Miracle-dz/UniFAR-FTUs huggingface dataset:
Changes in attitudes toward alcohol control policies in Lithuania: findings from two representative surveys in 2015 and 2020

ABSTRACT Background: A set of evidence‐based alcohol control policy measures was adopted in the period 2016–2020 in Lithuania. The present study fills a knowledge gap on how changes in alcohol control policy are associated with attitudes toward different alcohol policy measures. Objective: This study aims to explore whether support for key alcohol control policy measures in Lithuania declined following implementation of alcohol control measures. Methods: Data came from the Standard European Alcohol Survey. Two representative surveys with the same questionnaire, were conducted in Lithuania in 2015 (N = 1513, 51.7% female, response rate was 38.9%) and 2020 (N = 1015, 50.6% female, response rate was 38.0%). Multi-stage stratified probability sampling was applied. Surveys were carried out using computer-assisted face-to-face interviews, descriptive statistics and multiple logistic regression analyses was applied. We used a binomial logistic regression analysis and the Pearson chi-square test. Results: There was a significant decline in a proportion of respondents who agreed that the number of alcohol selling places should be kept low (OR: 0.84, p = .032), alcohol prices should be kept high (OR: 0.83, p = .027), and the police should be allowed to randomly check whether the driver is sober (OR: 0.65, p < .001). The proportion of respondents who agree that individuals are responsible enough with their drinking significantly declined (OR: 0.76, p = .003). Conclusion: Support for restrictions on alcohol-selling points, increase in alcohol price, and random alcohol testing of drivers declined following the adoption of new alcohol control policy measures. Our findings might be beneficial for policy-makers planning alcohol control policies and information campaigns.
Fungal COP9 signalosome assembly requires connection of two trimeric intermediates for integration of intrinsic deneddylase

Significance Controlled protein degradation is a key element for adaptation, stress response, development, and growth of eukaryotic organisms. The conserved protein complex COP9 signalosome (CSN) functions as an important regulator of this process. We took advantage of the filamentous fungus Aspergillus nidulans that has a complete eight-subunit CSN, which, in contrast to higher eukaryotes, is not essential. Dissection of the assembly pathway, the control of protein amounts, and nuclear entry of subunits or subcomplexes revealed a complex interconnected regulation. Our findings show that the CSN is formed by two trimeric subcomplexes, which are further assembled to a heptamer before the enzymatically active subunit is integrated. This study serves as a blueprint for CSN assembly in higher eukaryotes.
Recent advances towards natural plants as potential inhibitors of SARS-Cov-2 targets

Abstract Context Coronavirus disease 2019 (COVID-19), caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), is still ongoing and currently the most striking epidemic disease. With the rapid global spread of SARS-CoV-2 variants, new antivirals are urgently needed to avert a more serious crisis. Inhibitors from traditional medicines or natural plants have shown promising results to fight COVID-19 with different mechanisms of action. Objectives To provide comprehensive and promising approaches to the medical community in the fight against this epidemic by reviewing potential plant-derived anti-SARS-CoV-2 inhibitors. Methods Structural databases such as TCMSP (http://lsp.nwu.edu.cn/tcmsp.php), TCM Database @ Taiwan (http://tcm.cmu.edu.tw/), BATMAN-TCM (http://bionet.ncpsb.org/batman-tcm/) and TCMID (http://www.megabionet.org/tcmid/), as well as PubMed, Sci Finder, Research Gate, Science Direct, CNKI, Web of Science and Google Scholar were searched for relevant articles on TCMs and natural products against SARS-CoV-2. Results Seven traditional Chinese medicines formulas have unique advantages in regulating the immune system for treating COVID-19. The plant-derived natural compounds as anti-SARS-CoV-2 inhibitors were identified based on 5 SARS-CoV-2 key proteins, namely, angiotensin-converting enzyme 2 (ACE2), 3 C-like protease (3CLpro), papain-like protease (PLpro), spike (S) protein, and nucleocapsid (N) protein. Conclusions A variety of natural products, such as flavonoids, terpenoids, phenols, and alkaloids, were identified, which could be used as potential SASR-Cov-2 inhibitors. These shed new light on the efficient discovery of SASR-Cov-2 inhibitors from natural products.
Cycle exercise training and muscle mass: A preliminary investigation of 17 lower limb muscles in older men

