Miracle-dz/UniFAR-FTUs huggingface dataset:
RANK 2 SHEAVES ON K3 SURFACES: A SPECIAL CONSTRUCTION

Let X be a K3 surface of degree 8 in P^5 with hyperplane section H. We associate to it another K3 surface M which is a double cover of P^2 ramified on a sextic curve C. In the generic case when X is smooth and a complete intersection of three quadrics, there is a natural correspondence between M and the moduli space M' of rank two vector bundles on X with Chern classes c_1=H and c_2=4. We build on previous work of Mukai and others, giving conditions and examples where M' is fine, compact, non-empty; and birational or isomorphic to M. We also present an explicit calculation of the Fourier-Mukai transform when X contains a line and has Picard number two.
An empirical analysis of consumer resistance to smart-lighting products

Considering the low market penetration of smart-lighting technology, this study investigates the drivers and consequences of consumer resistance to these innovative lighting products. The paper builds on research on passive and active consumer resistance and on the technology acceptance theory to analyse individuals’ inclination to adopt or reject smart-lighting products. Moreover, the paper evaluates the moderating effects of gender and age. In addition to the influence of passive and active innovation resistance and based on a representative survey of German consumers (N = 653), the empirical results identify performance expectancy, social pressure and compatibility and health concerns as major determinants of consumer behaviour. Furthermore, the results vary among consumers of different ages and genders. A follow-up study (N = 115) investigates consumers’ health concerns more deeply. This study provides practical implications and avenues for future research.
Taxonium, a web-based tool for exploring large phylogenetic trees

The COVID-19 pandemic has resulted in a step change in the scale of sequencing data, with more genomes of SARS-CoV-2 having been sequenced than any other organism on earth. These sequences reveal key insights when represented as a phylogenetic tree, which captures the evolutionary history of the virus, and allows the identification of transmission events and the emergence of new variants. However, existing web-based tools for exploring phylogenies do not scale to the size of datasets now available for SARS-CoV-2. We have developed Taxonium, a new tool that uses WebGL to allow the exploration of trees with tens of millions of nodes in the browser for the first time. Taxonium links each node to associated metadata and supports mutation-annotated trees, which are able to capture all known genetic variation in a dataset. It can either be run entirely locally in the browser, from a server-based backend, or as a desktop application. We describe insights that analysing a tree of five million sequences can provide into SARS-CoV-2 evolution, and provide a tool at cov2tree.org for exploring a public tree of more than five million SARS-CoV-2 sequences. Taxonium can be applied to any tree, and is available at taxonium.org, with source code at github.com/theosanderson/taxonium.
vsRNAfinder: a novel method for identifying high-confidence viral small RNAs from small RNA-Seq data

Virus-encoded small RNAs (vsRNA) have been reported to play an important role in viral infection. Unfortunately, there is still a lack of an effective method for vsRNA identification. Herein, we presented vsRNAfinder, a de novo method for identifying high-confidence vsRNAs from small RNA-Seq (sRNA-Seq) data based on peak calling and Poisson distribution and is public available at https://github.com/ZenaCai/vsRNAfinder. vsRNAfinder outperformed two widely-used methods namely miRDeep2 and ShortStack in identifying viral miRNAs with a significantly improved sensitivity. It can also be used to identify sRNAs in animals and plants with similar performance to miRDeep2 and ShortStack. The study would greatly facilitate effective identification of vsRNAs.
Opto-MASS: a high-throughput engineering platform for genetically encoded fluorescent sensors enabling all-optical in vivo detection of monoamines and opioids

Fluorescent sensor proteins are instrumental for detecting biological signals in vivo with high temporal accuracy and cell-type specificity. However, engineering sensors with physiological ligand sensitivity and selectivity is difficult because they need to be optimized through individual mutagenesis in vitro to assess their performance. The vast mutational landscape proteins constitute an obstacle that slows down sensor development. This is particularly true for sensors that require mammalian host systems to be screened. Here, we developed a novel high-throughput engineering platform that functionally tests thousands of variants simultaneously in mammalian cells and thus allows the screening of large variant numbers. We showcase the capabilities of our platform, called Optogenetic Microwell Array Screening System (Opto-MASS), by engineering novel monoamine and neuropeptide in vivo capable sensors with distinct physiological roles at high-throughput.
Integration of single-cell RNA and protein data identifies novel clinically-relevant lymphocyte phenotypes in breast cancers

