Miracle-dz/UniFAR-FTUs huggingface dataset:
Minnesota peat viromes reveal terrestrial and aquatic niche partitioning for local and global viral populations

Background Peatlands are expected to experience sustained yet fluctuating higher temperatures due to climate change, leading to increased microbial activity and greenhouse gas emissions. Despite mounting evidence for viral contributions to these processes in peatlands underlain with permafrost, little is known about viruses in other peatlands. More generally, soil viral biogeography and its potential drivers are poorly understood at both local and global scales. Here, 87 metagenomes and five viral size-fraction metagenomes (viromes) from a boreal peatland in northern Minnesota (the SPRUCE whole-ecosystem warming experiment and surrounding bog) were analyzed for dsDNA viral community ecological patterns, and the recovered viral populations (vOTUs) were compared with our curated PIGEON database of 266,125 vOTUs from diverse ecosystems. Results Within the SPRUCE experiment, viral community composition was significantly correlated with peat depth, water content, and carbon chemistry, including CH 4 and CO 2 concentrations, but not with temperature during the first 2 years of warming treatments. Peat vOTUs with aquatic-like signatures (shared predicted protein content with marine and/or freshwater vOTUs) were significantly enriched in more waterlogged surface peat depths. Predicted host ranges for SPRUCE vOTUs were relatively narrow, generally within a single bacterial genus. Of the 4326 SPRUCE vOTUs, 164 were previously detected in other soils, mostly peatlands. None of the previously identified 202,371 marine and freshwater vOTUs in our PIGEON database were detected in SPRUCE peat, but 0.4% of 80,714 viral clusters (VCs, grouped by predicted protein content) were shared between soil and aquatic environments. On a per-sample basis, vOTU recovery was 32 times higher from viromes compared with total metagenomes. Conclusions Results suggest strong viral “species” boundaries between terrestrial and aquatic ecosystems and to some extent between peat and other soils, with differences less pronounced at higher taxonomic levels. The significant enrichment of aquatic-like vOTUs in more waterlogged peat suggests that viruses may also exhibit niche partitioning on more local scales. These patterns are presumably driven in part by host ecology, consistent with the predicted narrow host ranges. Although more samples and increased sequencing depth improved vOTU recovery from total metagenomes, the substantially higher per-sample vOTU recovery after viral particle enrichment highlights the utility of soil viromics. Video abstract The importance of Minnesota peat viromes in revealing terrestrial and aquatic niche partitioning for viral populations
Genome architecture shapes evolutionary adaptation to DNA replication stress

In haploid budding yeast, evolutionary adaptation to constitutive DNA replication stress alters three genome maintenance modules: DNA replication, the DNA damage checkpoint, and sister chromatid cohesion. We asked how these trajectories depend on a population’s genome architecture by comparing the adaptation of haploids to that diploids and recombination deficient haploids. In all three genome architectures, adaptation happens within 1000 generations at rates that are linearly correlated with the initial fitness defect of the ancestors. Mutations in individual genes are selected at different frequencies in different architectures, but the benefits these mutations confer are similar in all three architectures, and combinations of these mutations reproduce the fitness gains of evolved populations. Despite the differences in the selected mutations, the evolutionary trajectories target the same three functional modules in all architectures revealing a common evolutionary response to DNA replication stress.
Redistribution of FLAgellar Member 8 during the trypanosome life cycle: Consequences for cell fate prediction

The single flagellum of African trypanosomes is essential in multiple aspects of the parasite development. The FLAgellar Member 8 protein (FLAM8), localised to the tip of the flagellum in cultured insect forms, was identified as a marker of the locking event that controls flagellum length. Here, we investigated whether FLAM8 could also reflect the flagellum maturation state in other stages. We observed that FLAM8 distribution extended along the entire flagellar cytoskeleton in mammalian infective forms. Then, a rapid FLAM8 concentration to the distal tip occurs during differentiation into early insect forms, illustrating for the first time the remodeling of an existing flagellum in trypanosomes. In the tsetse cardia, FLAM8 further localizes to the entire length of the new flagellum during an asymmetric division. Strikingly, in parasites dividing in the tsetse midgut and in the salivary glands, the amount and distribution of FLAM8 in the new flagellum was seen to predict the daughter cell fate. We propose and discuss how FLAM8 could be considered as a meta-marker of the flagellum stage and maturation state in trypanosomes. Summary statement The trypanosome protein FLAM8 displays a dynamic and stage-specific distribution during the entire parasite cycle, representing a novel marker of the flagellum stage and maturation state.
Sugarcane Nitrogen and Irrigation Level Prediction Based on UAV-Captured Multispectral Images at the Elongation Stage

