Miracle-dz/UniFAR-FTUs huggingface dataset:
Structural and functional characterization of AIP56, an apoptogenic exotoxin of Photobacterium damselae piscicida

Bacterial exotoxins are amongst the principal virulence factors of pathogenic bacteria and usually contribute for the majority of symptoms and lesions caused by infection. The AIP56 toxin (apoptosis-inducing protein of 56 kDa) is a major virulence factor of Photobacterium damselae piscicida (Phdp), a Gram-negative bacterium causing a septicaemic infection that is one of the most threatening diseases in mariculture. During infection, the toxin is secreted by Phdp and is systemically disseminated, triggering apoptosis of macrophages and neutrophils. The destruction of both professional phagocytes of the host allows bacterial evasion from phagocytosis, promoting bacterial septicaemic spread. This process culminates in cell lysis by secondary necrosis with release of the phagocyte’s cytotoxic contents, thus contributing to the necrotic lesions observed in the diseased animals. Despite the toxic effects of AIP56 and their contribution to the pathology of Phdp infections are well known, knowledge about the toxin’s structure-function relationships and the molecular determinants beyond its biologic activity was still missing. The work herein presented shows that AIP56 is a zinc-metalloprotease that cleaves NF-κB p65 at Cys, an evolutionarily conserved residue of the Rel homology domain, removing crucial residues involved in DNA interaction and compromising the NF-κB activity. Cleavage was observed upon incubation of sea bass cell lysates or recombinant p65 Rel homology domain with AIP56 and was dependent on the metalloprotease signature of the toxin. Furthermore, the proteolytic activity towards NFκB p65 was found to be essential for toxicity, since mutations within the toxin’s metalloprotease signature completely abolished its apoptogenic activity. Given the central role of the NF-κB family of transcription factors in the host responses to microbial pathogen invasion (particularly regulating the expression of inflammatory and anti-apoptotic genes) it is not surprising that depletion of NF-κB p65 is involved in the disseminated apoptosis observed in Phdp infections. Bacterial toxins acting on intracellular targets enter cells through a multistep process involving specific binding to the cell surface, internalization via receptormediated endocytosis and translocation into the cytosol from an intracellular compartment. The structure of those toxins is adapted to their entry mechanisms and, to this end, these toxins are organized in a so-called AB-type structure consisting of a catalytic A subunit bound to a cell interacting B subunit(s) that mediate toxin binding to a cell receptor and delivery of the catalytic A subunit into the cytosol. Homology
Artificial Neural Network Based Path Loss Prediction for Wireless Communication Network

Accurate path loss (PL) prediction is essential for predicting transmitter coverage and optimizing wireless network performance. Traditional PL models are difficult to cope with the development trend of diversity, time-varying and mass wireless channels. In this work, the most widely used multilayer perceptron (MLP) neural network in artificial neural network (ANN) is employed to accurately predict PL. Three types of environmental features are defined and extracted, which describe the propagation environment only by considering limited environmental types instead of complex 3D environment modeling. Principal component analysis (PCA) is used to generate the low-dimensional environmental features, and eliminate redundant information among similar environmental types. Moreover, the information of base station (BS) and the receiver (Rx), including 3D locations, frequency, the transmitted power of BS, the antenna information, the feeder loss, and the received power of all the locations are obtained from the measurements. Different environmental features are combined with the information of BS and Rx to construct seven datasets for PL prediction models based on MLP neural networks. The impacts of the number of neurons in the hidden layer, the number of hidden layers, the number of training samples, and environmental features on PL prediction models are explored by considering the absolute value of mean error (AME), the mean absolute error (MAE), the standard deviation (STD) of error, the correlation coefficient, and the time ratio, respectively. This work aims to understand the propagation characteristics of radio waves, which can provide a theoretical basis for wireless network optimization and communication system design.
Life on parole: The quality of experiences soon after release contributes to a conviction-free re-entry.

BACKGROUND People returning to the community after prison face many challenges, including finding suitable accommodation and employment, and accessing good social support. The prospects are particularly poor for high-risk offenders with up to a third of those released in New Zealand returning to prison within 100 days. AIMS/HYPOTHESES We developed the Parole Experiences Measure (PEM) to quantify the quality of men's life experiences during the first weeks of re-entry from prison. We aimed to answer the question, can the quality of life experiences differentiate men who survive in the community without reconviction from those who do not? METHODS Using a longitudinal design, we examined whether PEM scores for 178 men with extensive histories of crime and violence predicted three recidivism indices (breach of parole, reconviction and reimprisonment) over a 12-month follow-up period. RESULTS We found that PEM scores predicted all three indices of recidivism. Of the two PEM subscales, external circumstances (finances, support, accommodation) were more predictive of recidivism than subjective wellbeing (mental and physical health). CONCLUSIONS/IMPLICATIONS The results suggest that meeting basic practical needs in the early months of parole may be more important to avoiding reconviction than attending only to mental and physical health.
Non-heuristic Identification of the Optimal Number of Oscillation Patterns in Uncertain Power Systems

