bluuebunny/crossref_metadata_2025 huggingface dataset:
The Genomic Diversity of theEliurusgenus in northern Madagascar with a Putative New Species
Authors: Sgarlata Gabriele Maria , Rasolondraibe Emmanuel , Salmona Jordi , Le Pors Barbara , Ralantoharijaona Tantely , Rakotonanahary Ando , Jan Fabien , Manzi Sophie , Iribar-Pelozuelo Amaya , Zaonarivelo John Rigobert , Andriaholinirina Nicole Volasoa , Rasoloharijaona Solofonirina , Chikhi Lounès
Published: October 2022

Madagascar exhibits extraordinarily high level of species richness and endemism, while being severely threatened by habitat loss and fragmentation (HL&F). In front of such threat to biodiversity, conservation effort can be directed, for instance, in the documentation of species that are still unknown to science, or in investigating how species respond to HL&F. The tufted-tail rats genus (Eliurusspp.) is the most speciose genus of endemic rodents in Madagascar, with 13 described species, which occupy two major habitat types: dry or humid forests. The large species diversity and association to specific habitat types make theEliurusgenus a suitable model for investigating species adaptation to new environments, as well as response to HL&F (dryvshumid). In the present study, we investigatedEliurusspp. genomic diversity across northern Madagascar, a region covered by both dry and humid fragmented forests. From the mitochondrial DNA (mtDNA) and nuclear genomic (RAD-seq) data of 124Eliurusindividuals sampled in poorly studied forests of northern Madagascar, we identified an undescribedEliurustaxon (Eliurus sp. nova). We tested the hypothesis of a newEliurusspecies using several approaches: i) DNA barcoding; ii) phylogenetic inferences; iii) species delimitation tests based on the Multi-Species Coalescent (MSC) model, iv) genealogical discordance index (gdi); v) thead-hoctest of isolation-by-distance withinversusbetween sister-taxa, vi) comparisons of %GC content patterns and vii) morphological analyses. All analyses support the recognition of the undescribed lineage as a distinct species. In addition, we show thatEliurus myoxinus, a species known from the dry forests of western Madagascar, is, surprisingly, found mostly in humid forests in northern Madagascar. In conclusion, we discuss the implications of such findings in the context ofEliurusspecies evolution and diversification, and use the distribution of northernEliurusspecies as a proxy for reconstructing past changes in forest cover and vegetation type in northern Madagascar.
A positive statistical benchmark to assess network agreement
Authors: Hao Bingjie , Kovács István A.
Published: October 2022

As the current best practice, an experimental network dataset is validated by showing significant overlap with a gold standard network. Significance is assessed by comparison to a negative benchmark, often a randomized version of the same gold standard. While such analysis can reliably indicate the presence of signal, it is illsuited to assess how much signal there is. As an alternative, here we introduce a positive statistical benchmark corresponding to the best-case scenario, capturing the maximum possible overlap between two networks. Such a positive benchmark can be efficiently generated in a maximum entropy framework and opens the way to assess if the observed overlap is significantly different from the best-case scenario. In combination with the negative benchmark, we provide a normalized overlap score (Normlap). As an application, we compare molecular and functional networks, resulting in anagreement networkof human as well as yeast network datasets. Although the number of shared interactions between most networks within the same organism is relatively small, we find that it is typically close to the best-case scenario. This paradox is resolved by the underlyingdegree inconsistency, meaning that highly connected hubs in one network often have small degrees in another, limiting the potential overlap. Furthermore, we illustrate how Normlap improves the quality assessment of experimental networks, fostering the creation of future high-quality networks.
Smart Glasses for Gait Analysis in Parkinson’s Disease: A preliminary study
Authors: Kiprijanovska Ivana , Stankoski Simon , Gjoreski Martin , William Archer James Archer , Broulidakis John , Mavridou Ifigeneia , Hayes Bradley , Nduka Charles , Gjoreski Hristijan
Published: October 2022

Parkinson’s disease (PD) is one of the most common neurodegenerative disorders of the central nervous system, which predominantly affects patients’ motor functions, movement, and stability. Monitoring movement in patients with PD is crucial for inferring motor state fluctuations throughout daily life activities, which aids in disease progression analysis and assessing how patients respond to medications over time. In recent years, there has been an increase in the usage of wearable sensors for PD symptom monitoring. In this study, we present a preliminary analysis of smart glasses equipped with IMU sensors to provide objective information on the motor state in patients with PD. Data were collected from seven Parkinson’s patients with varying levels of symptom severity. The patients performed the Timed-Up-and-Go (TUG) Test while wearing IMU-equipped glasses. Our analysis indicates that smart glasses can provide information about patients’ gait that can be used to assess the severity level of the PD as measured by two standardized questionnaires. Furthermore, patient-specific clusters can be easily detected in the sensor data, hinting at the development of personalized models for patient-specific monitoring of symptom progression. Therefore, smart glasses have the potential to be used as an unobtrusive and continuous screening tool for PD patients’ gait, enhancing medical assessment and treatment.

