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Background: With recent technological advancement, new and improved endoscopic instruments and laser devices have catapulted flexible ureteroscopy (fURS) to the forefront, hence making retrograde i...

Importance There is great concern about the impact of COVID-19 in pregnancy due to the high morbidity and mortality associated with prior coronavirus infections. Objective The objective of this review is to summarize the current literature on the impact of COVID-19 on pregnant women and their newborns. Evidence acquisition The search terms COVID-19 and pregnancy were used in Medline and Clinical Key databases. Only articles written in English with outcome data on both mothers and their newborns were incorporated. Results Pregnant women generally experience COVID-19 as a mild-moderate illness. However, approximately 5% become critically ill. Women with underlying comorbidities seem more likely to experience severe morbidity. Newborns also generally have a favorable course. Vertical transmission in the intrauterine period is possible but rare. Infection control measures need to be taken to prevent transmission during the peripartum period. There is a paucity of data on infections in the first and second trimesters, but research from those infected in the third trimester indicates a possible link with preterm birth. There is a significant percentage of asymptomatic cases. Racial disparities are also being noted with disproportionate numbers of racial/ethnic minorities being affected. Conclusions COVID-19 is generally experienced by pregnant women and their newborns as a mild to moderate illness, although a minority become critically ill and mortality does occur. This is more likely among those with underlying comorbidities, as in the general population. Asymptomatic cases heighten the need for increased testing and infection control measures. Racial disparities highlight underlying vulnerabilities and the need for increased research and policy changes.

Background Brain metastases frequently occur in patients with non‐small cell lung cancer (NSCLC) resulting in a poor prognosis. Here, we investigated the association between PD‐L1 expression and brain metastasis in patients with NSCLC and its clinical significance. Methods A total of 270 patients diagnosed with metastatic NSCLC who underwent PD‐L1 testing on their tumor tissue between January 2017 and March 2019 were retrospectively reviewed. The VENTANA PD‐L1 (SP263) assay was used, and positive PD‐L1 expression was defined as staining in ≥1% of tumor cells. Results Positive PD‐L1 expression was observed in 181 (67.0%) patients, and 74 (27.4%) patients had brain metastasis at diagnosis. Synchronous brain metastases were more frequently observed in PD‐L1‐positive compared with PD‐L1‐negative patients (31.5% vs. 19.1%, p = 0.045). Multiple logistic regression analysis identified positive PD‐L1 expression (odds ratio [OR]: 2.24, p = 0.012) as an independent factor associated with synchronous brain metastasis, along with the histological subtype of nonsquamous cell carcinoma (OR: 2.84, p = 0.003). However, the incidence of central nervous system (CNS) progression was not associated with PD‐L1 positivity, with a two‐year cumulative CNS progression rate of 26.3% and 28.4% in PD‐L1‐positive and PD‐L1‐negative patients, respectively (log rank p = 0.944). Furthermore, positive PD‐L1 expression did not affect CNS progression or overall survival in patients with synchronous brain metastasis (long rank p = 0.513 and 0.592, respectively). Conclusions Initial brain metastases are common in NSCLC patients with positive PD‐L1 expression. Further studies are necessary to understand the relationship between early brain metastasis and cancer immunity., We investigated the association between PD‐L1 expression and brain metastasis in patients with non‐small cell lung cancer (NSCLC) and its clinical significance. Patients with tumors exhibiting positive PD‐L1 expression (TPS ≥ 1%) were found to have a higher frequency and risk of synchronous brain metastasis when diagnosed with advanced NSCLC. No prognostic impact of PD‐L1 expression was observed on either CNS progression in the entire cohort of patients or OS in patients presenting with synchronous brain metastasis.

Background and objectives Combined sequential treatments with multiple modalities such as lasers and soft-tissue fillers are commonly required for the treatment of atrophic acne scars. Recently, fractional treatment with picosecond-domain lasers has proven to be effective for skin rejuvenation and scar treatment. However, little is known about the effects of picosecond-domain fractional laser treatment over hyaluronic acid fillers (HAFs). We aimed to evaluate the in vivo tissue responses to 1064 nm picosecond-domain fractional neodymium:yttrium-aluminum-garnet (Nd:YAG) laser treatments using microlens array (MLA) applied over pre-injected HAF in rats. In addition, we evaluated the efficacy and safety of this combined same-day treatment for atrophic acne scars in patients. Study design/materials and methods Sprague-Dawley rats were subjected to 1064 nm picosecond-domain fractional Nd:YAG laser treatment immediately after HAF dermal injection. Skin specimens were histologically evaluated on days 0, 7, and 21. In a clinical study, 36 patients with acne scars were treated concurrently with 1064 nm MLA-type picosecond lasers and HAFs. The patients were scheduled to receive two consecutive treatments at 4-week intervals, with a follow-up visit at 12 weeks after the final treatment. Acne scar photographs were graded using the Goodman and Baron's qualitative and quantitative scales at baseline and 12 weeks post-procedure. Results Picosecond-domain fractional laser treatment immediately after the dermal injection of HAF into rats did not cause any histological changes in the filler or surrounding skin. In a clinical study, treated subjects (n = 36) achieved significant improvement in acne scars and patient satisfaction. No serious adverse events were observed. Conclusions Combined picosecond laser and HAF treatment were proven to be safe and effective based on in vivo and clinical study results. Facial rejuvenation and scar treatment using a picosecond-domain fractional laser may be performed immediately after HAF injection. Lasers Surg. Med. © 2020 Wiley Periodicals, Inc.

