Miracle-dz/UniFAR-FTUs huggingface dataset:
Effects of ECM proteins (laminin, fibronectin, and type IV collagen) on the biological behavior of Schwann cells and their roles in the process of remyelination after peripheral nerve injury

Introduction: It is important to note that complete myelination and formation of myelinated fibers are essential for functional nerve regeneration after peripheral nerve injury (PNI). However, suboptimal myelin regeneration is common and can hinder ideal nerve regeneration. Therefore, it is important to closely monitor and support myelin regeneration in patients with PNI to achieve optimal outcomes. Methods: This study analyzed the effects of three extracellular matrix (ECM) proteins on Schwann cells (SCs) in the nerve regeneration environment, including their adhesion, proliferation, and migration. The study also explored the use of composite sodium alginate hydrogel neural scaffolds with ECM components and investigated the effects of ECM proteins on remyelination following peripheral nerve injury. Results: The results showed that laminin (LN), fibronectin (FN), and collagen Ⅳ (type IV Col) promoted the early adhesion of SCs in 2-dimensional culture but the ratios of early cell adhesion were quite different and the maintenance of cells’ morphology by different ECM proteins were significantly different. In transwell experiment, the ability of LN and FN to induce the migration of SCs was obviously higher than that of type IV Col. An vitro co-culture model of SCs and dorsal root ganglia (DRG) neurons showed that LN promoted the transition of SCs to a myelinated state and the maturation of the myelin sheath, and increased the thickness of neurofilaments. Animal experiments showed that LN had superior effects in promoting myelin sheath formation, axon repair, and reaching an ideal G-ratio after injury compared to FN and Col IV. The situation of gastrocnemius atrophy was significantly better in the LN group. Notably, the thickness of the regenerated myelin sheaths in the type IV Col group was the thickest. Conclusion: In this experiment, we analyzed and compared the effects of LN, FN, and type IV Col on the biological behavior of SCs and their effects on remyelination after PNI and further clarified their unique roles in the process of remyelination. Further research is necessary to explore the underlying mechanisms.
Singular Vectors, Metrics, and Adaptive Observations.

Singular vectors of the linearized equations of motion have been used to study the instability properties of the atmosphere‐ocean system and its related predictability. A third use of these singular vectors is proposed here: as part of a strategy to target adaptive observations to ‘‘sensitive’’ parts of the atmosphere. Such observations could be made using unmanned aircraft, though calculations in this paper are motivated by the upstream component of the Fronts and Atlantic Storm-Track Experiment. Oceanic applications are also discussed. In defining this strategy, it is shown that there is, in principle, no freedom in the choice of inner product or metric for the singular vector calculation. However, the correct metric is dependent on the purpose for making the targeted observations (to study precursor developments or to improve forecast initial conditions). It is argued that for predictability studies, where both the dynamical instability properties of the system and the specification of the operational observing network and associated data assimilation system are important, the appropriate metric will differ from that appropriate to a pure geophysical fluid dynamics (GFD) problem. Based on two different sets of calculations, it is argued that for predictability studies (but not for GFD studies), a first-order approximation to the appropriate metric can be based on perturbation energy. The role of observations in data assimilation procedures (constraining large scales more than small scales) is fundamental in understanding reasons for the requirement for different metrics for the two classes of problems. An index-based tensor approach is used to make explicit the role of the metric. The strategy for using singular vectors to target adaptive observations is discussed in the context of other possible approaches, specifically, based on breeding vectors, potential vorticity diagnosis, and sensitivity vectors. The basic premises underlying the use of breeding and singular vectors are discussed. A comparison of the growth rates of breeding and singular vectors is made using a T21 quasigeostrophic model. Singular vectors and subjective potential vorticity (PV) diagnosis are compared for a particular case study. The areas of sensitivity indicated by the two methods only partially agree. Reasons for disagreement hinge around the fact that subjective PV diagnosis emphasizes Lagrangian advection, whereas singular vector analysis emphasizes wave propagation. For the latter, areas of sensitivity may be associated with regions of weak PV gradient, for example, mid to lower troposphere. Amplification of singular vectors propagating from regions of weak PV gradient to regions of strong PV gradient is discussed in terms of pseudomomentum conservation. Evidence is shown that analysis error may be as large in the lower midtroposphere as in the upper troposphere.
Performance Evaluation of a New Fluorescent-Based Lateral Flow Immunoassay for Quantification of Hemoglobin A1c (HBA1c) in Diabetic Patients.