Cycling exercise in older individuals is beneficial for the cardiovascular system and quadriceps muscles, including partially reversing the age‐related loss of quadriceps muscle mass. However, the effect of cycling exercise on the numerous other lower limb muscles is unknown. Six older men (74 ± 8 years) underwent MRI before and after 12‐weeks of progressive aerobic cycle exercise training (3–4 days/week, 60–180 min/week, 60%–80% heart rate reserve, VO2max: +13%) for upper (rectus femoris, vastii, adductor longus, adductor magnus, gracilis, sartorius, biceps femoris long head, biceps femoris short head, semimembranosus, semitendinosus) and lower (anterior tibial, posterior tibialis, peroneals, flexor digitorum longus, lateral gastrocnemius, medial gastrocnemius, soleus) leg muscle volumes. In the upper leg, cycle exercise training induced hypertrophy (p ≤ 0.05) in the vastii (+7%) and sartorius (+6%), with a trend to increase biceps femoris short head (+5%, p = 0.1). Additionally, there was a trend to decrease muscle volume in the adductor longus (−6%, p = 0.1) and biceps femoris long head (−5%, p = 0.09). In the lower leg, all 7 muscle volumes assessed were unaltered pre‐ to post‐training (−2% to −3%, p > 0.05). This new evidence related to cycle exercise training in older individuals clarifies the specific upper leg muscles that are highly impacted, while revealing all the lower leg muscles do not appear responsive, in the context of muscle mass and sarcopenia. This study provides information for exercise program development in older individuals, suggesting other specific exercises are needed for the rectus femoris and adductors, certain hamstrings, and the anterior and posterior lower leg muscles to augment the beneficial effects of cycling exercise for older adults.
Determinants of measles persistence in Beijing, China: A modelling study

Abstract In Beijing, the capital of China, routine measles mass vaccination has been in place for decades with high coverage; and since the 2000s, catch-up vaccination programmes have been implemented for migrant workers coming to the city. However, measles epidemics in Beijing persisted. Here, we explored the contributing factors of persistent measles transmission in Beijing using an epidemic model in conjunction with a particle filter. Model inputs included data on birth, death, migration, and vaccination. We formulated a series of hypotheses covering the impact of migrant influx, early waning of maternal immunity, and increased mixing among infants; we compared the plausibility of the hypotheses based on model fit to age-grouped, weekly measles incidence data from January 2005 to December 2014, and out-of-fit prediction during 2015–2019. Our best models showed close agreement with the data, and the out-of-fit prediction generally captured the trend of measles incidence from 2015 to 2019. We found that large influx of migrants with considerably higher susceptibility likely contributed to the persistent measles transmission in Beijing. Our findings suggest that stronger catch-up vaccination programmes for migrants may help eliminate measles transmission in Beijing.
Design and Performance Analysis of a Geographic Routing Protocol for Highly Dynamic MANETs

Efficient multi-hop routing has become important for airborne telemetry networks. The highly dynamic nature in these scenarios results in short-lived links. Geographic-based routing has an advantage over topology-based routing to make rapid forwarding decisions based on neighbor and destination position. The AeroRP geographic routing protocol is detailed, which uses a heuristic metric for forwarding decisions that takes transmission range and a neighbor’s location and velocity into consideration. The main contributions of this work include detailing and finalizing the routing decision metrics, design, and simulation implementation of AeroRP. The analysis of the simulations shows AeroRP has several advantages over other MANET routing protocols and offers tradeoffs for different performance metrics in the form of different AeroRP modes. Specifically, AeroRP yields higher accuracy than all compared routing protocols and various AeroRP modes can be chosen depending on how packet delivery and delay are prioritized.
Eye movement patterns during viewing face images with neutral expressions in patients with early‐stage Alzheimer's disease and amnestic mild cognitive impairment

