Pre-defined Set Research Articles

Plan2Dance: Planning Based Choreographing from Music

The field of dancing robots has drawn much attention from numerous sources. Despite the success of previous systems on choreography for robots to dance with external stimuli, they are often either limited to a pre-defined set of movements or lack of considering “hard” relations among dancing motions. In the demonstration, we design a planning based choreographing system, which views choreography with music as planning problems and solve the problems with off-the-shelf planners. Our demonstration exhibits the effectiveness of our system via evaluating our system with various music.

Robust Scheduling of Hot Rolling Production by Local Search Enhanced Ant Colony Optimization Algorithm

Scheduling of a hot strip mill is an important decision problem in the steel manufacturing industry. Previous studies on the hot strip mill scheduling problem have mostly neglected the random factors in production. However, random variations in processing times are inevitable due to unpredictable delays and disturbances. In this article, we adopt a robust optimization approach to deal with the uncertainty in processing times. The advantage is that no assumption has to be made regarding the distribution of random data, and the obtained schedule will remain strictly feasible when the variations have not exceeded a predefined uncertainty set. First, a mixed-integer linear programming model is presented to formulate the robust scheduling problem. Then, a hybrid metaheuristic algorithm, which combines ant colony system (ACS) and enhanced local search, is proposed to provide an efficient solution to the problem. Finally, extensive computational experiments involving both randomly generated and real-world instances have been conducted to verify the effectiveness of the proposed algorithm. It is shown that the algorithm achieves optimality for small instances and outperforms two state-of-the-art metaheuristics when used to solve large instances.

The determinants of handwashing behaviour in domestic settings: An integrative systematic review.

Hygiene promotion interventions are likely to be more effective if they target the determinants of handwashing behaviour. Synthesis of the evidence on the determinants of handwashing behaviour is needed to enable practitioners to use evidence in hygiene promotion programming. To identify, define and categorise the determinants of handwashing behaviour in domestic settings and to appraise the quality of this evidence. We conducted an integrative review, searching three databases for terms related to handwashing and behaviour change determinants. Studies were summarised and their quality assessed against a pre-defined set of criteria for qualitative, quantitative and mixed-method studies. Data on determinants were extracted and classified according to a predefined theoretical taxonomy. The effect of each association between a determinant and handwashing behaviour was summarised and weighted based on the quality of evidence provided. Determinants that were reported more than three times were combined into a meta-association and included in the main analysis. Sub-analyses were done for studies conducted during outbreaks or humanitarian crises. Seventy-eight studies met the criteria. Of these, 18% were graded as 'good quality' and 497 associations between determinants and handwashing behaviour were extracted. We found that 21% of these associations did not clearly define the determinant and 70% did not use a valid or reliable method for assessing determinants and/or behaviour. Fifty meta-associations were included in the main analysis. The determinants of handwashing that were most commonly reported were knowledge, risk, psychological trade-offs or discounts, characteristic traits (like gender, wealth and education), and infrastructure. There was insufficient data to draw conclusions about the determinants of behaviour in outbreaks or crises. This review demonstrates that our understanding of behavioural determinants remains sub-optimal. We found that there are limitations in the way behavioural determinants are conceptualised and measured and that research is biased towards exploring a narrow range of behavioural determinants. Hygiene promotion programmes are likely to be most successful if they use multi-modal approaches, combining infrastructural improvement with 'soft' hygiene promotion which addresses a range of determinants rather than just education about disease transmission.

A knowledge-light approach to personalised and open-ended human activity recognition

Human Activity Recognition (HAR) is a core component of clinical decision support systems that rely on activity monitoring for self-management of chronic conditions such as Musculoskeletal Disorders. Deployment success of such applications in part depend on their ability to adapt to individual variations in human movement and to facilitate a range of human activity classes. Research in personalised HAR aims to learn models that are sensitive to the subtle nuances in human movement whilst Open-ended HAR learns models that can recognise activity classes out of the pre-defined set available at training. Current approaches to personalised HAR impose a data collection burden on the end user; whilst Open-ended HAR algorithms are heavily reliant on intermediary-level class descriptions. Instead of these “knowledge-intensive” HAR algorithms; in this article, we propose a “knowledge-light” method. Specifically, we show how by using a few seconds of raw sensor data, obtained through micro-interactions with the end-user, we can effectively personalise HAR models and transfer recognition functionality to new activities with zero re-training of the model after deployment. We introduce a Personalised Open-ended HAR algorithm, MNZ, a user context aware Matching Network architecture and evaluate on 3 HAR data sources. Performance results show up to 48.9% improvement with personalisation and up to 18.3% improvement compared to the most common “knowledge-intensive” Open-ended HAR algorithms.

