Miracle-dz/UniFAR-FTUs huggingface dataset:
Novel Prognosis and Therapeutic Response Model of Immune-Related lncRNA Pairs in Clear Cell Renal Cell Carcinoma

Clear cell renal cell carcinoma (ccRCC) is the most common type of renal carcinoma. It is particularly important to accurately judge the prognosis of patients. Since most tumor prediction models depend on the specific expression level of related genes, a better model therefore needs to be constructed. To provide an immune-related lncRNA (irlncRNAs) tumor prognosis model that is independent of the specific gene expression levels, we first downloaded and sorted out the data on ccRCC in the TCGA database and screened irlncRNAs using co-expression analysis and then obtained the differently expressed irlncRNA (DEirlncRNA) pairs by means of univariate analysis. In addition, we modified LASSO penalized regression. Subsequently, the ROC curve was drawn, and we compared the area under the curve, calculated the Akaike information standard value of the 5-year receiver operating characteristic curve, and determined the cut-off point to establish the best model to distinguish the high- or low-disease-risk group of ccRCC. Subsequently, we reassessed the model from the perspectives of survival, clinic-pathological characteristics, tumor-infiltrating immune cells, chemotherapeutics efficacy, and immunosuppressed biomarkers. A total of 17 DEirlncRNAs pairs (AL031710.1|AC104984.5, AC020907.4|AC127-24.4,AC091185.1|AC005104.1, AL513218.1|AC079015.1, AC104564.3|HOXB-AS3, AC003070.1|LINC01355, SEMA6A-AS1|CR936218.1, AL513327.1|AS005785.1, AC084876.1|AC009704.2, IGFL2-AS1|PRDM16-DT, AC011462.4|MMP25-AS1, AL662844.3I|TGB2-AS1, ARHGAP27P1|AC116914.2, AC093788.1|AC007098.1, MCF2L-AS1|AC093001.1, SMIM25|AC008870.2, and AC027796.4|LINC00893) were identified, all of which were included in the Cox regression model. Using the cut-off point, we can better distinguish patients according to different factors, such as survival status, invasive clinic-pathological features, tumor immune infiltration, whether they are sensitive to chemotherapy or not, and expression of immunosuppressive biomarkers. We constructed the irlncRNA model by means of pairing, which can better eliminate the dependence on the expression level of the target genes. In other words, the signature established by pairing irlncRNA regardless of expression levels showed promising clinical prediction value.
Beneficial Shifts in Gut Microbiota by Lacticaseibacillus rhamnosus R0011 and Lactobacillus helveticus R0052 in Alcoholic Hepatitis

Gut microbiota performs indispensable functions in the pathophysiology of alcoholic hepatitis (AH). We investigated the effects of Lacticaseibacillus rhamnosus R0011 and Lactobacillus helveticus for gut microbial restoration toward eubiosis in patients with AH. A multicenter, double-blind, and randomized trial was conducted. Probiotics (n = 44) and placebo (n = 45) groups received, during 7 days, L. rhamnosus R0011/L. helveticus R0052 at 120 mg/day and placebo. All patients were hospitalized to ensure abstinence. Liver function, lipopolysaccharide level, and stool analysis were evaluated in patients before and after 7 days of treatment. At baseline, the dominant bacteria were Gram-negative in both groups which decreased after the probiotics treatment and exhibited a significant reduction in lipopolysaccharide level (p < 0.001). The probiotics ameliorated the Child–Pugh scores (p < 0.001). Furthermore, the probiotics group showed a decline in the levels of alanine aminotransferase and gamma-glutamyltranspeptidase (p < 0.05). The probiotics changed the gut microbial composition at various taxonomical levels. The proportion of Bacteroidetes (147%) was increased after 7 days of probiotics supplementation while Proteobacteria (30%) and Fusobacteria (0%) were decreased. Administration of L. rhamnosus R0011 and L. helveticus R0052 conceivably associated with restoration of gut microbiome in AH patients and improved AH by modulating the gut–liver axis.
Experimental Infection of Mice and Ticks with the Human Isolate of Anaplasma phagocytophilum NY-18