Alzheimer’s disease (AD) neuropathology affects the brain regions responsible for visuospatial skills. Accumulating evidence points to visual difficulties involving face processing in AD and amnestic mild cognitive impairment (aMCI). No study has so far examined eye movement patterns when viewing faces with neutral expressions in patients with AD.
Buthionine sulfoximine and chemoresistance in cancer treatments: a systematic review with meta-analysis of preclinical studies

ABSTRACT Buthionine sulfoximine (BSO) is a synthetic amino acid that blocks the biosynthesis of reduced glutathione (GSH), an endogenous antioxidant cellular component present in tumor cells. GSH levels have been associated with tumor cell resistance to chemotherapeutic drugs and platinum compounds. Consequently, by depleting GSH, BSO enhances the cytotoxicity of chemotherapeutic agents in drug-resistant tumors. Therefore, the aim of this study was to conduct a systematic review with meta-analysis of preclinical studies utilizing BSO in cancer treatments. The systematic search was carried out using the following databases: PubMed, Web of Science, Scopus, and EMBASE up until March 20, 2023, in order to collect preclinical studies that evaluated BSO, alone or in association, as a strategy for antineoplastic therapy. One hundred nine investigations were found to assess the cytotoxic potential of BSO alone or in combination with other compounds. Twenty-one of these met the criteria for performing the meta-analysis. The evidence gathered indicated that BSO alone exhibits cytotoxic activity. However, this compound is generally used in combination with other antineoplastic strategies, mainly chemotherapy ones, to improve cytotoxicity to carcinogenic cells and treatment efficacy. Finally, this review provides important considerations regarding BSO use in cancer treatment conditions, which might optimize future studies as a potential adjuvant antineoplastic therapeutic tool.
An alternative to SVM Method for Data Classification

Support vector machine (SVM), is a popular kernel method for data classification that demonstrated its efficiency for a large range of practical applications. The method suffers, however, from some weaknesses including; time processing, risk of failure of the optimization process for high dimension cases, generalization to multi-classes, unbalanced classes, and dynamic classification. In this paper an alternative method is proposed having a similar performance, with a sensitive improvement of the aforementioned shortcomings. The new method is based on a minimum distance to optimal subspaces containing the mapped original classes.
Meta-Stock: Task-Difficulty-Adaptive Meta-learning for Sub-new Stock Price Prediction

Sub-new stock price prediction, forecasting the price trends of stocks listed less than one year, is crucial for effective quantitative trading. While deep learning methods have demonstrated effectiveness in predicting old stock prices, they require large training datasets unavailable for sub-new stocks. In this paper, we propose Meta-Stock: a task-difficulty-adaptive meta-learning approach for sub-new stock price prediction. Leveraging prediction tasks formulated by old stocks, our meta-learning method aims to acquire the fast generalization ability that can be further adapted to sub-new stock price prediction tasks, thereby solving the data scarcity of sub-new stocks. Moreover, we enhance the meta-learning process by incorporating an adaptive learning strategy sensitive to varying task difficulties. Through wavelet transform, we extract high-frequency coefficients to manifest stock price volatility. This allows the meta-learning model to assign gradient weights based on volatility-quantified task difficulty. Extensive experiments on datasets collected from three stock markets spanning twenty-two years prove that our Meta-Stock significantly outperforms previous methods and manifests strong applicability in real-world stock trading. Besides, we evaluate the reasonability of the task difficulty quantification and the effectiveness of the adaptive learning strategy.
Beyond Discriminative Regions: Saliency Maps as Alternatives to CAMs for Weakly Supervised Semantic Segmentation

