Mass Segmentation Research Articles

Rapid evaluation of immobilized immunoglobulins using automated mass-segmented ToF-SIMS

Surface interactions largely control how biomaterials interact with biology and how many other types of materials function in industrial applications. ToF-SIMS analysis is extremely useful for interrogating the surfaces of complex materials and shows great promise in analyzing biological samples. Previously, the authors demonstrated that segmentation (between 1 and 0.005 m/z mass bins) of the mass spectral axis can be used to differentiate between polymeric materials with both very similar and dissimilar molecular compositions. Here, the same approach is applied for the analysis of proteins on surfaces, focusing on the effect of binding and orientation of an antibody on the resulting ToF-SIMS spectrum. Due to the complex nature of the samples that contain combinations of only 20 amino acids differing in sequence, it is enormously challenging and prohibitively time-consuming to distinguish the minute variances presented in each dataset through manual analysis alone. Herein, the authors describe how to apply the newly developed rapid data analysis workflow to previously published ToF-SIMS data for complex biological materials, immobilized antibodies. This automated method reduced the analysis time by two orders of magnitudes while enhancing data quality and allows the removal of any user bias. The authors used mass segmentation at 0.005 m/z over a 1-300 mass range to generate 60 000 variables. In contrast to the previous manual binning approach, this method captures the entire mass range of the spectrum resulting in an information-rich dataset rather than specifically selected mass spectral peaks. This work constitutes an additional proof of concept that rapid and automated data analyses involving mass-segmented ToF-SIMS spectra can efficiently and robustly analyze a broader range of complex materials, ranging from generic polymers to complicated biological samples. This automated analysis method is also ideally positioned to provide data to train machine learning models of surface-property relationships that can greatly enhance the understanding of how the surface interacts with biology and provides more accurate and robust quantitative predictions of the biological properties of new materials.

Kinematic and kinetic analysis of knee joint during squatting

Knee function in squatting is an important consideration for activity of daily living. However, there are only a limited number of studies reporting biomechanics of deep knee flexion beyond 900. The joint and muscle forces calculated from the data collected from six healthy subjects performing squatting and rising from a deep squat using a force plate, a marker system and EMG. The estimated tibio-femoral joint forces were between 4.7 and 5.6 times body weight in vertical direction and 2.9-3.5 times body weight in horizontal direction. These forces are much larger than the forces during normal walking. A mathematical model shows the external forces acting on the segments of lower limbs are the gravity forces and inertia forces due to mass segments of limb and the reactive forces transmitted from the ground. The ground reaction forces have the enormous effect on the leg during stance phase of walking whereas the gravity forces and the inertia forces are predominantly responsible for the forces at joints during swing phase of walking. The moment developed by the external forces at the respective joint is counterbalanced by the moment developed by the contraction of muscle forces about the joint to keep the joint in dynamic equilibrium.

Rhapso: Automatic Stitching of Mass Segments from Fourier Transform Ion Cyclotron Resonance Mass Spectra.

Fourier transform ion cyclotron resonance mass spectrometry (FTICR MS) provides the resolution and mass accuracy needed to analyze complex mixtures such as crude oil. When mixtures contain many different components, a competitive effect within the ICR cell takes place that hampers the detection of a potentially large fraction of the components. Recently, a new data collection technique, which consists of acquiring several spectra of small mass ranges and assembling a complete spectrum afterward, enabled the observation of a record number of peaks with greater accuracy compared to broadband methods. There is a need for statistical methods to combine and preprocess segmented acquisition data. A particular challenge of quadrupole isolation is that near the window edges there is a drop in intensity, hampering the stitching of consecutive windows. We developed an algorithm called Rhapso to stitch peak lists corresponding to multiple different m/z regions from crude oil samples. Rhapso corrects potential edge effects to enable the use of smaller windows and reduce the required overlap between windows, corrects mass shifts between windows, and generates a single peak list for the full spectrum. Relative to a stitching performed manually, Rhapso increased the data processing speed and avoided potential human errors, simplifying the subsequent chemical analysis of the sample. Relative to a broadband spectrum, the stitched output showed an over 2-fold increase in assigned peaks and reduced mass error by a factor of 2. Rhapso is expected to enable routine use of this spectral stitching method for ultracomplex samples, giving a more detailed characterization of existing samples and enabling the characterization of samples that were previously too complex to analyze.

