bluuebunny/crossref_metadata_2025 huggingface dataset:
Validity of Administrative Coding for Nonarteritic Ischemic Optic Neuropathy
Authors: Hamedani Ali G. , Kim Dale S. , Chaitanuwong Pareena , Gonzalez Lizbeth A. , Moss Heather E. , DeLott Lindsey B.
Published: May 2024

Background:

Administrative claims have been used to study the incidence and outcomes of nonarteritic ischemic optic neuropathy (NAION), but the validity of International Classification of Diseases (ICD)-10 codes for identifying NAION has not been examined.

Methods:

We identified patients at 3 academic centers who received ≥1 ICD-10 code for NAION in 2018. We abstracted the final diagnosis from clinical documentation and recorded the number of visits with an NAION diagnosis code. We calculated positive predictive value (PPV) for the overall sample and stratified by subspecialty and the number of diagnosis codes. For patients with ophthalmology or neuro-ophthalmology visit data, we recorded presenting symptoms, examination findings, and laboratory data and calculated PPV relative to case definitions of NAION that incorporated sudden onset of symptoms, optic disc edema, afferent pupillary defect, and other characteristics.

Results:

Among 161 patients, PPV for ≥1 ICD-10 code was 74.5% (95% CI: 67.2%–80.7%). PPV was similar when restricted to patients who had visited an ophthalmologist (75.8%, 95% CI: 68.4%–82.0%) but increased to 86.8% when restricted to those who had visited neuro-ophthalmologists (95% CI: 79.2%–91.9%). Of 113 patients with >1 ICD-10 code and complete examination data, 37 (32.7%) had documented sudden onset, optic disc swelling, and an afferent pupillary defect (95% CI: 24.7%–42.0%). Of the 76 patients who did not meet these criteria, 54 (71.0%) still received a final clinical diagnosis of NAION; for most (41/54, 75.9%), this discrepancy was due to lack of documented optic disc edema.

Conclusions:

The validity of ICD-10 codes for NAION in administrative claims data is high, particularly when combined with provider specialty.
The global fish and invertebrate abundance value of mangroves
Authors: Ermgassen Philine zu , Worthington Thomas A. , Gair Jonathan R. , Garnett Emma E. , Mukherjee Nibedita , Longley-Wood Kate , Nagelkerken Ivan , Abrantes Kátya , Aburto-Oropeza Octavio , Acosta Alejandro , da Rocha Araujo Ana Rosa , Baker Ronald , Barnett Adam , Beitl Christine M. , Benzeev Rayna , Brookes Justin , Castellanos-Galindo Gustavo A. , Chong Ving Ching , Connolly Rod M. , Cunha-Lignon Marília , Dahdouh-Guebas Farid , Diele Karen , Dwyer Patrick G. , Friess Daniel A. , Grove Thomas , Hoq M. Enamul , Huijbers Chantal , Hutchinson Neil , Johnson Andrew F. , Johnson Ross , Knight Jon , Krumme Uwe , Kuguru Baraka , Lee Shing Yip , Lobo Aaron Savio , Lugendo Blandina R. , Meynecke Jan-Olaf , Munga Cosmas Nzaka , Olds Andrew D. , Parrett Cara L. , Reguero Borja G. , Rönnbäck Patrik , Safryghin Anna , Sheaves Marcus , Taylor Matthew D. , Mendonça Jocemar Tomasino , Waltham Nathan J. , Wolff Matthias , Spalding Mark D.
Published: May 2024

Mangroves are a critical coastal habitat that provides a suite of ecosystem services and supports livelihoods. We undertake the first global analysis to estimate density and abundance of 37 commercially important fish and invertebrates that are known to extensively use mangroves. Geomorphic mangrove type, sea surface salinity and temperature, and length of mangrove forest edge were important in predicting the density of commercial fish and invertebrates, with deltaic systems supporting the highest densities. The model predicted high densities throughout parts of southeast Asia, the northern coast of South America, the Red Sea, and the Caribbean and Central America. Application of our model onto the global mangrove extent, estimates that mangroves support the annual abundance of nearly 800 billion young-of-year fish and invertebrates contained in our model. Our results confirm the critical role of mangroves globally in supporting fish and fisheries, and further builds the case for their conservation and restoration.
Genetic Explication of Impaired Insulin Resistance in Genesis of Metabolic Diseases
Authors: Soomro Naveed Iqbal , Bakhtiar Syeda Marriam
Published: May 2024

This study aims to investigate the role of impaired insulin resistance in the onset and progression of metabolic diseases such as prediabetes, diabetes, and cardiovascular diseases. Insulin resistance occurs when insulin is unable to effectively stimulate glucose uptake, and if the body is unable to produce sufficient insulin to compensate, type 2 diabetes may develop. This research endeavors to elucidate the molecular and genetic underpinnings of insulin resistance and its association with metabolic disorders. Employing various tools and databases, gene interaction data was procured through GeneMania, and pathway validation was conducted using KEGG. Construction of gene regulatory networks employed GEPHI 0.9.2, with centralities statistical analysis identifying hub genes. Enrichment analysis and literature validation substantiated the significance of these hubs, resulting in the refinement of the initially identified seven genes to five with interaction data. The implicated hub genes were discerned to play roles in inflammation, either directly or indirectly. Future prospects involve further genetic analysis across diverse populations, utilizing PCR to discern the allelic variations of these identified hub genes. Ultimately, this research may shed light on the underlying genetic and molecular mechanisms of insulin resistance and metabolic syndrome, and contribute to the development of targeted treatments for these conditions.
Peromyscus leucopus,Mus musculus, and humans have distinct transcriptomic responses to larvalIxodes scapularisbites
Authors: Bourgeois Jeffrey S. , McCarthy Julie E. , Turk Siu-Ping , Bernard Quentin , Clendenen Luke H. , Wormser Gary P. , Marcos Luis A. , Dardick Kenneth , Telford Sam R. , Marques Adriana R. , Hu Linden T.
Published: May 2024

