ahmedabdelwahed/Medical_papers_title_and_abstract_NLP_dataset huggingface dataset:
The Mediating Effect of Social Anxiety on the Relationship Between Internet Addiction and Aggression in Teenagers

The purpose of this study was to investigate the effect of "internet addiction level" on "aggression level" among teenagers and to examine the mediating role of "social anxiety level" on this effect. The study participants consisted of 958 students attending private teaching institutions for university preparation in Turkey. Data was collected from voluntary participants through data collection forms delivered to the management of these institutions. "Sociodemographic Characteristics Questionnaire Form", "Young's Internet Addiction Test", "Buss-Perry Aggression Questionnaire" and "Social Anxiety Scale" were used as data collection tools. A regression analysis based on the bootstrap method was implemented to test whether "social anxiety level" had a mediating role on the influence of students' "internet addiction level" on "aggression level". According to the findings, it was determined that the indirect effect of "internet addiction" on "aggression" was significant, and therefore, "social anxiety level" mediated the relationship between "internet addiction" and "aggression".
Characterness: An Indicator of Text in the Wild

Text in an image provides vital information for interpreting its contents, and text in a scene can aid a variety of tasks from navigation to obstacle avoidance and odometry. Despite its value, however, detecting general text in images remains a challenging research problem. Motivated by the need to consider the widely varying forms of natural text, we propose a bottom-up approach to the problem, which reflects the characterness of an image region. In this sense, our approach mirrors the move from saliency detection methods to measures of objectness. In order to measure the characterness, we develop three novel cues that are tailored for character detection and a Bayesian method for their integration. Because text is made up of sets of characters, we then design a Markov random field model so as to exploit the inherent dependencies between characters. We experimentally demonstrate the effectiveness of our characterness cues as well as the advantage of Bayesian multicue integration. The proposed text detector outperforms state-of-the-art methods on a few benchmark scene text detection data sets. We also show that our measurement of characterness is superior than state-of-the-art saliency detection models when applied to the same task.
Polynomial matrix decompositions and semi-blind channel estimation for MIMO frequency-selective channels

The authors propose a semi-blind channel estimation (semi-BCE) and precoding/decoding technique for frequency selective (FS) multiple-input multiple-output (MIMO) channels. A FS MIMO channel can be represented using a matrix whose elements are polynomials; hence their method is based on polynomial matrix decomposition. Polynomial eigenvalue decomposition (PEVD) and polynomial QR decomposition (PQRD) are the generalisation of eigenvalue decomposition and QR decomposition; they are suitable for decoupling and precoding of FS MIMO channels. As the coding of communication channels requires reliable estimation of the channel, a semi-BCE scheme, coupled with PQRD/PEVD-based MIMO-channel decomposition, is attractive since this reduces training overhead (pilot transmission) considerably, resulting in higher spectral efficiency. The proposed semi-BCE algorithm is a generalisation of a recently developed single-input single output BCE method to MIMO systems. A new class of PQRD algorithms is introduced, which is based on the recently-developed sequential matrix diagonalisation (SMD). The decoders produced by the proposed SMD-based PQRD algorithm are shown to be more suitable (efficient) for MIMO-channel equalisation than those generated by the prior art. Computer simulations show that the proposed MIMO-channel coding strategy compares favourably to state-of-the-art MIMO systems, in terms of bit error rate performance, while reducing the overhead.
TREATMENT WITH THERESIENöL OF SURGICAL DISEASES OF THE SKIN

Theresienöl is a 100 % natural product representing a mixture of animal and vegetable raw materials from Tyrol. Its exact recipe has been preserved untouched and in deep secret for more than six centuries yet, and has been passed down from generation to generation. Six patients were included in this case series one patient with malignant melanoma of the skin after re-excision with subsequent non-free skin surgical plastic, two patients with III degree skin burning and three patients with infected wound successfully treated with Theresienöl. All of them - before the application of Theresienöl - were treated with different operative methods. The treatment of scars from operative interventions with Theresienöl is very effective. That is why it must start directly after the operative intervention. The therapeutic effect of Theresienöl for postoperative scars is commensurable with and even better than the one of all applied until now local medicines, which makes it an agent of choice in those cases. Theresienöl represents a good alternative to the free skin surgical plastic for small burns of III degree. The local treatment of infected wounds with Theresienoil is more effective and economically sound than the treatment with all the rest types of dressings. The effects from the treatment of different surgical diseases with Theresienöl occur very rapidly, while there is a very good response to local hematomas, pain, and itchiness by the medicine, and there are no side effects from its administration.
Identification and Validation of Yak ( Bos grunniens) Frozen-Thawed Sperm Proteins Associated with Capacitation and the Acrosome Reaction

