Miracle-dz/UniFAR-FTUs huggingface dataset:
Infectious Salmon Anemia Virus Infectivity Is Determined by Multiple Segments with an Important Contribution from Segment 5

Infectious salmon anemia virus (ISAV) is the etiological agent of infectious salmon anemia. It belongs to the genus isavirus, one of the genera of the Orthomyxoviridae family, as does Influenzavirus A. The ISAV genome comprises eight negative-sense single-stranded RNA segments that code for at least 10 proteins. Although some ISAV strains can reach 100% mortality rates, the factors that determine isavirus infectivity remain unknown. However, some studies suggest that segments 5 and 6 are responsible for the different degrees of virulence and infectivity among ISAV subtypes, unlike the influenza A virus, where most segments are involved in the virus infectivity. In this work, synthetic reassortant viruses for the eight segments of ISAV were generated by reverse genetics, combining a highly virulent virus, ISAV 752_09 (HPR7b), and an avirulent strain, SK779/06 (HPR0). We characterized the rescued viruses and their capacity to replicate and infect different cell lines, produce plaques in ASK cells, and their ability to induce and modulate the cellular immune response in vitro. Our results show that the majority of ISAV segments are involved in at least one of the analyzed characteristics, segment 5 being one of the most important, allowing HPR0 viruses, among other things, to produce plaques and replicate in CHSE-214 cells. We determined that segments 5 and 6 participate in different stages of the viral cycle, and their compatibility is critical for viral infection. Additionally, we demonstrated that segment 2 can modulate the cellular immune response. Our results indicate a high degree of genetic compatibility between the genomic segments of HPR7b and HPR0, representing a latent risk of reassortant that would give rise to a new virus with an unknown phenotype.
UHPLC–MS Characterization, and Antioxidant and Nutritional Analysis of Cocoa Waste Flours from the Peruvian Amazon

Cocoa (Theobroma cacao) is a food product used worldwide and a key raw material for chocolate manufacturing. Cocoa possesses bioactive compounds such as methylxanthines, flavonoids, procyanidins, and related molecules with medicinal or health-promoting properties. Cocoa shell and pod husk have been proposed as a by-product with several interesting bioactivities, and the gummy residue or glue (a sticky, gluey by-product known as “mucilage” in Spanish) is used to produce liquors and is eaten as a food in Perú. However, little is known about the chemical composition and bioactivity of flours made from Peruvian cocoa ecotype wastes such as those from the vein and pod husk of the fruits. This study aimed to characterize the in vitro antioxidant properties and nutritional values of flours made from the waste from a special ecotype of cocoa (CCN-51). The chemical fingerprinting was performed using UHPLC–HESI orbitrap mass spectrometry and allowed the detection of 51 compounds. GC-FID was used for the determination of individual fatty acid contents, and the antioxidant activity was assessed by several assays (DPPH, FRAP, and ABTS). The flours obtained were composed of a good amount of dietary fiber, carbohydrates, and minerals, as well as several bioactive polyphenolic compounds, fatty acids, and amino acids with nutraceutical properties, making the flours a rich and promising food as well as a good source for the preparation of functional foods or nutraceuticals.
Investigation on the Thermal Characteristics of Enhancement-Mode p-GaN HEMT Device on Si Substrate Using Thermoreflectance Microscopy

In this paper, thermoreflectance microscopy was used to measure the high spatial resolution temperature distribution of the p-GaN HEMT under high power density. The maximum temperature along the GaN channel was located at the drain-side gate edge region. It was found that the thermal resistance (Rth) of the p-GaN HEMT device increased with the increase of channel temperature. The Rth dependence on the temperature was well approximated by a function of Rth~Ta (a = 0.2). The three phonon Umklapp scattering, point mass defects and dislocations scattering mechanisms are suggested contributors to the heat transfer process for the p-GaN HEMT. The impact of bias conditions and gate length on the thermal characteristics of the device was investigated. The behaviour of temperature increasing in the time domain with 50 µs pulse width and different drain bias voltage was analysed. Finally, a field plate structure was demonstrated for improving the device thermal performance.
Beliefs, performance, and applicability of mathematics in learning for life: The multi-case study at secondary education institutes in Italy

