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Around 80 % of global soybean supply is produced in southeast South America (SESA), central Brazil (CB) and the United States (US) alone. This concentration of production in few regions makes global soybean supply sensitive to spatially compounding harvest failures. Weather variability is a key driver of soybean variability, with soybeans being especially vulnerable to hot and dry conditions during the reproductive growth stage in summer. El Niño–Southern Oscillation (ENSO) teleconnections can influence summer weather conditions across the Americas, presenting potential risks for spatially compounding harvest failures. Here, we develop causal structural models to quantify the influence of ENSO on soybean yields via mediating variables like local weather conditions and extratropical sea surface temperatures (SSTs). We show that soybean yields are predominately driven by soil moisture conditions in summer, explaining ∼50 %, 18 % and 40 % of yield variability in SESA, CB and the US respectively. Summer soil moisture is strongly driven by spring soil moisture, as well as by remote extratropical SST patterns in both hemispheres. Both of these soil moisture drivers are again influenced by ENSO. Our causal models show that persistent negative ENSO anomalies of -1.5 standard deviation (SD) lead to a -0.4 SD soybean reduction in the US and SESA. When spring soil moisture and extratropical SST precursors are pronouncedly negative (-1.5 SD), then estimated soybean losses increase to -0.9 SD for the US and SESA. Thus, by influencing extratropical SSTs and spring soil moisture, persistent La Niñas can trigger substantial soybean losses in both the US and SESA, with only minor potential gains in CB. Our findings highlight the physical pathways by which ENSO conditions can drive spatially compounding events. Such information may increase preparedness against climate-related global soybean supply shocks. [ABSTRACT FROM AUTHOR]

Around 80% of global soybean supply is produced in southeast South America (SESA), central Brazil (CB) and the United States (US) alone. This concentration of production in few regions makes global soybean supply sensitive to spatially compounding harvest failures. Weather variability is a key driver of soybean yield variability, with soybean especially vulnerable to hot and dry conditions during the reproductive growth stage in summer. El Niño Southern Oscillation (ENSO) teleconnections can influence summer weather conditions across the Americas presenting potential risks for spatially compounding harvest failures. Here, we develop causal structural models to quantify the influence of ENSO on crop yields via mediating variables like local weather conditions and extratropical sea-surface temperatures (SST). We show that soybean yields are predominately driven by soil moisture conditions in summer explaining ~50%, 18% and 40% of yield variability in SESA, CB and US respectively. Summer soil moisture is strongly driven by spring soil moisture as well as remote extra tropical SST patterns in both hemispheres. Both of these soil moisture drivers are again influenced by ENSO. Our causal models show that persistent negative ENSO anomalies of -1.5 standard deviation (SD) lead to a -0.4 SD soybean reductions in the US and SESA. When spring soil moisture and extratropical SST precursors are pronouncedly negative (-1.5 SD), then estimated soybean losses increase to -0.9 SD for US and SESA. Thus, by influencing extratropical SSTs and spring soil moisture, persistent La Niña's can trigger substantial soybean losses in both the US and SESA, with only minor potential gains in CB. Our findings highlight the physical pathways by which ENSO conditions can drive spatially compounding events. Such information may increase preparedness against climate related global soybean supply shocks. [ABSTRACT FROM AUTHOR]

