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ObjectivesThis study investigates perceived barriers towards the implementation of multiprofessional team briefings (MPTB) in operating theatres, as well as ways to overcome these perceived barriers. Previous research shows that MPTB can enhance teamwork and communication, but are underused in operating theatres. By adopting a multilevel systems perspective, this study examines perceived barriers and solutions for MPTB implementation.DesignParticipants completed open-ended survey questions. Responses were coded via qualitative content analysis. The analysis focused on themes in the responses and the systems level at which each barrier and solution operates.SettingFour tertiary hospitals in Australia.Participants103 operating theatre staff, including nurses, surgeons, anaesthetists, technicians and administrators.ResultsParticipants identified barriers and solutions at the organisational (15.81% of barriers; 74.10% of solutions), work group (61.39% of barriers; 25.09% of solutions) and individual level (22.33% of barriers; 0% of solutions). Of all the perceived barriers to MPTB occurrence, a key one is getting everyone into the room at the same time . Matching of perceived barriers and solutions shows that higher systems-level solutions can address lower level barriers, thereby showing the relevance of implementing such wider reaching solutions to MPTB occurrence (including work practices at occupational level and above) as well as addressing more local issues.ConclusionsSuccessful MPTB implementation requires changes at various systems levels. Practitioners can strategically prepare and plan for systems-based strategies to overcome barriers to MPTB implementation. Future research can build on this study’s findings by directly examining higher systems-level barriers and solutions via detailed case analyses.

IntroductionGynaecological cancers collectively account for almost 10% of cancer diagnoses made in Australian women. The extent of variation in gynaecological cancer survival rates and treatment outcomes across Australia is not well documented. The purpose of the clinical quality registry described in this paper is to systematically monitor and improve quality of care provided to these women, and facilitate clinical process improvements to ensure better patient outcomes and greater adherence to best practice care. The registry infrastructure has been developed in conjunction alongside the inaugural ovarian, tubal and peritoneal (OTP) module, allowing for concurrent piloting of the methodology and one module. Additional tumour modules will be developed in time to cover the other gynaecological tumour types.Method and analysisThe National Gynae-Oncology Registry (NGOR) aims to capture clinical data on all newly diagnosed cancers of the uterus, ovary, fallopian tubes, peritoneum, cervix, vulva and vagina in Australia with a view to using these data to support improved clinical care and increased adherence to ‘best practice’. Data are sourced from existing clinical databases maintained by clinicians and/or hospital gynaecological cancer units. A pilot phase incorporating only OTP cancers has recently been conducted to assess the feasibility of the registry methodology and assess the support of a quality initiative of this nature among clinicians and other key stakeholders.Ethics and disseminationThe NGOR has received National Mutual Acceptance (NMA) ethics approval from Monash Health Human Research Ethics Committee (HREC), NMA HREC Reference Number: HREC/17/MonH/198. We also have approval from Mercy Health HREC and University of Tasmania HREC. Data will be routinely reported back to participating sites illustrating their performance against measures of agreed best practice. It is through this feedback system that the registry will support changes to quality of care and improved patient outcomes.

OBJECTIVE:To investigate morbidity for patients after the primary surgical management of cervical cancer in low and middle-income countries (LMIC). METHODS:The Pubmed, Cochrane, the Cochrane Central Register of Controlled Trials, Embase, LILACS and CINAHL were searched for published studies from 1st Jan 2000 to 30th June 2017 reporting outcomes of surgical management of cervical cancer in LMIC. Random-effects meta-analytical models were used to calculate pooled estimates of surgical complications including blood transfusions, ureteric, bladder, bowel, vascular and nerve injury, fistulae and thromboembolic events. Secondary outcomes included five-year progression free (PFS) and overall survival (OS). FINDINGS:Data were available for 46 studies, including 10,847 patients from 11 middle income countries. Pooled estimates were: blood transfusion 29% (95%CI 0.19-0.41, P = 0.00, I2 = 97.81), nerve injury 1% (95%CI 0.00-0.03, I2 77.80, P = 0.00), bowel injury, 0.5% (95%CI 0.01-0.01, I2 = 0.00, P = 0.77), bladder injury 1% (95%CI 0.01-0.02, P = 0.10, I2 = 32.2), ureteric injury 1% (95%CI 0.01-0.01, I2 0.00, P = 0.64), vascular injury 2% (95% CI 0.01-0.03, I2 60.22, P = 0.00), fistula 2% (95%CI 0.01-0.03, I2 = 77.32, P = 0.00,), pulmonary embolism 0.4% (95%CI 0.00-0.01, I2 26.69, P = 0.25), and infection 8% (95%CI 0.04-0.12, I2 95.72, P = 0.00). 5-year PFS was 83% for laparotomy, 84% for laparoscopy and OS was 85% for laparotomy cases and 80% for laparoscopy. CONCLUSION:This is the first systematic review and meta-analysis of surgical morbidity in cervical cancer in LMIC, which highlights the limitations of the current data and provides a benchmark for future health services research and policy implementation.

