Recovery of Metal Values from Useless Printed Circuit Boards

This study provides a hydrometallurgical method to recover copper, lead, tin and gold from useless printed circuit boards. Metals in the board were leached with different mineral acids. Gold, if present, was first recovered by filtering from the acid solution, washed and polished. Metal salts went into the acidic leachant were separately recovered, washed and dried. These were thermally reduced using carbon to obtain reduced metals. The polymeric base material was found safe for feasible for reuse in the manufacture of new printed circuit boards. Parameters affecting the recovery factor were studied. Results obtained showed that nitric acid was more effective compared to sulfuric or hydrochloric acid. The extent of metals dissolution increases with increase in acid molarity, stoichiometric ratio, temperature and time of leaching. With sulfuric acid, copper dissolved in > 6 M solution at > 75 °C whereas lead and tin did not. With nitric acid, all metals dissolved on hot conditions whereby tin deposited upon cooling as basic oxide. Lead was separated from copper as chloride. Copper was separated as solid sulfide. The recovered compounds were reduced with hydrogen gas or by carbon at temperatures up to 1000 °C. A separation factor of 98.4-96.2% was achieved

The effect of chronic administration of l-arginine on the learning and memory of estradiol-treated ovariectomized rats tested in the morris water maze

OBJECTIVE: The present study was carried out to evaluate the effect of L-arginine on the learning and memory of estradiol-treated ovariectomized (OVX) rats. METHODS: Forty-eight rats were divided into six groups: (1) sham, (2) OVX, (3) sham-Est, (4) OVX-Est, (5) sham-Est-LA, and (6) OVX-Est-LA. The animals of the sham-Est and OVX-Est groups were treated by weekly injection of estradiol valerate (2mg/kg). The sham-Est-LA and OVX-Est-LA groups were treated in the same manner but with an additional daily injection of L-arginine (200mg/kg). After eight weeks, animals of all groups were tested in the Morris water maze. The escape latency and path traveled to reach the platform were compared between groups. RESULTS: Time latency and path length in the OVX group were significantly higher than in the sham group (P<0.05). The OVX-Est group had a significantly shorter traveled path length and time latency compared to the OVX group (P<0.001). Time latency and path length in the sham-Est group was significantly higher than in the sham group (P<0.001). Time latency and path length in the OVX-Est-LA group were significantly higher than in the OVX-Est group. CONCLUSIONS: These results allow us to propose that chronic treatment with estradiol enhances the spatial learning and memory of OVX rats, and that long term L-arginine treatment attenuates the effects of improvement produced by estradiol in OVX rats

Henry Gas Solubility Optimization Double Machine Learning Classifier for Neurosurgical Patients

This study aims to predict head trauma outcome for Neurosurgical patients in children, adults, and elderly people. As Machine Learning (ML) algorithms are helpful in healthcare field, a comparative study of various ML techniques is developed. Several algorithms are utilized such as k-nearest neighbor, Random Forest (RF), C4.5, Artificial Neural Network, and Support Vector Machine (SVM). Their performance is assessed using anonymous patients\u27 data. Then, a proposed double classifier based on Henry Gas Solubility Optimization (HGSO) is developed with Aquila optimizer (AQO). It is implemented for feature selection to classify patients\u27 outcome status into four states. Those are mortality, morbidity, improved, or the same. The double classifiers are evaluated via various performance metrics including recall, precision, F-measure, accuracy, and sensitivity. Another contribution of this research is the original use of hybrid technique based on RF-SVM and HGSO to predict patient outcome status with high accuracy. It determines outcome status relationship with age and mode of trauma. The algorithm is tested on more than 1000 anonymous patients\u27 data taken from a Neurosurgical unit of Mansoura International Hospital, Egypt. Experimental results show that the proposed method has the highest accuracy of 99.2% (with population size = 30) compared with other classifiers

Nitric oxide contributes to learning and memory deficits observed in hypothyroid rats during neonatal and juvenile growth

