustment remain major challenges. BD is known to be comorbid with many medical and psychiatric conditions and they may share inflammatory and stress-related aetiologies, which could give rise to this association. The integrated stress response (ISR) responds to various stress conditions that lead to alterations in cellular homeostasis. However, as a causative mechanism underlying cognitive deficits and neurodegeneration in a broad range of brain disorders, the impact of ISR on BD is understudied. Mendelian randomization has been widely used to repurpose licensed drugs and discover novel therapeutic targets. Thus, we aimed to identify novel therapeutic targets for BD and analyze their pathophysiological mechanisms, using the summary data-based Mendelian Randomization (SMR) and Bayesian colocalization (COLOC) methods to integrate the summary-level data of the GWAS on BD and the expression quantitative trait locus (eQTL) study in blood. We utilized the GWAS data including 41,917 BD cases and 371,549 controls from the Psychiatric Genomics Consortium and the eQTL data from 31,684 participants of predominantly European ancestry from the eQTLGen consortium. The SMR analysis identified the EIF2B5 gene that was associated with BD due to no linkage but pleiotropy or causality. The COLOC analysis strongly suggested that EIF2B5 and the trait of BD were affected by shared causal variants, and thus were colocalized. Utilizing data in EpiGraphDB we find other putative causal BD genes (EIF2AK4 and GSK3B) to prioritize potential alternative drug targets.

Comparing type 1 and type 2 error rates of different tests for heterogeneous treatment effects

Psychologists are increasingly interested in whether treatment effects vary in randomized controlled trials. A number of tests have been proposed in the causal inference literature to test for such heterogeneity, which differ in the sample statistic they use (either using the variance terms of the experimental and control group, their empirical distribution functions, or specific quantiles), and in whether they make distributional assumptions or are based on a Fisher randomization procedure. In this manuscript, we present the results of a simulation study in which we examine the performance of the different tests while varying the amount of treatment effect heterogeneity, the type of underlying distribution, the sample size, and whether an additional covariate is considered. Altogether, our results suggest that researchers should use a randomization test to optimally control for type 1 errors. Furthermore, all tests studied are associated with low power in case of small and moderate samples even when the heterogeneity of the treatment effect is substantial. This suggests that current tests for treatment effect heterogeneity require much larger samples than those collected in current research.

The English Sublexical Toolkit: Methods for indexing sound-spelling consistency

This work introduces the English Sublexical Toolkit, a suite of tools that utilizes an experience-dependent learning framework of sublexical knowledge to extract regularities from the English lexicon. The Toolkit quantifies the empirical regularity of sublexical units in both the reading and spelling directions (i.e., grapheme-to-phoneme and phoneme-to-grapheme) and at multiple grain sizes (i.e., phoneme/grapheme and onset/rime unit size). It can extract multiple experience-dependent regularity indices for words or pseudowords, including both frequency indices (e.g., grapheme frequency) and conditional probability indices (e.g., grapheme-to-phoneme probability). These tools provide (1) superior estimates of the regularities that better reflect the complexity of the sublexical system relative to previously published indices and (2) completely novel indices of sublexical units such as phonographeme frequency (i.e., combined units of individual phonemes and graphemes that are independent of processing direction). We demonstrate that measures from the toolkit explain significant amounts of variance in empirical data (naming of real words and lexical decision), and either outperform or are comparable to the best available consistency measures. The flexibility of the toolkit is further demonstrated by its ability to readily index the probability of different pseudowords pronunciations, and we report that the measures account for the majority of variance in these empirically observed probabilities. Overall, this work provides a framework and resources that can be flexibly used to identify optimal corpus-based consistency measures that help explain reading/spelling behaviors for real and pseudowords.

Cycling safely: Examining the factors associated with bicycle accidents in Seoul, South Korea

This study investigates the factors contributing to bicycle accidents, focusing on four types of bicycle lanes and other exposure and built environment characteristics of census blocks. Using Seoul as a case study, three years of bicycle accident spot data from 2018 to 2020 was collected, resulting in 1,330 bicycle accident spots and a total of 2,072 accidents. The geographically weighted Poisson regression (GWPR) model was used as a methodological approach to investigate the spatially varying relationships between the accident frequency and explanatory variables across the space, as opposed to the Poisson regression model. The results indicated that the GWPR model outperforms the global Poisson regression model in capturing unobserved spatial heterogeneity. For example, the value of deviance that determines the goodness of fit for a model was 0.244 for the Poisson regression model and 0.500 for the far better-fitting GWPR model. Further findings revealed that the factors affecting bicycle accidents have varying impacts depending on the location and distribution of accidents. For example, despite the presence of bicycle lanes, some census blocks, particularly in the northeast part of the city, still pose a risk for bicycle accidents. These findings can provide valuable insights for urban planners and policymakers in developing bicycle safety measures and regulations.