BACKGROUND Rapid hemoglobin A1C (HbA1c) level monitoring is essential in slowing the progression of diabetes. This need becomes challenging in low resources countries where the social burden of the disease is overwhelming. Recently, fluorescent-based lateral flow immunoassays (LFIAs) gained wide attention for small laboratories and population surveillance. AIM We aim to evaluate the performance of Finecare™ HbA1c Rapid Test, certified by CE, NGSP, and IFCC, for the quantitative measurement of hemoglobin A1C (HbA1c) along with its reader. METHODS A total of 100 (fingerstick and venepuncture whole blood) samples were analyzed by Wondfo Finecare™ HbA1c Rapid Quantitative Test and the results were compared with the reference assay Cobas Pro c503. RESULTS A strong correlation was observed between Finecare™/Cobas Pro c503 with fingerstick (r > 0.93, p < 0.0001) and venous (r > 0.97, p < 0.0001) blood samples. Finecare™ measurements showed excellent agreement and compliance with Roche Cobas Pro c503 as the mean bias was negligible; 0.05 (Limits-of-agreement: -0.58-0.68) with fingerstick and 0.003 (Limits-of-agreement: -0.49-0.50) with venous blood. Interestingly, a very small mean bias (0.047) was also shown between the fingerstick and the venepuncture data, indicating that the type of sample used does not affect the results and the high reproducibility of the assay. Finecare™ showed 92.0% (95% CI: 74.0-99.0) sensitivity and 94.7% (95% CI: 86.9-98.5) specificity compared to the Roche Cobas Pro c503 using fingerstick whole blood samples. Finecare™ showed 100% (95% CI: 86.3-100) sensitivity and 98.7% (95% CI: 92.8-100) specificity compared to the Cobas Pro c503 using venepuncture samples. Cohen's Kappa denoted excellent agreement with Cobas Pro c503; 0.84 (95% CI: 0.72-0.97) and 0.97 (95% CI: 0.92-1.00) using fingerstick and venous blood samples, respectively. Most importantly, Finecare™ showed a significant difference between normal, pre-diabetic, and diabetic samples (p < 0.0001). Similar results were obtained when an additional 47 samples (from different participants; mainly diabetic) were analyzed in a different lab using different Finecare™ analyzer and different kit lot number. CONCLUSIONS Finecare™ is a reliable and rapid assay (5 min) which can be easily implemented for long-term monitoring of HbA1c in diabetic patients, particularly in small laboratory settings.
Cross-Talking of Pathway-Specific Regulators in Glycopeptide Antibiotics (Teicoplanin and A40926) Production

Teicoplanin and A40926 (natural precursor of dalbavancin) are clinically relevant glycopeptide antibiotics (GPAs) produced by Actinoplanes teichomyceticus NRRL B-16726 and Nonomuraea gerenzanensis ATCC 39727. Their biosynthetic enzymes are coded within large biosynthetic gene clusters (BGCs), named tei for teicoplanin and dbv for A40926, whose expression is strictly regulated by pathway-specific transcriptional regulators (PSRs), coded by cluster-situated regulatory genes (CSRGs). Herein, we investigated the “cross-talk” between the CSRGs from tei and dbv, through the analysis of GPA production levels in A. teichomyceticus and N. gerenzanensis strains, with knockouts of CSRGs cross-complemented by the expression of heterologous CSRGs. We demonstrated that Tei15* and Dbv4 StrR-like PSRs, although orthologous, were not completely interchangeable: tei15* and dbv4 were only partially able or unable to cross-complement N. gerenzanensis knocked out in dbv4 and A. teichomyceticus knocked out in tei15*, implying that the DNA-binding properties of these PSRs are more different in vivo than it was believed before. At the same time, the unrelated LuxR-like PSRs Tei16* and Dbv3 were able to cross-complement corresponding N. gerenzanensis knocked out in dbv3 and A. teichomyceticus knocked out in tei16*. Moreover, the heterologous expression of dbv3 in A. teichomyceticus led to a significant increase in teicoplanin production. Although the molecular background of these events merits further investigations, our results contribute to a deeper understanding of GPA biosynthesis regulation and offer novel biotechnological tools to improve their production.
Structural properties of self-organized semiconductor nanostructures

