Miracle-dz/UniFAR-FTUs huggingface dataset:
Critical Quality Control Methods for a Novel Anticoagulant Candidate LFG-Na by HPSEC-MALLS-RID and Bioactivity Assays

A low molecular weight fucosylated glycosaminoglycan sodium (LFG-Na) is a novel anticoagulant candidate from the sea cucumber Holothuria fuscopunctata that selectively inhibits intrinsic tenase (iXase). The molecular weight, molecular weight distribution and bioactivities are the critical quality attributes of LFG-Na. The determination of these quality attributes of such an oligosaccharides mixture drug is challenging but critical for the quality control process to ensure its safety and efficacy in clinical use. Herein, the molecular weight and molecular weight distribution of LFG-Na were successfully determined using high performance size exclusion chromatography coupled with multi angle laser light scattering and refractive index detector (HPSEC-MALLS-RID). Comparing to the conventional method, HPSEC-MALLS-RID based on the refractive index increment (dn/dc) did not require the reference substances to establish the calibration curve. The acceptance criteria of LFG-Na were established, the weight-average molecular weight (Mw) should be 4000 to 6000 Da, the polydispersity (Mw/Mn) < 1.40, and the fraction with molecular weights of 1500 to 8000 Da should be no less than 80% of the total. HPSEC-MALLS-RID was also utilized for the determination of the starting material native fucosylated glycosaminoglycan (NFG) to choose a better manufacturing process. Furthermore, APTT assay was selected and the potency of anti-iXase, referring to the parallel line assay (PLA) method, was established to clarify the consistency of its biological activities. The results suggest that HPSEC-MALLS-RID and bioactivity assays are critical quality control methods for multi-component glycosaminoglycan LFG-Na. The methods also provide a feasible strategy to control the quality of other polysaccharide medicines.
Research on the Influence of Whether Rebalance Model on Quantitative Trading Investment Strategy Based on BEKK-MGARCH and Multi-Factor Model

The quantitative strategy of asset pricing risk management is one of the financial theory research hot spots. In this paper, we use the BEKK-GARCH model based on the relative economic theory to predict the daily volatility of gold and bitcoin; Secondly, we establish a multi-factor model with 16 factors to predict the return rate of the two assets; Finally, we establish WR model to determine the optimal position proportion of investors every day. In building the model, we use the data in the previous period to continuously slide and train our model to prevent future information leakage. In data processing, we choose the base point (0.01%) as the measurement unit, and the missing value is filled in by the average of the data of two days before and after it.
Head circumference as an epigenetic risk factor for maternal nutrition

Nutrition indicators for malnutrition can be screened by many signs such as stunting, underweight or obesity, muscle wasting, and low caloric and nutrients intake. Those deficiencies are also associated with low socioeconomic status. Anthropometry can assess nutritional status by maternal weight measurements during pregnancy. However, most studies have focused primarily on identifying changes in weight or Body Mass Index (BMI), and their effects on neonatal measures at present time. Whereas head circumference (HC) has been associated with nutrition in the past. When the mother was exposed to poor nutrition and unfavorable social conditions during fetal life, it was hypothesized that the intergenerational cycle was potentially mediated by epigenetic mechanisms. To investigate this theory, maternal head circumference (MHC) was associated with neonatal head circumference (NHC) in pregnant women without preexisting chronic conditions, differentiated by sociodemographic characteristics. A multiple linear regression model showed that each 1 cm-increase in MHC correlated with a 0.11 cm increase in NHC (β95% CI 0.07 to 0.15). Notwithstanding, associations between maternal and neonatal anthropometrics according to gestational age at birth have been extensively explained. Path analysis showed the influence of social status and the latent variable was socioeconomic status. A model of maternal height and head circumference was tested with effects on neonatal HC. The social variable lacked significance to predict neonatal HC in the total sample (p = 0.212) and in the South/Southeast (p = 0.095), in contrast to the Northeast (p = 0.047). This study highlights the potential intergenerational influence of maternal nutrition on HC, suggesting that maternal nutrition may be more relevant in families with major social vulnerability.
Recovery of Waste with a High Iron Content in the Context of the Circular Economy

