Miracle-dz/UniFAR-FTUs huggingface dataset:
The predicted secretome of Lactobacillus plantarum WCFS1 sheds light on interactions with its environment.

The predicted extracellular proteins of the bacterium Lactobacillus plantarum were analysed to gain insight into the mechanisms underlying interactions of this bacterium with its environment. Extracellular proteins play important roles in processes ranging from probiotic effects in the gastrointestinal tract to degradation of complex extracellular carbon sources such as those found in plant materials, and they have a primary role in the adaptation of a bacterium to changing environmental conditions. The functional annotation of extracellular proteins was improved using a wide variety of bioinformatics methods, including domain analysis and phylogenetic profiling. At least 12 proteins are predicted to be directly involved in adherence to host components such as collagen and mucin, and about 30 extracellular enzymes, mainly hydrolases and transglycosylases, might play a role in the degradation of substrates by L. plantarum to sustain its growth in different environmental niches. A comprehensive overview of all predicted extracellular proteins, their domains composition and their predicted function is provided through a database at http://www.cmbi. ru.nl/secretome which could serve as a basis for targeted experimental studies into the function of extracellular proteins.
A DLTS study on Deep Trench Processing induced Trap States in Silicon Photodiodes

Deep Trench Isolation (DTI) is commonly employed to separate individual devices or different blocks on a monolithic semiconductor chip. However, trap states at the DTI sidewalls may impact the performance of minority carrier based devices by means of interface recombination. To extract parameters of traps located at or nearby the DTI sidewalls we perform a Deep Level Transient Spectroscopy (DLTS) study on dedicated test structures. Furthermore, we exploit the trap parameters in computer simulations to evaluate the influence of sidewall trap states on the performance of DTI-terminated island photodiodes. Our results suggest that the carrier recombination at the DTI interface states is responsible for a non-linear diode behavior with respect to the optical radiant flux.
GeV Emission from Prompt and Afterglow Phases of Gamma-Ray Bursts

We investigate the GeV emission from gamma-ray bursts (GRBs) using the results from the Energetic Gamma Ray Experimental Telescope (EGRET) and in view of the Gamma-Ray Large Area Space Telescope (GLAST). Assuming that the conventional prompt and afterglow photons originate from synchrotron radiation, we compare an accompanying inverse-Compton component with EGRET measurements and upper limits on GeV fluence, taking Klein-Nishina feedback into account. We find that the EGRET constraints are consistent with the theoretical framework of the synchrotron self-Compton model for both prompt and afterglow phases, and discuss constraints on microphysical parameters in both phases. Based on the inverse-Compton model and using EGRET results, we predict that GLAST would detect GRBs with GeV photons at a rate of ≳20 yr−1 from both the prompt and afterglow phases. This rate applies to the high-energy tail of the prompt synchrotron emission and to the inverse-Compton component of the afterglow. Theory predicts that in a large fraction of the cases where synchrotron GeV prompt emission would be detected by GLAST, inverse-Compton photons should also be detected at high energies (≳10 GeV). Therefore, GLAST will enable a more precise test of the high-energy emission mechanism. Finally, we show that the contribution of GRBs to the flux of the extragalactic gamma-ray background measured with EGRET is at least 0.01%, and likely around 0.1%.
Tissue crosslinks concentrations in normal joints and chronic articular diseases

