Miracle-dz/UniFAR-FTUs huggingface dataset:
Clinical implications of antitelomeric drugs with respect to the nontelomeric functions of telomerase in cancer

: Telomerase is responsible for maintaining the length of telomeres at the ends of chromosomes. Although most somatic cells do not exhibit telomerase activity, it is reactivated in approximately 85% of cancers. This simple and attractive phenomenon steers the development of anticancer drugs targeting telomeres and telomerase. Recent studies have been revealing extratelomeric roles of telomerase in normal tissues, affecting processes that are critical for survival and aging of organisms. In this review, we will discuss the current therapeutic strategies targeting telomeres and telomerase and evaluate their potential advantages and risks with respect to nontelomeric functions.
Cross-modal Networks and Dual Softmax Operation for MediaEval NewsImages 2022

Matching images to articles is challenging and can be considered a special version of the cross-media retrieval problem. This working note paper presents our solution for the MediaEval NewsImages benchmarking task. We investigated the performance of two cross-modal networks, a pre-trained network and a trainable one, the latter originally developed for text-video retrieval tasks and adapted to the NewsImages task. Moreover, we utilize a method for revising the similarities produced by either one of the cross-modal networks, i
Impact of Dark and Light Graphical User Interface Modes on System Usability: Preliminary Findings of an Experimental Study

In recent years, an increasing number of software systems offer users the ability to switch from the more conventional light color background graphical user interface (GUI) to the dark mode. In this mode, black, dark grey, or other similar colors are used for background, while text and other interface objects are typically displayed in white or other light colors. However, the impact of the mode on user experience and usability of systems is still under-researched, meaning that the dark mode is often implemented without knowing if it won’t cause unwanted effects. This is especially important on the web, where GUI design trends are particularly dynamic, while interface changes can be implemented relatively quickly and completely one-sidedly by system owners. In this paper, preliminary results of the study aimed at determining the connections between GUI mode and web system usage efficiency or productivity are presented. The study was conducted in the form of an experimental survey and provided valuable insights that could be valuable to GUI developers and web system owners when making informed decisions on usage of dark and/or light color modes.
Evaluation of AlphaFold antibody–antigen modeling with implications for improving predictive accuracy

High resolution antibody–antigen structures provide critical insights into immune recognition and can inform therapeutic design. The challenges of experimental structural determination and the diversity of the immune repertoire underscore the necessity of accurate computational tools for modeling antibody–antigen complexes. Initial benchmarking showed that despite overall success in modeling protein–protein complexes, AlphaFold and AlphaFold‐Multimer have limited success in modeling antibody–antigen interactions. In this study, we performed a thorough analysis of AlphaFold's antibody–antigen modeling performance on 427 nonredundant antibody–antigen complex structures, identifying useful confidence metrics for predicting model quality, and features of complexes associated with improved modeling success. Notably, we found that the latest version of AlphaFold improves near‐native modeling success to over 30%, versus approximately 20% for a previous version, while increased AlphaFold sampling gives approximately 50% success. With this improved success, AlphaFold can generate accurate antibody–antigen models in many cases, while additional training or other optimization may further improve performance.
The KNOB is Broken: Exploiting Low Entropy in the Encryption Key Negotiation Of Bluetooth BR/EDR

