Miracle-dz/UniFAR-FTUs huggingface dataset:
A comparison of faecal glucocorticoid metabolite concentration and gut microbiota diversity in bonobos (Pan paniscus).

Sex, age, diet, stress and social environment have all been shown to influence the gut microbiota. In several mammals, including humans, increased stress is related to decreasing gut microbial diversity and may differentially impact specific taxa. Recent evidence from gorillas shows faecal glucocorticoid metabolite concentration (FGMC) did not significantly explain gut microbial diversity, but it was significantly associated with the abundance of the family Anaerolineaceae. These patterns have yet to be examined in other primates, like bonobos (Pan paniscus). We compared FGMC to 16S rRNA amplicons for 202 bonobo faecal samples collected across 5 months to evaluate the impact of stress, measured with FGMC, on the gut microbiota. Alpha diversity measures (Chao's and Shannon's indexes) were not significantly related to FGMC. FGMC explained 0.80 % of the variation in beta diversity for Jensen-Shannon and 1.2% for weighted UniFrac but was not significant for unweighted UniFrac. We found that genus SHD-231, a member of the family Anaerolinaceae had a significant positive relationship with FGMC. These results suggest that bonobos are relatively similar to gorillas in alpha diversity and family Anaerolinaceae responses to FGMC, but different from gorillas in beta diversity. Members of the family Anaerolinaceae may be differentially affected by FGMC across great apes. FGMC appears to be context dependent and may be species-specific for alpha and beta diversity but this study provides an example of consistent change in two African apes. Thus, the relationship between physiological stress and the gut microbiome may be difficult to predict, even among closely related species.
Change in reflective functioning in interpersonal psychotherapy and cognitive behavioral therapy for major depressive disorder

Abstract Background Patients with Major Depressive Disorder (MDD) have been found to have restricted capacity for mentalization, and it is possible that this constitutes a vulnerability factor for developing depression. Due to its focus on linking depressive symptomatology to emotions and interpersonal relations, it was hypothesized that Interpersonal Psychotherapy (IPT) would improve mentalization more than Cognitive Behavioral Therapy (CBT). Methods In a randomized controlled trial of 90 patients undergoing IPT and CBT for MDD, Reflective Functioning (RF) was rated from Adult Attachment and from Depression-Specific Reflective Functioning (DSRF) Interviews before and after therapy. Treatment outcome was assessed using the Beck Depression Inventory-II. Results The interaction between time and treatment approach was statistically significant, with RF improving significantly more in IPT than in CBT. Change in RF was not correlated with change in depression. The difference in DSRF ratings before and after therapy was not statistically significant for any of the treatments. Conclusions IPT may improve mentalization more than CBT. However, although RF increased significantly in IPT, the mean level was still low after therapy. A limitation of the study is the large amount of post-treatment missing data. More research is needed to understand the potential role of mentalization in symptom reduction.
Translationally controlled tumor protein: the mediator promoting cancer invasion and migration and its potential clinical prospects

Translationally controlled tumor protein (TCTP) is a highly conserved multifunctional protein localized in the cytoplasm and nucleus of eukaryotic cells. It is secreted through exosomes and its degradation is associated with the ubiquitin-proteasome system (UPS), heat shock protein 27 (Hsp27), and chaperone-mediated autophagy (CMA). Its structure contains three α-helices and eleven β-strands, and features a helical hairpin as its hallmark. TCTP shows a remarkable similarity to the methionine-R-sulfoxide reductase B (MsrB) and mammalian suppressor of Sec4 (Mss4/Dss4) protein families, which exerts guanine nucleotide exchange factor (GEF) activity on small guanosine triphosphatase (GTPase) proteins, suggesting that some functions of TCTP may at least depend on its GEF action. Indeed, TCTP exerts GEF activity on Ras homolog enriched in brain (Rheb) to boost the growth and proliferation of Drosophila cells. TCTP also enhances the expression of cell division control protein 42 homolog (Cdc42) to promote cancer cell invasion and migration. Moreover, TCTP regulates cytoskeleton organization by interacting with actin microfilament (MF) and microtubule (MT) proteins and inducing the epithelial-mesenchymal transition (EMT) process. In essence, TCTP promotes cancer cell movement. It is usually highly expressed in cancerous tissues and thus reduces patient survival; meanwhile, drugs can target TCTP to reduce this effect. In this review, we summarize the mechanisms of TCTP in promoting cancer invasion and migration, and describe the current inhibitory strategy to target TCTP in cancerous diseases.
A qualitative study into the experience of living with acalculia after stroke and other forms of acquired brain injury

