bluuebunny/crossref_metadata_2025 huggingface dataset:
Distinct neurotoxic TDP-43 fibril polymorphs can be generated by heterotypic interactions with α-synuclein
Authors: Dhakal Shailendra , Robang Alicia S. , Bhatt Nemil , Puangamali Nicha , Fung Leiana , Kayed Rakez , Paravastu Anant K. , Rangachari Vijayaraghavan
Published: June 2022

Amyloid aggregates of specific proteins form important pathological hallmarks in many neurodegenerative diseases, defining neuronal degeneration and disease onset. Recently, increasing numbers of patients show co-morbidities and overlaps between multiple neurodegenerative diseases, presenting distinct phenotypes. Such overlaps are often accompanied by co-localizations of more than one amyloid protein, prompting the question of whether direct interactions between different amyloid proteins could generate heterotypic amyloids. To answer this question, we investigated the effect of α-synuclein (αS) on TDP-43 aggregation inspired by their co-existence in pathologies such as Lewy body dementia and limbic predominant age-related TDP-43 encephalopathy. We previously showed that αS and prion-like C-terminal domain (PrLD) of TDP-43 synergistically interact with one another to generate toxic heterotypic aggregates in vitro. Here, we extend these studies to investigate whether αS induces structurally and functionally distinct polymorphs of PrLD aggregates. Using αS –PrLD heterotypic aggregates generated in two different stoichiometric proportions, we show that αS can effect PrLD fibril forms. The fibril samples have distinctive residue-level structural signatures in NMR spectra, dye-binding capability, proteinase K (PK) stability, and SDS-sensitive thermal stability. By gold nanoparticle labeling and TEM, we show the presence of both αS and PrLD proteins within the same fibrils, and thus the existence of hetertypic hybrid fibrils. We also observe that αS and PrLD co-localize in the cytosol of SH-SY5Y neuroblastoma cells, and show that the heterotypic PrLD fibrils selectively induce synaptic dysfunction in primary cortical neurons. These findings establish the existence of heterotypic amyloid polymorphs and provide a molecular basis for the observed overlap between synucleinopathies and TDP-43 proteinopathies.
Data Processing Choices Can Affect Findings in Differential Methylation Analyses: An Investigation Using Data from the LIMIT RCT
Authors: Louise Jennie , Deussen Andrea R , Dodd Jodie M
Published: June 2022

Objective

A wide array of methods exist for processing and analysing DNA methylation data. We aimed to perform a systematic comparison of the behaviour of these methods, using cord blood DNAm from the LIMIT RCT, in relation to detecting hypothesised effects of interest (intervention and pre-pregnancy maternal BMI) as well as effects known to be spurious, and known to be present.

Methods

DNAm data, from 645 cord blood samples analysed using Illumina 450K BeadChip arrays, were normalised using three different methods (with probe filtering undertaken pre- or post-normalisation). Batch effects were handled with a supervised algorithm, an unsupervised algorithm, or adjustment in the analysis model. Analysis was undertaken with and without adjustment for estimated cell type proportions. The effects estimated included intervention and BMI (effects of interest in the original study), infant sex and randomly assigned groups. Data processing and analysis methods were compared in relation to number and identity of differentially methylated probes, rankings of probes by p value and log-fold-change, and distributions of p values and log-fold-change estimates.

Results

There were differences corresponding to each of the processing and analysis choices. Importantly, some combinations of data processing choices resulted in a substantial number of spurious ‘significant’ findings. We recommend greater emphasis on replication and greater use of sensitivity analyses.

Clinical Trials Registration

ACTRN12607000161426
Porcine ANTXR1, Heparan Sulfate and Neu5Gc act as entry factors for Seneca Valley virus invasion
Authors: Tang Wenda , Wang Yanchao , Qi Xiaolan , Gu Fengxing , Li Kangli , Han Haitang , Du Xuguang , Zhu Zixiang , Wu Sen , Zhao Yaofeng , Zheng Haixue
Published: June 2022

Seneca Valley virus (SVV) disease is a newly emerging infectious disease of pigs caused by SVV, which seriously endangers the pig industry. This study was set out to identify the essential host factors required for SVV entering porcine cells. Using a CRISPR/Cas9 library containing 93,859 sgRNAs that were designed to target approximately 22,707 porcine genes, we generated mutated porcine cell libraries, which were subjected to SVV challenge for enrichment of cells resistant to SVV infection. These resistant cells were subsequently analyzed to identify genes essential for SVV infection. We demonstrated that ANTXR1, a type I transmembrane protein encoded by ANTXR1, heparan sulfate (HS), glycosaminoglycans modified by acetylation and sulfation of HS2ST1, and Neu5Gc, a non-human sialic acid catalyzed by CMAH, were the essential host factors for SVV entry into porcine cells. These results will be helpful to elucidate the pathogenesis of SVV and the development of prevention and control measures.
Hnf1b renal expression directed by a distal enhancer responsive to Pax8
Authors: Goea L. , Buisson I. , Bello V. , Eschstruth A. , Paces-Fessy M. , Le Bouffant R. , Chesneau A. , Cereghini S. , Riou J.F. , Umbhauer M.
Published: June 2022

