Miracle-dz/UniFAR-FTUs huggingface dataset:
Comparative Characterization of Aspergillus Pectin Lyases by Discriminative Substrate Degradation Profiling

Fungal genomes often contain several copies of genes that encode carbohydrate active enzymes having similar activity. The copies usually have slight sequence variability, and it has been suggested that the multigenecity represents distinct reaction optima versions of the enzyme. Whether the copies represent differences in substrate attack proficiencies of the enzyme have rarely been considered. The genomes of Aspergillus species encode several pectin lyases (EC 4.2.2.10), which all belong to polysaccharide lyase subfamily PL1_4 in the CAZy database. The enzymes differ in terms of sequence identity and phylogeny, and exhibit structural differences near the active site in their homology models. These enzymes catalyze pectin degradation via eliminative cleavage of the α-(1,4) glycosidic linkages in homogalacturonan with a preference for linkages between methyl-esterified galacturonate residues. This study examines four different pectin lyases (PelB, PelC, PelD, and PelF) encoded by the same Aspergillus sp. (namely A. luchuensis), and further compares two PelA pectin lyases from two related Aspergillus spp. (A. aculeatus and A. tubingensis). We report the phylogeny, enzyme kinetics, and enzymatic degradation profiles of the enzymes’ action on apple pectin, citrus pectin, and sugar beet pectin. All the pectin lyases exerted highest reaction rate on apple pectin [degree of methoxylation (DM) 69%, degree of acetylation (DAc) 2%] and lowest reaction rate on sugar beet pectin (DM 56%, DAc 19%). Activity comparison at pH 5–5.5 produced the following ranking: PelB > PelA > PelD > PelF > PelC. The evolution of homogalacturonan-oligomer product profiles during reaction was analyzed by liquid chromatography with mass spectrometry (LC-MS) detection. This analyses revealed subtle differences in the product profiles indicating distinct substrate degradation preferences amongst the enzymes, notably with regard to acetyl substitutions. The LC-MS product profiling analysis thus disclosed that the multigenecity appears to provide the fungus with additional substrate degradation versatility. This product profiling furthermore represents a novel approach to functionally compare pectin-degrading enzymes, which can help explain structure-function relations and reaction properties of disparate copies of carbohydrate active enzymes. A better understanding of the product profiles generated by pectin modifying enzymes has significant implications for targeted pectin modification in food and biorefinery processes.
Detection of Depression in Thai Social Media Messages using Deep Learning

: Depression problems can severely affect not only personal health, but also society. There is evidence that shows people who suffer from depression problems tend to express their feelings and seek help via online posts on online platforms. This study is conducted to apply Natural Language Processing (NLP) with messages associated with depression problems. Feature extractions, machine learning, and neural network models are applied to carry out the detection. The CNN-LSTM model, a unified model combining Convolutional Neural Networks (CNN) and Long Short-Term Memory Networks (LSTM), is used sequentially and in parallel as branches to compare the outcomes with baseline models. In addition, different types of activation functions are applied in the CNN layer to compare the results. In this study, the CNNLSTM models show improvement over the classical machine learning method. However, there is a slight improvement among the CNN-LSTM models. The three-branch CNN-LSTM model with the Rectified Linear Unit (ReLU) activation function is capable of achieving the F1-score of 83.1%.
Prediction of Soybean Yield using Self-normalizing Neural Networks

Nowadays, agriculture around the world is facing severe challenges because of global warming and rapid population growth. In order to maximize the agricultural production and minimize the environmental degradation at the same time, careful land-use planning and crop selection come to be crucial, where accurate crop yield prediction plays a key role. This research applies an emerging Deep Learning architecture called Self-normalizing neural networks (SNN) for yield prediction of the soybean, using numerical data obtained from official statistics to ensure high reliability and availability of data. Plentiful work has been done to improve the performance, including careful parameter tuning and application of early stopping and the learning rate scheduler. This study conducts an experiment to evaluate the performance of the model, and the results show that SNN can achieve a lower prediction error with sufficiently large training data compared with traditional machine learning methods, such as Support Vector Regression, and other Deep Learning techniques, such as Batch Normalization.
Accelerating Matrix Factorization by Overparameterization

