Miracle-dz/UniFAR-FTUs huggingface dataset:
Model inclusion lattice of coloured Gaussian graphical models for paired data

We consider the problem of learning a graphical model when the observations come from two groups sharing the same variables but, unlike the usual approach to the joint learning of graphical models, the two groups do not correspond to diﬀerent populations and therefore produce dependent samples. A Gaussian graphical model for paired data may be implemented by applying the methodology developed for the family of graphical models with edge and vertex symmetries, also known as coloured graphical models. We identify a family of coloured graphical models suited for the paired data problem and investigate the structure of the corresponding model space. More speciﬁcally, we provide a comprehensive description of the lattice structure formed by this family of models under the model inclusion order. Furthermore, we give rules for the computation of the join and meet operations between models, which are useful in the exploration of the model space. These are then applied to implement a stepwise model search procedure and an application to the identiﬁcation of a brain network from fMRI data is given.
Nonparametric exponential family graph embeddings for multiple representation learning

In graph data, each node often serves multiple functionalities. However, most graph embedding models assume that each node can only possess one representation. We address this issue by proposing a nonparametric graph embedding model. The model allows each node to learn multiple representations where they are needed to represent the complexity of random walks in the graph. It extends the Exponential family graph embedding model with two nonparametric prior settings, the Dirichlet process and the uniform process. The model combines the ability of Exponential family graph embedding to take the number of occurrences of context nodes into account with nonparametric priors giving it the flexibility to learn more than one latent representation for each node. The learned embeddings outperforms other state of the art approaches in link prediction and node classification tasks.
Learning sparse representations of preferences within Choquet expected utility theory

This paper deals with preference elicitation within Choquet Expected Utility (CEU) theory for decision making under uncertainty. We consider the Savage’s framework with a finite set of states and assume that preferences of the Decision Maker over acts are observable. The CEU model involves two parameters that must be tuned to the value system of the decision maker: a set function (capacity) modeling weights attached to events, of size exponential in the number of states, and a utility function defined on the space of outcomes. Our aim is to learn a sparse representation of the CEU model from preference data. We propose and test a preference learning approach based on a spline representation of utilities and the sparse learning of capacities to obtain CEU models achieving a good tradeoff between the aim of sparsity and the expressivity required by preference data.
Evolutive Adversarially-Trained Bayesian Network Autoencoder for Interpretable Anomaly Detection

Semi-supervised detection of outliers with only positive and unlabeled data, which is among the most frequent forms of the anomaly detection (AD) problem in real scenarios, requires for a model to capture the normal behaviour of data from a training set exclusively comprised of normal-labelled data, so new unseen data can be afterwards compared to the induced notion of normality to be flagged -or not-as anomalous. In modelling a certain pattern of behaviour, generative models such as generative-adversarial networks (GANs) have proved to have great performance. Thus, numerous AD algorithms with GANs at its core have been proposed, most of them powered by deep neural networks and relying on an autoencoder for the AD task. In the present work, a novel approach to semi-supervised AD with Bayesian networks using generative-adversarial training and an evolutive strategy is proposed, which aims to palliate the intrinsic lack of interpretability of deep neural networks. The proposed model is tested on a real-world AD problem in cybersecurity.
Enhanced adaptive optics control with image to image translation

We aim to signiﬁcantly enhance the science return of astronomical observatories, and in particular giant terrestrial optical telescopes. Observatories employ Adaptive Optics (AO) systems in order to acquire high sensitivity diffraction limited images of the sky. The incumbent “workhorse” for control of AO systems employs a linear real-time controller in a closed loop, with sensing of state performed via a ( Shack-Hartmann ) wavefront sensor (WFS). The actuators of a deformable mirror (DM) are driven, with the action performed in each iteration having a continuous representation as an array of DC voltages. The typical control regime is practical and scalable, nonetheless, there remains a residual uncompensated turbulence that leads to optical aberrations limiting the class of scientiﬁc assets that can be acquired. We have developed and trained a translational GAN model that accurately estimates residual perturbations from WFS images. Model inference occurs in 0.34 milliseconds using off-the-shelf GPU hardware, and is applicable for use in AO control where the control loop might be running at 500Hz. We develop an AO control regime with a second controller stage actuating a second DM controlled in an open loop according to the estimated residual turbulence. Using the open-source COMPASS tool for simulation, we are able to signiﬁcantly improve the performance using our new regime.
Healthcare professionals’ and patients’ attitudes toward the causes and management of aggression in Estonian psychiatric clinics: a quantitative cross-sectional comparative study

