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With new innovations and technologies world besides being facilitated is also subjected to face the threat of major misadventures and disasters. Likewise, Liquefied Petroleum Gas (LPG) is generally used in household for kitchen in general and gas geysers or heaters in winter. Similarly, industries use it for different causes for instance furnace, boiling and get higher production at cheaper rate. Besides, this gas is igneous gas and can cause potential hazard at any time. Hence, in this paper research is made on Internet of Things (IOT) to reduce the chances of such mishaps by presenting a model of microcontroller-based LPG gas detecting and warning system. Furthermore, it explicates the development of self-directed android based mobile device for gas leakage detection that can be installed in all sort of places. Moreover, this device would be containing MQ6 sensor to detect the unwanted outflow and GPS module to locate particular location of leakage what is more Arduino is installed to disseminate information of leakage to different servers like Smartphones, Email and buzzer and shall give display on LCD. Subsequently, it will send the mobile alert to authorized and concerned authorities to take action with in time and there would be buzzer installed. In addition, this device promises automatic working with no need of human interference and assures reliability, efficiency along with cost-effectiveness check-ups of pipeline.

Abstract Despite the existence of established standards and guidelines for pathology reporting, many pathology reports are still written in unstructured free text. Extracting information from these reports and formatting it according to a standard is crucial for consistent interpretation. Automated information extraction from unstructured pathology reports is a challenging task, as it requires accurately interpreting medical terminologies and context‐dependent details. In this work, we present a practical approach for automatically extracting information from unstructured pathology reports or scanned paper reports utilising a large multimodal model. This framework uses context‐aware prompting strategies to extract values of individual fields, such as grade, size, etc. from pathology reports. A unique feature of the proposed approach is that it assigns a confidence value indicating the correctness of the model's extraction for each field and generates a structured report in line with national pathology guidelines in human and machine‐readable formats. We have analysed the extraction performance in terms of accuracy and kappa scores, and the quality of the confidence scores assigned by the model. We have also evaluated the prognostic value of the extracted fields and feature embeddings of the raw text. Results showed that the model can accurately extract information with an accuracy and kappa score up to 0.99 and 0.98, respectively. Our results indicate that confidence scores are an effective indicator of the correctness of the extracted information achieving an area under the receiver operating characteristic curve up to 0.93 thus enabling automatic flagging of extraction errors. Our analysis further reveals that, as expected, information extracted from pathology reports is highly prognostically relevant. The framework demo is available at: https://labieb.dcs.warwick.ac.uk/. Information extracted from pathology reports of colorectal cancer cases in the cancer genome atlas using the proposed approach and its code are available at: https://github.com/EtharZaid/Labieb.

Abstract Oral epithelial dysplasia (OED) is a premalignant histopathological diagnosis given to lesions of the oral cavity. Its grading suffers from significant inter-/intra-observer variability, and does not reliably predict malignancy progression, potentially leading to suboptimal treatment decisions. To address this, we developed an artificial intelligence (AI) algorithm, that assigns an Oral Malignant Transformation (OMT) risk score based on the Haematoxylin and Eosin (H&E) stained whole slide images (WSIs). Our AI pipeline leverages an in-house segmentation model to detect and segment both nuclei and epithelium. Subsequently, a shallow neural network utilises interpretable morphological and spatial features, emulating histological markers, to predict progression. We conducted internal cross-validation on our development cohort (Sheffield; n = 193 cases) and independent validation on two external cohorts (Birmingham and Belfast; n = 89 cases). On external validation, the proposed OMTscore achieved an AUROC = 0.75 (Recall = 0.92) in predicting OED progression, outperforming other grading systems (Binary: AUROC = 0.72, Recall = 0.85). Survival analyses showed the prognostic value of our OMTscore (C-index = 0.60, p = 0.02), compared to WHO (C-index = 0.64, p = 0.003) and binary grades (C-index = 0.65, p

Quantum machine learning with quantum kernels for classification problems is a growing area of research. Recently, quantum kernel alignment techniques that parameterise the kernel have been developed, allowing the kernel to be trained and therefore aligned with a specific dataset. While quantum kernel alignment is a promising technique, it has been hampered by considerable training costs because the full kernel matrix must be constructed at every training iteration. Addressing this challenge, we introduce a novel method that seeks to balance efficiency and performance. We present a sub-sampling training approach that uses a subset of the kernel matrix at each training step, thereby reducing the overall computational cost of the training. In this work, we apply the sub-sampling method to synthetic datasets and a real-world breast cancer dataset and demonstrate considerable reductions in the number of circuits required to train the quantum kernel while maintaining classification accuracy.

