Activation of Human CD8+ T Cells with Nitroso Dapsone-Modified HLA-B*13:01-Binding Peptides.

Previous studies have shown that cysteine-reactive drug metabolites bind covalently with protein to activate patient T cells. However, the nature of the antigenic determinants that interact with HLA and whether T cell stimulatory peptides contain the bound drug metabolite has not been defined. Because susceptibility to dapsone hypersensitivity is associated with the expression of HLA-B*13:01, we have designed and synthesized nitroso dapsone-modified, HLA-B*13:01 binding peptides and explored their immunogenicity using T cells from hypersensitive human patients. Cysteine-containing 9-mer peptides with high binding affinity to HLA-B*13:01 were designed (AQDCEAAAL [Pep1], AQDACEAAL [Pep2], and AQDAEACAL [Pep3]), and the cysteine residue was modified with nitroso dapsone. CD8+ T cell clones were generated and characterized in terms of phenotype, function, and cross-reactivity. Autologous APCs and C1R cells expressing HLA-B*13:01 were used to determine HLA restriction. Mass spectrometry confirmed that nitroso dapsone-peptides were modified at the appropriate site and were free of soluble dapsone and nitroso dapsone. APC HLA-B*13:01-restricted nitroso dapsone-modified Pep1- (n = 124) and Pep3-responsive (n = 48) CD8+ clones were generated. Clones proliferated and secreted effector molecules with graded concentrations of nitroso dapsone-modified Pep1 or Pep3. They also displayed reactivity against soluble nitroso dapsone, which forms adducts in situ, but not with the unmodified peptide or dapsone. Cross-reactivity was observed between nitroso dapsone-modified peptides with cysteine residues in different positions in the peptide sequence. These data characterize a drug metabolite hapten CD8+ T cell response in an HLA risk allele-restricted form of drug hypersensitivity and provide a framework for structural analysis of hapten HLA binding interactions.

Dapsone- and nitroso dapsone-specific activation of T cells from hypersensitive patients expressing the risk allele HLA-B*13:01.

BACKGROUND: Research into drug hypersensitivity associated with the expression of specific HLA alleles has focussed on the interaction between parent drug and the HLA with no attention given to reactive metabolites. For this reason, we have studied HLA-B*13:01-linked dapsone hypersensitivity to (a) explore whether the parent drug and/or nitroso metabolite activate T cells and (b) determine whether HLA-B*13:01 is involved in the response. METHODS: Peripheral blood mononuclear cells (PBMC) from six patients were cultured with dapsone and nitroso dapsone, and proliferative responses and IFN-γ release were measured. Dapsone- and nitroso dapsone-specific T-cell clones were generated and phenotype, function, HLA allele restriction, and cross-reactivity assessed. Dapsone intermediates were characterized by mass spectrometry. RESULTS: Peripheral blood mononuclear cells from six patients and cloned T cells proliferated and secreted Th1/2/22 cytokines when stimulated with dapsone (clones: n = 395; 80% CD4+ CXCR3hi CCR4hi , 20% CD8+CXCR3hi CCR4hi CCR6hi CCR9hi CCR10hi ) and nitroso dapsone (clones: n = 399; 78% CD4+, 22% CD8+ with same chemokine receptor profile). CD4+ and CD8+ clones were HLA class II and class I restricted, respectively, and displayed three patterns of reactivity: compound specific, weakly cross-reactive, and strongly cross-reactive. Nitroso dapsone formed dimers in culture and was reduced to dapsone, providing a rationale for the cross-reactivity. T-cell responses to nitroso dapsone were dependent on the formation of a cysteine-modified protein adduct, while dapsone interacted in a labile manner with antigen-presenting cells. CD8+ clones displayed an HLA-B*13:01-restricted pattern of activation. CONCLUSION: These studies describe the phenotype and function of dapsone- and nitroso dapsone-responsive CD4+ and CD8+ T cells from hypersensitive patients. Discovery of HLA-B*13:01-restricted CD8+ T-cell responses indicates that drugs and their reactive metabolites participate in HLA allele-linked forms of hypersensitivity.

Novel artificial intelligence assisted Landsat-8 imagery analysis for mango orchard detection and area mapping.