Immune cells are critical determinants of solid tumour aetiology, but the diverse phenotypes of intra-tumoural immune cells remain incompletely characterised. We applied integrated single cell RNA sequencing (scRNA-Seq) and highly multiplexed protein epitope analysis to a cohort of breast cancer samples to resolve cell states within the tumour microenvironment. We reveal novel protein markers for resting and activated tumour infiltrating lymphocytes, and show that high expression of CD103 primarily marks exhausted CD8 rather than tissue resident CD8 T-cells in human breast cancers. We identify two distinct states of activated CD4+ T follicular helper (Tfh) cells. A population resembling conventional Tfh (cTfh) cells were localised primarily to lymphoid aggregates by spatial transcriptomics. In contrast, cancer associated Tfh (caTfh) cells expressing markers of tissue residency and exhaustion co-localized with cancer foci and signalled to macrophages. Importantly, increased caTfh : cTfh ratio associated with improved disease outcome and response to checkpoint immunotherapy.
Leveraging place field repetition to understand positional versus nonpositional inputs to hippocampal field CA1

The hippocampus is believed to encode episodic memory by binding information about the content of experience within a spatiotemporal framework encoding the location and temporal context of that experience. Previous work implies a distinction between positional inputs to the hippocampus that provide information about an animal’s location and nonpositional inputs which provide information about the content of experience, both sensory and navigational. Here we leverage the phenomenon of “place field repetition” to better understand the functional dissociation between positional and nonpositional inputs to CA1. Rats navigated freely on a novel maze consisting of linear segments arranged in a rectilinear, city-block configuration, which combined elements of open-field foraging and linear-track tasks. Unlike typical results in open-field foraging, place fields were directionally tuned on the maze, even though the animal’s behavior was not constrained to 1-D trajectories. Repeating fields from the same cell tended to have the same directional preference when the fields were aligned along a linear corridor of the maze, but they showed uncorrelated directional preferences when they were unaligned across different corridors. Lastly, individual fields displayed complex time dynamics which resulted in the population activity changing gradually over the course of minutes. These temporal dynamics were evident across repeating fields of the same cell. These results demonstrate that the positional inputs that drive a cell to fire in similar locations across the maze can be behaviorally and temporally dissociated from the nonpositional inputs that alter the firing rates of the cell within its place fields, thereby increasing the flexibility of the system to encode episodic variables within a spatiotemporal framework provided by place cells.
Association of Striatal Connectivity Gradients to Functional Domains Across Psychiatric Disorders

Objective To uncover transdiagnostic domains of functioning across stress- and neurodevelopmental disorders, and to map these on to the topographic functional organization of cortico-striatal circuitry. Methods In a clinical sample (n=186) of subjects with high rates of comorbidity of major depressive disorder, anxiety disorder, attention-deficit/hyperactivity disorder and/or autism spectrum disorder, we use exploratory factor analysis on a wide range of clinical questionnaires to identify consistent functional domains of symptomatology across disorders, then replicate these functional domains in an independent dataset (n=188). Then, we use canonical correlation analysis link these functional domains to the topographic organization of the striatum as represented by connectopic maps. Results We reveal four functional domains that transcend current diagnostic categories relating to negative valence, cognition, social functioning and inhibition/arousal. These functional domains are replicated in an independent sample and are associated with the fine-grained topographical organization of functional connectivity in the striatum (out of sample r=0.20, p=0.026), a central hub in motor, cognitive, affective and reward-related brain circuits. Conclusions and relevance Functional domains across stress- and neurodevelopmental disorders are associated with the functional organization of the striatum. We propose that investigating psychiatric symptoms across disorders is a promising path to linking them to underlying biology, and can help bridge the gap between neuroscience and clinical psychiatry.
Effect of channel density, inverse solutions and connectivity measures on EEG resting-state networks: a simulation study

Along with the study of brain activity evoked by external stimuli, the past two decades witnessed an increased interest in characterizing the spontaneous brain activity occurring during resting conditions. The identification of the connectivity patterns in this so-called “resting-state” has been the subject of a great number of electrophysiology-based studies, using the Electro/Magneto-Encephalography (EEG/MEG) source connectivity method. However, no consensus has been reached yet regarding a unified (if possible) analysis pipeline, and several involved parameters and methods require cautious tuning. This is particularly challenging when different choices induce significant discrepancy in results and drawn conclusions, thereby hindering reproducibility of neuroimaging research. Hence, our objective in this study was to evaluate some of the parameters related to the EEG source connectivity analysis and shed light on their implications on the accuracy of the resulting networks. We simulated, using neural mass models, EEG data corresponding to two of the resting-state networks (RSNs), namely the default mode network (DMN) and the dorsal attentional network (DAN). We investigated the impact of five channel densities (19, 32, 64, 128, 256), three inverse solutions (weighted minimum norm estimate (wMNE), exact low resolution brain electromagnetic tomography (eLORETA), and linearly constrained minimum variance (LCMV) beamforming) and four functional connectivity measures (phase-locking value (PLV), phase-lag index (PLI), and amplitude envelope correlation (AEC) with and without source leakage correction), on the correspondence between reconstructed and reference networks. We showed that, with different analytical choices, a high variability is present in the results. More specifically, our results show that a higher number of EEG channels significantly increased the accuracy of the reconstructed networks. Additionally, our results showed a significant variability in the performance of the tested inverse solutions and connectivity measures. In our specific simulation context, eLORETA and wMNE combined with AEC computed between orthogonalized time series exhibited the highest performance in terms of similarity between reconstructed and reference connectivity matrices. Results were similar for both DMN and DAN. We believe that this work could be useful for the field of electrophysiology connectomics, by shedding light on the challenge of analytical variability and its consequences on the reproducibility of neuroimaging studies.
Reconstructing human Brown Fat developmental trajectory in vitro