Abstract Background To enhance patient aggression management efficacy and to reduce the use of coercive interventions in inpatient psychiatric care, it is important to know the attitudes of all involved parties towards the aggression and its management. Aims This study aimed to explore attitudes of the psychiatrists, nurses and inpatients toward the causes and management of patient aggression in Estonian psychiatric clinics. A frequency of aggressive behaviour experienced by professionals, the relationships between the attitudes, participation in coercion, and a frequency of aggression, were also identified. Methods To assess attitudes towards the causes and management of patient aggression, the Management of Aggression and Violence Attitude Scale was implemented. Results The survey comprised of 63 psychiatrists, 197 nurses and 199 inpatients. The patients and the professionals had similar as well as different attitudes toward aggression and its management. Patients expected professionals to take a more personal approach when aggression occurs; in some cases, allowing the patients to deal with negative emotions on their own but, if necessary, isolating or restraining the violent people. Conclusions There is a need of training programs that focus on dealing patient aggression to foster a culture of safe, patient-centred care and to reduce aggression in psychiatric clinics.
Live-cell tracking of γ-H2AX kinetics reveals the distinct modes of ATM and DNA-PK in the immediate response to DNA damage

DNA double-strand break (DSB) is a serious form of DNA damage that can cause genetic mutation. On the induction of DSBs, histone H2AX becomes phosphorylated by kinases, including ataxia telangiectasia-mutated (ATM), ataxia telangiectasia and Rad3-related (ATR), and DNA-dependent protein kinase (DNA-PK). Phosphorylated H2AX (γ-H2AX) can be a platform to recruit DNA repair machinery. Here we analyzed the immediate early kinetics of γ-H2AX upon laser-induced DNA damage in ATM-proficient and -deficient living cells by using fluorescently labeled antigen-binding fragments specific for γ-H2AX. The accumulation kinetics of γ-H2AX were similar in both ATM-proficient and -deficient cells. However, γ-H2AX accumulation was delayed when the cells were treated with a DNA-PK inhibitor, suggesting that DNA-PK rapidly phosphorylates H2AX at DSB sites. Ku80, a DNA-PK subunit, diffused freely in the nucleus without DNA damage, whereas ATM repeatedly bound to and dissociated from chromatin. The H2AX phosphorylation activity of ATM, but not DNA-PK, depended on a histone H4K16 acetyltransferase, males absent on the first (MOF). These results suggest distinct actions of ATM and DNA-PK that plays a primary role in immediate early γ-H2AX accumulation.
The Genome of the Wasp Anastatus disparis Reveals Energy Metabolism Adaptations for Extreme Aggression

Extremely aggressive behavior is rare in most species, as contestants can be severely injured or killed. Such high level of aggression can evolve when critical resources are limited, as the benefits of winning outweigh the potential costs of conflict. Currently, studies of extreme aggression are mainly from the perspectives of behavioral ecology and evolutionary biology, displaying distinct results from common nonextreme aggression. Here, we provide a high-quality genome of the generalist endoparasitoid Anastatus disparis, the males of which exhibit extreme mate-competition aggression, to shed light on possible genomic adaptations for extreme aggressive behavior. We combined Nanopore PromethION sequencing with Hi-C technology to assemble a high-quality chromosome-level genome of A. disparis. The size of the genome of this species (939.58 Mb) is larger than that of most hymenopterans (ranging from 180 Mb to 340 Mb) due to the expansion of repeated sequences (612.90 Mb, 65.23% of the whole genome). With the aid of RNA sequencing, 19,246 protein-coding genes were identified, and a great expansion of genes involved in detoxification was detected, which could represent an adaptation of this species to exploit a diverse range of known hosts. The integrated multiomics analysis highlighted genes involved in energy metabolism (especially from lipids) and antibacterial activity, both of which are possibly major aspects of adaptation for extreme aggression in A. disparis. Our study provides insight into molecular and evolutionary studies of extreme aggression in A. disparis and provides a valuable genomic resource for further research into the molecular basis of trait evolution in Hymenoptera.
RNetDys: identification of disease-related impaired regulatory interactions due to SNPs