CCS CONCEPTS • Applied computing • Life and medical sciences • Health informatics

ACM Reference Format

First Author’s Name, Initials, and Last Name, Second Author’s Name, Initials, and Last Name, and Third Author’s Name, Initials, and Last Name. 2022. The Title of the Paper: ACM Conference Proceedings Manuscript Submission Template: This is the subtitle of the paper, this document both explains and embodies the submission format for authors using Word. In Woodstock ‘18: ACM Symposium on Neural Gaze Detection, June 03–05, 2018, Woodstock, NY. ACM, New York, NY, USA, 10 pages. NOTE: This block will be automatically generated when manuscripts are processed after acceptance.
Hemochromatosis Neural Archetype Reveals Iron Disruption in Motor Circuits
Authors: Loughnan Robert , Ahern Jonathan , Boyle Mary , Jernigan Terry L. , Hagler Donald J. , Iversen John R. , Frei Oleksandr , Smith Diana M. , Andreassen Ole , Zaitlen Noah , Sugrue Leo , Thompson Wesley K. , Dale Anders , Schork Andrew J. , Fan Chun Chieh
Published: October 2022

Our understanding of brain iron regulation and its disruption in disease is currently lacking. We previously found that motor circuitry is susceptible to the cumulative neurotoxic effects of excessive iron, leading to the manifestation of Parkinson’s disease. However, beyond a few well-known genes involved in peripheral iron metabolism, the underlying molecular mechanisms regulating central iron levels remain unclear. To bridge this gap, we generated scores in neurotypical individuals based on the archetypal brain iron accumulation observed in magnetic resonance imaging scans of individuals who exhibit excessive absorption of dietary iron and hemochromatosis risk. Genome-wide analysis, using common-variant SNP-array and rare-variant exome data, demonstrated this archetypal brain iron accumulation pattern is highly heritable, revealing both known and novel loci associated with iron homeostasis, and causally driven by peripheral iron levels. Our score predicted abnormalities in gait and revealed a U-shape relationship with PD risk - identifying a group of individuals with a 3-fold increased risk for this disorder. Taken together these results establish a hormetic relationship between brain iron and PD risk, in which central iron levels are strongly determined by genetics via peripheral iron. We believe this framework combining forward and reverse genetics represents a powerful new study design to understand genomic drivers underlying high dimensional phenotypes.
Pod-based e-cigarette use among college-aged adults in the United States: A survey on the perception of health effects, sociodemographic correlates, and interplay with other tobacco products
Authors: Obisesan Olufunmilayo H. , Uddin S.M. Iftekhar , Boakye Ellen , Osei Albert D. , Mirbolouk Mohammadhassan , Orimoloye Olusola A. , Dzaye Omar , Shahawy Omar El , Stokes Andrew , DeFilippis Andrew P. , Benjamin Emelia J. , Blaha Michael J.
Published: October 2022

Introduction

E-cigarette use among youth and young adults remains of public health concern. Pod-based e-cigarettes, including JUUL, significantly changed the e-cigarette landscape in the United States (US). Using an online survey, we aimed to explore the socio-behavioral correlates, predisposing factors, and addictive behaviors among young adult pod-mod users within a University in Maryland, USA.

Methods

112 eligible college students aged 18-24 years recruited from a University in Maryland who reported using pod-mods were included in this study. Participants were categorized into current/non-current users based on past-30-day use. Descriptive statistics were used to analyze participants’ responses.

Results

Of the 112 participants with mean age 20.5±1.2 years, 56.3% were female, 48.2% White, and 40.2% reported past-30-day (current) use of pod-mods. The mean age of first experimentation with pod-mods was 17.8±1.4 years, while the mean age of regular use was 18.5±1.4 years, with the majority (67.9%) citing social influence as the reason for initiation. Of current users, 62.2% owned their own devices, and 82.2% predominantly used JUUL and menthol flavor (37.8%). A significant proportion of current users (73.3%) reported buying pods in person, 45.5% of whom were below the age of 21. Among all participants, 67% had had a past serious quit attempt. Among them, 89.3% neither used nicotine replacement therapy nor prescription medications. Finally, current use (adjusted odds ratio (aOR):4.52; 95%CI:1.76,11.64), JUUL use (aOR:2.56; 95%CI:1.08,6.03), and menthol flavor (aOR:6.52; 95%CI:1.38,30.89) were associated with reduced nicotine autonomy, a measure of addiction.

Conclusion

Our findings provide specific data to inform the development of public health interventions targeted at college youth, including the need for more robust cessation support for pod-mod users.
Advancing Post-Traumatic Seizure Classification and Biomarker Identification: Information Decomposition Based Multimodal Fusion and Explainable Machine Learning with Missing Neuroimaging Data
Authors: Akbar Md Navid , Ruf Sebastian F. , Singh Ashutosh , Faghihpirayesh Razieh , Garner Rachael , Bennett Alexis , Alba Celina , La Rocca Marianna , Imbiriba Tales , Erdoğmuş Deniz , Duncan Dominique
Published: October 2022

