ahmedabdelwahed/Medical_papers_title_and_abstract_NLP_dataset huggingface dataset:
Learning Geodesic Active Contours for Embedding Object Global Information in Segmentation CNNs

Most existing CNNs-based segmentation methods rely on local appearances learned on the regular image grid, without consideration of the object global information. This article aims to embed the object global geometric information into a learning framework via the classical geodesic active contours (GAC). We propose a level set function (LSF) regression network, supervised by the segmentation ground truth, LSF ground truth and geodesic active contours, to not only generate the segmentation probabilistic map but also directly minimize the GAC energy functional in an end-to-end manner. With the help of geodesic active contours, the segmentation contour, embedded in the level set function, can be globally driven towards the image boundary to obtain lower energy, and the geodesic constraint can lead the segmentation result to have fewer outliers. Extensive experiments on four public datasets show that (1) compared with state-of-the-art (SOTA) learning active contour methods, our method can achieve significantly better performance; (2) compared with recent SOTA methods that are designed for reducing boundary errors, our method also outperforms them with more accurate boundaries; (3) compared with SOTA methods on two popular multi-class segmentation challenge datasets, our method can still obtain superior or competitive results in both organ and tumor segmentation tasks. Our study demonstrates that introducing global information by GAC can significantly improve segmentation performance, especially on reducing the boundary errors and outliers, which is very useful in applications such as organ transplantation surgical planning and multi-modality image registration where boundary errors can be very harmful.
Effects of Nature-Based Group Art Therapy Programs on Stress, Self-Esteem and Changes in Electroencephalogram (EEG) in Non-Disabled Siblings of Children with Disabilities

The purpose of the present study was to examine changes in brain waves, stress, and self-esteem after a continuous eight-week nature-based art therapy program in the forest in non-disabled siblings of children with disabilities. A total of 29 participants participated in this study (art therapy program group, n = 18; control group, n = 11). The art therapy program group received eight weekly sessions of art therapy lasting 60 min each. Pre- and post-test results showed positive changes in the brain function index and stress levels of the participants in the art therapy program group. On the self-esteem scale, overall and social self-esteem increased significantly. In conclusion, creative activities in the forest can increase resistance to diseases through mechanisms that relieve stress and increase self-esteem. If art therapy that emphasizes somatosensory experience, creative expression, and self-motivation is accompanied by forest activities, this combined intervention can elicit positive physical and psychological changes.
Sketch-and-Fill Network for Semantic Segmentation

Recent efforts in semantic segmentation using deep learning framework have made notable advances. While achieving high performance, however, they often require heavy computation, making them impractical to be used in real world applications. There are two reasons that produce prohibitive computational cost: 1) heavy backbone CNN to create high resolution of contextual information and 2) complex modules to aggregate multi-level features. To address these issues, we propose the computationally efficient architecture called "Sketch-and-Fill Network (SFNet)" with a three-stage Coarse-to-Fine Aggregation (CFA) module for semantic segmentation. In the proposed network, lower-resolution contextual information is first produced so that the overall computation in the backbone CNN is largely reduced. Then, to alleviate the detail loss of the lower-resolution contextual information, the CFA module forms global structures and fills fine details in a coarse-to-fine manner. To preserve global structures, the contextual information is passed without any reduction to the CFA module. Experimental results show that the proposed SFNet achieves significantly lower computational loads while delivering comparable or improved segmentation performance with state-of-the-art methods. Qualitative results show that our method is superior to state-of-the-art methods in capturing fine detail while keeping global structures on Cityscapes, ADE20K and RUGD benchmarks.
Growth of desert varnish on petroglyphs from Jubbah and Shuwaymis, Ha'il region, Saudi Arabia