To achieve fertilization, mammalian spermatozoa must undergo capacitation and the acrosome reaction (AR) within the female reproductive tract. However, the effects of cryopreservation on sperm maturation and fertilizing potential have yet to be established. To gain insight into changes in protein levels within sperm cells prepared for use in the context of fertilization, a comprehensive quantitative proteomic profiling approach was used to analyze frozen-thawed Ashidan yak spermatozoa under three sequential conditions: density gradient centrifugation-based purification, incubation in a capacitation medium, and treatment with the calcium ionophore A23187 to facilitate AR induction. In total, 3280 proteins were detected in these yak sperm samples, of which 3074 were quantified, with 68 and 32 being significantly altered following sperm capacitation and AR induction. Differentially abundant capacitation-related proteins were enriched in the metabolism and PPAR signaling pathways, while differentially abundant AR-related proteins were enriched in the AMPK signaling pathway. These data confirmed a role for superoxide dismutase 1 (SOD1) as a regulator of sperm capacitation while also offering indirect evidence that heat shock protein 90 alpha (HSP90AA1) regulates the AR. Together, these findings offer a means whereby sperm fertility-related marker proteins can be effectively identified. Data are available via Proteome Xchange with identifier PXD035038.
Aspergilloma in a hydatid cavity

Fungal colonization by aspergillus usually occurs in pre-existing lung cavities mostly due to post-tubercular sequelae. Colonization of a hydatid cavity is very rare. We hereby report this unusual co-infection in a 55 years old diabetic male patient who was diagnosed pre-operatively and was managed with surgery, anti-fungal agents, and anthelminthics. The possibility of this co-infection should make clinicians more vigilant in managing hydatid cysts in diabetics and immunocompromised, as they may have concomitant fungal infestation of the hydatid cavity.
Sulfuretin induces osteoblast differentiation through activation of TGF-β signaling

The identification and examination of potential determinants controlling the progression of cell fate toward osteoblasts can be intriguing subjects. In this study, the effects of sulfuretin, a major compound isolated from Rhus verniciflua Stokes, on osteoblast differentiation were investigated. Treatments of sulfuretin induced alkaline phosphatase (ALP) activity in mesenchymal C3H10T1/2 cells and mineralization in preosteoblast MC3T3-E1 cells. Pro-osteogenic effects of sulfuretin were consistently observed in freshly isolated primary bone marrow cells. In mechanical studies, sulfuretin specifically induced expression of TGF-β target genes, such as SMAD7 and PAI-1, but not other signaling pathway-related genes. Similar to the results of gene expression analysis, reporter assays further demonstrated TGF-β-specific induction by sulfuretin. Furthermore, disruption of TGF-β signaling using treatment with TGF-β-specific inhibitor, SB-431542, and introduction of SMAD2/3 small interfering RNA impaired the effects of sulfuretin in inducing ALP activity and expression of ALP mRNA. Together, these data indicate that the pro-osteogenic effects of sulfuretin are mediated through activation of TGF-β signaling, further supporting the potential of sulfuretin in the prevention of bone-related diseases such as bone fracture and osteoporosis.
Aligned visual semantic scene graph for image captioning

Image captioning is a multi-modal task to describe an image into natural language. Many state-of-the-art methods generally take the encoder-decoder architecture, encode an image by the convolution neural networks, or by the structured semantic scene graph that contains the object, relationship and the attribute information. The image scene graph constructed by the existing scene graph generation models are generally too noisy. To alleviate the phenomenon, we propose a multi-level cross-modal alignment (MCA) module to align the image scene graph with the sentence scene graph at different level. MCA can distill the redundant information of the image scene graph according to the sentence scene graph, and providing the commonsense knowledge for the decoder. Except for the semantic relationships, we take advantage of the bounding boxes with the visual objects to compute the implicit spatial relationships for the detected objects. With the aligned scene graph features and the implicit spatial relationship information, our decoder fused them via the dynamic mixtured attention to translate these features into descriptions. Extensive experiments on the MSCOCO dataset got the promising result compared with the state-of-the-art methods, which verified the effectiveness of our method.
Simple Yet Effective Fine-Tuning of Deep CNNs Using an Auxiliary Classification Loss for Remote Sensing Scene Classification