Brown adipocytes represent a specialized type of mammalian adipocytes able to uncouple nutrient catabolism from ATP generation to dissipate energy as heat. They play an important role in mammals, allowing non-shivering thermogenesis to regulate body temperature in response to cold exposure. In humans, the brown fat tissue is composed of small discrete depots found mostly throughout the neck and trunk region. Increasing brown fat activity either with drug treatment or cell therapy is considered a potential approach for the treatment of metabolic syndrome and obesity. The recent development of in vitro differentiation strategies relying on human pluripotent stem cells (hPSCs) offers the possibility to produce unlimited amounts of brown adipocytes. A strategy efficiently applied to several tissues is to recapitulate step by step the development of the tissue of interest by exposing hPSCs to the signaling cues used during normal embryonic development. However, this strategy has proven difficult to implement for brown fat as the development of this tissue is poorly understood. Here, we first used single cell RNA sequencing to characterize the development of interscapular brown fat in mouse. Our analysis identified a previously unrecognized population of brown adipocytes precursors characterized by expression of the transcription factor GATA6. We show that this precursor population can be efficiently generated from paraxial mesoderm precursors differentiated in vitro from hPSCs by modulating the signaling pathways identified in our transcriptomic analysis. These precursors can in turn be efficiently converted into functional brown adipocytes which can respond to adrenergic stimuli by increasing their metabolism resulting in heat production.
The coarse classification of countable abelian groups

We classify up to coarse equivalence all countable abelian groups of finite torsion free rank. The Q-cohomological dimension and the torsion free rank are the two invariants that give us such classification. We also prove that any countable abelian group of finite torsion free rank is coarsely equivalent to Z^n + H where H is a direct sum (possibly infinite) of cyclic groups. A partial generalization to countable abelian groups of the Gromov rigidity theorem for abelian groups is shown.
Wide genome transcription and metabolome profiles reprogrammed by sucrose under xenobiotic constraints

Plants have developed strategies to adapt quickly to environmental changes. However, the regulation of these adaptive responses and coordination of signals network remains poorly understood for many environmental constraints. Indeed, signalling molecules play a central role in environmental stimuli and may coordinate plants development under environmental cues. Sucrose is the major transport carbohydrate in higher plants, in addition to its metabolic role; recent investigations suggested that sucrose impact the plasticity of plant development by controlling gene expression. Sucrose was also suggested as a ROS scavenger and as a signalling molecule. In this paper we showed that sucrose mitigated phenanthrene induced stress. Since, exogenous sucrose allowed growth and chlorophyll accumulation in the presence of high phenanthrene concentrations, whereas Arabidopsis development was blocked and seedlings were unable to accumulate chlorophyll in the presence of phenanthrene solely. To decipher the earlier molecular changes involved in sucrose tolerance to phenanthrene, wide transcriptional genes analysis and targeted metabolic profiling were carried out. We showed that sucrose driving plant response was associated with a deep reconfiguration of both genes expression and metabolites accumulation during the first hours of treatment, suggesting that sucrose, allowed plants to sustain the primary metabolism and to overcome the phenanthrene induced injuries.
Diversity-Generating Retroelements in Prokaryotic Immunity

Adaptive immunity systems found in different organisms fall into two major types. Prokaryotes possess CRISPR-Cas systems, recognizing former invaders using the memorized (captured) pieces of their DNA as the pathogen signatures. Mammals possess a vast repertory of antibodies and T-cell receptors variants generated in advance. In this second type of adaptive immunity, a pathogen presentation to the immune system specifically activates the cells expressing matching antibodies or receptors. These cells proliferate to fight the infection and to form the immune memory. The principle of preemptive production of diverse defense proteins for future use can hypothetically take place in microbes too. In this study we test the hypothesis that prokaryotes employ diversity-generating retroelements to prepare defense proteins against yet-unknown invaders. We identified several candidate defense systems and characterised them.
Maximal Cohen-Macaulay modules over non-isolated surface singularities

