Miracle-dz/UniFAR-FTUs huggingface dataset:
Modelling three‐dimensional cancer‐associated cachexia and therapy: The molecular basis and therapeutic potential of interleukin‐6 transignalling blockade

Causes and mechanisms underlying cancer cachexia are not fully understood, and currently, no therapeutic approaches are available to completely reverse the cachectic phenotype. Interleukin‐6 (IL‐6) has been extensively described as a key factor in skeletal muscle physiopathology, exerting opposite roles through different signalling pathways.
Using the E. coli Alleleome in Strain Design

Leveraging observed variants in strain design is a promising technique for creating strains with specific properties. Adaptive laboratory evolution (ALE) experiments generate variants that enhance fitness under specific conditions and can contribute to application-specific strain designs. Further, the wild-type (WT) coding alleleome of an organism, the complete set of its genes’ WT alleles, can provide an additional amount and diversity of variants not yet accessible from the aggregation of ALE experiment results. This study used both an ALE mutation database (3093 genomes) and a large set of WT genomes (12,661 genomes) to explore the sequence solution space of genes involved in tolerance to 10 conditions of industrial importance. To accomplish this, ALE variants for 22 genes previously identified as potentially important for industrial chemical tolerance were collected and supplemented with all available variants from the WT coding alleleome. A total of 4879 variants were reintroduced and used in 10 selection experiments. Both ALE and WT contributed highly enriched variants, where the enrichment and benefits depended on the conditions, genes, and gene product regions. The results also revealed that variants not originating from the initial experiment could potentially confer substantially greater benefits. Additionally, ALE and WT variants rarely overlapped on AA positions, but their clustering did coincide with where highly enriched variants were ultimately located. For genes primarily hosting potential gain-of-function variations, substitutions predicted to have a conservative impact frequently outperformed more radical substitutions. Case studies demonstrated that maximizing the amount of variants enabled easier identification of variant trends, which in turn can be used to better understand areas and characteristics of genes that can be feasibly varied, representing what could be thought of as a genome design variable. The combination of ALE and WT variants is a promising approach for use in future projects to better constrain and ultimately achieve practical coverage in the exploration of feasible sequence solution space. Visual Abstract
Rethinking Sensors Modeling: Hierarchical Information Enhanced Traffic Forecasting

With the acceleration of urbanization, traffic forecasting has become an essential role in smart city construction. In the context of spatio-temporal prediction, the key lies in how to model the dependencies of sensors. However, existing works basically only consider the micro relationships between sensors, where the sensors are treated equally, and their macroscopic dependencies are neglected. In this paper, we argue to rethink the sensor's dependency modeling from two hierarchies: regional and global perspectives. Particularly, we merge original sensors with high intra-region correlation as a region node to preserve the inter-region dependency. Then, we generate representative and common spatio-temporal patterns as global nodes to reflect a global dependency between sensors and provide auxiliary information for spatio-temporal dependency learning. In pursuit of the generality and reality of node representations, we incorporate a Meta GCN to calibrate the regional and global nodes in the physical data space. Furthermore, we devise the cross-hierarchy graph convolution to propagate information from different hierarchies. In a nutshell, we propose a Hierarchical Information Enhanced Spatio-Temporal prediction method, HIEST, to create and utilize the regional dependency and common spatio-temporal patterns. Extensive experiments have verified the leading performance of our HIEST against state-of-the-art baselines. We publicize the code to ease reproducibility1.
XR Input Error Mediation for Hand-Based Input: Task and Context Influences a User’s Preference

Many XR devices use bare-hand gestures to reduce the need for handheld controllers. Such gestures, however, lead to false positive and false negative recognition errors, which detract from the user experience. While mediation techniques enable users to overcome recognition errors by clarifying their intentions via UI elements, little research has explored how mediation techniques should be designed in XR and how a user’s task and context may impact their design preferences. This research presents empirical studies about the impact of user perceived error costs on users’ preferences for three mediation technique designs, under different simulated scenarios that were inspired by real-life tasks. Based on a large-scale crowd-sourced survey and an immersive VR-based user study, our results suggest that the varying contexts within each task type can impact users’ perceived error costs, leading to different preferred mediation techniques. We further discuss the study implications of these results on future XR interaction design.
Digital twins of nonlinear dynamical systems: a perspective

