Miracle-dz/UniFAR-FTUs huggingface dataset:
Widespread latent hyperactivity of nociceptors outlasts enhanced avoidance behavior following incision injury

Nociceptors with somata in dorsal root ganglia (DRGs) exhibit an unusual readiness to switch from an electrically silent state to a hyperactive state of tonic, nonaccommodating, low-frequency, irregular discharge of action potentials (APs). Ongoing activity (OA) during this state is present in vivo in rats months after spinal cord injury (SCI), and has been causally linked to SCI pain. OA induced by various neuropathic conditions in rats, mice, and humans is retained in nociceptor somata after dissociation and culturing, providing a powerful tool for investigating its mechanisms and functions. An important question is whether similar nociceptor OA is induced by painful conditions other than neuropathy. The present study shows that probable nociceptors dissociated from DRGs of rats subjected to postsurgical pain (induced by plantar incision) exhibit OA. The OA was most apparent when the soma was artificially depolarized to a level within the normal range of membrane potentials where large, transient depolarizing spontaneous fluctuations (DSFs) can approach AP threshold. This latent hyperactivity persisted for at least 3 weeks, whereas behavioral indicators of affective pain – hindpaw guarding and increased avoidance of a noxious substrate in an operant conflict test – persisted for 1 week or less. An unexpected discovery was latent OA in neurons from thoracic DRGs that innervate dermatomes distant from the injured tissue. The most consistent electrophysiological alteration associated with OA was enhancement of DSFs. Potential in vivo functions of widespread, low-frequency nociceptor OA consistent with these and other findings are to amplify hyperalgesic priming and to drive anxiety-related hypervigilance.
Projections from thalamic nucleus reuniens to hippocampal CA1 area enable context value updating

The ability to extinguish contextual fear in a changing environment is crucial for animal survival. The ‘classic’ neuronal circuit of contextual fear extinction includes the medial prefrontal cortex (mPFC), amygdala, and hippocampus. However, recent data also support the role of the thalamic nucleus reuniens (RE) in this process. Here, we aimed to determine whether the projections from RE to the dorsal hippocampal CA1 area (RE→dCA1) play a role in updating context-dependent memories and to characterize the underlying synaptic processes. We used chemogenetic manipulations and conducted ex vivo field recordings in mice to analyze the impact of RE afferents on synaptic transmission in the dCA1. Next, we employed virally mediated labeling of RE afferents in Thy1-GFP mice to analyze the RE buttons and dendritic spines (as a proxy for glutamatergic synapses) in dCA1 during contextual fear memory extinction. This approach was complemented by 3D electron microscopy to analyze synaptic changes with nano-scale resolution. Finally, using projection-specific chemogenetic manipulation, we tested the role of the RE→CA1 projection in the classic contextual fear extinction paradigm, as well as in the extinction of aversive and appetitive contextual memories in a close-to-ecological environment of IntelliCages. Overall, our data demonstrate that RE regulates synaptic transmission in the stratum oriens (SO) and lacunosum-moleculare of the CA1 area (SLM). Extinction of contextual fear induces synaptic plasticity in these regions. However, only the changes in SLM are specific for the dendrites innervated by RE. Finally, we demonstrate that the RE→dCA1 pathway contributes to updating contextual memories independent of their valence.
Gene-specific RNA homeostasis revealed by perturbation of the NuA4/Tip60 acetyltransferase complex

Transcript buffering entails the reciprocal modulation of mRNA synthesis and degradation rates, ensuring a constant RNA concentration amidst changes in cellular conditions. While an increasing body of research supports a global, non-sequence-specific linkage between mRNA synthesis and degradation, the underlying mechanisms remain elusive. To explore this, we investigated alterations in RNA metabolism following the acute depletion of TIP60/KAT5, the transcriptional coactivator and acetyltransferase subunit of the NuA4 complex, in mouse embryonic stem cells. By combining RNA sequencing of nuclear, cytoplasmic, and newly synthesised transcript fractions with biophysical modelling, we show that TIP60 activates transcription of numerous genes, with substantially fewer genes undergoing transcriptional repression. Surprisingly, specific RNA species’ transcription changes triggered by TIP60 depletion were counterbalanced by compensatory adjustments in RNA export and/or stability within the nucleus and in RNA stability within the cytoplasm. These discoveries imply that transcript buffering operates on a gene-specific level and suggest that cells continually monitor RNA molecule counts in nuclear and cytoplasmic compartments to maintain cellular homeostasis.
Discovery and Characterization of Terpene Synthases Powered by Machine Learning