Petroglyphs, engraved throughout the Holocene into rock varnish coatings on sandstone, were investigated in the Ha'il region of northwestern Saudi Arabia, at Jabal Yatib, Jubbah, and Shuwaymis. The rock art has been created by removing the black varnish coating and thereby exposing the light sandstone underneath. With time, the varnish, a natural manganese (Mn)-rich coating, grows back. To study the rate of regrowth, we made 234 measurements by portable X-ray fluorescence (pXRF) on intact varnish and engraved petroglyphs. Since many petroglyphs can be assigned to a specific time period, a relationship between their ages and the Mn surface densities (D-Mn) of the regrown material could be derived. This relationship was improved by normalizing the D-Mn in the petroglyphs with the D-Mn of adjacent intact varnish. In turn, we used this relationship to assign a chronologic context to petroglyphs of unknown ages. Following the removal of the varnish by the artist and prior to the beginning of Mn oxyhydroxide regrowth, a thin Fe-rich film forms on the underlying rock. This initial Fe oxyhydroxide deposit may act as catalyst for subsequent fast Mn oxidation. After a few decades of relatively rapid growth, the regrowth of the Mn-rich varnish slows down to about 0.017 mu g cm(-2) a(-1) Mn, corresponding to about 0.012% a(-1) Mn of the intact varnish density, or about 1.2 nm a(-1), presumably due to a change of the catalytic process. Our results suggest that petroglyphs were engraved almost continuously since the pre-Neolithic period, and that rock varnish growth seems to proceed roughly linear, without detectable influences of the regional Holocene climatic changes.
Model-assisted content adaptive detail enhancement and quadtree decomposition for image visibility enhancement

In this work, a simple and unique, yet effective framework for visibility enhancement, using image decomposition, adaptive boundary constraint and quadtree-based dehazing, detail enhancement and fusion is proposed. The input image firstly undergoes image decomposition, to be split into its its ambient illumination and reflex lightness components. For increasing brightness and contrast, contrast-based enhancement algorithm is applied to the reflex lightness component and the ambient illumination component is dehazed through the application of atmospheric scattering model. In order to estimate the airlight, instead of using the whole image, a simple and efficient method based on quadtree decomposition is used. The transmission map is computed through contextual regularization using adaptive boundary constraints. The dehazed ambient illumination component is passed through detail enhancement for enhancement of sharp edges. The resultant image and the enhanced reflex lightness component are then combined together through fusion to obtain the final, artifact free, enhanced image with preserved colors and details. The proposed methodology is evaluated using numerous images and compared with 8 different state-of-the-art techniques. Visual and quantitative comparison of the proposed methodology with existing state-of-the-art techniques demonstrates the effectiveness of the proposed technique.
DTS-SNN: Spiking Neural Networks With Dynamic Time-Surfaces

Convolution helps spiking neural networks (SNNs) capture the spatio-temporal structures of neuromorphic (event) data as evident in the convolution-based SNNs (C-SNNs) with the state-of-the-art classification-accuracies on various datasets. However, the efficacy aside, the efficiency of C-SNN is questionable. In this regard, we propose SNNs with novel trainable dynamic time-surfaces (DTS-SNNs) as efficient alternatives to convolution. The novel dynamic time-surface proposed in this work features its high responsiveness to moving objects given the use of the zero-sum temporal kernel that is motivated by the simple cells' receptive fields in the early stage visual pathway. We evaluated the performance and computational complexity of our DTS-SNNs on three real-world event-based datasets (DVS128 Gesture, Spiking Heidelberg dataset, N-Cars). The results highlight high classification accuracies and significant improvements in computational efficiency, e.g., merely 1.51% behind of the state-of-the-art result on DVS128 Gesture but a x 18 improvement in efficiency. The code is available online (https://github.com/dooseokjeong/DTS-SNN).
Guided Zoom: Zooming into Network Evidence to Refine Fine-Grained Model Decisions

In state-of-the-art deep single-label classification models, the top-k (k = 2,3,4,....) accuracy is usually significantly higher than the top-1 accuracy. This is more evident in fine-grained datasets, where differences between classes are quite subtle. Exploiting the information provided in the top k predicted classes boosts the final prediction of a model. We propose Guided Zoom, a novel way in which explainabitity could be used to improve model performance. We do so by making sure the model has "the right reasons" fora prediction. The reason/evidence upon which a deep neural network makes a prediction is defined to be the grounding, in the pixel space, for a specific class conditional probability in the model output. Guided Zoom examines how reasonable the evidence used to make each of the top-k predictions is. Test time evidence is deemed reasonable if it is coherent with evidence used to make similar correct decisions at training time. This leads to better informed predictions. We explore a variety of grounding techniques and study their complementarity for computing evidence. We show that Guided Zoom results in an improvement of a model's classification accuracy and achieves state-of-the-art classification performance on four fine-grained classification datasets.
GII.17[P17] and GII.8[P8] noroviruses showed different RdRp activities associated with their epidemic characteristics

