Conservation of data deficient species under multiple threats: Lessons from an iconic tropical butterfly (Teinopalpus aureus)
حفاظت از گونه های کمبود داده در معرض تهدیدات متعدد: درسهایی از یک پروانه گرمسیری نمادین (Teinopalpus aureus)-2019
With increasing pressure from wildlife trade, conservation eﬀorts must balance deﬁciencies in distribution data for species (the Wallacean shortfall) with the risk of increasing accessibility of locality for collectors. The Golden Kaiser-I-Hind (Teinopalpus aureus Mell) is an iconic butterﬂy restricted to Southeast Asia, popular in trade markets but lacking in ecological and conservation information. We compiled occurrence records and used them to assess multiple threats of T. aureus distribution-wide and at the national level. Results of species distribution models suggest that suitable habitats of T. aureus are montane forests in mid to high elevations in Southern China, Laos and Vietnam. However, habitat networks for the species are poorly connected, with some portions of its distribution experiencing intensive deforestation and threatened by climate change. The trade assessment results showed specimens of T. aureus were available for sale with high prices, indicating potential pressure from trade markets. We also found diﬀerent conservation statuses and eﬀorts to protect T. aureus across countries; the species is under strict protection in China, moderate protection in Vietnam and has no protection in Laos. Both recorded locations and projected distribution in the three countries were poorly covered by protected areas. These results together demonstrate the importance of distribution data in conservation management of threa- tened species while highlighting trade-oﬀs inherent in not making location information widely available when trade pressure is present. Finally, we strongly encourage cross-border cooperation in sharing ecological in- formation for consistent conservation management of species under multiple threats from habitat loss, climate change and illegal wildlife trade.
Keywords: Climate change | Cross-border conservation | Habitat loss | Insect conservation | Southeast Asia | Wildlife trade
سیستم پشتیبانی از تصمیم برای خطرات و اقدامات متقابل ایمنی جاده ای اروپا
سال انتشار: 2019 - تعداد صفحات فایل pdf انگلیسی: 8 - تعداد صفحات فایل doc فارسی: 32
سیستم پشتیبانی از تصمیم درباره ایمنی جاده ای اروپا (roadsafety-dss.eu) یک سیستم نوآورانه است که شواهد و مدارک دسترس پذیری را درباره گستره وسیعی از خطرات جاده ای و اقدامات متقابل امکانپذیر فراهم می کند. این مقاله پایه و اساس علمی سیستم پشتیبانی از تصمیم را توصیف می کند. ساختار موجود در سیستم پشتیبانی از تصمیم شامل (1) یک طبقه بندی که به شناسایی عوامل خطر و اقدامات متقابل آن می پردازد و آنها را به همدیگر مرتبط می کند، (2) یک مجموعه ای از مطالعات، و (3) خلاصه هایی که تاثیرات تخمین زده شده در منابع علمی را برای هر عامل و سنجه خطر خلاصه بندی می کنند و (4) یک ابزار ارزیابی کارآمدی اقتصادی (محاسبه گر E3) می شود. سیستم پشتیبانی از تصمیم در یک ابزار نوین مبتنی بر وب با فصل مشترک بسیار انسانی اجرا می شود که به کاربران اجازه می دهد تا مرور اجمالی سریعی داشته باشند یا نتایج هر مطالعه را برطبق نیازهای مخصوص آنها عمیق تر بررسی کنند.
