bluuebunny/crossref_metadata_2025 huggingface dataset:
Comparative evaluation of soil DNA extraction kits for long read metagenomic sequencing
Authors: Child Harry T. , Wierzbicki Lucy , Joslin Gabrielle R. , Tennant Richard K.
Published: September 2024

Metagenomics has been transformative in our understanding of the diversity and function of soil microbial communities. Applying long read sequencing to whole genome shotgun metagenomics has the potential to revolutionise soil microbial ecology through improved taxonomic classification, functional characterisation and metagenome assembly. However, optimisation of robust methods for long read metagenomics of environmental samples remains undeveloped. In this study, Oxford Nanopore sequencing using samples from five commercially available soil DNA extraction kits was compared across four soil types, in order to optimise read length and reproducibility for comparative long read soil metagenomics. Average extracted DNA lengths varied considerably between kits, but longer DNA fragments did not translate consistently into read lengths. Highly variable decreases in the length of resulting reads from some kits were associated with poor classification rate and low reproducibility in microbial communities identified between technical repeats. Replicate samples from other kits showed more consistent conversion of extracted DNA fragment size into read length and resulted in more congruous microbial community representation. Furthermore, extraction kits showed significant differences in the community representation and structure they identified across all soil types. Overall, the QIAGEN DNeasy PowerSoil Pro Kit displayed the best suitability for reproducible long-read WGS metagenomic sequencing, although further optimisation of DNA purification and library preparation may enable translation of higher molecular weight DNA from other kits into longer read lengths. These findings provide a novel insight into the importance of optimising DNA extraction for achieving replicable results from long read metagenomic sequencing of environmental samples.
Screening and identification of microbes and evaluation of potential probiotic characteristics of microorganisms from Innovative Indigenous Non-Alcoholic Fermented Beverages
Authors: Dlamini Nothando Delight , Kolanisi Unathi , Siwela Muthulisi , Dlamini Nkosinathi Goodman
Published: September 2024

The consumption of probiotics has garnered significant attention due to their potential health benefits, with fermented beverages emerging as a promising source of these beneficial microorganisms. This research paper delves into the probiotic qualities of innovative non-alcoholic fermented beverages rooted in indigenous traditions. It focuses on their distinct microbial compositions and their potential to promote health. This study created three samples of non-alcoholic fermented beverages using ProVitamin A biofortified maize (PVABM) and Sclerocarya birrea (Amarula) juice. These samples were prepared using three distinct techniques: roasting, boiling, and milling, all followed by the fermentation process. We employed a serial dilution technique to isolate the microorganisms in the samples. Saline solution and nutrient agar medium were used to screen for microbes in the samples, including 16S rRNA sequencing. The colony-forming unit (CFU) method was employed to assess cell viability and determine the isolated cells' antimicrobial activity through a disc diffusion method. The isolates did not exhibit antimicrobial activity against E. coli, B. subtilis and salmonella. These isolates were identified as Pichia sp. from all three samples. The presence of these beneficial microorganisms in indigenous fermented beverages can enhance their status as functional foods, contributing to improved gut health, immunity, and overall well-being. This research paper underscores the significance of indigenous, non-alcoholic fermented beverages as a natural and culturally important source of probiotics.
Current Status of Health Care Bioethics Committees in Chile: Suggestions for Improvement in Public Health Management
Authors: Barra Novoa Rodrigo
Published: September 2024

This study examines the current functioning of Health Care Bioethics Committees (HBCs) in Chile, with the objective of providing recommendations for improving public health care management. Through a comprehensive literature review and a case analysis of the ‘Hospital Clínico de la Universidad de Chile’, the structure, functions and challenges of the CEA are explored. The research reveals the importance of the multidisciplinary composition of the committees, the need for continuing education in bioethics, and the relevance of the consultative role in ethical decision making. The results suggest that the effectiveness of CABs does not necessarily depend on the number of members or the length of their terms of office, but on the quality of their training and experience. It is concluded that for adequate health care management, it is essential to strengthen the role of the CEA in the ethical education of health care personnel and in the development of institutional regulations, as well as to improve their visibility and accessibility within the Chilean health care system.
Microscopic Origins of Flow Activation Energy in Biomolecular Condensates
Authors: Yang Sean , Potoyan Davit A
Published: September 2024

Material properties of biomolecular condensates dictate their form and function, influencing the diffusion of regulatory molecules and the dynamics of biochemical reactions. The increasing quality and quantity of microrheology experiments on biomolecular condensates necessitate a deeper understanding of the molecular grammar that encodes their material properties. Recent reports have identified a characteristic timescale related to network relaxation dynamics in condensates, which governs their temperature-dependent viscoelastic properties. This timescale is intimately connected to an activated process involving the dissociation of sticker regions, with the energetic barrier referred to as flow activation energy. The microscopic origin of activation energy is a complex function of sequence patterns, component stoichiometry, and external conditions. This study elucidates the microscopic origins of flow activation energy in single and multicomponent condensates composed of model peptide sequences with varying sticker and spacer motifs, with RNA as a secondary component. We dissected the effects of condensate density, RNA stoichiometry, and peptide sequence patterning using extensive sequence-resolved coarse-grained simulations. We found that flow activation energy is closely linked to the lifetime of sticker-sticker pairs under certain conditions, though the presence of multiple competing stickers further complicates this relationship. The insights gained in this study should help establish predictive multiscale models for the material properties and serve as a valuable guide for the programmable design of condensates.
Caspase-Activated DNase localizes to cancer causing translocation breakpoints during cell differentiation
Authors: Alsowaida Dalal , Larsen Brian D. , Hachmer Sarah , Azimi Mehri , Arezza Eric , Brunette Steve , Tur Steven , Palii Carmen G. , Albraidy Bassam , Sorensen Claus S. , Brand Marjorie , Dilworth F. Jeffrey , Megeney Lynn A.
Published: September 2024

