Frequency versus quantity: phenotypic response of two wheat varieties to water and nitrogen variability

Due to climate change, water availability will become increasingly variable, affecting nitrogen (N) availability. Therefore, we hypothesised watering frequency would have a greater impact on plant growth than quantity, affecting N availability, uptake and carbon allocation. We used a gravimetric platform, which measures the unit of volume per unit of time, to control soil moisture and precisely compare the impact of quantity and frequency of water under variable N levels. Two wheat genotypes (Kukri and Gladius) were used in a factorial glasshouse pot experiment, each with three N application rates (25, 75 and 150 mg N kg−1 soil) and five soil moisture regimes (changing water frequency or quantity). Previously documented drought tolerance, but high N use efficiency, of Gladius as compared to Kukri provides for potentially different responses to N and soil moisture content. Water use, biomass and soil N were measured. Both cultivars showed potential to adapt to variable watering, producing higher specific root lengths under low N coupled with reduced water and reduced watering frequency (48 h watering intervals), or wet/dry cycling. This affected mineral N uptake, with less soil N remaining under constant watering × high moisture, or 48 h watering intervals × high moisture. Soil N availability affected carbon allocation, demonstrated by both cultivars producing longer, deeper roots under low N. Reduced watering frequency decreased biomass more than reduced quantity for both cultivars. Less frequent watering had a more negative effect on plant growth compared to decreasing the quantity of water. Water variability resulted in differences in C allocation, with changes to root thickness even when root biomass remained the same across N treatments. The preferences identified in wheat for water consistency highlights an undeveloped opportunity for identifying root and shoot traits that may improve plant adaptability to moderate to extreme resource limitation, whilst potentially encouraging less water and nitrogen use

Factors Associated with Revision Surgery after Internal Fixation of Hip Fractures

Background: Femoral neck fractures are associated with high rates of revision surgery after management with internal fixation. Using data from the Fixation using Alternative Implants for the Treatment of Hip fractures (FAITH) trial evaluating methods of internal fixation in patients with femoral neck fractures, we investigated associations between baseline and surgical factors and the need for revision surgery to promote healing, relieve pain, treat infection or improve function over 24 months postsurgery. Additionally, we investigated factors associated with (1) hardware removal and (2) implant exchange from cancellous screws (CS) or sliding hip screw (SHS) to total hip arthroplasty, hemiarthroplasty, or another internal fixation device. Methods: We identified 15 potential factors a priori that may be associated with revision surgery, 7 with hardware removal, and 14 with implant exchange. We used multivariable Cox proportional hazards analyses in our investigation. Results: Factors associated with increased risk of revision surgery included: female sex, [hazard ratio (HR) 1.79, 95% confidence interval (CI) 1.25-2.50; P = 0.001], higher body mass index (fo

Hippocampal deformation mapping in MRI-negative PET-positive temporal lobe epilepsy

Objectives: To compare hippocampal surface structure, using large deformation high dimensional mapping (HDM-LD), in subjects with temporal lobe epilepsy (TLE) with (HS+ve) and without (HS&minus;ve) hippocampal sclerosis.Methods: The study included 30 HS&minus;ve subjects matched with 30 HS+ve subjects from the previously reported epilepsy patient cohort. To control for normal right&ndash;left asymmetries of hippocampal surface structure, subjects were regrouped based on laterality of onset of epileptic seizures and presence of HS. Gender ratio, age, duration of epilepsy and seizure frequency were calculated for each of the four groups. Final HDM-LD surface maps of the right and left TLE groups were compared to define differences in subregional hippocampal involvement within the groups.Results: There were no significant differences in comparisons of the left TLE (left HS&minus;ve compared with HS+ve) or right TLE (right HS&minus;ve compared with HS+ve) groups with respect to age, duration of epilepsy or seizure severity scores. HDM-LD maps showed accentuated surface changes over the lateral hippocampal surface, in the region of the Sommer sector, in the hippocampi affected by HS. However, HS&minus;ve hippocampi showed maximal surface changes in a different pattern, and did not involve the region of Sommer sector.Conclusion: We conclude that differences in segmental volume loss between the HS&minus;ve and HS+ve groups are suggestive that the underlying pathophysiology of hippocampal changes in the two groups is different, and not related to chronic seizure duration or severity. </div

Genetic correlation between amyotrophic lateral sclerosis and schizophrenia

We have previously shown higher-than-expected rates of schizophrenia in relatives of patients with amyotrophic lateral sclerosis (ALS), suggesting an aetiological relationship between the diseases. Here, we investigate the genetic relationship between ALS and schizophrenia using genome-wide association study data from over 100,000 unique individuals. Using linkage disequilibrium score regression, we estimate the genetic correlation between ALS and schizophrenia to be 14.3% (7.05-21.6; P=1 × 10) with schizophrenia polygenic risk scores explaining up to 0.12% of the variance in ALS (P=8.4 × 10). A modest increase in comorbidity of ALS and schizophrenia is expected given these findings (odds ratio 1.08-1.26) but this would require very large studies to observe epidemiologically. We identify five potential novel ALS-associated loci using conditional false discovery rate analysis. It is likely that shared neurobiological mechanisms between these two disorders will engender novel hypotheses in future preclinical and clinical studies

At-admission prediction of mortality and pulmonary embolism in an international cohort of hospitalised patients with COVID-19 using statistical and machine learning methods

By September 2022, more than 600 million cases of SARS-CoV-2 infection have been reported globally, resulting in over 6.5 million deaths. COVID-19 mortality risk estimators are often, however, developed with small unrepresentative samples and with methodological limitations. It is highly important to develop predictive tools for pulmonary embolism (PE) in COVID-19 patients as one of the most severe preventable complications of COVID-19. Early recognition can help provide life-saving targeted anti-coagulation therapy right at admission. Using a dataset of more than 800,000 COVID-19 patients from an international cohort, we propose a cost-sensitive gradient-boosted machine learning model that predicts occurrence of PE and death at admission. Logistic regression, Cox proportional hazards models, and Shapley values were used to identify key predictors for PE and death. Our prediction model had a test AUROC of 75.9% and 74.2%, and sensitivities of 67.5% and 72.7% for PE and all-cause mortality respectively on a highly diverse and held-out test set. The PE prediction model was also evaluated on patients in UK and Spain separately with test results of 74.5% AUROC, 63.5% sensitivity and 78.9% AUROC, 95.7% sensitivity. Age, sex, region of admission, comorbidities (chronic cardiac and pulmonary disease, dementia, diabetes, hypertension, cancer, obesity, smoking), and symptoms (any, confusion, chest pain, fatigue, headache, fever, muscle or joint pain, shortness of breath) were the most important clinical predictors at admission. Age, overall presence of symptoms, shortness of breath, and hypertension were found to be key predictors for PE using our extreme gradient boosted model. This analysis based on the, until now, largest global dataset for this set of problems can inform hospital prioritisation policy and guide long term clinical research and decision-making for COVID-19 patients globally. Our machine learning model developed from an international cohort can serve to better regulate hospital risk prioritisation of at-risk patients. © The Author(s) 2024