The Incidence of Lymphoma in Children in Gezira State During 2005-2014:A general Population-Based Study

Introduction: Lymphomas are the third most often diagnosed malignant neoplasm among children and adolescents. They constitute about 10-15% of childhood malignancies. We aimed to quantify incidence of Lymphomas (HD and NHD) in the Gezira State and their difference with gender and age. Methods: The data was abstracted and classified accordingly to the third revision of the International Classification of Childhood Cancer. Age-standardised rates (ASR) for three 5-year age groups (0–4 years, 5–9 years and 10–14 years) calculated for males and females.  Results: The total number of children diagnosed with lymphoma was 140 patients.     NHL forms 75/140(53%) and HD 65/140(46%). Incidence of NHL was 6.68/million. Males with NHL was 48/75 (64%) with an ASR of 5.71/million and females 27/75(36%) with ASR of 4.04/million and a ratio of 1.7:1. The most common age group of presentation of NHL in males was 5-9 years of age, while in females was from 10-14 years of age. Incidence of HD was 4.22/million. Males constituted about 40/65 (62%) with an ASR of 4.72/million, while females were 25/65(38%) with ASR of 3.72 and males to females ratio of 1.6:1. The common age of presentation of HD in both males and females was 5-9 years of age. In conclusion: The results presented in this study were similar with international results and comparable with them. Implemented analytical studies to clarify the different types of haematological malignancies will help to choose the right treatment and better cure.&nbsp

Green intellectual capital and sustainability in manufacturing industries in Saudi Arabia

The objective of the present study was to promote Sustainability through green intellectual capital (GIC) and its dimensions. In addition, this study intends to examine the mediating effect of green culture (GC) between GIC dimensions and Sustainability. The research employed quantitative techniques and acquired cross-sectional data from Saudi Arabian employees of large manufacturing companies (LMCs). The study used a technique of convenience sampling to obtain responses from respondents. The final sample size for the investigation was 268 valid cases. Green human capital (GHC), green structural capital (GSC), and green relational capital (GRC) have a positive and statistically significant effect on green capital (GC), economic performance (EP), environmental performance (ENP), and social performance (SP), as determined by structural equation modeling (SEM). Additionally, the study demonstrates that GC has a positive and significant influence on EP and ENP but a negligible impact on SP. Concerning mediating effects, it has been determined that GC is an effective mediator in forming the association between GHC and EP, GSC, and ENP. Conversely, GC does not form a positive association between GRC and SP. The study's findings would aid policymakers and administrators in understanding the contribution of GIC to GC and Sustainability. The study would contribute to the management, environmental science, and sustainability literature based on empirical findings. The study contributes to developing a green environment by promoting green culture, which ultimately improves the Sustainability of businesses.Nadia Abdelhamid Abdelmegeed Abdelwahed (Department of Management, College of Business, King Faisal University), Mohammed A. Al Doghan (Department of Management, College of Business, King Faisal University), Bahadur Ali Soomro (Department of Economics, Abdul Haq Campus, Federal Urdu University of Arts, Science and Technology)Includes bibliographical references

Genetic diversity of Schistosoma haematobium parasite IS NOT associated with severity of disease in an endemic area in Sudan

BACKGROUND: Over 650 million people globally are at risk of schistosomiasis infection, while more than 200 million people are infected of which the higher disease rates occur in children. Eighty three students between 6-20 years (mean 12.45 ± 3.2) from Quran School for boys in Radwan village, Gezira state were recruited to investigate for the relationship between the genetic diversity of Schistosoma haematobium strains and the severity of the disease. METHOD: Schistosoma haematobium infection was detected by filtration of urine. Ultrasonography was done on each study subject, while PCR technique was used for genotyping via random amplified polymorphic DNA (RAPD) with A01, A02, A12, Y20 and A13 primers. A01 primer gave three different genotypes (A01-1, A01-2 and A01-3). RESULTS: About 54.2% (45/83) were S. haematobium egg positive by urine filtration. On assessment of the upper and lower urinary tract by ultrasound technique, 61.4% (51/83) were positiveand73.3% (60/83) samples were PCR positive. No significant difference was found when comparing the three different genotypes with severity of the disease. CONCLUSION: This study concludes that no association was found between the different genotypes of S.haemtobium and the severity of the disease. Examination of more samples from different areas to identify any possible differences between the parasites genes and disease severity was recommended. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/1471-2334-14-469) contains supplementary material, which is available to authorized users

Parallelizing the Simulation of Shipboard Power Systems

As a result of this research the Navy has a simulation approach for ship power systems that is computationally effective enough to permit efficient simulation. In addition to simulating the basic power system, significant progress has been made in the simulation of the control system.United States Office of Naval ResearchCenter for Electromechanic

Chemical composition and quality of rapeseed meal as affected by genotype and nitrogen fertilization☆

Rapeseed meal (RSM) is known for its high nutritional quality as animal feed. However, there has been little studies on the effect of nitrogen fertilization on RSM chemical composition, mainly neutral detergent fiber (NDF), acid detergent fiber (ADF), acid detergent lignin (ADL), and ash content. Therefore, this study was carried out to evaluate the effect of nitrogen application rate on chemical composition of RSM in interaction with different spring rapeseed varieties (Brassica napus L). A field experiment was conducted during 2018/19 cropping season at the experimental station of the Ecole Nationale d’Agriculture de Meknès” according to a split-plot design with three replications, using six nitrogen application rates, as main plot, and six rapeseed varieties, as subplot. After seeds harvest and oil extraction, meals derived from the different treatments were used in this study. Results showed that increasing nitrogen rate from 0 to 120 kg N ha−1 led to a significant rise in meal yield up to 74.58%. A positive effect of nitrogen fertilization was observed on dry matter, protein content and ash content, recording the highest values at 120 kg N ha−1 treatment. However, cellulose and lignin content were affected negatively by nitrogen fertilization. The nitrogen supply of 150 kg N ha−1 resulted in a reduction of ADF and ADL contents by 23% and 28%, respectively, compared to the unfertilized control (N0). Variability within rapeseed varieties for all parameters except dry matter and ADL content was highlighted

