Miracle-dz/UniFAR-FTUs huggingface dataset:
Rv1453 is associated with clofazimine resistance in Mycobacterium tuberculosis

ABSTRACT Clofazimine (CFZ) has been repurposed for treating tuberculosis (TB), especially multidrug-resistant tuberculosis (MDR-TB). However, the mechanisms of resistance to clofazimine are poorly understood. We previously reported a mutation located in the intergenic region of Rv1453 that was linked to resistance to CFZ and demonstrated that an Rv1453 knockout resulted in an increased minimum inhibitory concentration (MIC) of CFZ. The current study aims to go back and describe in detail how the mutation was identified and further explore its association with CFZ resistance by testing additional 30 isolates. We investigated MICs of clofazimine against 100 clinical strains isolated from MDR-TB patients by microplate alamarBlue assay. Whole-genome sequencing (WGS) was performed on 11 clofazimine-resistant and 7 clofazimine-susceptible strains, including H37Rv. Among the 11 clofazimine-resistant mutants subjected to WGS, the rate of mutation in the intergenic region of the Rv1453 gene was 55% (6/11) in clofazimine-resistant strains. Among another 30 clofazimine-resistant clinical isolates, 27 had mutations in the intergenic region of the Rv1453 gene. A mutation in the Rv1453 gene associated with clofazimine resistance was identified, which shed light on the mechanisms of action and resistance of clofazimine. IMPORTANCE Tuberculosis (TB) is an infectious disease caused by the bacterium Mycobacterium tuberculosis, especially the emergence of multidrug-resistant tuberculosis (MDR-TB) brings great distress to humans. Clofazimine (CFZ) plays an important role in the treatment of MDR-TB. To understand the underlying mechanism of clofazimine resistance better, in this study, we review and detail the findings of the mutation of intergenic region of Rv1453 and find additional evidence that this mutation is related to clofazimine resistance in 30 additional isolates. The significance of our research is to contribute to a comprehensive understanding of clofazimine-resistant mechanisms, which is critical for reducing the emergence of resistance and for anti-TB drug discovery. Tuberculosis (TB) is an infectious disease caused by the bacterium Mycobacterium tuberculosis, especially the emergence of multidrug-resistant tuberculosis (MDR-TB) brings great distress to humans. Clofazimine (CFZ) plays an important role in the treatment of MDR-TB. To understand the underlying mechanism of clofazimine resistance better, in this study, we review and detail the findings of the mutation of intergenic region of Rv1453 and find additional evidence that this mutation is related to clofazimine resistance in 30 additional isolates. The significance of our research is to contribute to a comprehensive understanding of clofazimine-resistant mechanisms, which is critical for reducing the emergence of resistance and for anti-TB drug discovery.
Mésolithique final et Néolithique ancien autour du détroit : une perspective septentrionale (Atlantique / Méditerranée)

Several recent excavations allow us to identify, within the first components of the Portuguese Neolithic, some particularities that seem to represent a rupture with the "franco-iberian" Cardial. We can cite, for example, the abundance of arrowheads of segment type, the heat treatment of flint or even some pottery shapes and decorations which diversify dramatically on the transition from the 6th to 5th millennium BC ("bag-like shapes", the use of la almagra, varieties of impressed and incised techniques). In order to explain this recomposition of the Neolithic package in Southern Iberia, it seemed to us interesting to explore various African lines of evidence. It seems therefore that the recomposition of the Neolithic technological system is done both by the conjunction of elements of diverse origins and by local mutations, which is not in full accord with the rapid pioneer displacement often used to model the Mediterranean Neolithic
Produção de mudas de cupuaçuzeiro em diferentes substratos e tubetes.

