Browse Articles

Transcriptomic analysis reinforces the implication of spatacsin in neuroinflammation and neurodevelopment

Refining potential energy surface through dynamical properties via differentiable molecular simulation

Public attitude and media governance of biometric information dissemination in the era of digital intelligence

Early attainment of 20-hydroxyecdysone threshold shapes mosquito sexual dimorphism in developmental timing

Analyzing the relationship between gene expression and phenotype in space-flown mice using a causal inference machine learning ensemble

Exercise induces dynamic changes in intra-articular metabolism and inflammation associated with remodeling of the infrapatellar fat pad in mice

DeepLabCut custom-trained model and the refinement function for gait analysis

Unlocking the Key to Photocatalytic Hydrogen Production Using Electronic Mediators for Z-Scheme Water Splitting

Explainable human-centered traits from head motion and facial expression dynamics

We explore the efficacy of multimodal behavioral cues for explainable prediction of personality and interview-specific traits. We utilize elementary head-motion units named kinemes, atomic facial movements termed action units and speech features to estimate these human-centered traits. Empirical results confirm that kinemes and action units enable discovery of multiple trait-specific behaviors while also enabling explainability in support of the predictions. For fusing cues, we explore decision and feature-level fusion, and an additive attention-based fusion strategy which quantifies the relative importance of the three modalities for trait prediction. Examining various long-short term memory (LSTM) architectures for classification and regression on the MIT Interview and First Impressions Candidate Screening (FICS) datasets, we note that: (1) Multimodal approaches outperform unimodal counterparts, achieving the highest PCC of 0.98 for Excited-Friendly traits in MIT and 0.57 for Extraversion in FICS; (2) Efficient trait predictions and plausible explanations are achieved with both unimodal and multimodal approaches, and (3) Following the thin-slice approach, effective trait prediction is achieved even from two-second behavioral snippets. Our implementation code is available at: https://github.com/deepsurbhi8/Explainable_Human_Traits_Prediction.

The severity of coronary heart disease and the incidence of later diabetic retinopathy in diabetic population: A retrospective cohort study

Objectives The coronary heart disease (CHD) can influence the development of several diseases. The presence of CHD is correlated to a higher incidence of concurrent diabetic retinopathy (DR) in previous study. Herein, we aim to analyze the relationship between the CHD severity and following DR with different severity. Methods A retrospective cohort study was conducted with the usage of Taiwan National Health Insurance Research Database (NHIRD). The CHD patients with DM were categorized into those with medical treatments and those received percutaneous coronary intervention (PCI) management with a 1:1 ratio. The major outcome was the development of DR, diabetic macular edema (DME) and proliferative diabetic retinopathy (PDR) 6 months after the onset of CHD. Results There was 7317, 316, and 386 episodes of DR, DME and PDR in the mild CHD groups and 8568, 411, and 508 events of DR, DME and PDR in the severe CHD groups, respectively. The severe CHD group showed a significantly higher incidence of DR (aHR: 1.063, 95% CI: 1.038–1.089, P = 0.0324), DME (aHR: 1.412, 95% CI: 1.252–1.594, P = 0.0092) and PDR (aHR: 1.314, 95% CI: 1.172–1.473, P = 0.0113) compared to the mild CHD group. The cumulative incidence of DR was significantly higher in the severe CHD group (P < 0.001). In the subgroup analysis, the association between CHD severity and DR was more prominent in the female population (P = 0.0224). Conclusions The severe CHD is associated with higher incidence of following DR, DME and PDR, while the incidence of DR in CHD is positively correlated to longer disease period.