Poland's syndrome (PS; OMIM 173800) is a rare congenital syndrome which consists of absence or hypoplasia of the pectoralis muscle. Other features can be variably associated, including rib defects. On the affected side other features (such as of breast and nipple anomalies, lack of subcutaneous tissue and skin annexes, hand anomalies, visceral, and vertebral malformation) have been variably documented. To date, association of PS with central nervous system malformation has been rarely reported remaining poorly understood and characterized. We report a left-sided PS patient carrying a de novo 1.5 Mb Xp22.31 duplication diagnosed in addiction to strabismus, optic nerves and chiasm hypoplasia, corpus callosum abnormalities, ectopic neurohypophysis, pyelic ectasia, and neurodevelopmental delay. Since, to our knowledge, this features' association has not been previously reported, we argue that this case may contribute to further widening of the variability of PS phenotype.

This study aimed to determine how the muscle mass of the lower leg affects the preservation of the lower extremities in patients with diabetic foot ulcer. This study analyzed patients with diabetic foot ulcer between January 2014 and June 2018 with a follow-up of at least 2 years. Of these 181 patients whose ulcer is located distal to the metatarsophalangeal joint, which was categorized as grade ≤2 by the Wagner classification were classified into 4 grades: grade 0 (treated without amputation), grade 1 (amputation distal to the metatarsophalangeal joint), grade 2 (Ray, transmetatarsal, Lisfranc, and Chopart amputation), and grade 3 (Syme, below-knee, and above-knee amputation) according to the final amputation degree. The muscles of the lower leg were classified into 4 compartments: anterior, lateral, deep posterior, and superficial posterior. The cross-sectional area and attenuation to estimate the muscle volume and density were measured at the axial image of computed tomography (CT) angiography. No significant differences were observed in the sex ratio and mean age among the grades ( P = .966 and .962). The cross-sectional area of the anterior, lateral, and posterior compartments demonstrated no significant differences, but that of the superficial posterior compartment exhibited significant differences among the grades ( P

A Wireless Sensor Network (WSN) is a wireless network that monitors physical environment conditions through resource-constrained sensor nodes and delivers data to a sink node through the network. One of the most important constraints on sensor nodes is their limited power source, which consists of small and irreplaceable batteries. Energy conservation is thus a dominant factor in WSN. Therefore, when designing a routing protocol for WSNs, it is necessary to consider the energy constraint of sensor nodes. In this paper, we consider the energy constraint of sensor nodes and propose an Energy Balancing Routing Protocol using reinforcement learning. The performance of the proposed protocol is compared to other existing energy-efficient routing protocols and the results show that the proposed protocol performs better with regards to energy saving and network lifetime.

Adding ovarian function suppression (OFS) after chemotherapy improves survival in young women with moderate- and high-risk breast cancer. Assessment of ovarian function restoration after chemotherapy becomes critical for subsequent endocrine treatment and addressing fertility issues.In the adding OFS after chemotherapy trial, patients who resumed ovarian function up to 2 years after chemotherapy were randomised to receive either 5 years of tamoxifen or adding 2 years of OFS with tamoxifen. Ovarian function was evaluated from enrolment to randomisation, and patients who did not randomise because of amenorrhoea for 2 years received tamoxifen and were followed up for 5 years. Prospectively collected consecutive hormone levels (proportion of patients with premenopausal follicle-stimulating hormone [FSH] levels 30 mIU/mL and oestradiol [E2] levels ≥40 pg/mL) and history of menstruation were available for 1067 patients with breast cancer.Over 5 years of tamoxifen treatment, 69% of patients resumed menstruation and 98% and 74% of patients satisfied predefined ovarian function restoration as per serum FSH and E2 levels, respectively. Menstruation was restored in 91% of patients younger than 35 years at baseline, but in only 33% of 45-year-old patients over 5 years. Among these patients, 41% experienced menstruation restoration within 2 years after chemotherapy and 28% slowly restored menstruation after 2-5 years. Younger age (35 years) at baseline, anthracycline without taxanes and ≤90 days of chemotherapy were predictors of menstruation restoration.During 5 years of tamoxifen treatment after chemotherapy, two-thirds of the patients experienced menstruation restoration, especially patients younger than 35 years. Young age, Adriamycin without taxanes and short duration of chemotherapy appeared to have a positive effect on ovarian reserves in the long term.ClinicalTrials.gov identifier: NCT00912548.

