Miracle-dz/UniFAR-FTUs huggingface dataset:
Prospects for a high-field, compact break-even axisymmetric mirror (BEAM) and applications

This paper explores the feasibility of a break-even-class mirror referred to as BEAM (break-even axisymmetric mirror): a neutral-beam-heated simple mirror capable of thermonuclear-grade parameters and $Q\sim 1$ conditions. Compared with earlier mirror experiments in the 1980s, BEAM would have: higher-energy neutral beams, a larger and denser plasma at higher magnetic field, both an edge and a core and capabilities to address both magnetohydrodynamic and kinetic stability of the simple mirror in higher-temperature plasmas. Axisymmetry and high-field magnets make this possible at a modest scale enabling a short development time and lower capital cost. Such a $Q\sim 1$ configuration will be useful as a fusion technology development platform, in which tritium handling, materials and blankets can be tested in a real fusion environment, and as a base for development of higher-$Q$ mirrors.
Organizational Resilience in Public Sector Organizations: Destination Management Organizations

This qualitative exploratory study examined the effectiveness of organizational resilience processes adopted by Destination Management Organizations (DMOs) in Small Island Developing States (SIDS) during a global crisis. Study results showed that the DMOs’ organizational resilience engaged strategies fostered within a positive work climate (PWC) where dynamic processes of resilience capabilities emerged for Caribbean SIDS. These organizational resilience processes are complex and dynamic, with an interplay of both internal and external influences. The study offers practical and theoretical implications for public organizations in the service sector, where an integrated framework for engaging resilience strategies and capabilities processes emerges.
Predicting Hospital Closure Using Popular Financial Indicators: An Exploratory Study of Muhlenberg Hospital

The role of government in the U.S. healthcare system is critical and hospitals play fundamental roles in our communities, among them critical healthcare provider and major employer. The federal funding program Medicare, and the federal-state funding source, Medicaid, are the two public health financing mechanisms that comprise significant funding to hospitals. Further, Medicare and Medicaid account for approximately 37 percent of all health expenditures in the U.S. (United States Centers for Medicare and Medicaid Services, 2018), and more than 60 percent of all hospital care (American Hospital Association, 2016). The public administration roles in the $3.4 trillion U.S. healthcare industry span that of provider, purchaser of services, initiatives and research funder, regulator, and policy implementer. In today's precarious political climate, amidst layers of government bureaucracies and an increased focus on better and fairer healthcare outcomes, the integration of public policy, administration, and healthcare has become increasingly important to practitioners and researchers. Indeed, the American Hospital Association tallies 5,564 hospitals in the United States, with 16 million employees and accounting for about one-sixth of the U.S. economy. Recently, of particular importance is the rise in U.S. hospital closures that impact healthcare and the economy in our local communities. This study contributes to the literature by identifying indicators for detecting warning trends in hospital financial condition before closures occur using a parsimonious system that can be replicated by both practitioners and policymakers with minimal data gathering efforts. The findings discussed here suggest that in the case of Muhlenberg Hospital, a clear trend of worsening financial condition could be detected at least four years prior to its closure. Current ratio, total margin, and debt to net asset ratio for Muhlenberg Hospital evidenced very clear indicators of financial instability. Consequently, the question emerges as to why neither the hospital nor the community was able to adequately negotiate these treacherous financial challenges to retain this valuable community asset.
Decision tree‐based identification of important molecular fragments for protein‐ligand binding

