Management of stripe rust of barley (Hordeum vulgare L.) using fungicides

Under field conditions, various fungicide molecules were validated for their effectiveness on barley (Hordeum vulgare L.) stripe rust Puccinia striiformis f. sp. consecutively for three years under artificial field epiphytotic conditions. Seven fungicides viz., propiconazole 25%EC (tilt @ 0.1%), tebuconazole 25.9% m/m EC (folicur @ 0.1%), triademefon 25%WP (bayleton @ 0.1%), propiconazole 25%EC (tilt @ 0.05%), tebuconazole 25.9% m/m EC (folicur @ 0.05%), triademefon 25%WP (bayleton@ 0.05%), and mancozeb 75%WP (dithane M45 @ 0.2%) with variousconcentrations were tested for their effectiveness in controlling barley stripe rust severity. All fungicide applications resulted in lower disease severity and higher grain yields than untreated check plots. All the fungicides @ 0.1% concentrations reduced disease severity ranging from 87.8% to 95.6% except Mancozeb @ 0.2% (34.4%). Significant higher yield was obtained with Propiconazole @ 0.1% (26.7 q/ha) followed by Tebuconazole @ 0.1% (25.2 q/ha) and Triademefon @ 0.1% (24.5 q/ha). The present study revealed propiconazole as the most effective fungicide for the control of stripe rust of barley under epiphytotic conditions

Rede neural convolucional eficiente para detecção e contagem dos glóbulos sanguíneos

Blood cell analysis is an important part of the health and immunity assessment. There are three major components of the blood: red blood cells, white blood cells, and platelets. The count and density of these blood cells are used to find multiple disorders like blood infections (anemia, leukemia, among others). Traditional methods are time-consuming, and the test cost is high. Thus, it arises the need for automated methods that can detect different kinds of blood cells and count the number of cells. A convolutional neural network-based framework is proposed for detecting and counting the cells. The neural network is trained for the multiple iterations, and a model having lower validation loss is saved. The experiments are done to analyze the performance of the detection system and results with high accuracy in the counting of the cells. The mean average precision is achieved when compared to ground truth provided to respective labels. The value of the average precision is found to be ranging from 70% to 99.1%, with a mean average precision value of 85.35%. The proposed framework had much less time complexity: it took only 0.111 seconds to process an image frame with dimensions of 640×480 pixels. The system can also be implemented in low-cost, single-board computers for rapid prototyping. The efficiency of the proposed framework to identify and count different blood cells can be utilized to assist medical professionals in finding disorders and making decisions based on the obtained report.El análisis de células sanguíneas es una parte importante de la evaluación de la salud y la inmunidad. Hay tres componentes principales de los glóbulos rojos, los glóbulos blancos y las plaquetas. El recuento y la densidad de estas células sanguíneas se utilizan para encontrar múltiples trastornos como infecciones de la sangre como anemia, leucemia, etc. Los métodos tradicionales consumen mucho tiempo y el costo de las pruebas es alto. Por tanto, surge la necesidad de métodos automatizados que puedan detectar diferentes tipos de células sanguíneas y contar el número de células. Se propone un marco basado en una red neuronal convolucional para la detección y el recuento de las células. La red neuronal se entrena para las múltiples iteraciones y se guarda un modelo que tiene una menor pérdida de validación. Los experimentos se realizan con el fin de analizar el rendimiento del sistema de detección y los resultados con alta precisión en el recuento de células. La precisión promedio se logra al analizar las respectivas etiquetas que hay en la imagen. Se ha determinado que el valor de la precisión promedio, oscila entre el 70% y el 99,1% con un valor medio de 85,35%. El coste computacional de la propuesta fue de 0.111 segundos, procesar una imagen con dimensiones de 640 × 480 píxeles. El sistema también se puede implementar en ordenadores con CPU de bajo costo, para la creación rápida de prototipos. La eficiencia de la propuesta, para identificar y contar diferentes células sanguíneas, se puede utilizar para ayudar a los profesionales médicos a encontrar los trastornos y la toma decisiones, a partir de la identificación automática.O exame de células sanguíneas é uma parte importante da avaliação de saúde e imunidade. Há três componentes principais dos glóbulos vermelhos, glóbulos brancos e plaquetas. A contagem e a densidade dessas células sanguíneas são usadas para encontrar múltiplos distúrbios, tais como infecções no sangue: anemia, leucemia, etc. Os métodos tradicionais são demorados e o custo dos testes é alto. Portanto, surge a necessidade de métodos automatizados que possam detectar diferentes tipos de células sanguíneas e contar o número de células. É proposta uma estrutura baseada em rede neural convolucional para a detecção e contagem de células. A rede neural é treinada para múltiplas iterações e é salvo um modelo que tem uma menor perda de validação. São realizados experimentos para analisar o desempenho do sistema de detecção e os resultados com alta precisão na contagem de células. A precisão média é obtida analisando os respectivos rótulos na imagem. Foi determinado que o valor médio de precisão oscila entre 70 % e 99,1 % com um valor médio de 85,35 %. O custo computacional da proposta foi de 0,111 segundos, processando uma imagem com dimensões de 640 × 480 pixels. O sistema também pode ser implementado em computadores com CPUs de baixo custo para prototipagem rápida. A eficiência da proposta, para identificar e contar diferentes células sanguíneas, pode ser usada para ajudar os profissionais médicos a encontrar distúrbios e tomar decisões, com base na identificação automática

