Cetacean sightings within the Great Pacific Garbage Patch

Here, we report cetacean sightings made within a major oceanic accumulation zone for plastics, often referred to as the ‘Great Pacific Garbage Patch’ (GPGP). These cetacean records occurred in October 2016 and were made by sensors and trained observers aboard a Hercules C-130 aircraft surveying the GPGP at 400 m height and 140 knots speed. Four sperm whales (including a mother and calf pair), three beaked whales, two baleen whales, and at least five other cetaceans were observed. Many surface drifting plastics were also detected, including fishing nets, ropes, floats and fragmented debris. Some of these objects were close to the sighted mammals, posing entanglement and ingestion risks to animals using the GPGP as a migration corridor or core habitat. Our study demonstrates the potential exposure of several cetacean species to the high levels of plastic pollution in the area. Further research is required to evaluate the potential effects of the GPGP on marine mammal populations inhabiting the North Pacific

Maternal Humoral Immune Correlates of Peripartum Transmission of Clade C HIV-1 in the Setting of Peripartum Antiretrovirals

ABSTRACT Despite the widespread use of antiretrovirals (ARV), more than 150,000 pediatric HIV-1 infections continue to occur annually. Supplemental strategies are necessary to eliminate pediatric HIV infections. We previously reported that maternal HIV envelope-specific anti-V3 IgG and CD4 binding site-directed antibodies, as well as tier 1 virus neutralization, predicted a reduced risk of mother-to-child transmission (MTCT) of HIV-1 in the pre-ARV era U.S.-based Women and Infants Transmission Study (WITS) cohort. As the majority of ongoing pediatric HIV infections occur in sub-Saharan Africa, we sought to determine if the same maternal humoral immune correlates predicted MTCT in a subset of the Malawian Breastfeeding, Antiretrovirals, and Nutrition (BAN) cohort of HIV-infected mothers ( n = 88, with 45 transmitting and 43 nontransmitting). Women and infants received ARV at delivery; thus, the majority of MTCT was in utero (91%). In a multivariable logistic regression model, neither maternal anti-V3 IgG nor clade C tier 1 virus neutralization was associated with MTCT. Unexpectedly, maternal CD4 binding-site antibodies and anti-variable loop 1 and 2 (V1V2) IgG were associated with increased MTCT, independent of maternal viral load. Neither infant envelope (Env)-specific IgG levels nor maternal IgG transplacental transfer efficiency was associated with transmission. Distinct humoral immune correlates of MTCT in the BAN and WITS cohorts could be due to differences between transmission modes, virus clades, or maternal antiretroviral use. The association between specific maternal antibody responses and in utero transmission, which is distinct from potentially protective maternal antibodies in the WITS cohort, underlines the importance of investigating additional cohorts with well-defined transmission modes to understand the role of antibodies during HIV-1 MTCT

Role of Artificial Intelligence in Radiogenomics for Cancers in the Era of Precision Medicine

Radiogenomics, a combination of “Radiomics” and “Genomics,” using Artificial Intelligence (AI) has recently emerged as the state-of-the-art science in precision medicine, especially in oncology care. Radiogenomics syndicates large-scale quantifiable data extracted from radiological medical images enveloped with personalized genomic phenotypes. It fabricates a prediction model through various AI methods to stratify the risk of patients, monitor therapeutic approaches, and assess clinical outcomes. It has recently shown tremendous achievements in prognosis, treatment planning, survival prediction, heterogeneity analysis, reoccurrence, and progression-free survival for human cancer study. Although AI has shown immense performance in oncology care in various clinical aspects, it has several challenges and limitations. The proposed review provides an overview of radiogenomics with the viewpoints on the role of AI in terms of its promises for computa-tional as well as oncological aspects and offers achievements and opportunities in the era of precision medicine. The review also presents various recommendations to diminish these obstacles

