Miracle-dz/UniFAR-FTUs huggingface dataset:
Human nasoseptal chondrocytes maintain their differentiated phenotype on PLLA scaffolds produced by thermally induced phase separation and supplemented with bioactive glass 1393

ABSTRACT Damage of hyaline cartilage such as nasoseptal cartilage requires proper reconstruction, which remains challenging due to its low intrinsic repair capacity. Implantation of autologous chondrocytes in combination with a biomimetic biomaterial represents a promising strategy to support cartilage repair. Despite so far mostly tested for bone tissue engineering, bioactive glass (BG) could exert stimulatory effects on chondrogenesis. The aim of this work was to produce and characterize composite porous poly(L-lactide) (PLLA)/1393BG scaffolds via thermally induced phase separation (TIPS) technique and assess their effects on chondrogenesis of nasoseptal chondrocytes. The PLLA scaffolds without or with 1, 2.5, 5% BG1393 were prepared via TIPS technique starting from a ternary solution (polymer/solvent/non-solvent) in a single step. Scaffolds were characterized by scanning electron microscopy (SEM), X-ray diffraction (XRD) and differential scanning calorimetric analysis (DSC). Human nasoseptal chondrocytes were seeded on the scaffolds with 1 and 2.5% BG for 7 and 14 days and cell survival, attachment, morphology and expression of SOX9 and cartilage-specific extracellular cartilage matrix (ECM) components were monitored. The majority of chondrocytes survived on all PLLA scaffolds functionalized with BG for the whole culture period. Also inner parts of the scaffold were colonized by chondrocytes synthesizing an ECM which contained glycosaminoglycans. Type II collagen and aggrecan gene expression increased significantly in 1% BG scaffolds during the culture. Chondrocyte protein expression for cartilage ECM proteins indicated that the chondrocytes maintained their differentiated phenotype in the scaffolds. BG could serve as a cytocompatible basis for future scaffold composites for osteochondral cartilage defect repair. Abbreviations: AB: alcian blue ACAN: gene coding for aggrecan; BG: Bioactive glass; 2D: two-dimensional; 3D: three-dimensional; COL2A1: gene coding for type II collagen; DAPI: 4ʹ,6-diamidino-2-phenylindole; DMEM: Dulbecco’s Modified Eagle’s Medium; DMMB: dimethylmethylene blue; DSC: Differential scanning calorimetric analysis; ECM: extracellular matrix; EDTA: ethylenediaminetetraacetic acid; EtBr: ethidium bromide; FCS: fetal calf serum; FDA: fluorescein diacetate; GAG: glycosaminoglycans; HDPE: high density polyethylene; HE: hematoxylin and eosin staining; HCA: hydoxylapatite; PBE: phosphate buffered EDTA100 mM Na2HPO4 and 5 mM EDTA, pH8; PBS: phosphate buffered saline; PFA: paraformaldehyde; PG: proteoglycans; PI: propidium iodide; PLLA: Poly-L-Lactic Acid Scaffold; RT: room temperature; SD: standard deviation; SEM: scanning electron microscopy; sGAG: sulfated glycosaminoglycans; SOX9/Sox9: SRY (sex-determining region Y)-box 9 protein; TBS: TRIS buffered saline; TIPS: Thermally Induced Phase Separation; XRD: X-ray diffraction analysis
Dynamic Features of Problematic Drinking: Alcohol Use Disorder Latent Classes Across Ages 18–64

