Search

The current Indian stock market structure is fully centralized, with each party's government, financial institutions, and private sector relying on one another to authenticate transactions. Because of the prior party's reliance, the settlement in client Demat accounts takes T + 2. Most of the time, the T + 2 rule causes retail short-term investors or traders to lose money or fail to make a profit. The current settlement method is extremely difficult to comprehend, resulting in anomalies in customer Demat accounts. Blockchain is an immutable append-only data chain that is always developing. The SHA-256 cryptographic technique prevents data from being removed or changed after it has been inserted. There is no central database to maintain the chain; instead, each node stores a copy and processes data locally, updating it in milliseconds. In the form of fresh blocks, new information is added to the chain, ensuring that all parties have the same picture of the blockchain at all times. Everyone has access to the information, so they may verify and confirm transactions, in the end, resulting in complete transparency for all transactions. This work proposes a decentralized stock exchange architecture based on permissioned blockchain to address the T + 2 settlement cycle rule problem. Before designing this architecture it considers every aspect so, it excludes the traditional party name custodian member party because of its traditional functionality so that our architecture becomes cost-efficient at the time of implementation. [ABSTRACT FROM AUTHOR]

The work presents a newly designed penalty function to be added with an existing Cross Entropy based fitness function [3] for optimal selection of multi-level thresholds for image segmentation. The extended fitness function so designed is tested here for Brain magnetic resonance (MR) image segmentation using nature-inspired meta-heuristics such as Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Whale Optimization Algorithm (WOA) and Moth-flame Optimization (MFO), and MFO is finally selected. The proposed method excels in Brain MR image segmentation when tested on WBA database and BrainWeb MR image database with other nature-inspired meta-heuritic based methods. It outperformed the other methods in terms of the three performance metrics Peak Signal to Noise Ratio (PSNR), Structural Similarity Index (SSIM) and Feature Similarity Index (FSIM). The best results shown by the proposed method on WBA database in terms of these metrics - PSNR, SSIM and FSIM - are 29.77, 0.927 and 0.899 respectively. On BrainWeb database, the proposed method yields 26.91, 0.864 and 0.892 for the three respective metrics. [ABSTRACT FROM AUTHOR]

The world population is growing very fast and the lifestyle of human beings is changing with time and place. So, there is a need for disease management which includes disease diagnosis, its detection and classification, cure and lastly for future disease prevention. The outermost protective layer of a human body is the skin. Skin not only impacts a person's health but also psychologically impacts one's life. Computer-aided systems are very helpful in skin disease detection and classification and their application is growing rapidly in healthcare. This literature review paper aims to help the researchers to get a synthesized and appropriate information for the same. We have included papers from 2021 to 2023 for the review from the Scopus database. 45 studies are selected for the review of which 32 studies use deep learning techniques, 11 use machine learning techniques and 2 studies use a hybrid approach. The studies are compared on various parameters like models, datasets, and performance metrics. The work also identified some of the challenges like dealing with noise and also explained disease symptoms. [ABSTRACT FROM AUTHOR]

Software reliability is a far more important factor that influences quality of the software. Software bug identification is a critical aspect of software development method. Software reliability is greatly impacted by the existence of defects in the software, so must anticipate bugs in software. Nevertheless, software bug detection may be insufficiently accurate for practical application, and wide benefits of various may be used. To address these concerns, Modified Deep Neural Network (MDNN) is suggested for predicting software defects. Initially, raw data's are collected and pre-processed by using min–max normalisation that reorganises the data as in database. Then, utilizing principal component evaluation for reduce the dimensionality of pre-processed data. After reducing dimension select appropriate features using correlation based Fuzzy C-means Clustering Method (FCM). First, unnecessary characteristics are removed employing FCM, and then non-redundant features were extracted from every cluster utilising correlations value. After that selected features are given as an input for MDNN. MDNN is developed through optimal selection of weight parameter using Reptile Search Optimization (RSA) algorithm providing error as fitness. Finally, classifier predict bugs in software module which is used to improve software reliability performance is achieved. According to the simulation study, the proposed method achieves 98% accuracy, 0.02% error, 95% specificity, 90% recall, and 95% precision. This indicates that the proposed approach performs better than all prior options. Based on this proposed classification bugs are predicted and software reliability performance is improved. [ABSTRACT FROM AUTHOR]

Optical character recognition is a way of converting scanned images of printed or handwritten documents into machine-encoded text, making it easier to store, browse, retrieve, and process electronic data. In this research, a Printed Amharic Characters Recognition dataset is prepared to train and test a model. Images in the dataset only contain 231 basic Amharic characters that are normalized to 32 × 32 pixels. In this work, a hybrid model of the two super classifiers is developed: the convolutional neural network (CNN) and the support vector machine (SVM). In this novel hybrid CNN-SVM model, CNN works as an automatic feature extractor from the raw images, and then, the extracted feature vectors are given as input to SVM for classification and recognition. A 99.84% accuracy was achieved on the own-prepared dataset and 95.59% accuracy on the benchmark Amharic Optical Character Recognition Database in classifying the testing dataset images. The proposed hybrid CNN-SVM model gave better results than the CNN with a fully connected layer. Moreover, the proposed model outperforms previously existing works attempted by others to recognize printed Amharic characters on the same and different datasets. [ABSTRACT FROM AUTHOR]

Online social networks are becoming more and more popular, according to recent trends. The user's primary concern is the secure preservation of their data and privacy. A well-known method for preventing individual identity in publicly accessible datasets is K-anonymization. Existing anonymity techniques' primary drawback is that they're susceptible to similarity and attribute disclosure attacks. Furthermore, a large amount of data has been lost in the given database. To address this limitation, the proposed model integrated an anonymizing algorithm with Manhattan-based k-member clustering and an enhanced rabbit optimization algorithm (MKCAEROA). The Manhattan distance-based k member (MKCA) clustering algorithm is employed in this case to cluster the initial database. The resulting clusters are then further optimized, and the original database is made anonymous, using the enhanced rabbit optimization algorithm (EROA). In the suggested model, EROA is included to satisfy the specified anonymity constraints while concurrently minimizing the clustering error rate and the generated information loss. When compared to existing approaches, the experimental results demonstrate that the suggested method provides the highest clustering accuracy with the least amount of information loss. The MATLAB workbench will be used to implement the suggested model. [ABSTRACT FROM AUTHOR]

Because the complete decompression of images encoded through the HEVC standard is time-consuming, image retrieval encoded through the HEVC standard is one of the most challenging topics in existing database management. Our proposed method is a content-based image retrieval method in the compressed domain, leveraging feature vectors constructed from the I-frame information encoded in HEVC. We introduce a novel concept called auto-correloblock to form these feature vectors. These feature vectors, which represent HEVC coded images in the compressed domain, are based on block sizes and intra-prediction modes. In our method, we utilize the feature vector of the query image to generate a histogram, which is then compared to the feature vectors of images stored in the database. To evaluate our approach, we conduct experiments using the INRIA HOLIDAY database. The results demonstrate the effectiveness of our proposed method, achieving an ANMRR (Average Normalized Modified Retrieval Rank) of 0.23 in coded image retrieval and outperforms its counterparts in this domain. [ABSTRACT FROM AUTHOR]

We introduce a deep learning-driven framework for creating an adaptably applicable importance map (A2R-Map) that can be integrated with existing image and video retargeting operators. A conventional retargeting algorithm uses a heuristic approach to seek an off-the-self algorithm used into their retargeting system. The extracted importance map of the image does not match the characteristics of the input image; therefore, it affects the retargeting results and limits the performance of the retargeting method. Our designed framework attempts to minimize the artifacts/distortions caused by inappropriate energy, e.g., the shrunk phenomenon in warping-based results and carving-through-object distortion in the seam carving-based approach. Our proposed framework focuses on capturing sensitive distortion regions and activating their energy to solve this challenge. We verify the effectiveness of our proposed scheme by plugging it in three typical retargeting methods: seam carving-based, warping-based for image, and video retargeting. Extensive experiments and evaluations are conducted on two widely used databases. On the one hand, A2R-Map significantly reduces the time of importance map generation in retargeting systems to ∼ 9 times compared to the baseline saliency map. On the other hand, our A2R-Map achieves improvement over the baseline methods with an average of 11% and 9% in terms of image and video quality, respectively. The experimental results and evaluations demonstrate that our strategy for A2R-Map substantially outperforms the previous works and significantly boosts the visual quality of video/image retargeting. [ABSTRACT FROM AUTHOR]