EpiCOLOC: Integrating Large-Scale and Context-Dependent Epigenomics Features for Comprehensive Colocalization Analysis.

High-throughput genome-wide epigenomic assays, such as ChIP-seq, DNase-seq and ATAC-seq, have profiled a huge number of functional elements across numerous human tissues/cell types, which provide an unprecedented opportunity to interpret human genome and disease in context-dependent manner. Colocalization analysis determines whether genomic features are functionally related to a given search and will facilitate identifying the underlying biological functions characterizing intricate relationships with queries for genomic regions. Existing colocalization methods leveraged diverse assumptions and background models to assess the significance of enrichment, however, they only provided limited and predefined sets of epigenomic features. Here, we comprehensively collected and integrated over 44,385 bulk or single-cell epigenomic assays across 53 human tissues/cell types, such as transcription factor binding, histone modification, open chromatin and transcriptional event. By classifying these profiles into hierarchy of tissue/cell type, we developed a web portal, epiCOLOC (http://mulinlab.org/epicoloc or http://mulinlab.tmu.edu.cn/epicoloc), for users to perform context-dependent colocalization analysis in a convenient way.

Quantity vs. quality: an assessment of the current pipeline for rabies.

To gain insight in the potential of the current pipeline for rabies to decrease the burden of disease by evaluating its relevance to high-risk countries. Rabies-related patent documents and clinical trials were retrieved from Espacenet and the WHO ICTRP, respectively. Data were cleaned, modulated and categorised into a pre-defined set of indicators those were used for (statistical) analyses on the number of patent applications, patent quality and type of stakeholders involved for different geographical areas. Analysis of 583 unique patent families applied for in the period 1954-2017 showed a steep growth in the yearly number of patent applications. A significant portion of new patent applications concern Chinese patents with relatively low quality that are filed by a dispersed group of applicants. Excluding these patents, the number of patent applications has been virtually stable over the years. A shift is seen in public stakeholders becoming more prolific as patent applicants. This shift is also reflected in clinical trials; key sponsors of clinical trials include public and private stakeholders originating from high-risk rabies countries. The majority of clinical trials investigate adjustments to existing vaccines that may improve accessibility. The results show a discrepancy between the quantity and quality of rabies patent applications that reflects national patent regulations rather than real progress in decreasing the burden of disease. This is in contrast to clinical trials, which focus on incremental innovations that are tested in clinical trials but may nevertheless have a potentially strong impact in high-risk countries.

Low Apgar score in term newborns and long-term infectious morbidity: a population-based cohort study with up to 18years of follow-up.

Since introduced, the Apgar score has remained the most widespread predictor for neonatal morbidity and mortality. We aimed to investigate the association between low 5-min Apgar score and long-term infectious pediatric morbidity. A population-based cohort analysis was performed comparing total and specific subtypes of infectious morbidity leading to hospitalization among term newborns with normal (≥ 7) and low (< 7) 5-min Apgar scores, born between 1999 and 2014 at a single tertiary regional hospital. Infectious morbidity included hospitalizations involving a pre-defined set of infection-related ICD-9 codes. A Kaplan-Meier survival curve was constructed to compare cumulative infectious morbidity incidence and a Cox proportional hazards model to adjust for confounders. The long-term analysis of 223,335 children (excluding perinatal death cases) yielded 585 (0.3%) infants with low 5-min Apgar scores. The rate of infection-related hospitalizations was 9.8% and 12.4% among newborns with normal and low 5-min Apgar scores, respectively (p = 0.06). Adjusting for maternal age, gestational age, hypertension, diabetes, cesarean delivery, and fertility treatments, the association proved to be statistically significant (adjusted HR = 1.28; 95% CI 1.01-1.61).Conclusion: Term infants with low 5-min Apgar scores may be at an increased risk for long-term pediatric infectious morbidity.What is Known:• Though not meant to be a prognostic tool for long-term morbidity, studies assessing the correlation between low Apgar score and long-term outcomes were and are being performed, reporting significant associations with many outcomes-such as cerebral palsy (CP), ophthalmic disorders, GI disorders, and several types of malignancies.• Yet, an association between low Apgar scores and future health remains a matter of controversy.What is New:• Our work shows that a low 5-min Apgar score is independently associated with long-term pediatric infection-related hospitalizations among term singleton newborns.