The current literature of remote sensing (RS) scene classification shows that state-of-the-art results are achieved using feature extraction methods, where convolutional neural networks (CNNs) (mostly VGG16 with 138.36 M parameters) are used as feature extractors and then simple to complex handcrafted modules are added for additional feature learning and classification, thus coming back to feature engineering. In this paper, we revisit the fine-tuning approach for deeper networks (GoogLeNet and Beyond) and show that it has not been well exploited due to the negative effect of the vanishing gradient problem encountered when transferring knowledge to small datasets. The aim of this work is two-fold. Firstly, we provide best practices for fine-tuning pre-trained CNNs using the root-mean-square propagation (RMSprop) method. Secondly, we propose a simple yet effective solution for tackling the vanishing gradient problem by injecting gradients at an earlier layer of the network using an auxiliary classification loss function. Then, we fine-tune the resulting regularized network by optimizing both the primary and auxiliary losses. As for pre-trained CNNs, we consider in this work inception-based networks and EfficientNets with small weights: GoogLeNet (7 M) and EfficientNet-B0 (5.3 M) and their deeper versions Inception-v3 (23.83 M) and EfficientNet-B3 (12 M), respectively. The former networks have been used previously in the context of RS and yielded low accuracies compared to VGG16, while the latter are new state-of-the-art models. Extensive experimental results on several benchmark datasets reveal clearly that if fine-tuning is done in an appropriate way, it can settle new state-of-the-art results with low computational cost.
Proximity Detection During Epidemics: Direct UWB TOA Versus Machine Learning Based RSSI

In this paper, we compare the direct TOA-based UWB technology with the RSSI-based BLE technology using machine learning algorithms for proximity detection during epidemics in terms of complexity of implementation, availability in existing smart phones, and precision of the results. We establish the theoretical limits on the precision and confidence of proximity estimation for both technologies using the Cramer Rao Lower Bound (CRLB) and validate the theoretical foundations using empirical data gathered in diverse practical operating scenarios. We perform our empirical experiments at eight distances in three flat environments and one non-flat environment encompassing both Line of Sight (LOS) and Obstructed-LOS (OLOS) situations. We also analyze the effects of various postures (eight angles) of the person carrying the sensor, and four on-body locations of the sensor. To estimate the range with BLE RSSI, we use 14 features for training the Gradient Boosted Machines (GBM) learning algorithm and we compare the precision of results with those obtained from memoryless UWB TOA ranging algorithm. We show that the memoryless UWB TOA algorithm achieves 93.60% confidence, slightly outperforming the 92.85% confidence of the BLE RSSI with more complex GBM machine learning (ML) algorithm and the need for substantial training. The training process for the RSSI-based BLE social distance measurements involved 3000 measurements to create a training dataset for each scenario and post-processing of data to extract 14 features of RSSI, and the ML classification algorithm consumed 200 s of computational time. The memoryless UWB ranging algorithm achieves more robust results without any need for training in less than 0.5 s of computation time.
Mansonic neuroschistosomiasis in the childhood: an undiagnosed pathology?

Schistosomiasis is an endemic parasitic disease in several tropical countries. In Brazil, the only prevalent species of parasite responsible for schistosomiasis is Schistosoma mansoni. Neuroschistosomiasis is the second most frequent form of infection and the primary ectopic manifestation, with predominant involvement of the lower thoracic spinal cord and lumbar and lumbosacral regions. The frequent contact of children with contaminated ponds and the immaturity of their immune systems make this age group especially susceptible to infection by this parasite. Therefore, neuroschistosomiasis mansoni should always be considered in cases of transverse myelitis in children from endemic regions. The treatment for this condition is quite simple and effective, resulting in total recovery of neurological deficits if the diagnosis is made early.
Knowledge-Based Representation Learning for Nucleus Instance Classification From Histopathological Images

The classification of nuclei in H&E-stained histopathological images is a fundamental step in the quantitative analysis of digital pathology. Most existing methods employ multi-class classification on the detected nucleus instances, while the annotation scale greatly limits their performance. Moreover, they often downplay the contextual information surrounding nucleus instances that is critical for classification. To explicitly provide contextual information to the classification model, we design a new structured input consisting of a content-rich image patch and a target instance mask. The image patch provides rich contextual information, while the target instance mask indicates the location of the instance to be classified and emphasizes its shape. Benefiting from our structured input format, we propose Structured Triplet for representation learning, a triplet learning framework on unlabelled nucleus instances with customized positive and negative sampling strategies. We pre-train a feature extraction model based on this framework with a large-scale unlabeled dataset, making it possible to train an effective classificationmodel with limited annotated data. We also add two auxiliary branches, namely the attribute learning branch and the conventional self-supervised learning branch, to further improve its performance. As part of this work, we will release a new dataset of H&E-stained pathology images with nucleus instance masks, containing 20,187 patches of size 1024 x 1024, where each patch comes from a different whole- slide image. The model pre-trained on this dataset with our framework significantly reduces the burden of extensive labeling. We show a substantial improvement in nucleus classification accuracy compared with the state-ofthe-art methods.
Latent-Class Hough Forests for 6 DoF Object Pose Estimation

