Miracle-dz/UniFAR-FTUs huggingface dataset:
STX5’s flexibility in SNARE pairing supports Golgi functions

The intracellular transport system is an evolutionally conserved, essential, and highly regulated network of organelles and transport vesicles that traffic protein and lipid cargoes within the cell. The events of vesicle formation, budding and fusion are orchestrated by the trafficking machinery – an elaborate set of proteins including small GTPases, vesicular coats, tethers, and SNAREs. The Golgi - the central organelle in this transport network, receives, modifies and sorts secretory and endocytic cargo. Glycosylation is one of the major modifications that occur within the Golgi, which houses enzymes and other components of glycosylation machinery. According to the current Golgi maturation model, Golgi resident proteins are constantly recycled from the late (trans) Golgi compartments to the early compartment (cis) by the evolutionary conserved vesicular trafficking machinery. The key modulator of vesicular trafficking and glycosylation at the Golgi is the Conserved Oligomeric Golgi (COG) complex – its interaction vesicular trafficking machinery particularly Golgi SNAREs (STX5, GS28 (GOSR1), GS15 (BET1L) and YKT6) that drive fusion of incoming vesicles. Since the COG complex functions upstream of SNARE-mediated vesicle fusion, we hypothesize that depletion of Golgi v-SNAREs would mirror defects observed in COG deficient cells. To test this, we created single and double knockouts (KO) of GS28 and GS15 in HEK293T cells and analyzed resulting mutants using a comprehensive set of biochemical, mass-spectrometry (MS) and microscopy approaches. Deletion of GS28 significantly affected GS15, but not the other two partners, STX5 and YKT6. Surprisingly, our analysis revealed that COG dysfunction is more deleterious for Golgi function than disrupting the canonical Golgi SNARE complex. Quantitative MS analysis of STX5-interacting SNAREs revealed unexpected flexibility of Golgi SNARE pairing in mammalian cells. We uncovered two novel non-canonical Golgi SNARE complexes – STX5/VTI1B/GS15/YKT6 and STX5/SNAP29/VAMP7 which were upregulated in GS28 KO cells. Analysis of cells co-depleted for GS28/SNAP29 or GS28/VTI1B SNAREs revealed escalated defects in Golgi glycosylation, indicating that upregulation of these complexes functionally substitutes deleted GS28. Our data points to the remarkable plasticity in the intra-Golgi membrane fusion machinery which is controlled by the COG complex.
Cultural transmission of reproductive success impacts genomic diversity, coalescent tree topologies and demographic inferences

Cultural Transmission of Reproductive Success (CTRS) has been observed in many human populations as well as other animals. It consists in a positive correlation of non-genetic origin between the progeny size of parents and children. This correlation can result from various factors, such as the social influence of parents on their children, the increase of children’s survival through allocare from uncle and aunts, or the transmission of resources. Here, we study the evolution of genomic diversity through time under CTRS. We show that CTRS has a double impact on population genetics: (1) effective population size decreases when CTRS starts, mimicking a population contraction, and increases back to its original value when CTRS stops; (2) coalescent trees topologies are distorted under CTRS, with higher imbalance and higher number of polytomies. Under long-lasting CTRS, effective population size stabilises but the distortion of tree topology remains, which yields U-shaped Site Frequency Spectra (SFS) under constant population size. We show that this CTRS’ impact yields a bias in SFS-based demographic inference. Considering that CTRS was detected in numerous human and animal populations worldwide, one should be cautious that inferring population past histories from genomic data can be biased by this cultural process.
Perceptual similarity judgments do not predict the distribution of errors in working memory