Machine learning-aided unveiling the relationship between chemical pretreatment and methane production of lignocellulosic waste

Chemical pretreatment is a common method to enhance the cumulative methane yield (CMY) of lignocellulosic waste (LW) but its effectiveness is subject to various factors, and accurate estimation of methane production of pretreated LW remains a challenge. Here, based on 254 LW samples, a machine learning (ML) model to predict the methane production performance of pretreated feedstock was constructed using two automated ML platforms (tree-based pipeline optimization tool and neural network intelligence). Furthermore, the interactive effects of pretreatment conditions, feedstock properties, and digestion conditions on methane production of pretreated LW were studied through model interpretability analysis. The optimal ML model performed well on the validation set, and the digestion time, pretreatment agent, and lignin content (LC) were found to be key factors affecting the methane production of pretreated LW. If the LC in the raw LW was lower than 15%, the maximum CMY might be achieved using the NaOH, KOH, and alkaline hydrogen peroxide (AHP) with concentrations of 3.8%, 4.4%, and 4.5%, respectively. On the other hand, if LC was higher than 15%, only high concentrations of AHP exceeding 4% could significantly increase methane production. This study provides valuable guidance for optimizing pretreatment process, comparing different chemical pretreatment approaches, and regulating the operation of large-scale biogas plants.

A causal counterfactual graph neural network for arising-from-chair abnormality detection in parkinsonians

The arising-from-chair task assessment is a key aspect of the evaluation of movement disorders in Parkinson's disease (PD). However, common scale-based clinical assessment methods are highly subjective and dependent on the neurologist's expertise. Alternate automated methods for arising-from-chair assessment can be established based on quantitative susceptibility mapping (QSM) images with multiple-instance learning. However, performance stability for such methods can be typically undermined by the presence of irrelevant or spuriously-relevant features that mask the intrinsic causal features. Therefore, we propose a QSM-based arising-from-chair assessment method using a causal graph-neural-network framework, where counterfactual and debiasing strategies are developed and integrated into this framework for capturing causal features. Specifically, the counterfactual strategy is proposed to suppress irrelevant features caused by background noise, by producing incorrect predictions when dropping causal parts. The debiasing strategy is proposed to suppress spuriously relevant features caused by the sampling bias and it comprises a resampling guidance scheme for selecting stable instances and a causal invariance constraint for improving stability under various interferences. The results of extensive experiments demonstrated the superiority of the proposed method in detecting arising-from-chair abnormalities. Its clinical feasibility was further confirmed by the coincidence between the selected causal features and those reported in earlier medical studies. Additionally, the proposed method was extensible for another motion task of leg agility. Overall, this study provides a potential tool for automated arising-from-chair assessment in PD patients, and also introduces causal counterfactual thinking in medical image analysis. Our source code is publicly available at https://github.com/SJTUBME-QianLab/CFGNN-PDarising.

Differential interactions between gene expressions and stressors across the lifespan in major depressive disorder

BACKGROUND

Both genetic predispositions and exposures to stressors have collectively contributed to the development of major depressive disorder (MDD). To deep dive into their roles in MDD, our study aimed to examine which susceptible gene expression interacts with various dimensions of stressors in the MDD risk among a large population cohort.

METHODS

Data analyzed were from a longitudinal community-based cohort from Southwest Montreal, Canada (N = 1083). Latent profile models were used to identify distinct patterns of stressors for the study cohort. A transcriptome-wide association study (TWAS) method was performed to examine the interactive effects of three dimensions of stressors (threat, deprivation, and cumulative lifetime stress) and gene expression on the MDD risk in a total of 48 tissues from GTEx. Additional analyses were also conducted to further explore and specify these associations including colocalization, and fine-mapping analyses, in addition to enrichment analysis investigations based on TWAS.