Abstract Breast cancer (BC) grade is a well-established subjective prognostic indicator of tumour aggressiveness. Tumour heterogeneity and subjective assessment result in high degree of variability among observers in BC grading. Here we propose an objective Haematoxylin & Eosin (H&E) image-based prognostic marker for early-stage luminal/Her2-negative BReAst CancEr that we term as the BRACE marker. The proposed BRACE marker is derived from AI based assessment of heterogeneity in BC at a detailed level using the power of deep learning. The prognostic ability of the marker is validated in two well-annotated cohorts (Cohort-A/Nottingham: n = 2122 and Cohort-B/Coventry: n = 311) on early-stage luminal/HER2-negative BC patients treated with endocrine therapy and with long-term follow-up. The BRACE marker is able to stratify patients for both distant metastasis free survival (p = 0.001, C-index: 0.73) and BC specific survival (p

Abstract Early‐stage estrogen receptor positive and human epidermal growth factor receptor negative (ER+/HER2−) luminal breast cancer (BC) is quite heterogeneous and accounts for about 70% of all BCs. Ki67 is a proliferation marker that has a significant prognostic value in luminal BC despite the challenges in its assessment. There is increasing evidence that spatial colocalization, which measures the evenness of different types of cells, is clinically important in several types of cancer. However, reproducible quantification of intra‐tumor spatial heterogeneity remains largely unexplored. We propose an automated pipeline for prognostication of luminal BC based on the analysis of spatial distribution of Ki67 expression in tumor cells using a large well‐characterized cohort (n = 2,081). The proposed Ki67 colocalization (Ki67CL) score can stratify ER+/HER2− BC patients with high significance in terms of BC‐specific survival (p

The prevailing barrier to produce hydrogen from water splitting is disappointingly sluggish kinetics. Furthermore, a lack of understanding of the key obstacle in the oxygen evolution reaction (OER), is an obstruction to the design of efficient water‐splitting electrocatalysts. Here, we present the single‐step and fast fabrication of ready‐made Ni(OH)2/nickel foam (NF) electrocatalysts for overall water splitting by using the environmentally friendly and sustainable approach of electric‐field‐assisted pulsed laser ablation in liquid for the generation of nanoparticles (NPs) and the simultaneous electrodeposition of the NPs on the NF substrate. The as‐fabricated electrocatalyst is found to be highly efficient and stable in alkaline medium for hydrogen evolution reaction (HER) and OER kinetics with remarkably low overpotentials of 187 mV (vs. RHE for HER) and 166 mV (vs. RHE for OER) to reach a current density of 20 mA cm−2, with HER and OER Tafel slopes of 82 and 69 mV dec−1, respectively. The two‐electrode electrochemical cell made of the bifunctional Ni(OH)2/NF electrocatalyst requires an overpotential as low as 168 mV to drive a 10 mA/cm2 current density over a long period. These results reveal the long‐term stability and activity of the Ni(OH)2)/NF electrocatalyst towards overall water (OW) splitting. The developed sustainable approach of electrocatalyst fabrication can be extended to a range of materials, beyond Ni(OH)2/NF, to boost a range of chemical reactions, beyond water splitting. [ABSTRACT FROM AUTHOR]

Pocock, Graham et al. present TIAToolbox, a Python toolbox for computational pathology. The extendable library can be used for data loading, pre-processing, model inference, post-processing, and visualization.