Population coding models provide a quantitative account of visual working memory (VWM) retrieval errors with a plausible link to the response characteristics of sensory neurons. Recent work has provided an important new perspective linking population coding to variables of signal detection, including d-prime, and put forward a new hypothesis: that the distribution of recall errors on, e.g., a color wheel, is a consequence of the psychological similarity between points in that stimulus space, such that the exponential-like psychophysical distance scaling function can fulfil the role of population tuning and obviate the need to fit a tuning width parameter to recall data. Using four different visual feature spaces, we measured psychophysical similarity and memory errors in the same participants. Our results revealed strong evidence for a common source of variability affecting similarity judgments and recall estimates, but did not support any consistent relationship between psychophysical similarity functions and VWM errors. At the group level, the responsiveness functions obtained from the psychophysical similarity task diverged strongly from those that provided the best fit to working memory errors. At the individual level, we found convincing evidence against an association between observed and best-fitting similarity functions. Finally, our results show that the newly proposed exponential-like responsiveness function has in general no advantage over the canonical von Mises (circular normal) function assumed by previous population coding models.
Genetic diversity of Staphylococcus aureus wall teichoic acid glycosyltransferases affects immune recognition

Staphylococcus aureus is a leading cause of skin and soft tissue infections and systemic infections. Wall teichoic acids (WTAs) are cell wall-anchored glycopolymers that are important for S. aureus nasal colonization, phage-mediated horizontal gene transfer, and antibiotic resistance. WTAs consist of a polymerized ribitol phosphate (RboP) chain that can be glycosylated with N-acetylglucosamine (GlcNAc) by three glycosyltransferases: TarS, TarM, and TarP. TarS and TarP modify WTA with β-linked GlcNAc at the C-4 (β1,4-GlcNAc) and the C-3 position (β1,3-GlcNAc) of the RboP subunit, respectively, whereas TarM modifies WTA with α-linked GlcNAc at the C-4 position (α1,4-GlcNAc). Importantly, these WTA glycosylation patterns impact immune recognition and clearance of S. aureus . Previous studies suggest that tarS is near-universally present within the S. aureus population, whereas a smaller proportion co-contain either tarM or tarP. To gain more insight into the presence and genetic variation of tarS, tarM and tarP in the S. aureus population, we analysed a collection of 25 652 S . aureus genomes within the PubMLST database. Over 99 % of isolates contained tarS. Co-presence of tarS/tarM or tarS/tarP occurred in 37 and 7 % of isolates, respectively, and was associated with specific S. aureus clonal complexes. We also identified 26 isolates (0.1 %) that contained all three glycosyltransferase genes. At sequence level, we identified tar alleles with amino acid substitutions in critical enzymatic residues or with premature stop codons. Several tar variants were expressed in a S. aureus tar-negative strain. Analysis using specific monoclonal antibodies and human langerin showed that WTA glycosylation was severely attenuated or absent. Overall, our data provide a broad overview of the genetic diversity of the three WTA glycosyltransferases in the S. aureus population and the functional consequences for immune recognition.
Neural signatures of auditory hypersensitivity following acoustic trauma

Throughout the brain, neurons exhibit a remarkable capacity to maintain stable firing rates despite large perturbations in afferent activity levels. As an exception, homeostatic regulation of neural activity often fails in the adult auditory system after hearing loss. Cochlear deafferentation caused by aging or noise exposure triggers widespread neural hyperactivity, particularly in the auditory cortex (ACtx), which underlies perceptual disorders including tinnitus and hyperacusis. Here, we show that mice with noise-induced damage of the high-frequency cochlear base were behaviorally hypersensitive to spared mid-frequency tones and to direct optogenetic stimulation of auditory thalamocortical neurons. Chronic 2-photon calcium imaging from ACtx pyramidal neurons (PyrNs) revealed an initial stage of diffuse hyperactivity, hypercorrelation, and hyperresponsivity that consolidated around deafferented map regions three or more days after acoustic trauma. Deafferented PyrN ensembles displayed hypersensitive decoding of spared mid-frequency tones, mirroring behavioral hypersensitivity. At the level of individual PyrNs, some exhibited stable, homeostatic gain control after acoustic trauma, while others showed non-homeostatic excess gain. Interestingly, factors such as baseline spontaneous activity levels and sound level encoding could account for 40% of the variability in PyrN gain regulation after acoustic trauma. These findings suggest that non-homeostatic regulation of cortical sound intensity coding following sensorineural loss may underlie the well-established clinical phenomenon of loudness hypersensitivity. Further, while cortical gain changes are triggered by reduced bottom-up afferent input, their subsequent stabilization is also shaped by their local circuit milieu, where baseline response features can identify neurons with the greatest propensity for developing pathological hyperactivity following sensorineural hearing loss.
Optimum time for hand pollination in yam (Dioscorea spp.)

