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A solar electric vehicle (SEV) is an electric vehicle (EV) with onboard photovoltaic cells charging a set of batteries for extended driving range. This study aimed to improve the energy management system of a SEV, called YILDIZ, using a fuzzy logic control system (FLC). A MATLAB based simulation model of three basic components of a solar car: solar cell modules, batteries and motor drive system was performed. An original FLC was developed. For proving its applicability, the performances of the SEV were tested by simulation, in accordance with the standard test drive cycle ECE-15. The characteristics obtained with the original Proportional Integral Fuzzy Logic Control (PI-FLC) were compared with those obtained with a classical Proportional Integral (PI) controller. Using the designed model, we calculated the range of YILDIZ with and without PV feeding which gave us an opportunity to study and compare both SEV and EV models on real race-track situation. Then the optimum speed, at any time, which enabled the vehicle to reach a chosen destination as quickly as possible, while fully using the available energy, was calculated. Proposed solutions tested on YILDIZ. Results of simulations were compared with YILDIZ run on the Formula-G race track in Izmit, Turkey.

Stimulated by climate change, sustainability concerns have increased the literature on socioeconomic impact assessments of solar energy and Photovoltaic (PV) technologies. PV systems have gained prominence as a key renewable energy source since the 2000s. This paper conducts a bibliometric analysis of this literature and the main objective is to review the existing studies on socioeconomic impacts systematically. For the research design, we adopted a non-traditional review method and used scientometric analysis tools using HistCite and VosViewer programs. According to the findings, there has been a significant increase in publications on the socioeconomic impact assessment of Solar Energy and PV technologies from the 2000s to 2020. The top ten research countries are developed, except for China and India. The findings from the narrative analysis of the most frequently cited articles suggest that public policies and patent structures play a prominent role in improving the social acceptance of solar technologies. The paper highlights the most influential works and authors in the field emphasize their essential findings on solar energy's socioeconomic impact and provides recommendations for future research. The literature of 964 articles reviewed for this paper indicates an emerging bridge between the photovoltaic/solar energy technical research and social sciences. [ABSTRACT FROM AUTHOR]

Introduction: It is known that combat athletes frequently lose weight before a competition. This study aimed to determine the weight loss methods before an official championship and the effects of these weight loss methods on the performance of wrestlers., Method: The sample of the study consisted of 350 competitive wrestlers. The "Athlete Weight Loss Methodology and Effects Scale" and personal information form were used as data collection tools in the study. In the data analysis, independent samples T-test, One-way ANOVA, and Pearson correlation analysis were used in addition to descriptive statistics., Results: Wrestlers generally reported dieting by restricting fatty (89.1%) and carbohydrate (83.4%) foods. It was observed that they preferred jogging with a raincoat (89.1%) and/or using the sauna (79.7%) as a weight loss method. Wrestlers also reported that they performed weight loss, although at a low level, by using ergogenic aids such as laxatives (31.7%) and/or diuretic pills (28.0%). They were observed to experience physiological effects such as muscle cramps (72.9%), injury (71.1%), and/or difficulty breathing (60.9%), as well as psychological effects such as feeling extremely tired (81.7%), stress (79.7%), decreased performance (78.6%) and/or extremely irritability (71.4%). It was determined that there was no difference between the weight loss methods and effects of the wrestlers according to their gender and wrestling style ( p  > 0.05). On the other hand, it was determined that the diet level of U20 wrestlers was higher than U17 ( p  < 0.05) and Senior wrestlers and that U17 and U20 wrestlers were exposed to higher levels of physiological and psychological effects than Senior wrestlers ( p  < 0.05)., Conclusion: It was determined that wrestlers preferred methods such as restricting food and fluids, using a sauna, and jogging with a raincoat to lose weight. It is necessary to prevent young wrestlers from losing weight before the competition. Thus, it is possible to prevent wrestlers in their development period (U17 and U20) from being exposed to physiological and psychological effects caused by losing weight., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2024 Seker, Isik, Durukan, Eraslan, Talaghir and Dorgan.)

