Miracle-dz/UniFAR-FTUs huggingface dataset:
Guided Generative Adversarial Neural Network for Representation Learning and Audio Generation Using Fewer Labelled Audio Data

The Generation power of Generative Adversarial Neural Networks (GANs) has shown great promise to learn representations from unlabelled data while guided by a small amount of labelled data. We aim to utilise the generation power of GANs to learn Audio Representations. Most existing studies are, however, focused on images. Some studies use GANs for speech generation, but they are conditioned on text or acoustic features, limiting their use for other audio, such as instruments, and even for speech where transcripts are limited. This paper proposes a novel GAN-based model that we named Guided Generative Adversarial Neural Network (GGAN), which can learn powerful representations and generate good-quality samples using a small amount of labelled data as guidance. Experimental results based on a speech [Speech Command Dataset (S09)] and a non-speech [Musical Instrument Sound dataset (Nsyth)] dataset demonstrate that using only 5% of labelled data as guidance, GGAN learns significantly better representations than the state-of-the-art models.
A Compact and Wideband Filtenna Using Elliptical Patch and CSRR Structure for Wireless Application

In this paper a compact wideband Filtenna structure with stopband characteristics has been presented. The proposed filtenna is a planar elliptical patch with a CSRR structure embedded in the partial ground. The elliptical shape is utilized to attain the wideband characteristics whereas the CSRR is etched out to filter out the undesired bands. This filtenna design exhibits an impedance bandwidth of 3.3 GHz ranging from 2.47 GHz to 5.77 GHz covering the WLAN (2.4/5.2 GHz) and WiMAX (2.5/ 3.5/ 5.5 GHz) bands and effectively rejects the higher bands beyond 5.77 GHz. The designed filtenna achieves a maximum gain of 4.11 dBi and a bidirectional radiation pattern over the desired bandwidth. The overall dimensions of the proposed filtenna is 40 mm x 50 mm x 1.6 mm with the patch area 14 mm x 10 mm. The proposed filtenna is designed, simulated and optimized using CST Microwave Studio and has been fabricated and tested to validate the proposed design.
Artificial Intelligence for Enhanced Mobility and 5G Connectivity in UAV-Based Critical Missions

In the context of Fifth Generation mobile networks (5G), Search and Rescue (SAR) missions using Unmanned Aerial Vehicles (UAVs) can benefit from a dynamic, intelligent, and autonomous placement of both Network Functions (NFs) and Artificial Intelligence (AI) systems to quickly adapt in minimal human intervention scenarios. This article examines current 5G architectures and timely standardization efforts within this context. The contribution of this work is to identify associated 5G components and propose AI modules that enable efficient UAV-based SAR missions: the System Intelligence (SI) and Edge Intelligence (EI) concepts. SI is conceived as the entity responsible for defining and orchestrating the placement and processing tasks of NFs and AI systems, while EI is responsible for the optimization of AI-based end-user applications. The article also presents an open-source virtualized testbed that enables a concrete example of SI and EI roles in a SAR mission based on object detection with Deep Neural Networks (DNNs). In this proof-of-concept, the DNN layers are partitioned and the tradeoffs between communication and computational costs are highlighted. For instance, the results indicate that the latency can severely degrade the UAV trajectory and different DNN partitioning options can reduce the required bit rate to transmit DNN scores by more than three times.
Leveraging Natural Language Processing Algorithms to Understand the Impact of the COVID-19 Pandemic and Related Policies on Public Sentiment in India

Understanding the effects of a pandemic on the public sentiment is an important challenge in the study of social dynamics during a global pandemic. This paper puts forward a case study that throws light on the psychological impact of the COVID-19 pandemic on the people living in the Indian subcontinent. The study is based on a pipeline that involves preprocessing, sentiment analysis, topic modelling, natural language processing and statistical analysis of Twitter data extracted in the form of tweets. The results demonstrate the effectiveness of this pipeline in understanding the temporal impact of the different lockdowns implemented in the span of the pandemic on the public sentiment, which can be useful for healthcare workers, authorities, and researchers.
NOMA-Based Integrated Satellite Terrestrial Networks With Relay Selection and Imperfect SIC

