Miracle-dz/UniFAR-FTUs huggingface dataset:
Investigation of CRISPR-Independent Phage Resistance Mechanisms Reveals a Role for FtsH in Phage Adsorption to Streptococcus thermophilus

Streptococcus thermophilus is an important organism for production of cultured dairy foods, but it is susceptible to lytic phages which can lead to failed products. Consequently, mechanisms for phage resistance are an active area of research. ABSTRACT Prokaryotes are under constant pressure from phage infection and thus have evolved multiple means of defense or evasion. While CRISPR-Cas constitutes a robust immune system and appears to be the predominant means of survival for Streptococcus thermophilus when facing lytic phage infection, other forms of phage resistance coexist in this species. Here, we show that S. thermophilus strains with deleted CRISPR-Cas loci can still give rise to phage-resistant clones following lytic phage challenge. Notably, non-CRISPR phage-resistant survivors had multiple mutations which would truncate or recode a membrane-anchored host protease, FtsH. Phage adsorption was dramatically reduced in FtsH mutants, implicating this protein in phage attachment. Phages were isolated which could bypass FtsH-based resistance through mutations predicted to alter tape measure protein translation. Together, these results identify key components in phage propagation that are subject to mutation in the molecular arms race between phage and host cell. IMPORTANCE Streptococcus thermophilus is an important organism for production of cultured dairy foods, but it is susceptible to lytic phages which can lead to failed products. Consequently, mechanisms for phage resistance are an active area of research. One such mechanism is CRISPR-Cas, and S. thermophilus is a model organism for the study of this form of adaptive immunity. Here, we expand on known mechanisms with our finding that spontaneous mutations in ftsH, a gene encoding a membrane-anchored protease, protected against phage infection by disrupting phage adsorption. In turn, mutations in phage tail protein genes allowed phages to overcome ftsH-based resistance. Our results identified components in phage propagation that are subject to mutation in the molecular arms race between phage and host.
Water and sediment quality of an agro‐industrial area through different approaches: Physicochemical parameters and ecotoxicity with amphibians

Amphibians are subject to several stressors in the aquatic and terrestrial environments, and human activities have profoundly impacted this vertebrate group. The aim of the present study was to analyze physicochemical parameters, metals and pesticide residues, and the toxicity of water and sediment samples from an environment with high agricultural activity (S1: Salto stream; S2: drainage channel downstream from S1) by means of bioassays using Rhinella arenarum (Amphibia: Anura) larvae. Metals and pesticides were analyzed in water and sediment samples by fluorescence spectrometer of X‐ray by total reflection and ultra‐high‐performance liquid chromatography‐MS/MS, respectively. For lethality bioassays, 10 larvae (in triplicate) were exposed for 504 h to water and sediment samples. Also, 50 larvae were exposed for 96 h (in triplicate) to water and sediment samples for the evaluation of biomarkers of neurotoxicity, oxidative stress, and genotoxicity. Twenty‐six different pesticides (mainly herbicides) were detected in both sites, and Cu, Zn, and Pb exceeded the limit for protection of aquatic life. Lethality was observed in larvae exposed to water and sediment samples from both sites at chronic exposure. Oxidative stress was observed in larvae exposed to both sites. In larvae exposed to samples from S1, alterations in the neurotoxicity biomarkers were observed. These results alert about the degradation of the sites and highlight the need to monitor and control the use of pesticides.
Schistosome egg antigen stimulates the secretion of miR-33-carrying extracellular vesicles from macrophages to promote hepatic stellate cell activation and liver fibrosis in schistosomiasis

