Miracle-dz/UniFAR-FTUs huggingface dataset:
Sound Localization in Single-Sided Deaf Participants Provided With a Cochlear Implant

Spatial hearing is crucial in real life but deteriorates in participants with severe sensorineural hearing loss or single-sided deafness. This ability can potentially be improved with a unilateral cochlear implant (CI). The present study investigated measures of sound localization in participants with single-sided deafness provided with a CI. Sound localization was measured separately at eight loudspeaker positions (4°, 30°, 60°, and 90°) on the CI side and on the normal-hearing side. Low- and high-frequency noise bursts were used in the tests to investigate possible differences in the processing of interaural time and level differences. Data were compared to normal-hearing adults aged between 20 and 83. In addition, the benefit of the CI in speech understanding in noise was compared to the localization ability. Fifteen out of 18 participants were able to localize signals on the CI side and on the normal-hearing side, although performance was highly variable across participants. Three participants always pointed to the normal-hearing side, irrespective of the location of the signal. The comparison with control data showed that participants had particular difficulties localizing sounds at frontal locations and on the CI side. In contrast to most previous results, participants were able to localize low-frequency signals, although they localized high-frequency signals more accurately. Speech understanding in noise was better with the CI compared to testing without CI, but only at a position where the CI also improved sound localization. Our data suggest that a CI can, to a large extent, restore localization in participants with single-sided deafness. Difficulties may remain at frontal locations and on the CI side. However, speech understanding in noise improves when wearing the CI. The treatment with a CI in these participants might provide real-world benefits, such as improved orientation in traffic and speech understanding in difficult listening situations.
Can Priming Multiple Identities Enhance Divergent Thinking for Middle School Students?

Previous studies have found that promoting multiple identities can improve children’s creative performance (divergent thinking). The present study employed a priming paradigm to design two experiments and investigate whether promoting a sense of multiple identities in middle school students could enhance their divergent thinking, a key component of creativity. In Experiment 1, 77 junior high school students were divided into multiple identities and physical trait condition groups. They were instructed to think about a child with multiple identities or physical traits. The results showed that there were no differences in divergent thinking (DT) scores between the two groups. In Experiment 2, we modified the priming method by asking participants to think about and write a description of the various identities or physical traits and employed a subjective top-scoring method to make up for shortcomings in the traditional scoring method when applied to originality. The results still showed no significant difference in scores between the identity and physical trait groups. Thus, the results of this study contradict those of previous research, which found that the identity group demonstrated significantly higher scores on a creativity test than did those in the physical trait group. Several potential factors affect this outcome, but it seems that priming to enhance divergent thinking is not particularly effective. Thus, the social priming effect should be pursued with caution regarding both replicability and generalizability.
Rapid Detection and Quantification of Plant Innate Immunity Response Using Raman Spectroscopy

We have developed a rapid Raman spectroscopy-based method for the detection and quantification of early innate immunity responses in Arabidopsis and Choy Sum plants. Arabidopsis plants challenged with flg22 and elf18 elicitors could be differentiated from mock-treated plants by their Raman spectral fingerprints. From the difference Raman spectrum and the value of p at each Raman shift, we derived the Elicitor Response Index (ERI) as a quantitative measure of the response whereby a higher ERI value indicates a more significant elicitor-induced immune response. Among various Raman spectral bands contributing toward the ERI value, the most significant changes were observed in those associated with carotenoids and proteins. To validate these results, we investigated several characterized Arabidopsis pattern-triggered immunity (PTI) mutants. Compared to wild type (WT), positive regulatory mutants had ERI values close to zero, whereas negative regulatory mutants at early time points had higher ERI values. Similar to elicitor treatments, we derived an analogous Infection Response Index (IRI) as a quantitative measure to detect the early PTI response in Arabidopsis and Choy Sum plants infected with bacterial pathogens. The Raman spectral bands contributing toward a high IRI value were largely identical to the ERI Raman spectral bands. Raman spectroscopy is a convenient tool for rapid screening for Arabidopsis PTI mutants and may be suitable for the noninvasive and early diagnosis of pathogen-infected crop plants.
FUCA2 Is a Prognostic Biomarker and Correlated With an Immunosuppressive Microenvironment in Pan-Cancer

