The poverty impact of rural roads : evidencefrom Bangladesh

The rationale for public investment in rural roads is that households can better exploit agricultural and nonagricultural opportunities to use labor and capital more efficiently. But significant knowledge gaps remain as to how opportunities provided by roads actually filter back into household outcomes and their distributional consequences. This paper examines the impacts of rural road projects using household-level panel data from Bangladesh. Rural road investments are found to reduce poverty significantly through higher agricultural production, higher wages, lower input and transportation costs, and higher output prices. Rural roads also lead to higher girls'and boys'schooling. Road investments are pro-poor, meaning the gains are proportionately higher for the poor than for the non-poor.Transport Economics Policy&Planning,Rural Roads&Transport,Economic Theory&Research,Rural Transport,Rural Poverty Reduction

A Neuron as a Signal Processing Device

A neuron is a basic physiological and computational unit of the brain. While much is known about the physiological properties of a neuron, its computational role is poorly understood. Here we propose to view a neuron as a signal processing device that represents the incoming streaming data matrix as a sparse vector of synaptic weights scaled by an outgoing sparse activity vector. Formally, a neuron minimizes a cost function comprising a cumulative squared representation error and regularization terms. We derive an online algorithm that minimizes such cost function by alternating between the minimization with respect to activity and with respect to synaptic weights. The steps of this algorithm reproduce well-known physiological properties of a neuron, such as weighted summation and leaky integration of synaptic inputs, as well as an Oja-like, but parameter-free, synaptic learning rule. Our theoretical framework makes several predictions, some of which can be verified by the existing data, others require further experiments. Such framework should allow modeling the function of neuronal circuits without necessarily measuring all the microscopic biophysical parameters, as well as facilitate the design of neuromorphic electronics.Comment: 2013 Asilomar Conference on Signals, Systems and Computers, see http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=681029

Non-Local Deformation of a Supersymmetric Field Theory

In this paper, we will analyse a supersymmetric field theory deformed by generalized uncertainty principle and Lifshitz scaling. It will be observed that this deformed supersymmetric field theory contains non-local fractional derivative terms. In order to construct such deformed N=1 supersymmetric theory, a harmonic extension of functions will be used. However, the supersymmetry will be only preserved for a free theory and will be broken by the inclusion of interaction terms.Comment: 12 pages, pulished versio

Estimating Blood Pressure from Photoplethysmogram Signal and Demographic Features using Machine Learning Techniques

Hypertension is a potentially unsafe health ailment, which can be indicated directly from the Blood pressure (BP). Hypertension always leads to other health complications. Continuous monitoring of BP is very important; however, cuff-based BP measurements are discrete and uncomfortable to the user. To address this need, a cuff-less, continuous and a non-invasive BP measurement system is proposed using Photoplethysmogram (PPG) signal and demographic features using machine learning (ML) algorithms. PPG signals were acquired from 219 subjects, which undergo pre-processing and feature extraction steps. Time, frequency and time-frequency domain features were extracted from the PPG and their derivative signals. Feature selection techniques were used to reduce the computational complexity and to decrease the chance of over-fitting the ML algorithms. The features were then used to train and evaluate ML algorithms. The best regression models were selected for Systolic BP (SBP) and Diastolic BP (DBP) estimation individually. Gaussian Process Regression (GPR) along with ReliefF feature selection algorithm outperforms other algorithms in estimating SBP and DBP with a root-mean-square error (RMSE) of 6.74 and 3.59 respectively. This ML model can be implemented in hardware systems to continuously monitor BP and avoid any critical health conditions due to sudden changes.Comment: Accepted for publication in Sensor, 14 Figures, 14 Table

Transfer Learning with Deep Convolutional Neural Network (CNN) for Pneumonia Detection using Chest X-ray

Pneumonia is a life-threatening disease, which occurs in the lungs caused by either bacterial or viral infection. It can be life-endangering if not acted upon in the right time and thus an early diagnosis of pneumonia is vital. The aim of this paper is to automatically detect bacterial and viral pneumonia using digital x-ray images. It provides a detailed report on advances made in making accurate detection of pneumonia and then presents the methodology adopted by the authors. Four different pre-trained deep Convolutional Neural Network (CNN)- AlexNet, ResNet18, DenseNet201, and SqueezeNet were used for transfer learning. 5247 Bacterial, viral and normal chest x-rays images underwent preprocessing techniques and the modified images were trained for the transfer learning based classification task. In this work, the authors have reported three schemes of classifications: normal vs pneumonia, bacterial vs viral pneumonia and normal, bacterial and viral pneumonia. The classification accuracy of normal and pneumonia images, bacterial and viral pneumonia images, and normal, bacterial and viral pneumonia were 98%, 95%, and 93.3% respectively. This is the highest accuracy in any scheme than the accuracies reported in the literature. Therefore, the proposed study can be useful in faster-diagnosing pneumonia by the radiologist and can help in the fast airport screening of pneumonia patients.Comment: 13 Figures, 5 tables. arXiv admin note: text overlap with arXiv:2003.1314

Lupus cystitis presenting with urinary symptoms.

