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Controlled Environment Agriculture (CEA) is a method of increasing crop productivity per unit area of cultivated land by extending crop production into the vertical dimension and enabling year-round production. Light emitting diodes (LED) are frequently used as the source of light energy in CEA systems and light is commonly the limiting factor for production under CEA conditions. In the current study, the impact of different spectra was compared with the use of white LED light. The various spectra were white; white supplemented with ultraviolet b for a week before harvest; three combinations of red/blue lights (red 660 nm with blue 450 nm at 1:1 ratio; red 660 nm with blue 435 nm 1:1 ratio; red 660 nm with blue at mix of 450 nm and 435 nm 1:1 ratio); and red/blue supplemented with green and far red (B/R/G/FR, ratio: 1:1:0.07:0.64). The growth, yield, physiological and chemical profiles of two varieties of lettuce, Carmoli (red) and Locarno (green), responded differently to the various light treatments. However, white (control) appeared to perform the best overall. The B/R/G/FR promoted the growth and yield parameters in both varieties of lettuce but also increased the level of stem elongation (bolting), which impacted the quality of grown plants. There was no clear relationship between the various physiological parameters measured and final marketable yield in either variety. Various chemical traits, including vitamin C content, total phenol content, soluble sugar and total soluble solid contents responded differently to the light treatments, where each targeted chemical was promoted by a specific light spectrum. This highlights the importance of designing the light spectra in accordance with the intended outcomes. The current study has value in the field of commercial vertical farming of lettuce under CEA conditions.

This paper presents a comprehensive study on the classification of brain tumor images using five pre-trained vision transformer (ViT) models, namely R50-ViT-l16, ViT-l16, ViT-l32, ViT-b16, and ViT-b32, employing a fine-tuning approach. The objective of this study is to advance the state-of-the-art in brain tumor classification by harnessing the power of these advanced models. The dataset utilized for experimentation consists of a total of 4855 images in the training set and 857 images in the testing set, encompassing four distinct tumor classes. The performance evaluation of each model is conducted through an extensive analysis encompassing precision, recall, F1-score, accuracy, and confusion matrix metrics. Among the models assessed, ViT-b32 demonstrates exceptional performance, achieving a high accuracy of 98.24% in accurately classifying brain tumor images. Notably, the obtained results outperform existing methodologies, showcasing the efficacy of the proposed approach. The contributions of this research extend beyond conventional methods, as it not only employs cutting-edge ViT models but also surpasses the performance of existing approaches for brain tumor image classification. This study not only demonstrates the potential of ViT models in medical image analysis but also provides a benchmark for future research in the field of brain tumor classification.

The Saudi Initiative for Asthma 2021 (SINA-2021) is the fifth version of asthma guidelines for the diagnosis and management of asthma for adults and children, which is developed by the SINA group, a subsidiary of the Saudi Thoracic Society. The main objective of the SINA is to have guidelines that are up to date, simple to understand, and easy to use by healthcare workers dealing with asthma patients. To facilitate achieving the goals of asthma management, the SINA panel approach is mainly based on the assessment of symptom control and risk for both adults and children. The approach to asthma management is aligned for age groups: adults, adolescents, children aged 5-12 years, and children aged less than 5 years. SINA guidelines have focused more on personalized approaches reflecting better understanding of disease heterogeneity with the integration of recommendations related to biologic agents, evidence-based updates on treatment, and the role of immunotherapy in management. Medication appendix has also been updated with the addition of recent evidence, new indications for existing medication, and new medications. The guidelines are constructed based on the available evidence, local literature, and the current situation at national and regional levels. There is also an emphasis on patient-doctor partnership in the management that also includes a self-management plan.

Nowadays, brain tumors have become a leading cause of mortality worldwide. The brain cells in the tumor grow abnormally and badly affect the surrounding brain cells. These cells could be either cancerous or non-cancerous types, and their symptoms can vary depending on their location, size, and type. Due to its complex and varying structure, detecting and classifying the brain tumor accurately at the initial stages to avoid maximum death loss is challenging. This research proposes an improved fine-tuned model based on CNN with ResNet50 and U-Net to solve this problem. This model works on the publicly available dataset known as TCGA-LGG and TCIA. The dataset consists of 120 patients. The proposed CNN and fine-tuned ResNet50 model are used to detect and classify the tumor or no-tumor images. Furthermore, the U-Net model is integrated for the segmentation of the tumor regions correctly. The model performance evaluation metrics are accuracy, intersection over union, dice similarity coefficient, and similarity index. The results from fine-tuned ResNet50 model are IoU: 0.91, DSC: 0.95, SI: 0.95. In contrast, U-Net with ResNet50 outperforms all other models and correctly classified and segmented the tumor region.