Intersection between individual, household, environmental and system level factors in defining risk and resilience for children in Kenya’s ASAL: A qualitative study

Introduction Children growing up in arid and semi-arid regions of Sub-Saharan Africa (SSA) face heightened risks, often resulting in poor developmental outcomes. In Kenya, the arid and semi-arid lands (ASAL) exhibit the lowest health and developmental indicators among children. Despite these risks, some children grow up successfully and overcome the challenges. However, there is limited comprehensive data on sources of risks and resilience in these children, particularly research that incorporates community perspectives and indigenous knowledge. Systematic documentation of factors influencing child outcomes is crucial for understanding the overall burden, informing policy and implementing targeted interventions. This study aimed at bridging this gap. Methods The study was conducted in 10 ASAL counties in Kenya. Purposive and snowballing techniques were used to recruit 11 key informants per site with varied levels of involvement in early childhood development and primary caregivers. Using a semi-structured interview guide, 103 telephonic interviews were conducted between June and August 2022, involving 68 key informants and 35 caregivers. Thematic approach was used to analyze the data, using NVIVO software. Results The mean age of the participants was 44years (SD = 11 years). The findings, viewed through the lens of Bronfenbrenner’s ecological systems theory, reveal a complex interplay of contextual factors impacting children’s development. These factors range from individual and biological aspects to family, community, systemic, and environmental level, including climatic challenges. Identified risk factors encompassed issues such as young caregiver’s age, low literacy, mental health issues, drug abuse, domestic violence, malnutrition, poverty, lack of paternal involvement, early marriages, female genital mutilation, drought, heat stress, and insecurity. Nonetheless, sources of resilience include breastfeeding, immunization, responsive caregiving, family and community support, higher socio-economic status (SES), cultural practices, self-motivation, hard work, community role models, religious activities and quality service provision. Conclusion Urgent attention is needed to address the multifaceted challenges faced by children in ASAL regions. The study underscores the importance of addressing the root causes of risks while harnessing community strengths and resources to safeguard and promote the holistic development of these children.

Composition and functional diversity of soil and water microbial communities in the rice-crab symbiosis system

Rice-crab co-culture is an environmentally friendly agricultural and aquaculture technology with high economic and ecological value. In order to clarify the structure and function of soil and water microbial communities in the rice-crab symbiosis system, the standard rice-crab field with a ring groove was used as the research object. High-throughput sequencing was performed with rice field water samples to analyze the species and abundance differences of soil bacteria and fungi. The results showed that the OTU richness and community diversity in soil were significantly higher than those in water, while there were significant differences in soil microbial diversity and OTU richness in water sediments. The dominant species at the bacterial phylum level were Amoebacteria, Cyanobacteria, Actinomycetes, Synechococcus and Greenbacteria, and at the genus level the dominant species were norank_f_norank_o_Chloroplast, unclassified_f_Rhodobacteraceae, LD29, Cyanobium_PCC-6307, and norank_f_MWH-UniP1_aquatic_group. The dominant species at the fungal phylum level are unclassified_k_Fungi, Ascomycota, Rozellomycota, Phaeomycota and Stenotrophomonas, and at the genus level the dominant species are unclassified_k_Fungi, unclassified_p_Rozellomycota, Metschnikowia, Cladosporium, unclassified_p_Chytridiomycota. The dominant phylum may rely on mechanisms such as organic matter catabolism, secretion of secondary metabolites and phototrophic autotrophy, as predicted by functional gene analysis. The main functional genes are related to metabolic functions, including secondary product metabolism, energy metabolism, and amino acid metabolism.

What are the challenges that social prescribers face when supporting people within dementia and how can these be addressed? A qualitative study