Random Forests machine learning applied to gas chromatography – Mass spectrometry derived average mass spectrum data sets for classification and characterisation of essential oils

Differences in chemical profiles of various essential oils (EOs) come from the fact that each plant species and chemotype has a distinctive secondary metabolism. Therefore, these differences can be used as the chemical markers for EO classification and determination of their quality. Herein, the Random Forests (RF) machine learning algorithm was applied to the classification of 20 different EOs. From three-way raw gas chromatography - mass spectra data, total chromatogram average mass spectra (TCAMS) and segment average mass spectra (SAMS) were created. TCAMS was generated by averaging response of each m/z over the whole chromatogram and SAMS by averaging the response of each fragment across a certain time segment within the chromatogram. The RF model was applied to the two data sets and optimised through the evaluation of pre-processed data, number of trees, and number of variables used in each node split. The performance of the model was evaluated through a cross-validation process, repeated 50 times by dividing the whole sample set into training and validation subsets. The calculated average out-of-bag error (OOBE), over 50 different training TCAMS data sets was 3.22 ± 1.29%, while for SAMS it was found to be 2.28 ± 1.33%. The minimal number of variables necessary for EO classification was determined by a nested cross-validation process. The amount of reduced variables in each step was 10%. It was shown that the TCAMS data set with 6 variables had similar prediction power as the SAMS with 30 variables. OOBE for classification of 20 EOs was 2.89 ± 1.44% and 3.70 ± 1.73%, for TCAMS and SAMS, respectively. Proximity between samples was used to evaluate their qualities. Samples with greater intra-class proximity had good similarity, while the lower ones indicated greater variations in the chemical profiles. The SAMS data set showed superior potential for quality assurance, compared with TCAMS.

Techniques to Master A Difficult Bladder Flap

Video Objective The purpose of this video is to review techniques utilized to decrease the risk of bleeding or bladder injury when encountering a difficult bladder flap. Setting This video reviews the presentation, imaging, and surgery of a patient with a history of three prior cesarean deliveries and a suspected lower uterine segment mass. She was referred to MIGS for concern that this lower uterine segment mass was an isthmocele in the setting of a scarred bladder flap and would require surgical expertise. This video also discusses techniques that are utilized to decrease the risk of bleeding or bladder injury in a patient with a scarred bladder flap and/or lower uterine segment mass. These techniques can be used alone or in combination to safely dissect around abnormal pathology. Interventions The techniques utilized to decrease the risk of bleeding or bladder injury include isolating the uterine artery at its origin, backfilling the bladder, and using the low lateral technique. Conclusion This is a unique case in which a patient had both a lower uterine segment mass and a history of three prior cesarean sections. This case highlights numerous techniques that can be utilized to safely dissect a difficult bladder flap.

Scrutiny of Breast Cancer Detection Techniques of Deeplearning and Machine Learning

Breast cancer is one of the most widely recognized tumors globally among ladies with the data available that one of every eight ladies is influenced by this illness during their lifetime. Mammography is the best imaging methodology for early location of the disease in beginning times. On account of poor complexity and low perceivability in the mammographic pictures, early discovery of the cancer malignant growth is a huge challenge to effective cure of the disease. Distinctive CAD (computer aided detection) supported algorithms have been developed to enable radiologists to give an exact determination. This paper highlights the study of the most widely recognized methodologies of image segmentation created for recognition of calcifications and masses. The principle focal point of this survey is on picture theof strategies and the factors utilized for early bosom disease identification. Surface investigation is the vital advance in any picture division strategies of image segmentation which depend on a nearby spatial variety of color or shading. Subsequently, different techniques for texture investigation for small scale calcification and mass identification in mammography are talked about in the mechanism of mammography. The point of this paper is to audit existing ways to deal with the segmentation of masses and automated detection in mammographic pictures, underlining the key-focuses and primary contrasts among the utilized systems. The key goal is to bring up the preferences and drawbacks of the different methodologies. Conversely with different surveys which just portray and think about various methodologies subjectively, this audit likewise gives a quantifiable examination.In proposed research use deep learning base network for classification of mammography images . In previous approaches use machine learning base learning. The Main drawback of machine learning is selection of features manualy or by functions but in deep learning automatic feature detect and its vary according to image. The demonstration of seven mass recognition techniques is thought about utilizing two distinctive databases of mammography: an open digitized database and a full-field (local) advanced digitized database. The outcomes are given as far as Free reaction Receiver Operating Characteristic (FROC) and Receiver Operating Characteristic (ROC) examination.

