Miracle-dz/UniFAR-FTUs huggingface dataset:
Vaginal Lactobacillus iners abundance is associated with outcome in antibiotic treatment of bacterial vaginosis and capable of inhibiting Gardnerella

Bacterial vaginosis is characterized as a polymicrobial dysbiosis with the loss of Lactobacillus spp. and growth of multiple anerobic bacteria, including Gardnerella, Prevotella and Atopobium ranked as the top three most abundant. A total of nine Gardnerella genomospecies have been identified, yet the association between their distribution or any exact Lactobacillus species with BV occurrence or prognosis remains controversial. A total of 308 patients and 62 healthy women who sought annual examinations were recruited, with 130 BV patients and 41 healthy women who met our inclusion criteria finally included. Vaginal samples were used for microscopic examination, 16S rRNA sequencing, bacterial culture and isolation. Isolates of Gardnerella vaginalis, Fannyhessae vaginae (used to be called Atopobium vaginae) and Lactobacillus iners were used for competition tests. We found that the relative abundances of Gardnerella, Prevotella and Atopobium were elevated in BV patients compared to healthy people (p<0.0001), yet no significant differences were found among patients with different clinical outcomes (p>0.05). Seven out of nine Gardnerella genomospecies were present in both BV patients and healthy women, and the relative abundances of all detected genomospecies were higher in BV patients (p<0.05). Cured patients possessed higher GS03 than intermediate and failed patients (p=0.005, 0.0337). L. iners was significantly higher in cured patients than in the other two groups (p=0.0021, p<0.0001), and its ability to inhibit the growth of G. vaginalis and F. vaginae was validated. In summary, seven Gardnerella genomospecies were detected in Chinese BV patients, but no association of its distribution and BV occurrence or prognosis was found. The relative abundance of L. iners was higher in cured patients, and its antimicrobial activity against G. vaginalis and F. vaginae was validated through in vitro inhibition experiment. L. iners could become a predictive indicator of clinical outcomes of BV patients, and its antimicrobial function might be beneficial to BV patients.
How Will Blockchain Technology Impact Auditing and Accounting: Permissionless versus Permissioned Blockchain

Blockchain offers a drastically new way to record, process, and store financial transactions and information, and has the potential to fundamentally change the landscape of the accounting profession and reshape the business ecosystem. In this article, we introduce two types (i.e., permissionless and permissioned) of blockchain and lay out their technological features. We further discuss implications of blockchain to auditing and elaborate on opportunities and challenges of the two types of blockchain to auditors. We conclude by making specific recommendations for auditors to adapt, adjust, and elevate themselves to the role of strategic partners in blockchain implementation. JEL Classifications: M15; M41; M42; O14; O33; O55.
Crosstalk between cytotoxic CD8+ T cells and stressed cardiomyocytes triggers development of interstitial cardiac fibrosis in hypertensive mouse hearts

