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BackgroundInborn errors of immunity (IEI) predispose patients to various infectious and non-infectious complications. Thanks to the development and expanding use of flow cytometry and increased awareness, the diagnostic rate of IEI has markedly increased in Algeria the last decade.AimThis study aimed to describe a large cohort of Algerian patients with probable IEI and to determine their clinical characteristics and outcomes.MethodsWe collected and analyzed retrospectively the demographic data, clinical manifestations, immunologic, genetic data, and outcome of Algerian IEI patients - diagnosed in the department of medical immunology of Beni Messous university hospital center, Algiers, from 2008 to 2021.ResultsEight hundred and seven patients with IEI (482 males and 325 females) were enrolled, 9.7% of whom were adults. Consanguinity was reported in 50.3% of the cases and a positive family history in 32.34%. The medium age at disease onset was 8 months and at diagnosis was 36 months. The median delay in diagnosis was 16 months. Combined immunodeficiencies were the most frequent (33.8%), followed by antibody deficiencies (24.5%) and well-defined syndromes with immunodeficiency (24%). Among 287 patients tested for genetic disorders, 129 patients carried pathogenic mutations; 102 having biallelic variants mostly in a homozygous state (autosomal recessive disorders). The highest mortality rate was observed in patients with combined immunodeficiency (70.1%), especially in patients with severe combined immunodeficiency (SCID), Omenn syndrome, or Major Histocompatibility Complex (MHC) class II deficiency.ConclusionThe spectrum of IEI in Algeria is similar to that seen in most countries of the Middle East and North Africa (MENA) region, notably regarding the frequency of autosomal recessive and/or combined immunodeficiencies.

Several Artificial Intelligence-based models have been developed for cancer prediction. In spite of the promise of artificial intelligence, there are very few models which bridge the gap between traditional human-centered prediction and the potential future of machine-centered cancer prediction. In this study, an efficient and effective model is developed for gene selection and cancer prediction. Moreover, this study proposes an artificial intelligence decision system to provide physicians with a simple and human-interpretable set of rules for cancer prediction. In contrast to previous deep learning-based cancer prediction models, which are difficult to explain to physicians due to their black-box nature, the proposed prediction model is based on a transparent and explainable decision forest model. The performance of the developed approach is compared to three state-of-the-art cancer prediction including TAGA, HPSO and LL. The reported results on five cancer datasets indicate that the developed model can improve the accuracy of cancer prediction and reduce the execution time.

Abstract Positioning and location estimation of targets either for mobile or stationary nodes is very important, especially after the astonishing developments of the wired and/or wireless communications, as well as the popularity of handheld devices. To assist in accessing services or managing resources, numerous users, application developers, and service providers require accurate position information. Owing to the boom of multisensory systems, wireless technology, and autonomous systems, many positioning systems have been proposed. These positioning systems differ based on the adopted technology, type of sensory information, communication protocol, algorithm, and accuracy level. These different approaches impose a non-negligible amount of uncertainty and challenges; in such case, fuzzy-logic-based localization systems offer an attractive and feasible approach to overcome some of the reported challenges and handle uncertainties, depending on its proven mathematical framework for handling vagueness and uncertainty. Intuitively, fuzzy sets and fuzzy inference systems are initially intended to incorporate granularity and flexibility of human knowledge, which characterize complex system behaviors without explicitly requiring precise mathematical models or when a complete and exhaustive list of comprehending factors are ill known. This study demonstrates the benefits of fuzzy sets, fuzzy logic, and fuzzy inference systems in wireless positioning problems. Notably, as a way to benefit both the academic community and practitioners of system positioning, we review and investigate various fuzzy-related techniques and methodologies. To the best of our knowledge, this is the first comprehensive review that focuses solely on fuzzy-based positioning systems.

Several Artificial Intelligence-based models have been developed for COVID-19 disease diagnosis. In spite of the promise of artificial intelligence, there are very few models which bridge the gap between traditional human-centered diagnosis and the potential future of machine-centered disease diagnosis. Under the concept of human-computer interaction design, this study proposes a new explainable artificial intelligence method that exploits graph analysis for feature visualization and optimization for the purpose of COVID-19 diagnosis from blood test samples. In this developed model, an explainable decision forest classifier is employed to COVID-19 classification based on routinely available patient blood test data. The approach enables the clinician to use the decision tree and feature visualization to guide the explainability and interpretability of the prediction model. By utilizing this novel feature selection phase, the proposed diagnosis model will not only improve diagnosis accuracy but decrease the execution time as well.

The research focus in remote sensing scene image classification has been recently shifting towards deep learning (DL) techniques. However, even the state-of-the-art deep-learning-based models have shown limited performance due to the inter-class similarity and the intra-class diversity among scene categories. To alleviate this issue, we propose to explore the spatial dependencies between different image regions and introduce patch-based discriminative learning (PBDL) for remote sensing scene classification. In particular, the proposed method employs multi-level feature learning based on small, medium, and large neighborhood regions to enhance the discriminative power of image representation. To achieve this, image patches are selected through a fixed-size sliding window, and sampling redundancy, a novel concept, is developed to minimize the occurrence of redundant features while sustaining the relevant features for the model. Apart from multi-level learning, we explicitly impose image pyramids to magnify the visual information of the scene images and optimize their positions and scale parameters locally. Motivated by this, a local descriptor is exploited to extract multi-level and multi-scale features that we represent in terms of a codeword histogram by performing k-means clustering. Finally, a simple fusion strategy is proposed to balance the contribution of individual features where the fused features are incorporated into a bidirectional long short-term memory (BiLSTM) network. Experimental results on the NWPU-RESISC45, AID, UC-Merced, and WHU-RS datasets demonstrate that the proposed approach yields significantly higher classification performance in comparison with existing state-of-the-art deep-learning-based methods.