Theory suggests an inverse relation between the stiffness and the energy storage capacity for linear helical springs: reducing the active length of the spring by 50% increases its stiffness by 100%, but reduces its energy storage capacity by 50%. State-of-the-art variable stiffness actuators used to drive robots are characterized by a similar inverse relation, implying reduced energy storage capacity for increased spring stiffness. This relation limits the potential of the variable stiffness actuation technology when it comes to human performance augmentation in natural tasks, e.g., jumping, weight-bearing and running, which may necessitate a spring exoskeleton with large stiffness range and high energy storage capacity. In this paper, we theoretically show that the trade-off between stiffness range and energy storage capacity is not fundamental; it is possible to develop variable stiffness springs with simultaneously increasing stiffness and energy storage capacity. Consistent with the theory, we experimentally show that a controllable volume air spring, has a direct relation between its stiffness range and energy storage capacity. The mathematical conditions presented in this paper may be used to develop actuators that could bypass the limited energy storage capacity of current variable stiffness spring technology.

License plate recognition is the most promising application in IoT camera. The existing recognition algorithm shows good performance when it is used in the constrained situation such as at the entrance to the parking lot. The number plate images taken from the cameras of the parking lot system have a constant size and brightness. The accuracy of the license plate recognition system installed at the entrance to the parking lot is more than 98%. However, the recognition accuracy of license plate number may drop by about 50% to 70% in video stream taken on roads, where the size and the brightness of number plate images in video stream are not constant. They are very different to each other due to the camera angle, the distance to the vehicles and the light. This situation makes some objects to be recognized as license plates compared to the constrained situation. A major cause of this low recognition accuracy is the inability to filter out noise like an outlier that has a similar pattern to license plates. More deeper and wider CNN technique can be useful to increase the accuracy in these situation. But, the IoT camera generally has a restricted resource and is unable to use too large deep CNN, which requires a lot of computation power and memory. This paper suppresses outliers, non-license plate objects and increases the recognition accuracy through time series analysis. We use the time series analysis as the statistical methods to solve this problem of the outlier occurrence. Experimental results show that the recognition accuracy in video stream taken on ordinary roads is 77% in the case of the absence of outlier filters, but 85% in the case of including the filters proposed in the paper.

The size distribution of income is the basis of income inequality measures which in turn are needed for evaluation of social welfare. Therefore, proper specification of the income density function is of special importance. In this paper, using information theoretic approach, first, we provide a maximum entropy (ME) characterization of some well-known income distributions. Then, we suggest a class of flexible parametric densities which satisfy certain economic constraints and stylized facts of personal income data such as the weak Pareto law and a decline of the income-share elasticities. Our empirical results using the U.S. family income data show that the ME principle provides economically meaningful and a very parsimonious and, at the same time, flexible specification of the income density function.

Machine-to-machine (M2M) communications and applications are expected to be a significant part of the Internet of Things (IoT). However, conventional network gateways reported in the literature are unable to provide sustainable solutions to the challenges posted by the massive amounts of M2M communications requests, especially in the context of the IoT for smart cities. In this paper, we present an admission control model for M2M communications. The model differentiates all M2M requests into delay-sensitive and delay-tolerant first, and then aggregates all delay-tolerant requests by routing them into one low-priority queue, aiming to reduce the number of requests from various devices to the access point in the IoT for smart cities. Also, an admission control algorithm is devised on the basis of this model to prevent access collision and to improve the quality of service. Performance evaluations by network calculus, numerical experiments, and simulations show that the proposed model is feasible and effective.

In the field of risk management, it is of great importance to obtain an efficient portfolio when market participants invest in a variety of assets. In this study, we propose a simple double-shrinkage portfolio selection rule to improve the out-of-sample performance of the portfolio. The double-shrinkage portfolio is obtained by a convex combination between highly structured covariance matrices and sample covariance matrix. Using various real datasets we show that the proposed portfolio strategy is found to be comparatively stable and yields higher values of Sharpe-ratio and lower values of conditional value at risk. Thus, the double-shrinkage selection rule improves the performances of the portfolios significantly.

This prospective cohort study compared women participating in CenteringPregnancy® group prenatal care (N = 120) with those in standard individual care (N = 221) to determine if participation in Centering was associated with improvements in perceived social support and quality of life, with concomitant decreases in screens of postpartum depression and improvements in breastfeeding rates. Participants completed surveys at the onset of prenatal care, in the late third trimester and in the postpartum period. Centering participants had higher scores of perceived social support from friends after participating in group care (p

Real-time object detection models based on deep learning are being studied. However, when using deep learning, the user must directly select one of the various object detection models, and the result of object detection may vary depending on the selected object detection model. Therefore, in this paper, we propose an efficient deep learning platform for object detection technology. The proposed platform estimates learning results based on benchmark results and recommends proper object detection model based on deep learning to minimizes user intervention.