Objetivou-se avaliar mudas de cupuacuzeiro utilizando diferentes substratos e recipientes. O experimento foi conduzido no viveiro de producao de mudas da Embrapa Acre, localizada em Rio Branco - Acre. O substrato utilizado foi composto por uma mistura de terra de subsolo peneirada, esterco de curral peneirado e curtido, numa proporcao de 3:1, acrescido de 2 kg de superfosfato simples por metro cubico da mistura e o segundo o substrato comercial Plantmax©. O delineamento utilizado foi o inteiramente casualizado, no esquema de parcela subdividida, com seis tratamentos e quatro repeticoes.A parcela constituiu-se de dois tipos de substratos (Substrato 1 e Substrato 2) e, as subparcelas de tres tamanhos de tubetes, por que variaram em tres niveis de volume (recipiente 1: 288 cm3 ; recipiente 2: 180 cm3 ; e recipiente 3: 120 cm3 ). Com termino do periodo de observacao (180 dias apos a repicagem), realizou-se a analise destrutiva de tres plantas de cada tratamento, em cada repeticao. As caracteristicas avaliadas foram: altura de plantas (cm), diâmetro na altura do colo (mm), numero de folhas, massa seca da raiz (g), massa seca do caule (g), massa seca das folhas (g), massa seca total (g) e area foliar (dm2). As mudas de cupuacuzeiro produzidas nos maiores tubetes apresentaram melhor desempenho. Entre as mudas cultivadas nos dois substratos nao houve diferenca para: altura das plantas e diâmetro na altura do colo. Houve diferenca para massa seca de folhas em todos os recipientes.
Basic life support: knowledge and attitude of medical/paramedical professionals.

BACKGROUND Basic life support (BLS), a key component of the chain of survival decreases the arrest - cardiopulmonary resuscitation interval and increases the rate of hospital discharge. The study aimed to explore the knowledge of and attitude towards basic life support (BLS) among medical/paramedical professionals. METHODS An observational study was conducted by assessing response to self prepared questionnaire consisting of the demographic information of the medical/paramedical staff, their personnel experience/attitude and knowledge of BLS based on the 2005 BLS Guidelines of European Resuscitation Council. RESULTS After excluding incomplete questionnaires, the data from 121 responders (27 clinical faculty members, 21 dental and basic sciences faculty members, 29 house officers and 44 nurses and health assistants) were analyzed. Only 9 (7.4%) of the 121 responders answered ≥11, 53 (43%) answered 7-10, and 58 (48%) answered <7 of 15 questions correctly. The clinical faculty members, house officers and nurses/HA had a mean score of 7.4±3.15, 7.37±2.02 and 6.63±2.16 respectively, while dental/basic sciences faculty members attained a least mean score of 4.52 ±2.13 (P<0.001). Those who had received cardiopulmonary resuscitation (CPR) training within 5 years obtained a highest mean score of 8.62±2.49, whereas those who had the training more than 5 years back or no training obtained a mean score of 5.54±2.38 and 6.1±2.29 respectively (P=0.001). Those who were involved in resuscitation frequently had a higher median score of 8 in comparison to those who were seldom involved or not involved at all (P<0.001). CONCLUSIONS The average health personnel in our hospital lack adequate knowledge in CPR/BLS. Training and experience can enhance knowledge of CPR of these personnel. Thus standard of CPR/BLS training and assessment are recommended at our hospital.
Language as Reality: A Co-Creative Storytelling Game Experience in 1001 Nights using Generative AI

Generative AI (GenAI), encompassing image generation and large language models (LLMs), has opened new avenues for gameplay experiences. This paper introduces "1001 Nights", a narrative game centered on GenAI. Drawing inspiration from Wittgenstein's note, "The limits of my language mean the limits of my world", the game exemplifies the concept of language as reality. The protagonist, Shahrzad, possesses a unique power: specific keywords, such as "sword" or "shield", when spoken by others in tales, materialize as tangible weapons, serving as battle equipment against the King. Players guide the LLM-driven King in co-creating narratives, with GPT-4 employing LLM reasoning methods to ensure story consistency. As these narratives progress, the depicted world is dynamically generated and visualized through Stable Diffusion, blurring the boundaries between narrative and in-game reality. This fusion of interactive storytelling combines gameplay paradigms and story together with dynamic content generation. Players not only aim to alter Shahrzad's fate from the original folklore, but also leverage the power of natural language to shape the game's world. With this example, we propose the term "AI-Native games" to categorize innovative games where GenAI is fundamental to the game's novel mechanics and very existence.
A Model of Sequential Learning based on Non-Axiomatic Logic