Pierwotny śródczaszkowy bazaloidalny rak płaskonabłonkowy

Primary intracranial squamous cell carcinoma is extremely rare, with most cases arising from malignant transformation of dysembryogenetic lesions such as epidermoid and dermoid cysts. Intracranial squamous cell neoplasm arising de novo is even rarer and has been reported in only four patients to date. We herein describe a case of primary intracranial squamous cell carcinoma arising de novo in the right frontal lobe in a 35-year-old woman treated with a combination of surgery and postoperative conformal radiation. We have also shed light on the biology and the therapeutic options of this enigmatic tumour.Pierwotny śródczaszkowy rak płaskonabłonkowy jest wyjątkową rzadkością i w większości przypadków rozwija się w wyniku zezłośliwienia zmian o charakterze dysembriogenetycznym, np. torbieli naskórkowej lub skórzastej. Śródczaszkowy rak płaskonabłonkowy powstały de novo jest jeszcze rzadszy – dotąd opisano 4 takie przypadki. W niniejszej pracy przedstawiono przypadek nowotworu powstałego de novo w prawym płacie czołowym u 35-letniej chorej, którą z tego powodu poddano leczeniu chirurgicznemu i pooperacyjnej radioterapii konformalnej. Podano również informacje na temat biologii i możliwości leczenia tego zagadkowego guza

The impact of a pharmacist intervention at an intensive care rehabilitation clinic

Objective: While disruptions in medications are common among patients who survive critical illness, there is limited information about specific medication-related problems among survivors of critical care. This study sought to determine the prevalence of specific medication-related problems detected in patients, seen after critical care discharge. Design: Consecutive patients attending an intensive care unit (ICU) follow-up programme were included in this single-centre service evaluation. Setting: Tertiary care regional centre in Scotland (UK). Participants: 47 patients reviewed after critical care discharge at an ICU follow-up programme. Interventions: Pharmacists conducted a full medication review, including: medicines reconciliation, assessing the appropriateness of each prescribed medication, identification of any medication-related problems and checking adherence. Measurements: Medication-related problems in patients following critical care discharge. Interventions and medication-related problems were systematically graded and risk factors were identified using an adapted version of the National Patient Safety Agency Risk Matrix. Main results: 69 medication-related problems were identified in 38 (81%) of the 47 patients. The most common documented problem was drug omission (29%). 64% of the medication-related problems identified were classified as either moderate or major. The number of pain medications prescribed at discharge from intensive care was predictive of medication-related problems (OR 2.02, 95% CI 1.14 to 4.26, p=0.03). Conclusions: Medication problems are common following critical care. Better communication of medication changes both to patients and their ongoing care providers may be beneficial following a critical care admission. In the absence of highly effective communication, a pharmacy intervention may contribute substantially to an intensive care rehabilitation or recovery programme

Risk of secondhand smoke exposure and severity of COVID-19 infection: multicenter case–control study