In order to apply the concepts that allow the transition from a linear to a circular economy, waste generators and/or processors must identify those variants that generate products that can be used as secondary raw materials, thus also respecting the actions governing sustainable development. This paper presents such a variant, the briquetting of waste with high iron content, waste generated on current flows in steel enterprises or deposited in industrial sites. The obtained briquettes are analyzed for chemical and mechanical characteristics so that can be used as secondary materials in the steel production. An optimization of the chemical composition using generic algorithms is also proposed in order to obtain the mechanical characteristics necessary for the proper handling of these products.
Speculation on How RIC-3 and Other Chaperones Facilitate α7 Nicotinic Receptor Folding and Assembly

The process of how multimeric transmembrane proteins fold and assemble in the endoplasmic reticulum is not well understood. The alpha7 nicotinic receptor (α7 nAChR) is a good model for multimeric protein assembly since it has at least two independent and specialized chaperones: Resistance to Inhibitors of Cholinesterase 3 (RIC-3) and Nicotinic Acetylcholine Receptor Regulator (NACHO). Recent cryo-EM and NMR data revealed structural features of α7 nAChRs. A ser-ala-pro (SAP) motif precedes a structurally important but unique “latch” helix in α7 nAChRs. A sampling of α7 sequences suggests the SAP motif is conserved from C. elegans to humans, but the latch sequence is only conserved in vertebrates. How RIC-3 and NACHO facilitate receptor subunits folding into their final pentameric configuration is not known. The artificial intelligence program AlphaFold2 recently predicted structures for NACHO and RIC-3. NACHO is highly conserved in sequence and structure across species, but RIC-3 is not. This review ponders how different intrinsically disordered RIC-3 isoforms from C. elegans to humans interact with α7 nAChR subunits despite having little sequence homology across RIC-3 species. Two models from the literature about how RIC-3 assists α7 nAChR assembly are evaluated considering recent structural information about the receptor and its chaperones.
PROGNOSTIC ROLE OF THE BIOMARKER FOR SQUAMOUS CELL CARCINOMA ANTIGEN IN ORAL LICHEN PLANUS

Lichen planus refers to chronic dermatoses with manifestation in the oral mucosa. Its clinical manifestations are displayed as disruption of the immune system balance. A prolonged course of lichen planus causes deregulation of the cell growth gene, dysplasia of the epithelium of the oral mucosa and the development of squamous cell carcinoma. Identification of circulating biological markers in blood and saliva enables to detect stable and reliable predictors of clinical significance in the diagnosis and the prognosis of diseases of the oral mucosa, including the onset of tumor transformation. Squamous cell carcinoma (SCC) antigen is one of these cancer markers. Saliva is an "ultrafiltrate of blood plasma", which makes it possible to evaluate biological markers by a safe non-invasive method. The aim of this study is to assess the concentration of squamous cell carcinoma antigen in mixed saliva samples of patients with various forms of lichen planus at the stages of diagnosis and treatment. Materials and methods. 47 women with a clinical diagnosis of "oral lichen planus" were examined and randomized in groups. They described the dental status, identified the causes of chronic injury, and made a comprehensive treatment plan. Monitoring was carried out at the diagnostic stage, after 14 and 90 days. During the first visit and after treatment, unstimulated mixed saliva was collected on an empty stomach. Results. During the diagnosis, the content of SC in the oral fluid of patients with lichen planus was <500 mcg/ml (489 mcg/ml) and a decrease in its concentration after treatment is characteristic of a benign course and susceptibility to therapy. The concentration of the marker >500 mcg/ml (527 mcg/ml) and an increase in the value after the treatment course indicates a severe recurrent nature of the disease and low efficacy of prescribed medications. Conclusion. The study of squamous cell carcinoma (SCC) antigen in oral fluid expands the applications of the biomarker in determining the prognosis of the course and effectiveness of therapy for oral lichen planus.
Soluble Solids Content Binary Classification of Miyagawa Satsuma in Chongming Island Based on Near Infrared Spectroscopy