Sequential learning is a fundamental function of an intelligent agent. This technical report introduces a model of sequential learning, which is interpretable through Non-Axiomatic Logic. The learning procedure includes three steps, hypothesizing, revising, and recycling, and can work under the Assumption of Insufficient Knowledge and Resources. Although there are limitations for the current design, the model has been proven effective in some simple cases.
An interlacing property of the signless Laplacian of threshold graphs

We show that for threshold graphs, the eigenvalues of the signless Laplacian matrix interlace with the degrees of the vertices. As an application, we show that the signless Brouwer conjecture holds for threshold graphs, i.e., for threshold graphs the sum of the k largest eigenvalues is bounded by the number of edges plus k + 1 choose 2.
New time domain decomposition methods for parabolic control problems I: Dirichlet-Neumann and Neumann-Dirichlet algorithms

We present new Dirichlet-Neumann and Neumann-Dirichlet algorithms with a time domain decomposition applied to unconstrained parabolic optimal control problems. After a spatial semi-discretization, we use the Lagrange multiplier approach to derive a coupled forward-backward optimality system, which can then be solved using a time domain decomposition. Due to the forward-backward structure of the optimality system, three variants can be found for the Dirichlet-Neumann and Neumann-Dirichlet algorithms. We analyze their convergence behavior and determine the optimal relaxation parameter for each algorithm. Our analysis reveals that the most natural algorithms are actually only good smoothers, and there are better choices which lead to efficient solvers. We illustrate our analysis with numerical experiments.
Master-slave Deep Architecture for Top-K Multi-armed Bandits with Non-linear Bandit Feedback and Diversity Constraints

We propose a novel master-slave architecture to solve the top- K combinatorial multiarmed bandits (CMABs) problem with nonlinear bandit feedback and diversity constraints, which, to the best of our knowledge, is the first combinatorial bandits setting considering diversity constraints under bandit feedback. Specifically, to efficiently explore the combinatorial and constrained action space, we introduce six slave models with distinguished merits to generate diversified samples well balancing rewards and constraints as well as efficiency. Moreover, we propose teacher learning-based optimization and the policy cotraining technique to boost the performance of the multiple slave models. The master model then collects the elite samples provided by the slave models and selects the best sample estimated by a neural contextual UCB-based network (NeuralUCB) to decide on a tradeoff between exploration and exploitation. Thanks to the elaborate design of slave models, the cotraining mechanism among slave models, and the novel interactions between the master and slave models, our approach significantly surpasses existing state-of-the-art algorithms in both synthetic and real datasets for recommendation tasks. The code is available at https://github.com/huanghanchi/Master-slave-Algorithm-for-Top-K-Bandits.
More considerations about the symmetry of the stress tensor of fluids

Regarding a recent dispute about the symmetry of the stress tensor of fluids, more considerations are presented. The usual proofs of this symmetry are reviewed, and contradictions between this symmetry and the mechanism of gas viscosity are analyzed for simple gas flows. It is emphasized that these proofs may not be valid because they depend on the theorem of angular momentum which preassumes that the internal forces between any two fluid particles are always along the line connecting them. From Newton's laws of motion, however, one can only obtain the theorem of angular momentum with an additional term. It is proved that this additional term represents the total moment of internal forces in a fluid, both by using continuum model and by considering the microscopic structure of the fluid. In the latter case, this term has the form of the total moment of the forces exerted on the nuclei by the electrons. This moment of internal forces may lead to nonsymmetry of the stress tensor and is in general nonzero as long as shear stress exists. A nonsymmetrical stress tensor suggested in the literature is discussed in terms of its effect in eliminating the contradictions and simplifying the Navier-Stokes equation. The derivation of this stress tensor for ideal gases based on the kinetic theory of gas molecules is presented, and its form in a general orthogonal curvilinear coordinate system is given. Finally, a possible experimental verification of this nonsymmetrical stress tensor is discussed.
Reinforcement Learning Informed Evolutionary Search for Autonomous Systems Testing