Ixodes scapularisticks are an important vector for at least six tick-borne human pathogens, including the predominant North American Lyme disease spirocheteBorrelia burgdorferi. The ability for these ticks to survive in nature is credited, in part, to their ability to feed on a variety of hosts without excessive activation of the proinflammatory branch of the vertebrate immune system. While the ability for nymphal ticks to feed on a variety of hosts has been well-documented, the host-parasite interactions between larvalI. scapularisand different vertebrate hosts is relatively unexplored. Here we report on the changes in the vertebrate transcriptome present at the larval tick bite site using the naturalI. scapularishostPeromyscus leucopusdeermouse, a non-natural rodent hostMus musculus(BALB/c), and humans. We note substantially less evidence of activation of canonical proinflammatory pathways inP. leucopuscompared to BALB/c mice and pronounced evidence of inflammation in humans. Pathway enrichment analyses revealed a particularly strong signature of interferon gamma, tumor necrosis factor, and interleukin 1 signaling at the BALB/c and human tick bite site. We also note that bite sites on BALB/c mice and humans, but not deermice, show activation of wound-healing pathways. These data provide molecular evidence of the coevolution between larvalI. scapularisandP. leucopusas well as expand our overall understanding ofI. scapularisfeeding.

Significance

Ixodes scapularistick bites expose humans to numerous diseases in North America. While larval tick feeding enables pathogens to enter the tick population and eventually spread to humans, how larval ticks interact with mammals has been understudied compared to other tick stages. Here we examined the transcriptomic response of a naturalI. scapularisrodent host (Peromyscus leucopus), a non-nativeI. scapularisrodent host (Mus musculus), and an incidental host (humans). We find that there are differences in how all three species respond to larvalI. scapularis, with the natural host producing the smallest transcriptomic signature of a canonical proinflammatory immune response and the incidental human host producing the most robust signature of inflammation in response to the larval tick. These data expand our understanding of the pressures on ticks in the wild and inform our ability to model these interactions in laboratory settings.
NK Cell Exhaustion in Wilson’s Disease Revealed by Single-cell RNA Sequencing Predicts the Prognosis of Cholecystitis
Authors: Jin Yong , Xing Jiayu , Dai Chenyu , Jin Lei , Zhang Wanying , Tao Qianqian , Hou Mei , Li Ziyi , Yang Wen , Feng Qiyu , Wang Hongyang , Yu Qingsheng
Published: May 2024

Metabolic abnormalities associated with liver disease have a significant impact on the risk and prognosis of cholecystitis. However, the underlying mechanism is complicated and remains to be elucidated. In particular, the effect of metabolic abnormalities on the progression of cholecystitis through the regulation of immune cell function is poorly understood. In this study, we investigated this issue using Wilson’s disease (WD) as a model. Wilson’s disease is a genetic disorder characterized by impaired mitochondrial function and abnormal copper metabolism. Our retrospective clinical study of over 600 patients with WD found that they have a significantly higher incidence of cholecystitis and a poorer prognosis. The immune cell landscape in the hepatic mesenchymal stromal microenvironment of WD patients was shown using single-cell RNA. A major change is the constitution and function of the innate immune system, including enhanced antigen presentation process, activation of the immune response, and activation of lymphocytes. The proportion of mononuclear phagocytes and natural killer (NK) cells is increased, and the primary characteristic and function of macrophage, Kupffer cell, neutrophil, and NK cell are altered. Exhaustion of NK cell is the fundamental factor, supported by an increase in the expression of the inhibitory receptors KLRC1 and TIGIT and a decrease in the expression of cytotoxic molecules. Clinical tissue and blood samples verified increased KLRC1+and TIGIT+NK cells and decreased IFNγ+NK cells in WD. Further bioinformatic analysis has confirmed a positive correlation between NK cell exhaustion and poor prognosis in cholecystitis and other inflammatory diseases. The study demonstrated abnormal function of liver mesenchymal immune cells triggered by specific metabolic dysfunction in WD, with a focus on the correlation between NK cell exhaustion and poor healing of cholecystitis. Our findings highlight the immune cell dysfunction due to metabolic changes in hepatocytes and provide new insights into the improvement of inflammatory diseases by assessing immune cell function.
Formation of Multinucleated Osteoclasts Depends on an Oxidized Species of Cell Surface Associated La Protein
Authors: Leikina Evgenia , Whitlock Jarred M. , Melikov Kamran , Zhang Wendy , Bachmann Michael P. , Chernomordik Leonid V.
Published: May 2024