We present an attack on the encryption key negotiation protocol of Bluetooth BR/EDR. The attack allows a third party, without knowledge of any secret material (such as link and encryption keys), to make two (or more) victims agree on an encryption key with only 1 byte (8 bits) of entropy. Such low entropy enables the attacker to easily brute force the negotiated encryption keys, decrypt the eavesdropped ciphertext, and inject valid encrypted messages (in real-time). The attack is stealthy because the encryption key negotiation is transparent to the Bluetooth users. The attack is standard-compliant because all Bluetooth BR/EDR versions require to support encryption keys with entropy between 1 and 16 bytes and do not secure the key negotiation protocol. As a result, the attacker completely breaks Bluetooth BR/EDR security without being detected. We call our attack Key Negotiation Of Bluetooth (KNOB) attack. The attack targets the firmware of the Bluetooth chip because the firmware (Bluetooth controller) implements all the security features of Bluetooth BR/EDR. As a standard-compliant attack, it is expected to be effective on any firmware that follows the specification and on any device using a vulnerable firmware. We describe how to perform the KNOB attack, and we implement it. We evaluate our implementation on more than 14 Bluetooth chips from popular manufacturers such as Intel, Broadcom, Apple, and Qualcomm. Our results demonstrate that all tested devices are vulnerable to the KNOB attack. We discuss countermeasures to fix the Bluetooth specification and its implementation.
Adapting to climate change in West Africa

Impacts of climate change vary from region to region. The 4th Assessment Report of the IPCC mentions that drier areas will be affected by more droughts and that the rainfall regime, in general, will become "rougher". In West Africa, specifically the area below the Sahel, the climate change signal may be more subtle. Anecdotal evidence from farmers suggest that the onset of the rainy season has been shifting forward in time over the past two generations. Recently, detailed atmospheric modeling over the region (Jung and Kunstmann, 2008) shows that also in the near future, the onset of the rainy season will shift to later in the year, roughly from April towards May. The end of the rainy season as well as the total amount of rainfall will remain more or less fixed. This implies that adaptation strategies should be twofold. The first part of a comprehensive adaptation strategy would be a continuation of the efforts to produce faster growing rainfed crop cultivars, mainly corn and sorghum. The second part would consist of increased water storage during the wet season for use in the dry season. River runoff in West Africa is very sensitive to the rainfall distribution. When the same amount of rain falls within a shorter period, as is suggested by climate projections, runoff will show an important increase. Also the recharge of groundwater will improve under these circumstances. Storage of surface runoff in small reservoirs would be an important part of climate change adaptation. Extensive use of (shallow) groundwater in the dry season could be a second, highly complimentary adaptation strategy. The development of large dams would probably be less successful given the flatness of the landscape and the move towards decentralized development in most West African countries. Shortening of the rainy season will reduce rainfed agriculture, which is the dominant mode of food production in the region. Use of surface and groundwater in the dry season may partially offset this negative effect. Success of any of these adaptation strategies will to a large extent depend on institutional and socio-economic developments within the region.
Role of non-invasive objective markers for the rehabilitative diagnosis of central sensitization in patients with fibromyalgia: A systematic review.

BACKGROUND Central sensitization cannot be demonstrated directly in humans. Therefore, studies used different proxy markers (signs, symptoms and tools) to identify factors assumed to relate to central sensitization in humans, that is, Human Assumed Central Sensitization (HACS). OBJECTIVE The aims of this systematic review were to identify non-invasive objective markers of HACS and the instruments to assess these markers in patients with fibromyalgia (FM). METHODS A systematic review was conducted with the following inclusion criteria: (1) adults, (2) diagnosed with FM, and (3) markers and instruments for HACS had to be non-invasive. Data were subsequently extracted, and studies were assessed for risk of bias using the quality assessment tools developed by the National Institute of Health. RESULTS 78 studies (n= 5234 participants) were included and the findings were categorized in markers identified to assess peripheral and central manifestations of HACS. The identified markers for peripheral manifestations of HACS, with at least moderate evidence, were pain after-sensation decline rates, mechanical pain thresholds, pressure pain threshold, sound 'pressure' pain threshold, cutaneous silent period, slowly repeated evoked pain sensitization and nociceptive flexion reflex threshold. The identified markers for central manifestations of HACS were efficacy of conditioned pain modulation with pressure pain conditioning and brain perfusion analysis. Instruments to assess these markers are: pin-prick stimulators, cuff-algometry, repetitive pressure stimulation using a pressure algometer, sound, electrodes and neuroimaging techniques. CONCLUSIONS This review provides an overview of non-invasive markers and instruments for the assessment of HACS in patients with FM. Implementing these findings into clinical settings may help to identify HACS in patients with FM.
Diagnostic Accuracy of Office Blood Pressure Measurement and Home Blood Pressure Monitoring for Hypertension Screening Among Adults: Results From the IDH Study