Caspase activated DNase (CAD) induced DNA breaks promote cell differentiation and therapy-induced cancer cell resistance. CAD targeting activity is assumed to be unique to each condition, as differentiation and cancer genesis are divergent cell fates. Here, we made the surprising discovery that a subset of CAD-bound targets in differentiating muscle cells are the same genes involved in the genesis of cancer-causing translocations. In muscle cells, a prominent CAD-bound gene pair isPax7andFoxo1a, the mismatched reciprocal loci that give rise to alveolar rhabdomyosarcoma. We show that CAD-targeted breaks in thePax7gene are physiologic to reducePax7expression, a prerequisite for muscle cell differentiation. A cohort of these CAD gene targets are also conserved in early differentiating T cells and include genes that spur leukemia/lymphoma translocations. Our results suggest the CAD targeting of translocation prone oncogenic genes is non-pathologic biology and aligns with initiation of cell fate transitions.
Cell-type-specific mapping of enhancers and target genes from single-cell multimodal data
Authors: Su Chang , Lee Dongsoo , Jin Peng , Zhang Jingfei
Published: September 2024

Mapping enhancers and target genes in disease-related cell types has provided critical insights into the functional mechanisms of genetic variants identified by genomewide association studies (GWAS). However, most existing analyses rely on bulk data or cultured cell lines, which may fail to identify cell-type-specific enhancers and target genes. Recently, single-cell multimodal data measuring both gene expression and chromatin accessibility within the same cells have enabled the inference of enhancer-gene pairs in a cell-type-specific and context-specific manner. However, this task is challenged by the data’s high sparsity, sequencing depth variation, and the computational burden of analyzing a large number of enhancer-gene pairs. To address these challenges, we propose scMultiMap, a statistical method that infers enhancer-gene association from sparse multimodal counts using a joint latent-variable model. It adjusts for technical confounding, permits fast moment-based estimation and provides analytically derivedp-values. In systematic analyses of blood and brain data, scMultiMap shows appropriate type I error control, high statistical power with greater reproducibility across independent datasets and stronger consistency with orthogonal data modalities. Meanwhile, its computational cost is less than 1% of existing methods. When applied to single-cell multimodal data from postmortem brain samples from Alzheimer’s disease (AD) patients and controls, scMultiMap gave the highest heritability enrichment in microglia and revealed new insights into the regulatory mechanisms of AD GWAS variants in microglia.
OPUS-DSD2: Disentangling Dynamics and Compositional Heterogeneity for Cryo-EM/ET
Authors: Luo Zhenwei , Chen Xiangru , Zhang Yiqiu , Wang Qinghua , Ma Jianpeng
Published: September 2024

Cryo-electron microscopy and tomography (cryo-EM/ET) capture structural heterogeneities in macromolecules, ranging from dynamic motions to compositional changes— key to understanding biological mechanisms. While the deep learning framework, OPUS-DSD, advanced heterogeneity analysis for cryo-EM, it conflates different types of heterogeneities, and remains incompatible with cryo-ET which can elucidate macromolecular functions in their native cellular environments. Here, we present OPUS-DSD2, a unified framework for disentangling structural heterogeneity in both cryo-EM and cryo-ET data. OPUS-DSD2 augments a 3D convolutional neural network with a multi-layer perceptron based rigid-body dynamics model, advocating the separation of subunit level rigid-body dynamics from other heterogeneities. Tests on real datasets demonstrate that OPUS-DSD2 effectively captures large-scale subunit motions while isolating spatially localized structural variations into distinct principal components of the composition latent space. Critically, OPUS-DSD2 enables direct analysis of noisy cryo-ET template-matching results, bypassing labor-intensive subtomogram classification and averaging and unlocking high-throughput visual proteomics. Backward-compatible with OPUS-DSD, OPUS-DSD2 is available athttps://github.com/alncat/opusDSD.
Alcohol, smoking, and brain structure: common or substance specific associations
Authors: Thornton Vera , Chang Yoonhoo , Chaloemtoem Ariya , Anokhin Andrey P , Bijsterbosch Janine D , Foraker Randi , Hancock Dana B , Johnson Eric Otto , White Julie D , Hartz Sarah M , Bierut Laura J
Published: September 2024

