Miracle-dz/UniFAR-FTUs huggingface dataset:
Microbial metabolites in the pathogenesis of periodontal diseases: a narrative review

The purpose of this narrative review is to highlight the importance of microbial metabolites in the pathogenesis of periodontal diseases. These diseases, involving gingivitis and periodontitis are inflammatory conditions initiated and maintained by the polymicrobial dental plaque/biofilm. Gingivitis is a reversible inflammatory condition while periodontitis involves also irreversible destruction of the periodontal tissues including the alveolar bone. The inflammatory response of the host is a natural reaction to the formation of plaque and the continuous release of metabolic waste products. The microorganisms grow in a nutritious and shielded niche in the periodontal pocket, protected from natural cleaning forces such as saliva. It is a paradox that the consequences of the enhanced inflammatory reaction also enable more slow-growing, fastidious, anaerobic bacteria, with often complex metabolic pathways, to colonize and thrive. Based on complex food chains, nutrient networks and bacterial interactions, a diverse microbial community is formed and established in the gingival pocket. This microbiota is dominated by anaerobic, often motile, Gram-negatives with proteolytic metabolism. Although this alternation in bacterial composition often is considered pathologic, it is a natural development that is promoted by ecological factors and not necessarily a true “dysbiosis”. Normal commensals are adapting to the gingival crevice when tooth cleaning procedures are absent. The proteolytic metabolism is highly complex and involves a number of metabolic pathways with production of a cascade of metabolites in an unspecific manner. The metabolites involve short chain fatty acids (SCFAs; formic, acetic, propionic, butyric, and valeric acid), amines (indole, scatole, cadaverine, putrescine, spermine, spermidine) and gases (NH3, CO, NO, H2S, H2). A homeostatic condition is often present between the colonizers and the host response, where continuous metabolic fluctuations are balanced by the inflammatory response. While it is well established that the effect of the dental biofilm on the host response and tissue repair is mediated by microbial metabolites, the mechanisms behind the tissue destruction (loss of clinical attachment and bone) are still poorly understood. Studies addressing the functions of the microbiota, the metabolites, and how they interplay with host tissues and cells, are therefore warranted.
Tumour associated endothelial cells: origin, characteristics and role in metastasis and anti-angiogenic resistance

Tumour progression and metastasis remain the leading causes of cancer-related death worldwide. Tumour angiogenesis is essential for tumour progression. The vasculature surrounding tumours is not only a transport channel for nutrients, oxygen, and metabolites, but also a pathway for metastasis. There is a close interaction between tumour cells and endothelial cells in the tumour microenvironment. Recent studies have shown that tumour-associated endothelial cells have different characteristics from normal vascular endothelial cells, play an important role in tumour progression and metastasis, and are expected to be a key target for cancer therapy. This article reviews the tissue and cellular origin of tumour-associated endothelial cells and analyses the characteristics of tumour-associated endothelial cells. Finally, it summarises the role of tumour-associated endothelial cells in tumour progression and metastasis and the prospects for their use in clinical anti-angiogenic therapy.
A deep learning approach for state-of-health estimation of lithium-ion batteries based on differential thermal voltammetry and attention mechanism

Accurate estimation of the State of Health (SOH) of lithium-ion batteries is crucial for ensuring their safe and reliable operation. Data-driven methods have shown excellent performance in estimating SOH, but obtaining high-quality and strongly correlated features remains a major challenge for these methods. Moreover, different features have varying importance in both spatial and temporal scales, and single data-driven models are unable to capture this information, leading to issues with attention dispersion. In this paper, we propose a data-driven method for SOH estimation leveraging the Bi-directional Long Short-Term Memory (Bi-LSTM) that uses the Differential Thermal Voltammetry (DTV) analysis to extract features, and incorporates attention mechanisms (AM) at both temporal and spatial scales to enable the model focusing on important information in the features. The proposed method is validated using the Oxford Battery degradation Dataset, and the results show that it achieves high accuracy and robustness in SOH estimation. The Root Mean Squared Error (RMSE) and Mean Absolute Error (MAE) are around 0.4% and 0.3%, respectively, indicating the potential for online application of the proposed method in the cyber hierarchy and interactional network (CHAIN) framework.
Keratinoctye-neuro-immune-units (KNICUs): collaborative impact on the initiation and maintenance of psoriasis

The skin is the outermost barrier that separates the human body from the external environment. In psoriasis, immune cells reside within or infiltrate the epidermis to form the epidermal (epithelial) immunological microenvironment (EIME) and engage in complex interactions with keratinocytes, nerves, and microbiota. The proposed hypothesis is that psoriasis is a chronic inflammatory disease mainly mediated by a specific inflammatory environment composed of keratinocyte–neuro–immune cell units (KNICUs). These KNICUs arise from the interaction between activated epidermal keratinocytes, nerves, immune cells, and the skin microbiota, forming a complex interaction framework. Multiple units gather to complete the circulatory and amplified loops, consequently serving as a group army to initiate and maintain psoriasis.
A study to characterize the mechanical properties and material constitution of adult descending thoracic aorta based on uniaxial tensile test and digital image correlation