RESULTS

We identified 3321 genes linked to MDD at the nominal p-value <0.05 and found that different patterns of stressors can amplify the genetic susceptibility to MDD. We also observed specific genes and pathways that interacted with deprivation and cumulative lifetime stressors, particularly in specific brain tissues including basal ganglia, prefrontal cortex, brain amygdala, brain cerebellum, brain cortex, and the whole blood. Colocalization analysis also identified these genes as having a high probability of sharing MDD causal variants.

LIMITATIONS

The study cohort was composed exclusively of individuals of Caucasians, which restricts the generalizability of the findings to other ethnic population groups.

CONCLUSIONS

The findings of the study unveiled significant interactions between potential tissue-specific gene expression × stressors in the MDD risk and shed light on the intricate etiological attributes of gene expression and specific stressors across the lifespan in MDD. These genetic and environmental attributes in MDD corroborate the vulnerability-stress theory and direct future stress research to have a closer examination of genetic predisposition and potential involvements of omics studies to specify the intricate relationships between genes and stressful environments.

Bayesian hybrid gamma-GPD model for extreme traffic conflict threshold determination in the peak over threshold approach

Extreme value theory (EVT) models have been frequently utilized to estimate crash risk from traffic conflicts with the peak over threshold commonly used to identify conflict extremes. However, a common problem for the peak over threshold method is the selection of a suitable threshold to distinguish general and extreme conflicts. Subjective and arbitrary selection of the threshold in peak over threshold method can result in bias and unstable estimation results. The primary objective of the study is to propose a hybrid modelling approach for the threshold determination in peak over threshold method. The hybrid model consists of a joint gamma distribution and generalized Pareto distribution (GPD). The gamma distribution is used to fit general conflicts while the GPD is used to fit extreme conflicts. Specially, discontinued, continued and differentiable gamma-GPD models are developed with the threshold being treated as a model parameter. Traffic conflict data collected from three signalized intersections in the city of Surrey, British Columbia were used for the study. The modified time to collision (MTTC) was employed as conflict indicator. The Bayesian approach was employed to estimate the threshold as well as other hybrid gamma-GPD model parameters. The results show that the discontinued gamma-GPD model is superior to the continued and differentiable gamma-GPD models for determining the threshold in terms of crash estimation accuracy and model fit. The crash estimates using the threshold determined by the hybrid gamma-GPD model outperform those estimated based on the traditional quantile plots method, indicating that the superiority of the proposed threshold determination approach based on gamma-GPD hybrid model. The proposed hybrid gamma-GPD model could determine the threshold parameter in peak over threshold method for traffic conflicts extremes automatically in an objective and quantitative way. It contributes to existing peak over threshold method for producing reliable crash estimation.

High PEEP/low FiO2 ventilation is associated with lower mortality in COVID-19

RATIONALE

The positive end-expiratory pressure (PEEP) strategy in patients with coronavirus 2019 (COVID-19) acute respiratory distress syndrome (ARDS) remains debated. Most studies originate from the initial waves of the pandemic. Here we aimed to assess the impact of high PEEP/low FiO2 ventilation on outcomes during the second wave in the Netherlands.

METHODS

Retrospective observational study of invasively ventilated COVID-19 patients during the second wave. Patients were categorized based on whether they received high PEEP or low PEEP ventilation according to the ARDS Network tables. The primary outcome was ICU mortality, and secondary outcomes included hospital and 90-day mortality, duration of ventilation and length of stay, and the occurrence of kidney injury. Propensity matching was performed to correct for factors with a known relationship to ICU mortality.

RESULTS

This analysis included 790 COVID-ARDS patients. At ICU discharge, 32 (22.5%) out of 142 high PEEP patients and 254 (39.2%) out of 848 low PEEP patients had died (HR 0.66 [0.46-0.96]; P = 0.03). High PEEP was linked to improved secondary outcomes. Matched analysis did not change findings.

CONCLUSIONS

High PEEP ventilation was associated with improved ICU survival in patients with COVID-ARDS.