Instabilities in semiconductor heterostructure growth can be exploited for the self-organized formation of nanostructures, allowing for carrier confinement in all three spatial dimensions. Beside the description of various growth modes, the experimental characterization of structural properties, such as size and shape, chemical composition, and strain distribution is presented. The authors discuss the calculation of strain fields, which play an important role in the formation of such nanostructures and also influence their structural and optoelectronic properties. Several specific materials systems are surveyed together with important applications.
An Ultrathin Internal Dielectric Actuator for Extended Range Deflection Pull-in Free Actuation

MEMS electrostatic actuators suffer from pull-in instability, making them highly nonlinear and limiting their useful range of motion. This report resolves the performance-limiting issues of the gap-closing transducer by using an ultrathin dielectric layer (UDL) for actuation. The device has an electromechanical coupling coefficient of 4.26% and an average deflection sensitivity of 119 nm/V over a range of 350 nm for upto 3.3 V dc bias. The UDL capacitance and deflection show a square law dependence on voltage. The device can thus be used as an actuator upto the full range of motion with better performance. Additionally, the UDL or gap capacitance can be used for feedback to maintain constant gap or strain. UDL actuation may also be combined with gap actuation to lower the pull-in voltage without changing the gap or the actuator thickness. [2022-0213]
Suppressing Viscous Fingering in Porous Media with Wetting Gradient

The viscous fingering phenomenon often occurs when a low-viscosity fluid displaces a high-viscosity fluid in a homogeneous porous media, which is an undesirable displacement process in many engineering applications. The influence of wetting gradient on this process has been studied over a wide range of capillary numbers (7.5 × 10−6 to 1.8 × 10−4), viscosity ratios (0.0025 to 0.04), and porosities (0.48 to 0.68), employing the lattice Boltzmann method. Our results demonstrate that the flow front stability can be improved by the gradual increase in wettability of the porous media. When the capillary number is less than 3.5 × 10−5, the viscous fingering can be successfully suppressed and the transition from unstable to stable displacement can be achieved by the wetting gradient. Moreover, under the conditions of high viscosity ratio (M > 0.01) and large porosity (Φ > 0.58), wetting gradient improves the stability of the flow front more significantly.
Machine Learning for Brain MRI Data Harmonisation: A Systematic Review

Background: Magnetic Resonance Imaging (MRI) data collected from multiple centres can be heterogeneous due to factors such as the scanner used and the site location. To reduce this heterogeneity, the data needs to be harmonised. In recent years, machine learning (ML) has been used to solve different types of problems related to MRI data, showing great promise. Objective: This study explores how well various ML algorithms perform in harmonising MRI data, both implicitly and explicitly, by summarising the findings in relevant peer-reviewed articles. Furthermore, it provides guidelines for the use of current methods and identifies potential future research directions. Method: This review covers articles published through PubMed, Web of Science, and IEEE databases through June 2022. Data from studies were analysed based on the criteria of Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA). Quality assessment questions were derived to assess the quality of the included publications. Results: a total of 41 articles published between 2015 and 2022 were identified and analysed. In the review, MRI data has been found to be harmonised either in an implicit (n = 21) or an explicit (n = 20) way. Three MRI modalities were identified: structural MRI (n = 28), diffusion MRI (n = 7) and functional MRI (n = 6). Conclusion: Various ML techniques have been employed to harmonise different types of MRI data. There is currently a lack of consistent evaluation methods and metrics used across studies, and it is recommended that the issue be addressed in future studies. Harmonisation of MRI data using ML shows promises in improving performance for ML downstream tasks, while caution should be exercised when using ML-harmonised data for direct interpretation.
Improved dynamic characteristics on four-wave mixing wavelength conversion in light-holding SOAs

