Artificial intelligence classifies primary progressive aphasia from connected speech

Neurodegenerative dementia syndromes, such as primary progressive aphasias (PPA), have traditionally been diagnosed based, in part, on verbal and non-verbal cognitive profiles. Debate continues about whether PPA is best divided into three variants and regarding the most distinctive linguistic features for classifying PPA variants. In this cross-sectional study, we initially harnessed the capabilities of artificial intelligence and natural language processing to perform unsupervised classification of short, connected speech samples from 78 pateints with PPA. We then used natural language processing to identify linguistic features that best dissociate the three PPA variants. Large language models discerned three distinct PPA clusters, with 88.5% agreement with independent clinical diagnoses. Patterns of cortical atrophy of three data-driven clusters corresponded to the localization in the clinical diagnostic criteria. In the subsequent supervised classification, 17 distinctive features emerged, including the observation that separating verbs into high- and low-frequency types significantly improved classification accuracy. Using these linguistic features derived from the analysis of short, connected speech samples, we developed a classifier that achieved 97.9% accuracy in classifying the four groups (three PPA variants and healthy controls). The data-driven section of this study showcases the ability of large language models to find natural partitioning in the speech of patients with PPA consistent with conventional variants. In addition, the work identifies a robust set of language features indicative of each PPA variant, emphasizing the significance of dividing verbs into high- and low-frequency categories. Beyond improving diagnostic accuracy, these findings enhance our understanding of the neurobiology of language processing.

Experience shapes wild boar spatial response to drive hunts

Human-induced disturbances of the environment are rapid and often unpredictable in space and time, exposing wildlife to strong selection pressure favouring plasticity in specific traits. Measuring wildlife behavioural plasticity in response to human-induced disturbances such as hunting pressures is crucial in understanding population expansion in the highly plastic wild boar species. We collected GPS-based movement data from 55 wild boars during drive hunts over three hunting seasons (2019-2022) in the Czech Republic and Sweden to identify behavioural plasticity in space use and movement strategies over a range of experienced hunting disturbances. Daily distance, daily range, and daily range overlap with hunting area were not affected by hunting intensity but were clearly related to wild boar hunting experience. On average, the post-hunt flight distance was 1.80 km, and the flight duration lasted 25.8 h until they returned to their previous ranging area. We detected no relationship in flight behaviour to hunting intensity or wild boar experience. Wild boar monitored in our study showed two behavioural responses to drive hunts, "remain" or "leave". Wild boars tended to "leave" more often with increasing hunting experience. Overall, this study highlights the behavioural plasticity of wild boar in response to drive hunts.

Development of QSARs for cysteine-containing di- and tripeptides with antioxidant activity:influence of the cysteine position

Antioxidants agents play an essential role in the food industry for improving the oxidative stability of food products. In the last years, the search for new natural antioxidants has increased due to the potential high toxicity of chemical additives. Therefore, the synthesis and evaluation of the antioxidant activity in peptides is a field of current research. In this study, we performed a Quantitative Structure Activity Relationship analysis (QSAR) of cysteine-containing 19 dipeptides and 19 tripeptides. The main objective is to bring information on the relationship between the structure of peptides and their antioxidant activity. For this purpose, 1D and 2D molecular descriptors were calculated using the PaDEL software, which provides information about the structure, shape, size, charge, polarity, solubility and other aspects of the compounds. Different QSAR model for di- and tripeptides were developed. The statistic parameters for di-peptides model (R2train = 0.947 and R2test = 0.804) and for tripeptide models (R2train = 0.923 and R2test = 0.847) indicate that the generated models have high predictive capacity. Then, the influence of the cysteine position was analyzed predicting the antioxidant activity for new di- and tripeptides, and comparing them with glutathione. In dipeptides, excepting SC, TC and VC, the activity increases when cysteine is at the N-terminal position. For tripeptides, we observed a notable increase in activity when cysteine is placed in the N-terminal position.

Malaria seroepidemiology in very low transmission settings in the Peruvian Amazon

Despite progress towards malaria reduction in Peru, measuring exposure in low transmission areas is crucial for achieving elimination. This study focuses on two very low transmission areas in Loreto (Peruvian Amazon) and aims to determine the relationship between malaria exposure and proximity to health facilities. Individual data was collected from 38 villages in Indiana and Belen, including geo-referenced households and blood samples for microscopy, PCR and serological analysis. A segmented linear regression model identified significant changes in seropositivity trends among different age groups. Local Getis-Ord Gi* statistic revealed clusters of households with high (hotspots) or low (coldspots) seropositivity rates. Findings from 4000 individuals showed a seropositivity level of 2.5% (95%CI: 2.0%-3.0%) for P. falciparum and 7.8% (95%CI: 7.0%-8.7%) for P. vivax, indicating recent or historical exposure. The segmented regression showed exposure reductions in the 40-50 age group (β1 = 0.043, p = 0.003) for P. vivax and the 50-60 age group (β1 = 0.005, p = 0.010) for P. falciparum. Long and extreme distance villages from Regional Hospital of Loreto exhibited higher malaria exposure compared to proximate and medium distance villages (p < 0.001). This study showed the seropositivity of malaria in two very low transmission areas and confirmed the spatial pattern of hotspots as villages become more distant.

Five years of Hospital at Home adoption in Catalonia: impact, challenges, and proposals for quality assurance

BACKGROUND

Hospital at home (HaH) was increasingly implemented in Catalonia (7.7 M citizens, Spain) achieving regional adoption within the 2011-2015 Health Plan. This study aimed to assess population-wide HaH outcomes over five years (2015-2019) in a consolidated regional program and provide context-independent recommendations for continuous quality improvement of the service.