Evolutionary search-based techniques are commonly used for testing autonomous robotic systems. However, these approaches often rely on computationally expensive simulator-based models for test scenario evaluation. To improve the computational efficiency of the search-based testing, we propose augmenting the evolutionary search (ES) with a reinforcement learning (RL) agent trained using surrogate rewards derived from domain knowledge. In our approach, known as RIGAA (Reinforcement learning Informed Genetic Algorithm for Autonomous systems testing), we first train an RL agent to learn useful constraints of the problem and then use it to produce a certain part of the initial population of the search algorithm. By incorporating an RL agent into the search process, we aim to guide the algorithm towards promising regions of the search space from the start, enabling more efficient exploration of the solution space. We evaluate RIGAA on two case studies: maze generation for an autonomous ant robot and road topology generation for an autonomous vehicle lane keeping assist system. In both case studies, RIGAA converges faster to fitter solutions and produces a better test suite (in terms of average test scenario fitness and diversity). RIGAA also outperforms the state-of-the-art tools for vehicle lane keeping assist system testing, such as AmbieGen and Frenetic.
Unveiling the Evolutionary State of Three B Supergiant Stars: PU Gem, ϵ CMa, and η CMa

We aim to combine asteroseismology, spectroscopy, and evolutionary models to establish a comprehensive picture of the evolution of Galactic blue supergiant stars (BSG). To start such an investigation, we selected three BSG candidates for our analysis: HD 42087 (PU Gem), HD 52089 (ϵ CMa), and HD 58350 (η CMa). These stars show pulsations and were suspected to be in an evolutionary stage either preceding or succeding the red supergiant (RSG) stage. For our analysis, we utilized the 2-min cadence TESS data to study the photometric variability, and we obtained new spectroscopic observations at the CASLEO observatory. We used non-LTE radiative transfer models calculated with CMFGEN to derive their stellar and wind parameters. For the fitting procedure, we included CMFGEN models in the iterative spectral analysis pipeline XTgrid to determine their CNO abundances. The spectral modeling was limited to changing only the effective temperature, surface gravity, CNO abundances, and mass-loss rates. Finally, we compared the derived metal abundances with prediction from Geneva stellar evolution models. The frequency spectra of all three stars show stochastic oscillations and indications of one nonradial strange mode, fr= 0.09321 d−1 in HD 42087 and a rotational splitting centred in f2= 0.36366 d−1 in HD 52089. We conclude that the rather short sectoral observing windows of TESS prevent establishing a reliable mode identification of low frequencies connected to mass-loss variabilities. The spectral analysis confirmed gradual changes in the mass-loss rates, and the derived CNO abundances comply with the values reported in the literature. We were able to achieve a quantitative match with stellar evolution models for the stellar masses and luminosities. However, the spectroscopic surface abundances turned out to be inconsistent with the theoretical predictions. The stars show N enrichment, typical for CNO cycle processed material, but the abundance ratios did not reflect the associated levels of C and O depletion. We found HD 42087 to be the most consistent with a pre-RSG evolutionary stage, HD 58350 is most likely in a post-RSG evolution and HD 52089 shows stellar parameters compatible with a star at the TAMS.
3D Pose Nowcasting: Forecast the Future to Improve the Present

Technologies to enable safe and effective collaboration and coexistence between humans and robots have gained significant importance in the last few years. A critical component useful for realizing this collaborative paradigm is the understanding of human and robot 3D poses using non-invasive systems. Therefore, in this paper, we propose a novel vision-based system leveraging depth data to accurately establish the 3D locations of skeleton joints. Specifically, we introduce the concept of Pose Nowcasting, denoting the capability of the proposed system to enhance its current pose estimation accuracy by jointly learning to forecast future poses. The experimental evaluation is conducted on two different datasets, providing accurate and real-time performance and confirming the validity of the proposed method on both the robotic and human scenarios.
Differentiable Microscopy Designs an All Optical Phase Retrieval Microscope

