Sex ratio estimations of Chelonia mydas hatchlings at Samandağ Beach, Turkey

Samandağ Beach, an important nesting location for endangered green turtles (Chelonia mydas), is located at the most eastern part of the Mediterranean in Turkey. Hatchling sex ratio can be one of the key factors for green turtle protection, which is under the threat of global climate change. In this study, sex ratios of hatchlings were estimated by histological examination of dead hatchlings (n = 190) and by nest temperatures (n = 14) at Samandağ Beach between the 2003 and 2007 nesting seasons. The histological examination of dead C. mydas hatchlings and temperature-recorded nests indicated a female-biased sex ratio on Samandağ Beach. By histological examination, top and bottom levels of nests were recorded as 94% and 64% of females. Sex ratios during the middle third of the incubation period ranged from 39% to 97% for females with a mean of 74%. Distance from the sea was correlated with the middle third of incubation temperature (r = –0.604, P = 0.022) and female ratio (r = –0.573, P = 0.032). While nest temperatures and nest parameters such as hatching success, clutch size, nest distance from the sea and from vegetation, nest depth, and the day of emergence were not found significantly different among years (P > 0.05), incubation duration was found significantly different among years (P < 0.05). The results showed a female-dominated sex ratio by both histology and nest temperature. The knowledge of hatchling sex ratios provides information on demographic parameters, and coupled with appropriate conservation measures this can make important contributions to studies of climate change effects on green sea turtle populations. © TÜBİTAK

Work-related injuries sustained by emergency medical technicians and paramedics in Turkey

BACKGROUND: Evaluated in the present study were locations, descriptions, and results of work-related injuries (WRIs) sustained by emergency medical technicians (EMTs) and paramedics in Turkey’s most crowded city, İstanbul. METHODS: After the present study had been accepted by the urban health authority, a questionnaire was emailed to the healthcare personnel of İstanbul’s 195 ambulance stations. RESULTS: Included in the present study were the responses of 901 members of staff (660 EMTs and 241 paramedics), with a mean age of 29.5±6.1 (min: 18; max: 61). The majority of participants (94.9%) had encountered verbal abuse from the public, and 39.8% had encountered physical violence from patients’ relatives. Levels of satisfaction with work in emergency medical services (EMS) was also evaluated, and 510 participants (57.6%) were unhappy. Regarding gender, female employees were more likely to be verbally attacked (p=0.01), while males were more likely to be physically attacked (p=0.001). It was reported that motor vehicle accidents (MVAs) were the most common cause of WRIs (81.4%), followed by needle-stick injuries (52.2%), ocular exposure to blood and other fluids (30.9%), and sharp injuries (22.5%). Only 10.5% (n=95) of WRIs were reported to authorities; 488 (54.2%) of participants just attended to the practice to prevent possible WRIs. CONCLUSION: For paramedics and EMTs, risk of WRI is obviously high. Strategies to decrease and prevent verbal and physical violence should be developed. © 2016 TJTES

Categorized priority systems: a new tool for fairly allocating scarce medical resources in the face of profound social inequities

The coronavirus disease 2019 (COVID-19) pandemic has motivated medical ethicists and several task forces to revisit or issue new guidelines on allocating scarce medical resources. Such guidelines are relevant for the allocation of scarce therapeutics and vaccines and for allocation of ICU beds, ventilators, and other life-sustaining treatments or potentially scarce interventions. Principles underlying these guidelines, like saving the most lives, mitigating disparities, reciprocity to those who assume additional risk (eg, essential workers and clinical trial participants), and equal access may compete with one another. We propose the use of a “categorized priority system” (also known as a “reserve system”) as an improvement over existing allocation methods, particularly because it may be able to achieve disparity mitigation better than other methods

Categorized priority systems: a new tool for fairly allocating scarce medical resources in the face of profound social inequities

The coronavirus disease 2019 (COVID-19) pandemic has motivated medical ethicists and several task forces to revisit or issue new guidelines on allocating scarce medical resources. Such guidelines are relevant for the allocation of scarce therapeutics and vaccines and for allocation of ICU beds, ventilators, and other life-sustaining treatments or potentially scarce interventions. Principles underlying these guidelines, like saving the most lives, mitigating disparities, reciprocity to those who assume additional risk (eg, essential workers and clinical trial participants), and equal access may compete with one another. We propose the use of a “categorized priority system” (also known as a “reserve system”) as an improvement over existing allocation methods, particularly because it may be able to achieve disparity mitigation better than other methods