Piloting wastewater-based surveillance of norovirus in England

Wastewater-based epidemiology (WBE) gained widespread use as a tool for supporting clinical disease surveillance during the COVID-19 pandemic. There is now significant interest in the continued development of WBE for other pathogens of clinical significance. In this study, approximately 3,200 samples of wastewater from across England, previously collected for quantification of SARS-CoV-2, were re-analysed for the quantification of norovirus genogroup I (GI) and II (GII). Overall, GI and GII were detected in 93% and 98% of samples respectively, and at least one of the genogroups was detected in 99% of samples. GI was found at significantly lower concentrations than GII, but the proportion of each genogroup varied over time, with GI becoming more prevalent than GII in some areas towards the end of the study period (May 2021 - March 2022). Using relative strength indices (RSI), it was possible to study the trends of each genogroup, and total norovirus over time. Increases in norovirus levels appeared to coincide with the removal of COVID-19 related lockdown restrictions within England. Local Moran's I analyses indicated several localised outbreaks of both GI and GII across England, notably the possible GI outbreak in the north of England in early 2022. Comparisons of national average norovirus concentrations in wastewater against concomitant norovirus reported case numbers showed a significant linear relationship. This highlights the potential for wastewater-based monitoring of norovirus as a valuable approach to support surveillance of norovirus in communities.

An abbreviated Chinese dyslexia screening behavior checklist for primary school students using a machine learning approach

To increase early identification and intervention of dyslexia, a prescreening instrument is critical to identifying children at risk. The present work sought to shorten and validate the 30-item Mandarin Dyslexia Screening Behavior Checklist for Primary School Students (the full checklist; Fan et al., , 19, 521-527, 2021). Our participants were 15,522 Mandarin-Chinese-speaking students and their parents, sampled from classrooms in grades 2-6 across regions in mainland China. A machine learning approach (lasso regression) was applied to shorten the full checklist (Fan et al., , 19, 521-527, 2021), constructing grade-specific brief checklists first, followed by a compilation of the common brief checklist based on the similarity across grade-specific checklists. All checklists (the full, grade-specific brief, and common brief versions) were validated and compared with data in our sample and an external sample (N = 114; Fan et al., , 19, 521-527, 2021). The results indicated that the six-item common brief checklist showed consistently high reliability (αs > .82) and reasonable classification performance (about 60% prediction accuracy and 70% sensitivity), comparable to that of the full checklist and all grade-specific brief checklists across our current sample and the external sample from Fan et al., , 19, 521-527, (2021). Our analysis showed that 2.42 (out of 5) was the cutoff score that helped classify children's reading status (children who scored higher than 2.42 might be considered at risk for dyslexia). Our final product is a valid, accessible, common brief checklist for prescreening primary school children at risk for Chinese dyslexia, which can be used across grades and regions in mainland China.

Covariate-adjusted generalized pairwise comparisons in small samples

Semiparametric probabilistic index models allow for the comparison of two groups of observations, whilst adjusting for covariates, thereby fitting nicely within the framework of generalized pairwise comparisons (GPC). As with most regression approaches in this setting, the limited amount of data results in invalid inference as the asymptotic normality assumption is not met. In addition, separation issues might arise when considering small samples. In this article, we show that the parameters of the probabilistic index model can be estimated using generalized estimating equations, for which adjustments exist that lead to estimators of the sandwich variance-covariance matrix with improved finite sample properties and that can deal with bias due to separation. In this way, appropriate inference can be performed as is shown through extensive simulation studies. The known relationships between the probabilistic index and other GPC statistics allow to also provide valid inference for example, the net treatment benefit or the success odds.

Prefiltered component-based greedy (PreCoG) scan method

The spatial distribution of disease cases can provide important insights into disease spread and its potential risk factors. Identifying disease clusters correctly can help us discover new risk factors and inform interventions to control and prevent the spread of disease as quickly as possible. In this study, we propose a novel scan method, the Prefiltered Component-based Greedy (PreCoG) scan method, which efficiently and accurately detects irregularly shaped clusters using a prefiltered component-based algorithm. The PreCoG scan method's flexibility allows it to perform well in detecting both regularly and irregularly-shaped clusters. Additionally, it is fast to apply while providing high power, sensitivity, and positive predictive value for the detected clusters compared to other scan methods. To confirm the effectiveness of the PreCoG method, we compare its performance to many other scan methods. Additionally, we have implemented this method in the smerc R package to make it publicly available to other researchers. Our proposed PreCoG scan method presents a unique and innovative process for detecting disease clusters and can improve the accuracy of disease surveillance systems.

