Miracle-dz/UniFAR-FTUs huggingface dataset:
Immunostimulatory membrane proteins potentiate H. pylori-induced carcinogenesis by enabling CagA translocation

ABSTRACT Infection with Helicobacter pylori is the single greatest risk factor for developing gastric adenocarcinoma. In prospective, population-based studies, seropositivity to the uncharacterized H. pylori proteins Hp0305 and Hp1564 was significantly associated with cancer risk in East Asia. However, the mechanism underlying this observation has not been elucidated. Here, we show that Hp0305 and Hp1564 act in concert with previously ascribed H. pylori virulence mechanisms to orchestrate cellular alterations that promote gastric carcinogenesis. In samples from 546 patients exhibiting premalignant gastric lesions, seropositivity to Hp0305 and Hp1564 was significantly associated with increased gastric atrophy across all stomach conditions. In vitro, depletion of Hp0305 and Hp1564 significantly reduced levels of gastric cell-associated bacteria and markedly impaired the ability of H. pylori to stimulate pro-inflammatory cytokine production. Remarkably, our studies revealed that Hp1564 is required for translocation of the oncoprotein CagA into gastric epithelial cells. Our data provide experimental insight into the molecular mechanisms governing novel H. pylori pathogenicity factors that are strongly associated with gastric disease and highlight the potential of Hp0305 and Hp1564 as robust molecular tools that can improve identification of individuals that are highly susceptible to gastric cancer. We demonstrate that Hp0305 and Hp1564 augment H. pylori-mediated inflammation and gastric cancer risk by promoting key bacteria-gastric cell interactions that facilitate delivery of oncogenic microbial cargo to target cells. Thus, therapeutically targeting microbial interactions driven by Hp0305/Hp1564 may enable focused H. pylori eradication strategies to prevent development of gastric malignancies in high-risk populations.
Melatonin downregulates TRPC6, impairing store-operated calcium entry in triple-negative breast cancer cells

Melatonin has been reported to induce effective reduction in growth and development in a variety of tumors, including breast cancer. In triple-negative breast cancer (TNBC) cells, melatonin attenuates a variety of cancer features, such as tumor growth and apoptosis resistance, through a number of still poorly characterized mechanisms. One biological process that is important for TNBC cells is store-operated Ca2+ entry (SOCE), which is modulated by TRPC6 expression and function. We wondered whether melatonin might intersect with this pathway as part of its anticancer activity. We show that melatonin, in the nanomolar range, significantly attenuates TNBC MDA-MB-231 cell viability, proliferation, and migration in a time- and concentration-dependent manner, without having any effect on nontumoral breast epithelial MCF10A cells. Pretreatment with different concentrations of melatonin significantly reduced SOCE in MDA-MB-231 cells without altering Ca2+ release from the intracellular stores. By contrast, SOCE in MCF10A cells was unaffected by melatonin. In the TNBC MDA-MB-468 cell line, melatonin not only attenuated viability, migration, and SOCE, but also reduced TRPC6 expression in a time- and concentration-dependent manner, without altering expression or function of the Ca2+ channel Orai1. The expression of exogenous TRPC6 overcame the effect of melatonin on SOCE and cell proliferation, and silencing or inhibition of TRPC6 impaired the inhibitory effect of melatonin on SOCE. These findings indicate that TRPC6 downregulation might be involved in melatonin's inhibitory effects on Ca2+ influx and the maintenance of cancer hallmarks and point toward a novel antitumoral mechanism of melatonin in TNBC cells.
Factors Affecting Complementary Feeding of Infants. A Pilot Study Conducted after the Introduction of New Infant Feeding Guidelines in Poland