INTRODUCTION: Severe cognitive impairment follows thyroid hormone deficiency during the neonatal period. The role of nitric oxide (NO) in learning and memory has been widely investigated. METHODS: This study aimed to investigate the effect of hypothyroidism during neonatal and juvenile periods on NO metabolites in the hippocampi of rats and on learning and memory. Animals were divided into two groups and treated for 60 days from the first day of lactation. The control group received regular water, whereas animals in a separate group were given water supplemented with 0.03% methimazole to induce hypothyroidism. Male offspring were selected and tested in the Morris water maze. Samples of blood were collected to measure the metabolites of NO, NO2, NO3 and thyroxine. The animals were then sacrificed, and their hippocampi were removed to measure the tissue concentrations of NO2 and NO3. DISCUSSION: Compared to the control group's offspring, serum thyroxine levels in the methimazole group's offspring were significantly lower (P<0.01). In addition, the swim distance and time latency were significantly higher in the methimazole group (P<0.001), and the time spent by this group in the target quadrant (Q1) during the probe trial was significantly lower (P<0.001). There was no significant difference in the plasma levels of NO metabolites between the two groups; however, significantly higher NO metabolite levels in the hippocampi of the methimazole group were observed compared to controls (P<0.05). CONCLUSION: These results suggest that the increased NO level in the hippocampus may play a role in the learning and memory deficits observed in childhood hypothyroidism; however, the precise underlying mechanism(s) remains to be elucidated

MICROALBUMINURIA BESIDES TO URINARY ENZYMATIC PROTEIN LEVELS INCREASE IN DIABETIC KIDNEY DISEASE WITH TYPE II DIABETICS

Background: Diabetic kidney disease (DKD) is a time progressive problem, give rise in uncontrolled Diabetics increasing risks for chronic kidney disease (CKD) and /or end-stage renal disease (ESRD). The vulnerability to renal dysfunction manifested with sudden glomerular hypofiltration associated with micro-to macroalbuminuria passing to renal failure. So that, screening of specific enzymes shifts, or urinary albumin may predict onset diabetic nephropathy. Objective:The assessment of urinary alkaline phosphatase (ALP), alanine aminopeptidase (AAP), acid phosphatase (ACP) and microalbuminuria (MAU) for type II diabetic patients. Patients and Methods: In this study,120 type II diabetic patients were compared to 90 healthy volunteers of matched age and sex in Al-Leith General Hospital, Al-Leith Kidney Unit (AKU), Al-Leith, Makkah area, KSA in which random urine samples were collected for testing of MAU, ALP, AAP, ACP and Cr. Results: Mean values of measured biomarkers in patient group for MAU, ALP, AAP, ACP and Cr were 51.92 mg/I, 41.55 U/L, 20.17 U/L, 570.10 U/L and 2.92 mg/dl VS in control group were 12.59 mg/I, 8.84 U/L, 6.94 U/L, 385.87U/L and 1.07 mg/dl respectively. Additionally, there were statistically positive correlation between AAP with MAU and ALP; ACP with MAU, ALP and AAP; Cr level with MAU, ALP, AAP and ACP; on the other hand, there were positive significant correlation between duration of diabetes with all studied markers. Conclusion: Using of MAU in addition to other urinary enzymes could be beneficial non-invasive indicators for renal deterioration in type II diabetics

The Promise of Molecular and Genomic Techniques for Biodiversity Research and DNA Barcoding of the Arabian Peninsula Flora

The Arabian Peninsula is known to have a comprehensive and rich endowment of unique and genetically diverse plant genetic resources. Analysis and conservation of biological diversity is a crucial issue to the whole Arabian Peninsula. The rapid and accurate delimitation and identification of a species is crucial to genetic diversity analysis and the first critical step in the assessment of distribution, population abundance and threats related to a particular target species. During the last two decades, classical strategies of evaluating genetic variability, such as morphology and physiology, have been greatly complemented by phylogenetic, taxonomic, genetic diversity and breeding research molecular studies. At present, initiatives are taking place around the world to generate DNA barcode libraries for vascular plant flora and to make these data available in order to better understand, conserve and utilize biodiversity. The number of herbarium collection-based plant evolutionary genetics and genomics studies being conducted has been increasing worldwide. The herbaria provide a rich resource of already preserved and identified material, and these as well as freshly collected samples from the wild can be used for creating a reference DNA barcode library for the vascular plant flora of a region. This review discusses the main molecular and genomic techniques used in plant identification and biodiversity analysis. Hence, we highlight studies emphasizing various molecular techniques undertaken during the last 10 years to study the plant biodiversity of the Arabian Peninsula. Special emphasis on the role of DNA barcoding as a powerful tool for plant biodiversity analysis is provided, along with the crucial role of herbaria in creating a DNA barcode library

Nanopesticides in comparison with agrochemicals: Outlook and future prospects for sustainable agriculture