This paper introduces a non-heuristic methodology using Hierarchical Clustering (HC) for identifying the optimal number of oscillation patterns in uncertain power systems due to transient stability phenomena, for subsequent control and stabilization. The HC application parameters are statistically defined after comparing the quality of different clustering partitions using appropriate indicators, so that the most suitable patterns can be automatically obtained. The methodology is illustrated on a modified IEEE 68 bus NETS-NYPS test network with renewable energy sources and realistically modelled uncertainties, resulting in the identification of a reduced (optimal) number of unstable generator patterns which are fully representative of the overall multi-machine transient behavior of the system.
Effective Data Transmission Based on Cluster User Communications in Opportunistic Complexity Social Networks

Social networks are very important information dissemination platforms. Messages in social networks are forwarded through encounters formed by moving nodes in opportunistic complexity network. However, with the gradual expansion of the network scale, the amount of data to be transmitted has increased dramatically, which has brought greater load to the entire network. In order to improve this transmission environment, this paper proposes a transmission mechanism that selects cooperative nodes based on the user’s interest. In communication, the degree of matching of node preferences is calculated, and highly matched nodes are selected for priority transmission. This method reduces the occupation of cache resources by redundant information in the network by filtering the message transmission mode of the cooperative nodes, and improves the space utilization of the message cache. Through the comparison of experiments and social network algorithms, this method improves the node’s cache utilization, reduces the data transmission delay, and improves the overall network efficiency.
BLDnet: Robust Learning-Based Detection for High-Order QAM With Nonlinear Distortion

The performances of wireless communication systems are strongly limited by the nonlinearities that exist in the transceiver. We first study the effect of nonlinearities of power amplifiers on high-order quadrature amplitude modulation (QAM) signals and then propose a bit-level demodulator network (BLDnet) to reduce the nonlinear interference. More specifically, the BLDnet can not only perform hard decisions but also provide the soft outputs for further processing in channel decoder. From the simulations, the BLDnet is observed to have a better performance than the conventional scheme in the Rapp model and the Saleh model. Compared to other detection schemes, the BLDnet has a comparatively low computation complexity without performance loss in the case of high-order modulation, such as 1024QAM.
Cellular and Network Mechanisms May Generate Sparse Coding of Sequential Object Encounters in Hippocampal-Like Circuits

Abstract The localization of distinct landmarks plays a crucial role in encoding new spatial memories. In mammals, this function is performed by hippocampal neurons that sparsely encode an animal’s location relative to surrounding objects. Similarly, the dorsolateral pallium (DL) is essential for spatial learning in teleost fish. The DL of weakly electric gymnotiform fish receives both electrosensory and visual input from the preglomerular nucleus (PG), which has been hypothesized to encode the temporal sequence of electrosensory or visual landmark/food encounters. Here, we show that DL neurons in the Apteronotid fish and in the Carassius auratus (goldfish) have a hyperpolarized resting membrane potential (RMP) combined with a high and dynamic spike threshold that increases following each spike. Current-evoked spikes in DL cells are followed by a strong small-conductance calcium-activated potassium channel (SK)-mediated after-hyperpolarizing potential (AHP). Together, these properties prevent high frequency and continuous spiking. The resulting sparseness of discharge and dynamic threshold suggest that DL neurons meet theoretical requirements for generating spatial memory engrams by decoding the landmark/food encounter sequences encoded by PG neurons. Thus, DL neurons in teleost fish may provide a promising, simple system to study the core cell and network mechanisms underlying spatial memory.
Increased sympathetic and haemodynamic responses to exercise and muscle metaboreflex activation in post‐menopausal women with rheumatoid arthritis

Rheumatoid arthritis (RA) patients present exacerbated blood pressure responses to exercise, but little is known regarding the underlying mechanisms involved. This study assessed autonomic and haemodynamic responses to exercise and to the isolated activation of muscle metaboreflex in post‐menopausal women with RA. Participants with RA showed augmented pressor and sympathetic responses to exercise and to the activation of muscle metaboreflex. These responses were associated with multiple pro‐ and anti‐inflammatory cytokines and with pain. The results of the present study support the suggestion that an abnormal reflex control of circulation is an important mechanism underlying the exacerbated cardiovascular response to exercise and increased cardiovascular risk in RA.
Ligand-Specific Factors Influencing GLP-1 Receptor Post-Endocytic Trafficking and Degradation in Pancreatic Beta Cells