Predictive regression modeling of body segment parameters using individual-based anthropometric measurements

Body segment parameters such as segment mass, center of mass, and radius of gyration are used as inputs in static and dynamic ergonomic and biomechanical models used to predict joint and muscle forces, and to assess risks of musculoskeletal injury. Previous work has predicted body segment parameters (BSPs) in the general population using age and obesity levels as statistical predictors (Merrill et al., 2017). Estimated errors in the prediction of BSPs can be as large as 40%, depending on age, and the prediction method employed (Durkin and Dowling, 2003). Thus, more accurate and representative segment parameter inputs are required for attempting to predict modeling outputs such as joint contact forces, muscle forces, and injury risk in individuals. This study aims to provide statistical models for predicting torso, thigh, shank, upper arm, and forearm segment parameters in working adults using whole body dual energy x-ray absorptiometry (DXA) scan data along with a set of anthropometric measurements. The statistical models were developed on a training data set, and independently validated on a separate test data set. The predicted BSPs in validation data were, on average, within 5% of the actual in vivo DXA-based BSPs, while previously developed predictions (de Leva, 1996) had average errors of up to 60%, indicating that the new models greatly increase the accuracy in predicting segment parameters. These final developed models can be used for calculating representative BSPs in individuals for use in modeling applications dependent on these parameters.

1404 Sclerotic Bone Metastasis From Cholangiocarcinoma: A Case Report

INTRODUCTION: Cholangiocarcinoma (CCA) account for 3% of gastrointestinal malignancies. Commonly it metastasizes to liver, peritoneum and lymph nodes. Rarely it metastasizes to bone with lytic lesions. We are presenting a case of metastatic CCA with sclerotic bone metastasis. CASE DESCRIPTION/METHODS: 59-year-old female with no medical history presented with diffuse abdominal pain for 2 weeks. She denied other complaints, illicit drug use, personal or family of history of malignancy. She was a 40-pack year smoker and social drinker. Vitals and physical exam were unremarkable except minimally diffuse tender abdomen. Labs revealed elevated AST, ALT, ALP and Bilirubin which were 524, 640, 2300 and 1.7 respectively. CT abdomen with IV contrast showed intrahepatic biliary ductal dilatation and prominence of the common hepatic duct with abrupt tapering of the duct proximal to a small, high density mass versus short segment ductal wall thickening, normal Pancreatic duct and widespread sclerotic bone lesions. MRCP showed 3 cm long nonspecific stricture involving the mid CBD regional to the insertion of the cystic duct. No associated mass or abnormal enhancement with moderate intrahepatic biliary duct dilatation in both liver lobes. CA 19-9, CEA, and AFP were elevated to 492, 700, and 7 respectively. Bone scan showed extensive diffuse increased activity over multiple focal areas within the calvarium, sternum, bilateral ribs, scapulae, entire spine, pelvis, diffusely involving most femurs. ERCP with spyglass was done along with brush biopsy and metal stent placement. Biopsy samples were not adequate for tissue diagnosis. Iliac crest biopsy was sent for pathology, immunohistochemistry (IHC), flow cytometry and chromosome analysis which showed metastatic adenocarcinoma, by IHC (pankeratin+ CK7+, CK20+, MOC31+, CK19+, CA19-9+) most compatible with pancreatobilliary primary. Flow cytometry did not show any evidence of myeloproliferative disorder and chromosome analysis could not be done due to nondividing cells. Pt underwent palliative chemotherapy for metastatic CCA with gemcitabine and cisplatin. DISCUSSION: Metastatic CCA to the bone is rare. In the few case reports describing this finding, bony lesions are usually lytic in nature. Sclerotic bone metastasis from CCA has never been reported in literature. Prognosis is extremely poor due to the minimal response to conventional chemotherapy or radiotherapy, symptom control, particularly pain, is the mainstay of treatment for improving the quality of life of the patients.