Aims Alcohol use disorders (AUDs) are linked with numerous severe detrimental outcomes. Evidence suggests that there is a typology of individuals with an AUD based on the symptoms they report. Scant research has identified how these groups may vary in prevalence by age, which could highlight aspects of problematic drinking behavior that are particularly salient at different ages. Our study aimed to (a) identify latent classes of drinkers with AUD that differ based on symptoms of AUD and (b) examine prevalences of latent classes by age. Short summary Our findings advocate for personalized treatment approaches for AUD and highlight the need for carefully considering the role of age in prevention and intervention efforts. Methods We used data from the National Epidemiologic Survey on Alcohol and Related Conditions (NESARC-III). Current drinkers aged 18-64 who met criteria for a past-year AUD were included (n = 5402). Results Latent class analysis (LCA) based on 11 AUD criteria revealed 5 classes: 'Alcohol-Induced Injury' (25%), 'Highly Problematic, Low Perceived Life Interference' (21%), 'Adverse Effects Only' (34%), 'Difficulty Cutting Back' (13%) and 'Highly Problematic' (7%). Using time-varying effect modeling (TVEM), each class was found to vary in prevalence across age. The Adverse Effects Only and Highly Problematic, Low Perceived Life Interference classes were particularly prevalent among younger adults, and the Difficulty Cutting Back and Alcohol-Induced Injury classes were more prevalent as age increased. Conclusions Findings suggest that experience of AUD is not only heterogeneous in nature but also that the prevalence of these subgroups vary across age.
Crossing-Domain Generative Adversarial Networks for Unsupervised Multi-Domain Image-to-Image Translation

State-of-the-art techniques in Generative Adversarial Networks (GANs) have shown remarkable success in image-to-image translation from peer domain X to domain Y using paired image data. However, obtaining abundant paired data is a non-trivial and expensive process in the majority of applications. When there is a need to translate images across n domains, if the training is performed between every two domains, the complexity of the training will increase quadratically. Moreover, training with data from two domains only at a time cannot benefit from data of other domains, which prevents the extraction of more useful features and hinders the progress of this research area. In this work, we propose a general framework for unsupervised image-to-image translation across multiple domains, which can translate images from domain X to any a domain without requiring direct training between the two domains involved in image translation. A byproduct of the framework is the reduction of computing time and computing resources since it needs less time than training the domains in pairs as is done in state-of-the-art works. Our proposed framework consists of a pair of encoders along with a pair of GANs which learns high-level features across different domains to generate diverse and realistic samples from. Our framework shows competing results on many image-to-image tasks compared with state-of-the-art techniques.
Multi-modal Multi-cultural Dimensional Continues Emotion Recognition in Dyadic Interactions

Automatic emotion recognition is a challenging task which can make great impact on improving natural human computer interactions. In this paper, we present our solutions for the Cross-cultural Emotion Sub-challenge (CES) of Audio/Visual Emotion Challenge (AVEC) 2018. The videos were recorded in dyadic human-human interaction scenarios. In these complicated scenarios, a person's emotion state will be influenced by the interlocutor's behaviors, such as talking style/prosody, speech content, facial expression and body language. In this paper, we highlight two aspects of our solutions: 1) we explore multiple modalities's efficient deep learning features and use the LSTM network to capture the long-term temporal information. 2) we propose several multimodal interaction strategies to imitate the real interaction patterns for exploring which modality information of the interlocutor is effective, and we find the best interaction strategy which can make full use of the interlocutor's information. Our solutions achieve the best CCC performance of 0.704 and 0.783 on arousal and valence respectively on the challenge testing set of German, which significantly outperform the baseline system with corresponding CCC of 0.524 and 0.577 on arousal and valence, and which outperform the winner of the AVEC2017 with corresponding CCC of 0.675 and 0.756 on arousal and valence. The experimental results show that our proposed interaction strategies have strong generalization ability and can bring more robust performance.
Automated Screening for Bipolar Disorder from Audio/Visual Modalities

This paper addresses the Bipolar Disorder sub-challenge of the Audio/Visual Emotion recognition Challenge (AVEC) 2018, where the objective is to classify patients suffering from bipolar disorder into states of remission, hypo-mania, and mania, from audio-visual recordings of structured interviews. To this end, we propose 'turbulence features' to capture sudden, erratic changes in feature contours from audio and visual modalities, and demonstrate their efficacy for the task at hand. We introduce Fisher Vector encoding of ComParE low level descriptors (LLDs) and demonstrate that these features are viable for screening of bipolar disorder from speech. We also perform several experiments with standard feature sets from the OpenSmile toolkit as well as multi-modal fusion. The best result achieved on the test set is a UAR = 57.41%, which matches the best result published as the official baseline.
Random Forest Exploiting Post-related and User-related Features for Social Media Popularity Prediction