Background The expression of Fucosidase, alpha-L-2 (FUCA2) varies across tumors. However, its role in various tumor types and relationship with the tumor immune microenvironment (TIME) is poorly defined. Methods We analyzed profiles of FUCA2 expression using datasets from The Cancer Genome Atlas (TCGA) and Genotype-Tissue Expression (GTEx) databases. Next, gene alteration, clinical characteristics and prognostic values of FUCA2 were elucidated based on TCGA pan-cancer data. This was followed by gene set enrichment analysis by R software. Relationships between FUCA2 expression and immune infiltration and immune-related genes were also evaluated. Moreover, the association of immune cell infiltration with FUCA2 expression was evaluated across three different sources of immune cell infiltration data, namely the TIMER online, ImmuCellAI databases, as well as a published study. In addition, MTT assays was also conducted to validate the oncogene role of FUCA2 in lung cancer cells. Results FUCA2 was upregulated in most tumors, and this was significantly associated with poor survival rates. Gene set enrichment analysis uncovered that FUCA2 correlated with immune pathways in different tumor types. FUCA2 expression was positively related to tumor associated macrophages (TAMs), especially M2-like TAMs. Moreover, FUCA2 level showed a positive relationship with most immunosuppression genes, including programmed death-ligand 1 (PD-L1), transforming growth factor beta 1 (TGFB1), and interleukin-10 (IL10) in most cancer types. FUCA2 knockdown inhibited the cell viability in lung cancer cells. Conclusions Our study reveals that FUCA2 is a potential oncogene and is indicative biomarker of a worse prognosis in pan-cancer. High FUCA2 expression may contribute to increased infiltration of TAMs and associates with an immunosuppressive microenvironment, providing a potential target for tumor therapy.
Alternative Energy: Breaking Down the Diverse Metabolic Features of Lung Cancers

Metabolic reprogramming is a hallmark of cancer initiation, progression, and relapse. From the initial observation that cancer cells preferentially ferment glucose to lactate, termed the Warburg effect, to emerging evidence indicating that metabolic heterogeneity and mitochondrial metabolism are also important for tumor growth, the complex mechanisms driving cancer metabolism remain vastly unknown. These unique shifts in metabolism must be further investigated in order to identify unique therapeutic targets for individuals afflicted by this aggressive disease. Although novel therapies have been developed to target metabolic vulnerabilities in a variety of cancer models, only limited efficacy has been achieved. In particular, lung cancer metabolism has remained relatively understudied and underutilized for the advancement of therapeutic strategies, however recent evidence suggests that lung cancers have unique metabolic preferences of their own. This review aims to provide an overview of essential metabolic mechanisms and potential therapeutic agents in order to increase evidence of targeted metabolic inhibition for the treatment of lung cancer, where novel therapeutics are desperately needed.
RISK FACTORS OF DIASTOLIC HEART FAILURE

Background and objective: Isolated diastolicdysfunction /heart failure is the cause of congestive heart failure in 50% of patients with normal systolic function. Severalfactors have been shown to be predisposing conditions associated with the development of diastolic dysfunction anddiastolic heart failure. This study was designed to study the common and important factors leading to diastolicdysfunction of the heart. Study design: This was a descriptive analytic study. Place & Duration: This study wasconducted at Cardiology Department of Mayo Hospital Lahore from January 1998 to September 1998. Material andmethods: One hundred patients of diastolic heart failure who fulfilled our inclusion criteria were studied when theypresented to echo room for echocardiography. Detailed Echocardiography examination of all patients included in thisstudy was done to measure Doppler parameters of diastolic dysfunction like deceleration time, isovolumic relaxationtime, S & D wave measurements on pulmonary vein. Detailed history was taken and physical examination wasperformed to evaluate the risk factor associated with diastolic dysfunction in each patient. Results Mean age of thestudy population was 511 18 years. Majority of patients 50(50%) had age range from 36 to 55 years. There were62(62%) male and 38(38%) female patients. Majority of patients 34(34%) had NYHA class I symptoms and 30(30%)patients were in NYHA class II. Ejection fraction was normal in 54(54%) of patients while it was less than 40% in 24%of patients. Majority of patients, 28(28%) had Ischemic heart disease while 24(24%) patients had hypertension and14(14%) had diabetes mellitus. Hypertension and Ischemic heart disease were present in 14(14%) patients.Conclusion Approximately 40% - 50% of patients with or without overt clinical features of heart failure have isolateddiastolic dysfunction / DHF with normal systolic function. Coronary artery disease, arterial hypertension and diabetesmellitus are the major factors associated with diastolic dysfunction and DHF. Obesity and aging also contributeindependently to the development of diastolic dysfunction in a reasonable number of patients.
Combining content and social features in a deep learning approach to Vietnamese email prioritization

The email overload problem has been discussed in numerous email-related studies. One of the possible solutions to this problem is email prioritization, which is the act of automatically predicting the importance levels of received emails and sorting the user’s inbox accordingly. Several learning-based methods have been proposed to address the email prioritization problem using content features as well as social features. Although these methods have laid the foundation works in this field of study, the reported performance is far from being practical. Recent works on deep neural networks have achieved good results in various tasks. In this paper, the authors propose a novel email prioritization model which incorporates several deep learning techniques and uses a combination of both content features and social features from email data. This method targets Vietnamese emails and is tested against a self-built Vietnamese email corpus. Conducted experiments explored the effects of different model configurations and compared the effectiveness of the new method to that of a previous work.
Using Distribution Alliance to Signal the Seller's Service Quality in Online Retailing Platforms