A late post-traumatic seizure (LPTS), a consequence of traumatic brain injury (TBI), can potentially evolve into a lifelong condition known as post-traumatic epilepsy (PTE). Presently, the mechanism that triggers epileptogenesis in TBI patients remains elusive, inspiring the epilepsy community to devise ways to predict which TBI patients will develop PTE and to identify potential biomarkers. In response to this need, our study collected comprehensive, longitudinal multimodal data from 48 TBI patients across multiple participating institutions. A supervised binary classification task was created, contrasting data from LPTS patients with those without LPTS. To accommodate missing modalities in some subjects, we took a two-pronged approach. Firstly, we extended a graphical model-based Bayesian estimator to directly classify subjects with incomplete modality. Secondly, we explored conventional imputation techniques. The imputed multimodal information was then combined, following several fusion and dimensionality reduction techniques found in the literature, and subsequently fitted to a kernel- or a tree-based classifier. For this fusion, we proposed two new algorithms: recursive elimination of correlated components (RECC) that filters information based on the correlation between the already selected features, and information decomposition and selective fusion (IDSF), which effectively recombines information from decomposed multimodal features. Our cross-validation findings showed that the proposed IDSF algorithm delivers superior performance based on the area under the curve (AUC) score. Ultimately, after rigorous statistical comparisons and interpretable machine learning examination using Shapley values of the most frequently selected features, we recommend the two following magnetic resonance imaging (MRI) abnormalities as potential biomarkers: the left anterior limb of internal capsule in diffusion MRI (dMRI), and the right middle temporal gyrus in functional MRI (fMRI).
Meta-analysis of preclinical pharmacogenomic studies to discover robust and translatable biomarkers of drug response
Authors: Smirnov Petr , Nair Sisira Kadambat , Abbas-Aghababazadeh Farnoosh , Feizi Nikta , Smith Ian , Pugh Trevor J. , Haibe-Kains Benjamin
Published: October 2022

Preclinical pharmacogenomic studies provide an opportunity to discover novel biomarkers for drug response. However, pharamcogenomic studies linking gene expression profiles to drug response do not always agree on the significance or strength of biomarkers. We apply a statistical meta-analysis approach to 7 large independent pharmacogenomic studies, testing for tissue-specific gene expression markers predictive of response among cancer cell lines. We found 4,338 statistically-significant biomarkers across 8 tissue types and 34 drugs. Significant biomarkers were found to be closer than random to drug targets in a gene network built on pathway co-membership (average distance of 2 vs 2.9). However, functional relationships with the drug target did not predict reproducibility across studies. To validate these biomarkers, we utilized 10 clinical datasets, allowing 42/4338 biomarkers to be assessed for clinical translation. Of the 42 candidate biomarkers, the expression ofODC1was found to be significantly predictive of Paclitaxel response as a neoadjuvant treatment of breast carcinoma across 2 independent clinical studies of>200 patients each. We expect that as more clinical transcriptomics data matched with response are available, our results can be used to prioritize which genes to evaluate as clinical biomarkers of drug response.
Temporal single cell transcriptome atlas of zebrafish anterior segment development reveals high degree of conservation between the trabecular meshwork and the annular ligament
Authors: Vöcking Oliver , Famulski J.K.
Published: October 2022

Anterior segment dysgenesis (ASD), resulting in vision impairment, stems from maldevelopment of anterior segment (AS) tissues. Incidence of ASD has been linked to malfunction of periocular mesenchyme cells (POM). POM cells specify into anterior segment mesenchyme (ASM) cells which colonize and produce AS tissues. In this study we uncover ASM developmental trajectories associated with formation of the AS. Using a transgenic line of zebrafish that fluorescently labels the ASM throughout development, Tg[foxc1b:GFP], we isolated GFP+ ASM cells at several developmental timepoints (48-144hpf) and performed single cell RNA sequencing. Clustering analysis indicates subdifferentiation of ASM as early as 48hpf and subsequent diversification into corneal, epithelium/endothelium/stroma, or annular ligament (AL) lineages. Tracking individual clusters reveals common developmental pathways, up to 72hpf, for the AL and corneal endothelium/stroma, and distinct pathways for corneal epithelium starting at 48hpf. Spatiotemporal validation of over 80 genes found associated AS development demonstrates high degree of conservation with mammalian trabecular meshwork and corneal tissues. In addition, we characterize thirteen novel genes associated with AL and seven with corneal development. Overall, the data provide a molecular verification of the long-standing hypothesis that POM derived ASM give rise to AS tissues and highlight the high degree of conservation between zebrafish and mammals.
Evaluation of a structured screening assessment to detect patients with isolated REM Sleep Behavior Disorder
Authors: Seger Aline , Ophey Anja , Heitzmann Wiebke , Doppler Christopher E. J. , Lindner Marie-Sophie , Brune Corinna , Kickartz Johanna , Dafsari Haidar S. , Oertel Wolfgang H. , Fink Gereon R. , Jost Stefanie T. , Sommerauer Michael
Published: October 2022

Background

Isolated rapid eye movement (REM) sleep behavior disorder (iRBD) cohorts have provided novel insights in the earliest neurodegenerative processes in α-synucleinopathies. Even though polysomnography remains the gold standard for diagnosis, an accurate questionnaire-based algorithm to identify eligible subjects could facilitate efficient recruitment in research.

Objectives

This study aimed to optimize the identification of subjects with iRBD from the general population.

Methods

Between June 2020 and July 2021, we placed newspaper advertisements including the single-question screen for RBD (RBD1Q). Participants’ evaluations included a structured telephone screening consisting of the RBD screening questionnaire (RBDSQ) and additional sleep-related questionnaires. We examined anamnestic information predicting polysomnography-proven iRBD using logistic regressions and receiver operating characteristic curves.

