Search

Brain-Computer Interface (BCI) is a system that provides a link between the brain of humans and the hardware directly. The recorded brain data is converted directly to the machine that can be used to control external devices. There are four major components of the BCI system: acquiring signals, preprocessing of acquired signals, features extraction, and classification. In traditional machine learning algorithms, the accuracy is insignificant and not up to the mark for the classification of multi-class motor imagery data. The major reason for this is, features are selected manually, and we are not able to get those features that give higher accuracy results. In this study, motor imagery (MI) signals have been classified using different deep learning algorithms. We have explored two different methods: Artificial Neural Network (ANN) and Long Short-Term Memory (LSTM). We test the classification accuracy on two datasets: BCI competition III-dataset IIIa and BCI competition IV-dataset IIa. The outcome proved that deep learning algorithms provide greater accuracy results than traditionalmachine learning algorithms. Amongst the deep learning classifiers, LSTM outperforms the ANN and gives higher classification accuracy of 96.2%. [ABSTRACT FROM AUTHOR]

Background: Initiation, promotion, progression, and metastasis of mammary tumors are mediated by dysregulation of multiple genes involved in various signaling pathways. Expressional variation of these molecules significantly influences cancer cell proliferation in breast cancer.Aims and Objectives: In the current study, tumor necrosis factor-alpha (TNF-α) and its downstream effector nuclear factor kappa-B1 (NF-κB1) mean transcript levels were explored and associated with molecular subtypes in breast cancer cohort of Pakistan. Freshly excised tumors (n = 150) along with background tissues were collected for RNA isolation and cDNA synthesis.Materials and Methods: Quantitative polymerase chain reaction was carried out for quantification of TNF-α, NF-κB1, and β-actin gene transcripts along with estrogen receptor, progesterone receptor, HER2, and Ki-67, followed by statistical analysis.Results: For TNF-α and NF-κB1, 95% and 77% of the cohort was found to be positive, respectively. Both of these molecules were found to be significantly upregulated in tumors when compared against their respective controls (P < 0.0001). Association of TNF-α and NF-κB1 with late clinical stages, poorly differentiated tumors, increased tumor size, nodal involvement, and metastasis was observed to be statistically significant (P < 0.05). Strong positive correlation was established between TNF-α and NF-κB1(r = 0.465, P< 0.05). Moreover, mean transcript levels of TNF-α and NF-κB1 were significantly elevated in Luminal A and Luminal B subtypes of breast cancer patients, respectively.Conclusion: Strong positive correlation between TNF-α and NF-κB1 proposed the putative role of these molecules as prognostic biomarkers in breast cancer. [ABSTRACT FROM AUTHOR]

Onboard attitude estimation for a ground vehicle is persuaded by its application in active anti-roll bar design. Conventionally, the attitude estimation problem for a ground vehicle is a complex one, and computationally, its solution is very intensive. Lateral load transfer is an important parameter which should be taken in account for all roll stability control systems. This parameter is directly related to vehicle roll angle, which can be measured using devices such as dual antenna global positioning system (GPS) which is a costly technique, and this led to the current work in which we developed a simple and robust attitude estimation technique that is tested on a ground vehicle for roll mitigation. In the first phase Luenberger and Sliding mode observer is implemented using simplest roll dynamics model to measure the roll angle of a vehicle and the validation of results is carried using commercial software, CarSim® (CarSim, Ann Arbor, MI, USA). In the second phase of research, complementary and Kalman filters have been designed for attitude estimation. In the third phase, a low-cost inertial measurement unit (IMU) is mounted on a vehicle, and both the complementary filter (CF) and Kalman filter (KF) are applied independently to measure the data for both smooth and uneven terrains at four different frequencies. We compared the simulated and real-time results of roll and pitch angles obtained using the complementary and Kalman filters. Using the proposed method, the achieved root mean square error (RMSE) is less than 0.73 degree for pitch and 0.68 degree for roll, with a sample time of 2 ms. Thus, a warning signal can be generated to mitigate roll over. Hence, we claim that our proposed method can provide a low-cost solution to the roll-over problem for a road vehicle. [ABSTRACT FROM AUTHOR]