The standard method for identifying active Brown Adipose Tissue (BAT) is [ 18 F]-Fluorodeoxyglucose ([ 18 F]-FDG) PET/CT imaging, which is costly and exposes patients to radiation, making it impractical for population studies. These issues can be addressed with computational methods that predict [ 18 F]-FDG uptake by BAT from CT; earlier population studies pave the way for developing such methods by showing some correlation between the Hounsfield Unit (HU) of BAT in CT and the corresponding [ 18 F]-FDG uptake in PET. In this study, we propose training convolutional neural networks (CNNs) to predict [ 18 F]-FDG uptake by BAT from unenhanced CT scans in the restricted regions that are likely to contain BAT. Using the Attention U-Net architecture, we perform experiments on datasets from four different cohorts, the largest study to date. We segment BAT regions using predicted [ 18 F]-FDG uptake values, achieving 23% to 40% better accuracy than conventional CT thresholding. Additionally, BAT volumes computed from the segmentations distinguish the subjects with and without active BAT with an AUC of 0.8, compared to 0.6 for CT thresholding. These findings suggest CNNs can facilitate large-scale imaging studies more efficiently and cost-effectively using only CT., (© 2024. The Author(s).)

Supervised deep learning-based methods yield accurate results for medical image segmentation. However, they require large labeled datasets for this, and obtaining them is a laborious task that requires clinical expertise. Semi/self-supervised learning-based approaches address this limitation by exploiting unlabeled data along with limited annotated data. Recent self-supervised learning methods use contrastive loss to learn good global level representations from unlabeled images and achieve high performance in classification tasks on popular natural image datasets like ImageNet. In pixel-level prediction tasks such as segmentation, it is crucial to also learn good local level representations along with global representations to achieve better accuracy. However, the impact of the existing local contrastive loss-based methods remains limited for learning good local representations because similar and dissimilar local regions are defined based on random augmentations and spatial proximity; not based on the semantic label of local regions due to lack of large-scale expert annotations in the semi/self-supervised setting. In this paper, we propose a local contrastive loss to learn good pixel level features useful for segmentation by exploiting semantic label information obtained from pseudo-labels of unlabeled images alongside limited annotated images with ground truth (GT) labels. In particular, we define the proposed contrastive loss to encourage similar representations for the pixels that have the same pseudo-label/GT label while being dissimilar to the representation of pixels with different pseudo-label/GT label in the dataset. We perform pseudo-label based self-training and train the network by jointly optimizing the proposed contrastive loss on both labeled and unlabeled sets and segmentation loss on only the limited labeled set. We evaluated the proposed approach on three public medical datasets of cardiac and prostate anatomies, and obtain high segmentation performance with a limited labeled set of one or two 3D volumes. Extensive comparisons with the state-of-the-art semi-supervised and data augmentation methods and concurrent contrastive learning methods demonstrate the substantial improvement achieved by the proposed method. The code is made publicly available at https://github.com/krishnabits001/pseudo&#95;label&#95;contrastive&#95;training., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 The Author(s). Published by Elsevier B.V. All rights reserved.)

Live fluorescence imaging has demonstrated the dynamic nature of dendritic spines, with changes in shape occurring both during development and in response to activity. The structure of a dendritic spine correlates with its functional efficacy. Learning and memory studies have shown that a great deal of the information stored by a neuron is contained in the synapses. High precision tracking of synaptic structures can give hints about the dynamic nature of memory and help us understand how memories evolve both in biological and artificial neural networks. Experiments that aim to investigate the dynamics behind the structural changes of dendritic spines require the collection and analysis of large time-series datasets. In this paper, we present an open-source software called SpineS for automatic longitudinal structural analysis of dendritic spines with additional features for manual intervention to ensure optimal analysis. We have tested the algorithm on in-vitro, in-vivo, and simulated datasets to demonstrate its performance in a wide range of possible experimental scenarios., (© 2022. The Author(s).)