ABSTRACT Acalculia, an acquired disability following a brain injury, involves difficulty processing numerical information and/or calculations. Acalculia is not routinely screened for, and as a result there is a lack of understanding about the nature and prevalence and the impact of the condition. This qualitative study was initiated by stroke survivors with a strong interest in acalculia. Sixteen stroke/brain injury survivors with acalculia and seven carers were interviewed using semi-structured online interviews. Participants ranged in age, gender, time post-onset, country of residence and numeracy level prior to brain injury. Data were analysed using thematic analysis. Three main themes were identified: Awareness and Diagnosis; Emotional and Practical Impact (independence); Support, Coping Strategies and Self-training. Participants and carers repeatedly referred to the lack of awareness and treatment for acalculia and the impact acalculia has had on their lives and independence. Practical impacts included managing money, making appointments, using timetables, organizing social activities and employment, and managing medication. Our results highlight the urgent need to develop suitable assessments and interventions for acalculia and the scope for this to be Patient, Carer and Public involvement (PCPI)-led. The data also reveal useful strategies and suggestions regarding effective timing, targets and approaches for intervention.
Correlation between pathological features and protein expressions of TfR1, VEGF and MMP-9 in patients with osteosarcoma.

OBJECTIVE To analyze the pathological features of patients with osteosarcoma and the correlation of expressions of transferrin receptor 1 (TfR1), vascular endothelial growth factor (VEGF) and matrix metalloproteinase (MMP)-9 in tumor tissue with the pathological features. METHODS The tumor tissue and paracancerous tissue samples from 31 patients with osteosarcoma were collected before treatment to measure the expressions of TfR1, VEGF and MMP-9. Kaplan-meier curve was used to analyze the relationship of TfR1, VEGF and MMP-9 with the survival time of patients. Log-rank test was used to test the difference, and Spearman test was used to test the correlation. RESULTS Among the 31 osteosarcoma samples, 23 (74.19%), 24 (77.42%) and 17 (54.84%) samples showed medium-high expressions of VEGF, TfR1 and MMP-9, respectively, which were significantly higher than those in the paracancerous samples (P<0.05). Furthemore, the medium-high expression rates of VEGF, TfR1 and MMP-9 were significantly different in patients with different Enneking stages, different histological differentiation degrees, and with or without distant metastasis. Besides, the expression of VEGF was also significantly different in different-size osteosarcoma samples (P<0.05). The survival time of patients with low expressions of TfR1, VEGF and MMP-9 was significantly longer than that of patients with medium-high expressions (P<0.05). Additionally, in osteosarcoma samples, significant positive correlations were shown between the expressions of TfR1 and VEGF, as well as between TfR1 and MMP-9 (P<0.05), but there was no significant correlation between VEGF and MMP-9 (P>0.05). CONCLUSION TfR1, VEGF and MMP-9 are abnormally expressed in osteosarcoma lesions, suggesting that they all play a role in the occurrence and development of osteosarcoma.
Sodium‐glucose co‐transporter 2 inhibitors and hematopoiesis

Many patients with diabetes mellitus, especially those with chronic kidney disorders, have some degree of anemia due to a spectrum of causes and underlying pathophysiologic pathways. As such, enhancement in erythropoiesis is important in these patients. Sodium‐glucose cotransporter 2 inhibitors (SGLT2i) are a relatively new class of antidiabetic drugs with confirmed protective effects in kidney and cardiovascular tissues. Recent evidence suggests that these drugs may provide additional benefits in enhancing hematopoietic processes in diabetic patients. Though the exact mediating pathways have not been fully elucidated, cellular mechanisms are likely involved. In the current study, we present the potential pathways by which SGLT2i may modulate hematopoiesis and stimulate erythropoiesis.
Epidemiologic characteristics of SARS-CoV-2 in Wuhan, other regions of China, and globally based on data gathered from January 2020 to February 2021

This observation study examines coronavirus disease 2019 (COVID-19) data from outbreak and other sites in China and worldwide in order to examine the epidemiological pattern of COVID-19 before the acquisition of immunity through widespread vaccination and infection. COVID-19–related morbidity and mortality data for January 2020 to February 2021 were obtained from the Chinese Center for Disease Control and Prevention, Hubei Provincial Center for Disease Control and Prevention, and the World Health Organization. The number of cases was logarithmically transformed for comparison of the rate of increase or decrease with time across areas. From January to February 2020, the number of new confirmed cases in Wuhan grew substantially but returned to zero by May 2020. In other parts of China, the rate of decrease was lower than that in Wuhan, and the mortality rate was lower outside Wuhan (1.93%) than in Wuhan (7.68%). The influenza trends were similar to those of COVID-19, but the mortality rate of influenza was much lower (0.011%) than that of COVID-19. After the early stage, similar increase in the incidence rate with time was observed globally, although the total number of cases differed between regions. The outbreak severe acute respiratory syndrome coronavirus 2 strain in Wuhan had low epidemic intensity and high virulence, but the epidemiological characteristics of severe acute respiratory syndrome coronavirus 2 may not be associated with race, geography, or economic status. Importantly, more effective prevention and control measures and vaccines should be applied for controlling the variants.
Risk Factors, Temporal Dependence, and Seasonality of Human Extended-Spectrum β-Lactamases-Producing Escherichia coli and Klebsiella pneumoniae Colonization in Malawi: A Longitudinal Model-Based Approach