The glucagon-like peptide-1 receptor (GLP-1R) is an important regulator of blood glucose homeostasis. Ligand-specific differences in membrane trafficking of the GLP-1R influence its signalling properties and therapeutic potential in type 2 diabetes. Here, we have evaluated how different factors combine to control the post-endocytic trafficking of GLP-1R to recycling versus degradative pathways. Experiments were performed in primary islet cells, INS-1 832/3 clonal beta cells and HEK293 cells, using biorthogonal labelling of GLP-1R to determine its localisation and degradation after treatment with GLP-1, exendin-4 and several further GLP-1R agonist peptides. We also characterised the effect of a rare GLP1R coding variant, T149M, and the role of endosomal peptidase endothelin-converting enzyme-1 (ECE-1), in GLP1R trafficking. Our data reveal how treatment with GLP-1 versus exendin-4 is associated with preferential GLP-1R targeting towards a recycling pathway. GLP-1, but not exendin-4, is a substrate for ECE-1, and the resultant propensity to intra-endosomal degradation, in conjunction with differences in binding affinity, contributes to alterations in GLP-1R trafficking behaviours and degradation. The T149M GLP-1R variant shows reduced signalling and internalisation responses, which is likely to be due to disruption of the cytoplasmic region that couples to intracellular effectors. These observations provide insights into how ligand- and genotype-specific factors can influence GLP-1R trafficking.
Endophytic bacterial communities and spatiotemporal variations in cotton roots in Xinjiang, China.

Endophytic bacteria may be important for plant health and other ecologically relevant functions of cotton. However, the endophytic bacterial community structure and diversity in cotton is still poorly characterized. We investigated the community structure of endophytic bacteria in cotton roots growing in Xinjiang, China, using the Illumina amplicon sequencing. A total of 60.84 M effective sequences of 16S rRNA gene V3 region were obtained from cotton samples. These sequences revealed huge amount of operational taxonomic units (OTUs) in cotton, that is, 81-338 OTUs in a cotton sample, at 3% cutoff level and sequencing depth of 50000 sequences. We identified 23 classes from the resulting 2,723,384 sequences. Gammaproteobacteria were the dominant class in all cottons, followed by Alphaproteobacteria, Actinobacteria and Bacilli. A marked difference in the diversity of endophytic bacteria in cotton for different growth periods was evident. The greatest number of OTUs was detected during seedling (654 OTUs) and budding (381 OTUs). Endophytic bacteria diversity was reduced during flowering (350 OTUs) and boll-opening (351 OTUs). 217 OTUs were common to all four periods. There were more tags of Pantoea in Shihezi than other locations. While there were more tags of Erwinia in Hami than other locations. The dynamics of endophytic bacteria communities were influenced by plant growth stage. These results show the complexity of the bacterial populations present in inner tissues of cotton.
Nonlinear Properties of Superconducting Microstrip Hilbert Fractal Resonators

The series of nonlinear amplitude-frequency characteristics are investigated for two superconducting niobium microstrip fractal Hilbert resonators with a large number of sharp or smooth turns. A different extent of local and global nonlinearity dominance, depending on the type of turns, was found. A significant increase in the nonlinear response was observed during the transition from smooth to sharp turns.
Performance Assessment of Hybrid Precoders in the Presence of Artificial Noise

The demands of users are gradually increasing with the passage of time and with the continuous evaluation in wireless communication technology (i.e., from first to fifth-generation (1G to 5G)). To acquire extreme capacity, extra-high data rates, massive connectivity, extremely low latency and energy consumption, the utilization of a suitable frequency band of the electromagnetic spectrum is inevitable. An appropriate solution is to utilize millimeter-waves (mmWaves) that can offer 200 times more bandwidth than the existing technology to meet the growing demands of applicants. However, these waves have certain degradation problems, which can be solved by deploying multiple-antenna technology owning to short wavelengths offered by mmWave bands. Still, security is one of the major problems associated with the mmWaves and has got a lot of attention from the researchers in recent times. The primary objectives of this study are to first present a secure hybrid precoder design in mmWave massive multiple-input multiple-output (MIMO) communication systems with the aid of artificial noise (AN), and then, compare the results with the most recent method in the literature. Extensive simulation studies confirm that the AN based hybrid precoder provides more secure communication than its counterpart in terms of secrecy rate versus signal-to-noise ratio (SNR).
Achieving Efficient Structured Concurrency through Lightweight Fibers in Java Virtual Machine