Schistosomiasis is a serious and neglected disease with a high prevalence in tropical and subtropical countries. The primary pathology of hepatic schistosomiasis caused by Schistosoma japonicum (S. japonicum) or Schistosoma mansoni (S. mansoni) infection is egg-induced granuloma and subsequent fibrosis in the liver. Activation of hepatic stellate cells (HSCs) is the central driver of liver fibrosis. Macrophages (Mφ), making up 30% of cells in hepatic granulomas, directly or indirectly regulate HSC activation by paracrine mechanisms, via secreting cytokines or chemokines. Currently, Mφ-derived extracellular vesicles (EVs) are broadly involved in cell communication with adjacent cell populations. However, whether Mφ-derived EVs could target neighboring HSCs to regulate their activation during schistosome infection remains largely unknown. Schistosome egg antigen (SEA) is considered to be the main pathogenic complex mixture involved in liver pathology. Here, we demonstrated that SEA induced Mφ to produce abundant extracellular vesicles, which directly activated HSCs by activating their autocrine TGF-β1 signaling. Mechanistically, EVs derived from SEA-stimulated Mφ contained increased miR-33, which were transferred into HSCs and subsequently upregulated autocrine TGF-β1 in HSCs through targeting and downregulating SOCS3 expression, thereby promoting HSC activation. Finally, we validated that EVs derived from SEA-stimulated Mφ utilized enclosed miR-33 to promote HSC activation and liver fibrosis in S. japonicum-infected mice. Overall, our study indicates that Mφ-derived EVs play important roles in the paracrine regulation of HSCs during the progression of hepatic schistosomiasis, representing a potential target for the prevention of liver fibrosis in hepatic schistosomiasis.
Single-View 3D Mesh Reconstruction for Seen and Unseen Categories

Single-view 3D object reconstruction is a fundamental and challenging computer vision task that aims at recovering 3D shapes from single-view RGB images. Most existing deep learning based reconstruction methods are trained and evaluated on the same categories, and they cannot work well when handling objects from novel categories that are not seen during training. Focusing on this issue, this paper tackles Single-view 3D Mesh Reconstruction, to study the model generalization on unseen categories and encourage models to reconstruct objects literally. Specifically, we propose an end-to-end two-stage network, GenMesh, to break the category boundaries in reconstruction. Firstly, we factorize the complicated image-to-mesh mapping into two simpler mappings, i.e., image-to-point mapping and point-to-mesh mapping, while the latter is mainly a geometric problem and less dependent on object categories. Secondly, we devise a local feature sampling strategy in 2D and 3D feature spaces to capture the local geometry shared across objects to enhance model generalization. Thirdly, apart from the traditional point-to-point supervision, we introduce a multi-view silhouette loss to supervise the surface generation process, which provides additional regularization and further relieves the overfitting problem. The experimental results show that our method significantly outperforms the existing works on the ShapeNet and Pix3D under different scenarios and various metrics, especially for novel objects.
Solución de problemas complejos de ingeniería empleando sistemas cognitivos especializados como motivación en la enseñanza de matemáticas avanzadas para ingeniería

In this article the solution of two-dimensional heat transfer equation with heat generation or Poisson equation for a geometry with straight sides and curved ends, with Dirichlet boundary conditions and Neumann, using an artificial cognitive system (PDE Toolbox for Matlab) as inspiration in teaching advanced mathematics to engineering problems is presented. The proposed methodology is based on the theory for teaching mathematics based on Polya troubleshooting. It was found that the artificial cognitive system (SCA) employed proved to be an ideal to encourage the student's ability to move in different forms of mathematical and physical concepts representation involved in the solution process.
Bidirectional Modulation of Nociception by GlyT2+ Neurons in the Ventrolateral Periaqueductal Gray

Abstract The midbrain periaqueductal gray (PAG), particularly its ventrolateral column (vlPAG), is part of a key descending pathway that modulates nociception, fear and anxiety behaviors in both humans and rodents. It has been previously demonstrated that inhibitory GABAergic neurons within the vlPAG have a major role in this nociceptive modulation. However, the PAG contains a diverse range of neuronal subtypes and the contribution of different subtypes of inhibitory neurons to nociceptive control has not been investigated. Here, we employed a chemogenetic strategy in mice that express Cre recombinase under the promotor for the glycine transporter 2 (GlyT2::cre) to modulate a novel group of glycinergic neurons within the vlPAG and then investigate their role in nociceptive control. We show that activation of GlyT2-PAG neurons enhances cold and noxious heat responses and increases locomotor activity (LMA) in both male and female mice. In contrast, inhibition of GlyT2-PAG neurons reduced nociceptive responses, while locomotor behaviors were unaffected. Our findings demonstrate that GlyT2+ neurons in the vlPAG modulate nociception and suggest that strategies targeting GlyT2-PAG neurons could be used to design novel analgesic therapies.
Lived Experience of Violence Perpetrated by Treated Patients and Their Visitors in Intensive Care Units: A Qualitative Study of Nurses