Characteristics and partitions of traditional and emerging organophosphate esters in soil and groundwater based on machine learning

Organophosphate esters (OPEs) pose hazards to both humans and the environment. This study applied target screening to analyze the concentrations and detection frequencies of OPEs in the soil and groundwater of representative contaminated sites in the Pearl River Delta. The clusters and correlation characteristics of OPEs in soil and groundwater were calculated by self-organizing map (SOM). The risk assessment and partitions of OPEs in industrial park soil and groundwater were conducted. The results revealed that 14 out of 23 types of OPEs were detected. The total concentrations (Σ23OPEs) ranged from 1.931 to 743.571 ng/L in the groundwater, and 0.218 to 79.578 ng/g in the soil, the former showed highly soluble OPEs with high detection frequencies and concentrations, whereas the latter exhibited the opposite trend. SOM analysis revealed that the distribution of OPEs in the soil differed significantly from that in the groundwater. In the industrial park, OPEs posed acceptable risks in both the soil and groundwater. The soil could be categorized into Zone I and II, and the groundwater into Zone I, II, and III, with corresponding management recommendations. Applying SOM to analyze the characteristics and partitions of OPEs may provide references for other new pollutants and contaminated sites.

Relationship between sleep, physical fitness, brain microstructure, and cognition in healthy older adults: A pilot study

BACKGROUND

There is a critical need for neuroimaging markers of brain integrity to monitor effects of modifiable lifestyle factors on brain health. This observational, cross-sectional study assessed relationships between brain microstructure and sleep, physical fitness, and cognition in healthy older adults.

METHODS

Twenty-three adults aged 60 and older underwent whole-brain multi-shell diffusion imaging, comprehensive cognitive testing, polysomnography, and exercise testing. Neurite Orientation Dispersion and Density Imaging (NODDI) was used to quantify neurite density (NDI) and orientation dispersion (ODI). Diffusion tensor imaging (DTI) was used to quantify axial diffusivity (AxD), fractional anisotropy (FA), mean diffusivity (MD), and radial diffusivity (RD). Relationships between sleep efficiency (SE), time and percent in N3 sleep, cognitive function, physical fitness (VO2 peak) and the diffusion metrics in regions of interest and the whole brain were evaluated.

RESULTS

Higher NDI in bilateral white and gray matter was associated with better executive functioning. NDI in the right anterior cingulate and adjacent white matter was positively associated with language skills. Higher NDI in the left posterior corona radiata was associated with faster processing speed. Physical fitness was positively associated with NDI in the left precentral gyrus and corticospinal tract. N3 % was positively associated with NDI in the left caudate and right pre- and postcentral gyri. Higher ODI in the left putamen and adjacent white matter was associated with better executive function.

CONCLUSION

NDI and ODI derived from NODDI are potential neuroimaging markers for associations between brain microstructure and modifiable risk factors in aging. If these associations are observable in clinical samples, NODDI could be incorporated into clinical trials assessing the effects of modifiable risk factors on brain integrity in aging and neurodegenerative diseases.

Applicability of machine learning techniques to analyze Microplastic transportation in open channels with different hydro-environmental factors

This research utilized machine learning to analyze experiments conducted in an open channel laboratory setting to predict microplastic transport with varying discharge, velocity, water depth, vegetation pattern, and microplastic density. Four machine learning (ML) models, incorporating Random Forest (RF), Decision Tree (DT), Extreme Gradient Boost (XGB) and K-Nearest Neighbor (KNN) algorithms, were developed and compared with the Linear Regression (LR) statistical model, using 75% of the data for training and 25% for validation. The predictions of ML algorithms were more accurate than the LR, while XGB and RF provided the best predictions. To explain the ML results, Explainable artificial intelligence (XAI) was employed by using Shapley Additive Explanations (SHAP) to predict the global behavior of variables. RF was the most reliable model, with a coefficient of correlation of 0.97 and a mean absolute percentage error of 1.8% after hyperparameter tuning. Results indicated that discharge, velocity, water depth, and vegetation all influenced microplastic transport. Discharge and vegetation enhanced and reduced microplastic transport, respectively, and showed a response to different vegetation patterns. A strong linear positive correlation (R2 = 0.8) was noted between microplastic density and retention. In the absence of dedicated microplastic transport analytical models and infeasibility of using classical sediment transport models in predicting microplastic transport, ML proved to be helpful. Moreover, the use of XAI will reduce the black-box nature of ML models with effective interpretation enhancing the trust of domain experts in ML predictions. The developed model offers a promising tool for real-world open channel predictions, informing effective management strategies to mitigate microplastic pollution.