Social media headline prediction (SMHP) is a thriving application scenario, which aims to predict the popularity of the post data shared on social media. In this paper, we propose to use multi-aspect features combined with the random forest (RF) model for popularity predictions. Firstly, we extract features by combining both metadata of the posts and users' features. More specifically, we adopt the binary coding strategy for dimensionality reduction and deal with the missing values by using some strategies, i.e., estimating the missing geographic information according to the information of users, and filling the missing features with median. Furthermore, regression models can be used directly to make predictions. In particular, a random forest (RF) model is adopted since it does not require much effort in tuning hyper-parameters and performs effectively. Extensive experiments conducted on the SMHP dataset consisting of 340K image posts shared by 80K users manifest the effectiveness of our method. Our approach achieves the 4nd place in the leader board of the Grand Challenge of SMHP in ACM Multimedia 2018.
Hope and caution: rewilding to mitigate the impacts of biological invasions

Rewilding is a novel approach to ecological restoration. Trophic rewilding in particular aims to reinstate ecological functions, especially trophic interactions, through the introduction of animals. We consider the potential for trophic rewilding to address biological invasions. In this broad review, we note some of the important conceptual and ethical foundations of rewilding, including a focus on ecosystem function rather than composition, reliance on animal agency, and an appeal to an ethic of coexistence. Second, we use theory from invasion biology to highlight pathways by which rewilding might prevent or mitigate the impacts of an invasion, including increasing biotic resistance. Third, we use a series of case studies to illustrate how reintroductions can mitigate the impacts of invasions. These include reintroductions and positive management of carnivores and herbivores including European pine martens (Martes martes), Eurasian otters (Lutra lutra), dingoes (Canis dingo), Tasmanian devils (Sarcophilus harrisii) and tule elk (Cervus canadensis nannodes). Fourth, we consider the risk that rewilding may enable a biological invasion or aggravate its impacts. Lastly, we highlight lessons that rewilding science might take from invasion biology. This article is part of the theme issue ‘Trophic rewilding: consequences for ecosystems under global change’.
Genetic Predisposition vs Individual-Specific Processes in the Association Between Psychotic-like Experiences and Cannabis Use

Importance Previous research indicates that cannabis use is associated with psychotic-like experiences (PLEs). However, it is unclear whether this association results from predispositional (ie, shared genetic) factors or individual-specific factors (eg, causal processes, such as cannabis use leading to PLEs). Objectives To estimate genetic and environmental correlations between cannabis use and PLEs, and to examine PLEs in twin and nontwin sibling pairs discordant for exposure to cannabis use to disentangle predispositional from individual-specific effects. Design, Setting, and Participants In this cross-sectional analysis, diagnostic interviews and self-reported data were collected from 2 separate population-based samples of twin and nontwin sibling pairs. Data from the Human Connectome Project were collected between August 10, 2012, and September 29, 2015, and data from the Australian Twin Registry Cohort 3 (ATR3) were collected between August 1, 2005, and August 31, 2010. Data were analyzed between August 17, 2017, and July 6, 2018. The study included data from 1188 Human Connectome Project participants and 3486 ATR3 participants, totaling 4674 participants. Main Outcomes and Measures Three cannabis-involvement variables were examined: frequent use (ie, ≥100 times), a DSM-IV lifetime cannabis use disorder diagnosis, and current cannabis use. Genetic and environmental correlations between cannabis involvement and PLEs were estimated. Generalized linear mixed models examined PLE differences in twin and nontwin sibling pairs discordant for cannabis use. Results Among the 4674 participants, the mean (SD) age was 30.5 (3.2) years, and 2923 (62.5%) were female. Data on race/ethnicity were not included as a covariate owing to lack of variability within the ATR3 sample; among the 1188 participants in the Human Connectome Project, 875 (73.7%) were white. Psychotic-like experiences were associated with frequent cannabis use (&bgr; = 0.11; 95% CI, 0.08-0.14), cannabis use disorder (&bgr; = 0.13; 95% CI, 0.09-0.16), and current cannabis use (&bgr; = 0.07; 95% CI, 0.04-0.10) even after adjustment for covariates. Correlated genetic factors explained between 69.2% and 84.1% of this observed association. Within discordant pairs of twins/siblings (Npairs, 308-324), Psychotic-like experiences were more common in cannabis-exposed individuals compared with their relative who used cannabis to a lesser degree (&bgr; ≥ .23, P < .05; eg, frequent and infrequent cannabis-using relatives significantly differed, z = −5.41; P < .001). Conclusions and Relevance Despite the strong contribution of shared genetic factors, frequent and problem cannabis use also appears to be associated with PLEs via person-specific pathways. This study’s findings suggest that policy discussions surrounding legalization should consider the influence of escalations in cannabis use on traitlike indices of vulnerability, such as PLEs, which could contribute to pervasive psychological and interpersonal burden.
Association of thrombocytopenia with in-hospital outcome in patients with acute ST-segment elevated myocardial infarction