The risk of developing post-traumatic osteoarthritis (PTOA) following joint injury is high. Furthering our understanding of the molecular mechanisms underlying PTOA and/or identifying novel biomarkers for early detection may help to improve treatment outcomes. Increased expression of integrin α1β1 and inhibition of epidermal growth factor receptor (EGFR) signaling protect the knee from spontaneous OA; however, the impact of the integrin α1β1/EGFR axis on PTOA is currently unknown. We sought to determine metabolic changes in serum samples collected from wild-type and integrin α1-null mice that underwent surgery to destabilize the medial meniscus and were treated with the EGFR inhibitor erlotinib. Following (1)H nuclear magnetic resonance spectroscopy, we generated multivariate statistical models that distinguished between the metabolic profiles of erlotinib- versus vehicle-treated mice and the integrin α1-null versus wild-type mouse genotype. Our results show the sex-dependent effects of erlotinib treatment and highlight glutamine as a metabolite that counteracts this treatment. Furthermore, we identified a set of metabolites associated with increased reactive oxygen species production, susceptibility to OA, and regulation of TRP channels in α1-null mice. Our study indicates that systemic pharmacological and genetic factors have a greater effect on serum metabolic profiles than site-specific factors such as surgery.

The Precision Agriculture (PA) plays a crucial part in the agricultural industry about improving the decision-making process. It aims to optimally allocate the resources to maintain the sustainable productivity of farmland and reduce the use of chemical compounds. [17] However, the on-site inspection of vegetations often falls to researchers' trained eye and experience, when it deals with the identification of the non-crop vegetations. Deep Convolution Neural Network (CNN) can be deployed to mitigate the cost of manual classification. Although CNN outperforms the other traditional classifiers, such as Support Vector Machine, it is still in question whether CNN can be deployable in an industrial environment. In this paper, we conducted a study on the feasibility of CNN for Vegetation Mapping on lawn inspection for weeds. We would like to study the possibility of expanding the concept to the on-site, near realtime, crop site inspections, by evaluating the generated results.

As the Internet of Things (IoT) technology evolves, it is no longer appropriate to process real-time, large-volume data generated by numerous IoT devices in a cloud computing environment. To solve this problem, fog computing has been proposed which minimizes response time and makes real - time processing suitable. However, there is still a lack of research on techniques for efficiently managing various services and monitoring IoT devices in real time. Therefore, this paper proposes an efficient framework to monitor IoT devices in fog computing environment. The proposed monitoring framework is based on the oneM2M standard and consists of three detailed frameworks: device manager, monitoring manager, and data manager.

Background Cardiac and cerebrovascular complications are major causes of adverse outcomes following thoracic endovascular aortic repair (TEVAR). The benefits of statins have been established, but little is known about their impact on patients undergoing TEVAR. We investigated whether chronic statin use protected against early postoperative major adverse cardiac and cerebrovascular events (MACCEs) after TEVAR. Methods We retrospectively reviewed 211 patients who underwent TEVAR between February 2013 and March 2017 classified into two groups, those with acute aortic syndrome (AAS, N.=79) and those without (non-AAS, N.=132). Patients were subdivided according to preoperative statin therapy for ≥3 months or not. The primary endpoint was 30-day MACCE, defined as myocardial infarction, stroke, arrhythmia, cardiovascular death, or cerebrovascular death. Acute kidney injury (AKI) occurrence within 48 hours was also evaluated. Multivariate logistic regression analysis was performed to identify independent risk factors for MACCEs and AKI. Results Incidence of MACCEs (1% vs. 11%, P=0.019) was significantly lower in the statin group than in the no-statin group in non-AAS patients. Multivariate logistic regression analysis revealed statin use (odds ratio 0.85, 95% confidence interval 0.01-0.95, P=0.046) as an independent predictor for MACCE in non-AAS patients. The AKI incidence was significantly higher in the statin group than in the no-statin group in AAS patients (44% vs. 15%, P=0.018). Conclusions In patients undergoing TEVAR, chronic statin use was associated with reduced 30-day MACCEs in non-AAS patients, but not in AAS patients. It might rather be associated with increased risk of AKI in AAS patients.

Skin stretch haptic devices are well-suited for transmitting information through touch, a promising avenue in prosthetic research, addressing the lack of feedback in myoelectric designs. Rocker-based skin stretch devices have been proposed for sensory substitution and navigational feedback, but the designs vary in their geometry. Other works create torsional stretch, and utilize nonlinear mappings to enhance perception. This work investigates parameters of rocker geometry and mapping functions, and how they impact user perception. We hypothesize that perceptual changes are dependent on the choice of stretch increment sizes over the range of motion. The rocker geometry is varied with an offset between the rotational and geometric axes, and three rocker designs are evaluated during a targeting task implemented with a nonlinear or linear mapping. The rockers with no offset and a positive offset (wide) perform better than the negative offset (narrow) case, though the mapping method does not affect target accuracy.

Background: Despite the development of molecular research and targeted therapy, patients with wild-type epidermal growth factor receptor (EGFR) non-small cell lung cancer (NSCLC) still receive platinum doublet chemotherapy as the standard first-line treatment. We investigated the efficacy of first-line regimens in patients with wild-type EGFR nonsquamous NSCLC. Methods: We retrospectively analyzed the efficacy of various platinum doublet regimens as first-line treatments. Between 2007 and 2013, a total of 165 patients with wild-type EGFR nonsquamous NSCLC were included in this study. Results: Seventy-one (43.0%) patients were treated with pemetrexed plus platinum (PP) and 94 (57.0%) with non-pemetrexed plus platinum (NPP). The overall response rate was not different between the PP- and NPP-treated groups (26.8 vs. 28.7%, respectively; p = 0.78). The median progression-free survival (PFS) and overall survival (OS) also showed no differences between the two treatment groups (p = 0.12 for PFS, p = 0.42 for OS). The median PFS and OS for the PP group were 4.6 months (95% CI, 3.8-5.4) and 18.7 months (95% CI, 11.7-25.8), respectively, and for the NPP group, they were 4.2 months (95% CI, 3.4-5.0) and 12.2 months (95% CI, 10.3-14.1), respectively. In the subgroup analysis, most subgroups showed no significant difference in PFS and OS between the two treatment groups. Conclusion: Our data showed that the efficacy of various platinum doublet regimens was similar in patients with wild-type EGFR nonsquamous NSCLC.

