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Entity resolution (ER) identifies data instances referring to the same real-world entity and has received enormous research attention. In this paper, we examine the task of ER from a broader perspective, with its input extended from textual records, which are conventionally studied in the literature, to other modalities such as check-in sequences, GPS trajectories and surveillance video frames to generate new applications. Our goal in this paper is to design an effective model to uniformly support all these ER applications with different input formats. Technically, we fully exploit the semantic contexts of embedding vectors for the pair of input instances. In particular, we propose an integrated multi-context attention framework that takes into account self-attention, pair-attention and global-attention from three types of context. The idea can be further extended to incorporate attribute attention in order to support structured datasets. We conduct extensive experiments on a diverse class of entity resolutions tasks, including tasks on unstructured, structured and dirty textual records, check-in sequences, GPS trajectories and surveillance video frames. The experimental results verified the effectiveness and generality of our model. When compared with strong baselines in these applications, our model can achieve superior or comparative performance.

In this paper, we study a new variant of trajectory similarity search from the context of continuous query processing. Given a moving object from s to $d$ , following a reference route $T_r$ , we monitor the trajectory similarity between the reference route and the current partial route at each timestamp for online detour detection. Since existing trajectory distance measures fail to adequately capture the deviation between a partial route and a complete route, we propose a partial trajectory similarity measure to bridge the gap. In particular, we enumerate all the possible routes extended from the partial route to reach the destination d and calculate their minimum distance to $T_r$ . We consider deviation calculation in both Euclidean space and road networks. In Euclidean space, we can directly infer the optimal future path with the minimum trajectory distance. In road networks, we propose an efficient expansion algorithm with a suite of pruning rules. Furthermore, we propose efficient incremental processing strategies to facilitate continuous query processing for moving objects. Our experiments are conducted on multiple real datasets and the experimental results verify the efficiency of our query processing algorithms.

Subgraph enumeration is important for many applications such as network motif discovery, community detection, and frequent subgraph mining. To accelerate the execution, recent works utilize graphics processing units (GPUs) to parallelize subgraph enumeration. The performances of these parallel schemes are dominated by the set intersection operations which account for up to $95\%$ of the total processing time. (Un)surprisingly, a significant portion (as high as $99\%$) of these operations is actually redundant, i.e., the same set of vertices is repeatedly encountered and evaluated. Therefore, in this paper, we seek to salvage and recycle the results of such operations to avoid repeated computation. Our solution consists of two phases. In the first phase, we generate a reusable plan that determines the opportunity for reuse. The plan is based on a novel reuse discovery mechanism that can identify available results to prevent redundant computation. In the second phase, the plan is executed to produce the subgraph enumeration results. This processing is based on a newly designed reusable parallel search strategy that can efficiently maintain and retrieve the results of set intersection operations. Our implementation on GPUs shows that our approach can achieve up to $5$ times speedups compared with the state-of-the-art GPU solutions.

There is currently no standardized way to determine suitability for extubation of pediatric ICU (PICU) patients, potentially resulting in prolonged duration of mechanical ventilation. We aimed to design and implement a protocol for screening all intubated PICU patients for extubation readiness.We adopted the quality improvement (QI) Model for Improvement with Plan-Do-Study-Act (PDSA) cycles to achieve this aim. This QI project was conducted over 11 months in a multidisciplinary PICU. Outcome measures included the (1) development of a standardized extubation readiness test (ERT) that was acceptable and safe; (2) performance of ERT on80% of all mechanically ventilated subjects; and (3) maintenance or reduction in mechanical ventilation duration, extubation failure (non-elective re-intubation within 48 h of extubation), and need for rescue noninvasive ventilation (NIV). Balancing measures were to ensure (1) no compromise of the subject's clinical status; and (2) acceptability of the ERT workflow by medical, nursing, and respiratory therapist (RT) teams.Four PDSA cycles were necessary to achieve the aims of this study. During the QI period, 438 subjects were admitted to the PICU. The ERT was championed by the RTs who conducted the test during office hours. ERT performance increased from 0% (baseline) to 90% (fourth PDSA cycle). Extubation failure rate after implementing ERT was reduced compared to baseline (4/31 [12.9%] vs 3/127 [2.4%],An acceptable and safe ERT protocol was implemented and found to improve outcomes in PICU subjects on mechanical ventilation.

