Agriculture plays a vital role in the economic growth of any country. With the increase of population, frequent changes in climatic conditions and limited resources, it becomes a challenging task to ...fulfil the food requirement of the present population. Precision agriculture also known as smart farming have emerged as an innovative tool to address current challenges in agricultural sustainability. The mechanism that drives this cutting edge technology is machine learning (ML). It gives the machine ability to learn without being explicitly programmed. ML together with IoT (Internet of Things) enabled farm machinery are key components of the next agriculture revolution. In this article, authors present a systematic review of ML applications in the field of agriculture. The areas that are focused are prediction of soil parameters such as organic carbon and moisture content, crop yield prediction, disease and weed detection in crops and species detection. ML with computer vision are reviewed for the classification of a different set of crop images in order to monitor the crop quality and yield assessment. This approach can be integrated for enhanced livestock production by predicting fertility patterns, diagnosing eating disorders, cattle behaviour based on ML models using data collected by collar sensors, etc. Intelligent irrigation which includes drip irrigation and intelligent harvesting techniques are also reviewed that reduces human labour to a great extent. This article demonstrates how knowledge-based agriculture can improve the sustainable productivity and quality of the product.
Perioperative myocardial infarction or cardiac arrest is associated with significant morbidity and mortality. The Revised Cardiac Risk Index is currently the most commonly used cardiac risk ...stratification tool; however, it has several limitations, one of which is its relatively low discriminative ability. The objective of the present study was to develop and validate a predictive cardiac risk calculator.
Patients who underwent surgery were identified from the American College of Surgeons' 2007 National Surgical Quality Improvement Program database, a multicenter (>250 hospitals) prospective database. Of the 211 410 patients, 1371 (0.65%) developed perioperative myocardial infarction or cardiac arrest. On multivariate logistic regression analysis, 5 predictors of perioperative myocardial infarction or cardiac arrest were identified: type of surgery, dependent functional status, abnormal creatinine, American Society of Anesthesiologists' class, and increasing age. The risk model based on the 2007 data set was subsequently validated on the 2008 data set (n=257 385). The model performance was very similar between the 2007 and 2008 data sets, with C statistics (also known as area under the receiver operating characteristic curve) of 0.884 and 0.874, respectively. Application of the Revised Cardiac Risk Index to the 2008 National Surgical Quality Improvement Program data set yielded a relatively lower C statistic (0.747). The risk model was used to develop an interactive risk calculator.
The cardiac risk calculator provides a risk estimate of perioperative myocardial infarction or cardiac arrest and is anticipated to simplify the informed consent process. Its predictive performance surpasses that of the Revised Cardiac Risk Index.
Carotenoids comprise the most widely distributed natural pigments. In plants, they play indispensable roles in photosynthesis, furnish colors to flowers and fruit and serve as precursor molecules for ...the synthesis of apocarotenoids, including aroma and scent, phytohormones and other signaling molecules. Dietary carotenoids are vital to human health as a source of provitamin A and antioxidants. Hence, the enormous interest in carotenoids of crop plants. Over the past three decades, the carotenoid biosynthesis pathway has been mainly deciphered due to the characterization of natural and induced mutations that impair this process. Over the year, numerous mutations have been studied in dozens of plant species. Their phenotypes have significantly expanded our understanding of the biochemical and molecular processes underlying carotenoid accumulation in crops. Several of them were employed in the breeding of crops with higher nutritional value. This compendium of all known random and targeted mutants available in the carotenoid metabolic pathway in plants provides a valuable resource for future research on carotenoid biosynthesis in plant species.
Abstract Objective To identify preoperative factors associated with an increased risk of postoperative pneumonia and subsequently develop and validate a risk calculator. Patients and Methods The ...American College of Surgeons’ National Surgical Quality Improvement Program, a multicenter, prospective data set (2007-2008) was used. Univariate and multivariate logistic regression analyses were performed. The 2007 data set (N=211,410) served as the training set, and the 2008 data set (N=257,385) served as the validation set. Results In the training set, 3825 patients (1.8%) experienced postoperative pneumonia. Patients who experienced postoperative pneumonia had a significantly higher 30-day mortality (17.0% vs 1.5%; P <.001). On multivariate logistic regression analysis, 7 preoperative predictors of postoperative pneumonia were identified: age, American Society of Anesthesiologists class, chronic obstructive pulmonary disease, dependent functional status, preoperative sepsis, smoking before operation, and type of operation. The risk model based on the training data set was subsequently validated on the validation data set, with model performance being very similar (C statistic: 0.860 and 0.855, respectively). The high C statistic indicates excellent predictive performance. The risk model was used to develop an interactive risk calculator. Conclusion Preoperative variables associated with an increased risk of postoperative pneumonia include age, American Society of Anesthesiologists class, chronic obstructive pulmonary disease, dependent functional status, preoperative sepsis, smoking before operation, and type of operation. The validated risk calculator provides a risk estimate for postoperative pneumonia and is anticipated to aid in surgical decision making and informed patient consent.
