Miracle-dz/UniFAR-FTUs huggingface dataset:
A Comprehensive Framework for the Evaluation of Individual Treatment Rules From Observational Data

Individualized treatment rules (ITRs) are deterministic decision rules that recommend treatments to individuals based on their characteristics. Though ubiquitous in medicine, ITRs are hardly ever evaluated in randomized controlled trials. To evaluate ITRs from observational data, we introduce a new probabilistic model and distinguish two situations: i) the situation of a newly developed ITR, where data are from a population where no patient implements the ITR, and ii) the situation of a partially implemented ITR, where data are from a population where the ITR is implemented in some unidentified patients. In the former situation, we propose a procedure to explore the impact of an ITR under various implementation schemes. In the latter situation, on top of the fundamental problem of causal inference, we need to handle an additional latent variable denoting implementation. To evaluate ITRs in this situation, we propose an estimation procedure that relies on an expectation-maximization algorithm. In Monte Carlo simulations our estimators appear unbiased with confidence intervals achieving nominal coverage. We illustrate our approach on the MIMIC-III database, focusing on ITRs for dialysis initiation in patients with acute kidney injury.
The physiotherapy workforce in Nepal: A national survey

Abstract Background Physiotherapy is a relatively young profession in Nepal. Education standards, and workforce organization and representation have improved in recent years, but there is no detailed workforce data to support decision‐making and long‐term planning. Therefore, this study aimed to describe the demographic characteristics, physiotherapy practice areas, settings and employment characteristics of physiotherapists in Nepal; and to document job satisfaction and the factors that influence it. Methods In a cross‐sectional survey, we asked Nepali physiotherapists about their sociodemographic characteristics, employment or physiotherapy practice area and settings, and job satisfaction. We assessed the associations between sociodemographic factors and job satisfaction among physiotherapists using univariable and multivariable logistic regressions. We invited all Nepali physiotherapists to participate via email, social media and visits to hospitals. Results Of the 1120 physiotherapists who were contacted, 52% (female: 287; male: 293) completed the survey. The majority of the participants (75%) were less than 30 years old. The majority (73%) held a Bachelor of Physiotherapy degree. Eighty percent were employed at the time of data collection; 58% had 1–4 years of experience. The majority (77%) practiced in or near the capital Kathmandu. Most physiotherapist worked either in private hospitals (46%) or in private physiotherapy practices (26%), rehabilitation centres (19%) and 11% practiced in public hospitals. The most common areas of practice were musculoskeletal physiotherapy (90%) and adult neurology (67%). More than half of the physiotherapist (53%) felt that they were underpaid and 41% were not satisfied with their current job status. The majority of the physiotherapists (67%) reported that they participated in continuing professional development activities. Multivariable logistic regression showed that physiotherapists who had a higher remuneration were more likely to be satisfied with their job. Conclusions The majority of the physiotherapy workforce in Nepal is well‐educated, young and at an early career stage. Most physiotherapists work in musculoskeletal and adult neurological physiotherapy, in private practices or private hospitals near Kathmandu. Job dissatisfaction was common and was related to low annual income. This first nationwide survey described the current physiotherapy workforce and provides comparison data for future physiotherapy workforce surveys.
In-vitro activity of plazomicin, meropenem-vaborbactam, and omadacycline against carbapenem-resistant Gram-negative isolates in Egypt

Abstract Anti-microbial resistance is an escalating worldwide threat. Thus, there is an utmost necessity for the introduction of novel anti-microbial agents. This research aimed to evaluate the in-vitro activity of plazomicin, meropenem-vaborbactam, and omadacycline against carbapenem-resistant Gram-negative isolates gathered from one Egyptian University group of Hospitals. 210 intensive care units (ICU) and 113 non-ICU samples were included. Resistance to carbapenems was reported in 37.5% and 21.05% of the isolated Gram-negative ICU and non–ICU organisms respectively (P < 0.001). Via disc diffusion, the sensitivity rates for carbapenem-resistant non-ICU and ICU strains were 68.8% & 64.4% for plazomicin, 68.8% & 48.9% for meropenem-vaborbactam, and 56.25% & 44.5% for omadacycline, respectively. Regarding MIC50/90, it was 1/16 μg/mL for plazomicin and 4/32 μg/mL for both meropenem-vaborbactam and omadacycline separately. In conclusion, plazomicin displayed potent activity against carbapenem-resistant strains. Moreover, meropenem-vaborbactam and omadacycline demonstrated satisfactory results.
Complementary representations of time in the prefrontal cortex and hippocampus

