ahmedabdelwahed/Medical_papers_title_and_abstract_NLP_dataset huggingface dataset:
A Novel Weakly Supervised Multitask Architecture for Retinal Lesions Segmentation on Fundus Images

Obtaining the complete segmentation map of retinal lesions is the first step toward an automated diagnosis tool for retinopathy that is interpretable in its decision-making. However, the limited availability of ground truth lesion detection maps at a pixel level restricts the ability of deep segmentation neural networks to generalize over large databases. In this paper, we propose a novel approach for training a convolutional multi-task architecture with supervised learning and reinforcing it with weakly supervised learning. The architecture is simultaneously trained for three tasks: segmentation of red lesions and of bright lesions, those two tasks done concurrently with lesion detection. In addition, we propose and discuss the advantages of a new preprocessing method that guarantees the color consistency between the raw image and its enhanced version. Our complete system produces segmentations of both red and bright lesions. The method is validated at the pixel level and per-image using four databases and a cross-validation strategy. When evaluated on the task of screening for the presence or absence of lesions on the Messidor image set, the proposed method achieves an area under the ROC curve of 0.839, comparable with the state-of-the-art.
The History of Acta geographica Slovenica

Acta geographica Slovenica is a research journal for geography and related disciplines published by the Anton Melik Geographical Institute of Scientific Research Centre of the Slovenian Academy of Sciences and Arts. It has been published since 1952 and is the second-oldest Slovenian geographical journal. Volume 50 was published in 2010, and this article is dedicated to this special anniversary. The journal was only published occasionally until 1976, when the volume 14 appeared, but afterwards it began to be published annually, with two volumes a year since 2003 (volume 43). With volume 43, the journal was included in Science Citation Index Expanded (SCIE). Since 2010, it has also had an impact factor. For 2009, this factor was 0.714, which ranks the journal in third place among all indexed Slovenian journals. In all the volumes, a total of 273 research articles have been published on more than 12,000 pages; half of these articles were written by the institute members.
A Particle Swarm Optimization-Based Flexible Convolutional Autoencoder for Image Classification

Convolutional autoencoders (CAEs) have shown their remarkable performance in stacking to deep convolutional neural networks (CNNs) for classifying image data during the past several years. However, they are unable to construct the state-of-the-art CNNs due to their intrinsic architectures. In this regard, we propose a flexible CAE (FCAE) by eliminating the constraints on the numbers of convolutional layers and pooling layers from the traditional CAE. We also design an architecture discovery method by exploiting particle swarm optimization, which is capable of automatically searching for the optimal architectures of the proposed FCAE with much less computational resource and without any manual intervention. We test the proposed approach on four extensively used image classification data sets. Experimental results show that our proposed approach in this paper significantly outperforms the peer competitors including the state-of-the-art algorithms.
Total Deep Variation: A Stable Regularization Method for Inverse Problems

Various problems in computer vision and medical imaging can be cast as inverse problems. A frequent method for solving inverse problems is the variational approach, which amounts to minimizing an energy composed of a data fidelity term and a regularizer. Classically, handcrafted regularizers are used, which are commonly outperformed by state-of-the-art deep learning approaches. In this work, we combine the variational formulation of inverse problems with deep learning by introducing the data-driven general-purpose total deep variation regularizer. In its core, a convolutional neural network extracts local features on multiple scales and in successive blocks. This combination allows for a rigorous mathematical analysis including an optimal control formulation of the training problem in a mean-field setting and a stability analysis with respect to the initial values and the parameters of the regularizer. In addition, we experimentally verify the robustness against adversarial attacks and numerically derive upper bounds for the generalization error. Finally, we achieve state-of-the-art results for several imaging tasks.
Fuzzy ART neural network algorithm for classifying the power system faults