Results

543 participants answered the advertisements and 185 subjects fulfilling in- and exclusion criteria were screened. Of these, 124 received polysomnography after expert selection and 78 (62.9%) were diagnosed with iRBD. Selected items of the RBDSQ, the Pittsburgh Sleep Quality Index, the STOP-Bang questionnaire, and age predicted iRBD with high accuracy in a multiple logistic regression model (area under the curve >80%). Comparing the algorithm to the sleep expert decision, 77 instead of 124 polysomnographies (62.1%) would have been carried out, while 63 (80.8%) of iRBD patients would have been identified. 32 of 46 (69.6%) unnecessary polysomnography examinations could have been avoided.

Conclusions

Our proposed algorithm displayed high diagnostic accuracy for polysomnography-proven iRBD in a cost-effective manner and may be a convenient tool for application in research and clinical settings. External validation sets are warranted to prove its reliability.
A SEIRD+V Model for the Effect of Vaccination and Social Distancing on SARS-CoV-2 Infection and Mortality
Authors: Jin Alexander , Sharifi Husham
Published: October 2022

We present a deterministic, calibrated Susceptible-Exposed-Infected-Recovered-Dead + Vaccinated (SEIRD+V) model that simulates the spread and containment of COVID-19. We use the model to compare the effectiveness of vaccination vs. social distancing on death prevention and total and peak infection reduction. We find that vaccination drastically reduces total deaths from COVID-19, as well as total and peak infections with Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2). We find that social distancing plays a role in reducing total COVID-19 deaths, but its impact is less pronounced when vaccine efficacy and vaccination fraction are both high. Social distancing also plays a role in reducing total and peak infections, which is attenuated in the presence of vaccination. We employed a thresholding methodology to assess the requirements of vaccine efficacy and the vaccination fraction to limit total COVID-19 deaths and peak infections to a 5% threshold. Our thresholding results quantify the impact of social distancing on total COVID-19 deaths and peak infections and are significant in their ability to inform public health policy for future outbreaks, as well as for SARS-CoV-2 itself as it continues to mutate and alter its transmissibility.
Prevalence and associated factors of depression among junior healthcare professionals of public teaching hospitals of Bangladesh: An analytical cross-sectional study
Authors: Mamun Gazi Md. Salahuddin , Hassan Md. Zakiul , Khatun Aysha , Kabir Md. Farhad , Shaima Shamsun Nahar , Afrin Sadia , Seoty Nuhad Raisa
Published: October 2022

Due to the significant number and its effects on quality of life, depression is becoming a major concern worldwide. Though its prevalence among junior healthcare professionals is also increasing day by day, still very few data are available regarding this. So, we’ve conducted a study to find out the prevalence and associated factors of depression among this vulnerable population. A total of 218 participants were enrolled from two public teaching tertiary-level hospitals in Dhaka, Bangladesh from October 2018 to April 2019. Data were collected by using a self-administered questionnaire including the WHO-5 well-being index. Prevalence of major depression was found at 17.9% and poor-emotional well-being was 25.2%. Factors associated independently with major depression were those thinking to be a doctor as the wrong decision (aRRR: 6.85, 95% CI: 1.40-33.45, p=0.017) and taking sedative or anxiolytic drugs (aRRR: 4.54, 95% CI: 1.50-13.73, p=0.007). On the other hand, doing physical exercise (aRRR: 0.32, 95% CI: 0.12-0.89, p=0.028) and being satisfied in their current job position (aRRR: 0.07, 95% CI: 0.02-0.29, p<0.001) had significantly less chance of being suffering of major depression. Suicidal and self-hurting ideation was also found among 23.4% of participants. If these modifiable factors can be addressed properly and by taking necessary steps against these simply identifiable factors, unwanted incidences can be prevented especially in low- and middle-income countries.

What is already known on this topic

Depression is common among healthcare professionals but is still neglected especially in low- and middle-income countries.

What this study adds

Thinking of being a doctor as the wrong decision, taking sleeping pills, not doing physical exercise, and being not satisfied in their current job position are associated with depression among junior healthcare professionals. Suicidal and self-hurting ideation were also found high among the participants.

How this study might affect research, practice or policy

Early identification of major depression by simple factors may help to initiate prompt strategies that will reduce the burden of depression among junior healthcare professionals and may improve the healthcare services of low- and middle-income countries.
Multiomic single-cell lineage tracing to dissect fate-specific gene regulatory programs
Authors: Jindal Kunal , Adil Mohd Tayyab , Yamaguchi Naoto , Yang Xue , Wang Helen C. , Kamimoto Kenji , Rivera-Gonzalez Guillermo C. , Morris Samantha A.
Published: October 2022