Episodic memory binds the spatial and temporal relationships between the elements of experience. The hippocampus encodes space through place cells that fire at specific spatial locations. Similarly, time cells fire sequentially at specific time points within a temporally organized experience. Recent studies in rodents, monkeys, and humans have identified time cells with discrete firing fields and cells with monotonically changing activity in supporting the temporal organization of events across multiple timescales. Using in vivo electrophysiological tetrode recordings, we simultaneously recorded neurons from the prefrontal cortex and dorsal CA1 of the hippocampus while rats performed a delayed match to sample task. During the treadmill mnemonic delay, hippocampal time cells exhibited sparser firing fields with decreasing resolution over time, consistent with previous results. In comparison, temporally modulated cells in the prefrontal cortex showed more monotonically changing firing rates, ramping up or decaying with the passage of time, and exhibited greater temporal precision for Bayesian decoding of time at long time lags. These time cells show exquisite temporal resolution both in their firing fields and in the fine timing of spikes relative to the phase of theta oscillations. Here, we report evidence of theta phase precession in both the prefrontal cortex and hippocampus during the temporal delay, however, hippocampal cells exhibited steeper phase precession slopes and more punctate time fields. To disentangle whether time cell activity reflects elapsed time or distance traveled, we varied the treadmill running speed on each trial. While many neurons contained multiplexed representations of time and distance, both regions were more strongly influenced by time than distance. Overall, these results demonstrate the flexible integration of spatiotemporal dimensions and reveal complementary representations of time in the prefrontal cortex and hippocampus in supporting memory‐guided behavior.
Examining Data Imbalance in Crowdsourced Reports for Improving Flash Flood Situational Awareness

The use of crowdsourced data has been finding practical use for enhancing situational awareness during disasters. While recent studies have shown promising results regarding the potential of crowdsourced data for flood mapping, little attention has been paid to data imbalances issues that could introduce biases. We examine biases present in crowdsourced reports to identify data imbalances with a goal of improving disaster situational awareness. Sample bias, spatial bias, and demographic bias are examined as we analyzed reported flooding from 3-1-1, Waze reports, and FEMA damage data collected in the aftermaths of Tropical Storm Imelda in 2019 and Hurricane Ida in 2021. Integrating other flooding related topics from 3-1-1 reports into the Global Moran's I and Local Indicator of Spatial Association (LISA) test revealed more communities that were impacted by floods. To examine spatial bias, we perform the LISA and BI-LISA tests on the three datasets at the census tract and census block group level. By looking at two geographical aggregations, we found that the larger spatial aggregations, census tracts, show less data imbalance in the results. Finally, one-way analysis of Variance (ANOVA) test performed on the clusters generated from the BI-LISA shows that data imbalance exists in areas where minority populations reside. Through a regression analysis, we found that 3-1-1 and Waze reports have data imbalance limitations in areas where minority populations reside. The findings of this study advance understanding of data imbalances and biases in crowdsourced datasets that are growingly used for disaster situational awareness.
A zero-sum game or an interactive frame? Iron competition between bacteria and humans in infection war

Abstract Iron is an essential trace element for both humans and bacteria. It plays a vital role in life, such as in redox reactions and electron transport. Strict regulatory mechanisms are necessary to maintain iron homeostasis because both excess and insufficient iron are harmful to life. Competition for iron is a war between humans and bacteria. To grow, reproduce, colonize, and successfully cause infection, pathogens have evolved various mechanisms for iron uptake from humans, principally Fe3+-siderophore and Fe2+-heme transport systems. Humans have many innate immune mechanisms that regulate the distribution of iron and inhibit bacterial iron uptake to help resist bacterial invasion and colonization. Meanwhile, researchers have invented detection test strips and coupled antibiotics with siderophores to create tools that take advantage of this battle for iron, to help eliminate pathogens. In this review, we summarize bacterial and human iron metabolism, competition for iron between humans and bacteria, siderophore sensors, antibiotics coupled with siderophores, and related phenomena. We also discuss how competition for iron can be used for diagnosis and treatment of infection in the future.
Polygenic score for Alzheimer’s disease identifies differential atrophy in hippocampal subfield volumes

