Miracle-dz/UniFAR-FTUs huggingface dataset:
Exploring Cinnamoyl-Substituted Mannopyranosides: Synthesis, Evaluation of Antimicrobial Properties, and Molecular Docking Studies Targeting H5N1 Influenza A Virus

The pursuit of innovative combinations for the development of novel antimicrobial and antiviral medications has garnered worldwide interest among scientists in recent times. Monosaccharides and their glycosides, such as methyl α-d-mannopyranoside derivatives, play a significant role in the potential treatment of viral respiratory pathologies. This study was undertaken to investigate and assess the synthesis and spectral characterization of methyl α-d-mannopyranoside derivatives 2–6, incorporating various aliphatic and aromatic groups. The investigation encompassed comprehensive in vitro antimicrobial screening, examination of physicochemical properties, molecular docking analysis, molecular dynamics simulations, and pharmacokinetic predictions. A unimolar one-step cinnamoylation reaction was employed under controlled conditions to produce methyl 6-O-cinnamoyl-α-d-mannopyranoside 2, demonstrating selectivity at the C-6 position. This represented a pivotal step in the development of potential antimicrobial derivatives based on methyl α-d-mannopyranoside. Subsequently, four additional methyl 6-O-cinnamoyl-α-d-mannopyranoside derivatives were synthesized with reasonably high yields. The chemical structures of these novel analogs were confirmed through a thorough analysis of their physicochemical properties, elemental composition, and spectroscopic data. In vitro antimicrobial assays were conducted against six bacterial strains and two fungal strains, revealing promising antifungal properties of these methyl α-d-mannopyranoside derivatives in comparison to their antibacterial activity. Moreover, cytotoxicity testing revealed that the compounds are less toxic. Further supporting these findings, molecular docking studies were performed against the H5N1 influenza A virus, indicating significant binding affinities and nonbonding interactions with the target protein 6VMZ. Notably, compounds 4 (−7.2) and 6 (−7.0) exhibited the highest binding affinities. Additionally, a 100 ns molecular dynamics simulation was conducted to assess the stability of the complex formed between the receptor 6VMZ and methyl α-d-mannopyranoside derivatives under in silico physiological conditions. The results revealed a stable conformation and binding pattern within the stimulating environment. In silico pharmacokinetic and toxicity assessments of the synthesized molecules were performed using Osiris software (version 2.9.1). Compounds 4 and 6 demonstrated favorable computational and pharmacological activities, albeit with a low drug score, possibly attributed to their higher molecular weight and irritancy. In conclusion, this study showcases the synthesis and evaluation of methyl α-d-mannopyranoside derivatives as promising candidates for antimicrobial and antifungal agents. Molecular docking and dynamics simulations, along with pharmacological predictions, contribute to our understanding of their potential therapeutic utility, although further research may be warranted to address certain pharmacological aspects.
The Role of Archaeology in the Recovery and Interpretation of Human Remains from an Outdoor Forensic Setting

In a forensic setting, much contextual information can only be acquired during the scene recovery phase of the investigation and is of most value when the information is gathered following standard archaeological methodological principles. This applies equally to urban or rural settings, fire scenes, and multiple fatality situations. Benefits are also realized by active participation in the autopsy and through postrecovery visits to the site for additional evidence. When included in the early stages, a forensic anthropologist who is trained in archaeological methodologies can make a significant impact on a forensic investigation in terms of: (1) locating and defining the scene; (2) ensuring maximal evidence recovery at the scene; and (3) more thoroughly addressing issues of personal identity and events surrounding and subsequent to the death, including postmortem interval or PMI (see also Killam 1990). The use of archaeological methodologies during recovery has its greatest potential effects on the reconstruction of perimortem and postmortem events affecting the remains of the victim (more properly termed “taphonomy”). This chapter discusses basic archaeological protocols or “research paradigms” to be employed at forensic scenes and the benefits derived from their application to forensic investigation in general, and taphonomic reconstructions in particular. Discussions of case studies conducted by the authors illustrate concepts introduced in the text. The recovery methodologies discussed in this chapter have been very successful in producing rigorous data sets in strictly archaeological research contexts (e.g., Adovasio 1982; Adovasio et al. 1985, 1990; Carlisle and Adovasio 1982; East et al. 1989; Jorstad et al. 1986), and can be expected to do the same in the realm of forensic taphonomy. It is hoped that what follows will demonstrate that:
A Case Study Comparing Activity and Participation Measurement in Two Subjects