We investigate theoretically and experimentally the dynamic characteristics of the wavelength conversion that is based on four-wave mixing (FWM) in a light-holding semiconductor optical amplifier (LHSOA). Two FWM conversion techniques, including the single-pump (SP) and two orthogonal-polarized pumps (OPP) schemes, are investigated using a sophisticated time-domain simulation model. The simulation indicates that the holding light gives an improved eye opening and signal-to-noise ratio for both the SP and OPP schemes. The chirp response for both schemes are also presented. The performance improvement are verified from dynamic measurements such as eye diagram and bit-error-rate (BER) tests. Clearer eye openings and reduced power penalties are obtained with the injection of a holding light. By comparing to the experimental results, we also prove that our numerical modeling can predict the device performance and can be used for optimizing the wavelength conversion process.
Acoustic Feedback Cancellation Algorithm for Hearing Aids Based on a Weighted Error Adaptive Filter

Acoustic feedback is a common phenomenon that occurs during hearing aid use, limiting the maximum gain that a hearing aid can provide. Effective cancellation of acoustic feedback is an essential feature of hearing aids. However, due to the complex environments in which hearing aids are used and the frequently changing acoustic feedback path, it is difficult for existing adaptive filter-based acoustic feedback cancellation algorithms to balance both convergence speed and steady-state error. For this reason, based on the nonparametric variable step size (NPVSS) algorithm, a weighted NPVSS algorithm that also introduces a prediction error method is proposed in this paper. First, by introducing the prediction error method, the adaptive filter bias caused by the nonwhite source signal is effectively reduced. Second, the proposed weighting mechanism weights the error signal according to the adaptive filter misalignment, which enhances the steady-state robustness of the algorithm while accelerating its convergence. In addition, a new low-complexity method is herein proposed for source signal energy estimation by reusing the misalignment information to solve the step size calculation problem of the NPVSS algorithm. Simulation results show that the new algorithm exhibits greater robustness and faster convergence than similar algorithms. The proposed algorithm is implemented with a real hearing aid and its performance is measured on a dummy head in a soundproof room. The test results demonstrate that the proposed algorithm achieves a 35% reduction in convergence time compared with PEM-IMLMS and a 60% reduction compared with PEM-NLMS. Furthermore, the proposed algorithm reduces the sound pressure level of acoustic feedback residues compared with PEM-IMLMS and PEM-NLMS by approximately 2 dB SPL and 6 dB SPL, respectively. These results indicate that the new algorithm can provide timely and stable cancellation of acoustic feedback.
Predicting Consumption Intention of Consumer Relationship Management Users Using Deep Learning Techniques: A Review

Consumer/customer relationship management (CRM) can potentially influence business as it predicts changes in people’s perspectives, which could impact future sales. Accordingly, advancements in Information Technology are under investigation to see their capabilities to improve the work of CRM. Many prediction techniques, such as Data Mining, Machine Learning (ML), and Deep Learning (DL), were found to be utilized with CRM. ML methods were found to dominate other approaches in terms of the prediction of consumers’ intention to purchase. This review provides DL algorithms that are mostly used in the last five years, to support CRM to predict purchase intention for better product sales decisions. Prediction criteria related to online activities and behavior were found to be the most inputs of prediction models. DL approaches are slowly applied within purchase intention prediction due to their advanced capabilities in handling large and complicated datasets with minimum human supervision. DL models such as CNN and LSTM result in high accuracy in prediction intention with 98%. Future research uses the two algorithms (CNN, LSTM) compiled to make the best prediction consumption in CRM. Additionally, an effort is being made to create a framework for predicting purchases based on many DL algorithms and the most pertinent characteristics.
The Surgical and Therapeutic Activities of Non-Functional Pancreatic Neuroendocrine Tumors at a High-Volume Institution