Alcohol use and smoking are common substance-use behaviors with well-established negative health effects, including decreased brain health. We examined whether alcohol use and smoking were associated with the same neuroimaging-derived brain measures. We further explored whether the effects of alcohol use and smoking on the brain were additive or interactive. We leveraged a cohort of 36,309 participants with neuroimaging data from the UK Biobank. We used linear regression to determine the association between 354 neuroimaging-derived brain measures and alcohol use defined as drinks per week, pack years of smoking, and drinks per week x pack years smoking interaction. To assess whether the brain associations with alcohol are broadly similar or different from the associations with smoking, we calculated the correlation between z-scores of association for drinks per week and pack years smoking. Results indicated overall moderate positive correlation in the associations across measures representing brain structure, magnetic susceptibility, and white matter tract microstructure, indicating greater similarity than difference in the brain measures associated with alcohol use and smoking. The only evidence of an interaction between drinks per week and pack years smoking was seen in measures representing magnetic susceptibility in subcortical structures. The effects of alcohol use and smoking on brain health appeared to be additive rather than multiplicative for all other brain measures studied. 97% (224/230) of associations with alcohol and 100% (167/167) of the associations with smoking that surpassed a p value threshold are in a direction that can be interpreted to reflect reduced brain health. Our results underscore the similarity of the adverse associations between use of these substances and neuroimaging derived brain measures.
Neurocognitive and resting-state functional MRI changes in patients with diffuse gliomas after chemoradiotherapy
Authors: Liu Zhihua , Mitchell Timothy J. , Luo Chongliang , Park Ki Yun , Shimony Joshua S. , Fucetola Robert , Leuthardt Eric C. , Perkins Stephanie M. , Snyder Abraham Z. , Zhu Tong , Huang Jiayi
Published: September 2024

Background

This prospective observational study employed resting-state functional MRI (rs-fMRI) to investigate network-level disturbances associated with neurocognitive function (NCF) changes in patients with gliomas following radiation therapy (RT).

Methods

Adult patients with either IDH-wildtype or IDH-mutant gliomas underwent computerized NCF testing and rs-fMRI before and 6 months after RT. NCF changes were quantified by the percent change in age-normalized composite scores from baseline (ΔNCFcomp). rs-fMRI data underwent seed-based functional connectivity (FC) analysis. Whole-brain connectivity regression analysis assessed the association between network FC changes and NCF changes, using a split-sample approach with a 26-patient training set and a 6-patient validation set, iterated 200 times. Permutation tests evaluated the significance of network selection.

Results

Between September 2020 and December 2023, 43 patients were enrolled, with 32 completing both initial and follow-up evaluations. The mean ΔNCFcompwas 2.9% (SD: 13.7%), with 38% experiencing a decline. Intra-hemispheric FC remained similar between ipsilateral and contralateral hemispheres for most patients at both time points. FC changes accounted for a moderate amount of variance in NCF changes (mean R2: 0.301, SD: 0.249), with intra-network FC of the Parietal Memory Network (PMN-PMN,P=0.001) and inter-network FC between the PMN and the Visual Network (PMN-VN,P=0.002) as the most significant factors. Similar findings were obtained by sensitivity analyses using only the FC data from the hemisphere contralateral to the tumor.

Conclusions

Post-RT rs-fMRI changes significantly predicted NCF decline, highlighting rs-fMRI as a promising imaging biomarker for neurocognitive decline after RT.
A scoping review of remote and unsupervised digital cognitive assessments in preclinical Alzheimer’s disease
Authors: Polk S. E. , Öhman F. , Hassenstab J. , König A. , Papp K. V. , Schöll M. , Berron D.
Published: September 2024

Subtle cognitive changes in preclinical Alzheimer’s disease (AD) are difficult to detect using traditional pen-and-paper neuropsychological assessments. Remote and unsupervised digital assessments can improve scalability, measurement reliability, and ecological validity, enabling the detection and monitoring of subtle cognitive change. Here, we evaluate such tools deployed in preclinical AD samples, defined as cognitively unimpaired individuals with abnormal levels of amyloid-β (Aβ), or Aβ and tau.

In this scoping review, we screened 1,680 unique reports for studies using remote and unsupervised cognitive assessment tools in preclinical AD samples; 23 tools were found. We describe each tool’s usability, validity, and reported metrics of reliability.

Construct and criterion validity according to associations with established neuropsychological assessments and measures of Aβ and tau are reported.

With this review, we aim to present a necessary update to a rapidly evolving field, following a previous review by Öhman and colleagues (2021;Alzheimers Dement. Diagn. Assess. Dis. Monit) and addressing the open questions of feasibility and reliability of remote testing in the target population. We discuss future directions for using remote and unsupervised digital cognitive assessments in preclinical AD and how such tools may be used for longitudinal monitoring of cognitive function, scalable case finding, and individualized prognostics in both clinical trials and healthcare contexts.
Trajectories of mHealth-tracked mental health symptoms and their predictors in chronic pelvic pain
Authors: Leventhal Emily L. , Nukavarapu Nivedita , Elhadad Noemie , Bakken Suzanne , Elovitz Michal , Hirten Robert , Rodrigues Jovita , Danieletto Matteo , Landell Kyle , Ensari Ipek
Published: September 2024

Background.

Female chronic pelvic pain disorders (CPPDs) affect 1 in 7 women worldwide and are characterized by psychosocial comorbidities, including reduced quality of life and 2-10 fold increased risk of depression and anxiety. Despite its prevalence and morbidity, CPPDs are often inadequately managed with few patients experiencing relief from any medical intervention. Characterizing mental health symptom trajectories and lifestyle predictors of mental health is a starting point to enhancing patient self-efficacy in managing symptoms. Here, we investigate the association between mental health, pain, and physical activity (PA) in females with CPPD and demonstrate a method for handling multi-modal mobile health (mHealth) data.

Method.