The US agriculture system supplies more than one-third of globally traded soybean, and with 90 % of US soybean produced under rainfed agriculture, soybean trade is particularly sensitive to weather and climate variability. Average growing season climate conditions can explain about one-third of US soybean yield variability. Additionally, crops can be sensitive to specific short-term weather extremes, occurring in isolation or compounding at key moments throughout crop development. Here, we identify the dominant within-season climate drivers that can explain soybean yield variability in the US, and we explore the synergistic effects between drivers that can lead to severe impacts. The study combines weather data from reanalysis and satellite-informed root zone soil moisture fields with subnational crop yields using statistical methods that account for interaction effects. On average, our models can explain about two-thirds of the year-to-year yield variability (70 % for all years and 60 % for out-of-sample predictions). The largest negative influence on soybean yields is driven by high temperature and low soil moisture during the summer crop reproductive period. Moreover, due to synergistic effects, heat is considerably more damaging to soybean crops during dry conditions and is less problematic during wet conditions. Compounding and interacting hot and dry (hot–dry) summer conditions (defined by the 95th and 5th percentiles of temperature and soil moisture respectively) reduce yields by 2 standard deviations. This sensitivity is 4 and 3 times larger than the sensitivity to hot or dry conditions alone respectively. Other relevant drivers of negative yield responses are lower temperatures early and late in the season, excessive precipitation in the early season, and dry conditions in the late season. We note that the sensitivity to the identified drivers varies across the spatial domain. Higher latitudes, and thus colder regions, are positively affected by high temperatures during the summer period. On the other hand, warmer southeastern regions are positively affected by low temperatures during the late season. Historic trends in identified drivers indicate that US soybean production has generally benefited from recent shifts in weather except for increasing rainfall in the early season. Overall, warming conditions have reduced the risk of frost in the early and late seasons and have potentially allowed for earlier sowing dates. More importantly, summers have been getting cooler and wetter over the eastern US. Nevertheless, despite these positive changes, we show that the frequency of compound hot–dry summer events has remained unchanged over the 1946–2016 period. In the longer term, climate models project substantially warmer summers for the continental US, although uncertainty remains as to whether this will be accompanied by drier conditions. This highlights a critical element to explore in future studies focused on US agricultural production risk under climate change. [ABSTRACT FROM AUTHOR]

The US agriculture system supplies more than one-third of globally-traded soybean and with 90 % of US soybean produced under rainfed agriculture, soybean trade is particularly sensitive to weather and climate variability. Average growing season climate conditions can explain about one-third of US soybean yield variability. Additionally, crops can be sensitive to specific short-term weather extremes, occurring in isolation or compounding at key moments throughout crop development. Here, we identify the dominant within-season climate drivers that can explain soybean yield variability in the US, and explore synergistic effects between drivers that can lead to severe impacts. The study combines weather data from reanalysis, satellite-based evapotranspiration and root-zone soil moisture with sub-national crop yields using statistical methods that account for interaction effects. Our model can explain on average about half of the year-to-year yield variability (60 % on all years and 40 % on out-of-sample predictions). The largest negative influence on soybean yields is driven by high temperature and low soil moisture during the summer crop reproductive period. Moreover, due to synergistic effects, heat is considerably more damaging to soybean crops during dry conditions, and less so during wet conditions. Compound and interacting hot and dry August conditions (defined by the 95th and 5th percentiles of temperature and soil moisture, respectively) reduce yields by 1.25 standard deviation. This sensitivity is, respectively, 6 and 3 times larger than the sensitivity to hot or dry conditions alone. Other important drivers of negative yield responses are lower evapotranspiration early in the season and lower minimum temperature late in the season, both likely reflecting an increased risk of frost. The sensitivity to the identified drivers varies across the spatial domain with higher latitudes, and thus colder regions, being less sensitive to hot-dry August months. Historic trends in identified drivers indicates that US soybean has generally benefited from recent shifts in weather. Overall warming conditions have reduced the risk of frost in early and late-season and potentially allowed for earlier sowing dates. More importantly, summers have been getting cooler and wetter over eastern US. Still, despite these positive changes, we show that the frequency of compound hot-dry August month has remained unchanged over 1946-2016. Moreover, in the longer term, climate models project substantially warmer summers for the continental US which likely creates risks for soybean production. [ABSTRACT FROM AUTHOR]

Background Preoperative computed tomography angiography (CTA) of the abdominal wall vessels is used when planning free flap breast reconstruction (FFBR) because it provides a surgical road map which facilitates flap harvest. However, there are few reports on the effect of abnormal findings on the operative plan. Methods We conducted a retrospective study of all FFBRs performed at a tertiary referral center over a 6-year period (November 2011 to June 2017). One consultant radiologist reported on the findings. Details on patient demographics, CTA reports, and intraoperative details were collected. Results Two hundred patients received preoperative CTAs. Fourteen percent of patients (n=28) had abnormal findings. Of these findings, 18% were vascular anomalies; 36% tumorrelated and 46% were "miscellaneous." In four patients, findings subsequently prevented surgery; they comprised a mesenteric artery aneurysm, absent deep inferior epigastric (DIE) vessels, bilateral occluded DIE arteries, and significant bone metastases. Another patient had no suitable vessels for a free flap and the surgical plan converted to a pedicled transverse rectus abdominis musculocutaneous flap. The remaining incidental findings had no impact on the surgical plan or appropriateness of FFBR. More than one in 10 of those with abnormal findings went on to have further imaging before their operation. Conclusions CTA in FFBR can have a wider impact than facilitating surgical planning and reducing operative times. Incidental findings can influence the surgical plan, and in some instances, avoid doomed-to-fail and unsafe surgery. It is therefore important that these scans are reported by an experienced radiologist. [ABSTRACT FROM AUTHOR]