The bone-resorbing activity of osteoclasts plays a critical role in the life-long remodeling of our bones that is perturbed in many bone loss diseases. Multinucleated osteoclasts are formed by the fusion of precursor cells, and larger cells - generated by an increased number of cell fusion events - have higher resorptive activity. We find that osteoclast fusion and bone-resorption are promoted by reactive oxygen species (ROS) signaling and by an unconventional low molecular weight species of La protein, located at the osteoclast surface. Here, we develop the hypothesis that La’s unique regulatory role in osteoclast multinucleation and function is controlled by a ROS switch in La trafficking. Using antibodies that recognize reduced or oxidized species of La, we find that differentiating osteoclasts enrich an oxidized species of La at the cell surface, which is distinct from the reduced La species conventionally localized within cell nuclei. ROS signaling triggers the shift from reduced to oxidized La species, its dephosphorylation and delivery to the surface of osteoclasts, where La promotes multinucleation and resorptive activity. Moreover, intracellular ROS signaling in differentiating osteoclasts oxidizes critical cysteine residues in the C-terminal half of La, producing this unconventional La species that promotes osteoclast fusion. Our findings suggest that redox signaling induces changes in the location and function of La and may represent a promising target for novel skeletal therapies.
Predicting Differentially Methylated Cytosines in TET and DNMT3 Knockout Mutants via a Large Language Model
Authors: Sereshki Saleh , Lonardi Stefano
Published: May 2024

DNA cytosine methylation is an epigenetic marker which regulates many cellular processes. Mammalian genomes typically maintain consistent methylation patterns over time, except in specific regulatory regions like promoters and certain types of enhancers. The dynamics of DNA methylation is controlled by a complex cellular machinery, in which the enzymes DNMT3 and TET play a major role. This study explores the identification of differentially methylated cytosines (DMCs) in TET and DNMT3 knockout mutants in mice and human embryonic stem cells. We investigate (i) whether a large language model can be trained to recognize DMCs in human and mouse from the sequence surrounding the cytosine of interest, (ii) whether a classifier trained on human knockout data can predict DMCs in the mouse genome (and vice versa), (iii) whether a classifier trained on DNMT3 knockout can predict DMCs for TET knockout (and vice versa). Our study identifies statistically significant motifs associated with the prediction of DMCs each mutant, casting a new light on the understanding of DNA methylation dynamics in stem cells. Our software tool is available athttps://github.com/ucrbioinfo/dmc_prediction.
Fast, Accurate, and Versatile Data Analysis Platform for the Quantification of Molecular Spatiotemporal Signals
Authors: Mi Xuelong , Chen Alex Bo-Yuan , Duarte Daniela , Carey Erin , Taylor Charlotte R. , Braaker Philipp N. , Bright Mark , Almeida Rafael G. , Lim Jing-Xuan , Ruetten Virginia M. S. , Zheng Wei , Wang Mengfan , Reitman Michael E. , Wang Yizhi , Poskanzer Kira E. , Lyons David A. , Nimmerjahn Axel , Ahrens Misha B. , Yu Guoqiang
Published: May 2024

Optical recording of intricate molecular dynamics is becoming an indispensable technique for biological studies, accelerated by the development of new or improved biosensors and microscopy technology. This creates major computational challenges to extract and quantify biologically meaningful spatiotemporal patterns embedded within complex and rich data sources, many of which cannot be captured with existing methods. Here, we introduce Activity Quantification and Analysis (AQuA2), a fast, accurate, and versatile data analysis platform built upon advanced machine learning techniques. It decomposes complex live imaging-based datasets into elementary signaling events, allowing accurate and unbiased quantification of molecular activities and identification of consensus functional units. We demonstrate applications across a wide range of biosensors, cell types, organs, animal models, and imaging modalities. As exemplar findings, we show how AQuA2 identified drug-dependent interactions between neurons and astroglia, and distinct sensorimotor signal propagation patterns in the mouse spinal cord.
Sleep and Activity Patterns in Autism Spectrum Disorder
Authors: Weissenkampen J. Dylan , Ghorai Arpita , Fasolino Maria , Gehringer Brielle , Rajan Maya , Dow Holly C. , Sebro Ronnie , Rader Daniel J. , Keenan Brendan T. , Almasy Laura , Brodkin Edward S. , Bucan Maja
Published: May 2024

Background

Autism spectrum disorder (ASD) is a highly heritable and heterogeneous neurodevelopmental disorder characterized by impaired social interactions, repetitive behaviors, and a wide range of comorbidities. Between 44-83% of autistic individuals report sleep disturbances, which may share an underlying neurodevelopmental basis with ASD.

Methods

We recruited 382 ASD individuals and 223 of their family members to obtain quantitative ASD-related traits and wearable device-based accelerometer data spanning three consecutive weeks. An unbiased approach identifying traits associated with ASD was achieved by applying the elastic net machine learning algorithm with five-fold cross-validation on 6,878 days of data. The relationship between sleep and physical activity traits was examined through linear mixed-effects regressions using each night of data.

Results

This analysis yielded 59 out of 242 actimetry measures associated with ASD status in the training set, which were validated in a test set (AUC: 0.777). For several of these traits (e.g. total light physical activity), the day-to-day variability, in addition to the mean, was associated with ASD. Individuals with ASD were found to have a stronger correlation between physical activity and sleep, where less physical activity decreased their sleep more significantly than that of their non-ASD relatives.

