A disease predictive model based on epidemiological factors for the management of bacterial leaf blight of rice / Um modelo preditivo de doenças baseado em fatores epidemiológicos para o manejo da queima bacteriana das folhas do arroz

Rice is a widely consumed staple food for a large part of the world's human population. Approximately 90% of the world's rice is grown in Asian continent and constitutes a staple food for 2.7 billion people worldwide. Bacterial leaf blight (BLB) caused by Xanthomonas oryzae pv. oryzae is one of the devastating diseases of rice. A field experiment was conducted during the year 2016 and 2017 to investigate the influence of different meteorological parameters on BLB development as well as the computation of a predictive model to forecast the disease well ahead of its appearance in the field. The seasonal dataset of disease incidence and environmental factors was used to assess five rice varieties/ cultivars (Basmati-2000, KSK-434, KSK-133, Super Basmati, and IRRI-9). The accumulated effect of two year environmental data; maximum and minimum temperature, relative humidity, wind speed, and rainfall, was studied and correlated with disease incidence. Average temperature (maximum & minimum) showed a negative significant correlation with BLB disease and all other variables; relative humidity, rainfall, and wind speed had a positive correlation with BLB disease development on individual varieties. Stepwise regression analysis was performed to indicate potentially useful predictor variables and to rule out incompetent parameters. Environmental data from the growing seasons of July to October 2016 and 2017 revealed that, with the exception of the lowest temperature, all environmental factors contributed to disease development throughout the cropping season. A disease prediction multiple regression model was developed based on two-year data (Y = 214.3-3.691 Max T-0.508 Min T + 0.767 RH + 2.521 RF + 5.740 WS), which explained 95% variability. This disease prediction model will not only help farmers in early detection and timely management of bacterial leaf blight disease of rice but may also help reduce input costs and improve product quality and quantity. The model will be both farmer and environmentally friendly.

O arroz é um alimento básico amplamente consumido por grande parte da população humana mundial. Aproximadamente 90% do arroz do mundo é cultivado no continente asiático e constitui um alimento básico para 2,7 bilhões de pessoas em todo o mundo. O crestamento bacteriano das folhas (BLB) causado por Xanthomonas oryzae pv. oryzae é uma das doenças devastadoras do arroz. Um experimento de campo foi realizado durante os anos de 2016 e 2017 para investigar a influência de diferentes parâmetros meteorológicos no desenvolvimento do BLB, bem como o cálculo de um modelo preditivo para prever a doença bem antes de seu aparecimento em campo. O conjunto de dados sazonais de incidência de doenças e fatores ambientais foi usado para avaliar cinco variedades/cultivares de arroz (Basmati-2000, KSK-434, KSK-133, Super Basmati e IRRI-9). O efeito acumulado de dados ambientais de dois anos; temperatura máxima e mínima, umidade relativa do ar, velocidade do vento e precipitação pluviométrica foram estudados e correlacionados com a incidência da doença. A temperatura média (máxima e mínima) apresentou correlação significativa negativa com a doença BLB e todas as outras variáveis; umidade relativa, precipitação e velocidade do vento tiveram uma correlação positiva com o desenvolvimento da doença BLB em variedades individuais. A análise de regressão stepwise foi realizada para indicar variáveis preditoras potencialmente úteis e para descartar parâmetros incompetentes. Os dados ambientais das safras de julho a outubro de 2016 e 2017 revelaram que, com exceção da temperatura mais baixa, todos os fatores ambientais contribuíram para o desenvolvimento da doença ao longo da safra. Um modelo de regressão múltipla de previsão de doença foi desenvolvido com base em dados de dois anos (Y = 214,3-3,691 Max T-0,508 Min T + 0,767 RH + 2,521 RF + 5,740 WS), que explicou 95% de variabilidade. Este modelo de previsão de doenças não só ajudará os agricultores na detecção precoce e gestão atempada da doença bacteriana das folhas do arroz, mas também pode ajudar a reduzir os custos de insumos e melhorar a qualidade e a quantidade do produto. O modelo será agricultor e ambientalmente amigável.

A disease predictive model based on epidemiological factors for the management of bacterial leaf blight of rice

Abstract Rice is a widely consumed staple food for a large part of the worlds human population. Approximately 90% of the worlds rice is grown in Asian continent and constitutes a staple food for 2.7 billion people worldwide. Bacterial leaf blight (BLB) caused by Xanthomonas oryzae pv. oryzae is one of the devastating diseases of rice. A field experiment was conducted during the year 2016 and 2017 to investigate the influence of different meteorological parameters on BLB development as well as the computation of a predictive model to forecast the disease well ahead of its appearance in the field. The seasonal dataset of disease incidence and environmental factors was used to assess five rice varieties/ cultivars (Basmati-2000, KSK-434, KSK-133, Super Basmati, and IRRI-9). The accumulated effect of two year environmental data; maximum and minimum temperature, relative humidity, wind speed, and rainfall, was studied and correlated with disease incidence. Average temperature (maximum & minimum) showed a negative significant correlation with BLB disease and all other variables; relative humidity, rainfall, and wind speed had a positive correlation with BLB disease development on individual varieties. Stepwise regression analysis was performed to indicate potentially useful predictor variables and to rule out incompetent parameters. Environmental data from the growing seasons of July to October 2016 and 2017 revealed that, with the exception of the lowest temperature, all environmental factors contributed to disease development throughout the cropping season. A disease prediction multiple regression model was developed based on two-year data (Y = 214.3-3.691 Max T-0.508 Min T + 0.767 RH + 2.521 RF + 5.740 WS), which explained 95% variability. This disease prediction model will not only help farmers in early detection and timely management of bacterial leaf blight disease of rice but may also help reduce input costs and improve product quality and quantity. The model will be both farmer and environmentally friendly.