Introduction Evidence suggests that social prescribing might have a positive impact on identity, control, creativity and quality of life in people with dementia. While evidence on the benefits of social prescribing is accumulating, there is a sparsity of research on the experiences of social prescribers. This study aims to identify the challenges that social prescribers face when supporting people with dementia and their families and strategies to address these. Materials and methods A qualitative study involving 24 social prescribers from all regions in England. Semi-structured interviews investigated challenges and strategies that social prescribers experience in their own practice. Data were analysed through thematic analysis. Results on “Challenges” and the respective “Strategies” are presented in a chronological order that reflects the different stages of contact with and support for the client with dementia, from referral to discharge. Results and discussion This study identified unique barriers that social prescribers face when working with people with dementia, particularly around communication, motivation, engagement and overdependency. It identified person and system-level strategies that can be used to address these challenges. These include expanding opportunities for dementia training, offering in-person support, including social prescribing in annual dementia reviews, and increasing integration of services within Integrated Care Systems and collaborations between health care service providers and with the third sector. Improving delivery and effectiveness of services is crucial to ensure that social prescribing fulfils its ethos of personalised care approach for all, including people with dementia, as envisioned in the NHS long term plan.

Neural plasticity in early potters: Shape analysis and TMS-EEG co-registration trace the rise of a new motor skill

In this study, we explored the biocultural mechanisms underlying ancient craft behaviours. Archaeological methods were integrated with neuroscience techniques to explore the impact on neuroplasticity resulting from the introduction of early pottery techniques. The advent of ceramic marked a profound change in the economy and socio-cultural dynamics of past societies. It may have also played a central role in developing new craft skills that influenced the neural plasticity of the potters. Coiling, one of the most widespread neolithic techniques, requires precise hand movements and the ability to regulate finger pressure to shape the clay without deformation. In a pilot study involving intensive training in neolithic pottery, we used TMS-EEG co-registration to monitor a group of participants and we examined the shape of the artefacts they made before and after training. Our findings suggest changes in the functional properties of the primary motor cortex (M1) responsible for the control and execution of actions. We also observed an improvement in symmetry and consistency of the artefacts and a significant reduction in errors. This multidisciplinary approach sheds light on the mechanisms of material culture’s variation in the archaeological field and provides promising insights into the co-evolution of technology and human skill.

FBXW7 metabolic reprogramming inhibits the development of colon cancer by down-regulating the activity of arginine/mToR pathways

FBXW7 is a tumor suppressor gene that regulates metabolism and is associated with the onset and progression of colorectal cancer (CRC)), however, the precise mechanism whereby FBXW7 participates in the metabolic reprogramming of CRC remains unclear. Here, the research aims to reveal the association between the expression of FBXW7 and clinical variables and to investigate the molecular mechanism by which FBXW7 plays a critical role in the development of CRC. The clinical importance of FBXW7 in CRC was determined by immunohistochemistry. Non-targeted metabolomics was utilized to explore the role of FBXW7 in the metabolic regulation of CRC. Low expression of FBXW7 was associated with poor prognosis in individuals with CRC, both at the mRNA and protein levels. FBXW7 over-expression inhibited CRC cell growth, colony formation, migration, and invasion. Non-targeted metabolomics unveiled that FBXW7 over-expression directly caused the deprivation of arginine which led to downmodulation of mTOR signaling pathway; meanwhile, FBXW7-related metabolites were primarily concentrated in the mTOR signaling pathway. In summary, the research identified a novel mechanism of action of FBXW7 in CRC. The research findings provide a theoretical foundation for the prognostic prediction and therapeutic planning of CRC based on metabolic reprogramming.

Unhealthy behaviours associated with uncontrolled hypertension among adults in India- Insights from a national survey

