Inclusiveness by design? Reviewing sustainable electricity access and entrepreneurship from a gender perspective
شمول طراحی شده توسط؟ بررسی دسترسی پایدار به برق و کارآفرینی از منظر جنسیت-2019
There is a substantial literature analysing the role of electricity as a catalyst for economic development. However, there are significant knowledge gaps in whether such systems are or can indeed be designed in a gender sensitive way to promote equal opportunity for socially inclusive entrepreneurship at the local level. We make three main contributions with this paper. First, we carry out a literature review to unpack the genderelectricity- entrepreneurship nexus by identifying the agenda of the gender-energy and gender-entrepreneurship literature respectively and how they intersect and understand gender over time. Second, we synthesise key factors identified as hindering and driving empowerment in relation to electricity and entrepreneurship and identify the weaknesses of the respective literature. Third, we outline the contours of the conceptual intersection and develop a framework which shows how electricity systems can be designed to become favourable and economically empowering for both men and women. Furthermore, we demonstrate how local value chains can benefit from this electric inclusiveness. Finally, with our framework, we develop recommendations for strategic action and identify points of intervention in policy, planning, design and operation of electricity systems.
Keywords: Gender and energy | Gender and entrepreneurship | Electricity access | Women’s empowerment
Improving Workflow Efficiency for Mammography Using Machine Learning
بهبود بهره وری گردش کار برای ماموگرافی با استفاده از یادگیری ماشین-2019
Objective: The aim of this study was to determine whether machine learning could reduce the number of mammograms the radiologist must read by using a machine-learning classifier to correctly identify normal mammograms and to select the uncertain and abnormal examinations for radiological interpretation. Methods: Mammograms in a research data set from over 7,000 women who were recalled for assessment at six UK National Health Service Breast Screening Program centers were used. A convolutional neural network in conjunction with multitask learning was used to extract imaging features from mammograms that mimic the radiological assessment provided by a radiologist, the patient’s nonimaging features, and pathology outcomes. A deep neural network was then used to concatenate and fuse multiple mammogram views to predict both a diagnosis and a recommendation of whether or not additional radiological assessment was needed. Results: Ten-fold cross-validation was used on 2,000 randomly selected patients from the data set; the remainder of the data set was used for convolutional neural network training. While maintaining an acceptable negative predictive value of 0.99, the proposed model was able to identify 34% (95% confidence interval, 25%-43%) and 91% (95% confidence interval: 88%-94%) of the negative mammograms for test sets with a cancer prevalence of 15% and 1%, respectively. Conclusion: Machine learning was leveraged to successfully reduce the number of normal mammograms that radiologists need to read without degrading diagnostic accuracy.
Key Words: Breast cancer | deep learning | machine learning | mammography | radiology
Extracting comprehensive clinical information for breast cancer using deep learning methods
استخراج اطلاعات جامع بالینی برای سرطان پستان با استفاده از روشهای یادگیری عمیق-2019
Objective Breast cancer is the most common malignant tumor among women. The diagnosis and treatment information of breast cancer patients is abundant in multiple types of clinical fields, including clinicopathological data, genotype and phenotype information, treatment information, and prognosis information. However, current studies are mainly focused on extracting information from one specific type of clinical field. This study defines a comprehensive information model to represent the whole-course clinical information of patients. Furthermore, deep learning approaches are used to extract the concepts and their attributes from clinical breast cancer documents by fine-tuning pretrained Bidirectional Encoder Representations from Transformers (BERT) language models. Materials and Methods The clinical corpus that was used in this study was from one 3A cancer hospital in China, consisting of the encounter notes, operation records, pathology notes, radiology notes, progress notes and discharge summaries of 100 breast cancer patients. Our system consists of two components: a named entity recognition (NER) component and a relation recognition component. For each component, we implemented deep learning-based approaches by fine-tuning BERT, which outperformed other state-of-the-art methods on multiple natural language processing (NLP) tasks. A clinical language model is first pretrained using BERT on a large-scale unlabeled corpus of Chinese clinical text. For NER, the context embeddings that were pretrained using BERT were used as the input features of the Bi-LSTM-CRF (Bidirectional long-short-memory-conditional random fields) model and were fine-tuned using the annotated breast cancer notes. Furthermore, we proposed an approach to fine-tune BERT for relation extraction. It was considered to be a classification problem in which the two entities that were mentioned in the input sentence were replaced with their semantic types. Results Our best-performing system achieved F1 scores of 93.53% for the NER and 96.73% for the relation extraction. Additional evaluations showed that the deep learning-based approaches that fine-tuned BERT did outperform the traditional Bi-LSTM-CRF and CRF machine learning algorithms in NER and the attention-Bi-LSTM and SVM (support vector machines) algorithms in relation recognition. Conclusion In this study, we developed a deep learning approach that fine-tuned BERT to extract the breast cancer concepts and their attributes. It demonstrated its superior performance compared to traditional machine learning algorithms, thus supporting its uses in broader NER and relation extraction tasks in the medical domain.
Keywords: clinical information extraction | breast cancer | deep learning | fine-tuning BERT | information model
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
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