Fragment‐based drug design is an emerging technology in pharmaceutical research and development. One of the key aspects of this technology is the identification and quantitative characterization of molecular fragments. This study presents a strategy for identifying important molecular fragments based on molecular fingerprints and decision tree algorithms and verifies its feasibility in predicting protein‐ligand binding affinity. Specifically, the three‐dimensional (3D) structures of protein‐ligand complexes are encoded using extended‐connectivity fingerprints (ECFP), and three decision tree models, namely Random Forest, XGBoost, and LightGBM, are used to quantitatively characterize the feature importance, thereby extracting important molecular fragments with high reliability. Few‐shot learning reveals that the extracted molecular fragments contribute significantly and consistently to the binding affinity even with a small sample size. Despite the absence of location and distance information for molecular fragments in ECFP, 3D visualization, in combination with the reverse ECFP process, shows that the majority of the extracted fragments are located at the binding interface of the protein and the ligand. This alignment with the distance constraints critical for binding affinity further supports the reliability of the strategy for identifying important molecular fragments.
The fnr-like mutants enhance isoxaben tolerance by initiating mitochondrial retrograde signalling

Isoxaben is a pre-emergent herbicide used to control broadleaf weeds. While the phytotoxic mechanism is not completely understood, isoxaben interferes with cellulose synthesis. Certain mutations in cellulose synthase complex proteins can confer isoxaben tolerance; however, these mutations cause compromised cellulose synthesis and perturbed plant growth, rendering them unsuitable as herbicide tolerance traits. We conducted a genetic screen to identify new genes associated with isoxaben tolerance by screening a selection of Arabidopsis thaliana T-DNA mutants. We found that mutations in a FERREDOXIN-NADP(+) OXIDOREDUCTASE-LIKE (FNRL) gene enhanced tolerance to isoxaben, exhibited as a reduction in primary root stunting, reactive oxygen species accumulation, and ectopic lignification. The fnrl mutant did not exhibit a reduction in cellulose levels following exposure to isoxaben, indicating that FNRL operates upstream of isoxaben-induced cellulose inhibition. In line with these results, transcriptomic analysis revealed a highly reduced response to isoxaben treatment in fnrl mutant roots. The fnrl mutants displayed constitutively induced mitochondrial retrograde signalling, and the observed isoxaben tolerance is partially dependent on the transcription factor ANAC017, a key regulator of mitochondrial retrograde signalling. Moreover, FNRL is highly conserved across all plant lineages, implying conservation of its function. Notably, fnrl mutants did not show a growth penalty in shoots, making FNRL a promising target for biotechnological applications in breeding isoxaben tolerance in crops.
Little evidence for inbreeding depression for birth mass, survival and growth in Antarctic fur seal pups

Inbreeding depression, the loss of offspring fitness due to consanguineous mating, is generally detrimental for individual performance and population viability. We therefore investigated inbreeding effects in a declining population of Antarctic fur seals at Bird Island, South Georgia. Here, localised warming has reduced the availability of the seal’s staple diet, Antarctic krill, leading to a temporal increase in the strength of viability selection against inbred offspring, which are increasingly failing to recruit into the adult breeding population. However, it remains unclear whether viability selection operates before or after nutritional independence at weaning. We therefore used microsatellite data from 884 pups and their mothers, and SNP array data from 100 mother-offspring pairs, to quantify the effects of individual and maternal inbreeding on three important neonatal fitness traits: birth mass, survival and growth. We did not find any clear or consistent effects of inbreeding on any of these traits. This suggests that viability selection filters inbred individuals out of the population as juveniles during the time window between weaning and recruitment. Our study brings into focus a poorly understood life-history stage and emphasises the importance of understanding the ecology and threats facing juvenile pinnipeds.
Divisome minimization shows that FtsZ and SepF can form an active Z-ring, and reveals BraB as a new cell division influencing protein in Bacillus subtilis