Graphical Abstract Cardiac fibrosis developed from fibrotic remodelling due to hypertension leads to heart failure. Immune cells are abundantly accumulated in fibrotic regions, but exact mechanisms how they contributed to cardiac fibrosis are not clear. Here we demonstrated that CD8 T cells are major contributor to cardiac fibrosis in hypertensive hearts. Stressed cardiomyocytes express STING-dependent RAE-1 that activates NKG2D + CD8 T cells to induce apoptosis of stressed cardiomyocytes. Preventing STING signalling in stressed cardiomyocytes attenuates cardiac fibrosis. Pharmacologically inhibiting cardiomyocayte-RAE-1 and CD8+ T cell-NKG2D axis may be a potential therapeutic strategy to prevent cardiac fibrosis in HFpEF patients. Aims Cardiac fibrosis is central to heart failure (HF), especially HF with preserved ejection fraction (HFpEF), often caused by hypertension. Despite fibrosis causing diastolic dysfunction and impaired electrical conduction, responsible for arrhythmia-induced sudden cardiac death, the mechanisms are poorly defined and effective therapies are lacking. Here we show that crosstalk between cardiac cytotoxic memory CD8+ T cells and overly stressed cardiomyocytes is essential for development of non-ischemic hypertensive cardiac fibrosis. Methods and results CD8 T cell depletion in hypertensive mice, strongly attenuated CF, reduced cardiac apoptosis and improved ventricular relaxation. Interaction between cytotoxic memory CD8+ T cells and overly stressed cardiomyocytes is highly dependent on the CD8+ T cells expressing the innate stress-sensing receptor NKG2D and stressed cardiomyocytes expressing the NKG2D activating ligand RAE-1. The interaction between NKG2D and RAE-1 results in CD8+ T cell activation, release of perforin, cardiomyocyte apoptosis, increased numbers of TGF-β1 expressing macrophages and fibrosis. Deleting NKG2D or perforin from CD8+ T cells greatly attenuates these effects. Activation of the cytoplasmic DNA-STING-TBK1-IRF3 signaling pathway in overly stressed cardiomyocytes is responsible for elevating RAE-1 and MCP-1, a macrophage attracting chemokine. Inhibiting STING activation greatly attenuates cardiomyocyte RAE-1 expression, the cardiomyocyte apoptosis, TGF-β1 and fibrosis. Conclusion Our data highlight a novel pathway by which CD8 T cells contribute to an early triggering mechanism in CF development; preventing CD8+ T cell activation by inhibiting the cardiomyocyte RAE-1-CD8+ T cell-NKG2D axis holds promise for novel therapeutic strategies to limit hypertensive cardiac fibrosis.
Building team effectiveness through adaptation: Team knowledge and implicit and explicit coordination

We develop a theory of team adaptation that centers on team knowledge structures and coordination processes. Specifically, we explain that when a team’s task changes, there may be a disruption in the extent to which their team mental model (TMM) fits the current situation. Whether this is the case is likely to depend on team compositional factors, emergent states, and structural characteristics of the team. When there is a lack of correspondence between the TMM and the situation, this then requires a shift in the extent to which the team uses implicit or explicit coordination processes. We also explain that the team performance phase matters, such that during action phases, a prevalence of implicit coordination relative to explicit coordination results in greater effectiveness; during a transition phase, the opposite is likely. In this way, we address central questions in the field: what types of task changes require team adaptive response, what happens during the adaptation process, and how this influences team effectiveness over time.
Local Entropy Statistics for Point Processes

Point processes are often described with functionals, such as the probability generating functional, the Laplace functional, and the factorial cumulant generating functional. These are used to facilitate modelling of different processes and to determine important statistics via functional differentiation. In information theory, generating functions have also been defined for probability densities to determine information quantities such as the Shannon information and Kullback-Leibler divergence, though as yet there are no such analogues for point processes. The purpose of this article is to exploit the advantages of both types of generating function to facilitate the derivation of information statistics for point processes. In particular, a generating functional for point processes is introduced for determining statistics related to entropy and relative entropy based on Golomb’s information function and Moyal’s probability generating functional. It is shown that the information generating functional permits the derivation of a suite of statistics, including localised Shannon entropy and Kullback-Leibler divergence calculations.
Fuzzy Semantic Web Architecture for Activity Detection in Wireless Multimedia Sensor Network Applications

This study aims to increase the reliability of activity detection in Wireless Multimedia Sensor Networks (WMSNs) by using Semantic Web technologies extended with fuzzy logic. The proposed approach consists of three layers: the sensor layer, the data layer, and the Semantic Web layer. The sensor layer comprises a WMSN comprising sensor nodes with multimedia and scalar sensors. The data layer retrieves and stores data from the sink of WMSN. At the top of the architecture, there is a semantic web layer that includes a semantic web application server, a fuzzy reasoning engine, and a semantic knowledge base. When a new entity is detected at the sensor layer, the associated data produced by the sensors and the sink are collected in the data layer and transmitted to the semantic web application server where the data is converted into subjects, predicates, and objects, according to the ontology conceived and recorded in RDF format. Then, the fuzzy reasoning engine is automatically activated and fuzzy rules are executed to determine if there is an activity in the monitored area. Our implementation confirms that extended semantic Web technologies with fuzzy logic can have a significant impact on the detection of activities in WMSNs.
Using Speech to Predict Sequentially Measured Cortisol Levels During a Trier Social Stress Test