In recent years, DNA microarray is a recent research technique aimed at classifying gene expression. This paper is to develop Optimal Bidirectional Long Short-Term Memory with Self Attention Mechanism (OBiLSTM-SAM) for gene expression data classification. In the initial phase, the database is gathered from online sources. The projected technique is proceeding with three stages pre-processing, Gene extraction, and classification. In the pre-processing phase, the MIN–MAX normalization techniques are considered. After that, gene extraction is achieved with the consideration of Mutual Information Maximization (MIM). The extracted data will be utilized aimed at gene expression data classification. In the classification stage, the OBiLSTM-SAM is utilized. The OBiLSTM-SAM is a grouping of Bidirectional Long Short-Term Memory with Self Attention Mechanism (BiLSTM-SAM) and Remora Optimization Algorithm (ROA). In the BiLSTM-SAM, the weight parameters are selected with the assistance of ROA. Based on the ODSNN, gene expression data classification is achieved. The presentation of the proposed methodology is evaluated in f-measure, recall, precision, specificity, sensitivity, and accuracy. To validate the projected technique, it is compared with optimized long short-term memory, algorithm (OLSTM) and Adaptive Salp Swarm Optimization algorithm (ASSA) respectively. [ABSTRACT FROM AUTHOR]

The evaluation of uterine contraction offers significant information regarding the progression of labour. The occurrence of deliveries before the expected dates leads to undesirable consequences for the mother and fetus. Electrohysterography (EHG) is a non-invasive monitoring scheme generally preferred to detect preterm delivery and reduce the hostile consequences. This paper proposed a novel term and preterm prediction method for predicting preterm deliveries by employing EHG signals. The proposed scheme involves three phases: pre-processing, feature extraction and prediction. Initially, the acquired EHG signals are pre-processed using a band pass filter and wavelet transform to remove noise and artefacts from the EHG signal. The second stage is feature extraction, where the representative features, including Shannon energy, median frequency, time-varying centroid frequency, etc., are extracted. At last, the prediction is carried out using an enhanced sheep flock optimized hybrid extreme artificial neural learning network (ESFHEANL). ESFHEANL is the combination of a hybrid extreme artificial neural learning network (HEANL) and enhanced sheep flock optimization (ESFO) algorithm. It assists in diagnosing the term and preterm birth more accurately. The proposed scheme is implemented in the Python platform and assessed the performance in terms of accuracy, recall, specificity and f-measure using the term-preterm EHG (TPEHG) database. Finally, the experimental outcomes evidenced that the proposed scheme achieved better performance and was employed to diagnose term and preterm births accurately. [ABSTRACT FROM AUTHOR]

Facial recognition is a rapidly growing field with applications in security, surveillance, and human-computer interaction. The performance of the Histogram of Oriented Gradients (HOG) algorithm, a popular feature extraction method for facial recognition, heavily relies on its parameter settings. Optimizing these parameters is crucial for achieving high accuracy. In this paper, we propose a novel approach to optimize HOG algorithm parameters and image size for facial recognition using a Tree-structured Parzen Estimator (TPE)-based Bayesian optimization technique. This optimization process effectively harnesses machine learning performance metrics as the objective function, enabling the algorithm to adapt and enhance its feature extraction capabilities. Extensive experiments conducted across multiple databases, including the AR Face Database, Extended Yale B Database, ORL Database, Yale Face Database, and Facepix, have yielded an exceptional recognition accuracy of 100%. Our optimized handcrafted HOG algorithm highlights the potential and continued relevance of parameter-based handcrafted feature extraction algorithms in computer vision, particularly in resource-constrained environments or applications requiring interpretability. [ABSTRACT FROM AUTHOR]

Advancements in Machine Learning (ML) and Computer Vision have led to notable improvements in the detection of breast cancer. However, the accuracy of the classifier is limited due to imbalanced datasets that cause overfitting. Thus, additional images are needed to improve the classifier's performance. Generative Adversarial Networks (GAN) are used for image augmentation. Still, its limitations, such as the random use of different types of GAN, can lead to a too-good discriminator, causing generator training to fail due to vanishing gradients. Therefore, selecting the appropriate GAN model for the given scenario is crucial for optimal performance. This paper proposes a novel Smart Generative Adversarial Network (Smart GAN) architecture to develop an efficient and computational classification model for a limited imbalanced dataset. Smart GAN uses a three-fold approach. Different types of GAN augment the dataset in the first phase of experimental work, and their evaluation metrics are calculated. In the second phase, the metric scores are used as rewards for the Reinforcement learning model (Q-learning approach). The best augmentation is chosen based on the best Q-values for each metric score. It compares three different Convolutional Neural Networks (CNN) and selects the best-suited network to classify the augmented datasets. The proposed Smart GAN architecture outperforms other existing approaches by giving a better accuracy of 89.62% and 89.91% on Mammographic Image Analysis Society (MIAS) and Digital Database for Screening Mammography (DDSM) augmented datasets, respectively, representing an approximately 10% increment in detection rate compared to non-augmented datasets. [ABSTRACT FROM AUTHOR]

This paper examines the problem of unobserved heterogeneity in urban water demand. It uses a panel quantile regression (QR) approach to focus on segments of consumers with different levels of water consumption. This estimation strategy is applied to a rich set of panel microdata capturing the consumption of water for 4,023 households in Valencia (Spain) between the years 2009 and 2011. To capture heterogeneity in a city's residential household water consumption, a QR approach is applied to the specified water demand model, enabling analysis for different quantiles (levels) of consumption. The QR shows the behaviour of the parameters for different consumption levels. It enables differentiation of consumer reactions to different independent variables at each quantile of the distribution of the dependent variable. The results provide strong evidence of unobserved heterogeneity at different levels. This approach is useful in that it can lead to better-informed tariff design by providing an understanding of heterogeneity in price elasticities. [ABSTRACT FROM AUTHOR]

Acute kidney injury (AKI) due to vitamin D therapy for osteoporosis is encountered in clinical practice, but epidemiological studies are scarce. We aimed to determine the association between AKI and vitamin D therapy and to identify risk factors for AKI using the Japanese Adverse Drug Event Report database. We used reporting odds ratios (RORs) to detect signals and evaluate risk factors using multiple logistic regression analysis. Among 298,891 reports from April 2004 to September 2023, 1071 implicated active vitamin D3 analogs as suspect drugs for adverse events. There was a significant association between AKI and active vitamin D3 analogs (ROR [95% confidence interval {CI}], eldecalcitol: 16.75 [14.23–19.72], P < 0.001; alfacalcidol: 5.29 [4.07–6.87], P < 0.001; calcitriol: 4.46 [1.88–10.59], P < 0.001). The median duration of administration before AKI onset was 15.4 weeks. Multiple logistic regression analysis showed a significant association between AKI and age ≥ 70 years (odds ratio [95% CI], 1.47 [1.04–2.07]; P = 0.028), weight < 50 kg (1.55 [1.12–2.13]; P = 0.007), hypertension (1.90 [1.42–2.54]; P < 0.001), and concomitant use of nonsteroidal anti-inflammatory drugs (1.58 [1.10–2.25], P = 0.012) and magnesium oxide (1.96 [1.38–2.78]; P < 0.001). Our results suggest that active vitamin D3 analogs are associated with AKI development. Physicians prescribing these medications to patients with risk factors should consider the possibility of AKI, especially during the first 6 months. [ABSTRACT FROM AUTHOR]

Single-cell RNA sequencing (scRNA-seq) has emerged as a pivotal tool for exploring cellular landscapes across diverse species and tissues. Precise annotation of cell types is essential for understanding these landscapes, relying heavily on empirical knowledge and curated cell marker databases. In this study, we introduce MarkerGeneBERT, a natural language processing (NLP) system designed to extract critical information from the literature regarding species, tissues, cell types, and cell marker genes in the context of single-cell sequencing studies. Leveraging MarkerGeneBERT, we systematically parsed full-text articles from 3702 single-cell sequencing-related studies, yielding a comprehensive collection of 7901 cell markers representing 1606 cell types across 425 human tissues/subtissues, and 8223 cell markers representing 1674 cell types across 482 mouse tissues/subtissues. Comparative analysis against manually curated databases demonstrated that our approach achieved 76% completeness and 75% accuracy, while also unveiling 89 cell types and 183 marker genes absent from existing databases. Furthermore, we successfully applied the compiled brain tissue marker gene list from MarkerGeneBERT to annotate scRNA-seq data, yielding results consistent with original studies. Conclusions: Our findings underscore the efficacy of NLP-based methods in expediting and augmenting the annotation and interpretation of scRNA-seq data, providing a systematic demonstration of the transformative potential of this approach. The 27323 manual reviewed sentences for training MarkerGeneBERT and the source code are hosted at https://github.com/chengpeng1116/MarkerGeneBERT. [ABSTRACT FROM AUTHOR]