Complex gene regulatory mechanisms underlie differentiation and reprogramming. Contemporary single-cell lineage tracing (scLT) methods use expressed, heritable DNA barcodes to combine cell lineage readout with single-cell transcriptomics enabling high-resolution analysis of cell states while preserving lineage relationships. However, reliance on transcriptional profiling limits their adaptation to an ever-expanding tool kit of multiomic single-cell assays. With CellTag-multi, we present a novel approach for independently profiling lineage barcodes with single-cell chromatin accessibility without relying on co-assay of transcriptional state, paving the way for truly multiomic lineage tracing. We validate CellTag-multi in mouse hematopoiesis, characterizing transcriptional and epigenomic lineage priming across progenitor cell populations. In direct reprogramming of fibroblasts to endoderm progenitors, we use CellTag-multi to comprehensively link early cell state with reprogramming outcomes, identifying core regulatory programs underlying on-target and off-target reprogramming. Further, we reveal the Transcription Factor (TF) Zfp281 as a novel regulator of reprogramming outcome, biasing cells towards an off-target mesenchymal fate via its regulation of TGF-β signaling. Together, these results establish CellTag-multi as a novel lineage tracing method compatible with multiple single-cell modalities and demonstrate its utility in revealing fate-specifying gene regulatory changes across diverse paradigms of differentiation and reprogramming.
Inferring time-aware models of cancer progression using Timed Hazard Networks
Authors: Chen Jian
Published: October 2022

Analysis of the sequential accumulation of genetic events, such as mutations and copy number alterations, is key to understanding disease dynamics and may provide insights into the design of targeted therapies. Oncogenetic graphical models are computational methods that use genetic event profiles from cross-sectional genomic data to infer the statistical dependencies between events and thereby deduce their temporal order of occurrence. Existing research focuses mainly on the development of graph structure learning algorithms. However, no algorithm explicitly links the oncogenetic graph with the temporal differences of samples in an analytic way. In this paper, we propose a novel statistical framework Timed Hazard Networks (TimedHN), that treat progression times as hidden variables and jointly infers oncogenetic graph and pseudo-time order of samples. We modeled the accumulation process as a continuous-time Markov chain and developed an efficient gradient computation algorithm for the optimization. Experiment results using synthetic data showed that our method outperforms the state-of-the-art in graph reconstruction. We highlighted the differences between TimedHN and competing methods on a luminal breast cancer dataset and illustrated the potential utility of the proposed method. Implementation and data are available athttps://github.com/puar-playground/TimedHN
Which curve are we flattening? The disproportionate impact of COVID-19 among economically marginalized communities in Ontario, Canada, was unchanged from wild-type to omicron
Authors: Ma Huiting , Chan Adrienne K. , Baral Stefan D. , Fahim Christine , Straus Sharon , Sander Beate , Mishra Sharmistha
Published: October 2022

Economically marginalized communities have faced disproportionately higher risks for infection and death from COVID-19 across Canada. It was anticipated that health disparities would dissipate over time and during subsequent waves. We used person-level surveillance and neighbourhood-level income data to explore, using Lorenz curves and Gini coefficients, magnitude of inequalities in COVID-19 hospitalizations and deaths over five waves of COVID-19 in Ontario, Canada (population 14 million) between February 26, 2020 and February 28, 2022. We found that despite attempts at equity-informed policies alongside fluctuating levels of public health measures, inequalities in hospitalizations and deaths by income remained at levels observed during the first wave - prior to vaccination, discussion or implementation of equity-informed policies - and despite rising levels of hybrid immunity. There was no change in the magnitude of inequalities across all waves evaluated. Our findings indicate that interventions did not sufficiently address differential exposure risks amplified at the intersections of household crowding and size, workplace exposures, and systemic barriers to prevention and care (including access to therapeutics). Equity and effectiveness of programs are inherently linked and ongoing evaluation of both is central to inform the public health response to future waves of COVID-19 and other rapidly emergent pandemics.
A deep learning approach for metabolic rate prediction
Authors: Kontopoulos Ioannis , Valagkouti Christina-Ariadni , Kontopoulos Panagiotis
Published: October 2022

Abstract

The sudden increase of wearable devices has led to the generation of an abundance of data. As a result, researchers can use such data to perform analyses and generate recommendations. A crucial factor in the research field of nutrition and dietetics is the accurate measurement of metabolic rate, as it can be used to estimate several other variables, e.g. calorie expenditure. Nonetheless, limited studies have been conducted to examine the use of machine learning models for metabolic rate prediction based on data generated from wearable devices. Therefore, in this paper, a neural network architecture is proposed, able to predict a subject’s metabolic rate exploiting only such data. Experimental results demonstrated that the proposed methodology can outperform conventional algorithms in prediction accuracy of real-world data. Furthermore, results indicated that the trivial time taken for the network to predict the metabolic rate makes it suitable for wearable devices deployment.
Exploring Digital Speech Biomarkers of Hypokinetic Dysarthria in a Multilingual Cohort
Authors: Kovac Daniel , Mekyska Jiri , Aharonson Vered , Harar Pavol , Galaz Zoltan , Rapcsak Steven , Orozco-Arroyave Juan Rafael , Brabenec Lubos , Rektorova Irena
Published: October 2022

