PETROLOGY AND GEOCHEMISTRY OF THE LATE CRETACEOUS PAB FORMATION, WESTERN SULAIMAN FOLD- THRUST- BELT, PAKISTAN: IMPLICATIONS FOR PROVENANCE AND PALEO-WEATHERING

Late Cretaceous sandstone succession of the Pab Formation in western Sulaiman Fold Thrust belt Pakistan was investigated for petrology and bulk rock chemistry to determine its source terrain, paleo-weathering and tectonic setting. The formation is mainly comprised of sandstone with reddish to maroon color shale and arenaceous limestone. Texturally, the sandstone is fine to coarse grained, sub-angular to well-rounded and moderately to well sorted. The sandstone is petrologically and geochemically classified as quartz arenite to sub lithic arenite. The detritus was mainly derived from plutonic acidic source. QtFL and QmFLt suggests that recycled orogeny and Craton Interior setting were major sources of sediments. Geochemical models support that the detritus was derived from quartzose sedimentary source terrain, suggest deposition in a passive continental margin setting. Average values of chemical indices are CIA 59% CIW  67% and CIV 12.70%, which suggest moderate to high degree of chemical weathering in source area, that may reflect humid climate condition in the source area. The petrographic study and geochemical models demonstrate that the Pab Formation is mostly composed of mature sandstone from acidic plutonic and low-grade metamorphic rocks terrain in recycled and Craton Interior setting deposited on western passive margin of Indian plate in Tethys Ocean

Comparative study of Internet Protocol

In today's generation, most of today's Internet is using IPv4, Now twenty years old. IPv4 is now uploading with the Problem of meeting growing Internet requirements and it is a shortage of IPv4 addresses, which are necessary for all new the machines added to the Internet.IPv6, fixes a series of problems In IPv4 addresses. It also adds many improvements to IPv4 and provides a better network. IPv6 is expected to gradually replace IPv4, with the two coexisting for several years during a transition period. As the population is increasing day by day, similarly the Internet isAlso growing and expanding more and more and more and more,Government, scientists and universities are looking for new waysTo send information quickly and powerfully The two new InternetsDevelop new and faster technologies to improve research andCommunication, and both projects are expected toEventually improve the current commercial Internet. A big advantage of IPv6 is that it simplifies and solves the problem. The scarcity of IP addresses. In today's Internet technology, Controls in the United States 74% of the 4 million IP addresses, while the amount that China has is equal only to the University California, but its share of 80 million users. This is the main reason Asian countries, especially China, Japan and South Korea, Show interest in IPv6 version technology

Predicting the main pollen season of Broussonetia Papyrifera (paper mulberry) tree

Paper mulberry pollen, declared a pest in several countries including Pakistan, can trigger severe allergies and cause asthma attacks. We aimed to develop an algorithm that could accurately predict high pollen days to underpin an alert system that would allow patients to take timely precautionary measures. We developed and validated two prediction models that take historical Nov 15, 2023 2/18 pollen and weather data as their input to predict the start date and peak date of the pollen season in Islamabad, the capital city of Pakistan. The first model is based on linear regression and the second one is based on phenological modelling. We tested our models on an original and comprehensive dataset from Islamabad. The mean absolute errors (MAEs) for the start day are 2.3 and 3.7 days for the linear and phenological models, respectively, while for the peak day, the MAEs are 3.3 and 4.0 days, respectively. These encouraging results could be used in a website or app to notify patients and healthcare providers to start preparing for the paper mulberry pollen season. Timely action could reduce the burden of symptoms, mitigate the risk of acute attacks and potentially prevent deaths due to acute pollen-induced allergy

The nutritional composition of the vegetable soybean (maodou) and its potential in combatting malnutrition

IntroductionGlobal malnutrition continues to be a canker owing to poor eating habits and over-reliance on the major staple crops. Vegetable soybean (maodou) is gaining popularity globally as an affordable snack and vegetable.MethodsIn this study, we profiled the nutritional composition of 12 soybean cultivars at the vegetable (R6-R7) and mature (R8) stages. We also conducted an RNA-seq analysis during seed development, focusing on key biosynthesis enzymes for quality traits.ResultsThe results showed that 100 g of maodou contained 66.54% moisture, 13.49% protein, 7.81% fatty acids, 2.47% soluble sugar, abundant content of minerals, and micronutrients, including folate (462.27 μg FW) and carotenoids (3,935.41 μg FW). Also, the isoflavone content of maodou ranged between 129.26 and 2,359.35 μg/g FW. With regard to the recommended daily allowance, 100 g fresh weight of maodou can contribute 26.98, 115.57, and 11.60% of protein, folate, and zinc, respectively, and significant proportions of other nutrients including linoleic acid (21.16%), linolenic acid (42.96%), zinc (11.60%), and iron (18.01%). On a dry weight basis, maodou has two to six folds higher contents of folate, tocopherol, and carotenoid than the mature soybean. Furthermore, RNA-seq analysis revealed that key biosynthesis enzymes of quality traits are differentially expressed during seed development and may contribute to variations in the content of quality traits at the vegetable and mature stages. Correlation analysis of quality traits at both stages revealed that protein only correlated positively with zinc at the vegetable stage but negatively correlated with total tocopherol and total fatty acid at the mature stage. Complex associations among folates, soluble sugar, and isoflavones were also identified.DiscussionThis study provides insight into the nutritional contents of vegetable soybean and demonstrates that maodou is essential for meeting the nutritional requirements of most countries