Development and validation of a pancreatic cancer prediction model from electronic health records using machine learning.

679 Background: Pancreatic Adenocarcinoma (PDAC) is often diagnosed at an advanced stage. We sought to develop a model for early PDAC prediction in the general population, using electronic health records (EHRs) and machine learning. Methods: We used three EHR datasets from Beth-Israel Deaconess Medical Center (BIDMC) and Partners Healthcare (PHC): 1. “BIDMC-Development-Data” (BIDMC-DD) for model development, using a feed-forward neural network (NN) and L2-regularized logistic regression,randomly split (80:20) into training and test groups. We tuned hyperparameters using cross-validation in training, and report performance on the test split. 2. “BIDMC-Large-Data” (BIDMC-LD) to re-fit and calibrate models. 3. “PHC-Data” for external validation. We evaluate using Area Under the Receiver Operating Characteristic Curve (AUC) and compute 95% CI using empirical bootstrap over test data. PDAC patients were selected using ICD9/-10 codes and validated with tumor registries. In contrast to prior work, we did not predefine feature sets based on known clinical correlates and instead employed data-driven feature selection, specifically importance-based feature pruning, regularization, and manual validation, to identify diagnostic-based features. Results: BIDMC-DD included demographics, diagnoses, labs and medications for 1018 patients (cases = 509; age-sex paired controls). BIDMC-LD included diagnoses for 547,917 patients (cases = 509), and PHC included diagnoses for 160,593 patients (cases = 408). We compared our approach to adapted and re-fitted published baselines. With a 365-day lead-time, NN obtained a BIDMC-DD test AUC of 0.84 (CI 0.79 - 0.90) versus the previous best baseline AUC of 0.70 (CI 0.62 - 0.78). We also validated using BIDMC-DD’s test cancer patients and BIDMC LD controls. The AUC was 0.71 (CI 0.67 - 0.76) at the 365-day cutoff. NN’s external validation AUC on PHC-Data was 0.71 (CI 0.63 - 0.79), outperforming an existing model’s AUC of 0.61 (CI 0.52 - 0.70) (Baecker et al, 2019). Conclusions: Models based on data-driven feature selection outperform models that use predefined sets of known clinical correlates and can help in early prediction of PDAC development.

Maternal history of recurrent pregnancy loss and long-term risk of thromboembolic events

BackgroundRecurrent pregnancy loss is a multifactorial disorder with potential underlying maternal hypercoagulability. ObjectiveWe aimed to investigate whether a history of recurrent pregnancy loss poses an independent risk for future maternal long-term thromboembolic morbidity. Study designA population-based study compared the incidence of long-term thromboembolic morbidity in a cohort of women with and without a history of recurrent pregnancy loss. Data were collected from two databases that were cross-linked and merged: a computerized hospitalization database and a computerized obstetrics and gynecology perinatal database. Deliveries occurred between the years 1991-2017. The risk for long-term thromboembolic morbidity was based on the hospital’s database and a pre-defined set of ICD codes of thromboembolic related hospitalizations. A Kaplan-Meier survival curve was constructed to compare the cumulative incidence of significant thromboembolic morbidity in the exposed and unexposed groups, and a Cox proportional hazards model, to control for confounders. ResultsDuring the study period, 123,791 parturients met the inclusion criteria; 6.7 % n = 8247 of which experienced previous recurrent pregnancy loss-the exposed group. The rate of thromboembolic related hospitalizations was almost double in exposed women as compared to non-exposed 1.1 % vs. 0.6 %, OR = 1.8; 95 % 1.4–2.2; p < 0.001). Cumulative thromboembolic event incidence, as depicted in the survival curves, was also significantly higher among patients with recurrent pregnancy loss history (Log rank p < 0.001). The Cox model confirmed recurrent pregnancy loss history to be an independent risk factor for later maternal thromboembolic morbidity, while controlling for maternal age, known thrombophilia, ethnicity, smoking, hypertension, diabetes, and fertility treatment (aHR 1.27, 95 %CI 1.02–1.59, p=0.034). ConclusionA history of recurrent pregnancy loss is an independent risk factor for long-term thromboembolic morbidity, even in the absence of known maternal thrombophilia.

