Miracle-dz/UniFAR-FTUs huggingface dataset:
The discovery of genome-wide mutational dependence in naturally evolving populations

Background Evolutionary pressures on bacterial pathogens can result in phenotypic change including increased virulence, drug resistance, and transmissibility. Understanding the evolution of these phenotypes in nature and the multiple genetic changes needed has historically been difficult due to sparse and contemporaneous sampling. A complete picture of the evolutionary routes frequently travelled by pathogens would allow us to better understand bacterial biology and potentially forecast pathogen population shifts. Methods In this work, we develop a phylogeny-based method to assess evolutionary dependency between mutations. We apply our method to a dataset of 31,428 Mycobacterium tuberculosis complex (MTBC) genomes, a globally prevalent bacterial pathogen with increasing levels of antibiotic resistance. Results We find evolutionary dependency within simultaneously- and sequentially-acquired variation, and identify that genes with dependent sites are enriched in antibiotic resistance and antigenic function. We discover 20 mutations that potentiate the development of antibiotic resistance and 1,003 dependencies that evolve as a consequence antibiotic resistance. Varying by antibiotic, between 9% and 80% of resistant strains harbor a dependent mutation acquired after a resistance-conferring variant. We demonstrate that mutational dependence can not only improve prediction of phenotype (e.g. antibiotic resistance), but can also detect sequential environmental pressures on the pathogen (e.g. the pressures imposed by sequential antibiotic exposure during the course of standard multi-antibiotic treatment). Taken together, our results demonstrate the feasibility and utility of detecting dependent events in the evolution of natural populations. Data and code available at: https://github.com/farhat-lab/DependentMutations
Inhibitors of ROCK kinases induce multiple mitotic defects and synthetic lethality in BRCA2-deficient cells

BRCA2-deficient cells are highly sensitive to poly-ADP-ribose polymerase inhibitors (PARPi) due to their impaired homologous recombination repair. This increased cytotoxicity is triggered by DNA replication stress induced by PARP trapping on DNA. Thus, it is broadly assumed that DNA damage is a prerequisite for BRCA2 synthetic lethality (SL). Here we show that inhibiting ROCK kinases in BRCA2 deficient cells, triggers SL independently from acute replication stress. In contrast, such SL is preceded by enhanced M-phase defects such as anaphase bridges, and abnormal mitotic figures, which were associated with multipolar spindles, supernumerary centrosomes and multinucleation. SL was also triggered by inhibiting Citron Rho-interacting kinase, another enzyme which, similarly to ROCK kinases, regulates cytokinesis. Together, these observations suggest cytokinesis failure as trigger of mitotic abnormalities and SL in BRCA2 cells. Furthermore, preventing mitotic entry by Early mitotic inhibitor 1 (EMI1) depletion promoted survival of BRCA2 deficient cells treated with inhibitors of ROCK kinases, thus reinforcing the association between M-phase and the cell death in BRCA2 deficient cells. This novel mechanism of SL induction is in contrast to the one triggered by PARPi and uncovers mitosis as an Achilles heel of BRCA2 deficient cells.
Early visual cortex tracks speech envelope in the absence of visual input

Neural entrainment to continuous speech is typically observed within the language network and can be modulated by both low-level acoustic features and high-level meaningful linguistic units (e.g., phonemes, phrases, and sentences). Recent evidence showed that visual cortex may entrain to speech envelope, however its putative role in the hierarchy of speech processing remains unknown. We tested blindfolded participants who listened to semantically meaningful or meaningless stories, either in quiet or embedded in multi-talker babble noise. Entrainment to speech was assessed with forward linear modeling of participants’ EEG activity. We investigated (1) low-level acoustic effects by contrasting neural tracking of speech presented in quiet or noise and (2) high-level linguistic effects by contrasting neural tracking to meaningful or meaningless stories. Results showed that envelope tracking was enhanced and delayed for speech embedded in noise compared to quiet. When semantic information was missing, entrainment to speech envelope was fastened and reduced. Source modeling revealed that envelope tracking engaged wide neural networks beyond the auditory cortex, including early visual cortex. Surprisingly, while no clear influence of semantic content was found, the magnitude of visual cortex entrainment was affected by low-level features. The decrease of sound SNR-level dampened visual cortex tracking, suggesting an active suppressing mechanism in challenging listening conditions. Altogether, these findings provide further evidence of a functional role of early visual cortex in the entrainment to continuous speech.
Assessing the pathogenic potential of less common Salmonella enterica serotypes circulating in the Thai pork production chain