Background Antimicrobial resistance (AMR) represents an important threat to achieving the sustainable development goals in Sub-Saharan Africa (sSA). sSA is reported to have the highest estimated death rate attributable to AMR, with Extended-Spectrum Beta-Lactamase-producing Enterobacterales, such as Klebsiella pneumoniae and Escherichia coli, representing the greatest challenge. However, the dynamics of human colonisation with such bacteria in the sSA community setting are not well known. Inadequate water, sanitation and hygiene (WASH) infrastructure and associated behaviours are thought to play an important role in transmission of AMR-bacteria, and an improved understanding of the temporal dynamics of within-household transmission could help inform the design of public health policies that interrupt transmission of AMR-bacteria. Methods and Findings In this 18-month study, individuals from households in diverse areas of Southern Malawi were recruited and human stool samples were longitudinally collected. Using microbiological data and household surveys, we built a multivariable hierarchical harmonic logistic regression model to identify risk factors for colonisation with ESBL-producing E. coli and K. pneumoniae, reflecting household structure and temporal correlation of colonisation status between timepoints. Important risk factors were identified, with men having a lower risk of becoming colonised with ESBL-producing E. coli (OR 0.786 CrI[0.678-0.910]) and the use of a tube well or a borehole as a water drinking source highly increasing the risk of becoming colonised (OR 1.550 CrI[1.003-2.394]). Coming into contact with standing water also appeared to be negatively associated with colonisation status (OR 0.749 CrI[0.574-0.978]). For ESBL-producing K. pneumoniae, having recently taken a course of antibiotics increased the risk of being colonised (OR 1.281 CrI[1.049-1.565]). We also found a negative association between eating from shared plates and colonisation with ESBL-producing K. pneumoniae (OR 0.672 CrI[0.460- 0.980]). Finally, we detected a temporal correlation range of eight to eleven weeks, providing evidence that within-household transmission occurs within this time frame. Conclusions We suggest that interventions aimed at preventing transmission might have the best impact when targeted at the household-level and focused on a combination of improving WASH infrastructure and modifying associated behaviours. Additionally, we showed that antibiotic use is important when looking at colonisation with ESBL-producing K. pneumoniae and therefore infection prevention and control measures and antibiotic use and stewardship training could help in reducing transmission.
A phase‐field approach for the modeling of nematic liquid crystal elastomers

Liquid crystal elastomers combine the elastic properties of rubber with the orientational properties of liquid crystals. Amongst the various types of liquid crystal elastomers, nematic elastomers are one special class. Nematic elastomers undergo a phase transition from a high‐temperature isotropic state to a low‐temperature nematic state, which is generally accompanied by large deformations. In the present contribution, a phase‐field model for the continuum‐mechanical description of nematic elastomers will be introduced. The model is implemented into the finite element method and employed for the simulation of domain evolution under specified initial boundary conditions. (© 2014 Wiley‐VCH Verlag GmbH & Co. KGaA, Weinheim)
Fair Representation Learning: An Alternative to Mutual Information

Learning fair representations is an essential task to reduce bias in data-oriented decision making. It protects minority subgroups by requiring the learned representations to be independent of sensitive attributes. To achieve independence, the vast majority of the existing work primarily relaxes it to the minimization of the mutual information between sensitive attributes and learned representations. However, direct computation of mutual information is computationally intractable, and various upper bounds currently used either are still intractable or contradict the utility of the learned representations. In this paper, we introduce distance covariance as a new dependence measure into fair representation learning. By observing that sensitive attributes (e.g., gender, race, and age group) are typically categorical, the distance covariance can be converted to a tractable penalty term without contradicting the utility desideratum. Based on the tractable penalty, we propose FairDisCo, a variational method to learn fair representations. Experiments demonstrate that FairDisCo outperforms existing competitors for fair representation learning.
TaxoTrans: Taxonomy-Guided Entity Translation

