Browse Articles

Evaluation of nanopore sequencing for increasing accessibility of eDNA studies in biodiverse countries

Biodiversity loss is a global challenge of the 21st century. Environmental DNA (eDNA)-based metabarcoding offers a cost- and time-efficient alternative to conventional biodiversity surveys, enabling detection of rare, cryptic, and elusive species from environmental samples. However, limited access to genomic technologies restricts the application of eDNA metabarcoding in highly biodiverse remote regions and low- and middle-income countries (LMICs). Here, we directly compared the latest portable nanopore sequencing methods with established Illumina sequencing for vertebrate eDNA metabarcoding of Zambian water samples. Our results show that due to recent improvements in sequencing chemistry and optimized basecalling, nanopore sequencing data can recapitulate or even surpass established protocols, demonstrating the feasibility of in situ biodiversity assessments. eDNA- and camera trap-based species detections had minimal overlap in species detections, suggesting a complementary rather than substituting application of these biodiversity monitoring technologies. We finally demonstrate that our entire eDNA workflow can be successfully implemented in a mobile laboratory under remote field conditions by completing all steps—from sample collection to data analysis—within the Luambe National Park in Zambia. This approach has important implications for capacity building in LMICs and for overcoming limitations associated with sample export.

Spatiotemporal dependency data imputation for long-term health monitoring of concrete arch bridges

Lizard polymorphic throat colors are distinguishable by conspecifics and predators across variable light environments

Color polymorphisms, or distinct color variants within a population, provide tractable study systems for studying the generation, maintenance, and loss of phenotypic diversity in nature because biologists can easily observe changes in the number and frequency of discrete variants over time. However, many color polymorphisms are studied in the context of the human visual system and do not consider how conspecifics or potential predators view morph variation. The visual systems of predators and conspecifics may be sensitive to different aspects of coloration, which can influence the evolution and maintenance of morph diversity and phenotypic variation within and between populations. The Aegean wall lizard (Podarcis erhardii) is a color polymorphic lizard that exhibits co-occurring orange, white, and yellow throat color morphs. Here, we measured the reflectance of P. erhardii throat color patches and used visual modelling to determine if lizards and their bird and snake predators can visually discriminate between morph colors across different lighting contexts. Our results suggest that P. erhardii and their violet-sensitive bird and snake predators can distinguish chromatically between each color morph pair in standard daylight and forest shade illuminance contexts. However, only P. erhardii can distinguish achromatic morph colors in both illuminance contexts (except for white and yellow morphs in forest shade). These results indicate that P. erhardii morphs are most difficult for predators to distinguish in low lighting conditions and could help explain previously observed morph differences in microhabitat usage.

‘Google for DNA’ brings order to biology’s big data

Radix Scrophulariae regulates proliferation, apoptosis, and autophagy of rat thyroid cells via the MST1/Hippo signaling pathway

Social norms and security and justice services for gender-based violence survivors in Nepal: Programmatic implications from a mixed-methods assessment

Background Gender-based violence (GBV) is highly prevalent throughout the world. Only a small fraction of survivors seek help from security and justice (S&J) providers such as the police or courts, due in part to social norms that discourage help-seeking. The prevention of GBV requires attention to both demand- and supply-side factors and programming is moving toward this integration, including in Nepal. However, little research exists at the nexus of these issues. To address this gap, we provide a comprehensive mixed-methods situation analysis of GBV-related social norms, help-seeking, and S&J service provision. Methods Data included a household survey (N = 3830), a sub-study of youth (N = 143) and married adults (N = 464) in one site and qualitative data collection including interviews with S&J service providers, help-seeking GBV survivors and families (N = 68), and focus group discussions with police, youth groups, and school management committees (N = 20) in four sites. Descriptive analysis of survey data was triangulated with findings from a modified grounded theory analysis of the qualitative data to elucidate the role of social norms and other barriers limiting help-seeking. Results GBV was perceived to be common, especially child marriage, domestic violence, eve-teasing, and dowry-related violence. Formal help-seeking was low, despite positive attitudes towards S&J providers. Participants described injunctive norms discouraging formal reporting in cases of GBV and sanctions for women violating these norms. Conclusions Norms favoring family- and community-based mediation remain strong. Sanctions for formal reporting remain a deterrent to help-seeking. Leveraging gender-equitable role models, such as female S&J providers, and connecting S&J providers to women and youth may capitalize on existing shifts.

