Miracle-dz/UniFAR-FTUs huggingface dataset:
ARG1 Is a Peripheral Membrane Protein That Modulates Gravity-Induced Cytoplasmic Alkalinization and Lateral Auxin Transport in Plant Statocytes

ARG1 ( ALTERED RESPONSE TO GRAVITY ) is required for normal root and hypocotyl gravitropism. Here, we show that tar-geting ARG1 to the gravity-perceiving cells of roots or hypocotyls is sufficient to rescue the gravitropic defects in the corresponding organs of arg1-2 null mutants. The cytosolic alkalinization of root cap columella cells that normally occurs very rapidly upon gravistimulation is lacking in arg1-2 mutants. Additionally, vertically grown arg1-2 roots appear to accumulate a greater amount of auxin in an expanded domain of the root cap compared with the wild type, and no detectable lateral auxin gradient develops across mutant root caps in response to gravistimulation. We also demonstrate that ARG1 is a peripheral membrane protein that may share some subcellular compartments in the vesicular trafficking pathway with PIN auxin efflux carriers. These data support our hypothesis that ARG1 is involved early in gravitropic signal transduction within the gravity-perceiving cells, where it influences pH changes and auxin distribution. We propose that ARG1 affects the localization and/or activity of PIN or other proteins involved in lateral auxin transport. We analyzed the and of the Arabidopsis protein in We showed that ARG1 in the is to mediate a wild-type response. ARG1 is required for the gravity-induced cytosolic alkalinization of the columella We also showed that arg1-2 a of auxin than do and they seem in the generation of a ARG1 is a peripheral membrane protein that is with interact with the cytoskeleton. observations support a role for in transduction within For visualization of GFP signals, excised roots from 4-day-old seed- lings were mounted live in water and examined with a Bio-Rad MRC-1024 confocal microscope (Keck Biological Imaging Laboratory) with a 488-nm excitation line and a 522-nm emission band-path filter or a multiphoton optical workstation equipped with a Ti:sapphire laser tuned to 900 nm (Wokosin 2003) (Laboratory
MSDAR: Multi-Stage Dynamic Architecture Intrusion Detection System

—Ad hoc networks have been through extensive research in the last decade. Even with their desirable characteristics, major issues related to their security need to be considered. Various security solutions have been proposed to reduce the risks of malicious actions. They mainly focus on key management, authentication, secure localization, and aggregation techniques. These techniques have been proposed to secure wireless communications but they can only deal with external threats. Therefore, they are considered the first line of defense. Intrusion detection systems are always required to safeguard ad hoc networks as such threats cannot be completely avoided. In this paper, we present a comprehensive survey on intrusion detection systems in ad hoc networks. The intrusion detection systems and components and taxonomy as well as different implementations and types of IDSs are studied and categorized. In addition, we provide a comparison between different Intrusion Detection Systems’ architectures. We also propose a Multi Stage Dynamic Architecture intrusion detection system (MSDAR), designed with a multi-stage detection approach making use of both signature-based and anomaly detection benefits. Our proposed intrusion detection system MSDAR is featured by its dynamic architecture as it can be deployed in the network using the Distributed Hierarchical Architecture. The viability and performance of the proposed system MSDAR are tested against the Distributed Denial of Service Attacks through simulations. Advanced performance parameters were used to evaluate the proposed scheme MSDAR. Experimental results have shown that the performance of MSDAR improves by using multiple stages of different detection mechanisms. In addition, based on simulations, the Detection Rate increases when the sensitivity level increases.
Neural Representations of Post-Decision Choice Confidence and Reward Expectation in the Caudate Nucleus and Frontal Eye Field

Performance monitoring that supports ongoing behavioral adjustments is often examined in the context of either choice confidence for perceptual decisions (i.e., “did I get it right? ”) or reward expectation for reward-based decisions (i.e., “what reward will I receive? ”). However, our understanding of how the brain encodes these distinct evaluative signals remains limited because they are easily conflated, particularly in commonly used two-alternative tasks with symmetric rewards for correct choices. Previously we reported behavioral and neural results related to decision formation by monkeys performing a visual motion discrimination task with asymmetric rewards (Doi et al., 2020; Fan et al., 2020). Here we leveraged this task design to partially decouple trial-by-trial estimates of choice confidence and reward expectation and examine their impacts on behavior and their representations in the caudate nucleus (part of the striatum in the basal ganglia) and the frontal eye field (FEF, in prefrontal cortex). We found that these evaluative signals had infrequent, but consistent, effects on the behavior of well-trained monkeys. We also identified distinguishable representations of the two types of signals in FEF and caudate neurons, including different distribution patterns, time courses, and relationships to behavior in the two brain areas. These results suggest that the cortico-striatal decision network may use diverse evaluative signals for performance monitoring and add to our understanding of the different roles of the FEF and caudate nucleus in decision-related computations.
Development of Explainable Data-Driven Turbulence Models with Application to Liquid Fuel Nuclear Reactors