The interplay between the immune system, sleep dysfunction and cognitive impairment participates in the progression of disability in multiple sclerosis (MS). Our aim was to identify molecular pathways and B cell associated with separate components of MS disability. Benign MS, non-benign MS patients and healthy controls were recruited. Patients underwent polysomnography and cognitive studies. Microarray and bioinformatics analysis performed using peripheral blood mononuclear cell samples identified B cell-associated genes with the most significantly altered expression. Expression levels of these genes were validated by real-time PCR and peripheral blood cell subsets were examined by flow cytometry. Putative correlations among clinical and laboratory parameters were investigated by correlation network analysis. Sleep and cognitive functions were equally impaired in BMS and NBMS. BMS patients showed significantly reduced memory B cell and increased regulatory B cell percentages than NBMS patients. Among genes that were selected by bioinformatics, levels of BLK, BLNK, BANK1, FCRL2, TGFB1 and KCNS3 genes were significantly different among study subgroups. Correlation network analysis showed associations among physical-cognitive disability and sleep dysfunction measures of MS versus expression levels of selected genes. BMS and NBMS differ by physical disability but not cognitive and sleep dysfunction. Different components of disability in MS are associated with peripheral blood B cell ratios and B cell related gene expression levels. Thus, it is likely that altered B cell functions participate in the progression of disability in MS., (Copyright © 2021 Elsevier B.V. All rights reserved.)

Context/Objective : Clinical disease activity in multiple sclerosis (MS) may manifest as predominant involvement of optic nerves and spinal cord, as exemplified by opticospinal multiple sclerosis (OSMS) often encountered in Asian countries. Our aim was to compare the clinical features, neuropsychological profile and cytokine/chemokine levels of patients with conventional MS (CMS) and MS presenting predominantly with spinal cord and optic nerve attacks (MS-SCON). Design: Cross-sectional study. Setting: MS Outpatient Clinic. Participants: Fourteen MS-SCON patients, 20 CMS patients without myelitis and optic neuritis attacks and 21 healthy individuals. Outcome measures: IL-8, IL-10, IFN-γ, IL-17 and TNF-α levels were measured by multiplex assay and CXCL2 and CXCL5 levels were measured by ELISA. A panel of neuropsychological tests, Beck depression inventory, 9-hole peg and timed 25-foot walk tests were employed. Results: CMS and MS-SCON patients showed similar clinical features. Both CMS and MS-SCON patients displayed reduced IL-8 and CXCL2 and increased TNF-α levels, while IL-10 and CXCL5 levels were identical among all groups. Conclusion: Neuropsychological and motor function test performances of CMS and MS-SCON patients were highly comparable. CMS and MS-SCON present with similar clinical, neuropsychological and immunological features. Therefore, optic nerve and spinal cord-dominant form of MS does not necessarily establish a distinct entity in our region. Cognitive networks of the central nervous system may be damaged during the disease course of MS, despite the absence of cerebral or cerebellar clinical attacks.

Supervised learning-based segmentation methods typically require a large number of annotated training data to generalize well at test time. In medical applications, curating such datasets is not a favourable option because acquiring a large number of annotated samples from experts is time-consuming and expensive. Consequently, numerous methods have been proposed in the literature for learning with limited annotated examples. Unfortunately, the proposed approaches in the literature have not yet yielded significant gains over random data augmentation for image segmentation, where random augmentations themselves do not yield high accuracy. In this work, we propose a novel task-driven data augmentation method for learning with limited labeled data where the synthetic data generator, is optimized for the segmentation task. The generator of the proposed method models intensity and shape variations using two sets of transformations, as additive intensity transformations and deformation fields. Both transformations are optimized using labeled as well as unlabeled examples in a semi-supervised framework. Our experiments on three medical datasets, namely cardiac, prostate and pancreas, show that the proposed approach significantly outperforms standard augmentation and semi-supervised approaches for image segmentation in the limited annotation setting. The code is made publicly available at https://github.com/krishnabits001/task&#95;driven&#95;data&#95;augmentation., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2020. Published by Elsevier B.V.)