Contemporary concurrent server applications, commonly built of smaller and independent services, are using concurrent threads to serve many incoming requests and often have to perform under excessive load. Those applications are relatively easy to develop in general-purpose, imperative programming languages such as Java, and have great tooling support. However, such applications are not easily scalable, mostly due to relying on oversized OS kernel threads, which can be created only in a limited number on finite hardware resources. Furthermore, heavyweight OS threads are implemented so robustly and generically to support various usage scenarios, and are, therefore, wasteful in resources and often inefficient in addressing specific application demands.Those challenges in software development resulted in various asynchronous programming techniques. This paper presents an exploration of a novel structured concurrency model in the Java Virtual Machine (JVM), introduced within OpenJDK's Project Loom. It is focused on the exploitation of fibers, new lightweight implementation of virtual threads within the JVM depending on delimited continuations. Furthermore, on several applications' benchmark cases, we analyzed performance with traditional threads and new fibers in different configurations. Finally, we discussed the current challenges of implementing fibers as a feasible approach for the more efficient future of Java.
Identifying unpredictable test examples with worst-case guarantees

Often times, whether it be for adversarial or natural reasons, the distributions of test and training data differ. We give an algorithm that, given sets of training and test examples, identifies regions of test examples that cannot be predicted with low error. These regions are classified as ƒ or equivalently omitted from classification. Assuming only that labels are consistent with a family of classifiers of low VC dimension, the algorithm is shown to make few misclassification errors and few errors of omission in both adversarial and covariate-shift settings. Previous models of learning with different training and test distributions required assumptions connecting the two.
A Decision-dependent Stochastic Approach for Wind Farm Maintenance Scheduling Considering Wake Effect

With higher proportion of wind generation, the operation and maintenance plan of wind farms plays an increasingly critical role in power systems. Failure probability based risk management of wind turbines (WT) contributes to cost efficient maintenance schedules and reliable operation of a wind farm. In this paper, focusing on a large-scale wind farm, a decision-dependent stochastic wind farm maintenance strategy is studied considering the influence of wake effect. Based on the failure probability expression of WTs and wake effect model, the expected wind speed at every WT is formulated as a decision-dependent uncertain parameter, which depends on the failure probability and maintenance status of its upstream WTs. Then, considering uncertainties in electricity price and wind speed and failure probability of WTs, the maintenance decision-making model of the wind farm is established as a decision-dependent stochastic programming model to maximize total expected wind power revenue. The established non-convex stochastic optimization problem is solved using particle swarm algorithm, and methods to improve computation efficiency are presented. Case study results verified the validity of the proposed method.
Edge-supported Microservice-based Resource Discovery for Mist Computing

Mist computing extends the Internet of Things computing infrastructures to the IoT devices at the edges of the networks. The dynamic characteristics of the IoT environments and resource limitations of the devices introduce challenges to the orchestration of the mist platform resources. In this paper, we present a hybrid resource discovery solution for mist, based on the IETF CoRE Resource Directories deployed as containerized Microservices to the supporting edge devices. This way the directory instances can be deployed on-demand as part of the edge platform, where each instance serves a mist network connected to the hosting edge device. This enables low latency resource queries at one-hop distance for the mist applications. At the edge layer, the directories form a distributed discovery infrastructure, connecting resources in disparate mist networks with each other and cloud and edge applications. A real-world prototype of such discovery infrastructure is implemented, based on low resource edge devices hosting the directory instances and low-power embedded devices as the mist resource servers and clients. The prototype is evaluated with latency and power consumption measurements, where the results show that discovery latency is as low as half a millisecond with a low power consumption.
Optical Camera Communications with Convolutional Neural Network for Vehicle-toVehicle Links

This paper describes a vehicle-to-vehicle (V2V) communication system, employing optical camera communications (OCC). The system comprises the light emitting diode (LED)-based taillights and a raspberry camera used as the transmitter (Tx) and the receiver (Rx), respectively. The sectorized taillights (i.e., Tx) are intensity modulated at different frequencies, and a convolutional neural network (CNN) at the Rx is used for scene analysis, the region of interest (RoI) selection, and symbol detection. Results show that, the system data rates are constrained by the camera frame rate and symbol duration. The link performance is dependent on the CNN training set and we show that, the use of CNN allows a robust implementation, able to provide response under multiple situations: taillight obstruction, variable link distances, and misaligned Tx-Rx. Furthermore, CNN enables multiple input multiple output (MIMO) signal detection without the need for dedicated training.