Background Postoperative respiratory failure (PRF) (requiring mechanical ventilation > 48 h after surgery or unplanned intubation within 30 days of surgery) is associated with significant morbidity ...and mortality. The objective of this study was to identify preoperative factors associated with an increased risk of PRF and subsequently develop and validate a risk calculator. Methods The American College of Surgeons National Surgical Quality Improvement Program (NSQIP), a multicenter, prospective data set (2007-2008), was used. The 2007 data set (n = 211,410) served as the training set and the 2008 data set (n = 257,385) as the validation set. Results In the training set, 6,531 patients (3.1%) developed PRF. Patients who developed PRF had a significantly higher 30-day mortality (25.62% vs 0.98%, P < .0001). On multivariate logistic regression analysis, five preoperative predictors of PRF were identified: type of surgery, emergency case, dependent functional status, preoperative sepsis, and higher American Society of Anesthesiologists (ASA) class. The risk model based on the training data set was subsequently validated on the validation data set. The model performance was very similar between the training and the validation data sets (c-statistic, 0.894 and 0.897, respectively). The high c-statistics (area under the receiver operating characteristic curve) indicate excellent predictive performance. The risk model was used to develop an interactive risk calculator. Conclusions Preoperative variables associated with increased risk of PRF include type of surgery, emergency case, dependent functional status, sepsis, and higher ASA class. The validated risk calculator provides a risk estimate of PRF and is anticipated to aid in surgical decision making and informed patient consent.
Objective Two randomized trials to date have compared open surgery (OS) and endovascular (EVAR) repair for ruptured abdominal aortic aneurysm (rAAA); however, neither addressed optimal management of ...unstable patients. Single-center reports have produced conflicting data regarding the superiority of one vs the other, with the lack of statistical power due to low patient numbers. Furthermore, previous studies have not delineated between the outcomes of stable patients with a contained rupture vs those patients with instability. Our objective was to compare 30-day outcomes in patients undergoing OS vs EVAR for all rAAAs, focusing specifically on patients with instability. Methods Patients who underwent repair of rAAA were identified from the American College of Surgeons (ACS) National Surgical Quality Improvement Program (NSQIP) database (2005 to 2010). Unstable patients with rupture were identified as those who were American Society of Anesthesiologists Physical Status Classification 4 or 5 requiring emergency repair with at least one of the following: preoperative shock, preoperative transfusion of >4 units, preoperative intubation, or preoperative coma or impaired sensorium. Univariable and multivariable logistic regression analyses were performed. Results Of the 1447 patients with rAAA, 65.5% underwent OS and 34.5% EVAR. Forty-five percent were unstable, and for these patients, OS was performed in 71.3% and EVAR in 28.7%. The 30-day mortality rate was 47.9% (OS, 52.8%; EVAR, 35.6%; P < .0001) for unstable rAAAs and was 22.4% for stable rAAAs (OS, 26.3%; EVAR, 16.4%; P = .001). Amongst patients with unstable rAAA, 26% had a myocardial infarction or cardiac arrest ≤30 days (OS, 29.0%; EVAR, 19.1%; P = .006), and 17% needed postoperative dialysis (OS, 18.7%; EVAR, 12.8%; P = .04). Amongst patients with stable rAAA, 13.6% had a myocardial infarction or cardiac arrest ≤30 days (OS, 14.9%; EVAR, 11.6%; P = .20), and 11.5% needed postoperative dialysis (OS, 13.3%; EVAR, 8.7%; P = .047). Multivariable analyses showed OS was a predictor of 30-day mortality for unstable rAAA (odds ratio, 1.74; 95% confidence interval, 1.16-2.62) and stable rAAA (odds ratio, 1.64; 95% confidence interval, 1.10-2.43). Conclusions Approximately one-third of patients treated for rAAA undergo EVAR in NSQIP participating hospitals. Not surprisingly, unstable patients have less favorable outcomes. In both stable and unstable rAAA patients, EVAR is associated with a diminished 30-day mortality and morbidity.
