Miracle-dz/UniFAR-FTUs huggingface dataset:
Differential regulation of microglial states by colony stimulating factors

Recent studies have emphasized the role of microglia in the progression of many neurodegenerative diseases. The colony stimulating factors, CSF-1 (M-CSF), granulocyte-macrophage CSF (GM-CSF) and granulocyte CSF (G-CSF) regulate microglia through different cognate receptors. While the receptors for GM-CSF (GM-CSFR) and G-CSF (G-CSFR) are specific for their ligands, CSF-1 shares its receptor, the CSF-1 receptor-tyrosine kinase (CSF-1R), with interleukin-34 (IL-34). All four cytokines are expressed locally in the CNS. Activation of the CSF-1R in macrophages is anti-inflammatory. In contrast, the actions of GM-CSF and G-CSF elicit different activated states. We here review the roles of each of these cytokines in the CNS and how they contribute to the development of disease in a mouse model of CSF-1R-related leukodystrophy. Understanding their roles in this model may illuminate their contribution to the development or exacerbation of other neurodegenerative diseases.
Impact of Changes in Detection Effort on Control of Visceral Leishmaniasis in the Indian Subcontinent

Coffeng, Luc E; Le Rutte, Epke A; Muñoz, Johanna; Adams, Emily R; Prada, Joaquin M; de Vlas, Sake J; Medley, Graham F; (2019) Impact of Changes in Detection Effort on Control of Visceral Leishmaniasis in the Indian Subcontinent. The Journal of infectious diseases. ISSN 0022-1899 DOI: https://doi.org/10.1093/infdis/jiz644 Downloaded from: http://researchonline.lshtm. ac.uk/id/eprint/4655621/ DOI: https://doi.org/10.1093/infdis/jiz644
Unveiling Therapeutic Targets for Esophageal Cancer: A Comprehensive Review

Esophageal cancer is a highly aggressive and deadly disease, ranking as the sixth leading cause of cancer-related deaths worldwide. Despite advances in treatment, the prognosis remains poor. A multidisciplinary approach is crucial for achieving complete remission, with treatment options varying based on disease stage. Surgical intervention and endoscopic treatment are used for localized cancer, while systemic treatments like chemoradiotherapy and targeted drug therapy play a crucial role. Molecular markers such as HER2 and EGFR can be targeted with drugs like trastuzumab and cetuximab, and immunotherapy drugs like pembrolizumab and nivolumab show promise by targeting immune checkpoint proteins. Epigenetic modifications offer new avenues for targeted therapy. Treatment selection depends on factors like stage, tumor location, and patient health, with post-operative and rehabilitation care being essential. Early diagnosis, appropriate treatment, and supportive care are key to improving outcomes. Continued research is needed to develop effective targeted drugs with minimal side effects. This review serves as a valuable resource for clinicians and researchers dedicated to enhancing esophageal cancer treatment outcomes.
Enterprise Architecture Trends in the Digital Transformation Era

Enterprise Architecture (EA) is a solution to build alignment between business strategy and information technology in dealing with digital transformation that causes fundamental changes for companies and businesses. The purpose of this study is to examine the trends and progress of EA in responding to the challenges of the digital transformation era, which is carried out through SLRs using the PRISMA method. According to the research findings, implementing EA in businesses serves the following purposes: establishing clear objectives and strategies for the development of business and technology, achieving harmony between business and information technology, time and cost efficiency, and effectiveness in business development processes and information technology. Then, it was concluded that there are nine components of EA, namely, vision, mission, and strategic objectives; business architecture; data & information architecture; information system architecture; infrastructure and network architecture; security architecture; human resources architecture; governance, legal, and compliance; and standards & regulations.
Adverse effects on consumer ’ s health caused by hormones administered in cattle

In today’s digital age to feed this ever increasing population there is a high demand for more production of food. To meet this task, artificial hormones are being used to increase the production of milk and meat. In this review, we address the controversial issue of adverse effects of hormones being administered in cattle. Oestradiol, Progesterone, Testosterone, Zeranol, Trenbolone and Melengestrol hormones are approved by U.S. food and drug administration (FDA) for commercial use. These have been found to be used to increase the quantity of milk and meat in cattle but their adverse effects being observed on the consumers as they cause cancer and premature puberty in children. Similarly the utilization of recombinant bovine growth hormone (rBGH) in cattle is seen to be a burning issue as it causes cancer. Here, we present a deeper insight to above mentioned content
IEEE 802.15.4z UWB Angle of Departure Tag Design for Indoor Positioning