Purpose: To report the oncological outcomes and the tolerance between 6 instillations and more than 6 cycles of hyperthermic intravesical chemotherapy(HIVEC) in patients with non-muscle invasive bladder cancer(NMIBC). Methods: This is a multicenter retrospective study from a national database including 9 expert centers. All patients treated with HIVEC between 2016 and 2023 for NMIBC were included. Patients were classified into two groups according to the total number of HIVEC instillations, including induction plus maintenance. Kaplan-Meier curves were computed to present survival outcomes. Results: 261 patients with a median follow-up of 25.5 months were included. 199(76.2%) and 62(23.8%) were treated by 6 and more than 6 cycles of HIVEC, respectively. The 2-years RFS(40.2% vs. 34.4%,p = 0.3) and the 2-years PFS(86% vs. 87%,p = 0.85) were similar between group treated with 6 and more than 6 instillations. 2-years CSS and OS were also similar between both groups. Univariate Cox regression showed no association between the number of bladder instillation and RFS (HR = 1.2 95%CI[0.8–1.84], p = 0.3) or PFS (HR = 0.8 95%CI[0.29–2.02], p = 0.2). In the group treated with more than 6 cycles, 2-years RFS and 2-years PFS were similar between patients who received induction plus maintenance compared to those treated with induction only. Finally, hematuria and urinary burning were significantly higher in the group treated by more than 6 cycles (21% vs. 8.5%(p < 0.01),and 29% vs. 17% (p = 0.03), respectively). Serious side effects(grade ≥ 3) are rare(3.1%) and similar in both groups. Conclusions: Results show no significant difference in two years RFS, PFS, CSS and OS according to number of instillations received, while toxicity profile seems better in the group receiving six instillations only. [ABSTRACT FROM AUTHOR]

Introduction: The aim of this study is to compare outcomes of SRP (salvage radical prostatectomy) with SCAP (salvage cryoablation of the prostate) in local radio-recurrent PCa (prostate cancer) patients. Materials and methods: A retrospective analysis of a multicentric European Society of Uro-technology (ESUT) database was performed. Data on patients with local recurrent PCa after radiotherapy who underwent salvage treatment were collected. Patients and their respective disease characteristics, perioperative complications as well as oncological outcomes were then described. The treatment success rate was defined as PSA nadir < 0,4 ng/ml. Any complications were graded according to the modified Clavien system. A descriptive and comparative analysis was performed using SPSS software. Results: 25 patients underwent SRP and 71 patients received SCAP. The mean follow-up was 24 months. The median PSA level before initial treatment was 8.3 (range 7-127) ng/ml. The success rates of SRP and SCAP were largely comparable (88% (22 patients) vs. 67.7% (48 patients), respectively, p = 0.216). The mean serum PSA levels at 12 months after salvage treatment were 1.2 ± 0.2 ng/mL vs. 0.25 ± 0.5 ng/mL, p > 0.05). During the follow-up period, only 3 (12%) patients in the SRP group had PSA recurrence compared with 21 patients (29.6%) in the SCAP group. The 5-year BRFS was similar (51,6% and 48,2%, p = 0,08) for SRP and SCAP respectively. The 5-year overall survival rate was 91.7%, and 89,7% (p = 0.669) and the 5-year cancer-specific survival was 91.7%, and 97,1% (p = 0.077), after SRP and SCAP respectively. No difference was found regarding the complications. Conclusions: Both SRP and SCAP should be considered as valid treatment options for patients with local recurrence of PCa after radiotherapy. SCAP has a potentially lower risk of morbidity and acceptable intermediate-term oncological efficacy, but a longer follow up and a higher number of patients is ideally needed to draw any long-term conclusions regarding the oncological data. [ABSTRACT FROM AUTHOR]

Fatty acid-binding protein 4 (FABP4) plays an essential role in metabolism and inflammation. However, the role of FABP4 in alcoholic steatohepatitis (ASH) remains unclear. This study aimed to investigate the function and underlying mechanisms of FABP4 in the progression of ASH. We first obtained alcoholic hepatitis (AH) datasets from the National Center for Biotechnology Information–Gene Expression Omnibus database and conducted bioinformatics analysis to identify critical genes in the FABP family. We then established ASH models of the wild-type (WT) and Fabp4-deficient (Fabp4−/−) mice to investigate the role of FABP4 in ASH. Additionally, we performed transcriptional profiling of mouse liver tissue and analyzed the results using integrative bioinformatics. The FABP4-associated signaling pathway was further verified. FABP4 was upregulated in two AH datasets and was thus identified as a critical biomarker for AH. FABP4 expression was higher in the liver tissues of patients with alcoholic liver disease and ASH mice than in the corresponding control samples. Furthermore, the Fabp4−/− ASH mice showed reduced hepatic lipid deposition and inflammation compared with the WT ASH mice. Mechanistically, Fabp4 may be involved in regulating the p53 and sirtuin-1 signaling pathways, subsequently affecting lipid metabolism and macrophage polarization in the liver of ASH mice. Our results demonstrate that Fabp4 is involved in the progression of ASH and that Fabp4 deficiency may ameliorate ASH. Therefore, FABP4 may be a potential therapeutic target for ASH treatment. [ABSTRACT FROM AUTHOR]

Diabetic nephropathy (DN) is one of the most important comorbidities of diabetic patients, which places large physiological and economic burdens on patients. Safflower yellow, a natural pigment extracted from the petals of safflower, has been put into adjuvant therapy. Databases including Chinese National Knowledge Infrastructure (CNKI), Wanfang Database, MEDLINE and etc. will be searched for relevant articles. A meta-analysis was carried out to assess the efficacy and safety of safflower yellow adjuvant to conventional treatment regimen using mean differences (MD) and rate ratios (RR). A cost-effectiveness analysis was also conducted based on the result of meta-analysis. Finally, 28 articles involving 2251 patients were included in meta-analysis. The results showed that compared with conventional treatment, the fasting blood-glucose (FBG) [MD = 0.40], urinary albumin ejection rate (UAER) within 24 h [MD = 48.16], serum creatinine (Scr) [MD = 9.63], blood urea nitrogen (BUN) [MD = 1.73] were significantly lower and the clinical efficacy [RR = 1.28] was more remarkable in safflower yellow adjuvant to conventional treatment group. Our analysis suggested that safflower yellow adjuvant to conventional treatment regimen not only had better clinical efficacy but more cost-effective than conventional treatment regimen. [ABSTRACT FROM AUTHOR]

Aquatic foods are highly traded, with nearly 60 million tonnes exported in 2020, representing 11% of global agriculture trade by value. Despite the vast scale, basic characteristics of aquatic food trade, including species, origin, and farmed vs wild sourcing, are largely unknown due to the reporting of trade data. Consequently, we have a coarse picture of aquatic food trade and consumption patterns. Here, we present results from a database on species trade that aligns production, conversion factors, and trade to compute apparent consumption for all farmed and wild aquatic foods from 1996 to 2020. Over this period, aquatic foods became increasingly globalized, with the share of production exported increasing by 40%. Importantly, trends differ across aquatic food sectors. Global consumption also increased by 19.4% despite declining marine capture consumption, and some regions became increasingly reliant on foreign-sourced aquatic foods. To identify sustainable diet opportunities among aquatic foods, our findings, and underlying database enable a greater understanding of the role of trade in rapidly evolving aquatic food systems. Despite high levels of trade, the basic characteristics of the aquatic food trade are largely unknown. Here, the authors present a global seafood trade database showing the increasing globalization of farmed and wild aquatic foods. [ABSTRACT FROM AUTHOR]

Multiple object tracking (MOT), as a typical application scenario of computer vision, has attracted significant attention from both academic and industrial communities. With its rapid development, MOT has becomes an hot topic. However, maintaining robust MOT in complex scenarios still faces significant challenges, such as irregular motion patterns, similar appearances, and frequent occlusions. Based on an extensive investigation into the state-of-the-art MOT, this survey has made the following efforts: 1) listing down preceding MOT approaches and current classifications; 2) surveying the MOT metrics and benchmark databases; 3) evaluating the MOT approaches frequently employed; 4) discussing the main challenges for MOT; and 5) putting forward potential directions for the development of future MOT approaches. By doing so, it strives to provide a systematic and comprehensive overview of existing MOT methods from SDE to TBA perspectives, thereby promoting further research into this emerging and important field. [ABSTRACT FROM AUTHOR]