Abstract This study investigated the association of thrombocytopenia (TP) with in-hospital medication and outcome of patients with acute ST-segment elevated myocardial infarction (STEMI). A total of 16,678 consecutive patients with STEMI from multiple centers that participated in the China Acute Myocardial Infarction registry was included. In-hospital adverse event rates were compared between patients with TP and those with a normal platelet count. Multivariate logistic regression was applied to adjust for confounders. Propensity score matching (PSM) was applied to control for baseline differences. There were 359 patients with baseline TP, accounting for 2.2% of the total cohort. The risk of in-hospital death (11.1% vs 6.0%, P < 0.001); major adverse cardiovascular events (MACE) (11.7% vs 6.4%, P < 0.001); and newly occurred or aggravated heart failure, cardiogenic shock, malignant arrhythmia, acute pulmonary embolism, and bleeding (3.6% vs 1.8%, P = 0.024) were significantly higher in the TP group than in the normal platelet group. After multivariate adjustment, TP was independently associated only with malignant arrhythmia (odds ratio: 1.49; 95% confidence interval: 1.09–2.05, P = 0.014). A total of 289 patients in each group were matched by PSM. The risk of all endpoints was not significantly different between the two matched groups before and after multivariate adjustment. In-hospital outcomes were worse in patients with STEMI and TP than in those with a normal platelet count. However, baseline TP was not independently associated with in-hospital death, MACE, or bleeding risk after multivariate adjustment and controlling for baseline differences.
School gardening increases knowledge of primary school children on edible plants and preference for vegetables

Abstract At least 10% of children worldwide are diagnosed with overweight. Part of this problem is attributed to low vegetable intake, for which preference at a younger age is an indicator. Few studies examined long‐term effects of school garden interventions on the knowledge about and preference for vegetables. Therefore, in this study, an intervention period of 7 months (17 lessons) was organized for primary school students (n = 150) of age 10–12 years in the Municipality of Nijmegen (the Netherlands). Surveys were conducted before and after the intervention period to test the ability of students to identify vegetables, to measure their self‐reported preference for vegetables, and to analyze students’ attitudes toward statements about gardening, cooking, and outdoor activity. The long‐term effects were measured by repeating the survey 1 year after the intervention (n = 52). Results were compared with a control group of students (n = 65) with similar background and tested for significance with α = 0.05. School gardening significantly increases the knowledge of primary schoolchildren on 10 vegetables as well as their ability to self‐report preference for the vegetables. The short‐term (n = 106) and long‐term (n = 52) preference for vegetables increased (p < 0.05) in comparison with the control group. The latter did not show a significant learning effect (p > 0.05). This implies that the exposure to vegetables generated by school gardening programs may increase willingness to taste and daily intake of vegetables on the long term. Students’ attitudes toward gardening, cooking, and outdoor activity were unaffected by the intervention.
Assessing and minimizing the risk of percutaneous coronary intervention in patients with chronic kidney disease