STRATEGIC EVALUATION IN OPTIMIZING THE INTERNAL SUPPLY CHAIN USING TOPSIS: EVIDENCE IN A COIL-WINDING MACHINE MANUFACTURER

Most of manufacturing firm aims to optimize their Supply Chain in-terms of improved profitability of their products through value Addition. This study takes a critical look into the factors that affect the Performance of internal supply chain with respect to specific criteria’s. Accordingly, ranking these factors to get the critical dimensions of supply chain performance in the manufacturing industry. A semi-structured interview with the pre-defined set of questions used to collect the responses from decision makers of the firm. Multi criteria decision-making tool called TOPSIS is used to evaluate the responses and rank the factors. The results of this indicate that supplier relationship and inventory planning were most principal factors positively influencing on-time delivery of the product, production flexibility, cost savings, additional costs. This study helps to identify and optimize the process parameters using objective and subjective evaluation approach. The combined influence of thought process of manager to optimize the internal supply chain is extracted in this work

Dynamic scheduling model for the construction industry

PurposeBasic project control through traditional methods is not sufficient to manage the majority of real-time events in most construction projects. The purpose of this paper is to propose a Dynamic Scheduling (DS) model that utilizes multi-objective optimization of cost, time, resources and cash flow, throughout project construction.Design/methodology/approachUpon reviewing the topic of DS, a worldwide internet survey with 364 respondents was conducted to define end-user requirements. The model was formulated and solution algorithms discussed. Verification was reported using predefined problem sets and a real-life case. Validation was performed via feedback from industry experts.FindingsThe need for multi-objective dynamic software optimization of construction schedules and the ability to choose among a set of optimal alternatives were highlighted. Model verification through well-known test cases and a real-life project case study showed that the model successfully achieved the required dynamic functionality whether under the small solved example or under the complex case study. The model was validated for practicality, optimization of various DS schedule quality gates, ease of use and software integration with contemporary project management practices.Practical implicationsOptimized real-time scheduling can provide better resources management including labor utilization and cost efficiency. Furthermore, DS contributes to optimum materials procurement, thus minimizing waste.Social implicationsOptimized real-time scheduling can provide better resources management including labor utilization and cost efficiency. Furthermore, DS contributes to optimum materials procurement, thus minimizing waste.Originality/valueThe paper illustrates the importance of DS in construction, identifies the user needs and overviews the development, verification and validation of a model that supports the generation of high-quality schedules beneficial to large-scale projects.

Acupoint embedding therapy improves nonalcoholic fatty liver disease with abnormal transaminase: A PRISMA-compliant systematic review and meta-analysis.

Non-alcoholic fatty liver disease (NAFLD) with abnormal transaminase were main targeted disorder in clinical intervention. Acupuncture embedding has been used as a modified acupuncture therapy in current management, while no comprehensive summarization has been established. Hence, we conducted a systematic review and meta-analysis to evaluate the effectiveness and safety of acupoint embedding alone or in combination for NAFLD with abnormal transaminase, and to provide potential regimen for further verification. Seven English and Chinese databases were systematically researched from inception to February 28, 2019, including PubMed, Embase, Cochrane Central Register of Controlled Trials (CENTRAL), Chinese biomedical literature database (SinoMed), Chinese National Knowledge Infrastructure (CNKI), Chinese VIP information (VIP), and WanFang database. Academic dissertations were also searched as supplement. The searching terms included "nonalcoholic fatty liver disease," "acupoint embedding," "clinical trial," with their corresponding synonyms. Randomized controlled trials (RCTs) and quasi-RCTs involving acupoint embedding alone or in combination for adult patients with NAFLD with abnormal transaminase were included. The diagnosis of NAFLD should be confirmed by radiologic evidence. Two researchers independently completed predefined data sets extraction and quality assessment. STATA 15.0 was applied to estimate the combined effect presented as odds ratio or mean difference (MD) with a 95% confidence interval (CI). The primary outcome was the change of serum alanine aminotransferase (ALT). A total of fifteen studies with 1349 patients were included. Meta-analysis reported that acupoint embedding alone or in combination was superior to conventional medications on ALT change (MD: 16.58, 95%CI: [10.42, 22.74], P < .001). The benefits were also demonstrated in other outcomes, including aspartate aminotransferase, triglyceride, and total cholesterol, total efficacy rate and radiological efficacy rate. The safety profile of acupoint embedding was satisfactory. BL18 (Ganshu) was the most frequently utilized acupoint. To some extent, the systematic review supported the application of acupoint embedding in management of NAFLD, while further high-quality studies should be designed to evaluate the practical effect of acupoint embedding.