Our study stars with the aim of discovering if there is a relationship between the beliefs and perceptions that Italian High School students have towards mathematics and some factors as grade, macro area, type of school and academic performance. The methodological proposal that this study follows is based on the combination of quantitative and qualitative method. Regarding the quantitative design, the instrument used has been a survey filled out by 4845 students living in Italy. The qualitative methodology has focused on the discourse analysis of 12 in-depth interviews with the participation of secondary school teachers. Among the main results we found that the perception of mathematics as a useful subject just to count, calculate and measure can be considered among the causes of the decrease academic performance of the students of the Second Grade Secondary School compared to the Primary School, because many students consider utility of math just in its arithmetic aspect. In addition, while changes about beliefs by grade, macro area and type of school are not significant, we found correlations between some particular beliefs and performance in mathematics, confirming a connection between beliefs towards this discipline and academic performance. The novelty of our research is found in having specifically detected which are beliefs and perceptions related to academic performance. That opens the door for future research.
Cell Therapy Based on Gingiva-Derived Mesenchymal Stem Cells Seeded in a Xenogeneic Collagen Matrix for Root Coverage of RT1 Gingival Lesions: An In Vivo Experimental Study

(1) Background: To investigate the effect of a xenogeneic collagen matrix (CMX) seeded with autologous gingiva-derived mesenchymal cells (GMSCs) when combined with a coronally advanced flap (CAF) in the treatment of localized gingival recession type 1 (RT1). (2) Methods: Dehiscence-type defects were created in seven dogs. GMSCs were isolated, transfected with a vector carrying green fluorescent protein (GFP) and expanded. Once chronified, the defects were randomly treated with (1) CAF plus the combination of CMX and GFP+ GMSCs, (2) CAF plus CMX with autologous fibroblasts, (3) CAF plus CMX and (4) CAF alone. Histological and clinical outcomes at 2- and 6-week healing periods were analyzed and compared among groups. (3) Results: Histologically, the addition of autologous cells to the CMX resulted in reduced inflammation and a variable degree of new cementum/bone formation. CMX plus GMSCs resulted in greater mean recession reduction (1.42; SD = 1.88 mm) and percentage of teeth with recession reduction of ≥2 mm (57%) when compared to the other groups, although these differences were not statistically significant. (4) Conclusions: The histometric and clinical results indicated a positive trend favouring the combination of CMX and GMSCs with the CAF when compared to the groups without cells, although these differences were not statistically significant.
Network user interest mining method based on Neutrosophic cluster analysis

These days’ user interests have become more critical for companies and firms to introduce their content due to the growth in networks and the internet. So this method used neutrosophic sets for network user interest. In this paper, we proposed five main criteria and seventeen sub-criteria to show user interest in the network. The multi-criteria decision-making (MCDM) method is used to deal with various criteria and sub-criteria. So the Analytical Hierarchal Process (AHP) is used to show weights of criteria and sub-criteria to present the user interest in the network. An illustrative example provides to show calculations of the proposed method.
A Four-Port CSRR-Loaded Dual-Band MIMO Antenna with Suppressed Higher Order Modes

In this paper, we propose a compact four-port dual-band MIMO antenna with suppressed higher order modes (HOMs). First, complementary split ring resonator (CSRR) loading is used on a square microstrip antenna to achieve simultaneous miniaturization and dual-band response. Next, the HOMs in the proposed CSRR loaded MIMO configuration are analysed using equivalent circuit model as well as surface current distribution plots. By placing a single shorting post close to antenna center line, these HOMs of the four-port dual-band MIMO antenna are then suppressed, while maintaining satisfactory mutual coupling (< –11 dB) and impedance matching (< –15 dB) performance in the operating band. Furthermore, simulated and measured values of total active reflection coefficient (TARC), envelop correlation coefficient (ECC) and channel capacity loss (CCL) are within the desired levels of –10 dB, 0.5 and 0.5 bits/s/Hz respectively, signifying good diversity performance of the proposed MIMO antenna.
The Proteogenome of Symbiotic Frankia alni in Alnus glutinosa Nodules