Digital twins have attracted a great deal of recent attention from a wide range of fields. A basic requirement for digital twins of nonlinear dynamical systems is the ability to generate the system evolution and predict potentially catastrophic emergent behaviors so as to provide early warnings. The digital twin can then be used for system “health” monitoring in real time and for predictive problem solving. For example, if the digital twin forecasts a possible system collapse in the future due to parameter drifting as caused by environmental changes or perturbations, an optimal control strategy can be devised and executed as early intervention to prevent the collapse. Two approaches exist for constructing digital twins of nonlinear dynamical systems: sparse optimization and machine learning. The basics of these two approaches are described and their advantages and caveats are discussed.
Living Longer But Frailer? Temporal Trends in Life Expectancy and Frailty in Older Swedish Adults

Abstract Background This study aims to examine temporal trends in frailty state transitions, and years spent frail, in older Swedish adults. Methods We followed the Swedish National Study on Aging and Care in Kungsholmen participants from baseline (2001–2004) for 15 (median: 9.6) years. A 40-deficit frailty index (FI) was constructed to identify 3 frailty states: robust (FI ≤ 0.125), mild frailty (0.125 < FI ≤ 0.25), and moderate and severe frailty (FI > 0.25). Multistate survival analyses were implemented to obtain hazard ratios (HRs) for frailty state transitions, with birth year and sex as predictors. To examine temporal trends, frailty state-specific life expectancies at age 60 were forecasted for robust persons born in different years (1900, 1910, 1920, 1930, and 1940), also by sex. Results At baseline, the 2 941 participants’ mean age was 75 years and 65% were women. Predicted life expectancy and time spent frail from age 60 followed an increasing trend by birth year. Hazards of transitioning from mild frailty to death (HR: 0.89; 95% confidence interval [CI]: 0.83–0.97) and moderate and severe frailty to death (HR: 0.98; 95% CI: 0.97–1.00) were lower for those born later. Women were less likely to transition from robust to mild frailty (HR: 0.81; 95% CI: 0.70–0.93), mild frailty to moderate and severe frailty (HR: 0.80; 95% CI: 0.68–0.93), and moderate and severe frailty to death (HR: 0.68; 95% CI: 0.59–0.78), but spent more time frail. Conclusions Our results point to an expansion of time spent frail among older Swedish adults over time.
Necroptosis in Organ Transplantation: Mechanisms and Potential Therapeutic Targets

Organ transplantation remains the only treatment option for patients with end-stage organ dysfunction. However, there are numerous limitations that challenge its clinical application, including the shortage of organ donations, the quality of donated organs, injury during organ preservation and reperfusion, primary and chronic graft dysfunction, acute and chronic rejection, infection, and carcinogenesis in post-transplantation patients. Acute and chronic inflammation and cell death are two major underlying mechanisms for graft injury. Necroptosis is a type of programmed cell death involved in many diseases and has been studied in the setting of all major solid organ transplants, including the kidney, heart, liver, and lung. It is determined by the underlying donor organ conditions (e.g., age, alcohol consumption, fatty liver, hemorrhage shock, donation after circulatory death, etc.), preservation conditions and reperfusion, and allograft rejection. The specific molecular mechanisms of necroptosis have been uncovered in the organ transplantation setting, and potential targeting drugs have been identified. We hope this review article will promote more clinical research to determine the role of necroptosis and other types of programmed cell death in solid organ transplantation to alleviate the clinical burden of ischemia–reperfusion injury and graft rejection.
Evaluation of bergenin in amylase enzyme inhibition and lipid uptake in liver cells

Changes in the global economy contribute to a sedentary lifestyle and higher caloric intake, increasing the incidence of metabolic diseases. It is estimated that the number of individuals diagnosed with metabolic diseases will increase by 25% by 2030 and 51% by 2045. Natural products have been tested in metabolic diseases with positive results, as shown by bergenin. Thus, this work aimed to evaluate the possible effects of bergenin in models of metabolic disease through in silico and in vitro experimental procedures. Bergenin showed the ability to inhibit protein glycation by 19.88 ± 4.22% in the oxidative pathway and 8.32 ± 1.85% in the non-oxidative pathway. This study showed for the first time that bergenin has the potential to inhibit the α-amylase enzyme (33.89 ± 0.96%) using in silico and in vitro assays. Bergenin was not able to significantly inhibit lipid uptake by the Oil Red method. After these results, it is understood that bergenin has the potential to be better studied as a therapeutic option in metabolic diseases. However, additional in vivo studies are needed to better elucidate the possible pharmacological effects of bergenin in metabolic diseases.
A Wideband and Flexible Testing System Based on a Quasi-Coaxial Structure and Machine Learning