Adequate nutrition in the first year of life is the key prerequisite for a child’s healthy growth and development. The success of complementary feeding is influenced by various factors, including the family’s socioeconomic status, maternal age, place of residence and educational level, older children and duration of breastfeeding. Modified infant feeding guidelines were introduced in Poland in 2016. The aim of this study was to identify the factors that exert the greatest influence on infant feeding practices in the Polish population. A thorough understanding of maternal factors that are responsible for undesirable feeding practices is required to improve the mothers’ knowledge and to promote healthy feeding practices. This study was carried out in March–June 2018 and between November 2018 and March 2019 in the Region of Lublin in southeastern Poland. The mothers of children aged 9–14 months, who had introduced complementary foods, were invited to the study. A total of 441 mothers agreed to participate, and 289 of them fully and correctly completed the questionnaires. Logistic regression models were developed to assess the association between maternal factors, such as age, educational level and the nutrition knowledge score, and introduction of solid foods. The infant’s sex and birth weight and the mother’s place of residence had no significant influence on the duration of breastfeeding. The mother’s age and educational level, the number of children in the family and maternal nutrition knowledge scores contributed to significant differences in breastfeeding duration. Older mothers (30–34 years vs. 25–29 years, p = 0.001), better educated mothers (university vs. secondary school education p = 0.002) and mothers with one or two children exclusively breastfed their children for longer (17 weeks vs. 11 weeks, p = 0.002) than younger mothers with secondary school education and more than three children. Exclusive formula feeding was significantly correlated with untimely introduction of solid foods compared to exclusive breastfeeding (13 weeks vs. 19.7 weeks, p = 0.001). Mothers with nutrition knowledge scores in the upper tertile were more likely to adapt food consistency to the skills manifested by the child (88%) (OR = 1.88; Cl: 1.53–2.26, p < 0.05) and were less likely to delay the introduction of new foods that required chewing and biting (84%) (OR = 0.22; Cl: 0.09–0.34, p < 0.05) than mothers with nutrition knowledge scores in the bottom tertile. Maternal age, educational level and nutrition knowledge significantly increased the age at which infants were introduced to solid foods and the correct identification of the signs indicative of the child’s readiness to explore new tastes and foods with a new consistency. The above factors contributed to delayed, but not untimely, introduction of complementary foods (6 months of age or later), including gluten.
EEG-Inception: A Novel Deep Convolutional Neural Network for Assistive ERP-Based Brain-Computer Interfaces

In recent years, deep-learning models gained attention for electroencephalography (EEG) classification tasks due to their excellent performance and ability to extract complex features from raw data. In particular, convolutional neural networks (CNN) showed adequate results in brain-computer interfaces (BCI) based on different control signals, including event-related potentials (ERP). In this study, we propose a novel CNN, called EEG-Inception, that improves the accuracy and calibration time of assistive ERP-based BCIs. To the best of our knowledge, EEG-Inception is the first model to integrate Inception modules for ERP detection, which combined efficiently with other structures in a light architecture, improved the performance of our approach. The model was validated in a population of 73 subjects, of which 31 present motor disabilities. Results show that EEG-Inception outperforms 5 previous approaches, yielding significant improvements for command decoding accuracy up to 16.0%, 10.7%, 7.2%, 5.7% and 5.1% in comparison to rLDA, xDAWN + Riemannian geometry, CNN-BLSTM, DeepConvNet and EEGNet, respectively. Moreover, EEG-Inception requires very few calibration trials to achieve state-of-the-art performances taking advantage of a novel training strategy that combines cross-subject transfer learning and fine-tuning to increase the feasibility of this approach for practical use in assistive applications.
The relationship between smartphone addiction and musculoskeletal pain prevalence among young population: a cross-sectional study

Background In the literature, there have been debates as to whether smartphone use has negative effects on physical and mental health. The present study investigated the extent to which smartphone addiction impacts on musculoskeletal pain prevalence among university students. Methods The questionnaire consisted of three sections demographic information, the Smartphone Addiction Scale (SAS), and the modified Nordic Musculoskeletal Questionnaire. Results A total of 249 participants were included in this cross-sectional study. The body parts that were reported with highest prevalence of musculoskeletal pain were the upper back (70.3%), neck (65.9%), and wrists/hands (68.7%). The SAS scores were correlated with duration of smartphone use on a typical day (P = 0.001), duration of owning a smartphone (P = 0.027), and musculoskeletal pain prevalence in the neck (P = 0.001), wrists/hands (P = 0.001), shoulders (P = 0.025), and upper back (P = 0.023). The SAS score was significantly associated with prevalence of musculoskeletal pain in the neck (odd ratio [OR], 1.08; 95% confidence interval [CI], 0.98-1.10; P = 0.002), wrists/hands (OR, 1.07; 95% CI, 0.97-1.09; P = 0.001), and upper back (OR, 1.10; 95% CI, 0.98-1.11; P = 0.033). Conclusions The findings indicated that the upper back, neck, and wrists/hands have a higher prevalence of musculoskeletal pain among smartphone users, particularly those with a smartphone addiction. Smartphone addiction scores were correlated with duration of smartphone use on a typical day, duration of owning smartphone, and musculoskeletal pain prevalence in the neck, wrists/hands, shoulders, and upper back.
Doses e épocas de aplicação de nitrogênio e teores foliares deste nutriente e de clorofila em feijoeiro

