Climate change impacts on crop breeding: Targeting interacting biotic and abiotic stresses for wheat improvement

Wheat (Triticum aestivum L.) as a staple crop is closely interwoven into the development of modern society. Its influence on culture and economic development is global. Recent instability in wheat markets has demonstrated its importance in guaranteeing food security across national borders. Climate change threatens food security as it interacts with a multitude of factors impacting wheat production. The challenge needs to be addressed with a multidisciplinary perspective delivered across research, private, and government sectors. Many experimental studies have identified the major biotic and abiotic stresses impacting wheat production, but fewer have addressed the combinations of stresses that occur simultaneously or sequentially during the wheat growth cycle. Here, we argue that biotic and abiotic stress interactions, and the genetics and genomics underlying them, have been insufficiently addressed by the crop science community. We propose this as a reason for the limited transfer of practical and feasible climate adaptation knowledge from research projects into routine farming practice. To address this gap, we propose that novel methodology integration can align large volumes of data available from crop breeding programs with increasingly cheaper omics tools to predict wheat performance under different climate change scenarios. Underlying this is our proposal that breeders design and deliver future wheat ideotypes based on new or enhanced understanding of the genetic and physiological processes that are triggered when wheat is subjected to combinations of stresses. By defining this to a trait and/or genetic level, new insights can be made for yield improvement under future climate conditions

Frequency of five cardiovascular/hemostatic entities as primary manifestations of SARS-CoV-2 infection: Results of the UMC-19-S2

Infection by SARS-Cov-2 is mainly characterized by fever and respiratory symptoms, with dyspnea and lung infiltrates in more severe cases [1,2]. Many patients also present a pro-coagulant state, which is biochemically detected by increased D-dimer levels and is related to complications and a worse prognosis [1,3]. In this context, isolated case reports and short case series have suggested an increased risk of patients with COVID-19 to develop clinically relevant cardiovascular and hemostatic disturbances [3–7]. Nonetheless, many of these reports refer to hospitalized patients, and as hospitalization itself usually increases complications in bedridden patients with multidrug treatmentor in very poor condition, it is unknown if such cardiovascular/hemostatic processes are related to the pathogenesis of SARS-Cov-2. Focus on patients with COVID-19 at emergency department (ED) arrival could help to answer this question

Incidence, risk factors, clinical characteristics and outcomes of deep venous thrombosis in patients with COVID-19 attending the Emergency Department: results of the UMC-19-S8

Background and importance: A higher incidence of venous thromboembolism [both pulmonary embolism and deep vein thrombosis (DVT)] in patients with coronavirus disease 2019 (COVID-19) has been described. But little is known about the true frequency of DVT in patients who attend emergency department (ED) and are diagnosed with COVID-19. Objective: We investigated the incidence, risk factors, clinical characteristics and outcomes of DVT in patients with COVID-19 attending the ED before hospitalization. Methods: We retrospectively reviewed all COVID patients diagnosed with DVT in 62 Spanish EDs (20% of Spanish EDs, case group) during the first 2 months of the COVID-19 outbreak. We compared DVT-COVID-19 patients with COVID-19 without DVT patients (control group). Relative frequencies of DVT were estimated in COVID and non-COVID patients visiting the ED and annual standardized incidences were estimated for both populations. Sixty-three patient characteristics and four outcomes were compared between cases and controls. Results: We identified 112 DVT in 74 814 patients with COVID-19 attending the ED [1.50‰; 95% confidence interval (CI), 1.23-1.80‰]. This relative frequency was similar than that observed in non-COVID patients [2109/1 388 879; 1.52‰; 95% CI, 1.45-1.69‰; odds ratio (OR) = 0.98 [0.82-1.19]. Standardized incidence of DVT was higher in COVID patients (98,38 versus 42,93/100,000/year; OR, 2.20; 95% CI, 2.03-2.38). In COVID patients, the clinical characteristics associated with a higher risk of presenting DVT were older age and having a history of venous thromboembolism, recent surgery/immobilization and hypertension; chest pain and desaturation at ED arrival and some analytical disturbances were also more frequently seen, d-dimer >5000 ng/mL being the strongest. After adjustment for age and sex, hospitalization, ICU admission and prolonged hospitalization were more frequent in cases than controls, whereas mortality was similar (OR, 1.37; 95% CI, 0.77-2.45). Conclusions: DVT was an unusual form of COVID presentation in COVID patients but was associated with a worse prognosis