Xenopus provides a simple and efficient model system to study nephrogenesis and explore the mechanisms causing renal developmental defects in human. Hnf1b (hepatocyte nuclear factor 1 homeobox b), a gene whose mutations are the most commonly identified genetic cause of developmental kidney disease, is required for the acquisition of a proximo-intermediate nephron segment in Xenopus as well as in mouse. Genetic networks involved in Hnf1b expression during kidney development remain poorly understood. We decided to explore the transcriptional regulation of Hnf1b in the developing Xenopus pronephros and mammalian renal cells. Using phylogenetic footprinting, we identified an evolutionary conserved sequence (CNS1) located several kilobases (kb) upstream the Hnf1b transcription start and harboring epigenomic marks characteristics of a distal enhancer in embryonic and adult renal cells in mammals. By means of functional expression assays in Xenopus and mammalian renal cell lines we showed that CNS1 displays enhancer activity in renal tissue. Using CRISPR/cas9 editing in Xenopus tropicalis, we demonstrated the in vivo functional relevance of CNS1 in driving hnf1b expression in the pronephros. We further showed the importance of Pax8-CNS1 interaction for CNS1 enhancer activity allowing us to conclude that Hnf1b is a direct target of Pax8. Our work identified for the first time a Hnf1b renal specific enhancer and may open important perspectives into the diagnosis for congenital kidney anomalies in human, as well as modeling HNF1B-related diseases.
EEG-based Graph Neural Network Classification of Alzheimer’s Disease: An Empirical Evaluation of Functional Connectivity Methods
Authors: Klepl Dominik , He Fei , Wu Min , Blackburn Daniel J. , Sarrigiannis Ptolemaios G.
Published: June 2022

Alzheimer’s disease (AD) is the leading form of dementia worldwide. AD disrupts neuronal pathways and thus is commonly viewed as a network disorder. Many studies demonstrate the power of functional connectivity (FC) graph-based biomarkers for automated diagnosis of AD using electroencephalography (EEG). However, various FC measures are commonly utilised, as each aims to quantify a unique aspect of brain coupling. Graph neural networks (GNN) provide a powerful framework for learning on graphs. While a growing number of studies use GNN to classify EEG brain graphs, it is unclear which method should be utilised to estimate the brain graph. We use eight FC measures to estimate FC brain graphs from sensor-level EEG signals. GNN models are trained in order to compare the performance of the selected FC measures. Additionally, three baseline models based on literature are trained for comparison. We show that GNN models perform significantly better than the other baseline models. Moreover, using FC measures to estimate brain graphs improves the performance of GNN compared to models trained using a fixed graph based on the spatial distance between the EEG sensors. However, no FC measure performs consistently better than the other measures. The best GNN reaches 0.984 area under sensitivity-specificity curve (AUC) and 92% accuracy, whereas the best baseline model, a convolutional neural network, has 0.924 AUC and 84.7% accuracy.
No effects of offline high frequency transcranial magnetic stimulation to posterior parietal cortex on the choice of which hand to use to perform a reaching task
Authors: Fitzpatrick Aoife M. , Dundon Neil M. , Valyear Kenneth F.
Published: June 2022

The current study used a high frequency TMS protocol known as continuous theta burst stimulation (cTBS) to test a model of hand choice that relies on competing interactions between the hemispheres of the posterior parietal cortex. Based on the assumption that cTBS reduces cortical excitability, the model predicts a significant decrease in the likelihood of selecting the hand contralateral to stimulation. An established behavioural paradigm was used to estimate hand choice in each individual, and these measures were compared across three stimulation conditions: cTBS to the left posterior parietal cortex, cTBS to the right posterior parietal cortex, or sham cTBS. Our results provide no supporting evidence for the interhemispheric competition model. We find no effects of cTBS on hand choice, independent of whether the left or right posterior parietal cortex was stimulated. Our results are nonetheless of value as a point of comparison against prior brain stimulation findings that, in contrast, provide evidence for a causal role for the posterior parietal cortex in hand choice.

Highlights

High-frequency TMS applied to the left and right posterior parietal cortex, separately, did not produce reliable aftereffects on hand choice.

Response times to initiate actions were significantly increased when reaching near the point in space where hand choice was equally probable.
Pan-cancer classification of single cells in the tumour microenvironment
Authors: Nofech-Mozes Ido , Soave David , Awadalla Philip , Abelson Sagi
Published: June 2022

Single-cell RNA sequencing reveals valuable insights into cellular heterogeneity within tumour microenvironments (TMEs), paving the way for a deep understanding of cellular mechanisms contributing to cancer. However, high heterogeneity among the same cancer types and low transcriptomic variation in immune cell subsets present challenges for accurate, high-resolution confirmation of cells’ identities. Here we present scATOMIC; a modular annotation tool for malignant and non-malignant cells. We trained scATOMIC on >250,000 cancer, immune, and stromal cells defining a pan-cancer reference across 19 common cancer types and employed a novel hierarchical approach, outperforming current classification methods. We extensively confirmed scATOMIC’s accuracy on 198 tumour biopsies and 54 blood samples encompassing >420,000 cancer and a variety of TME cells. Lastly, we demonstrate scATOMIC’s practical significance to accurately subset breast cancers into clinically relevant subtypes and predict tumours’ primary origin across metastatic cancers. Our approach represents a broadly applicable strategy to analyze multicellular cancer TMEs.
Candesartan treatment preserves learning and working memory in female TgF344-AD rats
Authors: Sinon Christopher G , Carter Kathleen , Ma Jing , Bagchi Pritha , Zhang Xiancong , Williams Peter-Jon C. , Dammer Eric B , Seyfried Nicholas T , García Paul S , Sutliff Roy L , Hajjar Ihab M
Published: June 2022

