Miracle-dz/UniFAR-FTUs huggingface dataset:
Microfluidics-Based Single-Cell Research for Intercellular Interaction

Intercellular interaction between cell–cell and cell–ECM is critical to numerous biology and medical studies, such as stem cell differentiation, immunotherapy and tissue engineering. Traditional methods employed for delving into intercellular interaction are limited by expensive equipment and sophisticated procedures. Microfluidics technique is considered as one of the powerful measures capable of precisely capturing and manipulating cells and achieving low reagent consumption and high throughput with decidedly integrated functional components. Over the past few years, microfluidics-based systems for intercellular interaction study at a single-cell level have become frequently adopted. This review focuses on microfluidic single-cell studies for intercellular interaction in a 2D or 3D environment with a variety of cell manipulating techniques and applications. The challenges to be overcome are highlighted.
Anti-inflammatory Activity of a Polypeptide Fraction From Achyranthes bidentate in Amyloid β Oligomers Induced Model of Alzheimer’s Disease

Neuroinflammation plays a crucial role in neurodegenerative diseases such as Alzheimer’s disease (AD) and Parkinson’s disease (PD), and anti-inflammation has been considered as a potential therapeutic strategy. Achyranthes bidentate polypeptide fraction k (ABPPk) was shown to protect neurons from death and suppress microglia and astrocyte activation in PD model mice. However, how ABPPk regulates neuroinflammation to exert a neuroprotective role remains unclear. Toxic Aβ oligomers (AβOs) can trigger inflammatory response and play an important role in the pathogenesis of AD. In the present study, for the first time, we investigated the effects and underlying mechanisms of ABPPk on neuroinflammation in AβOs-induced models of AD. In vitro, ABPPk pretreatment dose-dependently inhibited AβOs-induced pro-inflammatory cytokines mRNA levels in BV2 and primary microglia. ABPPk pretreatment also reduced the neurotoxicity of BV2 microglia-conditioned media on primary hippocampal neurons. Furthermore, ABPPk down-regulated the AβOs-induced phosphorylation of IκBα and NF-κB p65 as well as the expression of NLRP3 in BV2 microglia. In vivo, ABPPk pre-administration significantly improved locomotor activity, alleviated memory deficits, and rescued neuronal degeneration and loss in the hippocampus of AβOs-injected mice. ABPPk inhibited the activation of microglia in hippocampal CA3 region and suppressed the activation of NF-κB as well as the expression of NLRP3, cleaved caspase-1, and ASC in the brain after AβOs injection. ABPPk hindered the release of pro-inflammatory cytokines and promoted the release of anti-inflammatory cytokines in the brain. Notably, the polarization experiment on BV2 microglia demonstrated that ABPPk inhibited M1-phenotype polarization and promoted M2-phenotype polarization by activating the LPS- or AβOs-impaired autophagy in microglia. Taken together, our observations indicate that ABPPk can restore the autophagy of microglia damaged by AβOs, thereby promoting M2-phenotype polarization and inhibiting M1-phenotype polarization, thus playing a role in regulating neuroinflammation and alleviating neurotoxicity.
A Policy-Driven Approach to Secure Extraction of COVID-19 Data From Research Papers

The entire scientific and academic community has been mobilized to gain a better understanding of the COVID-19 disease and its impact on humanity. Most research related to COVID-19 needs to analyze large amounts of data in very little time. This urgency has made Big Data Analysis, and related questions around the privacy and security of the data, an extremely important part of research in the COVID-19 era. The White House OSTP has, for example, released a large dataset of papers related to COVID research from which the research community can extract knowledge and information. We show an example system with a machine learning-based knowledge extractor which draws out key medical information from COVID-19 related academic research papers. We represent this knowledge in a Knowledge Graph that uses the Unified Medical Language System (UMLS). However, publicly available studies rely on dataset that might have sensitive data. Extracting information from academic papers can potentially leak sensitive data, and protecting the security and privacy of this data is equally important. In this paper, we address the key challenges around the privacy and security of such information extraction and analysis systems. Policy regulations like HIPAA have updated the guidelines to access data, specifically, data related to COVID-19, securely. In the US, healthcare providers must also comply with the Office of Civil Rights (OCR) rules to protect data integrity in matters like plasma donation, media access to health care data, telehealth communications, etc. Privacy policies are typically short and unstructured HTML or PDF documents. We have created a framework to extract relevant knowledge from the health centers’ policy documents and also represent these as a knowledge graph. Our framework helps to understand the extent to which individual provider policies comply with regulations and define access control policies that enforce the regulation rules on data in the knowledge graph extracted from COVID-related papers. Along with being compliant, privacy policies must also be transparent and easily understood by the clients. We analyze the relative readability of healthcare privacy policies and discuss the impact. In this paper, we develop a framework for access control decisions that uses policy compliance information to securely retrieve COVID data. We show how policy compliance information can be used to restrict access to COVID-19 data and information extracted from research papers.
SYNTHESIS AND CHARACTERIZATION OF POLY(VINYL ALCOHOL)-HYALURONIC ACID BLENDED HYDROGEL MEMBRANES