In this paper we present Latent-Class Hough Forests, a method for object detection and 6 DoF pose estimation in heavily cluttered and occluded scenarios. We adapt a state of the art template matching feature into a scale-invariant patch descriptor and integrate it into a regression forest using a novel template-based split function. We train with positive samples only and we treat class distributions at the leaf nodes as latent variables. During testing we infer by iteratively updating these distributions, providing accurate estimation of background clutter and foreground occlusions and, thus, better detection rate. Furthermore, as a by-product, our Latent-Class Hough Forests can provide accurate occlusion aware segmentation masks, even in the multi-instance scenario. In addition to an existing public dataset, which contains only single-instance sequences with large amounts of clutter, we have collected two, more challenging, datasets for multiple-instance detection containing heavy 2D and 3D clutter as well as foreground occlusions. We provide extensive experiments on the various parameters of the framework such as patch size, number of trees and number of iterations to infer class distributions at test time. We also evaluate the Latent-Class Hough Forests on all datasets where we outperform state of the art methods.
The growth-regulating factor PdbGRF1 positively regulates the salt stress response in Populus davidiana × P. bolleana

Growth-regulating factor (GRF) is a transcription factor unique to plants that plays a crucial role in the growth, development and stress adaptation of plants. However, information on the GRFs related to salt stress in Populus davidiana × P. bolleana is lacking. In this study, we characterized the activity of PdbGRF1 in transgenic Populus davidiana × P. bolleana under salt stress. qRTPCR analyses showed that PdbGRF1 was highly expressed in young leaves and that the pattern of PdbGRF1 expression was significantly changed at most time points under salt stress, which suggests that PdbGRF1 expression may be related to the salt stress response. Moreover, PdbGRF1 overexpression enhanced tolerance to salt stress. A physiological parameter analysis showed that the overexpression of PdbGRF1 significantly decreased the contents of hydrogen peroxide (H2O2) and malondialdehyde (MDA) and increased the activities of antioxidant enzymes (SOD and POD) and the proline content. A molecular analysis showed that PdbGRF1 regulated the expression of PdbPOD17 and PdbAKT1 by binding to the DRE ('A/GCCGAC') in their respective promoters. Together, our results demonstrate that the binding of PdbGRF1 to DRE regulates genes related to stress tolerance and activates the associated physiological pathways, and these effects increase the ROS scavenging ability, reduce the degree of damage to the plasma membrane and ultimately enhance the salt stress response in Populus davidiana × P. bolleana.
Challenges in the diagnosis and management of immune-mediated necrotising myopathy (IMNM) in a patient on long-term statins

Immune-mediated necrotising myopathy (IMNM) is a severe and poorly understood complication of statin use. Prompt management with immunosuppressive treatment is often needed to control the condition, which differs from the management of the more commonly recognised statin-induced myopathy. We present a case report and brief review of the literature regarding the pathogenesis, diagnosis, and management of anti-3-hydroxy-3-methylglutaryl-coenzyme A reductase (HMGCR) positive IMNM (HMGCR IMNM). There are no randomised clinical trials, but several smaller studies and cases suggest a triple therapy of corticosteroids, IVIG, and a corticosteroid-sparing immunosuppressant appears efficacious in patients with IMNM and proximal weakness. The mechanism of statin-induced IMNM is uncertain, and this is further complicated by the reports of HMGCR IMNM in statin-naïve patients, including children. We present a case of biopsy-confirmed HMGCR IMNM in a woman taking daily statins for treatment of hypercholesterolaemia for 4 years. She presented with symptoms consistent with a urinary tract infection (UTI), including muscle weakness. She was treated as an isolated case of UTI. One month later, she presented again with worsening weakness in her shoulders and hips. Creatine kinase was elevated, and MRI showed increased signal with STIR sequences in both thighs. Anti-HMGCR was positive and leg biopsy-confirmed necrotising changes. Stopping her statin prescription and a short course of prednisolone did not improve her muscle weakness. Adding methotrexate resulted in eventual resolution of her symptoms. IMNM should be considered as a differential in any patient taking statins presenting with muscle weakness, and this case suggests that immunosuppressant therapy in addition to cessation of statins is effective at treating IMNM. Clinical trials are needed to further investigate the efficacy of different combinations of immunosuppressants.
The intestinal microbiota in colorectal cancer metastasis - Passive observer or key player?

The association between colorectal cancer (CRC) and alterations in intestinal microbiota has been demonstrated by several studies, and there is increasing evidence that bacteria are an important component of the tumour microenvironment. Bacteria may contribute to the development of CRC metastasis by signalling through metabolites, promoting epithelial-mesenchymal transition, creating an immunosuppressive microenvironment and through the impairment of the gut-vascular barrier. Host immunity and intestinal microbiome symbiosis play a key role in determining innate and adaptive immune responses at the local and systemic level. How this gut-systemic axis might contribute to the development of CRC metastasis is however unclear. Several clinical trials are investigating the impact of microbiome-targeted interventions on the systemic inflammatory response, treatment-related complications, and side effects. This review examines pre-clinical and clinical studies which have examined the role of microbes in relation to CRC metastasis, the mechanisms which may contribute to tumour dissemination, and directions for future work.