The mango fruit plays a crucial role in providing essential nutrients to the human body and Pakistani mangoes are highly coveted worldwide. The escalating demand for agricultural products necessitates enhanced methods for monitoring and managing agricultural resources. Traditional field surveys are labour-intensive and time-consuming whereas remote sensing offers a comprehensive and efficient alternative. The field of remote sensing has witnessed substantial growth over time with satellite technology proving instrumental in monitoring crops on a large scale throughout their growth stages. In this study, we utilize novel data collected from a mango farm employing Landsat-8 satellite imagery and machine learning to detect mango orchards. We collected a total of 2,150 mango tree samples from a farm over six months in the province of Punjab, Pakistan. Then, we analyzed each sample using seven multispectral bands. The Landsat-8 framework provides high-resolution land surface imagery for detecting mango orchards. This research relies on independent data, offering an advantage for training more advanced machine learning models and yielding reliable findings with high accuracy. Our proposed optimized CART approach outperformed existing methods, achieving a remarkable 99% accuracy score while the k-Fold validation score also reached 99%. This research paves the way for advancements in agricultural remote sensing, offering potential benefits for crop management yield estimation and the broader field of precision agriculture.

An improved fused feature residual network for 3D point cloud data.

Point clouds have evolved into one of the most important data formats for 3D representation. It is becoming more popular as a result of the increasing affordability of acquisition equipment and growing usage in a variety of fields. Volumetric grid-based approaches are among the most successful models for processing point clouds because they fully preserve data granularity while additionally making use of point dependency. However, using lower order local estimate functions to close 3D objects, such as the piece-wise constant function, necessitated the use of a high-resolution grid in order to capture detailed features that demanded vast computational resources. This study proposes an improved fused feature network as well as a comprehensive framework for solving shape classification and segmentation tasks using a two-branch technique and feature learning. We begin by designing a feature encoding network with two distinct building blocks: layer skips within, batch normalization (BN), and rectified linear units (ReLU) in between. The purpose of using layer skips is to have fewer layers to propagate across, which will speed up the learning process and lower the effect of gradients vanishing. Furthermore, we develop a robust grid feature extraction module that consists of multiple convolution blocks accompanied by max-pooling to represent a hierarchical representation and extract features from an input grid. We overcome the grid size constraints by sampling a constant number of points in each grid using a simple K-points nearest neighbor (KNN) search, which aids in learning approximation functions in higher order. The proposed method outperforms or is comparable to state-of-the-art approaches in point cloud segmentation and classification tasks. In addition, a study of ablation is presented to show the effectiveness of the proposed method.

Evaluation of wind power generation projects to reduce air pollution using multi-criteria decision-making methods in Saudi Arabia.

Currently, Saudi Arabia has very limited renewable energy generation capacity, as most of the country's electricity sector is dependent on cheap fossil fuels. However, in recent years, the Saudi government has announced a national development program called "the Saudi Vision 2030," whereby the country intends to increase the share of renewable energies in its total power generation to 20% by 2030. This research is aimed on the possibility of developing wind farms in Saudi Arabia's Al-Jawf area, which is known to be rich in wind sources. The potential of wind energy in the region was examined in the first phase of the research, which focused at the environmental, economic, and technical aspects. For this goal, the two-parameter Weibull function was used to model wind energy in the area. The economic assessment was performed in terms of the Levelized Cost of Energy and payback period. Multi-criteria decision-making approaches were employed in the second phase of the study to determine the most proper sites for harvesting wind energy in the study region based on eight factors including technical, economic, environmental, and social aspects. The most proper site for wind farms was identified by the combined use of Stepwise Weight Assessment Ratio Analysis and Weighted Aggregated Sum Product Assessment. The results showed that the most proper site for locating wind farms in the study area is the city of Al-Qurrayyat, where, using 1 MW turbines, it will be possible to produce 2357 MWh/year of electricity at a cost of 0.092 $/kWh, resulting in a payback period of 8.1 years. From the environmental perspective, wind power generation in Al-Qurrayyat will result in 1124.15 ton/year of CO2 emissions reduction.

Incorporating CNN Features for Optimizing Performance of Ensemble Classifier for Cardiovascular Disease Prediction.

