Miracle-dz/UniFAR-FTUs huggingface dataset:
Influenza A Virus NS1 Protein Structural Flexibility Analysis According to Its Structural Polymorphism Using Computational Approaches

Influenza A viruses are highly contagious RNA viruses that cause respiratory tract infections in humans and animals. Their non-structural protein NS1, a homodimer of two 230-residue chains, is the main viral factor in counteracting the antiviral defenses of the host cell. Its RNA-binding domain is an obligate dimer that is connected to each of the two effector domains by a highly flexible unstructured linker region of ten amino acids. The flexibility of NS1 is a key property that allows its effector domains and its RNA binding domain to interact with several protein partners or RNAs. The three-dimensional structures of full-length NS1 dimers revealed that the effector domains could adopt three distinct conformations as regards their mutual interactions and their orientation relative to the RNA binding domain (closed, semi-open and open). The origin of this structural polymorphism is currently being investigated and several hypotheses are proposed, among which one posits that it is a strain-specific property. In the present study, we explored through computational molecular modeling the dynamic and flexibility properties of NS1 from three important influenza virus A strains belonging to three distinct subtypes (H1N1, H6N6, H5N1), for which at least one conformation is available in the Protein Data Bank. In order to verify whether NS1 is stable in three forms for the three strains, we constructed homology models if the corresponding forms were not available in the Protein Data Bank. Molecular dynamics simulations were performed in order to predict the stability over time of the three distinct sequence variants of NS1, in each of their three distinct conformations. Our results favor the co-existence of three stable structural forms, regardless of the strain, but also suggest that the length of the linker, along with the presence of specific amino acids, modulate the dynamic properties and the flexibility of NS1.
Toxigenic Properties of Yersinia enterocolitica Biotype 1A

Yersinia (Y.) enterocolitica, an etiological agent of yersiniosis, is a bacterium whose pathogenicity is determined, among other things, by its ability to produce toxins. The aim of this article was to present the most important toxins that are produced by biotype 1A strains of Y. enterocolitica, and to discuss their role in the pathogenesis of yersiniosis. Y. enterocolitica biotype 1A strains are able to synthesize variants of thermostable YST enterotoxin and play a key role in the pathogenesis of yersiniosis. Biotype 1A strains of Y. enterocolitica also produce Y. enterocolitica pore-forming toxins, YaxA and YaxB. These toxins form pores in the cell membrane of host target cells and cause osmotic lysis, which is of particular importance in systemic infections. Insecticidal toxin complex genes have been detected in some clinical biotype 1A strains of Y. enterocolitica. However, their role has not yet been fully elucidated. Strains belonging to biotype 1A have long been considered non-pathogenic. This view is beginning to change due to the emerging knowledge about the toxigenic potential of these bacteria and their ability to overcome the defense barriers of the host organism.
Dual Attention Modeling between Users and Items Using Review for Recommendation

Recommendation systems were developed to predict the users’ preferences by tracking the interactions between users and items. While the interaction history and the basic information have been the prevailing data of the predictive models, other available types of information, like user feedback, could also provide abundant and subjective information. However, there is no efficient way to extract emotional information, and hence user comments are rarely fully utilized. With the recent development of Natural Language Processing (NLP) in sentiment extraction, user and item reviews could be processed to sequential word embeddings via Transformer encoders and then further extracted as features, which enables itself as a piece of auxiliary information added to the recommendation systems. Thus, this paper aims to propose a dual attention model using review for recommendation called DAM4R, which employs deep bidirectional self-attention to model user preferences and item characters respectively from review text and predict the score via an MLP layer. Through evaluation on Yelp and Amazon datasets, we show that our model can match the predictive performance of prior work.
Towards Integration of Two-Dimensional Hexagonal Boron Nitride (2D h-BN) in Energy Conversion and Storage Devices