کلیدواژه ها: اقدامات متقابل ایمنی جاده | خطرات جاده ای | سودمندی | سیستم آنلاین | مرور | هزینه – سود
|مقاله ترجمه شده|
Mining patient-specific and contextual data with machine learning technologies to predict cancellation of children’s surgery
استخراج داده های خاص و اختصاصی بیمار با فناوری های یادگیری ماشین برای پیش بینی لغو جراحی کودکان-2019
Background: Last-minute surgery cancellation represents a major wastage of resources and can cause significant inconvenience to patients. Our objectives in this study were: 1) To develop predictive models of last-minute surgery cancellation, utilizing machine learning technologies, from patient-specific and contextual data from two distinct pediatric surgical sites of a single institution; and 2) to identify specific key predictors that impact children’s risk of day-of-surgery cancellation. Methods and findings: We extracted five-year datasets (2012–2017) from the Electronic Health Record at Cincinnati Children’s Hospital Medical Center. By leveraging patient-specific information and contextual data, machine learning classifiers were developed to predict all patient-related cancellations and the most frequent four cancellation causes individually (patient illness, “no show,” NPO violation and refusal to undergo surgery by either patient or family). Model performance was evaluated by the area under the receiver operating characteristic curve (AUC) using ten-fold cross-validation. The best performance for predicting all-cause surgery cancellation was generated by gradient-boosted logistic regression models, with AUC 0.781 (95% CI: [0.764,0.797]) and 0.740 (95% CI: [0.726,0.771]) for the two campuses. Of the four most frequent individual causes of cancellation, “no show” and NPO violation were predicted better than patient illness or patient/family refusal. Models showed good cross-campus generalizability (AUC: 0.725/0.735, when training on one site and testing on the other). To synthesize a human-oriented conceptualization of pediatric surgery cancellation, an iterative step-forward approach was applied to identify key predictors which may inform the design of future preventive interventions. Conclusions: Our study demonstrated the capacity of machine learning models for predicting pediatric patients at risk of last-minute surgery cancellation and providing useful insight into root causes of cancellation. The approach offers the promise of targeted interventions to significantly decrease both healthcare costs and also families’ negative experiences.
Keywords: Pediatric surgery cancellation | Quality improvement | Predictive modeling | Machine learning
Malpractice risk and medical treatment selection
خطر سوء استفاده و انتخاب درمان پزشکی-2019
Westudy howlegal and financial incentives affectmedical decisions. Using patient-level data fromItaly, weidentify the effect of a change in medical liability pressure by exploiting the geographical distribution of hospitals across court districts, where some districts increase the predictability of expected damages per injury while others do not. Using a difference-in-differences identification strategy, we show that as certainty of compensation increases, c-sections increase by 6.5 percentage points. There is no statistically significant effect on secondary health outcomes of either mothers or newborns, but the increase is higher for low-risk than high-risk mothers. The increase is driven by hospitals that have lower quality, are governed by inefficient court districts, face lower expected damages, and are paid more per c-section.
Keywords: Scheduled damages | Cesarean sections | Difference in differences
The legal determinants of health: harnessing the power of law for global health and sustainable development
عوامل قانونی سلامت: بهره گیری از قدرت قانون برای سلامت جهانی و توسعه پایدار-2019
Health risks in the 21st century are beyond the control of any government in any country. In an era of globalisation, promoting public health and equity requires cooperation and coordination both within and among states. Law can be a powerful tool for advancing global health, yet it remains substantially underutilised and poorly understood. Working in partnership, public health lawyers and health professionals can become champions for evidence-based laws to ensure the public’s health and safety. This Lancet Commission articulates the crucial role of law in achieving global health with justice, through legal instruments, legal capacities, and institutional reforms, as well as a firm commitment to the rule of law. The Commission’s aim is to enhance the global health community’s understanding of law, regulation, and the rule of law as effective tools to advance population health and equity.