Eighteen lactating Beetal goats of the almost same age, body weight, and parity were selected and divided into three groups (A, B and C) of six animals each. All the animals were given ad libitum fodder supplemented with 500g concentrate per animal. The animals of group C were treated as the control group, while those of A and B were supplemented with live yeast (Saccharomyces cerevisiae) @ 1.5 and 3 gm/day per animal, respectively up to 45 days. Results showed that dry matter intake, weight gain (kg), milk yield (kg), milk fat, milk protein, lactose, ash, and total solid% were ascertained better in group B followed by A and C. Findings of the present study suggest that the use of yeast @ 3gm/day can improve the milk yield and its composition in Beetal goats. [ABSTRACT FROM AUTHOR]

In this article, non-invasive hybrid brain-computer interface (hBCI) technologies for improving classification accuracy and increasing the number of commands are reviewed. Hybridization combining more than two modalities is a new trend in brain imaging and prosthesis control. Electroencephalography (EEG), due to its easy use and fast temporal resolution, is most widely utilized in combination with other brain/non-brain signal acquisition modalities, for instance, functional near infrared spectroscopy (fNIRS), electromyography (EMG), electrooculography (EOG), and eye tracker. Three main purposes of hybridization are to increase the number of control commands, improve classification accuracy and reduce the signal detection time. Currently, such combinations of EEG + fNIRS and EEG + EOG are most commonly employed. Four principal components (i.e., hardware, paradigm, classifiers, and features) relevant to accuracy improvement are discussed. In the case of brain signals, motor imagination/movement tasks are combined with cognitive tasks to increase active brain-computer interface (BCI) accuracy. Active and reactive tasks sometimes are combined: motor imagination with steady-state evoked visual potentials (SSVEP) and motor imagination with P300. In the case of reactive tasks, SSVEP is most widely combined with P300 to increase the number of commands. Passive BCIs, however, are rare. After discussing the hardware and strategies involved in the development of hBCI, the second part examines the approaches used to increase the number of control commands and to enhance classification accuracy. The future prospects and the extension of hBCI in real-time applications for daily life scenarios are provided. [ABSTRACT FROM AUTHOR]

Millions of people across the globe have been affected by coronavirus disease 2019 (COVID-19), which began in Wuhan, China, and is caused by SARS-CoV-2. COVID-19 has a variety of clinical characteristics and triggers immune responses required for the elimination of the viral agent. Currently, no effective treatment options are available for targeting SARS-CoV-2 infection. Repurposing of drugs such as chloroquine, thalidomide, and leflunomide alongside convalescent plasma is being employed as a therapeutic strategy. Clinical studies have shown that both asymptomatic and symptomatic patients can have an extremely active immune response that is largely attributable to immune system modulations. This includes cytokine storm syndrome (CSS), which affects the adaptive immune system, leading to exhaustion of natural killer (NK) cells and thrombocytopenia in some cases. This review examines the interaction of SARS-CoV-2 with the host immune system and the potential for the development of appropriate immunotherapy for the treatment of COVID-19. [ABSTRACT FROM AUTHOR]

Antibiotic resistance is an increasing threat, requiring novel therapeutic solutions. Metal nanoparticles e.g., zinc oxide nanoparticles (ZnO NPs) exhibited the potential against many bacterial pathogens. Strains of Salmonella enterica serovar Typhi resistant to ceftriaxone were reported first from Pakistan in 2016. Since then, S. Typhi is a pathogen of concern globally owing to its rapidly emerging resistance potential against many last resort antibiotics. In the present study, in vitro and in vivo antimicrobial activity of ZnO NPs against multidrug resistant (MDR) and extensively drug resistant (XDR) Salmonella Typhi strains from Pakistan was evaluated. Zinc oxide green nanoparticles (ZnO GNPs), synthesized from Aloe vera, were characterized by SEM, XRD, UV-vis and Raman spectroscopy. In vitro antibacterial activity of two different concentrations of ZnO GNPs (7 and 15%) was checked using agar well diffusion method. Further, broth microdilution and time kill assays were performed using the ZnO GNPs. In vivo assays were conducted in BALB/c mice sepsis models. In all the three methods, agar well diffusion assay broth microdilution and time kill assay, different zinc oxide dihydrate precursor concentrations had shown the antibacterial activity. The minimum inhibitory concentration (MIC) of ZnO GNPs nanoparticles against MDR and XDR S. Typhi strains was found as 16 to 64 µg/ml. In vivo experiment has shown a significant decrease in CFU/ml in the mice treated with ZnO GNPs as compared to the control group. Our findings have revealed that ZnO GNPs have significant antibacterial activity against MDR and XDR S. Typhi, both in vitro and in vivo., (© 2024. The Author(s) under exclusive licence to Sociedade Brasileira de Microbiologia.)