Ultra-WideBand (UWB) is becoming a major indoor positioning technology. UWB indoor positioning technology initially used only distance information, but recently uses direction information using an antenna array along with distance information. Until now, most studies using direction information in UWB have been Angle of Arrival (AoA) using the phase difference of incident wave between antennas. However, AoA is not suitable for positioning applications such as indoor navigation because of angle changes that occurs when the UWB anchor is moved. These applications require the use of Angle of Departure (AoD) concept to measure the angle of departure of a fixed UWB device. In this paper, a UWB AoD tag is designed and implemented using two switched array antennas. The implemented UWB AoD tag uses latest IEEE 802.15. 4z standard. We have successfully implemented an indoor positioning system using these fixed AoD tag.
On the Poisson equation for nonreversible Markov jump processes

We study the solution $V$ of the Poisson equation $LV + f=0$ where $L$ is the backward generator of an irreducible (finite) Markov jump process and $f$ is a given centered state function. Bounds on $V$ are obtained using a graphical representation derived from the Matrix Forest Theorem and using a relation with mean first-passage times. Applications include estimating time-accumulated differences during relaxation toward a steady nonequilibrium regime.
Airborne measurements of gravity wave breaking at the tropopause

A breaking atmospheric gravity wave was investigated with a combination of airborne in‐situ dynamical measurements and ground‐based VHF radar observations. The wave was generated by flow over mountains and it was observed to break near the tropopause. The measurements reveal that the wave was overturning at the tropopause and that the initial breakdown into turbulence involved the generation of smaller oscillations with a horizontal wavelength of around 500 m. There was also evidence that the turbulence associated with the wave breaking can cause substantial mixing in the tropopause region.
Explaining Rebel Behavior in Goal Reasoning Agents

Generating human-comprehensible explanations is an important requirement for autonomous systems in human-agent teaming environments. Humans and agents often have their own knowledge of the world, knowledge of objectives being pursued and tasks being performed, and their own constraints. Given these differences, an agent may be issued goals that violate its own constraints or preferences, or are undesirable for the team’s task. Numerous situations may arise where rebellion by dropping or changing goals leads to a more beneficial outcome. Agents with goal reasoning capabilities may rebel by rejecting or altering the goals and plans expected of them by human teammates. Explanations help build trust and understanding between the human and agent, leading to greater overall effectiveness. In this paper we outline motivating examples for explainable rebellious behavior in goal reasoning systems and identify open research questions.
A Review of State-of-the-Art Malware Attack Trends and Defense Mechanisms

The increasing sophistication of malware threats has led to growing concerns in the anti-malware community, as malware poses a significant danger to online users despite the availability of numerous defense solutions. This study aims to comprehensively review malware evolution and current attack trends to identify effective defense mechanisms. It reviews the most recent journal articles, conference proceedings, reports, and online resources published during the last five years. We extensively review the malware landscape from 1970 to the present and analyze malware types, operational mechanisms, attack vectors, and vulnerabilities. Furthermore, we explore different defensive strategies developed in response to these evolving threats. Our findings highlight the increasing sophistication of malware attack trends, including a surge in cryptojacking, attacks on mobile devices, Internet of Things devices, ransomware, advanced persistent threats, supply chain attacks, fileless malware, cloud-based attacks, exploitation of remote employees, and attack trends on edge networks. Defense strategies have also evolved in parallel, emphasizing multilayered security measures to counter these dynamic threats. This study highlights the critical need for robust, multilayered security measures to combat dynamic malware. Despite these advancements, some open challenges and significant research gaps remain, which require further innovation. This review serves as a valuable guide for cybersecurity professionals by identifying the key trends, challenges, limitations, and future cybersecurity research opportunities.
Open Problems in DAOs

Decentralized autonomous organizations (DAOs) are a new, rapidly-growing class of organizations governed by smart contracts. Here we describe how researchers can contribute to the emerging science of DAOs and other digitally-constituted organizations. From granular privacy primitives to mechanism designs to model laws, we identify high-impact problems in the DAO ecosystem where existing gaps might be tackled through a new data set or by applying tools and ideas from existing research fields such as political science, computer science, economics, law, and organizational science. Our recommendations encompass exciting research questions as well as promising business opportunities. We call on the wider research community to join the global effort to invent the next generation of organizations.
Tassement d'un milieu granulaire : chargement surfacique et chargement volumique

The settlement of an immersed granular material can be achieved either by a boundary loading, when adding an overload at the top surface of the medium, or by a volume loading induced by an extra body force due to a downward hydraulic flow. This paper investigates theoretical predictions for both loading modes in terms of overall settlement, vertical displacement profile and solid volume fraction profile. The results are confronted to experimental results and a satisfying agreement is obtained. Mots clefs : tassement, materiau granulaire, compression, velocimetrie par correlation d’images
Uplifting edges in higher order networks: spectral centralities for non-uniform hypergraphs

