Miracle-dz/UniFAR-FTUs huggingface dataset:
Linking Signal Relevancy and Intensity in Predictive Tactile Suppression

Predictable somatosensory feedback leads to a reduction in tactile sensitivity. This phenomenon, called tactile suppression, relies on a mechanism that uses an efference copy of motor commands to help select relevant aspects of incoming sensory signals. We investigated whether tactile suppression is modulated by (a) the task-relevancy of the predicted consequences of movement and (b) the intensity of related somatosensory feedback signals. Participants reached to a target region in the air in front of a screen; visual or tactile feedback indicated the reach was successful. Furthermore, tactile feedback intensity (strong vs. weak) varied across two groups of participants. We measured tactile suppression by comparing detection thresholds for a probing vibration applied to the finger either early or late during reach and at rest. As expected, we found an overall decrease in late-reach suppression, as no touch was involved at the end of the reach. We observed an increase in the degree of tactile suppression when strong tactile feedback was given at the end of the reach, compared to when weak tactile feedback or visual feedback was given. Our results suggest that the extent of tactile suppression can be adapted to different demands of somatosensory processing. Downregulation of this mechanism is invoked only when the consequences of missing a weak movement sequence are severe for the task. The decisive factor for the presence of tactile suppression seems not to be the predicted action effect as such, but the need to detect and process anticipated feedback signals occurring during movement.
Where National Medicines Policies Have Taken Us With Patient Involvement and Health Technology Assessment in Africa

The Covid-19 pandemic has highlighted global knowledge about, but lack of equitable access to, life-changing medicines, and other innovative medical products by populations in African low and middle income countries. The World Health Organization (WHO) and other international non-profit foundations and organizations are constantly striving to address inequity. In the 1970s, WHO initiated a regularly updated essential medicines list, together with the concept of national medicines policies (NMPs) to ensure access and availability, affordability, rational, and effective use of medicines which are considered essential in addressing predominant population health issues and disease burden. We studied the NMPs of Ghana, South Africa, Uganda and Zimbabwe to highlight some of the important issues that these countries experience in the safe and effective use of medical products. Thailand is an example of how health technology assessment (HTA) can provide a country with an internationally supported, clearly defined and transparent process to broaden access to medicines and services. These medical services can add considerable value in accordance with local values and priorities. Involvement of civil society adds democratic legitimacy to such processes. Community health workers and patient advocacy groups are important in raising awareness and knowledge of safety issues and the effective use of quality medicines. They can apply pressure for increased funding to improve access to healthcare. Medicines and services that contribute to supported self-care are of benefit in any setting. Joint efforts across African countries such as with the African Medicines Agency are important in addressing some of the major health issues.
Calcium-Mediated Regulation Promotes the Biofilm Formation of Two Novel Pyridine-Degrading Bacteria

In bioaugmented wastewater treatment systems, it is essential for recalcitrant pollutant-degrading bacteria to form biofilms. Inducing biofilm formation in these bacteria, however, is challenging as it involves multiple inter-related regulating pathways and environmental factors. Herein, we report the remarkable promoting effect of Ca2+ on biofilm formation of two novel pyridine-degrading bacteria with poor innate biofilm-forming capabilities, Pseudomonas sp. ZX01 and Arthrobacter sp. ZX07. The roles of Ca2+ in different biofilm development stages were investigated. Our data showed strong influences of Ca2+ on the initial attachment of the two strains onto positively charged glass surfaces by altering cell surface charge as well as the cation bridging effect. Contrary to many other biofilm promoting mechanisms, Ca2+ downregulated the extracellular polymeric substances (EPS) production per cell in both Pseudomonas sp. ZX01 and Arthrobacter sp. ZX07, while increasing biofilm biomass. This is attributed to the strong cationic bridging between Ca2+ and EPS which can elevate the efficiency of the extracellular products in binding bacterial cells. Furthermore, Ca2+ increased the protein-to-polysaccharide (PN/PS) ratio in biofilm EPS of both strains, which favored cell aggregation, and biofilm establishment by increasing the hydrophobicity of cell surfaces. More intriguingly, the intracellular c-di-GMP, which can drive the switch of bacterial lifestyle from planktonic state to biofilm state, was also elevated markedly by exogenous Ca2+. Taken together, these results would be of guidance for applying the two strains into bioaugmented biofilm reactors where Ca2+ supplement strategy can be employed to facilitate their biofilm formation on the surfaces of engineering carriers.
Characterizing Heparin Tetrasaccharides Binding to Amyloid-Beta Peptide

