Miracle-dz/UniFAR-FTUs huggingface dataset:
Evaluation of a computer-aided diagnosis system for diabetic retinopathy screening on public data.

PURPOSE To evaluate the performance of a comprehensive computer-aided diagnosis (CAD) system for diabetic retinopathy (DR) screening, using a publicly available database of retinal images, and to compare its performance with that of human experts. METHODS A previously developed, comprehensive DR CAD system was applied to 1200 digital color fundus photographs (nonmydriatic camera, single field) of 1200 eyes in the publicly available Messidor dataset (Methods to Evaluate Segmentation and Indexing Techniques in the Field of Retinal Ophthalmology (http://messidor.crihan.fr). The ability of the system to distinguish normal images from those with DR was determined by using receiver operator characteristic (ROC) analysis. Two experts also determined the presence of DR in each of the images. RESULTS The system achieved an area under the ROC curve of 0.876 for successfully distinguishing normal images from those with DR with a sensitivity of 92.2% at a specificity of 50%. These compare favorably with the two experts, who achieved sensitivities of 94.5% and 91.2% at a specificity of 50%. CONCLUSIONS This study shows, for the first time, the performance of a comprehensive DR screening system on an independent, publicly available dataset. The performance of the system on this dataset is comparable with that of human experts.
Expert Systems With Applications

We offer a systematic analysis of the use of deep learning networks for stock market analysis and pre- diction. Its ability to extract features from a large set of raw data without relying on prior knowledge of predictors makes deep learning potentially attractive for stock market prediction at high frequencies. Deep learning algorithms vary considerably in the choice of network structure, activation function, and other model parameters, and their performance is known to depend heavily on the method of data repre- sentation. Our study attempts to provides a comprehensive and objective assessment of both the advantages and drawbacks of deep learning algorithms for stock market analysis and prediction. Using high- frequency intraday stock returns as input data, we examine the effects of three unsupervised feature extraction methods—principal component analysis, autoencoder, and the restricted Boltzmann machine— on the network’s overall ability to predict future market behavior. Empirical results suggest that deep neural networks can extract additional information from the residuals of the autoregressive model and improve prediction performance; the same cannot be said when the autoregressive model is applied to the residuals of the network. Covariance estimation is also noticeably improved when the predictive net- work is applied to covariance-based market structure analysis. Our study offers practical insights and potentially useful directions for further investigation into how deep learning networks can be effectively used for stock market analysis and prediction.
Correlation between fat mass estimated by skin-fold thickness and blood pressure and serum biomarker in Syrian adolescents

The purpose of this study was to assess the association of body fat percentage estimated by skin-fold thickness (SFT) measurements with blood pressure and blood serum characterization of Syrian adolescents. SFT, blood pressure, and biochemical analyses of blood were determined using standard methods. Receiver operating characteristics (ROC) curve drawn to determine appropriate cut-off points of the Σ2 limb SFT (biceps and triceps), Σ2 trunk SFT (subscapula and suprailiac), and Σ4 SFT for identifying cut-offs to predict health outcomes. Our results showed that the overweight and obese group estimated by SFT had significantly (p < 0.05) higher levels of fasting blood sugar, triglycerides (TG), cholesterol (Chol), low-density lipoprotein cholesterol, and TG/high-density lipoprotein cholesterol than the normal weight group. Based on ROC calculation for SFT and some metabolic risks, the data suggest the best Σ2 limb SFT, Σ2 trunk SFT, Σ4 SFT, and logarithm of Σ4 SFT cutoffs for population studied ranged between 16.75–19.75 mm, 24.25–30.75 mm, 41.75–52.75 mm, and 1.62–1.72 respectively. Our results showed that, in Syrian adolescents, SFT is a good indicator for association with some metabolic syndrome indicators.
Homecare Nurses' Attitudes Toward Palliative Care in a Rural Community in Western Quebec

Homecare nurses in rural areas play a key role in providing patients with continuous, personalized, and holistic care. However, palliative care for terminally ill patients and their families can be difficult to obtain and provide in geographically isolated areas of Canada. This study explored homecare nurses' attitudes toward palliative care in a rural community in Western Quebec. A qualitative design was used, which incorporated semistructured interviews and a focus group with five homecare nurses within the community's health and social service organization. Data were analyzed using a thematic content analysis technique. Six themes emerged: (1) experiencing community embeddedness, (2) maintaining patients' quality of life, (3) collaborating with a multidisciplinary team, (4) experiencing emotional and physical isolation, (5) acknowledging the need for professional development, and (6) dealing with organizational issues. Findings identified the need to develop a palliative homecare team and context-specific professional development activities for the nurses.
The transitional pattern of pain and disability, from perceived pain to sick leave. Experience from a longitudinal study.