Conclusions

The average duration of sleep/physical activity and the variation in the average duration of sleep/physical activity strongly predict ASD status. Physical activity measures were correlated with sleep quality, traits, and regularity, with ASD individuals having stronger correlations. Interventional studies are warranted to investigate whether improvements in both sleep and increased physical activity may improve the core symptoms of ASD.
Strengths and limitations of using participatory science data to characterize a wildlife mass mortality event
Authors: Taylor Liam U. , Barychka Tatsiana , McKeon Seabird , Bartolotta Natasha , Avery-Gomm Stephanie
Published: May 2024

Large participatory science (i.e., “community science” or “citizen science”) platforms are increasingly used at every level of ecological and conservation research, including disease monitoring. Here, we used a comprehensive, ground-truthed mortality dataset to judge how well participatory science data from iNaturalist represented the magnitude, taxonomic, temporal, and spatial patterns of waterbird mortality associated with a mass mortality event following the incursion of Highly Pathogenic Avian Influenza in eastern Canada in 2022. The iNaturalist dataset was effective at identifying species with high mortality (especially Northern Gannets,Morus bassanus), along with the time period and spatial regions with high concentrations of avian deaths. However, iNaturalist data severely underestimated the magnitude, overestimated the taxonomic breadth, and poorly represented the full geographic scope of disease-related deaths. Our results suggest iNaturalist can be used to identify the species, timing, and location of relatively high mortality in situations where no other information is available, and to supplement conventional sources of data. However, iNaturalist alone can neither quantify the magnitude nor pinpoint the mechanisms of mortality and therefore is not a viable substitute for comprehensive mortality assessments.
Multilevel clinical fingerprinting: uncovering longitudinal changes in the functional connectome of the brain along the migraine cycle
Authors: Esteves Inês , Fouto Ana R. , Ruiz-Tagle Amparo , Caetano Gina , Nunes Rita G. , da Silva Nuno A. , Vilela Pedro , Gil-Gouveia Raquel , Martins Isabel Pavão , Caballero-Gaudes César , Figueiredo Patrícia
Published: May 2024

Migraine is a common neurological disorder characterized by recurrent headache episodes alternating with symptom-free periods, which has been associated with alterations across large-scale functional brain networks albeit with variable findings. Critically, despite the cyclic nature of the disorder, longitudinal studies spanning the various phases of the migraine cycle are scarce. Here, we leverage the identifiability of individual functional connectomes (FC) to investigate changes along the migraine cycle. For this purpose, we employ a case-control longitudinal design to study a group of 10 patients with episodic menstrual or menstrual-related migraine without aura, in the 4 phases of their spontaneous migraine cycle (preictal, ictal, postictal, interictal), and a group of 14 healthy controls in corresponding phases of the menstrual cycle, using resting-state fMRI. We propose a novel multilevel clinical fingerprinting approach to analyse the differential FC identifiability within-subject, as well as within-session and within-group. The individual FC matrices are then reconstructed with 19 principal components maximizing identifiability at all levels, and analyzed with Network-Based Statistic to identify significant changes in FC strength. We observe decreased FC identifiability for patients in the preictal phase relative to controls, which increases with the progression of the attack and becomes comparable to controls in the interictal phase. Regarding the FC strength, is increased in the ictal and postictal phases relative to controls across several networks. Our novel multilevel clinical fingerprinting approach captures FC variations along the migraine cycle in a case-control longitudinal study, bringing new insights into the cyclic nature of the disorder.
High fat diet consumption and social instability stress impair stress adaptation and maternal care in C57Bl/6 mice
Authors: Bucknor Morgan C , Gururajan Anand , Dale Russell C. , Hofer Markus J
Published: May 2024

Poor maternal diet and psychosocial stress represent two environmental factors that can significantly impact maternal health during pregnancy. While various mouse models have been developed to study the relationship between maternal health and offspring development, few incorporate multiple sources of stress that mirror the complexity of human experiences. Maternal high-fat diet (HF) models in rodents are well-established, whereas maternal psychosocial stress models are still emerging. The social instability stress (SIS) paradigm, serves as a chronic and unpredictable form of social stress. To evaluate the combined effects of a poor maternal diet and social stress on maternal health and behaviour, we developed a novel maternal stress model in adult female C57Bl/6 mice. We observed that all HF+ mice demonstrated rapid weight gain, elevated fasting blood glucose levels and impaired glucose tolerance independent of the presence (+) or absence (-) of SIS. Behavioural testing revealed anxiety-like behaviors remained across all groups prior to pregnancy. However, we did observe a trend of poorer nest quality among all HF+ mice compared to HF-mice following nest building testing. Unlike the other HF+ and HF-stress groups, which exhibited significantly reduced plasma ACTH and corticosterone levels following SIS exposure, we did not observe this reduction in HF+/SIS+ females. In addition, HF+/SIS+ females demonstrated significant postpartum maternal neglect, resulting in fewer numbers of live offspring. These findings suggest that prolonged maternal HF diet consumption, coupled with SIS, places a significant burden on the maternal stress response system, resulting in reduced parental investment and negative postpartum behaviour towards offspring.
A New Differential Gene Expression Based Simulated Annealing for Solving Gene Selection Problem: A Case Study on Eosinophilic Esophagitis and Few Other Gastro-Intestinal Diseases
Authors: Sinha Koushiki , Chakraborty Sanchari , Bardhan Arohit , Saha Riju , Chakraborty Srijan , Biswas Surama
Published: May 2024

Background

Identifying the set of disease-causing genes is crucial for understanding pathogenesis and developing therapies. This is particularly important to understand the pathophysiology of Eosinophilic Esophagitis (EoE) and other gastrointestinal diseases. Comparing and contrasting gene selection methods across these diseases can enhance our knowledge to identify potential therapeutic targets.