Development of the APBD-SQ, a novel patient-reported outcome for health-related quality of life in adult polyglucosan body disease

Adult polyglucosan body disease (APBD) is a rare autosomal recessive glycogen storage disorder that leads to slowly progressive multi-organ dysfunction in adulthood. A novel disease-specific patient-reported outcome measure was created and administered to assess symptom burden and health-related quality of life (HR-QOL) in APBD. Thirty-six participants between 30 and 79 years of age (83% ≥60 years, 56% male) completed the anonymous questionnaire independently or with a caregiver proxy (75% self-report). Unemployment predicted an 18.3 (95% CI: 2.8, 33.8; p = 0.028) higher composite disease severity score and a 28.8 (95% CI: 8.2, 49.4; p = 0.010) higher composite HR-QOL score. Use of one or more assistive devices also predicted a 29.3 (95% CI: 8.3, 50.4; p = 0.011) higher composite disease severity score and a 41.8 (95% CI: 10.9, 72.8; p = 0.013) higher composite HR-QOL score. Proxy survey completion predicted a 19.4 (95% CI: 4.1, 34.7; p = 0.020) higher composite disease severity score compared to self-report. Older age at survey completion predicted a 27.4 higher composite HR-QOL score (95% CI: 2.5, 52.4; p = 0.039) for participants in their sixties compared to those between 30 and 59 years old. The development of the Adult Polyglucosan Body Disease questionnaire on Symptom burden and health-related Quality of life (APBD-SQ) marks an important stride forward in capturing the patient experience as a tool for disease monitoring and future research.

Renewable risk assessment of heterogeneous streaming time-to-event cohorts

The analysis of streaming time-to-event cohorts has garnered significant research attention. Most existing methods require observed cohorts from a study sequence to be independent and identically sampled from a common model. This assumption may be easily violated in practice. Our methodology operates within the framework of online data updating, where risk estimates for each cohort of interest are continuously refreshed using the latest observations and historical summary statistics. At each streaming stage, we introduce parameters to quantify the potential discrepancy between batch-specific effects from adjacent cohorts. We then employ penalized estimation techniques to identify nonzero discrepancy parameters, allowing us to adaptively adjust risk estimates based on current data and historical trends. We illustrate our proposed method through extensive empirical simulations and a lung cancer data analysis.

Addressing dispersion in mis-measured multivariate binomial outcomes: A novel statistical approach for detecting differentially methylated regions in bisulfite sequencing data

Motivated by a DNA methylation application, this article addresses the problem of fitting and inferring a multivariate binomial regression model for outcomes that are contaminated by errors and exhibit extra-parametric variations, also known as dispersion. While dispersion in univariate binomial regression has been extensively studied, addressing dispersion in the context of multivariate outcomes remains a complex and relatively unexplored task. The complexity arises from a noteworthy data characteristic observed in our motivating dataset: non-constant yet correlated dispersion across outcomes. To address this challenge and account for possible measurement error, we propose a novel hierarchical quasi-binomial varying coefficient mixed model, which enables flexible dispersion patterns through a combination of additive and multiplicative dispersion components. To maximize the Laplace-approximated quasi-likelihood of our model, we further develop a specialized two-stage expectation-maximization (EM) algorithm, where a plug-in estimate for the multiplicative scale parameter enhances the speed and stability of the EM iterations. Simulations demonstrated that our approach yields accurate inference for smooth covariate effects and exhibits excellent power in detecting non-zero effects. Additionally, we applied our proposed method to investigate the association between DNA methylation, measured across the genome through targeted custom capture sequencing of whole blood, and levels of anti-citrullinated protein antibodies (ACPA), a preclinical marker for rheumatoid arthritis (RA) risk. Our analysis revealed 23 significant genes that potentially contribute to ACPA-related differential methylation, highlighting the relevance of cell signaling and collagen metabolism in RA. We implemented our method in the R Bioconductor package called "SOMNiBUS."

Bayesian survival analysis with INLA

This tutorial shows how various Bayesian survival models can be fitted using the integrated nested Laplace approximation in a clear, legible, and comprehensible manner using the INLA and INLAjoint R-packages. Such models include accelerated failure time, proportional hazards, mixture cure, competing risks, multi-state, frailty, and joint models of longitudinal and survival data, originally presented in the article "Bayesian survival analysis with BUGS." In addition, we illustrate the implementation of a new joint model for a longitudinal semicontinuous marker, recurrent events, and a terminal event. Our proposal aims to provide the reader with syntax examples for implementing survival models using a fast and accurate approximate Bayesian inferential approach.