Hippocampal subfield atrophy is a prime structural change in the brain, associated with cognitive aging and neurodegenerative diseases such as Alzheimer’s disease. Recent developments in genome-wide association studies (GWAS) have identified genetic loci that characterize the risk of hippocampal volume loss based on the processes of normal and abnormal aging. Polygenic risk scores are the genetic proxies mimicking the genetic role of the pre-existing vulnerabilities of the underlying mechanisms influencing these changes. Discriminating the genetic predispositions of hippocampal subfield atrophy between cognitive aging and neurodegenerative diseases will be helpful in understanding the disease etiology. In this study, we evaluated the polygenic risk of Alzheimer’s disease (AD PGRS) for hippocampal subfield atrophy in 1,086 individuals (319 cognitively normal (CN), 591 mild cognitively impaired (MCI), and 176 Alzheimer’s disease dementia (ADD)). Our results showed a stronger association of AD PGRS effect on the left hemisphere than on the right hemisphere for all the hippocampal subfield volumes in a mixed clinical population (CN+MCI+ADD). The subfields CA1, CA4, hippocampal tail, subiculum, presubiculum, molecular layer, GC-ML-DG, and HATA showed stronger AD PGRS associations with the MCI+ADD group than with the CN group. The subfields CA3, parasubiculum, and fimbria showed moderately higher AD PGRS associations with the MCI+ADD group than with the CN group. Our findings suggest that the eight subfield regions, which were strongly associated with AD PGRS are likely involved in the early stage ADD and a specific focus on the left hemisphere could enhance the early prediction of ADD.
Effectiveness and cost-effectiveness of universal school-based mindfulness training compared with normal school provision in reducing risk of mental health problems and promoting well-being in adolescence: the MYRIAD cluster randomised controlled trial

Background Systematic reviews suggest school-based mindfulness training (SBMT) shows promise in promoting student mental health. Objective The My Resilience in Adolescence (MYRIAD) Trial evaluated the effectiveness and cost-effectiveness of SBMT compared with teaching-as-usual (TAU). Methods MYRIAD was a parallel group, cluster-randomised controlled trial. Eighty-five eligible schools consented and were randomised 1:1 to TAU (43 schools, 4232 students) or SBMT (42 schools, 4144 students), stratified by school size, quality, type, deprivation and region. Schools and students (mean (SD); age range=12.2 (0.6); 11–14 years) were broadly UK population-representative. Forty-three schools (n=3678 pupils; 86.9%) delivering SBMT, and 41 schools (n=3572; 86.2%) delivering TAU, provided primary end-point data. SBMT comprised 10 lessons of psychoeducation and mindfulness practices. TAU comprised standard social-emotional teaching. Participant-level risk for depression, social-emotional-behavioural functioning and well-being at 1 year follow-up were the co-primary outcomes. Secondary and economic outcomes were included. Findings Analysis of 84 schools (n=8376 participants) found no evidence that SBMT was superior to TAU at 1 year. Standardised mean differences (intervention minus control) were: 0.005 (95% CI −0.05 to 0.06) for risk for depression; 0.02 (−0.02 to 0.07) for social-emotional-behavioural functioning; and 0.02 (−0.03 to 0.07) for well-being. SBMT had a high probability of cost-effectiveness (83%) at a willingness-to-pay threshold of £20 000 per quality-adjusted life year. No intervention-related adverse events were observed. Conclusions Findings do not support the superiority of SBMT over TAU in promoting mental health in adolescence. Clinical implications There is need to ask what works, for whom and how, as well as considering key contextual and implementation factors. Trial registration Current controlled trials ISRCTN86619085. This research was funded by the Wellcome Trust (WT104908/Z/14/Z and WT107496/Z/15/Z).
Predicting Stability of Community Members in Complex Networks