In recent years, several Weakly Supervised Semantic Segmentation (WS3) methods have been proposed that use class activation maps (CAMs) generated by a classifier to produce pseudo-ground truths for training segmentation models. While CAMs are good at highlighting discriminative regions (DR) of an image, they are known to disregard regions of the object that do not contribute to the classifier's prediction, termed non-discriminative regions (NDR). In contrast, attribution methods such as saliency maps provide an alternative approach for assigning a score to every pixel based on its contribution to the classification prediction. This paper provides a comprehensive comparison between saliencies and CAMs for WS3. Our study includes multiple perspectives on understanding their similarities and dissimilarities. Moreover, we provide new evaluation metrics that perform a comprehensive assessment of WS3 performance of alternative methods w.r. t. CAMs. We demonstrate the effectiveness of saliencies in addressing the limitation of CAMs through our empirical studies on benchmark datasets. Furthermore, we propose random cropping as a stochastic aggregation technique that improves the performance of saliency, making it a strong alternative to CAM for WS3.
Smoothing of Binary Codes, Uniform Distributions, and Applications

The action of a noise operator on a code transforms it into a distribution on the respective space. Some common examples from information theory include Bernoulli noise acting on a code in the Hamming space and Gaussian noise acting on a lattice in the Euclidean space. We aim to characterize the cases when the output distribution is close to the uniform distribution on the space, as measured by the Rényi divergence of order α∈(1,∞]. A version of this question is known as the channel resolvability problem in information theory, and it has implications for security guarantees in wiretap channels, error correction, discrepancy, worst-to-average case complexity reductions, and many other problems. Our work quantifies the requirements for asymptotic uniformity (perfect smoothing) and identifies explicit code families that achieve it under the action of the Bernoulli and ball noise operators on the code. We derive expressions for the minimum rate of codes required to attain asymptotically perfect smoothing. In proving our results, we leverage recent results from harmonic analysis of functions on the Hamming space. Another result pertains to the use of code families in Wyner’s transmission scheme on the binary wiretap channel. We identify explicit families that guarantee strong secrecy when applied in this scheme, showing that nested Reed–Muller codes can transmit messages reliably and securely over a binary symmetric wiretap channel with a positive rate. Finally, we establish a connection between smoothing and error correction in the binary symmetric channel.
Masked Cross-image Encoding for Few-shot Segmentation

Few-shot segmentation (FSS) is a dense prediction task that aims to infer the pixel-wise labels of unseen classes using only a limited number of annotated images. The key challenge in FSS is to classify the labels of query pixels using class prototypes learned from the few labeled support exemplars. Prior approaches to FSS have typically focused on learning class-wise descriptors independently from support images, thereby ignoring the rich contextual information and mutual dependencies among support-query features. To address this limitation, we propose a joint learning method termed Masked Cross-Image Encoding (MCE), which is designed to capture common visual properties that describe object details and to learn bidirectional inter-image dependencies that enhance feature interaction. MCE is more than a visual representation enrichment module; it also considers cross-image mutual dependencies and implicit guidance. Experiments on FSS benchmarks PASCAL-5i and COCO-20i demonstrate the advanced meta-learning ability of the proposed method.
AdS/CFT Correspondence and Double-Copy Relations: A Unified Perspective on QCD and QED

This study utilizes the double copy relation to derive on-shell gravity as product of gauge fields in Anti-de Sitter(AdS)space. Comparing with its corresponding conformal field theory (CFT) gauge field, we establish a unique connection between CFT gauge field and another gauge field in on-shell AdS space, and confirmed it as a strong-weak correspondence having associated thermodynamic properties. Also, it provides gravity theory coupling to a tensor field instead of a scalar field, to be more complex but perhaps more accurate.
Enriched homotopy-coherent structures

We introduce a general notion of enrichment for homotopy-coherent algebraic structures described by Segal conditions, using the framework of"algebraic patterns"developed in our previous work. This recovers several known examples of enriched structures, including enriched $\infty$-categories, enriched $\infty$-operads, and enriched $\infty$-properads. As new examples we discuss enriched modular $\infty$-operads, enriched $n$-fold $\infty$-categories, and a non-iterative definition of enriched $(\infty,n)$-categories.
A Simple Framework for Multi-mode Spatial-Temporal Data Modeling