One of the aims of synthetic biology is the construction of a minimal cell. Since bacteria are the simplest life forms, they are the preferred blueprint for such a cell. In this study, we tried to find the minimal set of proteins required for division of a cell wall containing cell, using the bacterial model system Bacillus subtilis. Bacterial cytokinesis begins with polymerization of the tubulin homologue FtsZ at midcell into the Z-ring, which recruits the late cell division protein that synthesize the division septum. Assembly of FtsZ is carefully regulated, involving a dozen conserved cell division proteins. These proteins are not essential, but removing more than one is in many cases lethal. We made use of known suppressor mutations to find a gene deletion route that eventually enabled us to remove eight conserved cell division proteins: ZapA, MinC, MinJ, UgtP, ClpX, Noc, EzrA and FtsA. Only FtsZ and its membrane anchor SepF appeared to be required for Z-ring formation. Interestingly, SepF is also the FtsZ anchor in archaea, and both proteins may date back to the Last Universal Common Ancestor (LUCA). Curiously, viability was not greatly affected by the multiple deletions, although the frequency of cell division was considerably reduced. However, genome sequencing exposed the accumulation of several suppressor mutations, and revealed an unexpected cell division regulation function for the branched chain amino acid transporter BraB. The implications of these findings for the role of SepF in cell division, and the construction of a minimal cell division machinery are discussed.
Cell-type-specific origins of spinal rhythmicity at different locomotor speeds in larval zebrafish

Different speeds of locomotion require heterogeneous spinal populations, but a common mode of rhythm generation is presumed to exist. Here, we explore the cellular versus synaptic origins of spinal rhythmicity at different speeds by performing electrophysiological recordings from premotor excitatory interneurons in larval zebrafish. Chx10-labeled V2a neurons are divided into at least two subtypes proposed to play distinct roles in timing and intensity control. Consistent with distinct rhythm generating and output patterning functions within the spinal V2a population, we find that one subtype is recruited exclusively at slow or fast speeds and exhibits intrinsic cellular properties suitable for rhythmogenesis at those speeds, while the other subtype is recruited more reliably at all speeds and lacks appropriate rhythmogenic cellular properties. Unexpectedly, however, phasic firing patterns during locomotion in rhythmogenic and non-rhythmogenic subtypes are best explained by distinct modes of synaptic inhibition linked to cell-type and speed. At fast speeds reciprocal inhibition in rhythmogenic V2a neurons supports phasic firing, while recurrent inhibition in non-rhythmogenic V2a neurons helps pattern motor output. In contrast, at slow speeds recurrent inhibition in rhythmogenic V2a neurons supports phasic firing, while non-rhythmogenic V2a neurons rely on reciprocal inhibition alone to pattern output. Our findings suggest cell-type-specific, not common, modes of rhythmogenesis generate and coordinate different speeds of locomotion.
Strategies to Enhance Knowledge Transfer: Lessons from a Multinational Corporation

Overview: To maximize the rent-yield of the innovation and product development process, firms attempt to commercialize knowledge through activities that require effective knowledge transfer. Knowledge transfer remains a challenge for most firms, however, due to the recipient organization’s lack of ability to absorb and apply new knowledge. We examined four knowledge-transfer projects within a large multinational corporation. We identified important actions, lessons learned, and recommendations that practitioners can use to enhance their own knowledge-transfer processes.
Distinct alterations in white matter properties and organization related to maternal treatment initiation in neonates exposed to HIV but uninfected

HIV exposed-uninfected (HEU) infants and children are at risk of developmental delays as compared to uninfected unexposed (HUU) populations. The effects of exposure to in utero HIV and ART regimens on the HEU the developing brain are not well understood. In a cohort of 2-week-old newborns, we used diffusion tensor imaging (DTI) tractography and graph theory to examine the influence of HIV and ART exposure in utero on neonate white matter integrity and organisation. The cohort included HEU infants born to mothers who started ART before conception (HEUpre) and after conception (HEUpost), as well as HUU infants from the same community. We investigated HIV exposure and ART duration group differences in DTI metrics (fractional anisotropy (FA) and mean diffusivity (MD)) and graph measures across white matter. We found increased MD in white matter connections involving the thalamus and limbic system in the HEUpre group compared to HUU. We further identified reduced nodal efficiency in the basal ganglia. Within the HEUpost group, we observed reduced FA in cortical-subcortical and cerebellar connections as well as decreased transitivity in the hindbrain area compared to HUU. Overall, our analysis demonstrated distinct alterations in white matter integrity related to the timing of maternal ART initiation that influence regional brain network properties.
Event-triggered consensus control of multi-agent systems under denial-of-service jamming attacks

