Machine learning for synergistic network pharmacology: a comprehensive overview.

Network pharmacology is an emerging area of systematic drug research that attempts to understand drug actions and interactions with multiple targets. Network pharmacology has changed the paradigm from 'one-target one-drug' to highly potent 'multi-target drug'. Despite that, this synergistic approach is currently facing many challenges particularly mining effective information such as drug targets, mechanism of action, and drug and organism interaction from massive, heterogeneous data. To overcome bottlenecks in multi-target drug discovery, computational algorithms are highly welcomed by scientific community. Machine learning (ML) and especially its subfield deep learning (DL) have seen impressive advances. Techniques developed within these fields are now able to analyze and learn from huge amounts of data in disparate formats. In terms of network pharmacology, ML can improve discovery and decision making from big data. Opportunities to apply ML occur in all stages of network pharmacology research. Examples include screening of biologically active small molecules, target identification, metabolic pathways identification, protein-protein interaction network analysis, hub gene analysis and finding binding affinity between compounds and target proteins. This review summarizes the premier algorithmic concepts of ML in network pharmacology and forecasts future opportunities, potential applications as well as several remaining challenges of implementing ML in network pharmacology. To our knowledge, this study provides the first comprehensive assessment of ML approaches in network pharmacology, and we hope that it encourages additional efforts toward the development and acceptance of network pharmacology in the pharmaceutical industry.

In Situ Imaging of Two-Dimensional Crystal Growth Using a Heat-Resistant Optical Microscope.

Revealing low-dimensional material growth dynamics is critical for crystal growth engineering. However, in a practical high-temperature growth system, the crystal growth process is a black box because of the lack of heat-resistant imaging tools. Here, we develop a heat-resistant optical microscope and embed it in a chemical vapor deposition (CVD) system to investigate two-dimensional (2D) crystal growth dynamics. This in situ optical imaging CVD system can tolerate temperatures of ≤900 °C with a spatial resolution of ∼1 µm. The growth of monolayer MoS2 crystals was studied as a model for 2D crystal growth. The nucleation and growth process have been imaged. Model analysis and simulation have revealed the growth rate, diffusion coefficient, and spatial distribution of the precursor. More importantly, a new vertex-kink-ledge model has been suggested for monolayer crystal growth. This work provides a new technique for in situ microscopic imaging at high temperatures and fundamental insight into 2D crystal growth.

Covalent Organic Framework Membranes and Water Treatment.

Covalent organic frameworks (COFs) are an emerging class of highly porous crystalline organic polymers comprised entirely of organic linkers connected by strong covalent bonds. Due to their excellent physicochemical properties (e.g., ordered structure, porosity, and stability), COFs are considered ideal materials for developing state-of-the-art separation membranes. In fact, significant advances have been made in the last six years regarding the fabrication and functionalization of COF membranes. In particular, COFs have been utilized to obtain thin-film, composite, and mixed matrix membranes that could achieve effective rejection (mostly above 80%) of organic dyes and model organic foulants (e.g., humic acid). COF-based membranes, especially those prepared by embedding into polyamide thin-films, obtained adequate rejection of salts in desalination applications. However, the claims of ordered structure and separation mechanisms remain unclear and debatable. In this perspective, we analyze critically the design and exploitation of COFs for membrane fabrication and their performance in water treatment applications. In addition, technological challenges associated with COF properties, fabrication methods, and treatment efficacy are highlighted to redirect future research efforts in realizing highly selective separation membranes for scale-up and industrial applications.

Role of UDP-Glycosyltransferase (ugt) Genes in Detoxification and Glycosylation of 1-Hydroxyphenazine (1-HP) in Caenorhabditis elegans.

Caenorhabditis elegans is a useful model organism to study the xenobiotic detoxification pathways of various natural and synthetic toxins, but the mechanisms of phase II detoxification are understudied. 1-Hydroxyphenazine (1-HP), a toxin produced by the bacterium Pseudomonas aeruginosa, kills C. elegans. We previously showed that C. elegans detoxifies 1-HP by adding one, two, or three glucose molecules in N2 worms. Our current study evaluates the roles that some UDP-glycosyltransferase (ugt) genes play in 1-HP detoxification. We show that ugt-23 and ugt-49 knockout mutants are more sensitive to 1-HP than reference strains N2 or PD1074. Our data also show that ugt-23 knockout mutants produce reduced amounts of the trisaccharide sugars, while the ugt-49 knockout mutants produce reduced amounts of all 1-HP derivatives except for the glucopyranosyl product compared to the reference strains. We characterized the structure of the trisaccharide sugar phenazines made by C. elegans and showed that one of the sugar modifications contains an N-acetylglucosamine (GlcNAc) in place of glucose. This implies broad specificity regarding UGT function and the role of genes other than ogt-1 in adding GlcNAc, at least in small-molecule detoxification.