Cardiovascular diseases (CVDs) have been regarded as the leading cause of death with 32% of the total deaths around the world. Owing to the large number of symptoms related to age, gender, demographics, and ethnicity, diagnosing CVDs is a challenging and complex task. Furthermore, the lack of experienced staff and medical experts, and the non-availability of appropriate testing equipment put the lives of millions of people at risk, especially in under-developed and developing countries. Electronic health records (EHRs) have been utilized for diagnosing several diseases recently and show the potential for CVDs diagnosis as well. However, the accuracy and efficacy of EHRs-based CVD diagnosis are limited by the lack of an appropriate feature set. Often, the feature set is very small and unable to provide enough features for machine learning models to obtain a good fit. This study solves this problem by proposing the novel use of feature extraction from a convolutional neural network (CNN). An ensemble model is designed where a CNN model is used to enlarge the feature set to train linear models including stochastic gradient descent classifier, logistic regression, and support vector machine that comprise the soft-voting based ensemble model. Extensive experiments are performed to analyze the performance of different ratios of feature sets to the training dataset. Performance analysis is carried out using four different datasets and results are compared with recent approaches used for CVDs. Results show the superior performance of the proposed model with 0.93 accuracy, and 0.92 scores each for precision, recall, and F1 score. Results indicate both the superiority of the proposed approach, as well as the generalization of the ensemble model using multiple datasets.

Analyses of Public Attention and Sentiments towards Different COVID-19 Vaccines Using Data Mining Techniques.

COVID-19 is a widely spread disease, and in order to overcome its spread, vaccination is necessary. Different vaccines are available in the market and people have different sentiments about different vaccines. This study aims to identify variations and explore temporal trends in the sentiments of tweets related to different COVID-19 vaccines (Covaxin, Moderna, Pfizer, and Sinopharm). We used the Valence Aware Dictionary and Sentiment Reasoner (VADER) tool to analyze the public sentiments related to each vaccine separately and identify whether the sentiments are positive (compound ≥ 0.05), negative (compound ≤ −0.05), or neutral (−0.05 < compound < 0.05). Then, we analyzed tweets related to each vaccine further to find the time trends and geographical distribution of sentiments in different regions. According to our data, overall sentiments about each vaccine are neutral. Covaxin is associated with 28% positive sentiments and Moderna with 37% positive sentiments. In the temporal analysis, we found that tweets related to each vaccine increased in different time frames. Pfizer- and Sinopharm-related tweets increased in August 2021, whereas tweets related to Covaxin increased in July 2021. Geographically, the highest sentiment score (0.9682) is for Covaxin from India, while Moderna has the highest sentiment score (0.9638) from the USA. Overall, this study shows that public sentiments about COVID-19 vaccines have changed over time and geographically. The sentiment analysis can give insights into time trends that can help policymakers to develop their policies according to the requirements and enhance vaccination programs.

A generative adversarial network for synthetization of regions of interest based on digital mammograms.

Deep learning (DL) models are becoming pervasive and applicable to computer vision, image processing, and synthesis problems. The performance of these models is often improved through architectural configuration, tweaks, the use of enormous training data, and skillful selection of hyperparameters. The application of deep learning models to medical image processing has yielded interesting performance, capable of correctly detecting abnormalities in medical digital images, making them surpass human physicians. However, advancing research in this domain largely relies on the availability of training datasets. These datasets are sometimes not publicly accessible, insufficient for training, and may also be characterized by a class imbalance among samples. As a result, inadequate training samples and difficulty in accessing new datasets for training deep learning models limit performance and research into new domains. Hence, generative adversarial networks (GANs) have been proposed to mediate this gap by synthesizing data similar to real sample images. However, we observed that benchmark datasets with regions of interest (ROIs) for characterizing abnormalities in breast cancer using digital mammography do not contain sufficient data with a fair distribution of all cases of abnormalities. For instance, the architectural distortion and breast asymmetry in digital mammograms are sparsely distributed across most publicly available datasets. This paper proposes a GAN model, named ROImammoGAN, which synthesizes ROI-based digital mammograms. Our approach involves the design of a GAN model consisting of both a generator and a discriminator to learn a hierarchy of representations for abnormalities in digital mammograms. Attention is given to architectural distortion, asymmetry, mass, and microcalcification abnormalities so that training distinctively learns the features of each abnormality and generates sufficient images for each category. The proposed GAN model was applied to MIAS datasets, and the performance evaluation yielded a competitive accuracy for the synthesized samples. In addition, the quality of the images generated was also evaluated using PSNR, SSIM, FSIM, BRISQUE, PQUE, NIQUE, FID, and geometry scores. The results showed that ROImammoGAN performed competitively with state-of-the-art GANs. The outcome of this study is a model for augmenting CNN models with ROI-centric image samples for the characterization of abnormalities in breast images.

Ensemble learning-based feature engineering to analyze maternal health during pregnancy and health risk prediction.