Signal plays a significant role in the online retailing market, especially where the service quality of sellers is unobservable. In the current study, a game-theoretical model was formulated to help examine whether the new delivery service called distribution alliance in the electronic market can serve as a superior signal in revealing online seller’s service quality. Our results showed that the certification accuracy and the application fee are closely related to the signaling effect of the distribution alliance. Specifically, we found a concrete analytical boundary where a certain high level of certification accuracy is required to guarantee the existence of market equilibrium, and a corresponding application fee can convoy the signal’s effectiveness. In addition, the potential extensions and limitations of this research were also discussed.
IMPACTS OF INFORMATION AND COMMUNICATION TECHNOLOGY ON DEVELOPING QUALITY OF TEACHING

Information and communication technology (ICT) brings about magnificent improvement in the field of education. It also provides opportunities for innovations in the process of teaching and learning for the sake of achieving the desired learning in students. The approaches of teaching mainly transformed from the teacher-centred class into the student-centred one, with students actively engaging in learning. The teachers who use ICT are expected to effectively employ it in teaching and learning processes to improve not only the students’ learning performance but also their own teaching proficiency. First, this study aims at demonstrating the effects of ICT on the development of teaching quality that could enable teachers to improve their pedagogical beliefs and practices. Second, it suggests the factors that result in progressing ICT teachers’ teaching quality in educational settings. A qualitative methodology was adopted in this study, and the adopted analysis procedure includes a description of ICT, ICT and quality of teaching, the impacts of ICT on improving teaching quality (benefits of ICT in the teaching context), factors that result in advancing ICT and teaching quality in the educational settings and conclusions. This paper has drawn on educational research and emerging new practices of ICT in teaching and learning processes to investigate the impacts of ICT on developing quality of teaching.
Experiment analysis on the characteristic of empty fruit bunch, palm kernel shell, coconut shell, and rice husk for biomass boiler fuel

It has been a necessary option for most developing countries moving towards renewable energy options as part of the Paris Agreement, which minimizes conventional energy sources’ reliance. In Malaysia, biomass is a profitable renewable option compared to solar and hydro sources for energy production due to the abundance of agricultural biomass availability for immediate use. However, most of the biomass power plants in Malaysia depend on empty fruit bunch as fuel, causing problems when there is a shortage of fuel supply and other circumstances. Variations in the fuels’ properties provide a new challenge to the power plant output; however, mixing biomass fuels can overcome the issue. Hence, this article aims to study the empty fruit bunch (EFB) with other abundant biomass fuels like “palm kernel shell (PKS),” “rice husk (RH),” and “coconut shell (CS)” for biomass boiler fuel. Therefore, the biomass’s composition and characteristics need to be known, which was done through the proximate analysis (PA), ultimate analysis (UA), and high heating value (HHV). As a result of PA, UA, and HHV, RH is the least favourable fuel due to lowest ((moisture (4.92%), volatile matter (63.20%), carbon (42.50%), hydrogen (5.42%), nitrogen (0.43%) and sulphur (0.01%)) and highest ash content (18.19%), whereas CS exhibits the most favourable option with highest (carbon (50.25%) and oxygen (42.57%)) and second highest in HHV (20.53%) compared with PKS. Thus, the experiments have provided the least and highest favourable feedstock ratios option for biomass boiler fuel application.
Prognostic Value of Metabolism-Related Genes and Immune Infiltration in Clear Cell Renal Cell Carcinoma

Background Clear cell renal cell carcinoma (ccRCC) is one of the most prevalent cancers. Thus, it is warranted to detect the status of metabolism-related genes (MRGs) and infiltrating immune cells in ccRCC progression for the prognosis of ccRCC. This research was designed to establish and verify the prognostic signature of ccRCC using MRGs. In addition, we investigated the potential link between the relative proportion of tumor infiltrated immune cells (TIICs) and ccRCC prognosis. Methods Sequencing data of metabolism-related gene sets in ccRCC cases were obtained from The Cancer Genome Atlas database (TCGA) and Gene Expression Omnibus Database (GEO). The R Programming Language software packages were applied for differential analysis of MRGs. First, a univariate Cox regression model was applied to determine the MRGs linked with overall survival (OS). Then, the multivariate Cox regression model was applied to establish the prognostic signature. Finally, the CIBERSORT algorithm was used to determine the proportion of TIICs. Results Overall, 286 differentially expressed MRGs were identified in the TCGA dataset. Univariate and multivariate Cox regression models were applied to develop a prognostic signature with six MRGs. The predictive capability of the prognostic signature was further verified by TCGA and GEO database. In addition, RS positively correlated with memory B cells, plasma cells, activated memory CD4+ T cells, follicular helper T cells, regulatory T cells, CD8+ T cells, and M0 macrophages, and were negatively associated with resting memory CD4+ T cells, resting dendritic cells, activated dendritic cells, M2 macrophages, monocytes, resting mast cells, and eosinophils. Conclusion Herein, a prognostic signature was developed using MRGs for ccRCC prognosis. The proportion of 22 TIICs in ccRCC and the association between TIICs and clinical outcomes were also determined. The identified genes and cells could guide future targeted therapy and immunotherapy.
Acceleration based black-box accident detection and warning system