Integrated satellite terrestrial networks (ISTNs) are becoming a hot research topic in recent years due to the capabilities of high quality, high throughput, and seamless coverage. However, the limited spectrum resources are difficult to meet the needs of a large number of users in ISTNs, and the obstacles and shadowing will seriously affect the communication quality of networks. In this regard, cooperative transmission of non-orthogonal multiple access (NOMA)-assisted ISTNs with the help of multiple terrestrial relays are established to enhance the spectrum efficiency and enlarge the transmission coverage. In this paper, we investigate the performance of NOMA-based ISTNs with relay selection and imperfect successive interference cancellation (SIC). Particularly, the partial relay selection (PRS) scheme is used in the considered system to reach the balance of system performance and complexity. Owing to practical constraints, the imperfect SIC is analyzed for the networks. Based on the PRS scheme and imperfect SIC, we obtain the closed-form expressions for the outage probability (OP) and ergodic capacity (EC) of the considered networks. Besides, to get further insights of key system parameters, the asymptotic analysis for the OP is also derived. Finally, numerical and simulation results are presented to validate the correctness of our analytical results.
Efficient Design of Adaptable Deep Learning Accelerator

With the evolving arena of Machine Learning (ML), Deep Learning (DL) has shown wonderful promise to solve complicated problems involving large amounts of data. But practical implementations increase the size of the networks exponentially creating problems in creating sustainable systems. One of the ways in which a sustainable system can be designed with optimum performance has been demonstrated through the design of DLAU which is scalable accelerator architecture which can be executed on an adaptable device like field-programmable gate array. The basic idea behind the architecture is to use three pipelined processing units to improve processing speed as well as throughput.
Embedded System for Eye Blink Detection Using Machine Learning Technique

Nowadays, eye tracking and blink detection are increasingly popular among researchers and have the potential to become a more important component of future perceptual user interfaces. The real-time eye-tracking system has been a fundamental and challenging problem for machine learning problems. The main purpose of this paper is to propose a new method to design an embedded eye blink detection system that can be used for various applications with the lowest cost. This study presents an efficient technique to determine the level of eyes that are closed and opened. We offered a real-time blink detection method by using machine learning and computer vision libraries. The proposed method consists of four phases: (1) taking frame by employing a raspberry pi camera that slotted to the raspberry pi 3 platform, (2) utilizing haar cascade algorithm to identify faces in the captured frames, (3) find facial landmarks by utilizing facial landmark detector algorithm, (4) detect the eyes’ region and calculate the eye aspect ratio. The proposed method obtained a high accuracy to indicate eye closing or opening. In this study, an aspect ratio method was used to implement a robust and low-cost embedded eye blink detection system on the raspberry pi platform. This method is exact resourceful, fast, and easy to perform eye blink detection.
Improved Hybrid Genome Assemblies of Two Strains of Bacteroides xylanisolvens, SD_CC_1b and SD_CC_2a, Obtained Using Illumina and 454 Sequencing Technologies

ABSTRACT Bacteroides xlyanisolvens strains (SD_CC_1b, SD_CC_2a) isolated from human feces were grown on crystalline cellulose. Cellulolytic properties are not common in Bacteroides species. Here, we report improved genome sequences of both of the B. xlyanisolvens strains.
Accurate Offline Electrical Parameters Identification for Air-Bearing PMLSM

The accurate electrical parameters of PMLSM is important for precision control. This paper studied the electrical parameter identification, and discussed the method that injects “DC+AC” voltage to identify the d-q axis incremental inductance. In order to improve the identification accuracy, the injected signal characteristics and inverter nonlinearity were considered. Finally, the resistance and d-q axis incremental inductance were identified, and the q-axis identification experiments were implemented in different current values. Through Piecewise Cubic Hermite interpolation, a smooth q-axis current-incremental inductance curve was achieved. The effectiveness of the proposed identification scheme was confirmed on an air-bearing PMLSM.
Analysis and prevention of IoT vulnerabilities by implementing a lightweight AD-IoT intrusion detection system model