Non-small cell lung cancer (NSCLC) constitutes a significant portion of lung cancers and cytotoxic drugs (e.g. cisplatin) are currently the first-line treatment. However, NSCLC has developed resistance to this drug, which limits the therapeutic effect and thus affects prognosis. NSCLC sc-RNA-seq data were downloaded from the GEO database and Ku Leuven Laboratory for Functional Epigenetics, and bulk RNA-seq data were obtained from the TCGA database. The "Seurat" package was employed for scRNA-seq data processing, and the uniform manifold approximation and projection (UMAP) were applied for downscaling and cluster identification. Use the FindAllMarkers function to find differential genes (DEGs) for tumor stem cells. Then, we performed univariate regression analyses on the DEGs to identify potential prognostic genes. We created a machine learning framework based on potential prognostic genes, which combines 10 machine learning methods and their 101 combinations to get the optimal prognostic risk model. The model was evaluated in the training set and validation set. A nomogram was developed to provide physicians with a quantitative tool for prognosis prediction. Finally, we evaluated the expression and functionality of SLC2A1. We discovered 22 cell clusters containing 218379 cells by examining single-cell RNA sequencing datasets (GSE148071, KU_lom, GSE131907, GSE136246, GSE127465). Tumour cells were isolated for subpopulation analysis and 162 differential genes from SOX2_cancer were obtained. After univariate Cox analysis, we found 23 genes with prognostic potential prognostic value and utilized them to develop 101‑combination machine learning computational framework. We eventually picked the best performing 'StepCox[both] + RSF', which includes 8 genes. The model has a relatively high prediction accuracy in both TCGA and GEO datasets. In in vitro investigations, targeted suppression of the SLC2A1 gene resulted in significant reductions in proliferation, invasion and migration in A549 cells. In addition, a significant reduction in cisplatin resistance was seen in A549/DDP cells. The outcomes demonstrated the precision and credibility of the prognostic model for NSCLC, highlighting its potential significance in the treatment and prognosis of individuals affected by this disease. SLC2A1 may become a promising prognostic marker and a potential therapeutic target, offering valuable insights to inform clinical treatment decisions. [ABSTRACT FROM AUTHOR]

1,4-Naphthoquinone scaffold-derived compounds has shown considerable pharmacological properties against cancer, including acute myeloid leukemia (AML) However, its impact and mechanisms in AML are uncertain. In this study, the mechanisms of 1,4-naphthoquinone scaffold-derived compounds against AML were investigated via network pharmacology, molecular docking and molecular dynamics simulation. ASINEX database was used to collect the 1,4-naphthoquinone scaffold-derived compounds, and compounds were extracted from the software to evaluate their drug similarity and toxicity. The potential targets of compounds were retrieved from the SwissTargetPrediction Database and the Similarity Ensemble Approach Database, while the potential targets of AML were obtained from the GeneCards databases and Gene Expression Omnibus. The STRING database was used to construct a protein–protein interaction (PPI) network, topologically and Cyto Hubb plugin of Cytoscape screen the central targets. After selecting the potential key targets, the gene ontology (GO) function annotation and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis were performed for the intersection targets, and a network map of "compounds-potential targets-pathway-disease" were constructed. Molecular docking of the compounds with the core target was performed, and core target with the strongest binding force and 1,4-naphthoquinone scaffold-derived compounds was selected for further molecular dynamics simulation and further molecular mechanics/Poisson–Boltzmann surface area (MM/PBSA) approach verification. In addition, the Bloodspot database was applied to perform the overall survival of core targets. A total of 19 1,4-naphthoquinone scaffold-derived compounds were chosen out, and then 836 targets of compounds, 96 intersection targets of AML were screened. Core targets include STAT3, TLR4, HSP90AA1, JUN, MMP9, PTPRC, JAK2, PTGS2, KIT and CSF1R. GO functional enrichment analysis revealed that 90 biological processes, 10 cell components and 12 molecular functions were enriched while KEGG pathway enrichment analysis revealed 34 enriched signaling pathways. Analysis of KEGG enrichment hinted that these 10 core genes were located in the pathways in cancer, suggesting that 1,4-naphthoquinone scaffold-derived compounds had potential activity against AML. Molecular docking analysis revealed that the binding energies between 1,4-naphthoquinone scaffold-derived compounds and the core proteins were all higher than − 6 kcal/mol, indicating that the 10 core targets all had strong binding ability with compounds. Moreover, a good binding capacity was inferred from molecular dynamics simulations between compound 7 and MMP9. The total binding free energy calculated using the MM/GBSA approach revealed values of − 6356.865 kcal/mol for the MMP9-7 complex. In addition, Bloodspot database results exhibited that HSP90AA1, MMP9 and PTPRC were associated with overall survival. The findings provide foundations for future studies into the interaction underlying the anti-AML potential of compounds with 1,4-naphthoquinone-based scaffold structures. Compounds with 1,4-naphthoquinone-based scaffold structures exhibits considerable potential in mitigating and treating AML through multiple targets and pathways. [ABSTRACT FROM AUTHOR]

Parkinson's is the second most common neurodegenerative disease, affecting nearly 8.5M people and steadily increasing. In this research, Multimodal Deep Learning is investigated for the Prodromal stage detection of Parkinson's Disease (PD), combining different 3D architectures with the novel Excitation Network (EN) and supported by Explainable Artificial Intelligence (XAI) techniques. Utilizing data from the Parkinson's Progression Markers Initiative, this study introduces a joint co-learning approach for multimodal fusion, enabling end-to-end training of deep neural networks and facilitating the learning of complementary information from both imaging and clinical modalities. DenseNet with EN outperformed other models, showing a substantial increase in accuracy when supplemented with clinical data. XAI methods, such as Integrated Gradients for ResNet and DenseNet, and Attention Heatmaps for Vision Transformer (ViT), revealed that DenseNet focused on brain regions believed to be critical to prodromal pathophysiology, including the right temporal and left pre-frontal areas. Similarly, ViT highlighted the lateral ventricles associated with cognitive decline, indicating their potential in the Prodromal stage. These findings underscore the potential of these regions as early-stage PD biomarkers and showcase the proposed framework's efficacy in predicting subtypes of PD and aiding in early diagnosis, paving the way for innovative diagnostic tools and precision medicine. [ABSTRACT FROM AUTHOR]

We report a novel deep learning (DL) method for classifying inorganic compounds using 3D electron density data. We transform Density Functional Theory (DFT)-derived CHGCAR files from the Materials Project (MP) and experimental data from the Inorganic Crystal Structure Database (ICSD) into point clouds and sparse tensors, optimized for use in DL models such as PointNet and Sparse 3D CNN. This approach effectively overcomes the limitations of handling the dense 3D data, a common challenge in DL. Contrasting with traditional 1D or 2D X-ray diffraction (XRD) patterns that necessitate complex reciprocal space analysis, our method utilizes 3D density data for direct interpretation in real lattice space. This shift significantly enhances classification accuracy, outperforming traditional XRD-driven DL methods. We achieve accuracies of 97.28%, 90.77%, and 90.10% for crystal system, extinction group, and space group classifications, respectively. Our 3D electron density-based DL approach not only showcases improved accuracy but also contributes a more intuitive and effective framework for materials discovery. [ABSTRACT FROM AUTHOR]

Fuel moisture content (FMC) is important for the ignitability, behaviour and severity of wildfires. Understanding the drivers of FMC and its spatial and temporal variability can help us develop fuel moisture models and inform assessments of wildfire behaviour and danger. Here we present the first United Kingdom (UK) national-scale temperate FMC dataset of 8,057 samples of eighteen different fuel constituents collected across 58 sampling sites between 2021–2023. We sampled fuels across emerging fire-prone ecosystems in the UK across three studies: (1) UK-wide longer-term sampling characterising the spatio-temporal drivers of FMC; (2) landscape-scale measurement through the North Yorkshire Moors to investigate landscape-driven variability in FMC; (3) plot-scale intensive sampling in the West Midlands to quantify diurnal patterns and among-sampler variability in fuel measurements. This database addresses a global fuel moisture measurement gap within traditionally non-fire prone regions. The database will advance our understanding of temperate fuel moisture dynamics and forms a fundamental contribution towards the development of a fire danger rating system for traditionally non-fire prone regions such as the UK. [ABSTRACT FROM AUTHOR]