Liquid fuel nuclear reactors offer innovative possibilities in terms of nuclear reactor designs and passive safety systems. Molten Salts Reactors (MSRs) with a fast spectrum are a particular type of these reactors using liquid fuel. MSFRs often involve large open cavities in their core in which the liquid fuel circulates at a high speed to transport the heat generated by the nuclear reactions into the heat exchangers. This high-speed flow yields a turbulent field with large Reynolds numbers in the reactor core. Since the nuclear power, the neutron precursor’s transport and the thermal exchanges are strongly coupled in the MSFR’s core cavity, having accurate turbulent models for the liquid fuel flow is necessary to avoid introducing significant errors in the numerical simulations of these reactors. Nonetheless, high-accuracy simulations of the turbulent flow field in the reactor cavity of these reactors are usually prohibitively expensive in terms of computational resources, especially when performing multiphysics numerical calculations. Therefore, in this work, we propose a novel method using a modified genetic algorithm to optimize the calculation of the Reynolds Shear Stress Tensor (RST) used for turbulence modeling. The proposed optimization methodology is particularly suitable for advanced liquid fuel reactors such as the MSFRs since it allows the development of high-accuracy but still low-computational-cost turbulence models for the liquid fuel. We demonstrate the applicability of this approach by developing high accuracy Reynolds-Averaged Navier–Stokes (RANS) models (averaged flow error less than 5%) for a low and a large aspect ratio in a Backward-Facing Step (BFS) section particularly challenging for RANS models. The newly developed turbulence models better capture the flow field after the boundary layer tipping, over the extent of the recirculation bubble, and near the boundary layer reattachment region in both BFS configurations. The main reason for these improvements is that the developed models better capture the flow field turbulent anisotropy in the bulk region of the BFS. Then, we illustrate the interest in using this turbulence modeling approach for the case of an MSFR by quantifying the impact of the turbulence modeling on the reactor key parameters.
Lifting Markov Chains To Mix Faster: Limits and Opportunities

Lifted Markov chains are Markov chains on graphs with added local "memory" and can be used to mix towards a target distribution faster than their memoryless counterparts. Upper and lower bounds on the achievable performance have been provided under specific assumptions. In this paper, we analyze which assumptions and constraints are relevant for mixing, and how changing these assumptions affects those bounds. Explicitly, we find that requesting mixing on either the original or the full lifted graph, and allowing for reducible lifted chains or not, have no essential influence on mixing time bounds. On the other hand, allowing for suitable initialization of the lifted dynamics and/or imposing invariance of the target distribution for any initialization do significantly affect the convergence performance. The achievable convergence speed for a lifted chain goes from diameter-time to no acceleration over a standard Markov chain, with conductance bounds limiting the effectiveness of the intermediate cases. In addition, we show that the relevance of imposing ergodic flows depends on the other criteria. The presented analysis allows us to clarify in which scenarios designing lifted dynamics can lead to better mixing, and provide a flexible framework to compare lifted walks with other acceleration methods.
Nested circuits mediate the decision to vocalize

Vocalizations facilitate mating and social affiliation but may also inadvertently alert predators and rivals. Consequently, the decision to vocalize depends on brain circuits that can weigh and compare these potential benefits and risks. Male mice produce ultrasonic vocalizations (USVs) during courtship to facilitate mating, and previously isolated female mice produce USVs during social encounters with novel females. Earlier we showed that a specialized set of neurons in the midbrain periaqueductal gray (PAG-USV neurons) are an obligatory gate for USV production in both male and female mice, and that both PAG-USV neurons and USVs can be switched on by their inputs from the preoptic area (POA) of the hypothalamus and switched off by their inputs from neurons on the border between the central and medial amygdala (AmgC/M-PAG neurons) (Michael et al., 2020). Here, we show that the USV-suppressing AmgC/M-PAG neurons are strongly activated by predator cues or during social contexts that suppress USV production in male and female mice. Further, we explored how vocal promoting and vocal suppressing drives are weighed in the brain to influence vocal production in male mice, where the drive and courtship function for USVs are better understood. We found that AmgC/M-PAG neurons receive monosynaptic inhibitory input from POA neurons that also project to the PAG, that these inhibitory inputs are active in USV-promoting social contexts, and that optogenetic activation of POA cell bodies that make divergent axonal projections to the amygdala and PAG is sufficient to elicit USV production in socially isolated male mice. Accordingly, AmgC/M-PAG neurons, along with POAPAG and PAG-USV neurons, form a nested hierarchical circuit in which environmental and social information converges to influence the decision to vocalize.
Weed Plant Detection from Agricultural Field Images using YOLOv3 Algorithm