Bovine Serum Albumin Template-Mediated Fabrication of Ruthenium Dioxide/Multiwalled Carbon Nanotubes: High-Performance Electrochemical Dopamine Biosensing in Human Serum.

The presence of abnormal dopamine (DA) levels may cause serious neurological disorders, therefore, the quantitative analysis of DA and its related research are of great significance for ensuring health. Herein, the bovine serum albumin (BSA) template method has been proposed for the preparation of catalytically high-performance ruthenium dioxide/multiwalled carbon nanotube (RuO2/MWCNT) nanocomposites. The incorporation of MWCNTs has improved the active surface area and conductivity while effectively preventing the aggregation of RuO2 nanoparticles. The outstanding electrocatalytic performance of RuO2/MWCNTs has promoted the electro-oxidation of DA at neutral pH. The electrochemical sensing platform based on RuO2/MWCNTs has demonstrated a wide linear range (0.5 to 111.1 µM), low detection limit (0.167 µM), excellent selectivity, long-term stability, and good reproducibility for DA detection. The satisfactory recovery range of 94.7% to 103% exhibited by the proposed sensing podium in serum samples signifies its potential for analytical applications. The aforementioned results reveal that RuO2/MWCNT nanostructures hold promising aptitude in the electrochemical sensor to detect DA in real samples, further offering broad prospects in clinical and medical diagnosis.

Assessment of Genomic Diversity and Selective Pressures in Crossbred Dairy Cattle of Pakistan.

Improving the low productivity levels of native cattle breeds in smallholder farming systems is a pressing concern in Pakistan. Crossbreeding high milk-yielding holstein friesian (HF) breed with the adaptability and heat tolerance of Sahiwal cattle has resulted in offspring that are well-suited to local conditions and exhibit improved milk yield. The exploration of how desirable traits in crossbred dairy cattle are selected has not yet been investigated. This study aims to provide the first overview of the selective pressures on the genome of crossbred dairy cattle in Pakistan. A total of eighty-one crossbred, thirty-two HF and twenty-four Sahiwal cattle were genotyped, and additional SNP genotype data for HF and Sahiwal were collected from a public database to equate the sample size in each group. Within-breed selection signatures in crossbreds were investigated using the integrated haplotype score. Crossbreds were also compared to each of their parental breeds to discover between-population signatures of selection using two approaches: cross-population extended haplotype homozygosity and fixation index. We identified several overlapping genes associated with production, immunity, and adaptation traits, including U6, TMEM41B, B4GALT7, 5S_rRNA, RBM27, POU4F3, NSD1, PRELID1, RGS14, SLC34A1, TMED9, B4GALT7, OR2AK3, OR2T16, OR2T60, OR2L3, and CTNNA1. Our results suggest that regions responsible for milk traits have generally experienced stronger selective pressure than others.

Launaea fragilis extract attenuated arthritis in rats through modulation of IL-1ß, TNF-α, IL-6, NF-κB, COX-2, IL-4, and IL-10.