Until a few years, the Internet of Things (IoT) has been continually evolving and today it is giving a great welcome to a paradigmatic style of technology, for this reason new electronic devices are being invented that are used continuously by many people. But these devices must be protected in a certain way since there are many attacks on the IoT web, with which, by integrating new algorithms, certain network security will be obtained. In addition, the use of new model systems; such as the IoT Anomaly Detection System (AD-IoT) that uses the Random Forest (RF) machine learning algorithm to detect web attacks; It will help us reduce attacks from bad IoT computer users. In this present article, a very effective evaluative metric was obtained through a group of data that will help us develop and modify the communication security paradigms of IoT devices.
Investigation on Design of Novel Step-Up 18-Pulse Auto-Transformer Rectifier

The multi-pulse rectifier has gained a wide range of applications in the high-power rectification system due to the advantages like simple structure, high efficiency, and high reliability. In this paper, an 18-pulse auto-transformer rectifier unit (ATRU) with adjustable output DC voltage was studied, to meet the requirements of high efficiency and low harmonic pollution of ATRU. Firstly, the basic operating principle of the proposed ATRU was analyzed in detail. The essential relationship between the step-up ratio factor and the output DC voltage, the total harmonic distortion (THD) of the input line current, and the equivalent kVA rating of the transformer was deduced considerately, which provides a theoretical basis for the optimization design of the system. Finally, the correctness of the theoretical analysis was verified by simulation and experiment. The experimental results show that under the 6kVA rating condition when the step-up ratio is 1.137, the transformer equivalent kVA rating is only 27.8% of the load output power, the input line current THD of ATRU is 6.58%, and the efficiency is 98.5%.
The microstructure of magnetorheological materials characterized by means of computed X-ray microtomography

Abstract Magnetorheological materials are a class of “smart materials”, where mechanical material properties can be tuned by the application of externally applied fields. To accomplish the magneto-sensitive quality, magnetic particlesare distributed in a host matrix. In the last year’s interest gained in materials based on solid matrices. In contrast to fluid systems, within a solid matrix, the particles are fixed within the material. This enables an evaluation of the structures formed by the particles by means of computed X-ray microtomography. As known from past investigations, the arrangement and movement of the magnetic particles within the matrix play a major role in determining the overall material properties. Computed X-ray microtomography proved to be a convenient tool, providing important new knowledge about those materials. This paper gives an overview of the application of the method of computed X-ray microtomography on several kinds of solid magnetorheological materials, the broad possibilities of data evaluation, and fundamental results obtained with this method and the described materials.
Nuclear SUN1 stabilizes endothelial cell junctions via microtubules to regulate blood vessel formation

Endothelial cells line all blood vessels, where they coordinate blood vessel formation and the blood-tissue barrier via regulation of cell-cell junctions. The nucleus also regulates endothelial cell behaviors, but it is unclear how the nucleus contributes to endothelial cell activities at the cell periphery. Here we show that the nuclear-localized LINC complex protein SUN1 regulates vascular sprouting and barrier function via effects on endothelial cell-cell junction morphology and function. Loss of murine endothelial Sun1 impaired blood vessel formation and destabilized junctions, angiogenic sprouts formed but retracted in SUN1-depleted sprouts, and zebrafish vessels lacking Sun1b had aberrant junctions and defective cell-cell connections. At the cellular level, SUN1 stabilized endothelial cell-cell junctions, promoted barrier function, and regulated contractility. Mechanistically, SUN1 depletion altered cell behaviors via the cytoskeleton without changing transcriptional profiles. Reduced peripheral microtubule density, fewer junction contacts and increased catastrophes accompanied SUN1 loss, and microtubule depolymerization phenocopied effects on junctions. Depletion of GEF-H1, a microtubule-regulated Rho activator, or the LINC complex protein nesprin-1 rescued defective junctions of SUN1-depleted endothelial cells. Thus, endothelial SUN1 regulates peripheral cell-cell junctions from the nucleus via LINC complex-based microtubule interactions that affect peripheral microtubule dynamics and Rho-regulated contractility, and this long-range regulation is important for proper blood vessel sprouting and barrier function. SUMMARY The nuclear membrane protein SUN1 promotes blood vessel formation and barrier function by stabilizing endothelial cell-cell junctions. Communication between SUN1 and endothelial cell junctions relies upon proper microtubule dynamics and Rho signaling far from the nucleus, revealing long-range cellular communication from the nucleus to the cell periphery that is important for vascular development and function.
Molecular basis of immune evasion by the delta and kappa SARS-CoV-2 variants