OBJECTIVES To investigate the prospective value of the transitional and dynamic patterns of pain disability over time on sick leave in chronic recurrent back/neck pain cases. METHODS The material used was based on a longitudinal study with three repeated measurements. The graded Chronic Pain Scale was used to assess levels of pain disability. The relationship between the transitional patterns of the pain disability score (ten defined states of decrease, increase or no change, between two time points) and sick leave was analyzed for 909 chronic/recurrent cases in three different models using logistic regression. RESULTS Those with high level of pain disability have a more transitional pattern and their pain level changed during the time period studied. When adjusting for age, gender, education and previous sick leave, the final model indicated that the current level of pain disability was a risk factor in taking sick leave. The likelihood of sick leave was highest in the transition of pain into the highest levels of disability, independent of past disability level of pain. Earlier sick leave remained as an important predictor of sick leave. CONCLUSIONS From a clinical and prognostic perspective the probability of sick leave will be different and can be predicted based on previous sick leave but not from former history of pain disability level or its transitional pattern.
Exoskeleton Technology in Rehabilitation: Towards an EMG-Based Orthosis System for Upper Limb Neuromotor Rehabilitation

The rehabilitation of patients should not only be limited to the first phases during intense hospital care but also support and therapy should be guaranteed in later stages, especially during daily life activities if the patient’s state requires this. However, aid should only be given to the patient if needed and as much as it is required. To allow this, automatic self-initiated movement support and patient-cooperative control strategies have to be developed and integrated into assistive systems. In this work, we first give an overview of different kinds of neuromuscular diseases, review different forms of therapy, and explain possible fields of rehabilitation and benefits of robotic aided rehabilitation. Next, the mechanical design and control scheme of an upper limb orthosis for rehabilitation are presented. Two control models for the orthosis are explained which compute the triggering function and the level of assistance provided by the device. As input to the model fused sensor data from the orthosis and physiology data in terms of electromyography (EMG) signals are used.
Validation of a Coding Algorithm to Identify Bladder Cancer and Distinguish Stage in an Electronic Medical Records Database

Studies on outcomes in bladder cancer rely on accurate methods to identify patients with bladder cancer and differentiate bladder cancer stage. Medical record and administrative databases are increasingly used to study cancer incidence, but few have distinguished cancer stage, and none have focused on bladder cancer. In this study, we used data from The UK Health Improvement Network (THIN) to identify patients with bladder cancer using at least one diagnostic code for bladder cancer, and distinguish muscle-invasive from non-invasive disease using a subsequent code for cystectomy. Algorithms were validated against a gold standard of physician-completed questionnaires, pathology reports, and consultant letters. Algorithm performance was evaluated by measuring positive predictive value (PPV) and corresponding 95% confidence interval (CI). Among all patients coded with bladder cancer (n = 194), PPV for any bladder cancer was 99.5% (95% CI, 97.2–99.9). PPV for incident bladder cancer was 93.8% (95% CI, 89.4–96.7). PPV for muscle-invasive bladder cancer was 70.1% (95% CI, 59.4–79.5) in patients with cystectomy (n = 95) and 83.9% (95% CI, 66.3–94.5) in those with cystectomy plus additional codes for metastases and death (n = 31). Using our codes for bladder cancer, the age- and sex-standardized incidence rate (SIR) of bladder cancer in THIN approximated that measured by cancer registries (SIR within 20%), suggesting that sensitivity was high as well. THIN is a valid and novel database for the study of bladder cancer. Our algorithm can be used to examine the epidemiology of muscle-invasive bladder cancer or outcomes following cystectomy for patients with muscle invasion. Cancer Epidemiol Biomarkers Prev; 24(1); 303–7. ©2014 AACR.
Hidden Markov Mined Activity Model for Human Activity Recognition