Launaea fragilis (Asso) Pau is a Cholistan desert medicinal plant. Launaea species are used as traditional remedies against various inflammatory conditions. The current research was designed to evaluate the anti-nociceptive, anti-inflammatory, and anti-arthritic potential of ethanolic extract of L. fragilis (Et-LF). The plant extract was prepared by triple maceration. GC-MS screening explored the presence of various bioactive phytoconstituents including n-tetracosanol-1, 1-heptacosanol, and n-hexadecanoic acid. DPPH assay demonstrated the antioxidant potential of Et-LF. Safety profile data indicated that Et-LF was safe up to the oral dose of 5000 mg/kg in female rats. Anti-nociceptive activity of Et-LF was assessed in hot plate method and acetic acid-induced writhing model and the results suggested that Et-LF had significant analgesic effects in both animal models. Carrageenan, histamine, and serotonin-induced edema models were used to estimate the anti-inflammatory effects of Et-LF and were found to prevent paw edema development dose dependently. The anti-arthritic effect of Et-LF was estimated in CFA-induced arthritic rat model. Treatment with Et-LF 125, 250, 500 and flurbiprofen (FP) 10 mg/kg/day significantly attenuated the paw edema, reversed the reduced body weight, and restored the altered hematological parameters in arthritic rats. Gene expression studies revealed the significant downregulation of IL-1ß, TNF-α, IL-6, NF­κB, and COX-2, and upregulation of IL-4 and IL-10 in arthritic rats treated with various doses of plant extract. Histological evaluation of ankle joints showed that Et-LF mitigated pannus formation, infiltration of inflammatory cells, and fibrous connective tissue formation in the diseased rats. Thereof, it may be concluded that the recent study demonstrated the anti-nociceptive, anti-inflammatory, and anti-arthritic effects ascribed to the signifying presence of phytoconstituents in L. fragilis.

Development of Biocompatible Electrospun PHBV-PLLA Polymeric Bilayer Composite Membranes for Skin Tissue Engineering Applications.

Bilayer electrospun fibers aimed to be used for skin tissue engineering applications were fabricated for enhanced cell attachment and proliferation. Different ratios of PHBV-PLLA (70:30, 80:20, and 90:10 w/w) blends were electrospun on previously formed electrospun PHBV membranes to produce their bilayers. The fabricated electrospun membranes were characterized with FTIR, which conformed to the characteristic peaks assigned for both PHBV and PLLA. The surface morphology was evaluated using SEM analysis that showed random fibers with porous morphology. The fiber diameter and pore size were measured in the range of 0.7 ± 0.1 µm and 1.9 ± 0.2 µm, respectively. The tensile properties of the bilayers were determined using an electrodynamic testing system. Bilayers had higher elongation at break (44.45%) compared to the monolayers (28.41%) and improved ultimate tensile strength (7.940 MPa) compared to the PHBV monolayer (2.450 MPa). In vitro cytotoxicity of each of the scaffolds was determined via culturing MC3T3 (pre-osteoblastic cell line) on the membranes. Proliferation was evaluated using the Alamar Blue assay on days 3, 7, and 14, respectively. SEM images of cells cultured on membranes were taken in addition to bright field imaging to visually show cell attachment. Fluorescent nuclear staining performed with DAPI was imaged with an inverted fluorescent microscope. The fabricated bilayer shows high mechanical strength as well as biocompatibility with good cell proliferation and cell attachment, showing potential for skin substitute applications.

Membrane distillation of wastewater: comparison of model and real organics.

Fouling behaviour in membrane distillation (MD) processes plays a crucial role in determining their widespread acceptability. Most studies have primarily focused on model organic foulants, such as humic acid (HA) and sodium alginate (SA). This study investigates the fouling of a polytetrafluoroethylene membrane in a direct contact MD (DCMD) using model organics (i.e., HA and SA) and real wastewater. The results indicated that the flux decline (5-60%) was only observed during the initial phase of the operation with model organic foulants. In contrast, real wastewater caused a gradual decline in flux throughout the experiment in both the concentrate (40%) and continuous (90%) modes. The study also found significant differences in the fouling layer morphology, composition, and hydrophobicity between the model organic foulants and real wastewater. Fourier transform infrared spectroscopy findings demonstrated that the fouling layer formed by real wastewater varied significantly from model organics, which primarily comprised of protein-like and polysaccharide-like functional groups. Finally, liquid chromatography-organic carbon detection revealed that the fouling layer of the MD membrane with real wastewater was composed of 40.7% hydrophobic and 59.3% hydrophilic organics. This study suggests that model organics may not accurately reflect real wastewater fouling.

DGH-GO: dissecting the genetic heterogeneity of complex diseases using gene ontology.