Understanding risky behaviors during adolescence: A model of self-discovery through experimentation
درک رفتارهای مخاطره آمیز در دوران نوجوانی: یک مدل از کشف خود از طریق آزمایش-2019
This paper presents a theory of risky “anti-social” behavior that links economic motives with aspects ofpersonality development during adolescence. We show that a model in which adolescents go through aprocess of self-discovery and the costs and benefits of risky behavior (such as engaging in a criminal act)are revealed to them through experiential learning can replicate the pattern of initiation and desistancein such conduct typically observed in the data. In this setting, farsighted individuals have an incentiveto experiment with risky behavior to resolve the uncertainty over their personality; contrary to popularbelief, this means that a greater concern for the future may be linked to a stronger incentive to pursuerisky activities at a young age. We also show that while stigma reduces the incentive to experiment,its effect on the prevalence of risky behavior is ambiguous. We discuss how imperfect self-knowledgemay interact with other factors associated with risky behavior over the life course, including increasingpenalties for repeat offenders and self-control problems
Keywords:Risky behavior | Crime | Age-crime curve | Recidivism | Stigma | Punishment | Penaltiesa
A machine-learning-based prediction model of fistula formation after interstitial brachytherapy for locally advanced gynecological malignancies
یک مدل پیش بینی مبتنی بر یادگیری ماشینی از تشکیل فیستول پس از براکی تراپی بینابینی برای بدخیمی های ژنتیکی بومی محلی-2019
PURPOSE: External beam radiotherapy combined with interstitial brachytherapy is commonly used to treat patients with bulky, advanced gynecologic cancer. However, the high radiation dose needed to control the tumor may result in fistula development. There is a clinical need to identify patients at high risk for fistula formation such that treatment may be managed to prevent this toxic side effect. This work aims to develop a fistula prediction model framework using machine learning based on patient, tumor, and treatment features. METHODS AND MATERIALS: This retrospective study included 35 patients treated at our institution using interstitial brachytherapy for various gynecological malignancies. Five patients developed rectovaginal fistula and two developed both rectovaginal and vesicovaginal fistula. For each patient, 31 clinical features of multiple data types were collected to develop a fistula prediction framework. A nonlinear support vector machine was used to build the prediction model. Sequential backward feature selection and sequential floating backward feature selection methods were used to determine optimal feature sets. To overcome data imbalance issues, the synthetic minority oversampling technique was used to generate synthetic fistula cases for model training. RESULTS: Seven mixed data features were selected by both sequential backward selection and sequential floating backward selection methods. Our prediction model using these features achieved a high prediction accuracy, that is, 0.904 area under the curve, 97.1% sensitivity, and 88.5% specificity. CONCLUSIONS: A machine-learningebased prediction model of fistula formation has been developed for patients with advanced gynecological malignancies treated using interstitial brachytherapy. This model may be clinically impactful pending refinement and validation in a larger series.
Keywords: Machine learning | Support vector machine | Interstitial brachytherapy | Gynecologic cancer
Child sexual abuse in Indonesia: A systematic review of literature, law and policy
سوء استفاده جنسی از کودکان در اندونزی: مرور نظاممند ادبیات، حقوق و سیاست-2019
Background: Like many middle-income countries, knowledge about child sexual abuse (CSA) is limited in Indonesia. The national government has stated a commitment to protect children from the worst forms of abuse, yet the sensitivity of CSA along with the complexity of culture and law, present substantial challenges. Objective: This article reviews current knowledge about CSA in Indonesia, in the context of existing laws and policies that influence CSA prevention and intervention. Method: A systematic review of this research was conducted in the following manner: a review of scholarly literature and grey literature in English (19 papers) and in Bahasa Indonesian (11 papers), and a review of CSA-related Indonesian laws (4 documents) and policies (5 documents). Results: This review finds that knowledge about CSA in Indonesia is still limited. The taboos on discussing sexual matters were identified as factors that impede reporting of CSA. Poverty also leads to increasing children’s risk of sexual abuse. There was less attention to CSA occurring within family contexts and focus was more upon its occurrence outside of the family. The study identified that contradictory definitions of children within the law add to children’s vulnerability to CSA; this is especially the case for girls. Current child protection strategies in prevention and intervention lack specific focus on CSA. Conclusion: Further research is needed to enable the development of evidence-based approaches to better harmonize the development of law and policy with contemporary knowledge about CSA.