METHODS

A mixed-methods approach was adopted, combining population-based retrospective analyses of registry information with qualitative research. HaH (admission avoidance modality) was compared with a conventional hospitalization group using propensity score matching techniques. We evaluated the 12-month period before the admission, the hospitalization, and use of healthcare resources at 30 days after discharge. A panel of experts discussed the results and provided recommendations for monitoring HaH services.

RESULTS

The adoption of HaH steadily increased from 5,185 episodes/year in 2015 to 8,086 episodes/year in 2019 (total episodes 31,901; mean age 73 (SD 17) years; 79% high-risk patients. Mortality rates were similar between HaH and conventional hospitalization within the episode [76 (0.31%) vs. 112 (0.45%)] and at 30-days after discharge [973(3.94%) vs. 1112(3.24%)]. Likewise, the rates of hospital re-admissions at 30 days after discharge were also similar between groups: 2,00 (8.08%) vs. 1,63 (6.58%)] or ER visits [4,11 (16.62%) vs. 3,97 (16.03%). The 27 hospitals assessed showed high variability in patients' age, multimorbidity, severity of episodes, recurrences, and length of stay of HaH episodes. Recommendations aiming at enhancing service delivery were produced.

CONCLUSIONS

Besides confirming safety and value generation of HaH for selected patients, we found that this service is delivered in a case-mix of different scenarios, encouraging hospital-profiled monitoring of the service.

Using Generative Artificial Intelligence to Classify Primary Progressive Aphasia from Connected Speech

Neurodegenerative dementia syndromes, such as Primary Progressive Aphasias (PPA), have traditionally been diagnosed based in part on verbal and nonverbal cognitive profiles. Debate continues about whether PPA is best subdivided into three variants and also regarding the most distinctive linguistic features for classifying PPA variants. In this study, we harnessed the capabilities of artificial intelligence (AI) and natural language processing (NLP) to first perform unsupervised classification of concise, connected speech samples from 78 PPA patients. Large Language Models discerned three distinct PPA clusters, with 88.5% agreement with independent clinical diagnoses. Patterns of cortical atrophy of three data-driven clusters corresponded to the localization in the clinical diagnostic criteria. We then used NLP to identify linguistic features that best dissociate the three PPA variants. Seventeen features emerged as most valuable for this purpose, including the observation that separating verbs into high and low-frequency types significantly improves classification accuracy. Using these linguistic features derived from the analysis of brief connected speech samples, we developed a classifier that achieved 97.9% accuracy in predicting PPA subtypes and healthy controls. Our findings provide pivotal insights for refining early-stage dementia diagnosis, deepening our understanding of the characteristics of these neurodegenerative phenotypes and the neurobiology of language processing, and enhancing diagnostic evaluation accuracy.

Exploration of semen quality analyzed by casa-mot systems of brahman bulls infected with BLV and BHV-1

Enzootic bovine leukosis virus (BLV) and bovine herpesvirus 1 (BHV-1) are very important infectious agents for the livestock industry worldwide. The present study aimed to explore the association between natural exposure to BLV and BHV-1 with sperm quality analyzed by Computer-Assisted Semen Analysis (CASA) systems. Ten sexually mature Brahman bulls, with sanitary status BLV+/BHV-1+ (n = 2), BLV-/BHV-1+ (n = 6) and BLV-/BHV-1- (n = 2) were evaluated twice, 30 days apart. Results showed that sanitary status of each bull was not associated with semen quality. It was found that the quality of the semen from the second collection was better due to the interruption of sexual rest. The evidence thus revealed that a bull infected with BLV generated good-quality contaminated semen and, therefore, that it is essential to detect contaminated seminal samples to prevent the spread of BLV. A multivariate analysis showed the presence of four sperm subpopulations in Brahman bulls that differ significantly in their kinematic patterns and with respect to sanitary status (P < 0.05), indicating that infection-free and seronegative bulls present the best kinematic parameters, which improved discrimination of sperm quality according to sanitary status. Overall, the analyses indicate that the seropositive-infected bulls with BLV and BHV-1 should be excluded from beef cattle farms.

Morphometric assessment of cryopreserved livestock bull spermatozoa in the tropics

The identification of different morphometric patterns of spermatozoa serves as a basis for improving our understanding of the diversity in an ejaculate and to relate them to the potential fertility of males. In this study, we aimed to examine the semen subpopulation structure, following dilution in semen of extenders, using a mathematical approach a possible application to fertility analyses. Ten sexually mature Bos taurus bulls were randomly allotted to one of three groups: (1) Tris-citric acid-egg yolk extender (Tris-EY); (2) commercial egg yolk extender OptiXcell® and (3) commercial egg yolk extender Triladyl®. The results showed significant differences (p < .05) between extenders in terms of values for head size and head shape variables of individual sperm, indicating an influence of extender composition. Sperm head width was found to significantly differ (p < .05) according to the extender, decreasing in the following order: OptiXcell® (4.836 ± 0.017 μm), Triladyl® (4.695 ± 0.012 μm) and Tris-EY (4.638 ± 0.010 μm). Principal component analysis allowed us to identify two subpopulations in OptiXcell®, and three subpopulations were each found in Triladyl® and Tris-EY. Overall, we observed significant differences between sperm subpopulations within each extender (p < .05), with differences in sperm head size and shape between bovine species that can be related to functionality and fertility capabilities.

