Miracle-dz/UniFAR-FTUs huggingface dataset:
Rearrangement operations on unrooted phylogenetic networks

Rearrangement operations transform a phylogenetic tree into another one and hence induce a metric on the space of phylogenetic trees. Popular operations for unrooted phylogenetic trees are NNI (nearest neighbour interchange), SPR (subtree prune and regraft), and TBR (tree bisection and reconnection). Recently, these operations have been extended to unrooted phylogenetic networks, which are generalisations of phylogenetic trees that can model reticulated evolutionary relationships. Here, we study global and local properties of spaces of phylogenetic networks under these three operations. In particular, we prove connectedness and asymptotic bounds on the diameters of spaces of different classes of phylogenetic networks, including tree-based and level-$k$ networks. We also examine the behaviour of shortest TBR-sequence between two phylogenetic networks in a class, and whether the TBR-distance changes if intermediate networks from other classes are allowed: for example, the space of phylogenetic trees is an isometric subgraph of the space of phylogenetic networks under TBR. Lastly, we show that computing the TBR-distance and the PR-distance of two phylogenetic networks is NP-hard.
NAS-FCOS: Fast Neural Architecture Search for Object Detection

The success of deep neural networks relies on significant architecture engineering. Recently neural architecture search (NAS) has emerged as a promise to greatly reduce manual effort in network design by automatically searching for optimal architectures, although typically such algorithms need an excessive amount of computational resources, e.g., a few thousand GPU-days. To date, on challenging vision tasks such as object detection, NAS, especially fast versions of NAS, is less studied. Here we propose to search for the decoder structure of object detectors with search efficiency being taken into consideration. To be more specific, we aim to efficiently search for the feature pyramid network (FPN) as well as the prediction head of a simple anchor-free object detector, namely FCOS, using a tailored reinforcement learning paradigm. With carefully designed search space, search algorithms and strategies for evaluating network quality, we are able to efficiently search a top-performing detection architecture within 4 days using 8 V100 GPUs. The discovered architecture surpasses state-of-the-art object detection models (such as Faster R-CNN, RetinaNet and FCOS) by 1.5 to 3.5 points in AP on the COCO dataset, with comparable computation complexity and memory footprint, demonstrating the efficacy of the proposed NAS for object detection.
An Examination of Motivation to Change and Neural Alcohol Cue Reactivity Following a Brief Intervention

Background: Brief interventions represent a promising psychological intervention targeting individuals with heavy alcohol use. Motivation to change represents an individual’s openness to engage in a behavior change strategy and is thought to be a crucial component of brief interventions. Neuroimaging techniques provide a translational tool to investigate the neurobiological mechanisms underlying potential mediators of treatment response, including motivation to change. Therefore, this study aimed to examine the effect of a brief intervention on motivation to change drinking behavior and neural alcohol taste cue reactivity. Methods: Non–treatment-seeking heavy drinkers were randomized to receive a brief drinking intervention (n = 22) or an attention-matched control (n = 24). Three indices of motivation to change were assessed at baseline and after the intervention or control session: importance, confidence, and readiness. Immediately following the intervention or control session, participants also underwent an functional magnetic resonance imaging (fMRI) during which they completed an alcohol taste cues paradigm. Results: There was a significant effect of the brief intervention on increasing ratings of importance of changing drinking behavior, but not on ratings of confidence or readiness to change. Ratings of importance after the intervention or control session were associated with neural alcohol taste cue reactivity, but notably, this effect was only significant for participants who received the intervention. Individuals in the intervention condition showed a positive association between ratings of importance and activation in the precuneus, posterior cingulate, and insula. Conclusions: The brief drinking intervention was successful at improving one dimension of motivation to change among non–treatment-seeking heavy drinkers. The brief intervention moderated the relationship between ratings of importance and brain activation in circuitry associated with interoceptive awareness and self-reflection. Together, findings represent an initial step toward understanding the neurobiological mechanisms through which a brief intervention may improve motivation to change.
Large-Scale Validation of the Paddling Pool Task in the Clockmaze for Studying Hippocampus-Based Spatial Cognition in Mice