The effect of stress on the human body is substantial, potentially resulting in serious health implications. Furthermore, with modern stressors seemingly on the increase, there is an abundance of contributing factors which lead to a diagnosis of acute stress. However, observing biological stress reactions usually includes costly and time consuming sequential fluidbased samples to determine the degree of biological stress. On the contrary, a speech monitoring approach would allow for a non-invasive indication of stress. To evaluate the efficacy of the speech signal as a marker of stress, we explored, for the first time, the relationship between sequential cortisol samples and speech-based features. Utilising a novel corpus of 43 individuals undergoing a standardised Trier Social Stress Test (TSST), we extract a variety of feature sets and observe a correlation between speech and sequential cortisol measurements. For prediction of mean cortisol levels from speech, results show that for the entire TSST oral presentation, handcrafted COMPARE features achieve best results of 0.244 root mean square error [0 ;1] for the sample 20 minutes after the TSST. Correlation also increases at minute 20, with a Spearman’s correlation coefficient of 0.421, and Cohen’s d of 0.883 between the baseline and minute 20 cortisol predictions.
Pixelwise Estimation of Signal-Dependent Image Noise Using Deep Residual Learning

In traditional image denoising, noise level is an important scalar parameter which decides how much the input noisy image should be smoothed. Existing noise estimation methods often assume that the noise level is constant at every pixel. However, real-world noise is signal dependent, or the noise level is not constant over the whole image. In this paper, we attempt to estimate the precise and pixelwise noise level instead of a simple global scalar. To the best of our knowledge, this is the first work on the problem. Particularly, we propose a deep convolutional neural network named “deep residual noise estimator” (DRNE) for pixelwise noise-level estimation. We carefully design the architecture of the DRNE, which consists of a stack of customized residual blocks without any pooling or interpolation operation. The proposed DRNE formulates the process of noise estimation as pixel-to-pixel prediction. The experimental results show that the DRNE can achieve better performance on nonhomogeneous noise estimation than state-of-the-art methods. In addition, the DRNE can bring denoising performance gains in removing signal-dependent Gaussian noise when working with recent deep learning denoising methods.
Human T-Lymphotropic Virus-1/2 Detected in Drug Abused Prisoners Imprisoned in Correctional Facilities in Central of Java Indonesia

Introduction: Human T cell lymphotropic virus types 1 and 2 (HTLV-1/2) has been believed not circulate in Indonesia yet. This study evaluated the prevalence of HTLV-1 and 2 in drug abused prisoners imprisoned in correctional facilities in Central of Java Indonesia, to track the presentation of HTLV-1/2 in Indonesia. Methods: A cross-sectional study was conducted in two prisons (Kedung Pane Male Prison Semarang and Women Prison Semarang) in Central of Java Indonesia between August 2009 and October 2009. All drug abused prisoners in both prisons were enrolled in this study (n = 148). Questionnaires were retrieved and bloods were tested in the period using enzyme linked immuno sorbent assays. The serological positive results were then confirmed by RT-PCR nested addressed the part of HTLV-1 LTR and HTLV-2 LTR region, respectively. Results: The average seroprevalence in the period was 1.4% (2/148). All positive serological samples were confirmed by nested RT-PCR. Of these, one was HTLV-1 positive and one was HTLV-2 positive. Molecular analysis of positive PCR products revealed that the HTLV-1 isolate had close relationship with HTLV-1 isolated in Japan while the HTLV-2 isolate with that of isolated in USA. Conclusions: HTLV-1 and HTLV-2 were detected in drug abused prisoners imprisoned in correctional facilities in Central of Java Indonesia indicated that these viruses were circulated in Indonesia.
Enhancing User Experience of Task Assignment in Spatial Crowdsourcing: A Self-Adaptive Batching Approach