Because of the limitations of hardware devices, such as the sensors, processing capacity, and high accuracy altitude control equipment, traditional optical remote sensing (RS) imageries capture information regarding the same scene from mostly one single angle or a very small number of angles. Nowadays, with video satellites coming into service, obtaining imageries of the same scene from a more-or-less continuous array of angles has become a reality. In this paper, we analyze the differences between the traditional RS data and continuous multi-angle remote sensing (CMARS) data, and unravel the characteristics of the CMARS data. We study the advantages of using CMARS data for classification and try to capitalize on the complementarity of multi-angle information and, at the same time, to reduce the embedded redundancy. Our arguments are substantiated by real-life experiments on the employment of CMARS data in order to classify urban land covers while using a support vector machine (SVM) classifier. They show the superiority of CMARS data over the traditional data for classification. The overall accuracy may increase up to about 9% with CMARS data. Furthermore, we investigate the advantages and disadvantages of directly using the CMARS data, and how such data can be better utilized through the extraction of key features that characterize the variations of spectral reflectance along the entire angular array. This research lay the foundation for the use of CMARS data in future research and applications.

Abstract Background Progressive supranuclear palsy (PSP) is a rare neurodegenerative disease characterized by the accumulation of aggregated tau proteins in astrocytes, neurons, and oligodendrocytes. Previous genome-wide association studies for PSP were based on genotype array, therefore, were inadequate for the analysis of rare variants as well as larger mutations, such as small insertions/deletions (indels) and structural variants (SVs). Method In this study, we performed whole genome sequencing (WGS) and conducted association analysis for single nucleotide variants (SNVs), indels, and SVs, in a cohort of 1,718 cases and 2,944 controls of European ancestry. Of the 1,718 PSP individuals, 1,441 were autopsy-confirmed and 277 were clinically diagnosed. Results Our analysis of common SNVs and indels confirmed known genetic loci at MAPT, MOBP, STX6, SLCO1A2, DUSP10, and SP1, and further uncovered novel signals in APOE, FCHO1/MAP1S, KIF13A, TRIM24, TNXB, and ELOVL1. Notably, in contrast to Alzheimer’s disease (AD), we observed the APOE ε2 allele to be the risk allele in PSP. Analysis of rare SNVs and indels identified significant association in ZNF592 and further gene network analysis identified a module of neuronal genes dysregulated in PSP. Moreover, seven common SVs associated with PSP were observed in the H1/H2 haplotype region (17q21.31) and other loci, including IGH, PCMT1, CYP2A13, and SMCP. In the H1/H2 haplotype region, there is a burden of rare deletions and duplications (P = 6.73 × 10–3) in PSP. Conclusions Through WGS, we significantly enhanced our understanding of the genetic basis of PSP, providing new targets for exploring disease mechanisms and therapeutic interventions.

The air quality in urban areas is a major concern in modern cities due to significant impacts of air pollution on public health, global environment, and worldwide economy. Recent studies reveal the importance of micro-level pollution information, including human personal exposure and acute exposure to air pollutants. A real-time system with high spatio-temporal resolution is essential because of the limited data availability and non-scalability of conventional air pollution monitoring systems. Currently, researchers focus on the concept of The Next Generation Air Pollution Monitoring System (TNGAPMS) and have achieved significant breakthroughs by utilizing the advance sensing technologies, MicroElectroMechanical Systems (MEMS) and Wireless Sensor Network (WSN). However, there exist potential problems of these newly proposed systems, namely the lack of 3D data acquisition ability and the flexibility of the sensor network. In this paper, we classify the existing works into three categories as Static Sensor Network (SSN), Community Sensor Network (CSN) and Vehicle Sensor Network (VSN) based on the carriers of the sensors. Comprehensive reviews and comparisons among these three types of sensor networks were also performed. Last but not least, we discuss the limitations of the existing works and conclude the objectives that we want to achieve in future systems.