We read with interest the recent paper by Takahashi et al 1 concerning joint tissue concentrations of collagen crosslinks (Pyr, Dpyr) in patients with osteoarthritis (OA) and rheumatoid arthritis (RA). The main discovery of this study was the presence of Dpyr also in extra-osseous tissues, namely cartilage and synovium in patients with joint disorders and in the synovium of non- arthritic controls. These data suggest that extra-skeletal sources may contribute to the increased urinary excretion of the two crosslinks in joint disorders, as several observers have reported in recent years.2-4 We recently performed a similar study on samples of subchondral bone, cartilage, and synovium of six patients with OA (mean (SD) age 58.1 (10.7)) and six patients with RA (age 61.1 (18.1)) who underwent total hip arthroplasty. In contrast with the study by Takahashi, as a control group we studied joints from six young patients (aged 37.5 (13.5)) who underwent surgical amputation of an extremity for malignant osteosarcoma. All patients were physically active before surgery, permitting the sampling of normal ‘healthy’ joint tissues. Briefly, tissue specimens of articular cartilage, subchondral bone, and synovium were collected, cleaned, dried, and immediately weighed and stored at −80°C. The assay of their crosslinks content was performed by high performance liquid chromatography according to a published method 5 after an overnight hydrolysis. Data …
Conal contribution to the electrocardiogram of chick embryo hearts

Contraction of the conus arteriosus (conus) is visibly delayed in the tubular heart of the chick embryo, and since the electromechanical coupling interval of isolated conal segment is similar to other areas of the embryonic heart, the delayed contraction of the conus indicated an impedance in the spread of the depolarization wave across the ventriculo‐conal (VC) junction.
Research article SENSORY ATTRIBUTES OF INDIAN FRIED FOODS INCORPORATED WITH DIFFERENT LEVELS OF FRUCTOOLIGOSACCHARIDES

Prebiotics are the non-digestible fibers which have the potential to exert a positive effect on human health. They serve as a substrate for microflora in the large intestine and are known to improve overall gut health. Fried foods contribute greatly to the snack foods industries and are specially liked for their crispy texture and unique flavor. Addition of fructooligosaccharides in fried food products has a potential to be developed into a therapeutic food. Hence, the effect of incorporating fructooligosaccharide on sensory qualities of fried products namely vegetable paratha (Shallow fried) and lilva kachori (Deep fried) was investigated. Fructooligosaccharides was added to the one serving of vegetable paratha and lilva kachori's stuffing at 10 g, 15 g and 5 g, 10 g, and 15 g respectively and were compared with the standard recipes. Three successive trials were conducted for screening the panelist through threshold test. Sensory evaluation was carried out at nine point hedonic scale test for the two products in triplicates. An internal panel of 25 semi trained members evaluated the products for color and appearance, mouthfeel, texture, taste, after taste, and overall acceptability. The results revealed a non-significant reduction in most of the sensory attributes for vegetable paratha, whereas, a significant reduction was observed in texture scores (p<.05). A significant reduction (p<.001) was found in all sensory attribute scores of lilva kachori. Addition upto 5 g fructooligosaccharide did not altered sensory qualities of lilva kachori. The most affected attribute for vegetable parantha was texture (8.6 percent reduction) and for lilva kachori it was color and appearance. Thus it can be concluded that fructooligosaccharides can be incorporated in vegetable paratha upto 15 g and upto 5 g in lilva kachori. An addition of fructooligosaccharide in stuffed fried snacks may serve to fulfill consumer demand for health foods.
Large-scale image classiﬁcation with trace-norm regularization

With the advent of larger image classiﬁcation datasets such as ImageNet, designing scalable and efﬁcient multi-class classiﬁcation algorithms is now an important challenge. We introduce a new scalable learning algorithm for large-scale multi-class image classiﬁcation, based on the multinomial logistic loss and the trace-norm regularization penalty. Reframing the challenging non-smooth optimization problem into a surrogate inﬁnite-dimensional optimization problem with a regular ℓ 1 -regularization penalty, we propose a simple and provably efﬁcient accelerated coordinate descent algorithm. Furthermore, we show how to perform efﬁcient matrix computations in the compressed domain for quantized dense visual features, scaling up to 100,000s examples, 1,000s-dimensional features, and 100s of categories. Promising experimental results on the “Fun-gus”, “Ungulate”, and “Vehicles” subsets of ImageNet are presented, where we show that our approach performs sig-niﬁcantly better than state-of-the-art approaches for Fisher vectors with 16 Gaussians.
Spike Train Auto-Structure Impacts Post-Synaptic Firing and Timing-Based Plasticity