Faced with the explosive demand of real-world applications, spatial crowdsourcing has attracted much attention, in which task assignment algorithms take the dominant role in the past few years. On the one hand, most recent studies concentrate on maximizing the overall benefits of the platform, ignoring the fact that user experience also plays an essential role in task allocation. On the other hand, they focus on matching, that is, how to assign tasks, rather than batching, that is, when to make assignment. In fact, user experience also depends on batching, but this is largely overlooked by current studies. In this paper, we propose a self-adaptive batching mechanism to enhance user experience in spatial crowdsourcing. With appropriate start-up timestamps, previous matching methods can perform better. Multi-armed bandit algorithm in reinforcement learning is adopted to split the batch dynamically according to historical current states. Extensive experimental results on both real and synthetic datasets demonstrate the effectiveness and efficiency of the proposed approach.
Drivers of farmers' intentions to use eco-breeding: Integrating the theory of planned behavior and the norm activation model

Ecological breeding (eco-breeding) is a systematic ecological farming method for the benefit of the environment and human health, but farmers have adopted this method only to a lesser extent. The main objective of this study was to examine the factors that influence farmers' intentions to use eco-breeding methods. The study model was a combined application of the theory of planned behavior (TPB) and the normative activation model (NAM) to investigate the intentions of 527 Chinese farmers to engage in eco-breeding practices. Structural equation modeling analysis revealed that although the farmers' intention to adopt eco-breeding practices was influenced by both self-interested and altruistic motives, self-interested motives had a greater impact on the farmers' choices. Furthermore, favorable attitudes had the greatest effect on the farmers' intentions to implement eco-breeding, while past habits had no statistically significant effect on the intentions. Nonetheless, past habits significantly influenced attitudes and perceived behavior control in the combined model. In addition, the findings indicated that awareness of consequences had a significant effect on personal norms and the attribution of responsibility. Overall, the findings demonstrate the good efficiency as well as comprehensiveness of the integrated TPB-NAM in explaining the farmers' intentions to engage in eco-breeding practices. This study increases our understanding of the factors influencing the farmers' adoption of eco-breeding practices and helps to promote the adoption of eco-breeding in rural areas while providing a basis for the development of eco-breeding policy interventions.
Lenvatinib as second-line treatment in patients with unresectable hepatocellular carcinoma: A retrospective analysis

Objective The purpose of this study is to determine the efficacy and safety of lenvatinib as second-line therapy in Chinese patients with unresectable hepatocellular carcinoma (HCC). Methods We performed a retrospective analysis of Chinese patients with unresectable HCC who received second-line treatment of lenvatinib at three institutions from November 2018 to February 2022. Demographic and clinicopathologic characteristics, data on the treatment regimens were obtained from medical records. Tumor response was evaluated every 4-6 weeks by modified Response Evaluation Criteria in Solid Tumors (mRECIST). Results In total, 50 patients with unresectable HCC who received second-line treatment of lenvatinib were enrolled in this study. The objective response rate (ORR) was 18.0% and the disease control rate (DCR) was 74.0%, respectively. The duration of response (DoR) was 6.0 months. The median progression-free survival (PFS) and overall survival (OS) were 5.0 and 8.5 months, respectively. Patients who received ICIs combined with anti-angiogenic inhibitors as first-line therapy, achieving CR/PR at first-line therapy, with PFS≥6months at first-line therapy had a higher DCR. Univariate and multivariate analysis showed that AFP (ng/ml)<400, absence of extrahepatic metastasis, Child-Pugh A, tumor number<3, ICIs combined with anti-angiogenic inhibitors as first-line therapy, CR/PR to first-line therapy, and PFS≥6months at first-line therapy were independent factors of favorable PFS. Univariate analysis showed that absence of extrahepatic metastasis, tumor number<3, ICIs combined with anti-angiogenic inhibitors as first-line therapy, and PFS≥6months at first-line therapy were significantly associated with longer OS. Multivariate analysis showed that absence of extrahepatic metastasis, Child-Pugh A, tumor number<3, CR/PR to first-line therapy and PFS≥6months at first-line therapy were independent prognostic factors of OS. The majority of AEs were grade 1-2, and were reversible. Grade 3/4 AEs occurred in 12 patients (24.0%) and were mostly connected with hand-foot skin reactions (10.0%), and 10 patients had lenvatinib dose reductions. Two toxicity-related treatment interruptions were attributed to grade 3 hand-foot skin reaction, and grade 4 proteinuria, respectively. Conclusion This study confirms the efficacy and safety of lenvatinib as second-line therapy after progression on sorafenib or ICIs combined with anti-angiogenic inhibitors.
Makeup based on segmentation and local transfer