The mechanical properties and material constitution of the aorta are important in forensic science and clinical medicine. Existing studies on the material constitution of the aorta do not satisfy the practical requirements of forensic and clinical medicine, as the reported failure stress and failure strain values for human aortic materials have a high dispersion. In this study, descending thoracic aortas were obtained from 50 cadavers (dead within 24 h) free of thoracic aortic disease, aged from 27 to 86 years old, which were divided into six age groups. The descending thoracic aorta was divided into proximal and distal segments. A customized 4-mm cutter was used to punch a circumferential and an axial dog-bone-shaped specimen from each segment; the aortic ostia and calcification were avoided. Instron 8,874 and digital image correlation were used to perform a uniaxial tensile test on each sample. Four samples from each descending thoracic aorta produced ideal stress-strain curves. All parameter-fitting regressions from the selected mathematical model converged, and the best-fit parameters of each sample were obtained. The elastic modulus of collagen fiber, failure stress, and the strain showed a decreasing trend with age, while the elastic modulus of elastic fiber showed an increasing trend with age. The elastic modulus of collagen fiber, failure stress, and strain of circumferential tensile were all greater than those for axial tensile. There was no statistical difference in model parameters and physiological moduli between the proximal and distal segments. The failure stress and strain in the proximal circumferential, distal circumferential, and distal axial tensile were all greater for the male group than for the female group. Finally, the Fung-type hyperelastic constitutive equations were fitted for the different segments in different age groups.
EFFECTS OF OBLIQUITY ON THE HABITABILITY OF EXOPLANETS AROUND M DWARFS

Most previous studies on how obliquity affects planetary habitability focused on planets around Sun-like stars. Their conclusions may not be applicable to habitable planets around M dwarfs due to the tidal-locking feature and associated insolation pattern of these planets. Here we use a comprehensive three-dimensional atmospheric general circulation model to investigate this issue. We find that the climates of planets with higher obliquities are generally warmer, consistent with previous studies. The mechanism of warming is, however, completely different. Significant reduction of low clouds, instead of sea-ice cover, within the substeller region (which moves if the obliquity is non-zero) is the key in warming M-dwarf planets with high obliquities. For a total insolation of 1237 W m−2, the climate warms by 21 K when the obliquity increases from 0° to 90°. Correspondingly, the runaway greenhouse inner edge of the habitable zone shifts outward from 2500 to 2100 W m−2. The moist greenhouse inner edge, based on our crude estimation, shifts less, from 2180 to 2075 W m−2. Near the outer edge, in contrast, the climates of planets with higher obliquities are colder due to their reduced ability to maintain a hotspot at the surface. Therefore, the outer edge moves inward when obliquity is increased, opposite to the finding of previous studies on planets around Sun-like stars. Our results thus indicate that the habitable zone for M dwarfs narrows if the obliquity of their planets increases.
Visualizing design project team and individual progress using NLP: a comparison between latent semantic analysis and Word2Vector algorithms

Abstract Design has always been seen as an inherently human activity and hard to automate. It requires a lot of traits that are seldom attributable to machines or algorithms. Consequently, the act of designing is also hard to assess. In particular in an educational context, the assessment of progress of design tasks performed by individuals or teams is difficult, and often only the outcome of the task is assessed or graded. There is a need to better understand, and potentially quantify, design progress. Natural Language Processing (NLP) is one way of doing so. With the advancement in NLP research, some of its models are adopted into the field of design to quantify a design class performance. To quantify and visualize design progress, the NLP models are often deployed to analyze written documentation collected from the class participants at fixed time intervals through the span of a course. This paper will explore several ways of using NLP in assessing design progress, analyze its advantages and shortcomings, and present a case study to demonstrate its application. The paper concludes with some guidelines and recommendations for future development.
Segmentation Method for Car Emblem Based on Texture Homogeneity Measure

The car emblem is a label of the car and contains important information about the car.Because car emblem exchange is very difficult,it can provide a crucial basis for classifying and identify a car.This method is based on texture homogeneity measure which is defined in the paper.The main steps of the proposed method are:firsthy,perform wavelet transform on the original image and calculate its vertical texture measure;secondly,select a appropriate threshold to segment the measure image and then implement morphological closing operation on the binary image;thirdly,compute the coarse position of the car emblem;in the end,extract the car emblem from the background with homogeneity measure.The experimental results show that the proposed algorithm is robust to segmentation of the car emblem.
Biomimetic Scaffolds for Tendon Tissue Regeneration