Background. Recently, dentists can utilize three-dimensional printing technology in fabricating dental restoration. However, to date, there is a lack of evidence regarding the effect of printing layer thicknesses and postprinting on the mechanical properties of the 3D-printed temporary restorations with the additive manufacturing technique. So, this study evaluated the mechanical properties of a 3D-printed dental resin material with different printing layer thicknesses and postprinting methods. Methods. 210 specimens of a temporary crown material (A2 EVERES TEMPORARY, SISMA, Italy) were 3D-printed with different printing layer thicknesses (25, 50, and 100 μm). Then, specimens were 3D-printed using DLP technology (EVERES ZERO, DLP 3D printer, SISMA, Italy) which received seven different treatment conditions after printing: water storage for 24 h or 1 month, light curing or heat curing for 5 or 15 minutes, and control. Flexural properties were evaluated using a three-point bending test on a universal testing machine (ISO standard 4049). The Vickers hardness test was used to evaluate the microhardness of the material system. The degree of conversion was measured using an FT-IR ATR spectrophotometer. Statistical analysis was performed using two-way analysis of variance (ANOVA) and Tukey’s honestly significant difference (HSD) test (p≤0.05). Results. The 100 μm printing layer thickness had the highest flexural strength among the other thickness groups. As a combined effect printing thickness and postprinting conditions, the 100 μm with the dry storage group has the highest flexural strength among the tested groups (94.60 MPa). Thus, the group with 100 μm thickness that was heat cured for 5 minutes (HC 5 min 100 μm) has the highest VHN value (VHN=17.95). Also, the highest mean DC% was reported by 50 μm layer thickness (42.84%).Conclusions. The thickness of the 100 μm printing layer had the highest flexural strength compared to the 25 μm and 50 μm groups. Also, the postprinting treatment conditions influenced the flexural strength and hardness of the 3D-printed resin material.

A novel coronavirus (MERS-CoV) is causing severe disease in the Middle East. In this report on a hospital outbreak of MERS-CoV infection, 23 confirmed cases and evidence of person-to-person transmission were identified. The median incubation period was 5.2 days. Respiratory viruses are an emerging threat to global health security and have led to worldwide epidemics with substantial morbidity, mortality, and economic consequences. Since the severe acute respiratory syndrome (SARS) pandemic in 2003–2004, 1 – 3 two additional human coronaviruses — HKU-1 and NL-63 — have been identified, both of which cause mild respiratory infection and are distributed worldwide. 4 , 5 In September 2012, the World Health Organization (WHO) reported two cases of severe community-acquired pneumonia caused by a novel human β-coronavirus, subsequently named the Middle East respiratory syndrome coronavirus (MERS-CoV). 6 – 8 Since then, MERS-CoV has been identified as the cause of pneumonia . . .

The yield and quality of field crops are affected by abiotic stresses such as water deficit, which can negatively impact crop growth, productivity, and quality. However, nanotechnology holds great promise for increasing crop yield, maintaining quality, and thus mitigating abiotic stresses. Therefore, the current study was conducted to examine the influences of 0, 50, and 100 mg L−1 zinc oxide (ZnO) nanoparticles and 0, 25, and 50 mg L−1 silicon dioxide (SiO2) nanoparticles on the yield and quality traits of potato plants grown under water deficit conditions (100%, 75%, and 50% ETc). Water deficit significantly reduced yield traits (average tuber weight, number of plant tubers, and tuber yield) and quality traits (tuber diameter, crude protein, and mineral content). However, it enhanced tuber dry weight, specific gravity, ascorbic acid, starch, and total soluble solids. Foliar applications of ZnO and SiO2 nanoparticles under water deficit treatments significantly enhanced yield and improved quality traits of potato plants. Moreover, significant and positive correlations were found among yield traits. Thus, it can be concluded that using ZnO NPs at 100 mg L−1 significantly improves potato productivity and quality traits by mitigating the negative effects of water deficit in arid regions.