This paper presents a collection of datasets holding information on the energy and climate action plans of 6,850 municipalities, taking part in the transnational initiative of the Global Covenant of Mayors (GCoM). This collection includes commitments for reducing net GHG emissions by at least 20% by 2020, 55% by 2030 and becoming climate neutral by 2050. The signatories commit to addressing any of the three pillars of the initiative, namely climate change mitigation, adaptation and energy access. Following two previous releases, the third release of the GCoM collection is introduced, with closing date September 2022. The datasets include information on the action plans and monitoring reports as they are self-reported by signatories, undergoing a quality-harnessing procedure before publication. Additionally, an external comparison is developed with the Emissions Database for Global Atmospheric Research (EDGAR v7), controlling for comparable sources and activity sectors, ensuring the usability of the GCoM datasets for relevant research on local policies and their effects on reducing the impact of climate change. [ABSTRACT FROM AUTHOR]

The aim of this study was to construct the sixth in a series of guidelines on the treatment of urolithiasis by the International Alliance of Urolithiasis (IAU) that by providing a clinical framework for the management of pediatric patients with urolithiasis based on the best available published literature. All recommendations were summarized following a systematic review and assessment of literature in the PubMed database from January 1952 to December 2023. Each generated recommendation was graded using a modified GRADE methodology. Recommendations are agreed upon by Panel Members following review and discussion of the evidence. Guideline recommendations were developed that addressed the following topics: etiology, risk factors, clinical presentation and symptoms, diagnosis, conservative management, surgical interventions, prevention, and follow-up. Similarities in the treatment of primary stone episodes between children and adults, incorporating conservative management and advancements in technology for less invasive stone removal, are evident. Additionally, preventive strategies aiming to reduce recurrence rates, such as ensuring sufficient fluid intake, establishing well-planned dietary adjustments, and selective use pharmacologic therapies will also result in highly successful outcomes in pediatric stone patients. Depending on the severity of metabolic disorders and also anatomical abnormalities, a careful and close follow-up program should inevitably be planned in each pediatric patient to limit the risk of future recurrence rates. [ABSTRACT FROM AUTHOR]

Bioinoculants of Sphingobium indicum B90A have been used to decontaminate hexachlorocyclohexane (HCH)-contaminated soils in the past. There is no selective or convenient method available to track the added B90A in HCH-contaminated soils in the presence of several native sphingomonads. Here, we describe a method, BioMarkTrack, for tracking B90A bioinoculant by simple amplification of the B90A specific biomarker genes. Whole-genome sequence data of 120 different genera of sphingomonads (Sphingobium, Novosphingobium, Sphingomonas, Sphingopyxis, and Sphingosinicella) were retrieved from the NCBI database and annotated. Intra- and inter-genus similarity searches, including the genome of B90A as a reference was conducted. 122 unique gene sequences were identified in strain B90A, out of which 45 genes were selected that showed no similarity with the NCBI non-redundant (NR) database or gene sequences in the publicly available database. Primers were designed for amplification of 4 biomarkers. To validate the biomarkers B90A tracking efficacy in bioaugmented soils, a microcosm study was conducted in which sterile garden and HCH-contaminated dumpsite soils were amended with strain B90A. Amplification of the biomarker was observed both in sterile garden soil and HCH-contaminated dumpsite soil but not in control (lacking B90A) samples. Further, the primer set was used to track B90A in a bioremediation field trial soil, demonstrating the convenience and efficiency of the simple PCR-based method, which can be employed for tracking B90A in bioaugmented soils. The approach as presented here can be employed on different bioinoculants to identify unique biomarkers and then tracking these organisms during bioremediation. [ABSTRACT FROM AUTHOR]

Purpose: This review aims to investigate the effects of the Gufoni maneuver on horizontal semicircular canal benign paroxysmal positional vertigo (HC-BPPV). Methods: A comprehensive search, including PubMed, PEDro, REHABDATA, SCOPUS, EMBASE, and Web of Science, was conducted to determine randomized clinical trials (RCTs) studying the effects of the Gufoni maneuver for HC-BPPV from inception to March 1, 2024. The quality of the included studies was estimated using the Physiotherapy Evidence Database (PEDro) scale. Results: Ten randomized controlled trials (RCTs) were included in this review with a total of 1025 HC-BPPV patients (mean age = 58.58 years; 63% female; 55% right-side HC-HBPPV; 49% geotropic HC-BPPV). The included RCTs ranged from 4 to 9 out of 10 (median = 6.5) on the PEDro scale. The included studies showed that the Gufoni maneuver revealed efficacy when compared to the sham maneuver but not when compared to other maneuvers such as the Barbecue roll maneuver, the Appiani maneuver, the Mastoid oscillation, the head shaking, and the modified Gufoni maneuver. Conclusions: The Gufoni maneuver is considered an option for treating patients with geotropic or apogeotropic HC-BPPV. Precise diagnosis of the BPPV, the subtype of HC-BPPV, symptom duration, history of previous BPPV attacks, the applied methods of maneuver and the proficiency of the clinician performing the maneuver, proper diagnosis, presence of any underlying health conditions are critical for successful treatment. Further studies are strongly warranted. [ABSTRACT FROM AUTHOR]

Purpose: This study investigated the impacts of the number of positive lymph nodes (NPLN) and lymph node ratio (LN ratio) for patients with hypopharyngeal squamous cell carcinoma (HPSCC) based on SEER database, which were validated in the real-world data of China. Methods: A total of 520 patients from SEER database were analyzed. Then 195 patients with pathologically stage III or IV HPSCC in our center were retrospectively studied. Results: In the SEER database, NPLN ≥ 3 was found in 36.9% of patients. Multivariate analysis revealed that LN ratio ≥ 0.138 was significant with poorer overall survival (OS) (hazard ratio [HR] = 1.525, p = 0.001) and cancer-specific survival (CSS) (HR = 1.697, p < 0.001), so was the NPLN ≥ 3 (HR = 1.388, p = 0.013; HR = 1.479, p = 0.008). Patients with NPLN ≥ 3 were found in 103 (52.8%) in our center. Multivariate analysis confirmed a significant association regarding OS (p = 0.005) or CSS (p = 0.003) between patients with LN ratio ≥ 0.138 or not. In addition, disease recurrence rate differed significantly between the patients with NPLN ≥ 3 (27.2%) and NPLN < 3 (14.1%, p = 0.026). Moreover, postoperative chemoradiotherapy (CCRT) was significantly associated with better prognosis in patients with NPLN ≥ 3. Conclusion: In the SEER database, NPLN ≥ 3 and LN ratio ≥ 0.138 were independent poor prognostic factors for patients with HPSCC. Whereas identifying worldwide cut-off values for LN ratio is difficult and surgeon-dependent. In our cohort, adjuvant CCRT was beneficial for OS in patients with NPLN ≥ 3. [ABSTRACT FROM AUTHOR]