Disclosing transcriptomics network-based signatures of glioma heterogeneity using sparse methods

Gliomas are primary malignant brain tumors with poor survival and high resistance to available treatments. Improving the molecular understanding of glioma and disclosing novel biomarkers of tumor development and progression could help to find novel targeted therapies for this type of cancer. Public databases such as The Cancer Genome Atlas (TCGA) provide an invaluable source of molecular information on cancer tissues. Machine learning tools show promise in dealing with the high dimension of omics data and extracting relevant information from it. In this work, network inference and clustering methods, namely Joint Graphical lasso and Robust Sparse K-means Clustering, were applied to RNA-sequencing data from TCGA glioma patients to identify shared and distinct gene networks among different types of glioma (glioblastoma, astrocytoma, and oligodendroglioma) and disclose new patient groups and the relevant genes behind groups' separation. The results obtained suggest that astrocytoma and oligodendroglioma have more similarities compared with glioblastoma, highlighting the molecular differences between glioblastoma and the others glioma subtypes. After a comprehensive literature search on the relevant genes pointed our from our analysis, we identified potential candidates for biomarkers of glioma. Further molecular validation of these genes is encouraged to understand their potential role in diagnosis and in the design of novel therapies.

Absence of enterotypes in the human gut microbiomes reanalyzed with non-linear dimensionality reduction methods

Enterotypes of the human gut microbiome have been proposed to be a powerful prognostic tool to evaluate the correlation between lifestyle, nutrition, and disease. However, the number of enterotypes suggested in the literature ranged from two to four. The growth of available metagenome data and the use of exact, non-linear methods of data analysis challenges the very concept of clusters in the multidimensional space of bacterial microbiomes. Using several published human gut microbiome datasets of variable 16S rRNA regions, we demonstrate the presence of a lower-dimensional structure in the microbiome space, with high-dimensional data concentrated near a low-dimensional non-linear submanifold, but the absence of distinct and stable clusters that could represent enterotypes. This observation is robust with regard to diverse combinations of dimensionality reduction techniques and clustering algorithms.

Clinical Profiles and Patterns of Neurodegeneration in Amyotrophic Lateral Sclerosis: A Cluster-Based Approach Based on MR Imaging Metrics

BACKGROUND AND PURPOSE

The previous studies described phenotype-associated imaging findings in amyotrophic lateral sclerosis (ALS) with a prior categorization of patients based on clinical characteristics. We investigated the natural segregation of patients through a radiologic cluster-based approach without a priori patient categorization using 3 well-known prognostic MR imaging biomarkers in ALS, namely bilateral precentral and paracentral gyrus cortical thickness and medulla oblongata volume. We aimed to identify clinical/prognostic features that are cluster-associated.

MATERIALS AND METHODS

Bilateral precentral and paracentral gyri and medulla oblongata volume were calculated using FreeSurfer in 90 patients with amyotrophic lateral sclerosis and 25 healthy controls. A 2-step cluster analysis was performed using precentral and paracentral gyri (averaged pair-wise) and medulla oblongata volume.

RESULTS

We identified 3 radiologic clusters: 28 (31%) patients belonged to "cluster-1"; 51 (57%), to "cluster 2"; and 11 (12%), to "cluster 3." Patients in cluster 1 showed statistically significant cortical thinning of the analyzed cortical areas and lower medulla oblongata volume compared with subjects in cluster 2 and cluster 3, respectively. Patients in cluster 3 exhibited significant cortical thinning of both paracentral and precentral gyri versus those in cluster 2, and this latter cluster showed lower medulla oblongata volume than cluster 3. Patients in cluster 1 were characterized by older age, higher female prevalence, greater disease severity, higher progression rate, and lower survival compared with patients in clusters 2 and 3.

CONCLUSIONS

Patients with amyotrophic lateral sclerosis spontaneously segregate according to age and sex-specific patterns of neurodegeneration. Some patients with amyotrophic lateral sclerosis showed an early higher impairment of cortical motor neurons with relative sparing of bulbar motor neurons (cluster 3), while others expressed an opposite pattern (cluster 2). Moreover, 31% of patients showed an early simultaneous impairment of cortical and bulbar motor neurons (cluster 1), and they were characterized by higher disease severity and lower survival.

Prevention of Unplanned Hospital Admissions in Multimorbid Patients Using Computational Modeling: Observational Retrospective Cohort Study

BACKGROUND

Enhanced management of multimorbidity constitutes a major clinical challenge. Multimorbidity shows well-established causal relationships with the high use of health care resources and, specifically, with unplanned hospital admissions. Enhanced patient stratification is vital for achieving effectiveness through personalized postdischarge service selection.

OBJECTIVE

The study has a 2-fold aim: (1) generation and assessment of predictive models of mortality and readmission at 90 days after discharge; and (2) characterization of patients' profiles for personalized service selection purposes.

METHODS

Gradient boosting techniques were used to generate predictive models based on multisource data (registries, clinical/functional and social support) from 761 nonsurgical patients admitted in a tertiary hospital over 12 months (October 2017 to November 2018). K-means clustering was used to characterize patient profiles.

RESULTS

Performance (area under the receiver operating characteristic curve, sensitivity, and specificity) of the predictive models was 0.82, 0.78, and 0.70 and 0.72, 0.70, and 0.63 for mortality and readmissions, respectively. A total of 4 patients' profiles were identified. In brief, the reference patients (cluster 1; 281/761, 36.9%), 53.7% (151/281) men and mean age of 71 (SD 16) years, showed 3.6% (10/281) mortality and 15.7% (44/281) readmissions at 90 days following discharge. The unhealthy lifestyle habit profile (cluster 2; 179/761, 23.5%) predominantly comprised males (137/179, 76.5%) with similar age, mean 70 (SD 13) years, but showed slightly higher mortality (10/179, 5.6%) and markedly higher readmission rate (49/179, 27.4%). Patients in the frailty profile (cluster 3; 152/761, 19.9%) were older (mean 81 years, SD 13 years) and predominantly female (63/152, 41.4%, males). They showed medical complexity with a high level of social vulnerability and the highest mortality rate (23/152, 15.1%), but with a similar hospitalization rate (39/152, 25.7%) compared with cluster 2. Finally, the medical complexity profile (cluster 4; 149/761, 19.6%), mean age 83 (SD 9) years, 55.7% (83/149) males, showed the highest clinical complexity resulting in 12.8% (19/149) mortality and the highest readmission rate (56/149, 37.6%).