Entity resolution identifies all records in a database that refer to the same entity. In this paper, we propose an unsupervised framework for entity resolution using blocking and graph algorithms. The records are partitioned into blocks with no redundancy for efficiency improvement. For intra-block data processing, we propose a graph-theoretic fusion framework with two components, namely ITER and CliqueRank. Specifically, ITER constructs a weighted bipartite graph between terms and record-record pairs and iteratively propagates the node salience until convergence. Subsequently, CliqueRank constructs a record graph to estimate the likelihood of two records resident in the same clique. The derived likelihood from CliqueRank is fed back to ITER to rectify the edge weight until a joint optimum can be reached. Experimental evaluation was conducted with 4 real datasets. Results show that our unsupervised framework is comparable or even superior to state-of-the-art deep learning approaches.

Isatis indigotica Fortune is a biennial Chinese woad of the Cruciferae family. It is primarily cultivated in China, where it was a staple in indigo dye manufacture till the end of the 17th century. Today, I. indigotica is used primarily as a therapeutic herb in traditional Chinese medicine (TCM). The medicinal use of the plant is separated into its leaves (Da-Qing-Ye) and roots (Ban-Lan-Gen), whereas its aerial components can be processed into a dried bluish-spruce powder (Qing-Dai), following dehydration for long-term preservation. Over the past several decades, I. indigotica has been generally utilized for its heat-clearing effects and bodily detoxification in TCM, attributed to the presence of several classes of bioactive compounds, including organic acids, alkaloids, terpenoids, and flavonoids, as well as lignans, anthraquinones, glucosides, glucosinolates, sphingolipids, tetrapyrroles, and polysaccharides. This paper aims to delineate I. indigotica from its closely-related species (Isatis tinctoria and Isatis glauca) while highlighting the ethnomedicinal uses of I. indigotica from the perspectives of modern and traditional medicine. A systematic search of PubMed, Embase, PMC, Web of Science, and Google Scholar databases was done for articles on all aspects of the plant, emphasizing those analyzing the bioactivity of constituents of the plant. The various key bioactive compounds of I. indigotica that have been found to exhibit anti-inflammatory, antimicrobial, anticancer, and anti-allergic properties, along with the protective effects against neuronal injury and bone fracture, will be discussed. Collectively, the review hopes to draw attention to the therapeutic potential of I. indigotica not only as a TCM, but also as a potential source of bioactive compounds for disease management and treatment.

The 3D-printed iChip version made from thermoplastics or photopolymers can isolate microbial populations of a peat swamp in situ with a population profile different from that isolated via the standard in vitro Petri dish cultivation method.

In the past decade, researchers have focused on the emergence of drug resistance in fungal pathogens such as Candida albicans, also considered as pathobionts that occur harmlessly in the human body but could potentially be triggered to cause diseases. The increasing rate of antifungal resistance in commensal gut fungi is alarming and should be further investigated. Here, we report seven novel MLST (Multi Locus Sequence Typing) genotypes of multi-drug resistant C. albicans isolates obtained from participants of a community study in Segamat, a district in the state of Johor, Malaysia. A total of eight C. albicans were isolated from four individuals, which were found to express high resistance against fluconazole, itraconazole, voriconazole and 5-fluorocytosine antifungals. MLST was performed to assess the clonal relatedness of these drug resistant isolates among themselves and against other strains isolated from other geographical regions. The novel MLST C. albicans sequence types suggest significant genetic changes compared to previous genotypes. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s42770-021-00558-4.

The specific cytokines that regulate pediatric acute respiratory distress syndrome (PARDS) pathophysiology remains unclear. Here, we evaluated the respiratory cytokine profile in PARDS to identify the molecular signatures associated with severe disease. A multiplex suspension immunoassay was used to profile 45 cytokines, chemokines and growth factors. Cytokine concentrations were compared between severe and non-severe PARDS, and correlated with oxygenation index (OI). Partial least squares regression modelling and regression coefficient plots were used to identify a composite of key mediators that differentially segregated severe from non-severe disease. The mean (standard deviation) age and OI of this cohort was 5.2 (4.9) years and 17.8 (11.3), respectively. Early PARDS patients with severe disease exhibited a cytokine signature that was up-regulated for IL-12p70, IL-17A, MCP-1, IL-4, IL-1β, IL-6, MIP-1β, SCF, EGF and HGF. In particular, pro-inflammatory cytokines (IL-6, MCP-1, IP-10, IL-17A, IL-12p70) positively correlated with OI early in the disease. Whereas late PARDS was characterized by a differential lung cytokine signature consisting of both up-regulated (IL-8, IL-12p70, VEGF-D, IL-4, GM-CSF) and down-regulated (IL-1β, EGF, Eotaxin, IL-1RA, and PDGF-BB) profiles segregating non-severe and severe groups. This cytokine signature was associated with increased transcription, T cell activation and proliferation as well as activation of mitogen-activated protein kinase pathway that underpin PARDS severity.