The aggregation of β-amyloid peptide (Aβ) is one potential cause for Alzheimer’s disease (AD). Heparin can either promote or inhibit Aβ aggregation. The sulfation pattern and chain size determine its binding affinity and its role. Using 2D-NMR analysis and molecular modelling, the binding motif of heparin oligoaccharides to Aβ was determined to be HexA-GlcNS-IdoA2S-GlcNS6S. Iduronic acid epimerization and 6-O-sulfation are key factors for the binding affinity, while 3-O-sulfation of Arixtra (heparin pentasaccharide) is not involved in the binding to Aβ. Hydrogen-deuterium exchange mass spectrometry (HDX-MS) was used to study the glycosaminoglycan (GAG)-peptide complex and identified V12HHQKL17 as the binding site of GAG at Aβ. Furthermore, an MTT assay was applied to evaluate the anti-Aβ fibril formation function of heparin tetrasaccharide, and indicated that the heparin tetrasaccharide with the defined sequence represents a promising inhibitor of Aβ aggregation.
Self-Report Measures of Procrastination Exhibit Inconsistent Concurrent Validity, Predictive Validity, and Psychometric Properties

Procrastination is a chronic and widespread problem; however, emerging work raises questions regarding the strength of the relationship between self-reported procrastination and behavioral measures of task engagement. This study assessed the internal reliability, concurrent validity, predictive validity, and psychometric properties of 10 self-report procrastination assessments using responses collected from 242 students. Participants’ scores on each self-report instrument were compared to each other using correlations and cluster analysis. Lasso estimation was used to test the self-report scores’ ability to predict two behavioral measures of delay (days to study completion; pacing style). The self-report instruments exhibited strong internal reliability and moderate levels of concurrent validity. Some self-report measures were predictive of days to study completion. No self-report measures were predictive of deadline action pacing, the pacing style most commonly associated with procrastination. Many of the self-report measures of procrastination exhibited poor fit. These results suggest that researchers should exercise caution in selecting self-report measures and that further study is necessary to determine the factors that drive misalignment between self-reports and behavioral measures of delay.
Integrating Gemcitabine-Based Therapy With AdipoRon Enhances Growth Inhibition in Human PDAC Cell Lines

Pancreatic ductal adenocarcinoma (PDAC) accounts for 90% of all pancreatic cancers. Albeit its incidence does not score among the highest in cancer, PDAC prognosis is tremendously fatal. As a result of either aggressiveness or metastatic stage at diagnosis, chemotherapy constitutes the only marginally effective therapeutic approach. As gemcitabine (Gem) is still the cornerstone for PDAC management, the low response rate and the onset of resistant mechanisms claim for additional therapeutic strategies. The first synthetic orally active adiponectin receptor agonist AdipoRon (AdipoR) has recently been proposed as an anticancer agent in several tumors, including PDAC. To further address the AdipoR therapeutic potential, herein we investigated its pharmacodynamic interaction with Gem in human PDAC cell lines. Surprisingly, their simultaneous administration revealed a more effective action in contrasting PDAC cell growth and limiting clonogenic potential than single ones. Moreover, the combination AdipoR plus Gem persisted in being effective even in Gem-resistant MIA PaCa-2 cells. While a different ability in braking cell cycle progression between AdipoR and Gem supported their cooperating features in PDAC, mechanistically, PD98059-mediated p44/42 MAPK ablation hindered combination effectiveness. Taken together, our findings propose AdipoR as a suitable partner in Gem-based therapy and recognize the p44/42 MAPK pathway as potentially involved in combination outcomes.
Impact of anthropogenic disturbances on the liana diversity and need for conservation of resource valued species-Evidences from Indian tropical dry evergreen forests