Randomly Pre-Coded Packets Based Random Access Scheme for IoT-Oriented Satellite Networks

In this article, a novel random pre-coding (RPC) based random access (RA) scheme is proposed to improve the system performance in the Internet of Things-oriented satellite networks. In this scheme, each device randomly selects a non-orthogonal multi-level complex sequence from a pre-defined pre-coding sequence set to pre-code the transmitted packets in a sub-frame, by which multiple superimposed packets in the sub-frame can be successfully decoded by using the minimum mean squared error (MMSE) based successive interference cancellation (SIC) detection algorithm. Besides, by combining the proposed RPC with the existing contention resolution diversity slotted ALOHA (CRDSA) scheme, which the RPC-CRDSA scheme is generated. The SIC processing should be carried out across sub-frames to remove the received signals that are already recovered, which can further significantly improve the system throughput. The performance of the RPC-RA scheme is evaluated via both theoretical analysis and computer simulation. The collision probabilities of both preamble and pre-coding sequences and an upper bound of throughput are derived, respectively. Simulation results show that the peak normalized throughput of RPC-CRDSA with the pre-coding sequence length $L = 8$ can reach 1.87 bits/symbol, which largely outperforms traditional schemes.

Similar Fault Isolation of Discrete-Time Nonlinear Uncertain Systems: An Adaptive Threshold Based Approach

In this paper, a new concept of “similar fault” is introduced to the field of fault isolation (FI) of discrete-time nonlinear uncertain systems, which defines a new and important class of faults that have small mutual differences in fault magnitude and fault-induced system trajectories. Effective isolation of such similar faults is rather challenging as their small mutual differences could be easily concealed by other system uncertainties (e.g., modeling uncertainty/disturbances). To this end, a novel similar fault isolation (sFI) scheme is proposed based on an adaptive threshold mechanism. Specifically, an adaptive dynamics learning approach based on the deterministic learning theory is first introduced to locally accurately learn/identify the uncertain system dynamics under each faulty mode using radial basis function neural networks (RBF NNs). Based on this, a bank of sFI estimators are then developed using a novel mechanism of absolute measurement of fault dynamics differences. The resulting residual signals can be used to effectively capture the small mutual differences of similar faults and distinguish them from other system uncertainties. Finally, an adaptive threshold is designed for real-time sFI decision making. One important feature of the proposed sFI scheme is that: it is capable of not only isolating similar faults that belong to a pre-defined fault set (used in the training/learning process), but also identifying new faults that do not match any pre-defined faults. Rigorous analysis on isolatability conditions and isolation time is conducted to characterize the performance of the proposed sFI scheme. Simulation results on a practical application example of a single-link flexible joint robot arm are used to show the effectiveness and advantages of the proposed scheme over existing approaches.

Flexible Robust Risk-Constrained Unit Commitment of Power System Incorporating Large Scale Wind Generation and Energy Storage

With the increasing penetration of wind power in the power systems, the uncertainties in wind power significantly challenge the reliable and economic operation of power systems. Recently, the worst scenario-based robust optimization approaches have been employed to manage the uncertainties in the unit commitment problem. To further improve the robustness and economic efficiency of power system operation, this article proposes a flexible robust risk-constrained unit commitment formulation, in which flexible reserve capacities of conventional generators and energy storage are allocated to cope with the uncertainty of wind power. The proposed model optimizes the unit commitment and dispatch solutions for the base case while guaranteeing that the flexible reserve capacity can be adaptively adjusted after wind generation realization. In contrast to the predefined uncertainty set in the conventional robust unit commitment, the proposed model constructs an adjustable and flexible uncertainty set via balancing the operational costs and the operational risk. The model establishes worst-case constraints to optimally allocate the flexible reserve capacity. The proposed model can be equivalently transformed into a single-level optimization problem using the strong duality theory. Numerical case studies on a modified standard test system demonstrate the effectiveness and the efficiency of the proposed model.