In this work, a wideband system with flexible test sample sizes is proposed for retrieving the complex permittivity values of materials. It includes a well-designed testing holder and a customized machine learning-based reconstruction algorithm for the inversion. The holder is implemented on a quasi-coaxial structure consisting of three parts, i.e., a quasi-coaxial structure and two coaxial connectors (inner conductors joined together). The testing procedure consists of a simple calibration without additional assembling, a measurement without dielectric under testing (DUT), placement of the DUT, and another measurement with the DUT. This procedure suppresses system complexity and mitigates systematic errors. The reconstruction algorithm is developed based on a Gaussian process regression (GPR) machine learning technique to determine the complex permittivity of the DUT in the partially filled holder. The reconstruction results from numerical simulations and measured data with several nonmagnetic dielectric materials demonstrate the effectiveness of the proposed method.
Computer Analysis Of Images And Patterns

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An Empirical Study of Malicious Code In PyPI Ecosystem

PyPI provides a convenient and accessible package management platform to developers, enabling them to quickly implement specific functions and improve work efficiency. However, the rapid development of the PyPI ecosystem has led to a severe problem of malicious package propagation. Malicious developers disguise malicious packages as normal, posing a significant security risk to end-users. To this end, we conducted an empirical study to understand the characteristics and current state of the malicious code lifecycle in the PyPI ecosystem. We first built an automated data collection framework and collated a multi-source malicious code dataset containing 4,669 malicious package files. We preliminarily classified these malicious code into five categories based on malicious behaviour characteristics. Our research found that over 50 % of malicious code exhibits multiple malicious behaviours, with information stealing and command execution being particularly prevalent. In addition, we observed several novel attack vectors and anti-detection techniques. Our analysis revealed that 74.81 % of all malicious packages successfully entered end-user projects through source code installation, thereby increasing security risks. A real-world investigation showed that many reported malicious packages persist in PyPI mirror servers globally, with over 72 % remaining for an extended period after being discovered. Finally, we sketched a portrait of the malicious code lifecycle in the PyPI ecosystem, effectively reflecting the characteristics of malicious code at different stages. We also present some suggested mitigations to improve the security of the Python open-source ecosystem.
TRAVID: An End-to-End Video Translation Framework

In today's globalized world, effective communication with people from diverse linguistic backgrounds has become increasingly crucial. While traditional methods of language translation, such as written text or voice-only translations, can accomplish the task, they often fail to capture the complete context and nuanced information conveyed through nonverbal cues like facial expressions and lip movements. In this paper, we present an end-to-end video translation system that not only translates spoken language but also synchronizes the translated speech with the lip movements of the speaker. Our system focuses on translating educational lectures in various Indian languages, and it is designed to be effective even in low-resource system settings. By incorporating lip movements that align with the target language and matching them with the speaker's voice using voice cloning techniques, our application offers an enhanced experience for students and users. This additional feature creates a more immersive and realistic learning environment, ultimately making the learning process more effective and engaging.
Deep Reinforcement Learning for Infinite Horizon Mean Field Problems in Continuous Spaces

We present the development and analysis of a reinforcement learning (RL) algorithm designed to solve continuous-space mean field game (MFG) and mean field control (MFC) problems in a unified manner. The proposed approach pairs the actor-critic (AC) paradigm with a representation of the mean field distribution via a parameterized score function, which can be efficiently updated in an online fashion, and uses Langevin dynamics to obtain samples from the resulting distribution. The AC agent and the score function are updated iteratively to converge, either to the MFG equilibrium or the MFC optimum for a given mean field problem, depending on the choice of learning rates. A straightforward modification of the algorithm allows us to solve mixed mean field control games (MFCGs). The performance of our algorithm is evaluated using linear-quadratic benchmarks in the asymptotic infinite horizon framework.
Discussion on the mechanism of action of Salvia miltiorrhiza in the treatment of prostate cancer based on network pharmacology

: The purpose of this study was to explore the mechanism of action of Salvia miltiorrhiza in the treatment of prostate cancer (PCa) based on the network pharmacology approach, and to provide a basis for rational clinical use and cellular experiments. The method used in this study was to obtain the main active components and corresponding targets of Salvia miltiorrhiza through the Chinese Medicine System Pharmacology Database and Analysis Platform (TCMSP), Perl, UniProt and other databases. Disease targets for PCa were obtained from GeneCards, OMIM, TTD and DrugBank databases. Using bioinformatics online database mapping tool, Venn diagram was drawn to screen the cross targets of Salvia miltiorrhiza and PCa active components. active components of Salvia miltiorrhiza-PCa related target network and cross target protein interaction (PPI) network were constructed using STRING platform, and Cytoscape 3.8. 2 software was used to screen key targets, and R 4.3. 0 software was used to perform gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis. A total of 58 active components of Salvia miltiorrhiza and their corresponding target genes and 2142
Road Descriptors for Fast Global Localization on Rural Roads Using OpenStreetMap