Re-inventory of lianas were performed in two 1-ha permanent plots at a 19-year interval (2001–2020) in two tropical dry evergreen forest sites (TDEF; Oorani -OR and Puthupet PP) of Coromandel Coast of India. Overall, 1964 lianas representing 30 species belonging to 24 genera and 18 families were recorded in this study. Liana species richness declined in both the sites since initial inventory. The TDEF sites revealed an overall increase in stem density for the families Loganiaceae, Papilionaceae, Tiliaceae, Combretaceae, and Celastraceae. The site PP subjected to extreme anthropogenic disturbances reported considerable decrease in its stem density. The diameter class 1–3 cm also revealed substantial decline in its stem density. The endemic species Derris ovalifolia showed an increase in its stem density in 19 years. Anthropogenic disturbances resulted in significant decrease in abundance of species such as Gymnema sylvestre and Jasminum angustifolium, which are known for their medicinal value. The present results provide insights on how anthropogenic disturbances alter species diversity, abundance, and resource values of lianas in TDEFs over a period of time, which will certainly be beneficial for proposing management strategies in the conservation of this forest type.
Enhancing the Explainability of AI Models in Nuclear Power Plants with Layer-wise Relevance Propagation

With the rapid advance of artificial intelligence (AI) technology, there have been enormous number of applications across the various fields. Nuclear field is also following this trend, and there have been many studies that utilize AI models for solving problems such as event diagnosis and automated/autonomous operation. However, deep neural network (DNN) which takes the biggest portion of recent AI technology applications has a limitation that it is not transparent and has low explainability. In the case of DNN-based model, it is difficult to know the internal logic of the model or how the model deduces outputs from given input. Due to this limitation, there is a hesitation on practical application of DNN-based models to the safety-critical fields and the fields related to moral/legal issues, although their performances are acceptable. To overcome the limitation of low explainability, a number of explainable AI (XAI) methods have been proposed. XAI methods can provide detail explanations such as model’s internal logics and relations between inputs and outputs. However, although the explainability issue is crucial for safety-critical nuclear field, there is a lack of studies that dealing with XAI. In this study, as an effort to enhance the explainability and the practicality of AI models in nuclear field, layerwise relevance propagation (LRP) [1] was investigated, which is one of the XAI methods that has shown better performance in many applications compared to other XAI methods. The rest of paper is organized as follows. In chapter 2, brief explanations on XAI and LRP are provided. In chapter 3, experiments for feasibility check are described and chapter 4 concludes the paper.
Modeling Characteristic Curves of Pump Station Using SCADA Operational Data

Characteristic curves of pump station are important indicators to evaluate the operational state.Based on the operational data of SCADA system in the waterworks,the least squares method was used to simulate characteristic curves of pump station in order to analyze its operational efficiency.The flow-lift curve and flow-power curve were simulated using the method and compared with the curves from the actual site test.The results show that the method can simulate the daily operation conditions of pump station,and offers a simple and effective evaluation measure for pump station.
Interval‐valued Pythagorean fuzzy linguistic KPCA model based on TOPSIS and its application for emergency group decision making