: This paper studies overparameterization on the matrix factorization (MF) model. We conﬁrm that overparameterization can signiﬁcantly accelerate the optimization of MF with no change in the expressiveness of the learning model. Consequently, modern applications on recommendations based on MF or its variants can largely beneﬁt from our discovery. Speciﬁcally, we theoretically derive that applying the vanilla stochastic gradient descent (SGD) on the overparameterized MF model is equivalent to employing gradient descent with momentum and adaptive learning rate on the standard MF model. We empirically compare the overparameterized MF model with the standard MF model based on various optimizers, including vanilla SGD, AdaGrad, Adadelta, RMSprop, and Adam, using several public datasets. The experimental results comply with our analysis – overparameterization converges faster. The overparameterization technique can be applied to various learning-based recommendation models, including deep learning-based recommendation models, e.g., SVD++, nonnegative matrix factorization (NMF), factorization machine (FM), NeuralCF, Wide&Deep, and DeepFM. Therefore, we suggest utilizing the overparameterization technique to accelerate the training speed for the learning-based recommendation models whenever possible, especially when the size of the training dataset is large.
Bacillus megaterium Strain CDK25, a Novel Plant Growth Promoting Bacterium Enhances Proximate Chemical and Nutritional Composition of Capsicum annuum L

The present study aimed to scrutinize the effect of different cow dung bacterial treatments on the nutritional value of Capsicum annuum L. Among all treatments, seeds inoculated with Bacillus megaterium (CDK25) showed significant enhancement in various proximate constituents viz., crude fiber (3.31%), crude protein (3.84%), and ash (2.53%) as compared to control. Likewise, significant increase in different nutrient contents viz., Ca (16.26 mg/100 g), Mg (17.37 mg/100 g), P (11.91 mg/100 g), K (0.47 mg/100 g), Fe (1.37 mg/100 g), and Zn (0.21 mg/100 g) was recorded over the control. Principal component analysis data depicts a positive correlation between different treatments with variables, validating enhancement in nutritional constituents by B. megaterium (CDK25) treatment. The study suggests the application of “B. megaterium” for achieving the persistent potential for augmenting and boosting up plant biological, functional, and nutritional assets, thereby enhancing the overall edible quality of C. annuum L. along with weathering of soil minerals.
Tocilizumab shortens time on mechanical ventilation and length of hospital stay in patients with severe COVID‐19: a retrospective cohort study

Background: Hyperinflammation is a key feature of the pathogenesis of COVID-19 with a central role of the interleukin-6 pathway. We aimed to study the impact of the IL-6 receptor antagonist tocilizumab on the outcome of patients admitted to the intensive care unit (ICU) with acute respiratory distress syndrome (ARDS) related to COVID-19. Methods: Eighty-seven patients with confirmed SARS-CoV-2 infection and moderate to severe ARDS were included (n tocilizumab = 29, n controls = 58). A matched cohort was created using a propensity score. The primary endpoint was 30-day all-cause mortality, secondary endpoints included ventilation-free days and length of stay. Results: No difference was found in 30-day all-cause mortality in patients treated with tocilizumab compared to controls (17.2% vs. 32.8%, p = 0.2; HR = 0.52 [0.19 - 1.39], p = 0.19). Ventilator-free days were 19.0 (IQR 12.5 - 20.0) versus 9 (IQR 0.0 - 18.5; p = 0.04), respectively. A higher rate of freedom from mechanical ventilation at 30 days was achieved in patients receiving tocilizumab (HR 2.83 [1.48 - 5.40], p < 0.002). Median length of stay in ICU and total length of stay were reduced by 8 and 9.5 days in patients treated with tocilizumab. Similar results were obtained in the analysis of the propensity score matched cohort. Conclusions: Treatment of critically ill patients with ARDS due to COVID-19 with tocilizumab was not associated with reduced 30-day all-cause mortality, but shorter duration on ventilatory support as well as shorter overall length of stay in hospital and in ICU.
Self-Supervised Encoder for Fault Prediction in Electrochemical Cells