ABSTRACT Introduction: Chronic kidney disease (CKD) is commonly present in patients undergoing percutaneous coronary intervention (PCI). These patients frequently present with more complex coronary artery disease (CAD) and higher risk of peri-procedural and post-procedural adverse events, including bleeding, thrombotic events, and contrast-induced acute kidney injury (CI-AKI). This article contains updated knowledge and management of patients with CKD undergoing PCI. Areas covered: In this article, the pathophysiological mechanisms behind the association of CKD, complex CAD lesions, and complications of PCI are reviewed and the different risk scores available to assess the occurrence of CI-AKI are detailed. Furthermore, various strategies developed to prevent or reduce the impact of complications of PCI are described. Expert commentary: Patients with CKD have remained a challenge in the field of PCI. Several strategies have been evaluated in the last 20 years, with uneven results. Intravascular expansion therapy remains the cornerstone of CI-AKI prevention although recent studies have emphasized the benefit of guided hydration rather than a one-size-fits-all model. N-acetylcysteine and sodium bicarbonate have recently been challenged while pretreatment with high-dose statins may be of interest. Finally, recent studies based on intravascular ultrasound and minimal-to-no-use of contrast media have yielded promising results.
Duplication of effort across Development Projects in Nigeria: An example using the Master Health Facility List

Objective Duplication of effort across development projects is often the resultant effect of poor donor coordination in low- and middle- income countries which receive development assistance. This paper examines the persistence of duplication through a case study of health facility listing exercises in Nigeria. Methods Document reviews, key informant interviews, and a stakeholder’s meeting were undertaken to identify similar health facility listing exercises between 2010 and 2016. Results As an outcome of this process, ten different health facility listing efforts were identified. Discussions Proper coordination and collaboration could have resulted in a single list grown over time, ensuring return on investments. This study provides evidence of the persistence of duplication, years after global commitment to harmonization, better coordination and efficient use of development assistance were agreed to. Conclusions The paper concludes by making a proposal for strategic leadership in the health sector and the need to leverage information and communications technology through the development of an electronic Health Facility Registry that can archive the data on health facilities, create opportunity for continuous updates of the list, and provide for easy sharing of the data across different country stakeholders thereby eliminating duplication. Keywords Aid Effectiveness, Donor coordination; Health Facilities; Health Information System; Health Systems; International Cooperation; Master Facility List
Unsupervised Evaluation of Text Co-clustering Algorithms Using Neural Word Embeddings

Text clustering, which allows to divide a dataset into groups of similar documents, plays an important role at various stages of the information retrieval process. Co-clustering is an extension of one-side clustering, and consists in simultaneously clustering the rows and columns of a data matrix. However, while co-clustering algorithms consider both dimensions of a document-term matrix, they are usually evaluated on the quality of the obtained document clusters alone. In this paper, we therefore propose an evaluation scheme that accounts for the two-dimensional nature of co-clustering algorithms, thus allowing for a more precise evaluation of their performance. Another important benefit of the proposed approach is that it does not require the use of any prior labels. This is achieved by leveraging large, public domain embedding matrices (GloVe, word2vec, FastText) to compute comparable representations of both document and term clusters. Experiments carried out on several textual datasets show that the proposed measures are both reliable and stable, and can even provide hints to improve co-clustering performance.
CoVieW'18: The 1st Workshop and Challenge on Comprehensive Video Understanding in the Wild

The 1st Workshop and Challenge on Comprehensive Video Understanding in the Wild, dubbed CoVieW'18, is held in Seoul, Korea on October 22, 2018, in conjuction with ACM Multimedia 2018. The workshop aims to solve the joint and comprehensive understanding problem in untrimmed videos with a particular emphasis on joint action and scene recognition. The workshop encourages researchers to participate in joint action and scene recognition challenge in untrimmed videos and to report their results. The workshop program includes 1 keynote speech, 2 invited speakers, 6 regular and challenge papers. The developments made in the workshop will deliver a step change in a variety of video applications.
Antibiotic resistance: a rundown of a global crisis

The advent of multidrug resistance among pathogenic bacteria is imperiling the worth of antibiotics, which have previously transformed medical sciences. The crisis of antimicrobial resistance has been ascribed to the misuse of these agents and due to unavailability of newer drugs attributable to exigent regulatory requirements and reduced financial inducements. Comprehensive efforts are needed to minimize the pace of resistance by studying emergent microorganisms, resistance mechanisms, and antimicrobial agents. Multidisciplinary approaches are required across health care settings as well as environment and agriculture sectors. Progressive alternate approaches including probiotics, antibodies, and vaccines have shown promising results in trials that suggest the role of these alternatives as preventive or adjunct therapies in future.