Object-usage-based human activity recognition systems require activity data for learning. Acquiring such data from the real world is expensive and time consuming. To overcome such difficulties, the exploitation of web activity data is gaining popularity. However, due to a lack of much real-world information in such data, existing activity models are not suitable for web data. In this paper, we propose a hidden Markov model- (HMM-) based activity model specially designed to use web activity data for activity recognition. It utilizes a sequence of object-usage information for activity recognition. We also propose a web activity data mining algorithm for this model. It is extremely fast and efficient in comparison with the existing algorithms. We perform three experiments to validate the proposed model. We show that the model can be effectively utilized by an activity recognition system.
Fabrication of Aluminium Nanowires by Differential Pressure Injection

The reported study aims to demonstrate the application of a simple technique, which is referred to as pressure differential injection, to prepare metallic nanowires. This technique relies on the difference in pressure between the inside of sealed nanochannels of an anodic aluminium oxide (AAO) substrate and the ambient atmosphere to inject a molten metal, which is previously deposited on the substrate, into the AAO pores. The application of this technique enabled the fabrication of nanowires in aluminium with diameters comprised between 55 nm and 65 nm.
Evaluation of Diagnostic Value of Elisa Method (EIA) & PCR in Diagnosis of Hepatitis C Virus In Hemodialysis Patients

Background and Aims: Since hepatitis C Virus has contaminated approximately 170 millions people over the world. Applying serological screening methods using EIA has led to decline the risk of transmission. However, in chronic renal disease and hemodialysis patients, the EIA method is not sufficient for diagnosis of HCV. Therefore, PCR method is suggested for rapid diagnosis of the disease in this group of patients. Methods: Of 50 collected blood specimens from hemodialysis patients serum separated and stored at -20o C. Then they were examined by the methods of EIA and PCR. Results: Using EIA method, 19 cases were positive (38%) and 31 cases were found negative (62%). In PCR method, 12 cases were found positive (24%) and 38 (76%) were negative. In this method, 12 cases were found real positive and 38 cases were found real negative. While, using EIA method, 5 cases were real positive and 24 cases were real negative and 14 cases were found false positive and 7 cases false negative. Conclusions: Considering the obtained results and their comparison, it is observed that the PCR method is accredited as a specific and reliable method suitable for public screening and that the PCR method is recommended as an exact and final diagnosis method of these patients.
Phylogeny of Lamiidae.

PREMISE OF THE STUDY The Lamiidae, a clade composed of approximately 15% of all flowering plants, consists of five orders: Boraginales, Gentianales, Garryales, Lamiales, and Solanales; and four families unplaced in an order: Icacinaceae, Metteniusiaceae, Oncothecaceae, and Vahliaceae. Our understanding of the phylogenetic relationships of Lamiidae has improved significantly in recent years, however, relationships among the orders and unplaced families of the clade remain partly unresolved. Here, we present a phylogenetic analysis of the Lamiidae based on an expanded sampling, including all families together, for the first time, in a single phylogenetic analyses. METHODS Phylogenetic analyses were conducted using maximum parsimony, maximum likelihood, and Bayesian approaches. Analyses included nine plastid regions (atpB, matK, ndhF, psbBTNH, rbcL, rps4, rps16, trnL-F, and trnV-atpE) and the mitochondrial rps3 region, and 129 samples representing all orders and unplaced families of Lamiidae. KEY RESULTS Maximum Likelihood (ML) and Bayesian trees provide good support for Boraginales sister to Lamiales, with successive outgroups (Solanales + Vahlia) and Gentianales, together comprising the core Lamiidae. Early branching patterns are less well supported, with Garryales only poorly supported as sister to the above 'core' and a weakly supported clade composed of Icacinaceae, Metteniusaceae, and Oncothecaceae sister to all other Lamiidae. CONCLUSIONS Our phylogeny of Lamiidae reveals increased resolution and support for internal relationships that have remained elusive. Within Lamiales, greater resolution also is obtained, but some family interrelationships remain a challenge.
多项肺系统肿瘤标志物异常在晚期肺腺癌治疗中的作用