Background

Targeting the renin angiotensin system, especially with angiotensin receptor II blockers (ARB), and related vascular dysfunction is a promising therapeutic intervention for cognitive impairment including Alzheimer’s Disease (AD). The underlying mechanisms of the effects of ARB is unclear. This study sought to examine if treatment with candesartan, an ARB, affects neurobehavioral manifestation and the underlying neuro- and vascular mechanisms in male and female TgF344-AD rats.

Methods

Candesartan or vehicle was administered to TgF344-AD rats (n=127) daily from 12-months to 18-months of age. Behavioral assays (spontaneous alternation test, novel object recognition, water radial arm maze) and neuropathologic assessment were completed along with brain proteome and measures of contractility in 12- and 18-month rat brains.

Results

Untreated 18-month TgF344-AD showed impairments in learning and increased perseverative working memory errors on the water radial arm maze (WRAM). These behavioral changes were corrected with candesartan treatment in female rats only. Treatment with candesartan was also associated with improved vascular reactivity and reduced blood pressure in both wild type and TgF344-AD male and female rats. Although there was no effect on amyloid-β, treatment with candesartan reduced whole brain clusterin, an AD-risk associated protein, and GFAP in female TgF344-AD.

Conclusions

Our results demonstrate that candesartan administered in the early stages of AD has a sexual dimorphic response in Tgf344-AD rat, where it reduced cognitive disturbances only in female TgF344-AD rat. These effects appear to be independent of changes in blood pressure and amyloid-β reduction and are likely mediated through mechanisms related to clusterin and GFAP pathways.
Immune gene expression in the mosquito vector Culex quinquefasciatus during an avian malaria infection
Authors: García-Longoria L , Ahrén D , Berthomieu A , Kalbskopf V , Rivero A , Hellgren O
Published: June 2022

Plasmodium relictum is the most widespread avian malaria parasite in the world. It is listed as one of the 100 most dangerous invasive species, having been responsible for the extinction of several endemic bird species, and the near-demise of several others. Here we present the first transcriptomic study examining the effect of P. relictum on the immune system of its vector (the mosquito Culex quinquefasciatus) at different times post-infection. We show that over 50% of immune genes identified as being part of the Toll pathway and 30-40% of the immune genes identified within the Imd pathway are overexpressed during the critical period spanning the parasite’s oocyst and sporozoite formation (8-12 days), revealing the crucial role played by both these pathways in this natural mosquito-Plasmodium combination. Comparison of infected mosquitoes with their uninfected counterparts also revealed some unexpected RNA expression patterns earlier and later in the infection: Significant differences in expression of several immune effectors were observed as early as 30 minutes after the ingestion of the infected blood meal. In addition, in the later stages of the infection (towards the end of the mosquito lifespan), we observed an unexpected increase in immune investment in uninfected, but not in infected, mosquitoes. In conclusion, our work extends the comparative transcriptomic analyses of malaria-infected mosquitoes beyond human and rodent parasites and provides insights into the degree of conservation of immune pathways and into the selective pressures exerted by Plasmodium parasites on their vectors.
Stable twin rDNA loci form a single nucleolus in brewer’s yeast
Authors: Lazar-Stefanita Luciana , Luo Jingchuan , Haase Max A. B. , Zhang Weimin , Boeke Jef D.
Published: June 2022

The nucleolus is the most prominent membraneless compartment within the nucleus1, 2 - dedicated to the metabolism of ribosomal RNA. Nucleoli are composed of hundreds of ribosomal DNA (rDNA) repeated genes that form large chromosomal clusters3–5, whose high recombination rates can cause nucleolar dysfunction and promote genome instability6–8 related to metabolic and genetic diseases9–13. Intriguingly, the evolving architecture of genomes appears to have favored two strategic rDNA locations in a broad range of species – where a single locus per chromosome is situated either near the centromere or the telomere14, 15. To delve into how organisms may benefit from these nuclear organizations, we used a fused-karyotype strain of Saccharomyces cerevisiae16 to megabase-engineer a chromosome with twin chromosome-collinear rDNA loci. We showed that the twin-rDNA yeast readily adapts exhibiting wild-type growth and maintaining rRNA homeostasis. Using imaging and chromosome conformation capture, we found that the twin loci merge into a single subnuclear compartment throughout the cell cycle. Unexpectedly, we found that rDNA locus size is dependent on its position relative to the centromere, whereby the locus that is centromere–distal undergoes size reduction at a higher frequency compared to the centromere-proximal counterpart. In sum, our work sheds light on the structural evolution of rDNA loci and provides new tools to study the rDNA dosage effect on cellular metabolism.
Modeling foot-and-mouth disease dissemination in Brazil and evaluating the effectiveness of control measures
Authors: Cardenas Nicolas C. , Lopes Francisco P. N. , Machado Alencar , Maran Vinicius , Trois Celio , Machado Felipe Amadori , Machado Gustavo
Published: June 2022