CONCLUSIONS

The results indicated the potential to predict mortality and morbidity-related adverse events leading to unplanned hospital readmissions. The resulting patient profiles fostered recommendations for personalized service selection with the capacity for value generation.

The prometastatic relevance of tumor-infiltrating B lymphocytes in laryngeal squamous cell carcinoma

Objectives

Laryngeal squamous cell carcinomas (LSCCs) typically have an excellent prognosis for stage I tumors but a significant risk of locoregional and distant recurrence for intermediate to advanced disease. This study will investigate the clinical relevance of the tumor microenvironment in a large cohort of treatment-naïve patients affected by stage II-IV LSCC.

Methods

Whole slide-based digital pathology analysis was applied to measure six immune cell populations identified by immunohistochemistry (IHC) staining for CD3, CD8, CD20, CD66b, CD163 and CD38. Survival analysis was performed by Cox proportional hazards models and unsupervised hierarchical clustering using the k-means method. Double IHC staining and in-situ hybridisation by RNAscope allowed further analysis of a protumoral B cell population.

Results

A cohort of 98 patients was enrolled and analysed. The cluster of immune-infiltrated LSCCs demonstrated a significantly worse disease-specific survival rate. We also discovered a new association between high CD20⁺ B cells and a greater risk of distant recurrence. The phenotypic analysis of infiltrating CD20⁺ B cells showed a naïve (BCL6^-CD27^-Mum1^-) regulatory phenotype, producing TGFβ but not IL10, according to an active TGFβ pathway, as proved by positive pSMAD2 staining.

Conclusion

The identification of regulatory B cells in the context of LSCC, along with the activation of the TGFβ pathway, could provide the basis for new trials investigating the efficacy of already available molecules targeting the TGFβ pathway in the treatment of LSCC.

How to select the optimal monitoring locations for an aerobiological network: A case of study in central northwest of Spain

Airborne pollen concentration varies depending on several factors, such as local plant biodiversity, geography and climatology. These particles are involved in triggering pollinosis in a share of worldwide human population, and adequate monitoring is, therefore, important. However, the pollen traps in aerobiological monitoring networks are usually installed in cities, and the features of the whole territory are not taken into account. The aim of this study was to analyze what environmental parameters are more suitable as regards setting up monitoring stations throughout a territory in order to obtain an aerobiological network that can represent environmental diversity. The analysis was carried out in 13 locations in Castilla y León over an 8 year period. This is a favorable territory in which to conduct this type of study owing to its climatic features, orography and biodiversity. The ten most abundant pollen types in the region were analyzed, and a clustering analysis was calculated with different distances so as to obtain homogeneous groups of stations. Moreover, the clusters obtained were analyzed in combination with altitudinal and different bioclimatic parameters, which derived from temperature and precipitation. The result here shows that the Castilla y León aerobiological network RACYL represents most of the environmental variability of the territory. Furthermore, it can be divided into two clusters and five sub-clusters for which the start of the main pollen season is different. This corresponds with the division of the territory as regards bioclimatic conditions. The most important bioclimatic parameters were the seasonality of the precipitation and the maximum temperature of the warmest month, although orography must also be taken into account. All of these help discover the optimal places in which to install traps and could reduce the number of monitoring stations. This study additionally provides data for unmonitored areas with similar bioclimatic conditions to those monitored.

Clusters of anatomical disease-burden patterns in ALS: a data-driven approach confirms radiological subtypes

Amyotrophic lateral sclerosis (ALS) is associated with considerable clinical heterogeneity spanning from diverse disability profiles, differences in UMN/LMN involvement, divergent progression rates, to variability in frontotemporal dysfunction. A multitude of classification frameworks and staging systems have been proposed based on clinical and neuropsychological characteristics, but disease subtypes are seldom defined based on anatomical patterns of disease burden without a prior clinical stratification. A prospective research study was conducted with a uniform imaging protocol to ascertain disease subtypes based on preferential cerebral involvement. Fifteen brain regions were systematically evaluated in each participant based on a comprehensive panel of cortical, subcortical and white matter integrity metrics. Using min–max scaled composite regional integrity scores, a two-step cluster analysis was conducted. Two radiological clusters were identified; 35.5% of patients belonging to ‘Cluster 1’ and 64.5% of patients segregating to ‘Cluster 2’. Subjects in Cluster 1 exhibited marked frontotemporal change. Predictor ranking revealed the following hierarchy of anatomical regions in decreasing importance: superior lateral temporal, inferior frontal, superior frontal, parietal, limbic, mesial inferior temporal, peri-Sylvian, subcortical, long association fibres, commissural, occipital, ‘sensory’, ‘motor’, cerebellum, and brainstem. While the majority of imaging studies first stratify patients based on clinical criteria or genetic profiles to describe phenotype- and genotype-associated imaging signatures, a data-driven approach may identify distinct disease subtypes without a priori patient categorisation. Our study illustrates that large radiology datasets may be potentially utilised to uncover disease subtypes associated with unique genetic, clinical or prognostic profiles.

COVID-19 and mental disorders in healthcare Personnel: A novel framework to develop Personas from an online survey

BACKGROUND

In this paper we propose a novel framework for the definition of Personas for healthcare workers based on an online survey, with the aim of highlighting different levels of risk of developing mental disorders induced by COVID-19 and tailor psychological support interventions.

