Vehicle automation and connectivity bring new opportunities for safe and sustainable mobility in urban and highway networks. Such opportunities are however not directly associated with traffic flow improvements. Research on exploitation of connected and automated vehicles (CAVs) toward a more efficient traffic currently remains at a theoretical level, and/or based on simulation models with limited reliability. Furthermore, testing CAVs in the real world is still costly and very challenging from an implementation perspective. A possible alternative is to use automated robots. By designing and testing both the low- and the high-level controllers of CAVs, it is indeed possible to reach a better understanding of the challenges that future vehicles will need to face. Robotic applications can effectively test these challenges within a wide variety of research communities—for example, via robotic competitions. Along this direction, the Joint Research Centre has organized the first European robotic traffic competition for automated miniature vehicles. Each team participated with four robots and was judged based on a set of indicators that assess the collective behaviors of the vehicles. Results show the suitability of the methodology with different teams proposing completely different approaches to deal with the challenge and thus achieving different results. Future competitions may further raise awareness about the possibility of using CAVs to improve traffic and to engage with a broader community to design systems that are really capable of achieving this goal.

Segmentation of specific brain tissue from MRI volumes is of great significance for brain disease diagnosis, progression assessment, and monitoring of neurological conditions. Manual segmentation is time-consuming, laborious, and subjective, which significantly amplifies the need for automated processes. Over the last decades, the active development in the field of deep learning, especially convolutional neural networks (CNNs), and the associated performance improvements have increased the demand for the application of CNN-based methods to provide consistent measurements and quantitative analyses. In this paper, we present an efficient deep learning approach for the segmentation of brain tissue. More specifically, we address the problem of segmentation of the posterior limb of the internal capsule (PLIC) in preterm neonates. To this end, we propose a CNN-based pipeline comprised of slice-selection modules and a multi-view segmentation model, which exploits the 3D information contained in the MRI volumes to improve segmentation performance. One special feature of the proposed method is its ability to identify one desired slice out of the whole image volume, which is relevant for pediatricians in terms of prognosis. To increase computational efficiency, we apply a strategy that automatically reduces the information contained in the MRI volumes to its relevant parts. Finally, we conduct an expert rating alongside standard evaluation metrics, such as dice score, to evaluate the performance of the proposed framework. We demonstrate the benefit of the multi-view technique by comparing it with its single-view counterparts, which reveals that the proposed method strikes a good balance between exploiting the available image information and reducing the required computing power compared to 3D segmentation networks. Standard evaluation metrics as, well as expert-based assessment, confirm the good performance of the proposed framework, with the latter being more relevant in terms of clinical applicability. We demonstrate that the proposed deep learning pipeline can compete with the experts in terms of accuracy. To prove the generalisability of the proposed method, we additionally assess our deep learning pipeline to data from the Developing Human Connectome Project (dHCP).

Aim To analyse biochemical markers as possible predictors of death before discharge in cooled newborns following perinatal asphyxia. Methods A total of 91 infants that underwent therapeutic hypothermia after perinatal asphyxia were included. Inclusion criteria for therapeutic hypothermia were Sarnat stage 2 or 3. Data were collected from medical histories regarding gender, gestational age, birth weight, Apgar and Sarnat score; additionally, gas analyses, liver and cardiac enzymes before, and in the first 12 hours after starting therapeutic hypothermia, were evaluated. The patients' characteristics were compared between two groups, survivors and non-survivors. Results Statistical difference was not found between groups regarding gender, gestational age, birth weight, delivery type, 1st and 5th minute Apgar score, seizures, alanine aminotransferase (ALT), creatine kinase (CK), troponin and fibrinogen level. Groups were significantly different regarding acid-base balance (p=0.012), base excess (BE) (p=0.025), lactate (p=0.002), aspartate aminotransferaze (AST), (p=0.011), lactate dehydrogenase (LDH) (p=0.006), activated partial thromboplastin clotting time (aPTT) (p=0.001) and international normalized ratio (INR) (p=0.001). Conclusion Acid-base balance, BE, lactate, AST, LDH, aPTT and INR were significantly higher in the group of cooled newborns after perinatal asphyxia (non-survivors), and can serve as predictors of death before discharge. Combining diagnostic modalities raises a chance for accurate prediction of outcomes of asphyxiated infants.

Background

BACKGROUND Attention-deficit/hyperactivity disorder (ADHD) and posttraumatic stress disorder (PTSD) are associated, but it is unclear if this is a causal relationship or confounding. We used genetic analyses and sibling comparisons to clarify the direction of this relationship. METHODS Linkage disequilibrium score regression and 2-sample Mendelian randomization were used to test for genetic correlation (rg) and bidirectional causal effects using European ancestry genome-wide association studies of ADHD (20,183 cases and 35,191 controls) and 6 PTSD definitions (up to 320,369 individuals). Several additional variables were included in the analysis to verify the independence of the ADHD-PTSD relationship. In a population-based sibling comparison (N = 2,082,118 individuals), Cox regression models were fitted to account for time at risk, a range of sociodemographic factors, and unmeasured familial confounders (via sibling comparisons). RESULTS ADHD and PTSD had consistent rg (rg range, 0.43-0.52; p <  .001). ADHD genetic liability was causally linked with increased risk for PTSD (β = 0.367; 95% CI, 0.186-0.552; p = 7.68 × 10-5). This result was not affected by heterogeneity, horizontal pleiotropy (Mendelian randomization Egger intercept = 4.34 × 10-4, p = .961), or other phenotypes and was consistent across PTSD datasets. However, we found no consistent associations between PTSD genetic liability and ADHD risk. Individuals diagnosed with ADHD were at a higher risk for developing PTSD than their undiagnosed sibling (hazard ratio = 2.37; 95% CI, 1.98-3.53). CONCLUSIONS Our findings add novel evidence supporting the need for early and effective treatment of ADHD, as patients with this diagnosis are at significantly higher risk to develop PTSD later in life.