Foot-and-mouth disease (FMD) is known to infect multiple food-animal species and to spread among ungulate populations. We introduce a multiscale compartmental stochastic model that incorporates population dynamics, births, deaths, and species-specific transmission dynamics. The model integrates disease dynamics at both the between-farm and the within-farm levels. We developed four scenarios to evaluate the effectiveness of countermeasures, including movement standstill, vaccination, and depopulation. The base scenario involved vaccinating the animals on 20 farms and depopulating four infected farms. The three alternative control scenarios included increasing vaccination and depopulation capacities and no vaccination. Our findings indicate that bovines were the most frequently infected species, followed by swine and small ruminants. After ten days of initial spread, the number of infected farms ranged from 1 to 123, with most simulations (90.12%) predicting fewer than 50 infected farms. Most of the secondary spread occurred within a range of 25 km. Early response to initiating the control action reduces the number of days spent working on control actions while intensifying daily depopulation and vaccination capacity; this may be worth considering in decision-making processes for future control of FMD. Emergency vaccination proved to be efficient in reducing the magnitude and duration of outbreaks, whereas increasing depopulation without the use of vaccination also proved to be effective in eliminating the outbreaks.
GAMBIT (Genomic Approximation Method for Bacterial Identification and Tracking): A methodology to rapidly leverage whole genome sequencing of bacterial isolates for clinical identification
Authors: Lumpe Jared , Gumbleton Lynette , Gorzalski Andrew , Libuit Kevin , Varghese Vici , Lloyd Tyler , Tadros Farid , Arsimendi Tyler , Wagner Eileen , Stephens Craig , Sevinsky Joel , Hess David , Pandori Mark
Published: June 2022

Whole genome sequencing of clinical bacterial isolates has the potential to transform the fields of medicine and public health, with particular impact on molecular epidemiology, infection control and assessing the spread of antibiotic resistance. To realize this potential, bioinformatic software needs to be developed that meets the quality standards of a diagnostic test to allow the reporting of identification results. Our research group has developed a methodology (GAMBIT: Genomic Approximation Method for Bacterial Identification and Tracking) using k-mer based strategies for identification of bacteria based on whole genome sequence reads. GAMBIT incorporates this algorithm with a highly curated searchable database of genomes, which is a subset of the NCBI RefSeq assembly database. In this manuscript, we describe the validation of the scoring methodology, robustness to chosen parameters, establishment of confidence thresholds and the curation of the reference database. We discuss a validation set with GAMBIT deployed as a laboratory-developed test at the Alameda County Public Health Laboratory in Oakland, California. Three advancements were required to build upon existing k-mer based strategies to allow GAMBIT to possess the quality control parameters desired for its use as a diagnostic laboratory-developed test. Firstly, we innovated the data structure used to store the database of known bacterial genomes. This allowed us to store 48,224 bacterial genomes in a k-mer based database—a majority of the bacterial genomes in the NCBI RefSeq database at the time of development. Secondly, curation of the NCBI RefSeq database was required to remove ambiguous or potentially incorrectly labeled bacterial genomes to greatly increase confidence in positive matches. Lastly, we used this curated version of the NCBI RefSeq database and our scoring method to generate confidence thresholds for identification. Thus, the end-user does not rely simply on the closest match, but is informed whether that closest match exceeds a threshold for highly confident identification. This method greatly reduces or eliminates false identifications which are often detrimental in a clinical setting.
Zoonotic avian influenza viruses evade human BTN3A3 restriction
Authors: Pinto Rute Maria , Bakshi Siddharth , Lytras Spyros , Zakaria Mohammad Khalid , Swingler Simon , Worrell Julie C , Herder Vanessa , Varjak Margus , Cameron-Ruiz Natalia , Rodriguez Mila Collados , Varela Mariana , Wickenhagen Arthur , Loney Colin , Pei Yanlong , Hughes Joseph , Valette Elise , Turnbull Matthew L , Furnon Wilhelm , Hargrave Kerrie E , Gu Quan , Orr Lauren , Taggart Aislynn , Boutell Chris , Grey Finn , Hutchinson Edward , Digard Paul , Monne Isabella , Wootton Sarah K , MacLeod Megan K L , Wilson Sam J , Palmarini Massimo
Published: June 2022

Cross-species transmission of avian influenza A viruses (IAVs) into humans could represent the first step of a future pandemic1. Multiple factors limiting the spillover and adaptation of avian IAVs in humans have been identified, but they are not sufficient to explain which virus lineages are more likely to cross the species barrier1,2. Here, we identified human BTN3A33 (butyrophilin subfamily 3 member A3) as a potent inhibitor of avian but not human IAVs. We determined that BTN3A3 is constitutively expressed in human airways and its antiviral activity evolved in primates. We show that BTN3A3 restriction acts at the early stages of virus replication by inhibiting avian IAV vRNA transcription. We identified residue 313 in the viral nucleoprotein (NP) as the genetic determinant of BTN3A3 sensitivity (313F, or rarely 313L in avian viruses) or evasion (313Y or 313V in human viruses). However, several serotypes of avian IAVs that spilled over into humans in recent decades evade BTN3A3 restriction. In these cases, BTN3A3 evasion is due to substitutions (N, H or Q) in NP residue 52 that is adjacent to residue 313 in the NP structure4. Importantly, we identified more than 150 avian IAV lineages with a BTN3A3-resistant genotype. In conclusion, sensitivity or resistance to BTN3A3 is another factor to consider in the risk assessment of the zoonotic potential of avian influenza viruses.
Cytomulate: accurate and efficient simulation of CyTOF data
Authors: Yang Yuqiu , Wang Kaiwen , Lu Zeyu , Wang Tao , Wang Xinlei
Published: June 2022