背景与目的 肺癌的常用肿瘤标志物中，癌胚抗原（carcinoembryonic antigen, CEA）与糖类抗原125（carbohydrate antigen 125, CA125）、细胞角蛋白19片段（cytokeratin 19, CYFRA21-1）与鳞状细胞癌抗原（squamous carcinoma antigen, SCC）、神经元特异性烯醇化酶（neuron specific enolase, NSE）与胃泌素释放肽前体（pro-gastrin-releasing peptide, ProGRP）分别在肺腺癌、肺鳞状细胞癌和小细胞肺癌中有较高表达。 本研究旨在通过对比多项肿瘤标志物异常（A组）和仅CEA和/或CA125异常（B组）的两组晚期肺腺癌患者，探讨多项肿瘤标志物异常在疗效评价和预测复发方面的价值。 方法 纳入中国医学科学院肿瘤医院的Ⅳ期肺腺癌初治病例，回顾性分析其临床数据，包括临床特征、治疗前的血清肿瘤标志物水平、疗效及无进展生存期。 结果 除CEA和CA125外，A组异常比率最高的肿瘤标志物是CYFRA21-1（93%），其次是NSE（36%）、SCC（13%）和ProGRP（12%）。 多项肿瘤标志物异常的患者更易出现远处多部位转移（P < 0.001），治疗后的无进展生存期更短（中位时间5. 3个月vs 7. 3个月，P=0.016）。 两组中进行维持治疗的患者均比未行维持治疗的患者复发风险低（P均 < 0.001）。 结论 多项肿瘤标志物异常患者复发风险高，维持治疗可降低复发风险。
Disadvantages of being a street child in Iran: a systematic review

In order to find related family factors and outcomes of being a street child in Iran, this study has classified and evaluated studies on this issue from a recent decade according to their strengths and weaknesses. We used a systematic review method in this study. By searching Iranian and international databases, and many universities and related organisations in Iran, and after their quality had been evaluated by a quality evaluation form, the findings of 40 studies were described and analysed. Street children in Iran are confronted with some serious family disadvantages such as family breakdown and abuse that may, along with poverty, push them to the streets. Many children also suffer from physical and mental health problems, and are involved in delinquency and drug abuse. It seems that both mechanisms of poverty and adverse familial conditions drive children to leave their homes for the streets as a way to solve their problems.
Loss of Nuclear Localized Parathyroid Hormone-Related Protein in Primary Breast Cancer Predicts Poor Clinical Outcome and Correlates with Suppressed Stat5 Signaling

Purpose: Parathyroid hormone-related protein (PTHrP) is required for normal mammary gland development and biology. A PTHLH gene polymorphism is associated with breast cancer risk, and PTHrP promotes growth of osteolytic breast cancer bone metastases. Accordingly, current dogma holds that PTHrP is upregulated in malignant primary breast tumors, but solid evidence for this assumption is missing. Experimental Design: We used quantitative IHC to measure PTHrP in normal and malignant breast epithelia, and correlated PTHrP levels in primary breast cancer with clinical outcome. Results: PTHrP levels were markedly downregulated in malignant compared with normal breast epithelia. Moreover, low levels of nuclear localized PTHrP in cancer cells correlated with unfavorable clinical outcome in a test and a validation cohort of breast cancer treated at different institutions totaling nearly 800 cases. PTHrP mRNA levels in tumors of a third cohort of 737 patients corroborated this association, also after multivariable adjustment for standard clinicopathologic parameters. Breast cancer PTHrP levels correlated strongly with transcription factors Stat5a/b, which are established markers of favorable prognosis and key mediators of prolactin signaling. Prolactin stimulated PTHrP transcript and protein in breast cancer cell lines in vitro and in vivo, effects mediated by Stat5 through the P2 gene promoter, producing transcript AT6 encoding the PTHrP 1-173 isoform. Low levels of AT6, but not two alternative transcripts, correlated with poor clinical outcome. Conclusions: This study overturns the prevailing view that PTHrP is upregulated in primary breast cancers and identifies a direct prolactin–Stat5–PTHrP axis that is progressively lost in more aggressive tumors.
Folate and other one-carbon metabolism-related nutrients and risk of postmenopausal breast cancer in the Cancer Prevention Study II Nutrition Cohort.