The effect of N applications (time and rates) on chlorophyll and N contents in bean leaves was evaluated and correlations among them and with the yield established. Carried out in the Selviria region, State of Minas Gerais, Brazil, the experiment was set up in a completely randomized block design in a factorial 3 x 5 scheme. Treatments consisted in the combination of N (urea) application by side dressing at five levels (0, 35, 70, 105, and 140 kg ha-1 of N) at three times (15, 30, and 15 + 30 days after plant emergence). Chlorophyll and N contents in leaves during the full flowering period were determined, as well as yields. All data were correlated to each other and to the N levels. The chlorophyll content presented a positive correlation with N leaf contents and with grain production. The portable chlorophyll meter proved its suitability as a tool to assess the nutritional state of N in bean plants.
The EBJIS definition of periprosthetic joint infection

Aims The diagnosis of periprosthetic joint infection (PJI) can be difficult. All current diagnostic tests have problems with accuracy and interpretation of results. Many new tests have been proposed, but there is no consensus on the place of many of these in the diagnostic pathway. Previous attempts to develop a definition of PJI have not been universally accepted and there remains no reference standard definition. Methods This paper reports the outcome of a project developed by the European Bone and Joint Infection Society (EBJIS), and supported by the Musculoskeletal Infection Society (MSIS) and the European Society of Clinical Microbiology and Infectious Diseases (ESCMID) Study Group for Implant-Associated Infections (ESGIAI). It comprised a comprehensive review of the literature, open discussion with Society members and conference delegates, and an expert panel assessment of the results to produce the final guidance. Results This process evolved a three-level approach to the diagnostic continuum, resulting in a definition set and guidance, which has been fully endorsed by EBJIS, MSIS, and ESGIAI. Conclusion The definition presents a novel three-level approach to diagnosis, based on the most robust evidence, which will be useful to clinicians in daily practice. Cite this article: Bone Joint J 2021;103-B(1):18–25.
Effects of Resveratrol on Thymic Stromal Lymphopoietin Expression in Mast Cells

Background and objectives: Cytokine thymic stromal lymphopoietin (TSLP) plays a pivotal role in the pathogenesis of atopic diseases such as atopic dermatitis, allergic rhinitis, and asthma. Resveratrol (RSV) exerts various pharmacological effects such as antioxidant, anti-inflammatory, neuroprotective, and anticancer. Although, it has been verified the beneficial effects of RSV on various subjects, the effect of RSV on thymic stromal lymphopoietin (TSLP) regulation has not been elucidated. Materials and Methods: Here, we examined how RSV regulates TSLP in HMC-1 cells. Enzyme-linked immunosorbent assay, real-time polymerase chain reaction, Western blotting, and calcium assay were performed to evaluate the effect of RSV. Results: TSLP production and mRNA expression were reduced by RSV. RSV down-regulated nuclear factor-κB activation, IκBα phosphorylation as well as activation of receptor-interacting protein2 and caspase-1 in HMC-1 cells. In addition, RSV treatment decreased the up-regulation of intracellular calcium in HMC-1 cells. Conclusions: These results suggest that RSV might be useful for the treatment of atopic diseases through blocking of TSLP.
Phenylhydrazone and Quinazoline Derivatives from the Cold-Seep-Derived Fungus Penicillium oxalicum