Background Non-communicable diseases (NCDs) are governed by a cluster of unhealthy behaviours and their determinants, like tobacco and alcohol, unhealthy diet, lack of physical activity, overweight and obesity, pollution (air, water, and soil), and stress. Regulation of these unhealthy behaviours plays a crucial role in blood pressure control among individuals on hypertensive treatment, especially those suffering from uncontrolled hypertension. Hence, the present study aims at identifying the unhealthy behaviours associated with uncontrolled hypertension. Materials and methods We did a secondary data analysis of the National Family Health Survey (NFHS) -5 data (2019–2021). Among those taking prescribed medication to lower blood pressure levels, SBP ≥140 mm Hg or DBP ≥90 mm Hg were considered uncontrolled hypertension. The other socio-demographic variables and unhealthy behaviours were used as independent variables for analysis. Results The proportion of uncontrolled hypertension was 49·5% (95% CI: 45·5–53·4) and 36·8% (95% CI: 35.8–37.8) among males and females, respectively. Alcohol consumption, clean fuel usage, and high BMI (≥30kg/m2) were the behavioural characteristics significantly associated with uncontrolled hypertension among males. In contrast, tobacco usage, alcohol consumption, coverage by Health insurance, presence of Diabetes, heart disease, usage of clean fuel, and high BMI (≥30kg/m2) were the behavioural characteristics significantly associated with uncontrolled hypertension among females. Regression results portrayed that higher age groups (45 and above) have found higher odds for men (OR: 7.6, CI: 4.6–12.3) and women (OR: 6.08, CI: 4.0–6.0) compared with 30 years and below age groups. Similarly, higher odds were found among the wealthiest wealth quintile than the poorest wealth quintile among men and women. Conclusion The current study reported a high proportion of uncontrolled hypertension. Providing opportunistic health education during blood pressure monitoring, regular screening, and targeted interventions will not only help to reduce its prevalence but also reduce the risk of developing related health implications.

Assessing the capture of sociodemographic information in electronic medical records to inform clinical decision making

There is a growing need to document sociodemographic factors in electronic medical records to produce representative cohorts for medical research and to perform focused research for potentially vulnerable populations. The objective of this work was to assess the content of family physicians’ electronic medical records and characterize the quality of the documentation of sociodemographic characteristics. Descriptive statistics were reported for each sociodemographic characteristic. The association between the completeness rates of the sociodemographic data and the various clinics, electronic medical record vendors, and physician characteristics was analyzed. Supervised machine learning models were used to determine the absence or presence of each characteristic for all adult patients over the age of 18 in the database. Documentation of marital status (51.0%) and occupation (47.2%) were significantly higher compared to the rest of the variables. Race (1.4%), sexual orientation (2.5%), and gender identity (0.8%) had the lowest documentation rates with a 97.5% missingness rate or higher. The correlation analysis for vendor type demonstrated that there was significant variation in the availability of marital and occupation information between vendors (χ2 > 6.0, P < 0.05). Variability in documentation between clinics indicated that the majority of characteristics exhibited high variation in completeness rates with the highest variation for occupation (median: 47.2, interquartile range: 60.6%) and marital status (median: 45.6, interquartile: 59.7%). Finally, physician sex, years since a physician graduated, and whether a physician was a foreign vs a Canadian medical graduate were significantly associated with documentation rates of place of birth, citizenship status, occupation, and education in the electronic medical records. Our findings suggest a crucial need to implement better documentation strategies for sociodemographic information in the healthcare setting. To improve completeness rates, healthcare systems should monitor, encourage, enforce, or incentivize sociodemographic data collection standards.

Field-scale detection of Bacterial Leaf Blight in rice based on UAV multispectral imaging and deep learning frameworks

Bacterial Leaf Blight (BLB) usually attacks rice in the flowering stage and can cause yield losses of up to 50% in severely infected fields. The resulting yield losses severely impact farmers, necessitating compensation from the regulatory authorities. This study introduces a new pipeline specifically designed for detecting BLB in rice fields using unmanned aerial vehicle (UAV) imagery. Employing the U-Net architecture with a ResNet-101 backbone, we explore three band combinations—multispectral, multispectral+NDVI, and multispectral+NDRE—to achieve superior segmentation accuracy. Due to the lack of suitable UAV-based datasets for rice disease, we generate our own dataset through disease inoculation techniques in experimental paddy fields. The dataset is increased using data augmentation and patch extraction methods to improve training robustness. Our findings demonstrate that the U-Net model incorporating ResNet-101 backbone trained with multispectral+NDVI data significantly outperforms other band combinations, achieving high accuracy metrics, including mean Intersection over Union (mIoU) of up to 97.20%, mean accuracy of up to 99.42%, mean F1-score of up to 98.56%, mean Precision of 97.97%, and mean Recall of 99.16%. Additionally, this approach efficiently segments healthy rice from other classes, minimizing misclassification and improving disease severity assessment. Therefore, the experiment concludes that the accurate mapping of the disease extent and severity level in the field is reliable to accurately allocating the compensation. The developed methodology has the potential for broader application in diagnosing other rice diseases, such as Blast, Bacterial Panicle Blight, and Sheath Blight, and could significantly enhance agricultural management through accurate damage mapping and yield loss estimation.