This paper investigates the emergency group decision‐making problem based on interval‐valued Pythagorean fuzzy language sets (IVPFLSs). The frequent occurrence of emergency events can bring huge economic damage to human beings. To reduce the loss, it is very important to make reasonable emergency decisions effectively and timely. In the emergency decision making (EDM), these problems are few studied, such as high dimension problem, data nonlinearity, and correlation. For EDM problems, the advantage of IVPFLSs is that it can reasonably express the evaluation information given by decision makers (DMs) through both qualitative and quantitative aspects. However, if the dimension and nonlinear relationship of the decision data keep growing, and the traditional decision‐making methods will fail. The distance measure between decision data is necessary to calculate in the process of dimensionality reduction, and the current research does not propose the definition of IVPFLSs distance measure. On the basis of this, this paper first uses the attributes and DMs as variables to define the standard Euclidean distance measure between IVPFLSs. For nonlinear features, we construct the interval‐valued Pythagorean fuzzy language kernel principal component analysis (IVPFL‐KPCA) model to reduce the dimensionality. What is more, we also obtain the reasonable weight vectors of the attribute and DMs from cumulative contribution rate. For low‐dimensional decision data, according to the technique for order performance by similarity to ideal solution method, the best emergency plan is selected for the information variables after dimensionality reduction. In sum, the IVPFL‐KPCA model not only avoids multicollinearity and nonlinear separability between decision data, but also obtains reasonable weights. It further improves the efficiency of decision‐making operations and reduces the difficulty of the algorithm. Finally, an example of earthquake emergency plan is shown to demonstrate the feasibility and practicability of the proposed method. Besides, we also compare it with the existing methods, which proves the effectiveness of the method.
Zebrafish xenograft model for studying the function of lncRNA SNHG4 in the proliferation and migration of colorectal cancer.

Background The zebrafish xenograft model has become a reliable in vivo model for human cancer research. Compared to a mouse model, the zebrafish xenograft has many advantages, including optical transparency, intuitive in vivo observation, and speed. Long noncoding RNAs (lncRNAs) have been identified as crucial regulatory factors in the progression of colorectal cancer (CRC). The biological function of lncRNA small nucleolar RNA host gene 4 (SNHG4) in CRC is still unclear. Methods We analyzed the expression of SNHG4 in CRC patient samples by the Gene Expression Profiling Interactive Analysis (GEPIA) software. The quantitative real time-polymerase chain reaction (qRT-PCR) was used to verify in CRC cell lines. The colony formation assay was used to study the cell proliferation, and we used the transwell assay to detect the migration ability. Then the zebrafish xenograft models were used to confirm these roles of SNHG4 in vivo. Moreover, we detected epithelial mesenchymal transition (EMT) related genes by qRT-PCR. Results We found the expression of SNHG4 was upregulated in CRC patient samples by analyzing GEPIA software, which was also verified in CRC cell lines. We also found that silencing SNHG4 inhibited the proliferation and migration of CRC cells, and its roles were verified in zebrafish xenografts in vivo. Further, we found that the expression of E-cadherin was significantly upregulated and N-cadherin was downregulated when knocking-down SNHG4 in CRC cells. Conclusions Our findings demonstrated that SNHG4 played oncogenic roles in CRC, which could be a potential target for treatment of CRC patients, and the results strongly revealed that zebrafish xenograft could be used for functional research of lncRNAs in human cancer.
Multimodel Integrated Enterprise Credit Evaluation Method Based on Attention Mechanism

Due to the difficulty of credit risk assessment, the current financing and loan difficulties of small- and medium-sized enterprises (SMEs) are particularly prominent, which hinders the operation and development of enterprises. Based on the previous researches, this paper first screens out features by correlation coefficient method and gradient boosting decision tree (GBDT). Then, with the help of SE-Block, the attention mechanism is added to the feature tensor of the subset separated from metadata. On this foundation, two models, XGBoost and LightGBM, are used to train four subsets, respectively, and Bayesian ridge regression is used to fuse the training results of single models under different subsets. In the simulation experiment, the AUC value of the NN-ATT-Bayesian-Stacking model reaches 0.9675 and the distribution of prediction results is ideal. The model shows good robustness, which could make a reliable assessment for the financing and loans of SMEs.
Interrogating Patterns of Cancer Disparities by Expanding the Social Determinants of Health Framework to Include Biological Pathways of Social Experiences