The Anthropometry of Economical Running.

To comprehensively investigate the relationships of anthropometry and body composition with running economy within a large heterogeneous sample of runners. Eighty-five runners (males [M], n = 45; females [F], n = 40), of diverse competitive standard, performed a discontinuous protocol of incremental treadmill running (4-min stages, 1 km·h increments) to establish locomotory energy cost (LEc) of running at submaximal speeds (averaged across 10-12 km·h; the highest common speed < lactate turnpoint). Measurements of anthropometry, including segment lengths, perimeters, masses and moments of inertia, and body composition were obtained using tape-based measurements and dual-energy x-ray absorptiometry. Absolute LEc (ABSLEc, kcal·km) was positively correlated with 21 (of 27) absolute anthropometric variables in both male and female cohorts. Multiple-regression analyses revealed that one variable (mean perimeter z score) explained 49.4% (M) and 68.9% (F) of the variance in ABSLEc. Relative LEc (RELLEc, kcal·kg·km) was also correlated with five (M) and seven (F) normalized anthropometric variables, and regression analyses explained 31.6% (M; percentage bone mass and normalized hip perimeter) and 33.3% (F, normalized forearm perimeter) of the variance in RELLEc. These findings provide novel and robust evidence that anthropometry and body composition variables, predominantly indicative of relative slenderness, explain a considerable proportion of the variance in running economy (i.e., more slender, lower energy cost). We, therefore, recommend that runners and coaches are attentive to relative slenderness in selecting and training athletes with the aim of enhancing running economy, and improving distance running performance.

Effect of microscopically assisted decompression with micro-hook scalpel in the surgical treatment of ossification of the posterior longitudinal ligament.

ObjectiveThis study was performed to investigate the effect of microscopically assisted decompression using a micro-hook scalpel on ossification of the posterior longitudinal ligament (OPLL).MethodsSixty-one patients with OPLL were divided into Group A (posterior surgery with laminectomy of the responsible segment and lateral mass screw fixation) and Group B (anterior cervical corpectomy with intervertebral titanium cage fusion). Neurological function was assessed by the Japanese Orthopaedic Association (JOA) score, visual analog scale (VAS) score, and recovery rate. The fixation status and the result of spinal canal decompression were radiographically assessed.ResultsIn Groups A and B, the JOA score was significantly higher and the VAS score was significantly lower at 1 week postoperatively and at the final follow-up than during the preoperative period. The mean recovery rate in Group A and B was 59.92% ± 13.46% and 62.28% ± 14.00%, respectively. Postoperative radiographs showed good positioning and no damage to the internal fixation materials. The spinal canal was also fully decompressed.ConclusionsMicroscopically assisted decompression with a micro-hook scalpel in both anterior and posterior surgeries achieved good clinical effects in patients with OPLL.