Omics are the most promising approaches to investigate microbes for which no genetic tools exist such as the nitrogen-fixing symbiotic Frankia. A proteogenomic analysis of symbiotic Frankia alni was done by comparing those proteins more and less abundant in Alnus glutinosa nodules relative to N-replete pure cultures with propionate as the carbon source and ammonium as the nitrogen-source. There were 250 proteins that were significantly overabundant in nodules at a fold change (FC) ≥ 2 threshold, and 1429 with the same characteristics in in vitro nitrogen-replete pure culture. Nitrogenase, SuF (Fe–Su biogenesis) and hopanoid lipids synthesis determinants were the most overabundant proteins in symbiosis. Nitrogenase was found to constitute 3% of all Frankia proteins in nodules. Sod (superoxide dismutase) was overabundant, indicating a continued oxidative stress, while Kats (catalase) were not. Several transporters were overabundant including one for dicarboxylates and one for branched amino acids. The present results confirm the centrality of nitrogenase in the actinorhizal symbiosis.
A Phosphosite Mutant Approach on LRRK2 Links Phosphorylation and Dephosphorylation to Protective and Deleterious Markers, Respectively

The Leucine Rich Repeat Kinase 2 (LRRK2) gene is a major genetic determinant of Parkinson’s disease (PD), encoding a homonymous multi-domain protein with two catalytic activities, GTPase and Kinase, involved in intracellular signaling and trafficking. LRRK2 is phosphorylated at multiple sites, including a cluster of autophosphorylation sites in the GTPase domain and a cluster of heterologous phosphorylation sites at residues 860 to 976. Phosphorylation at these latter sites is found to be modified in brains of PD patients, as well as for some disease mutant forms of LRRK2. The main aim of this study is to investigate the functional consequences of LRRK2 phosphorylation or dephosphorylation at LRRK2’s heterologous phosphorylation sites. To this end, we generated LRRK2 phosphorylation site mutants and studied how these affected LRRK2 catalytic activity, neurite outgrowth and lysosomal physiology in cellular models. We show that phosphorylation of RAB8a and RAB10 substrates are reduced with phosphomimicking forms of LRRK2, while RAB29 induced activation of LRRK2 kinase activity is enhanced for phosphodead forms of LRRK2. Considering the hypothesis that PD pathology is associated to increased LRRK2 kinase activity, our results suggest that for its heterologous phosphorylation sites LRRK2 phosphorylation correlates to healthy phenotypes and LRRK2 dephosphorylation correlates to phenotypes associated to the PD pathological processes.
Two-Way Feature Extraction for Speech Emotion Recognition Using Deep Learning

Recognizing human emotions by machines is a complex task. Deep learning models attempt to automate this process by rendering machines to exhibit learning capabilities. However, identifying human emotions from speech with good performance is still challenging. With the advent of deep learning algorithms, this problem has been addressed recently. However, most research work in the past focused on feature extraction as only one method for training. In this research, we have explored two different methods of extracting features to address effective speech emotion recognition. Initially, two-way feature extraction is proposed by utilizing super convergence to extract two sets of potential features from the speech data. For the first set of features, principal component analysis (PCA) is applied to obtain the first feature set. Thereafter, a deep neural network (DNN) with dense and dropout layers is implemented. In the second approach, mel-spectrogram images are extracted from audio files, and the 2D images are given as input to the pre-trained VGG-16 model. Extensive experiments and an in-depth comparative analysis over both the feature extraction methods with multiple algorithms and over two datasets are performed in this work. The RAVDESS dataset provided significantly better accuracy than using numeric features on a DNN.
Experimental Investigation on the Vertical Ductility of Rectangular CFST Columns Loaded Axially

