Renewable energy projects to electrify rural communities in Cape Verde

Abstract Even though Cape Verde has high wind and solar energy resources, the conventional strategy for increasing access to electricity in isolated rural areas is by centralized microgrids with diesel generators. In this study, the design of 2 off-grid electrification projects based on hybrid windphotovoltaic systems in Cape Verde is developed and analyzed. First a detailed wind resource assessment is carried out utilizing meso-scale wind atlas data combined with a specialized microscale wind flow model. Then a mathematical model is used for the design of off-grid projects considering a combination of individual systems and microgrids. Various design configurations are analyzed and compared. The proposed configurations exploit the highest wind potential areas and are economically beneficial in comparison with diesel generator systems

Biases in spatial bisection induced by viewing male and female faces

Research on visual attention triggered by face gender is still relatively sparse. In the present study, three experiments are reported in which male and female participants were required to estimate the midpoint of a line (i.e., the “line bisection task”): at each end of the line a face was presented. Depending on the experimental condition, faces could be of the same gender (i.e., two males or two females) or the opposite gender. Experiment 1 and 2 findings converged in showing that when a male face was presented at the right and a female face at the left endpoint of the line, a clear rightward bias emerged compared to the other experimental conditions, indicating that male faces captured attention more than female faces. Importantly, male faces used across Experiment 1 and 2 were rated as more threatening than female faces, suggesting that perceived level of threat may have been responsible for the observed bias toward the male face. Experiment 3 corroborated this hypothesis by finding an attentional bias toward the male face with high threat (angry) faces but not with low threat (smiling) faces

Transcriptomic profiling of white blood cells reveals new insights into the molecular mechanisms of thalidomide in children with inflammatory bowel disease

Thalidomide has emerged as an effective immunomodulator in the treatment of pediatric patients with inflammatory bowel disease (IBD) refractory to standard therapies. Cereblon, a component of E3 protein ligase complex that mediates ubiquitination and proteasomal degradation of target proteins, has been identified as the primary target of thalidomide. Cereblon plays a crucial role in thalidomide teratogenicity, however it is unclear whether it is also involved in the therapeutic effects in IBD patients. This study aimed at identifying the mechanisms underpinning thalidomide action in pediatric IBD. Ten IBD pediatric patients clinically responsive to thalidomide were prospectively enrolled. RNA-sequencing and functional enrichment analysis was carried out on peripheral blood mononuclear cells obtained before and after treatment with thalidomide. RNA-sequencing analysis revealed 378 differentially expressed genes after treatment with thalidomide. The most deregulated pathways were cytosolic calcium ion concentration, cAMP-mediated signaling, eicosanoid signaling and inhibition of matrix metalloproteinases. Neuronal signaling mechanisms such as CREB signaling in neurons and axonal guidance signaling also emerged. Connectivity Map analysis revealed that thalidomide gene expression changes were similar to those induced by MLN4924, an inhibitor of NEDD8 activating enzyme, suggesting that thalidomide exerts its immunomodulatory effects by acting on the ubiquitin-proteasome pathway. In vitro experiments on cell lines confirmed the effect of thalidomide on altered candidate pathways observed in patients. These results represent a unique resource for enhanced understanding of thalidomide mechanism in patients with IBD, providing novel potential targets associated with drug response.Book of abstract: 4th Belgrade Bioinformatics Conference, June 19-23, 202

Baseline risk factors of in-hospital mortality after surgery for acute type A aortic dissection: an ERTAAD study