Background To support its ageing population and the increasing need for chronic care in the community, Singapore needs to boost the number of doctors in its primary care workforce. To better understand how to improve doctor retention and build a more robust primary care system, we conducted a cross-sectional survey with doctors in general practice and family medicine to explore their career satisfaction, their career plans, factors related to their plans to leave, and their view on retaining GPs in primary care. Methods An anonymous online survey was distributed to general practitioners working in the public and private sectors. The survey contained questions on career satisfaction, career plans in the next 5 years, and factors important for retaining doctors in primary care. In addition, there were open-ended questions for respondents to elaborate on retention initiatives and other factors that may improve engagement among primary care doctors. Quantitative data was analyzed with descriptive statistics, principal component analysis, χ2 tests, t-tests, and Pearson’s correlations; qualitative data was analyzed thematically. Results The survey was attempted by 355 general practitioners and completed in full by 303. The respondents were most satisfied with rapport with patients and their current professional role; they were least satisfied with the amount of paperwork and the status of general practice in society. In terms of their career plans in the next 5 years, 49/341 (14.4%) of the respondents plan to leave general practice permanently, 43/341 (12.6%) plan to take a career break, and 175/341 (51.3%) plan to reduce their clinical hours. Higher remuneration, recognizing general practice and family medicine as a medical specialty, and reducing the litigious pressures on medical practice were rated as the most important factors for retaining primary care. Free-text responses also revealed a growing dissatisfaction with the Third-Party Administrators that manage insurance arrangements. Conclusion While the proportion of doctors who intend to leave is smaller than that reported in overseas studies, our findings highlight an urgent need for targeted interventions to engage and retain primary care doctors. Increasing recognition and support for general practitioners and their professional practice may contribute to strengthening community care for the ageing population.

Recent years have witnessed the advent and prevalence of deep learning that have provoked storms in ITS (Intelligent Transportation Systems). Consequently, traditional ML models in many applications have been replaced by the new learning techniques. In this paper, we focus on the problem of trajectory prediction, which is a cornerstone component to support many useful higher-level applications in ITS. We conduct so far the most comprehensive evaluation on various models proposed for trajectory prediction, including 5 statistic-based models and 10 deep-learning based networks. In addition, we propose a hybrid model that integrates MLP to extract local features and LSTM to capture long term dependency. It is also enhanced with a moving-state prediction model based on random forest to further reduce prediction error. We conduct extensive empirical evaluation on three real datasets with different motion patterns and reveal interesting findings. Furthermore, the experimental results show that our proposed hybrid model achieves superior performance over its competitors in all the datasets.

Toxic heavy metal pollution of water is a major environmental problem and the current remediation approaches are not optimal as they are non-eco-friendly and lacking in efficiency. As such phytoremediation, a green remediation technology is recognized as a better approach. In this study, both Fourier Transform Infrared (FTIR) Spectroscopy and Inductive Coupled Plasma-Optical Emission Spectroscopy (ICP-OES) were used to investigate the capability of an aquatic plant, water hyacinth (

Importance This study reports outcomes of myopic photorefractive keratectomy (PRK) in a mainly ethnic Chinese population in Singapore. Background To assess the incidence and associations of corneal haze and endothelial cell count (ECC) loss up to 12 months after PRK. Design This was a retrospective case series conducted in a tertiary eye centre. Participants A total of 158 patients (309 eyes) with a mean age of 22.1 ± 3.4 years were included. The majority was Chinese (97.4%) and male (97.4%). Mean preoperative spherical equivalent was -3.33 ± 1.15 D. Methods Eyes were categorized based on postoperative haze severity. A multivariate analysis adjusting for age and use of intraoperative mitomycin-C (MMC), preoperative sphere and cylinder was performed. Main outcome measures Refractive outcomes and corneal haze 3 and 12 months after PRK were assessed. ECC measurements were obtained before PRK and at variable periods postoperatively. Results At 12 months, overall efficacy index was 0.98, and safety index was 1.09. Eight (2.5%) eyes underwent enhancement surgery. An analysis was performed on 295 eyes that did not undergo enhancement and had complete clinical data, which demonstrated a decrease in the incidence of haze from 38.2% at 3 months to 9.3% at 12 months. Higher myopia was associated with increased haze severity at 3 months (OR, 1.36; P = .005). Higher astigmatism was associated with increased haze severity at 3 (OR, 1.65; P = .018) and 12 months (OR, 2.32; P = .015). Intraoperative MMC was not associated with haze severity or ECC loss. Conclusions and relevance Myopia and astigmatism were associated with increased corneal haze severity. Intraoperative MMC did not accelerate ECC loss. Overall, PRK is effective, predictable and safe in Asian eyes.