P666 left ventricular filling pressure using E/E" ratio as a useful marker for predicting periprocedural myocardial injury following percutaneous coronary intervention in diabetic patients

Abstract Background Identification of patients with risk of myocardial injury during PCI would allow targeted novel therapies capable of limiting the extent of this injury or reducing its patients number.CK-MB, Troponin-T and I have enabled the quantification of previously undetectable myocardial injury that can also be CMRI visualized.Elevated LV filling pressures are associated with adverse remodeling, HF, and worse survival.E" velocity (unlike others) appears to be relatively independent of preload, especially if myocardial relaxation decreased.In addition, early transmitral flow velocity /early diastolic mitral annulus velocity(E/E") ratio has recently been shown as the most accurate noninvasive predictor of high LV filling pressure.Purpose: evaluate the reliability and adverse outcome of high E/E" in predicting periprocedural myocardial injury. Methods 60 adult diabetic patients with troponin negative CAD admitted for PCI were studied,then analyzed on the basis of post PCI troponin level:(positive and negative group).E/E" ratio was measured and compared with both groups. Results 24 patients developed high troponin after PCI specially if PCI complicated or number of DES increased (p. value &amp;lt;0.001&amp;0.027 respectively).Septal E" velocity was improved after PCI(p. value 0.031).E/E" ratio was high in troponin positive group as. Average e/e" ratio can differentiate between patient"s with troponin negative and positive values (predict periprocedural myocardial injury) with high p. value &amp;lt;0.0001,with cuttof value &amp;gt;10.7, with sensitivity and specificity(79.17% and 87.11%) respectively. Positive and negative predictive value for average e/e" ratio was (79.2 and 86.1) respectively. Conclusions E/E" ratio can provide useful information on LV filling pressure and can be used as a reliable biomarker for early prediction of periprocedural myocardial injury. But, further studies on a larger number of patients are needed for reliability. Limitations small sample size and pre-defined set of study parameters. E/E" ratio and troponin Negative troponin Positive troponin p. value Mean± SD Median(iqr) Mean± SD Median(iqr) EF Biplane 55.8 ± 6.6 56(50:61.5) 53.8 ± 6.5 51(50:59.5) 0.289 LAV index 30.7 ± 4.6 29.5(28:35.5) 31.6 ± 3.6 33(28:35) 0.675 Average E/E" Post 9.1 ± 7.1 7.3(6.5:9.3) 15.9 ± 8.5 14.2(11.1:18.7) &amp;lt;0.001 Lateral E/E" Post 9 ± 6 7(6:9) 15 ± 7 13(11- 18) &amp;lt;0.001 Septal E/E" Post 10 ± 9 8(7:10) 17 ± 11 15(11:20) &amp;lt;0.001 comparison between focused echocardiographic variables &amp; troponin post PCI

A Novel Network Modelling for Metabolite Set Analysis: A Case Study on CRC Metabolomics

In metabolomics, pathway analysis normally refers to analysis of a pre-defined sets of metabolites (metabolite set) associated to the metabolic pathways. The metabolite set analysis is useful to facilitate biological interpretation of metabolomics data. The currently available methods may be divided into three generations: over-representation analysis, functional class scoring, and network topology analysis. Among the three generations of tools, the network topology methods have been shown to have lower false discovery rates and better biological interpretability than the other two earlier generations of tools. However, most of the current network topology methods focus the analysis only at the metabolite-level network. The interaction between pathways are not taken into consideration. To address this issue, we propose a new metabolite sets association network (MSAN) modelling scheme. In the developed method, the metabolite sets are defined based on the KEGG databases. By using the metabolite sets as vertexes, the MSAN network evaluated the relationships between pairs of metabolite sets based on their mutual information. The impact of a single metabolite set on the overall network was evaluated by the MSAN network, which may help to uncover differential metabolite sets relevant to the underlying biology mechanism of the study. A metabolomic dataset from a published colorectal cancer (CRC) study is used to evaluate the performance of MSAN network to identify perturbed metabolite sets in colorectal cancer patients. The current results are compared to that of two commonly used methods, NetGSA and MetaboAnalyst, which are based on the metabolite-level network approach. The current method highlights a number of metabolites sets consistent with recent published CRC reports. Taken together, the proposed method may provide an alternative tool for the identification of dysregulated pathways and facilitate biological interpretation of metabolomics data.

FCOS: A Simple and Strong Anchor-Free Object Detector.