Systematic, comprehensive, evidence-based approach to identify neuroprotective interventions for motor neuron disease: using systematic reviews to inform expert consensus

Objectives: Motor neuron disease (MND) is an incurable progressive neurodegenerative disease with limited treatment options. There is a pressing need for innovation in identifying therapies to take to clinical trial. Here, we detail a systematic and structured evidence-based approach to inform consensus decision making to select the first two drugs for evaluation in Motor Neuron Disease-Systematic Multi-arm Adaptive Randomised Trial (MND-SMART: NCT04302870), an adaptive platform trial. We aim to identify and prioritise candidate drugs which have the best available evidence for efficacy, acceptable safety profiles and are feasible for evaluation within the trial protocol. Methods: We conducted a two-stage systematic review to identify potential neuroprotective interventions. First, we reviewed clinical studies in MND, Alzheimer’s disease, Huntington’s disease, Parkinson’s disease and multiple sclerosis, identifying drugs described in at least one MND publication or publications in two or more other diseases. We scored and ranked drugs using a metric evaluating safety, efficacy, study size and study quality. In stage two, we reviewed efficacy of drugs in MND animal models, multicellular eukaryotic models and human induced pluripotent stem cell (iPSC) studies. An expert panel reviewed candidate drugs over two shortlisting rounds and a final selection round, considering the systematic review findings, late breaking evidence, mechanistic plausibility, safety, tolerability and feasibility of evaluation in MND-SMART. Results: From the clinical review, we identified 595 interventions. 66 drugs met our drug/disease logic. Of these, 22 drugs with supportive clinical and preclinical evidence were shortlisted at round 1. Seven drugs proceeded to round 2. The panel reached a consensus to evaluate memantine and trazodone as the first two arms of MND-SMART. Discussion: For future drug selection, we will incorporate automation tools, text-mining and machine learning techniques to the systematic reviews and consider data generated from other domains, including high-throughput phenotypic screening of human iPSCs

Global disparities in surgeons’ workloads, academic engagement and rest periods: the on-calL shIft fOr geNEral SurgeonS (LIONESS) study

: The workload of general surgeons is multifaceted, encompassing not only surgical procedures but also a myriad of other responsibilities. From April to May 2023, we conducted a CHERRIES-compliant internet-based survey analyzing clinical practice, academic engagement, and post-on-call rest. The questionnaire featured six sections with 35 questions. Statistical analysis used Chi-square tests, ANOVA, and logistic regression (SPSS® v. 28). The survey received a total of 1.046 responses (65.4%). Over 78.0% of responders came from Europe, 65.1% came from a general surgery unit; 92.8% of European and 87.5% of North American respondents were involved in research, compared to 71.7% in Africa. Europe led in publishing research studies (6.6 ± 8.6 yearly). Teaching involvement was high in North America (100%) and Africa (91.7%). Surgeons reported an average of 6.7 ± 4.9 on-call shifts per month, with European and North American surgeons experiencing 6.5 ± 4.9 and 7.8 ± 4.1 on-calls monthly, respectively. African surgeons had the highest on-call frequency (8.7 ± 6.1). Post-on-call, only 35.1% of respondents received a day off. Europeans were most likely (40%) to have a day off, while African surgeons were least likely (6.7%). On the adjusted multivariable analysis HDI (Human Development Index) (aOR 1.993) hospital capacity > 400 beds (aOR 2.423), working in a specialty surgery unit (aOR 2.087), and making the on-call in-house (aOR 5.446), significantly predicted the likelihood of having a day off after an on-call shift. Our study revealed critical insights into the disparities in workload, access to research, and professional opportunities for surgeons across different continents, underscored by the HDI

Reducing the environmental impact of surgery on a global scale: systematic review and co-prioritization with healthcare workers in 132 countries