Simple Summary Non-functional pancreatic neuroendocrine tumors (NF-PanNETs) are a highly heterogeneous group of tumors with an increasing incidence. This study aimed to summarize the surgical and therapeutic activities of NF-PanNETs and to perform survival analyses at Fudan University Shanghai Cancer Center (FUSCC) over the past 15 years. We screened 1001 patients with neuroendocrine neoplasms treated at FUSCC, and 509 patients with NF-PanNETs from 2006 to 2020 were included. Time trend analyses revealed an increasing number of diagnosed and resected NF-PanNETs. Among three randomly divided periods, a significant decrease in the tumor size and a substantial increase in minimally invasive techniques were observed. In the subgroup of non-metastatic and resected NF-PanNETs, tumor size, positive lymph node, adjuvant treatment, and tumor grade were independent prognostic factors for recurrence-free survival. Microvascular invasion and tumor grade were independent prognostic factors for overall survival (OS). Notably, a malignant transformation from NET into neuroendocrine carcinoma was observed. Abstract Background: This study aimed to summarize the surgical and therapeutic activities of non-functional pancreatic neuroendocrine tumors (NF-PanNETs) and perform survival analyses of a 15-year single-institutional cohort of NF-PanNETs. Methods: In total, 1001 patients with neuroendocrine neoplasms treated at Fudan University Shanghai Cancer Center were screened for inclusion, and 509 patients with NF-PanNETs from 2006 to 2020 were included. For time trend analyses, the 15-year study period was randomly divided into three periods. Survival analyses used the Kaplan–Meier method and Cox regression models. Results: The total number of resected NF-PanNETs increased over the 15-year study period, from 5 resections in 2006 to 94 resections in 2020. A significant decrease in the tumor size was observed, from a mean of 4.0 cm to 3.3 cm, and to 3.0 cm in the most recent period (p = 0.006). Minimally invasive techniques gradually increased from 3.5% to 12.9%, and finally to 46.4% in the most recent period (p < 0.001). In non-metastatic and resected tumors, the tumor size (p < 0.001), positive lymph node (p < 0.001), adjuvant treatment (p = 0.048), and tumor grade (p < 0.001) were independent prognostic factors for recurrence-free survival (RFS). The microvascular invasion (p = 0.024) and tumor grade (p = 0.013) were independent prognostic factors for overall survival (OS). A malignant transformation from NET into neuroendocrine carcinoma was observed. Conclusions: An increasing number of NF-PanNETs resection and minimally invasive surgery was shown. In non-metastatic and resected tumors NF-PanNETs, tumor size, positive lymph node, adjuvant treatment, and tumor grade were independent predictors of RFS. Microvascular invasion and tumor grade were independent prognostic factors for OS.
Salvia miltiorrhiza Alleviates Memory Deficit Induced by Ischemic Brain Injury in a Transient MCAO Mouse Model by Inhibiting Ferroptosis

Salvia miltiorrhiza (SM) has been used in oriental medicine for its neuroprotective effects against cardiovascular diseases and ischemic stroke. In this study, we investigated the therapeutic mechanism underlying the effects of SM on stroke using a transient middle cerebral artery occlusion (tMCAO) mouse model. Our results showed that SM administration significantly attenuated acute brain injury, including brain infarction and neurological deficits, 3 days after tMCAO. This was confirmed by our magnetic resonance imaging (MRI) study, which revealed a reduction in brain infarction with SM administration, as well as our magnetic resonance spectroscopy (MRS) study, which demonstrated the restoration of brain metabolites, including taurine, total creatine, and glutamate. The neuroprotective effects of SM were associated with the reduction in gliosis and upregulation of inflammatory cytokines, such as interleukin-6 (IL-6) and Tumor necrosis factor-α (TNF-α), along with the upregulation of phosphorylated STAT3 in post-ischemic brains. SM also reduced the levels of 4-Hydroxynonenal (4-HNE) and malondialdehyde (MDA), which are markers of lipid peroxidation, induced by oxidative stress upregulation in the penumbra of the tMCAO mouse brain. SM administration attenuated ischemic neuronal injury by inhibiting ferroptosis. Additionally, post-ischemic brain synaptic loss and neuronal loss were alleviated by SM administration, as demonstrated by Western blot and Nissl staining. Moreover, daily administration of SM for 28 days after tMCAO significantly reduced neurological deficits and improved survival rates in tMCAO mice. SM administration also resulted in improvement in post-stroke cognitive impairment, as measured by the novel object recognition and passive avoidance tests in tMCAO mice. Our findings suggest that SM provides neuroprotection against ischemic stroke and has potential as a therapeutic agent.