Abstract Background Abdominal scars can affect the patency of deep inferior epigastric artery (DIEA) perforators and are a concern when planning free flap breast reconstruction (FFBR). Computed tomography angiography (CTA) is routinely used for preoperative DIEA flap imaging. We investigated CTA utility in predicting the most clinically useful DIEA perforators in scarred abdomens. Methods A single surgeon's CTA FFBR patients were studied. All were imaged by one radiologist. CTA reports, abdominal scars, and flap intraoperative details were analyzed. The operative findings were then correlated with the CTA "predictions." Results A hundred and six patients with preoperative CTAs underwent 132 FFBRs, 44% (58) from scarred and 56% (74) from virgin abdomens. All flap transfers were successful. Concordance between perforators identified by CTA preoperatively and those selected by the surgeon intraoperatively was 95% (scarred 93%; non-scarred 96%, P = 0.470). There was a significant difference in the proportion of single-perforator flaps between the two groups (scarred 46%; non-scarred 28%, P = 0.041). "Scarred" flaps were heavier (789 vs 676 g, P = 0.0244) than those harvested from virgin abdomens. Conclusions CTA accurately predicted perforator choice in flaps from scarred and virgin abdomens. "Scarred" flaps are more likely to be heavier and based on one perforator suggesting that scarring may have an effect on intraflap vascular anatomy. Further investigations are needed to delineate the mechanism by which this occurs. [ABSTRACT FROM AUTHOR]

Nonparametric mathematical models have gained a very massive attention in the last two decades in solving regression problem. The application of soft computing methodologies produced a very remarkable assistance to human abilities especially in solving nonlinear and non-stationary engineering problems. The current article investigates the utility of k-nearest neighbor (k-nn) approach in predicting ultimate bearing capacity of shallow foundation. The inspected application involves an experimental data set of foundation dimension and soil properties that suggested and calculated via manual computational methods. The predictive model is established using dimensional shallow foundation, and soil properties are an inputs variable, whereas the bearing capacity is the output variable. For the purpose of comparison and evaluating the modeling accuracy, multiple linear regression (MLR) model is chosen to diagnose the result accuracies. Couple of statistical indicators are utilized to exhibit the performance criteria of the predictive model including coefficient of determination (r2), degree of agreement (d), root-mean-square error (RMSE) and mean absolute percentage error (MAPE). The results exhibited a very representable and high accuracies of the investigated k-nn model vis-à-vis MLR. For instance, the RMSE and MAPE were enhanced by 24 and 17%, respectively. In addition, the findings indicated that k-nn provides an accurate and reliable alternative predictive model to the manual computational methods. [ABSTRACT FROM AUTHOR]

Internal carotid artery (ICA) dissection is a dangerous condition that results from disruption of the intimal part of the wall of the internal carotid artery. It is a rare disease that may occur spontaneously or as a result of a trauma. Spontaneous dissections of the carotid artery are rare but important causes of ischemic stroke because they usually affect young and middle-aged patients. Up to date, only a few cases were described in the literature about ICA dissection causing isolated cranial nerve palsies, with the Hypoglossal nerve being the most affected. Here, we report a case of a 56-year-old man presenting with progressive dysarthria, dysphagia to liquid diet, and difficult mastication. He was diagnosed as a case of cervical internal carotid dissection with pseudoaneurysm formation causing mass effect resulting in a compressive ipsilateral Hypoglossal nerve palsy based on magnetic resonance imaging (MRI) findings. Angiography confirmed the presence of dissecting pseudoaneurysm which was eventually managed by stenting. This case was reported to highlight and emphasize the importance of radiology, whether diagnostic or interventional, in managing rare and challenging cases such as ICA dissection. [ABSTRACT FROM AUTHOR]