This research paper has been consecrated to design a black-box accident warning system that combines both Global System for Mobile Communication (GSM) and Global Positioning System (GPS) technologies to locate and send a Minimum Amount of Data containing important information about the accident gathered using different sensors such as the accident type, geographic coordinates, time and velocity of the vehicle to the intervention services (Hospitals, civil protection and police). In addition, the system exploits the data obtained via OBD-II standard to provide a reliable accident detection method based on the vehicle acceleration. Obviously, this system seemed to be the best solution for countries where the average age of vehicles is quite higher than others. The system has been placed in a real vehicle in order to test the accident detection algorithm by applying sudden medium braking.
Atiyah’s work on holomorphic vector bundles and gauge theories

The first part of the article surveys Atiyah’s work in algebraic geometry during the 1950s, mainly on holomorphic vector bundles over curves. In the second part we discuss his work from the late 1970s on mathematical aspects of gauge theories, involving differential geometry, algebraic geometry, and topology.
An effective face recognition method using guided image filter and convolutional neural network

In the area of computer vision, face recognition is a challenging task because of the pose, facial expression, and illumination variations. The performance of face recognition systems reduces in an unconstrained environment. In this work, a new face recognition approach is proposed using a guided image filter, and a convolutional neural network (CNN). The guided image filter is a smoothing operator and performs well near the edges. Initially, the ViolaJones algorithm is used to detect the face region and then smoothened by a guided image filter. Later the proposed CNN is used to extract the features and recognize the faces. The experiments were performed on face databases like ORL, JAFFE, and YALE and attained a recognition rate of 98.33%, 99.53%, and 98.65% respectively. The experimental results show that the suggested face recognition method attains good results than some of the state-of-the-art techniques.
DESIGN OF COMPACT CPW-FED SYMMETRICAL STAIRCASE-SHAPED UWB ANTENNA USING TRANSMISSION LINE MODEL

A co-planar waveguide-fed symmetrical staircase-shaped ultra-wideband antenna is proposed in this work. This antenna consists of three pairs of rectangular notches, two symmetrical C-shaped slots and two pairs of quarter-circular-ring-slits which are etched on the rectangular radiator and ground plane, respectively. By sequentially inserting three pairs of rectangular notches with proper positions, an excellent impedance bandwidth of 1.55–16.95GHz (166.51%), i.e., a 10.94 : 1 ratio bandwidth is obtained. The total volume of the prototype is merely 0.239 × 0.224 × 0. 004λl , λl wavelength of the free space at the lowest operating frequency (i.e., 1.55GHz). As a result, wider impedance bandwidth, fair gain, and better impedance matching of the proposed antenna are obtained. It is observed that the simulation results are in good agreement with the measurement results. The transmission line model (TLM) of the proposed antenna is presented, and it shows the antenna behavior based on the effect of each element. It is observed that the characteristics of the TLM model are close to the simulation result using the CST simulator. The prototype is successfully implemented, fabricated, and compared with the experimental results.
InFrag: Using Attribution-based Explainability to Guide Deep Molecular Optimization

The recently proposed Genetic expert guided learning (GEGL) framework has demonstrated impressive performances on several \textit{de novo} molecular design tasks. Despite the displayed state-of-the art results, the proposed system relies on an expert-designed Genetic expert. Although hand-crafted experts allow to navigate the chemical space efficiently, designing such experts requires a significant amount of effort and might contain inherent biases which can potentially slow down convergence or even lead to sub-optimal solutions. In this research, we propose a novel genetic expert named \textit{InFrag} which is free of design rules and can generate new molecules by combining promising molecular fragments. Fragments are obtained by using an additional graph convolutional neural network which computes attributions for each atom for a given molecule. Molecular substructures which contribute positively to the task score are kept and combined to propose novel molecules. We experimentally demonstrate that, within the GEGL framework, our proposed attribution-based genetic expert is either competitive or outperforms the original expert-designed genetic expert on goal-directed optimization tasks. When limiting the number of optimization rounds to one and three rounds, a performance increase of approximately 43% and 20% respectively is observed compared to the baseline genetic expert. Furthermore, we empirically show that combining several experts that share a fixed sampling budget at each optimization round generally improves or maintains the overall performance of the framework.