Maternal health is an important aspect of women's health during pregnancy, childbirth, and the postpartum period. Specifically, during pregnancy, different health factors like age, blood disorders, heart rate, etc. can lead to pregnancy complications. Detecting such health factors can alleviate the risk of pregnancy-related complications. This study aims to develop an artificial neural network-based system for predicting maternal health risks using health data records. A novel deep neural network architecture, DT-BiLTCN is proposed that uses decision trees, a bidirectional long short-term memory network, and a temporal convolutional network. Experiments involve using a dataset of 1218 samples collected from maternal health care, hospitals, and community clinics using the IoT-based risk monitoring system. Class imbalance is resolved using the synthetic minority oversampling technique. DT-BiLTCN provides a feature set to obtain high accuracy results which in this case are provided by the support vector machine with a 98% accuracy. Maternal health exploratory data analysis reveals that the health conditions which are the strongest indications of health risk during pregnancy are diastolic and systolic blood pressure, heart rate, and age of pregnant women. Using the proposed model, timely prediction of health risks associated with pregnant women can be made thus mitigating the risk of health complications which helps to save lives.

Transformational leadership and project success: The mediating role of trust and job satisfaction.

Transformational leadership (TFL) impacts on project and organizational success are well established. However, many underlying factors that make TFL effective are still missing. Therefore, we formulated hypotheses and tested the mediating role of trust (TS) and job satisfaction (JS) in linking TFL to project success (PS). A time-lagged methodology was used to collect quantitative data using a structured questionnaire from 326 project manager-team member dyads working in Pakistan's public sector. Our results showed that TS, JS, and TFL significantly impacted project success. Moreover, we found that TS and JS mediate the relationship between TFL and PS. These findings highlight the importance of trust and job satisfaction as mechanisms that translate TFL into the success of projects for organizations.

A novel state space reduction algorithm for team formation in social networks.

Team formation (TF) in social networks exploits graphs (i.e., vertices = experts and edges = skills) to represent a possible collaboration between the experts. These networks lead us towards building cost-effective research teams irrespective of the geolocation of the experts and the size of the dataset. Previously, large datasets were not closely inspected for the large-scale distributions & relationships among the researchers, resulting in the algorithms failing to scale well on the data. Therefore, this paper presents a novel TF algorithm for expert team formation called SSR-TF based on two metrics; communication cost and graph reduction, that will become a basis for future TF's. In SSR-TF, communication cost finds the possibility of collaboration between researchers. The graph reduction scales the large data to only appropriate skills and the experts, resulting in real-time extraction of experts for collaboration. This approach is tested on five organic and benchmark datasets, i.e., UMP, DBLP, ACM, IMDB, and Bibsonomy. The SSR-TF algorithm is able to build cost-effective teams with the most appropriate experts-resulting in the formation of more communicative teams with high expertise levels.

HLA Class-II‒Restricted CD8+ T Cells Contribute to the Promiscuous Immune Response in Dapsone-Hypersensitive Patients.

HLA-B∗13:01 is associated with dapsone (DDS)-induced hypersensitivity, and it has been shown that CD4+ and CD8+ T cells are activated by DDS and its nitroso metabolite (nitroso dapsone [DDS-NO]). However, there is a need to define the importance of the HLA association in the disease pathogenesis. Thus, DDS- and DDS-NO‒specific CD8+ T-cell clones (TCCs) were generated from hypersensitive patients expressing HLA-B∗13:01 and were assessed for phenotype and function, HLA allele restriction, and killing of target cells. CD8+ TCCs were stimulated to proliferate and secrete effector molecules when exposed to DDS and/or DDS-NO. DDS-responsive and several DDS-NO‒responsive TCCs expressing a variety of TCR sequences displayed HLA class-I restriction, with the drug (metabolite) interacting with multiple HLA-B alleles. However, activation of certain DDS-NO‒responsive CD8+ TCCs was inhibited with HLA class-II block, with DDS-NO binding to HLA-DQB1∗05:01. These TCCs were of different origin but expressed TCRs displaying the same amino acid sequences. They were activated through a hapten pathway; displayed CD45RO, CD28, PD-1, and CTLA-4 surface molecules; secreted the same panel of effector molecules as HLA class-I‒restricted TCCs; but displayed a lower capacity to lyse target cells. To conclude, DDS and DDS-NO interact with a number of HLA molecules to activate CD8+ TCCs, with HLA class-II‒restricted CD8+ TCCs that display hybrid CD4‒CD8 features also contributing to the promiscuous immune response that develops in patients.