Copyright © NCD Risk Factor Collaboration. Open Access article distributed under the terms of CC BY., Background: Underweight and severe and morbid obesity are associated with highly elevated risks of adverse health outcomes. We estimated trends in mean body-mass index (BMI), which characterises its population distribution, and in the prevalences of a complete set of BMI categories for adults in all countries. Methods: We analysed, with use of a consistent protocol, population-based studies that had measured height and weight in adults aged 18 years and older. We applied a Bayesian hierarchical model to these data to estimate trends from 1975 to 2014 in mean BMI and in the prevalences of BMI categories (, Wellcome Trust, Grand Challenges Canada

This paper proposes a Decision Management System to identify the white mold regions from the soybean fields using Autologistic Statistical Model (ASM) and Remote Sensing (RS) data analysis with commercially available Big Data sets as input data. In order to develop an identification model, numerous types of data need to be considered. In this study, the data that was used is satellite image pixel values, and data gathered from the field such as precipitation, yield, elevation, humidity, wind speed, wind direction and geospatial locations. The model evaluated the outcome using this information as input parameters and provided an overall estimation of the white mold region in the soybean fields. Based on the evaluation of the result, the accuracy rate of the proposed methods 84% which is a promising result due to the fact that each pixel of the satellite image is 30 by 30 meters.

This paper analyzes the determinants of housing prices in Hong Kong by using property transaction data of condominium units from Taikoo Shing, one of the largest real estate properties in Hong Kong. We use a hedonic pricing model for the empirical analysis and estimate the model by using the Box-Cox quantile regression method. The empirical results show that this method provides a more comprehensive description of housing price determinants. Housing prices and characteristics have a nonlinear relationship, and this relationship varies across all quantiles. In addition, the response of housing prices to various housing characteristics varies across quantiles. For example, an increase in the size of the gross floor area is more valued at higher quantiles. Other variables have differential effects on housing prices across the distribution of housing prices. We also perform a simple simulation for model predictability and show that our model outperforms other models which have been frequently used in the previous studies.

To compare previous studies, this study re-examines the determinants of systematic risk in the restaurant industry. To estimate systematic risk, the authors specify flexible models that take care of serial dependence, autoregressive conditional heteroskedasticity and non-normality of the time series data. Using the estimated systematic risk, they analyse the determinants of risk using a quantile regression approach. The empirical results show that a firm’s liquidity ratio, efficiency ratio, debt leverage ratio and size are the main determinants of systematic risk in the restaurant industry. Moreover, it turns out that the effects of liquidity, debt leverage and efficiency decrease as the considered risk levels increase.

Image classification and extracting the characteristics of a tumor are the powerful tools in medical science. In case of breast cancer medical treatment, the breast cancer classification methods can be used to classify input images as normal and abnormal classes for better diagnoses and earlier detection with breast tumors. However, classification process can be challenging because of the existence of noise in the images, and complicated structures of the image. Manual classification of the images is time-consuming, and need to be done only by medical experts. Hence using an automated medical image classification tool is useful and necessary. In addition, having a better training data set directly affect the quality of classification process. In this paper, a method is proposed based on supervised learning and automatic thresholding for both generating better training data set, and more accurate classification of the mammogram images into Normal/Abnormal classes. The procedure consists of preprocessing, removing noise, elimination of unwanted objects, features extraction, and classification. A Support Vector Machine (SVM) is used as the supervised model in two phases which are testing and training. Intensity value, auto-correlation matrix value of detected corners, and, energy, are three extracted features used to train the SVM. Experimental results show this method classify images with more accuracy and less execution time compared to existing method.

This paper proposes a sophisticated classification process to segment the leaves of carrots from weeds. In the early stages of the plants' development, the color of both the plants and the weeds are similar, making it difficult to differentiate between the two. The process becomes even harder if the weeds and plants overlap. The proposed system addresses this problem by creating a sophisticated mean for weed identification. The major components of this system are composed of three processes: image segmentation, feature extraction and decision-making. In the image segmentation process, the input images are processed into lower units where the relevant features are extracted. In the decision-making process, the system makes use of the Support Vector Machine to analyze and segregate the weeds from the plants. Afterward, the findings are used to dictate which plants receive herbicides and which do not. The main priority for the image segmentation process is on the overlapping images where weeds need to be isolated from plants so that they can be used for cultivation purpose. The evaluation of the approach is done using an open dataset of images consisting of carrot plants. The system is able to achieve 88.99% accuracy for weed classification using this dataset. This methodology will help to reduce the use of herbicides while improving the performance and costs of precision agriculture.