Introduction Sugarcane is the main industrial crop for sugar production; its growth status is closely related to fertilizer, water, and light input. Unmanned aerial vehicle (UAV)-based multispectral imagery is widely used for high-throughput phenotyping because it can rapidly predict crop vigor. This paper mainly studied the potential of multispectral images obtained by low-altitude UAV systems in predicting canopy nitrogen (N) content and irrigation level for sugarcane. Methods An experiment was carried out on sugarcane fields with three irrigation levels and five nitrogen levels. A multispectral image at a height of 40 m was acquired during the elongation stage, and the canopy nitrogen content was determined as the ground truth. N prediction models, including partial least square (PLS), backpropagation neural network (BPNN), and extreme learning machine (ELM) models, were established based on different variables. A support vector machine (SVM) model was used to recognize the irrigation level. Results The PLS model based on band reflectance and five vegetation indices had better accuracy (R=0.7693, root mean square error (RMSE)=0.1109) than the BPNN and ELM models. Some spectral information from the multispectral image had obviously different features among the different irrigation levels, and the SVM algorithm was used for irrigation level classification. The classification accuracy reached 77.8%. Conclusion Low-altitude multispectral images could provide effective information for N prediction and water irrigation level recognition.
Reconciling Membrane Protein Simulations with Experimental DEER Spectroscopy Data

Spectroscopy experiments are crucial to study membrane proteins for which traditional structure determination methods still prove challenging. Double electron-electron resonance (DEER) spectroscopy experiments provide protein residue-pair distance distributions that are indicative of their conformational heterogeneity. Atomistic molecular dynamics (MD) simulations are another tool that have proved vital to study the structural dynamics of membrane proteins such as to identify inward-open, occluded, and outward-open conformations of transporter membrane proteins, among other partially open or closed states of the protein. Yet, studies have reported that there is no direct consensus between distributional data from DEER experiments and MD simulations, which has challenged validation of structures obtained from long-timescale simulations and using simulations to design experiments. Current coping strategies for comparisons rely on heuristics, such as mapping nearest matching peaks between two ensembles or biased simulations. Here we examine the differences in residue-pair distance distributions arising due to choice of membrane around the protein and covalent modification of a pair of residues to nitroxide spin labels in DEER experiments. Through comparing MD simulations of two proteins, PepTSo and LeuT - both of which have been characterized using DEER experiments previously - we show that the proteins’ dynamics are similar despite the choice of the detergent micelle as a membrane mimetic in DEER experiments. On the other hand, covalently modified residues show slight local differences in their dynamics and a huge divergence when the spin labels’ anointed oxygen atom pair distances are measured rather than protein backbone distances. Given the computational expense associated with pairwise MTSSL labeled MD simulations, we examine the use of biased simulations to explore the conformational dynamics of the spin labels only to reveal that such simulations alter the underlying protein dynamics. Our study identifies the main cause for the mismatch between DEER experiments and MD simulations and will accelerate developing potential mitigation strategies to improve simulation observables match with DEER spectroscopy experiments.
Megadepth: efficient coverage quantification for BigWigs and BAMs