Recent studies challenge the previous view that apoptosis within the granulosa cells of the maturing ovarian follicle is a reflection of aging and consequently a marker for poor quality of the contained oocyte. On the contrary, apoptosis within the granulosa cells is an integral part of normal development and has limited predictive capability regarding oocyte quality or the ensuing pregnancy rate in in vitro fertilization programs. This review article covers our revised understanding of the process of apoptosis within the ovarian follicle, its three phenotypes, the major signaling pathways underlying apoptosis as well as the associated mitochondrial pathways.

Multiple Traveling Salesman Problem (MTSP) is able to model and solve various real-life applications such as multiple scheduling, multiple vehicle routing and multiple path planning problems, etc. While Traveling Salesman Problem (TSP) focuses on searching a path of minimum traveling distance to visit all cities exactly once by one salesman, the objective of the MTSP is to find m paths for m salesmen with a minimized total cost - the sum of traveling distances of all salesmen through all of the respective cities covered. They have to start from a designated depot which is the departing and returning location of all salesmen. Since the MTSP is a NP-hard problem, a new effective Genetic Algorithm with Local operators (GAL) is proposed in this paper to solve the MTSP and generate high quality solution within a reasonable amount of time for real-life applications. Two new local operators, Branch and Bound (BaB) and Cross Elimination (CE), are designed to speed up the convergence of the search process and improve the solution quality. Results demonstrate that GAL finds a better set of paths with a 9.62% saving on average in cost comparing to two existing MTSP algorithms.

In advanced lung cancer, epidermal growth factor tyrosine kinase inhibitors (EGFR TKIs) have extraordinary clinical efficacy. However, their usefulness is severely compromised by drug resistance mediated by various mechanisms, the most important of which is the secondary EGFR T790M mutation. The mutation blocks the binding of EGFR TKIs to the receptor kinase, thereby abolishing the therapeutic efficacy. In this study, we used our free and open-source protein-ligand docking software idock to screen worldwide approved small-molecule drugs against EGFR T790M. The computationally selected drug candidates were evaluated in vitro in resistant non-small cell lung cancer (NSCLC) cell lines. The specificity of the drugs toward the mutant EGFR was demonstrated by cell-free kinase inhibition assay. The inhibition of EGFR kinase activity and its downstream signaling pathways in NSCLC cells was shown by immunoblot analysis. The positive hints were revealed to be indacaterol, canagliflozin, and cis-flupenthixol, all of which were shown to induce apoptosis in NSCLC cells harboring the EGFR T790M mutation. Moreover, the combination of indacaterol with gefitinib was also found to produce synergistic anticancer effect in NSCLC cells bearing EGFR T790M. The observed synergistic effect was likely contributed by the enhanced inhibition of EGFR and its downstream signaling molecules.

Studies of land surface dynamics in heterogeneous landscapes often require satellite images with a high resolution, both in time and space. However, the design of satellite sensors often inherently limits the availability of such images. Images with high spatial resolution tend to have relatively low temporal resolution, and vice versa. Therefore, fusion of the two types of images provides a useful way to generate data high in both spatial and temporal resolutions. A Bayesian data fusion framework can produce the target high-resolution image based on a rigorous statistical foundation. However, existing Bayesian data fusion algorithms, such as STBDF (spatio-temporal Bayesian data fusion) -I and -II, do not fully incorporate the mixed information contained in low-spatial-resolution pixels, which in turn might limit their fusion ability in heterogeneous landscapes. To enhance the capability of existing STBDF models in handling heterogeneous areas, this study proposes two improved Bayesian data fusion approaches, coined ISTBDF-I and ISTBDF-II, which incorporate an unmixing-based algorithm into the existing STBDF framework. The performance of the proposed algorithms is visually and quantitatively compared with STBDF-II using simulated data and real satellite images. Experimental results show that the proposed algorithms generate improved spatio-temporal-resolution images over STBDF-II, especially in heterogeneous areas. They shed light on the way to further enhance our fusion capability.