Cortical neurons are typically driven by several thousand synapses. The precise spatiotemporal pattern formed by these inputs can modulate the response of a post-synaptic cell. In this work, we explore how the temporal structure of pre-synaptic inhibitory and excitatory inputs impact the post-synaptic firing of a conductance-based integrate and fire neuron. Both the excitatory and inhibitory input was modeled by renewal gamma processes with varying shape factors for modeling regular and temporally random Poisson activity. We demonstrate that the temporal structure of mutually independent inputs affects the post-synaptic firing, while the strength of the effect depends on the firing rates of both the excitatory and inhibitory inputs. In a second step, we explore the effect of temporal structure of mutually independent inputs on a simple version of Hebbian learning, i.e., hard bound spike-timing-dependent plasticity. We explore both the equilibrium weight distribution and the speed of the transient weight dynamics for different mutually independent gamma processes. We find that both the equilibrium distribution of the synaptic weights and the speed of synaptic changes are modulated by the temporal structure of the input. Finally, we highlight that the sensitivity of both the post-synaptic firing as well as the spike-timing-dependent plasticity on the auto-structure of the input of a neuron could be used to modulate the learning rate of synaptic modification.
Parallel Distributed Energy Generation Based Microgrid Incorporating Reactive Power Sharing and Grid Synchronisation Capability

This paper presents multiple parallel distributed energy sources (DERs) based generating unit connected to a common $AC$ bus for supplying uninterrupted power to $AC$ local loads while exercising on power quality of microgrid (MG). Main focus of this work is a) to share net reactive power demand of loads between parallel voltage source converters (which minimize burden of single converter) and 2) has capability to synchronize to the grid, when the grid is available (seamless grid synchronization ability). Sliding Goertzel pre-filter-based frequency locked loop is used to calculate fundamental component of local loads and also to obtain frequency during grid synchronization also when the grid is subjected to voltages unbalance. A brushless direct current (BLDC) machine is used as a wind generating unit because it is independent of voltage and frequency fluctuations at $\text{AC}$ bus unlike other conventional $\text{AC}$ generators. Wind energy conversion and solar energy conversion system along with a battery storage are connected to form a MG, which is modelled and its performance is simulated in MATLAB and adaptability of MG is found through obtained results.
Enhancing Mac OS Malware Detection through Machine Learning and Mach-O File Analysis

Malware research has predominantly focused on Windows and Android Operating Systems (OS), leaving Mac OS malware relatively unexplored. This paper addresses the growing threat of Mac OS malware by leveraging Machine Learning (ML) techniques. We propose a novel system for Mac malware detection that extends beyond traditional executables to include various Mach-O (Mach Object) file types. Our research encompasses feature selection, data sets, and the implementation of ML classifiers. We meticulously evaluate system performance using Precision, Recall, F1 score, and Accuracy metrics. Our findings highlight the challenges and opportunities in Mac malware detection and provide valuable insights for future research.
Generative Adversarial Networks to Secure Vehicle-to-Microgrid Services

As the use of Artificial Intelligence (AI) becomes more prevalent in the cyber-physical systems, there has been a rise in the number of adversarial attacks targeting Machine Learning (ML) models. Therefore, it has become imperative to safeguard ML models against such attacks. Our analysis under a vehicle-to-microgrid service setting shows that adversaries aim to deceive the victim's ML classifier at the network edge to misclassify the incoming energy requests from microgrid users. In this paper, we introduce an AI-powered framework to detect new instances of Adversarial attacks against Vehicle-to-Microgrid (V2M) systems. The proposed detection technique uses a Generative Adversarial Network (GAN) model and ML classifiers to accurately detect adversarial attacks. We test the proposed detection technique under three strategies of adversarial sample generation. Adversaries perform a two-stage adversarial attack beginning with an inference attack followed by an evasion attack against the victim's ML model. In addition, we investigate how the adversaries' knowledge of the victim's ML training dataset impacts the Adversarial Detection Rate (ADR). We assess five access cases to the victim's ML training dataset varying from a white-box to various levels of a gray-box attack. Moreover, we implement an unsupervised ML algorithm (i.e., DBSCAN) as a baseline method. Through simulations, we show that DBSCAN results in an ADR up to 69% and 17% for gray-box and white-box attacks, respectively. On the other hand, our proposed generative approach (i.e., GAN-based) outperforms DBSCAN resulting in an ADR up to 94.6% for the gray-box attack and 30.2% for the white-box attack.
Sensorless Control of High Voltage PMSM Drive with Bidirectional Converter for EV Application

