با سلام خدمت کاربران عزیز، به اطلاع می رساند ترجمه مقالاتی که سال انتشار آن ها زیر 2008 می باشد رایگان بوده و میتوانید با وارد شدن در صفحه جزییات مقاله به رایگان ترجمه را دانلود نمایید.
Factors associated with female genital cutting in Yemen and its policy implications
عوامل مرتبط با قطع دستگاه تناسلی زنان در یمن و پیامدهای سیاسی آن-2019
Background: A tremendous number of girls in Yemen are still subjected to female genital cutting (FGC), which carries an increased risk of health complications and violates children’s rights. This study describes the prevalence of FGC in four Yemeni provinces and investigates the determinants of FGC. Methods: We analyzed data from women aged 15 to 49 years who responded to a sub-national house- hold survey conducted in six rural districts of four Yemeni provinces in 20 08–20 09. Logistic regression was used to estimate the association between individual and household socioeconomic factors and FGC practices and attitudes. Results: The prevalence of women’s FGC was 48% while daughters’ FGC was 34%. Almost 45.8% of the women surveyed believe the FGC practice should discontinue. Higher odds of FGC practice and positive attitude towards it were associated with older age, family marriage, and lower tertiles of wealth and education indices. Early marriage was also associated with increased odds of FGC practice ( p < 0.01). Conclusions: Socioeconomic indices and other individual factors associated with FGC are differing and complex. Younger generations of women are more likely to not have FGC and to express negative atti- tudes towards the tradition. Appropriate strategies to invest in girls’ education and women’s empower- ment with effective engagement of religious and community leaders might support the change of atti- tudes and practice of FGC in the younger generation.
Keywords: Female genital mutilation | Circumcision | Women’s health | Socioeconomic factors | Equity | Yemen
The reality behind the Istanbul convention: Shattering conservative delusions
واقعیت پشت پرده کنوانسیون استانبول: شکستن پندار محافظه کاری-2019
This paper analyzes the long process of ratification of the Istanbul Convention in Croatia and its political instrumentalization. The Convention was ratified in 2018, following a strong anti-ratification campaign which exemplifies the strengthening of a global pushback against womens rights. The conservative movement behind this campaign, which is still ongoing - with the shifted goal of withdrawing from the Convention through the mechanism of referendum, spread a number of misconceptions about the Convention, based foremost on the narrative that the Convention would impose an undesirable “gender ideology”. The aim of this paper is to shatter these delusions by first deconstructing the notions of gender, gender ideology and gender-based violence, and then by exploring the extent to which gender (identity) already plays a role within Croatian legal system, including through the jurisprudence of the ECtHR. The last part focuses on particular positive novelties the Istanbul Convention will bring to Croatian society.
Keywords: Sex | Gender | Gender-based violence | Istanbul convention | Gender ideology | Stereotypes
From Slutwalks to Nirbhaya: Shifts in the Indian womens movement
از Slutwalks به Nirbhaya دگرگونی در جنبش زنان هند-2019
This paper takes a closer look at two significant mass-based feminist movements in India – Slutwalks and Nirbhaya agitation. Key questions guiding this paper are – why did Slut Walk Delhi, characterized as a ‘movement’ against victim blaming and sexual harassment fail to build momentum despite attempts at mobilization? What changed a year later that made Nirbhaya agitation a ‘successful mobilization’? In exploring these questions, the paper interrogates the tactics, discursive politics, and impact of these mobilizations on contemporary Indian womens movement; and argues that they mark an important moment of foregrounding debates on affirmative sexuality.