Rationally designed behavioral tests are important tools to assess the function of specific brain regions. The hippocampus is a crucial neural substrate for spatial cognition, and many studies have linked hippocampal dysfunction with defects on spatial learning and memory in neurological conditions ranging from Alzheimer’s disease to autoimmune syndromes, such as neuropsychiatric lupus. While our understanding of hippocampal function, from the molecular to the system levels, has increased dramatically over the last decades, this effort has not yet translated into efficacious therapies for cognitive impairment. We think that the availability of highly validated behavioral paradigms to measure cognition in mouse models is likely to enhance the potential success of preclinical therapeutic modalities. Here, we present an extensive study of the paddling pool task (PPT), first reported by Deacon and Rawlins, in which mice learn to escape from shallow water through a peripheral exit in a circular arena dubbed the clockmaze. We show that the PPT provides highly reliable results when assaying spatial cognition in C57/BL6 mice (120 males, 40 females) and BALB/c mice (40 males, 90 females). Additionally, we develop a robust algorithm for the assessment of escape strategies with clearly quantifiable readouts, enabling fine-granular phenotyping. Notably, the use of spatial strategy increases linearly across trials in the PPT. In a separate cohort of mice, we apply muscimol injections to silence the dorsal CA1 region of the hippocampus and show that the use of the spatial strategy in the PPT relies on the integrity of the dorsal hippocampus. Additionally, we compare directly the PPT and the Morris water maze (MWM) task in C57/BL6 mice (20 males, 20 females) and BALB/c mice (20 males, 20 females) and we find that the PPT induces significantly lower anxiety, exhaustion and hypothermia than the MWM. We conclude that the PPT provides a robust assessment of spatial cognition in mice, which can be applied in conjunction with other tests, to facilitate hypothesis testing and drug development to combat cognitive impairment.
A Proximal Point Dual Newton Algorithm for Solving Group Graphical Lasso Problems

Undirected graphical models have been especially popular for learning the conditional independence structure among a large number of variables where the observations are drawn independently and identically from the same distribution. However, many modern statistical problems would involve categorical data or time-varying data, which might follow different but related underlying distributions. In order to learn a collection of related graphical models simultaneously, various joint graphical models inducing sparsity in graphs and similarity across graphs have been proposed. In this paper, we aim to propose an implementable proximal point dual Newton algorithm (PPDNA) for solving the group graphical Lasso model, which encourages a shared pattern of sparsity across graphs. Though the group graphical Lasso regularizer is non-polyhedral, the asymptotic superlinear convergence of our proposed method PPDNA can be obtained by leveraging on the local Lipschitz continuity of the Karush-Kuhn-Tucker solution mapping associated with the group graphical Lasso model. A variety of numerical experiments on real data sets illustrates that the PPDNA for solving the group graphical Lasso model can be highly efficient and robust.
The complex chemistry of hot cores in Sgr B2(N): influence of cosmic-ray ionization and thermal history