Image segmentation is a challenging task in image processing. The purpose is to divide up pixels into different partitions in which members of each partition have similar characteristics and a unique label. Image segmentation and extracting the characteristics of a tumor are powerful tools that can be used in medical science. In the case of breast cancer medical treatment, segmentation methods can be used to extract and segment the tumor for better diagnoses and earlier detection of breast tumors. However, extracting and segmentation of the tumors or region of interest (RIO) can be challenging. This is due to the existence of noise in the images, along with the complicated structures of the image. Manual classification of the images is time-consuming, and needs to be done only by medical experts. Hence, using an automated medical image segmentation tool will be useful and necessary. In this paper, a method is proposed based on the well-known watershed technique and automated thresholding for single and multi-tumor segmentation in medical images. The procedure consists of pre-processing, removing noise, elimination of unwanted objects, generating and segmentation. Segmentation involves automatic thresholding, gradient magnitude, finding regional minimums, and recognition. Experimental results show that this method performs well in segmentation with efficient execution time and can be used for medical diagnostics of breast cancer.

Similarity measurements in images have always been a challenging task in the field of pattern recognition techniques. Shape based features is a widely adopted method in Content Based Image Retrieval (CBIR) for similarity measurement. In this paper we proposed an enhanced version of Mesh Distance Fourier Descriptor (MDFD) previously developed in our lab for the similarity measurement. Two extra levels of filters have been added to the output of MDFD so that the final output is more refined and the most similar image to the query image is selected from the database. The first level filter includes processing of images retrieved based on ratio of area of the image to the area of minimum bounding rectangle enclosing that image. The second level filtering includes calculation of average of absolute difference of global features like eccentricity, convexity and solidity of the query image and retrieved image. Adding these two extra filtering levels, the matching ratio has been increased from 84% to 88% which shows adding filters enhances the results of MDFD. In this paper we have used binary images extracted from region of interest (ROI) of mammogram which are classified into single objects using known classification methods such as K-means and SVM algorithms.

Background Prurigo pigmentosa (PP) is an inflammatory dermatosis characterized by recurrent pruritic erythematous papules, mainly located on the trunk. It was first described by Nagashima in 1971 in Japan. Since then, more than 300 cases have been reported in Japan, but reports from other parts of the world are quite rare. Materials and methods We studied clinical and histopathological data from six patients with PP diagnosed in our hospital and 43 patients (18 reports) who were diagnosed with PP in Korea between 1988 and 2008. Results The number of Korean patients reported in recent years is higher than the number of other non-Japanese patients reported. Clinicopathological characteristics in Korean patients were not significantly different from those previously reported. Therapeutic results with minocycline were successful in our patients. Conclusions We suspect that PP is not uncommon in Korea, and the disease may be underestimated. Strict restriction of diet as well as known associated factors like wet condition are suggested as one of the important factors contributing to the occurrence of PP in Korea.

Colposcopic grading provides an objective and meaningful guide to histologic severity and neoplastic progression of squamous intraepithelial lesions of the cervix. The objective of this study was to develop a more efficient and convenient method to overcome procedural complexities involved with the traditional Reid index in prediction of high-grade squamous intraepithelial lesion (HSIL). The Reid index uses four colposcopic signs (margin, color, vessel, and iodine staining). The proposed modified Reid index system specifically incorporates the location of the lesion within the transformation zone in place of iodine staining. Three hundred women with suspected or abnormal cytologies or abnormal cervicographic findings were evaluated by colposcopy, directed biopsy, and HPV testing by the Hybrid Capture II method, which detects high-risk HPV DNA types. The sensitivity of high-risk HPV testing for detecting HSIL was 94.4%, the specificity was 65.0%, the positive predictive value was 75.5%, and the negative predictive value was 91.0%. The results of the colposcopic impression using the modified Reid index were superior to HPV testing. The sensitivity, specificity, positive predictive value, and negative predictive value of the colposcopic impression for detecting HSIL were 91.3, 92.9, 93.6, and 90.3% respectively. These results strongly indicate that the modified Reid index can accurately predict the histologic grade of squamous intraepithelial lesions of the cervix and can be applied easily and objectively in clinical practice without affecting the diagnostic accuracy of the traditional Reid index.

Purpose: To evaluate the basic characteristics and reproducibility of anterior chamber angle (ACA) measurements determined by anterior-segment optical coherence tomography (AS-OCT) in open-angle and primary angle closure suspect (PACS) patients. Methods: Thirty-nine open-angle and 18 PACS patients were imaged for ACA by AS-OCT. Subjects underwent imaging of the nasal, temporal and inferior ACA under conditions of constant light, and darkness. For analysis, we used three ACA parameters handled by the Visante OCT software: angle opening distance at 500 μm (AOD500), trabecular-iris space area at 500 μm (TISA500) and angle recess area at 500 μm (ARA500). For determination of inter-session reproducibility, a single well-trained operator (D.Y.K.) scanned all patients at two different visits. For determination of inter-operator variability, a second operator (S.B.P.) acquired another set of images independently. Three sets of images were acquired at least 24 hour apart. Results: All parameters were significantly different when measured both in light and darkness, and in the nasal and temporal quadrants. There were no significant differences between the left and right eyes in the three ACA parameters in all quadrants. The temporal angle was wider than the nasal and inferior angles. All parameters of the nasal, temporal angles had excellent inter-session and inter-operator reproducibility [intra-class correlation coefficient (ICC) 0.796–0.981], but these values were slightly lower for inferior angle measurements (ICC 0.662–0.892) in both open-angle and PACS groups. Conclusion: AS-OCT provides quantitative and reproducible assessment of ACA. Reproducibility was lower in the inferior angle compared with the nasal and temporal angles, perhaps because of variable placement of the scleral spur.