Keywords: Slutwalks | Nirbhaya | Indian womens movement | Protests | Urban public spaces | Police order | Sexuality
The dilemma of rape avoidance advice: Acknowledging womens agency without blaming victims of sexual assault
معضل مشاوره برای جلوگیری از تجاوز جنسی: تصدیق آژانس زنان بدون مذمت قربانیان تجاوز جنسی-2019
This article addresses the question of whether there is a legitimate role for rape avoidance advice for women as part of a larger suite of efforts aimed at reducing the prevalence of mens sexual violence. It highlights an apparent dilemma between acknowledging womens agency and placing the blame for sexual violence on perpetrators rather than victims. The article builds upon analysis of the phenomenon of responsibility by moral and political philosophers to suggest a clearer way of thinking about this dilemma. I argue that because causal responsibility is a necessary but not sufficient element of moral responsibility, it is logically possible to hold that some victims could have prevented their rape and at the same time hold they are not blameworthy. I go on to argue that this poses a dilemma for feminists concerned to end rape, in that the practical interests of individual women in avoiding rape might at times be in conflict with womens strategic interests in ensuring that the burden (task responsibility) for ending rape rests with men (as potential perpetrators). I argue that while it is logically possible that some rape avoidance advice could help some women reduce their likelihood of being raped, the legitimate role for rape avoidance advice is circumscribed by its impact on womens strategic interests. The worth of rape avoidance advice in general should not be dismissed out of hand. However, the legitimacy of particular pieces of advice need to be assessed in terms of their impact on womens strategic and practical interests and this will vary depending on the quality and source of the advice
Keywords: Agency | Blame | Victims | Rape prevention | Sexual assault
Toward Robust Anxiety Biomarkers: A Machine Learning Approach in a Large-Scale Sample
به سمت نشانگرهای زیستی قوی اضطراب: یک رویکرد یادگیری ماشینی در یک نمونه مقیاس بزرگ-2019
BACKGROUND: The field of psychiatry has long sought biomarkers that can objectively diagnose patients, predict treatment response, or identify individuals at risk of illness onset. However, reliable psychiatric biomarkers have yet to emerge. The recent application of machine learning techniques to develop neuroimaging-based biomarkers has yielded promising preliminary results. However, much of the work in this domain has not met best practice standards from the field of machine learning. This is especially true for studies of anxiety, creating uncertainty about the potential for anxiety biomarker development. METHODS: We applied machine learning tools to predict trait anxiety from neuroimaging measurements in humans. Using publicly available data from the Brain Genomics Superstruct Project, we compared a suite of neuroimagingbased machine learning models predicting anxiety within a discovery sample (n = 531, 307 women) via k-fold cross-validation, and we tested the final model (a stacked model incorporating region-to-region functional connectivity, amygdala seed-to-voxel connectivity, and volumetric and cortical thickness data) in a held-out, unseen test sample (n = 348, 209 women). RESULTS: Though the best model was able to predict anxiety within the discovery sample (cross-validated R2 of .06, permutation test p , .001), the generalization test within the holdout sample failed (R2 of 2.04, permutation test p . .05). CONCLUSIONS: In this study, we did not find evidence of a generalizable anxiety biomarker. However, we encourage other researchers to investigate this topic, utilizing large samples and proper methodology, to clarify the potential of neuroimaging-based anxiety biomarkers.
Keywords: Anxiety | Biomarker | fMRI | Functional connectivity | Machine learning | Predictive modeling
Is mass classification in mammograms a solved problem? - A critical review over the last 20 years
آیا طبقه بندی دسته جمعی در ماموگرافی ها یک مشکل حل شده است؟ - بررسی انتقادی طی 20 سال گذشته-2019
Breast cancer is one of the most common and deadliest cancers that affect mainly women worldwide, and mammography examination is one of the main tools to help early detection. Several papers have been published in the last decades reporting on techniques to automatically recognize breast cancer by analyzing mammograms. These techniques were used to create computer systems to help physicians and radiologists obtain a more precise diagnosis. The objective of this paper is to present an overview re- garding the use of machine learning and pattern recognition techniques to discriminate masses in digi- tized mammograms. The main differences we found in the literature between the present paper and the other reviews are: 1) we used a systematic review method to create this survey; 2) we focused on mass classification problems; 3) the broad scope and spectrum used to investigate this theme, as 129 papers were analyzed to find out whether mass classification in mammograms is a problem solved. In order to achieve this objective, we performed a systematic review process to analyze papers found in the most im- portant digital libraries in the area. We noticed that the three most common techniques used to classify mammographic masses are artificial neural network, support vector machine and k-nearest neighbors. Furthermore, we noticed that mass shape and texture are the most used features in classification, al- though some papers presented the usage of features provided by specialists, such as BI-RADS descriptors. Moreover, several feature selection techniques were used to reduce the complexity of the classifiers or to increase their accuracies. Additionally, the survey conducted points out some still unexplored research opportunities in this area, for example, we identified that some techniques such as random forest and logistic regression are little explored, while others, such as grammars or syntactic approaches, are not being used to perform this task.