Integrative deep learning with prior assisted feature selection

Integrative analysis has emerged as a prominent tool in biomedical research, offering a solution to the "smalln $$ n $$ and largep $$ p $$ " challenge. Leveraging the powerful capabilities of deep learning in extracting complex relationship between genes and diseases, our objective in this study is to incorporate deep learning into the framework of integrative analysis. Recognizing the redundancy within candidate features, we introduce a dedicated feature selection layer in the proposed integrative deep learning method. To further improve the performance of feature selection, the rich previous researches are utilized by an ensemble learning method to identify "prior information". This leads to the proposed prior assisted integrative deep learning (PANDA) method. We demonstrate the superiority of the PANDA method through a series of simulation studies, showing its clear advantages over competing approaches in both feature selection and outcome prediction. Finally, a skin cutaneous melanoma (SKCM) dataset is extensively analyzed by the PANDA method to show its practical application.

Exploratory subgroup identification in the heterogeneous Cox model: A relatively simple procedure

For survival analysis applications we propose a novel procedure for identifying subgroups with large treatment effects, with focus on subgroups where treatment is potentially detrimental. The approach, termed forest search, is relatively simple and flexible. All-possible subgroups are screened and selected based on hazard ratio thresholds indicative of harm with assessment according to the standard Cox model. By reversing the role of treatment one can seek to identify substantial benefit. We apply a splitting consistency criteria to identify a subgroup considered "maximally consistent with harm." The type-1 error and power for subgroup identification can be quickly approximated by numerical integration. To aid inference we describe a bootstrap bias-corrected Cox model estimator with variance estimated by a Jacknife approximation. We provide a detailed evaluation of operating characteristics in simulations and compare to virtual twins and generalized random forests where we find the proposal to have favorable performance. In particular, in our simulation setting, we find the proposed approach favorably controls the type-1 error for falsely identifying heterogeneity with higher power and classification accuracy for substantial heterogeneous effects. Two real data applications are provided for publicly available datasets from a clinical trial in oncology, and HIV.

Differential network knockoff filter with application to brain connectivity analysis

The brain functional connectivity can typically be represented as a brain functional network, where nodes represent regions of interest (ROIs) and edges symbolize their connections. Studying group differences in brain functional connectivity can help identify brain regions and recover the brain functional network linked to neurodegenerative diseases. This process, known as differential network analysis focuses on the differences between estimated precision matrices for two groups. Current methods struggle with individual heterogeneity in measuring the brain connectivity, false discovery rate (FDR) control, and accounting for confounding factors, resulting in biased estimates and diminished power. To address these issues, we present a two-stage FDR-controlled feature selection method for differential network analysis using functional magnetic resonance imaging (fMRI) data. First, we create individual brain connectivity measures using a high-dimensional precision matrix estimation technique. Next, we devise a penalized logistic regression model that employs individual brain connectivity data and integrates a new knockoff filter for FDR control when detecting significant differential edges. Through extensive simulations, we showcase the superiority of our approach compared to other methods. Additionally, we apply our technique to fMRI data to identify differential edges between Alzheimer's disease and control groups. Our results are consistent with prior experimental studies, emphasizing the practical applicability of our method.

A multivariate to multivariate approach for voxel-wise genome-wide association analysis

The joint analysis of imaging-genetics data facilitates the systematic investigation of genetic effects on brain structures and functions with spatial specificity. We focus on voxel-wise genome-wide association analysis, which may involve trillions of single nucleotide polymorphism (SNP)-voxel pairs. We attempt to identify underlying organized association patterns of SNP-voxel pairs and understand the polygenic and pleiotropic networks on brain imaging traits. We propose a bi-clique graph structure (ie, a set of SNPs highly correlated with a cluster of voxels) for the systematic association pattern. Next, we develop computational strategies to detect latent SNP-voxel bi-cliques and an inference model for statistical testing. We further provide theoretical results to guarantee the accuracy of our computational algorithms and statistical inference. We validate our method by extensive simulation studies, and then apply it to the whole genome genetic and voxel-level white matter integrity data collected from 1052 participants of the human connectome project. The results demonstrate multiple genetic loci influencing white matter integrity measures on splenium and genu of the corpus callosum.