Generalized pustular psoriasis (GPP) is a rare, chronic and potentially life-threatening autoinflammatory skin disease characterized by widespread eruption of sterile pustules, with or without systemic inflammation. GPP can significantly reduce patients' quality of life (QoL). Several therapeutic approaches have been described in the literature, but there is no consensus on optimal treatment. In this review, we summarize published literature on efficacy, safety and QoL outcomes associated with current treatment of GPP with both approved and non-approved products. Embase and MEDLINE databases were searched (1980–September 2023). A search protocol was designed in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines and registered on the PROSPERO database (CRD42021215437). Details on publication, population, intervention, efficacy, safety and QoL were captured and checked by independent reviewers. In total, 118 publications were included, with only 19% of publications reporting on the results of clinical trials. Treatment modalities reported for GPP included non-biologic systemic therapies such as retinoids, cyclosporine and methotrexate, topical agents, biologics and small molecules, among others. Results were highly heterogeneous and methodological quality was very low, with only the interleukin-36R inhibitor spesolimab reporting results from placebo-controlled randomized trials; based on this, spesolimab is now approved for GPP treatment in regions including the USA, Japan, China, the EU and several other countries. Some other biologics are approved exclusively in Japan and Taiwan for the treatment of GPP based on open-label studies with small patient numbers in lieu of double-blind studies. Non-standardization of clinical outcomes across studies remains a major hurdle in reaching a consensus on optimal treatment. However, recently trials have been conducted using well-defined, disease-specific endpoints to evaluate GPP-targeted treatments, which will hopefully advance patient care. In conclusion, this review highlights the need for prospective randomized studies with GPP-specific endpoints to determine the optimal treatment strategy. Plain Language Summary: Generalized pustular psoriasis (GPP) is a rare, chronic skin condition characterized by painful, sterile pustules that can occur all over the body. These pustules may also be accompanied by systemic inflammation, which can lead to serious health complications. GPP significantly impacts patients' quality of life and can even be life-threatening. Because the disease is so rare, treatment guidelines have typically been based on those for plaque psoriasis. However, these guidelines do not specifically address the unique needs of GPP. In this review, we analysed the published literature on GPP management, focussing on treatment efficacy, safety and quality of life outcomes. We searched the literature databases Embase and MEDLINE for articles published between 1980 and September 2023. In total, we identified 118 publications on this topic, covering a wide range of therapies; only one of these therapies, spesolimab, reported results from placebo-controlled randomized trials. Based on these trials, spesolimab is now approved for GPP treatment in the USA, Japan, China, the EU and several other countries. Some other therapies are approved exclusively in Japan and Taiwan based on small, open-label studies in the absence of higher-quality data. To date, comparing treatments has been challenging because of different clinical outcomes used to measure effectiveness. However, well-defined endpoints specific to GPP have recently been developed and used in trials. In conclusion, our review highlights the need for prospective randomized studies with GPP-specific endpoints to determine the best treatment strategy. [ABSTRACT FROM AUTHOR]

Introduction: Pompe disease is caused by a rare biallelic mutation in the GAA gene resulting in acid α-glucosidase deficiency and glycogen accumulation. Aim: We analyzed hospital admissions associated with the administration of Myozyme®, utilizing the French hospital discharge database, known in France as the Programme de Médicalisation des Systèmes d'Information (PMSI), which comprehensively captures all hospital activity within the country. Methods: In this observational study, we examined hospitalization records from April 4, 2012, to December 31, 2019, within the PMSI database, focusing on admissions where Myozyme® was administered. We particularly investigated the incidence of critical care admissions and adverse events (AEs) related to Myozyme®. Results: From 2012 to 2019, approximately 26,714 hospital stays involving Myozyme® administration were recorded for 239 patients. Most (96.6%) of these were outpatient stays, with only 3.2% in critical care. Furthermore, hospitalizations without critical care needs increased from 96% in 2012 to 99% in 2019. Of the patients receiving at least one infusion, 997 critical care admissions were recorded, with 781 (78.3%) occurring concurrent with or the day after the Myozyme® treatment without directly correlating to adverse effects of enzyme therapy. Conclusions: The analysis of the French hospital discharge database indicated that Myozyme® was associated with a low incidence of AEs and complications in a hospital context, supporting the consideration of its safe use in home-infusion settings. [ABSTRACT FROM AUTHOR]

Purpose: Brain functional and physiological plasticity is essential to combat dynamic environmental challenges. The rhythmic dopamine signaling pathway, which regulates emotion, reward and learning, shows seasonal patterns with higher capacity of dopamine synthesis and lower number of dopamine transporters during dark seasons. However, seasonal variation of the dopamine receptor signaling remains to be characterized. Methods: Based on a historical database of healthy human brain [11C]raclopride PET scans (n = 291, 224 males and 67 females), we investigated the seasonal patterns of D2/3 dopamine receptor signaling. Daylength at the time of scanning was used as a predictor for brain regional non-displaceable binding of the radiotracer, while controlling for age and sex. Results: Daylength was negatively correlated with availability of D2/3 dopamine receptors in the striatum. The largest effect was found in the left caudate, and based on the primary sample, every 4.26 h (i.e., one standard deviation) increase of daylength was associated with a mean 2.8% drop (95% CI -0.042 to -0.014) of the receptor availability. Conclusions: Seasonally varying D2/3 receptor signaling may also underlie the seasonality of mood, feeding, and motivational processes. Our finding suggests that in future studies of brain dopamine signaling, especially in high-latitude regions, the effect of seasonality should be considered. [ABSTRACT FROM AUTHOR]

Purpose: The eccentric implantation of pregnancies in the upper lateral aspect of the uterine cavity is poorly defined clinically. The aim of the current study was to investigate whether differentiating between uterine anomalies that can lead to cavitary distortion has implications for the management of these pregnancies. Methods: Eight cases of first-trimester eccentric pregnancy implantation within the endometrial cavity (study group) were retrospectively identified. For each woman in the study group, 10 women identified as having a first-trimester concentric pregnancy implantation during the first-trimester US examination were retrieved from our database (control group). After delivery or pregnancy demise, the presence of uterine anomalies was assessed by a 3D-US examination in all patients. Results: In the study group patients, an increased incidence of uterine anomalies (50.0% vs. 8.8%, p = 0.007) was found, compared to the controls. In the study group, the eccentric location persisted in half of the pregnancies (n = 4; 50%), whereas the other half migrated to a more centric location within the endometrial cavity (n = 4; 50%). The follow-up examination showed that all the early pregnancy demises occurred in cases where the pregnancy persisted at the eccentric location. Uterine malformations were also detected in all these cases. Conclusion: The data point to a significantly higher incidence of uterine anomalies in patients diagnosed with eccentric pregnancy implantation within the endometrial cavity. These results advocate for the value of differentiating between eccentric pregnancies in non-anomalous versus anomalous uteri. [ABSTRACT FROM AUTHOR]

Purpose: This study investigates the effect of the body mass index (BMI) on the early (2 years) to midterm (5 years) results of medial opening wedge high tibial osteotomy (MOWHTO). Methods: A prospectively maintained single‐centre database of 1138 knee osteotomies, between 2002 and 2022, was retrospectively reviewed. Adult patients who underwent MOWHTO for symptomatic unicompartmental medial knee osteoarthritis (OA), with varus knee malalignment, having failed conservative management, were included. Patients were categorized into three groups according to their BMI as per the classification from the National Institute for Health and Care Excellence (NICE): the healthy weight (HW) group (BMI of 18.5–24.9 kg/m2), the overweight (OW) (BMI of 25–29.9 kg/m2), and the obesity (OB) group (BMI ≥ 30 kg/m2). Multiple patient‐reported outcome measures (n = 6) were recorded preoperatively and at 2 and 5 years postoperatively. Deformity analysis was undertaken preoperatively and postoperatively. The relative risk (RR) of the complications between the HW, OW and OB groups was calculated. The rate of conversion to arthroplasty, and 5 and 10 years survivorship were recorded, as well as the hazard ratio (HR) of BMI on survivorship. Results: A total of 574 cases were included in the study: the HW group (n = 96), the OW group (n = 233) and the OB group (n = 245), with a mean BMI of 23 ± 1.5 kg/m2, 27.4 ± 1.4 kg/m2 and 34.4 ± 3.8 kg/m2, respectively. The mean follow‐up was 13.3 years (4.8–20.3). The mean mechanical tibiofemoral angle corrections were in the HW group: 7.1 ± 3°, OW group: 6.6 ± 3.5° and OB group: 7.1 ± 3.8°, with no intergroup significant difference (p = n.s.). Clinically, despite lower preoperative scores in the OW and OB groups, no difference was observed postoperatively amongst the three groups. The overall complication rate was 12.5% in the HW group, 6.8% in the OW group, and 9.8% in the OB group. There was no significant difference in the RR of complications between the HW and OW groups (RR = 0.6, 95% confidence interval [CI] = 0.3–1.3) (p = 0.2), and the HW and OB groups (RR = 0.8; 95% CI = 0.3–2.2) (p = 0.7). There was no overall significant difference in survival outcomes based on the BMI between the three groups (p = 0.4). The HR of conversion to arthroplasty between the HW and OW groups was 1.4 [95% CI = 0.6–3.5](p = 0.5) and between the HW and OB groups was 1.8 [95% CI = 0.8–4.4] (p = 0.2). Conclusion: BMI has no significant effect on either the radiological corrections, clinical outcomes, complications or survivorship of MOWHTO at short‐ to medium‐term follow‐up. No specific cutoff point for BMI can be recommended as a contraindication to MOWHTO. Level of Evidence: Level IV, Retrospective cohort study. [ABSTRACT FROM AUTHOR]