Recently, many analysis tools have been devised to offer insights into data generated via Cytometry by time-of-flight (CyTOF). However, objective evaluations of these methods remain absent as most evaluations are conducted against real data where the ground truth is generally unknown. In this paper, we develop Cytomulate, a reproducible and accurate simulation algorithm of CyTOF data, which could serve as a foundation for future method development and evaluation. We demonstrate that Cytomulate can capture various characteristics of CyTOF data and is superior in learning overall data distributions than single-cell RNA-seq-oriented methods such as scDesign2, Splatter and generative models like LAMBDA.
AuCoMe: inferring and comparing metabolisms across heterogeneous sets of annotated genomes
Authors: Belcour Arnaud , Got Jeanne , Aite Méziane , Delage Ludovic , Collen Jonas , Frioux Clémence , Leblanc Catherine , Dittami Simon M. , Blanquart Samuel , Markov Gabriel V. , Siegel Anne
Published: June 2022

Comparative analysis of Genome-Scale Metabolic Networks (GSMNs) may yield important information on the biology, evolution, and adaptation of species. However, it is impeded by the high heterogeneity of the quality and completeness of structural and functional genome annotations, which may bias the results of such comparisons. To address this issue, we developed AuCoMe – a pipeline to automatically reconstruct homogeneous GSMNs from a heterogeneous set of annotated genomes without discarding available manual annotations. We tested AuCoMe with three datasets, one bacterial, one fungal, and one algal, and demonstrated that it successfully reduces technical biases while capturing the metabolic specificities of each organism. Our results also point out shared metabolic traits and divergence points among evolutionarily distant species, such as algae, underlining the potential of AuCoMe to accelerate the broad exploration of metabolic evolution across the tree of life.
Two-Step Machine Learning to Diagnose and Predict Involvement of Lungs in COVID-19 and Pneumonia using CT Radiomics
Authors: Khaniabadi Pegah Moradi , Bouchareb Yassine , Al-Dhuhli Humoud , Shiri Isaac , Al-Kindi Faiza , Khaniabadi Bita Moradi , Zaidi Habib , Rahmim Arman
Published: June 2022

Objective

We aimed to develop a two-step machine learning (ML) based model to diagnose and predict involvement of lungs in COVID-19 and non COVID-19 pneumonia patients using CT chest radiomic features.

Methods

Three hundred CT scans (3-classes: 100 COVID-19, 100 pneumonia, and 100 healthy subjects) were enrolled in this study. Diagnostic task included 3-class classification. For severity prediction, two radiologists scored involvement of lungs in COVID-19 and pneumonia scans based on percentage of involvement in all 5 lobes. Datasets were classified into mild (0-25%), moderate (26-50%), and severe (>50%). Whole lungs were segmented utilizing deep learning-based segmentation method. Altogether, 107 features including shape, first-order histogram, second and high order texture features were extracted. For both tasks, datasets were randomly divided into 90% training sets (70% and 30% for training and validation, respectively) and 10% test sets. Pearson correlation coefficient (PCC≥90%) was performed to exclude highly correlated features. Subsequently, different feature selection algorithms (Correlation attribute evaluation, Information gain attribute, Wrapper Subset selection algorithm, Relief method, and Correlation-based feature selection) were assessed. The most pertinent features were finally selected using voting method based on the evaluation of all algorithms. Several ML-based supervised algorithms were utilized, namely Naïve Bays, Support Vector Machine, Bagging, Random Forest, K-nearest neighbors, Decision Tree and Ensemble Meta voting. The synthetic minority oversampling technique (SMOTE) was used to balance the three classes in training sets. The optimal model was first selected based on precision, recall and area-under-curve (AUC) by randomizing the training/validation sets 20 times, followed by testing using the test set. To ensure the repeatability of the results, the entire process was repeated 50 times.

Results

Nine pertinent features (2 shape, 1 first-order, and 6 second-order features) were obtained after feature selection for both phases. In diagnostic task, the performance of 3-class classification using Random Forest was 0.909±0.026, 0.907±0.056, 0.902±0.044, 0.939±0.031, and 0.982±0.010 for precision, recall, F1-score, accuracy, and AUC, respectively. The severity prediction task using Random Forest achieved 0.868±0.123 precision, 0.865±0.121 recall, 0.853±0.139 F1-score, 0.934±0.024 accuracy, and 0.969±0.022 AUC.