Once-monthly paliperidone palmitate in recently diagnosed and chronic non-acute patients with schizophrenia

OBJECTIVE: To explore the treatment response, tolerability and safety of once-monthly paliperidone palmitate (PP1M) in non-acute patients switched to oral antipsychotics stratified by time since diagnosis as recently diagnosed (3 years). RESEARCH DESIGN AND METHODS: Post-hoc analysis of a prospective, interventional, single-arm, multicentre, open-label, 6-month study performed in 233 recently diagnosed and 360 chronic patients. MAIN OUTCOME MEASURES: The proportion achieving treatment response (defined as >/=20% improvement in Positive and Negative Syndrome Scale [PANSS] total score from baseline to endpoint) and maintained efficacy (defined as non-inferiority in the change in PANSS total score at endpoint [Schuirmann's test]). RESULTS: 71.4% of recently diagnosed and 59.2% of chronic patients showed a >/=20% decrease in PANSS total score (p=0.0028 between groups). Changes in PANSS Marder factors, PANSS subscales, and the proportion of patients with a Personal and Social Performance scale (PSP) total score of 71-100 were significantly greater in recently diagnosed compared with chronic patients. PP1M was well tolerated, presenting no new safety signals. CONCLUSION: These data show that recently diagnosed patients treated with PP1M had a significantly higher treatment response and improved functioning, as assessed by the PSP total score, than chronic patients

The validation service of the hydrological SAF geostationary and polar satellite precipitation products

Abstract. The development phase (DP) of the EUMETSAT Satellite Application Facility for Support to Operational Hydrology and Water Management (H-SAF) led to the design and implementation of several precipitation products, after 5 yr (2005–2010) of activity. Presently, five precipitation estimation algorithms based on data from passive microwave and infrared sensors, on board geostationary and sun-synchronous platforms, function in operational mode at the H-SAF hosting institute to provide near real-time precipitation products at different spatial and temporal resolutions. In order to evaluate the precipitation product accuracy, a validation activity has been established since the beginning of the project. A Precipitation Product Validation Group (PPVG) works in parallel with the development of the estimation algorithms with two aims: to provide the algorithm developers with indications to refine algorithms and products, and to evaluate the error structure to be associated with the operational products. In this paper, the framework of the PPVG is presented: (a) the characteristics of the ground reference data available to H-SAF (i.e. radar and rain gauge networks), (b) the agreed upon validation strategy settled among the eight European countries participating in the PPVG, and (c) the steps of the validation procedures. The quality of the reference data is discussed, and the efforts for its improvement are outlined, with special emphasis on the definition of a ground radar quality map and on the implementation of a suitable rain gauge interpolation algorithm. The work done during the H-SAF development phase has led the PPVG to converge into a common validation procedure among the members, taking advantage of the experience acquired by each one of them in the validation of H-SAF products. The methodology is presented here, indicating the main steps of the validation procedure (ground data quality control, spatial interpolation, up-scaling of radar data vs. satellite grid, statistical score evaluation, case study analysis). Finally, an overview of the results is presented, focusing on the monthly statistical indicators, referred to the satellite product performances over different seasons and areas

Occupancy Classification of Position Weight Matrix-Inferred Transcription Factor Binding Sites

BACKGROUND: Computational prediction of Transcription Factor Binding Sites (TFBS) from sequence data alone is difficult and error-prone. Machine learning techniques utilizing additional environmental information about a predicted binding site (such as distances from the site to particular chromatin features) to determine its occupancy/functionality class show promise as methods to achieve more accurate prediction of true TFBS in silico. We evaluate the Bayesian Network (BN) and Support Vector Machine (SVM) machine learning techniques on four distinct TFBS data sets and analyze their performance. We describe the features that are most useful for classification and contrast and compare these feature sets between the factors. RESULTS: Our results demonstrate good performance of classifiers both on TFBS for transcription factors used for initial training and for TFBS for other factors in cross-classification experiments. We find that distances to chromatin modifications (specifically, histone modification islands) as well as distances between such modifications to be effective predictors of TFBS occupancy, though the impact of individual predictors is largely TF specific. In our experiments, Bayesian network classifiers outperform SVM classifiers. CONCLUSIONS: Our results demonstrate good performance of machine learning techniques on the problem of occupancy classification, and demonstrate that effective classification can be achieved using distances to chromatin features. We additionally demonstrate that cross-classification of TFBS is possible, suggesting the possibility of constructing a generalizable occupancy classifier capable of handling TFBS for many different transcription factors