Background: There is a lack of literature reporting on long‐term outcomes following robotic‐arm‐assisted lateral unicompartmental knee arthroplasty (UKA). This study assessed the long‐term survivorship, patient‐reported satisfaction and pain scores following robotic‐arm‐assisted lateral UKA for lateral compartment osteoarthritis (OA). Methods: A single surgeon's database was reviewed to identify all patients who underwent robotic‐arm‐assisted lateral UKA with a cemented, fixed‐bearing prosthesis prior to May 2015. Patients were contacted to determine implant survivorship, satisfaction and pain. Kaplan–Meier models were applied to analyse survival. Results: A total of 77 knees (70 patients) with a mean follow‐up of 10.2 ± 1.5 years (range: 8.1–13.3) were included. Five knees were revised, corresponding to a 10‐year survivorship of 96.1% and estimated survival time of 12.7 ± 0.3 years (95% confidence interval: 12.2–13.2) with all‐cause revision as the endpoint. Unexplained pain (40.0%) and progression of OA (40.0%) in contralateral compartments were the most reported reasons for revision. Among patients without revision, 94.4% were either satisfied or very satisfied with their lateral UKA and the average pain score was 1.1. Conclusion: Robotic‐arm‐assisted lateral UKA led to high implant survivorship and patient satisfaction, and low pain scores at long‐term follow‐up. Progression of OA in contralateral compartments and unexplained pain were the most frequent reasons for revision. These findings support the continued use of robotic‐arm‐assisted lateral UKA for lateral compartment OA; however, its clinical value over conventional techniques remains to be established in prospective comparative studies. Level of Evidence: Therapeutic Level IV. [ABSTRACT FROM AUTHOR]

Automated handwritten digit identification has become essential for many daily operations such as verifying the authenticity of a cheque or deciphering a postal code. Researchers have been putting a lot of effort into figuring out how to automatically recognize and sort handwritten digits because of the advent of robotic technology in recent decades. Due to the complexity of the Arabic language and a lack of publicly available Arabic handwritten digit datasets, previous research has primarily concentrated on automating the recognition of English and European digits on availability of relevant datasets. Arabic handwritten recognition also plays a significant role in electronic-learning (e-learning) systems. It has not taken into account the recognition of handwritten Arabic digits. Arabic handwritten digits vary in size, form, slant, and image noise, which can cause changes in numeral topology. These factors make it difficult to classify and recognize Arabic handwritten digits. In order to overcome these challenges, we implemented a biologically plausible technique to classify Arabic handwritten digits. The objective of the research is to address the unique challenges of style, size, shape, slant variations, and image noise of Arabic handwritten digits in classification and recognition. To categorize Arabic handwritten digits, we employed convolutional spiking neural network (CSNN) and spike neural network (SNN) models. The reason for using SNN and SCNN is that the second-generation neural networks accumulate accolades on various computer vision tasks, i.e., pattern recognition, segmentation, and classification. However, there are certain challenges, i.e., energy inefficiency and computational cost, presented by these networks. The spiking neural network models also address these issues. These models are low-power, high-performance neural network models. We trained the spiking neural network model on Arabic digit datasets using a rate-based, non-spiking algorithm, i.e., backpropagation and soft-LIF, then integrated them with a spiking neural network. We have also used an STDP (spike time-dependent plasticity)-based spiking neural network for Arabic digit recognition. We attenuate the spiking variability in CSNN by adding noise during training, compensating the training errors, and bringing robustness against the spiking variability. We experimented with soft-LIF, a non-spiking version of the LIF neuron, and a variant of STDP rules for Arabic digit classification. Experimental results illustrate that CSNN achieves high performance at a significantly low energy and computational cost. We attained 98.98% and 91.16% recognition rates for the Arabic digit dataBase (ADBase) dataset using the convolutional spiking model and STDP spiking model, respectively. [ABSTRACT FROM AUTHOR]

Arabic text watermarking is the process of hiding private data for secure authentication and copyright protection. This research proposes different emerging watermarking approaches to hide secrecy using adjusted diacritical Arabic text hiding methodology. The study examines and compares two innovative watermarking schemes on the same platform testing its implementations utilizing Imam Nawawi's forty hadith text standard benchmark, remarking attractive contribution. The exploration remarked a significant improvement aiming to deal with its Arabic letters and diacritics, as for practical watermarking secrecy applications. The proposed hiding system models are investigated in terms of performance metrics of capacity, security, and robustness. The works are further compared to other state-of-the-art relative approaches showing very interesting remarks nominating one of our proposals as best applicable selection for 35% of the time and the second with different error rate percentages not remarking the best in many cases, but noted 7.5% as second rank, 10% as third assortment, and 17.5% as fourth preference, running the investigations fairly for all testing bench mark database analysis. [ABSTRACT FROM AUTHOR]

Nowadays, healthcare takes a great attention from people and governments due to the emergence of new diseases and viruses. Health Information Exchange (HIE) allows doctors, clinicians, and healthcare facilities (HCF) to exchange and share patient records according to patient permission. Unfortunately, the HIE systems suffer from several challenges such as security, privacy, latency, throughput, and scalability. Thus, researchers proposed many techniques for efficient HIE, but they did not cover all challenges. This paper proposes an efficient architecture called ELSO that combines between public blockchain and off-chain in order to enhance the integrity and privacy of HIE systems. In this architecture, the patient information is divided into two categories: relative data containing personal information that will be stored in the public blockchain; sensitive data describing the patient situation that will be stored in the off-chain database. Furthermore, ELSO is composed of three layers: The first is called the application layer and aims to create patient profiles; the second layer is called the control layer and aims to control the specialist access, and the third layer is called the storage layer and allows to store data in blockchain and off-chain. To evaluate the performance of our framework, we used real healthcare data where the obtained results show the effectiveness of ELSO in terms of all metrics compared to other techniques. [ABSTRACT FROM AUTHOR]

This study employs the Latent Dirichlet allocation method, a topic modeling technique, to reveal hidden patterns in Industry 4.0 research. The dataset comprises 8584 articles published in the Scopus database from 2011 to the end of 2022. The analysis categorized the articles into 12 distinct topics. The three most prominent topics identified are "Smart Cyber-Physical Systems," "Digital Transformation and Knowledge Management" and "Data Science in Energy," respectively. The findings from this topic modeling provide a comprehensive overview for researchers in the field of Industry 4.0, offering valuable insights into current trends and potential future research directions. [ABSTRACT FROM AUTHOR]

Analyzing many high-utility itemsets using HUIM methods is challenging and time-consuming. To solve this issue, Closed High-Utility Itemsets (CHUI), a condensed and lossless representation of high-utility itemsets, have been proposed. Using a matrix to hold both the total value of the transactions and the value of each item in transactions, we describe a new approach for the CHUI problem in this work. By merging the items and removing unnecessary values, one can use this matrix to enhance the mining of closed high-utility itemsets. The pruning approach in the suggested algorithm compares the aggregate value of the items with the minimum value, which can result in a more effective use of time and space. In contrast to other algorithms, which could only establish a minimum value for all the items, this method can set a minimum value for each item, which is one of its surprises. In addition to only scanning the database once, this approach is more straightforward and up to three times more time- and space-efficient than the prior algorithms. [ABSTRACT FROM AUTHOR]

Purpose: Total hip arthroplasty (THA) has demonstrated excellent results in elderly patients, however, the indications, outcomes, and long-term results in adolescent patients are less understood. This study aims to assess the outcomes of THA in patients under 21, providing insights for clinical decision-making in this exceptional population. Methods: A systematic review in PubMed, Ovid MEDLINE, and Embase database was performed. We included studies reporting clinical, radiological, and functional outcomes of THA in patients younger than 21 years, for any cause, with a with a minimum follow-up of one year. The ten year survivorship estimate was pooled using a meta-analysis methodology and each study was weighted according to its standard error, calculated from published confidence intervals. Results: We included 25 studies involving 1166 hips. Median age was 17 years old, 60% were females, and the average follow-up was 8.1 years. Juvenile inflammatory arthritis was the main indication for total hip arthroplasty (THA). The all-cause revision rate was 14.4% and aseptic loosening was the most common cause. Only eight studies reported ten year survival rates and form the pooled analysis an 84.91% survival rate (95% CI 70.56 – 99.27) was obtained. An average score of 88.08 in the Harris Hip Score (HHS) was observed. We found a 3.43% complication rate. Conclusions: Hip arthroplasty is an acceptable option for adolescents with end-stage arthritis. However, the altered hip anatomy, the elevated revision rate, and the long-term implant survival must be considered before performing a THA in adolescent patients. [ABSTRACT FROM AUTHOR]