Musculoskeletal full-body models including a detailed thoracolumbar spine for children and adolescents aged 6–18 years

Currently available musculoskeletal inverse-dynamics thoracolumbar spine models are entirely based on data from adults and might therefore not be applicable for simulations in children and adolescents. In addition, these models lack lower extremities, which are required for comprehensive evaluations of functional activities or therapeutic exercises. We therefore created OpenSim-based musculoskeletal full-body models including a detailed thoracolumbar spine for children and adolescents aged 6–18 years and validated by comparing model predictions to in vivo data.After combining our recently developed adult thoracolumbar spine model with a lower extremity model, children and adolescent models were created for each year of age by adjusting segmental length and mass distribution, center of mass positions and moments of inertia of the major body segments as well as sagittal pelvis and spine alignment based on literature data. Similarly, muscle strength properties were adjusted based on CT-derived cross-sectional area measurements. Simulations were conducted from in vivo studies reported in the literature involving children and adolescents evaluating maximum trunk muscle strength (MTMS), lumbar disc compressibility (LDC), intradiscal pressure (IDP) and trunk muscle activity (MA).Model predictions correlated highly with in vivo data (MTMS: r ≥ 0.82, p ≤ 0.03; LDC: r = 0.77, p < 0.001; IDP: r ≥ 0.78, p < 0.001; MA: r ≥ 0.90, p < 0.001), indicating suitability for the reasonably accurate prediction of maximal trunk muscle strength, segmental loading and trunk muscle activity in children and adolescents. When aiming at investigating children or adolescents with pathologies such as idiopathic scoliosis, our models can serve as a basis for the creation of deformed spine models and for comparative purposes.

Learning from adversarial medical images for X-ray breast mass segmentation

Background and ObjectiveSimulation of diverse lesions in images is proposed and applied to overcome the scarcity of labeled data, which has hindered the application of deep learning in medical imaging. However, most of current studies focus on generating samples with class labels for classification and detection rather than segmentation, because generating images with precise masks remains a challenge. Therefore, we aim to generate realistic medical images with precise masks for improving lesion segmentation in mammagrams. MethodsIn this paper, we propose a new framework for improving X-ray breast mass segmentation performance aided by generated adversarial lesion images with precise masks. Firstly, we introduce a conditional generative adversarial network (cGAN) to learn the distribution of real mass images as well as a mapping between images and corresponding segmentation masks. Subsequently, a number of lesion images are generated from various binary input masks using the generator in the trained cGAN. Then the generated adversarial samples are concatenated with original samples to produce a dataset with increased diversity. Furthermore, we introduce an improved U-net and train it on the previous augmented dataset for breast mass segmentation. ResultsTo demonstrate the effectiveness of our proposed method, we conduct experiments on publicly available mammogram database of INbreast and a private database provided by Nanfang Hospital in China. Experimental results show that an improvement up to 7% in Jaccard index can be achieved over the same model trained on original real lesion images. ConclusionsOur proposed method can be viewed as one of the first steps toward generating realistic X-ray breast mass images with masks for precise segmentation.

Mammogram Mass Segmentation Using Fractals

A new fractal-based technique for the segmentation of masses from digital mammograms is shown in this paper. This proposed fractal-based approach aims to detect and segment the masses from mammograms after successful suppression of pectoral muscle. This paper identifies and suppresses the pectoral muscle efficiently from the input mammogram and further segments the masses precisely with the help of fractal dimensions and fractal hurst based thresholding oriented computation. This approach is proved to be efficient in getting accurate segmentation results that guarantees the virtue of the projected technique.

Multi-level nested pyramid network for mass segmentation in mammograms

Mass segmentation in mammograms is an important and challenging topic in breast cancer computer-aided diagnosis. In this work, we propose a novel multi-level nested pyramid network (MNPNet) for dealing with the limitations of intra-class inconsistency and inter-class indistinction that commonly existed in mass segmentation in mammograms. The MNPNet includes an encoder and a decoder. The former encodes contextual information, low-level detail information, and high-level semantic information in a multi-level multi-scale manner by multi-level nesting atrous spatial pyramid pooling (ASPP) module on the feature pyramid generated by modified ResNet34. The latter consist of a series of simple yet effective bilinear upsampling and feature fusion operations to refine the segmentation results along mass boundaries. Our proposed MNPNet is greatly demonstrated on two public mammographic mass segmentation databases including INbreast and DDSM-BCRP, respectively achieving the Dice index of 91.10% and 91.69% without any pre/post-processing.

The Effects of Personalized Versus Generic Scaling of Body Segment Masses on Joint Torques During Stationary Wheelchair Racing.