Worldwide SARS-CoV-2 transmission leads to the recurrent emergence of variants, such as the recently described B.1. 617.1 (kappa), B.1. 617.2 (delta) and B.1. 617.2+ (delta+). The B.1. 617.2 (delta) variant of concern is causing a new wave of infections in many countries, mostly affecting unvaccinated individuals, and has become globally dominant. We show that these variants dampen the in vitro potency of vaccine-elicited serum neutralizing antibodies and provide a structural framework for describing the impact of individual mutations on immune evasion. Mutations in the B.1. 617.1 (kappa) and B.1. 617.2 (delta) spike glycoproteins abrogate recognition by several monoclonal antibodies via alteration of key antigenic sites, including an unexpected remodeling of the B.1. 617.2 (delta) N-terminal domain. The binding affinity of the B.1. 617.1 (kappa) and B.1. 617.2 (delta) receptor-binding domain for ACE2 is comparable to the ancestral virus whereas B.1. 617.2+ (delta+) exhibits markedly reduced affinity. We describe a previously uncharacterized class of N-terminal domain-directed human neutralizing monoclonal antibodies cross-reacting with several variants of concern, revealing a possible target for vaccine development.
The light chain of the L9 antibody is critical for binding circumsporozoite protein minor repeats and preventing malaria

L9 is a potent human monoclonal antibody (mAb) that preferentially binds two adjacent NVDP minor repeats and cross-reacts with NANP major repeats of the Plasmodium falciparum circumsporozoite protein (PfCSP) on malaria-infective sporozoites. Understanding this mAb’s ontogeny and mechanisms of binding PfCSP to neutralize sporozoites will facilitate vaccine development. Here, we isolated mAbs clonally related to L9 and showed that this B-cell lineage has baseline NVDP affinity and evolves to acquire NANP reactivity. Pairing the L9 kappa light chain (L9κ) with clonally-related heavy chains resulted in chimeric mAbs that cross-linked two NVDP, cross-reacted with NANP, and more potently neutralized sporozoites compared to their original light chain. Structural analyses revealed that chimeric mAbs bound the minor repeat motif in a type-1 β-turn seen in other repeat-specific antibodies. These data highlight the importance of L9κ in binding NVDP on PfCSP to neutralize SPZ and suggest that PfCSP-based immunogens might be improved by presenting ≥2 NVDP.
Exon-skipping antisense oligonucleotides for cystic fibrosis therapy

Significance Cystic fibrosis (CF) is caused by mutations in the cystic fibrosis transmembrane conductance regulator (CFTR) gene, which can lead to respiratory failure. To date, there is no treatment for CF caused by the CFTR-W1282X mutation located on CFTR exon 23. Nonsense-mediated messenger RNA (mRNA) decay (NMD) degrades the CFTR-W1282X mRNA, leading to low levels of functional CFTR protein. We developed a cocktail of two antisense oligonucleotides (ASOs) that promotes the skipping of exon 23 of the CFTR-W1282X mRNA. The resulting mRNA is NMD resistant and preserves the reading frame. Its translation produces CFTR-Δex23 protein that improves CFTR activity in human bronchial epithelial cells. Our results set the stage for developing an ASO therapy for CF caused by the W1282X mutation. Mutations in the cystic fibrosis transmembrane conductance regulator (CFTR) gene cause cystic fibrosis (CF), and the CFTR-W1282X nonsense mutation causes a severe form of CF. Although Trikafta and other CFTR-modulation therapies benefit most CF patients, targeted therapy for patients with the W1282X mutation is lacking. The CFTR-W1282X protein has residual activity but is expressed at a very low level due to nonsense-mediated messenger RNA (mRNA) decay (NMD). NMD-suppression therapy and read-through therapy are actively being researched for CFTR nonsense mutants. NMD suppression could increase the mutant CFTR mRNA, and read-through therapies may increase the levels of full-length CFTR protein. However, these approaches have limitations and potential side effects: because the NMD machinery also regulates the expression of many normal mRNAs, broad inhibition of the pathway is not desirable, and read-through drugs are inefficient partly because the mutant mRNA template is subject to NMD. To bypass these issues, we pursued an exon-skipping antisense oligonucleotide (ASO) strategy to achieve gene-specific NMD evasion. A cocktail of two splice-site–targeting ASOs induced the expression of CFTR mRNA without the premature-termination-codon–containing exon 23 (CFTR-Δex23), which is an in-frame exon. Treatment of human bronchial epithelial cells with this cocktail of ASOs that target the splice sites flanking exon 23 results in efficient skipping of exon 23 and an increase in CFTR-Δex23 protein. The splice-switching ASO cocktail increases the CFTR-mediated chloride current in human bronchial epithelial cells. Our results set the stage for developing an allele-specific therapy for CF caused by the W1282X mutation.
Investigate Inhibitory Effects of Ginger polyphenols compare to Simvastatin towards HMG-CoA reductase: An Integrated Molecular Docking and Molecular dynamic simulation