Taxonomies describe the definitions of entities, entities' attributes and the relations among the entities, and thus play an important role in building a knowledge graph. In this paper, we tackle the task of taxonomy entity translation, which is to translate the names of taxonomy entities in a source language to a target language. The translations then can be utilized to build a knowledge graph in the target language. Despite its importance, taxonomy entity translation remains a hard problem for AI models due to two major challenges. One challenge is understanding the semantic context in very short entity names. Another challenge is having deep understanding for the domain where the knowledge graph is built. We present TaxoTrans, a novel method for taxonomy entity translation that can capture the context in entity names and the domain knowledge in taxonomy. To achieve this, TaxoTrans creates a heterogeneous graph to connect entities, and formulates the entity name translation problem as link prediction in the heterogeneous graph: given a pair of entity names across two languages, TaxoTrans applies a graph neural network to determine whether they form a translation pair or not. Because of this graph, TaxoTrans can capture both the semantic context and the domain knowledge. Our offline experiments on LinkedIn's skill and title taxonomies show that by modeling semantic information and domain knowledge in the heterogeneous graph, TaxoTrans outperforms the state-of-the-art translation methods by ∼10%. Human annotation and A/B test results further demonstrate that the accurately translated entities significantly improves user engagements and advertising revenue at LinkedIn.
Vexation-Aware Active Learning for On-Menu Restaurant Dish Availability

Here we leverage the power of the crowd: online users who are willing to answer questions about dish availability at restaurants visited. While motivated users are happy to contribute knowledge, they are much less likely to respond to "silly'' or embarrassing questions (e.g., "DoesPizza Hut serve pizza? '' or "DoesMike's Vegan Restaurant serve steak? '') In this paper, we study the problem of Vexation-Aware Active Learning (VAAL), where judiciously selected questions are targeted towards improving restaurant-dish model prediction, subject to a limit on the percentage of "unsure'' answers or "dismissals'' (e.g., swiping the app closed) measuring user vexation. We formalize the selection problem as an integer program and solve it efficiently using a distributed solution that scales linearly with the number of candidate questions. Since our algorithm relies on an accurate estimation of the unsure-dismiss rate (UDR), we present a regression model that provides high-quality results compared to baselines including collaborative filtering. Finally, we demonstrate in a live system that our proposed VAAL strategy performs competitively against classical (margin-based) active learning approaches while reducing the UDR for the questions being asked.
Multi-task Hierarchical Classification for Disk Failure Prediction in Online Service Systems

One of the most common threats to online service system's reliability is disk failure. Many disk failure prediction techniques have been developed to predict failures before they actually occur, allowing proactive steps to be taken to minimize service disruption and increase service reliability. Existing approaches for disk failure prediction do not differentiate among various types of disk failure. In industrial practice, however, different product teams treat distinct types of disk failures as different prediction tasks in large-scale online service systems like Microsoft 365. For example, hardware operation team is concerned with physical disk errors, while database service team focuses on I/O delay. In this paper, we propose MTHC (Multi-Task Hierarchical Classification) to enhance the performance of disk failure prediction for each task via multi-task learning. In addition, MTHC introduces a novel hierarchy-aware mechanism to deal with the data imbalance problem, which is a severe issue in the area of disk failure prediction. We show that MTHC can be easily utilized to enhance most state-of-the-art disk failure prediction models. Our experiments on both industrial and public datasets demonstrate that such disk failure prediction models enhanced by MTHC performs much better than those models working without MTHC. Furthermore, our experiments also present that the hierarchical-aware mechanism underlying MTHC can alleviate the data imbalance problem and thus improve the practical performance of various disk failure prediction models. More encouragingly, the proposed MTHC has been successfully applied to Microsoft 365 online service systems, and averagely reduces the number of virtual machine interruptions by 10% per month.
Friend Recommendations with Self-Rescaling Graph Neural Networks

Friend recommendation service plays an important role in shaping and facilitating the growth of online social networks. Graph embedding models, which can learn low-dimensional embeddings for nodes in the social graph to effectively represent the proximity between nodes, have been widely adopted for friend recommendations. Recently, Graph Neural Networks (GNNs) have demonstrated superiority over shallow graph embedding methods, thanks to their ability to explicitly encode neighborhood context. This is also verified in our Xbox friend recommendation scenario, where some simplified GNNs, such as LightGCN and PPRGo, achieve the best performance. However, we observe that many GNN variants, including LightGCN and PPRGo, use a static and pre-defined normalizer in neighborhood aggregation, which is decoupled with the representation learning process and can cause the scale distortion issue. As a consequence, the true power of GNNs has not yet been fully demonstrated in friend recommendations. In this paper, we propose a simple but effective self-rescaling network (SSNet) to alleviate the scale distortion issue. At the core of SSNet is a generalized self-rescaling mechanism, which bridges the neighborhood aggregator's normalization with the node embedding learning process in an end-to-end framework. Meanwhile, we provide some theoretical analysis to help us understand the benefit of SSNet. We conduct extensive offline experiments on three large-scale real-world datasets. Results demonstrate that our proposed method can significantly improve the accuracy of various GNNs. When deployed online for one month's A/B test, our method achieves 24% uplift in adding suggested friends actions. At last, we share some interesting findings and hope the experience can motivate future applications and research in social link predictions.