Anti-cancer peptides (ACPs) are short peptides known for their ability to inhibit tumor cell proliferation, migration, and the formation of tumor blood vessels. In this study, we designed ACPs to target receptors often overexpressed in cancer using a systematic in silico approach. Three target receptors (CXCR1, DcR3, and OPG) were selected for their significant roles in cancer pathogenesis and tumor cell proliferation. Our peptide design strategy involved identifying interacting residues (IR) of these receptors, with their natural ligands serving as a reference for designing peptides specific to each receptor. The natural ligands of these receptors, including IL8 for CXCR1, TL1A for DcR3, and RANKL for OPG, were identified from the literature. Using the identified interacting residues (IR), we generated a peptide library through simple permutation and predicted the structure of each peptide. All peptides were analyzed using the web-based prediction server for Anticancer peptides, AntiCP. Docking simulations were then conducted to analyze the binding efficiencies of peptides with their respective target receptors, using VEGA ZZ and Chimera for interaction analysis. Our analysis identified HPKFIKELR as the interacting residues (IR) of CXCR-IL8. For DcR3, we utilized three domains from TL1A (TDSYPEP, TKEDKTF, LGLAFTK) as templates, along with two regions (SIKIPSS and PDQDATYP) from RANKL, to generate a library of peptide analogs. Subsequently, peptides for each receptor were shortlisted based on their predicted anticancer properties as determined by AntiCP and were subjected to docking analysis. After docking, peptides that exhibited the least binding energy were further analyzed for their detailed interaction with their respective receptors. Among these, peptides C9 (HPKFELY) and C7 (HPKFEWL) for CXCR1, peptides D6 (ADSYPQP) and D18 (AFSYPFP) for DcR3, and peptides P19 (PDTYPQDP) and p16 (PDQDATYP) for OPG, demonstrated the highest affinity and stronger interactions compared to the other peptides. Although in silico predictions indicated a favorable binding affinity of the designed peptides with target receptors, further experimental validation is essential to confirm their binding affinity, stability and pharmacokinetic characteristics., (© 2024. The Author(s).)

Despite the availability of technological advances in traditional anti-cancer therapies, there is a need for more precise and targeted cancer treatment strategies. The wide-ranging shortfalls of conventional anticancer therapies such as systematic toxicity, compromised life quality, and limited to severe side effects are major areas of concern of conventional cancer treatment approaches. Owing to the expansion of knowledge and technological advancements in the field of cancer biology, more innovative and safe anti-cancerous approaches such as immune therapy, gene therapy and targeted therapy are rapidly evolving with the aim to address the limitations of conventional therapies. The concept of immunotherapy began with the capability of coley toxins to stimulate toll-like receptors of immune cells to provoke an immune response against cancers. With an in-depth understating of the molecular mechanisms of carcinogenesis and their relationship to disease prognosis, molecular targeted therapy approaches, that inhibit or stimulate specific cancer-promoting or cancer-inhibitory molecules respectively, have offered promising outcomes. In this review, we evaluate the achievement and challenges of these technically advanced therapies with the aim of presenting the overall progress and perspective of each approach., (© 2024. The Author(s).)