Three new phenylhydrazones, penoxahydrazones A–C (compounds 1–3), and two new quinazolines, penoxazolones A (compound 4) and B (compound 5), with unique linkages were isolated from the fungus Penicillium oxalicum obtained from the deep sea cold seep. Their structures and relative configurations were assigned by analysis of 1D/2D NMR and mass spectroscopic data, and the absolute configurations of 1, 4, and 5 were established on the basis of X-ray crystallography or ECD calculations. Compound 1 represents the first natural phenylhydrazone-bearing steroid, while compounds 2 and 3 are rarely occurring phenylhydrazone tautomers. Compounds 4 and 5 are enantiomers that feature quinazoline and cinnamic acid units. Some isolates exhibited inhibition of several marine phytoplankton species and marine-derived bacteria.
Pathological Insight into 5-HT2B Receptor Activation in Fibrosing Interstitial Lung Diseases

Interstitial lung disease (ILD) encompasses a heterogeneous group of more than 200 conditions, of which primarily idiopathic pulmonary fibrosis (IPF), idiopathic nonspecific interstitial pneumonia, hypersensitivity pneumonitis, ILD associated with autoimmune diseases and sarcoidosis may present a progressive fibrosing (PF) phenotype. Despite different aetiology and histopathological patterns, the PF-ILDs have similarities regarding disease mechanisms with self-sustaining fibrosis, which suggests that the diseases may share common pathogenetic pathways. Previous studies show an enhanced activation of serotonergic signaling in pulmonary fibrosis, and the serotonin (5-HT)2 receptors have been implicated to have important roles in observed profibrotic actions. Our research findings in support by others, demonstrate antifibrotic effects with 5-HT2B receptor antagonists, alleviating several key events common for the fibrotic diseases such as myofibroblast differentiation and connective tissue deposition. In this review, we will address the potential role of 5-HT and in particular the 5-HT2B receptors in three PF-ILDs: ILD associated with systemic sclerosis (SSc-ILD), ILD associated with rheumatoid arthritis (RA-ILD) and IPF. Highlighting the converging pathways in these diseases discloses the 5-HT2B receptor as a potential disease target for PF-ILDs, which today have an urgent unmet need for therapeutic strategies.
Collision Prevention for Duty-Cycle Receiver-Initiation MAC Protocol via Multiple Access Reservation (MAR-RiMAC)

The prevalence of the Internet of Things (IoT) paradigm in more and more applications associated with our daily lives has induced a dense network in which numerous wireless devices, many of which have limited capabilities (e.g., power, memory, computation), need to communicate with the internet. One of the main bottlenecks of this setup is the wireless channel. Numerous medium access control (MAC) protocols have been devised to coordinate between devices that share the wireless channel. One prominent approach that is highly suitable for IoT and wireless sensor networks (WSNs), which rely on duty cycling, is the receiver-initiated approach, in which, rather than the transmitter, the receiver initiates the transaction. The problem with this approach is that when many devices are trying to respond to the receiver’s transmission invitation and transmit simultaneously, a collision occurs. When the network is highly loaded, resolving such collisions is quite tedious. In this paper, we devise an enhancement to the receiver-initiated approach that aims at preventing this inherent collision scenario. Our modification relies on multiple devices sending a short predefined signal, informing their intended receiver of their intention to transmit simultaneously. The data transaction is done via a four-way handshake in which, after all backlogged devices have informed their designated receiver of their desire to transmit simultaneously, the receiver identifies them and polls them one by one, avoiding the collision. We compare the performance of Receiver-Initiated-MAC protocol (RI-MAC), which is one of the prevalent receiver-initiated protocols, with and without the suggested enhancement, and show superior air-time utilization under high traffic loads, especially in the presence of hidden terminals.
Conditional out-of-distribution generation for unpaired data using transfer VAE.