In this article we develop a new method to deal with maximal Cohen-Macaulay modules over non-isolated surface singularities. In particular, we give a negative answer on an old question of Schreyer about surface singularities with only countably many indecomposable maximal Cohen-Macaulay modules. Next, we prove that the degenerate cusp singularities have tame Cohen-Macaulay representation type. Our approach is illustrated on the case of $k\llbracket x,y,z\rrbracket/(xyz)$ as well as several other rings. This study of maximal Cohen-Macaulay modules over non-isolated singularities leads to a new class of problems of linear algebra, which we call representations of decorated bunches of chains. We prove that these matrix problems have tame representation type and describe the underlying canonical forms.
Workload control in dual-resource constrained high-variety shops: an assessment by simulation

Workload Control (WLC) seeks to align capacity with demand, where capacity is typically assumed to be restricted by a single constraint – machine capacity. In practice, however, shops are often restricted by dual resource constraints: labour and machines. This study, therefore, uses simulation to investigate the performance of WLC in Dual Resource Constrained (DRC) high-variety shops with fully interchangeable labour. By considering several environmental factors and different labour assignment and dispatching rules, it is demonstrated that the order release function of WLC maintains its positive impact on performance in a DRC shop under different staffing levels. The positive effect of considering labour availability at release, as proposed in previous research, could not, however, be confirmed. Thus, the original release method can be applied if labour is fully interchangeable. In terms of labour assignment, we show that a distinct assignment pattern that differs between upstream and downstream stations improves performance if the routing is directed. Meanwhile, dispatching plays a less important role but creates important interaction effects with the assignment rule. Finally, the results suggest that increasing the service rate is a better response to the reduction in capacity that results from labour absenteeism than lowering the input frequency of work.
On the Effectiveness of airborne infrasound in eliciting vestibular-evoked myogenic responses

The use of airborne infrasound and other stimuli to elicit (cervical) vestibular-evoked myogenic potentials (cVEMPs) was studied to address the common proposition that infrasound may efficiently stimulate the vestibular system, an effect which may underlie the so-called wind-turbine syndrome. cVEMPs were measured for both ears of 15 normal-hearing subjects using three types of airborne sound stimulation: (1) 500-Hz tone bursts (transient); (2) 500-Hz sinusoidally amplitude-modulated tones at a 40-Hz rate (SAM); and (3) low-frequency and infrasound pure tones (LF/IS). The two former stimulation types served as control and allowed a systematic comparison with (3). It was found that SAM stimulation is effective and appears to be comparable to transient stimulation, as was previously observed in a yet small number of studies. Although the vestibular system is reported to be highly sensitive to low-frequency mechanical vibration, airborne LF/IS stimulation at ∼80–90-phon loudness levels did not elicit significant saccular vestibular responses.
On the conjugacy growth functions of groups

To every finitely generated group one can assign the conjugacy growth function that counts the number of conjugacy classes intersecting a ball of radius $n$. Results of Ivanov and Osin show that the conjugacy growth function may be constant even if the (ordinary) growth function is exponential. The aim of this paper is to provide conjectures, examples and statements that show that in "normal" cases, groups with exponential growth functions also have exponential conjugacy growth functions.
Optimization of Performance Parameter Design and Energy Use Prediction for Nearly Zero Energy Buildings

Optimizing key parameters with energy consumption as the control target can minimize the heating and cooling needs of buildings. In this paper we focus on the optimization of performance parameters design and the prediction of energy consumption for nearly Zero Energy Buildings (nZEB). The optimal combination of various performance parameters and the Energy Saving Ratio (ESR)are studied by using a large volume of simulation data. Artificial neural networks (ANNs) are applied for the prediction of annual electrical energy consumption in a nearly Zero Energy Building designs located in Shenyang (China). The data of the energy demand for our test is obtained by using building simulation techniques. The results demonstrate that the heating energy demand for our test nearly Zero Energy Building is 17.42 KW·h/(m2·a). The Energy Saving Ratio of window-to-wall ratios optimization is the most obvious, followed by thermal performance parameters of the window, and finally the insulation thickness. The maximum relative error of building energy consumption prediction is 6.46% when using the artificial neural network model to predict energy consumption. The establishment of this prediction method enables architects to easily and accurately obtain the energy consumption of buildings during the design phase.