Citrus is one of the most important fruits in China. Miyagawa Satsuma, one kind of citrus, is a nutritious agricultural product with regional characteristics of Chongming Island. Near-infrared Spectroscopy (NIR) is a proper method for studying the quality of fruits, because it is low-cost, efficient, non-destructive, and repeatable. Therefore, the NIR technique is used to detect citrus's soluble solid content (SSC) in this study. After obtaining the original spectral data, the first 70% of them are divided into the training set and 30% into the test set. Then, the Random Frog algorithm is chosen to select characteristic wavelengths, which reduces the dimension of the data and the complexity of the model, and accordingly makes the generalization of the classification model better. After comparing the performance of various classifiers (AdaBoost, KNN, LS-SVM, and Bayes) under different characteristic wavelength numbers, the AdaBoost classifier outperforms using 275 characteristic wavelengths for modeling eventually. The accuracy, precision, recall, and F1-score are 78.3%, 80.5%, 78.3%, and 0.780, respectively and the ROC (Receiver Operating Characteristic Curve, ROC curve) is close to the upper left corner, suggesting that the classification model is acceptable. The results demonstrate that it is feasible to use the NIR technique to estimate whether the citrus is sweet or not. Furthermore, it is beneficial for us to apply the obtained models for identifying the quality of citrus correctly. For fruit traders, the model helps them to determine the growth cycle of citrus more scientifically, improve the level of citrus cultivation and management and the final fruit quality, and thus increase the economic income of fruit traders.
Blockchain and Deep Learning for Secure Communication in Digital Twin Empowered Industrial IoT Network

The rapid expansion of the Industrial Internet of Things (IIoT) necessitates the digitization of industrial processes in order to increase network efficiency. The integration of Digital Twin (DT) with IIoT digitizes physical objects into virtual representations to improve data analytics performance. Nevertheless, DT empowered IIoT generates a massive amount of data that is mostly sent to the cloud or edge servers for real-time analysis. However, unreliable public communication channels and lack of trust among participating entities causes various types of threats and attacks on the ongoing communication. Motivated from the aforementioned discussion, we present a blockchain and Deep Learning (DL) integrated framework for delivering decentralized data processing and learning in IIoT network. The framework first present a new DT model that facilitates construction of a virtual environment to simulate and replicate security-critical processes of IIoT. Second, we propose a blockchain-based data transmission scheme that uses smart contracts to ensure integrity and authenticity of data. Finally, the DL scheme is designed to apply the Intrusion Detection System (IDS) against valid data retrieved from blockchain. In DL scheme, a Long Short Term Memory-Sparse AutoEncoder (LSTMSAE) technique is proposed to learn the spatial-temporal representation. The extracted characteristics are further used by the proposed Multi-Head Self-Attention (MHSA)-based Bidirectional Gated Recurrent Unit (BiGRU) algorithm to learn long-distance features and accurately detect attacks. The practical implementation of our proposed framework proves considerable enhancement of communication security and data privacy in DT empowered IIoT network.
Integration of robotic in the reprocessing and transfer of endoscopes

Background and study aims Optimal hygiene is crucial for patients undergoing flexible endoscopy. Reprocessing is currently influenced by manual procedures performed by endoscopy staff. To overcome this limitation, we designed and evaluated the integration of robotic application for an automated endoscope processing pathway. Methods We used an endoscope reprocessing pass through machine with drying cabinet and a Franka Emika Panda robot. The robot was programmed to interact with its environment in a compliant way, guaranteeing desired contact force thresholds and therefore ensuring safety of both robot and medical equipment. Results In an initial phase we tested the robots’ ability to handle a modified tray holding an endoscope as well as certain challenges (correct positioning, connection of tubing, undesired collisions). We added another Panda robot arm resulting in a device featuring two independent manipulators and tested the accuracy of each individual step. We evaluated 50 consecutive processing and transfer procedures, simulating the average daily throughput of an endoscopic unit. The endoscopes were removed in adapted tray using a specially designed lifting device and placed in an endoscope storage and venting cabinet. The mean time for the handling of the scope was 104.2 ± 1.2 seconds and an accuracy of 100 % (0 failures in 50 attempts) was achieved. Conclusions To the best of our knowledge, this is the first description and evaluation of an automated compliant robotic assistance in the processing of endoscopes. Further development could help to overcome shortcomings of the man handled endoscope processing and could lead to reproducible, standardized and certified endoscope processing.
Photo- and Radiofrequency-Induced Heating of Photoluminescent Colloidal Carbon Dots