Diabetes is an increasing problem in Ethiopia, affecting up to 6.5% of Ethiopian adults. There are serious complications associated with diabetes including macrovascular and microvascular. Controlling Lipid profiles and blood glucose significantly reduces the risk of complications. Statins are the only current treatment for both type 1 diabetes and Type 2 diabetes dyslipidemia. HMG-CoA reductase plays a central role in the production of cholesterol which, associated with cardiovascular disease (CVD). Statins have been found to reduce cardiovascular disease and mortality in those who are at high risk. Nonetheless, it has adverse effect, such as drug-related hypoglycemia and high cost. These situations lead to develop suitable phytotherapeutic agents with less frequent side effects. Ginger (Zingiber officinale) is widely consumed as a spice, and numerous studies suggest that ginger may have beneficial effects for diabetes and dyslipidemia. But, further studies are needed to investigate effects of binding affinity and binding site residues for major ginger extract polyphenols towards target HMG-CoA reductase. In this study, ADMET web server, Auto-Dock 5.4 and Gromacs 2020 were used. Out of eleven major gingers polyphenols screened three selected based on docking energy compare to Simvastatin for MD simulation. The predicted binding affinity for 6-paradol, 6-shogaol and gingerdione were −8.51, −6.93, −9.24 kcal/mol, respectively. The results of molecular dynamic simulation are consistence with docking. The predicted ligand binding site residues are Arg641, Gly808, Arg641, Met781, Ser794 and Arg595. In conclusion, 6-paradol, 6-shogaol and gingerdione could be possible therapy because, of interactions with target HMG-CoA reductase. Therefore, further wet lab study will be needed, for the better understanding of the mechanism of action of ginger extract by which it modulates liver and kidney vivo condition.
NSUN6, an RNA methyltransferase of 5-mC controls glioblastoma response to temozolomide (TMZ) via NELFB and RPS6KB2 interaction

Nop2/Sun RNA methyltransferase (NSUN6) is an RNA 5 - methyl cytosine (5mC) transferase with little information known of its function in cancer and response to cancer therapy. Here, we show that NSUN6 methylates both large and small RNA in glioblastoma and controls glioblastoma response to temozolomide with or without influence of the MGMT promoter status, with high NSUN6 expression conferring survival benefit to glioblastoma patients and in other cancers. Mechanistically, our results show that NSUN6 controls response to TMZ therapy via 5mC mediated regulation of NELFB and RPS6BK2. Taken together, we present evidence that show that NSUN6 mediated 5mC deposition regulates transcriptional pause (by accumulation of NELFB and the general transcription factor complexes (POLR2A, TBP, TFIIA, TFIIE) on the preinitiation complex at TATA binding site to control translation machinery in glioblastoma response to alkylating agents. Our findings open a new frontier into controlling of transcriptional regulation by RNA methyltransferase and 5mC.