X-ray induced electron and ion fragmentation dynamics in IBr

Characterization of the inner-shell decay processes in molecules containing heavy elements is key to understanding x-ray damage of molecules and materials and for medical applications with Auger-electron-emitting radionuclides. The 1s hole states of heavy atoms can be produced by absorption of tunable x-rays and the resulting vacancy decays characterized by recording emitted photons, electrons, and ions. The 1s hole states in heavy elements have large x-ray fluorescence yields that transfer the hole to intermediate electron shells that then decay by sequential Auger-electron transitions that increase the ion's charge state until the final state is reached. In molecules the charge is spread across the atomic sites, resulting in dissociation to energetic atomic ions. We have used x-ray/ion coincidence spectroscopy to measure charge states and energies of I$^{q+}$ and Br$^{q'+}$ atomic ions following 1s ionization at the I and Br \textit{K}-edges of IBr. We present the charge states and kinetic energies of the two correlated fragment ions associated with core-excited states produced during the various steps of the cascades. To understand the dynamics leading to the ion data, we develop a computational model that combines Monte-Carlo/Molecular Dynamics simulations with a classical over-the-barrier model to track inner-shell cascades and redistribution of electrons in valence orbitals and nuclear motion of fragments

Vitamin D deficiency prevalence and predictors in early pregnancy among Arab women

Data regarding the prevalence and predictors of vitamin D deficiency during early pregnancy are limited. This study aims to fill this gap. A total of 578 Saudi women in their 1st trimester of pregnancy were recruited between January 2014 and December 2015 from three tertiary care antenatal clinics in Riyadh, Saudi Arabia. Information collected includes socio-economic, anthropometric, and biochemical data, including serum vitamin D (25(OH)D) levels, intake of calcium and vitamin D, physical activity, and sun exposure indices. Pregnant women with 25(OH)D levels 3.5), low HDL-cholesterol, and living in West Riyadh were significant independent predictors for vitamin D deficiency, with odds ratios (ORs) (95% confidence interval) of 25.4 (5.5–117.3), 17.8 (2.3–138.5), 4.0 (1.7–9.5), 3.3 (1.4–7.9), 2.8 (1.2–6.4), and 2.0 (1.1–3.5), respectively. Factors like increased physical activity, sun exposure at noon, sunrise or sunset, high educational status, and residence in North Riyadh were protective against vitamin D deficiency with ORs 0.2 (0.1–0.5); 0.2 (0.1–0.6); 0.3 (0.1–0.9); and 0.4 (0.2–0.8), respectively. All ORs were adjusted for age, BMI, sun exposure, parity, summer season, vitamin D intake, multivitamin intake, physical activity, education, employment, living in the north, and coverage with clothing. In conclusion, the prevalence of vitamin D deficiency among Saudi women during early pregnancy was high (81%). Timely detection and appropriate supplementation with adequate amounts of vitamin D should reduce the risks of vitamin D deficiency and its complications during pregnancy

Seatbelt use and risk of major injuries sustained by vehicle occupants during motor-vehicle crashes: A systematic review and meta-analysis of cohort studies

BackgroundIn 2004, a World Health Report on road safety called for enforcement of measures such as seatbelt use, effective at minimizing morbidity and mortality caused by road traffic accidents. However, injuries caused by seatbelt use have also been described. Over a decade after publication of the World Health Report on road safety, this study sought to investigate the relationship between seatbelt use and major injuries in belted compared to unbelted passengers.MethodsCohort studies published in English language from 2005 to 2018 were retrieved from seven databases. Critical appraisal of studies was carried out using the Scottish Intercollegiate Guidelines Network (SIGN) checklist. Pooled risk of major injuries was assessed using the random effects meta-analytic model. Heterogeneity was quantified using I-squared and Tau-squared statistics. Funnel plots and Egger's test were used to investigate publication bias. This review is registered in PROSPERO (CRD42015020309).ResultsEleven studies, all carried out in developed countries were included. Overall, the risk of any major injury was significantly lower in belted passengers compared to unbelted passengers (RR 0.47; 95%CI, 0.29 to 0.80; I-2=99.7; P=0.000). When analysed by crash types, belt use significantly reduced the risk of any injury (RR 0.35; 95%CI, 0.24 to 0.52). Seatbelt use reduces the risk of facial injuries (RR=0.56, 95% CI=0.37 to 0.84), abdominal injuries (RR=0.87; 95% CI=0.78 to 0.98) and, spinal injuries (RR=0.56, 95% CI=0.37 to 0.84). However, we found no statistically significant difference in risk of head injuries (RR=0.49; 95% CI=0.22 to 1.08), neck injuries (RR=0.69: 95%CI 0.07 to 6.44), thoracic injuries (RR 0.96, 95%CI, 0.74 to 1.24), upper limb injuries (RR=1.05, 95%CI 0.83 to 1.34) and lower limb injuries (RR=0.77, 95%CI 0.58 to 1.04) between belted and non-belted passengers.ConclusionIn sum, the risk of most major road traffic injuries is lower in seatbelt users. Findings were inconclusive regarding seatbelt use and susceptibility to thoracic, head and neck injuries during road traffic accidents. Awareness should be raised about the dangers of inadequate seatbelt use. Future research should aim to assess the effects of seatbelt use on major injuries by crash type