Terpene synthases (TPSs) generate the scaffolds of the largest class of natural products, including several first-line medicines. The amount of available protein sequences is increasing exponentially, but computational characterization of their function remains an unsolved challenge. We assembled a curated dataset of one thousand characterized TPS reactions and developed a method to devise highly accurate machine-learning models for functional annotation in a low-data regime. Our models significantly outperform existing methods for TPS detection and substrate prediction. By applying the models to large protein sequence databases, we discovered seven TPS enzymes previously undetected by state-of-the-art computational tools and experimentally confirmed their activity. Furthermore, we discovered a new TPS structural domain and distinct subtypes of previously known domains. This work demonstrates the potential of machine learning to speed up the discovery and characterization of novel TPSs.
Influence of Agricultural Land Use on Allochthonous Input and Leaf Breakdown in Southern Appalachian Streams

Streams and terrestrial ecosystems are linked through allochthonous organic matter inputs from streamside vegetation. This allochthonous material makes up the energy base for forested aquatic food webs. Therefore, removal of riparian vegetation associated with agricultural land use affects stream ecosystem structure and function. The objectives of this study were to measure and compare allochthonous input and leaf breakdown rates along a gradient of agricultural land use in southern Appalachian streams. Study streams were placed into the following land use categories: forest and light, moderate, and heavy agriculture. Several physical, chemical, and biological parameters also were measured including discharge, temperature, nutrient concentrations, macroinvertebrate abundance and density, periphyton biomass, and chlorophyll a concentration. In forested, light agricultural, and moderate agricultural streams, the quantity and quality of allochthonous input were not significantly different. However, the timing and composition of allochthonous materials were related to land use. Chlorophyll a and periphyton biomass did not vary among land use types. Leaf breakdown rates were significantly faster in light and moderate agricultural streams in comparison to forested and heavy agricultural streams. Slow breakdown rates in forested streams resulted from low nutrient concentration and cool stream temperature. The scarcity of shredding macroinvertebrates and sedimentation probably limited leaf breakdown in heavy agricultural streams. Though limited riparian vegetation along agricultural streams resulted in an energy supply equivalent to forested streams, agricultural land use may still have long term impacts on stream structure including nutrient concentrations, temperature, macroinvertebrate community, and sedimentation thus affecting stream ecosystem function.
Research Paper The Effect of Licorice on Serum Lipid Peroxidation and Telomere Length of Immune Cells in Japanese Quail (as an Animal Model) Following Vaccination by Salmonella Bacterin

Background: Bacterin-based vaccination may be a stressful condition, leading to harmful effects, such as high lipid peroxidation and telomere shortening. This study aimed to reduce these probable side effects via the administration of different concentrations of licorice (because of its anti-oxidative and anti-aging properties). Materials and Methods: Japanese quails as animal models were reared for 42 days under standard conditions. The birds were divided into eight groups, including a control group, licorice treatments (licorice 0.02%, Licorice 0.04%, and licorice 0.08% groups), and Salmonella bacterin treatments (bacterin, bacterin+licorice 0.02%, bacterin+licorice 0.04%, and bacterin+licorice 0.08% groups). The Salmonella enterica bacterin-based vaccine was injected twice on days 14 and 28. Serum lipid peroxidation and telomere length of immune cells in all experimental groups were measured. Results: Lipid peroxidation and telomere shortening enhanced following bacterin vaccination. Licorice at doses of 0.04%, 0.08%, or both reduced the lipid peroxidation and telomere shortening in non-vaccinated and vaccinated birds, while the dose of 0.02% was not effective. Conclusion: This study confirmed the side effects of high lipid peroxidation and telomere attrition for S. enterica bacterin-based vaccination. Also, the improving properties of licorice for these side effects were considered to be effective.
Bio-Algorithms and Med-Systems Application of Quantum Entanglement Induced Polarization for Dual-Positron and Prompt Gamma Imaging

The intrinsic resolution of Positron Emission Tomography (PET) imaging is bound by positron range effects, wherein the radioactive decay of the imaging tracer occurs at a disjoint location from positron annihilation. Compounding this issue are the variable ranges positrons achieve, depending on tracer species (the energy they are emitted with) and the medium they travel in (bone vs soft tissue, for example) – causing the range to span more than an order of magnitude across various study scenarios (~0.19 mm to ~6.4 mm). Radioisotopes, such as Zr-89, exhibit dual emissions of positron and prompt gammas, offering an opportunity for accurate tracer positioning as prompt gammas originate from the tracer location. These multi-emission radiotracers have historically suffered from increased noise corresponding to the third gamma interfering in annihilation gamma coincidence pairing. Recent advancements, however, have brought to light the unique property of annihilation gammas having scattering kinematics distinct from random gamma pairs. These properties are born from the singular quantum entanglement state available to the gamma pair following para--positronium decay which prescribes linearly orthogonal polarization. Such coherent polarization is not shared by prompt gamma emissions, offering an opportunity for their discrimination. We present an investigation into this technique, comparing the distribution of relevant scattering kinematics of entangled annihilation gammas and corresponding prompt gammas via a Monte Carlo simulation.
Effectiveness of Holistic Community Development on Households` Well -B eing and Women Empowerment: Evidence from Panel Data