Methods

This study introduces two approaches for gene selection in gastrointestinal diseases: the Ranked Variance (RV) method and Differential Gene Expression Based Simulated Annealing (DGESA). RV acts as an initial screener by prioritizing genes based on variance. DGESA refines gene selection further by employing simulated annealing with differential expression data. We compared the outcomes of both methods through a case study on EoE and other gastrointestinal diseases.

Results

Result finds greater number of genes with negative fold changes compared to those with positive fold change in differential EoE dataset. RV Ranks top 40 genes with high variance of EoE which overlaps with the disease-causing gene set of EoE from DGESA. 40 gene pathways for each of EoE, Crohn’s Disease (CD), and Ulcerative Colitis (UC) were identified as execution outcome of our method DGESA. Among these, 10 genes for EoE, 8 for CD, and 7 for UC were confirmed in the literature for their connection with respective diseases. For EoE, 10 such confirmed genes include KRT79, CRISP2, IL36G, SPRR2B, SPRR2D and SPRR2E. For CD, the literature confirmed set encompasses NPDC1, SLC2A4RG, LGALS8, CDKN1A, XAF1, and CYBA. The validated genes in UC final gene set includes TRAF3, BAG6, CCDC80, CDC42SE2, and HSPA9.

Conclusion

The RV method, serving as an initial screener, and the more refined DGESA both effectively elucidate molecular signatures in gastrointestinal diseases. Identifying and validating genes like SPRR2B, SPRR2D, SPRR2E and STAT6 for EoE showcase efficacy of DGESA. Other genes in the same pathway are interesting targets for future laboratory validation.
Factors associated with tobacco consumption behavior of adolescent students
Authors: Mandal Anil Kumar
Published: May 2024

Although consumption of tobacco is attributed to 8 million deaths of people each year in the world, the consumption of tobacco by adolescent students in Nepal is seen in alarming stage. This paper aims to describe tobacco consumption behavior of adolescent students and its associated factors. For the study, cross-sectional survey design was followed. Students of community schools of Class 10 of Hanumannagar Kankalini Municipality, Saptari were the population of the study. Probability proportional to size method was used to select students. Sample size was 235 but only 225 were included in analysis due to incompleteness of questionnaire of 10 students. Self-administered anonymous questionnaire was used to collect data. Frequency and percentage; and chi-square test were used to analyze descriptive data and inferential data, respectively. Proportion of students who consumed tobacco was 24% (boy-18.7%, girl-5.3%). Sex of students and education of parents were significantly associated with tobacco consumption behavior of students. It is concluded that sex of students, and tobacco consumption behavior of parents are associated factors for high prevalence of tobacco consumption among adolescent students. It should be minimized by promoting girl as role model and educating parents of students.
Improving Predictability, Reliability and Generalisability of Brain-Wide Associations for Cognitive Abilities via Multimodal Stacking
Authors: Tetereva Alina , Knodt Annchen R. , Melzer Tracy R. , van der Vliet William , Gibson Bryn , Hariri Ahmad R. , Whitman Ethan T. , Li Jean , Deng Jeremiah , Ireland David , Ramrakha Sandhya , Pat Narun
Published: May 2024

Brain-wide association studies (BWASs) have attempted to relate cognitive abilities with brain phenotypes, but have been challenged by issues such as predictability, test-retest reliability, and cross-cohort generalisability. To tackle these challenges, we proposed a machine-learning “stacking” approach that draws information from whole-brain magnetic resonance imaging (MRI) across different modalities, from task-fMRI contrasts and functional connectivity during tasks and rest to structural measures, into one prediction model. We benchmarked the benefits of stacking, using the Human Connectome Projects: Young Adults (n=873, 22-35 years old) and Human Connectome Projects-Aging (n=504, 35-100 years old) and the Dunedin Multidisciplinary Health and Development Study (Dunedin Study, n=754, 45 years old). For predictability, stacked models led to out-of-sampler∼.5-.6 when predicting cognitive abilities at the time of scanning, primarily driven by task-fMRI contrasts. Notably, using the Dunedin Study, we were able to predict participants’ cognitive abilities at ages 7, 9, and 11 using their multimodal MRI at age 45, with an out-of-samplerof 0.52. For test-retest reliability, stacked models reached an excellent level of reliability (ICC>.75), even when we stacked only task-fMRI contrasts together. For generalisability, a stacked model with non-task MRI built from one dataset significantly predicted cognitive abilities in other datasets. Altogether, stacking is a viable approach to undertake the three challenges of BWAS for cognitive abilities.

Significance statement

For decades, psychologists and neuroscientists have attempted to predict cognitive abilities from brain magnetic resonance imaging (MRI) data but have had limited success, casting doubt on the predictive ability of brain MRI. Here we proposed a machine learning method, called stacking, which allows us to draw information across different types of brain MRI. Using three large databases (n=2,131, 22–100 years old), we found stacking to make the prediction of cognitive abilities 1) closer to actual cognitive scores when applied to a new individual, not part of the modelling process, 2) reliable over times and 3) applicable to the data collected from different age groups and MRI scanners. Indeed, stacking, especially with fMRI task contrasts, allowed us to use MRI of people aged 45 to predict their childhood cognitive abilities reasonably well. Accordingly, stacking may help MRI realise its potential to predict cognitive abilities.