Remote sensing is an important means to monitor the dynamics of the earth surface. It is still challenging for single-sensor systems to provide spatially high resolution images with high revisit frequency because of the technological limitations. Spatiotemporal fusion is an effective approach to obtain remote sensing images high in both spatial and temporal resolutions. Though dictionary learning fusion methods appear to be promising for spatiotemporal fusion, they do not consider the structure similarity between spectral bands in the fusion task. To capitalize on the significance of this feature, a novel fusion model, named the adaptive multi-band constraints fusion model (AMCFM), is formulated to produce better fusion images in this paper. This model considers structure similarity between spectral bands and uses the edge information to improve the fusion results by adopting adaptive multi-band constraints. Moreover, to address the shortcomings of the ℓ 1 norm which only considers the sparsity structure of dictionaries, our model uses the nuclear norm which balances sparsity and correlation by producing an appropriate coefficient in the reconstruction step. We perform experiments on real-life images to substantiate our conceptual augments. In the empirical study, the near-infrared (NIR), red and green bands of Landsat Enhanced Thematic Mapper Plus (ETM+) and Moderate Resolution Imaging Spectroradiometer (MODIS) are fused and the prediction accuracy is assessed by both metrics and visual effects. The experiments show that our proposed method performs better than state-of-the-art methods. It also sheds light on future research.

It has recently been claimed that the outstanding performance of machine-learning scoring functions (SFs) is exclusively due to the presence of training complexes with highly similar proteins to those in the test set. Here, we revisit this question using 24 similarity-based training sets, a widely used test set, and four SFs. Three of these SFs employ machine learning instead of the classical linear regression approach of the fourth SF (X-Score which has the best test set performance out of 16 classical SFs). We have found that random forest (RF)-based RF-Score-v3 outperforms X-Score even when 68% of the most similar proteins are removed from the training set. In addition, unlike X-Score, RF-Score-v3 is able to keep learning with an increasing training set size, becoming substantially more predictive than X-Score when the full 1105 complexes are used for training. These results show that machine-learning SFs owe a substantial part of their performance to training on complexes with dissimilar proteins to those in the test set, against what has been previously concluded using the same data. Given that a growing amount of structural and interaction data will be available from academic and industrial sources, this performance gap between machine-learning SFs and classical SFs is expected to enlarge in the future.

Remote sensing provides rich sources of data for the monitoring of land surface dynamics. However, single-sensor systems are constrained from providing spatially high-resolution images with high revisit frequency due to the inherent sensor design limitation. To obtain images high in both spatial and temporal resolutions, a number of image fusion algorithms, such as spatial and temporal adaptive reflectance fusion model (STARFM) and enhanced STARFM (ESTARFM), have been recently developed. To capitalize on information available in a fusion process, we propose a Bayesian data fusion approach that incorporates the temporal correlation information in the image time series and casts the fusion problem as an estimation problem in which the fused image is obtained by the Maximum A Posterior (MAP) estimator. The proposed approach provides a formal framework for the fusion of remotely sensed images with a rigorous statistical basis; it imposes no requirements on the number of input image pairs; and it is suitable for heterogeneous landscapes. The approach is empirically tested with both simulated and real-life acquired Landsat and Moderate Resolution Imaging Spectroradiometer (MODIS) images. Experimental results demonstrate that the proposed method outperforms STARFM and ESTARFM, especially for heterogeneous landscapes. It produces surface reflectances highly correlated with those of the reference Landsat images. It gives spatio-temporal fusion of remotely sensed images a solid theoretical and empirical foundation that may be extended to solve more complicated image fusion problems.

Urban air pollution has caused public concern globally because it seriously affects human life. Modern monitoring systems providing pollution information with high spatio-temporal resolution have been developed to identify personal exposures. However, these systems’ hardware specifications and configurations are usually fixed according to the applications. They can be inconvenient to maintain, and difficult to reconfigure and expand with respect to sensing capabilities. This paper aims at tackling these issues by adopting the proposed Modular Sensor System (MSS) architecture and Universal Sensor Interface (USI), and modular design in a sensor node. A compact MSS sensor node is implemented and evaluated. It has expandable sensor modules with plug-and-play feature and supports multiple Wireless Sensor Networks (WSNs). Evaluation results show that MSS sensor nodes can easily fit in different scenarios, adapt to reconfigurations dynamically, and detect low concentration air pollution with high energy efficiency and good data accuracy. We anticipate that the efforts on system maintenance, adaptation, and evolution can be significantly reduced when deploying the system in the field.