There has been marked progress on reducing poverty over the past decades. Heifer International Bangladesh has under taken an initiative which provides key income generating inputs to rural poor women. It is important to identify the effectiveness of these intervention programs for further policy intervention. This is the first paper examining the impact of the intervention programs of self help group on households` well-being and women empowerment in Bangladesh for panel data from 2013 to 2016. The We found that the intervention through community development program of self help group is statistically significantly positively associated with households` well-being improvement and women empowerment through participation of different activities. Moreover, we found no significant evidence of difference between male headed and female headed households in terms of intervention effect although there are significant differences among the locations. This indicates that female headed households are equally likely to adopt the intervention technologies.
A C-DCGAN-based method for generating extreme risk scenarios of high percentage new energy systems

Extreme events have occurred frequently in recent years, and the extreme uncertainty of source -loads of high percentage new energy systems brings risks to the safe and stable operation of power systems. Therefore, this paper proposes a C-DCGAN-based extreme risk scenario generation method for high percentage new energy systems. Firstly, an improved deep convolutional conditional adversarial generation network C-DCGAN suitable for new energy output and load scenario generation is designed by combining the advantages and disadvantages of CGAN and DCGAN. secondly, considering the source-load risk features of the extreme scenarios of the system, the respective extreme measures are established from the three risk perspectives reflecting the threats of wind and power abandonment, power supply tension, security and stability, which are used as the extra conditions for generating samples in line with the extreme scenario risk characteristics. Then, in order to improve the model training stability and reduce the metric error, the Wasserstein distance is adopted and the extreme loss term is added as the loss function of the adversarial generation network. Finally, training the historical data of a region's scenery in Python, and the source-load risk scenario close to the real scenario is generated using the power supply tension as the input of the extreme condition, thus verifying the stability of the model training and the validity of the method proposed in this paper.
Enhancing Career-Related Teacher Support Through Technologies: The NEFELE Training Model

The NEFELE project (Neet prevention in Educational systems through positive Future vision Enhancing Learning and teacher Education), co-funded by the Erasmus+ program of the European Union, aims to enhance Higher Education practices by providing innovative teacher methodologies for pre-service and in-service teachers to learn how to support adolescent career construction. The paper presents the NEFELE training model that designs, implements, and experiences learning opportunities that have the power to improve teaching and enhance student career planning. The NEFELE common framework will be delivered to teachers through Massive Open Online Courses. Moreover, during the MOOC lessons, teachers will be trained to use also the NEFELE game, based on the Tangible User Interfaces, as a supportive technology they can use in the classroom with their students.
Recognition of Greek Alphabet Characters with Memristive Neuromorphic Circuit

Pattern recognition is a critical aspect of modern computing and artificial intelligence, enabling robotic and computing systems to classify and categorize information based on its patterns and relationships. In this context, memristors are emerging as a promising new type of device for pattern recognition due to their memory capabilities as non-volatile two-terminal elements and in-memory computing potential. In this work, we present a novel approach to character recognition using CBRAM memristors by combining traditional machine learning techniques with a memristor-based oscillator exhibiting neuromorphic properties. The proposed approach demonstrates the feasibility of a pattern recognition specific hardware based on memristors that can be integrated in edge computing applications, aiming to bring computation and data storage closer to the source of data, rather than relying on centralized data centers, and consequently, leading to improved latency, increased reliability, and reduced costs.
IMTP: Intention-Matching Trajectory Prediction for Autonomous Vehicles

Trajectory prediction for surrounding vehicles is critical for ensuring the safety of autonomous driving. In this paper, we introduce a novel prediction framework named Intention-Matching Trajectory Prediction (IMTP). Different from existing results that predict trajectories based on only environmental information and historical trajectories, the proposed method initially identifies the possible intentions of surrounding vehicles based on the environment and generates intention-informed trajectories based on the physical vehicle model. Historical trajectories are then used to identify the intention and trajectory with the highest probability. The proposed framework effectively integrates the physical vehicle model, road-related environmental factors, and interactions among surrounding vehicles. A comparative study conducted on a public dataset demonstrates that our framework enhances both prediction accuracy and robustness.
Dynamic DNN Model Switching for Collaborative Edge Intelligence in UAV Swarm