Since the late 16th century, scientists have continuously innovated and developed new microscope types for various applications. Creating a new architecture from the ground up requires substantial scientific expertise and creativity, often spanning years or even decades. In this study, we propose an alternative approach called"Differentiable Microscopy,"which introduces a top-down design paradigm for optical microscopes. Using all-optical phase retrieval as an illustrative example, we demonstrate the effectiveness of data-driven microscopy design through $\partial\mu$. Furthermore, we conduct comprehensive comparisons with competing methods, showcasing the consistent superiority of our learned designs across multiple datasets, including biological samples. To substantiate our ideas, we experimentally validate the functionality of one of the learned designs, providing a proof of concept. The proposed differentiable microscopy framework supplements the creative process of designing new optical systems and would perhaps lead to unconventional but better optical designs.
Short Run Transit Route Planning Decision Support System Using a Deep Learning-Based Weighted Graph

Public transport routing plays a crucial role in transit network design, ensuring a satisfactory level of service for passengers. However, current routing solutions rely on traditional operational research heuristics, which can be time-consuming to implement and lack the ability to provide quick solutions. Here, we propose a novel deep learning-based methodology for a decision support system that enables public transport (PT) planners to identify short-term route improvements rapidly. By seamlessly adjusting specific sections of routes between two stops during specific times of the day, our method effectively reduces times and enhances PT services. Leveraging diverse data sources such as GTFS and smart card data, we extract features and model the transportation network as a directed graph. Using self-supervision, we train a deep learning model for predicting lateness values for road segments. These lateness values are then utilized as edge weights in the transportation graph, enabling efficient path searching. Through evaluating the method on Tel Aviv, we are able to reduce times on more than 9\% of the routes. The improved routes included both intraurban and suburban routes showcasing a fact highlighting the model's versatility. The findings emphasize the potential of our data-driven decision support system to enhance public transport and city logistics, promoting greater efficiency and reliability in PT services.
Distribution of Zeckendorf expressions

By Zeckendorf's Theorem, every positive integer is uniquely written as a sum of distinct non-adjacent Fibonacci terms. In this paper, we investigate the asymptotic formula of the number of binary expansions $<x$ that have no adjacent terms, and generalize the result to the setting of general linear recurrences with non-negative integer coefficients.
Practical limitations on robustness and scalability of quantum Internet

As quantum theory allows for information processing and computing tasks that otherwise are not possible with classical systems, there is a need and use of quantum Internet beyond existing network systems. At the same time, the realization of a desirably functional quantum Internet is hindered by fundamental and practical challenges such as high loss during transmission of quantum systems, decoherence due to interaction with the environment, fragility of quantum states, etc. We study the implications of these constraints by analyzing the limitations on the scaling and robustness of quantum Internet. Considering quantum networks, we present practical bottlenecks for secure communication, delegated computing, and resource distribution among end nodes. Motivated by the power of abstraction in graph theory (in association with quantum information theory), we consider graph-theoretic quantifiers to assess network robustness and provide critical values of communication lines for viable communication over quantum Internet. In particular, we begin by discussing limitations on usefulness of isotropic states as device-independent quantum key repeaters which otherwise could be useful for device-independent quantum key distribution. We consider some quantum networks of practical interest, ranging from satellite-based networks connecting far-off spatial locations to currently available quantum processor architectures within computers, and analyze their robustness to perform quantum information processing tasks. Some of these tasks form primitives for delegated quantum computing, e.g., entanglement distribution and quantum teleportation. For some examples of quantum networks, we present algorithms to perform different quantum network tasks of interest such as constructing the network structure, finding the shortest path between a pair of end nodes, and optimizing the flow of resources at a node.