Keywords: Mammography | Mammogram | Breast cancer | Classification | Diagnosis | Pattern recognition
Identification and Quantification of Cardiovascular Structures From CCTA
شناسایی و تعیین ساختارهای قلبی و عروقی از CCTA-2019
OBJECTIVES This study designed and evaluated an end-to-end deep learning solution for cardiac segmentation and quantification. BACKGROUND Segmentation of cardiac structures from coronary computed tomography angiography (CCTA) images is laborious. We designed an end-to-end deep-learning solution. METHODS Scans were obtained from multicenter registries of 166 patients who underwent clinically indicated CCTA. Left ventricular volume (LVV) and right ventricular volume (RVV), left atrial volume (LAV) and right atrial volume (RAV), and left ventricular myocardial mass (LVM) were manually annotated as ground truth. A U-Netinspired, deep-learning model was trained, validated, and tested in a 70:20:10 split. RESULTS Mean age was 61.1 8.4 years, and 49% were women. A combined overall median Dice score of 0.9246 (interquartile range: 0.8870 to 0.9475) was achieved. The median Dice scores for LVV, RVV, LAV, RAV, and LVM were 0.938 (interquartile range: 0.887 to 0.958), 0.927 (interquartile range: 0.916 to 0.946), 0.934 (interquartile range: 0.899 to 0.950), 0.915 (interquartile range: 0.890 to 0.920), and 0.920 (interquartile range: 0.811 to 0.944), respectively. Model prediction correlated and agreed well with manual annotation for LVV (r ¼ 0.98), RVV (r ¼ 0.97), LAV (r ¼ 0.78), RAV (r ¼ 0.97), and LVM (r ¼ 0.94) (p < 0.05 for all). Mean difference and limits of agreement for LVV, RVV, LAV, RAV, and LVM were 1.20 ml (95% CI: 7.12 to 9.51), 0.78 ml (95% CI: 10.08 to 8.52), 3.75 ml (95% CI: 21.53 to 14.03), 0.97 ml (95% CI: 6.14 to 8.09), and 6.41 g (95% CI: 8.71 to 21.52), respectively. CONCLUSIONS A deep-learning model rapidly segmented and quantified cardiac structures. This was done with high accuracy on a pixel level, with good agreement with manual annotation, facilitating its expansion into areas of research and clinical import. (J Am Coll Cardiol Img 2019;-:-–-) © 2019 by the American College of Cardiology Foundation.
Survival outcome prediction in cervical cancer: Cox models vs deep-learning model
پیش بینی نتیجه بقا در سرطان دهانه رحم: مدل های کاکس در مقابل مدل یادگیری عمیق-2019
BACKGROUND: Historically, the Cox proportional hazard regression model has been the mainstay for survival analyses in oncologic research. The Cox proportional hazard regression model generally is used based on an assumption of linear association. However, it is likely that, in reality, there are many clinicopathologic features that exhibit a nonlinear association in biomedicine. OBJECTIVE: The purpose of this study was to compare the deeplearning neural network model and the Cox proportional hazard regression model in the prediction of survival in women with cervical cancer. STUDY DESIGN: This was a retrospective pilot study of consecutive cases of newly diagnosed stage IeIV cervical cancer from 2000e2014. A total of 40 features that included patient demographics, vital signs, laboratory test results, tumor characteristics, and treatment types were assessed for analysis and grouped into 3 feature sets. The deeplearning neural network model was compared with the Cox proportional hazard regression model and 3 other survival analysis models for progression-free survival and overall survival. Mean absolute error and concordance index were used to assess the performance of these 5 models. RESULTS: There were 768 women included in the analysis. The median age was 49 years, and the majority were Hispanic (71.7%). The majority of tumors were squamous (75.3%) and stage I (48.7%). The median followup time was 40.2 months; there were 241 events for recurrence and progression and 170 deaths during the follow-up period. The deeplearning model showed promising results in the prediction of progression-free survival when compared with the Cox proportional hazard regression model (mean absolute error, 29.3 vs 316.2). The deep-learning model also outperformed all the other models, including the Cox proportional hazard regression model, for overall survival (mean absolute error, Cox proportional hazard regression vs deep-learning, 43.6 vs 30.7). The performance of the deep-learning model further improved when more features were included (concordance index for progression-free survival: 0.695 for 20 features, 0.787 for 36 features, and 0.795 for 40 features). There were 10 features for progression-free survival and 3 features for overall survival that demonstrated significance only in the deep-learning model, but not in the Cox proportional hazard regression model. There were no features for progression-free survival and 3 features for overall survival that demonstrated significance only in the Cox proportional hazard regression model, but not in the deep-learning model. CONCLUSION: Our study suggests that the deep-learning neural network model may be a useful analytic tool for survival prediction in women with cervical cancer because it exhibited superior performance compared with the Cox proportional hazard regression model. This novel analytic approach may provide clinicians with meaningful survival information that potentially could be integrated into treatment decision-making and planning. Further validation studies are necessary to support this pilot study.