Deploying machine learning (ML) algorithms on Unmanned Aerial Vehicle (UAV) swarm to perform real-time tasks such as object detection is currently a hot research topic. However, as the parameter size of deep neural network (DNN) models continues to grow rapidly, deploying complex DNN models on resource-constrained UAV swarm poses significant challenges. In this paper, we first study how to dynamically switch between small lightweight DNN model and large complex DNN model for efficient edge intelligence in a UAV swarm, where the former is executed by a single UAV and the latter executed by multiple UAVs collaboratively. Specifically, we formulate an optimization problem to maximize the utility capturing the tradeoff between the average accuracy and latency per DNN inference request, with the constraints of computation and memory, under the time-varying UAV-to-UAV communication links. To address it, we propose an algorithm called Dynamic Model Switching (DMS) based on genetic algorithms to select the best DNN model switching stragety, according to the current status of UAV communication network. Trace-driven simulation results validate the effectiveness of the proposed DMS algorithm by always achieving the highest utility value when varying number of DNN inference requests or number of UAVs, compared to four baseline algorithms.
Integrated Sensing and Communication Transmit Beamforming Design for Double-Function Receivers

This paper focuses on the beamforming design of a downlink integrated sensing and communication (ISAC) system considering multiple passive eavesdropping targets. In the considered model, we use sensing signals as artificial noise to conceal confidential information for secure transmission. For efficient signal processing, beamforming schemes are proposed for both conventional and ISAC-dedicated receivers. We aim to minimize the overall transmission rate of all eavesdroppers for conventional receivers that cannot perform noise cancellation. To solve this problem, an iterative algorithm based on successive convex approximations is proposed. As for ISAC-dedicated receivers that can cancel interference, we propose a low-complexity iterative algorithm. Simulation results verify the superiority of the proposed algorithms.
Stochastic Physics-Informed Deep Generative Network Scenario Generation: Application on Responsive Residential Load Management

This paper introduces a stochastic model for the optimal residential responsive loads power scheduling considering participants’ satisfaction. In this context, firstly, a physical-informed based generative adversarial network (PI-GAN) network is designed for scenario generation with a high correlation with the actual data. In this network, conventional GANs are improved to learn spatial-temporal features of the residential loads and physics-informed concepts. To realize the spatial feature of the complex and highly nonlinear time series like residential loads, a residual convolutional neural network (Res-CNN) is considered to learn the spatial features, while the fully temporal features are realized by gated recurrent neural networks (GNN). Then, generated scenarios are used to cover the uncertainty associated with residential loads and provide the optimal results for responsive loads, including shiftable and curtailable loads. The numerical results on actual data in London, England, verify the effectiveness of the proposed stochastic framework and superiority by comparison with conditional GAN and improved version of GAN in scenario generations impact of stochastic demand response program.
Time Series Database Optimization Based on InfluxDB

The performance issues of InfluxDB’s TSM (Time-Structured Merge) storage engine, which is based on the LSMTree architecture, have been addressed in this paper. The focus is on eliminating write stalls, reducing write amplification, and optimizing resource utilization in scenarios with continuous longterm writes. An optimization design and implementation have been presented for InfluxDB to achieve these objectives. Moreover, to further enhance the modifications made to InfluxDB’s TSM storage engine, a compute-storage separated cluster architecture has been introduced and implemented. The objective is to tackle the native clustering solution’s lack of scalability. Experimental results demonstrate that the transformations applied to the InfluxDB storage engine in this paper have successfully achieved the design objectives, leading to improved write performance.
Mechanical Categorization of Open Source Projects

Open source projects with open source software (OSS) have permeated society, and no industry can exist without OSS. At the same time, the risks and life cycle of OSS have severely impacted business and social life. Therefore, organizations utilizing OSS have started to analyze trends of OSS and use the analysis in their own organization's strategy. In an effort to look at OSS from a bird's eye view, OSS classification is important for human recognition. Without the OSS category, important OSS for the organization's business will be missed among too many OSS. At present, as multiple initiatives coexist, each uses its specialized OSS category, even in a single organization. For a long time, OSS stakeholders have manually organized OSS categories, and they have put in a great deal of effort. If manual work continues in the future, much effort will be required in the rapidly changing world of OSS. This research is fundamental and widely available for every analysis related to OSS, providing a universal methodology for OSS categorization with high validation accuracies of 0.98 by means of artificial intelligence technology. During this execution, there was an issue that the accuracy was low due to the small amount of suitable training data for OSS, but it was resolved with data augmentation.
Overview on Leakage Current Reduction Methods in Single-Phase Grid-Connected Inverters

Ground leakage currents can occur in transformerless grid-connected photovoltaic inverter systems, posing safety and performance issues. This paper provides a brief overview of recent research efforts focused on reducing or eliminating ground leakage currents in transformerless grid-connected single-phase voltage source inverters. The main objective of this study is to examine and describe the various methods used, including modifications or proposals for topologies, switching schemes, and filters. The paper aims at offering insights into the progress made to address the problem of ground leakage currents and their mitigation strategies in transformerless grid-connected PV inverters.