Label-free structural imaging of plant roots and microbes using third-harmonic generation microscopy

Abstract Root biology is pivotal in addressing global challenges including sustainable agriculture and climate change. However, roots have been relatively understudied among plant organs, partly due to the difficulties in imaging root structures in their natural environment. Here we used microfabricated ecosystems (EcoFABs) to establish growing environments with optical access and employed nonlinear multimodal microscopy of third-harmonic generation (THG) and three-photon fluorescence (3PF) to achieve label-free, in situ imaging of live roots and microbes at high spatiotemporal resolution. THG enabled us to observe key plant root structures including the vasculature, Casparian strips, dividing meristematic cells, and root cap cells, as well as subcellular features including nuclear envelopes, nucleoli, starch granules, and putative stress granules. THG from the cell walls of bacteria and fungi also provides label-free contrast for visualizing these microbes in the root rhizosphere. With simultaneously recorded 3PF signal, we demonstrated our ability to investigate root-microbe interactions by achieving single-bacterium tracking and subcellular imaging of fungal spores and hyphae in the rhizosphere.

Association of estrogen receptor single nucleotide polymorphisms and perinatal depression

Depression during pregnancy and in the postpartum period have been receiving increasing attention considering the possible complications for the mother and baby if left untreated. Genetic variations in the estrogen receptor genes (ESR) have been implicated in susceptibility to depression. However, only few studies investigated them in perinatal depression (PND) and none on its different trajectories (i.e., patterns of time of onset and persistency of depression). Here, we explored the association of single nucleotide polymorphisms (SNPs) of the ESR1 and ESR2 genes with PND among 2,973 women in Sweden. PND was defined using the Edinburgh Postnatal Depression Scale, the Depression Self-Rating Scale, use of selective serotonin reuptake inhibitor, and/or medical records. PND trajectories were identified as follows: controls (no depression at any point in the perinatal period), antepartum (depression during pregnancy and resolved postpartum), postpartum-onset (no depression during pregnancy with onset after delivery), and persistent (depression throughout the perinatal period). Multivariable logistic regression was performed. Out of 56 SNPs analyzed, one SNP in the ESR1 gene (rs2982712) was nominally significantly associated with PND (OR 0.83, 95% CI 0.71–0.98, p = 0.03) as well as with persistent depression (OR 0.77, 95% CI 0.61–0.98, p = 0.03) in the overdominant model (DD/dd vs. Dd). In addition, we also found two SNPs, namely rs1884051 (OR 0.74, 95% CI 0.56–0.98, p = 0.03) and rs2228480 (OR 0.77, 95% CI 0.60–0.99, p = 0.04) in the ESR1 gene, that were nominally significantly associated with persistent depression only. None of the ESR1 SNPs were associated with antepartum or postpartum-onset depression. None of the ESR2 SNPs, nor any haplotypes, were associated with PND or its trajectories. Our findings suggest a role of ESR1 in PND, especially its persistent trajectory.

Non-technical loss detection in power distribution networks using machine learning

Columbia suicide severity rating scale screen scores in adults with Chiari malformation Type 1

Adult Chiari malformation Type 1 is a neurological condition characterized by high levels of chronic pain, disability, and psychological distress, yet self-harming behaviors have not previously been studied in this patient group. The purpose of this study was to determine the prevalence of elevated suicide risk scores among adults with Chiari malformation Type I using the Columbia Suicide Severity Rating Scale – Screen and examine the association between risk scores and pain, disability, and psychological distress. A web-based, anonymous survey was administered comprised of validated scales and demographic questions. Suicide risk (Columbia Screen score) was dichotomized as Low or High-Risk and both crude and adjusted odds ratios were calculated to determine statistical associations with pain, disability, and psychological measures. Overall, 44% of 372 respondents scored in the High-Risk group. Logistic regression showed that depression at the moderate level or above (Adjusted Odd Ratio (AOR) = 4.27, 95% CI = 2.58–7.05, p < .0001), age younger than 30 years (AOR = 3.10, 95% CI = 1.67–5.78, p = .0003), and severe or complete neck related disability (AOR = 2.02, 95% CI = 1.22–3.33, p = .0056) were significant predictors of High-Risk scoring. This study is the first to examine suicidal ideation and risk in the adult CMI population. Clinicians should be aware that suicidal ideation is a serious morbidity in this patient population.