Conclusion

The two-phase ML-based model accurately classified COVID-19 and pneumonia patients using CT radiomics, and adequately predicted severity of lungs involvement. This 2-steps model showed great potential in assessing COVID-19 CT images towards improved management of patients.
Improving de novo Protein Binder Design with Deep Learning
Authors: Bennett Nathaniel , Coventry Brian , Goreshnik Inna , Huang Buwei , Allen Aza , Vafeados Dionne , Peng Ying Po , Dauparas Justas , Baek Minkyung , Stewart Lance , DiMaio Frank , De Munck Steven , Savvides Savvas N. , Baker David
Published: June 2022

We explore the improvement of energy-based protein binder design using deep learning. We find that using AlphaFold2 or RoseTTAFold to assess the probability that a designed sequence adopts the designed monomer structure, and the probability that this structure binds the target as designed, increases design success rates nearly 10-fold. We find further that sequence design using ProteinMPNN rather than Rosetta considerably increases computational efficiency.
Pre-neuronal processing of haptic sensory cues via dispersive high-frequency vibrational modes
Authors: Ding Yu , Vlasov Yurii
Published: June 2022

Sense of touch is one of the major perception channels. Neural coding of object textures conveyed by rodents’ whiskers has been a model to study early stages of haptic information uptake. While high-precision spike timing has been observed during whisker sweeping across textured surfaces, the exact nature of whisker micromotions that spikes encode remains elusive. Here, we discovered that a single micro-collision of a whisker with surface features generates vibrational eigenmodes spanning frequencies up to 10KHz. While propagating along the whisker, these high-frequency modes can carry up to 80% of shockwave energy, exhibit 100X smaller damping ratio, and arrive at the follicle 10X faster than low frequency components. The mechano-transduction of these energy bursts into a time-sequenced population spike trains may generate temporally unique “bar code” with ultra-high information capacity. This hypothesis of pre-neuronal processing of haptic signals based on dispersive temporal separation of the vibrational modal frequencies can shed light on neural coding of haptic signals in many whisker-like sensory organs across the animal world as well as in texture perception in primate’s glabrous skin.

Significance Statement

Understanding how the outside world is encoded in neurons spikes in sensory organs and how these neural codes contribute to perception remains elusive. Using a model system - a whisker of a mouse - we discovered that tiny whisker vibrations induced at the whisker tip by collisions with external objects generate a time series of energy bursts. This creates a temporally unique “bar code” of a time-sequenced population spike trains with ultra-high information capacity. We hypothesize that such a “pre-neuronal processing” of touch events into time-coded spikes can provide a conceptual link to understand neural coding in many whisker-like sensory organs across the animal world as well as in texture perception in primate’s glabrous skin.
A robust intensity distribution alignment for harmonization of T1w intensity values
Authors: Sederevičius Donatas , Bjørnerud Atle , Walhovd Kristine B. , Van Leemput Koen , Fischl Bruce , Fjell Anders M.
Published: June 2022

Variations in image intensities between magnetic resonance imaging (MRI) acquisitions affect the subsequent image processing and its derived outcomes. Therefore, it is necessary to normalize images of different scanners/acquisitions, especially for longitudinal studies where a change of scanner or pulse sequence often happens. Here, we propose a robust intensity distribution alignment (RIDA) method to remove between-scan effects. The method is based on MRI T1w images acquired in close succession and robustly aligns two cumulative distribution functions (CDF) of voxel intensities to improve image-derived outcomes of a range of subcortical brain structures with different acquisition parameters. We compare RIDA with the other image harmonization methods: mica and RAVEL. We study three intra-scanner and three inter-scanner protocol variations among the same 20 participants scanned with Siemens 1.5T Avanto, 3T Skyra, and 3T Prisma scanners on the same day and use image-derived volumetric outputs from the Sequence Adaptive Multimodal Segmentation (SAMSEG) method. We find that CDF-based intensity harmonization (mica and RIDA) significantly reduces intensity differences, improves consistency in volume quantification, and increases spatial overlap between two images acquired in close succession. The improvements are most considerable if the intensity normalization is based on subcortical structures only (RIDA), excluding cortical regions, instead of the whole brain. However, the effect of the corrections varies considerably as a function of the compared scanners and sequences. In conclusion, the RIDA scaneffect normalization improves the consistency of image-derived measures, but its performance depends on several factors.
DeMAG predicts the effects of variants in clinically actionable genes by integrating structural and evolutionary epistatic features
Authors: Luppino Federica , Adzhubei Ivan A. , Cassa Christopher A. , Toth-Petroczy Agnes
Published: June 2022

Despite an increasing use of genomic sequencing in clinical practice, interpretation of rare genetic variants remains challenging even in well-studied disease genes, resulting in many patients with Variants of Uncertain Significance (VUSs). Computational Variant Effect Predictors (VEPs) are currently used to provide valuable evidence in variant classifications, but they often misclassify benign variants, contributing to potential misdiagnoses. Here, we developed Deciphering Mutations in Actionable Genes (DeMAG), a supervised classifier for interpreting missense variants in actionable disease genes with improved performance over existing VEPs (20% decrease of false positive rate). Our tool has balanced specificity (82%) and sensitivity (94%) on clinical data, and the lowest misclassification rate on putatively benign variants among evaluated tools. DeMAG takes advantage of a novel epistatic feature, the ‘partners score’, which is based on evolutionary and structural partnerships of residues as estimated by evolutionary information and AlphaFold2 structural models. The ‘partners score’ as a general framework of epistatic interactions, can integrate not only clinical but functional information. We anticipate that our tool (demag.org) will facilitate the interpretation of variants and improve clinical decision-making.
Visuo-motor transformations in the intraparietal sulcus mediate the acquisition of endovascular medical skill
Authors: Paul Katja I. , Müller Karsten , Rousseau Paul-Noel , Glathe Annegret , Taatgen Niels A. , Cnossen Fokie , Lanzer Peter , Villringer Arno , Steele Christopher J.
Published: June 2022