Background Using high awake blood pressure (BP; ≥130/80 mm Hg) on ambulatory BP monitoring (ABPM) as a reference, the purpose of this study was to determine the accuracy of high office BP (≥130/80 mm Hg) at an initial visit and high confirmatory office BP (≥130/80 mm Hg), and separately, high home BP (≥130/80 mm Hg) among participants with high office BP (≥130/80 mm Hg) at an initial office visit. Methods and Results The accuracy of office BP measurements using the oscillometric method for detecting high BP on ABPM was determined among 379 participants with complete office BP and ABPM data in the IDH (Improving the Detection of Hypertension) study. For detecting high BP on ABPM, the accuracy of high confirmatory office BP using the oscillometric method and, separately, high home BP was also determined among the subgroup of 122 participants with high office BP at an initial visit and complete home BP monitoring data. High office BP had moderate sensitivity (0.61 [95% CI, 0.53–0.68]) and high specificity (0.85 [95% CI, 0.80–0.90]) for high awake BP. High confirmatory office BP and high home BP had moderate sensitivity (0.69 [95% CI, 0.59–0.79] and 0.79 [95% CI, 0.71–0.87], respectively) and low and moderate specificity (0.44 [95% CI, 0.27–0.61] and 0.72 [95% CI, 0.56–0.88], respectively). Conclusions Many individuals with high BP on ABPM do not have high office BP. Confirmatory office BP and home blood pressure monitoring also had limited ability to identify individuals with high BP on ABPM.
Severe Traumatic Brain Injury the Corpus Callosum in Mild, Moderate, and Diffusion Tensor Imaging Characteristics of

BACKGROUND AND PURPOSE: The corpus callosum is an important predilection site for traumatic axonal injury but may be unevenly affected in head trauma. We hypothesized that there were local differences in axonal injury within the corpus callosum as investigated with diffusion tensor imaging (DTI), varying among patients with differing severity of traumatic brain injury (TBI). MATERIALS AND METHODS: Ethics committee approval and informed consent were obtained. Ten control subjects (7 men, 3 women; mean age, 37 (cid:1) 9 years) and 39 patients with TBI (27 men, 12 women; 34 (cid:1) 12 years) were investigated, of whom 24 had mild; 9, moderate; and 6, severe TBI. Regions of interest were selected in the callosal genu, body, and splenium to calculate fractional anisotropy (FA), apparent diffusion coefficient (ADC), and the number of fibers passing through. Statistical comparison was made through analysis of variance with the Scheffe´ post hoc analysis. RESULTS: Compared with controls, patients with mild TBI investigated (cid:2) 3 months posttrauma ( n (cid:3) 12) had reduced FA ( P (cid:2) . 01) and increased ADC ( P (cid:2) . 05) in the genu, whereas patients with mild TBI investigated (cid:1) 3 months posttrauma ( n (cid:3) 12) showed no significant differences. Patients with moderate and severe TBI, all investigated (cid:2) 3 months posttrauma, had reduced FA ( P (cid:2) . 001) and increased ADC ( P (cid:2) . 01) in the genu compared with controls and reduced FA in the splenium ( P (cid:2) . 001) without significant ADC change. CONCLUSION: Mild TBI is associated with DTI abnormalities in the genu (cid:2) 3 months posttrauma. In more severe TBI, both the genu and splenium are affected. DTI suggests a larger contribution of vasogenic edema in the genu than in the splenium in TBI.
Basic density of Scots pine wood-relationships between values calculated at different heights of the trunk