Motivation A common way to summarize sequencing datasets is to quantify data lying within genes or other genomic intervals. This can be slow and can require different tools for different input file types. Results Megadepth is a fast tool for quantifying alignments and coverage for BigWig and BAM/CRAM input files, using substantially less memory than the next-fastest competitor. Megadepth can summarize coverage within all disjoint intervals of the Gencode V35 gene annotation for more than 19,000 GTExV8 BigWig files in approximately one hour using 32 threads. Megadepth is available both as a command-line tool and as an R/Bioconductor package providing much faster quantification compared to the rtracklayer package. Availability https://github.com/ChristopherWilks/megadepth, https://bioconductor.org/packages/megadepth. Contact chris.wilks@jhu.edu
Neuronal Correlates of the Perceptual Invariance of Words and Other Sounds in the Supplementary Motor Area of Macaques

The supplementary motor area (SMA) of the brain is critical for integrating memory and sensory signals into perceptual decisions. For example, in macaques, SMA activity correlates with decisions based on the comparison of sounds.1 In humans, functional MRI shows SMA activation during the invariant recognition of words pronounced by different speakers.2 Nevertheless, the neuronal correlates of perceptual invariance are unknown. Here we show that the SMA of macaques associates novel sounds with behaviors triggered by similar learned categories when recognizing sounds such as words. Notably, the neuronal activity at single and population levels correlates with the monkeys’ behaviors (e.g. hits and false alarms). Our results demonstrate that invariant recognitions of complex sounds involve premotor computations in areas other than the temporal and parietal speech areas. Therefore, we propose that perceptual invariance depends on motor predictions and not only sensory representations. We anticipate that studies on speech will observe sensory-motor transformations of acoustic information into motor skills.
Molecular topography of an entire nervous system

Nervous systems are constructed from a deep repertoire of neuron types but the underlying gene expression programs that specify individual neuron identities are poorly understood. To address this deficit, we have produced an expression profile of all 302 neurons of the C. elegans nervous system that matches the single cell resolution of its anatomy and wiring diagram. Our results suggest that individual neuron classes can be solely identified by combinatorial expression of specific gene families. For example, each neuron class expresses unique codes of ∼23 neuropeptide-encoding genes and ∼36 neuropeptide receptors thus pointing to an expansive “wireless” signaling network. To demonstrate the utility of this uniquely comprehensive gene expression catalog, we used computational approaches to (1) identify cis-regulatory elements for neuron-specific gene expression across the nervous system and (2) reveal adhesion proteins with potential roles in synaptic specificity and process placement. These data are available at cengen.org and can be interrogated at the web application CengenApp. We expect that this neuron-specific directory of gene expression will spur investigations of underlying mechanisms that define anatomy, connectivity and function throughout the C. elegans nervous system.
GNS561, a New Autophagy Inhibitor Active against Cancer Stem Cells in Hepatocellular Carcinoma and Hepatic Metastasis from Colorectal Cancer

Patients with advanced hepatocellular carcinoma (HCC) or metastatic colorectal cancer (mCRC) have a very poor prognosis due to the lack of efficient treatments. As observed in several other tumors, the effectiveness of treatments is mainly hampered by the presence of a highly tumorigenic sub-population of cancer cells called cancer stem cells (CSCs). Indeed, CSCs are resistant to chemotherapy and radiotherapy and have the ability to regenerate the tumor bulk. Hence, innovative drugs that are efficient against both bulk tumor cells and CSCs would likely improve cancer treatment. In this study, we demonstrated that GNS561, a new autophagy inhibitor that induces lysosomal cell death, showed significant activity against not only the whole tumor population but also a sub-population displaying CSC features (high ALDH activity and tumorsphere formation ability) in HCC and in liver mCRC cell lines. These results were confirmed in vivo in HCC from a DEN-induced cirrhotic rat model in which GNS561 decreased tumor growth and reduced the frequency of CSCs (CD90+CD45-). Accordingly, GNS561, which was in a global phase 1b clinical trial in liver cancers that was recently successful, offers great promise for cancer therapy by exterminating both the tumor bulk and the CSC sub-population.
Sequence determinants and evolution of constitutive and alternative splicing in yeast species