The anthropometries of elite wheelchair racing athletes differ from the generic, able-bodied anthropometries commonly used in computational biomechanical simulations. The impact of using able-bodied parameters on the accuracy of simulations involving wheelchair racing is currently unknown. In this study, athlete-specific mass segment inertial parameters of the head and neck, torso, upper arm, forearm, hand, thigh, shank, and feet for five elite wheelchair athletes were calculated using dual-energy X-ray absorptiometry (DXA) scans. These were compared against commonly used anthropometrics parameters of data presented in the literature. A computational biomechanical simulation of wheelchair propulsion using the upper extremity dynamic model in opensim assessed the sensitivity of athlete-specific mass parameters using Kruskal-Wallis analysis and Spearman correlations. Substantial between-athlete body mass distribution variances (thigh mass between 7.8% and 22.4% total body mass) and between-limb asymmetries (<62.4% segment mass; 3.1 kg) were observed. Compared to nonathletic able-bodied anthropometric data, wheelchair racing athletes demonstrated greater mass in the upper extremities (up to 3.8% total body mass) and less in the lower extremities (up to 9.8% total body mass). Computational simulations were sensitive to individual body mass distribution, with joint torques increasing by up to 31.5% when the scaling of segment masses (measured or generic) differed by up to 2.3% total body mass. These data suggest that nonathletic, able-bodied mass segment inertial parameters are inappropriate for analyzing elite wheelchair racing motion.

Selling the consumer: the marketing of advertising space in Sweden, ca. 1880-1939

PurposeBy studying the marketing of advertising space, this paper aims to study how class, gender and region were portrayed in terms of economic considerations in adverts selling advertising space to potential advertisers. The paper studies how readers were discursively transformed into consumers in this material and how different consumer groups were depicted, divided and framed during Sweden’s early consumer culture. By doing so, the paper highlights the tensions between aiming at a mass audience, on the one hand, and striving to reach more and more specific consumer groups on the other hand.Design/methodology/approachBoth qualitative and quantitative analyses are made in order to follow the changes of highlighted consumer groups in the ads. Intersectional analysis is used to see how notions of class and gender intersected during the analysed period.FindingsThe sectioning of the press is in the paper stressed as a prerequisite for market segmentation and the economic history of mass media is lifted as essential for understanding it. The gendering and classing of market segments were also based on how common interests were interpreted by political movements and their press forums. For surviving in the long run, however, the paper argues that the political press needed to commercialise their readerships to attract advertisers and survive economically.Originality/valueThe paper concludes that mass marketing and segmentation processes were in many senses parallel in the studied material. Statements of reaching all social classes diminished over time, but notions of the masses were prevalent in both the worker and the women categories. However, how advertisers choose between different media for their advertising campaigns or how they adopted different marketing methods towards different segments are beyond the scope of this paper.

Cascaded multi-scale convolutional encoder-decoders for breast mass segmentation in high-resolution mammograms.

This paper addresses breast mass segmentation from high-resolution mammograms. To cope with strong class imbalance, huge diversity of size, shape, texture and contour as well as limited receptive field, mass segmentation is achieved through a multi-scale cascade of deep convolutional encoder-decoders without any pre-detection scheme. Multi-scale information is integrated using auto-context to make long-range spatial context arising from lower scale impact training at higher resolution. The pipeline is trained end-to-end to benefit from simultaneous segmentation refinement performed at each level. It incorporates transfer learning and fine tuning from DDSM-CBIS to INbreast datasets to further improve mass delineations. The comprehensive evaluation provided for high-resolution INbreast images highlights promising model generalizability against standard encoder-decoder strategies.