Spatial-temporal data modeling aims to mine the underlying spatial relationships and temporal dependencies of objects in a system. However, most existing methods focus on the modeling of spatial-temporal data in a single mode, lacking the understanding of multiple modes. Though very few methods have been presented to learn the multi-mode relationships recently, they are built on complicated components with higher model complexities. In this paper, we propose a simple framework for multi-mode spatial-temporal data modeling to bring both effectiveness and efficiency together. Specifically, we design a general cross-mode spatial relationships learning component to adaptively establish connections between multiple modes and propagate information along the learned connections. Moreover, we employ multi-layer perceptrons to capture the temporal dependencies and channel correlations, which are conceptually and technically succinct. Experiments on three real-world datasets show that our model can consistently outperform the baselines with lower space and time complexity, opening up a promising direction for modeling spatial-temporal data. The generalizability of the cross-mode spatial relationships learning module is also validated.
Digital Twin-Oriented Complex Networked Systems based on Heterogeneous node features and interaction rules

This study proposes an extendable modelling framework for Digital Twin-Oriented Complex Networked Systems (DT-CNSs) with a goal of generating networks that faithfully represent real systems. Modelling process focuses on (i) features of nodes and (ii) interaction rules for creating connections that are built based on individual node's preferences. We conduct experiments on simulation-based DT-CNSs that incorporate various features and rules about network growth and different transmissibilities related to an epidemic spread on these networks. We present a case study on disaster resilience of social networks given an epidemic outbreak by investigating the infection occurrence within specific time and social distance. The experimental results show how different levels of the structural and dynamics complexities, concerned with feature diversity and flexibility of interaction rules respectively, influence network growth and epidemic spread. The analysis revealed that, to achieve maximum disaster resilience, mitigation policies should be targeted at nodes with preferred features as they have higher infection risks and should be the focus of the epidemic control.
Convoifilter: A case study of doing cocktail party speech recognition

This paper presents an end-to-end model designed to improve automatic speech recognition (ASR) for a particular speaker in a crowded, noisy environment. The model utilizes a single-channel speech enhancement module that isolates the speaker's voice from background noise (ConVoiFilter) and an ASR module. The model can decrease ASR's word error rate (WER) from 80% to 26.4% through this approach. Typically, these two components are adjusted independently due to variations in data requirements. However, speech enhancement can create anomalies that decrease ASR efficiency. By implementing a joint fine-tuning strategy, the model can reduce the WER from 26.4% in separate tuning to 14.5% in joint tuning. We openly share our pre-trained model to foster further research hf.co/nguyenvulebinh/voice-filter.
Development of a Novel Quantum Pre-processing Filter to Improve Image Classification Accuracy of Neural Network Models

This paper proposes a novel quantum pre-processing filter (QPF) to improve the image classification accuracy of neural network (NN) models. A simple four qubit quantum circuit that uses Y rotation gates for encoding and two controlled NOT gates for creating correlation among the qubits is applied as a feature extraction filter prior to passing data into the fully connected NN architecture. By applying the QPF approach, the results show that the image classification accuracy based on the MNIST (handwritten 10 digits) and the EMNIST (handwritten 47 class digits and letters) datasets can be improved, from 92.5% to 95.4% and from 68.9% to 75.9%, respectively. These improvements were obtained without introducing extra model parameters or optimizations in the machine learning process. However, tests performed on the developed QPF approach against a relatively complex GTSRB dataset with 43 distinct class real-life traffic sign images showed a degradation in the classification accuracy. Considering this result, further research into the understanding and the design of a more suitable quantum circuit approach for image classification neural networks could be explored utilizing the baseline method proposed in this paper.
Analyzing Complex Systems with Cascades Using Continuous-Time Bayesian Networks

Interacting systems of events may exhibit cascading behavior where events tend to be temporally clustered. While the cascades themselves may be obvious from the data, it is important to understand which states of the system trigger them. For this purpose, we propose a modeling framework based on continuous-time Bayesian networks (CTBNs) to analyze cascading behavior in complex systems. This framework allows us to describe how events propagate through the system and to identify likely sentry states, that is, system states that may lead to imminent cascading behavior. Moreover, CTBNs have a simple graphical representation and provide interpretable outputs, both of which are important when communicating with domain experts. We also develop new methods for knowledge extraction from CTBNs and we apply the proposed methodology to a data set of alarms in a large industrial system.