Stem cells are a population of undifferentiated cells with self-renewal and differentiation capacities. Normal and cancer stem cells share similar characteristics in relation to their stemness properties. One-carbon metabolism (OCM), a network of interconnected reactions, plays an important role in this dependence through its role in the endogenous synthesis of methionine and S-adenosylmethionine (SAM), the universal donor of methyl groups in eukaryotic cells. OCM genes are differentially expressed in stem cells, compared to their differentiated counterparts. Furthermore, cultivating stem cells in methionine-restricted conditions hinders their stemness capacities through decreased SAM levels with a subsequent decrease in histone methylation, notably H3K4me3, with a decrease in stem cell markers. Stem cells’ reliance on methionine is linked to several mechanisms, including high methionine flux or low endogenous methionine biosynthesis. In this review, we provide an overview of the recent discoveries concerning this metabolic dependence and we discuss the mechanisms behind them. We highlight the influence of SIRT1 on SAM synthesis and suggest a role of PGC-1α/PPAR-α in impaired stemness produced by methionine deprivation. In addition, we discuss the potential interest of methionine restriction in regenerative medicine and cancer treatment.

Medical image captioning is a very challenging task that has been rarely addressed in the literature on natural image captioning. Some existing image captioning techniques exploit objects present in the image next to the visual features while generating descriptions. However, this is not possible for medical image captioning when one requires following clinician-like explanations in image content descriptions. Inspired by the preceding, this paper proposes using medical concepts associated with images, in accordance with their visual features, to generate new captions. Our end-to-end trainable network is composed of a semantic feature encoder based on a multi-label classifier to identify medical concepts related to images, a visual feature encoder, and an LSTM model for text generation. Beam search is employed to ensure the best selection of the next word for a given sequence of words based on the merged features of the medical image. We evaluated our proposal on the ImageCLEF medical captioning dataset, and the results demonstrate the effectiveness and efficiency of the developed approach.

Accurately estimating snow hydrology parameters, including snow coverage mapping and snow depth, plays a significant role in comprehending water resource dynamics, flood forecasting, and environmental management in regions influenced by snow cover. These parameters are critical for hydrological models that simulate snowmelt and runoff, which are essential for predicting water availability and managing water resources in snow-covered areas. Traditional methods for estimating these parameters often rely on manual measurements or simplistic models, which can be inadequate for capturing the complexity of snow-related processes. In recent years, there has been a growing interest in leveraging deep learning techniques for snow hydrology parameter estimation, offering the potential to overcome these limitations. This review paper comprehensively analyzes the current state, challenges, and future directions of image-based approaches in snow hydrology parameter estimation. By harnessing the power of automated methods, particularly deep learning, these approaches demonstrate the ability to capture intricate spatial and temporal relationships present in image data. A comparative analysis between traditional and image-based methods highlights the strengths of automated approaches, including scalability and accuracy. Integration of image sensors, such as satellite imagery and crowd-sourced data, is explored as a crucial component of snow hydrology parameter estimation. Various satellite image sources, including Sentinel 1-2, Landsat, and MODIS, are discussed in terms of their suitability for snow hydrological applications. Despite the promise of image-based approaches, challenges remain, including data availability, model interpretability, and transferability. The paper identifies future research directions, emphasizing the exploration of novel deep learning architectures and uncertainty quantification techniques to address these challenges. In conclusion, this review underscores the importance of image-based approaches for advancing snow hydrology parameter estimation. By addressing challenges and maximizing potential impact, these approaches have the potential to revolutionize snow hydrological modeling and environmental management. [ABSTRACT FROM AUTHOR]

The development of sophisticated solar-blind photodetector devices is being motivated by the increasing need for solar-blind sensors with remarkable photosensitive qualities. This work is a modeling and simulation of electrical and optical properties of a Schottky photodetector based on gallium oxide (Ga2O3), one of the most promising wide-band-gap material. It focuses on the optimization of the device structure design, assumed fully transparent to visible light. In addition, analysis of electrical and optical properties of β-Ga2O3-based photodetector was carried out to accurately describe physical phenomenon through the semiconductor bulk. The impact of several parameters of the device on its performances is such as layer thickness, doping concentration, and electron affinity, anode work function and different cathode contact materials. Besides that, and for more understanding the real behaviors within different layers of the photodetector, diverse optical parameters were varied, for instance the light power intensity and the spectral response by changing the optical wavelength. The obtained results show a promising performance enhancement compared to previous works, for instance the photo to dark current ratio of 3 × 104, the good spectral responsivity in UV light illumination [250–350 nm], in addition to the relatively high detectivity of 2.5 × 109 (Jones). [ABSTRACT FROM AUTHOR]

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