Artificial Rabbit Optimizer with deep learning for fall detection of disabled people in the IoT Environment

Fall detection (FD) for disabled persons in the Internet of Things (IoT) platform contains a combination of sensor technologies and data analytics for automatically identifying and responding to samples of falls. In this regard, IoT devices like wearable sensors or ambient sensors from the personal space role a vital play in always monitoring the user's movements. FD employs deep learning (DL) in an IoT platform using sensors, namely accelerometers or depth cameras, to capture data connected to human movements. DL approaches are frequently recurrent neural networks (RNNs) or convolutional neural networks (CNNs) that have been trained on various databases for recognizing patterns connected with falls. The trained methods are then executed on edge devices or cloud environments for real-time investigation of incoming sensor data. This method differentiates normal activities and potential falls, triggering alerts and reports to caregivers or emergency numbers once a fall is identified. We designed an Artificial Rabbit Optimizer with a DL-based FD and classification (ARODL-FDC) system from the IoT environment. The ARODL-FDC approach proposes to detect and categorize fall events to assist elderly people and disabled people. The ARODL-FDC technique comprises a four-stage process. Initially, the preprocessing of input data is performed by Gaussian filtering (GF). The ARODL-FDC technique applies the residual network (ResNet) model for feature extraction purposes. Besides, the ARO algorithm has been utilized for better hyperparameter choice of the ResNet algorithm. At the final stage, the full Elman Neural Network (FENN) model has been utilized for the classification and recognition of fall events. The experimental results of the ARODL-FDC technique can be tested on the fall dataset. The simulation results inferred that the ARODL-FDC technique reaches promising performance over compared models concerning various measures

Variants of CTGF are associated with hepatic fibrosis in Chinese, Sudanese, and Brazilians infected with Schistosomes

Abnormal fibrosis occurs during chronic hepatic inflammations and is the principal cause of death in hepatitis C virus and schistosome infections. Hepatic fibrosis (HF) may develop either slowly or rapidly in schistosome-infected subjects. This depends, in part, on a major genetic control exerted by genes of chromosome 6q23. A gene (connective tissue growth factor [CTGF]) is located in that region that encodes a strongly fibrogenic molecule. We show that the single nucleotide polymorphism (SNP) rs9402373 that lies close to CTGF is associated with severe HF (P = 2 × 10−6; odds ratio [OR] = 2.01; confidence interval of OR [CI] = 1.51–2.7) in two Chinese samples, in Sudanese, and in Brazilians infected with either Schistosoma japonicum or S. mansoni. Furthermore, SNP rs12526196, also located close to CTGF, is independently associated with severe fibrosis (P = 6 × 10−4; OR = 1.94; CI = 1.32–2.82) in the Chinese and Sudanese subjects. Both variants affect nuclear factor binding and may alter gene transcription or transcript stability. The identified variants may be valuable markers for the prediction of disease progression, and identify a critical step in the development of HF that could be a target for chemotherapy

HER2/neu expression status of post BCG recurrent non-muscle-invasive bladder urothelial carcinomas in relation to their primary ones

Background: Transurethral resection (TUR) followed by adjuvant therapy is still the treatment of choice of Non-Muscle-Invasive Bladder Urothelial Carcinoma (NMIBUC). However, recurrence is one of the most troublesome features of these lesions. Early second resection and adjuvant BCG therapy has been shown to improve the outcome. Objective: To evaluate the prognostic value of C-erbB-2 (HER2/neu) expression status in Non-Muscle-Invasive Bladder Urothelial Carcinoma cases, before and after intravesical Bacillus Calmette Guerin (BCG immunotherapy). Materials and methods: HER2/neu expression was studied in 120 (Ta-T1) Non-Muscle-Invasive Urothelial Carcinoma cases. The expression was evaluated and compared to the expression after Bacillus Calmette Guerin (BCG) immunotherapy. Results: HER2/neu expression in low and high grade of the Non- Muscle-Invasive Urothelial Carcinoma was (38%) and (83%) respectively. The difference of the expression rates by tumor grade was statistically significant. In recurring lesions post BCG therapy, C-erbB-2 expression was markedly decreased (31.6%) when compared to its expression before therapy (65%). Conclusions: The HER2/neu expression increased as the tumor grade rose. The reduction in expression following BCG treatment in Non-Invasive transitional cell carcinoma cases could reflect a reduction of the potential malignancy of the tumor

ICML 2023 Topological Deep Learning Challenge : Design and Results

This paper presents the computational challenge on topological deep learning that was hosted within the ICML 2023 Workshop on Topology and Geometry in Machine Learning. The competition asked participants to provide open-source implementations of topological neural networks from the literature by contributing to the python packages TopoNetX (data processing) and TopoModelX (deep learning). The challenge attracted twenty-eight qualifying submissions in its two-month duration. This paper describes the design of the challenge and summarizes its main findings