RNA splicing is a key process in eukaryotic gene expression. Most Intron-containing genes are constitutively spliced, hence efficient splicing of an intron is crucial for efficient gene expression. Here we use a large synthetic oligo library of ~20,000 variants to explore how different intronic sequence features affect splicing efficiency and mRNA expression levels in S. cerevisiae. Using a combinatorial design of synthetic introns we demonstrate how non-consensus splice site sequences affect splicing efficiency in each of the three splice sites. We then show that S. cerevisiae splicing machinery tends to select alternative 3’ splice sites downstream of the original site, and we suggest that this tendency created a selective pressure, leading to the avoidance of cryptic splice site motifs near introns’ 3’ ends. We further use natural intronic sequences from other yeast species, whose splicing machineries have diverged to various extents, to show how intron architectures in the various species have been adapted to the organism’s splicing machinery. We suggest that the observed tendency for cryptic splicing is a result of a loss of a specific splicing factor, U2AF1. Lastly, we show that synthetic sequences containing two introns give rise to alternative RNA isoforms in S. cerevisiae, exposing intronic features that control and facilitate alternative splicing. Our study reveals novel mechanisms by which introns are shaped in evolution to allow cells to regulate their transcriptome.
Amorphous solid dispersions and the confounding effect of nanoparticles in in vitro dissolution and in vivo testing: Niclosamide as a case study

We developed an amorphous solid dispersion (ASD) of the poorly water-soluble molecule niclosamide that achieved more than a 2-fold increase in bioavailability. Notably, this niclosamide ASD formulation increased the apparent drug solubility about 60-fold relative to the crystalline material due to the generation of nanoparticles. Niclosamide is a weakly acidic drug, BCS class II, and a poor glass former with low bioavailability in vivo. Hot-melt extrusion is a high-throughput manufacturing method commonly used in the development of ASDs for increasing the apparent solubility and bioavailability of poorly water-soluble compounds. We utilized the polymer polyvinylpyrrolidone–vinyl acetate (PVP–VA) to manufacture niclosamide ASDs by extrusion. Samples were analyzed based on their microscopic and macroscopic behavior and their intermolecular interactions, using DSC, XRD, NMR, FTIR, and DLS. The niclosamide ASD generated nanoparticles with a mean particle size of about 100 nm in FaSSIF media. In a side-by-side diffusion test, these nanoparticles produced a 4-fold increase in niclosamide diffusion. We successfully manufactured amorphous extrudates of the poor glass former niclosamide that showed remarkable in vitro dissolution and diffusion performance. These in vitro tests were translated to a rat model that also showed an increase in oral bioavailability.
Through the Data Management Lens: Experimental Analysis and Evaluation of Fair Classification

Classification, a heavily-studied data-driven machine learning task, drives an increasing number of prediction systems involving critical human decisions such as loan approval and criminal risk assessment. However, classifiers often demonstrate discriminatory behavior, especially when presented with biased data. Consequently, fairness in classification has emerged as a high-priority research area. Data management research is showing an increasing presence and interest in topics related to data and algorithmic fairness, including the topic of fair classification. The interdisciplinary efforts in fair classification, with machine learning research having the largest presence, have resulted in a large number of fairness notions and a wide range of approaches that have not been systematically evaluated and compared. In this paper, we contribute a broad analysis of 13 fair classification approaches and additional variants, over their correctness, fairness, efficiency, scalability, robustness to data errors, sensitivity to underlying ML model, data efficiency, and stability using a variety of metrics and real-world datasets. Our analysis highlights novel insights on the impact of different metrics and high-level approach characteristics on different aspects of performance. We also discuss general principles for choosing approaches suitable for different practical settings, and identify areas where data-management-centric solutions are likely to have the most impact.
Web Application Firewalls and Ways of Seeing Imperfect Tools

— Common wisdom on how to evaluate preventative goods is weak, and as a result cybersecurity suppliers provide tools without hard evidence or guarantees. While it may be naive to expect any one tool to act as a silver bullet, information asymmetry is a problem that can and should be addressed. We argue that well-informed consumers are essential to responding to the security, privacy, and usability challenges associated with developing web applications hosted in the cloud. Accordingly, we study Web Application Firewalls to draw attention to the status quo, and provide questions that allow the public to readily identify information asymmetry in the goods they consider.
IMPACT OF CLIMATE CHANGE ON STREAM FLOW OF TIGRIS WATERSHED, BAGHDAD, IRAQ