MOTIVATION While generative models have shown great success in sampling high-dimensional samples conditional on low-dimensional descriptors (stroke thickness in MNIST, hair color in CelebA, speaker identity in WaveNet), their generation out-of-distribution poses fundamental problems due to the difficulty of learning compact joint distribution across conditions. The canonical example of the conditional variational autoencoder (CVAE), for instance, does not explicitly relate conditions during training and, hence, has no explicit incentive of learning such a compact representation. RESULTS We overcome the limitation of the CVAE by matching distributions across conditions using maximum mean discrepancy in the decoder layer that follows the bottleneck. This introduces a strong regularization both for reconstructing samples within the same condition and for transforming samples across conditions, resulting in much improved generalization. As this amount to solving a style-transfer problem, we refer to the model as transfer VAE (trVAE). Benchmarking trVAE on high-dimensional image and single-cell RNA-seq, we demonstrate higher robustness and higher accuracy than existing approaches. We also show qualitatively improved predictions by tackling previously problematic minority classes and multiple conditions in the context of cellular perturbation response to treatment and disease based on high-dimensional single-cell gene expression data. For generic tasks, we improve Pearson correlations of high-dimensional estimated means and variances with their ground truths from 0.89 to 0.97 and 0.75 to 0.87, respectively. We further demonstrate that trVAE learns cell-type-specific responses after perturbation and improves the prediction of most cell-type-specific genes by 65%. AVAILABILITY AND IMPLEMENTATION The trVAE implementation is available via github.com/theislab/trvae. The results of this article can be reproduced via github.com/theislab/trvae_reproducibility.
Identification of Dish-Based Dietary Patterns for Breakfast, Lunch, and Dinner and Their Diet Quality in Japanese Adults

We identified dish-based dietary patterns for breakfast, lunch, and dinner and assessed the diet quality of each pattern. Dietary data were obtained from 392 Japanese adults aged 20–69 years in 2013, using a 4 d dietary record. K-means cluster analysis was conducted based on the amount of each dish group, separately for breakfasts (n = 1462), lunches (n = 1504), and dinners (n = 1500). The diet quality of each dietary pattern was assessed using the Healthy Eating Index 2015 (HEI-2015) and Nutrient-Rich Food Index 9.3 (NRF9.3). The extracted dietary patterns were as follows: ‘bread-based’ and ‘rice-based’ for breakfast; ‘bread’, ‘rice-based’, ‘ramen’, ‘udon/soba’, and ‘sushi/rice bowl dishes’ for lunch; and ‘miscellaneous’, ‘meat dish and beer’, and ‘hot pot dishes’ for dinner. For breakfast, the HEI-2015 and NRF9.3 total scores were higher in the ‘rice-based’ pattern than the ‘bread-based’ pattern. For lunch, the HEI-2015 and NRF9.3 total scores were relatively high in the ‘rice-based’ pattern and low in the ‘ramen’ pattern. For dinner, the HEI-2015 total score was the highest in the ‘meat dish and beer’ pattern, and the NRF9.3 total score was higher in the ‘hot pot dishes’ than the ‘miscellaneous’ pattern. These results suggested that breakfast, lunch, and dinner have distinctive dietary patterns with different diet qualities.
The Heidelberg Spiking Data Sets for the Systematic Evaluation of Spiking Neural Networks

Spiking neural networks are the basis of versatile and power-efficient information processing in the brain. Although we currently lack a detailed understanding of how these networks compute, recently developed optimization techniques allow us to instantiate increasingly complex functional spiking neural networks in-silico. These methods hold the promise to build more efficient non-von-Neumann computing hardware and will offer new vistas in the quest of unraveling brain circuit function. To accelerate the development of such methods, objective ways to compare their performance are indispensable. Presently, however, there are no widely accepted means for comparing the computational performance of spiking neural networks. To address this issue, we introduce two spike-based classification data sets, broadly applicable to benchmark both software and neuromorphic hardware implementations of spiking neural networks. To accomplish this, we developed a general audio-to-spiking conversion procedure inspired by neurophysiology. Furthermore, we applied this conversion to an existing and a novel speech data set. The latter is the free, high-fidelity, and word-level aligned Heidelberg digit data set that we created specifically for this study. By training a range of conventional and spiking classifiers, we show that leveraging spike timing information within these data sets is essential for good classification accuracy. These results serve as the first reference for future performance comparisons of spiking neural networks.