Risk Variants Associated With Normal Pressure Hydrocephalus: Genome-Wide Association Study in the FinnGen Cohort

BACKGROUND AND OBJECTIVES

Large-scale genome-wide studies of chronic hydrocephalus have been lacking. We conducted a genome-wide association study (GWAS) in normal pressure hydrocephalus (NPH).

METHODS

We used a case-control study design implementing FinnGen data containing 473,691 Finns with genotypes and nationwide health records. Patients with NPH were selected based on ICD-10 G91.2 diagnosis. To select patients with idiopathic NPH (iNPH) for sensitivity analysis, we excluded patients with a potentially known etiology of the condition using an algorithm on their disease history. The controls were the remaining non-hydrocephalic participants. For a replication analysis, the NPH cohort from UK Biobank (UKBB) was used.

RESULTS

We included 1,522 patients with NPH (mean age 72.2 years, 53% women) and 451,091 controls (mean age 60.5 years, 44% women). In the GWAS comparing patients with NPH with the controls, we identified 6 gene regions significantly (p < 5.0e-8) associated with NPH that replicated in a meta-analysis with UKBB (NPH n = 173). The top loci near the following genes were rs7962263, SLCO1A2 (odds ratio [OR] 0.71, 95% CI 0.65-0.78, p = 1.0e-14); rs798495, AMZ1/GNA12 (OR 1.29, 95% CI 1.20-1.39, p = 2.9e-12); rs10828247, MLLT10 (OR 0.77, 95% CI 0.71-0.83, p = 1.5e-11); rs561699566 and rs371919113, CDCA2 (OR 0.76, 95% CI 0.70-0.82, p = 1.5e-11); rs56023709, C16orf95 (OR 1.24, 95% CI 1.16-1.33, p = 3.0e-9); and rs62434144, PLEKHG1 (OR 1.23, 95% CI 1.14-1.32, p = 1.4e-8). In the sensitivity analysis comparing only patients with iNPH (n = 1,055) with the controls (n = 451,091), 4 top loci near the following genes remained significant: rs7962263, SLCO1A2 (OR 0.70, 95% CI 0.63-0.78, p = 2.1e-11); rs10828247, MLLT10 (OR 0.74, 95% CI 0.62-0.82, p = 4.6e-10); rs798511, AMZ1/GNA12 (OR 1.28, 95% CI 1.17-1.39, p = 1.7e-8); and rs56023709, C16orf95 (OR 1.28, 95% CI 1.17-1.39, p = 1.7e-8).

DISCUSSION

We identified 6 loci significantly associated with NPH in the thus far largest GWAS in chronic hydrocephalus. The genes near the top loci have previously been associated with blood-brain barrier and blood-CSF barrier function and with increased lateral brain ventricle volume. The effect sizes and allele frequencies remained similar in NPH and iNPH cohorts, indicating the identified loci are risk determinants for iNPH and likely not explained by associations with other etiologies. However, the exact role of these loci is still unknown, warranting further studies.

Observational evidence for groundwater influence on crop yields in the United States

As climate change shifts crop exposure to dry and wet extremes, a better understanding of factors governing crop response is needed. Recent studies identified shallow groundwater-groundwater within or near the crop rooting zone-as influential, yet existing evidence is largely based on theoretical crop model simulations, indirect or static groundwater data, or small-scale field studies. Here, we use observational satellite yield data and dynamic water table simulations from 1999 to 2018 to provide field-scale evidence for shallow groundwater effects on maize yields across the United States Corn Belt. We identify three lines of evidence supporting groundwater influence: 1) crop model simulations better match observed yields after improvements in groundwater representation; 2) machine learning analysis of observed yields and modeled groundwater levels reveals a subsidy zone between 1.1 and 2.5 m depths, with yield penalties at shallower depths and no effect at deeper depths; and 3) locations with groundwater typically in the subsidy zone display higher yield stability across time. We estimate an average 3.4% yield increase when groundwater levels are at optimum depth, and this effect roughly doubles in dry conditions. Groundwater yield subsidies occur ~35% of years on average across locations, with 75% of the region benefitting in at least 10% of years. Overall, we estimate that groundwater-yield interactions had a net monetary contribution of approximately $10 billion from 1999 to 2018. This study provides empirical evidence for region-wide groundwater yield impacts and further underlines the need for better quantification of groundwater levels and their dynamic responses to short- and long-term weather conditions.

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.