Context. As the number of complex organic molecules (COMs) detected in the interstellar medium increases, it becomes even more important to place meaningful constraints on the origins and formation pathways of such chemical species. The molecular cloud Sagittarius B2(N) is host to several hot molecular cores in the early stage of star formation, where a great variety of COMs are detected in the gas phase. Given its exposure to the extreme conditions of the Galactic center (GC) region, Sgr B2(N) is one of the best targets to study the impact of environmental conditions on the production of COMs. Aims. Our main goal is to characterize the physico-chemical evolution of Sgr B2(N)’s sources in order to explain their chemical differences and constrain their environmental conditions. Methods. The chemical composition of Sgr B2(N)’s hot cores, N2, N3, N4, and N5 is derived by modeling their 3 mm emission spectra extracted from the Exploring Molecular Complexity with ALMA (EMoCA) imaging spectral line survey performed with the Atacama Large Millimeter/submillimeter Array (ALMA). We derived the density distribution in the envelope of the sources based on the masses computed from the ALMA dust continuum emission maps. We used the radiative transfer code RADMC-3D to compute temperature profiles and inferred the current luminosity of the sources based on the COM rotational temperatures derived from population diagrams. We used published results of 3D radiation-magnetohydrodynamical (RMHD) simulations of high-mass star formation to estimate the time evolution of the source properties. We employed the astrochemical code MAGICKAL to compute time-dependent chemical abundances in the sources and to investigate how physical properties and environmental conditions influence the production of COMs. Results. The analysis of the abundances of 11 COMs detected toward Sgr B2(N2-N5) reveals that N3 and N5 share a similar chemical composition while N2 differs significantly from the other sources. We estimate the current luminosities of N2, N3, N4, and N5 to be 2.6 × 105 L⊙, 4.5 × 104 L⊙, 3.9 × 105 L⊙, and 2.8 × 105 L⊙, respectively. We find that astrochemical models with a cosmic-ray ionization rate of 7 × 10−16 s−1 best reproduce the abundances with respect to methanol of ten COMs observed toward Sgr B2(N2-N5). We also show that COMs still form efficiently on dust grains with minimum dust temperatures in the prestellar phase as high as 15 K, but that minimum temperatures higher than 25 K are excluded. Conclusions. The chemical evolution of Sgr B2(N2-N5) strongly depends on their physical history. A more realistic description of the hot cores’ physical evolution requires a more rigorous treatment with RMHD simulations tailored to each hot core.
Interpersonal Fairness, Willingness-to-Stay and Organisation-Based Self-Esteem: The Mediating Role of Affective Commitment

This study examines the direct and indirect effects of interpersonal fairness on employees’ willingness-to-stay and organisation-based self-esteem through affective commitment among manufacturing workers in Tema, Ghana. Using the survey design, 300 manufacturing workers in Tema were conveniently sampled for the study. The confirmatory factor analysis and structural equation modelling were used to analyse the data. Results indicated that affective commitment partially mediated the relationship between interpersonal fairness and employees’ willingness-to-stay. Affective commitment also fully mediated the interpersonal fairness-organisation-based self-esteem relationship. Results further showed that organisation-based self-esteem partially mediated the affective commitment and willingness-to-stay relationship, such that, an increase in organisation-based self-esteem leads to a decrease in employees’ willingness-to-stay. These findings emphasised the roles of interpersonal fairness and affective commitment in organisations, where affective commitment increases as a result of an increase in interpersonal fairness and makes employees have an intention-to-stay. The findings imply that employees who are very confident and have higher organisation-based self-esteem stand lower chances of staying in their current organisations. This study is the first to examine how affective commitment transfers the effects of interpersonal fairness unto employees’ intention-to-stay among manufacturing workers in Ghana.
Improving the communication for children with speech disorder using the smart toys

An attempt is made to develop a smart toy to help the children suffering with communication disorders. The children suffering with such disorders need additional attention and guidance to understand different types of social events and life activities. Various issues and features of the children with speech disorders are identified in this study and based on the inputs from the study, a working architecture is proposed with suitable policies. A prediction module with a checker component is designed in this work to produce alerts in at the time of abnormal behaviour of the child with communication disorder. The model is designed very sensitively to the behaviour of the child for a particular voice tone, based on which the smart toy will change to tones automatically. Such an arrangement proved to be helpful for the children to improve the communication with other due to the inclusion of continuous training for the smart toy from the predictionmodule
Deep Reinforcement Learning With Discrete Normalized Advantage Functions for Resource Management in Network Slicing