Hand pollination success rate is low in yam (Dioscorea spp.), due partly to suboptimal weather conditions. Thus, determining the most suitable time for pollination could improve the pollination success in yam breeding programs. We performed continuous hand pollination within flowering windows of D. rotundata and D. alata for two consecutive years to determine the most appropriate month, week, and hours of the day allowing maximum pollination success. In D. alata crossing block, we observed significant differences among crossing hours for pollination success (p = 0.003); morning hours (8–12 a.m.) being more conducive than afternoons (12–5 p.m.). No significant differences existed between crossing hours in D. rotundata, though the mid-day seemed optimal. For both species, the time interval 11–12 a.m. was more appropriate for crossing while 4–5 p.m. was the poorest. However, in vitro pollen germination tests showed that mid-day pollen collection (12 noon – 2 p.m.) had better results than both extremes, though there were strong genotypic effects on outcomes. Pollination success rates differed significantly among months for D. alata (p < 0.001) but not for D. rotundata (p > 0.05). Differences in pollination success existed across weeks within flowering windows of both D. alata (p < 0.001) and D. rotundata (p = 0.004). The seed production efficiency (SPE) had a similar trend as the pollination success rate. No clear pattern existed between the pollination time and the seed setting rate (SSR) or seed viability (SV), though their dynamics varied with weeks and months. This study provided an insight on the dynamics of pollination outcomes under the influence of pollination times and allows detecting months, weeks, and hours of the day when hybridization activities should be focused for better results.
Thirty years of deforestation within the entire ranges of nine endangered lemur species (3 CR, 4 EN, 2 VU) in northwestern Madagascar

Forest cover change is of particular concern in tropical regions. In this study, we investigate the degree of deforestation in the entire ranges of nine highly threatened lemur species in northwestern Madagascar. Landsat satellite images were acquired from four different time stages (1990, 2000, 2011, 2020), classified into forest/non-forest, and changes quantified. Forest cover declined from 17.5% to 9.3% within the last 30 years. This decline varied across four protected areas (PAs) investigated: the forest cover of Ankarafantsika National Park (ANP) declined only moderately over time (from 76.3% to 67.4%), while it declined drastically in other PAs (e.g., from 54.9% to 18.9%, Bongolava Forest Corridor). Two lemur taxa are most affected (Lepilemur otto, Microcebus bongolavensis) by having only very few isolated forest patches left within their ranges (approximately 542.7 km²). For two other species (L. ahmansoni, L. aeeclis), most of the remaining forest is concentrated in two coastal PAs (in total 627.2 and 477.9 km², respectively), while those species occurring inside ANP (5 taxa) experienced rather stable forest coverage until 2020. A reversal of these deforestation trends and active reforestation measures are desperately needed to reduce habitat loss for these nine lemur species. A practical experience-based guideline is therefore provided.
Selective detection of m6A derived from mRNA using the Phospho-tag m6A assay

N6-methyladenosine (m6A) is a modified nucleotide found in mRNA, ribosome RNA (rRNA) and small nuclear RNA (snRNA). m6A in mRNA has important roles in regulating mRNA stability, splicing, and other processes. Numerous studies have described m6A as a dynamic modification using mass spectrometry-based quantification of m6A in mRNA samples prepared from different cellular conditions. However, these results have been questioned based on the finding that the mRNA purification protocols often result in varying levels of rRNA contamination. Additionally, mRNA purification protocols disproportionately enrich for the 3’ ends of mRNA, a region that is enriched in m6A. To address these problems, we developed the Phospho-tag m6A assay, a highly efficient method for quantifying m6A specifically from mRNA. In this assay, a series of selective RNase digestion steps is performed, which results in m6A from rRNA and snRNA being liberated as m6A monophosphate, while m6A from mRNA is mostly liberated as m6A nucleoside. m6A levels are normalized to transcript levels, using m7G monophosphate liberated by yDcpS decapping enzyme as a surrogate for mRNA levels. Notably, this approach uses total cellular RNA, rather than purified mRNA, which simplifies the steps for m6A detection and overcomes the 3’-end biases associated with mRNA purification. Overall, the Phospho-tag m6A provides a simple and efficient method for quantification of mRNA-derived m6A from total RNA samples.
The electric field generated by cationic TiO2 nanoparticles can be their key antimicrobial factor