In this paper, we introduce an automatic beauty system based on semantic segmentation and local transfer. The user provides a source image and a target image as a reference. Our system create a new makeup upon the face of source image with target image as reference. More precisely, we transfer each makeup areas(lip gloss, eye shadow, eyebrow)to corresponding parts of the face. One major advantages of our approach is we create this new makeup for source image locally, this not only caters to the requirement of users but also renders makeup upon face of source image more naturally. The effectiveness of the proposed are demonstrated in the experiments.
Identity Recognition Based on Face Image

This article is aimed at designing a human identity recognition algorithm based on face image, which will be used in indoor environment. As the working environment is set as indoor environment, the camera will not be affected much by illumination variation. The key is how to detect human face and handle the variation of facial pose. This article divides the whole recognition process into 4 parts: image pre-processing, face detection, face alignment, feature extraction and comparison. Face detection and feature extraction are the core functions and both realized by deep learning. The process of the whole algorithm can be described as following: images after pre-processing are fed to face detection network to get the locations of face and face landmarks. Then face alignment will be conducted. Finally, deep features of face will be extracted and compared. The unique features of this algorithm are its good performance of handling the variation of facial pose and its clear framework which allows the whole method can be easily adjusted and upgraded.
Innovative forms of rehabilitation: Enhancing communities of practice through the art of drumming

ABSTRACT Communities of place, practice, and interest have historically been impacted by mind and mood-altering substances for quite some time. The development and strength of informal social bonds are important in determining an individual’s behavior, and can explain changes in criminal behavior over time. This can impact the social fabric of community, as well as creating the possibility of a community of practice (COP) for rehabilitation. Blending the concepts of emotional intelligence, drumming, and community development, this study examines a community of practice in which individuals with a history of drug use engage in a collective learning process utilizing drumming as the context. The purpose was to explore how a community of practice could be developed through a rehabilitation group program. Results indicated themes of solidarity, development of agency, enhanced communication skills, and the meaning of community being particularly salient throughout the intervention. Future directions for research and practical applications are also presented.
Tax equity, green innovation and corporate sustainable development

In the context of increasing resource scarcity and environmental pollution, achieving sustainable development with environmental friendliness at its core is essential for maintaining long-term economic growth. However, the positive environmental externalities associated with sustainable development impose an additional cost burden on enterprises. Internalizing those positive environmental externalities in the form of corporate benefits through tax policies is the key to incentivizing sustainable development. Using the reform of China’s tax enforcement agency and tax enforcement system as a quasi-natural experimental context, this paper investigates the impact of tax equity on sustainable development. The empirical findings show that tax equity can promote the sustainable development of enterprises, and this promotion is achieved by enhancing corporate green innovation. Furthermore, tax equity has a stronger effect on the sustainable development of enterprises in a favorable market environment because it is more conducive to the protection of intellectual property and transformation of green innovation into tangible output. The tax equities brought about by different types of tax reforms have different effects on the sustainable development of enterprises in different life cycle stages because the types of tax inequities suffered by enterprises of different ages vary.
Brain organoids: Establishment and application

Brain organoids are produced by the differentiation of pluripotent stem cells under three-dimensional culture conditions by adding neurodevelopment-related regulatory signals. They are similar to the cell composition and anatomical structure of the brain, and can reflect the developmental process of the brain, as well as their physiology, pathology, and pharmacology. Brain organoids are good models to study human brain development and brain-related diseases in vitro. Here, we mainly focus on the construction of brain organoids and review the application of brain organoids in disease modelingand drug screening.