Energy efficient approximate compressor architectures for high performance image multiplication in CNTFET technology

Comparison of ischemic cardiovascular events between dapagliflozin and empagliflozin in combination with metformin: A nationwide population-based cohort study

The comparative effectiveness of individual sodium–glucose cotransporter-2 inhibitors (SGLT-2is) in preventing ischemic cardiovascular disease (CVD) remains uncertain. Thus, this study compared the incidence of ischemic CVD events in patients with type 2 diabetes mellitus (T2DM) treated with dapagliflozin or empagliflozin in combination with metformin. This retrospective cohort study analyzed national claims data from the Korean National Health Insurance Service. Patients with T2DM who received dapagliflozin or empagliflozin, combined with metformin, between 2014 and 2019 were included. The primary outcome was composite ischemic CVD events, defined as myocardial infarction, ischemic stroke, or coronary revascularization. Secondary outcomes included each component of composite ischemic CVD events, unstable angina, and all-cause mortality. Hazard ratios (HRs) and confidence intervals (CIs) were estimated using Cox proportional hazards models, adjusting for covariates in three stepwise models: Model 1 (age and sex), Model 2 (Model 1 variables plus patient characteristics), and Model 3 (Model 2 variables plus clinical parameters). In Model 3, after full adjustment for systolic blood pressure, low-density lipoprotein cholesterol, fasting blood glucose, and serum creatinine, no significant difference was observed in the incidence of composite ischemic CVD events between dapagliflozin and empagliflozin when each was used in combination with metformin (adjusted HR 0.50, 95% CI: 0.24–1.03). Additionally, no significant differences were observed in individual components of composite ischemic CVD events, unstable angina, and all-cause mortality. These real-world findings may help in selecting an SGLT-2is subtype for CVD prevention in Asian patients with T2DM.

Threat expectancies in a VR fear conditioning paradigm follow non-linear extinction patterns but are not influenced by intolerance of uncertainty

Abstract In exposure therapy, the degree to which individuals effectively correct their threat expectancies varies considerably. Using appropriate statistical models to capture this variability and investigating potential moderators (i.e. mechanisms of change) of the extinction learning process is therefore important. We hypothesized that a non-linear statistical model would better explain extinction learning trajectories indexed by unconditioned stimulus (US) expectancies than a linear model and that intolerance of uncertainty (IU) might negatively impact extinction. Seventy-one spider-fearful participants completed a two-day realistic virtual reality conditioning paradigm where a spider served as the US. Trial-by-trial US expectancy ratings were collected as the main outcome during acquisition and extinction. We used ordered beta regression in a Bayesian estimation framework to analyze data. As expected, trial-by-trial US expectancy ratings during extinction were substantially better explained by the non-linear ordered beta than a linear statistical model. Non-linear models can therefore more adequately capture interindividual differences in trial-by-trial extinction learning by providing a better fit to such data. However, IU did not moderate US expectancies in our study. Thus, our study supports the notion that US expectancies are largely insensitive to IU.

Investigating the common genetic basis between inflammatory bowel disease and metabolic syndrome through genomic structural equation modeling

Background Inflammatory bowel disease (IBD) and metabolic syndrome (MetS) exhibit a complex interplay, with clinical evidence indicating an increasing incidence of their co-occurrence. However, current research lacks a systematic framework to model the pleiotropic genetic architecture linking gastrointestinal and liver-metabolic phenotypes, thereby hindering a comprehensive understanding of how multiple genetic risk factors converge to drive IBD–MetS comorbidity. Methods This study employed genomic structural equation modeling (SEM) to integrate genome-wide association study (GWAS) summary datasets for IBD and MetS-related traits (body mass index, triglycerides, non-alcoholic fatty liver disease, hypertension, and type 2 diabetes), creating the multivariate GWAS summary datasets. Post-GWAS analytical approaches were subsequently utilized to assess risky loci, gene functionality, and tissue-specific regulatory networks, aiming to elucidate the pathological connections between chronic low-grade inflammation and the gut-liver-metabolic axis. Results Genomic SEM identified a shared latent genetic factor between IBD and MetS (Comparative Fit Index = 0.9864, Standardized Root Mean Square Residual = 0.0602). A total of 522 lead single nucleotide polymorphism (SNP) loci were identified, including 21 novel SNPs specific to the multivariate model that were not detected in univariate GWAS. Fine-mapping with SuSiE and FINEMAP identified 29 high-confidence causal SNPs. Integrating SNP fine-mapping with MAGMA, FUSION, and FOCUS analyses confirmed seven core genes. Conclusion To the best of our knowledge, this study provides the first comprehensive characterization of the shared genetic architecture of IBD and MetS through a multivariate genetic model. The results deepen the understanding of the genetic mechanisms underlying IBD and MetS and offer potential therapeutic targets and a conceptual framework for developing interventions for cross-system diseases.