Norovirus is the primary foodborne pathogenic agent causing viral acute gastroenteritis. It possesses broad genetic diversity and the prevalence of different genotypes varies substantially. However, the differences in RNA-dependent RNA polymerase (RdRp) activity among different genotypes of noroviruses remain unclear. In this study, the molecular mechanism of RdRp activity difference between the epidemic strain GII.17[P17] and the non-epidemic strain GII.8[P8] was characterized. By evaluating the evolutionary history of RdRp sequences with Markov Chain Monte Carlo method, the evolution rate of GII.17[P17] variants was higher than that of GII.8[P8] variants (1.22 × 10-3 nucleotide substitutions/site/year to 9.31 × 10-4 nucleotide substitutions/site/year, respectively). The enzyme catalytic reaction demonstrated that the Vmax value of GII.17[P17] RdRp was 2.5 times than that of GII.8[P8] RdRp. And the Km of GII.17[P17] and GII.8[P8] RdRp were 0.01 and 0.15 mmol/L, respectively. Then, GII.8[P8] RdRp fragment mutants (A-F) were designed, among which GII.8[P8]-A/B containing the conserved motif G/F were found to have significant effects on improving RdRp activity. The Km values of GII.8[P8]-A/B reached 0.07 and 0.06 mmol/L, respectively. And their Vmax values were 1.34 times than that of GII.8[P8] RdRp. In summary, our results suggested that RdRp activities were correlated with their epidemic characteristics. These findings will ultimately provide a better understanding in replication mechanism of noroviruses and development of antiviral drugs.
High-Rate Maximum Runlength Constrained Coding Schemes Using Nibble Replacement

In this paper, we will present coding techniques for the character-constrained channel, where information is conveyed using q-bit characters (nibbles), and where w prescribed characters are disallowed. Using codes for the character-constrained channel, we present simple and systematic constructions of high-rate binary maximum runlength constrained codes. The new constructions have the virtue that large lookup tables for encoding and decoding are not required. We will compare the error propagation performance of codes based on the new construction with that of prior art codes.
Towards Automatic and Fast Annotation of Seismocardiogram Signals Using Machine Learning

The automatic annotation of Seismocardiogram (SCG) potentially aid to estimate various cardiac health parameters continuously. However, the inter-subject variability of SCG poses great difficulties to automate its accurate annotation. The objective of the research is to design SCG peak retrieval methods on the top of the ensemble features extracted from the SCG morphology for the automatic annotation of SCG signals. The annotation scheme is formulated as a binary classification problem. Three binary classifiers such as Naive Bayes (NB), Support Vector Machine (SVM), and Logistic Regression (LR) are employed for the annotation and the results are compared with the recent state-of-the-art schemes. The performance evaluation is carried out using 9000 SCG signals of 20 presumably healthy volunteers with no known serious cardiac abnormalities. The SCG signals are acquired from the Physionet public repository "cebsdb". The models are rigorously validated using metrics "Precision", "Recall", and "F-measure" followed by 5-fold cross-validation. The experimental validation with recent state-of-the-art solutions establishes the robustness of the proposed NB, SVM and LRwith average annotation accuracy of 0.86, 0.925 and 0.935, respectively. The mean response time of proposed models is in the fraction of 1/10 sec, which establishes its application for the real-time annotation.
Strengthening Sensory Sustainability ScienceTheoretical and Methodological Considerations