The study sample included 4,270 person-level days and 799 person-level weeks of data from CPPD participants (N=76). Participants recorded PROMIS global mental health (GMH) and physical functioning, and pain weekly for 14 weeks using a research mHealth app, and moderate-to-vigorous PA (MVPA) was passively collected via activity trackers.

Data analysis.

We used penalized functional regression (PFR) to regress weekly GMH-T (GMH-T) on MVPA and weekly pain outcomes, while adjusting for baseline measures, time in study, and the random intercept of the individual. We converted 7-day MVPA data into a single smooth using spline basis functions to model the potential non-linear relationship.

Results

MVPA was a significant, curvilinear predictor of GMH-T (p<0.001), independent of pain measures and prior psychiatric diagnosis. Physical functioning was positively associated with GMH-T, while pain was negatively associated with GMH-T (β=2.24, β=-1.16, respectively; p<0.05).

Conclusion

These findings suggest that engaging in MVPA is beneficial to the mental health of females with CPPD. Additionally, this study demonstrates the potential of ambulatory mHealth-based data combined with functional models for delineating inter-individual and temporal variability.
Low Responsiveness of Machine Learning Models to Critical or Deteriorating Health Conditions
Authors: Pias Tanmoy Sarkar , Afrose Sharmin , Tuli Moon Das , Trisha Ipsita Hamid , Deng Xinwei , Nemeroff Charles B. , Yao Danfeng (Daphne)
Published: September 2024

Background

Machine learning (ML) based mortality prediction models can be immensely useful in intensive care units. Such a model should generate warnings to alert physicians when a patient’s condition rapidly deteriorates, or their vitals are in highly abnormal ranges. Before clinical deployment, it is important to comprehensively assess models’ ability to recognize critical patient conditions.

Methods

We develop testing approaches to systematically assess machine learning models’ ability to respond to serious medical emergencies by generating such conditions. We evaluated multiple machine learning models trained on four different datasets from two different clinical prediction tasks and evaluated ten different machine learning models including three resampling techniques.

Results

We identified serious deficiencies in the models’ responsiveness, i.e., the inability to recognize severely impaired medical conditions or rapidly deteriorating health. For in-hospital mortality prediction, the models tested using our synthesized cases fail to recognize 66% of the test cases involving injuries. In some instances, the models fail to generate adequate mortality risk scores for all test cases. Our testing methods identified similar kinds of deficiencies in the responsiveness of 5-year breast and lung cancer prediction models.

Conclusion

Using generated test cases, we found that statistical machine-learning models trained solely from patient data are grossly insufficient and have many dangerous blind spots. Despite their overall performance metrics, most ML models we tested failed to respond adequately to critically ill patients. Our proposed testing framework acts as a critical bridge between machine learning model development and clinical deployment, ensuring reliability and safety through rigorous evaluation.

Plain Language Summary

Machine learning models are increasingly used in healthcare to predict patients’ death risk or cancer survivability. These models could help doctors identify patients with worsening condition and take action to save lives. We developed a new method to test how well these models respond to severe health emergencies by creating life-threatening scenarios. We found most models failed to respond adequately to critical health events. For example, models missed 66% of cases involving serious injuries. This research emphasizes that current machine learning models have major limitations and could be dangerous if used in hospitals without thorough testing. Our testing framework can help improve these models to ensure they are safe and reliable before being used in real-life medical settings.
Combination of Imipridone ONC201 or ONC206 with Temozolomide and Radiotherapy in triple ITR therapy reduces intracranial tumor burden and prolongs survival in an orthotopic wild-type IDH GBM mouse model
Authors: Zhou Lanlan , Zhang Leiqing , Zhang Jun , Wu Laura Jinxuan , Zhang Shengliang , George Andrew , Hahn Marina , Safran Howard P. , Chen Clark C. , Seyhan Attila A. , Wong Eric T. , El-Deiry Wafik S.
Published: September 2024

Glioblastoma remains the most lethal common primary brain tumor in adults with limited therapeutic options. TIC10/ONC201, a first-in-class imipridone we discovered, achieved meaningful therapeutic effects in phase I/II trials in patients with diffuse gliomas harboring H3K27M mutations, and currently the drug is in randomized phase III testing (ACTION trial;NCT05580562). ONC201 targets mitochondrial protease ClpP to disrupt oxidative phosphorylation and trigger the integrated stress response (ISR), TRAIL/DR5, and tumor cell death. We hypothesized that ONC201 and its analogue ONC206 synergize with temozolomide (TMZ) and ionizing radiation (IR), standard-of-care glioblastoma therapies. ONC201 enhances TMZ or IR-induced apoptosis, and cytotoxicity. ClpP-silencing suppresses ONC201-induced cytotoxicity but not TMZ or RT. Both ONC201 and ONC206 reduce expression of TMZ-resistance mediator MGMT. Suppression of MGMT protein was observed in H3K27M-mutated DIPG cell lines following treatment with ONC201 or ONC206 with or without TMZ. Cytokine profiling indicates distinct ONC201 alterations relative to TMZ suggesting distinct anti-tumor immune mechanisms. Triple IR+TMZ+ONC201 (ITR) therapy prolongs median survival to 123 days with a tail on survival curve (3-of-7 mice alive beyond 200-days) in an orthotopic U251 GBM model versus ONC201 (44-days; p=0.000197), IR (63-days; p=0.0012), TMZ (78-days; p=0.0354), ONC201+IR (55-days; p=0.0004), ONC201+TMZ (80-days; p=0.0041) and IR+TMZ (103-days; p>0.05). By 231-days, the only surviving mice were in IRT group. Our results support investigation of ONC201/ONC206 in combination with TMZ and IR (ITR) in GBM or H3K27M mutated diffuse glioma therapy.
Multi-behavioral phenotyping in zebrafish identifies a novel disruptor of non-associative learning with environmental and human relevance
Authors: Leuthold David , Herold Nadia K. , Nerlich Jana , Bartmann Kristina , Scharkin Ilka , Hallermann Stefan J. , Schweiger Nicole , Fritsche Ellen , Tal Tamara
Published: September 2024