This paper introduces advanced pattern recognition algorithm for classifying the transmission line faults, based on combined use of neural network and fuzzy logic. The approach utilizes self-organized, supervised Adaptive Resonance Theory (ART) neural network with fuzzy decision rule applied on neural network outputs to improve algorithm selectivity for a variety of real events not necessarily anticipated during training. Tuning of input signal preprocessing steps and enhanced supervised learning are implemented, and their influence on the algorithm classification capability is investigated. Simulation results show improved algorithm recognition capabilities when compared to a previous version of ART algorithm for each of the implemented scenarios.
Socioeconomic inequalities in molecular risk for chronic diseases observed in young adulthood

Many common chronic diseases of aging are negatively associated with socioeconomic status (SES). This study examines whether inequalities can already be observed in the molecular underpinnings of such diseases in the 30s, before many of them become prevalent. Data come from the National Longitudinal Study of Adolescent to Adult Health (Add Health), a large, nationally representative sample of US subjects who were followed for over two decades beginning in adolescence. We now have transcriptomic data (mRNA-seq) from a random subset of 4,543 of these young adults. SES in the household-of-origin and in young adulthood were examined as covariates of a priori-defined mRNA-based disease signatures and of specific gene transcripts identified de novo. An SES composite from young adulthood predicted many disease signatures, as did income and subjective status. Analyses highlighted SES-based inequalities in immune, inflammatory, ribosomal, and metabolic pathways, several of which play central roles in senescence. Many genes are also involved in transcription, translation, and diverse signaling mechanisms. Average causal-mediated effect models suggest that body mass index plays a key role in accounting for these relationships. Overall, the results reveal inequalities in molecular risk factors for chronic diseases often decades before diagnoses and suggest future directions for social signal transduction models that trace how social circumstances regulate the human genome.
Quality of counselling assessed by patients after total knee arthroplasty: A cross-sectional study

Patient counselling is a key function in nursing. High-quality counselling promotes adherence to treatment and reduces complications. The purpose of the study was to describe the quality of counselling experienced by total knee arthroplasty patients following surgery. The study was a descriptive cross-sectional study. The data were collected from patients following total knee arthroplasty (N = 60) in 2016 with a modified Quality of Counselling Instrument, and analysed using statistical methods. Over half of the patients (58%) were women and the mean age was 68 years (range 49-84). Over a quarter of patients (28.9%) lived alone, and about two-thirds were overweight (42.1%), or obese (31.6%). After surgery, many patients (88%) experienced moderate pain. Half of patients (52.6%) received a good quality of counselling for the disease and its treatment, and counselling for recovery from treatment (81.6%) was good. Most patients (92.1%) received satisfactory counselling about physical activity. There was a correlation between the disease and its treatment counselling and quality of life (r = -0.553, p = 0.003) and pain (r = -0657, p = 0.000). Interaction during counselling was good (97.4%) and it was implemented in a patient-centred way (89.5%). High-quality counselling implemented in a patient-centred manner can play a part in reducing pain and increasing patients' quality of life.
How to activate intuitive and reflective thinking in behavior research? A comprehensive examination of experimental techniques

Experiments comparing intuitive and reflective decisions provide insights into the cognitive foundations of human behavior. However, the relative strengths and weaknesses of the frequently used experimental techniques for activating intuition and reflection remain unknown. In a large-scale preregistered online experiment (N = 3667), we compared the effects of eight reflection, six intuition, and two within-subjects manipulations on actual and self-reported measures of cognitive performance. Compared to the overall control, the long debiasing training was the most effective technique for increasing actual reflection scores, and the emotion induction was the most effective technique for increasing actual intuition scores. In contrast, the reason and the intuition recall, the reason induction, and the brief time delay conditions failed to achieve the intended effects. We recommend using the debiasing training, the decision justification, or the monetary incentives technique to activate reflection, and the emotion induction, the cognitive load, or the time pressure technique to activate intuition.
Looking through past records: The use of historical documents in cave art spatial studies and its application to La Pasiega (Puente Viesgo, Cantabria, Spain)