Tendon tissue connects muscle to bone and plays crucial roles in stress transfer. Tendon injury remains a significant clinical challenge due to its complicated biological structure and poor self-healing capacity. The treatments for tendon injury have advanced significantly with the development of technology, including the use of sophisticated biomaterials, bioactive growth factors, and numerous stem cells. Among these, biomaterials that the mimic extracellular matrix (ECM) of tendon tissue would provide a resembling microenvironment to improve efficacy in tendon repair and regeneration. In this review, we will begin with a description of the constituents and structural features of tendon tissue, followed by a focus on the available biomimetic scaffolds of natural or synthetic origin for tendon tissue engineering. Finally, we will discuss novel strategies and present challenges in tendon regeneration and repair.
the Subarachnoid Space and Computational Junction Studied with an Idealized Model of CSF Flow Dynamics at the Craniovertebral

BACKGROUND AND PURPOSE: How CSF flow varies with the anatomy of the subarachnoid space has not been sufficiently well studied. The goal of this study was to develop an idealized 3D computational model of the subarachnoid space and then to use this model to study the detailed spatiotemporal effects of anatomic variations on CSF pressures and velocities. MATERIALS AND METHODS: We created a geometric model with a computer-assisted design program. The model contained a central structure for the brain and spinal cord axis and a second surrounding structure for the peripheral borders of the subarachnoid space. Model dimensions were adjusted to capture the main characteristics of the normal human posterior fossa and cervical spinal anatomy. CSF flow was modeled as water with a sinusoidal flow pattern in time. Velocities and pressures during craniocaudal and caudocranial flow were calculated with computational fluid dynamics (CFD) software. Simulated flow was compared with published phase-contrast MR imaging measurements of CSF flow in healthy human subjects. RESULTS: The model contained geometric characteristics of the posterior fossa and spinal canal. Flow velocities varied with the time in the cycle and location in space. Flow velocities had spatial variations that resembled those in healthy human subjects. Reynolds numbers were moderate, showing a laminar flow regime. Pressure varied uniformly along the long axis of the model during craniocaudal and caudocranial flow. CONCLUSIONS: In an idealized geometric approximation of the human subarachnoid space, CSF velocities and pressures can be studied in spatiotemporal detail with mathematic models.
Improving Differential-Neural Distinguisher with Inception

In CRYPTO’19, Gohr proposed a new cryptanalysis method by building the differential-neural distinguisher with neural network. Gohr combined a differential-neural distinguisher with a classical differential, achieving a 12-round (out of 22) key recovery attack on Speck 32/64. Chen et al. improved the accuracy of the differential-neural distinguisher considering derived features from multiple-ciphertext pairs. Bao et al. improved the classical differential by generalizing the concept of neutral bits, leading to key recovery attacks for 13-round Speck 32/64 and 16-round (out of 32) Simon 32/64. We focus on training the distinguisher for more rounds or improving accuracy using deep learning methods. To capture information on multiple dimensions, we use multiple parallel convolutional layers with kernels of different sizes, placed in front of the Residual Network, to train differential-neural distinguisher inspired by the Inception in GoogLeNet. For Speck 32/64, we significantly improve the accuracy of the distinguisher in the 6, 7 and 8 rounds and train a 9-round differential-neural distin-guisher. For Simon 32/64, we obtain a new 12-round differential-neural distinguisher. In addition, we significantly improve the accuracy of the distinguisher in rounds 9, 10, and 11. In addition, we use neutral bits to solve the same distribution of data required to successfully launch a key recovery attack when using multiple-ciphertext pairs as the input of the neural network.
Role of Calcium-Activated Potassium Channels in Proliferation, Migration and Invasion of Human Chronic Myeloid Leukemia K562 Cells

Calcium-activated potassium channels (KCa) are important participants in calcium signaling pathways due to their ability to be activated by an increase in intracellular free calcium concentration. KCa channels are involved in the regulation of cellular processes in both normal and pathophysiological conditions, including oncotransformation. Previously, using patch-clamp, we registered the KCa currents in the plasma membrane of human chronic myeloid leukemia K562 cells, whose activity was controlled by local Ca2+ entry via mechanosensitive calcium-permeable channels. Here, we performed the molecular and functional identification of KCa channels and have uncovered their role in the proliferation, migration and invasion of K562 cells. Using a combined approach, we identified the functional activity of SK2, SK3 and IK channels in the plasma membrane of the cells. Selective SK and IK channel inhibitors, apamin and TRAM-34, respectively, reduced the proliferative, migratory and invasive capabilities of human myeloid leukemia cells. At the same time, the viability of K562 cells was not affected by KCa channel inhibitors. Ca2+ imaging showed that both SK and IK channel inhibitors affect Ca2+ entry and this could underlie the observed suppression of pathophysiological reactions of K562 cells. Our data imply that SK/IK channel inhibitors could be used to slow down the proliferation and spreading of chronic myeloid leukemia K562 cells that express functionally active KCa channels in the plasma membrane.
Linear Programming and Economic Analysis.