The vertebrate nervous system is vulnerable to chemical toxicity and the widespread release of chemicals into the environment outstrips the capacity to assess their safety. We devised a battery of automated behavior assays in larval zebrafish (Danio rerio), a 3R-compliant model amenable to higher-throughput chemical screens. The battery captures stereotypical visual and acoustic behaviors including habituation, a form of non-associative learning. Known pharmacological modulators of habituation in zebrafish evoked distinct behavioral patterns. By screening chemicals positive forex vivoN-methyl-D-aspartate receptor (NMDAR) modulation, we identified chlorophene, a biocide that caused sedation, paradoxical excitation, and reduced habituation in zebrafish. Usingin silicotarget predictions and pharmacological interventions, we discovered that chlorophene acts via gamma-aminobutyric acid A receptors (GABAARs), a previously unknown target site. Orthogonal validation in cultured mouse cortical neurons and human stem cell-derived BrainSpheres confirmed chlorophene’s interaction with GABAARs. Together, multi-behavioral phenotyping in zebrafish can accelerate the identification of neurotoxicants and their underlying mode of action.
Expression-based selection identifies a microglia-tropic AAV capsid for direct and CSF routes of administration in mice
Authors: Santoscoy Miguel C. , Espinoza Paula , Hanlon Killian S. , Yang Luna , Nieland Lisa , Ng Carrie , Badr Christian E. , Hickman Suzanne , de la Cruz Demitri , Griciuc Ana , Elkhoury Joseph , Bennett Rachel E. , Shen Shiqian , Maguire Casey A.
Published: September 2024

Microglia are critical innate immune cells of the brain.In vivotargeting of microglia using gene-delivery systems is crucial for studying brain physiology and developing gene therapies for neurodegenerative diseases and other brain disorders such as NeuroAIDS. Historically, microglia have been extremely resistant to transduction by viral vectors, including adeno-associated virus (AAV) vectors. Recently, there has been some progress demonstrating the feasibility and potential of using AAV to transduce microglia after direct intraparenchymal vector injection. Data suggests that combining specific AAV capsids with microglia-specific gene expression cassettes to reduce neuron off-targeting will be key. However, no groups have developed AAV capsids for microglia transduction after intracerebroventricular (ICV) injection. The ICV route of administration has advantages such as increased brain biodistribution while avoiding issues related to systemic injection. Here, we performed anin vivoselection using an AAV peptide display library that enables recovery of capsids that mediate transgene expression in microglia. Using this approach, we identified a capsid, MC5, which mediated enhanced transduction of microglia after ICV injection compared to AAV9. Furthermore, MC5 enhanced both the efficiency (85%) and specificity (93%) of transduction compared to a recently described evolved AAV9 capsid for microglia targeting after direct injection into the brain parenchyma. Exploration of the use of MC5 in a mouse models of Alzheimer’s disease revealed transduced microglia surrounding and within plaques. Overall, our results demonstrate that the MC5 capsid is a useful gene transfer tool to target microgliain vivoby direct and ICV routes of administration.
Mapping the aggregate g-ratio of white matter tracts using multi-modal MRI
Authors: Lu Wen Da , Nelson Mark C. , Leppert Ilana R. , Campbell Jennifer S.W. , Schiavi Simona , Pike G. Bruce , Rowley Christopher D. , Daducci Alessandro , Tardif Christine L.
Published: September 2024

The g-ratio of a myelinated axon is defined as the ratio of the inner-to-outer diameter of the myelin sheath and modulates conduction speed of action potentials along axons. This g-ratio can be mappedin vivoat the macroscopic scale across the entire human brain using multi-modal MRI and sampled along white matter streamlines reconstructed from diffusion-weighted images to derive the g-ratio of a white matter tract. This tractometry approach has shown spatiotemporal variations in myelin g-ratio across white matter tracts and networks. However, tractometry is biased by partial volume effects where voxels contain multiple fiber populations. To address this limitation, we used the Convex Optimization Modeling for Microstructure-Informed Tractography (COMMIT) framework to derive tract-specific axonal and myelin volumes, which are used to compute the tract-specific aggregate g-ratio. We compare our novel COMMIT-based tract-specific g-ratio mapping approach to conventional tractometry in a group of 10 healthy adults. Our findings demonstrate that the tract-specific g-ratio mapping approach preserves the overall spatial distribution observed in tractometry and enhances contrast between tracts. Additionally, our scan-rescan data shows high repeatability for medium to large caliber tracts. We show that short and large caliber tracts have a lower g-ratio, whereas tractometry results show the opposite trends. This technique advances tract-specific analysis by reducing biases introduced by the complex network of crossing white matter fibers.
Exploring Breast Cancer-Related Biochemical Changes in Circulating Extracellular Vesicles Using Raman Spectroscopy
Authors: Bonizzi Arianna , Signati Lorena , Grimaldi Maria , Truffi Marta , Piccotti Francesca , Gagliardi Stella , Dotti Giulia , Mazzucchelli Serena , Albasini Sara , Cazzola Roberta , Bhowmik Debanjan , Narayana Chandrabhas , Corsi Fabio , Morasso Carlo
Published: September 2024