A total of 5 steel and 21 rectangular composite concrete-filled steel tube (CFST) columns of moderate slenderness were tested to investigate their ductility under axial compression. The importance of the vertical ductility of columns was discussed, and a novel ductility measure was proposed and utilized to examine the ductility of tested specimens. The analyses showed that the ductility of axially compressed CFST columns highly depends on their failure mode. The key feature influencing the ductility is their ability to dissipate the energy of imposed loads. The larger the volume of a material that may permanently deform and consequently dissipate the energy, the greater this ability. In consequence, the ductility of specimens exhibiting local failure mode was higher in comparison to the columns that underwent global or mixed global—local failure. It was found that both steel and composite columns were able to carry axial loads in the post-critical state; but due to the limitations of local buckling of the steel cross-section in the concrete core and concrete confinement, all tested composite columns showed greater ductility than their steel counterparts.
Flexible Sensory Systems: Structural Approaches

Biology is characterized by smooth, elastic, and nonplanar surfaces; as a consequence, soft electronics that enable interfacing with nonplanar surfaces allow applications that could not be achieved with the rigid and integrated circuits that exist today. Here, we review the latest examples of technologies and methods that can replace elasticity through a structural approach; these approaches can modify mechanical properties, thereby improving performance, while maintaining the existing material integrity. Furthermore, an overview of the recent progress in wave/wrinkle, stretchable interconnect, origami/kirigami, crack, nano/micro, and textile structures is provided. Finally, potential applications and expected developments in soft electronics are discussed.
Smart three-dimensional processing of unconstrained cave scans using small unmanned aerial systems and red, green, and blue-depth cameras

This article focuses on a novel three-dimensional reconstruction system that maps large archeological caves using data collected by a small unmanned aircraft system with red, green, and blue-depth cameras. Cave sites often contain the best-preserved material in the archeological record. Yet few sites are fully mapped. Large caves environment usually contains complex geometric structures and objects, which must be scanned with long overlapped camera trajectories for better coverage. Due to the error in camera tracking of such scanning, reconstruction results often contain flaws and mismatches. To solve this problem, we propose a framework for surface loop closure, where loops are detected with a compute unified device architecture accelerated point cloud registration algorithm. After a loop is detected, a novel surface loop filtering method is proposed for robust loop optimization. This loop filtering method is robust to different scan patterns and can cope with tracking failure recovery so that there is more flexibility for unmanned aerial vehicles to fly and record data. We run experiments on public data sets and our cave data set for analysis and robustness tests. Experiments show that our system produces improved results on baseline methods.
A 2.5 V, 2.56 ppm/°C Curvature-Compensated Bandgap Reference for High-Precision Monitoring Applications

This work presents a high-precision high-order curvature-compensated bandgap voltage reference (BGR) for battery monitoring applications. The collector currents of bipolar junction transistor (BJT) pairs with different ratios and temperature characteristics can cause greater nonlinearities in ΔVEB. The proposed circuit additionally introduces high-order curvature compensation in the generation of ΔVEB, such that it presents high-order temperature effects complementary to VEB. Fabricated using a 0.18 µm BCD process, the proposed BGR generates a 2.5 V reference voltage with a minimum temperature coefficient of 2.65 ppm/°C in the range of −40 to 125 °C. The minimum line sensitivity is 0.023%/V when supply voltage varies from 4.5 to 5.5 V. The BGR circuit area is 382 × 270 μm2, and the BMIC area is 2.8 × 2.8 mm2.
A Hybrid RSM-ANN-GA Approach on Optimization of Ultrasound-Assisted Extraction Conditions for Bioactive Component-Rich Stevia rebaudiana (Bertoni) Leaves Extract