Hypokinetic dysarthria, a motor speech disorder characterized by reduced movement and control in the speech-related muscles, is mostly associated with Parkinson’s disease. Acoustic speech features thus offer the potential for early digital biomarkers to diagnose and monitor the progression of this disease. However, the influence of language on the successful classification of healthy and dysarthric speech remains crucial. This paper explores the analysis of acoustic speech features, both established and newly proposed, in a multilingual context to support the diagnosis of PD. The study aims to identify language-independent and highly discriminative digital speech biomarkers using statistical analysis and machine learning techniques. The study analyzes thirty-three acoustic features extracted from Czech, American, Israeli, Columbian, and Italian PD patients, as well as healthy controls. The analysis employs correlation and statistical tests, descriptive statistics, and the XGBoost classifier. Feature importances and Shapley values are used to provide explanations for the classification results. The study reveals that the most discriminative features, with reduced language dependence, are those measuring the prominence of the second formant, monopitch, and the frequency of pauses during text reading. Classification accuracies range from 67 % to 85 %, depending on the language. This paper introduces the concept of language robustness as a desirable quality in digital speech biomarkers, ensuring consistent behaviour across languages. By leveraging this concept and employing additional metrics, the study proposes several language-independent digital speech biomarkers with high discrimination power for diagnosing PD.
BugSigDB captures patterns of differential abundance across a broad range of host-associated microbial signatures
Authors: Geistlinger Ludwig , Mirzayi Chloe , Zohra Fatima , Azhar Rimsha , Elsafoury Shaimaa , Grieve Claire , Wokaty Jennifer , Gamboa-Tuz Samuel David , Sengupta Pratyay , Hecht Isaac , Ravikrishnan Aarthi , Gonçalves Rafael , Franzosa Eric , Raman Karthik , Carey Vincent , Dowd Jennifer B. , Jones Heidi E. , Davis Sean , Segata Nicola , Huttenhower Curtis , Waldron Levi
Published: October 2022

The literature of human and other host-associated microbiome studies is expanding rapidly, but systematic comparisons among published results of host-associated microbiome signatures of differential abundance remain difficult. We present BugSigDB, a community-editable database of manually curated microbial signatures from published differential abundance studies, accompanied by information on study geography, health outcomes, host body site, and experimental, epidemiological, and statistical methods using controlled vocabulary. The initial release of the database contains >2,500 manually curated signatures from >600 published studies on three host species, enabling high-throughput analysis of signature similarity, taxon enrichment, co-occurrence and co-exclusion, and consensus signatures. These data allow assessment of microbiome differential abundance within and across experimental conditions, environments, or body sites. Database-wide analysis reveals experimental conditions with the highest level of consistency in signatures reported by independent studies and identifies commonalities among disease-associated signatures including frequent introgression of oral pathobionts into the gut.
Oncogenic context shapes the fitness landscape of tumor suppression
Authors: Blair Lily M. , Juan Joseph M. , Sebastian Lafia , Tran Vy B. , Nie Wensheng , Wall Gregory D. , Gerceker Mehmet , Lai Ian K. , Apilado Edwin A. , Grenot Gabriel , Amar David , Foggetti Giorgia , Do Carmo Mariana , Ugur Zeynep , Deng Debbie , Chenchik Alex , Paz Zafra Maria , Dow Lukas E. , Politi Katerina , MacQuitty Jonathan J. , Petrov Dmitri A. , Winslow Monte M. , Rosen Michael J. , Winters Ian P.
Published: October 2022

Tumors acquire alterations in oncogenes and tumor suppressor genes in an adaptive walk through the fitness landscape of tumorigenesis. However, the features of this landscape remain poorly understood and cannot be revealed by human cancer genotyping alone. Here, we use a multiplexed, autochthonous mouse platform to model and quantify the initiation and growth of more than one hundred genotypes of lung tumors across four oncogenic contexts: KRAS G12D, KRAS G12C, BRAF V600E, and EGFR L858R. The resulting fitness landscape is rugged (the effect of tumor suppressor inactivation often switches between beneficial and deleterious depending on the oncogenic context), shows no evidence of diminishing-returns epistasis within variants of the same oncogene, and is inconsistent with expectations of a simple linear signaling relationship among these three oncogenes. Our findings suggest that tumor suppressor effects are strongly context-specific, which limits the set of evolutionary paths that can be taken through the fitness landscape.
A scalable, reproducible and open-source pipeline for morphologically profiling image cytometry data
Authors: Lippeveld Maxim , Peralta Daniel , Filby Andrew , Saeys Yvan
Published: October 2022

Due to high resolution and throughput of modern image cytometry platforms, morphologically profiling generated datasets poses a significant computational challenge. Here, we present Scalable Cytometry Image Processing (SCIP), an image processing software aimed at running on distributed high performance computing infrastructure. SCIP is scalable, flexible, open-source and enables reproducible image processing. It performs projection, illumination correction, segmentation, background masking and extensive morphological profiling on various imaging types.

We showcase SCIP’s capabilities on three large-scale image cytometry datasets. First, we process an imaging flow cytometry (IFC) dataset of human white blood cells and show how the obtained features are used to classify cells into 8 cell types based on bright- and darkfield imagery. Secondly, we process an automated microscopy dataset of human white blood cells to divide them into cell types in an unsupervised manner. Finally, a high-content screening dataset of breast cancer cells is processed to predict the mechanism-of-action of a large set of compound treatments.

The software can be installed from the PyPi repository. Its source code is available athttps://github.com/ScalableCytometryImageProcessing/SCIPunder the GNU General Public License version 3. It has been tested on Unix operating systems. Issues with the software can be submitted athttps://github.com/ScalableCytometryImageProcessing/SCIP/issues.