: Basic density of Scots pine wood – relationships between values calculated at different heights of the trunk. The aim of this study was to analyse relationships between basic density of wood determined at different heights on the trunks of Scots pines, particularly between breast height and other parts of the stem. Analyses were conducted on material from 63 sample trees from Central Pomerania (northern Poland). Five samples were collected from the trunk of each mean sample tree. Each sample was located at a different height (level). The first came from the breast height level, while the second and the successive ones were denoted with relative values, i.e. 20 (SII), 40 (SIII), 60 (SIII) and 80% (SV) tree height. It was found among other things that basic density decreased with a reduction of distance from the tree top. The dynamic of these changes was particularly evident in the butt end of the trunk. Linear dependencies, confirmed by high linear correlation coefficients, were found between wood density at breast height and in other parts of the stem. Values of linear correlation coefficients and coefficients of determination decreased with an increase in the distance between breast height and the section. Breast height provides a representative cross-section of the stem. Basic wood density determined at this level may be used to model density in other parts of the stem. effect of wind loading of trees on variation in selected technical parameters of wood in Scots pine sylvestris
United States Technological Superiority and the Losses From Migration

This study employs a new approach to examine the impact of immigration on the U.S. economy. Unlike earlier studies, we do not treat the movement of immigrant labor into this country in isolation. Older studies assumed that abundant resources and demand for labor was the primary reason for immigration, assumptions more appropriate to the 19 th century. We start by assuming that the technological superiority of the modern American economy and resulting high standard of living is the primary factor motivating immigration. The study also takes into account the new global economy, including the movement of capital as well as trade. Our findings show that immigration creates a net loss for natives of nearly $70 billion annually
Defining and Reasoning about Model-based Safety Analysis: A Review

14. ABSTRACT Model-based safety analysis (MBSA) has been around for over two decades. The benefits of MBSA have been well-documented in the literature, such as tackling complexity, introducing Formal Methods to eliminate the ambiguity in the traditional safety analysis, using automation to replace the error-prone manual safety modeling process, and ensuring consistency between the design model and the safety model. However, there is still a lack of consensus on what MBSA even is. This paper provides an approach towards developing such a consensus.
Leishmaniose dans les Alpes-Maritimes: caractéristiques épidémiologiques actuelles

Les veterinaires et les medecins du departement des Alpes-Maritimes sont de plus en plus souvent confrontes a la leishmaniose. Des enquetes prospectives revelent une seroprevalence de la leishmaniose canine de l'ordre de 10%. Dans plusieurs foyers d'enzootie canine, 30% de la population humaine reagit positivement a l'intradermoreaction a la leishmanine. On constate une nette progression de la frequence des cas annuels de leishmaniose viscerale humaine autochtone dont l'incidence est de 1,5 pour 100000 habitants en 1992. A partir de 1986 on observe une moyenne de trois cas par an de coinfections VIH 1-Leishmania dans ce departement qui est l'un des plus touches de France par le SIDA. Leishmania infantum MON-1 est identifie comme l'espece responsable de la maladie. Les vecteurs sont Phlebotomus pernicious et Phlebotomus ariasi
Using Sentiment Analysis to Spot Trending Products

Trend detection refers to the process of recognizing significant patterns or trends within large datasets, including changes over time and seasonal variations. In the context of product-related data, trend product detection is a specialized tool or software that is specifically designed to identify trends in sales, customer preferences, product reviews, and other relevant data points. The study focuses on the combination of trend detection and sentiment analysis as a powerful approach for trend spotting. In this paper, we present a study that uses natural language processing (NLP) techniques and the VADER sentiment analysis tool to identify product sentiment from customer reviews. We then applied decision tree analysis to predict whether a product is likely to trend or not based on its sentiment score. Additionally, we used clustering to group the top-rated products based on their sentiment scores and applied a moving average to determine which products are currently trending. Our results show that the combination of trend detection and sentiment analysis can effectively identify trending products with an accuracy of 93%. This study demonstrates the power of NLP and sentiment analysis in detecting trends and provides valuable insights for businesses to make data-driven decisions about their product offerings.
Automated Binary Program Partitioning through Structural Analysis