Predicting faults before they occur helps to avoid potential safety hazards. Furthermore, planning the required maintenance actions in advance reduces operation costs. In this article, the focus is on electrochemical cells. In order to predict a cell's fault, the typical approach is to estimate the expected voltage that a healthy cell would present and compare it with the cell's measured voltage in real-time. This approach is possible because, when a fault is about to happen, the cell's measured voltage differs from the one expected for the same operating conditions. However, estimating the expected voltage is challenging, as the voltage of a healthy cell is also affected by its degradation -- an unknown parameter. Expert-defined parametric models are currently used for this estimation task. Instead, we propose the use of a neural network model based on an encoder-decoder architecture. The network receives the operating conditions as input. The encoder's task is to find a faithful representation of the cell's degradation and to pass it to the decoder, which in turn predicts the expected cell's voltage. As no labeled degradation data is given to the network, we consider our approach to be a self-supervised encoder. Results show that we were able to predict the voltage of multiple cells while diminishing the prediction error that was obtained by the parametric models by 53%. This improvement enabled our network to predict a fault 31 hours before it happened, a 64% increase in reaction time compared to the parametric model. Moreover, the output of the encoder can be plotted, adding interpretability to the neural network model.
Sentiment Polarity Classification of Corporate Review Data with a Bidirectional Long-Short Term Memory (biLSTM) Neural Network Architecture

: A considerable amount of literature has been published on Corporate Reputation, Branding and Brand Image. These studies are extensive and focus particularly on questionnaires and statistical analysis. Although extensive research has been carried out, no single study was found which attempted to predict corporate reputation performance based on data collected from media sources. To perform this task, a biLSTM Neural Network extended with attention mechanism was utilized. The advantages of this architecture are that it obtains excellent performance for NLP tasks. The state-of-the-art designed model achieves highly competitive results, F1 scores around 72%, accuracy of 92% and loss around 20%.
Resource Allocation via Model-Free Deep Learning in Free Space Optical Networks

This paper investigates the general problem of resource allocation for mitigating channel fading effects in Free Space Optical (FSO) networks. The resource allocation problem is modelled with a constrained stochastic optimization framework, which we exemplify with problems in power adaptation and relay selection. Under this framework, we develop two algorithms to solve FSO resource allocation problems. We first present the Stochastic Dual Gradient algorithm that solves the problem exactly by exploiting the null duality gap but whose implementation necessarily requires explicit and accurate system models. As an alternative we present the Primal-Dual Deep Learning algorithm, which parametrizes the resource allocation policy with Deep Neural Networks (DNNs) and optimizes via a primal-dual method. The parametrized resource allocation problem incurs only a small loss of optimality due to the strong representational power of DNNs, and can be moreover implemented in an unsupervised manner without knowledge of system models. Numerical experiments are performed to exhibit superior performance of proposed algorithms compared to baseline methods in a variety of resource allocation problems in FSO networks, including both continuous power allocation and binary relay selection.
A Novel Approach for Android Malware Detection and Classification using Convolutional Neural Networks

: Malicious software or malware has been growing exponentially in the last decades according to antiviruses vendors. The growth of malware is due to advanced techniques that malware authors are using to evade detection. Hence, the traditional methods that antiviruses vendors deploy are insufﬁcient in protecting people’s digital lives. In this work, an attempt is made to address the problem of mobile malware detection and classiﬁcation based on a new approach to android mobile applications that uses Convolutional Neural Networks (CNN). The paper suggests a static analysis method that helps in malware detection using malware visualization. In our approach, ﬁrst, we convert android applications in APK format into gray-scale images. Since malware from the same family has shared patterns, we then designed a machine learning model to classify Android applications as malware or benign based on pattern recognition. The dataset used in this research is a combination of self-made datasets that used public APIs to scan the APK ﬁles downloaded from open sources on the internet, and a research dataset provided by the University of New Brunswick, Canada. Using our proposed solution, we achieved an 84.9% accuracy in detecting mobile malware.
Adapting Spectrum Resources using Predicted IP Traffic in Optical Networks