METHODS

Data were gathered from Italian healthcare workers between April and May 2020. Information about socio-demographic characteristics, current lifestyle, occupational, COVID-19 infection, and psychological indexes (Maslach Burnout Inventory, Impact of Event Scale and Patient Health Questionnaire) was collected. Respondents were divided in four subgroups based on their health profession: physicians (P), nurses (N), other medical professionals (OMP) and technical-administrative (TA). For each sub-group, collected variables (46) were reduced using Principal Component Analysis and clustered by means of k-medoids clustering. Statistical analysis was then applied to define which variables were able to differentiate among the k clusters, leading to the generation of a Persona card (i.e., a template with textual and graphical information) for each of the obtained clusters.

RESULTS

From the 538 respondents (153 P, 175 N, 176 OMP, 344 TA), the highest stress level, workload impact and risk of mental disorders were found in the N subgroup. Two clusters were identified for P, three clusters for N, two for OMP and one for TA.

CONCLUSIONS

The proposed framework was able to stratify different risk levels of possible development of mental health issues in healthcare workers due to COVID-19. This approach could represent the first step towards the development of mobile health tools to tailor psychological interventions in pandemic situations.

Maxmin Data Range Heuristic-Based Initial Centroid Method of Partitional Clustering for Big Data Mining

The centroid-based clustering algorithm depends on the number of clusters, initial centroid, distance measures, and statistical approach of central tendencies. The initial centroid initialization algorithm defines convergence speed, computing efficiency, execution time, scalability, memory utilization, and performance issues for big data clustering. Nowadays various researchers have proposed the cluster initialization techniques, where some initialization techniques reduce the number of iterations with the lowest cluster quality, and some initialization techniques increase the cluster quality with high iterations. For these reasons, this study proposed the initial centroid initialization based Maxmin Data Range Heuristic (MDRH) method for K-Means (KM) clustering that reduces the execution times, iterations, and improves quality for big data clustering. The proposed MDRH method has compared against the classical KM and KM++ algorithms with four real datasets. The MDRH method has achieved better effectiveness and efficiency over RS, DB, CH, SC, IS, and CT quantitative measurements.

SARS-CoV-2 Nsp16 activation mechanism and a cryptic pocket with pan-coronavirus antiviral potential

Coronaviruses have caused multiple epidemics in the past two decades, in addition to the current COVID-19 pandemic that is severely damaging global health and the economy. Coronaviruses employ between 20 and 30 proteins to carry out their viral replication cycle, including infection, immune evasion, and replication. Among these, nonstructural protein 16 (Nsp16), a 2'-O-methyltransferase, plays an essential role in immune evasion. Nsp16 achieves this by mimicking its human homolog, CMTr1, which methylates mRNA to enhance translation efficiency and distinguish self from other. Unlike human CMTr1, Nsp16 requires a binding partner, Nsp10, to activate its enzymatic activity. The requirement of this binding partner presents two questions that we investigate in this manuscript. First, how does Nsp10 activate Nsp16? Although experimentally derived structures of the active Nsp16/Nsp10 complex exist, structures of inactive, monomeric Nsp16 have yet to be solved. Therefore, it is unclear how Nsp10 activates Nsp16. Using over 1 ms of molecular dynamics simulations of both Nsp16 and its complex with Nsp10, we investigate how the presence of Nsp10 shifts Nsp16's conformational ensemble to activate it. Second, guided by this activation mechanism and Markov state models, we investigate whether Nsp16 adopts inactive structures with cryptic pockets that, if targeted with a small molecule, could inhibit Nsp16 by stabilizing its inactive state. After identifying such a pocket in SARS-CoV2 Nsp16, we show that this cryptic pocket also opens in SARS-CoV1 and MERS but not in human CMTr1. Therefore, it may be possible to develop pan-coronavirus antivirals that target this cryptic pocket.

Relationship between Fertility Traits and Kinematics in Clusters of Boar Ejaculates

Simple Summary

Swine reproduction efficiency is determined by the fertility potential of the sow and sperm quality. The objective of this study is to compare boar sperm motility and kinematic features to evaluate their relationships with reproductive success after artificial insemination (AI). In this study, the movement patterns of boar ejaculates were analyzed by a computer-assisted semen analysis (CASA)-Mot system, and the kinematic values of ejaculate clusters were assessed. The semen of the Pietrain boars showed more linear trajectory of the spermatozoa, while curvilinear velocity and oscillatory movement characterized the semen of the Duroc × Pietrain boars. The offspring of sows inseminated with Pietrain boars showed significantly lower number of stillbirths. In addition, ejaculate grouping into clusters did not have a predictive capacity on litter size variables. Nevertheless, the kinematic variables of the ejaculate may have a predictive, albeit reduced, capacity regarding litter size variables. The results of this study therefore open up possibilities for future assessments of fertility.

Abstract

The aim was to determine the relationship between kinematic parameters of boar spermatozoa and fertility rates of sow, as well as to assess the effect of sperm clusters on the fertility capacity of the ejaculate. Semen samples were collected from 11 sexually mature boars. Samples were analyzed by an ISAS^®v1 CASA-Mot system for eight kinematic parameters. Ejaculate clusters were characterized using multivariate procedures, such as principal factors (PFs) analysis and clustering methods (the k-means model). Four different ejaculate clusters were identified from two kinematic PFs which involved linear trajectory and velocity. There were differences (p < 0.05) between the sperm kinematic variables by sire line. There was no statistical difference (p > 0.05) between dam lines and ejaculate clusters in fertility variables. The discriminant ability of the different kinematics of sperm variables to predict litter size fertility was analyzed using receiver operating characteristics (ROC) curve analysis. Curvilinear velocity (VCL), average path velocity (VAP), amplitude of lateral head displacement (ALH), and beat-cross frequency (BCF) showed significant, albeit limited, predictive capacity for litter size fertility variables (range: 0.55–0.58 area under curve, AUC). The kinematic analysis of the ejaculates in clusters did not have a predictive capacity for litter size variables.