Electric vehicles (EVs) have gained significant popularity in recent years due to their environmental benefits, energy efficiency, motor control and advancements in battery technology. This paper presents sensorless vector control of high voltage permanent magnet synchronous motor (PMSM) utilizing bidirectional DC-DC converter. Bidirectional DC-DC converter helps to provide high voltage required DC link voltage from a low voltage battery as well as reverse power flow during braking condition. In addition to this, mechanical sensorless technique based on sliding mode observer with cascaded second order generalized integrator (CSOGI) is employed for rotor position and speed estimation. CSOGI effectively suppress harmonics present in back-EMF as well as DC- off set from both in phase as well as quadrature component, which improves sensorless speed and position estimation. Furthermore, to migrate disturbance and fluctuation caused due to current sensor measurement error, cogging torque and inverter nonlinearity, proportional integral in parallel with resonant controller (PIR) based speed controller is utilized for closed loop vector control. Topology analysis, controller performance and its comparison are implemented and verified with obtained results.
The paternal role in pre-eclampsia and giving birth to a small for gestational age infant; a population-based cohort study

Objective To estimate the effect of partner change on risks of pre-eclampsia and giving birth to a small for gestational age infant. Design Prospective population study. Setting Sweden. Participants Women with their first and second successive singleton births in Sweden between 1990 and 2006 without pregestational diabetes and/or hypertension (n=446 459). Outcome measures Preterm (<37 weeks) and term (≥37 weeks) pre-eclampsia, and giving birth to a small for gestational age (SGA) infant. Risks were adjusted for interpregnancy interval, maternal age, body mass index, height and smoking habits in second pregnancy, years of involuntary childlessness before second pregnancy, mother's country of birth, years of formal education and year of birth. Further, when we calculated risks of SGA we restricted the study population to women with non-pre-eclamptic pregnancies. Results In women who had a preterm pre-eclampsia in first pregnancy, partner change was associated with a strong protective effect for preterm pre-eclampsia recurrence (OR 0.24; 95% CI 0.07 to 0.88). Similarly, partner change was also associated with a protective effect of recurrence of SGA birth (OR 0.75; 95% CI 0.67 to 0.84). In contrast, among women without SGA in first birth, partner change was associated with an increased risk of SGA in second pregnancy. Risks of term pre-eclampsia were not affected by partner change. Conclusions There is a paternal effect on risks of preterm pre-eclampsia and giving birth to an SGA infant.
Intelligent Shared Spectrum Coordination in Heterogeneous Networks

Global connectivity requires reliable and affordable access to the internet for digital inclusiveness. Shared spectrum technologies are one of many technologies that can help provide affordable connectivity. They require available spectrum (channels) from Primary Users (PUs) and share these among future dynamic heterogeneous secondary user (SU) networks. Coordination of these transient available resources is exacerbated by dynamic SU network scenarios, thus raising the risk of poor SU experience and inefficient spectrum and power usage. Therefore, adopting reinforcement learning (RL), terrain-based propagation models, and IEEE 802.19 coexistence principles, a central intelligent real-time shared spectrum coordination algorithm is proposed to coordinate resource allocation among dynamic SUs operating at low bands. The proposed two-stage algorithm was compared to existing shared spectrum allocation techniques deployed in dynamic spectrum access (DSA) networks to quantify RL's impact on low-band wireless networks. 66% to 100% of SU nodes/access points (APs) that used the proposed algorithm experienced good quality of service (QoS) in most scenarios examined. A good QoS meant that 75% of APs receivers experienced signal-to-noise plus interference ratio (SINR) greater than 5. This was achieved using minimal AP transmission power.
IMA: Implicit Matching and Alignment for Multimodal Named Entity Recognition