Sustainability science is marked by a quarter century of conceptual and methodological development. Based on innovative approaches, such as transformative transdisciplinarity, sustainability science makes the claim to contribute solution-oriented knowledge to sustainable development. Despite successful expansion and promising experiences, there are limitations to be considered. This article argues that the multisensorial reality of human life in socio-material practices has not been adequately captured in sustainability science. Theoretical approaches addressing the sensoriality and corporality of human existence as well as methodological approaches of ethnography and arts-based research to access relevant human dimensions beyond the cognitive are discussed, and the perspective of sensory sustainability science is sketched.
Adaptive emotional memory: the key hippocampal-amygdalar interaction

For centuries philosophical and clinical studies have emphasized a fundamental dichotomy between emotion and cognition, as, for instance, between behavioral/emotional memory and explicit/representative memory. However, the last few decades cognitive neuroscience have highlighted data indicating that emotion and cognition, as well as their underlying neural networks, are in fact in close interaction. First, it turns out that emotion can serve cognition, as exemplified by its critical contribution to decision-making or to the enhancement of episodic memory. Second, it is also observed that reciprocally cognitive processes as reasoning, conscious appraisal or explicit representation of events can modulate emotional responses, like promoting or reducing fear. Third, neurobiological data indicate that reciprocal amygdalar-hippocampal influences underlie such mutual regulation of emotion and cognition. While supporting this view, the present review discusses experimental data, obtained in rodents, indicating that the hippocampal and amygdalar systems not only regulate each other and their functional outcomes, but also qualify specific emotional memory representations through specific activations and interactions. Specifically, we review consistent behavioral, electrophysiological, pharmacological, biochemical and imaging data unveiling a direct contribution of both the amygdala and hippocampal-septal system to the identification of the predictor of a threat in different situations of fear conditioning. Our suggestion is that these two brain systems and their interplay determine the selection of relevant emotional stimuli, thereby contributing to the adaptive value of emotional memory. Hence, beyond the mutual quantitative regulation of these two brain systems described so far, we develop the idea that different activations of the hippocampus and amygdala, leading to specific configurations of neural activity, qualitatively impact the formation of emotional memory representations, thereby producing either adaptive or maladaptive fear memories.
Improving mental and physical health outcomes in general healthcare settings: a Gedenkschrift in honor of Wayne Katon, MD (1950-2015)

This special article pays tribute to Wayne Katon, MD (1950-2015) with a Gedenkschrift, or review, of his prolific academic career. Abstracts of all of Dr. Katon's Medline citations were reviewed to develop a narrative of his seminal epidemiological and interventional research findings. Specifically, we describe: (a) how Dr. Katon's clinical work and observational epidemiology and health services research informed and guided interventional studies; (b) the evolution of multidisciplinary interventional trials from primary care-based psychiatric consultation to primary care-based collaborative care for depression to multicondition collaborative care; and (c) how Dr. Katon's research has informed the work of other leading researchers in the field of psychosomatic medicine and helped develop a new generation of researchers at the interface of psychiatry and primary care. For more than three decades, Dr. Katon led a multidisciplinary research team that conducted seminal epidemiological studies and randomized trials and that influenced the thinking and research in the field of psychiatry in a number of areas: (a) the importance and impact of mental disorders presenting in primary care settings and (b) the organization of effective multidisciplinary care for primary care patients with common mental disorders and comorbid medical conditions. Dr. Katon's work revolutionized the care of psychiatric illnesses in primary care and other medical care settings to the benefit of countless patients worldwide.
Identification and differential usage of a host metalloproteinase entry pathway by SARS-CoV-2 Delta and Omicron

The severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) spike glycoprotein (S) binds to angiotensin-converting enzyme 2 (ACE2) to mediate membrane fusion via two distinct pathways: 1) a surface, serine protease-dependent or 2) an endosomal, cysteine protease-dependent pathway. In this study, we found that SARS-CoV-2 S has a wider protease usage and can also be activated by TMPRSS13 and matrix metalloproteinases (MMPs). We found that MMP-2 and MMP-9 played roles in SARS-CoV-2 S cell-cell fusion and TMPRSS2- and cathepsin-independent viral entry in cells expressing high MMP levels. MMP-dependent viral entry required cleavage at the S1/S2 junction in viral producer cells, and differential processing of variants of concern S dictated its usage; the efficiently processed Delta S preferred metalloproteinase-dependent entry when available, and less processed Omicron S was unable to us metalloproteinases for entry. As MMP-2/9 are released during inflammation, they may play roles in S-mediated cytopathic effects, tropism, and disease outcome.
Albendazole exerts an anti-hepatocellular carcinoma effect through a WWOX-dependent pathway