BACKGROUND: Complex diseases such as neurodevelopmental disorders (NDDs) exhibit multiple etiologies. The multi-etiological nature of complex-diseases emerges from distinct but functionally similar group of genes. Different diseases sharing genes of such groups show related clinical outcomes that further restrict our understanding of disease mechanisms, thus, limiting the applications of personalized medicine approaches to complex genetic disorders. RESULTS: Here, we present an interactive and user-friendly application, called DGH-GO. DGH-GO allows biologists to dissect the genetic heterogeneity of complex diseases by stratifying the putative disease-causing genes into clusters that may contribute to distinct disease outcome development. It can also be used to study the shared etiology of complex-diseases. DGH-GO creates a semantic similarity matrix for the input genes by using Gene Ontology (GO). The resultant matrix can be visualized in 2D plots using different dimension reduction methods (T-SNE, Principal component analysis, umap and Principal coordinate analysis). In the next step, clusters of functionally similar genes are identified from genes functional similarities assessed through GO. This is achieved by employing four different clustering methods (K-means, Hierarchical, Fuzzy and PAM). The user may change the clustering parameters and explore their effect on stratification immediately. DGH-GO was applied to genes disrupted by rare genetic variants in Autism Spectrum Disorder (ASD) patients. The analysis confirmed the multi-etiological nature of ASD by identifying four clusters of genes that were enriched for distinct biological mechanisms and clinical outcome. In the second case study, the analysis of genes shared by different NDDs showed that genes causing multiple disorders tend to aggregate in similar clusters, indicating a possible shared etiology. CONCLUSION: DGH-GO is a user-friendly application that allows biologists to study the multi-etiological nature of complex diseases by dissecting their genetic heterogeneity. In summary, functional similarities, dimension reduction and clustering methods, coupled with interactive visualization and control over analysis allows biologists to explore and analyze their datasets without requiring expert knowledge on these methods. The source code of proposed application is available at https://github.com/Muh-Asif/DGH-GO.

Genetic insights into crossbred dairy cattle of Pakistan: exploring allele frequency, linkage disequilibrium, and effective population size at a genome-wide scale.

Linkage disequilibrium (LD) affects genomic studies accuracy. High-density genotyping platforms identify SNPs across animal genomes, increasing LD evaluation resolution for accurate analysis. This study aimed to evaluate the decay and magnitude of LD in a cohort of 81 crossbred dairy cattle using the GGP_HDv3_C Bead Chip. After quality control, 116,710 Single Nucleotide Polymorphisms (SNPs) across 2520.241 Mb of autosomes were retained. LD extent was assessed between autosomal SNPs within a 10 Mb range using the r2 statistics. LD value declined as inter-marker distance increased. The average r2 value was 0.24 for SNP pairs < 10 kb apart, decreasing to 0.13 for 50-100 kb distances. Minor allele frequency (MAF) and sample size significantly impact LD. Lower MAF thresholds result in smaller r2 values, while higher thresholds show increased r2 values. Additionally, smaller sample sizes exhibit higher average r2 values, especially for larger physical distance intervals (> 50 kb) between SNP pairs. Effective population size and inbreeding coefficient were 150 and 0.028 for the present generation, indicating a decrease in genetic diversity over time. These findings imply that the utilization of high-density SNP panels and customized/breed-specific SNP panels represent a highly favorable approach for conducting genome-wide association studies (GWAS) and implementing genomic selection (GS) in the Bos indicus cattle breeds, whose genomes are still largely unexplored. Furthermore, it is imperative to devise a meticulous breeding strategy tailored to each herd, aiming to enhance desired traits while simultaneously preserving genetic diversity.

Salt and drought stress-mitigating approaches in sugar beet (Beta vulgaris L.) to improve its performance and yield.

MAIN CONCLUSION: Although sugar beet is a salt- and drought-tolerant crop, high salinity, and water deprivation significantly reduce its yield and growth. Several reports have demonstrated stress tolerance enhancement through stress-mitigating strategies including the exogenous application of osmolytes or metabolites, nanoparticles, seed treatments, breeding salt/drought-tolerant varieties. These approaches would assist in achieving sustainable yields despite global climatic changes. Sugar beet (Beta vulgaris L.) is an economically vital crop for ~ 30% of world sugar production. They also provide essential raw materials for bioethanol, animal fodder, pulp, pectin, and functional food-related industries. Due to fewer irrigation water requirements and shorter regeneration time than sugarcane, beet cultivation is spreading to subtropical climates from temperate climates. However, beet varieties from different geographical locations display different stress tolerance levels. Although sugar beet can endure moderate exposure to various abiotic stresses, including high salinity and drought, prolonged exposure to salt and drought stress causes a significant decrease in crop yield and production. Hence, plant biologists and agronomists have devised several strategies to mitigate the stress-induced damage to sugar beet cultivation. Recently, several studies substantiated that the exogenous application of osmolytes or metabolite substances can help plants overcome injuries induced by salt or drought stress. Furthermore, these compounds likely elicit different physio-biochemical impacts, including improving nutrient/ionic homeostasis, photosynthetic efficiency, strengthening defense response, and water status improvement under various abiotic stress conditions. In the current review, we compiled different stress-mitigating agricultural strategies, prospects, and future experiments that can secure sustainable yields for sugar beets despite high saline or drought conditions.