Due to the limitation of insufficient textual information, Multimodal Named Entity Recognition (MNER), which introduces associated images to assist in the knowledge extraction of key entities, has increasingly received extensive attention from researchers. Existing MNER methods have made good progress based on the attention mechanism. However, there are still two shortcomings. One is that the text and its corresponding image are not perfectly matched, and the introduction of mismatched image information may lead to incorrect prediction by the model. Secondly, the text representation and image representation obtained by the pre-training encoder often have huge semantic differences, while existing methods usually lack the exploration of image representations that have a semantically consistent relationship with the textual representation. To address these two issues, we propose an implicit matching and alignment framework for the MNER task. Specifically, we propose a robust implicit alignment mechanism that synergistically aligns textual representations and image representations from both local and global perspectives based on contrastive learning to promote consistency in the representation of text and associated images in order to solve the problem of cross-modal mismatch. Meanwhile, we design a distribution matching module to promote the retention of distributional consistency information in image representations with high similarity to textual representations to address the problem of semantic divide. Extensive experiments on two datasets demonstrate the effectiveness of our proposed method.
Research on Edge-Cloud Collaborative Data Sharing Method Based on Federated Learning in Internet of Vehicles

With the development of Internet of Vehicles(IoV) technology and the popularization of intelligent vehicles, a large amount of vehicle data is generated and accumulated. Due to the privacy and sensitivity of vehicle datas, the emergence of Federated Learning(FL) technology can effectively protect user privacy. FL allows model training without the need to share raw data, protecting vehicle user data by sharing model parameters. However, within conventional FL methodologies, vehicle users are required to undertake significant computational tasks, and communication with cloud servers demands considerable time. Inspired by edge computing, we introduce a cloud-edge-end two-tier architecture based on federated learning(CEE-FLA). This structure transfers the model aggregation process originally completed by the cloud server to the edge server to reduce the number of communications with the cloud server, thereby improving the accuracy and efficiency of data sharing. Considering that model training efficiency is affected by vehicle performance, we improve the traditional FL algorithm in CEE-FLA and adopt a K-Vehicle random scheduling strategy to minimize the impact of vehicle performance differences on model convergence and data sharing efficiency. The experimental results demonstrate that our proposed CEE-FAL offers improvements in both accuracy and efficiency compared to the traditional cloud-end single-tier. Additionally, as the number of iterations between vehicles and edge servers increases, the model’s convergence speed also improves.
Latency-Optimized Multi-User Task Offloading Scheme Using Dynamic Priority and Duplication in Edge Computing

The extensive development of complex applications in embedded devices has driven the rapid development of edge computing, which provides powerful processing capabilities to the edge network. In this context of development, task offloading has received widespread attention as one of the key issues in edge computing. However, since a task usually consists of multiple subtasks with dependencies, the current computing subtask on the local or edge server must wait for the completion of the previous dependent subtask. As a result, existing offloading schemes are often constrained by the complexity of the task topology. Therefore, this paper is aimed at tasks with dependencies described as Directed Acyclic Graphs (DAGs) in devices. First, we optimize the order of subtasks with dynamic priorities. Meanwhile, we employ task duplication to reduce communication latency and thus overall task completion time. Additionally, to support the edge computing environment of multi-user and multi-server, game theory is used to find the optimal offload position for each user, so as to obtain the minimum average computing delay of all tasks. Experimental results show that the proposed algorithm outperforms existing algorithms in terms of task completion delay.