In computer vision, object detection is one of most important tasks, which underpins a few instance-level recognition tasks and many downstream applications. Recently one-stage methods have gained much attention over two-stage approaches due to their simpler design and competitive performance. Here we propose a fully convolutional one-stage object detector (FCOS) to solve object detection in a per-pixel prediction fashion, analogue to other dense prediction problems such as semantic segmentation. Almost all state-of-the-art object detectors such as RetinaNet, SSD, YOLOv3, and Faster R-CNN rely on pre-defined anchor boxes. In contrast, our proposed detector FCOS is anchor box free, as well as proposal free. By eliminating the pre-defined set of anchor boxes, FCOS completely avoids the complicated computation related to anchor boxes such as calculating the intersection over union (IoU) scores during training. More importantly, we also avoid all hyper-parameters related to anchor boxes, which are often sensitive to the final detection performance. With the only post-processing non-maximum suppression (NMS), we demonstrate a much simpler and flexible detection framework achieving improved detection accuracy. We hope that the proposed FCOS framework can serve as a simple and strong alternative for many other instance-level tasks. Code is available at: git.io/AdelaiDet.

Maternal obesity and long-term neuropsychiatric morbidity of the offspring.

To evaluate the long-term pediatric neuropsychiatric morbidity of children born to obese patients. A population-based cohort analysis was performed comparing all deliveries of obese (maternal pre-pregnancy body mass index of 30kg/m2 or more) and non-obese patients between 1991 and 2014 at a single tertiary medical center. Hospitalizations of the offspring up to the age of 18years involving neuropsychiatric morbidities were evaluated according to a pre-defined set of ICD-9 codes, including autistic, eating, sleeping and movement disorders, cerebral palsy, developmental disorders, and more. A Kaplan-Meier survival curve was used to compare cumulative hospitalization rate in exposed and unexposed offspring. A Cox regression model was used to control for confounders. During the study period, 242,342 deliveries met the inclusion criteria. Of them, 3290 were children of obese mothers. Hospitalizations involving neuropsychiatric morbidities were higher in children born to obese mothers compared with those born to non-obese mothers (3.95% vs. 3.10%, p < 0.01). Specifically, offspring of obese mothers had higher rates of autism spectrum disorders and psychiatric disorders. The Kaplan-Meier survival curve demonstrated a significantly higher cumulative incidence of neuropsychiatric-related hospitalizations in the obese group (Fig.1, log rank p < 0.05). Using a cox proportional hazard model, controlling for maternal age, preterm labor, maternal diabetes, hypertensive disorders of pregnancy, and birthweight, maternal obesity was found to be independently associated with long-term neuropsychiatric morbidity of the offspring (adjusted HR 1.24, 95% CI 1.04-1.47, p < 0.05). Maternal obesity is an independent risk factor for long-term neuropsychiatric morbidity of the offspring.

379: Maternal gestational diabetes mellitus and risk of long term pediatric infectious diseases of the offspring

To assess whether in utero exposure to GDM increases the risk for long-term infectious morbidity of the offspring. A population-based cohort analysis was performed comparing total and different subtypes of infectious related pediatric diagnoses among offspring of mothers with GDM vs. offspring of mothers with no diabetes. The analysis included all singletons born between the years 1991-2014. Infectious related morbidities included hospitalizations involving a pre-defined set of ICD-9 codes. Mothers with pregestational diabetes, insufficient prenatal care, infants with congenital malformations, multiple gestations, and perinatal deaths were excluded from the analysis. A Cox proportional hazards model was constructed to adjust for confounders. The study population included 220,105 newborns which met the inclusion criteria; among them 4.3% (9,566) were born to mothers with GDM controlled by diet and exercise (GDM A1) and 0.3% (732) were born to mothers with GDM requiring oral treatment or insulin (GDM A2). During the follow-up period, children exposed in utero to GDM did not have a higher rate of long- term hospitalizations with diagnoses of infectious morbidity (11.1% of those without exposure to GDM vs. 11.3% of those who were exposed to GDM, p=0.516). However, gynecologic infections and orthopedic infections were significantly higher among those who were exposed to GDM (OR 1.68 for orthopedic infections, p=0.009, OR 3.38 for gynecological infections, p=0.007). While adjusting for confounders such as gestational age, mother age at birth and maternal hypertension, the association remained non significant with an adjusted hazards ratio of 0.97 (95% CI 0.92-1.03, p=0.429). There is no significant association between GDM and the rate of infectious hospitalizations of the offspring. However, in our population there was higher rate of gynecological and orthopedic infectious morbidity of the offspring among those who were exposed to GDM.View Large Image Figure ViewerDownload Hi-res image Download (PPT)