1

Author Summary

Cytometry is a field of biology that studies cells by measuring their characteristics. In image cytometry, this is done by acquiring images of cells. In order to gain biological insight from a set of images, an extensive amount of measurements are derived from them describing the cells they contain. These measurements include, for instance, a cell’s area, diameter, or the average brightness of the cell image. These measurements can then be analyzed using automated software tools to understand, for example, how cells respond to drug treatments, or how cells differ between a healthy and a diseased person. In this work, we present a novel software tool that is able to efficiently compute image measurements on large datasets of images. We do this by harnessing the power of high performance computing infrastructure. By enabling image cytometry researchers to make use of more computational power, they can more efficiently process complex and large datasets, paving the way to novel, fascinating biological discoveries.
A frameshift inYersinia pestis rcsDleads to expression of a small HPt variant that alters canonical Rcs signalling to preserve flea-mammal plague transmission cycles
Authors: Guo Xiao-Peng , Yan Hai-Qin , Yang Wenhui , Yin Zhe , Vadyvaloo Viveka , Zhou Dongsheng , Sun Yi-Cheng
Published: October 2022

Multiple genetic changes in the enteric pathogenYersinia pseudotuberculosishave driven the emergence ofYesinia pestis, the arthropod-borne, etiological agent of plague. These include developing the capacity for biofilm-dependent blockage of the flea foregut to enable transmission by flea bite. Previously, we showed that pseudogenisation ofrcsA, encoding a component of the Rcs signalling pathway, is an important evolutionary step facilitatingY. pestisflea-borne transmission. Additionally,rcsD, another important gene in the Rcs system, harbours a frameshift mutation. Here, we demonstrated that thisrcsDmutation resulted in predominant production of a small protein composing the C-terminal RcsD histidine-phosphotransferase domain (designated RcsD-Hpt) and low levels of full-length RcsD. Genetic analysis revealed that thercsDframeshift mutation followed the emergence ofrcsApseudogenisation. It further altered the canonical Rcs phosphorylation signal cascade, fine-tuning biofilm production to be conducive with retention of thepgmlocus in modern lineages ofY. pestis. Taken together, our findings suggest that a frameshift mutation inrcsD, is an important evolutionary step that fine-tuned biofilm production to ensure perpetuation of flea-mammal plague transmission cycles.
Tau amyloid polymorphism is shaped by local structural propensities of its protein sequence
Authors: Louros Nikolaos , Wilkinson Martin , Tsaka Grigoria , Ramakers Meine , Morelli Chiara , Garcia Teresa , Gallardo Rodrigo U. , D’Haeyer Sam , Goossens Vera , Audenaert Dominique , Thal Dietmar Rudolf , Ranson Neil A. , Radford Sheena E. , Rousseau Frederic , Schymkowitz Joost
Published: October 2022

Different tauopathies are characterized by specific amyloid filament folds that are conserved between patients. Disease-specific tau filament folds probably reflect the specific pathological contexts leading to their formation including isoforms or post-translational modifications. Little is known, however, as to whether and how intrinsic conformational tendencies of the tau sequence itself contribute to its polymorphism. Using cryo-EM structure determination we find that a short amyloidogenic C-terminal peptide consisting of residues 350-362 of the tau repeat domain adopts the same polymorphic conformations in isolation as it does in the context of major disease-associated protofilament folds. Biophysical characterisation and molecular modelling show that the amyloid conformations adopted by this peptide constitute core structural motifs stabilizing distinct disease-associated tau filament folds. In accordance this segment also contributes to the efficient propagation of human AD tau seeds in tau reporter cells while it is irrelevant to heparin-induced recombinant seeds. Our findings suggest that tau 350-362 is key to the propagation of disease-associated tau polymorphs and that the conformational preferences of this segment predispose to the topological diversity observed in tau filament folds.
Regeneration of Starfish Radial Nerve Cord restores animal mobility and unveils a new coelomocyte population
Authors: Magalhães Filipe , Andrade Claúdia , Simões Beatriz , Brigham Fredi , Valente Ruben , Martinez Pedro , Rino José , Sugni Michela , Coelho Ana Varela
Published: October 2022

The potential to regenerate a damaged body part is expressed to a different extent in animals. Echinoderms, in particular starfish, are known for their outstanding potential to regenerate cell, tissue, organ, and body parts. For instance, humans have restricted abilities to restore organ systems being dependent on limited sources of stem cells. In particular, the potential to regenerate the central nervous system is extremely limited, explaining the lack of natural mechanisms that could overcome the development of neurodegenerative diseases and the presence of traumatisms. Therefore, understanding the molecular and cellular mechanisms of regeneration in starfish could lead to the development of new therapeutic approaches in humans. In this study, we tackle the problem of starfish central nervous system regeneration by examining anatomical and behavioral traits, including external anatomic anomalies, the dynamics of coelomocytes populations and neuronal tissue architecture. We noticed that several anatomic anomalies were evident and detected that the injured arm could not be used anymore to lead the starfish movement. Those seem to be related to defense mechanisms and protection of the wound. In particular, histology showed that tissue patterns during regeneration resemble those described in holothurians and in starfish arm tip regeneration. Flow cytometry coupled with imaging flow cytometry unveiled a new coelomocyte population during the late phase of the regeneration process. Morphotypes of previously characterized coelomocytes populations were described based on IFC data. Further studies of this new coelomocyte population might provide insights on their involvement in radial nerve cord regeneration.
Single-cell transcriptomics reveals striking heterogeneity and functional organization of dendritic and monocytic cells in the bovine mesenteric lymph node
Authors: Barut G.T. , Kreuzer M.C. , Bruggmann R. , Summerfield A. , Talker S.C.
Published: October 2022