Thirty-day outcomes in frail older patients discharged home from the emergency department with acute heart failure: effects of high-risk criteria identified by the DEED FRAIL-AHF trial

Objectives: To study the effect of high-risk criteria on 30-day outcomes in frail older patients with acute heart failure (AHF) discharged from an emergency department (ED) or an ED's observation and short-stay areas. Material and methods: Secondary analysis of discharge records in the Older AHF Key Data registry. We selected frail patients (aged > 70 years) discharged with AHF from EDs. Risk factors were categorized as modifiable or nonmodifiable. The outcomes were a composite endpoint for a cardiovascular event (revisits for AHF, hospitalization for AHF, or cardiovascular death) and the number of days alive out-of-hospital (DAOH) within 30 days of discharge. Results: We included 380 patients with a mean (SD) age of 86 (5.5) years (61.2% women). Modifiable risk factors were identified in 65.1%, nonmodifiable ones in 47.8%, and both types in 81.6%. The 30-day cardiovascular composite endpoint occurred in 83 patients (21.8%). The mean 30-day DAOH observed was 27.6 (6.1) days. Highrisk factors were present more often in patients who developed the cardiovascular event composite endpoint: the rates for patients with modifiable, nonmodifiable, or both types of risk were, respectively, as follows in comparison with patients not at high risk: 25.0% vs 17.2%, P = .092; 27.6% vs 16.7%, P = .010; and 24.7% vs 15.2%, P = .098). The 30-day DAOH outcome was also lower for at-risk patients, according to type of risk factor present: modifiable, 26.9 (7.0) vs 28.4 (4.4) days, P = .011; nonmodifiable, 27.1 (7.0) vs 28.0 (5.0) days, P = .127; and both, 27.1 (6.7) vs 28.8 (3.4) days, P = .005). After multivariate analysis, modifiable risk remained independently associated with fewer days alive (adjusted absolute difference in 30-day DAOH, -1.3 days (95% CI, -2.7 to -0.1 days). Nonmodifiable factors were associated with increased risk for the 30-day cardiovascular composite endpoint (adjusted absolute difference, 10.4%; 95% CI, -2.1% to 18.7%). Conclusion: Risk factors are common in frail elderly patients with AHF discharged home from hospital ED areas. Their presence is associated with a worse 30-day prognosis

Incidence, clinical characteristics, risk factors and outcomes of meningoencephalitis in patients with COVID-19

We investigated the incidence, clinical characteristics, risk factors, and outcome of meningoencephalitis (ME) in patients with COVID-19 attending emergency departments (ED), before hospitalization. We retrospectively reviewed all COVID patients diagnosed with ME in 61 Spanish EDs (20% of Spanish EDs, COVID-ME) during the COVID pandemic. We formed two control groups: non-COVID patients with ME (non-COVID-ME) and COVID patients without ME (COVID-non-ME). Unadjusted comparisons between cases and controls were performed regarding 57 baseline and clinical characteristics and 4 outcomes. Cerebrospinal fluid (CSF) biochemical and serologic findings of COVID-ME and non-COVID-ME were also investigated. We identified 29 ME in 71,904 patients with COVID-19 attending EDs (0.40‰, 95%CI=0.27-0.58). This incidence was higher than that observed in non-COVID patients (150/1,358,134, 0.11‰, 95%CI=0.09-0.13; OR=3.65, 95%CI=2.45-5.44). With respect to non-COVID-ME, COVID-ME more frequently had dyspnea and chest X-ray abnormalities, and neck stiffness was less frequent (OR=0.3, 95%CI=0.1-0.9). In 69.0% of COVID-ME, CSF cells were predominantly lymphocytes, and SARS-CoV-2 antigen was detected by RT-PCR in 1 patient. The clinical characteristics associated with a higher risk of presenting ME in COVID patients were vomiting (OR=3.7, 95%CI=1.4-10.2), headache (OR=24.7, 95%CI=10.2-60.1), and altered mental status (OR=12.9, 95%CI=6.6-25.0). COVID-ME patients had a higher in-hospital mortality than non-COVID-ME patients (OR=2.26; 95%CI=1.04-4.48), and a higher need for hospitalization (OR=8.02; 95%CI=1.19-66.7) and intensive care admission (OR=5.89; 95%CI=3.12-11.14) than COVID-non-ME patients. ME is an unusual form of COVID presentation (<0.5‰ cases), but is more than 4-fold more frequent than in non-COVID patients attending the ED. As the majority of these MEs had lymphocytic predominance and in one patient SARS-CoV-2 antigen was detected in CSF, SARS-CoV-2 could be the cause of most of the cases observed. COVID-ME patients had a higher unadjusted in-hospital mortality than non-COVID-ME patients