Agriculture is the only way for humans to survive in this world. Weed plant detection and classification are critical technical and economic issues in agriculture. Weed creates problems on the field as they extract good nutrients required by other crops. To encounter this, manual weed detection has recently been carried out with specialized people. Later, as technology advanced, people began using herbicides to kill weeds. People are trying to detect weeds without human intervention, but they were unable to reach the public due to a lack of precision. This paper focuses on weed plant detection techniques that can be used to supplement physical detection methods. We have attempted to use the CNN technique called YOLOV3 which is an object detection technique that will help us to accurately identify weed crops. We aim to counteract and provide a better solution for the existing problems to detect weeds in crops.
Finding vowels without phonology

3. Problems: how many pairs do you need to establish contrast? how close to a minimal pair do they have to be? e.g. does the minimal difference between “riding” [raiRIN] and “writing” [r! iRIN], or between “Cyclops” [saiklaps] and “micron” [m! ikran] mean that [ai] and [! i] are separate vowels in Canadian English (Mielke, Armstrong, & Hume 2003)? how do you tell if any given utterance contains one vowel or another?
Source-Free Multidomain Adaptation With Fuzzy Rule-Based Deep Neural Networks

Unsupervised domain adaptation deals with a task from an unlabeled target domain by leveraging the knowledge gained from labeled source domain(s). The fuzzy system is adopted in domain adaptation to better tackle the uncertainty caused by information scarcity in the transfer. Most existing fuzzy and nonfuzzy domain adaptation methods depend on data-level distribution matching to eliminate the domain shift. However, data sharing can trigger privacy concerns. This situation results in the unavailability of source data, wherein most domain adaptation methods cannot be applied. Source-free domain adaptation is then proposed to handle this problem. But the existing source-free domain adaptation methods rarely deal with any soft information component due to data imprecision. Besides, fewer methods handle multiple source domains that provide richer transfer information. Thus, in this article, we propose source-free multidomain adaptation with fuzzy rule-based deep neural networks, which takes advantage of a fuzzy system to handle data uncertainty in domain adaptation without source data. To learn source private models with high generality, which is important to collect low-noise pseudotarget labels, auxiliary tasks are designed by jointly training source models from multiple domains, which share source parameters and fuzzy rules while protecting source data. To transfer fuzzy rules and fit source private parameters to the target domain, self-supervised learning and anchor-based alignment are built to force target data into source feature spaces. Experiments on real-world datasets under both homogeneous and heterogeneous label space scenarios are carried out to validate the proposed method. The results indicate the superiority of the proposed fuzzy rule-based source-free multidomain adaptation method.
Marine Disaster Detection Using the Geostationary Ocean Color Imager (GOCI)

Recently, harmful algae (e.g., red tide) has damaged human and marine ecosystems. To address this, a response system should be developed to quickly cope with these ocean disasters. However, it is difficult to simultaneously monitor the vast ocean areas. Here, a marine disaster detection system can be developed through a convergence between the satellite-based ocean color remote sensing and the marine sensor network. The system architecture is divided into two steps: first, the system detects ocean anomalies in realtime using the satellite-based techniques, and secondly, the detected disaster information is transferred to the ships via the marine sensor networks. In this paper, we only focused on the first step and the second step is reserved for future work. Although the polar orbit satellite-based ocean color sensor platforms (e.g., MODIS, MERIS, and SeaWifs) can be used to simultaneously monitor the vast ocean areas, they are unsuitable for capturing subtle changes on a geographically equivalent area. On the other hand, the Geostationary Ocean Color Imager (GOCI), the world’s first ocean color remote sensor platform operated on a geostationary orbit, receives ocean color data around the Northeast Asia region every hour, eight times a day. Therefore, GOCI can be more effectively utilized to observe subtle changes and to detect anomalies in ocean environments in real-time. In this paper, we attempted to build a system to monitor marine disasters by detecting ocean anomalies using the ocean color data derived from GOCI. This system directly compares the test spectrum vectors (i.e. anomaly candidates) to a predefined reference spectrum vector (i.e. a target anomaly) through the cosine similarity. The experimental result showed that the proposed system could efficiently detect the disasters (e.g., the red tide) on the ocean environments.
Finite volumes and nonlinear diffusion