Background Surgery for type A aortic dissection (TAAD) is associated with high risk of mortality. Current risk scoring methods have a limited predictive accuracy.Methods Subjects were patients who underwent surgery for acute TAAD at 18 European centers of cardiac surgery from the European Registry of Type A Aortic Dissection (ERTAAD).Results Out of 3,902 patients included in the ERTAAD, 2,477 fulfilled the inclusion criteria. In the validation dataset (2,229 patients), the rate of in-hospital mortality was 18.4%. The rate of composite outcome (in-hospital death, stroke/global ischemia, dialysis, and/or acute heart failure) was 41.2%, and 10-year mortality rate was 47.0%. Logistic regression identified the following patient-related variables associated with an increased risk of in-hospital mortality [area under the curve (AUC), 0.755, 95% confidence interval (CI), 0.729-0.780; Brier score 0.128]: age; estimated glomerular filtration rate; arterial lactate; iatrogenic dissection; left ventricular ejection fraction <= 50%; invasive mechanical ventilation; cardiopulmonary resuscitation immediately before surgery; and cerebral, mesenteric, and peripheral malperfusion. The estimated risk score was associated with an increased risk of composite outcome (AUC, 0.689, 95% CI, 0.667-0.711) and of late mortality [hazard ratio (HR), 1.035, 95% CI, 1.031-1.038; Harrell's C 0.702; Somer's D 0.403]. In the validation dataset (248 patients), the in-hospital mortality rate was 16.1%, the composite outcome rate was 41.5%, and the 10-year mortality rate was 49.1%. The estimated risk score was predictive of in-hospital mortality (AUC, 0.703, 95% CI, 0.613-0.793; Brier score 0.121; slope 0.905) and of composite outcome (AUC, 0.682, 95% CI, 0.614-0.749). The estimated risk score was predictive of late mortality (HR, 1.035, 95% CI, 1.031-1.038; Harrell's C 0.702; Somer's D 0.403), also when hospital deaths were excluded from the analysis (HR, 1.024, 95% CI, 1.018-1.031; Harrell's C 0.630; Somer's D 0.261).Conclusions The present analysis identified several baseline clinical risk factors, along with preoperative estimated glomerular filtration rate and arterial lactate, which are predictive of in-hospital mortality and major postoperative adverse events after surgical repair of acute TAAD. These risk factors may be valuable components for risk adjustment in the evaluation of surgical and anesthesiological strategies aiming to improve the results of surgery for TAAD.Clinical Trial Registration https://clinicaltrials.gov, identifier NCT04831073

Outcome after Surgery for Iatrogenic Acute Type A Aortic Dissection

(1) Background: Acute Stanford type A aortic dissection (TAAD) may complicate the outcome of cardiovascular procedures. Data on the outcome after surgery for iatrogenic acute TAAD is scarce. (2) Methods: The European Registry of Type A Aortic Dissection (ERTAAD) is a multicenter, retrospective study including patients who underwent surgery for acute TAAD at 18 hospitals from eight European countries. The primary outcomes were in-hospital mortality and 5-year mortality. Twenty-seven secondary outcomes were evaluated. (3) Results: Out of 3902 consecutive patients who underwent surgery for acute TAAD, 103 (2.6%) had iatrogenic TAAD. Cardiac surgery (37.8%) and percutaneous coronary intervention (36.9%) were the most frequent causes leading to iatrogenic TAAD, followed by diagnostic coronary angiography (13.6%), transcatheter aortic valve replacement (10.7%) and peripheral endovascular procedure (1.0%). In hospital mortality was 20.5% after cardiac surgery, 31.6% after percutaneous coronary intervention, 42.9% after diagnostic coronary angiography, 45.5% after transcatheter aortic valve replacement and nihil after peripheral endovascular procedure (p = 0.092), with similar 5-year mortality between different subgroups of iatrogenic TAAD (p = 0.710). Among 102 propensity score matched pairs, in-hospital mortality was significantly higher among patients with iatrogenic TAAD (30.4% vs. 15.7%, p = 0.013) compared to those with spontaneous TAAD. This finding was likely related to higher risk of postoperative heart failure (35.3% vs. 10.8%, p < 0.0001) among iatrogenic TAAD patients. Five-year mortality was comparable between patients with iatrogenic and spontaneous TAAD (46.2% vs. 39.4%, p = 0.163). (4) Conclusions: Iatrogenic origin of acute TAAD is quite uncommon but carries a significantly increased risk of in-hospital mortality compared to spontaneous TAAD

Gamma-Ray Burst observations by the high-energy charged particle detector on board the CSES-01 satellite between 2019 and 2021

In this paper we report the detection of five strong Gamma-Ray Bursts (GRBs) by the High-Energy Particle Detector (HEPD-01) mounted on board the China Seismo-Electromagnetic Satellite (CSES-01), operational since 2018 on a Sun-synchronous polar orbit at a $\sim$ 507 km altitude and 97$^\circ$ inclination. HEPD-01 was designed to detect high-energy electrons in the energy range 3 - 100 MeV, protons in the range 30 - 300 MeV, and light nuclei in the range 30 - 300 MeV/n. Nonetheless, Monte Carlo simulations have shown HEPD-01 is sensitive to gamma-ray photons in the energy range 300 keV - 50 MeV, even if with a moderate effective area above $\sim$ 5 MeV. A dedicated time correlation analysis between GRBs reported in literature and signals from a set of HEPD-01 trigger configuration masks has confirmed the anticipated detector sensitivity to high-energy photons. A comparison between the simultaneous time profiles of HEPD-01 electron fluxes and photons from GRB190114C, GRB190305A, GRB190928A, GRB200826B and GRB211211A has shown a remarkable similarity, in spite of the different energy ranges. The high-energy response, with peak sensitivity at about 2 MeV, and moderate effective area of the detector in the actual flight configuration explain why these five GRBs, characterised by a fluence above $\sim$ 3 $\times$ 10$^{-5}$ erg cm$^{-2}$ in the energy interval 300 keV - 50 MeV, have been detected.Comment: Accepted for publication in The Astrophysical Journal (ApJ