Mobile robots are increasingly employed in today's environment. Perceiving the environment to perform a task plays a major role in the robots. The service robots are wisely employed in the fully (or) partially known user's environment. The exploration and exploitation of the unknown environment is a tedious task. This paper introduces a novel Trimmed Q-learning algorithm to predict interesting scenes via efficient memorability-oriented robotic behavioral scene activity training. The training process involves three stages: online learning and short-term and long-term learning modules. It is helpful for autonomous exploration and making wiser decisions about the environment. A simplified three-stage learning framework is introduced to train and predict interesting scenes using memorability. A proficient visual memory schema (VMS) is designed to tune the learning parameters. A role-based profile arrangement is made to explore the unknown environment for a long-term learning process. The online and short-term learning frameworks are designed using a novel Trimmed Q-learning algorithm. The underestimated bias in robotic actions must be minimized by introducing a refined set of practical candidate actions. Finally, the recalling ability of each learning module is estimated to predict the interesting scenes. Experiments conducted on public datasets, SubT, and SUN databases demonstrate the proposed technique's efficacy. The proposed framework has yielded better memorability scores in short-term and online learning at 72.84% and in long-term learning at 68.63%., (© 2023. The Author(s).)

Murine double minute 2 (MDM2) is a well-recognized molecule for its oncogenic potential. Since its identification, various cancer-promoting roles of MDM2 such as growth stimulation, sustained angiogenesis, metabolic reprogramming, apoptosis evasion, metastasis, and immunosuppression have been established. Alterations in the expression levels of MDM2 occur in multiple types of cancers resulting in uncontrolled proliferation. The cellular processes are modulated by MDM2 through transcription, post-translational modifications, protein degradation, binding to cofactors, and subcellular localization. In this review, we discuss the precise role of deregulated MDM2 levels in modulating cellular functions to promote cancer growth. Moreover, we also briefly discuss the role of MDM2 in inducing resistance against anti-cancerous therapies thus limiting the benefits of cancerous treatment., (© 2023. The Author(s).)

miR-17-92 cluster encodes six micro RNAs (miRNAs) and plays a crucial role in the regulation of various cellular processes. Aberrant expression of this cluster may result in the onset of several diseases. Initially, the role of miR-17-92 cluster in tumorigenesis was discovered but recent research has also uncovered its role in other diseases. Members of the cluster may serve as potential biomarkers in the prognosis, diagnosis, and treatment of several diseases and their complications. In this article, we have reviewed the recent research carried out on the expression pattern of miR-17-92 cluster in non-communicable diseases i.e., obesity, cardiovascular diseases (CVD), kidney diseases (KD) and diabetes mellitus (DM). We examined miR-17-92 role in pathological processes and their potential importance as biomarkers. Each member of the cluster miR-17-92 was upregulated in obesity. miR-18a, miR-19b-3p, miR20a, and miR92a were significantly upregulated in CVD. An equal fraction of the cluster was dysregulated (upregulated and downregulated) in diabetes; however, miR-17-92 was downregulated in most studies on CKD., (© 2023. The Author(s), under exclusive licence to Springer Nature B.V.)

The aim of this study was to find out the genetic polymorphism in β-casein gene CSN2 in Azi-Kheli buffaloes found in district Swat. Blood samples from 250 buffaloes were collected and processed in lab for sequencing to see the genetic polymorphism in CSN2 gene on 67 position of exon7. The β-casein is a milk second abundant protein having some variants, wherein A1 and A2 are the most common. After performing sequence analysis, it was found that Azi-Kheli buffaloes were homozygous for only A2 type variant. The amino acid change (proline to histadine) on 67 position of exon 7 was not found; however, three other novel SNPs at loci g.20545A > G, g.20570G > A, and g.20693C > A were identified in the study. Amino acid change due to SNPs were found as SNP1, valine > proline; SNP2, leucin > phenylalanine; and SNP3, threonine > valine. Allelic and genotypic frequencies' analysis exhibited that all three SNPs were following the Hardy-Weinberg equilibrium (HWE: P < 0.05). All the three SNPs showed medium PIC value and gene heterozygosity. The SNPs located on different position of exon 7 of CSN2 gene exhibited associations with some of the performance traits and milk composition. Higher daily milk yield of 9.86 ± 0.43 L and the peak milk yield of 13.80 ± 0.60 L were found in response to SNP3 followed by SNP2 and SNP1. The percentage of milk fat and protein was found significantly higher (P ≤ 0.05) in relation to SNP3 followed by SNP2 and SNP1 given as 7.88 ± 0.41, 7.48 ± 0.33, and 7.15 ± 0.48 for fat% and 4.00 ± 0.15, 3.73 ± 0.10 and 3.40 ± 0.10 for protein%. It was concluded that Azi-Kheli buffalo milk contains A2 genetic variant along with other useful novel variants indicating quality milk for human health. Genotypes of SNP3 should be given preference in selection both in indices and nucleotide polymorphism., (© 2023. The Author(s), under exclusive licence to Springer Nature B.V.)