In this work, we analyse and predict the stability of communities in complex networks. We use a variant of closeness centrality, known as profile closeness, to measure the loyalty of a member towards its community. We show that the profile closeness is an adequate indicator of how communities evolve in a network. We investigate this in static as well as dynamic (temporal) networks and establish the relevance of profile closeness in predicting the evolution of a complex network. Keywords: Small world networks , Centrality , Community , Closeness , Clustering
Verifying Attention Robustness of Deep Neural Networks against Semantic Perturbations

In this paper, we propose the first verification method for attention robustness, i.e., the local robustness of the changes in the saliency-map against combinations of semantic perturbations. Specmcally, our method determines the range of the perturbation parameters (e.g., the amount of brightness change) that maintains the difference between the actual saliencymap change and the expected saliency-map change below a given threshold value. Our method is based on linear activation region traversals, focusing on the outermost boundary of attention robustness for scalability on larger deep neural networks.
The transcription factors VaERF16 and VaMYB306 interact to enhance resistance of grapevine to Botrytis cinerea infection

Abstract Botrytis cinerea is a fungus that infects cultivated grape (Vitis vinifera); the identification and characterization of resistance mechanisms in the host is of great importance for the grape industry. Here, we report that a transcription factor in the ethylene‐responsive factor (ERF) family (VaERF16) from Chinese wild grape (Vitis amurensis ‘Shuang You’) is expressed during B. cinerea infection and in response to treatments with the hormones ethylene and methyl jasmonate. Heterologous overexpression of VaERF16 in Arabidopsis thaliana substantially enhanced resistance to B. cinerea and the bacterium Pseudomonas syringae DC3000 via the salicylic acid and jasmonate/ethylene signalling pathways. Yeast two‐hybrid, bimolecular fluorescence complementation, and co‐immunoprecipitation assays indicated that VaERF16 interacts with the MYB family transcription factor VaMYB306. Overexpression of VaERF16 or VaMYB306 in grape leaves increased resistance to B. cinerea and caused an up‐regulation of the defence‐related gene PDF1.2, which encodes a defensin‐like protein. Conversely, silencing of either gene resulted in increased susceptibility to B. cinerea. Yeast one‐hybrid and dual‐luciferase assays indicated that VaERF16 increased the transcript levels of VaPDF1.2 by binding directly to the GCC box in its promoter. Notably, VaMYB306 alone did not bind to the VaPDF1.2 promoter, but the VaERF16–VaMYB306 transcriptional complex resulted in higher transcript levels of VaPDF1.2, suggesting that the proteins function through their mutual interaction. Elucidation of this regulatory module may be of value in enhancing resistance of grapevine to B. cinerea infection.
Economic planning approach for electric vehicle charging stations integrating traffic and power grid constraints

Large-scale electric vehicle (EV) charging will bring new challenges to coordination of grid and transportation. To facilitate large-scale EV applications, optimal locating and sizing of charging stations have become essential. For the investors of a charging station, economic benefit is the primary and the only objective. In this context, this work studies the siting and sizing of EV stations based on the optimal economic benefit. Benefit changes with time, location and capacity. A planning model method considering net present value (NPV) and life cycle cost (LCC) is proposed to determine the site and the size of the charging stations. The model has integrated distribution network constraint, the user constraint and the traffic flow captured constraint. Origin–destination lines and voronoi diagram are selected to calculate the traffic flow and the service region of each charging station, respectively. The quantum genetic algorithm was adopted for a better convergence of the planning model. Finally, a coupled 33-node distribution system and a 36-node transportation system are used to simulate various scenarios in the coupled networks of grid and transportation. The simulation results show that by introducing a planning model method considering NPV and LCC, the charging station economical benefits can be further improved.
BASS: Broad Network Based on Localized Stochastic Sensitivity