Boosting Hydroxyl Radical Yield via Synergistic Activation of Electrogenerated HOCl/H2O2 in Electro-Fenton-like Degradation of Contaminants under Chloride Conditions.

Hydroxyl radical production via catalytic activation of HOCl is a new type of Fenton-like process. However, metal-chlorocomplex formation under high chloride conditions could deactivate the catalyst and reduce the process efficiency. Herein, in situ electrogenerated HOCl was activated to •OH via a metal-free, B/N-codoped carbon nanofiber cathode for the first time to degrade contaminant under high chloride condition. The results show 98% degradation of rhodamine B (RhB) within 120 min (k = 0.036 min-1) under sulfate conditions, while complete degradation (k = 0.188 min-1) was obtained in only 30 min under chloride conditions. An enhanced degradation mechanism consists of an Adsorb & Shuttle process, wherein adsorption concentrates the pollutants at the cathode surface and they are subsequently oxidized by the large amount of •OH produced via activation of HOCl and H2O2 at the cathode. Density functional theory calculations verify the pyridinic N as the active site for the activation of HOCl and H2O2. The process efficiency was also evaluated by treating tetracycline and bisphenol A as well as high chloride-containing real secondary effluents from a pesticide manufacturing plant. High yields of •OH and HOCl allow continuous regeneration of the cathode for several cycles, limiting its fast deactivation, which is promising for real application.

Can antidepressant use be associated with emotional blunting in a subset of patients with depression? A scoping review of available literature.

INTRODUCTION: Despite frequent recognition of emotional blunting in the published literature, either as a primary symptom of depression or as an adverse effect of antidepressants, there is no systematic synthesis on this topic to our knowledge. We undertook this scoping review to assess the prevalence, clinical features, implicated causes and management of emotional blunting, outlining the phenomenological and clinical gaps in research. METHOD: A systematic search was done until March 15, 2022, to include all original studies (i.e., interventional trials, cohort & cross-sectional studies, case reports, and case series). All reviewed data were delineated to answer pertinent clinical, phenomenological, and management questions related to the phenomenon of emotional blunting. RESULTS: A total of 25 original studies were included in our scoping review. Emotional blunting was described as a persistent diminution in both positive and negative feelings in depressed patients, who could subjectively differentiate it from their acute symptoms. However, the literature lacked the distinction between emotional blunting as a primary symptom of depression or an adverse effect of antidepressants. Common clinical strategies to manage antidepressant-induced emotional blunting included dose reduction or switching to a different antidepressant. CONCLUSION: Emotional blunting was a significant patient-reported concern with antidepressants. Future research should clarify phenomenological and neurobiological constructs underlying emotional blunting to improve diagnostic and management skills.

Absence of GSTT1 and polymorphisms in GSTP1 and TP53 are associated with the incidence of acne vulgaris.

BACKGROUNDS: Acne vulgaris is a chronic inflammatory skin disease of the pilosebaceous unit affecting most teenagers and numerous adults throughout the world. The present study was designed to assess the association of the presence or absence of GSTM1, GSTT1, and single nucleotide polymorphisms rs1695 in GSTP1 and rs1042522 in TP53 gene with acne vulgaris. METHODS: The cross-sectional case-control study was conducted at the Institute of Zoology from May 2020 to March 2021 and included acne vulgaris patients (N = 100) and controls (N = 100) enrolled in Dera Ghazi Khan district, Pakistan. Multiplex and tetra-primer amplification refractory mutation system-polymerase chain reactions were applied to investigate the genotype in analyzed genes. The association of rs1695 and rs1042522 with acne vulgaris was studied either individually or in various combinations with GATM1 and T1. RESULTS: A significant association of absence of GSTT1 and mutant genotype at rs1695 (GG) and at rs1042522 (CC) in GSTP1 and TP53, respectively, was found to be associated with acne vulgaris in enrolled subjects. Subjects aged 10-25 years and smokers were more susceptible to acne vulgaris. CONCLUSION: Our results suggest that genotypes of glutathione S-transferases (GSTs) and TP53 are involved in protection against oxidative stress and may influence disease progression in acne vulgaris.