Anaplasma phagocytophilum is the causative agent of tick-borne fever (TBF) and human granulocytic anaplasmosis (HGA) and is currently considered an emerging disease in the USA, Europe, and Asia. The increased prevalence of A. phagocytophilum as a human pathogen requires the detailed characterization of human isolates and the implementation of appropriate animal models. In this study, we demonstrated that the dynamics of infection with the human isolate of A. phagocytophilum NY-18 was variable in three different strains of mice (SCID, C3H/HeN, BALB/c). We further evaluated the ability of Ixodes ricinus to acquire and transmit A. phagocytophilum NY-18 and compared it with Ixodes scapularis. Larvae of both tick species effectively acquired the pathogen while feeding on infected mice. The infection rates then decreased during the development to nymphs. Interestingly, molted I. ricinus nymphs were unable to transmit the pathogen to naïve mice, which contrasted with I. scapularis. The results of our study suggest that I. ricinus is not a competent vector for the American human Anaplasma isolate. Further studies are needed to establish reliable transmission models for I. ricinus and European human isolate(s) of A. phagocytophilum.
Error-Free Three-Dimensional Multimode Crossover Graded-Index Polymer Waveguides for Board-Level Optical Circuitry

In this paper, we experimentally introduce a low-loss 3-dimensional (3D) symmetric crossover multimode optical waveguides realized with parabolic graded-index (GI) profiles using the Mosquito method. This compact crossover multimode waveguide is comprised of two S-bend core groups (6 Ch. × 6 Ch.), where the last six cores cross over the first six cores, maintaining a height gap at the crossover section to avoid the smudging from needle scan. The suitable height gap for this crossover multimode structure is 0.7 mm. The insertion loss of this multimode crossover waveguide is 1.64 dB at 850 nm wavelength. The insertion losses of this multimode crossover waveguide are compared with separately fabricated 3D S-bend waveguides, and it indicates that multimode crossover structures do not exhibit remarkable excess loss. The interchannel crosstalk of this waveguide is less than −45 dB. This crossover multimode waveguide demonstrates sharply open optical and electrical eye diagrams and transmits error-free data at 26 Gb/s.
A recommendation system of training data selection method for cross-project defect prediction

Cross-project defect prediction (CPDP) has been a popular approach to address the limited historical dataset when building a defect prediction model. Directly applying cross-project datasets to learn the prediction model produces an unsatisfactory predictive model. Therefore, the selection of training data is essential. Many studies have examined the effectiveness of training data selection methods, and the best-performing method varied across datasets. While no method consistently outperformed the others across all datasets, predicting the best method for a specific dataset is essential. This study proposed a recommendation system to select the most suitable training data selection method in the CPDP setting. We evaluated the proposed system using 44 datasets, 13 training data selection methods, and six classification algorithms. The findings concluded that the recommendation system effectively recommends the best method to select training data.
Accelerating Parallel Operation for Compacting Selected Elements on GPUs

Compacting is a common and heavily used operation in different application areas such as statistics, database systems, simulations, and artificial intelligence. The task of this operation is to write selected elements of an input array contiguously back to a new output array, producing a smaller (i.e., compact) array. The selected elements are usually defined by means of a bit mask. With the always increasing amount of data to be processed in different application areas, better performance becomes a key factor for this operation. Thus, exploiting the parallel capabilities of GPUs to speed up the compacting operation is of great interest. We introduce smart partitioning for GPU compaction (SPACE ) as a set of different optimization approaches for GPUs. A detailed guide about setting up and using the software is found in Fett et al. (n.d.). Fett et al. (2022) is a preprint of the published Euro-Par 2022 paper, in which SPACE is described in great detail.
BLENDED LEARNING: THE “NEW NORMAL” IN EDUCATION DELIVERY