Spectral analysis of networks states that many structural properties of graphs, such as centrality of their nodes, are given in terms of their adjacency matrices. The natural extension of such spectral analysis to higher order networks is strongly limited by the fact that a given hypergraph could have several different adjacency hypermatrices, hence the results obtained so far are mainly restricted to the class of uniform hypergraphs, which leaves many real systems unattended. A new method for analysing non-linear eigenvector-like centrality measures of non-uniform hypergraphs is presented in this paper that could be useful for studying properties of $\mathcal{H}$-eigenvectors and $\mathcal{Z}$-eigenvectors in the non-uniform case. In order to do so, a new operation - the $\textit{uplift}$ - is introduced, incorporating auxiliary nodes in the hypergraph to allow for a uniform-like analysis. We later argue why this is a mathematically sound operation, and we furthermore use it to classify a whole family of hypergraphs with unique Perron-like $\mathcal{Z}$-eigenvectors. We supplement the theoretical analysis with several examples and numerical simulations on synthetic and real datasets.
Near-Optimal Coverage Path Planning with Turn Costs

Coverage path planning is a fundamental challenge in robotics, with diverse applications in aerial surveillance, manufacturing, cleaning, inspection, agriculture, and more. The main objective is to devise a trajectory for an agent that efficiently covers a given area, while minimizing time or energy consumption. Existing practical approaches often lack a solid theoretical foundation, relying on purely heuristic methods, or overly abstracting the problem to a simple Traveling Salesman Problem in Grid Graphs. Moreover, the considered cost functions only rarely consider turn cost, prize-collecting variants for uneven cover demand, or arbitrary geometric regions. In this paper, we describe an array of systematic methods for handling arbitrary meshes derived from intricate, polygonal environments. This adaptation paves the way to compute efficient coverage paths with a robust theoretical foundation for real-world robotic applications. Through comprehensive evaluations, we demonstrate that the algorithm also exhibits low optimality gaps, while efficiently handling complex environments. Furthermore, we showcase its versatility in handling partial coverage and accommodating heterogeneous passage costs, offering the flexibility to trade off coverage quality and time efficiency.
A General Neural Causal Model for Interactive Recommendation

Survivor bias in observational data leads the optimization of recommender systems towards local optima. Currently most solutions re-mines existing human-system collaboration patterns to maximize longer-term satisfaction by reinforcement learning. However, from the causal perspective, mitigating survivor effects requires answering a counterfactual problem, which is generally unidentifiable and inestimable. In this work, we propose a neural causal model to achieve counterfactual inference. Specifically, we first build a learnable structural causal model based on its available graphical representations which qualitatively characterizes the preference transitions. Mitigation of the survivor bias is achieved though counterfactual consistency. To identify the consistency, we use the Gumbel-max function as structural constrains. To estimate the consistency, we apply reinforcement optimizations, and use Gumbel-Softmax as a trade-off to get a differentiable function. Both theoretical and empirical studies demonstrate the effectiveness of our solution.
Causal Interpretation of Self-Attention in Pre-Trained Transformers

We propose a causal interpretation of self-attention in the Transformer neural network architecture. We interpret self-attention as a mechanism that estimates a structural equation model for a given input sequence of symbols (tokens). The structural equation model can be interpreted, in turn, as a causal structure over the input symbols under the specific context of the input sequence. Importantly, this interpretation remains valid in the presence of latent confounders. Following this interpretation, we estimate conditional independence relations between input symbols by calculating partial correlations between their corresponding representations in the deepest attention layer. This enables learning the causal structure over an input sequence using existing constraint-based algorithms. In this sense, existing pre-trained Transformers can be utilized for zero-shot causal-discovery. We demonstrate this method by providing causal explanations for the outcomes of Transformers in two tasks: sentiment classification (NLP) and recommendation.
Counting triangles in graphs without vertex disjoint odd cycles

Given two graphs $H$ and $F$, the maximum possible number of copies of $H$ in an $F$-free graph on $n$ vertices is denoted by $\mathrm{ex}(n, H, F)$. Let $(\ell+1) \cdot F$ denote $\ell+1$ vertex disjoint copies of $F$. In this paper, we determine the exact value of $\mathrm{ex}(n, C_3, (\ell+1)\cdot C_{2k+1})$ and its extremal graph, which generalizes some known results.
Evaluating the externally environmental cost of cotton production in Xinjiang