Performing endovascular medical interventions safely and efficiently requires a diverse set of skills that need to be practised in dedicated training sessions. Here, we used multimodal magnetic resonance (MR) imaging to determine the structural and functional plasticity and core skills associated with skill acquisition. A training group learned to perform a simulator-based endovascular procedure, while a control group performed a simplified version of the task; multimodal MR images were acquired before and after training. Using a well-controlled interaction design, we found strong, multimodal evidence for the role of the intraparietal sulcus (IPS) in endovascular skill acquisition that is in line with previous work implicating the structure in simple visuo-motor and mental rotation tasks. Our results provide a unique window into the multimodal nature of rapid structural and functional plasticity of the human brain while learning a multifaceted and complex clinical skill. Further, our results provide a detailed description of the plasticity process associated with endovascular skill acquisition and highlight specific facets of skills that could enhance current medical pedagogy and be useful to explicitly target during clinical resident training.
Understanding the genome-wide transcription response to varying cAMP levels using phenomenological models in bacteria
Authors: Chakraborty Shweta , Singh Parul , Narain Seshasayee Aswin Sai
Published: June 2022

Attempts to understand gene regulation by global transcription factors (TF) have largely been limited to expression studies under binary conditions of presence and absence of the TF. Studies addressing genome-wide transcriptional responses to changing TF concentration at high resolution are lacking. Here, we create a dataset containing the entireE.colitranscriptome as it responds to 10 different cAMP concentrations spanning the biological range. We use the Hill’s model to accurately summarise individual gene responses into 3 intuitively understandable parameters -k, nandEmaxreflecting the midpoint of dynamic range, non-linearity and sensitivity of a gene. cAMP-regulated genes show a small dynamic range with midpoints centred around wild-type cAMP concentrations, with genes activating in a switch-like fashion. Using this approach we show that cAMP-CRP affinity at promoters is well correlated to the sensitivity(Emax) of genes but not to the midpoints of dynamic range(k). Finally, genes belonging to different functional classes are tuned to differentk, nandEmax. We show phenomenological models to be a better alternative for studying gene expression trends compared to classical clustering methods with the phenomenological constants providing greater insights into how genes are tuned in a regulatory network.
Publication games: in the web of reciprocity
Authors: Barta Zoltán
Published: June 2022

1

The present processes of research assessment, i.e. focusing on one or a few, related, sciento-metrics, foster questionable authorship practices, like gifting authorship to non-contributing people. An especially harmful one of these unethical practices is the formation of publication cartels, where authors offer gift authorship to each other reciprocally. Here, by developing a simple model and a simulation of the publication process I investigate how beneficial cartels can be and what measure can be used to restrict them. My results indicate that publication cartels can significantly boost members’ productivity even if paper counts are weighted by the inverse of author number (the 1/n rule). Nevertheless, applying the 1/n rule generates conflicts of interest both among cartel members themselves and between members and non-members which might lead to the self-purification of the academic publishing industry.
Increased severity of chronic kidney disease in response to high potassium intake is dependent on mineralocorticoid receptor activation
Authors: Olivier Valérie , Arnoux Gregoire , Ramakrishnan Suresh , Sassi Ali , Roth Isabelle , Chassot Alexandra , Tournier Malaury , Dizin Eva , Hummler Edith , Rutkowski Joseph M. , Feraille Eric
Published: June 2022

Dietary treatment is seminal for management of chronic kidney disease (CKD). The aim of our project was to assess the effects of potassium intake on the progression of CKD. We used 2 mouse CKD models to analyze the effects of potassium intake on CKD : the unilateral ureteral obstruction (UUO) and the POD-ATTAC models. POD-ATTAC mice display a podocyte-specific apoptosis after the administration of a chemical inducer. We also studied the effect of mineralocorticoid receptor (MR) using UUO in kidney tubule-specific MR knockout mice.

In both UUO and POD-ATTAC mice, high potassium diet increased interstitial fibrosis. High potassium diet also increased the abundance of the extracellular matrix protein fibronectin and decreased the abundance of the epithelial marker Na+-K+ ATPase. Consistently, POD-ATTAC mice fed with high potassium diet displayed lower glomerular filtration rate. Spironolactone, a MR antagonist, decreased fibrosis induced by high potassium diet in POD-ATTAC mice. However, kidney tubule-specific MR knockout did not improve the fibrotic lesions induced by UUO under normal or high potassium diets. Macrophages from high potassium-fed POD-ATTAC mice displayed higher mRNA levels of the pro-inflammatory chemokine MCP1. This effect was decreased by spironolactone, suggesting a role of MR signaling in myeloid cells in the pro-fibrotic effect of potassium-rich diet.

High potassium intake generates more fibrosis leading to decreased kidney function in experimental CKD. MR signaling plays a pivotal role in this potassium-induced fibrosis. The effect of reducing potassium intake on CKD progression should be assessed in future clinical trials.