The dysregulation of regulatory mechanisms due to Single Nucleotide Polymorphisms (SNPs) can lead to diseases and does not affect all cell (sub)types equally. Current approaches to study the impact of SNPs in diseases lack mechanistic insights. Indeed, they do not account for the regulatory landscape to decipher cell (sub)type specific regulatory interactions impaired due to disease-related SNPs. Therefore, characterizing the impact of disease-related SNPs in cell (sub)type specific regulatory mechanisms would provide novel therapeutical targets, such as promoter and enhancer regions, for the development of gene-based therapies directed at preventing or treating diseases. We present RNetDys, a pipeline to decipher cell (sub)type specific regulatory interactions impaired by disease-related SNPs based on multi-OMICS data. RNetDys leverages the information obtained from the generated cell (sub)type specific GRNs to provide detailed information on impaired regulatory elements and their regulated genes due to the presence of SNPs. We applied RNetDys in five disease cases to study the cell (sub)type differential impairment due to SNPs and leveraged the GRN information to guide the characterization of dysregulated mechanisms. We were able to validate the relevance of the identified impaired regulatory interactions by verifying their connection to disease-related genes. In addition, we showed that RNetDys identifies more precisely dysregulated interactions linked to disease-related genes than expression Quantitative Trait Loci (eQTL) and provides additional mechanistic insights. RNetDys is a pipeline available at https://github.com/BarlierC/RNetDys.git
A CREBZF/Zhangfei isoform activates CHOP during prolonged cellular stress

The basic leucine zipper transcription factor CREBZF (Zhangfei or ZF) was identified through its interaction with Herpes Simplex Virus-1 related cellular protein HCF-1. CREBZF has been implicated in cellular stress responses through its interaction with other proteins, such as CREB3/Luman and ATF4. Here we investigated the production of four CREBZF isoforms, which arise from translational initiation of a downstream AUG at codon 83, and mRNA alternative splicing that adds an IFFFR pentapeptidyl tail to the C-terminus. We found that in addition to transcriptional activation, the short-tailed CREBZF (stZF) isoform was specifically induced by prolonged ER stress treatment. This stZF isoform is a potent transcriptional activator of the pro-apoptotic protein CHOP. Overexpression of stZF activates transcription of CHOP through a CCAAT enhancer binding protein (C/EBP)-ATF site, and promotes apoptosis. We propose that 1) CREBZF is a key component of the Integrated Stress Response (ISR); 2) stZF is essential for the role of CREBZF in inducing CHOP and promoting cell death upon prolonged cellular stress.
Natural rubber reduces herbivory and alters the microbiome below ground

Laticifers are hypothesized to mediate both plant-herbivore and plant-microbe interactions. However, there is little evidence for the dual function of these secretory structures. We investigated whether the major constituent of natural rubber, cis-1,4-polyisoprene, a phylogenetically widespread and economically important latex polymer, alters plant resistance and the root microbiome of the Russian dandelion (Taraxacum koksaghyz) under attack of a root herbivore, the larva of the May cockchafer (Melolontha melolontha). Rubber-depleted transgenic plants lost more shoot and root biomass upon herbivory than normal rubber content near-isogenic lines. M. melolontha preferred to feed on artificial diet supplemented with rubber-depleted rather than normal rubber content latex. Likewise, adding purified cis-1,4-polyisoprene in ecologically relevant concentrations to diet deterred larval feeding and reduced larval weight gain. Metagenomics and metabarcoding revealed that abolishing biosynthesis of natural rubber alters the structure but not the diversity of the rhizosphere and root microbiota in a herbivore-dependent manner. Roots from rubber-depleted plants, however, did not exhibit a higher pathogen load compared to normal rubber content roots. Taken together, our data demonstrate that natural rubber biosynthesis reduces herbivory and alters the plant microbiota in a herbivore-dependent manner, which highlights the role of plant specialized metabolites and secretory structures in shaping multitrophic interactions.
Endosomal trafficking of two-pore K+ efflux channel TWIK2 to plasmalemma mediates NLRP3 inflammasome activation and inflammatory injury