In the course of the last decades, new cave art discoveries such as La Garma, Chauvet-Pont-d'Arc, Le Reseau Clastres in the Niaux Cave, Cosquer and Cussac have allowed researchers to advance in context and spatial studies related to the art. This has been possible because the decorated chambers were intact at the moment of the discovery and, soon after, protocols were put in place to protect these invaluable records. These types of caves are a minority. In the Cantabrian region, most of the discoveries took place at the beginning of the 20th century and, in some cases, a few years after the first studies were published, the caves were greatly modified to prepare them for tourist visits in the 1950s. However, the study of historical documents can provide information regarding the context and the original spatial distribution of the caves. Using the available data from different historical sources such as pictures, descriptions, sketches, plans, etc. available in publications and unpublished materials, we can reconstruct, to a limited extent, the appearance of a cave in the moment of its discovery. The information gathered by the different researchers in the last hundred years to advance in the knowledge of La Pasiega cave in Puente Viesgo (Cantabria) is used to prove the validity of this approach. The results, combining information from the available sources and careful observation in the cave, are positive, allowing us to advance significantly in the understanding of the cave's spatial characteristics. (C) 2016 Elsevier Ltd and INQUA. All rights reserved.
A Qualification Approach to DAC Mismatch-Shaping Methods

Linearizing a digital-to-analog converter by mismatch noise shaping is popular particularly in the art of Delta-Sigma converters. This work proposes a procedure for characterizing and benchmarking the shaping methods. The proposed approach provides five graphical figures of merit related to in-band spurious level and noise floor. In our case-studies, we will concentrate on the data-weighted averaging method with low-and bandpass shaping alongside a combination of two-tone suppression methods.
NSGA-II with objective-specific variation operators for multiobjective vehicle routing problem with time windows

Vehicle routing problem with time windows (VRPTW) is a pivotal problem in logistics domain as it possesses multiobjective characteristics in real-world applications. Literature contains a general multiobjective VRPTW (MOVRPTW) with five objectives along with MOVRPTW benchmark instances that are derived from real-world data. In this paper, we have proposed a nondominated sorting genetic algorithm II (NSGA-II) based approach with objective-specific variation operators to address the MOVRPTW. In the proposed NSGA-II approach, the crossover and mutation operators are designed by exploiting the problem characteristics as well as the attributes of each objective. The performance of the proposed approach is evaluated on the standard benchmark instances of the problem and compared with the state-of-the-art approach available in literature. The computational results demonstrate the superiority of our approach over the state-of-the-art approach for the MOVRPTW.
Toward Multicenter Skin Lesion Classification Using Deep Neural Network With Adaptively Weighted Balance Loss

Recently, deep neural network-based methods have shown promising advantages in accurately recognizing skin lesions from dermoscopic images. However, most existing works focus more on improving the network framework for better feature representation but ignore the data imbalance issue, limiting their flexibility and accuracy across multiple scenarios in multi-center clinics. Generally, different clinical centers have different data distributions, which presents challenging requirements for the network's flexibility and accuracy. In this paper, we divert the attention from framework improvement to the data imbalance issue and propose a new solution for multi-center skin lesion classification by introducing a novel adaptively weighted balance (AWB) loss to the conventional classification network. Benefiting from AWB, the proposed solution has the following advantages: 1) it is easy to satisfy different practical requirements by only changing the backbone; 2) it is user-friendly with no tuning on hyperparameters; and 3) it adaptively enables small intraclass compactness and pays more attention to the minority class. Extensive experiments demonstrate that, compared with solutions equipped with state-of-the-art loss functions, the proposed solution is more flexible and more competent for tackling the multi-center imbalanced skin lesion classification task with considerable performance on two benchmark datasets. In addition, the proposed solution is proved to be effective in handling the imbalanced gastrointestinal disease classification task and the imbalanced DR grading task. Code is available at https://github.com/Weipeishan2021.
A Case Report of a Normal Pregnancy in a Bicornuate Uterus Through In Vitro Fertilization