Selecting fusion levels based on the Luk et al criteria for operative management of thoracic adolescent idiopathic scoliosis (AIS) with hook and hybrid systems yields acceptable curve correction and balance parameters; however, it is unknown whether utilizing a purely pedicle screw strategy is effective. Utilizing the fulcrum bending radiographic (FBR) to assess curve flexibility to select fusion levels, the following study assessed the efficacy of pedicle screw fixation with alternate level screw strategy (ALSS) for thoracic AIS.A retrospective study with prospective radiographic data collection/analyses (preoperative, postoperative 1-week and minimum 2-year follow-up) of 28 operative thoracic AIS patients undergoing ALSS was performed. Standing coronal/sagittal and FBR Cobb angles, FBR flexibility, fulcrum bending correction index (FBCI), trunkal shift, radiographic shoulder height (RSH), and list were assessed on x-rays. Fusion level selection was based on the Luk et al criteria and compared to conventional techniques.In the primary curve, the mean preoperative and postoperative 1 week and last follow-up standing coronal Cobb angles were 59.9, 17.2 and 20.0 degrees, respectively. Eighteen patients (64.3%) had distal levels saved (mean: 1.6 levels) in comparison to conventional techniques. Mean immediate and last follow-up FBCIs were 122.6% and 115.0%, respectively. Sagittal alignment did not statistically differ between any assessment intervals (p>0.05). A decrease in trunkal shift was noted from preoperative to last follow-up (p = 0.003). No statistically significant difference from preoperative to last follow-up was noted in RSH and list (p>0.05). No "add-on" of other vertebra or decompensation was noted and all patients achieved fusion.This is the first report to note that using the FBR for decision-making in selecting fusion levels in thoracic AIS patients undergoing management with pedicle screw constructs (e.g. ALSS) is a cost-effective strategy that can achieve clinically-relevant deformity correction that is maintained and without compromising fusion levels.

Highly pathogenic avian influenza (HPAI) H5N1, a disease associated with high rates of mortality in infected human populations, poses a serious threat to public health in many parts of the world. This article reports findings from a study aimed at improving our understanding of the spatial pattern of the highly pathogenic avian influenza, H5N1, risk in East-Southeast Asia where the disease is both persistent and devastating. Though many disciplines have made important contributions to our understanding of H5N1, it remains a challenge to integrate knowledge from different disciplines. This study applies genetic analysis that identifies the evolution of the H5N1 virus in space and time, epidemiological analysis that determines socio-ecological factors associated with H5N1 occurrence, and statistical analysis that identifies outbreak clusters, and then applies a methodology to formally integrate the findings of the three sets of methodologies. The present study is novel in two respects. First it makes the initiative attempt to use genetic sequences and space-time data to create a space-time phylogenetic tree to estimate and map the virus' ability to spread. Second, by integrating the results we are able to generate insights into the space-time occurrence and spread of H5N1 that we believe have a higher level of corroboration than is possible when analysis is based on only one methodology. Our research identifies links between the occurrence of H5N1 by area and a set of socio-ecological factors including altitude, population density, poultry density, and the shortest path distances to inland water, coastlines, migrating routes, railways, and roads. This study seeks to lay a solid foundation for the interdisciplinary study of this and other influenza outbreaks. It will provide substantive information for containing H5N1 outbreaks.

Being a class of vitamin A’s main derivatives, retinoic acids (RAs) are important to animals’ growth and development. Previous studies demonstrated that exposure of excessive amounts of RAs would lead to malformation and abnormal development in aquatic animals such as amphibians and fishes. Currently, there are only limited toxicity data of RAs available for freshwater species, while those for marine species are seriously lacking. This study aimed to fill such data gap by conducting toxicity tests on six marine species (i.e., one microalga, four invertebrates and one fish) towards the exposure to all-trans-RA (at-RA), which is the most widely distributed RA in the environment. Results showed that the embryo of medaka fish Oryzias melastigma was the most sensitive towards the exposure of at-RA while the gastropod Monodonta labio was the least sensitive. A species sensitivity distribution (SSD) was constructed based on the experimental results generated from the present study. An interim marine-specific predicted no-effect concentration (PNEC) of at-RA was derived at 2300 ng/L. By computing the hazard quotients using the interim marine-specific PNEC and available measured and predicted concentrations of RAs, we found the current levels of RAs posed no immediate risks to the marine environment of Hong Kong. The interim marine-specific PNEC was more than 500-fold of freshwater-specific PNEC (i.e., 3.93 ng/L), indicating that marine species were generally less sensitive than their freshwater counterparts towards RAs. This was the first study to document the concentration-response of various marine species towards at-RA exposure and construct the marine-specific SSD for assessing the ecological risk of at-RA towards the marine environment. Since various forms of RAs and their metabolites often coexist in aquatic environments, further studies should investigate their combined toxicity to an array of marine species of different trophic levels with consideration of chronic exposure scenarios.