ABSTRACT This work focuses on a consensus problem of multi-agent systems (MASs) under an event-triggered control (ETC) subject to denial-of-service (DoS) jamming attacks. To reduce the communication cost, a novel event-triggering mechanism (ETM) is applied to determine whether the sampled signal should be transmitted or not. Unlike the periodic DoS jamming attacks strategy, the occurrence of DoS jamming attacks is irregular in this article, where attack attributes such as attack frequency and attack ratio are uncertain. Moreover, compared with some existing related results considering fixed undirected topologies, the communication topologies may change due to DoS jamming attacks in this work. In view of this, based on the piecewise Lyapunov function, sufficient conditions are derived to guarantee that the consensus problem of the MASs can be solved. Finally, the effectiveness and correctness of the theoretical results are verified by a numerical example.
EMG-to-torque models for exoskeleton assistance: a framework for the evaluation of in situ calibration

In the field of robotic exoskeleton control, it is critical to accurately predict the intention of the user. While surface electromyography (EMG) holds the potential for such precision, current limitations arise from the absence of robust EMG-to-torque model calibration procedures and a universally accepted model. This paper introduces a practical framework for calibrating and evaluating EMG-to-torque models, accompanied by a novel nonlinear model. The framework includes an in situ procedure that involves generating calibration trajectories and subsequently evaluating them using standardized criteria. A comprehensive assessment on a dataset with 17 participants, encompassing single-joint and multi-joint conditions, suggests that the novel model outperforms the others in terms of accuracy while conserving computational efficiency. This contribution introduces an efficient model and establishes a versatile framework for EMG-to-torque model calibration and evaluation, complemented by a dataset made available. This further lays the groundwork for future advancements in EMG-based exoskeleton control and human intent detection.
Oncogene-induced matrix reorganization controls CD8+ T cell function in the soft-tissue sarcoma microenvironment

CD8+ T cell dysfunction impedes anti-tumor immunity in solid cancers but the underlying mechanisms are diverse and poorly understood. Extracellular matrix (ECM) composition has been linked to both impaired T cell migration and enhanced tumor progression; however, impacts of individual ECM molecules on T cell function in the tumor microenvironment (TME) are only beginning to be elucidated. Upstream regulators of aberrant ECM deposition and organization in solid tumors are equally ill-defined. Therefore, we investigated how ECM composition modulates CD8+ T cell function in undifferentiated pleomorphic sarcoma (UPS), an immunologically active and desmoplastic tumor. Using an autochthonous murine model of UPS and data from multiple human patient cohorts, we discovered a multifaceted mechanism wherein the transcriptional co-activator YAP1 promotes collagen VI (COLVI) deposition in the UPS TME. In turn, COLVI induces CD8+ T cell dysfunction and immune evasion by remodeling fibrillar collagen and inhibiting T cell autophagic flux. Unexpectedly, collagen I (COLI) opposed COLVI in this setting, promoting CD8+ T cell function and acting as a tumor suppressor. Thus, CD8+ T cell responses in sarcoma depend upon oncogene-mediated ECM composition and remodeling.
Adenomatous polyposis coli (APC) regulates internalization and signaling of the chemorepellent receptor, Roundabout (ROBO) 1

The SLIT-ROBO signaling pathway regulates axon guidance and cell migration, and ROBO1 is a receptor for SLIT ligands. ROBO1 undergoes constitutive endocytosis which is enhanced upon SLIT2 binding, but the molecular mechanisms and functional consequences of this process are not well understood. Using pharmacologic inhibitors and molecular techniques, we found that clathrin-mediated endocytosis is necessary for SLIT2-induced inhibition of cell spreading. To explore the underlying mechanisms, we performed BioID to identify ROBO1-interacting proteins whose association with the cytoplasmic domain of ROBO1 is differentially regulated by SLIT2. We discovered that adenomatous polyposis coli (APC), a multifunctional tumor suppressor, constitutively interacts with ROBO1 but dissociates upon binding of SLIT2 and that this dissociation is necessary for clathrin-mediated endocytosis of ROBO1 and subsequent effects on cell morphology. These findings provide new insights into the functional mechanisms by which SLIT2 binding to ROBO1 effects changes in actin cytoskeletal architecture.
Propose-and-Complete: Auto-regressive Semantic Group Generation for Personalized Scene Synthesis