Preprint

biorXiv available under a CC-BY-NC 4.0 International license.https://doi.org/10.1101/2024.05.03.589404

Classification

Major Category: Biological Sciences

Minor Category: Psychological and Cognitive Sciences
Template switching during DNA replication is a prevalent source of adaptive gene amplification
Authors: Chuong Julie N. , Nun Nadav Ben , Suresh Ina , Matthews Julia Cano , De Titir , Avecilla Grace , Abdul-Rahman Farah , Brandt Nathan , Ram Yoav , Gresham David
Published: May 2024

Copy number variants (CNVs)—gains and losses of genomic sequences—are an important source of genetic variation underlying rapid adaptation and genome evolution. However, despite their central role in evolution little is known about the factors that contribute to the structure, size, formation rate, and fitness effects of adaptive CNVs. Local genomic sequences are likely to be an important determinant of these properties. Whereas it is known that point mutation rates vary with genomic location and local DNA sequence features, the role of genome architecture in the formation, selection, and the resulting evolutionary dynamics of CNVs is poorly understood. Previously, we have found that theGAP1gene inSaccharomyces cerevisiaeundergoes frequent and repeated amplification and selection under long-term experimental evolution in glutamine-limiting conditions. TheGAP1gene has a unique genomic architecture consisting of two flanking long terminal repeats (LTRs) and a proximate origin of DNA replication (autonomously replicating sequence, ARS), which are likely to promote rapidGAP1CNV formation. To test the role of these genomic elements on CNV-mediated adaptive evolution, we performed experimental evolution in glutamine-limited chemostats using engineered strains lacking either the adjacent LTRs, ARS, or all elements. Using a CNV reporter system and neural network simulation-based inference (nnSBI) we quantified the formation rate and fitness effect of CNVs for each strain. We find that althoughGAP1CNVs repeatedly form and sweep to high frequency in strains with modified genome architecture, removal of local DNA elements significantly impacts the rate and fitness effect of CNVs and the rate of adaptation. We performed genome sequence analysis to define the molecular mechanisms of CNV formation for 177 CNV lineages. We find that across all four strain backgrounds, between 26% and 80% of allGAP1CNVs are mediated by Origin Dependent Inverted Repeat Amplification (ODIRA) which results from template switching between the leading and lagging strand during DNA synthesis. In the absence of the local ARS, a distal ARS can mediate CNV formation via ODIRA. In the absence of local LTRs, homologous recombination mechanisms still mediate gene amplification followingde novoinsertion of retrotransposon elements at the locus. Our study demonstrates the remarkable plasticity of the genome and reveals that template switching during DNA replication is a frequent source of adaptive CNVs.
Cold adaptation in Upper Paleolithic hunter-gatherers of eastern Eurasia
Authors: Watanabe Yusuke , Wakiyama Yoshiki , Waku Daisuke , Valverde Guido , Tanino Akio , Nakamura Yuka , Suzuki Tsubasa , Koganebuchi Kae , Gakuhari Takashi , Katsumura Takafumi , Ogawa Motoyuki , Toyoda Atsushi , Mizushima Soichiro , Nagaoka Tomohito , Hirata Kazuaki , Yoneda Minoru , Nishimura Takayuki , Izuho Masami , Yamada Yasuhiro , Masuyama Tadayuki , Takahashi Ryuzaburo , Ohashi Jun ,   NCBN Controls WGS Consortium, Oota Hiroki
Published: May 2024

Previous genomic studies understanding the dispersal ofHomo sapienshave suggested that present-day East Eurasians and Native Americans can trace their ancestry to migrations from Southeast Asia. However, ineluctable adaptations during the Last Glacial Maximum (LGM) remain unclear. By analyzing 42 genomes of up to 30-fold coverage from prehistoric hunter-gatherers, Jomon, we reveal their descent from Upper Paleolithic (UP) foragers who migrated to and isolated in the Japanese archipelago during Late Pleistocene. We provide compelling evidence suggesting that these UP people underwent positive selection for cold environments, aiding their survival through the LGM facilitated by non-shivering thermogenesis and detecting it polygenically across multiple loci in the Jomon lineage. Our study pioneers the close estimation of the physiological adaptation of ancient humans by the paleogenomic approach.
Seabird and sea duck mortalities were lower during the second breeding season in eastern Canada following the introduction of Highly Pathogenic Avian Influenza A H5Nx viruses
Authors: Cormier Tabatha L. , Barychka Tatsiana , Beaumont Matthieu , Burt Tori V. , English Matthew D , Giacinti Jolene A. , Giroux Jean-François , Guillemette Magella , Hargan Kathryn E. , Jones Megan , Lair Stéphane , Lang Andrew S. , Lepage Christine , Montevecchi William A. , Rahman Ishraq , Rail Jean-François , Robertson Gregory J. , Ronconi Robert A. , Seyer Yannick , Taylor Liam U. , Ward Christopher R. E. , Wight Jordan , Wilhelm Sabina I. , Avery-Gomm Stephanie
Published: May 2024