Moderating effects of academic involvement in web-based learning management system success: A multigroup analysis

While several educational institutions in India, in accordance to global practices, have adopted Web-Based Learning Management Systems (WLMS) to supplement classroom courses, it is largely seen that these WLMSs fail in their objectives, leading to little or no return on investments. The study aims to define the factors that affect students’ acceptance of a web-based learning management system and test the moderating effect of their academic involvement in the success of a WLMS. 477 valid questionnaires were collected from university/college students to empirically test the research model using the structural equation modelling approach. The results concludes that indirect and direct effects account for 49% of the variation in the intention to use, which is explained by technical system quality, information quality, educational quality, service quality of the technical support team and user satisfaction. High academic involvement moderates the impact of different service qualities of the WLMS on user satisfaction, intention to use the system, and success of the WLMS. Based on the findings, theoretical and managerial implications are discussed.

Solution Selection from a Pareto Optimal Set of Multi-Objective Reservoir Operation via Clustering Operation Processes and Objective Values

Multi-objective evolutionary algorithms (MOEAs) are widely used to optimize multi-purpose reservoir operations. Considering that most outcomes of MOEAs are Pareto optimal sets with a large number of incomparable solutions, it is not a trivial task for decision-makers (DMs) to select a compromise solution for application purposes. Due to the increasing popularity of data-driven decision-making, we introduce a clustering-based decision-making method into the multi-objective reservoir operation optimization problem. Traditionally, solution selection has been conducted based on trade-off ranking in objective space, and solution characteristics in decision space have been ignored. In our work, reservoir operation processes were innovatively clustered into groups with unique properties in decision space, and the trade-off surfaces were analyzed via clustering in objective space. To attain a suitable performance, a new similarity measure, referred to as the Mei–Wang fluctuation similarity measure (MWFSM), was tailored to reservoir operation processes. This method describes time series in terms of both their shape and quantitative variation. Then, a compromise solution was selected via the joint use of two clustering results. A case study of the Three Gorges cascade reservoirs system under small and medium floods was investigated to verify the applicability of the proposed method. The results revealed that the MWFSM effectively distinguishes reservoir operation processes. Two more operation patterns with similar positions but different shapes were identified via MWFSM when compared with Euclidean distance and the dynamic time warping method. Furthermore, the proposed method decreased the selection range from the whole Pareto optimal set to a set containing relatively few solutions. Finally, a compromise solution was selected.

Ecological drivers switch from bottom-up to top-down during model microbial community successions

Bottom-up selection has an important role in microbial community assembly but is unable to account for all observed variance. Other processes like top-down selection (e.g., predation) may be partially responsible for the unexplained variance. However, top-down processes and their interaction with bottom-up selective pressures often remain unexplored. We utilised an in situ marine biofilm model system to test the effects of bottom-up (i.e., substrate properties) and top-down (i.e., large predator exclusion via 100 µm mesh) selective pressures on community assembly over time (56 days). Prokaryotic and eukaryotic community compositions were monitored using 16 S and 18 S rRNA gene amplicon sequencing. Higher compositional variance was explained by growth substrate in early successional stages, but as biofilms mature, top-down predation becomes progressively more important. Wooden substrates promoted heterotrophic growth, whereas inert substrates' (i.e., plastic, glass, tile) lack of degradable material selected for autotrophs. Early wood communities contained more mixotrophs and heterotrophs (e.g., the total abundance of Proteobacteria and Euglenozoa was 34% and 41% greater within wood compared to inert substrates). Inert substrates instead showed twice the autotrophic abundance (e.g., cyanobacteria and ochrophyta made up 37% and 10% more of the total abundance within inert substrates than in wood). Late native (non-enclosed) communities were mostly dominated by autotrophs across all substrates, whereas high heterotrophic abundance characterised enclosed communities. Late communities were primarily under top-down control, where large predators successively pruned heterotrophs. Integrating a top-down control increased explainable variance by 7-52%, leading to increased understanding of the underlying ecological processes guiding multitrophic community assembly and successional dynamics.

SARS-CoV-2 Nsp16 activation mechanism and a cryptic pocket with pan-coronavirus antiviral potential

Coronaviruses have caused multiple epidemics in the past two decades, in addition to the current COVID-19 pandemic that is severely damaging global health and the economy. Coronaviruses employ between twenty and thirty proteins to carry out their viral replication cycle including infection, immune evasion, and replication. Among these, nonstructural protein 16 (Nsp16), a 2'-O-methyltransferase, plays an essential role in immune evasion. Nsp16 achieves this by mimicking its human homolog, CMTr1, which methylates mRNA to enhance translation efficiency and distinguish self from other. Unlike human CMTr1, Nsp16 requires a binding partner, Nsp10, to activate its enzymatic activity. The requirement of this binding partner presents two questions that we investigate in this manuscript. First, how does Nsp10 activate Nsp16? While experimentally-derived structures of the active Nsp16/Nsp10 complex exist, structures of inactive, monomeric Nsp16 have yet to be solved. Therefore, it is unclear how Nsp10 activates Nsp16. Using over one millisecond of molecular dynamics simulations of both Nsp16 and its complex with Nsp10, we investigate how the presence of Nsp10 shifts Nsp16's conformational ensemble in order to activate it. Second, guided by this activation mechanism and Markov state models (MSMs), we investigate if Nsp16 adopts inactive structures with cryptic pockets that, if targeted with a small molecule, could inhibit Nsp16 by stabilizing its inactive state. After identifying such a pocket in SARS-CoV-2 Nsp16, we show that this cryptic pocket also opens in SARS-CoV-1 and MERS, but not in human CMTr1. Therefore, it may be possible to develop pan-coronavirus antivirals that target this cryptic pocket.