Convolutional Neural Networks (CNNs) work very well for supervised learning problems when the training dataset is representative of the variations expected to be encountered at test time. In medical image segmentation, this premise is violated when there is a mismatch between training and test images in terms of their acquisition details, such as the scanner model or the protocol. Remarkable performance degradation of CNNs in this scenario is well documented in the literature. To address this problem, we design the segmentation CNN as a concatenation of two sub-networks: a relatively shallow image normalization CNN, followed by a deep CNN that segments the normalized image. We train both these sub-networks using a training dataset, consisting of annotated images from a particular scanner and protocol setting. Now, at test time, we adapt the image normalization sub-network for each test image, guided by an implicit prior on the predicted segmentation labels. We employ an independently trained denoising autoencoder (DAE) in order to model such an implicit prior on plausible anatomical segmentation labels. We validate the proposed idea on multi-center Magnetic Resonance imaging datasets of three anatomies: brain, heart and prostate. The proposed test-time adaptation consistently provides performance improvement, demonstrating the promise and generality of the approach. Being agnostic to the architecture of the deep CNN, the second sub-network, the proposed design can be utilized with any segmentation network to increase robustness to variations in imaging scanners and protocols. Our code is available at: https://github.com/neerakara/test-time-adaptable-neural-networks-for-domain-generalization., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2020. Published by Elsevier B.V.)

Purpose: Multiple sclerosis (MS) patients whose first demyelinating event is optic neuritis have been claimed to display a milder disease course and reduced physical disability. Our aim was to investigate the impact of the clinical features of the first clinical episode on cognitive disability and sleep dysfunction in MS., Methods: A total of 26 (10 with optic neuritis as the first clinical event) MS patients were recruited. A comprehensive sleep study was performed, and a panel of tests were administered to examine cognitive and motor performance. Serum levels of sleep-related mediators orexin-A and melatonin were measured by enzyme-linked immunosorbent assay. Subjective sleep quality was evaluated by Pittsburgh sleep quality test, and daytime excessive sleepiness was tested by Epworth sleepiness scale., Results: MS patients with the first clinical episode of optic neuritis and patients with at least one optic neuritis attack exhibited increased daytime sleepiness, higher sleep efficiency and NREM duration and lower total wake time. Patients with a history of optic neuritis obtained more favorable scores in neuropsychological tests measuring executive functions and complex attention as compared to those who had never experienced optic neuritis. Melatonin and orexin-A levels were lower in patients with optic neuritis onset. The higher no. of optic neuritis attacks was associated with reduced wake time and higher symbol digit modalities test scores., Conclusions: Having a history of optic neuritis is associated with improved sleep quality and executive functions but increased daytime sleepiness. Reduction of orexin-A and melatonin levels might be one of the underlying mechanisms.

Segmenting images of low quality or with missing data is a challenging problem. In such scenarios, exploiting statistical prior information about the shapes to be segmented can improve the segmentation results significantly. Incorporating prior density of shapes into a Bayesian framework leads to the posterior density of segmenting shapes given the observed data. Most segmentation algorithms that exploit shape priors optimize a cost function based on the posterior density and find a point estimate (e.g., using maximum a posteriori estimation). However, especially when the prior shape density is multimodal leading to a multimodal posterior density, a point estimate does not provide a measure of the degree of confidence in that result, neither does it provide a picture of other probable solutions based on the observed data and the shape priors. With a statistical view, addressing these issues would involve the problem of characterizing the posterior distributions of the shapes of the objects to be segmented. An analytic computation of such posterior distributions is intractable; however, characterization is still possible through their samples. In this paper, we propose an efficient pseudo-marginal Markov chain Monte Carlo (MCMC) sampling approach to draw samples from posterior shape distributions for image segmentation. The computation time of the proposed approach is independent from the training set size. Therefore, it scales well for very large data sets. In addition to better characterization of the statistical structure of the problem, such an approach has the potential to address issues with getting stuck at local optima, suffered by existing shape-based segmentation methods. Our approach is able to characterize the posterior probability density in the space of shapes through its samples, and to return multiple solutions, potentially from different modes of a multimodal probability density, which would be encountered, e.g., in segmenting objects from multiple shape classes. We present promising results on a variety of synthetic and real data sets.