ABSTRACT Background Patient and visitor violence (PVV) is a widespread problem for health professionals. Nurses working in intensive care units (ICUs) face a relatively high risk of experiencing PVV, which significantly impacts both the health of nurses and the institution as a whole. The subjective perceptions of ICU nurses regarding PVV are inadequately explored in the literature. Purpose The purpose of this study was to explore the perspectives, experiences, and perceptions of PVV in ICU nurses and to better understand the precipitating factors of violence. Methods A phenomenological qualitative design and purposive sampling were used. A semistructured interview guide was used to conduct in-depth interviews with 12 ICU nurses with PVV experiences. Giorgi's method of analysis was used to discover and identify the essential categories of experience. Results Five main experience categories were identified: family and patient factors as flashpoints, managing suppressed emotions by weathering the emotional storm, spiritual awakening after violence, and strategies for surviving further violence. The participants' experiences with PVV included a range of caring and mental health difficulties. In ICU settings, patient progress is often unpredictable, resulting in discrepancies between patient/family expectations and reality. Because feelings of frustration and powerlessness can eventually cause exhaustion in ICU nurses, implementing effective emotional management, stress adjustment, psychological counseling, team support, and violence intervention programs are crucial. Conclusions/Implications for Practice This study provides new information on the process by which nurses can progress from inner trauma to self-recovery, moving from a negative affectivity disposition to a better understanding of threat appraisals and coping response options. Nurses should increase their awareness of the complexity of the phenomenon and of the interplay among the factors underlying PVV. The results of this study suggest that routine confusion and delirium assessments to rule out patients with ICU delirium in ICUs are important to preventing PVV. This study considers some of the implications of the research findings for nursing managers. Interventions, training programs, and/or management action should be used to ensure psychological and mental support is extended to all witnesses of PVV events and not only to those targeted by violence.
Low Precision Quantization-aware Training in Spiking Neural Networks with Differentiable Quantization Function

Deep neural networks have been proven to be highly effective tools in various domains, yet their computational and memory costs restrict them from being widely deployed on portable devices. The recent rapid increase of edge computing devices has led to an active search for techniques to address the abovementioned limitations of machine learning frameworks. The quantization of artificial neural networks (ANNs), which converts the full-precision synaptic weights into low-bit versions, emerged as one of the solutions. At the same time, spiking neural networks (SNNs) have become an attractive alternative to conventional ANNs due to their temporal information processing capability, energy efficiency, and high biological plausibility. Despite being driven by the same motivation, the simultaneous utilization of both concepts has yet to be thoroughly studied. Therefore, this work aims to bridge the gap between recent progress in quantized neural networks and SNNs. It presents an extensive study on the performance of the quantization function, represented as a linear combination of sigmoid functions, exploited in low-bit weight quantization in SNNs. The presented quantization function demonstrates the state-of-the-art performance on four popular benchmarks, CIFAR10-DVS, DVS128 Gesture, N-Caltech101, and N-MNIST, for binary networks (64.05%, 95.45%, 68.71%, and 99.43% respectively) with small accuracy drops and up to 31 × memory savings, which outperforms existing methods.
Integrated batch planning optimization based on particle swarm optimization and constraint satisfaction for steel production

The integrated batch planning of steel production continuous-casting hot-rolling was considered.By analyzing the essential operation properties of highlight problem,an integrated planning strategy was presented and a constraint satisfaction model was constructed.Considering the problem' s NP-hard feature,a hybrid algorithm combining Improved Discrete Particle Swarm Optimization(IDPSO),constraint satisfaction and neighborhood search was proposed to solve this problem.With natural-number-matrix representation,each particle represented one solution to corresponding task allocation problem.Neighborhood search method was used to calculate particle' s fitness value based on heuristic solution of the sort optimization problem.To improve the algorithm' s convergence,the constraint satisfaction technique was employed to generate the initial feasible particle swarms and to revise unfeasible solutions during iterations.Validity of the model and algorithm were tested by calculating the data from production practices.
On a class of elliptic equations with Critical Perturbations in the hyperbolic space