STATEMENT OF SIGNIFICANCE

Coronaviruses are a major threat to human health. These viruses employ molecular machines, called proteins, to infect host cells and replicate. Characterizing the structure and dynamics of these proteins could provide a basis for designing small molecule antivirals. In this work, we use computer simulations to understand the moving parts of an essential SARS-CoV-2 protein, understand how a binding partner turns it on and off, and identify a novel pocket that antivirals could target to shut this protein off. The pocket is also present in other coronaviruses but not in the related human protein, so it could be a valuable target for pan-coronavirus antivirals.

Expression Changes in Epigenetic Gene Pathways Associated With One-Carbon Nutritional Metabolites in Maternal Blood From Pregnancies Resulting in Autism and Non-Typical Neurodevelopment

The prenatal period is a critical window for the development of autism spectrum disorder (ASD). The relationship between prenatal nutrients and gestational gene expression in mothers of children later diagnosed with ASD or non-typical development (Non-TD) is poorly understood. Maternal blood collected prospectively during pregnancy provides insights into the effects of nutrition, particularly one-carbon metabolites, on gene pathways and neurodevelopment. Genome-wide transcriptomes were measured with microarrays in 300 maternal blood samples in Markers of Autism Risk in Babies-Learning Early Signs. Sixteen different one-carbon metabolites, including folic acid, betaine, 5'-methyltretrahydrofolate (5-MeTHF), and dimethylglycine (DMG) were measured. Differential expression analysis and weighted gene correlation network analysis (WGCNA) were used to compare gene expression between children later diagnosed as typical development (TD), Non-TD and ASD, and to one-carbon metabolites. Using differential gene expression analysis, six transcripts (TGR-AS1, SQSTM1, HLA-C, and RFESD) were associated with child outcomes (ASD, Non-TD, and TD) with genome-wide significance. Genes nominally differentially expressed between ASD and TD significantly overlapped with seven high confidence ASD genes. WGCNA identified co-expressed gene modules significantly correlated with 5-MeTHF, folic acid, DMG, and betaine. A module enriched in DNA methylation functions showed a suggestive protective association with folic acid/5-MeTHF concentrations and ASD risk. Maternal plasma betaine and DMG concentrations were associated with a block of co-expressed genes enriched for adaptive immune, histone modification, and RNA processing functions. These results suggest that the prenatal maternal blood transcriptome is a sensitive indicator of gestational one-carbon metabolite status and changes relevant to children's later neurodevelopmental outcomes. LAY SUMMARY: Pregnancy is a time when maternal nutrition could interact with genetic risk for autism spectrum disorder. Blood samples collected during pregnancy from mothers who had a prior child with autism were examined for gene expression and nutrient metabolites, then compared to the diagnosis of the child at age three. Expression differences in gene pathways related to the immune system and gene regulation were observed for pregnancies of children with autism and non-typical neurodevelopment and were associated with maternal nutrients.

Robust information for effective municipal solid waste policies: Identifying behaviour of waste generation across spatial levels of organization

Existing studies have studied influencing factors of MSW generation behaviour at different spatial levels of organization, but always one at a time and not simultaneously. Income is a strong influencing factor, affecting MSW generation from the individual to the country level, capable of hiding the effects of the others. This study shows that when MSW generation behaviour is holistically analysed across multiple levels of organization (individuals, households, and communities) hierarchically organized as functional units of MSW generation within a specific study area, it is possible to identify influencing factors in addition to income (education, demographic, health, ethnic, economic activity and financial types) as explanatory variables. Increasing the number of influencing factors of MSW generation makes it possible to create a robust knowledge base for MSW management policies in fast-growing urban areas of developing countries, improving the information used to select proper policies and plans within their MSW management systems and avoiding overlapping policies causing legal gaps. Betania, an urban area of the Panama City district, has been chosen as a case study area. The results show that the household income explains 86% of its memberś MSW generation and the community indigenous population explains 21% of householdś MSW generation. It is concluded that MSW generation is not linear across levels, it has as many degrees of freedom as influencing factors shaping the levels of organization where functional units generating waste exist. Influencing factors appearing at each spatial level affects MSW generation in an interdependent manner in variable degrees of magnitude.

Energy metabolism involved in fertility of the wheat TCMS line YS3038

In the wheat TCMS line YS3038, the anther development is inhibited from late uninucleate stage to the binucleate stage. The disruption of energy metabolism pathways by aberrant transcriptional regulation causes the male sterility under low temperatures. The utilization of thermosensitive male sterile (TMS) lines provides a basis for two-line breeding. Previous work, including morphological and cytological observations, has shown that the development process of the TMS line YS3038 is inhibited from the late uninucleate stage to the binucleate stage. Transcriptomics studies could now help to elucidate the overall expression of related genes in a specific reproductive process, revealing the metabolic network and its regulatory mechanism of the reproductive process from the transcription level. Considering the fertility characteristics of YS3038, three important stages for transcriptome analysis were determined to be the early uninucleate, late uninucleate and binucleate stages. The number of differentially expressed genes (DEGs) was found to be highest in the binucleate stage, and most were related to energy metabolism. Quantitative PCR analysis of selected genes related to energy metabolism revealed that their expression patterns were consistent with the sequencing results. Analysis of the fertility mechanism of YS3038 showed that although the tapetum of anthers was degraded in advance of the tetrad stage, the development of microspores did not result in obvious abnormalities until the binucleate stage, because the genes involved in energy metabolism pathways, including starch and sucrose metabolism (SSM), glycolysis, the tricarboxylic acid (TCA) cycle, oxidative phosphorylation, and respiration electron transport chain are differentially expressed under sterile and fertile conditions. Therefore, the pollen in YS3038 was sterile.