Background Patients presenting with acute mesenteric ischemia (AMI) sufficiently advanced to require bowel resection have a high morbidity and mortality. The objective of this study was to analyze ...these patients to determine if certain pre- or intraoperative variables are predictive of death or complications which could then be used to develop a predictive model to aid in surgical decision-making. Methods Patients undergoing bowel resection for AMI were identified from the American College of Surgeons’ National Surgical Quality Improvement Program database (2007–2008). Multiple logistic regression analysis was performed. Results The 861 patients identified had a median age of 69 years. Thirty-day postoperative morbidity and mortality were 56.6% and 27.9%, respectively. Pre- and intraoperative variables significantly associated with postoperative mortality (C statistic, 0.84) included preoperative do not resuscitate order, open wound, low albumin, dirty vs clean-contaminated case, and poor functional status. Pre- and intraoperative variables significantly associated with postoperative morbidity (C statistic, 0.79) included admission from chronic care facility, recent myocardial infarction, chronic obstructive pulmonary disease, requiring ventilator support, preoperative renal failure, previous cardiac surgery, and prolonged operative time. A predictive risk calculator was developed using these variables. Conclusion Mortality and morbidity rates after bowel resection for AMI are high. A risk calculator for prediction of postoperative mortality and morbidity has been developed and awaits validation in subsequent studies.
Although COPD affects large sections of the population, its effects on postoperative outcomes have not been rigorously studied. The objectives of this study were to describe the prevalence of COPD in ...patients undergoing surgery and to analyze the associations between COPD and postoperative morbidity, mortality, and hospital length of stay.
Patients with COPD who underwent surgery were identified from the National Surgical Quality Improvement Program database (2007-2008). Univariate and multivariate analyses were performed on this multicenter, prospective data set (N = 468,795).
COPD was present in 22,576 patients (4.82%). These patients were more likely to be older, men, white, smokers, and taking corticosteroids and had a lower BMI (P < .0001 for each). Median length of stay was 4 days for patients with COPD vs 1 day in those without COPD (P < .0001). Thirty-day morbidity rates were 25.8% and 10.2% for patients with and without COPD, respectively (P < .0001). Thirty-day death rates were 6.7% and 1.4% for patients with and without COPD, respectively (P < .0001). After controlling for > 50 comorbidities through logistic regression modeling, COPD was independently associated with higher postoperative morbidity (OR, 1.35; 95% CI, 1.30-1.40; P < .0001) and mortality (OR, 1.29; 95% CI, 1.19-1.39; P < .0001). Multivariate analyses with each individual postoperative complication as the outcome of interest showed that COPD was associated with increased risk for postoperative pneumonia, respiratory failure, myocardial infarction, cardiac arrest, sepsis, return to the operating room, and renal insufficiency or failure (P < .05 for each).
COPD is common among patients undergoing surgery and is associated with increased morbidity, mortality, and length of stay.
Background Although a risk score estimating postoperative mortality for patients undergoing gastric bypass exists, there is none predicting postoperative morbidity. Our objective was to develop a ...validated risk calculator for 30-day postoperative morbidity of bariatric surgery patients. Study Design We used the American College of Surgeons' 2007 National Surgical Quality Improvement Program (NSQIP) dataset. Patients undergoing bariatric surgery for morbid obesity were studied. Multiple logistic regression analysis was performed and a risk calculator was created. The 2008 NSQIP dataset was used for its validation. Results In 11,023 patients, mean age was 44.6 years, 20% were male, 77% were Caucasian, and mean body mass index (BMI; calculated as kg/m2 ) was 48.9. Thirty-day morbidity and mortality were 4.2% and 0.2%, respectively. Risk factors associated with increased risk of postoperative morbidity included recent MI/angina (odds ratio OR = 3.65; 95% CI 1.23 to 10.8), dependent functional status (OR = 3.48; 95% CI 1.78 to −6.80), stroke (OR = 2.89; 95% CI 1.09 to 7.67), bleeding disorder (OR = 2.23; 95% CI 1.47 to 3.38), hypertension (OR = 1.34; 95% CI 1.10 to 1.63), BMI, and type of bariatric surgery. Patients with BMI 35 to <45 and >60 had significantly higher adjusted OR compared with patients with BMI of 45 to 60 (p < 0.05 for all). These factors were used to create the risk calculator and subsequently validate it, with the model performance very similar between the 2007 training dataset and the 2008 validation dataset (c-statistics: 0.69 and 0.66, respectively). Conclusions NSQIP data can be used to develop and validate a risk calculator that predicts postoperative morbidity after various bariatric procedures. The risk calculator is anticipated to aid in surgical decision making, informed patient consent, and risk reduction.
PDF