Hepatocellular carcinoma (HCC) is the sixth most common cancer and the third most common cause of cancer-related deaths. The WW-domain containing oxidoreductase (WWOX) protein suppresses carcinogenesis and its absence is closely related to aggressive HCC phenotypes. In this study, by using SPR analysis, cell viability assay and xenograft mice models, we found that albendazole (ABZ), a safe and effective anthelmintic drug, exhibited the binding affinity with WWOX protein and potential inhibition effect on HCC cells in vitro and in vivo. Overexpression and knockdown of WWOX confirmed that the suppression of HCC by ABZ. Flow cytometric analysis, western blotting analysis and Co-IP were conducted to study the mechanism of ABZ. Our data showed that ABZ regulated the interaction between WWOX and its binding proteins including p53 and C-MYC. Furthermore, ABZ triggered p53-induced intrinsic apoptosis and suppressed EMT-mediated migration by C-MYC/Fibronectin axis. In addition, ∆NP73 expression was significantly inhibited by ABZ, which further sensitized p53-induced intrinsic apoptosis and cell cycle arrest. In summary, ABZ could suppress the proliferation and migration of HCC cells by regulating WWOX-dependent signaling pathway.
Spectral Response and Energy Output of Concentrator Multijunction Solar Cells

The spectral response of concentrator multijunction solar cells has been measured over a temperature range of 25-75 degrees C These data are combined with reference spectra representing the AMI.5 standard as well as annual spectral irradiance at representative geographical locations. The results suggest that higher performance in the field may be obtained if multijunction cells art, designed for an effective air mass higher than AMI.5. Copyright (C) 2008 John Wiley & Sons, Ltd.
Telomerase activity promotes osteoblast differentiation by modulating IGF-signaling pathway

The contribution of deficient telomerase activity to age-related decline in osteoblast functions and bone formation is poorly studied. We have previously demonstrated that telomerase over-expression led to enhanced osteoblast differentiation of human bone marrow skeletal (stromal) stem cells (hMSC) in vitro and in vivo. Here, we investigated the signaling pathways underlying the regulatory functions of telomerase in osteoblastic cells. Comparative microarray analysis and Western blot analysis of telomerase-over expressing hMSC (hMSC-TERT) versus primary hMSC revealed significant up-regulation of several components of insulin-like growth factor (IGF) signaling. Specifically, a significant increase in IGF-induced AKT phosphorylation and alkaline phosphatase (ALP) activity were observed in hMSC-TERT. Enhanced ALP activity was reduced in presence of IGF1 receptor inhibitor: picropodophyllin. In addition, telomerase deficiency caused significant reduction in IGF signaling proteins in osteoblastic cells cultured from telomerase deficient mice (Terc(-/-)). The low bone mass exhibited by Terc(-/-) mice was associated with significant reduction in serum levels of IGF1 and IGFBP3 as well as reduced skeletal mRNA expression of Igf1, Igf2, Igf2r, Igfbp5 and Igfbp6. IGF1-induced osteoblast differentiation was also impaired in Terc(-/-) MSC. In conclusion, our data demonstrate that impaired IGF/AKT signaling contributes to the observed decreased bone mass and bone formation exhibited by telomerase deficient osteoblastic cells.
Optimal Synthesis of Shaped Beams Through Concentric Ring Isophoric Sparse Arrays

An innovative deterministic approach to the optimal power synthesis of mask-constrained shaped beams through the concentric-ring isophoric sparse arrays is presented. The design procedure exploits at best the state-of-the-art techniques, respectively, available in the cases of circular-ring isophoric arrays radiating pencil beams and of linear isophoric arrays generating shaped beams. The technique avoids the exploitation of global-optimization algorithms and allows us to significantly outperform all the (few) available procedures.