Potassium efflux via the two pore K+ channel TWIK2 is a requisite step for the activation of the NLRP3 inflammasome, however it is unclear how the efflux is activated in response to cues. Here we report that during homeostasis, TWIK2 resides in endosomal compartments. TWIK2 is transported by endosomal fusion to the plasmalemma in response to increased extracellular ATP resulting in extrusion of K+ ATP-induced endosomal TWIK2 plasmalemma translocation is regulated by Rab11a. Deleting Rab11a or ATP ligated purinergic receptor P2X7 prevented endosomal fusion with the plasmalemma and K+ efflux and NLRP3 inflammasome activation in macrophages. Adoptive transfer of Rab11a-deleted macrophages into mouse lungs prevented NLRP3 inflammasome activation and inflammatory lung injury. Rab11a-mediated endosomal trafficking in macrophages thus regulates TWIK2 abundance and activity on the cell surface and downstream activation of the NLRP3 inflammasome. Endosomal trafficking of TWIK2 to the plasmalemma is therefore a potential therapy target in acute or chronic inflammatory states.
Novel Pore Size-Controlled, Susceptibility Matched, 3D-Printed MRI Phantoms

Diffusion magnetic resonance imaging (dMRI) methods are commonly employed to infer changes in tissue microstructure. Quantities like the apparent diffusion coefficient (mADC), and the fractional anisotropy (FA), derived from diffusion tensor imaging (DTI) data, characterize voxel-averaged diffusion properties, whereas double pulse field gradient (dPFG) or double diffusion encoded (DDE) MR methods can be used to characterize heterogeneous diffusion processes occurring within the voxel. Owing to its unique modular design, our novel 3D-printed dMRI phantom exhibits both macroscopic and microscopic anisotropy and can serve to calibrate measures of them. Our phantom susceptibility is close to that of water’s, enabling fast diffusion weighted echo-planar image (DW-EPI) acquisitions to be used to scan it. 3D printed microstructures offer a new medium with which to vet and validate theoretical models of diffusion and pipelines used to estimate it. Highlights Research highlight 1: We report the design concept and fabrication of dimensionally stable, uniformly oriented blocks or modules that can be assembled into large-scale MRI phantoms. Waffle-like structures containing blocks of aligned microcapillaries can be stacked into even larger arrays to construct diameter distribution phantoms, or fractured, to create a “powder-averaged” emulsion of randomly oriented blocks. Research highlight 2: This phantom can be used to vet and calibrate various MRI methods, such as DTI, AxCaliber MRI, MAP-MRI, and various multiple pulsed field gradient (PFG) or multiple diffusion-encoded microstructure imaging methods. Graphical Abstract
Principles of Modern Physics

Exams will be based on material in the text as well as material presented in class. Exams are cumulative. There will be two midterm exams and one final exam. The exams will be given in class, on paper, and will be closed book. You may bring one index card with equations for exam I, two index cards for exam II, and either 3 index cards or a full sheet of paper to the final exam. Physical constants will be provided. You will need a calculator with standard trigonometry functions, etc. Exams must be taken on the day scheduled unless you have a valid documented excuse. Please inform me as soon as possible if you have to miss an exam.
Leucism and stripe formation in ball pythons ( Python regius ) 1 are associated with variants affecting endothelin signaling