Key words: Cox proportional hazard | cervical cancer | deep learning | survival prediction
State of damage to and support for victims of motor vehicle accidents in Japan
وضعیت آسیب و پشتیبانی قربانیان حوادث وسایل نقلیه موتوری در ژاپن-2019
Individuals are likely to be involved in at least one motor vehicle accident (MVA) during their lifetime.MVAs can have a significant impact on both the victimsand their families; in the case of death, the bereaved familymay face mental health problems. Ongoing studies have focused on devising strategies to support victims and their families who face such problems. This paper clarifies the reality of mental health issues ofMVA victims and reviews the current state of victimsupport available in Japan, its significance and other relevant issues. The prevalence of post-traumatic stress disorder (PTSD) inMVA survivors has been estimated to be 8%–45% one month after the accident and 6%–40% six months after the accident. The mental health of the survivors families, bereaved families, and orphaned children are usually affected afterMVAs. Bereaved families experience not only PTSD but also symptoms of complicated grief. Based on studies using different scales to measure symptoms and other items, symptoms of PTSD and complicated grief have been seen in 17%–75% and 6%–61% of bereaved families, respectively, which were much higher than those observed in the general population. In addition to the actual physical andmental damage caused byMVAs, it is necessary to take notice of survivors who are exposed to post-accident secondary victimization. Justice agencies, such as the National Police Agency andMinistry of Justice Investigation Bureau, as well as victim support centers and self-help groups, provide support to MVA victims. To a certain extent, evaluating support provided to MVA victims and their families is possible by initiating assistance promptly and actively using leaflets, brochures, and other materials. The literature reports thatwomen are at increased risk for developing PTSD and complicated grief; also,men and women use differentmechanisms for coping with stress.Moreover, men tend not to express their pain and try to manage it on their own. Thus, support that is appropriate for both sexes must be provided. In the future, the effectiveness of the support provided should be evaluated by survivors. Whether acute-phase support leads to improvement in survivors long-term prognoses must also be investigated.
Keywords: Motor vehicle accidents | MVA victims | Bereaved family | Social support | Self-help groups | Sex differences
Gendered drug policy: Motherisk and the regulation of mothering in Canada
سیاست مواد مخدر جنسیتی: Motherisk و تنظیم مادرانگی در کانادا-2019
Background: Due to misinformation and enduring discourses about pregnant women and mothers suspected of using drugs, these women continue to experience systemic discrimination. In 2014, this fact was once again made public in Canada when the Ontario government established an independent review of hair testing practices conducted by Motherisk Drug Testing Laboratory (MDTL) at Torontos Hospital for Sick Kids. Between 2005 and 2015, MDTL tested the hair of more than 16,000 individuals for drug consumption. The results were introduced as evidence in court and resulted in both temporary and permanent loss of custody of children. Tragically, it was later discovered that the hair testing results were unreliable. This paper provides an analysis of child protection policies and practices directed at pregnant women and mothers suspected of using drugs, with a focus on the Motherisk tragedy in Ontario. Methods: Informed by feminist and critical drug perspectives, this study draws from findings in the 2015″Report of the Motherisk Hair Analysis Independent Review," produced by Honourable Susan Lang, and provides a Bacchi-informed critical analysis of Commissioner Beamans 2018 report of the Motherisk Commission, "Harmful Impacts: The Reliance on Hair Testing in Child Protection" (HI). Results: The HI report is quite sympathetic to the plight of families and it acknowledges systemic issues and unequal power relations between families, social workers and the courts. Even though drug testing is an inadequate measure of parenting capacity, the HI report does not recommend banning the practice. In the HI report, the themes of harm reduction and drug prohibition are notably absent — while the use of gender-neutral terms, such as "parent" and "families," render mothers invisible. Conclusions: The Motherisk tragedy cannot be understood as an isolated event, rather it is part of a continuum of state and gendered violence against poor, Indigenous, and Black women in Canada. The HI report fails to consider how prohibitionist discourses about drugs, addiction, mothering, and risk lead to institutional practices such as drug testing and child apprehension.
Keywords: Motherisk | Women | Race | Drug testing | Child apprehension