Caused by the new SARS-CoV-2 coronavirus, COVID-19 (coronavirus disease 2019) evolves with clinical symptoms that vary widely in severity, from mild symptoms to critical conditions, which can even result in the patient's death. A critical aspect related to an individual response to SARS-CoV-2 infection is the competence of the immune system, and it is well known that several trace elements are essential for an adequate immune response and have anti-inflammatory and antioxidant properties that are of particular importance in fighting infection. Thus, it is widely accepted that adequate trace element status can reduce the risk of SARS-CoV-2 infection and disease severity. In this study, we evaluated the serum levels of Cu, Zn, Se, Fe, I and Mg in patients (n = 210) with clinical conditions of different severity (“mild”, “moderate”, “severe” and “exitus letalis”, i.e., patients who eventually died). The results showed significant differences between the four groups for Cu, Zn, Se and Fe, in particular a significant trend of Zn and Se serum levels to be decreased and Cu to be increased with the severity of symptoms. For Mg and I, no differences were observed, but I levels were shown to be increased in all groups.

Since the outbreak of the COVID-19 pandemic, it has been obvious that virus infection poses a serious threat to human health on a global scale. Certain plants, particularly those rich in polyphenols, have been found to be effective antiviral agents. The effectiveness of Alchemilla viridiflora Rothm. (Rosaceae) methanol extract to prevent contact between virus spike (S)-glycoprotein and angiotensin-converting enzyme 2 (ACE2) and neuropilin-1 (NRP1) receptors was investigated. In vitro results revealed that the tested samples inhibited 50% of virus-receptor binding interactions in doses of 0.18 and 0.22 mg/mL for NRP1 and ACE2, respectively. Molecular docking studies revealed that the compounds from A. viridiflora ellagitannins class had a higher affinity for binding with S-glycoprotein whilst flavonoid compounds more significantly interacted with the NRP1 receptor. Quercetin 3-(6″-ferulylglucoside) and pentagalloylglucose were two compounds with the highest exhibited interfering potential for selected target receptors, with binding energies of −8.035 (S-glycoprotein) and −7.685 kcal/mol (NRP1), respectively. Furthermore, computational studies on other SARS-CoV-2 strains resulting from mutations in the original wild strain (V483A, N501Y-K417N-E484K, N501Y, N439K, L452R-T478K, K417N, G476S, F456L, E484K) revealed that virus internalization activity was maintained, but with different single compound contributions.

For more than two years, coronavirus disease 19 (COVID-19) has represented a threat to global health and lifestyles. Computed tomography (CT) imaging provides useful information in patients with COVID-19 pneumonia. However, this diagnostic modality is based on exposure to ionizing radiation, which is associated with an increased risk of radiation-induced cancer. In this study, we evaluated the common dose descriptors, CTDIvol and DLP, for 1180 adult patients. This data was used to estimate the effective dose, and risk of exposure-induced death (REID). Awareness of the extensive use of CT as a diagnostic tool in the management of COVID-19 during the pandemic is vital for the evaluation of radiation exposure parameters, dose reduction methods development and radiation protection.

Slaughterhouse wastewater (SWW) contains a significant volume of highly polluted organic wastes. These include blood, fat, soluble proteins, colloidal particles, suspended materials, meat particles, and intestinal undigested food that consists of higher concentrations of organics such as biochemical oxygen demand (BOD), chemical oxygen demand (COD), nitrogen and phosphorus hence an efficient treatment is required before discharging into the water bodies. The effluent concentrations and performance of simultaneous sequential batch biofilm reactor (SBBR) with recycled plastic carrier media support are better than the local single-stage sequential batch reactor (SBR), which is lacking in the literature in terms of COD, NH3, NO3, and PO4 treatment efficiency. In the present study, we report a novel strategy to remove the above-mentioned contaminants using an intermittently aerated SBBR with recycled plastic carrier media support along with simultaneous nitrification and denitrification. The central composite design was evaluated to optimize the treatment performance of seven different process variables including; different alternating conditions (Oxic/anoxic) for aeration cycles (3/2 h in a 6 h cycle, 6/5 h in a 12 h cycle, and 9/8 h in an 18 h cycle) and hydraulic retention time (6, 12 and 18 h). The average removal efficiencies are 94.5% for NH3, 93% for NO3 and 90.1% for PO4, and 99% for COD. The study reveals that the denitrification in the post-anoxic phase was more efficient than the pre-anoxic phase for pollutant removal and maintaining higher quality effluent. The effluent concentrations and performance of simultaneous SBBR with recycled polyethylene carrier support media were better than local SBR system in terms of COD, NH3, NO3 and PO4 treatment efficiency. Results stipulated the suitability of SBBR for wastewater treatment and reusability as a sustainable approach for wastewater management under optimum conditions.