Dendritic and monocytic cells co-operate to initiate and shape adaptive immune responses in secondary lymphoid tissue. The complexity of this system is poorly understood, also because of the high phenotypic and functional plasticity of monocytic cells. We have sequenced mononuclear phagocytes in mesenteric lymph nodes (LN) of three adult cows at the single-cell level, revealing ten dendritic-cell (DC) clusters and seven monocyte/macrophage clusters with clearly distinct transcriptomic profiles. Among DC, we defined LN-resident subsets and their progenitors, as well as subsets of highly activated migratory DC differing in transcript levels for T-cell attracting chemokines. Our analyses also revealed a potential differentiation path for cDC2, resulting in a cluster of inflammatory cDC2 with close transcriptional similarity to putative DC3 and monocyte-derived DC. Monocytes and macrophages displayed sub-clustering mainly driven by pro- or anti-inflammatory expression signatures, including a small cluster of cycling, presumably self-renewing, macrophages.

With this transcriptomic snapshot of LN-derived mononuclear phagocytes, we reveal functional properties and differentiation trajectories in a “command center of immunity” that are likely to be conserved across species.
High-throughput deep tissue two-photon microscopy at kilohertz frame rates
Authors: Xiao Sheng , Giblin John T. , Boas David A. , Mertz Jerome
Published: October 2022

Strategies to image biological phenomena at millisecond time scales are generally technically challenging and suffer from compromises between imaging field-of-view, depth penetration and excitation efficiency in thick tissue. We present a simple and cost-effective solution that enables a conventional video-rate two-photon microscope (2PM) to perform 2D scanning at kilohertz frame rates, while preserving all the benefits of standard 2PM, which we demonstrate by imaging neurovascular dynamics in mouse brains.
Deep Learning Reveals How Cells Pull, Buckle, and Navigate Tissue-Like Environments
Authors: Padhi Abinash , Daw Arka , Sawhney Medha , Talukder Maahi M. , Agashe Atharva , Pour Mehran Mohammad Hossein , Jafari Mohammad , Genin Guy M. , Alisafaei Farid , Kale Sohan , Karpatne Anuj , Nain Amrinder S.
Published: October 2022

Cells move within tissues by pulling on and reshaping their fibrous surroundings. Measuring the associated forces has been a fundamental challenge in cell biology. Here, we develop deep-learning-enabled live-cell fiber-force microscopy (DLFM), which computes forces produced by living cells in real time as they interact with tissue-like fiber networks. DLFM combines basic phase microscopy with novel deep learning to simultaneously track cell movement and fiber deformation without disruptive fluorescent labels or chemical modifications. This allowed us to measure forces in real-time situations that were previously impossible to study, revealing an intricate mechanical landscape: cells generate ten-fold changes in force as they change shape during migration, create force-dipoles during cell-cell interactions, and dramatically alter their force patterns during stem cell differentiation. Through integrated experiments and mathematical modeling, we discovered that cells in fibrous environments form force-generating adhesions throughout their body, strikingly different from the edge-only adhesions seen in traditional petri dish experiments. Results clarify cytoskeletal pathways by which cells adapt force-generating machinery to navigate the fibrous architecture of tissues.
P2RX7 signaling drives the differentiation of Th1 cells through metabolic reprogramming for aerobic glycolysis
Authors: de Salles Érika Machado , Raeder Paulo Lisboa , Angeli Claudia , Santiago Verônica Feijoli , de Souza Cristiane Naffah , Ramalho Theresa , Câmara Niels Olsen Saraiva , Palmisano Giuseppe , Álvarez José Maria , D’Império Lima Maria Regina
Published: October 2022

This study provides evidence on the molecular mechanisms by which P2RX7 signaling promotes the differentiation of Th1 cells.In vivoanalysis was performed in thePlasmodium chabaudimodel of malaria in view of the great relevance of this infectious disease for human health, as well as the great availability of data concerning Th1/Tfh differentiation. We show that P2RX7 induces T-bet expression and aerobic glycolysis in splenic CD4+T cells that respond to malaria, at a time prior to Th1/Tfh polarization. Cell-intrinsic P2RX7 signaling sustains the glycolytic pathway and causes bioenergetic mitochondrial stress in activated CD4+T cells. We also showin vitrothe phenotypic similarities of Th1-conditioned CD4+T cells that do not express P2RX7 and those in which the glycolytic pathway is pharmacologically inhibited. In addition,in vitroATP synthase blockade and the consequent inhibition of oxidative phosphorylation, which drives cellular metabolism for aerobic glycolysis, is sufficient to promote rapid CD4+T cell proliferation and polarization to the Th1 profile in the absence of P2RX7. These data demonstrate that P2RX7-mediated metabolic reprograming for aerobic glycolysis is a key event for Th1 differentiation and suggest that ATP synthase inhibition is a downstream effect of P2RX7 signaling that potentiates the Th1 response.