This case study compares the differences between complementary methodologies that measure activity and participation in two subjects who use wheeled mobility devices. Data collection consisted of 1) a self-report participation measure for people with mobility disabilities the Community Participation and Perceived Receptivity Survey (CPPRS) and 2) a passive instrumentation data-gathering methodology that includes a global positioning system (GPS) data logger, followed by a prompted recall interview (PRI) to confirm GPS data location and query activity purpose. Many of the destination types (e.g., grocery stores) named in the CPPRS were confirmed by GPS/PRI data. Issues affecting subject responses in both methods included ambiguous categories and researcher assumptions about the nature of activity. Differences between self-report and instrumentation data are discussed as they impact understanding activity and community participation among wheeled mobility users and the development of self-report participation instruments.
Evolution of physicochemical species concentration in streams based on heavy rainfall event data obtained for high-frequency monitoring

Estudos realizados em intervalos curtos de tempo auxiliam no entendimento dos processos biogeoquimicos, que ocorrem relativamente rapido em aguas naturais. Os dados de alta frequencia revelam maior variabilidade na qualidade da agua durante e imediatamente apos os eventos de precipitacao intensa. Este artigo busca avaliar as alteracoes dos parâmetros de qualidade de aguas superficiais, em bioma de Mata Atlântica, provocadas por eventos de precipitacao intensa. O trabalho foi realizado em duas secoes fluviometricas da bacia do Ribeirao Concordia, localizado no estado de Santa Catarina, sul do Brasil. A distribuicao espacial do uso do solo demonstra a predominância de Mata Atlântica na secao 1 e pastagem, reflorestamento, Mata Atlântica e agricultura na secao 2. Em cada evento de precipitacao intensa selecionado, foi analisada a evolucao temporal da altura da precipitacao, da cota fluviometrica e de cada parâmetro fisico-quimico da agua. Em todos os eventos a qualidade da agua sofreu alteracoes em virtude da precipitacao intensa. Apos os eventos, houve aumento da cota e da turbidez em ambas as secoes fluviometricas, ja o ion amonio teve acrescimo de concentracao no rio e o pH diminuiu. A condutividade eletrica apresentou comportamento diferente em cada secao. O nitrato diminuiu sua concentracao logo apos a precipitacao. O oxigenio dissolvido aumentou sua concentracao em 19 dos 27 eventos. A componente principal CP1 apresentou correlacao com a cota, nitrato e a turbidez, na secao 1 e com cota, turbidez e pH na secao 2.
Latent Skill Discovery for Chain-of-Thought Reasoning

Recent advances in Large Language Models (LLMs) have led to an emergent ability of chain-of-thought (CoT) prompting, a prompt reasoning strategy that adds intermediate rationale steps between questions and answers to construct prompts. Conditioned on these prompts, LLMs can effectively learn in context to generate rationales that lead to more accurate answers than when answering the same question directly. To design LLM prompts, one important setting, called demonstration selection, considers selecting demonstrations from an example bank. Existing methods use various heuristics for this selection, but for CoT prompting, which involves unique rationales, it is essential to base the selection upon the intrinsic skills that CoT rationales need, for instance, the skills of addition or subtraction for math word problems. To address this requirement, we introduce a novel approach named Reasoning Skill Discovery (RSD) that use unsupervised learning to create a latent space representation of rationales, called a reasoning skill. Simultaneously, RSD learns a reasoning policy to determine the required reasoning skill for a given question. This can then guide the selection of examples that demonstrate the required reasoning skills. Our approach offers several desirable properties: it is (1) theoretically grounded, (2) sample-efficient, requiring no LLM inference or manual prompt design, and (3) LLM-agnostic. Empirically, RSD outperforms existing methods by up to 6% in terms of the answer accuracy across multiple reasoning tasks.
Two-Timescale Q-Learning with Function Approximation in Zero-Sum Stochastic Games

We consider two-player zero-sum stochastic games and propose a two-timescale $Q$-learning algorithm with function approximation that is payoff-based, convergent, rational, and symmetric between the two players. In two-timescale $Q$-learning, the fast-timescale iterates are updated in spirit to the stochastic gradient descent and the slow-timescale iterates (which we use to compute the policies) are updated by taking a convex combination between its previous iterate and the latest fast-timescale iterate. Introducing the slow timescale as well as its update equation marks as our main algorithmic novelty. In the special case of linear function approximation, we establish, to the best of our knowledge, the first last-iterate finite-sample bound for payoff-based independent learning dynamics of these types. The result implies a polynomial sample complexity to find a Nash equilibrium in such stochastic games. To establish the results, we model our proposed algorithm as a two-timescale stochastic approximation and derive the finite-sample bound through a Lyapunov-based approach. The key novelty lies in constructing a valid Lyapunov function to capture the evolution of the slow-timescale iterates. Specifically, through a change of variable, we show that the update equation of the slow-timescale iterates resembles the classical smoothed best-response dynamics, where the regularized Nash gap serves as a valid Lyapunov function. This insight enables us to construct a valid Lyapunov function via a generalized variant of the Moreau envelope of the regularized Nash gap. The construction of our Lyapunov function might be of broad independent interest in studying the behavior of stochastic approximation algorithms.
Datasets, Models, and Algorithms for Multi-Sensor, Multi-agent Autonomy Using AVstack