Xinjiang,as the most important cotton production region in China,has contributed greatly to safeguarding the supply security of cotton for the whole China. But the cotton production in Xinjiang faces many new problems because of its slathering chemicals and its continuous cropping in a large area. To correctly judge the environmental problems that the cotton production in Xinjiang is faced with and to formulate the reasonable development ways of cotton producing,it is significant to study negative external cost of the cotton producing in Xinjiang and to evaluate it scientifically. Using Decompose Summation method,we listed the impacts of negative externality on cotton production in Xinjiang,and also evaluated its environmental cost. The results show that the externally environmental cost of cotton producing in Xinjiang was as high as 36. 9099 × 108 RMB,and the unit-area externally environmental cost of cotton production in Xinjiang was 2144. 9RMB·hm-2. Specially,the environmental value loss from water waste in irrigating of cotton fields was the leading externally environmental cost for cotton producing in Xinjiang,which was 24. 4827 × 108RMB; environmental cost from chemical pollution was also a major externally environmental cost for cotton producing in Xinjiang,which was 9. 4773 × 108 RMB. Finally,this research suggests that eco-environmental value of water in arid and semi-arid area should not be underestimated,and the sustainable development of cotton producing in Xinjiang will be challenged by chemical pollution as well as the contradiction between water supply and demand.
Private Variable-Length Coding with Non-Zero Leakage

A private compression design problem is studied, where an encoder observes useful data $Y$, wishes to compress it using variable length code and communicates it through an unsecured channel. Since $Y$ is correlated with private data $X$, the encoder uses a private compression mechanism to design encoded message $C$ and sends it over the channel. An adversary is assumed to have access to the output of the encoder, i.e., $\mathcal{C}$ and tries to estimate $X$. Furthermore, it is assumed that both encoder and decoder have access to a shared secret key $W$. In this work, we generalize the perfect privacy (secrecy) assumption and consider a non-zero leakage between the private data $X$ and encoded message $\mathcal{C}$. The design goal is to encode message $\mathcal{C}$ with minimum possible average length that satisfies non-perfect privacy constraints. We find upper and lower bounds on the average length of the encoded message using different privacy metrics and study them in special cases. For the achievability we use two-part construction coding and extended versions of Functional Representation Lemma. Lastly, in an example we show that the bounds can be asymptotically tight.
Harmonization-enriched domain adaptation with light fine-tuning for multiple sclerosis lesion segmentation

Deep learning algorithms utilizing magnetic resonance (MR) images have demonstrated cutting-edge proficiency in autonomously segmenting multiple sclerosis (MS) lesions. Despite their achievements, these algorithms may struggle to extend their performance across various sites or scanners, leading to domain generalization errors. While few-shot or one-shot domain adaptation emerges as a potential solution to mitigate generalization errors, its efficacy might be hindered by the scarcity of labeled data in the target domain. This paper seeks to tackle this challenge by integrating one-shot adaptation data with harmonized training data that incorporates labels. Our approach involves synthesizing new training data with a contrast akin to that of the test domain, a process we refer to as"contrast harmonization"in MRI. Our experiments illustrate that the amalgamation of one-shot adaptation data with harmonized training data surpasses the performance of utilizing either data source in isolation. Notably, domain adaptation using exclusively harmonized training data achieved comparable or even superior performance compared to one-shot adaptation. Moreover, all adaptations required only minimal fine-tuning, ranging from 2 to 5 epochs for convergence.
Energy-Aware Adaptive Sampling for Self-Sustainability in Resource-Constrained IoT Devices

In the ever-growing Internet of Things (IoT) landscape, smart power management algorithms combined with energy harvesting solutions are crucial to obtain self-sustainability. This paper presents an energy-aware adaptive sampling rate algorithm designed for embedded deployment in resource-constrained, battery-powered IoT devices. The algorithm, based on a finite state machine (FSM) and inspired by Transmission Control Protocol (TCP) Reno's additive increase and multiplicative decrease, maximizes sensor sampling rates, ensuring power self-sustainability without risking battery depletion. Moreover, we characterized our solar cell with data acquired over 48 days and used the model created to obtain energy data from an open-source world-wide dataset. To validate our approach, we introduce the EcoTrack device, a versatile device with global navigation satellite system (GNSS) capabilities and Long-Term Evolution Machine Type Communication (LTE-M) connectivity, supporting MQTT protocol for cloud data relay. This multi-purpose device can be used, for instance, as a health and safety wearable, remote hazard monitoring system, or as a global asset tracker. The results, validated on data from three different European cities, show that the proposed algorithm enables self-sustainability while maximizing sampled locations per day. In experiments conducted with a 3000 mAh battery capacity, the algorithm consistently maintained a minimum of 24 localizations per day and achieved peaks of up to 3000.