The segregation among benign and malignant nasopharyngeal carcinoma (NPC) from endoscopic images is one of the most challenging issues in cancer diagnosis because of the many conceivable shapes, regions, and image intensities, hence, a proper scientific technique is required to extract the features of cancerous NPC tumors. In the present research, a neural network-based automated discrimination system was implemented for the identification of malignant NPC tumors. In the proposed technique, five different types of qualities, such as local binary pattern, gray-level co-occurrence matrix, histogram of oriented gradients, fractal dimension, and entropy, were first determined from the endoscopic images of NPC tumors and then the following steps were executed: (1) an enhanced adaptive approach was employed as the post-processing method for the classification of NPC tumors, (2) an assessment foundation was created for the automated identification of malignant NPC tumors, (3) the benign and cancerous cases were discriminated by using region growing method and artificial neural network (ANN) approach, and (4) the efficiency of the outcomes was evaluated by comparing the results of ANN. In addition, it was found that texture features had significant effects on isolating benign tumors from malignant cases. It can be concluded that in our proposed method texture features acted as a pointer as well as a help instrument to diagnose the malignant NPC tumors. In order to examine the accuracy of our proposed approach, 159 abnormal and 222 normal cases endoscopic images were acquired from 249 patients, and the classifier yielded 95.66% precision, 95.43% sensitivity, and 95.78% specificity. [ABSTRACT FROM AUTHOR]

Abstract An accurate diagnosis of Parkinson's disease by specialists involves many neurological, psychological and physical examinations. The specialists investigate a number of symptoms and signs when examining the nervous system conditions of a person. The diagnosis involves reviewing the medical history and genetic factor of the person. The recent diagnosis methodology to Parkinson's disease relies on voice disorders analysis. This methodology entails extracting feature sets of a recorded person's voice then utilizing a machine learning technique to identify the healthy and Parkinson's cases from the voice. This paper attempts to improve the diagnoses of Parkinson's disease by testing multiple feature evaluation and classification machine learning methods based on the voice disorders analysis. The aim of this paper is to find the optimal solution to the problem by (i) proposing a new Multiple Feature Evaluation Approach (MFEA) of a multi-agent system (ii) implementing five independent classification schemas which are Decision Tree, Naïve Bayes, Neural Network, Random Forests, and Support Vector Machine on the Parkinson's diagnosis before and after applying the MFEA, and (iii) evaluating the diagnosis accuracy of the results. The methodology of the tests encompasses 10-fold cross-validation to evaluate the learning of methods and track variation in their performance. The test results show that the MFEA of the multi-agent system finds the best set of features and improves the performance of the classifiers. The average rate of improvement in the diagnostic accuracy of the classifiers are Decision Tree 10.51%, Naïve Bayes 15.22%, Neural Network 9.19%, Random Forests 12.75%, and Support Vector Machine 9.13%. These results show that the MFEA makes a significant improvement to the classifiers' diagnosis results. [ABSTRACT FROM AUTHOR]

Climate model-based seasonal forecasts are increasingly skilful and have the potential to inform basin-level drought management if communicated correctly. The uncertain and probabilistic information needs to be tailored in such a way that it is digestible and understandable for end-users in an organization like for example a river basin authority responsible for enforcing drought mitigation measures. We developed a winter seasonal risk outlook bulletin for the Segura River Basin authority, giving them information about the next three months on precipitation and streamflow anomalies. The goal was to co-design and test a climate service with the Segura river basin authority that provides ready-to-use information for drought risk management. The system relies on climate model-based forecasts of the NAO to resample the historical rainfall dataset. Then, these realizations are simulated through a hydrological model (SPHY) that was calibrated using historical streamflow data. Initial conditions are retrieved from the ECMWF EFAS system, and historical runs are forced with EOBS. The hydrological model runs on the FEWS system and calculates probabilities of above normal, near normal and below normal categories for both discharge and rainfall conditions for the winter period 2018-2019. The information has been communicated by means of monthly bulletins tailored to the needs of the end-user. The final result has been assessed to be of great value by the river basin authority offering a promising route for the transfer of such knowledge into operational practice. This work has been carried out within the H2020 project IMPREX (www.imprex.eu). [ABSTRACT FROM AUTHOR]