Poly(vinyl alcohol)PVA is a hydrophilic polymer and water soluble . It is used in many biomedical and pharmaceutical applications, due to its advantages such as: non-toxic, non-carcinogenic, and biodegradable characteristics with the ease of processing. Physically cross-linked hydrogel membranes composed of different amounts of hyaluronic acid (HA) blend with (PVA) were prepared by freeze–thawing method. This freezing–thawing cycle was repeated for three consecutive cycles. Properties of (PVA–HA) hydrogel membrane such as gel fraction, swelling, mechanical properties(tensile strength, elongation to break),degradation and protein adsorption were investigated. With the increasing of HA content, the gel fraction, the maximum tensile strength and elongation at break(%) of (PVAHA) hydrogel membranes were decreased. Furthermore, with the increase of HA content, the swelling, the protein adsorption and the hydrolytic degradation of PVA-HA hydrogel membrane were increased.After soaking of hydrogel membrane for three days in phosphate buffer saline (PBS), the maximum weight loss of PVA–HA hydrogel membranes ranged between 18% and 70% according to HA content, this indicates that they are biodegradable.
A Simple Approach to Relating the Optimal Learning and the Meaningful Learning Experience in Students Age 14-16

Using a questionnaire applied in real time to students in stages 14–16 during a distance class, the authors appraise whether they experience feelings that lead to a central experience of flow, according to the flow theory of positive psychology. Students are exposed to a planned session that considers the moments of the training sequence and consciously integrates technological tools to support learning. A formal evaluation system, which includes formative and summative evaluations, determines if students build meaningful learning. This research contributes to understanding that an optimal learning experience characterized by the pedagogical principles of curiosity, concentration, challenge, and enjoyment, favor the construction of meaningful learning. Furthermore, the simplicity of the proposed experimental design suggests a direct way to replicate the study in later learning stages and assess the efficiency of new technology-based pedagogies within the distance education paradigm imposed by the 2020 pandemic crisis.
Moderating Effect of Gender on the Relationship between Technology Readiness Index and Consumers' Continuous Use Intention of Self-Service Restaurant Kiosks

This study aims to analyze the moderating effect of gender on the relationship between technology readiness and willingness to continue using self-service kiosks in fast-food restaurants among middle-aged and older consumers. We conducted a survey from 1 May to 30 May 2020 among 320 consumers born in or before 1980 who only used kiosks in fast-food restaurants. The findings are as follows: First, the more innovative and optimistic the consumer, the more they are willing to continue using kiosks, whereas the more discomfort the consumer feels, the less likely they are to continue using them. Second, among technology readiness factors, a sense of insecurity does not have a significant effect on the willingness to continue to use kiosks. Third, among innovative consumers, men were found to be more likely to continue using kiosks than women. Thus, fast-food restaurant managers need to know that men and women perceive technology-based self-service differently.
Universal Substrate-Trapping Strategy To Grow Strictly Monolayer Transition Metal Dichalcogenides Crystals

Monolayer transition metal dichalcogenides (TMDs) possess great potential in the electronic and optoelectronic devices on account of their unique electronic structure as well as outstanding characteristics. However, presented growth approaches are hardly to avoid multilayers and the root cause of this thermodynamic growth process lies on the overflowing transition metal sources. Here, a novel substrate-trapping strategy (STS) is developed to achieve monolayer TMDs crystals over the whole substrate surface. A designed substrate with appropriate reaction activity to fix the extra precursors is the key, for which the dominance of the dynamics will be established, thus leading to strict self-limited monolayer growth behavior. The high-quality nature of the synthesized monolayer MoS2 crystals is also clarified by transmission electron microscopy characterizations and field-effect transistors performance. Excellent tolerance to variations in growth parameters of STS is therefore exhibited and, moreover, it is a.
Practical Applications of Artificial Intelligence in Spine Surgery

Lippincott Continuing Medical Education Institute, Inc., is accredited by the Accreditation Council for Continuing Medical Education to provide continuing medical education for physicians. Lippincott Continuing Medical Education Institute, Inc., designates this enduring material for a maximum of 1.5 AMA PRA Category 1 CreditsTM. Physicians should claim only the credit commensurate with the extent of their participation in the activity. To earn CME credit, you must read the CME article and complete the quiz and evaluation assessment survey on the enclosed form, answering at least 70% of the quiz questions correctly. This activity expires on August 31, 2023. LEARNING OBJECTIVES: After participating in this CME activity, the spine surgeon should be better able to: 1. Define artificial intelligence and identify its branches. 2. Describe the clinical applications of artificial intelligence in spine surgery. 3. Explain the difficulties and ethical dilemmas that can arise with the use of artificial intelligence.
Identification and Characterization of MADS-box Genes Involved in Floral Organ Development in Chinese Chestnut (Castanea mollissima Blume)