Abstract Due to COVID-19 outbreaks, most school pupils have had to be home-schooled for long periods of time. Two editions of a web-based competition “Beat the Pathologists” for school age participants in the UK ran to fill up pupils’ spare time after home-schooling and evaluate their ability on contributing to AI annotation. The two editions asked the participants to annotate different types of cells on Ki67 stained breast cancer images. The Main competition was at four levels with different level of complexity. We obtained annotations of four kinds of cells entered by school pupils and ground truth from expert pathologists. In this paper, we analyse school pupils’ performance on differentiating different kinds of cells and compare their performance with two neural networks (AlexNet and VGG16). It was observed that children tend to get very good performance in tumour cell annotation with the best F1 measure 0.81 which is a metrics taking both false positives and false negatives into account. Low accuracy was achieved with F1 score 0.75 on positive non-tumour cells and 0.59 on negative non-tumour cells. Superior performance on non-tumour cell detection was achieved by neural networks. VGG16 with training from scratch achieved an F1 score over 0.70 in all cell categories and 0.92 in tumour cell detection. We conclude that non-experts like school pupils have the potential to contribute to large-scale labelling for AI algorithm development if sufficient training activities are organised. We hope that competitions like this can promote public interest in pathology and encourage participation by more non-experts for annotation.

Abstract Recent advances in whole‐slide imaging (WSI) technology have led to the development of a myriad of computer vision and artificial intelligence‐based diagnostic, prognostic, and predictive algorithms. Computational Pathology (CPath) offers an integrated solution to utilise information embedded in pathology WSIs beyond what can be obtained through visual assessment. For automated analysis of WSIs and validation of machine learning (ML) models, annotations at the slide, tissue, and cellular levels are required. The annotation of important visual constructs in pathology images is an important component of CPath projects. Improper annotations can result in algorithms that are hard to interpret and can potentially produce inaccurate and inconsistent results. Despite the crucial role of annotations in CPath projects, there are no well‐defined guidelines or best practices on how annotations should be carried out. In this paper, we address this shortcoming by presenting the experience and best practices acquired during the execution of a large‐scale annotation exercise involving a multidisciplinary team of pathologists, ML experts, and researchers as part of the Pathology image data Lake for Analytics, Knowledge and Education (PathLAKE) consortium. We present a real‐world case study along with examples of different types of annotations, diagnostic algorithm, annotation data dictionary, and annotation constructs. The analyses reported in this work highlight best practice recommendations that can be used as annotation guidelines over the lifecycle of a CPath project.

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Field experiment was carried out to study the effect of different tillage systems and skipping irrigation at different growth stages along with or without foliar applied potassium on phenology, growth and yield of maize hybrid. Results revealed that maximum grains per cob (381), 1000-grains weight (281.00 g), grain yield (7.82 t ha-1) and biological yield (14.78 t ha-1) were observed in conventional tillage system as compared to zero tillage. Regarding the irrigation levels and potassium (K) spray maximum grains per cob (390), 1000-grains weight (309 g), grain yield (8 t ha-1) and biological yield (15.33 t ha-1) were recorded with full irrigation + 1% Sulphate of Potash (SOP) spray. Skipping irrigation at tasseling stage had the most negative effect on yield attributes. Foliar application of SOP at 1%with full irrigationsunder conventional tillage system significantly improved the phonological and yield attributes of autumn planted maize. [ABSTRACT FROM AUTHOR]

Sperm survival depending upon integral membranes and function is imperative for fertilization. This study was designed to augment survival of buffalo spermatozoa using alpha-lipoic acid ( ALA) and cholesterol-loaded cyclodextrin ( CLC) during cryopreservation. Semen was frozen using 0, 0.5, 1, 1.5, 2 and 2.5 mmol L−1 ALA (experiment 1) and ALA or CLC separately or together (experiment 2). Semen was assessed for post-thaw motility, plasma membrane integrity ( PMI), intact acrosome and plasma membrane ( IACR- IPM) and DNA integrity at 0, 1.5, 3 and 4.5 hr of incubation. In experiment 1, use of 0.5 mmol L−1 ALA enhanced the sperm cryosurvival and post-thaw longevity than other groups up to 4.5 hr of incubation, and this concentration of ALA was used in second experiment with CLC. The results revealed higher ( p < .05) sperm survival function and time of sperm attributes due to use of ALA than CLC and control. However, the sperm quality did not improve ( p > .05) when ALA was combined with CLC. In conclusion, survival of buffalo bull spermatozoa during freeze-thawing and post-thaw incubation can be enhanced more with ALA than CLC or control, followed by CLC than control. However, there is no synergistic effect on survival of buffalo bull spermatozoa due to ALA and CLC. [ABSTRACT FROM AUTHOR]