The prominence of two-dimensional hexagonal boron nitride (2D h-BN) nanomaterials in the energy industry has recently grown rapidly due to their broad applications in newly developed energy systems. This was necessitated as a response to the demand for mechanically and chemically stable platforms with superior thermal conductivity for incorporation in next-generation energy devices. Conventionally, the electrical insulation and surface inertness of 2D h-BN limited their large integration in the energy industry. However, progress on surface modification, doping, tailoring the edge chemistry, and hybridization with other nanomaterials paved the way to go beyond those conventional characteristics. The current application range, from various energy conversion methods (e.g., thermoelectrics) to energy storage (e.g., batteries), demonstrates the versatility of 2D h-BN nanomaterials for the future energy industry. In this review, the most recent research breakthroughs on 2D h-BN nanomaterials used in energy-based applications are discussed, and future opportunities and challenges are assessed.
Stacked Cross Validation with Deep Features

Detection of malignant skin lesions is important for early and accurate diagnosis of skin cancer. In this work, a hybrid method for malignant lesion detection from dermoscopy images is proposed. The method combines the feature extraction process of convolutional neural networks (CNN) with an ensemble learner called stacked cross-validation (CV). The features extracted by three different CNN architectures, namely, ResNet50, Xception, and VGG16 are used for training of four different baseline classifiers, which are support vector machines, k-nearest neighbors, artificial neural networks, and random forests. The stacked outputs of these classifiers are used to train a logistic regression model as a meta-classifier. The performance of the proposed method is compared with the baseline classifiers trained individually as well as AdaBoost classifier, another ensemble learner. Feature extraction with Xception architecture, outperforms all other benchmark models by achieving scores of 0.909, 0.896, 0.886, and 0.917 for accuracy, F1-score, sensitivity, and AUC, respectively.
A Survey on Machine Learning based Smart Maintenance and Quality Control Solutions

Machine learning aided tasks and processes have key roles in smart manufacturing, especially in controlling production and assembly lines, as well as smart maintenance and intelligent quality control. The last two ones are those tasks that nowadays are still performed manually by employees; however, there are numerous machine learning-based solutions that can automate these fields to optimize cost and performance. In this paper, we present an overview of smart manufacturing ecosystem and define the roles of maintenance and quality control in it. Up-to-date machine learning-based smart solutions will also be detailed while addressing current challenges and identifying hot research topics and possible gaps.
Cars in Europe: Supply Chains and Spillovers during COVID-19 Times

The auto sector is macro-critical in many European countries and constitutes one of the main supply chains in the region. Using a multi-sector and multi-country general equilibrium model, this paper presents a quantitative assessment of the impact of global pandemic-induced labor supply shocks—both directly and via supply chains—during the initial phase of the COVID-19 pandemic on the auto sector and aggregate activity in Europe. Our results suggest that these labor supply shocks would have a significant adverse impact on the major auto producers in Europe, with one-third of the decline in the value added of the car sector attributable to spillovers via supply chains within and across borders. Within borders, the pandemicinduced labor supply shocks in the services sector have a bigger adverse impact, reflecting their larger size and associated demand effects. Across borders, spillovers from the pandemic-induced labor supply shocks that originate in other European countries are larger than those that originate outside the region, though the latter are still sizable. JEL Classification Numbers: F42, F44
Use of Omics Technologies for the Detection of Colorectal Cancer Biomarkers