Agrochemicals are products of advanced technologies that use inorganic pesticides and fertilizers. Widespread use of these compounds has adverse environmental effects, leading to acute and chronic exposure. Globally, scientists are adopting numerous green technologies to ensure a healthy and safe food supply and a livelihood for everyone. Nanotechnologies significantly impact all aspects of human activity, including agriculture, even if synthesizing certain nanomaterials is not environmentally friendly. Numerous nanomaterials may therefore make it easier to create natural insecticides, which are more effective and environmentally friendly. Nanoformulations can improve efficacy, reduce effective doses, and extend shelf life, while controlled-release products can improve the delivery of pesticides. Nanotechnology platforms enhance the bioavailability of conventional pesticides by changing kinetics, mechanisms, and pathways. This allows them to bypass biological and other undesirable resistance mechanisms, increasing their efficacy. The development of nanomaterials is expected to lead to a new generation of pesticides that are more effective and safer for life, humans, and the environment. This article aims to express at how nanopesticides are being used in crop protection now and in the future. This review aims to shed some light on the various impacts of agrochemicals, their benefits, and the function of nanopesticide formulations in agriculture

NEW INSIGHT INTO THE MIDDLE EOCENE CALCAREOUS NANNOPLANKTON BIOSTRATIGRAPHY AND PALEOENVIRONMENT FROM FAYOUM AND BENI SUEF AREAS, EGYPT

The present study deals with calcareous nannoplankton paleoenvironmental and biostratigraphic implications as well as the genesis and the stratigraphic significance of an event bed recognized from the middle Eocene Beni Suef Formation in the sections of Gebel Na’alun (Fayoum area) and Gebel Homret Shaibun (Beni Suef area), Egypt. Calcareous nannoplankton biostratigraphy indicates that the Beni Suef Formation in the two areas is synchronous, covering an interval that may be correlated with the calcareous nannoplankton Zone NP17. Paleoenvironmental implications from calcareous nannoplankton suggests deposition of sediments in the Beni Suef Formation under relatively stable, temperate and mesotrophic conditions, with a short interval of eutrophication in the basal part of the Homret Shaibun section

Burden of disease scenarios for 204 countries and territories, 2022–2050: a forecasting analysis for the Global Burden of Disease Study 2021