Recent advancements in assured autonomy have brought autonomous vehicles (AVs) closer to fruition. Despite strong evidence that multi-sensor, multi-agent (MSMA) systems can yield substantial improvements in the safety and security of AVs, there exists no unified framework for developing and testing representative MSMA configurations. Using the recently-released autonomy platform, AVstack, this work proposes a new framework for datasets, models, and algorithms in MSMA autonomy. Instead of releasing a single dataset, we deploy a dataset generation pipeline capable of generating unlimited volumes of ground-truth-labeled MSMA perception data. The data derive from cameras (semantic segmentation, RGB, depth), LiDAR, and radar, and are sourced from ground-vehicles and, for the first time, infrastructure platforms. Pipelining generating labeled MSMA data along with AVstack's third-party integrations defines a model training framework that allows training multi-sensor perception for vehicle and infrastructure applications. We provide the framework and pretrained models open-source. Finally, the dataset and model training pipelines culminate in insightful multi-agent case studies. While previous works used specific ego-centric multi-agent designs, our framework considers the collaborative autonomy space as a network of noisy, time-correlated sensors. Within this environment, we quantify the impact of the network topology and data fusion pipeline on an agent's situational awareness.
ZePT: Zero-Shot Pan-Tumor Segmentation via Query-Disentangling and Self-Prompting

The long-tailed distribution problem in medical image analysis reflects a high prevalence of common conditions and a low prevalence of rare ones, which poses a significant challenge in developing a unified model capable of identifying rare or novel tumor categories not encountered during training. In this paper, we propose a new zero-shot pan-tumor segmentation framework (ZePT) based on query-disentangling and self-prompting to segment unseen tumor categories beyond the training set. ZePT disentangles the object queries into two subsets and trains them in two stages. Initially, it learns a set of fundamental queries for organ segmentation through an object-aware feature grouping strategy, which gathers organ-level visual features. Subsequently, it refines the other set of advanced queries that focus on the auto-generated visual prompts for unseen tumor segmentation. Moreover, we introduce query-knowledge alignment at the feature level to enhance each query's discriminative representation and generalizability. Extensive experiments on various tumor segmentation tasks demonstrate the performance superiority of ZePT, which surpasses the previous counterparts and evidence the promising ability for zero-shot tumor segmentation in real-world settings.
Evaluating The Accuracy of Classification Algorithms for Detecting Heart Disease Risk

The healthcare industry generates enormous amounts of complex clinical data that make the prediction of disease detection a complicated process. In medical informatics, making effective and efficient decisions is very important. Data Mining (DM) techniques are mainly used to identify and extract hidden patterns and interesting knowledge to diagnose and predict diseases in medical datasets. Nowadays, heart disease is considered one of the most important problems in the healthcare field. Therefore, early diagnosis leads to a reduction in deaths. DM techniques have proven highly effective for predicting and diagnosing heart diseases. This work utilizes the classification algorithms with a medical dataset of heart disease; namely, J48, Random Forest, and Naïve Bayes to discover the accuracy of their performance. We also examine the impact of the feature selection method. A comparative and analysis study was performed to determine the best technique using Waikato Environment for Knowledge Analysis (Weka) software, version 3.8. 6. The performance of the utilized algorithms was evaluated using standard metrics such as accuracy, sensitivity and specificity. The importance of using classification techniques for heart disease diagnosis has been highlighted. We also reduced the number of attributes in the dataset, which showed a significant improvement in prediction accuracy. The results indicate that the best algorithm for predicting heart disease was Random Forest with an accuracy of 99.24%.
Digital Epidemiology after COVID-19: impact and prospects

Epidemiology and Public Health have increasingly relied on structured and unstructured data, collected inside and outside of typical health systems, to study, identify, and mitigate diseases at the population level. Focusing on infectious disease, we review how Digital Epidemiology (DE) was at the beginning of 2020 and how it was changed by the COVID-19 pandemic, in both nature and breadth. We argue that DE will become a progressively useful tool as long as its potential is recognized and its risks are minimized. Therefore, we expand on the current views and present a new definition of DE that, by highlighting the statistical nature of the datasets, helps in identifying possible biases. We offer some recommendations to reduce inequity and threats to privacy and argue in favour of complex multidisciplinary approaches to tackling infectious diseases.
GraphMETRO: Mitigating Complex Distribution Shifts in GNNs via Mixture of Aligned Experts