Simple Summary Colorectal cancer (CRC) is one of the most frequent cancers worldwide. Early detection of CRC is crucial, as it greatly improves the survival of patients. Currently, the CRC screening programs consist of a stool test to detect the presence of blood in stool and a subsequent colonoscopy to confirm the diagnosis. However, CRC screening can be further improved with the use of new biomarkers. Omics technologies, that is, techniques that generate a vast amount of data, can help to establish these markers. Here, we discuss the use of omics with different types of samples (breath, urine, stool, blood, bowel lavage fluid, and tissue) and highlight some of the most relevant biomarkers that have been recently detected. Abstract Colorectal cancer (CRC) is one of the most frequently diagnosed cancers with high mortality rates, especially when detected at later stages. Early detection of CRC can substantially raise the 5-year survival rate of patients, and different efforts are being put into developing enhanced CRC screening programs. Currently, the faecal immunochemical test with a follow-up colonoscopy is being implemented for CRC screening. However, there is still a medical need to describe biomarkers that help with CRC detection and monitor CRC patients. The use of omics techniques holds promise to detect new biomarkers for CRC. In this review, we discuss the use of omics in different types of samples, including breath, urine, stool, blood, bowel lavage fluid, or tumour tissue, and highlight some of the biomarkers that have been recently described with omics data. Finally, we also review the use of extracellular vesicles as an improved and promising instrument for biomarker detection.
Forecast of port freight volume based on grey RBF neural network combination model

The prediction of port freight volume is of great significance to transportation and port planning. Based on the characteristics of grey GM (1,1) model and RBF neural network model, a combined forecasting model based on grey GM (1,1) model and RBF neural network model is constructed, and the port freight volume is predicted by field survey data. Experiments show that grey RBF neural network can improve the forecasting accuracy, which is effective and feasible for freight volume forecasting.
Allium ducissae (A. subgen. Polyprason, Amaryllidaceae) a New Species from the Central Apennines (Italy)

In this paper, Allium ducissae (the LSID for the name Allium ducissae is: 77254606-1) is described as a new species based on morphological and molecular analyses, and its taxonomic relationships are discussed. It grows in crevices on calcareous rocks, rocky slopes and grassy ledges in the subalpine belt, within two regional protected areas in the Lazio and Abruzzo administrative regions (Central Apennines, Italy). Previously, these populations were attributed to A. strictum, a species described from Siberia, belonging to A. sect. Reticulatobulbosa. The new species is distinct from A. strictum in the morphology of vegetative and reproductive structures. Indeed, it is close to A. palentinum, an endemic species to Cantabrian Mountains (NW Spain). Both molecular and morphological data support the recognition of the Allium populations coming from the Central Apennines as a new species. Allium ducissae can be clearly distinguished from A. palentinum by longer and wider tepals, longer filaments, tooth of inner filament, flower pedicels, spathe appendage, and smaller seeds. Moreover, seed testa micro-sculptures revealed slight differences between A. ducissae and A. palentinum. Chromosome counts showed that A. ducissae is diploid with 2n = 16 chromosomes, as already known for A. palentinum. Molecular analyses support the affiliation of A. ducissae and A. palentinum to A. sect. Falcatifolia, contrary to what is known for the latter species, usually included in A. sect. Daghestanica. Finally, the IUCN assessment for the newly described species is proposed and briefly discussed.
Path-Following of Quadrotor Aircraft Based on Vision

Aiming at the problem of detecting linear shaped structures in a landscape for indoor quadrotor aircraft,an improved method based on monocular camera is provided for line detection and following.Firstly,a Region of Interest( ROI) window is divided from the image. An advanced alternative of the HSV-color filter is applied on every pixel to see whether it belongs to the line. Secondly,Gaussian Smoothing is applied to remove small distortions in the ROI. Finally,a Probabilistic Hough Transform( PHT) is performed to extract the line from the pre-processed image and determine a directional vector for making the platform move accordingly. Test results show that this method can effectively improve the line detection efficiency and ensure an accurate line-following task.
A Genetic Algorithm-Based Approach for Fluctuating QoS Aware Selection of IoT Services