Body Composition Characteristics and Knee Injury Prevalence of NCAA Division I Women's Soccer and Lacrosse

Body composition is directly linked to athletic performance and may influence injury risk and recovery. Female Division I soccer and lacrosse players are at a high risk for lower limb injuries specific to the knee joint throughout their competitive careers. Return to play criterion vary; evaluation of body composition may be an important element of clearance. PURPOSE: The purpose of this study was to characterize pre-season body composition and injury prevalence among female Division I soccer and lacrosse players using dual-energy x-ray absorptiometry (DEXA). METHODS: Sixty Division I Women’s Soccer (n=27) and Lacrosse (n=33) athletes (Mean ± SD: age:19.8 ± 1.4 yrs, height, 167.3 ± 6.2 cm, weight, 64.9 ± 8.0 kg,) completed a whole body DEXA scan to determine fat mass (FM), percent body fat (%fat), lean mass (LM), segmental lean mass [right leg lean mass (RLM); left leg lean mass (LLM)], and bone mineral content (BMC). History of lower extremity injury (ACL and other knee injuries) throughout a career was self-reported using a validated questionnaire. Measurements were taken pre-season (August 2018). RESULTS: Soccer and lacrosse players combined (n=60) demonstrated the following body composition characteristics: total body FM (15.23 ± 4.8 kg), total body LM (46.96 ± 4.7 kg), and BMC (2.78 ±0 .3). There were significant differences between sports in %fat and segmental LM (LLM and RLM). Lacrosse athletes had higher %fat compared to soccer athletes: (mean difference [MD] ± SD: 2.5 ± 2.3%; p=0.034). Segmental leg LM (LLM, RLM) was higher in both legs of soccer athletes (LLM MD: 0.08 ± 0.54kg; p=0.004, RLM MD: 0.89 ± 5.2kg; p=0.001). In the full sample, there was no significant difference (p=0.139) between RLM and LLM. When evaluating injured vs. not injured athletes in the full sample, 45% of athletes reported a knee injury; %fat was significantly higher for athletes with no injury history (MD: 2.5±2.4%; p=0.046). CONCLUSIONS: These finding suggest that Division I soccer and lacrosse players who have returned to play following a lower extremity injury do not experience differences in segmental leg lean mass between the left and right leg. Based on the elite level of these teams, these data could portray optimal characteristics of successful athletes returned to play.

АНАЛИЗ ПОВЕДЕНИЯ РОССИЙСКИХ ПОТРЕБИТЕЛЕЙ ПАРФЮМЕРИИ И КОСМЕТИКИ

В статье дано описание результатов маркетингового исследования сети магазинов парфюмерии и косметики. Целью данной работы разработка рекомендаций для совершенствования маркетинговой политики розничных торговцев косметикой на основе анализа результатов проведенного опроса. При проведении исследования использовались социологические и статистические методы исследований.Новизна полученных результатов работы заключается в уточнении важных для маркетинговой деятельности характеристик покупателей, таких как источники получения информации о магазинах парфюмерии и косметики и факторы их выбора, осведомленность клиентов о характеристиках парфюмерно-косметической продукции и заинтересованность их в новинках, лояльность покупателей к магазинам сети и оценка их конкурентоспособность. Сделаны выводы, что наибольшая доля покупателей предпочитает продукцию среднего и массового сегмента, а наиболее эффективными способами продвижения являются наружная реклама и информация в сети интернет. В качестве основных выводов указаны тенденции индивидуализации «масс-маркета» и усиления внимания клиентов к качеству сервиса. Результаты работы представляют практический интерес для разработки решений в области маркетинговой деятельности компании.

Visibility improvement and mass segmentation of mammogram images using quantile separated histogram equalisation with local contrast enhancement

In this work, the authors develop a working software-based approach named ‘linearly quantile separated histogram equalisation-grey relational analysis’ for mammogram image (MI). This approach improves overall contrast (local and global) of given MI and segments breast-region with a specific end goal to acquire better visual elucidation, examination, and grouping of mammogram masses to help radiologists in settling on more precise choices. The fundamental commitment of this work is to demonstrate that results of good quality of breast-region segmentation can be accomplished from basic breast-region segmentation if the input image has good contrast and a better interpretation of hidden details. They have evaluated the proposed strategy for MIAS-MIs. Experimental results have shown that the proposed approach works better than state-of-the-art.