Salmonella is a frequent zoonotic foodborne pathogen, with swine and pork meats the most common source of human infection. In Chiang Mai and Lamphun Province in northern Thailand, there has been a high prevalence of salmonellosis for over a decade. Infection is usually with several dominant S. enterica serotypes, including serotypes Rissen and Monophasic Typhimurium. However, several less common serotypes also contribute to disease. Whole genome sequencing of 43 of these less common S. enterica serotypes isolated from the pork production chain through 2011-2014 were used to evaluate their genetic diversity and virulence potential. Salmonella contamination at local retail markets represented cross-contamination from multiple sources, including decontaminated foodstuff. Previous studies have highlighted the importance of host cell adhesion, invasion and intracellular survival for the development of clinical salmonellosis. We screened our dataset for known virulence genes and antimicrobial resistance genes, identifying at least 10 antimicrobial resistance genes in all isolates. These results indicate that these less common S. enterica serotypes also pose a significant public health risk. Our findings support the need for appropriate surveillance of food products going to market to reduce public exposure to highly pathogenic, multi-drug resistant Salmonella. Surveillance throughout the pork production chain would motivate stakeholders to reinforce sanitation standards and help reduce the risk of salmonellosis in humans.
Human electromagnetic and haemodynamic networks systematically converge in unimodal cortex and diverge in transmodal cortex

Whole-brain neural communication is typically estimated from statistical associations among electromagnetic or haemodynamic time-series. The relationship between functional network architectures recovered from these two types of neural activity remains unknown. Here we map electromagnetic networks (measured using magnetoencephalography; MEG) to haemodynamic networks (measured using functional magnetic resonance imaging; fMRI). We find that the relationship between the two modalities is regionally heterogeneous and systematically follows the cortical hierarchy, with close correspondence in unimodal cortex and poor correspondence in transmodal cortex. Comparison with the BigBrain histological atlas reveals that electromagnetic-haemodynamic coupling is driven by laminar differentiation and neuron density, suggesting that the mapping between the two modalities can be explained by cytoarchitectural variation. Importantly, haemodynamic connectivity cannot be explained by electromagnetic activity in a single frequency band, but rather arises from the mixing of multiple neurophysiological rhythms. Correspondence between the two is largely driven by MEG functional connectivity at the beta (15-29 Hz) frequency band. Collectively, these findings demonstrate highly organized but only partly overlapping patterns of connectivity in MEG and fMRI functional networks, opening fundamentally new avenues for studying the relationship between cortical microarchitecture and multi-modal connectivity patterns.
Characterizing The Landscape Of Viral Expression In Cancer By Deep Learning

About 15% of human cancer cases are attributed to viral infections. To date, virus expression in tumor tissues has been mostly studied by aligning tumor RNA sequencing reads to databases of known viruses. To allow identification of divergent viruses and rapid characterization of the tumor virome, we developed viRNAtrap, an alignment-free pipeline to identify viral reads and assemble viral contigs. We apply viRNAtrap, which is based on a deep learning model trained to discriminate viral RNAseq reads, to 14 cancer types from The Cancer Genome Atlas (TCGA). We find that expression of exogenous cancer viruses is associated with better overall survival. In contrast, expression of human endogenous viruses is associated with worse overall survival. Using viRNAtrap, we uncover expression of unexpected and divergent viruses that have not previously been implicated in cancer. The viRNAtrap pipeline provides a way forward to study viral infections associated with different clinical conditions.
Modeling nonsegmented negative-strand RNA virus (NNSV) transcription with ejective polymerase collisions and biased diffusion

Infections by nonsegmented negative-strand RNA viruses (NNSV) are widely thought to entail gradient gene expression from the well-established existence of a single promoter at the 3’ end of the viral genome and the assumption of constant transcriptional attenuation between genes. But multiple recent studies show viral mRNA levels in infections by respiratory syncytial virus (RSV), a major human pathogen and member of NNSV, that are inconsistent with a simple gradient. Here we integrate known and newly predicted phenomena into a biophysically reasonable model of NNSV transcription. Our model succeeds in capturing published observations of RSV and vesicular stomatitis virus (VSV) mRNA levels. We therefore propose a novel understanding of NNSV transcription based on the possibility of ejective polymerase-polymerase collisions and, in the case of RSV, biased polymerase diffusion.
Object Manipulation Learning by Imitation