The case report has been done to examine the possibility of normal pregnancy achieved in the case of a rare congenital anomaly, the bicornuate uterus. A bicornuate uterus is a very rare congenital anomaly of the uterus, which falls in the class 4 category according to the classification of Mullerian duct anomalies given by the American Society of Reproductive Medicine and is associated with several obstetrics complications like malpresentation, recurrent abortions, and growth restrictions. However, to have a normal pregnancy in a bicornuate uterus, close antenatal monitoring is required, and, depending on the individual, surgical unification can be done. A 30-year-old woman with G3A2 with 34.3 weeks of gestational age with in vitro fertilization (IVF) conception came with cervical stitch in situ and oligohydramnios with liquor index 7 for safe confinement. At the time of admission, amenorrhea was present for nine months. Ultrasound at 33 weeks three days showed a single uterine live fetus weighing about 2187 grams. The interpretation of the color Doppler was also normal. Fetal heart sound was heard in the Doppler. She was operated on at 36 weeks as an emergency lower-section cesarean section procedure. The indication was that it was an IVF baby, and the female had presented with oligohydramnios on performing investigations. The patient was counseled accordingly and discharged on 22 February 2022. She was advised to come back after 15 days or SOS at the time of emergency. All the measures were suggested, including adequate rest, plenty of fluids, and a good protein diet. Most cases of the bicornuate uterus do not present with any symptoms, i.e., they are asymptomatic and can be detected during routine evaluation of the patient. However, some patients can also have symptoms like menstrual problems such as dysmenorrhea and menorrhagia. Also, along with this anomaly, associated anomalies may be present, including agenesis of the kidney and ureter. The first and foremost investigation to be done is ultrasonography, which tells about the diagnosis of the bicornuate uterus. Magnetic resonance imaging is the gold standard test for its diagnosis. However, the diagnosis in the case of asymptomatic patients is relatively tricky and requires aggressive prenatal monitoring and needs to be kept in observation to make the pregnancy successful.
Ultra low phase noise sapphire - SiGe HBT oscillator

A state of the art C band oscillator is presented. It is based on a high Q WGM sapphire resonator and on a low residual phase noise SiGe HBT amplifier. A two oscillator experiment performed on this system has revealed a phase noise level of -133 dBc/Hz at 1 kHz offset from the 4.85 GHz carrier, which is the best published phase noise result for a single loop, free running microwave oscillator.
Beyond the science and art of the healthy buildings daylighting dynamic control's performance prediction and validation

Further advance of the healthy building's energy efficiency and sustainability is inextricably linked to the building's envelopes/facades physics study, particularly fundamentals of the dynamic control of sunlight and optimal control of solar heat gains. Based on the improved understanding of mechanism which physically control specific materials intensity, e.g. absorption, reflection and transmission of solar radiation, are to be improved strategies to dynamically control separation of the daylight admittance from the solar heat gains. Relevant dynamic control mathematical models and algorithms, as well as infrastructure/hardware and software integrated performance prediction and validation are to be further developed. This paper reviews the most recent research and development results, the current state of the science and art, as well as some of the ongoing R&D at the edge of new breakthroughs of the healthy buildings daylighting dynamic control's performance prediction and validation. Finally defined is a challenging future research goal-tuning control of buildings glazing's transmittance dependence on the solar radiation wavelength to optimize daylighting and building's energy efficiency. (c) 2011 Elsevier B.V. All rights reserved.
Robust Prostate Segmentation Using Intrinsic Properties of TRUS Images