IntroductionExposure to secondhand smoke (SHS) is an established causal risk factor for cardiovascular disease (CVD) and chronic lung disease. Numerous studies have evaluated the role of tobacco in COVID-19 infection, severity, and mortality but missed the opportunity to assess the role of SHS. Therefore, this study was conducted to determine whether SHS is an independent risk factor for COVID-19 infection, severity, mortality, and other co-morbidities.MethodologyMulticentric case–control study was conducted across six states in India. Severe COVID-19 patients were chosen as our study cases, and mild and moderate COVID-19 as control were evaluated for exposure to SHS. The sample size was calculated using Epi-info version 7. A neighborhood-matching technique was utilized to address ecological variability and enhance comparability between cases and controls, considering age and sex as additional matching criteria. The binary logistic regression model was used to measure the association, and the results were presented using an adjusted odds ratio. The data were analyzed using SPSS version 24 (SPSS Inc., Chicago, IL, USA).ResultsA total of 672 cases of severe COVID-19 and 681 controls of mild and moderate COVID-19 were recruited in this study. The adjusted odds ratio (AOR) for SHS exposure at home was 3.03 (CI 95%: 2.29–4.02) compared to mild/moderate COVID-19, while SHS exposure at the workplace had odds of 2.19 (CI 95%: 1.43–3.35). Other factors significantly related to the severity of COVID-19 were a history of COVID-19 vaccination before illness, body mass index (BMI), and attached kitchen at home.DiscussionThe results of this study suggest that cumulative exposure to secondhand cigarette smoke is an independent risk factor for severe COVID-19 illness. More studies with the use of biomarkers and quantification of SHS exposure in the future are needed

Challenges and opportunities in mixed method data collection on mental health issues of health care workers during COVID-19 pandemic in India

Background: The present paper describes the key challenges and opportunities of mixed method telephonic data collection for mental health research using field notes and the experiences of the investigators in a multicenter study in ten sites of India. The study was conducted in public and private hospitals to understand the mental health status, social stigma and coping strategies of different healthcare personnel during the COVID-19 pandemic in India.Methods: Qualitative and quantitative interviews were conducted telephonically. The experiences of data collection were noted as a field notes/diary by the data collectors and principal investigators.Results: The interviewers reported challenges such as network issues, lack of transfer of visual cues and sensitive content of data. Although the telephonic interviews present various challenges in mixed method data collection, it can be used as an alternative to face-to-face data collection using available technology.Conclusions: It is important that the investigators are well trained keeping these challenges in mind so that their capacity is built to deal with these challenges and good quality data is obtained

Medication related problems in ICU survivors: learning from a multi-centre programme

No abstract available

Software-Hardware Co-design for Fast and Scalable Training of Deep Learning Recommendation Models

Deep learning recommendation models (DLRMs) are used across many business-critical services at Facebook and are the single largest AI application in terms of infrastructure demand in its data-centers. In this paper we discuss the SW/HW co-designed solution for high-performance distributed training of large-scale DLRMs. We introduce a high-performance scalable software stack based on PyTorch and pair it with the new evolution of Zion platform, namely ZionEX. We demonstrate the capability to train very large DLRMs with up to 12 Trillion parameters and show that we can attain 40X speedup in terms of time to solution over previous systems. We achieve this by (i) designing the ZionEX platform with dedicated scale-out network, provisioned with high bandwidth, optimal topology and efficient transport (ii) implementing an optimized PyTorch-based training stack supporting both model and data parallelism (iii) developing sharding algorithms capable of hierarchical partitioning of the embedding tables along row, column dimensions and load balancing them across multiple workers; (iv) adding high-performance core operators while retaining flexibility to support optimizers with fully deterministic updates (v) leveraging reduced precision communications, multi-level memory hierarchy (HBM+DDR+SSD) and pipelining. Furthermore, we develop and briefly comment on distributed data ingestion and other supporting services that are required for the robust and efficient end-to-end training in production environments

Factors associated with stigma and manifestations experienced by Indian health care workers involved in COVID-19 management in India: A qualitative study

Healthcare personnel who deal with COVID-19 experience stigma. There is a lack of national-level representative qualitative data to study COVID-19-related stigma among healthcare workers in India. The present study explores factors associated with stigma and manifestations experienced by Indian healthcare workers involved in COVID-19 management. We conducted in-depth interviews across 10 centres in India, which were analysed using NVivo software version 12. Thematic and sentiment analysis was performed to gain deep insights into the complex phenomenon by categorising the qualitative data into meaningful and related categories. Healthcare workers (HCW) usually addressed the stigma they encountered when doing their COVID duties under the superordinate theme of stigma. Among them, 77.42% said they had been stigmatised in some way. Analyses revealed seven interrelated themes surrounding stigma among healthcare workers. It can be seen that the majority of the stigma and coping sentiments fall into the mixed category, followed by the negative sentiment category. This study contributes to our understanding of stigma and discrimination in low- and middle-income settings. Our data show that the emergence of fear of the virus has quickly turned into a stigma against healthcare workers