Extracellular vesicles (EVs) are a subgroup of the circulating particles, released by cells in both normal and diseased states, carrying active biomolecules. They have gained significant attention as potential cancer biomarkers, particularly in breast cancer (BC). Previous research showed variations in EVs content and quantity between BC patients and healthy controls (HC). However, studying EVs biochemical profile remains challenging due to their low abundance and complex composition. Additionally, EVs may interact with other plasma components, like lipoproteins (LPs), forming a so called “biomolecular corona” that further complicates their analysis. Here, Raman spectroscopy (RS) is proposed as a fast tool to obtain the biochemical profile of circulating EVs in the context of BC. RS was employed to differentiate various extracellular particles (EPs) in blood, including LPs and EVs. The study also evaluated RS’s capability to quantify major classes of biomolecules and compared these results with those obtained by traditional biochemical assays. Finally, compositional differences in large EVs (lEVs) and small EVs (sEVs) were assessed between 30 HC and 34 BC patients. RS revealed the existence of distinct biochemical signatures associated with BC, highlighting increased levels of nucleic acids and lipids in the BC group.
Multi-View Integrative Approach For Imputing Short-Chain Fatty Acids and Identifying Key factors predicting Blood SCFA
Authors: Liu Anqi , Tian Bo , Qiu Chuan , Su Kuan-Jui , Jiang Lindong , Zhao Chen , Song Meng , Liu Yong , Qu Gang , Zhou Ziyu , Zhang Xiao , Mungasavalli Gnanesh Shashank Sajjan , Thumbigere-Math Vivek , Luo Zhe , Tian Qing , Zhang Li-Shu , Wu Chong , Ding Zhengming , Shen Hui , Deng Hong-Wen
Published: September 2024

Short-chain fatty acids (SCFAs) are the main metabolites produced by bacterial fermentation of dietary fiber within gastrointestinal tract. SCFAs produced by gut microbiotas (GMs) are absorbed by host, reach bloodstream, and are distributed to different organs, thus influencing host physiology. However, due to the limited budget or the poor sensitivity of instruments, most studies on GMs have incomplete blood SCFA data, limiting our understanding of the metabolic processes within the host. To address this gap, we developed an innovative multi-task multi-view integrative approach (M2AE, Multi-task Multi-View Attentive Encoders), to impute blood SCFA levels using gut metagenomic sequencing (MGS) data, while taking into account the intricate interplay among the gut microbiome, dietary features, and host characteristics, as well as the nuanced nature of SCFA dynamics within the body. Here, each view represents a distinct type of data input (i.e., gut microbiome compositions, dietary features, or host characteristics). Our method jointly explores both view-specific representations and cross-view correlations for effective predictions of SCFAs. We applied M2AE to two in-house datasets, which both include MGS and blood SCFAs profiles, host characteristics, and dietary features from 964 subjects and 171 subjects, respectively. Results from both of two datasets demonstrated that M2AE outperforms traditional regression-based and neural-network based approaches in imputing blood SCFAs. Furthermore, a series of gut bacterial species (e.g.,Bacteroides thetaiotaomicronandClostridium asparagiforme), host characteristics (e.g., race, gender), as well as dietary features (e.g., intake of fruits, pickles) were shown to contribute greatly to imputation of blood SCFAs. These findings demonstrated that GMs, dietary features and host characteristics might contribute to the complex biological processes involved in blood SCFA productions. These might pave the way for a deeper and more nuanced comprehension of how these factors impact human health.
A massive community-science dataset reveals convergent evolution of delayed flowering phenology in North American red-flowering plants
Authors: McKenzie Patrick F. , Berardi Andrea E. , Hopkins Robin
Published: September 2024

The radiation of angiosperms is marked by a phenomenal diversity of floral size, shape, color, scent, and reward. Through hundreds of years of documentation and quantification, scientists have sought to make sense of this variation by defining pollination syndromes. These syndromes are the convergent evolution of common suits of floral traits across distantly related species that have evolved by selection to optimize pollination strategies. The availability of community-science datasets provides an opportunity to develop new tools and to examine new traits that may help further characterize broad patterns of flowering plant diversity. Here we test the hypothesis that flowering phenology can also be a pollination syndrome trait. We generate a novel flower color dataset by using GPT-4 with Vision (GPT-4V) to assign flower color to 11,729 North American species. We map these colors to 1,674,908 community-scientist observations of flowering plants to investigate patterns of phenology. We demonstrate constrained flowering time in the eastern United States for plants with red or orange flowers relative to plants with flowers of other colors. Red-and orange-colored flowers are often characteristic of the “hummingbird” pollination syndrome; importantly, the onset of red and orange flowers corresponds to the arrival of migratory hummingbirds. Our results suggest that the hummingbird pollination syndrome can include flowering phenology and reveal an opportunity to expand the suite of traits included in pollination syndromes. Our methods demonstrate an effective pipeline for leveraging enormous amounts of community science data by using artificial intelligence to extract information about patterns of trait variation.
WIMOAD: Weighted Integration of Multi-Omics data for Alzheimer’s Disease (AD) Diagnosis
Authors: Xiao Hanyu , Wang Jieqiong , Wan Shibiao
Published: September 2024