H5N1 clade 2.3.4.4b viruses have caused significant mortality events in various wild bird species across Europe, North America, South America, and Africa. In North America, the largest impacts on wild birds have been in eastern Canada, where over 40,391 wild birds were reported to have died from highly pathogenic avian influenza (HPAI) between April and September 2022. In the year following, we applied previously established methods to quantify total reported mortality in eastern Canada for a full year October 2022, to September 2023. In this study, we (i) document the spatial, temporal and taxonomic patterns of wild bird mortality in the 12 months that followed the mass mortality event in the summer of 2022 and (ii) quantify the observed differences in mortality across the breeding season (April to September) of 2022 and 2023. In eastern Canada, there was high uncertainty about whether 2023 would bring another year of devastating HPAI-linked mortalities. Mortalities in the breeding season were 93% lower in 2023 compared to 2022 but encompassed a more taxonomically diverse array of species. We found that mortalities in the fall and winter (non-breeding season) were dominated by waterfowl, while mortalities during the spring and summer (breeding season) were dominated by seabirds. Due to a low prevalence of HPAI among the subset of tested birds, we refrained from broadly attributing reported mortalities in 2023 to HPAI. However, our analysis did identify three notable mortality events linked to HPAI, involving at least 1,646 Greater Snow Geese, 232 Canada Geese, and 212 Northern Gannets.

This study emphasizes the ongoing need for H5NX surveillance and mortality assessments as the patterns of mortality in wild populations continue to change.
Prediction of KIR3DL1/Human Leukocyte Antigen binding
Authors: Maiers Martin , Louzoun Yoram , Pymm Philip , Vivian Julian P. , Rossjohn Jamie , Brooks Andrew G , Saunders Philippa M.
Published: May 2024

KIR3DL1 is a polymorphic inhibitory Natural Killer (NK) cell receptor that recognizes Human Leukocyte Antigen (HLA) class I allotypes that contain the Bw4 motif. Structural analyses have shown that in addition to residues 77-83 that span the Bw4 motif, polymorphism at other sites throughout the HLA molecule can influence the interaction with KIR3DL1. Given the extensive polymorphism of both KIR3DL1 and HLA class I, we built a machine learning prediction model to describe the influence of allotypic variation on the binding of KIR3DL1 to HLA class I. Nine KIR3DL1 tetramers were screened for reactivity against a panel of HLA class I molecules which revealed different patterns of specificity for each KIR3DL1 allotype. Separate models were trained for each of KIR3DL1 allotypes based on the full amino sequence of exons 2 and 3 encoding theα1 andα2 domains of the class I HLA allotypes, the set of polymorphic positions that span the Bw4 motif, or the positions that encodeα1 andα2 but exclude the connecting loops. The Multi-Label-Vector-Optimization (MLVO) model trained on all alpha helix positions performed best with AUC scores ranging from 0.74 to 0.974 for the 9 KIR3DL1 allotype models. We show that a binary division into binder and non-binder is not precise, and that intermediate levels exist. Using the same models, within the binder group, high- and low-binder categories can also be predicted, the regions in HLA affecting the high vs low binder being completely distinct from the classical Bw4 motif. We further show that these positions affect binding affinity in a nonadditive way and induce deviations from linear models used to predict interaction strength. We propose that this approach should be used in lieu of simpler binding models based on a single HLA motif.
Deep Imputation for Skeleton Data (DISK) for Behavioral Science
Authors: Rose France , Michaluk Monika , Blindauer Timon , Ignatowska-Jankowska Bogna M , O'Shaughnessy Liam , Stephens Greg J. , Pereira Talmo D , Uusisaari Marylka Y. , Bozek Katarzyna
Published: May 2024

Pose estimation methods and motion capture systems have opened doors to quantitative measurements of animal kinematics. However, these methods are not perfect and contain missing data. Our method, Deep Imputation for Skeleton data (DISK), leverages deep learning algorithms to learn dependencies between keypoints and their dynamics to impute missing tracking data. We developed an unsupervised training scheme, which does not rely on manual annotations, and tested several neural network architectures for the imputation task. We found that transformer outperforms other architectures including graph convolutional networks that were developed specifically for skeleton-based action recognition. We demonstrate the usability and performance of our imputation method on seven different animal skeletons including two multi-animal set-ups. With an optional estimated imputation error, DISK enables behavior scientists to assess the reliability of the imputed data. The imputed recordings allow to detect more episodes of motion, such as steps, and to obtain more statistically robust results when comparing these episodes between experimental conditions. While animal behavior experiments are expensive and complex, tracking errors make sometimes large portions of the experimental data unusable. DISK allows for filling in the missing information and for taking full advantage of the rich behavioral data. This stand-alone imputation package, freely available at https://github.com/bozeklab/DISK.git, is applicable to results of any tracking method (marker-based or markerless) and allows for any type of downstream analysis.
miRglmm: a generalized linear mixed model of isomiR-level counts improves estimation of miRNA-level differential expression and uncovers variable differential expression between isomiRs
Authors: Baran Andrea M. , Patil Arun H. , Aparicio-Puerta Ernesto , Halushka Marc K. , McCall Matthew N.
Published: May 2024

MicroRNA-seq data is produced by aligning small RNA sequencing reads of different miRNA transcript isoforms, called isomiRs, to known microRNAs. Aggregation to microRNA-level counts discards information and violates core assumptions of differential expression (DE) methods developed for mRNA-seq data. We establish miRglmm, a DE method for microRNA-seq data, that uses a generalized linear mixed model of isomiR-level counts, facilitating detection of miRNA with differential expression or differential isomiR usage. We demonstrate that miRglmm outperforms current DE methods in estimating DE for miRNA, whether or not there is significant isomiR variability, and simultaneously provides estimates of isomiR-level DE.
GUIDE deconstructs genetic architectures using association studies
Authors: Lazarev Daniel , Chau Grant , Bloemendal Alex , Churchhouse Claire , Neale Benjamin M
Published: May 2024