Accurate pose estimation is a fundamental ability that all mobile robots must posses in order to navigate a given environment. Much like a human, this ability is dependent on the robot’s understanding of a given scene. For autonomous vehicles (AVs), detailed 3D maps created beforehand are widely used to augment the perceptive abilities and estimate pose based on current sensor measurements. This approach, however, is less suited for rural communities that are sparsely connected and cover large areas. Topological maps such as OpenStreetMap have proven to be a useful alternative in these situations. However, vehicle localization using these maps is non-trivial, particularly for the global localization task, where the map spans large areas. To deal with this challenge, we propose road descriptors along with an initialization technique for localization that allows for fast global pose estimation. We test our algorithms on (real world) maps and benchmark them against other map-based localization as well as SLAM algorithms. Our results show that the proposed method can narrow down the pose to within 50 cm of the ground truth significantly faster than the state-of-the-art methods.
Non-Smooth Speed Control of Switched Reluctance Motor Based on Generalized Proportional Integral Observer

This paper investigates the speed regulation control of switched reluctance motor (SRM) systems. To improve the antidisturbance performance of SRM, a composite non-smooth control strategy is proposed. First, the structure of SRM is analyzed, and a simplified nonlinear model is obtained based on a segmented representation of the varying phase inductance. A virtual control function is introduced to represent the nonlinear part of the torque equation, whose inverse function is cascaded to linearized the nonlinear model. Second, a generalized proportional integral observer (GPIO) is constructed to estimate the lumped disturbance of the system, which is used for feedforward compensation design. Finally, a composite speed controller is designed based on a combination of finite time proportional feedback and feed-forward compensation based on GPIO (FTP+GPIO). The speed error closed-loop system can be regarded as a first-order finite time control system with bounded disturbances. Strict analysis shows that the proposed scheme can improve the anti-disturbance performance of the closed-loop system. The effectiveness of the proposed method is verified by simulation results. Moreover, it is compared with proportional feedback combining feed-forward compensation (P+GPIO) method and proportional integral (PI) control method.
Partition-A-Medical-Image: Extracting Multiple Representative Sub-regions for Few-shot Medical Image Segmentation

Few-shot Medical Image Segmentation (FSMIS) is a more promising solution for medical image segmentation tasks where high-quality annotations are naturally scarce. However, current mainstream methods primarily focus on extracting holistic representations from support images with large intra-class variations in appearance and background, and encounter difficulties in adapting to query images. In this work, we present an approach to extract multiple representative sub-regions from a given support medical image, enabling fine-grained selection over the generated image regions. Specifically, the foreground of the support image is decomposed into distinct regions, which are subsequently used to derive region-level representations via a designed Regional Prototypical Learning (RPL) module. We then introduce a novel Prototypical Representation Debiasing (PRD) module based on a two-way elimination mechanism which suppresses the disturbance of regional representations by a self-support, Multi-direction Self-debiasing (MS) block, and a support-query, Interactive Debiasing (ID) block. Finally, an Assembled Prediction (AP) module is devised to balance and integrate predictions of multiple prototypical representations learned using stacked PRD modules. Results obtained through extensive experiments on three publicly accessible medical imaging datasets demonstrate consistent improvements over the leading FSMIS methods. The source code is available at https://github.com/YazhouZhu19/PAMI.
Un posible síndrome de Morgagni- Stewart-Morel en un esqueleto de la colección de restos óseos de la Escuela de Medicina Legal de Madrid

The oseal remains’ study in Sciences like Forensic Anthropology, Paleopathology or Legal Medicine is utilized in order to typify the age, sex, height and race, as well as identify possible individuals’ pathologies suffered in their lives and even try to find out the possible causes of death. The objectives raised were the measurement, description and study of bone remains pertaining to the Universidad Complutense de Madrid’s Facultad de Medicina’s collection, using the Medicina Legal’s School’s protocol, to try to find out its age, sex, height, race and possible illness suffered. The characteristics and work's data, point to the conclusion that the remains belong to a mature Caucasoid woman who could suffer the called Morgagni-Stewart-Morel Syndrome or Hyperostosis Frontalis Interna.