Successful blended learning contexts engage students in highly interactive virtual learning environments through a mix of online and face-to-face teaching, learning and assessment modalities. This paper explores some of the affordances and constraints of blended learning for one Higher Education Institution in Ireland. TPACK (Mishra & Koehler, 2006) and Multimodal (Picciano, 2009) models frame the discussion opportunities and challenges more broadly for blended learning instructional and pedagogical design post pandemic in Higher Education.
The The Effect of Using Chain Story Technique In Writing Descriptive Text For The Grade Students of SMP Negeri 14 Medan

The study focused on finding out the effect of applying Chain Story Technique in writing descriptive text. The chainstory technique is a technique which is used to make writing class more interesting. this technique is suitable use inwriting activity. It can help teacher in teaching learning process and can help students to understand quickly to makesummary of the text. The population of the research was the eight grade students of SMP NEGERI 14 MEDAN.There were fourty eight students of the eight grade as the sample of the research. This study was conducted with tworandomized class namely experimental class and control class. The experimental class was taught by applying ChainStory Technique, while the control class was taught without Chain Story Technique. The instrument of collectingthe data was writing test. The data were analyzed by using reliability (KR21) and t-test formula. The calculationshown that the mean score of students in experimental class is 8,45 and the mean score of control class who aretaught without Chain Story technique is 3,25. It can be concluded that the mean score of experimental class is higherthan control class. There is a significant difference scores between experimental group and control group. Based onresearch finding, it is concluded that t-observed is higher than t-table. T-obs> t- table (p=0.05) 8.813>1.679. itmeans that H0 is rejected and Ha is accepted. Chain Story Technique significantly affects on the eight grade studentsof SMP NEGERI 14 MEDAN in writing descriptive text. It is suggested the English Department Students, lecturerand all of people to use this strategy
A Retrospective Study of the Clinical Characteristics of Japanese Encephalitis in Adults.

OBJECTIVE This study aimed to explore the diagnostic points and treatment modes of the clinical characteristics of Japanese encephalitis (JE) in the middle-aged and elderly population. METHODS Six patients aged 47-72 who were diagnosed with JE at the Beijing Chaoyang Hospital Affiliated with the Capital Medical University between August 2018 and September 2019 were enrolled in the study. Their clinical manifestations, biochemical indicators, imaging data, diagnostic methods, and the evolution and outcomes of the treatments they underwent were retrospectively analyzed. RESULTS (1) All six patients had severe clinical symptoms and poor prognoses that were more likely to be associated with other systemic diseases. (2) Lesions were most commonly distributed in the thalamus, basal ganglia, and midbrain. The appearance of hyperintensity in the corpus callosum, hippocampus, and subcortical white matter was more specific. The hyperperfusion metabolism in the lesion area in head computed tomography perfusion imaging indicated the state of inflammatory activity in the lesion. In cranial magnetic resonance imaging (MRI), T2 and fluid-attenuated inversion recovery (FLAIR) were more sensitive. (3) After a patient has been systematically treated in the intensive care unit (ICU), the patient gradually recovered and the level of consciousness improved (p < 0.05). CONCLUSIONS In brain MRI-especially T2 and FLAIR-intracranial infection is often accompanied by abnormal signals in the thalamus, midbrain, hippocampus, and white matter hyperintensity (WMH), which is highly suggestive of JE. The positive detection of anti-JE virus immunoglobulin M antibodies in a patient's serum and/or cerebrospinal fluid can confirm the diagnosis of JE, and comprehensive ICU treatment (hormones combined with anti-inflammatory, antiviral, and mild hypothermic cerebral protection therapies) can improve the survival rate.
Asymmetric Mach–Zehnder Interferometric Biosensing for Quantitative and Sensitive Multiplex Detection of Anti-SARS-CoV-2 Antibodies in Human Plasma

The Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) pandemic has once more emphasized the urgent need for accurate and fast point-of-care (POC) diagnostics for outbreak control and prevention. The main challenge in the development of POC in vitro diagnostics (IVD) is to combine a short time to result with a high sensitivity, and to keep the testing cost-effective. In this respect, sensors based on photonic integrated circuits (PICs) may offer advantages as they have features such as a high analytical sensitivity, capability for multiplexing, ease of miniaturization, and the potential for high-volume manufacturing. One special type of PIC sensor is the asymmetric Mach–Zehnder Interferometer (aMZI), which is characterized by a high and tunable analytical sensitivity. The current work describes the application of an aMZI-based biosensor platform for sensitive and multiplex detection of anti-SARS-CoV-2 antibodies in human plasma samples using the spike protein (SP), the receptor-binding domain (RBD), and the nucleocapsid protein (NP) as target antigens. The results are in good agreement with several CE-IVD marked reference methods and demonstrate the potential of the aMZI biosensor technology for further development into a photonic IVD platform.
Adaptive Scheduling and Power Control for Multi-Objective Optimization in IEEE 802.15.6 Based Personalized Wireless Body Area Networks

Multi-objective optimization (MOO) has been a topic of intense interest in providing flexible trade-offs between conflicting optimization criteria in wireless body area networks (WBANs). To solve diverse multi-objective optimization problems (MOPs), conventional resource management schemes have dealt with the classic issues of WBANs, such as traffic heterogeneity, emergency response, and body shadowing. However, existing approaches have difficulty achieving MOO because, despite the personalization of WBANs, they still miss the new constraints or considerations derived from user-specific characteristics. To address this problem, in this article, we propose an adaptive scheduling and power control scheme for MOO in personalized WBANs. Specifically, we investigate the existing scheduling and power control schemes for solving MOPs in WBANs, clarify their limitations, and present two feasible solutions: priority-based adaptive scheduling and deep reinforcement learning (DRL) power control. By integrating these two mechanisms in compliance with the IEEE 802.15. 6 standard, we can jointly improve the optimization criteria, that is, differentiated quality of service (QoS), transmission reliability, and energy efficiency. Through comprehensive simulations, we captured the performance variations under realistic WBAN deployment scenarios and verified that the proposed scheme can achieve a higher throughput and packet delivery ratio, lower power consumption ratio, and shorter delay compared with a conventional approach.
Towards Security Mechanism in D2D Wireless Communication: A 5G Network Approach

Device-to-device (D2D) communication has attracted many researchers, cellular operators, and equipment makers as mobile traffic and bandwidth demands have increased. It supports direct communication within devices with no need for any intermediate node and, therefore, offers advantage in 5G network while providing wide cell coverage range and frequency reuse. However, establishing acceptable and secure mechanism for D2D communication which ensures confidentiality, integrity, and availability is an issue encountered in this situation. Furthermore, in a resource-constrained IoT environment, these security challenges are more critical and difficult to mitigate, especially during emergence of IoT with 5G network application scenarios. To address these issues, this paper proposed a security mechanism in 5G network for D2D wireless communication dependent on lightweight modified elliptic curve cryptography (LMECC). The proposed scheme follows a proactive routing protocol to discover services, managing link setup, and for data transfer with the aim to reduce communication overhead during user authentication. The proposed approach has been compared against Diffie–Hellman (DH) and ElGamal (ELG) schemes to evaluate the protocol overhead and security enhancement at network edge. Results proved the outstanding performance of the proposed LMECC for strengthening data secrecy with approximate 13% and 22.5% lower overhead than DH and ELG schemes.
LLDM: Low-Latency DoS Attack Detection and Mitigation in SDN

Software-Defined Networking (SDN) is a new and highly flexible network architecture, but the bottleneck between the control plane and the data plane makes it vulnerable to the control plane saturation DoS attacks. When the attack happens, traditional schemes in DoS scrubbing agent use a binary classification and a First In First Out (FIFO) queue to filter attack flows. However, this scheme is inimical to the end-to-end latency of benign traffic. To tackle this issue, we propose LLDM, leveraging a dynamic priority scheme and a priority queue to detect, mitigate the attacks while ensuring low latency for benign traffic. After detecting the attack, LLDM leverages a two-phase scheme for mitigation. First, LLDM marks packets from the ports under attack as suspicious and migrates them to the mitigation agent. Then, the dynamic priority manager assigns each packet a priority corresponding to its legality, which is used in the priority queue for DoS scrubbing. We evaluate LLDM in a simulation SDN environment. The experimental results show that LLDM can reduce 90.4% of the queuing delay compared with the traditional scheme under a 5000 Packets Per Second (PPS) attack, and it is also resistant to more sophisticated attacks. Under the high rate attack of 50000 PPS, LLDM installs a flow rule for legitimate traffic in 0.2 seconds. Moreover, for benign HTTP requests, LLDM can keep the request time at 1.39 seconds.
Morphometric Analysis to Determine How the Shape of Tennis Rackets has Developed