Keywords: Child sexual abuse | Child abuse | Child protection | Indonesia
تعداد مورنیاز برای توئیت کردن: شبکه های اجتماعی و تاثیر آن روی علم جراحی
سال انتشار: 2019 - تعداد صفحات فایل pdf انگلیسی: 4 - تعداد صفحات فایل doc فارسی: 12
پزشکان جراح با استفاده از فیسبوک، توئیتر، لینکدین و اینستاگرام هم برای اهداف فردی و هم اهداف حرفه ای وارد شبکه های اجتماعی می شوند. در یک عصر دسترسی جهانی به هرچیز، خطر سرریز شدن اطلاعات وجود دارد و بنابراین نیاز به مقابله و همیاری داریم. هشتگ ها به صورت ویروسی تاثیر عظیم اجتماعی داشته اند مثلا" هشتگ #ILookLikeASurgeon. SoMe تبدیل به یک ابزاری برای برقراری ارتباط، به اشتراک گذاری و راهنمایی و آموزش شده است. این یک ابزاری برای آموزش نسل بعدی جراحان می باشد. برای محققان و مجلات، این سوال باقی مانده است که آیا ورودی مورد نیاز برای وارد شدن به بستر SoMe با یک بهره مشابه در خروجی، مثل شهرت و درمعرض دید قرار گرفتن جبران می شود یا خیر. اطلاعات خلاصه شده در چکیده های بصری می تواند به انتشار نتایج مطالعه برای طیف گسترده ای از مخاطبان کمک کند اما تاثیر یک هشتگ #visualabstract می تواند خاص و تخصصی باشد. درحال حاضر، اطلاعات و دانش اندکی درباره "تعداد موردنیاز برای توئیت کردن" به منظور اثرگذاشتن روی مواردی مثل دانلودها، ارجاع دهی ها و نهایتا" ضریب تاثیر وجود دارد.
|مقاله ترجمه شده|
Evidence-based clinical engineering: Machine learning algorithms for prediction of defibrillator performance
مهندسی بالینی مبتنی بر شواهد: الگوریتم های یادگیری ماشین برای پیش بینی عملکرد دفیبریلاتور-2019
tPoorly regulated and insufficiently supervised medical devices (MDs) carry high risk of performanceaccuracy and safety deviations effecting the clinical accuracy and efficiency of patient diagnosis and treat-ments. Even with the increase of technological sophistication of devices, incidents involving defibrillatormalfunction are unfortunately not rare.To address this, we have developed an automated system based on machine learning algorithms thatcan predict performance of defibrillators and possible performance failures of the device which can affectperformance. To develop an automated system, with high accuracy, overall dataset containing safety andperformance measurements data was acquired from periodical safety and performance inspections of1221 defibrillator. These inspections were carried out in period 2015–2017 in private and public health-care institutions in Bosnia and Herzegovina by ISO 17,020 accredited laboratory. Out of overall number ofsamples, 974 of them were used during system development and 247 samples were used for subsequentvalidation of system performance. During system development, 5 different machine learning algorithmswere used, and resulting systems were compared by obtained performance.The results of this study demonstrate that clinical engineering and health technology managementbenefit from application of machine learning in terms of cost optimization and medical device manage-ment. Automated systems, based on machine learning algorithms, can predict defibrillator performancewith high accuracy. Systems based on Random Forest classifier with Genetic Algorithm feature selectionyielded highest accuracy among other machine learning systems. Adoption of such systems will help inovercoming challenges of adapting maintenance and medical device supervision mechanism protocolsto rapid technological development of these devices. Due to increased complexity of healthcare institu-tion environment and increased technological complexity of medical devices, performing maintenancestrategies in traditional manner is causing a lot of difficulties.
Keywords:Automated system | Machine learning | Medical device | Maintenance | Managemen | tPrediction | Performance | Inspection | Evidence-based