Resumo O arroz é um alimento básico amplamente consumido por grande parte da população humana mundial. Aproximadamente 90% do arroz do mundo é cultivado no continente asiático e constitui um alimento básico para 2,7 bilhões de pessoas em todo o mundo. O crestamento bacteriano das folhas (BLB) causado por Xanthomonas oryzae pv. oryzae é uma das doenças devastadoras do arroz. Um experimento de campo foi realizado durante os anos de 2016 e 2017 para investigar a influência de diferentes parâmetros meteorológicos no desenvolvimento do BLB, bem como o cálculo de um modelo preditivo para prever a doença bem antes de seu aparecimento em campo. O conjunto de dados sazonais de incidência de doenças e fatores ambientais foi usado para avaliar cinco variedades/cultivares de arroz (Basmati-2000, KSK-434, KSK-133, Super Basmati e IRRI-9). O efeito acumulado de dados ambientais de dois anos; temperatura máxima e mínima, umidade relativa do ar, velocidade do vento e precipitação pluviométrica foram estudados e correlacionados com a incidência da doença. A temperatura média (máxima e mínima) apresentou correlação significativa negativa com a doença BLB e todas as outras variáveis; umidade relativa, precipitação e velocidade do vento tiveram uma correlação positiva com o desenvolvimento da doença BLB em variedades individuais. A análise de regressão stepwise foi realizada para indicar variáveis preditoras potencialmente úteis e para descartar parâmetros incompetentes. Os dados ambientais das safras de julho a outubro de 2016 e 2017 revelaram que, com exceção da temperatura mais baixa, todos os fatores ambientais contribuíram para o desenvolvimento da doença ao longo da safra. Um modelo de regressão múltipla de previsão de doença foi desenvolvido com base em dados de dois anos (Y = 214,3-3,691 Max T-0,508 Min T + 0,767 RH + 2,521 RF + 5,740 WS), que explicou 95% de variabilidade. Este modelo de previsão de doenças não só ajudará os agricultores na detecção precoce e gestão atempada da doença bacteriana das folhas do arroz, mas também pode ajudar a reduzir os custos de insumos e melhorar a qualidade e a quantidade do produto. O modelo será agricultor e ambientalmente amigável.

A disease predictive model based on epidemiological factors for the management of bacterial leaf blight of rice.

Rice is a widely consumed staple food for a large part of the world's human population. Approximately 90% of the world's rice is grown in Asian continent and constitutes a staple food for 2.7 billion people worldwide. Bacterial leaf blight (BLB) caused by Xanthomonas oryzae pv. oryzae is one of the devastating diseases of rice. A field experiment was conducted during the year 2016 and 2017 to investigate the influence of different meteorological parameters on BLB development as well as the computation of a predictive model to forecast the disease well ahead of its appearance in the field. The seasonal dataset of disease incidence and environmental factors was used to assess five rice varieties/ cultivars (Basmati-2000, KSK-434, KSK-133, Super Basmati, and IRRI-9). The accumulated effect of two year environmental data; maximum and minimum temperature, relative humidity, wind speed, and rainfall, was studied and correlated with disease incidence. Average temperature (maximum & minimum) showed a negative significant correlation with BLB disease and all other variables; relative humidity, rainfall, and wind speed had a positive correlation with BLB disease development on individual varieties. Stepwise regression analysis was performed to indicate potentially useful predictor variables and to rule out incompetent parameters. Environmental data from the growing seasons of July to October 2016 and 2017 revealed that, with the exception of the lowest temperature, all environmental factors contributed to disease development throughout the cropping season. A disease prediction multiple regression model was developed based on two-year data (Y = 214.3-3.691 Max T-0.508 Min T + 0.767 RH + 2.521 RF + 5.740 WS), which explained 95% variability. This disease prediction model will not only help farmers in early detection and timely management of bacterial leaf blight disease of rice but may also help reduce input costs and improve product quality and quantity. The model will be both farmer and environmentally friendly.

Anterior Cervical Osteophyte Resection for Treatment of Dysphagia.