Graph Neural Networks’ (GNNs) ability to generalize across complex distributions is crucial for real-world applications. However, prior research has primarily focused on specific types of distribution shifts, such as larger graph size, or inferred shifts from constructed data environments, which is highly limited when confronted with multiple and nuanced distribution shifts. For instance, in a social graph, a user node might experience increased interactions and content alterations, while other user nodes encounter distinct shifts. Neglect-ing such complexities significantly impedes generalization. To address it, we present GraphMETRO, a novel framework that enhances GNN generalization under complex distribution shifts in both node-and graph-level tasks. Our approach employs a mixture-of-experts (MoE) architecture with a gating model and expert models aligned in a shared representation space. The gating model identifies key mixture components governing distribution shifts, while each expert generates invariant representations w.r. t. a mixture component. Finally, GraphMETRO aggregates representations from multiple experts to generate the final invariant representation. Our experiments on synthetic and real-world datasets demonstrate GraphMETRO’s superiority and interpretability. To highlight, Graph-METRO achieves state-of-the-art performances on four real-world datasets from GOOD benchmark, outperforming the best baselines on WebKB and Twitch datasets by 67% and 4.2%, respectively.
Reconstruction from Noisy Substrings

This paper studies the problem of encoding messages into sequences which can be uniquely recovered from some noisy observations about their substrings. The observed reads comprise consecutive substrings with some given minimum overlap. This coded reconstruction problem has applications to DNA storage. We consider both single-strand reconstruction codes and multi-strand reconstruction codes, where the message is encoded into a single strand or a set of multiple strands, respectively. Various parameter regimes are studied. New codes are constructed, some of whose rates asymptotically attain the upper bounds.
Bell test of quantum entanglement in attosecond photoionization

Attosecond physics enables the study of ultrafast coherent electron dynamics in matter upon photoexcitation and photoionization, revealing spectacular effects such as hole migration and coherent Auger dynamics in molecules. In the photoionization scenario, there has been a strong focus on probing the physical manifestations of the internal quantum coherence within the individual parent ion and photoelectron systems. However, quantum correlations between these two subsystems emerging from the attosecond photoionization event have thus far remained much more elusive. In this work, we design theoretically and model numerically a direct probe of quantum entanglement in attosecond photoionization in the form of a Bell test. We simulate from first principles a Bell test protocol for the case of noble gas atoms photoionized by ultrashort, circularly polarized infrared laser pulses in the strong-field regime predicting robust violation of the Bell inequality. This theoretical result paves the way to the direct observation of entanglement in the context of ultrafast photoionization of many-electron systems. Our work provides a different perspective on attosecond physics directed towards the detection of quantum correlations between systems born during attosecond photoionization and unravelling the signatures of entanglement in the ultrafast coherent molecular dynamics, including in the chemical decomposition pathways of molecular ions.
Analysis on Effects of Fault Elements in Memristive Neuromorphic Systems

Nowadays, neuromorphic systems based on Spiking Neural Networks (SNNs) attract attentions of many researchers. There are many studies to improve performances of neuromorphic systems. These studies have been showing satisfactory results. To magnify performances of neuromorphic systems, developing actual neuromorphic systems is essential. For developing them, memristors play key role due to their useful characteristics. Although memristors are essential for actual neuromorphic systems, they are vulnerable to faults. However, there are few studies analyzing effects of fault elements in neuromorphic systems using memristors. To solve this problem, we analyze performance of a memristive neuromorphic system with fault elements changing fault ratios, types, and positions. We choose neurons and synapses to inject faults. We inject two types of faults to synapses: SA0 and SA1 faults. The fault synapses appear in random and important positions. Through our analysis, we discover the following four interesting points. First, memristive characteristics increase vulnerability of neuromorphic systems to fault elements. Second, fault neuron ratios reducing performance sharply exist. Third, performance degradation by fault synapses depends on fault types. Finally, SA1 fault synapses improve performance when they appear in important positions.
Nuevas determinaciones de velocidades de ondas P y ondas S para la corteza sísmica del terreno Cuyania en el retroarco andino

Fil: Venerdini, Agostina Lia. Consejo Nacional de Investigaciones Cientificas y Tecnicas. Centro Cientifico Tecnologico Conicet - San Juan. Centro de Investigaciones de la Geosfera y Biosfera. Universidad Nacional de San Juan. Facultad de Ciencias Exactas Fisicas y Naturales. Centro de Investigaciones de la Geosfera y Biosfera; Argentina
Non-trivial saddles in microscopic description of black holes

Non-trivial gravitational saddles have played a key role in the island proposal for the black hole information paradox. It is worth asking if non-trivial saddles exist in microscopic descriptions of black holes. We show this to be the case for 1/8 BPS black holes in N = 8 String Theory in a duality frame, where all charges are Ramond Ramond. The saddles are in the Coulomb branch, where they describe marginally stable bound states of the constituent branes, and correspond to vacua of the BFSS model. The non-perturbative suppression scale is determined by the binding energy.