Deep Learning Paradigm for Cardiovascular Disease/Stroke Risk Stratification in Parkinson’s Disease Affected by COVID‐19: A Narrative Review

Background and Motivation: Parkinson’s disease (PD) is one of the most serious, non-curable, and expensive to treat. Recently, machine learning (ML) has shown to be able to predict cardiovascular/stroke risk in PD patients. The presence of COVID‐19 causes the ML systems to be-come severely non‐linear and poses challenges in cardiovascular/stroke risk stratification. Further, due to comorbidity, sample size constraints, and poor scientific and clinical validation techniques, there have been no well‐explained ML paradigms. Deep neural networks are powerful learning machines that generalize non‐linear conditions. This study presents a novel investigation of deep learning (DL) solutions for CVD/stroke risk prediction in PD patients affected by the COVID‐19 framework. Method: The PRISMA search strategy was used for the selection of 292 studies closely associated with the effect of PD on CVD risk in the COVID‐19 framework. We study the hypothesis that PD in the presence of COVID‐19 can cause more harm to the heart and brain than in non‐ COVID‐19 conditions. COVID‐19 lung damage severity can be used as a covariate during DL training model designs. We, therefore, propose a DL model for the estimation of, (i) COVID‐19 lesions in computed tomography (CT) scans and (ii) combining the covariates of PD, COVID‐19 lesions, office and laboratory arterial atherosclerotic image‐based biomarkers, and medicine usage for the PD patients for the design of DL point‐based models for CVD/stroke risk stratification. Results: We validated the feasibility of CVD/stroke risk stratification in PD patients in the presence of a COVID‐ 19 environment and this was also verified. DL architectures like long short‐term memory (LSTM), and recurrent neural network (RNN) were studied for CVD/stroke risk stratification showing powerful designs. Lastly, we examined the artificial intelligence bias and provided recommendations for early detection of CVD/stroke in PD patients in the presence of COVID‐19. Conclusion: The DL is a very powerful tool for predicting CVD/stroke risk in PD patients affected by COVID‐19

COVLIAS 2.0-cXAI: Cloud-Based Explainable Deep Learning System for COVID-19 Lesion Localization in Computed Tomography Scans