We present a case of a young woman presenting with irritative lower urinary tract symptoms and microscopic hematuria who was diagnosed with systemic lupus erythematosus (SLE). Abdominal ultrasound revealed bilateral hydronephrosis and a thickened bladder wall. Cystoscopic evaluation revealed severe diffuse inflammation, erythema and hemorrhage at the trigone with punctate extensions to the bladder base. She was treated with prednisone and mycophenolate mofetil with improvements in her symptoms and ultrasound findings. Lupus cystitis is a rare manifestation of SLE

Cumulative Burden of Morbidity Among Testicular Cancer Survivors After Standard Cisplatin-Based Chemotherapy: A Multi-Institutional Study

Purpose In this multicenter study, we evaluated the cumulative burden of morbidity (CBM) among > 1,200 testicular cancer survivors and applied factor analysis to determine the co-occurrence of adverse health outcomes (AHOs). Patients and Methods Participants were ≤ 55 years of age at diagnosis, finished first-line chemotherapy ≥ 1 year previously, completed a comprehensive questionnaire, and underwent physical examination. Treatment data were abstracted from medical records. A CBM score encompassed the number and severity of AHOs, with ordinal logistic regression used to assess associations with exposures. Nonlinear factor analysis and the nonparametric dimensionality evaluation to enumerate contributing traits procedure determined which AHOs co-occurred. Results Among 1,214 participants, approximately 20% had a high (15%) or very high/severe (4.1%) CBM score, whereas approximately 80% scored medium (30%) or low/very low (47%). Increased risks of higher scores were associated with four cycles of either ifosfamide, etoposide, and cisplatin (odds ratio [OR], 1.96; 95% CI, 1.04 to 3.71) or bleomycin, etoposide, and cisplatin (OR, 1.44; 95% CI, 1.04 to 1.98), older attained age (OR, 1.18; 95% CI, 1.10 to 1.26), current disability leave (OR, 3.53; 95% CI, 1.57 to 7.95), less than a college education (OR, 1.44; 95% CI, 1.11 to 1.87), and current or former smoking (OR, 1.28; 95% CI, 1.02 to 1.63). CBM score did not differ after either chemotherapy regimen ( P = .36). Asian race (OR, 0.41; 95% CI, 0.23 to 0.72) and vigorous exercise (OR, 0.68; 95% CI, 0.52 to 0.89) were protective. Variable clustering analyses identified six significant AHO clusters (χ2 P < .001): hearing loss/damage, tinnitus (OR, 16.3); hyperlipidemia, hypertension, diabetes (OR, 9.8); neuropathy, pain, Raynaud phenomenon (OR, 5.5); cardiovascular and related conditions (OR, 5.0); thyroid disease, erectile dysfunction (OR, 4.2); and depression/anxiety, hypogonadism (OR, 2.8). Conclusion Factors associated with higher CBM may identify testicular cancer survivors in need of closer monitoring. If confirmed, identified AHO clusters could guide the development of survivorship care strategies

Validation of verbal autopsy tool for ascertaining the causes of stillbirth

Objective: To assess performance of the WHO revised verbal autopsy tool for ascertaining the causes of still birth in comparison with reference standard cause of death ascertained by standardized clinical and supportive data.Methods: All stillbirths at a tertiary hospital in Karachi, Pakistan were prospectively recruited into study from August 2006- February 2008. The reference standard cause of death was established by two senior obstetricians within 48 hours using the ICD coding system. Verbal autopsy interviews using modified WHO tool were conducted by trained health workers within 2- 6 weeks of still birth and the cause of death was assigned by second panel of obstetricians. The performance was assessed in terms of sensitivity, specificity and Kappa.Results: There were 204 still births. Of these, 80.8% of antepartum and 50.5% of intrapartum deaths were correctly diagnosed by verbal autopsy. Sensitivity of verbal autopsy was highest 68.4%, (95%CI: 46-84.6) for congenital malformation followed by obstetric complication 57.6%, (95%CI: 25-84.2). The specificity for all major causes was greater than 90%. The level of agreement was high (kappa=0.72) for anomalies and moderate (k=0.4) for all major causes of still birth, except asphyxia.Conclusion: Our results suggest that verbal autopsy has reasonable validity in identifying and discriminating between causes of stillbirth in Pakistan. On the basis of these findings, we feel it has a place in resource constrained areas to inform strategic planning and mobilization of resources to attain Millennium Development Goal