: Elastic optical networks provide the advantage of elaborate resource utilisation for achieving a wide range of performance goals. Cross-layer optimisation is feasible by exploiting high layer IP trafﬁc prediction for achieving efﬁcient lightpath establishment at the lower layer. Swarm Intelligence can provide a tool to adap-tively allocate spectrum resources according to trafﬁc analytics from the IP layer. A new algorithm is designed and evaluated that exploits these analytics using particle swarm optimisation to allocate spectrum.
Statin Use Improves Overall Survival of Patients with Gastric Cancer after Surgery and Adjuvant Chemotherapy in Taiwan: A Nationwide Matched Cohort Study

Background: Numerous studies have revealed that statins have antitumor effects in vivo and in vitro. However, few studies have explored the relationship between statin use and the mortality of gastric cancer (GC) patients after treatments. This study examines the relationship between statin use and the overall survival (OS) of GC patients after surgery and adjuvant chemotherapy, using data from the nationwide cohort database of Taiwan. Methods: All patients newly diagnosed with GC from 1999 to 2008 in Taiwan were identified from the Registry of Catastrophic Illness Patients Database. Through propensity score matching, statin users were matched to statin non-users at a 1:4 ratio. The relationship between statin use and the OS of patients with GC was estimated through Cox regression models. Results: The study cohort included 1835 patients with GC who had received therapies during the study period. The death numbers among statin users (defined as those who used more than 28 cumulative defined daily doses (cDDDs)) and statin non-users were 138 and 895, respectively. A dose–response association was noted between statin use and the OS of patients with GC after treatments. The adjusted hazard ratios were 0.62 (95% confidence intervals (CI), 0.50–0.78) and 0.34 (95% CI, 0.26–0.45) for statin users administered 28–167 cDDDs and >168 cDDDs, respectively, compared with no statin use (<28 cDDDs). Conclusions: This study highlights that statin use may dose-dependently improve the OS of patients with GC after surgery and adjuvant chemotherapy in Taiwan. Additional studies are required to confirm the efficacy and safety of statin use.
Molecular Epidemiology of Azole-Resistant Aspergillus fumigatus in Sawmills of Eastern France by Microsatellite Genotyping

Background: Wood chipping has been described as a potential hotspot for the selection of azole-resistant Aspergillus fumigatus (ARAf). We previously reported ARAf isolates in sawmills (Eastern France), most of which contained the TR34/L98H mutation. Methods: To study genotypic relatedness, microsatellite genotyping (short tandem repeat for A. fumigatus (STRAf)) was performed on 41 azole-susceptible A. fumigatus (ASAf) and 23 ARAf isolated from 18 sawmills and two clinical A. fumigatus (sensitive and resistant) isolated from a sinus sample of a woodworker. Results: Fifty-four unique multilocus genotypes (MLGs) were described among the 66 isolates: 13/24 ARAf and 41/42 ASAf. Allelic diversity was higher for ASAf than for ARAf. Among the 24 ARAf, five isolates had their own MLGs. Thirteen ARAf (54%) belonged to the same group, composed of four close MLGs, defined using Bruvo’s distance. Thirty-two of the 42 ASAf (76%) had their own MLGs and could not be grouped with the Bruvo’s distance cutoff used (0.2). Conclusion: Thus, at a regional scale and in the particular environment of the wood industry, common but also different distinct genotypes, even in the same sawmill, were identified. This suggests that the hypothesis of ARAf clonal expansion from a common strain is probably insufficient to explain genotype emergence and distribution.
Unravelling the complexities of the first breaths of life