Eventually, you will certainly discover a further experience and capability by spending more cash. nevertheless when? do you assume that you require to acquire those all needs in imitation of having significantly cash? Why don't you attempt to get something basic in the beginning? That's something that will lead you to comprehend even more all but the globe, experience, some places, similar to history, amusement, and a lot more?
City, University of London Institutional Repository

Christiansen & Chater (CC)’s key premise is that “if linguistic information is not processed rapidly, that information is lost for good”. From this “Now-or-Never Bottleneck” (NNB), CC derive “wide-reaching and fundamental implications for language processing, acquisition and change as well as for the structure of language itself”. We question both the premise and the consequentiality of its purported implications.
Instructions for use

Novel chondroitin sulfate (CS) chains with an average molecular mass of 79.6 kDa were purified from squid liver integument. A compositional analysis of the CS chains using chondroitinases (CSases) ABC and AC-I revealed a range of variably sulfated disaccharides with GlcA  1-3GalNAc(6- O -sulfate), GlcA  1-3GalNAc(4- O -sulfate), and GlcA  1-3GalNAc(4,6- O -disulfate) as the major ones, significant amounts of rare 3- O -sulfated GlcA-containing disaccharides, and a small amount of nonsulfated GlcA  1-3GalNAc. The CS chains exhibited neurite outgrowth-promoting activity toward embryonic mouse hippocampal neurons, which was abolished completely by digestion with CSase ABC or AC-I. Consequently, whether these CS chains interact with heparin-binding growth factors was tested in a BIAcore system. All of the growth factors exhibited concentration-dependent and specific binding. CS chains from squid liver integment, with their unique composition and strong biological activities, may be a good candidate for therapeutic application.
: CALLOUS-UNEMOTIONAL TRAITS AND PAIN RECOGNITION Recognition of Pain as another Deficit in Young Males with High Callous-Unemotional Traits

Prior research on callous-unemotional traits (CU) supports a deficit in recognizing fear in faces and body postures. Difficulties recognising others’ emotions may impair the typical behavioural inhibition for violent behaviour. However, recent research has begun to examine other distress cues such as pain. The present study examined emotion recognition skills, including pain, of school-excluded boys aged 11 to 16 years (N=50). Using dynamic faces and body poses, we examined the relation between emotion recognition and CU traits using the Youth Psychopathic Traits Inventory (YPI) and the Inventory of Callous-Unemotional Traits. Violent delinquency was covaried in regression analyses. Although fearful facial and fearful bodily expressions were unrelated to CU traits, recognition of dynamic pain facial expressions was negatively related to CU traits using the YPI. The failure to replicate a fear and sad deficit are discussed in relation to previous research. Also, findings are discussed in support of a general empathy deficit for distress cues which may underlie the problem behaviour of young males with CU traits.
Psychological distress in a Portuguese sample of cancer patients during the COVID-19 pandemic: a cross-sectional study

Abstract Objective: Considering the lack of studies exploring the psychological impact of COVID-19 on patients living with and beyond cancer, this study aimed to explore the levels of emotional distress, anxiety, and depression in patients with cancer and survivors during the COVID-19 pandemic and to identify psychological risk factors. Methods: Data for this cross-sectional study were collected through an online questionnaire comprised a self-report questionnaire and the Hospital Anxiety and Depression Scale (HADS). Descriptive statistics and linear regression models were used to characterize the sample and identify the determinants of emotional distress, depression, and anxiety. Statistical analyses were performed using R, version 4.1. 1. Results: The sample included 940 patients with cancer and survivors, mostly female (87.1%), with a mean age of 49.7 years. More than half of the patients were survivors (55.1%); 56.7% were diagnosed with breast cancer, and 35.0% had other chronic diseases. Most participants believed that they were more likely to be infected with COVID-19 (53.9%) and that they may have more complications if they were infected (76.7%). Approximately half of the participants revealed significant emotional distress (49.3%), 18.1% revealed significant depression, and 29.2% revealed significant anxiety. Female patients, participants with lower education levels, unemployed/lay-off, or in a medical leave with a chronic disease, taking care of someone with a chronic disease, or perceiving a higher risk of being infected with COVID-19 had higher levels of emotional distress, depression, and anxiety. Conclusions: This study provides useful information regarding the impact of the COVID-19 pandemic on the mental health of patients with cancer and survivors, showing that an increased number of patients experienced significant emotional distress during the pandemic.