Background: Signal transducer and activator of transcription 3 (STAT3) is an oncogenic transcription factor that promotes cell proliferation and immunomodulation in untransformed cells and maintains stemness of transformed cells, facilitating invasion and metastasis. Numerous point mutations in the STAT3 protein have been identified that drive malignancy in various tumor entities. The missense mutation D427H localized in the STAT3 DNA-binding domain has been previously reported in patients with NK/T cell lymphomas. To assess the biological activity of this missense mutation, we compared the STAT3-D427H mutant to wild-type (WT) protein as well as the known hyper-active mutant F174A., Results: Although previously reported as an activating mutation, the STAT3-D427H mutant neither showed elevated cytokine-induced tyrosine phosphorylation nor altered nuclear accumulation, as compared to the WT protein. However, the D427H mutant displayed enhanced binding to STAT-specific DNA-binding sites but a reduced sequence specificity and dissociation rate from DNA, which was demonstrated by electrophoretic mobility shift assays. This observation is consistent with the phenotype of the homologous E421K mutation in the STAT1 protein, which also displayed enhanced binding to DNA but lacked a corresponding increase in transcriptional activity., Conclusions: Based on our data, it is unlikely that the D427H missense mutation in the STAT3 protein possesses an oncogenic potential beyond the WT molecule., (© 2022. The Author(s).)

Non-coding RNAs (ncRNAs) participate in the regulation of several cellular processes including transcription, RNA processing and genome rearrangement. The aberrant expression of ncRNAs is associated with several pathological conditions. In this review, we focused on recent information to elucidate the role of various regulatory ncRNAs i.e., micro RNAs (miRNAs), circular RNAs (circRNAs) and long-chain non-coding RNAs (lncRNAs), in metabolic diseases, e.g., obesity, diabetes mellitus (DM), cardiovascular diseases (CVD) and metabolic syndrome (MetS). The mechanisms by which ncRNAs participated in disease pathophysiology were also highlighted. miRNAs regulate the expression of genes at transcriptional and translational levels. circRNAs modulate the regulation of gene expression via miRNA sponging activity, interacting with RNA binding protein and polymerase II transcription regulation. lncRNAs regulate the expression of genes by acting as a protein decoy, miRNA sponging, miRNA host gene, binding to miRNA response elements (MRE) and the recruitment of transcriptional element or chromatin modifiers. We examined the role of ncRNAs in the disease pathogenesis and their potential role as molecular markers for diagnosis, prognosis and therapeutic targets. We showed the involvement of ncRNAs in the onset of obesity and its progression to MetS and CVD. miRNA-192, miRNA-122, and miRNA-221 were dysregulated in all these metabolic diseases. Other ncRNAs, implicated in at least three diseases include miRNA-15a, miRNA-26, miRNA-27a, miRNA-320, and miRNA-375. Dysregulation of ncRNAs increased the risk of development of DM and MetS and its progression to CVD in obese individuals. Hence, these molecules are potential targets to arrest or delay the progression of metabolic diseases., (© 2022 The Authors. Publishing services by Elsevier B.V. on behalf of KeAi Communications Co., Ltd.)

