Distilling Information Reliability and Source Trustworthiness from Digital Traces

Online knowledge repositories typically rely on their users or dedicated editors to evaluate the reliability of their content. These evaluations can be viewed as noisy measurements of both information reliability and information source trustworthiness. Can we leverage these noisy evaluations, often biased, to distill a robust, unbiased and interpretable measure of both notions? In this paper, we argue that the temporal traces left by these noisy evaluations give cues on the reliability of the information and the trustworthiness of the sources. Then, we propose a temporal point process modeling framework that links these temporal traces to robust, unbiased and interpretable notions of information reliability and source trustworthiness. Furthermore, we develop an efficient convex optimization procedure to learn the parameters of the model from historical traces. Experiments on real-world data gathered from Wikipedia and Stack Overflow show that our modeling framework accurately predicts evaluation events, provides an interpretable measure of information reliability and source trustworthiness, and yields interesting insights about real-world events.Comment: Accepted at 26th World Wide Web conference (WWW-17

Residual Energy Based Cluster-head Selection in WSNs for IoT Application

Wireless sensor networks (WSN) groups specialized transducers that provide sensing services to Internet of Things (IoT) devices with limited energy and storage resources. Since replacement or recharging of batteries in sensor nodes is almost impossible, power consumption becomes one of the crucial design issues in WSN. Clustering algorithm plays an important role in power conservation for the energy constrained network. Choosing a cluster head can appropriately balance the load in the network thereby reducing energy consumption and enhancing lifetime. The paper focuses on an efficient cluster head election scheme that rotates the cluster head position among the nodes with higher energy level as compared to other. The algorithm considers initial energy, residual energy and an optimum value of cluster heads to elect the next group of cluster heads for the network that suits for IoT applications such as environmental monitoring, smart cities, and systems. Simulation analysis shows the modified version performs better than the LEACH protocol by enhancing the throughput by 60%, lifetime by 66%, and residual energy by 64%

Reproducibility of left atrial ablation with high-intensity focused ultrasound energy in a calf model

ObjectiveAchieving transmural tissue ablation might be necessary for successful treatment of atrial fibrillation. The purpose of this study was to evaluate the reproducibility of transmural left atrial ablation using a high-intensity focused ultrasound energy system in a calf model.MethodsNine heparinized bovines underwent a beating-heart left atrial ablation with a single application of the high-intensity focused ultrasound device. All animals were acutely killed, and the left atrium was fixed in formalin. Protocolized histological sections (5 μm) were obtained throughout each lesion and prepared with Masson trichrome and hematoxylin and eosin staining. Measurements were performed on a total of 359 slides from the 9 lesions. In addition, fresh left atrial tissues from 18 unused human donor hearts that did not meet the criteria for cardiac transplantation were measured at the site where the high-intensity focused ultrasound device is normally applied.ResultsCalf left atrial thickness ranged between 2.5 and 20.1 mm, with a mean of 9.10 mm. High-intensity focused ultrasound ablation consistently produced a 100% transmural lesion in left atrial thickness up to 6 mm. In addition, a transmural lesion was observed in 91% of tissues that were up to 10 mm thick and in 85% that were up to 15 mm thick. Human left atrial thickness ranged between 1.2 to 6 mm, with a mean of 3.7 mm.ConclusionsCalf left atrial thickness in this study was greater than human left atrial thickness. Human left atrial thickness is generally less than 6 mm, and in this range high-intensity focused ultrasound ablation achieved 100% transmurality. These histological results might correlate with a high success rate of atrial fibrillation ablation by using the high-intensity focused ultrasound system

Giant secreting adrenal myelolipoma in a man: a case report

<p>Abstract</p> <p>Introduction</p> <p>Adrenal myelolipoma is a rare, benign neoplasm that is usually asymptomatic, unilateral and nonsecreting. It develops within the adrenal gland and is composed of mature adipose tissue with elements of the hematopoietic series. We describe the case of what is, to the best of our knowledge, one of the largest secreting adrenal myelolipomas reported in the literature.</p> <p>Case presentation</p> <p>A 52-year-old Caucasian man of medium build who had had moderate hypertension for three years presented to our hospital. He had no other significant symptoms. His hypertension was pharmacologically treated. He came to our hospital to undergo abdominal ultrasonography during a clinical checkup. The ultrasound scan showed the presence of a voluminous hyperechoic mass interposed between the spleen and the left kidney. It was reported as a myelolipoma of the left kidney on the basis of its structural characteristics and position. Computed tomography confirmed our diagnosis. All preoperative biochemical tests were normal, with the exception of high serum cortisol, which was being overproduced by the lesion and was probably responsible for the patient's hypertension. He underwent successful surgery, and his postoperative course was uneventful. The pathologic examination of the lesion confirmed the diagnosis of adrenal myelolipoma. The patient's blood pressure returned to within the normal range.</p> <p>Conclusions</p> <p>The "incidental" discovery of an adrenal mass requires careful diagnostic study to plan adequate therapeutic management. Both of the primary investigations at our disposal, ultrasound and blood tests (adrenal hormones), helped in rendering the diagnosis and allowed us to move toward the most appropriate treatment, taking into account the size of the tumor and its probable hormonal production.</p

Multiparametric, Longitudinal Optical Coherence Tomography Imaging Reveals Acute Injury and Chronic Recovery in Experimental Ischemic Stroke

Progress in experimental stroke and translational medicine could be accelerated by high-resolution in vivo imaging of disease progression in the mouse cortex. Here, we introduce optical microscopic methods that monitor brain injury progression using intrinsic optical scattering properties of cortical tissue. A multi-parametric Optical Coherence Tomography (OCT) platform for longitudinal imaging of ischemic stroke in mice, through thinned-skull, reinforced cranial window surgical preparations, is described. In the acute stages, the spatiotemporal interplay between hemodynamics and cell viability, a key determinant of pathogenesis, was imaged. In acute stroke, microscopic biomarkers for eventual infarction, including capillary non-perfusion, cerebral blood flow deficiency, altered cellular scattering, and impaired autoregulation of cerebral blood flow, were quantified and correlated with histology. Additionally, longitudinal microscopy revealed remodeling and flow recovery after one week of chronic stroke. Intrinsic scattering properties serve as reporters of acute cellular and vascular injury and recovery in experimental stroke. Multi-parametric OCT represents a robust in vivo imaging platform to comprehensively investigate these properties

Identification and prediction of Parkinson's disease subtypes and progression using machine learning in two cohorts.