Binary code has emerged as the standard format for storing various applications, both in industry and academia, owing to its lightweight nature, easy installation, and faster processing. The increasing number of outdated legacy applications in binary format has created a demand for efficient tools capable of automatically processing and updating them. How-ever, accurately processing these binaries poses challenges due to the presence of hidden functionality embedded in multiple sub tasks within the program. This paper presents a methodol-ogy for the automated structural analysis and partitioning of binary programs. Through an iterative analysis of subgraphs extracted from the complete binary's Control-Flow graph using a hybrid approach that combines a Deep Graph Convolution Neural Network model and a formal analysis module, we successfully identify graph structures representing different functionalities. Specifically, this study focuses on four functionalities: matrix multiplication, matrix transposition, array processing, and mathematical functions. Experimental results on a synthetic dataset demonstrate the high performance of the proposed method in accurately isolating the relevant nodes associated with these functionalities. As a result, these nodes are annotated within the Control-Flow graph, facilitating easier processing by legacy-code analysis tools.
Design of Propulsion Motor for EV to Mitigate Shaft Voltage Considering Electromagnetic Performance

This research proposes a design method to reduce shaft voltage of the propulsion motors based on the motor configuration considering electromagnetic performances. First, design variables that affect parasitic capacitance are investigated by FEM. Many articles have been submitted, in which they used the insulated rotor, ceramic bearing, and ground ring. However, since the methods require manufacturing costs, it is not effective to mitigate the shaft voltage. In addition, electro-magnetic performances according to the variables are analyzed by FEM. As a result, Lcr is the most significant variable to decrease the shaft voltage. optimization for reduction of the shaft voltage considering the electromagnetic charateristisc is performed. As a result, the shaft voltage of the optimization model has 22.52 % lesser shaft voltage maintaining the electromagnetic characteristics.
Cow Face Recognition with Noise Labels Using Vision Transformer

Animal face recognition is a crucial step for people to monitor and manage livestock more accurately and conveniently, playing an important role in intelligent breeding. Traditionally, the method of labeling animals with a different type of marks such as tags, tattoos and brands is generally applied to uniquely identify individual livestock. However, the traditional way of identification is high cost, low efficiency and unreliable. In this paper, we propose a deep learning approach based on a framework of semi-supervised noise-label learning to realize noisy cow face recognition, which adopts the Vision Transformer model that has recently become popular in the filed of computer vision. Our network is trained on a dataset that consists of 6 individual cows with a total of 3556 facial images. Due to the lack of adequate datasets, this is a challenging task for achieving high performance. To evaluate the robustness of our approach to noise, we compared it with several other noisy learning methods. Our results demonstrate that Vision Transformer can be used effectively for cow face recognition, and can achieve good performance even in the presence of noise.
Obstacle Detection in Off-road Environments Based on LiDAR

Mapping off-road terrain is a challenging task, especially when compared to urban terrain. The complexity and roughness of off-road terrain make it difficult to achieve accurate mapping results. Obstacle detection in such environments is also a challenging task, as the performance of LiDAR-based methods depends on the point cloud density at the location of the obstacle. High line beam LiDAR is expensive, while low line beam LiDAR is sparse, making it difficult to obtain dense point clouds. In this paper, we propose a multi-LiDAR fusion approach that uses roof LiDAR as the primary sensor and front LiDAR as a supplement. The approach employs point cloud filtering, dedistortion, and point cloud registration to fuse data from multiple sensors. This results in a richer view of the environment and more intensive measurement data. We combine the voxel map with a twodimensional grid map and accelerate the voxel map using a GPU to generate accurate maps. Experimental results show that our proposed method achieves the highest accuracy and recall rates, improving the accuracy and robustness of obstacle detection in off-road environments.