Summary: Background: Determining the status of molecular pathways and key mutations in colorectal cancer is crucial for optimal therapeutic decision making. We therefore aimed to develop a novel deep learning pipeline to predict the status of key molecular pathways and mutations from whole-slide images of haematoxylin and eosin-stained colorectal cancer slides as an alternative to current tests. Methods: In this retrospective study, we used 502 diagnostic slides of primary colorectal tumours from 499 patients in The Cancer Genome Atlas colon and rectal cancer (TCGA-CRC-DX) cohort and developed a weakly supervised deep learning framework involving three separate convolutional neural network models. Whole-slide images were divided into equally sized tiles and model 1 (ResNet18) extracted tumour tiles from non-tumour tiles. These tumour tiles were inputted into model 2 (adapted ResNet34), trained by iterative draw and rank sampling to calculate a prediction score for each tile that represented the likelihood of a tile belonging to the molecular labels of high mutation density (vs low mutation density), microsatellite instability (vs microsatellite stability), chromosomal instability (vs genomic stability), CpG island methylator phenotype (CIMP)-high (vs CIMP-low), BRAFmut (vs BRAFWT), TP53mut (vs TP53WT), and KRASWT (vs KRASmut). These scores were used to identify the top-ranked titles from each slide, and model 3 (HoVer-Net) segmented and classified the different types of cell nuclei in these tiles. We calculated the area under the convex hull of the receiver operating characteristic curve (AUROC) as a model performance measure and compared our results with those of previously published methods. Findings: Our iterative draw and rank sampling method yielded mean AUROCs for the prediction of hypermutation (0·81 [SD 0·03] vs 0·71), microsatellite instability (0·86 [0·04] vs 0·74), chromosomal instability (0·83 [0·02] vs 0·73), BRAFmut (0·79 [0·01] vs 0·66), and TP53mut (0·73 [0·02] vs 0·64) in the TCGA-CRC-DX cohort that were higher than those from previously published methods, and an AUROC for KRASmut that was similar to previously reported methods (0·60 [SD 0·04] vs 0·60). Mean AUROC for predicting CIMP-high status was 0·79 (SD 0·05). We found high proportions of tumour-infiltrating lymphocytes and necrotic tumour cells to be associated with microsatellite instability, and high proportions of tumour-infiltrating lymphocytes and a low proportion of necrotic tumour cells to be associated with hypermutation. Interpretation: After large-scale validation, our proposed algorithm for predicting clinically important mutations and molecular pathways, such as microsatellite instability, in colorectal cancer could be used to stratify patients for targeted therapies with potentially lower costs and quicker turnaround times than sequencing-based or immunohistochemistry-based approaches. Funding: The UK Medical Research Council.

Introduction: Pre-eclampsia is a serious complication of pregnancy and is associated with increased maternal and perinatal morbidity and mortality. Increased iron and ferritin concentration is associated to a higher risk of pre-eclampsia and prophylactic iron supplementation can sometimes be harmful to pregnant women who are otherwise not iron deficient. Aim: To analyse serum iron, serum ferritin, serum Transferrin Receptor (sTFR) levels, Total Iron Binding Capacity (TIBC) among iron supplemented pre-eclamptic cases and normotensive pregnant controls. Materials and Methods: This prospective, observational, case-control study was conducted in Jaipur National University Institute of Medical Sciences and Research Centre (JNUIMSRC), Jaipur, Rajasthan, India for a period of one year from (February 2019- January 2020). A total of 200 pregnant females, all on iron supplementation during the course of their pregnancy, 100 in pre-eclampsia group (case group); 100 in normotensive group (control group), in third trimester and between 18-45 years of age were enrolled in the study. Serum iron, serum ferritin, sTFR and TIBC were estimated using fully automated analysers. Results: Median Interquartile Range (IQR) of serum ferritin levels in cases were higher than the controls [41 (30-70) vs 24 (17-44); p40 ng/mL had three times increased risk of PE (Odds ratio =3, 95% Cl=1.571- 5.157). Conclusion: Elevated serum ferritin, decreased sTFR levels and correlation of Ferritin and TIBC to BP in pre-eclampsia compels us to put cautionary remarks over testing these parameters before continuing with iron supplements. This may prevent a possible iron related added complications in pre-eclampsia and help clinicians decide about restricting/lowering iron supplement doses in case a situation of iron overload arise.