Our research goal is to build a novel scene synthesis framework enabling the ﬂex-ible generation of individualized indoor virtual environments. Current deep methods only learn the layout patterns from training scene samples, affording only partial co-occurrence possibilities while ignoring any user intent. In contrast, this paper devises a novel framework by ﬂexibly combining and generating function-oriented semantic object groups while accommodating strong user intent. Conforming to this group-centric design paradigm, we consider different strategies for proposing group-level locations and completing semantic clusters with intra-group relationships. The entire framework hinges upon two technical innovations. First, we design a conditional normalizing ﬂow-based ProposeNet to learn the exact distribution of semantic groups, by which we sample potentially plausible group-level locations constrained by user-desirable room functionalities. Second, we design a conditional graph variational auto-encoder, CompleteNet , to instantiate each semantic group with the user-speciﬁc complexity (e.g., graph size). With the complete groups readily available, we then recursively select the most plausible proposals and optimize the ﬁnal layout subject to a collision-free, accessible room space and an arbitrary ﬂoor plan. Comprehensive experiments have conﬁrmed that our new framework can produce
E XPLAIN Y OUR M OVE : U NDERSTANDING A GENT A CTIONS U SING S ALIENT AND R ELEVANT F EATURE A TTRIBUTION

As deep reinforcement learning (RL) is applied to more tasks, there is a need to visualize and understand the behavior of learned agents. Saliency maps explain agent behavior by highlighting the features of the input state that are most relevant for the agent in taking an action. Existing perturbation-based approaches to compute saliency often highlight regions of the input that are not relevant to the action taken by the agent. Our approach, SARFA generates more focused saliency maps by balancing two aspects (specificity and relevance) that capture different desiderata of saliency. The first captures the impact of perturbation on the relative expected reward of the action to be explained. The second downweighs irrelevant features that alter the relative expected rewards of actions other than the action to be explained. We compare SARFA with existing approaches on agents trained to play board games (Chess and Go) and Atari games (Breakout, Pong and Space Invaders). We show through illustrative examples (Chess, Atari, Go), human studies (Chess), and automated evaluation methods (Chess) that SARFA generates saliency maps that are more interpretable for humans than existing approaches. For the code release and demo videos, see https://nikaashpuri.github. io/sarfa-saliency/.
Masked Attention ConvNeXt Unet with Multi-Synthesis Dynamic Weighting for Anomaly Detection and Localization

In recent years, self-supervised models like Cutpaste, Mask, NSA, and Perlin have gained popularity for anomaly detection. These models generate synthetic data by employing various data augmentation strategies, demonstrating their potential for improving anomaly detection through learned representations. In this study, we introduce an algo-rithm called Multi-Synthesis Dynamic Weighting (MSdW) to leverage the advantages of diverse synthetic data. MSdW enables the model to learn various abnormal conditions during training, thereby enhancing accuracy. Our model architecture consists of reconstructive and discriminative subnetworks, both utilizing the UNet architecture. The encoders in both subnetworks employ modern ConvNets, specifically ConvNeXtV2, for proficient feature extraction. Additionally, we propose an attention mechanism known as Self-Supervised Predictive Convolutional Block with Multi-Attentions (SSPCBMA), which is seamlessly integrated into the reconstructive subnetwork to enhance feature extraction capabilities. We evaluate our proposed model on multiple datasets designed for anomaly detection and segmentation tasks, including MVTecAD, BTAD, and KSDD2. These datasets serve various purposes, and our model outperforms the state-of-the-art methods, particularly in terms of Pixel AP and PRO indices.