Background: Sesame is an ancient oilseed crop, known for its high oil content and quality. Its sensitivity to drought at early seedling stage is one of the limiting factors affecting its world-wide growth and productivity. Among plant specific transcription factors, the association of HD-ZIPs with sesame drought responses at early seedling stage is not well-established yet and is very important to develop our molecular understanding on sesame drought tolerance., Methods and Results: In this study, total 61 sesame HD-ZIP proteins were identified, based on their protein sequence homology with Arabidopsis and protein domain(s) architecture prediction, followed by their phylogenetic, conserved domain(s) motifs and gene structure analyses to classify them into four classes (HD-ZIP Class I-IV). HD-ZIP Class I was also subdivided into four subgroups: α (SiHZ25, SiHZ43, SiHZ9 and SiHZ16), β1 (SiHZ10, SiHZ30, SiHZ32 and SiHZ26), β2 (SiHZ42 and SiHZ45) and γ (SiHZ17, SiHZ7 and SiHZ35) by a comparative phylogenetic analysis of sesame with Arabidopsis and maize. Afterwards, twenty-one days old sesame seedlings were exposed to drought stress by withholding water for 7 days (when soil moisture content reduced to ~16%) and gene expression of HD-ZIP Class I (13 members) was performed in well- watered (control) and drought stressed seedlings. The gene expression analysis showed that the expressions of SiHZ7 (6.8 fold) and SiHZ35 (2.6 fold) from γ subgroup were significantly high in drought seedlings., Conclusions: This study is useful in demonstrating the role of SiHD-ZIP Class I in sesame drought responses at early seedling stage and to develop its novel drought tolerant varieties., (© 2021. The Author(s), under exclusive licence to Springer Nature B.V.)

Background: Neonatal seizures are a common occurrence in clinical settings, requiring immediate attention and detection. Previous studies have proposed using manual feature extraction coupled with machine learning, or deep learning to classify between seizure and non-seizure states., New Method: In this paper a deep learning based approach is used for neonatal seizure classification using electroencephalogram (EEG) signals. The architecture detects seizure activity in raw EEG signals as opposed to common state-of-art, where manual feature extraction with machine learning algorithms is used. The architecture is a two-dimensional (2D) convolutional neural network (CNN) to classify between seizure/non-seizure states., Results: The dataset used for this study is annotated by three experts and as such three separate models are trained on individual annotations, resulting in average accuracies (ACC) of 95.6 %, 94.8 % and 90.1 % respectively, and average area under the receiver operating characteristic curve (AUC) of 99.2 %, 98.4 % and 96.7 % respectively. The testing was done using 10-cross fold validation, so that the performance can be an accurate representation of the architectures classification capability in a clinical setting. After training/testing of the three individual models, a final ensemble model is made consisting of the three models. The ensemble model gives an average ACC and AUC of 96.3 % and 99.3 % respectively., Comparison With Existing Methods: This study outperforms previous studies, with increased ACC and AUC results coupled with use of small time windows (1 s) used for evaluation., Conclusion: The proposed approach is promising for detecting seizure activity in unseen neonate data in a clinical setting., (Copyright © 2021 Elsevier B.V. All rights reserved.)

Regulation of gene expression is vital to maintain normal cellular functions and its dysregulation leads to molecular pathogenesis of many diseases and disorders. Non-coding RNAs regulate the expression of approximately 60% of protein-coding genes and their malfunction contribute to the development of numerous diseases. The involvement of variant forms of circulating non-coding RNAs in diseases has been established. However, their function as biomarkers or therapeutic targets in metabolic disorders are underexploited. The aim of this study was to predict therapeutic targets and construction of biomarker panel for early detection of metabolic syndrome (MS). Non-coding RNAs including circular RNAs (circRNAs), long chain non-coding RNAs (lncRNA) and micro RNAs (miRNAs) were extracted from intensive literature search and experimentally supported databases. Raw data of gene expression profiles of MS were obtained from the GEO dataset and analyzed to get differentially expressed genes (DEGs). Functional enrichment analysis, network illustration of non-coding RNAs and predicted target DEGs were performed. Furthermore, a few numbers of miRNAs targeted DEGs were subjected to homology study. The strong association of hsa-miR-548c-3p, hsa-miR-579-3p, hsa-miR-17-5p and hsa-miR-320a was observed with the pathogenesis of MS. It includes the regulation of genes in glucose and lipid homeostasis, MAPKK activity, regulation of inflammatory responses and many signaling pathways such as insulin resistance, JAK/STAT and mTOR. Finally, interactions of hsa-miR-17-5p:STAT3, hsa-miR-320:JAK2, hsa-miR-320:S6K and hsa-let-7:DVL hybrids were predicted. Results of this study suggest the designing of a biomarker panel to detect early onset and molecular approach for the management of MS., (Copyright © 2021 Elsevier Ltd. All rights reserved.)