Background: The transition to air-breathing at birth is a seminal, but poorly understood, respiratory event common to all humans. The objectives of this prospective, observational study were to describe the spatiotemporal gas flow, aeration and ventilation patterns occurring within the lung in neonates during successful respiratory transition. Methods: Electrical impedance tomography was used to image intrathoracic volume patterns for every breath until six minutes from birth in term infants not needing resuscitation. Breaths were classified by video data, and measures of lung aeration, tidal flow conditions and intrathoracic volume distribution calculated for each inflation. Findings: 1401 breaths (n=17 neonates) met eligibility and data analysis criteria. Stable functional residual capacity was obtained by median (IQR) 43 (21, 77) breaths. Breathing patterns changed from predominantly crying (80.9% first minute) to tidal breathing (65.3% sixth minute). Tidal ventilation was inhomogeneous at birth, favouring the right and non-dependent lung; p<0.001 versus left and dependent lung (mixed effects model). Initial crying created a unique pattern with delayed mid-expiratory gas flow associated with intrathoracic volume redistribution (pendelluft flow) within the lung. This preserved functional residual capacity (70.8% cries), especially within the dorsal and right lung. Interpretation: The commencement of air-breathing at birth generates unique flow and volume states associated with marked spatiotemporal ventilation inhomogeneity not seen elsewhere in respiratory physiology. At birth neonates innately brake expiratory flow to defend functional residual capacity gains and redistribute gas to less aerated regions.
Two distinct amphipathic peptide antibiotics with systemic efficacy

Significance It is the dream of many scientists to develop antimicrobial peptides (AMPs) into potent antibiotics to combat resistant pathogens and their biofilms. However, the success is low due to limited knowledge on peptide design. Natural AMPs are diverse in sequence, activity, and source. We found a linear relationship between the averaged contents of arginine (R) and hydrophobic amino acids (Pho) of over 3,000 AMPs in the current Antimicrobial Peptide Database (http://aps.unmc.edu/AP). Based on this R–Pho relationship, we identified a peptide template and designed two representative amphipathic peptides with distinct structures and activities. Remarkably, horine and verine showed systemic efficacy in mice, but no nephrotoxicity to rodents. Our study advances peptide design and provides two antibiotics to combat drug-resistant pathogens. Antimicrobial peptides are important candidates for developing new classes of antibiotics because of their potency against antibiotic-resistant pathogens. Current research focuses on topical applications and it is unclear how to design peptides with systemic efficacy. To address this problem, we designed two potent peptides by combining database-guided discovery with structure-based design. When bound to membranes, these two short peptides with an identical amino acid composition can adopt two distinct amphipathic structures: A classic horizontal helix (horine) and a novel vertical spiral structure (verine). Their horizontal and vertical orientations on membranes were determined by solid-state 15N NMR data. While horine was potent primarily against gram-positive pathogens, verine showed broad-spectrum antimicrobial activity. Both peptides protected greater than 80% mice from infection-caused deaths. Moreover, horine and verine also displayed significant systemic efficacy in different murine models comparable to conventional antibiotics. In addition, they could eliminate resistant pathogens and preformed biofilms. Significantly, the peptides showed no nephrotoxicity to mice after intraperitoneal or intravenous administration for 1 wk. Our study underscores the significance of horine and verine in fighting drug-resistant pathogens.
Ultrasound Imaging of the Trunk Muscles in Acute Stroke Patients and Relations With Balance Scales

Objective To examine the correlation between ultrasonographic trunk muscle parameters and balance scales in mild acute stroke patients. Methods A total of 55 stroke patients with hemiparesis and motor power grade ≥4 in the manual motor test were included. The Scale for the Assessment and Rating of Ataxia (SARA), Berg Balance Scale (BBS), Timed Up and Go Test (TUG), and Trunk Control Test (TCT) were used to evaluate patient balance function. Ultrasonographic parameters were measured on both non-paretic and paretic sides of the rectus abdominis, external oblique, internal oblique, transversus abdominis, and erector spinae muscles. Resting thickness and contraction thickness were measured in all muscles, and contractility and contractility ratio were calculated based on measured thicknesses. The differences between paretic and non-paretic muscle parameters, and the correlation between ultrasonographic parameters and balance scales were analyzed. Stroke patients were divided into two groups according to their fall risk. Ultrasonographic measurements between the two groups were compared. Results All muscles’ contraction thickness and contractility were significantly different between paretic and non-paretic sides (p<0.001). Contractility ratios of all trunk muscles showed a significant correlation with SARA, BBS, TUG, and TCT (p<0.05). Contractility ratios of all muscles were significantly different between high- and low-risk fall groups (p<0.05). Conclusion The contractility ratio in stroke patients reflects their balance disturbance and fall risk and it may serve as a new parameter for ultrasound imaging of trunk muscles.