STUDY DESIGN: This was a retrospective cohort study. OBJECTIVES: When anterior cervical osteophytes become large enough, they may cause dysphagia. There is a paucity of work examining outcomes and complications of anterior cervical osteophyte resection for dysphagia. METHODS: Retrospective review identified 19 patients who underwent anterior cervical osteophyte resection for a diagnosis of dysphagia. The mean age was 71 years and follow-up, 4.7 years. The most common level operated on was C3-C4 (13, 69%). RESULTS: Following anterior cervical osteophyte resection, 79% of patients had improvement in dysphagia. Five patients underwent cervical fusion; there were no episodes of delayed or iatrogenic instability requiring fusion. Fusion patients were younger (64 vs 71 years, P = .05) and had longer operative times (315 vs 121 minutes, P = .01). Age of 75 years or less trended toward improvement in dysphagia (P = .09; OR = 18.8; 95% CI 0.7-478.0), whereas severe dysphagia trended toward increased complications (P = .07; OR = 11.3; 95% CI = 0.8-158.5). Body mass index, use of an exposure surgeon, diffuse idiopathic skeletal hyperostosis diagnosis, surgery at 3 or more levels, prior neck surgery, and fusion were not predictive of improvement or complication. CONCLUSIONS: Anterior cervical osteophyte resection improves swallowing function in the majority of patients with symptomatic osteophytes. Spinal fusion can be added to address stenosis and other underlying cervical disease and help prevent osteophyte recurrence, whereas intraoperative navigation can be used to ensure complete osteophyte resection without breaching the cortex or entering the disc space. Because of the relatively high complication rate, patients should undergo thorough multidisciplinary workup with swallow evaluation to confirm that anterior cervical osteophytes are the primary cause of dysphagia prior to surgery.

Central retinal artery occlusion during vitrectomy: Immediate retinal revascularization following induction of posterior vitreous detachment.

PURPOSE: To describe a patient with acute central retinal artery occlusion (CRAO) during vitrectomy surgery and the possible role of vitrectomy in acute CRAO management. OBSERVATIONS: An 84-year-old man presented with broad vitreomacular traction and epiretinal membrane in the right eye. Preoperative assessment clearly showed normal retinal vasculature. On starting vitrectomy, complete CRAO with marked segmentation of all retinal vessels was noted. Vitrectomy was performed in the usual manner and once the posterior hyaloid detached from the disc, immediate complete revascularization of the retinal vessels was noted. The patient had a complete visual recovery. CONCLUSIONS AND IMPORTANCE: Immediate vitrectomy with induction of posterior vitreous detachment may have a role in selected cases of acute CRAO, particularly if performed within a short window.

Clinical and demographic characteristics of psychiatric inpatients admitted via emergency and non-emergency routes at a university hospital in Pakistan.

OBJECTIVE: To examine the differences in the demographic and clinical characteristics of patients admitted through emergency versus non-emergency routes and see if these two groups of patients were significantly different from each other with respect to criteria mentioned in the title. METHODS: Retrospective data was analysed in all 2576 patient records were reviewed and these patients were divided in two groups with respect to their mode of admission (emergency vs non emergency). These groups were then compared with respect to sex, age, length of stay, discharge status and diagnostic categories. Statistical package for social sciences version 8.0 (SPSS 8.0) was used to analyze the data. SETTING: The study was conducted at the Aga Khan University Hospital, is a private tertiary care hospital with a 13 bed psychiatric facility. RESULTS: When these two groups were compared, significant differences were found, with ER patients having a shorter length of stay and youngest mean age, proportion of females admitted via ER was greater than those in non-ER group. There were significantly more women in each group who were married. The percentage of patients who left against medical advice was greater in the ER group. In both the groups mood disorders including Bipolar disorder and major depressive disorders was the most prevalent category with psychotic disorders to follow. The percentage of patients in both these categories was greater in the non-ER group where as percentage of patients with conversion disorder was higher in the ER group. CONCLUSION: Significant differences were found in the patient characteristics admitted via ER versus Non ER. Shorter length of stay in ER group might indicate an acute episode resolving quickly. Studies need to be done prospectively to determine the difference in the two groups thus ascertaining the level of care needed for each group. Lastly, the high proportion of patients coming through the ER also indicates that there exists a need for primary care involvement in mental health care thus reducing the need for emergency room usage. Clinical and Demographic Characteristics of inpatients admitted via emergency and non-emergency routes at a university hospital in Pakistan.

Conversion disorder: difficulties in diagnosis using DSM-IV/ICD-10.

OBJECTIVE: Conversion disorder presents differently in various cultures. The commonest symptoms in the Asian subcontinent may be very different from those seen in Western Hemisphere. This causes some difficulty in making a diagnosis while using DSM-IV and ICD-10. METHOD: This study searched inpatient records for the last 10 years at the Aga Khan University and collected some demographic data as well as assessed the phenomenology of conversion disorder in the patient population. RESULTS: We found unresponsiveness to be the most common symptom in this sample thus not exactly fitting the DSM-IV/ICD-10 picture. CONCLUSION: We observed that current criteria of conversion disorder as stated in two major classification systems are not totally relevant to the clinical practice in Pakistan and other parts of subcontinent.