Abstract Background Healthcare cannot achieve net-zero carbon without addressing operating theatres. The aim of this study was to prioritize feasible interventions to reduce the environmental impact of operating theatres. Methods This study adopted a four-phase Delphi consensus co-prioritization methodology. In phase 1, a systematic review of published interventions and global consultation of perioperative healthcare professionals were used to longlist interventions. In phase 2, iterative thematic analysis consolidated comparable interventions into a shortlist. In phase 3, the shortlist was co-prioritized based on patient and clinician views on acceptability, feasibility, and safety. In phase 4, ranked lists of interventions were presented by their relevance to high-income countries and low–middle-income countries. Results In phase 1, 43 interventions were identified, which had low uptake in practice according to 3042 professionals globally. In phase 2, a shortlist of 15 intervention domains was generated. In phase 3, interventions were deemed acceptable for more than 90 per cent of patients except for reducing general anaesthesia (84 per cent) and re-sterilization of ‘single-use’ consumables (86 per cent). In phase 4, the top three shortlisted interventions for high-income countries were: introducing recycling; reducing use of anaesthetic gases; and appropriate clinical waste processing. In phase 4, the top three shortlisted interventions for low–middle-income countries were: introducing reusable surgical devices; reducing use of consumables; and reducing the use of general anaesthesia. Conclusion This is a step toward environmentally sustainable operating environments with actionable interventions applicable to both high– and low–middle–income countries

Plant Glycomics

Glycomics is the comprehensive study of glycomes (the entire complement of sugars, whether free or present in more complex molecules of an organism), including genetic, physiologic, pathologic, and other aspects in living organisms. Carbohydrates being most abundant macromolecules are found in all organisms’ organs, tissues, and cells. They are present in almost every organelle of the cell in varying amounts depending on the type of organelle. Carbohydrates are not directly synthesized by genes; rather they are formed by gene products. Sometime carbohydrates are present in free form and also exist in the form of conjugates. Plants being the largest producer of carbohydrates on earth are of particular importance. Plants are rich in complex carbohydrates molecules. Complex biopolymers like cellulose, lignin, and hemicelluloses are being studied along with their structure and type of linkages present between them. The presence of these carbohydrates is somewhat linked to the survival of these plants under extreme conditions and stresses. Understanding these carbohydrates has allowed us to find answers on how plants survived severe climate changes in the past. These complex molecules form linkages with non-carbohydrate molecules and understanding the structure of these conjugates is a challenging task to the scientific community. Glycomics approach regarding the structural and functional analysis of these carbohydrates has been revolutionized by the modification in techniques like mass spectrophotometry, high pressure liquid chromatography, and capillary electrophoresis. Still some improvements are needed in these techniques to make glycomic approach less time-consuming and more specific and sensitive

The Prevalence of Parkinson Disease Among Patients With Hepatitis C Infection

Introduction: HCV has been suspected to potentially cause degenerations in the central nervous system. Parkinson's disease is the second most common neurodegenerative disorder. Our aim was to assess the prevalence of Parkinson's disease among patients with HCV infection. Material and methods: For this study, we used Medicare database from 2005-2010. Medicare database contains information on enrollment, coverage, diagnosis recorded with International Classification of Disease, Ninth Revision (ICD-9). From combined inpatient and outpatient files, Parkinson's disease was identified as the first diagnosis by ICD-9 code 332.0. Other study variables were; age, gender, race (White and No White), and Medicare eligibility status. Simple distribution comparison by HCV status examined with t-test for numerical variables and χ2 test for categorical variables in the main analytical cohort as well as in the propensity score matched cohort. Results: A total of 1,236,734 patients (median age 76 years, 41% male, and 85% White) was identified among over 47 million claims. Of these, 6040 patients (0.5%) were infected with HCV. Overall, 0.8% (N = 49) of the HCV group and 1.3% (N = 16,004) of the Non-HCV group had Parkinson's disease (P 0.05). Discussion: This study revealed that, among Medicare population, HCV was not associated with Parkinson disease

Cucumber Leaf Diseases Recognition Using Multi Level Deep Entropy-ELM Feature Selection

Agriculture has becomes an immense area of research and is ascertained as a key element in the area of computer vision. In the agriculture field, image processing acts as a primary part. Cucumber is an important vegetable and its production in Pakistan is higher as compared to the other vegetables because of its use in salads. However, the diseases of cucumber such as Angular leaf spot, Anthracnose, blight, Downy mildew, and powdery mildew widely decrease the quality and quantity. Lately, numerous methods have been proposed for the identification and classification of diseases. Early detection and then treatment of the diseases in plants is important to prevent the crop from a disastrous decrease in yields. Many classification techniques have been proposed but still, they are facing some challenges such as noise, redundant features, and extraction of relevant features. In this work, an automated framework is proposed using deep learning and best feature selection for cucumber leaf diseases classification. In the proposed framework, initially, an augmentation technique is applied to the original images by creating more training data from existing samples and handling the problem of the imbalanced dataset. Then two different phases are utilized. In the first phase, fine-tuned four pre-trained models and select the best of them based on the accuracy. Features are extracted from the selected fine-tuned model and refined through the Entropy-ELM technique. In the second phase, fused the features of all four fine-tuned models and apply the Entropy-ELM technique, and finally fused with phase 1 selected feature. Finally, the fused features are recognized using machine learning classifiers for the final classification. The experimental process is conducted on five different datasets. On these datasets, the best-achieved accuracy is 98.4%. The proposed framework is evaluated on each step and also compared with some recent techniques. The comparison with some recent techniques showed that the proposed method obtained an improved performance