Background: Increasing global consumption of fossil fuels leads to greenhouse gas emissions, climate change and environmental pollution. Agricultural, animal and food industrial waste is one of the main sources of pollution. The bioethanol industry is one of 17 highly polluted industries. In the process of producing bioethanol, vinasse is produced, and so far 22.4 Giga litter of vinasse has been produced worldwide. With fossil fuel resources running out, energy production from renewable sources, including organic waste, has been considered in recent years and has transformed biological hydrogen production into a new approach to replace it with fossil fuels. The purpose of this study was to evaluate Biohydrogen production from Vinasse bioethanol processing industry as clean and renewable energy. Methods: In this review study conducted in 1398, 150 indexed articles from the past 15 years were used in the databases of ProQuest, Science Direct, Pubmed, Google Scholar and Scopus. Results: The results showed that vinasse is the most effective organic material for high-efficiency Biohydrogen production and dark fermentation hydrogen is the most promising biological hydrogen production method. Microbial species, reactor type, pH, temperature, substrate type and concentration, organic loading rate (OLR), hydraulic retention time (HRT), nitrogen, phosphate and iron concentrations are among the parameters influencing the process to maximize biohydrogen productivity and efficiency and concentration. High volatile fatty acids are also a major inhibitor of Biohydrogen production. Conclusion: Compared to the thermal energy content of methane, ethanol and gasoline, hydrogen thermal energy is high (142 kj/g) and water is the only by-product of hydrogen burning. Anaerobic treatment is the most effective way to reduce the pollution of Vinasse. In addition to reducing environmental pollution, the economical result of Biohydrogen production as a clean energy source is that by optimizing the operating conditions of the process we can achieve maximum efficiency and productivity of Biohydrogen production.

During the survey a total 69 soil samples were collected from various agro-ecological areas with different cropping history in six districts comprising Mianwali, Bhakkar, Gujranwala, Sialkot, Gujrat and Mandi Bahauddin of Punjab province, Pakistan and assessed for nematode populations. Analysis of soil samples revealed the presence of eight genera of plant parasitic and soil nematodes. The most frequently isolated nematode population from all the soil samples were Tylenchorhynchus sp., Xiphinema sp., and Cephalobus sp. Prevalence of nematodes were high in districts Mandi Bahauddin and Gujranwala with soil cropping history of maize and rice cultivation. [ABSTRACT FROM AUTHOR]

The study was aimed to examine the nutrients intake, nutrients utilization, milk production, milk fat content, nitrogen balance and hematological response of buffaloes to varying rumen undegradable protein (RUP) concentrations, frequently referred as bypass protein, in Completely Randomized Design. Fifteen dry pregnant Nili-Ravi buffaloes in their last month of pregnancy with almost similar milk production record of their previous lactation were divided into three groups, five animals in each. Three iso-caloric and iso-nitrogenous diets with 50, 40 and 30% concentrations of RUP were formulated and denoted as RUP50, RUP40 and RUP30, respectively. The study lasted for 120 days, first ten days were adaptation period while every six days after every fifteen days of the remaining period served as collection periods. The findings revealed unaltered nutrients intake in buffaloes fed different RUP concentrations, except RUP intake which decreased (P<0.01) with decreasing the dietary concentration of RUP. However, dry matter digestibility increased (P<0.01) with gradual decreased dietary RUP concentration while crude protein and neutral detergent fiber digestibilities remained unchanged. Slight decrease in milk yield was noticed in buffalo fed on diets with gradual decreased RUP concentrations, however, statistically it remained non-significant. Hematological analysis indicated decreased (P<0.01) mean corpuscular volume and hemoglobin concentrations in buffaloes fed on gradual decreased dietary RUP concentrations. Milk fat content, weight gain and nitrogen balance in buffaloes receiving different RUP concentration didn't show any variation. [ABSTRACT FROM AUTHOR]