We aim to enable robot to learn object manipulation by imitation. Given external observations of demonstrations on object manipulations, we believe that two underlying problems to address in learning by imitation is 1) segment a given demonstration into skills that can be individually learned and reused, and 2) formulate the correct RL (Reinforcement Learning) problem that only considers the relevant aspects of each skill so that the policy for each skill can be effectively learned. Previous works made certain progress in this direction, but none has taken private information into account. The public information is the information that is available in the external observations of demonstration, and the private information is the information that are only available to the agent that executes the actions, such as tactile sensations. Our contribution is that we provide a method for the robot to automatically segment the demonstration of object manipulations into multiple skills, and formulate the correct RL problem for each skill, and automatically decide whether the private information is an important aspect of each skill based on interaction with the world. Our experiment shows that our robot learns to pick up a block, and stack it onto another block by imitating an observed demonstration. The evaluation is based on 1) whether the demonstration is reasonably segmented, 2) whether the correct RL problems are formulated, 3) and whether a good policy is learned.
Transcriptomic and functional mapping of autism associated environmental factors in differentiating human neural progenitors

Autism spectrum disorder (ASD) is a heterogenous, early-onset neurodevelopmental disorder with multifactorial aetiology. There are hundreds of genes that contribute to ASD. Additionally, several environmental factors, such as neurotoxic compounds, endocrine modulators, and neuropsychiatric medications, have been associated with ASD outcomes. We investigate a highly efficient and multiplexable, fractional factorial experimental design (FFED) to study six ASD-associated environmental factors (lead, valproic acid, bisphenol A, ethanol, zinc deficiency and fluoxetine) and four human induced pluripotent stem cell (iPSC) lines. RNA sequencing analysis after five-day exposures on differentiating human neural progenitors with a layered analytical approach detected several convergent and divergent, gene and pathway level responses across the factors. In particular, we revealed significant upregulation of pathways related to synaptic function and lipid metabolism, following exposure to lead and fluoxetine, respectively. Furthermore, we validated our findings using mass spectrometry-based metabolomics after fluoxetine exposure. The study demonstrates that the FFED can be used for multiplexed transcriptomic analyses to detect relevant changes at the pathway level. Our study provides a roadmap for understanding the impact of low-grade environmental exposures on human neurodevelopment. Expansive future studies will require multiple cell lines with different genetic backgrounds for characterising environmental exposures in ASD and other neurodevelopmental disorders.
Functional unknomics: closing the knowledge gap to accelerate biomedical research

The human genome encodes ∼20,000 proteins, many still uncharacterised. Scientific and social factors have resulted in a focus on well-studied proteins, leading to a concern that poorly understood genes are unjustifiably neglected. To address this, we have developed an “Unknome database” that ranks proteins based on how little is known about them. We applied RNAi in Drosophila to 260 unknown genes that are conserved between flies and humans. About a quarter are required for viability, and functional screening of the rest revealed hits for fertility, development, locomotion, protein quality control and resilience to stress. CRISPR/Cas9 gene disruption validated a component of Notch signalling and two genes contributing to male fertility. Our work demonstrates the importance of poorly understood genes, provides a resource for future research acceleration, and highlights a need for our awareness of ignorance to be protected from erosion by automated database annotation.
Tree-Based Methods: Concepts, Uses and Limitations under the Framework of Resource Selection Models

The use of empirical models to predict species distribution is recognized as an important tool in wildlife management. Tree-based methods gained considerable attention in the last years mostly due to their flexibility and robustness. Here, we provide an overview of tree-based methods by addressing some of their concepts, uses and limitations. For illustrative purposes, we modelled the distribution of a red deer (Cervus elaphus) population using fine-scale predictors while applying four modelling methods: three treebased methods (classification trees, random forests and boosted trees) and the generalized linear model by stepwise regression. In order to explore alternative trees and achieve the best model performance, a series of classifiers were run with different tuning parameters. The random forests and boosted trees models were the most accurate classifiers followed by classification trees and generalized linear model by stepwise regression. Despite differences in the predictive accuracy, the results of the four models were consistent with the species ecological requirements. Red deer occurred further away from disturbed areas (e.g. villages and other human settlements), agricultural fields and near shrubs and forest patches. Furthermore, the species often occurred in areas with gentle slopes, preferentially with a southern exposure. We observed that classification trees are easy to interpret but may produce unstable decision trees and unwieldy results in the presence of sharp discontinuities. We state that ensemble methods such as random forests and boosted trees are valuable tools in predicting species distributions. This study provides the necessary background for the understanding of tree-based methods, which will be of great help in further studies in ecological modelling, as it will shed light in the most appropriate technique to be used.
Tunable energy harvesting on UHF bands especially for GSM frequencies