A brain tumor is a significant health concern that directly or indirectly affects thousands of people worldwide. The early and accurate detection of brain tumors is vital to the successful treatment of brain tumors and the improved quality of life of the patient. There are several imaging techniques used for brain tumor detection. Among these techniques, the most common are MRI and CT scans. To overcome the limitations associated with these traditional techniques, computer-aided analysis of brain images has gained attention in recent years as a promising approach for accurate and reliable brain tumor detection. In this study, we proposed a fine-tuned vision transformer model that uses advanced image processing and deep learning techniques to accurately identify the presence of brain tumors in the input data images. The proposed model FT-ViT involves several stages, including the processing of data, patch processing, concatenation, feature selection and learning, and fine tuning. Upon training the model on the CE-MRI dataset containing 5712 brain tumor images, the model could accurately identify the tumors. The FT-Vit model achieved an accuracy of 98.13%. The proposed method offers high accuracy and can significantly reduce the workload of radiologists, making it a practical approach in medical science. However, further research can be conducted to diagnose more complex and rare types of tumors with more accuracy and reliability.

Advanced developments in the field of enzyme technology have increased the use of enzymes in industrial applications, especially in detergents. Enzymes as detergent additives have been extensively studied and the demand is considerably increasing due to its distinct properties and potential applications. Enzymes from microorganisms colonized at various geographical locations ranging from extreme hot to cold are explored for compatibility studies as detergent additives. Especially psychrophiles growing at cold conditions have cold-active enzymes with high catalytic activity and their stability under extreme conditions makes it as an appropriate eco-friendly and cost-effective additive in detergents. Adequate number of reports are available on cold-active enzymes such as proteases, lipases, amylases, and cellulases with high efficiency and exceptional features. These enzymes with increased thermostability and alkaline stability have become the premier choice as detergent additives. Modern approaches in genomics and proteomics paved the way to understand the compatibility of cold-active enzymes as detergent additives in broader dimensions. The molecular techniques such as gene coding, amino acid sequencing, and protein engineering studies helped to solve the mysteries related to alkaline stability of these enzymes and their chemical compatibility with oxidizing agents. The present review provides an overview of cold-active enzymes used as detergent additives and molecular approaches that resulted in development of these enzymes as commercial hit in detergent industries. The scope and challenges in using cold-active enzymes as eco-friendly and sustainable detergent additive are also discussed.

Middle East respiratory syndrome (MERS) is a new human disease caused by a novel coronavirus (CoV). Clinical data on MERS-CoV infections are scarce. We report epidemiological, demographic, clinical, and laboratory characteristics of 47 cases of MERS-CoV infections, identify knowledge gaps, and define research priorities. We abstracted and analysed epidemiological, demographic, clinical, and laboratory data from confirmed cases of sporadic, household, community, and health-care-associated MERS-CoV infections reported from Saudi Arabia between Sept 1, 2012, and June 15, 2013. Cases were confirmed as having MERS-CoV by real-time RT-PCR. 47 individuals (46 adults, one child) with laboratory-confirmed MERS-CoV disease were identified; 36 (77%) were male (male:female ratio 3·3:1). 28 patients died, a 60% case-fatality rate. The case-fatality rate rose with increasing age. Only two of the 47 cases were previously healthy; most patients (45 [96%]) had underlying comorbid medical disorders, including diabetes (32 [68%]), hypertension (16 [34%]), chronic cardiac disease (13 [28%]), and chronic renal disease (23 [49%]). Common symptoms at presentation were fever (46 [98%]), fever with chills or rigors (41 [87%]), cough (39 [83%]), shortness of breath (34 [72%]), and myalgia (15 [32%]). Gastrointestinal symptoms were also frequent, including diarrhoea (12 [26%]), vomiting (ten [21%]), and abdominal pain (eight [17%]). All patients had abnormal findings on chest radiography, ranging from subtle to extensive unilateral and bilateral abnormalities. Laboratory analyses showed raised concentrations of lactate dehydrogenase (23 [49%]) and aspartate aminotransferase (seven [15%]) and thrombocytopenia (17 [36%]) and lymphopenia (16 [34%]). Disease caused by MERS-CoV presents with a wide range of clinical manifestations and is associated with substantial mortality in admitted patients who have medical comorbidities. Major gaps in our knowledge of the epidemiology, community prevalence, and clinical spectrum of infection and disease need urgent definition. None.