Alzheimer's disease clinical variants show distinct regional patterns of neurofibrillary tangle accumulation

The clinical spectrum of Alzheimer's disease (AD) extends well beyond the classic amnestic-predominant syndrome. The previous studies have suggested differential neurofibrillary tangle (NFT) burden between amnestic and logopenic primary progressive aphasia presentations of AD. In this study, we explored the regional distribution of NFT pathology and its relationship to AD presentation across five different clinical syndromes. We assessed NFT density throughout six selected neocortical and hippocampal regions using thioflavin-S fluorescent microscopy in a well-characterized clinicopathological cohort of pure AD cases enriched for atypical clinical presentations. Subjects underwent apolipoprotein E genotyping and neuropsychological testing. Main cognitive domains (executive, visuospatial, language, and memory function) were assessed using an established composite z score. Our results showed that NFT regional burden aligns with the clinical presentation and region-specific cognitive scores. Cortical, but not hippocampal, NFT burden was higher among atypical clinical variants relative to the amnestic syndrome. In analyses of specific clinical variants, logopenic primary progressive aphasia showed higher NFT density in the superior temporal gyrus (p = 0.0091), and corticobasal syndrome showed higher NFT density in the primary motor cortex (p = 0.0205) relative to the amnestic syndrome. Higher NFT burden in the angular gyrus and CA1 sector of the hippocampus were independently associated with worsening visuospatial dysfunction. In addition, unbiased hierarchical clustering based on regional NFT densities identified three groups characterized by a low overall NFT burden, high overall burden, and cortical-predominant burden, respectively, which were found to differ in sex ratio, age, disease duration, and clinical presentation. In comparison, the typical, hippocampal sparing, and limbic-predominant subtypes derived from a previously proposed algorithm did not reproduce the same degree of clinical relevance in this sample. Overall, our results suggest domain-specific functional consequences of regional NFT accumulation. Mapping these consequences presents an opportunity to increase understanding of the neuropathological framework underlying atypical clinical manifestations.

A primer in artificial intelligence in cardiovascular medicine

Driven by recent developments in computational power, algorithms and web-based storage resources, machine learning (ML)-based artificial intelligence (AI) has quickly gained ground as the solution for many technological and societal challenges. AI education has become very popular and is oversubscribed at Dutch universities. Major investments were made in 2018 to develop and build the first AI-driven hospitals to improve patient care and reduce healthcare costs. AI has the potential to greatly enhance traditional statistical analyses in many domains and has been demonstrated to allow the discovery of 'hidden' information in highly complex datasets. As such, AI can also be of significant value in the diagnosis and treatment of cardiovascular disease, and the first applications of AI in the cardiovascular field are promising. However, many professionals in the cardiovascular field involved in patient care, education or science are unaware of the basics behind AI and the existing and expected applications in their field. In this review, we aim to introduce the broad cardiovascular community to the basics of modern ML-based AI and explain several of the commonly used algorithms. We also summarise their initial and future applications relevant to the cardiovascular field.

Assisting radiologists with reporting urgent findings to referring physicians: A machine learning approach to identify cases for prompt communication

Radiologists are expected to expediently communicate critical and unexpected findings to referring clinicians to prevent delayed diagnosis and treatment of patients. However, competing demands such as heavy workload along with lack of administrative support resulted in communication failures that accounted for 7% of the malpractice payments made from 2004 to 2008 in the United States. To address this problem, we have developed a novel machine learning method that can automatically and accurately identify cases that require prompt communication to referring physicians based on analyzing the associated radiology reports. This semi-supervised learning approach requires a minimal amount of manual annotations and was trained on a large multi-institutional radiology report repository from three major external healthcare organizations. To test our approach, we created a corpus of 480 radiology reports from our own institution and double-annotated cases that required prompt communication by two radiologists. Our evaluation on the test corpus achieved an F-score of 74.5% and recall of 90.0% in identifying cases for prompt communication. The implementation of the proposed approach as part of an online decision support system can assist radiologists in identifying radiological cases for prompt communication to referring physicians to avoid or minimize potential harm to patients.

Theoretical Studies on the Engagement of Interleukin 18 in the Immuno-Inflammatory Processes Underlying Atherosclerosis

Interleukin 18 (IL-18) is one of the pro-inflammatory cytokines expressed by macrophages, suggesting that it plays important physiological and immunological functions, among the others: stimulation of natural killers (NKs) and T cells to interferon gamma (IFN- γ ) synthesis. IL-18 was originally identified as interferon gamma inducing factor and now it is recognized as multifunctional cytokine, which has a role in regulation of innate and adaptive immune responses. Therefore, in order to investigate IL-18 contribution to the immuno-inflammatory processes underlying atherosclerosis, a systems approach has been used in our studies. For this purpose, a model of the studied phenomenon, including selected pathways, based on the Petri-net theory, has been created and then analyzed. Two pathways of IL-18 synthesis have been distinguished: caspase 1-dependent pathway and caspase 1-independent pathway. The analysis based on t-invariants allowed for determining interesting dependencies between IL-18 and different types of macrophages: M1 are involved in positive regulation of IL-18, while M2 are involved in negative regulation of IL-18. Moreover, the obtained results showed that IL-18 is produced more often via caspase 1-independent pathway than caspase 1-dependent pathway. Furthermore, we found that this last pathway may be associated with caspase 8 action.