We study the existence and non-existence of positive solutions for the following class of nonlinear elliptic problems in the hyperbolic space $$ -\Delta_{\mathbb{B}^N} u-\lambda u=a(x)u^{p-1} \, + \, \varepsilon u^{2^*-1} \,\;\;\text{in}\;\mathbb{B}^{N}, \quad u \in H^{1}{(\mathbb{B}^{N})}, $$ where $\mathbb{B}^N$ denotes the hyperbolic space, $2<p<2^*:=\frac{2N}{N-2}$, if $N \geqslant 3; 2<p<+\infty$, if $N = 2,\;\lambda<\frac{(N-1)^2}{4}$, and $0<a\in L^\infty(\mathbb{B}^N). $ We first prove the existence of a positive radially symmetric ground-state solution for $a(x) \equiv 1. $ Next, we prove that for $a(x) \geq 1$, there exists a ground-state solution for $\varepsilon$ small. For proof, we employ ``conformal change of metric"which allows us to transform the original equation into a singular equation in a ball in $\mathbb R^N$. Then by carefully analysing the energy level using blow-up arguments, we prove the existence of a ground-state solution. Finally, the case $a(x) \leq 1$ is considered where we first show that there is no ground-state solution, and prove the existence of a \it bound-state solution \rm (high energy solution) for $\varepsilon$ small. We employ variational arguments in the spirit of Bahri-Li to prove the existence of high energy-bound-state solutions in the hyperbolic space.
SPECTRAL EVOLUTION OF BREAKING-NON BREAKING WAVES IN THE SURF ZONE

In the present study the spectral evolution of breaking-non breaking, regular and irregular waves in the surf zone is studied experimentally in a large scale facility. A great number of numerical and experimental studies exists in the literature concerning surf zone dynamics. A part of them is focused on the evolution of variance spectra (Becq-Girard et al, 1999, Beji & Nadaoka K, 1997, Ozanne et al, 1997). The experimental study was conducted in the CIEM flume of the Catalonia University of Technology, Barcelona (fig. 1). Regular and irregular waves of various heights and periods were generated for breaking and non breaking conditions in order to study the spectral variances and the energy decay especially in the surf zone. The irregular waves were generated according to JONSWAP spectrum with a shape parameter γ equal to 3.3. Approximately 1200 waves were generated in order to obtain the appropriate statistical information for the reconstruction of the energy spectrum. It is revealed that for breaking conditions (plunging breaker) the spectrums undergo significant changes under the combined effects on non-linear energy transfer and dissipation (fig.2 & fig.3). The phenomena of higher frequency harmonics generation is more evident in the case of regular waves (fig.3B). Spectra in the outer and in the inner region of breaking for regular and irregular waves verify the intense energy decay (fig.2A-fig.2B & fig.3Afig.3B). The experimental results are compared with an existing Boussinesq type model which incorporates higher-order non-linear terms and additional dispersion terms (according to Madsen & Sorensen, 1992). In the model a new method is used to simulate wave breaking, proposed by Karambas and Tozer (2003). Both turbulent stresses and non-uniform vertical distribution of the horizontal velocity due to the presence of roller are incorporated in the momentum equation. The eddy viscosity coefficient is calculated using a k-model, while the velocity distribution of the breaker is derived adopting an analytical solution of the vorticity transport equation for the turbulent flow, according to Veeramony and Svendsen (2000). The numerical scheme proposed by Wei and Kirby (1995) is used. Wave generation is implemented inside the computational domain using the source function method as described by Wei et al. (1999). This method employs a mass source term in the continuity equation that acts on a limited ‘source region’ while it is combined with wave damping sponge layers at the boundaries. The above method is adapted to be consistent with the Madsen & Sorensen (1992) equations, used in the present work, instead of the Nwogu type of equations, used by Wei et al. (1999). The comparisons in the outer and inner breaking region are shown in Figure 3.
Alkohol- und andere Abhängigkeitserkrankungen in der allgemeinmedizinischen Praxis