Outcome after Surgery for Iatrogenic Acute Type A Aortic Dissection

(1) Background: Acute Stanford type A aortic dissection (TAAD) may complicate the outcome of cardiovascular procedures. Data on the outcome after surgery for iatrogenic acute TAAD is scarce. (2) Methods: The European Registry of Type A Aortic Dissection (ERTAAD) is a multicenter, retrospective study including patients who underwent surgery for acute TAAD at 18 hospitals from eight European countries. The primary outcomes were in-hospital mortality and 5-year mortality. Twenty-seven secondary outcomes were evaluated. (3) Results: Out of 3902 consecutive patients who underwent surgery for acute TAAD, 103 (2.6%) had iatrogenic TAAD. Cardiac surgery (37.8%) and percutaneous coronary intervention (36.9%) were the most frequent causes leading to iatrogenic TAAD, followed by diagnostic coronary angiography (13.6%), transcatheter aortic valve replacement (10.7%) and peripheral endovascular procedure (1.0%). In hospital mortality was 20.5% after cardiac surgery, 31.6% after percutaneous coronary intervention, 42.9% after diagnostic coronary angiography, 45.5% after transcatheter aortic valve replacement and nihil after peripheral endovascular procedure (p = 0.092), with similar 5-year mortality between different subgroups of iatrogenic TAAD (p = 0.710). Among 102 propensity score matched pairs, in-hospital mortality was significantly higher among patients with iatrogenic TAAD (30.4% vs. 15.7%, p = 0.013) compared to those with spontaneous TAAD. This finding was likely related to higher risk of postoperative heart failure (35.3% vs. 10.8%, p &lt; 0.0001) among iatrogenic TAAD patients. Five-year mortality was comparable between patients with iatrogenic and spontaneous TAAD (46.2% vs. 39.4%, p = 0.163). (4) Conclusions: Iatrogenic origin of acute TAAD is quite uncommon but carries a significantly increased risk of in-hospital mortality compared to spontaneous TAAD

Evaluation of individual and ensemble probabilistic forecasts of COVID-19 mortality in the United States

Short-term probabilistic forecasts of the trajectory of the COVID-19 pandemic in the United States have served as a visible and important communication channel between the scientific modeling community and both the general public and decision-makers. Forecasting models provide specific, quantitative, and evaluable predictions that inform short-term decisions such as healthcare staffing needs, school closures, and allocation of medical supplies. Starting in April 2020, the US COVID-19 Forecast Hub (https://covid19forecasthub.org/) collected, disseminated, and synthesized tens of millions of specific predictions from more than 90 different academic, industry, and independent research groups. A multimodel ensemble forecast that combined predictions from dozens of groups every week provided the most consistently accurate probabilistic forecasts of incident deaths due to COVID-19 at the state and national level from April 2020 through October 2021. The performance of 27 individual models that submitted complete forecasts of COVID-19 deaths consistently throughout this year showed high variability in forecast skill across time, geospatial units, and forecast horizons. Two-thirds of the models evaluated showed better accuracy than a naïve baseline model. Forecast accuracy degraded as models made predictions further into the future, with probabilistic error at a 20-wk horizon three to five times larger than when predicting at a 1-wk horizon. This project underscores the role that collaboration and active coordination between governmental public-health agencies, academic modeling teams, and industry partners can play in developing modern modeling capabilities to support local, state, and federal response to outbreaks

The United States COVID-19 Forecast Hub dataset

Academic researchers, government agencies, industry groups, and individuals have produced forecasts at an unprecedented scale during the COVID-19 pandemic. To leverage these forecasts, the United States Centers for Disease Control and Prevention (CDC) partnered with an academic research lab at the University of Massachusetts Amherst to create the US COVID-19 Forecast Hub. Launched in April 2020, the Forecast Hub is a dataset with point and probabilistic forecasts of incident cases, incident hospitalizations, incident deaths, and cumulative deaths due to COVID-19 at county, state, and national, levels in the United States. Included forecasts represent a variety of modeling approaches, data sources, and assumptions regarding the spread of COVID-19. The goal of this dataset is to establish a standardized and comparable set of short-term forecasts from modeling teams. These data can be used to develop ensemble models, communicate forecasts to the public, create visualizations, compare models, and inform policies regarding COVID-19 mitigation. These open-source data are available via download from GitHub, through an online API, and through R packages