According to Intergovernmental Panel on Climate Change (IPCC) future projections, precipitation and temperature will increase over Baghdad in the coming century. The main objectives of the study was to investigate the possible impact of climate change on runoff and stream flow of Tigris river basin, by downscaling can ESM2 global climate model using Statistical Downscaling Model (SDSM). Based on IPCC recommendation baseline periods (2008-2018) were used for baseline scenario analysis. Future scenario analysis was performed for the 2020s and 2050s. Globally, canESM2 model is widely applied for climate change studies and it consists of Representative Concentration Pathway (RCP) RCP 8.5 (medium to high emission) and RCP 4.5 (medium low emission) scenarios. Impact assessment on stream flow and sediment yield was done by Soil and Water Assessment Tool (SWAT) hydrological model. To set up the model for simulation a 20×20m DEM (Digital Elevations Model), land use map and soil map were used. The daily recorded weather data from 2008 to 2018 were used as input to the model. Monthly stream flow and sediment yield data were available from 2008 to 2018. In this study both local (one-at-a-time) and global sensitivity analysis were performed and the ranking of the parameters in both cases compared. The model was calibrated by using both automated and manual calibration for monthly stream flow and sediment yield by using stream flow and sediment yield data from 2008 to 2010. SWAT model performance in simulating monthly stream flow for the study area was satisfactory with Nash-Sutcliffe Efficiency (NSE) coefficient of determination (R2) and percent error (D). Mean annual changes of precipitation and temperature (maximum and minimum) were applied to quantify these impacts. The result of down scaled precipitation and temperature reveals a systematic increase in all future time periods for both RCP 6.5 and RCP 3.5 scenarios.
Power-law decay in epidemics is likely due to interactions with the time-variant contact graph

Reported COVID-19 data from o¢ cial health agencies in several countries suggest that the prevalence, the average fraction of infected people, decays over time with power-law tails. Moreover, the prevalence over the entire outbreak in a country seems reasonably well tted by a lognormal function. The major consequence of the observation is that Markovian modeling on xed graphs, characterized by exponential times such as initial exponential increase and eventual exponential decrease (in addition to exponential infection and recovery times), fails. As a result, an epidemic will last much longer than predicted by simple but widely used, mean- eld Markovian SIR (or variants) equations on a xed graph. Surprisingly, extensive simulations of non-Markovian SIR epidemics on xed graphs exhibit exponential decays. Thus, also a realistic non-Markovian description fails. Therefore, we are led to conjecture that power-law decay is caused by interactions between the viral transmission and the time-varying, human contact graph. The current Corona pandemic places virus spread in networks as part of Network Science in the scienti c spotlights. The network science [3] de nition of a network rests upon the duality between the networks graph, also called the structure or topology, and the networks process, also called the function or service that runs over the graph. The graph on N nodes is speci ed [34] by an N N adjacency matrix A, where the element aij 2 f0; 1g expresses link existence, reecting a relation or interaction between node i and j. In general, the graph is not xed, but changes over time. In epidemics, besides the viral transmission process, a human mobility process (HMP) generates the contact graph in two di¤erent ways: (a) in temporal networking, the viral process and HMP are independent and (b) in adaptive networking, a third interaction process couples the viral process and HMP. For example, if people sense an infected, they adapt their behavior by avoiding contacts, otherwise they proceed independently of the viral process, e.g. in case of asymptomatics. One of the simpler viral processes on networks is SIS epidemic spread [38], which consists of two competing processes [30]: an infection and curing or recovery process, characterized by an infection time distribution fT (t) and a recovery time distribution fR (t), respectively. Epidemic spread on Faculty of Electrical Engineering, Mathematics and Computer Science, P.O Box 5031, 2600 GA Delft, The Netherlands; email : P.F. A.VanMieghem@tudelft.nl