Chinese chestnut (Castanea mollissima) is a woody plant species with important economic and ecological value and is widely cultivated for nut production. Due to its unique flowering structure, the identification of genes related to flower development is extremely important in Chinese chestnut, and MADS-box genes are directly involved in floral organ morphogenesis. In this study, we report a MADS-box gene family in Chinese chestnut. According to their gene structure and phylogenetic trees, 53 CmMADS proteins were identified and clustered into five groups in Chinese chestnut. The MIKC C group was further divided into 12 subgroups, and the BS subgroup was not found in Chinese chestnut. The classification and motif composition of Chinese chestnut MADS proteins were also analyzed. Additionally, we evaluated the expression of all the MADS-box genes in floral organs by quantitative real-time PCR. Overall, most of the type II genes had higher expression levels in the floral organs compared with leaves and cotyledons. These results provide valuable information for future studies of the regulation of MADS-box genes in Chinese chestnut during flower development. Additional key words: expression patterns, MADS proteins, motifs, phylogenetic analysis, reproductive organs
A Retrospective Study of 158 Cases on the Risk Factors for Recurrence in Ameloblastoma

Background: Ameloblastoma is an odontogenic tumor occurring in jaws, with local aggressiveness and postoperative recurrence. This study was aim to investigate the clinical and radiographic risk factors for recurrence in ameloblastoma. Methods: Patients diagnosed with ameloblastoma between March 2009 and March 2019 were retrospectively analyzed. Clinical and Radiological data and follow-up records were collected. Survival analyses were performed by Kaplan-Meier and log-rank tests, as well as Cox proportional hazards model. Results: One hundred and fifty-eight patients (104 males and 54 females were enrolled. The overall recurrence rate for ameloblastoma was 13.29%, and 10.76% recurred within 5 years. Most of the tumors were located in mandible (86.71%), while the rest 21 cases were in maxilla (13.29%). More than half cases (55.06%) showed multilocular radiolucency, 61 cases (38.61%) showed unilocular radiolucency. Significant differences were found with amelobastoma recurrence rate related to treatment modality, impacted tooth and root resorption (P =0.002, 0.022 and 0.007 respectively). Conclusions: Treatment modality, impacted tooth and root resorption all showed statistically significant associations with the recurrence rate in ameloblastoma. However, due to the limitation of this study, further studies are needed to reveal the true mechanism of ameloblastoma recurrence.
Feature Engineering Framework to detect Phishing Websites using URL Analysis

Phishing is a most popular and dangerous cyberattack in the world of internet. One of the most common attacks in cyber security is to access the personal information of internet users through “Phishing Website”. The major element through which hacker can do this job is through URL. Hacker creates an almost replica of original URL in which there is a very small difference, generally not revealed without keen observation. By pipelining various machine learning algorithms, the proposed model aims to recognize the important features to classify the URL using a recursive feature elimination process. In this work the data set of various URL records has been collected with 112 features including one target value. In this work a Machine Learning based model is proposed to identify the significant features, used to classify a URL, the wrapper method recursive feature elimination compares different bagging and boosting machine learning approaches . Ensemble algorithms, Bootstrap Aggregation Algorithms, Boosting and stacking algorithms are used for feature selection. The proposed work has five sections: work on the pre-processing phase, finding the relation between the features of the dataset, automatic selection of number of features using Extra Tree Classifier, comparison of the various ensemble algorithm and finally generates the best features for URL analysis. This paper, designs meta learner with XG BOOST classifier as base classifier and achieved an accuracy of 93% Out of 112 features, this model has performed an extensive comparative study on feature selection and identified 29 features as core features by performing URL analysis. Keywords—Recursive feature elimination; principal component analysis; standard scalar transformation; eXtreme gradient boosting classifier; correlation matrix
On-premises Analysis of Advanced Threat Prevention Appliances

Cyberattacks are becoming increasingly diverse and serious. To counter them, many organizations run multiple appliances, rather than a singular appliance, because their standard is that there should never be an undetected threat. Although applying those various security appliances has increased organizations’ security, their security operations are experiencing alert fatigue, possibly causing incidents due to missing critical threat information or human error. In this research, we investigated how much of the alerts issued by different security devices installed on the same network can be considered duplicates or unique. We obtained the alert data for an organization with multiple appliances for a period of 10 months and extracted all the sets of alerts that could be inferred to refer to the same event to analyze the extent to which the alert types that they generate co-occur across the appliances. According to the analysis of the similarity between alert types based on their co-occurrence, we mapped the alert types in 2 dimensions to discuss the appliances’ correlation. We observed that some appliances completely overlap with the alerting behaviors of other appliances and identified appliances that produce many useful alerts with high uniqueness.
Lodestar: Supporting Independent Learning and Rapid Experimentation Through Data-Driven Analysis Recommendations