Purposes: The present study evaluated the impact of clinical guidelines for gastric cancer surgery on surgeons' choice of procedure in real-world practice. We focused on the 2014 guideline revision recommending laparoscopic surgery and the evidence concerning splenectomy for prophylactic lymphadenectomy reported in 2015 using the National Clinical Database, which is the most comprehensive database in Japan. Methods: We investigated the monthly percentages of laparoscopic distal gastrectomies performed for stage I gastric cancer (LDG%) and splenectomies performed during total gastrectomy for advanced cancer (TGS%) between 2014 and 2017. We evaluated the descriptive statistics of the time-series changes in the LDG%, TGS%, and annual trends of outcomes. Results: In total, 124,787 patients were enrolled. The mean LDG% and TGS% were 69.8% and 9.2%, respectively. The LDG% and TGS% were 66.4% and 16.7%, respectively, in January 2014 and 73.1% and 5.9%, respectively, in December 2017. LDG% consistently increased, and TGS% showed a consistent downward trend throughout the observation period. There was no significant change in this trend after the publication of the guideline recommendations or clinical trial results. Conclusion: No significant changes in surgical procedures were observed after publication of the guidelines or results of clinical trials. [ABSTRACT FROM AUTHOR]

Background and Objective: CDKL5 deficiency disorder presents as a challenging condition with early-onset refractory seizures, severe developmental delays, and a range of other neurological symptoms. Our study aimed to explore the benefits and side effects of anti-seizure medications (ASMs) in managing seizures among individuals with CDKL5 deficiency disorder, drawing on data from the International CDKL5 Disorder Database. Methods: Data for this retrospective cohort study were obtained from the International CDKL5 Disorder Database, which contains responses from a baseline questionnaire administered between 2012 and 2022 and a follow-up questionnaire administered between 2018 and 2019. Families of eligible individuals were asked to provide information on ASMs that were previously and currently taken, the dose prescribed, the age at starting the medications, and the age at discontinuation for past medications. The outcome variables of interest were perceived seizure-related benefits for the current and past use of ASMs and caregiver-reported side effects. Rescue medications and infrequently used ASMs were excluded from the analysis. Descriptive statistics were used to summarise the characteristics of the study population. Results: The study included 399 children and adults with CDKL5 deficiency disorder, descriptively analysing the perceived benefits and side effects of 23 unique ASMs based on caregiver reports. The study identified levetiracetam, topiramate, sodium valproate, vigabatrin, phenobarbital and clobazam as the most used ASMs. Notably, cannabidiol showed highly beneficial outcomes with few side effects, whereas levetiracetam and phenobarbital exhibited less favourable benefit-to-side-effect ratios. Dual therapy involving sodium valproate and levetiracetam was only used a small number (n = 5) of times but appeared effective in reducing seizure activity with relatively few side effects. Compared with monotherapy, polytherapy had a relatively higher likelihood of reported side effects than benefits. Conclusions: The study, leveraging a large sample size that exceeds that of previous research, emphasises the complex nature of seizure management in CDKL5 deficiency disorder. Our findings underscore the necessity of ongoing research to optimise treatment strategies, considering both the efficacy of seizure control and the potential for adverse effects. The study also points to the need for future investigations into the therapeutic potential of emerging treatments such as ganaxolone and the unresolved efficacy of cannabis products in seizure management. [ABSTRACT FROM AUTHOR]

Background: In 2019, the Gastrectomy Complications Consensus Group (GCCG) published a standardized set of complications aiming toward uniform reporting of post-gastrectomy complications. This study aimed to report outcomes after gastrectomy in the Netherlands according to GCCG definitions and compare them to previously reported national results and the European database reported by the GCCG. Methods: This nationwide, population-based cohort study included all patients undergoing gastrectomy for gastric cancer registered in the DUCA in 2020–2021. Postoperative morbidity and 30-day/in-hospital mortality were analyzed according to the GCCG definitions. For all patients, baseline characteristics and outcomes were compared with the GCCG cohort consisting of 27 European expert centers (GASTRODATA; 2017–2018). Results: In 2020–2021, 782 patients underwent gastrectomy in the Netherlands. Variation was seen in baseline characteristics between the Dutch and the GCCG cohort (N = 1349), most notably in minimally invasive surgery (80.6% vs 19.6%, p < 0.001). In the Netherlands, 223 (28.5%) patients developed a total of 407 complications, the most frequent being non-surgical infections (28.5%) and anastomotic leakage (13.4%). The overall complication and 30-day mortality rates were similar between the Dutch and GCCG cohort (28.5% vs 29.8%, p = 0.563; 3.7% vs 3.6%, p = 0.953). Higher surgical and endoscopic/radiologic reintervention rates were observed in the Netherlands compared to the GCCG cohort (10.7% vs 7.8%, p = 0.025; 10.9% vs 2.9%, p < 0.001). Conclusion: Reporting outcomes according to the standardized GCCG definitions allows for international benchmarking. Postoperative outcomes were comparable between Dutch and GCCG cohorts, but both exceed the international benchmark for expert gastrectomy care, highlighting targets for national and international quality improvement. [ABSTRACT FROM AUTHOR]

Dam failure may lead to significant casualties among downstream residents. Therefore, it is crucial to study a reliable method to quantitatively assess the loss of life (LOL) caused by dam failure for emergency response to dam failure incidents. Based on a statistical analysis of typical dam failure accidents in China and the research on the formation mechanism of LOL, the study quantified the factors influencing LOL using fuzzy theory and constructed a quantitative database for the LOL. Then, it proposed an innovative algorithm integrating the grey wolf optimization (GWO) algorithm and the random forest (RF) model. Finally, a data-driven assessment model for the LOL caused by dam failure was developed by combining the gray correlation analysis of the factors. The performance of the GWO-RF model was validated using a dataset of the LOL caused. The proposed model was used to assess the LOL in typical dam failure events. The results indicate that the model has higher accuracy, with an average absolute error of approximately 945 persons, significantly lower than 2529 persons in the Graham method. Thus, it can effectively estimate the LOL caused by dam failure. This study developed a novel method for quantitatively assessing the LOL caused by dam failure, which could also serve as a reference for modeling disaster consequences in other fields. [ABSTRACT FROM AUTHOR]

Social work regulatory rules and practices should balance the social values of promoting consumer safety and workforce equity. As the two values often compete, the balancing act should be guided by empirical evidence about the effects of regulations on consumers and the workforce. Unfortunately, however, empirical studies investigating the relationships between various regulatory rules and practices and consumer safety and workforce equity are rare in the existing literature. This paper identifies critical gaps in the current knowledge base and proposes a series of future research agendas that call for empirical evidence, including detailed data on jurisdictional regulatory rules and practices, the measures of consumer safety, and licensed social workers' demographic, employment, and practice characteristics. The paper concludes by calling out to all social work stakeholders about the need for empirical evidence on the effects of social work regulatory rules and practices. [ABSTRACT FROM AUTHOR]

Prostatitis represents a common disease of the male genitourinary system, significantly impacting the physical and mental health of male patients. While numerous studies have suggested a potential link between immune cell activity and prostatitis, the exact causal role of immune cells in prostatitis remains uncertain. This study aims to explore the causal relationship between immune cell characteristics and prostatitis using a bidirectional Mendelian randomization approach. This study utilizes data from the public GWAS database and employs bidirectional Mendelian randomization analysis to investigate the causal relationship between immune cells and prostatitis. The causal relationship between 731 immune cell features and prostatitis was primarily investigated through inverse variance weighting (IVW), complemented by MR-Egger regression, a simple model, the weighted median method, and a weighted model. Ultimately, the results underwent sensitivity analysis to assess the heterogeneity, horizontal pleiotropy, and stability of Single Nucleotide Polymorphisms (SNPs) in immune cells and prostatitis. MR analysis revealed 17 immune cells exhibiting significant causal effects on prostatitis. In contrast, findings from reverse MR indicated a significant causal relationship between prostatitis and 13 immune cells. Our study utilizes bidirectional Mendelian Randomization to establish causal relationships between specific immune cell phenotypes and prostatitis, highlighting the reciprocal influence between immune system behavior and the disease. Our findings suggest targeted therapeutic approaches and the importance of including diverse populations for broader validation and personalized treatment strategies. [ABSTRACT FROM AUTHOR]