When studying sports equipment mechanics, object shapes are often simplified; for example, implements like rackets are often modelled as beams [1]. Such beam models cannot account for differences in racket shape that could influence mechanics, such as asymmetry and variations in head width. Indeed, the shape of lawn tennis rackets has changed considerably since the origins of the game in the 1870s [2], with implications for player performance and injury risk [3]. Nineteenth century rackets were wooden and often had small, asymmetric heads. From the 1900s to 1960s, rackets tended to be wooden with small, symmetrical heads. A period of rapid development in racket design began in the 1960s, as engineers trialled different materials and experimented with new shapes. In response to these developments, the International Tennis Federation started limiting the size of tennis rackets in the 1980s, with current limits on overall length and width set at 73.7 and 31.7 cm, respectively [4]. Most high-end tennis rackets are now made from fibre-polymer composites, and tend to be lighter and stiffer [1] and have larger heads than their wooden predecessors.
Role of Mitochondrial Mutations in Ocular Aggregopathy

Background Mitochondria are essential cellular organelles that are responsible for oxidative stress-induced damage in age-dependent neurodegenerations such as glaucoma. Previous studies have linked mitochondrial DNA (mtDNA) mutations to cellular energy shortages that result in eye degeneration. Methodology To look for nucleotide variations in mtDNA in exfoliation syndrome/glaucoma (XFS/XFG), we performed a polymerase chain reaction (PCR) to amplify the entire coding region of the mitochondrial genome from peripheral blood of XFS/XFG (n = 25) patients and controls (n = 25). Results This study identified a total of 65 variations in XFS/XFG patients, of which 25 (38%) variations were non-synonymous single-nucleotide polymorphism (nsSNPs). Out of 25 nsSNPs, seven (five nsSNP in MT-ND4 and two in MT-ATP6 gene) were predicted as pathogenic using four different software, namely, SIFT, Polyphene2, mutation taster, and MutPred2. The pathogenic nsSNPs were then subjected to structural change analysis using online tools. Conclusions The pathogenic nsSNPs were found in both proteins’ transmembrane domains and were expected to be conserved, but with lower protein stability (ΔΔG <− 0.5), indicating a possibly harmful effect in exfoliation. However, three-dimensional protein analysis indicated that the predicted mutations in MT-ND4 and MT-ATP6 were unlikely to alter the protein function.
A Survey of Deep Learning Applications for COVID-19 Detection Techniques Based on Medical Images

The outbreak of the new coronavirus (COVID-19) has created a disaster worldwide and it became a very severe and acute disease. COVID-19 prevalence is rapidly increasing around the world. Deep learning (DL) technology had become a hot topic in the context of computing and is widely applied in various medical applications. These techniques have proven to be one of the effective tools for clinicians in the automatic diagnoses of COVID-19. The goal of the present paper is to provide an overview of recently developed systems based on DL techniques that use various medical imaging modalities such as Computer Tomography (CT) and Chest X-Rays (CXR). This review focuses on systems that had been developed for the diagnosis of COVID-19 with the use of the DL methods, as well as the well-known datasets that are utilized for the training of those networks. Finally, the researcher reviewed 58 research papers based on different medical images. Overall, this article aims to assist experts (medical or otherwise) and technicians to understand how the DL approaches are utilized in this context and the way that they can potentially be expanded to combat COVID-19 outbreaks.