Accurate segmentation is usually crucial in transrectal ultrasound (TRUS) image based prostate diagnosis; however, it is always hampered by heavy speckles. Contrary to the traditional view that speckles are adverse to segmentation, we exploit intrinsic properties induced by speckles to facilitate the task, based on the observations that sizes and orientations of speckles provide salient cues to determine the prostate boundary. Since the speckle orientation changes in accordance with a statistical prior rule, rotation-invariant texture feature is extracted along the orientations revealed by the rule. To address the problem of feature changes due to different speckle sizes, TRUS images are split into several arc-like strips. In each strip, every individual feature vector is sparsely represented, and representation residuals are obtained. The residuals, along with the spatial coherence inherited from biological tissues, are combined to segment the prostate preliminarily via graph cuts. After that, the segmentation is fine-tuned by a novel level sets model, which integrates 1) the prostate shape prior, 2) dark-to-light intensity transition near the prostate boundary, and 3) the texture feature just obtained. The proposed method is validated on two 2-D image datasets obtained from two different sonographic imaging systems, with the mean absolute distance on the mid gland images only 1.06 +/- 0.53 mm and 1.25 +/- 0.77 mm, respectively. The method is also extended to segment apex and base images, producing competitive results over the state of the art.
Information overload's double-edged sword effect on sense of safety: Examining the moderating role of hypervigilance

Since the COVID-19 pandemic outbreak, long-term overlooked motives concerning a sense of safety have become a primary concern. People's sense of safety largely depends on the information they receive. Indeed, a tsunami of information about the virus has been disseminated by all forms of media to people's electronic devices, thus permeating their lives. This study proposed that the over-abundance of information, known as information overload, could endanger individuals' sense of safety by increasing their rumination about COVID-19. However, it could also enhance their sense of safety by increasing their positive attitudes toward COVID-19 precautions. Furthermore, we proposed that individuals' hypervigilance could strengthen the relationship between information overload and rumination about COVID-19 and attitudes toward COVID-19 precautions. We tested these hypotheses using a cross-sectional survey study (N = 403) in February 2021 and a diary study (N = 98) in July 2021 in China. The results of both studies support the dual mediating paths of the relationship between information overload and sense of safety. We also found that hypervigilance moderated the relationship between information overload and rumination about COVID-19. Overall, our study offers insights into how social media may influence people's sense of safety and how individual differences in hypervigilance play a role in the process.
Association between vitamin D status and serum parathyroid hormone concentration and calcaneal stiffness in Japanese adolescents: sex differences in susceptibility to vitamin D deficiency

There is currently insufficient information on serum 25-hydroxyvitamin D (25OHD) and parathyroid hormone (PTH) concentrations, and bone mineral status in healthy adolescents to allow reference values to be set. This study aimed to provide comparable data on vitamin D status in Japanese adolescents and to assess sex differences in susceptibility to vitamin D insufficiency. Serum 25OHD and PTH concentrations were measured in 1,380 healthy adolescents (aged 12-18 years). Subjects completed a questionnaire on exercise history, diet, and lifestyle factors. Calcaneal stiffness was evaluated by quantitative ultrasound. Serum 25OHD concentrations in boys and girls were 60.8 ± 18.3 and 52.8 ± 17.0 nmol/L, respectively. Approximately 30 % of boys and 47 % of girls had suboptimal 25OHD concentrations (<50 nmol/L). Serum PTH concentration was negatively correlated with serum 25OHD concentration in boys, but negatively correlated with calcium intake rather than serum 25OHD in girls. In contrast, the increment in calcaneal stiffness as a result of elevation of serum 25OHD was higher in girls than in boys. As vitamin D deficiency is common in Japanese adolescents, it was estimated that intakes of ≥12 and ≥14 μg/day vitamin D would be required to reach 25OHD concentrations of 50 nmol/L in boys and girls, respectively. Moreover, the results of the present study indicate that vitamin D deficiency has a greater association with calcaneal stiffness in girls than in boys.
[Innovating in the hospital of tomorrow]

The hospital of tomorrow must aim not only to be an innovative establishment, but also to become an establishment that innovates. The objective is to bring up initiatives and the expression of needs of the hospital community, to evaluate and prioritize them, and then to implement the selected projects.