Network slicing promises to provision diversified services with distinct requirements in one infrastructure. Deep reinforcement learning (e.g., deep <inline-formula> <tex-math notation="LaTeX">$\mathcal {Q}$ </tex-math></inline-formula>-learning, DQL) is assumed to be an appropriate algorithm to solve the demand-aware inter-slice resource management issue in network slicing by regarding the varying demands and the allocated bandwidth as the environment <italic>state</italic> and the <italic>action</italic>, respectively. However, allocating bandwidth in a finer resolution usually implies larger action space, and unfortunately DQL fails to quickly converge in this case. In this letter, we introduce discrete normalized advantage functions (DNAF) into DQL, by separating the <inline-formula> <tex-math notation="LaTeX">$\mathcal {Q}$ </tex-math></inline-formula>-value function as a state-value function term and an advantage term and exploiting a deterministic policy gradient descent (DPGD) algorithm to avoid the unnecessary calculation of <inline-formula> <tex-math notation="LaTeX">$\mathcal {Q}$ </tex-math></inline-formula>-value for every state-action pair. Furthermore, as DPGD only works in continuous action space, we embed a k-nearest neighbor algorithm into DQL to quickly find a valid action in the discrete space nearest to the DPGD output. Finally, we verify the faster convergence of the DNAF-based DQL through extensive simulations.
Physical Harm and Death in the Context of Coercive Measures in Psychiatric Patients: A Systematic Review

Background: For centuries coercive measures in psychiatry have been means of averting acute danger. It has been known for almost as long that these measures can lead to harm or even death to those affected. Over the past two decades the topic has increasingly been the subject of scientific discussion and research. While the legal and ethical preconditions for coercive measures in psychiatry as well as epidemiological studies on their incidence and patients’ subjective experiences have increasingly come into focus, research on possible adverse events has lagged behind. To our knowledge there is no systematic review on the harmful or even fatal physical adverse effects of coercive interventions in psychiatry. Methods: We searched the databases PubMed and CINAHL for primary literature with a search string based on the PICO framework including key words describing different psychiatric diagnoses, coercive measures, and harms. Results: In total, 67 eligible studies (mainly case reports and case series) of very heterogeneous quality were included. Two RCTs were found reporting position-dependent cardiac deterioration, but were, however, carried out with healthy people and were characterized by a small number of cases. Death was the most frequently reported harm: cardiac arrest by chest compression in 14 studies, cardiac arrest by strangulation in 9, and pulmonary embolism in 8 studies. Further harms were, among others, venous thromboembolism and injuries. Injuries during physical restraint were reported in 0.8–4% of cases. For other kinds of coercive interventions, there are no sufficient data. Venous thromboembolism occurred in a considerable percentage of cases during mechanical restraint, also under prophylaxis. The most commonly reported coercive measure was restraint, distinguishing in mechanical restraint (43 studies), physical restraint (22 studies), bedrails (eight studies), vest restraint (7 studies), and chair restraint (6 studies). Forced medication was explicitly mentioned only in two, but seems to have occurred in nine studies. Six studies included seclusion. Conclusion: Coercive measures can lead to physical harm or even death. However, there is a significant lack of data on the incidence of such adverse events related to coercive interventions. Though reported anecdotally, physical adverse events during seclusion appear to be highly underresearched.
Progression of Blood Flow Restricted Resistance Training in Older Adults at Risk of Mobility Limitations