Translational statement

Dietetic approach is a cheap and effective therapy to slow down the development of chronic kidney diseases and kidney fibrosis. Potassium-rich diets are protective against renal and cardiovascular events in the general population, albeit some conflicting data were obtained in patients with chronic kidney disease. We showed that potassium-rich diet accelerates fibrosis development, by enhancing kidney inflammation in two mouse models of chronic kidney disease. These data suggest that potassium-rich diets should not be advised in patients with chronic kidney disease, unless future clinical trials demonstrate any beneficial effect in these patients.
Biphasic JNK–Erk Signaling Separates Induction and Maintenance of Cell Senescence after DNA Damage
Authors: Netterfield Tatiana S. , Ostheimer Gerard J. , Tentner Andrea R. , Sorger Peter K. , Janes Kevin A. , Lauffenburger Douglas A. , Yaffe Michael B.
Published: June 2022

Genotoxic stress in mammalian cells, including that caused by anti-cancer chemotherapy, can induce temporary cell cycle arrest, DNA damage-induced senescence (DDIS) or apoptotic cell death. Despite obvious clinical importance, it is unclear how the signals emerging from DNA damage are integrated together with other cellular signaling pathways monitoring the cell’s environment and/or internal state to control these different cell fates. Here, using a combination of single cell-based signaling measurements and tensor PLSR/PCA computational approaches, we show that the JNK and Erk MAPK signaling pathways regulate the initiation of senescence through the transcription factor AP-1 at early times after extrinsic DNA damage, and the Senescence Associated Secretory Phenotype, a hallmark of DDIS, at late times after damage. These results identify a time-based separation of function for the same signaling pathways beyond the classic DNA damage response that control the cell senescence decision and modulate the tumor microenvironment following genotoxic stress, and reveal a fundamental similarity between signaling mechanisms responsible for oncogene-induced senescence and senescence caused by extrinsic DNA damaging agents.
Automating clinical assessments of memory deficits: Deep Learning based scoring of the Rey-Osterrieth Complex Figure
Authors: Langer Nicolas , Weber Maurice , Vieira Bruno Hebling , Strzelczyk Dawid , Wolf Lukas , Pedroni Andreas , Heitz Jonathan , Müller Stephan , Schultheiss Christoph , Tröndle Marius , Arango-Lasprilla Juan Carlos , Rivera Diego , Scarpina Federica , Zhao Qianhua , Leuthold Rico , Wehrle Flavia , Jenni Oskar G. , Brugger Peter , Zaehle Tino , Lorenz Romy , Zhang Ce
Published: June 2022

Background

Memory deficits are a hallmark of many different neurological and psychiatric conditions. The Rey-Osterrieth complex figure (ROCF) is the state–of-the-art assessment tool for neuropsychologists across the globe to assess the degree of non-verbal visual memory deterioration. To obtain a score, a trained clinician inspects a patient’s ROCF drawing and quantifies deviations from the original figure. This manual procedure is time-consuming, slow and scores vary depending on the clinician’s experience, motivation and tiredness.

Methods

Here, we leverage novel deep learning architectures to automatize the rating of memory deficits. For this, we collected more than 20k hand-drawn ROCF drawings from patients with various neurological and psychiatric disorders as well as healthy participants. Unbiased ground truth ROCF scores were obtained from crowdsourced human intelligence. This dataset was used to train and evaluate a multi-head convolutional neural network.

Results

The model performs highly unbiased as it yielded predictions very close to the ground truth and the error was similarly distributed around zero. The neural network outperforms both online raters and clinicians. The scoring system can reliably identify and accurately score individual figure elements in previously unseen ROCF drawings, which facilitates explainability of the AI-scoring system. To ensure generalizability and clinical utility, the model performance was successfully replicated in a large independent prospective validation study that was pre-registered prior to data collection.

Conclusions

Our AI-powered scoring system provides healthcare institutions worldwide with a digital tool to assess objectively, reliably and time-efficiently the performance in the ROCF test from hand-drawn images.
Temperature impacts on dengue incidence are nonlinear and mediated by climatic and socioeconomic factors
Authors: Kirk Devin , Straus Samantha , Childs Marissa L. , Harris Mallory , Couper Lisa , Davies T. Jonathan , Forbes Coreen , Gehman Alyssa-Lois , Groner Maya L. , Harley Christopher , Lafferty Kevin D. , Savage Van , Skinner Eloise , O’Connor Mary , Mordecai Erin A.
Published: June 2022

Temperature can influence mosquito-borne diseases like dengue. These effects are expected to vary geographically and over time in both magnitude and direction and may interact with other environmental variables, making it difficult to anticipate changes in response to climate change. Here, we investigate global variation in temperature–dengue relationship by analyzing published correlations between temperature and dengue and matching them with remotely sensed climatic and socioeconomic data. We found that the correlation between temperature and dengue was most positive at intermediate (near 24°C) temperatures, as predicted from the thermal biology of the mosquito and virus. Positive temperature–dengue associations were strongest when temperature variation and population density were high and decreased with infection burden and rainfall mean and variation, suggesting alternative limiting factors on transmission. Our results show that while climate effects on diseases are context-dependent they are also predictable from the thermal biology of transmission and its environmental and social mediators.