Keeping abreast of current trends, technologies, and best practices in visualization and data analysis is becoming increasingly difficult, especially for fledgling data scientists. In this paper, we propose Lodestar, an interactive computational notebook that allows users to quickly explore and construct new data science workflows by selecting from a list of automated analysis recommendations. We derive our recommendations from directed graphs of known analysis states, with two input sources: one manually curated from online data science tutorials, and another extracted through semi-automatic analysis of a corpus of over 6,000 Jupyter notebooks. We evaluate Lodestar in a formative study guiding our next set of improvements to the tool. Our results suggest that users find Lodestar useful for rapidly creating data science workflows.
A Case Study for Progressive Algorithms - An Investigation into Progressive Extraction of Intermediate Solutions for The Weighted Interval Scheduling Problem

Asserted convergence characteristics shared by all serial algorithms have traditionally not been as prominent a measure of quality as time complexity has been. In instances relying on the exploration of large datasets, a convergence bound on inter-actively guided exploration approaches could act complementary to traditional time complexity, and constitute a alternative starting point for algorithm design. In an attempt to further explore the plausibility of this conjecture, a theoretical framework (Alewijnse, 2014) for convergence analysis through decomposition into consecutive intermediate computations is adopted, and its resulting intermediate solutions are used as a mean of empirical algorithm convergence analysis and categorization. Different scenarios related to the weighted interval scheduling problem are explored in this light, which is chosen primarily based on its documented compatibility with dynamic programming approaches. The result is presented as asymptotic upper bound functions on the convergence along with conjectures on upper bound hardness with respect to two diﬀerent error functions, one adapted for stochastically and one for deterministically rooted algorithms.
Is the impact of transport modes on health an individual determinant of transport mode choice

High modal share of the private car has two important health consequences. First, air pollution, particularly fine particulate matter in urban areas, leads to increased mortality and morbidity (cardiovascular or pulmonary diseases, etc.). Second, the increase in sedentary lifestyles is partly due to the lack of physical activity in our mobility behaviour with the widespread use of the private car. This also leads to exacerbated morbidity (cardiovascular diseases, type 2 diabetes, cancer and mental illnesses). In this article, we try to evaluate the extent to which the impact of mode choice on public or individual health influences mode choice. in other words, does the fact that taking the car increase the risk of cardiovascular disease for the user, through physical activity, and for his co-citizens, through pollution, have an influence on the choice of alternative modes? We address this question using a Stated Preferences survey, considering car, public transport, walking and cycling modes.
GEDet: Detecting Erroneous Nodes with A Few Examples

Detecting nodes with erroneous values in real-world graphs remains challenging due to the lack of examples and various error scenarios. We demonstrate GEDet , an error detection engine that can detect erroneous nodes in graphs with a few examples. The GEDet framework tackles error detection as a few-shot node classification problem. We invite the attendees to experience the following unique features. (1) Few-shot detection . Users only need to provide a few examples of erroneous nodes to perform error detection with GEDet . GEDet achieves desirable accuracy with (a) a graph augmentation module, which automatically generates synthetic examples to learn the classifier, and (b) an adversarial detection module, which improves classifiers to better distinguish erroneous nodes from both cleaned nodes and synthetic examples. We show that GEDet significantly improves the state-of-the-art error detection methods. (2) Diverse error scenarios . GEDet profiles data errors with a built-in library of transformation functions from correct values to errors. Users can also easily “plug in” new error types or examples. (3) User-centric detection . GEDet supports (a) an active learning mode to engage users to verify detected results, and adapts the error detection process accordingly; and (b) visual interfaces to interpret and track detected errors.
Probabilistic Attack Sequence Generation and Execution Based on MITRE ATT&CK for ICS Datasets

To practically leverage a dataset, various attack situations should be created according to the user’s objective and how realistic the generated attack sequence is should be expressed. However, there is a limit to manually generating various attack sequences that can be credible by reflecting the characteristics of the intrusion process for known cyber attacks and the field’s constraints. In this paper, we propose an automatic generation method of various attack sequences that satisfy the characteristics of the attack desired by the user based on the tactics and techniques of MITRE ATT&CK, and introduce the application method through a case study. To collect the industrial control system security dataset based on the attack sequence for future work, an attack sequence executor is applied to automatically drive the attack sequence on the HAI testbed.