Objective: Diencephalon is frequently affected in multiple sclerosis (MS), and lesions of this region are associated with increased disability. Orexin-A and melatonin, two foremost mediators of diencephalon, modulate cognitive and motor functions through several pathways including the brain-derived neurotrophic factor (BDNF)-cAMP response element-binding protein (CREB) signaling pathway. In this pilot study, our aim was to investigate the prognostic value of these factors in progression of cognitive and physical disability., Methods: Levels of BDNF, melatonin, CREB, and orexin-A were determined by ELISA in sera of 25 relapsing remitting MS (RRMS) patients, 15 secondary progressive MS (SPMS) patients, and 20 healthy controls. Cognitive and motor functions were assessed by a neuropsychological test battery, timed 25-ft walk (T25-FW) and 9-hole peg (9-HP) tests., Results: MS patients had significantly lower serum levels of orexin-A and BDNF than healthy controls, and SPMS patients had significantly lower levels of melatonin and orexin-A than RRMS patients. Serum orexin-A levels were negatively correlated with 9-HP, T25-FW test scores, and progression index in RRMS patients. BDNF, CREB, and melatonin levels did not show any significant correlation with clinical features including EDSS and cognitive/motor performance of the patients., Conclusion: Our results suggest that orexin-A levels are decreased in parallel to disease progression and motor system deterioration in the earlier stages of the disease. Thus, orexin-A might be used as a potential biomarker of physical disability.

Detecting morphological changes of dendritic spines in time-lapse microscopy images and correlating them with functional properties such as memory and learning, are fundamental and challenging problems in neurobiology research. In this paper, we propose an algorithm for dendritic spine detection in time series. The proposed approach initially performs spine detection at each time point and improves the accuracy by exploiting the information obtained from tracking of individual spines over time. To detect dendritic spines in a time point image we employ an SVM classifier trained by pre-labeled SIFT feature descriptors in combination with a dot enhancement filter. Second, to track the growth or loss of spines, we apply a SIFT-based rigid registration method for the alignment of time-series images. This step takes into account both the structure and the movement of objects, combined with a robust dynamic scheme to link information about spines that disappear and reappear over time. Next, we improve spine detection by employing a probabilistic dynamic programming approach to search for an optimum solution to accurately detect missed spines. Finally, we determine the spine location more precisely by performing a watershed-geodesic active contour model. We quantitatively assess the performance of the proposed spine detection algorithm based on annotations performed by biologists and compare its performance with the results obtained by the noncommercial software NeuronIQ. Experiments show that our approach can accurately detect and quantify spines in 2-photon microscopy time-lapse data and is able to accurately identify spine elimination and formation., (Copyright © 2018 IBRO. Published by Elsevier Ltd. All rights reserved.)

Background/aim: Benign multiple sclerosis (BMS) patients display preserved somatic neurological functions but nevertheless may develop cognitive dysfunction. Our aim was to explore the impact of computer-assisted cognitive rehabilitation (CCR) on cognitive functions of BMS patients. Materials and methods: Age- and sex-matched BMS patients (n = 21), non-BMS patients (n = 22), and healthy individuals (n = 38) were recruited for evaluation of cognitive functions. CCR was administered to 10 BMS patients and a panel of neuropsychological tests were employed at baseline and 6 months. CCR was based on mental exercise software containing attention, memory, reasoning, visual, and verbal task modules. Results: BMS and non-BMS patients showed impaired selective reminding, spatial recall, symbol digit modalities (SDMTs), controlled oral word association (COWAT), paced auditory serial addition-3 (PASAT-3), and Stroop tests. Timed 25-foot walk and 9-hole peg test results of BMS patients were comparable to those of healthy controls. BMS patients with CCR showed significantly improved SDMTs, COWAT, and Stroop test results compared to those without CCR. Conclusion: Several cognitive domains including memory and executive functions are impaired in BMS patients. CCR has an ameliorating impact particularly on sustained attention, information processing speed, verbal fluency, categorical reasoning, and executive functions of BMS patients., (CC-BY 4.0)