As the most common subtype of dementia, Alzheimer’s disease (AD) is characterized by a progressive decline in cognitive functions, especially in memory, thinking, and reasoning ability. Early diagnosis and interventions enable the implementation of measures to reduce or slow further regression of the disease, preventing individuals from severe brain function decline. The current framework of AD diagnosis depends on A/T/(N) biomarkers detection from cerebrospinal fluid or brain imaging data, which is invasive and expensive during the data acquisition process. Moreover, the pathophysiological changes of AD accumulate in amino acids, metabolism, neuroinflammation, etc., resulting in heterogeneity in newly registered patients. Recently, next generation sequencing (NGS) technologies have found to be a non-invasive, efficient and less-costly alternative on AD screening. However, most of existing studies rely on single omics only. To address these concerns, we introduce WIMOAD, a weighted integration of multi-omics data for AD diagnosis. WIMOAD synergistically leverages specialized classifiers for patients’ paired gene expression and methylation data for multi-stage classification. The resulting scores were then stacked with MLP-based meta-models for performance improvement. The prediction results of two distinct meta-models were integrated with optimized weights for the final decision-making of the model, providing higher performance than using single omics only. Remarkably, WIMOAD achieves significantly higher performance than using single omics alone in the classification tasks. The model’s overall performance also outperformed most existing approaches, highlighting its ability to effectively discern intricate patterns in multi-omics data and their correlations with clinical diagnosis results. In addition, WIMOAD also stands out as a biologically interpretable model by leveraging the SHapley Additive exPlanations (SHAP) to elucidate the contributions of each gene from each omics to the model output. We believe WIMOAD is a very promising tool for accurate AD diagnosis and effective biomarker discovery across different progression stages, which eventually will have consequential impacts on early treatment intervention and personalized therapy design on AD.
CLYBL averts methylmalonyl-CoA mutase inhibition and loss of vitamin B12 by repairing malyl-CoA
Authors: Griffith Corey M. , Conrotte Jean-François , Paydar Parisa , Xie Xinqiang , Heins-Marroquin Ursula , Gavotto Floriane , Jäger Christian , Ellens Kenneth W. , Linster Carole L.
Published: September 2024

Citrate lyase beta-like protein (CLYBL) is a ubiquitously expressed mammalian enzyme known for its role in the degradation of itaconate, a bactericidal immunometabolite produced in activated macrophages. The association ofCLYBLloss-of-function with reduced circulating vitamin B12 levels was proposed to result from inhibition of the B12-dependent enzyme methylmalonyl-CoA mutase (MCM) by itaconyl-CoA. The discrepancy between the highly inducible and locally confined production of itaconate and the broad expression profile ofCLYBLacross tissues, suggested a role for this enzyme beyond itaconate catabolism. We discovered that CLYBL additionally functions as a metabolite repair enzyme for malyl-CoA, a side-product of promiscuous TCA cycle enzymes. We found thatCLYBLknockout cells, accumulating malyl-CoA but not itaconyl-CoA, show decreased levels of adenosylcobalamin and that malyl-CoA is a more potent inhibitor of MCM than itaconyl-CoA. Our work thus suggests that malyl-CoA plays a role in the B12 deficiency observed in individuals withCLYBLloss-of-function.

Graphical Abstract
Development of nanobodies against human Survivin, applicable to suppress cell proliferation
Authors: Tagawa Ayako , Fuchigami Takeshi , Miyanari Yusuke
Published: September 2024

Survivin, a member of the inhibitor of apoptosis protein (IAP) family, is overexpressed in many cancers, making it a valuable target for therapy and a biomarker for cancer diagnostics. As Survivin contributes to cancer development by inhibiting apoptosis and promoting rapid cell division, inhibiting its activity is a promising strategy to overcome cancer resistance to apoptosis and reduce tumor growth. In this study, we developed nanobodies targeting human Survivin using M13 phage display and deep sequencing to identify high-affinity binders. Seven nanobody clones were selected and evaluated through ELISA, western blotting, and immunostaining. Clones 7 and 15, which specifically bind the alpha-helix region of Survivin, effectively suppressed cell proliferation by disrupting cell cycle regulation, likely by interfering interaction with intracellular binding partners. These findings suggest that Survivin-targeting nanobodies hold potential as therapeutic agents for cancer treatment, offering a novel approach to inhibiting tumor growth through the disruption of Survivin-mediated processes.
Fascicle stretch during partial muscle deactivation does not enhance the human tibialis anterior’s subsequent neuromechanical output
Authors: Raiteri Brent James , De Lorenzo Ricardo , Kraul Malte , Hahn Daniel
Published: September 2024