Association of genotypes at rs438228855 in bovine SLC35A3 receptor gene of Pakistani cattle with the susceptibility to develop complex vertebral malformation.

The aim of this study was to report the genotype and allelic frequency at rs438228855 (G > T) in SLC35A3 receptor gene and its association with a complex vertebral malformation (CMV) in the enrolled Pakistani cattle. Our results indicated that allelic and genotype frequency at rs438228855 varied non-significantly (p > .05) among the three enrolled cattle breeds. GT (heterozygous) genotype was most abundant (0.54) followed by GG (wild type) genotype (0.45) while the mutant genotype (TT) was not observed among the enrolled cattle. It was observed that the Holstein Friesian breed had more GG (wild) than GT (heterozygous) genotypes while Sahiwal and cross cattle breed had more heterozygous (GT) combination at rs438228855 than the wild (GG) genotype. Significant variations in white blood cell count, % lymphocytes, red blood cell count, % monocytes, haemoglobin, mean corpuscular volume and mean corpuscular haemoglobin concentration were observed when compared between the enrolled cattle breeds. Most of the studied haematological parameters showed no association with the genotype at rs438228855. In conclusion, the heterozygosity at rs438228855 is not limited to the Holstein Friesian breed as local Sahiwal and crossbred cattle had also higher heterozygosity at rs438228855. We recommend that animals must be genotyped for rs438228855 before their selection as breeders to prevent economic losses.

Colorimetric platform based on synergistic effect between bacteriophage and AuPt nanozyme for determination of Yersinia pseudotuberculosis.

The development of a novel colorimetric method is reported, using vB_YepM_ZN18 phages along with AuPt nanozyme for the sensitive detection of Y. pseudotuberculosis. The phage used in this work has been extracted from hospital sewer water and is highly specific toward Y. pseudotuberculosis. The synthesized AuPt NPs possess peroxidase-like activity, which is suitable in the development of nanozyme based detection system. Furthermore, phages@MB and AuPt@phages are added into the bacterial samples for co-incubation, forming an intercalated complex. The magnetic separation and absorbance analysis of enzymatic reaction are carried out for the detection of targeted bacteria. The proposed method has a limit of detection of 14 CFU/mL, a wide linear range from 2.50 × 101 ~ 2.50 × 107 CFU/mL and the assay completion time is 40 min. Benefitting from the outperformance of this sensor, we have successfully employed the developed sensing platform for the detection of Y. pseudotuberculosis in food industry and hospital specimens.

Hybrid Deep Learning and Discrete Wavelet Transform-Based ECG Biometric Recognition for Arrhythmic Patients and Healthy Controls.

The intrinsic and liveness detection behavior of electrocardiogram (ECG) signals has made it an emerging biometric modality for the researcher with several applications including forensic, surveillance and security. The main challenge is the low recognition performance with datasets of large populations, including healthy and heart-disease patients, with a short interval of an ECG signal. This research proposes a novel method with the feature-level fusion of the discrete wavelet transform and a one-dimensional convolutional recurrent neural network (1D-CRNN). ECG signals were preprocessed by removing high-frequency powerline interference, followed by a low-pass filter with a cutoff frequency of 1.5 Hz for physiological noises and by baseline drift removal. The preprocessed signal is segmented with PQRST peaks, while the segmented signals are passed through Coiflets' 5 Discrete Wavelet Transform for conventional feature extraction. The 1D-CRNN with two long short-term memory (LSTM) layers followed by three 1D convolutional layers was applied for deep learning-based feature extraction. These combinations of features result in biometric recognition accuracies of 80.64%, 98.81% and 99.62% for the ECG-ID, MIT-BIH and NSR-DB datasets, respectively. At the same time, 98.24% is achieved when combining all of these datasets. This research also compares conventional feature extraction, deep learning-based feature extraction and a combination of these for performance enhancement, compared to transfer learning approaches such as VGG-19, ResNet-152 and Inception-v3 with a small segment of ECG data.