16 Color patterns in non-avian reptiles are beautifully diverse, but little is known about the genetics and development of these patterns. Here we investigated color patterning in pet ball 18 pythons ( Python regius ), which have been bred to show color phenotypes that differ dramatically from the wildtype form. We report that several color phenotypes in pet animals are 20 associated with putative loss-of-function variants in the gene encoding endothelin receptor EDNRB1: (i) frameshift variants in EDNRB1 are associated with conversion of the normal mottled color pattern to all-white skin (leucism), (ii) missense variants affecting conserved sites 23 of the EDNRB1 protein are associated with dorsal, longitudinal stripes, and (iii) substitutions at 24 EDNRB1 splice donors are associated with subtle changes in patterning compared to wildtype. We propose that these phenotypes are caused by loss of specialized color cells 26 (chromatophores), with loss ranging from severe (leucism) to moderate (dorsal striping) to mild (subtle changes in patterning). Our study is the first to describe variants affecting endothelin signaling in a non-avian reptile and suggests that reductions in endothelin signaling in ball 29 pythons can produce a variety of color phenotypes, depending on the degree of color cell loss. These prioritized given their in chromatophore regulation in zebrafish al. 2000; Spiewak al. 2018) and axolotl (Woodcock et al. 2017). Samples of pet ball pythons (shed skins) were recruited from pet owners across the United States, and genetic variants were tested for association with phenotypes in the Yellowbelly series.
High-momentum distribution with a subleading k−3 tail in odd-wave interacting one-dimensional Fermi gases

We study the odd-wave interacting identical fermions in one-dimension with finite effective range. We show that to fully describe the high-momentum distribution $\rho(k)$ up to $k^{-4}$, one needs four parameters characterizing the properties when two particles {\it contact} with each other. Two parameters are related to the variation of energy with respect to the odd-wave scattering length and the effective range, respectively, determining the $k^{-2}$ tail and part of $k^{-4}$ tail in $\rho(k)$. The other two parameters are related to the center-of-mass motion of the system, respectively determining the $k^{-3}$ tail and the other part of $k^{-4}$ tail. We point out that the unusual $k^{-3}$ tail, which has not been discovered before in atomic systems, is an intrinsic component to complete the general form of $\rho(k)$ and also realistically detectable under certain experimental conditions. Various other universal relations are also derived in terms of those contact parameters, and finally the results are confirmed through the exact solution of a two-body problem.
HES1 contributes to high salt stress response as an enhancer of NFAT5-DNA binding

High salt conditions and subsequent hyperosmolarity are injurious cellular stresses but can activate immune signaling. Nuclear factor of activated T-cells 5 (NFAT5) is an essential transcription factor that induces osmoprotective genes such as aldose reductase (AR) and betaine-GABA transporter 1 (BGT1). High salt stress-mediated NFAT5 activation is also reported to accelerate the inflammatory response and autoimmune diseases. However, the systemic regulation of NFAT5 remains unclear. Here, we performed a genome-wide siRNA screen to comprehensively identify the upstream factors of NFAT5. We monitored NFAT5 nuclear translocation and identified one of the Notch signaling effectors, Hairy and enhancer of split-1 (HES1), as a novel positive regulator of NFAT5. HES1 was induced by high salinity via ERK signaling and facilitated NFAT5 recruitment to its target promoter region, resulting in the proper induction of osmoprotective genes and cytoprotection under high salt stress. These findings suggest that although HES1 is well known as a transcriptional repressor, it positively regulates NFAT5-dependent transcription in the context of a high salinity/hyperosmotic response. One Sentence Summary HES1 contributes to high salinity/hyperosmotic response through positive regulation of NFAT5-dependent transcription.
Associative memory retrieval resolves anticipated uncertainty of upcoming perceptual decisions.

Expectations can inform fast, accurate decisions. But what informs expectations? Here we test the hypothesis that expectations are set by dynamic inference from memory. Participants performed a cue-guided perceptual decision task with independently-varying memory and sensory evidence. Cues established expectations by reminding participants of past stimulus-stimulus pairings, which predicted the likely target in a subsequent noisy image stream. Participant’s responses used both memory and sensory information, weighted by their relative reliability. Formal model comparison showed that the sensory evidence accumulation was best explained when its parameters were set dynamically at each trial by evidence accumulated from memory. Supporting this model, neural pattern analysis revealed that responses to the probe were modulated by the speciﬁc content and ﬁdelity of memory reinstatement that occurred before the probe appeared. Together, these results suggest that perceptual decisions arise from the continuous and integrated accumulation of memory and sensory evidence.