The objective of this article is to call for integrating biological pathways of social experiences in the concept model of cancer disparities and social determinants of health (SDH) fields. Black, Indigenous, and People of Color (BIPOC) populations experience more negative outcomes across the cancer continuum. Social conditions are instrumental in better understanding the contemporary and historical constructs that create these patterns of disparities. There is an equally important body of evidence that points to the ways that social conditions shape biological pathways. To date, these areas of research are, for the most part, separate. This paper calls for a bridging of these two areas of research to create new directions for the field of cancer disparities. We discuss inflammation, epigenetic changes, co-morbidities, and early onset as examples of the biological consequences of social conditions that BIPOC populations experience throughout their lifespan that may contribute to disproportionate tumorigenesis and tumor progression.
Auditory Property-Based Features and Artificial Neural Network Classifiers for the Automatic Detection of Low-Intensity Snoring/Breathing Episodes

The definitive diagnosis of obstructive sleep apnea syndrome (OSAS) is made using an overnight polysomnography (PSG) test. This test requires that a patient wears multiple measurement sensors during an overnight hospitalization. However, this setup imposes physical constraints and a heavy burden on the patient. Recent studies have reported on another technique for conducting OSAS screening based on snoring/breathing episodes (SBEs) extracted from recorded data acquired by a noncontact microphone. However, SBEs have a high dynamic range and are barely audible at intensities >90 dB. A method is needed to detect SBEs even in low-signal-to-noise-ratio (SNR) environments. Therefore, we developed a method for the automatic detection of low-intensity SBEs using an artificial neural network (ANN). However, when considering its practical use, this method required further improvement in terms of detection accuracy and speed. To accomplish this, we propose in this study a new method to detect low SBEs based on neural activity pattern (NAP)-based cepstral coefficients (NAPCC) and ANN classifiers. Comparison results of the leave-one-out cross-validation demonstrated that our proposed method is superior to previous methods for the classification of SBEs and non-SBEs, even in low-SNR conditions (accuracy: 85.99 ± 5.69% vs. 75.64 ± 18.8%).
Acute Changes in Heart Rate Variability to Glucose and Fructose Supplementation in Healthy Individuals: A Double-Blind Randomized Crossover Placebo-Controlled Trial

Simple Summary In this study, we investigated the cardio-autonomic stress responses to the ingestion of liquid glucose, fructose, a combination thereof and a placebo in healthy individuals at rest. The cardio-autonomic response was more pronounced in all groups with carbohydrates compared to placebo indicating an increased cardio-autonomic stress response resulting in a reduced heart-rate variability. When investigating different levels of blood glucose, the findings showed a significant decline in heart-rate variability with increasing blood glucose levels. This was also seen with severely low levels of blood glucose. The speed of how quick blood glucose increased and decreased also impacted the cardio-autonomic response which further deteriorated heart-rate variability. These findings indicate that healthy human’s autonomic system responds quickly to changes in their blood glucose. Abstract Background: It is unknown how different types of carbohydrates alter the cardio-autonomic system in healthy individuals. Therefore, the aim of this study was to investigate how heart-rate variability changes to single dose ingestion of glucose, fructose, glucose and fructose, and an artificial sweetener (sucralose). Methods: In a double-blind randomized crossover placebo-controlled setting, 15 participants received all study-specific substances in liquid form. During each 2-h visit, venous blood glucose was measured in a 5-min interval while heart-rate variability was measured continuously via Holter-electrocardiograph. Results: Ingestion of different types of carbohydrates and sucralose showed significant differences for heart rate (p < 0.001), SDNN (p < 0.008), RMSSD (p < 0.001), pNN50 (p < 0.001) and blood pressure (p < 0.001). Different glucose levels significantly altered parameters of heart-rate variability and blood pressure (all p < 0.001), while the rate of change in blood glucose led to changes in heart rate variability, but not in heart rate (p = 0.25) or blood pressure (p = 0.99). Conclusions: Ingestion of different types of carbohydrates lead to reductions in heart-rate variability compared to a placebo. Blood glucose values above or below 70–90 mg/dL decreased heart rate variability while this was also seen for rapid glucose changes, yet not as pronounced. Healthy individuals should be conscious about carbohydrate intake while maintaining blood glucose levels between 70–90 mg/dL.