The training of the standard broad learning system (BLS) concerns the optimization of its output weights via the minimization of both training mean square error (MSE) and a penalty term. However, it degrades the generalization capability and robustness of BLS when facing complex and noisy environments, especially when small perturbations or noise appear in input data. Therefore, this work proposes a broad network based on localized stochastic sensitivity (BASS) algorithm to tackle the issue of noise or input perturbations from a local perturbation perspective. The localized stochastic sensitivity (LSS) prompts an increase in the network’s noise robustness by considering unseen samples located within a $Q$ -neighborhood of training samples, which enhances the generalization capability of BASS with respect to noisy and perturbed data. Then, three incremental learning algorithms are derived to update BASS quickly when new samples arrive or the network is deemed to be expanded, without retraining the entire model. Due to the inherent superiorities of the LSS, extensive experimental results on 13 benchmark datasets show that BASS yields better accuracies on various regression and classification problems. For instance, BASS uses fewer parameters (12.6 million) to yield 1% higher Top-1 accuracy in comparison to AlexNet (60 million) on the large-scale ImageNet (ILSVRC2012) dataset.
Shape-Aware Masking for Inpainting in Medical Imaging

Inpainting has recently been proposed as a successful deep learning technique for unsupervised medical image model discovery. The masks used for inpainting are generally independent of the dataset and are not tailored to perform on diﬀerent given classes of anatomy. In this work, we introduce a method for generating shape-aware masks for inpainting, which aims at learning the statistical shape prior. We hypothesize that although the variation of masks improves the generaliz-ability of inpainting models, the shape of the masks should follow the topology of the organs of interest. Hence, we propose an unsupervised guided masking approach based on an oﬀ-the-shelf inpainting model and a superpixel over-segmentation algorithm to generate a wide range of shape-dependent masks. Experimental results on abdominal MR image reconstruction show the superiority of our proposed masking method over standard methods using square-shaped or dataset of irregular shape masks.
Unsupervised Recognition of Informative Features via Tensor Network Machine Learning and Quantum Entanglement Variations

Given an image of a white shoe drawn on a blackboard, how are the white pixels deemed (say by human minds) to be informative for recognizing the shoe without any labeling information on the pixels? Here we investigate such a “white shoe” recognition problem from the perspective of tensor network (TN) machine learning and quantum entanglement. Utilizing a generative TN that captures the probability distribution of the features as quantum amplitudes, we propose an unsupervised recognition scheme of informative features with variations of entanglement entropy (EE) caused by designed measurements. In this way, a given sample, where the values of its features are statistically meaningless, is mapped to the variations of EE that statistically characterize the gain of information. We show that the EE variations identify the features that are critical to recognize this specific sample, and the EE itself reveals the information distribution of the probabilities represented by the TN model. The signs of the variations further reveal the entanglement structures among the features. We test the validity of our scheme on a toy dataset of strip images, the MNIST dataset of hand-drawn digits, the fashion-MNIST dataset of the pictures of fashion articles, and the images of nerve cord. Our scheme opens the avenue to the quantum-inspired and interpreted unsupervised learning, which can be applied to, e.g., image segmentation and object detection.
NEC: Speaker Selective Cancellation via Neural Enhanced Ultrasound Shadowing

In this paper, we propose NEC (Neural Enhanced Cancellation), a defense mechanism, which prevents unautho-rized microphones from capturing a target speaker’s voice. Compared with the existing scrambling-based audio cancellation approaches, NEC can selectively remove a target speaker’s voice from a mixed speech without causing interference to others. Specifically, for a target speaker, we design a Deep Neural Network (DNN) model to extract high-level speaker-specific but utterance-independent vocal features from his/her reference audios. When the microphone is recording, the DNN generates a shadow sound to cancel the target voice in real-time. Moreover, we modulate the audible shadow sound onto an ultrasound frequency, making it inaudible for humans. By leveraging the non-linearity of the microphone circuit, the microphone can accurately decode the shadow sound for target voice cancellation. We implement and evaluate NEC comprehensively with 8 smartphone microphones in different settings. The results show that NEC effectively mutes the target speaker at a microphone without interfering with other users’ normal conversations.