Nitrogen- and oxygen-containing carbon nanoparticles (O, N-CDs) were prepared by a facile one-step solvothermal method using urea and citric acid precursors. This method is cost-effective and easily scalable, and the resulting O, N-CDs can be used without additional functionalization and sample pretreatment. The structure of O, N-CDs was characterized by TEM, AFM, Raman, UV-vis, and FTIR spectroscopies. The obtained O, N-CDs with a mean diameter of 4.4 nm can be easily dispersed in aqueous solutions. The colloidal aqueous solutions of O, N-CDs show significant photothermal responses under red-IR and radiofrequency (RF) irradiations. The as-prepared O, N-CDs have a bright temperature-dependent photoluminescence (PL). PL/PLE spectral maps were shown to be used for temperature evaluation purposes in the range of 30–50 °C. In such a way, the O, N-CDs could be used for biomedicine-related applications such as hyperthermia with simultaneous temperature estimation with PL imaging.
Viral Vector Vaccine Development and Application during the COVID-19 Pandemic

With the accumulation of mutations in SARS-CoV-2 and the continuous emergence of new variants, the importance of developing safer and effective vaccines has become more prominent in combating the COVID-19 pandemic. Both traditional and genetically engineered vaccines have contributed to the prevention and control of the pandemic. However, in recent years, the trend of vaccination research has gradually transitioned from traditional to genetically engineered vaccines, with the development of viral vector vaccines attracting increasing attention. Viral vector vaccines have several unique advantages compared to other vaccine platforms. The spread of Omicron has also made the development of intranasal viral vector vaccines more urgent, as the infection site of Omicron is more prominent in the upper respiratory tract. Therefore, the present review focuses on the development of viral vector vaccines and their application during the COVID-19 pandemic.
A Study on the Influence of Organizational Innovation Climate on Employee’s Innovation Performance

In the era of knowledge-driven economy, employees’ innovation performance is the decisive factor of enterprise competitiveness. In order to further enrich the research achievements of the innovation performance of employees, this paper tests and verifies the relationship between the organizational innovation climate, thriving at work, inclusive leadership and the innovation. The results establish that the organizational innovation climate is significantly and positively connected to employee innovation performance, and thriving at work plays a moderating role. In addition, inclusive leadership mediates the influence of organizational innovation climate on employees thriving at work, thus mediating the moderator role of thriving at work of the model.
Oxidative Glucose Metabolism Promotes Senescence in Vascular Endothelial Cells

Vascular aging is based on the development of endothelial dysfunction, which is thought to be promoted by senescent cells accumulating in aged tissues and is possibly affected by their environment via inflammatory mediators and oxidative stress. Senescence appears to be closely interlinked with changes in cell metabolism. Here, we describe an upregulation of both glycolytic and oxidative glucose metabolism in replicative senescent endothelial cells compared to young endothelial cells by employing metabolic profiling and glucose flux measurements and by analyzing the expression of key metabolic enzymes. Senescent cells exhibit higher glycolytic activity and lactate production together with an enhanced expression of lactate dehydrogenase A as well as increases in tricarboxylic acid cycle activity and mitochondrial respiration. The latter is likely due to the reduced expression of pyruvate dehydrogenase kinases (PDHKs) in senescent cells, which may lead to increased activity of the pyruvate dehydrogenase complex. Cellular and mitochondrial ATP production were elevated despite signs of mitochondrial dysfunction, such as an increased production of reactive oxygen species and extended mitochondrial mass. A shift from glycolytic to oxidative glucose metabolism induced by pharmacological inhibition of PDHKs in young endothelial cells resulted in premature senescence, suggesting that alterations in cellular glucose metabolism may act as a driving force for senescence in endothelial cells.
Localization of Dielectric Anomalies with Multi-Monostatic S11 Using 2D MUSIC Algorithm with Spatial Smoothing