— In this paper we prove the convergence of a finite volume scheme to the solution of a Stefan problem, namely the nonlinear diffusion équation u t - Aç>(u) = v, together with a homogeneous Neumann boundary condition and an initial condition. This is done by means of a priori estimâtes in L°° and use of Kolmogorov's theorem on relative compactness of subsets of L . © Elsevier, Paris Résumé. — On démontre la convergence d'un schéma de volumes finis pour un problème de Stefan, défini par Véquation de diffusion non linéaire u t — Aç? ( u) = v avec une condition aux limites de Neumann homogène et une condition initiale bornée. La démonstration de la convergence s'appuie sur des estimations a priori dans L°° et sur l'application du théorème de Kolmogorov sur la compacité relative d'ensembles de L 1 . © Elsevier
137 Cs content in mushrooms from localities in eastern Slovakia

Monitoring of environmental radioactivity is necessary because of development of nuclear energy and exploitation of radioactive materials in various parts of popular activity. Its aim is to determine the influence of radioactivity on health of today’s and future population. Our laboratory is one of the permanent component of radioactivity monitoring network, that carries out regular measurements of soils, hydrosphere, individual parts of food chains as well as aerosols, fallout etc. The amount of artificial radionuclides in the individual compartments of food chains is at present at a very low level, generally at the limit of detection. Values of radiocaesium in individual samples of fruits, vegetables and cereals are within 0.06−0.1 Bq kg. Mushrooms are characterised by high ability to accumulate radiocaesium [1, 3, 7]. The reason why mushrooms work as such good indicators of radioactivity in general is connected with their structure. Their bodies consist of gentle fibres, hyphae. Fungal metabolism differs from that of green plants. Mushrooms are heterotrophic organisms and depend on supply of organic compounds. Water constitutes about 90−95% of mushroom fresh weight. Consumption of wild mushrooms as a delicacy has been high in many countries, mainly in central and eastern Europe and collecting mushrooms has become a national hobby in many countries. A possible risk of radioactivity for human health is expressed by the effective dose [3, 4]. The activity of Cs concentrations depends on several factors, e.g. mushroom species, contamination of soil, moisture, etc. The aim of this study was to determine the amount of radiocaesium in the selected mushroom species from localities in the East of Slovakia. The process of soaking of Cs content in mushrooms from localities in eastern Slovakia Andrea C ∨ ipáková
Attention in Attention for Hyperspectral With High Spatial Resolution (H) Image Classification

An inevitable trend of hyperspectral remote sensing has been toward hyperspectral with high spatial resolution (H2) images. However, the higher resolution also brings higher spatial/spectral heterogeneity of surface features, which increases the difficulty of fine classification. Fully using global spatial–spectral features and contextual information is an effective method to alleviate spatial/spectral heterogeneity. Recently, to extract global spatial–spectral features with long-range dependencies, the self-attention mechanism has been widely used in H2 image classification and has achieved excellent results. As is well known, the simultaneous use of spatial and spectral information has always been a key aspect of hyperspectral image (HSI) processing; however, the current spatial and spectral attention modules only focus on the spatial and spectral features separately. This prevents further improvement in network performance, especially when the sample size is small. Therefore, a spatial–spectral attention-in-attention network (S2AiANet) is proposed, which solves the problem of the current spatial–spectral attention maps only focusing on single features through the spatial–spectral attention-in-attention (S2AiA) module. In addition, a multiscale attention (MSA) module is proposed to enhance the network’s adaptability to various complex scenarios. The experiments on two H2 datasets and one classic HSI dataset demonstrate that S2AiANet can achieve a significant performance improvement compared with the state-of-the-art hyperspectral classifiers.
Personalized Prediction of Stress-Induced Blood Pressure Spikes in Real Time from FitBit Data using Artificial Intelligence: A Research Protocol

Background: Referred to as the "silent killer," elevated blood pressure often goes unnoticed due to the absence of apparent symptoms, resulting in cumulative harm over time. While various health conditions contribute to hypertension, they collectively account for a minority of cases. Chronic stress has been identified as a significant factor in increased blood pressure, and the heterogeneous nature of stress responses makes it challenging to identify specific deleterious behaviors through traditional clinical interviews. Objective: We aim to leverage machine learning algorithms for real-time predictions of stress-induced blood pressure spikes using consumer wearable devices such as FitBit, providing actionable insights to both patients and clinicians to improve diagnostics and enable proactive health monitoring. Methods: The study proposes the development of machine learning algorithms to analyze biosignals obtained from these wearable devices, aiming to make real-time predictions about blood pressure spikes. Results: We have developed the core study application, named CardioMate. CardioMate will be used to remind participants to initiate blood pressure readings using an Omron HeartGuide wearable monitor. The project described is supported as a pilot project from the Robert C. Perry Fund of the Hawaii Community Foundation. This protocol was approved by the University of Hawaii Institutional Review Board (IRB) under protocol #2023-00130. Conclusions: Personalized machine learning when applied to biosignals is a promising approach to providing the mobile sensing backend support for real-time digital health interventions for chronic stress and its corresponding symptoms.