Blood flow restriction (BFR) resistance training leads to increased muscle mass and strength but the progression leading to adaptations may be different as strength gains are often to a lesser magnitude than high-load (HL) training. The impact of training loads and repetitions on older adults’ muscle mass and strength following BFR or HL training was evaluated. Twenty-one older adults (67–90 years) classified as being at risk of mobility limitations were randomly assigned to HL (n = 11) or BFR (n = 10) knee extension (KE) and flexion (KF) training twice per week for 12 weeks. Strength was measured with 10-repetition maximum (10-RM) tests and isometric contractions. Cross-sectional area (CSA) of the quadriceps and hamstrings was measured. HL and BFR interventions increased 10-RM KF and isometric strength (P < 0.05) and hamstrings CSA increased an average of 4.8 ± 5.9% after HL and BFR training (time main effect P < 0.01). There were no differences between the training groups (time x group interactions P > 0.05). The rate of progression of KF training load and repetitions was comparable (time × group interactions of each variable P > 0.05). The groups averaged an increase of 0.50 ± 25 kg⋅week-1 and 1.8 ± 0.1. 7 repetitions⋅week-1 of training (time main effects P < 0.05). The HL training group experienced greater improvements in KE 10-RM strength than the BFR group (60.7 ± 36.0% vs. 35.3 ± 25.5%; P = 0.03). In both groups, isometric KE strength increased 17.3 ± 18.5% (P = 0.001) and there were no differences between groups (P = 0.24). Quadriceps CSA increased (time main effect P < 0.01) and to similar magnitudes (time x group interaction P = 0.62) following HL (6.5 ± 3.1%) and BFR training (7.8 ± 8.2%). The HL group experienced accelerated progression of load when compared to BFR (0.90 ± 0.60 kg⋅week-1 vs. 30 ± 0.21 kg⋅week-1; P = 0.006) but was not different when expressed in relative terms. BFR training progressed at a rate of 3.6 ± 1.3 repetitions⋅week-1 while the HL group progressed at 2.2 ± 0.43 repetitions⋅week-1 (P = 0.003). HL training led to greater increases in KE 10-RM and it may be attributed to the greater load and/or faster rate of progression of the load throughout the 12-week training period and the specificity of the testing modality. Incorporating systematic load progression throughout BFR training periods should be employed to lead to maximal strength gains.
PDIA1/P4HB is required for efficient proinsulin maturation and ß cell health in response to diet induced obesity

Regulated proinsulin biosynthesis, disulfide bond formation and ER redox homeostasis are essential to prevent Type two diabetes. In ß cells, protein disulfide isomerase A1 (PDIA1/P4HB), the most abundant ER oxidoreductase of over 17 members, can interact with proinsulin to influence disulfide maturation. Here we find Pdia1 is required for optimal insulin production under metabolic stress in vivo. ß cell-specific Pdia1 deletion in young high-fat diet fed mice or aged mice exacerbated glucose intolerance with inadequate insulinemia and increased the proinsulin/insulin ratio in both serum and islets compared to wildtype mice. Ultrastructural abnormalities in Pdia1-null ß cells include diminished insulin granule content, ER vesiculation and distention, mitochondrial swelling and nuclear condensation. Furthermore, Pdia1 deletion increased accumulation of disulfide-linked high molecular weight proinsulin complexes and islet vulnerability to oxidative stress. These findings demonstrate that PDIA1 contributes to oxidative maturation of proinsulin in the ER to support insulin production and ß cell health.
Sequential Source Coding for Stochastic Systems Subject to Finite Rate Constraints

In this article, we revisit the sequential source-coding framework to analyze fundamental performance limitations of discrete-time stochastic control systems subject to feedback data-rate constraints in finite-time horizon. The basis of our results is a new characterization of the lower bound on the minimum total-rate achieved by sequential codes subject to a total (across time) distortion constraint and a computational algorithm that allocates optimally the rate-distortion, for a given distortion level, at each instant of time and any fixed finite-time horizon. The idea behind this characterization facilitates the derivation of analytical, nonasymptotic, and finite-dimensional lower and upper bounds in two control-related scenarios: a) A parallel time-varying Gauss–Markov process with identically distributed spatial components that are quantized and transmitted through a noiseless channel to a minimum mean-squared error decoder; and b) a time-varying quantized linear quadratic Gaussian (LQG) closed-loop control system, with identically distributed spatial components and with a random data-rate allocation. Our nonasymptotic lower bound on the quantized LQG control problem reveals the absolute minimum data-rates for (mean square) stability of our time-varying plant for any fixed finite-time horizon. We supplement our framework with illustrative simulation experiments.