Der Früherkennung von Suchterkrankungen stehen die Betroffenen oft aufgrund massiver Schuld- und Schamgefühle und abwehrenden Verhaltens selbst im Wege. Insofern ist es im ärztlichen Gespräch wichtig, Suchtprobleme behutsam und empathisch anzusprechen. Denn tatsächlich ist die Prognose bei Suchterkrankungen, werden diese frühzeitig erkannt und behandelt, ausgesprochen gut. Deshalb ist dieser Thematik ein besonders hoher Stellenwert beizumessen.
External observers' reactions to abusive supervision in the workplace: the impact of racial differences

Abstract This research examines external observers' reactions to abusive supervision in the workplace while accounting for the impact of the abusive supervisor's race and the abused employee's race. We conducted four different studies to examine differences in external observers' protective behavior across the four possible abusive supervisor–abused employee racial combinations. The focus of these studies is on the two largest racial groups in the US: White Americans and African Americans. Our findings reveal that external observers' willingness to protect an abused employee depends significantly on the abused employee's race and the abusive supervisor's race.
Mindfulness older workers and relational leadership

Abstract There is scant research examining both the psychological (individual) and leadership (environmental) influences on older workers. We firstly examine the influence of older workers' mindfulness on their job engagement, job satisfaction and turnover intentions. Secondly, we address effective leadership approaches for older workers, comparing two positive relational leadership styles, leader member exchange and leader autonomy support (LAS). We survey 1,237 participants from 28 organisations in New Zealand and employ structural equation modelling to test our hypotheses using AMOS 24.0. We find that mindful older workers enjoy greater wellbeing and are discerning of the leadership styles that most benefit their engagement, satisfaction and intentions to stay within the organisation. We find that mindfulness has direct importance and LAS has indirect importance on advancing the wellbeing of older workers. Mindful older workers exhibit greater work wellbeing than non-mindful workers, but they also demonstrate greater expectations and discernment of the leadership styles they encounter.
Scalable Hybrid CMOS-Diamond Quantum Magnetometers

Nitrogen-vacancy (NV) centers in diamond have demonstrated outstanding sensing and imaging capabilities. However, their conventional implementations have used discrete instrumentation for microwave generation and optical readout, which increases the system scale. This work presents a chip-scale complementary-metal-oxide-semiconductor (CMOS) platform that integrates the necessary components for NV quantum state control and measurement. Specifically, a scalable CMOS integrated system capable of the control and readout of an ensemble of NV centers for vector magnetic field sensing is discussed. We demonstrate a sensitivity of 245~nT/Hz1/2, representing 130× improvement over our previous CMOS-diamond sensor prototype. This scalable architecture opens the door for sub-nT/Hz1/2 sensitivity in the future and the possibility of incorporating additional functionalities.
SiFI-AI: A Fast and Flexible RTL Fault Simulation Framework Tailored for AI Models and Accelerators

For AI-based systems in safety-critical domains, it is inevitable to understand the impact of random hardware faults affecting the target hardware accelerators. The high degree of data reuse makes Deep Neural Network (DNN) accelerators susceptible to significant fault propagation and hence hazardous predictions. Therefore, we present SiFI-AI, a simulation framework for fault injection in DNN accelerators. SiFI-AI proposes a hybrid simulation approach combining fast AI inference with cycle-accurate RTL simulation. Time-expensive RTL simulation is only used to accurately target registers in the hardware through condition-based fault injection. This enables to reveal vulnerable DNN layers and the related fault origin. In a resilience study with 1.5~M fault injection experiments, we analyze representative DNNs and a state-of-the-art DNN accelerator to identify vulnerable layers. The study only takes 1.15 days which is 7x faster than state-of-the-art. Our experiments show the high impact of control register faults and that narrow and deep layers are 10x more resilient compared to the wide and shallow layers of a DNN.
On the Random Disorientation of two Cubes

We are given two identical symmetrical bodies (e.g., cubes) with independent random orientations; then we can always, in several ways, turn one of these bodies about some axis through its centre of gravity, so as to bring it into the same orientation as the other body. The smallest angle of rotation needed will be called the disorientation, d, of the two bodies, and we shall be concerned with the distribution of d under these conditions.