Background: The previous COVID-19 lung diagnosis system lacks both scientific validation and the role of explainable artificial intelligence (AI) for understanding lesion localization. This study presents a cloud-based explainable AI, the “COVLIAS 2.0-cXAI” system using four kinds of class activation maps (CAM) models. Methodology: Our cohort consisted of ~6000 CT slices from two sources (Croatia, 80 COVID-19 patients and Italy, 15 control patients). COVLIAS 2.0-cXAI design consisted of three stages: (i) automated lung segmentation using hybrid deep learning ResNet-UNet model by automatic adjustment of Hounsfield units, hyperparameter optimization, and parallel and distributed training, (ii) classification using three kinds of DenseNet (DN) models (DN-121, DN-169, DN-201), and (iii) validation using four kinds of CAM visualization techniques: gradient-weighted class activation mapping (Grad-CAM), Grad-CAM++, score-weighted CAM (Score-CAM), and FasterScore-CAM. The COVLIAS 2.0-cXAI was validated by three trained senior radiologists for its stability and reliability. The Friedman test was also performed on the scores of the three radiologists. Results: The ResNet-UNet segmentation model resulted in dice similarity of 0.96, Jaccard index of 0.93, a correlation coefficient of 0.99, with a figure-of-merit of 95.99%, while the classifier accuracies for the three DN nets (DN-121, DN-169, and DN-201) were 98%, 98%, and 99% with a loss of ~0.003, ~0.0025, and ~0.002 using 50 epochs, respectively. The mean AUC for all three DN models was 0.99 (p < 0.0001). The COVLIAS 2.0-cXAI showed 80% scans for mean alignment index (MAI) between heatmaps and gold standard, a score of four out of five, establishing the system for clinical settings. Conclusions: The COVLIAS 2.0-cXAI successfully showed a cloud-based explainable AI system for lesion localization in lung CT scans

Cardiovascular Risk Stratification in Diabetic Retinopathy via Atherosclerotic Pathway in COVID-19/non-COVID-19 Frameworks using Artificial Intelligence Paradigm: A Narrative Review

Diabetes is one of the main causes of the rising cases of blindness in adults. This microvascular complication of diabetes is termed diabetic retinopathy (DR) and is associated with an expanding risk of cardiovascular events in diabetes patients. DR, in its various forms, is seen to be a powerful indicator of atherosclerosis. Further, the macrovascular complication of diabetes leads to coronary artery disease (CAD). Thus, the timely identification of cardiovascular disease (CVD) complications in DR patients is of utmost importance. Since CAD risk assessment is expensive for lowincome countries, it is important to look for surrogate biomarkers for risk stratification of CVD in DR patients. Due to the common genetic makeup between the coronary and carotid arteries, lowcost, high-resolution imaging such as carotid B-mode ultrasound (US) can be used for arterial tissue characterization and risk stratification in DR patients. The advent of artificial intelligence (AI) techniques has facilitated the handling of large cohorts in a big data framework to identify atherosclerotic plaque features in arterial ultrasound. This enables timely CVD risk assessment and risk stratification of patients with DR. Thus, this review focuses on understanding the pathophysiology of DR, retinal and CAD imaging, the role of surrogate markers for CVD, and finally, the CVD risk stratification of DR patients. The review shows a step-by-step cyclic activity of how diabetes and atherosclerotic disease cause DR, leading to the worsening of CVD. We propose a solution to how AI can help in the identification of CVD risk. Lastly, we analyze the role of DR/CVD in the COVID-19 framework

Direct enzymatic esterification of cotton and Avicel with wild-type and engineered cutinases

In this work, the surface of cellulose, either Avicel or cotton fabric, was modified using cutinases without any previous treatment to swell or to solubilise the polymer. Aiming further improvement of cutinase ester synthase activity on cellulose, an engineered cutinase was investigated. Wild-type cutinase from Fusarium solani and its fusion with the carbohydrate-binding module N1 from Cellulomonas fimi were able to esterify the hydroxyl groups of cellulose with distinct efficiencies depending on the acid substrate/solvent system used, as shown by titration and by ATR-FTIR. The carbonyl stretching peak area increased significantly after enzymatic treatment during 72 h at 30 °C. Cutinase treatment resulted in relative increases of 31 and 9 % when octanoic acid and vegetable oil were used as substrates, respectively. Cutinase-N1 treatment resulted in relative increases of 11 and 29 % in the peak area when octanoic acid and vegetable oil were used as substrates, respectively. The production and application of cutinase fused with the domain N1 as a cellulose ester synthase, here reported for the first time, is therefore an interesting strategy to pursuit.This work was co-funded by the European Social Fund through the management authority POPH and FCT, Postdoctoral fellowship reference: SFRH/BPD/47555/2008. The authors also want to thank Doctor Raul Machado for his valuable help on FTIR spectral data treatment