Genome-wide association studies have revealed that the genetic architecture of most complex traits is characterized by a large number of distinct effects scattered across the genome. Functional enrichment analyses of these results suggest that the associations for any given complex trait are not purely random. Thus, we set out to leverage the genetic association results from many traits with a view to identifying the set of modules, or latent factors, that mediate these associations. The identification of such modules may aid in disease classification as well as the elucidation of complex disease mechanisms. We propose a method, Genetic Unmixing by Independent Decomposition (GUIDE), to estimate a set of statistically independent latent factors that best express the patterns of association across many traits. The resulting latent factors not only have desirable mathematical properties, such as sparsity and a higher variance explained (for both traits and variants), but are also able to single out and prioritize key biological features or pathophysiological mechanisms underlying a given trait or disease. Moreover, we show that these latent factors can index biological pathways as well as epidemiological and environmental influences that compose the genetic architecture of complex traits.
Validating the Target Functions and Synergistic Multi-Target, Multi-Pathway Action Mode of Compound Kushen Injection Using CRISPR/CAS
Authors: Shen Hanyuan , Nourmohammadi Saeed , Zhou Yan , Harata-Lee Yuka , Qu Zhipeng , Wang Wei , Yool Andrea J , Adelson David L.
Published: May 2024

Background and Purpose

Due to the complexity of traditional Chinese medicine based on complex mixtures of natural products and their multi-target mechanism of action, the discovery and validation of relevant targets have always been challenging. In previous studies, using transcriptomic methods and Compound Kushen Injection (CKI) as a model drug, we identified multiple pathways and target genes through which CKI exerts its pharmacological effects. Therefore, we wished to verify these targets by perturbing those genetic pathways.

Experimental Approach

In this study, we selected eight key genes from four candidate pathways and used CRISPR/CAS technology to knock out these genes in four cell lines, validating their role in CKI activity.

Key Results

Firstly, we found that although the sensitivity of different cell lines to gene knockout varied, overall, it led to a reduction in various cellular activities. After the addition of CKI, we observed that, except for the minor impact of CDKN1A gene knockout on the effect of CKI, knocking out the other genes significantly affected the pharmacological efficacy of CKI in different assays. Among them, knockout of MYD88 and NFkB genes enhanced the efficacy of CKI. At the same time, we found that the genes IL24 and CYP1B1 play a crucial role in CKI inhibition of tumour cell migration, and the CYP1A1 gene is critical for the cell cycle arrest induced by CKI.

Conclusion and Implications

These findings not only validate the results of our previous transcriptomic analysis but also further demonstrate the complexity of pharmacological mechanisms of multi-target synergistic action of natural product mixtures.

What is already known

CKI demonstrates antitumor effects in both clinical and pharmacological research. Transcriptomic analysis shows CKI can perturb the expression of numerous genes in pathways related to cancer.

What does this study add

The knockout of most selected genes whose expression is altered by CKI can significantly affect the pharmacological effects of CKI.

IL24 and CYP1B1genes are essential to CKI’s inhibition of cancer cell migration and CYP1A1 is important for CKI’s G2M cell cycle arrest effect.

What is the clinical significance

The efficacy of CKI is demonstrated to arise from the synergistic action of multiple pathways and targets.
LBFextract: unveiling transcription factor dynamics from liquid biopsy data
Authors: Lazzeri Isaac , Spiegl Benjamin Gernot , Hasenleithner Samantha O. , Speicher Michael R. , Kircher Martin
Published: May 2024

Structured abstract

Motivation

The analysis of circulating cell-free DNA (cfDNA) holds immense promise as a non-invasive diagnostic tool across various human conditions. However, extracting biological insights from cfDNA fragments entails navigating complex and diverse bioinformatics methods, encompassing not only DNA sequence variation but also epigenetic characteristics like nucleosome footprints, fragment length, and methylation patterns.

Results

We introduce LBFextract, a comprehensive package designed to streamline feature extraction from cfDNA data, with the aim of enhancing the reproducibility and comparability of liquid biopsy studies. LBFextract facilitates the integration of preprocessing and postprocessing steps through alignment fragment tags and a hook mechanism. It incorporates various methods, including coverage-based and fragment length-based approaches, alongside two novel feature extraction methods: an entropy-based method to infer TF activity from fragmentomics data and a technique to amplify signals from nucleosome dyads. Additionally, it implements a method to extract condition-specific differentially active TFs based on these features for biomarker discovery. We demonstrate the use of LBFextract for the subtype classification of advanced prostate cancer patients using coverage signals at transcription factor binding sites from cfDNA. We show that LBFextract can generate robust and interpretable features that can discriminate between different clinical groups. LBFextract is a versatile and user-friendly package that can facilitate the analysis and interpretation of liquid biopsy data.

Data and Code Availability and Implementation

LBFextract is freely accessible athttps://github.com/Isy89/LBF. It is implemented in Python and compatible with Linux and Mac operating systems. Code and data to reproduce these analyses have been uploaded to 10.5281/zenodo.10964406.

Contact

For further information, contactisaac.lazzeri@medunigraz.at,samantha.hasenleithner@medunigraz.at,martin.kircher@bih-charite.de.

Supplementary Information

For additional details see Supplementary Information. For usage of the package, refer tohttps://lbf.readthedocs.io/.