Central venous pressure as a method of optimising atrio-ventricular delay after cardiac surgery

Introduction Haemodynamic atrioventricular delay (AVD) optimisation has primarily focussed on signals that are not easy to acquire from a pacing system itself, such as invasive left ventricular catheterisation or arterial blood pressure (ABP). In this study, standard clinical central venous pressure (CVP) signals are tested as a potential alternative. Methods Sixteen patients with a temporary pacemaker after cardiac surgery were studied. AV delay optimisation was performed by alternating between a reference AVD of 120ms and tested settings ranging from 40 to 280ms, with 8 replicates for each setting. Alongside (a) the raw data, three methods of correcting for respiration were tested: (b) limiting analysis to a respiratory cycle, (c) asymmetric least squares (ALS) and (d) discrete wavelet transform (DWT). The utility of a quality control step was tested. Results CVP signals were a mirror image of the systolic ABP signals: The four R values were -0.674, -0.692, -0.631, -0.671 respectively (all p<0.001). With quality control, the mirror image was best for DWT (R = -0.76, p<0.001), with the CVP and ABP optima agreeing well (R = 0.78, p<0.001). The automated quality control signal correctly predicted the gap between the AVD optima calculated from ABP and CVP (R = 0.8, p<0.001). Conclusions Central venous pressure signals could be used to optimise AVD, because they have a reliable inverse relationship with ABP when pacemaker settings undergo protocolised testing. However, protocols need careful design to circumvent spontaneous biological variability.

Exploring the impact of grazing on fecal and soil microbiome dynamics in small ruminants in organic crop-livestock integration systems

In integrated crop-livestock systems, livestock graze on cover crops and deposit raw manure onto fields to improve soil health and fertility. However, enteric pathogens shed by grazing animals may be associated with foodborne pathogen contamination of produce influenced by fecal-soil microbial interactions. We analyzed 300 fecal samples (148 from sheep and 152 from goats) and 415 soil samples (272 from California and 143 from Minnesota) to investigate the effects of grazing and the presence of non-O157 Shiga toxin-producing Escherichia coli (STEC) or generic E. coli (gEc) in fecal and soil microbiomes. We collected samples from field trials of three treatments (fallow, a cover crop without grazing (non-graze CC), and a cover crop with grazing (graze CC)) grazed by sheep or goats between 2020 and 2022. No significant differences in non-O157 STEC prevalence were found between pre- and post-grazing fecal samples in either sheep or goats. However, gEc was more prevalent in graze CC soils compared to fallow or non-graze CC soils. Alpha diversity was influenced by the species of grazing animals and the region, as sheep fecal samples and soil from the California trials had greater alpha diversity than goat fecal samples and soil from the Minnesota trials. Beta diversity in sheep fecal samples differed by the presence or absence of non-O157 STEC, while in goat fecal samples, it differed between pre- and post-grazing events. Actinobacteria was negatively associated with non-O157 STEC presence in sheep fecal samples and decreased in post-grazing goat fecal samples. Grazing did not significantly affect soil microbial diversity or composition, and no interaction was observed between post-grazing fecal samples and the graze CC soil. The results suggest that soil contamination by foodborne pathogens and microbiome dynamics in ICLS are influenced by grazing animal species and regional factors, with interactions between fecal and soil microbial communities having minimal impact.