The antimicrobial effect of TiO2 nanoparticles (TiO2 NPs) has been reliably established. However, the mechanism of this action remains unclear. Currently, according to the dominant hypothesis, it is assumed that the key destructive mechanism is the photocatalytic oxidation of cell components, and the key antimicrobial factor is the reactive oxygen species (ROS) formed on the surface of TiO2 NPs as a result of photocatalysis. In this communication, for the first time, the influence of the electric field created by cationic TiO2 NPs is suggested as a key killing factor, and the key antimicrobial mechanism - the electrical breakdown of the cell membranes and the destruction of the DNA molecule under the action of this field. Here, it was shown by mathematical calculation that TiO2 NPs are able to generate an electric field with a strength sufficient for an electrical breakdown of bilayer lipid membranes, which can lead to a violation of the structure and vital functions of the cytoplasmic membrane (CPM) and to collapse of DNA molecule. GRAPHICAL ABSTRACT
The Effects of Rest Breaks on Driver Fatigue

Driver sleepiness contributes substantially to road death and trauma. Effective countermeasures to reduce driver sleepiness are critical to reducing the incidence of driver sleepiness. Study one determined the effectiveness of a nap break and an active rest break. It was found that a nap break provided objective benefit for reducing driver sleepiness. Study two examined drivers’ ability to recognise increasing sleepiness, and to self-regulate their behaviour by taking a break. The results suggest that drivers were able to identify increasing sleepiness during the test period, and could make the decision to cease driving. However, the ability among participants to identify their increasing sleepiness varied. Strategies to improve perception, detection and mitigation of sleepiness while driving should be pursued.
DeepPHiC: predicting promoter-centered chromatin interactions using a novel deep learning approach

Motivation Promoter-centered chromatin interactions, which include promoter-enhancer and promoter-promoter interactions, are important to decipher gene regulation and disease mechanisms. The development of next generation sequencing technologies such as promoter capture Hi-C (pcHi-C) leads to the discovery of promoter-centered chromatin interactions. However, pcHi-C experiments are expensive and thus may be unavailable for tissues or cell types of interest. In addition, these experiments may be underpowered due to insufficient sequencing depth or various artifacts, which results in a limited finding of interactions. Results To overcome these challenges, we develop a supervised multi-modal deep learning model, which utilizes a comprehensive set of features including genomic sequence, epigenetic signal and anchor distance to predict tissue/cell type-specific genome-wide promoter-enhancer and promoter-promoter interactions. We further extend the deep learning model in a multi-task learning and a transfer learning framework. We demonstrate that the proposed approach outperforms state-of-the-art deep learning methods and is robust to the inclusion of anchor distance as a feature. In addition, we find that the proposed approach can achieve comparable prediction performance using biologically relevant tissues/cell types compared to using all tissues/cell types especially for predicting promoter-enhancer interactions. Availability https://github.com/lichen-lab/DeepPHiC
Morphodynamic Atlas for Drosophila Development