In recent years, the Internet of Things (IoT) has evolved at an exceptional speed, which enables to interconnect a very large number of heterogeneous, distributed, and mobile devices. This number will exceed 70 billion by 2025 according to Statista.a with this huge amount of connected objects, the fulfillment of complex IoT applications, which usually requires a combination of several IoT objects, remains a real challenge. Besides, several requirements of Quality of Service (QoS) must be fulfilled, which makes the problem of selecting the appropriate IoT services NP-hard. In the literature, two main techniques for QoS-driven service selection are proposed: global selection characterized by a poor performance in dynamic and distributed huge environments and local selection which considers pre-defined local QoS constraints. Mainly, the existing works consider static QoS. However, in real life scenarios, QoS of IoT services can be fluctuating. To enhance the reliability of IoT applications, it is of paramount importance to consider the fluctuation dimension. In this context, we propose a QoS fluctuation-aware selection approach of IoT services. To do so, we propose a near-to optimal distributed approach that relies on decomposing the global QoS into distinct local constraints that serve as upper/lower bounds for selection while enhancing the reliability of the resulting composition by considering the QoS fluctuation of the candidate IoT services. The approach, we propose is based on a multi-objective evolutionary algorithm (MOEA) to solve the global QoS decomposition problem. Then a local selection using the obtained local constraints is performed in a parallel and distributed way. The performance of the proposed approach is evaluated and validated via experiment series.ahttps://www.statista. com/statistics/471264/iot-number-of-connected-devices-worldwide/
Optimization of Silicon Nitride Waveguide Platform for On-Chip Virus Detection

This work presents a rigorous and generic sensitivity analysis of silicon nitride on silicon dioxide strip waveguide for virus detection. In general, by functionalizing the waveguide surface with a specific antibodies layer, we make the optical sensor sensitive only to a particular virus. Unlike conventional virus detection methods such as polymerase chain reaction (PCR), integrated refractive index (RI) optical sensors offer cheap and mass-scale fabrication of compact devices for fast and straightforward detection with high sensitivity and selectivity. Our numerical analysis includes a wide range of wavelengths from visible to mid-infrared. We determined the strip waveguide’s single-mode dimensions and the optimum dimensions that maximize the sensitivity to the virus layer attached to its surface at each wavelength using finite difference eigenmode (FDE) solver. We also compared the strip waveguide with the widely used slot waveguide. Our theoretical study shows that silicon nitride strip waveguide working at lower wavelengths is the optimum choice for virus detection as it maximizes both the waveguide sensitivity (Swg) and the figure of merit (FOM) of the sensor. The optimized waveguides are well suited for a range of viruses with different sizes and refractive indices. Balanced Mach–Zehnder interferometer (MZI) sensors were designed using FDE solver and photonic circuit simulator at different wavelengths. The designed sensors show high FOM at λ = 450 nm ranging from 500 RIU−1 up to 1231 RIU−1 with LMZI = 500 µm. Different MZI configurations were also studied and compared. Finally, edge coupling from the fiber to the sensor was designed, showing insertion loss (IL) at λ = 450 nm of 4.1 dB for the design with FOM = 500 RIU−1. The obtained coupling efficiencies are higher than recently proposed fiber couplers.
A Technology of Pipeline Alignment in Subsea

Under peculiar sea condition and complicated geological situation at the sea floor,tie-in technology is essential to the issue that how to joint the newly-developed oil and gas pipeline with the previously-built subsea pipeline.Pipeline alignment should be completed before pipeline connection in deepwater. The deepwater pipeline alignment technology was introduced.The working principle and structure of the pipeline alignment tool were analyzed. The importance of alignment technology in deepwater operation was introduced. The key techniques of pipeline alignment were put forward.
Demystifying DPP III Catalyzed Peptide Hydrolysis—Computational Study of the Complete Catalytic Cycle of Human DPP III Catalyzed Tynorphin Hydrolysis