ResBoost: characterizing and predicting catalytic residues in enzymes

Abstract Background Identifying the catalytic residues in enzymes can aid in understanding the molecular basis of an enzyme's function and has significant implications for designing new drugs, identifying genetic disorders, and engineering proteins with novel functions. Since experimentally determining catalytic sites is expensive, better computational methods for identifying catalytic residues are needed. Results We propose ResBoost, a new computational method to learn characteristics of catalytic residues. The method effectively selects and combines rules of thumb into a simple, easily interpretable logical expression that can be used for prediction. We formally define the rules of thumb that are often used to narrow the list of candidate residues, including residue evolutionary conservation, 3D clustering, solvent accessibility, and hydrophilicity. ResBoost builds on two methods from machine learning, the AdaBoost algorithm and Alternating Decision Trees, and provides precise control over the inherent trade-off between sensitivity and specificity. We evaluated ResBoost using cross-validation on a dataset of 100 enzymes from the hand-curated Catalytic Site Atlas (CSA). Conclusion ResBoost achieved 85% sensitivity for a 9.8% false positive rate and 73% sensitivity for a 5.7% false positive rate. ResBoost reduces the number of false positives by up to 56% compared to the use of evolutionary conservation scoring alone. We also illustrate the ability of ResBoost to identify recently validated catalytic residues not listed in the CSA

Commissioning and operation of the readout system for the solid neutrino detector

The SoLid experiment aims to measure neutrino oscillation at a baseline of 6.4 m from the BR2 nuclear reactor in Belgium. Anti-neutrinos interact via inverse beta decay (IBD), resulting in a positron and neutron signal that are correlated in time and space. The detector operates in a surface building, with modest shielding, and relies on extremely efficient online rejection of backgrounds in order to identify these interactions. A novel detector design has been developed using 12800 5 cm cubes for high segmentation. Each cube is formed of a sandwich of two scintillators, PVT and 6LiF:ZnS(Ag), allowing the detection and identification of positrons and neutrons respectively. The active volume of the detector is an array of cubes measuring 80x80x250 cm (corresponding to a fiducial mass of 1.6 T), which is read out in layers using two dimensional arrays of wavelength shifting fibres and silicon photomultipliers, for a total of 3200 readout channels. Signals are recorded with 14 bit resolution, and at 40 MHz sampling frequency, for a total raw data rate of over 2 Tbit/s. In this paper, we describe a novel readout and trigger system built for the experiment, that satisfies requirements on: compactness, low power, high performance, and very low cost per channel. The system uses a combination of high price-performance FPGAs with a gigabit Ethernet based readout system, and its total power consumption is under 1 kW. The use of zero suppression techniques, combined with pulse shape discrimination trigger algorithms to detect neutrons, results in an online data reduction factor of around 10000. The neutron trigger is combined with a large per-channel history time buffer, allowing for unbiased positron detection. The system was commissioned in late 2017, with successful physics data taking established in early 2018