We were interested in whether stretching the muscle via elastic tissue recoil during partial muscle deactivation could trigger stretch-induced mechanisms that subsequently enhance the muscle’s steady-state neuromechanical output. Two torque-controlled experiments were conducted to test this aim. In Experiment 1, fifteen participants performed fixed-end dorsiflexion contractions to a moderate or high-then-moderate level with torque-drop rates of 0, 10, 20, 40, or 80% MVC·s-1from 60-40% MVC, while net ankle joint torque, tibialis anterior (TA) muscle activity level, and TA ultrasound images were recorded. The same measurements were performed in Experiment 2, which tested twelve different participants who performed fixed-end contractions to three reference levels or to a higher-then-lower level (with torque-drop amplitudes of 85-45, 85-30, and 85-15% MVC at 20% MVC·s-1). Increased fascicle shortening amplitudes (Experiment 1: 1-2 mm,p≤.049; Experiment 2: 5-10 mm,p<.001) to initially higher joint torques and different fascicle stretch rates (0.6 to 4.3 mm·s-1,p≤.041) and amplitudes (4-9 mm;p≤.001) did not significantly affect TA’s subsequent muscle activity level relative to the reference contractions at similar joint torques (0-1% MVC,p≥.659 andp≥.626). However, the torque steadiness relative to the reference conditions was significantly reduced after the 85-15% MVC (p=.036) and 85-30% MVC (p=.036) torque drops. Consequently, the history of force production affected the control of muscle force more than TA’s steady-state neuromuscular output. These findings indicate that assessing fascicle kinematics concurrently with motor unit behavior during fixed-end contractions with large torque drops might provide unique insights into the neuromechanical contributors to impaired torque control.

NEW & NOTEWORTHY

The history of force production affected the subsequent control of dorsiflexion torque more than tibialis anterior’s (TA’s) neuromuscular output following larger amounts of fascicle stretch during fixed-end contractions. Larger fascicle stretches also reduced the median frequency of TA’s EMG signal, which tentatively indicates that the voluntary control of muscle force was altered in the steady state. Together, these findings indicate that voluntary force control might be impaired by stretch-induced and/or activity-level-dependent mechanisms during fixed-end contractions.
Assessing coverage of the Monitoring Framework of the Kunming-Montreal Global Biodiversity Framework and opportunities to fill gaps
Authors: Affinito F. , Butchart S. H. M. , Nicholson E. , Hirsch T. , Williams J. M. , Campbell J. , Ferrari M. F. , Gabay M. , Gorini L. , Kalamujic Stroil B. , Kohsaka R. , Painter B. , Pinto J. C. , Scholz A. H. , Straza T. R. A. , Tshidada N. , Vallecillo S. , Widdicombe S. , Gonzalez A.
Published: September 2024

The Kunming-Montreal Global Biodiversity Framework (GBF) is the most ambitious agreement on biodiversity conservation and sustainable use to date. It calls for a whole-of government and whole-of-society approach to halt and reverse biodiversity loss worldwide. The Monitoring Framework of the GBF lays out how Parties to the Convention on Biological Diversity (CBD) are expected to report their progress. A CBD expert group provided guidance on its implementation, including a gap analysis to identify the strengths and limitations of the indicators in the Monitoring Framework. We present the results of the gap analysis, highlight where more work is needed and provide recommendations on implementing and improving monitoring to allow effective and comprehensive tracking of the GBF’s ambition. We find that with the headline and binary indicators, which Parties are required to use, the Monitoring Framework fully covers 19% of the GBF’s ambition and partially covers an additional 40%. Including disaggregations of the headline indicators improves coverage to 22% fully and an additional 41% partially. Adding optional (component and complementary) indicators brings full coverage to 29% with an additional 47% partial coverage. No indicators are available for 12% of the GBF. In practice, the coverage of the Monitoring Framework will depend on which indicators (headline and binary as well as component and complementary) and disaggregations are used by countries. Disaggregations are particularly relevant to monitor the cross-cutting considerations defined under section C. Substantial investment is required to collect the necessary data to compute indicators, infer change, and effectively monitor progress. We highlight important next steps to progressively improve the efficacy of the Monitoring Framework.
3D photogrammetry as a low cost, portable and noninvasive method for acoustic modeling of hearing
Authors: Vesterholm Karsten Krautwald , Häfele Felix T. , Figeac Florence , Jakobsen Lasse
Published: September 2024

Animals with specialized hearing such as bats utilize the directionality of their hearing for complicated tasks such as navigation and foraging. The directionality of hearing can be described through the head related transfer function (HRTF). Current state of the art for obtaining the HRTF involves either direct measurement with a microphone at the eardrum, or a μCT (micro computed tomography) scan to create a 3D model of the head for acoustic modelling. Both methods usually involve dead animals.

We developed a 3D photogrammetry approach to create scaled 3D models of bats with sufficient detail to simulate the HRTF using the boundary element method (BEM). We designed a setup of 28 cameras to obtain 3D models and HRTF from live awake bats. We directly compare the mesh models generated by our photogrammetry method and from μCT scans as well as the simulated HRTFs from both with measurements using an in-ear microphone.

Geometries of the mesh models match well between photogrammetry and μCT, but with increasing errors where line of sight is compromised for photogrammetry. The resulting HRTFs are in great agreement when comparing μCT and in-ear measurements to photogrammetry (correlation coefficients above 0.6). The 3D model and simulated HRTF of the live and awake bat likewise aligns well to the results from the deceased animals.

Photogrammetry is a viable alternative to μCT scans for the generation of surface models of small animals. These models allow numerical modelling of HRTFs at biologically relevant frequencies. Moreover, photogrammetry allows for model generation and subsequent HRTF simulation of live, awake animals, abolishing the need for euthanasia and anesthesia. It paves the way for large scale acquisition of 3D models for various purposes including HRTFs.