Speech Emotion Recognition Using Convolution Neural Networks and Multi-Head Convolutional Transformer.

Speech emotion recognition (SER) is a challenging task in human-computer interaction (HCI) systems. One of the key challenges in speech emotion recognition is to extract the emotional features effectively from a speech utterance. Despite the promising results of recent studies, they generally do not leverage advanced fusion algorithms for the generation of effective representations of emotional features in speech utterances. To address this problem, we describe the fusion of spatial and temporal feature representations of speech emotion by parallelizing convolutional neural networks (CNNs) and a Transformer encoder for SER. We stack two parallel CNNs for spatial feature representation in parallel to a Transformer encoder for temporal feature representation, thereby simultaneously expanding the filter depth and reducing the feature map with an expressive hierarchical feature representation at a lower computational cost. We use the RAVDESS dataset to recognize eight different speech emotions. We augment and intensify the variations in the dataset to minimize model overfitting. Additive White Gaussian Noise (AWGN) is used to augment the RAVDESS dataset. With the spatial and sequential feature representations of CNNs and the Transformer, the SER model achieves 82.31% accuracy for eight emotions on a hold-out dataset. In addition, the SER system is evaluated with the IEMOCAP dataset and achieves 79.42% recognition accuracy for five emotions. Experimental results on the RAVDESS and IEMOCAP datasets show the success of the presented SER system and demonstrate an absolute performance improvement over the state-of-the-art (SOTA) models.

Anti-inflammatory mechanisms of eucalyptol rich Eucalyptus globulus essential oil alone and in combination with flurbiprofen.

Inflammation is the core contributor in the pathogenesis of various acute and chronic illness including appendicitis, bronchitis, arthritis, cancer and neurological diseases. NSAIDs, commonly used medications for inflammatory diseases, on prolonged use cause GI bleeding, ulcers and many more issues. Plant-based therapeutic agents including essential oils in combination with low-dose synthetic drugs have been shown to produce synergistic effects and reduce complications of synthetic drugs. This study was designed to evaluate the anti-inflammatory, analgesic and anti-pyretic properties of Eucalyptus globulus essential oil alone and in combination with flurbiprofen. GC-MS analysis was performed to screen chemical composition of oil. In vitro anti-inflammatory assay (membrane stabilization assay) and in vivo inflammatory acute (carrageenan and histamine-induced paw oedema) and chronic (cotton pellet-induced granuloma and Complete Freund's adjuvant-induced arthritis) models were performed to check anti-inflammatory properties. Acetic acid-induced algesia and yeast-induced pyrexia models were performed to check analgesic and anti-pyretic properties. qRT-PCR was performed to study the effect of treatments on the expression of inflammatory biomarkers. GC-MS analysis of E. globulus essential oil showed the presence of eucalyptol along with other active biomolecules. 500 + 10 mg/kg of oil-drug combination showed significantly (p < 0.05) better in vitro membrane stabilization effects as compared with groups treated with 500 mg/kg of E. globulus oil and 10 mg/kg of Flurbiprofen alone. 500 + 10 mg/kg of oil-drug combination showed significantly (p < 0.05) better anti-inflammatory, analgesic and antipyretic effects as compared to 500 mg/kg of E. globulus oil alone in all in vivo models. When comparison was done between 500 + 10 mg/kg of oil-drug combination-treated and 10 mg/kg Flurbiprofen-treated group, the former group showed significantly (p < 0.05) better anti-inflammatory and anti-pyretic effects, but there were non-significant differences in the analgesic model. Animal group treated with 10 mg/kg of Flurbiprofen showed significantly (p < 0.05) better anti-inflammatory and analgesic effects than group treated with 500 mg/kg of oil alone while, there were non-significant differences in anti-pyretic effects. qRT-PCR analysis showed significant (p < 0.05) down-regulation in the expression of IL-4 and TNF-α in serum samples of animals treated with 500 + 10 mg/kg of oil-drug combination as compared to the diseased control (arthritic) group. Overall, the current research demonstrates that Eucalyptus globulus essential oil in combination with flurbiprofen showed better anti-inflammatory, analgesic and anti-pyretic effects than oil and flurbiprofen alone which is attributed to the down-regulation of pro-inflammatory biomarkers (IL-4 and TNF-α). Further studies are required to formulate a stable dosage form and to check the anti-inflammatory efficacy in different inflammatory disorders.