Time-domain response bounds for linear time invariant systems with application to transmission line traveling waves

Partial volume effect on kidney stones and lung nodules in CT imaging

Rationale and Objectives To examine the impact of the partial volume effect (PVE) on the imaging of spherical objects depending on their size, density and center voxel position. Materials and Methods We developed an algorithm for calculating the volume of a sphere wrapped by voxels. The algorithm measured the internal volume of each voxel cut by the sphere and automatically attributed the average voxel density. The sphere volume was simulated by the sum of voxels with an average density above the Hounsfield Unit (HU) cutoff level for that object. Various sphere sizes, densities and positions in the voxel grid were examined. The two clinical settings used were nodules (0 HU) in the lung (−1000 HU) and kidney stones (1000 HU) embedded in the renal parenchyma (30 HU). Results Small kidney stones appeared magnified by the PVE when a stone cutoff level of 130 HU was used: the smallest stone simulated with a diameter of 1.4 mm demonstrated a volume that was 231% the size of the ground truth (sphere volume as measured with the classical formula). A hypothetical stone of 10 cm would still have a PVE of 2%. The PVE did not affect lung nodules if the cutoff level for the nodule fraction was set to the exact mean of both the internal and external density (−500 HU). Lung nodules were more affected by the geometrical effect, where tiny nodules appeared smaller because of the greater curvature of smaller spheres, often cutting less than 50% of the volume of a surface voxel. Conclusions This study highlights the potential risks associated with inaccurate raw data postprocessing of CT images with objects that are particularly sensitive to the PVE, such as kidney stones and high-density calcifications (Agatston score).

A modified deep learning approach for seminal vesicle region localization in prostate MRI

Retraction: IoT based battery energy monitoring and management for electric vehicles with improved converter efficiency

Exposome variations affect Drosophila bristle patterning via the regulation of proneural genes through distinct mechanisms

Abstract How developing organisms respond to a changing environment is a fundamental question. Pollutants and temperature are major environmental factors. Using the bristle patterning of Drosophila as a model system, we observed that cold temperature and methotrexate, a medical drug that contaminates wastewaters, increase dorsocentral (DC) bristle number, a trait normally robust. The patterning of bristles is well understood and involves the achaete-scute (ac-sc) proneural genes. Modular enhancers activate ac-sc expression in groups of cells, called proneural clusters, from which bristle precursors are selected by lateral inhibition, a process involving Notch signalling and ac-sc auto-activation. In addition, ac-sc basal expression is controlled by a cocktail of repressive factors. We observed that the deletion of the DC enhancer prevents the induction of ectopic DC bristles by methotrexate but does not stop low temperature to induce DC bristles. Indeed, we show that methotrexate has a strong synergy with mutants of factors that regulate the DC enhancer and extends the zone of activity of this enhancer. In contrast, temperature interacts with repressors of ac-sc basal expression. Thus, methotrexate and temperature both affect DC bristle patterning but by distinct mechanisms, methotrexate on the DC enhancer and cold independently of this enhancer.

MG-DIFF: A novel molecular graph diffusion model for molecular generation and optimization

Recent advancements in denoising diffusion models have revolutionized image, text, and video generation. Inspired by these achievements, researchers have extended denoising diffusion models to the field of molecule generation. However, existing molecular generation diffusion models are not fully optimized according to the distinct features of molecules, leading to suboptimal performance and challenges in conditional molecular optimization. In this paper, we introduce the MG-DIFF model, a novel approach tailored for molecular generation and optimization. Compared to previous methods, MG-DIFF incorporates three key improvements. Firstly, we propose a mask and replace discrete diffusion strategy, specifically designed to accommodate the complex patterns of molecular structures, thereby enhancing the quality of molecular generation. Secondly, we introduce a graph transformer model with random node initialization, which can overcome the expressiveness limitations of regular graph neural networks defined by the first-order Weisfeiler-Lehman test. Lastly, we present a graph padding strategy that enables our method to not only do conditional generation but also optimize molecules by adding certain atomic groups. In several molecular generation benchmarks, the proposed MG-DIFF model achieves state-of-the-art performance and demonstrates great potential molecular optimization.