Background: Future trends in disease burden and drivers of health are of great interest to policy makers and the public at large. This information can be used for policy and long-term health investment, planning, and prioritisation. We have expanded and improved upon previous forecasts produced as part of the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) and provide a reference forecast (the most likely future), and alternative scenarios assessing disease burden trajectories if selected sets of risk factors were eliminated from current levels by 2050. Methods: Using forecasts of major drivers of health such as the Socio-demographic Index (SDI; a composite measure of lag-distributed income per capita, mean years of education, and total fertility under 25 years of age) and the full set of risk factor exposures captured by GBD, we provide cause-specific forecasts of mortality, years of life lost (YLLs), years lived with disability (YLDs), and disability-adjusted life-years (DALYs) by age and sex from 2022 to 2050 for 204 countries and territories, 21 GBD regions, seven super-regions, and the world. All analyses were done at the cause-specific level so that only risk factors deemed causal by the GBD comparative risk assessment influenced future trajectories of mortality for each disease. Cause-specific mortality was modelled using mixed-effects models with SDI and time as the main covariates, and the combined impact of causal risk factors as an offset in the model. At the all-cause mortality level, we captured unexplained variation by modelling residuals with an autoregressive integrated moving average model with drift attenuation. These all-cause forecasts constrained the cause-specific forecasts at successively deeper levels of the GBD cause hierarchy using cascading mortality models, thus ensuring a robust estimate of cause-specific mortality. For non-fatal measures (eg, low back pain), incidence and prevalence were forecasted from mixed-effects models with SDI as the main covariate, and YLDs were computed from the resulting prevalence forecasts and average disability weights from GBD. Alternative future scenarios were constructed by replacing appropriate reference trajectories for risk factors with hypothetical trajectories of gradual elimination of risk factor exposure from current levels to 2050. The scenarios were constructed from various sets of risk factors: environmental risks (Safer Environment scenario), risks associated with communicable, maternal, neonatal, and nutritional diseases (CMNNs; Improved Childhood Nutrition and Vaccination scenario), risks associated with major non-communicable diseases (NCDs; Improved Behavioural and Metabolic Risks scenario), and the combined effects of these three scenarios. Using the Shared Socioeconomic Pathways climate scenarios SSP2-4.5 as reference and SSP1-1.9 as an optimistic alternative in the Safer Environment scenario, we accounted for climate change impact on health by using the most recent Intergovernmental Panel on Climate Change temperature forecasts and published trajectories of ambient air pollution for the same two scenarios. Life expectancy and healthy life expectancy were computed using standard methods. The forecasting framework includes computing the age-sex-specific future population for each location and separately for each scenario. 95% uncertainty intervals (UIs) for each individual future estimate were derived from the 2·5th and 97·5th percentiles of distributions generated from propagating 500 draws through the multistage computational pipeline. Findings: In the reference scenario forecast, global and super-regional life expectancy increased from 2022 to 2050, but improvement was at a slower pace than in the three decades preceding the COVID-19 pandemic (beginning in 2020). Gains in future life expectancy were forecasted to be greatest in super-regions with comparatively low life expectancies (such as sub-Saharan Africa) compared with super-regions with higher life expectancies (such as the high-income super-region), leading to a trend towards convergence in life expectancy across locations between now and 2050. At the super-region level, forecasted healthy life expectancy patterns were similar to those of life expectancies. Forecasts for the reference scenario found that health will improve in the coming decades, with all-cause age-standardised DALY rates decreasing in every GBD super-region. The total DALY burden measured in counts, however, will increase in every super-region, largely a function of population ageing and growth. We also forecasted that both DALY counts and age-standardised DALY rates will continue to shift from CMNNs to NCDs, with the most pronounced shifts occurring in sub-Saharan Africa (60·1% [95% UI 56·8–63·1] of DALYs were from CMNNs in 2022 compared with 35·8% [31·0–45·0] in 2050) and south Asia (31·7% [29·2–34·1] to 15·5% [13·7–17·5]). This shift is reflected in the leading global causes of DALYs, with the top four causes in 2050 being ischaemic heart disease, stroke, diabetes, and chronic obstructive pulmonary disease, compared with 2022, with ischaemic heart disease, neonatal disorders, stroke, and lower respiratory infections at the top. The global proportion of DALYs due to YLDs likewise increased from 33·8% (27·4–40·3) to 41·1% (33·9–48·1) from 2022 to 2050, demonstrating an important shift in overall disease burden towards morbidity and away from premature death. The largest shift of this kind was forecasted for sub-Saharan Africa, from 20·1% (15·6–25·3) of DALYs due to YLDs in 2022 to 35·6% (26·5–43·0) in 2050. In the assessment of alternative future scenarios, the combined effects of the scenarios (Safer Environment, Improved Childhood Nutrition and Vaccination, and Improved Behavioural and Metabolic Risks scenarios) demonstrated an important decrease in the global burden of DALYs in 2050 of 15·4% (13·5–17·5) compared with the reference scenario, with decreases across super-regions ranging from 10·4% (9·7–11·3) in the high-income super-region to 23·9% (20·7–27·3) in north Africa and the Middle East. The Safer Environment scenario had its largest decrease in sub-Saharan Africa (5·2% [3·5–6·8]), the Improved Behavioural and Metabolic Risks scenario in north Africa and the Middle East (23·2% [20·2–26·5]), and the Improved Nutrition and Vaccination scenario in sub-Saharan Africa (2·0% [–0·6 to 3·6]). Interpretation: Globally, life expectancy and age-standardised disease burden were forecasted to improve between 2022 and 2050, with the majority of the burden continuing to shift from CMNNs to NCDs. That said, continued progress on reducing the CMNN disease burden will be dependent on maintaining investment in and policy emphasis on CMNN disease prevention and treatment. Mostly due to growth and ageing of populations, the number of deaths and DALYs due to all causes combined will generally increase. By constructing alternative future scenarios wherein certain risk exposures are eliminated by 2050, we have shown that opportunities exist to substantially improve health outcomes in the future through concerted efforts to prevent exposure to well established risk factors and to expand access to key health interventions

Brain Tumor Segmentation Using Deep Capsule Network and Latent-Dynamic Conditional Random Fields

Because of the large variabilities in brain tumors, automating segmentation remains a difficult task. We propose an automated method to segment brain tumors by integrating the deep capsule network (CapsNet) and the latent-dynamic condition random field (LDCRF). The method consists of three main processes to segment the brain tumor—pre-processing, segmentation, and post-processing. In pre-processing, the N4ITK process involves correcting each MR image’s bias field before normalizing the intensity. After that, image patches are used to train CapsNet during the segmentation process. Then, with the CapsNet parameters determined, we employ image slices from an axial view to learn the LDCRF-CapsNet. Finally, we use a simple thresholding method to correct the labels of some pixels and remove small 3D-connected regions from the segmentation outcomes. On the BRATS 2015 and BRATS 2021 datasets, we trained and evaluated our method and discovered that it outperforms and can compete with state-of-the-art methods in comparable conditions