During morphogenesis, diverse cell-scale and tissue-scale processes couple to dynamically sculpt organs. In this coupling, genetic expression patterns and biochemical signals regulate and respond to mechanical deformations to ensure reproducible and robust changes in tissue geometry. A long-standing approach to characterize these interactions has been the construction of expression atlases, and these atlases have necessarily relied on fixed snapshots of embryogenesis. Addressing how expression profiles relate to tissue dynamics, however, requires a scheme for spatiotemporal registration across different classes of data that incorporates both live samples and fixed datasets. Here, we construct a morphodynamic atlas that unifies fixed and live datasets – from gene expression profiles to cytoskeletal components – into a single, morphological consensus timeline. This resource and our computational approach to global alignment facilitate hypothesis testing using quantitative comparison of data both within and across ensembles, with resolution in both space and time to relate genes to tissue rearrangement, cell behaviors, and out-of-plane motion. Examination of embryo kinematics reveals stages in which tissue flow patterns are quasi-stationary, arranged as a sequence of ‘morphodynamic modules’. Temperature perturbations tune the duration of one such module – during body axis elongation – according to a simple, parameter-free scaling in which the total integrated tissue deformation is achieved at a temperature-dependent rate. By extending our approach to visceral organ formation during later stages of embryogenesis, we highlight how morphodynamic atlases can incorporate complex shapes deforming in 3D. In this context, morphodynamic modules are reflected in some, but not all, measures of tissue motion. Our approach and the resulting atlas opens up the ability to quantitatively test hypotheses with resolution in both space and time, relating genes to tissue rearrangement, cell behaviors, and organ motion.
The substrate scopes of enzymes: a general prediction model based on machine and deep learning

For a comprehensive understanding of metabolism, it is necessary to know all potential substrates for each enzyme encoded in an organism’s genome. However, for most proteins annotated as enzymes, it is unknown which primary and/or secondary reactions they catalyze [1], as experimental characterizations are time-consuming and costly. Machine learning predictions could provide an efficient alternative, but are hampered by a lack of information regarding enzyme non-substrates, as available training data comprises mainly positive examples. Here, we present ESP, a general machine learning model for the prediction of enzyme-substrate pairs, with an accuracy of over 90% on independent and diverse test data. This accuracy was achieved by representing enzymes through a modified transformer model [2] with a trained, task-specific token, and by augmenting the positive training data by randomly sampling small molecules and assigning them as non-substrates. ESP can be applied successfully across widely different enzymes and a broad range of metabolites. It outperforms recently published models designed for individual, well-studied enzyme families, which use much more detailed input data [3, 4]. We implemented a user-friendly web server to predict the substrate scope of arbitrary enzymes, which may support not only basic science, but also the development of pharmaceuticals and bioengineering processes.
Altered dynamical integration/segregation balance during anesthesia-induced loss of consciousness

In recent years, brain imaging studies have begun to shed light on the neural correlates of physiologically-reversible altered states of consciousness such as deep sleep, anesthesia, psychedelic experiences. The emerging consensus is that normal waking consciousness requires the exploration of a dynamical repertoire enabling both global integration i.e. long-distance interactions between brain regions, and segregation, i.e. local processing in functionally specialized clusters. Altered states of consciousness have notably been characterized by a tipping of the integration/segregation balance away from this equilibrium. Historically, functional MRI (fMRI) has been the modality of choice for such investigations. However, fMRI does not enable characterization of the integration/segregation balance at sub-second temporal resolution. Here, we investigated global brain spatiotemporal patterns in electrocorticography (ECoG) data of a monkey (Macaca fuscata) under either ketamine or propofol general anaesthesia. We first studied the effects of these anesthetics from the perspective of band-specific synchronization across the entire ECoG array, treating individual channels as oscillators. We further aimed to determine whether synchrony within spatially localized clusters of oscillators was differently affected by the drugs in comparison to synchronization over spatially distributed subsets of ECoG channels, thereby quantifying changes in integration/segregation balance on physiologically-relevant time scales. The findings reflect global brain dynamics characterized by a loss of long-range integration in multiple frequency bands under both ketamine and propofol anaesthesia, most pronounced in the beta (13-30Hz) and low-gamma bands (30-80Hz), and with strongly preserved local synchrony in all bands.
RoboCupRescue - Robot League Team RFC Uppsala, Sweden

The robot system designed by RFC Uppsala for the 2004 RoboCup Rescue Real League is a fully autonomous team of four robots. Based on ultra- sonic sensor data, the robots cooperatively generate a map and mark victim po- sitions. Employing wireless ad-hoc networking, communication between the robots is upheld even if contact with the network infrastructure is lost.