Four measures of inequality in the distribution of income, income sources, consumption, and saving for salary-and-wage-earner households in cities of Korea are reported. Polarization measures are also computed. Income distribution shows improvement during the early part of the 1990s, but modest deterioration during the period 1998-2005. The income inequality variations are found to result mostly from variations in wage inequality. We find that income gaps between the top 10 percent and the bottom 10 percent groups have been widening. Income inequality and polarization did increase in the early 2000s. However, no definitive evidence is found on the presence of a rising trend of polarization. Comparing the Gini and real mean income per household for the United States, Taiwan, and Korea from 1984 to 2003 indicates that Koreai¯s Gini varied relatively favorably.

The objective of this retrospective study was to investigate clinical features and treatment outcomes in patients with angioimmunoblastic T-cell lymphoma (AITL), data of which were collected over a 15-year period. Sixty-five patients diagnosed with AITL were included in the study. About half of the patients (46.2%) presented with poor performance status (ECOGor = 2); 72.3% of patients belonged to high intermediate or high-risk of IPI and same proportion belonged to Class 2 of PIT (Prognostic index for PTCL-U), and most patients (95.4%) were diagnosed at an advanced stage. At diagnosis, 27 patients (41.5%) presented with malignant pleural effusion, and 22 patients (33.8%) had skin involvement. Most of the initial chemotherapy regimens were anthracycline-based (88.2%). Overall response rate to initial chemotherapy was 86.2% (64.7% of complete response, 21.5% of partial response). The median progression-free survival and overall survival of all patients was 7.1 months (95% CI, 2.8 - 11.4) and 15.1 months (95% CI, 6.7 - 23.5), respectively. Age, performance status, and PIT scores were predictive prognostic factors for survival. In conclusion, although AITLs showed a good response to the initial chemotherapy, their response durations were short; therefore, chemotherapy for AITL should be modified or intensified as in high-dose chemotherapy.

Automated vehicle detection and classification is one of many highly researched areas in Computer Science. This paper proposes and presents a hybridized method for classifying objects based on spatial orientation. Specifically, the proposed classification system explores the Histogram of Oriented Gradients feature extractor conjoined with a clustering algorithm to classify vehicle images in an unsupervised manner. HOG is a well-suited feature extractor for dense images rich with contours and edges. The pairing provides an efficacious orientation classifier for vehicle images. Training and sample data exceeded 1.8 million images and was provided by Carsforsale.com, Inc.'s vast catalogue of historical vehicle images.

Forming a single currency region in East Asia is desirable. The lack of political commitment and experience with political cooperation in East Asia constitute the decisive factors against the formation of a common currency area. The formation of a quasi-monetary bloc remains a viable option to East Asia. It is suggested that a coordinating institution be formed to assess needs and formulate the steps necessary for the formation of a quasi-monetary union. The implementation by the East Asian countries and the coordinating institution will lead to political convergence as well as economic convergence, a necessary process toward establishing common monetary standards. China and Japan continue to play the leadership role in the push for greater regional monetary integration and cooperation.

Augmentation of the partially edentulous ridge can significantly improve the final prosthodontic rehabilitation. For enhancing soft tissue contours in the anterior region, the subepithelial connective tissue graft is the treatment of choice. The combination of connective tissue grafts with alloplastic bone graft material can optimize the ridge augmentation and reduce post extraction defects. The aim of this clinical report is to describe the use of subepithelial connective tissue in conjunction with an alloplastic bone graft for augmentation of a maxillary anterior ridge prior to prosthetic rehabilitation.CitationBreault LG, Lee SY, Mitchell NE. Fixed Prosthetics with a Connective Tissue and Alloplastic Bone Graft Ridge Augmentation: A Case Report. J Contemp Dent Pract 2004 November;(5)4:111-122.

In this paper, we present a shape based image retrieval method based on chain code representing irregularity of an object. A distinctive chain code is introduced as a main extracted feature of the object. All objects used in this paper are binary object images extracted by well-known classification algorithm, Support Vector Machine (SVM). From these classified binary images, we propose a modified shape based image retrieval method with the unique chain code interpreting irregularity of object. Proposed method is experimented along with known shape based image retrieval method using characteristic point features. The experimental result shows that proposed method exceed matching rate that of conventional contour to centroid triangulation (CTCT) method showing proposed method has higher matching rate.