Dipeptidyl peptides III (DPP III) is a dual-domain zinc exopeptidase that hydrolyzes peptides of varying sequence and size. Despite attempts to elucidate and narrow down the broad substrate-specificity of DPP III, there is no explanation as to why some of them, such as tynorphin (VVYPW), the truncated form of the endogenous heptapeptide spinorphin, are the slow-reacting substrates of DPP III compared to others, such as Leu-enkephalin. Using quantum molecular mechanics calculations followed by various molecular dynamics techniques, we describe for the first time the entire catalytic cycle of human DPP III, providing theoretical insight into the inhibitory mechanism of tynorphin. The chemical step of peptide bond hydrolysis and the substrate binding to the active site of the enzyme and release of the product were described for DPP III in complex with tynorphin and Leu-enkephalin and their products. We found that tynorphin is cleaved by the same reaction mechanism determined for Leu-enkephalin. More importantly, we showed that the product stabilization and regeneration of the enzyme, but not the nucleophilic attack of the catalytic water molecule and inversion at the nitrogen atom of the cleavable peptide bond, correspond to the rate-determining steps of the overall catalytic cycle of the enzyme.
Endophytic, Non-endophytic Fungal Alkaloids and its Applications

Alkaloids are a secondary metabolite, which are extracted from a wide – array of organisms include Bacteria, Fungi, Plants and Animals. Here in present review mainly focused on the alkaloids derived from endophytic fungi and non – endophytic fungi. Fungi have provided a great source of spirit for novel drug compounds as fungi derived alkaloids have done major boon to human health and well - being. In the present review listed 35 different group of alkaloids from endophytes. In which 12 different alkaloids are kept under derivatives of Indole group, 4 different alkaloids are kept under Quinoline group, 4 different alkaloids kept under amines and amide group, from non-endophytic alkaloid 27 different group of alkaloids are reported. In which 9 different type of alkaloid are mentioned under indole group of alkaloids, 4 alkaloids are reported under diketopiperazine alkaloids, 4 different alkaloids are mentioned under ergot group of alkaloids, 3 alkaloids under prenylated indole alkaloids, 3 alkaloids are mentioned under pyridine classes. The main applications of these alkaloids are antimicrobial, Antibacterial, Antifungal, Antiviral Activity Antihelmenthic, Anticancerous, Nutrient Pedaling, Photo stimulation, Endophytes in Tissue Culture Antidiabetic Activity, and Immunosuppressive Activity; hence all the alkaloids should be used as medicines in Ayurvedam. The alkaloids derived from different fungi were reviewed and their uses in medical fields were also report.
Organizational response to the covid-19 pandemic: Does it affect hospitality industry employees' outcomes?

The purpose of this study was to analyze the relationships between hospitality organizations' crisis response during the COVID-19 pandemic with hospitality employees' attitudes and behaviors. Data was collected using a survey distributed on Amazon Mechanical Turk (MTurk) targeting hospitality industry employees. The findings reveal that crisis response is related to psychological well-being, job satisfaction, and organizational commitment among hospitality industry employees. This study broadens the scope of crisis management literature in the hospitality field. Furthermore, it establishes the significance of effective crisis response for hospitality industry employees' well-being and positive outcomes.
Opto-Microfluidic Integration of the Bradford Protein Assay in Lithium Niobate Lab-on-a-Chip

This paper deals with the quantification of proteins by implementing the Bradford protein assay method in a portable opto-microfluidic platform for protein concentrations lower than 1.4 mg/mL. Absorbance is measured by way of optical waveguides integrated to a cross-junction microfluidic circuit on a single lithium niobate substrate. A new protocol is proposed to perform the protein quantification based on the high correlation of the light absorbance at 595 nm, as commonly used in the Bradford method, with the one achieved at 633 nm with a cheap commercially available diode laser. This protocol demonstrates the possibility to quantify proteins by using nL volumes, 1000 times less than the standard technique such as paper-analytical devices. Moreover, it shows a limit of quantification of at least 0.12 mg/mL, which is four times lower than the last literature, as well as a better accuracy (98%). The protein quantification is obtained either by using one single microfluidic droplet as well by performing statistical analysis over ensembles of several thousands of droplets in less than 1 min. The proposed methodology presents the further advantage that the protein solutions can be reused for other investigations and the same pertains to the opto-microfluidic platform.