Metamaterial-based energy harvesting structures operating at GSM 900 and GSM 1800 frequency bands have been proposed. The basic structure is composed of four nested U-shaped elements on the front and back side of the dielectric substrate, with a 180° relationship between the front and back resonators. As energy harvesting capability is directly related to the quality of absorption, with the aim being perfect absorption, there is an air gap to allow tuning of the resonance frequency, and copper plate follows the air gap to facilitate the perfect absorption of electromagnetic waves, which is verified by means of simulation and experimental tests. A set of simulations and experimental study were further carried out to demonstrate incident and polarization angle dependency, the effects of Schottky diodes, and the optimal orientation of the U-shapes. Spectrum analysis and DC voltage measurements across the HSMS 2860 Schottky diode are also presented to test the energy harvesting capability: the spectrum analyzer shows a 40 dBm difference, while 81.7 mV maximum DC voltage is observed at 900 MHz. The simulation and experimental study results, while displaying several differences, do suggest that the proposed design can be used for running low-energy consumption devices such as sensors and switches.
Understanding Anti-COVID-19 Vaccination Protest Slogans in the US

This study aims to investigate public protest slogans in response to the Biden Administration's announcement of two COVID-19 vaccination regulations on November 4, 2021. We identified three protest themes: (1) support for individual freedom/rights, (2) opposition to government's control, and (3) anti-science reasoning/misinformation/disinformation. Major policy recommendations include implementing efforts to dispel unscientific misinformation/disinformation and to emphasize individuals' civic responsibilities for vaccination during the COVID-19 pandemic, especially considering the current spreading of the Omicron variants and the relaxing of mask mandates across the US.
Surprisingness and Occupational Engagement Influence Affective Forecasting in Career-Relevant Contexts

People tend to misestimate their future emotions. This phenomenon is thought to be associated with information accessibility. However, few studies have demonstrated the impact of context-specific information accessibility on affective forecasting. This research investigated the effects of information accessibility on affective forecasting in career context (i.e., occupational engagement was seen as information accessibility), during which surprise or not surprise context was played simultaneously. We found that affective forecasting appeared stably across emotional response types. Specifically, there was an underestimation in interest appraisals and an overestimation in satisfaction. These biases were influenced by occupational engagement, which only worked in career interest appraisals. High occupational engagement made people estimate their future emotions more accurately and overcome their forecasting bias. Surprisingness was then manipulated further to explain whether it could impact the effect of occupational engagement on affective forecasting. The emotional responses in both prediction and experience were affected by surprisingness, thus causing no affective forecasting biases. These results suggest the role of occupational engagement in affective forecasting and provide evidence supporting the information accessibility model about the mechanism in affective forecasting.
DCBLD1 Overexpression Is Associated With a Poor Prognosis in Head and Neck Squamous Cell Carcinoma

Background DCBLD1 is highly expressed in several kinds of cancer and plays a potential prognostic factor. However, the prognostic value and immune infiltration in head and neck squamous cell carcinoma remain unclear and need further research. Materials and Methods DCBLD1 expression and clinical information were obtained from the Cancer Genome Atlas (TCGA) database. The mRNA level in cell lines (SCC25 and CAL27) and gingival fibroblasts were detected using quantitative PCR. Cox regression analysis was used to evaluate the prognostic values of DCBLD1 and clinical data in HNSCC. A nomogram was also established to predict the impact of DCBLD1 on prognosis based on Cox multivariate results. The methylation level of DCBLD1 in HNSC and its prognosis were analyzed in UALACN and MethSurv. Finally, the potential biological functions of DCBLD1 were investigated using gene set enrichment analysis (GSEA) and single-sample GSEA (ssGSEA). Results The mRNA and protein expression levels of DCBLD1 were highly expressed in HNSCC tissue and cell lines. The Cox analyses demonstrate that highly expressed DCBLD1 is an independent prognosis marker (p < 0.05). ROC curve analysis showed the performance of DCBLD1 (area under the ROC curve: 0.948, sensitivity: 93.2%, specificity: 84.7%). The methylation was increased in HNSCC patients compared with normal subjects (p < 0.05) and was associated with poor prognosis at sites cg27642470 and cg21104965. Additionally, DCBLD1 expression is poorly associated with immune cell infiltration and immunological checkpoints PD-L1 and TIM-3. Conclusion In head and neck squamous cell carcinoma, DCBLD1 is overexpressed, associated with poor patient prognosis. The detailed underlying mechanism merits further research.
Biochemistry of Aminoacyl tRNA Synthetase and tRNAs and Their Engineering for Cell-Free and Synthetic Cell Applications

Cell-free biology is increasingly utilized for engineering biological systems, incorporating novel functionality, and circumventing many of the complications associated with cells. The central dogma describes the information flow in biology consisting of transcription and translation steps to decode genetic information. Aminoacyl tRNA synthetases (AARSs) and tRNAs are key components involved in translation and thus protein synthesis. This review provides information on AARSs and tRNA biochemistry, their role in the translation process, summarizes progress in cell-free engineering of tRNAs and AARSs, and discusses prospects and challenges lying ahead in cell-free engineering.