In this paper, we propose a new image retrieval method based on Sectored Contour to Centroid Triangulation (SCTCT) using distinctive shape feature, named Arc Difference Rate (ADR). We utilized Support Vector Machine (SVM) method as an extraction tool to extract suspicious tumor area as binary object image from the breast MRI. Therefore extracted 100 binary object images are used as test cases in the experimental study. The results from proposed method show the improvement in finding correct matches compare to the traditional SCTCT.

Multimedia applications based on cloud services for mobile devices have recently gained considerable popularity. However, the increasing density of devices leads to a high level of interference, which reduces the performance of wireless communication between devices and cloud. In this paper, we propose a new approach which allows the network to adaptively find a transmission opportunity scheduling strategy by choosing the most valuable transmission request opportunity. In this approach, the transmission request selection strategy is optimized by considering multimedia distortion reduction, hidden node problem, transmission interference, and signal coverage. Simulation results show that the proposed request selecting strategy significantly improves the systems overall data transmission quality by exploring the tradeoff between communication node pair and their neighbor nodes.

Spinal dysraphism is a group of congenital anomalies involving incomplete midline closure of bony, neural, and soft tissue elements.1–5 The condition represents one of the more common congenital malformations in the Western world. The dysraphic states can be classified into open or closed forms. Open defects can be further subdivided into either meningoceles or myelomeningoceles, depending on whether or not these meninges lined defects contain neural tissue. The majority of these open dysplasias are associated with the Chiari II malformation, in which the posterior fossa contents are displaced into the upper spinal canal and accompanied by ventriculomegaly. In 1986, Nicolaides and associates described two characteristic cranial findings associated with the Chiari II malformation, the “lemon sign” and the “banana sign.”6 These signs have been shown to be very sensitive in the detection of Chiari malformations and the associated open form of spinal dysraphism. Occult dysraphic lesions, however, compose a more heterogeneous spectrum of abnormalities, including dermal sinus, lipomyelomeningocele, diastematomyelia, and various types of spinal lipomas. Unlike their open counterparts, these closed neurulation defects do not have accompanying cranial findings, making prenatal diagnosis more challenging. We present an unusual case of a lipomyelomeningocele with tethered cord, which was diagnosed on prenatal ultrasonography. An ultrasonogram showed the posterior fossa to be normal. The abnormality was identified only upon imaging of the lumbosacral spine.

The common neurological manifestations of pre-eclampsia include headache, confusion, and visual disturbance; while isolated abducens nerve palsy in pre-eclampsia is very rare. We report one case of a severe pre-eclampsia with abducens nerve palsy at 39 weeks' gestation. There was no specific pathology, except hypertension, and the palsy resolved spontaneously.

Recently, it becomes increasingly important to secure user private data in mobile devices. To protect user private data, one possible approach is to implement a secure file storage in the mobile devices based on mandatory access control (MAC), but the device manufacturers seldom implement it because of high pressure of time-to-market, frequent version upgrade of the operating system, and no unanimous agreement in the MAC standard software. In this paper, we propose an implementation study of a secure file storage, called a ghost drive, which can facilitate the implementation of MAC in the mobile devices by unburdening the manufacturers from aggressive instrumentation of the whole operating system. Since our implementation is in form of a loadable kernel module, separated from the main kernel, it can be deployed even to commercial mobile devices already in use by installing it over the air. Our experiments show that the performance of our secure storage implementation is not worse than the original unmodified implementation.

About 1 in 8 women in the United States is expected to develop breast cancer over the course of herentire lifetime but a few medical imaging techniques have been applied for breast cancer screening. In addition, the feature extraction and comparison in medical images for breast cancer detection haverarely been reported in literature. We propose a new framework toextract agglomerated features in medical imagesand comparethem by relating original characteristic patterns thereof. Our method concentrates on three key aspects and they are: a comparison between intensity distributions of pixels collected by the hexagonal mask, detecting minimum gradient points in a radial intensity series, and generatinga characteristic pattern of the feature. The main contribution of ourproposed approach is improving a method of identifying features which is lesssensitive to noise in medical images for breast cancerdetectionand presenting an original design of relating features which is consistent to the orientation and size of the feature. Experimental results demonstrate that our proposed approach is more tolerant of image noise than prior research and generates an invariant characteristic pattern of various orientations and sizes.

The segmentation of breast Magnetic Resonance Imaging (MRI) has been a long term challenge due to the fuzzy boundaries among objects, small spots, and irregular object shapes in breast MRI. Even though intensity-based clustering algorithms such as K-means clustering and Fuzzy C-means clustering have been used widely for basic image segmentation, they resulted in complicated patterns for computer aided breast MRI diagnosis.In this paper, we propose a new segmentation algorithm to improve the clustering results from K-means clustering algorithm with breast MRI. The major contribution of the proposed algorithm is that it simplifies breast MRI for the computer aided object analysis without loss of original MRI information. The proposed algorithm follows K-means clustering algorithm and explores neighbors and boundary information to redistribute unexpectedly clustered pixels and merge over-segmented objects from K-means clustering algorithm. We will discuss the results from the proposed algorithm and compare them with the result of K-means clustering algorithm.