Stevia rebaudiana (Bertoni) leaves consist of dietetically important diterpene steviol glycosides (SGs): stevioside (ST) and rebaudioside-A (Reb-A). ST and Reb-A are key sweetening compounds exhibiting a sweetening potential of 100 to 300 times more intense than that of table sucrose. Ultrasound-assisted extraction (UAE) of SGs was optimized by effective process optimization techniques, such as response surface methodology (RSM) and artificial neural network (ANN) modeling coupled with genetic algorithm (GA) as a function of ethanol concentration (X1: 0–100%), sonication time (X2: 10–54 min), and leaf–solvent ratio (X3: 0.148–0.313 g·mL−1). The maximum target responses were obtained at optimum UAE conditions of 75% (X1), 43 min (X2), and 0.28 g·mL−1 (X3). ANN-GA as a potential alternative indicated superiority to RSM. UAE as a green technology proved superior to conventional maceration extraction (CME) with reduced resource consumption. Moreover, UAE resulted in a higher total extract yield (TEY) and SGs including Reb-A and ST yields as compared to those that were obtained by CME with a marked reduction in resource consumption and CO2 emission. The findings of the present study evidenced the significance of UAE as an ecofriendly extraction method for extracting SGs, and UAE scale-up could be employed for effectiveness on an industrial scale. These findings evidenced that the UAE is a high-efficiency extraction method with an improved statistical approach.
Porcine Circovirus Type 4 Strains Circulating in China Are Relatively Stable and Have Higher Homology with Mink Circovirus than Other Porcine Circovirus Types

Porcine circovirus type 4 (PCV4) is a newly identified porcine circovirus (PCV) belonging to the Circovirus genus Circoviridae family. Although several groups have conducted epidemiological investigations on PCV4 and found that PCV4 also exists widely in pigs, there are few reports on the origin and evolution of PCV4. In this study, the genetic relationship between PCV4, mink circovirus (MiCV), bat circovirus (BtCV), PCV1, PCV2, and PCV3 was analyzed, and the consistency of viral proteins in three-dimensional (3D) structure and epitopes was predicted. We found that the genome and protein structure of PCV4 was relatively stable among current circulating PCV4 strains. Furthermore, PCV4 was more similar to MiCV in terms of its genome, protein structure, and epitope levels than other PCVs and BtCVs, suggesting that PCV4 may be derived from MiCV or have a common origin with MiCV, or mink may be an intermediate host of PCV4, which may pose a great threat to other animals and/or even human beings. Therefore, it is necessary to continuously monitor the infection and variation of PCV4, analyze the host spectrum of PCV4, and establish the prevention and treatment methods of PCV4 infection in advance.
Fake News Detection: GA-Transformer And IG-Transformer Based Approach

A significant part of the information available on the internet is fake news which is generated to influence the decision of individuals politically and socially. Detecting fake news from social media has become a very important problem. It can be done either manually or by using Deep Learning. In this work, we focus on detecting fake news with Transformer models of Deep Learning. We analyze the performance of Transformer models like BERT, XLNet, RoBERTa, and Longformer. We also propose two novel approaches by combining Information Gain and Genetic Algorithm with all these models for further improving their results. The models based on our proposed approaches show better results than the previous models. Among all the models, Longformer and RoBERTa using Genetic Algorithm give the best result when 80% unique keywords are used. The micro average F1-score of both methods is 0.62. Our proposed approaches also reduce the training text dimension due to which computing time for training the models reduce too.
SleepQual and B.Health: Smartwatch and Smartphone based Behavioral Datasets of Youth

Wearable sensors recording different components of peoples’ daily activity are commonly used these days for collecting motion data. These provide objective measures of behavioral components like sleep and physical activity. Smartphones are being used increasingly contributing to users’ screen viewing and hence are capable of providing precise measures of users’ screen time. Disruption in the key lifestyle patterns like sleep, physical activity and screen viewing may result in health impairments as these correspond to the utmost engaged times during the day. This study presents a novel dataset containing attributes of physical activity, sleep, and smartphone-based screen viewing collected from users’ smartphones and smartwatches. The dataset contains real time objective measures of activities of 24 undergraduate students. A sleep quality indicator and a behavioral health indicator namely SleepQual and B.Health respectively are evaluated using the extracted features. The instances are labelled based on the scores of SleepQual and B.Health. We have made the dataset publicly available which opens avenues for researchers to employ various intelligent data analysis techniques in order to develop systems capable of automatically assessing sleep quality and behavioral health using digital data without the need of any self-reporting questionnaires and tools. To provide baseline performance four machine learning classifiers are implemented on the proposed dataset.