The clinical manifestations of Parkinson's disease (PD) are characterized by heterogeneity in age at onset, disease duration, rate of progression, and the constellation of motor versus non-motor features. There is an unmet need for the characterization of distinct disease subtypes as well as improved, individualized predictions of the disease course. We used unsupervised and supervised machine learning methods on comprehensive, longitudinal clinical data from the Parkinson's Disease Progression Marker Initiative (n = 294 cases) to identify patient subtypes and to predict disease progression. The resulting models were validated in an independent, clinically well-characterized cohort from the Parkinson's Disease Biomarker Program (n = 263 cases). Our analysis distinguished three distinct disease subtypes with highly predictable progression rates, corresponding to slow, moderate, and fast disease progression. We achieved highly accurate projections of disease progression 5 years after initial diagnosis with an average area under the curve (AUC) of 0.92 (95% CI: 0.95 ± 0.01) for the slower progressing group (PDvec1), 0.87 ± 0.03 for moderate progressors, and 0.95 ± 0.02 for the fast-progressing group (PDvec3). We identified serum neurofilament light as a significant indicator of fast disease progression among other key biomarkers of interest. We replicated these findings in an independent cohort, released the analytical code, and developed models in an open science manner. Our data-driven study provides insights to deconstruct PD heterogeneity. This approach could have immediate implications for clinical trials by improving the detection of significant clinical outcomes. We anticipate that machine learning models will improve patient counseling, clinical trial design, and ultimately individualized patient care

Modified Back Projection Kernel Based Image Super Resolution

In this paper, we propose a new super resolution technique based on iterative interpolation followed by registering them using back projection (BP). Firstly the low resolution image is interpolated and then decimated to four low resolution images. The four low resolution images are interpolated and registered by using BP in order to generate a sharper high resolution image then high resolution image is down sampled and back to the first step. The proposed method has been tested on some bench mark images. The peak signal-to-noise ratio (PSNR) and structural similarity index (SSIM) results as well as the visual results shows the superiority of the proposed technique over the conventional and state-of-art image super resolution techniques. In Average, the PSNR is 2.72 dB higher than the bicubic interpolation

Impact of perioperative chemotherapy on survival in patients with advanced primary urethral cancer: results of the international collaboration on primary urethral carcinoma

This is the first series that suggests a prognostic benefit of neoadjuvant treatment in a consecutive series of patients who underwent perioperative chemotherapy plus surgery for advanced primary urethral carcinoma. Further studies should yield a better understanding of how perioperative chemotherapy exerts a positive effect on survival in order to selectively advocate its use in advanced primary urethral carcinom

Concomitant CIS on TURBT does not impact oncological outcomes in patients treated with neoadjuvant or induction chemotherapy followed by radical cystectomy

© Springer-Verlag GmbH Germany, part of Springer Nature 2018Background: Cisplatin-based neoadjuvant chemotherapy (NAC) for muscle invasive bladder cancer improves all-cause and cancer specific survival. We aimed to evaluate whether the detection of carcinoma in situ (CIS) at the time of initial transurethral resection of bladder tumor (TURBT) has an oncological impact on the response to NAC prior to radical cystectomy. Patients and methods: Patients were identified retrospectively from 19 centers who received at least three cycles of NAC or induction chemotherapy for cT2-T4aN0-3M0 urothelial carcinoma of the bladder followed by radical cystectomy between 2000 and 2013. The primary and secondary outcomes were pathological response and overall survival, respectively. Multivariable analysis was performed to determine the independent predictive value of CIS on these outcomes. Results: Of 1213 patients included in the analysis, 21.8% had concomitant CIS. Baseline clinical and pathologic characteristics of the ‘CIS’ versus ‘no-CIS’ groups were similar. The pathological response did not differ between the two arms when response was defined as pT0N0 (17.9% with CIS vs 21.9% without CIS; p = 0.16) which may indicate that patients with CIS may be less sensitive to NAC or ≤ pT1N0 (42.8% with CIS vs 37.8% without CIS; p = 0.15). On Cox regression model for overall survival for the cN0 cohort, the presence of CIS was not associated with survival (HR 0.86 (95% CI 0.63–1.18; p = 0.35). The presence of LVI (HR 1.41, 95% CI 1.01–1.96; p = 0.04), hydronephrosis (HR 1.63, 95% CI 1.23–2.16; p = 0.001) and use of chemotherapy other than ddMVAC (HR 0.57, 95% CI 0.34–0.94; p = 0.03) were associated with shorter overall survival. For the whole cohort, the presence of CIS was also not associated with survival (HR 1.05 (95% CI 0.82–1.35; p = 0.70). Conclusion: In this multicenter, real-world cohort, CIS status at TURBT did not affect pathologic response to neoadjuvant or induction chemotherapy. This study is limited by its retrospective nature as well as variability in chemotherapy regimens and surveillance regimens.Peer reviewedFinal Accepted Versio