The use of appearance and shape priors in image segmentation is known to improve accuracy; however, existing techniques have several drawbacks. For instance, most active shape and appearance models require landmark points and assume unimodal shape and appearance distributions, and the level set representation does not support construction of local priors. In this paper, we present novel appearance and shape models for image segmentation based on a differentiable implicit parametric shape representation called a disjunctive normal shape model (DNSM). The DNSM is formed by the disjunction of polytopes, which themselves are formed by the conjunctions of half-spaces. The DNSM's parametric nature allows the use of powerful local prior statistics, and its implicit nature removes the need to use landmarks and easily handles topological changes. In a Bayesian inference framework, we model arbitrary shape and appearance distributions using nonparametric density estimations, at any local scale. The proposed local shape prior results in accurate segmentation even when very few training shapes are available, because the method generates a rich set of shape variations by locally combining training samples. We demonstrate the performance of the framework by applying it to both 2-D and 3-D data sets with emphasis on biomedical image segmentation applications.

In many image segmentation problems involving limited and low-quality data, employing statistical prior information about the shapes of the objects to be segmented can significantly improve the segmentation result. However, defining probability densities in the space of shapes is an open and challenging problem, especially if the object to be segmented comes from a shape density involving multiple modes (classes). Existing techniques in the literature estimate the underlying shape distribution by extending Parzen density estimator to the space of shapes. In these methods, the evolving curve may converge to a shape from a wrong mode of the posterior density when the observed intensities provide very little information about the object boundaries. In such scenarios, employing both shape- and class-dependent discriminative feature priors can aid the segmentation process. Such features may involve, e.g., intensity-based, textural, or geometric information about the objects to be segmented. In this paper, we propose a segmentation algorithm that uses nonparametric joint shape and feature priors constructed by Parzen density estimation. We incorporate the learned joint shape and feature prior distribution into a maximum a posteriori estimation framework for segmentation. The resulting optimization problem is solved using active contours. We present experimental results on a variety of synthetic and real data sets from several fields involving multimodal shape densities. Experimental results demonstrate the potential of the proposed method.

Motivation: Clustering methods including k-means, SOM, UPGMA, DAA, CLICK, GENECLUSTER, CAST, DHC, PMETIS and KMETIS have been widely used in biological studies for gene expression, protein localization, sequence recognition and more. All these clustering methods have some benefits and drawbacks. We propose a novel graph-based clustering software called COMUSA for combining the benefits of a collection of clusterings into a final clustering having better overall quality., Results: COMUSA implementation is compared with PMETIS, KMETIS and k-means. Experimental results on artificial, real and biological datasets demonstrate the effectiveness of our method. COMUSA produces very good quality clusters in a short amount of time., Availability: http://www.cs.umb.edu/∼smimarog/comusa, Contact: selim.mimaroglu@bahcesehir.edu.tr

Aim: Television (TV) viewing has been implicated in children's weight gain. We aimed to conduct a comprehensive content analysis of TV food advertising in Turkey., Methods: TV advertisements (ads) in the four most popular national free to air Turkish TV channels, were assessed on two weekdays and two weekend days at four time periods of the day; 0800-1200,1200-1600,1600-2000 and 2000-2400 h for each TV channel (64 h assessed for each TV channel), making a total of 256 h. Each ad was analysed for food and drink content, duration and audiovisual properties., Results: There were 8853 TV ads and 2848 of these were related to food (32.1%). A majority of food ads included high-calorie, high-fat, high-sugar food and drink rather than core/healthy foods (81%). Chocolate and chocolate bars were the most frequently advertised food/drink product, followed by cakes, coffee, tea, candies, gum, fast food, chips, juices/carbonated beverages, margarine and ice-cream formed the highest rate of food products advertised in decreasing order. Thirty per cent of all obesogenic/unhealthy ads targeted childhood by using audiovisual techniques. The proportion of total advertisements which were for food or drink, and the proportion of food advertisements that were for unhealthy foods were both much higher at the weekend (33% vs. 30% and 84% vs. 78%, respectively). The time period between 1600 and 2000 h was the most concentrated time slot (33%) for food advertising., Conclusions: This study provides data for the first time on the high levels of obesogenic food advertising on Turkish TV. This should alarm policy-makers to set limits on food advertising targeted towards children especially in countries like Turkey in which childhood obesity is emerging as an important public health issue.