BACKGROUND Epidemiologic studies of the association of folate intake with breast cancer risk have been inconclusive, and few have investigated how related nutrients modify this association. OBJECTIVE We investigated the association of dietary (food folate plus folic acid from fortification) and total folate (food folate, folic acid from fortification, and folic acid from supplements), vitamin B-6, vitamin B-12, methionine, and alcohol intakes with postmenopausal breast cancer among women in the Cancer Prevention Study II Nutrition Cohort. The modification of the folate associations by the other nutrients was also investigated. DESIGN This prospective cohort study included 70,656 postmenopausal women for whom dietary information was collected in 1992. Of these, 3898 developed breast cancer between enrollment in 1992 and June 2005. Cox proportional hazards modeling was used to calculate multivariate-adjusted hazard rate ratios and 95% CIs. RESULTS Compared with the lowest quintile, the highest quintile of dietary folate intake was associated with a higher risk of breast cancer (rate ratio: 1.12; 95% CI: 1.01, 1.24). However, the test for trend was not significant (P for trend = 0.15). No association was found for total folate, vitamin B-6, or vitamin B-12, but methionine was inversely associated with breast cancer risk (P for trend = 0.04). The association of dietary folate with breast cancer was not modified by other nutrients or alcohol. CONCLUSIONS This study suggests that dietary folate intake may be positively associated with postmenopausal breast cancer. However, no dose-response relation was observed. The extent to which increased supplement use and folate fortification contributes to breast cancer risk warrants further research.
Improving breast and lung cancer services in hospital using experience based co-design (EBCD)

Abstract Background and aims This project sought to design better experiences for patients and health care staff from the breast and lung cancer services within two large teaching hospitals in England. Experience based co-design (EBCD) was the chosen action research approach.1 EBCD is a new and innovative methodology combining (1) a user-centred orientation (by adopting a narrative storytelling approach) and (2) a participatory, collaborative change process, allowing staff to ‘see the person in the patient’ and placing patient and staff experience at the centre of service development. Methods and results The project involved an in-depth qualitative study of how care was delivered by staff and received by patients, focusing on patients' emotional ‘journey’. It included 36 filmed patient narratives, capturing the key emotional ‘touch points’, 60 staff interviews about their experience of providing services, and ethnographic observation of clinical areas. Patient and staff interviews were analysed to identify themes and issues for which were feedback to patients and staff at various group events. For example, a composite 30 min film of breast and lung cancer patients’ experiences, was created and used to feedback patient narratives to staff. Through a facilitated three-stage change process which will be described, patients and staff agreed on joint priorities for improvement and then worked together in co-design groups that focused on identified priority areas (for example information provision, day surgery, continuity of care, diagnosis and outpatient care). Discussion and conclusions The paper reflects on lessons learned for improving patient/staff experiences through the use of EBCD. It explores the value of the EBCD approach, the use of narratives, observation and film (excerpts will be shown) as a way of humanising health care and engaging staff and patients in a change process to facilitate meaningful and lasting improvements in service provision.
Distributed Binary Quantization of a Noisy Source in Wireless Sensor Networks

In distributed (decentralized) estimation in wireless sensor networks, an unknown parameter must be estimated from some noisy measurements collected at different sensors. Due to limited communication resources, these measurements are typically quantized before being sent to a fusion center, where an estimation of the unknown parameter is calculated. In the most stringent condition, each measurement is converted to a single bit. In this study, we propose a distributed quantization scheme which is based on single-bit quantized data from each sensor and achieves high estimation accuracy at the fusion centre. We do this by designing some local binary quantizers which define a multithreshold quantization rule for each sensor. These local binary quantizers are initially designed so that together they mimic the functionality of a multilevel quantizer. Later, their design is improved to include some error-correcting capability, which further improves the estimation accuracy from the sensors’ binary data. The distributed quantization formed by such local binary quantizers along with the proper estimator proposed in this work achieves better performance, compared to the existing distributed binary quantization methods, specially when fewer sensors with low measurement noise are available.