This article demonstrates that the complex value of S11 of an antenna, acquired in a multi-monostatic configuration, can be used for localization of a dielectric anomaly hidden inside a dielectric background medium when the antenna is placed close (~5 mm) to the geometry. It uses an Inverse Synthetic Aperture Radar (ISAR) imaging framework where data is acquired at multiple frequencies and look-angles. Initially, near-field scattering data are used for simulation to validate this methodology since the basic derivation of the Multiple Signal Classification (MUSIC) algorithm is based on the plain wave assumption. Later on, from an applications perspective, data acquisition is performed using an antipodal Vivaldi antenna that has eight constant-width slots on each arm. This antenna operates in a frequency range of 1 to 8.5 GHz and its S11 is fed to the 2D MUSIC algorithm with spatial smoothing whereas the antenna artifact and background effect are removed by subtracting the average S11 at each antenna location. Measurements reveal that this methodology gives accurate results with both homogeneous and inhomogeneous backgrounds because the size of data sub-arrays trades between the image noise and resolution, hence reducing the effect of inhomogeneity in the background. In addition to near-field ISAR imaging, this study can be used in the ongoing research on breast tumors and brain stroke detection, among others.
Fabrication of a Cell-Friendly Poly(dimethylsiloxane) Culture Surface via Polydopamine Coating

In this study, we fabricated a poly(dimethylsiloxane) (PDMS) surface coated with polydopamine (PDA) to enhance cell adhesion. PDA is well known for improving surface adhesion on various surfaces due to the abundant reactions enabled by the phenyl, amine, and catechol groups contained within it. To confirm the successful surface coating with PDA, the water contact angle and X-ray photoelectron spectroscopy were analyzed. Human umbilical vein endothelial cells (HUVECs) and human-bone-marrow-derived mesenchymal stem cells (MSCs) were cultured on the PDA-coated PDMS surface to evaluate potential improvements in cell adhesion and proliferation. HUVECs were also cultured inside a cylindrical PDMS microchannel, which was constructed to mimic a human blood vessel, and their growth and performance were compared to those of cells grown inside a rectangular microchannel. This study provides a helpful perspective for building a platform that mimics in vivo environments in a more realistic manner.
DeepMatch: Toward Lightweight in Point Cloud Registration

From source to target, point cloud registration solves for a rigid body transformation that aligns the two point clouds. IterativeClosest Point (ICP) and other traditional algorithms require a long registration time and are prone to fall into local optima. Learning-based algorithms such as Deep ClosestPoint (DCP) perform better than those traditional algorithms and escape from local optimality. However, they are still not perfectly robust and rely on the complex model design due to the extracted local features are susceptible to noise. In this study, we propose a lightweight point cloud registration algorithm, DeepMatch. DeepMatch extracts a point feature for each point, which is a spatial structure composed of each point itself, the center point of the point cloud, and the farthest point of each point. Because of the superiority of this per-point feature, the computing resources and time required by DeepMatch to complete the training are less than one-tenth of other learning-based algorithms with similar performance. In addition, experiments show that our algorithm achieves state-of-the-art (SOTA) performance on both clean, with Gaussian noise and unseen category datasets. Among them, on the unseen categories, compared to the previous best learning-based point cloud registration algorithms, the registration error of DeepMatch is reduced by two orders of magnitude, achieving the same performance as on the categories seen in training, which proves DeepMatch is generalizable in point cloud registration tasks. Finally, only our DeepMatch completes 100% recall on all three test sets.