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Objective: A Serous Tubal Intraepithelial Carcinoma (STIC) without concomitant invasive carcinoma is occasionally identified and associated with a high risk of subsequent peritoneal carcinomatosis. Management needs optimisation. This study explores professionals' opinions and clinical practices regarding the diagnosis, counselling, treatment and follow-up of isolated STIC to facilitate clinical decision making and optimise the direction of future research. A secondary aim is to assess international clinical guidelines. Design: Focus group study. Setting: Four online sessions. Population: International panel (n = 12 countries) of gynaecologists, gynaecologic oncologists, pathologists and medical oncologists (n = 49). Methods: A semi-structured interview guide was used. Two independent researchers analysed transcripts by open and axial coding. Results were organised in domains. Relevant (inter)national guidelines were screened for recommendations regarding isolated STIC. Main Outcome Measures: Professionals' opinions and clinical practices regarding isolated STIC management. Results: Regarding pathology, most professionals identified the SEE-FIM protocol as standard of care for high-risk patients, whereas variation exists in the histopathological examination of fallopian tubes in the general population. Confirmation of STIC diagnosis by a specialised pathologist was recommended. Regarding work-up and follow-up after STIC diagnosis, there was variety and discordance. Data on outcomes is limited. As for treatment, chemotherapy and PARP inhibitors were not recommended by most. Eleven guidelines provided limited recommendations. Conclusions: We identified recommendations and highlighted knowledge gaps in the diagnosis and management of isolated STIC. Moreover, recommendations in clinical guidelines are limited. There is an agreed need for international collaboration for the prospective registration of isolated STIC.

Cyclin-dependent kinase 2 (CDK2) is thought to play an important role in driving proliferation of certain cancers, including those harboring CCNE1 amplification and breast cancers that have acquired resistance to CDK4/6 inhibitors (CDK4/6i). The precise impact of pharmacologic inhibition of CDK2 is not known due to the lack of selective CDK2 inhibitors. Here we describe INX-315, a novel and potent CDK2 inhibitor with high selectivity over other CDK family members. Using cell-based assays, patient-derived xenografts (PDX), and transgenic mouse models, we show that INX-315 (i) promotes retinoblastoma protein hypophosphorylation and therapy-induced senescence (TIS) in CCNE1-amplified tumors, leading to durable control of tumor growth; (ii) overcomes breast cancer resistance to CDK4/6i, restoring cell cycle control while reinstating the chromatin architecture of CDK4/6i-induced TIS; and (iii) delays the onset of CDK4/6i resistance in breast cancer by driving deeper suppression of E2F targets. Our results support the clinical development of selective CDK2 inhibitors.

Cardiovascular and cerebrovascular pathology may accelerate brain aging and cognitive decline. Structural cerebrovascular imaging biomarkers, such as white matter hyperintensities, reflect mostly irreversible accumulated damage. In contrast, cerebral blood flow (CBF), measured with arterial spin labelling (ASL) perfusion MRI, is a potential early haemodynamic biomarker of cognitive impairment. However, our understanding of physiological CBF variability and its relation with cognition is limited.

Cardiovascular and cerebrovascular pathology may accelerate brain aging and cognitive decline. Structural cerebrovascular imaging biomarkers, such as white matter hyperintensities, reflect mostly irreversible accumulated damage. In contrast, cerebral blood flow (CBF), measured with arterial spin labelling (ASL) perfusion MRI, is a potential early haemodynamic biomarker of cognitive impairment. However, our understanding of physiological CBF variability and its relation with cognition is limited.

Background: Risk-reducing salpingectomy with delayed oophorectomy has gained interest for individuals at high risk for tubo-ovarian cancer as there is compelling evidence that especially high-grade serous carcinoma originates in the fallopian tubes. Two studies have demonstrated a positive effect of salpingectomy on menopause-related quality of life and sexual health compared with standard risk-reducing salpingo-oophorectomy. Primary Objective: To investigate whether salpingectomy with delayed oophorectomy is non-inferior to the current standard salpingo-oophorectomy for the prevention of tubo-ovarian cancer among individuals at high inherited risk. Study Hypothesis: We hypothesize that postponement of oophorectomy after salpingectomy, to the age of 40-45 (BRCA1) or 45-50 (BRCA2) years, compared with the current standard salpingo-oophorectomy at age 35-40 (BRCA1) or 40-45 (BRCA2) years, is non-inferior in regard to tubo-ovarian cancer risk. Trial Design: In this international prospective preference trial, participants will choose between the novel salpingectomy with delayed oophorectomy and the current standard salpingo-oophorectomy. Salpingectomy can be performed after the completion of childbearing and between the age of 25 and 40 (BRCA1), 25 and 45 (BRCA2), or 25 and 50 (BRIP1, RAD51C, and RAD51D pathogenic variant carriers) years. Subsequent oophorectomy is recommended at a maximum delay of 5 years beyond the upper limit of the current guideline age for salpingo-oophorectomy. The current National Comprehensive Cancer Network (NCCN) guideline age, which is also the recommended age for salpingo-oophorectomy within the study, is 35-40 years for BRCA1, 40-45 years for BRCA2, and 45-50 years for BRIP1, RAD51C, and RAD51D pathogenic variant carriers. Major Inclusion/Exclusion Criteria: Premenopausal individuals with a documented class IV or V germline pathogenic variant in the BRCA1, BRCA2, BRIP1, RAD51C, or RAD51D gene who have completed childbearing are eligible for

GRB~230812B is a bright and relatively nearby ($z =0.36$) long gamma-ray burst (GRB) that has generated significant interest in the community and has thus been observed over the entire electromagnetic spectrum. We report over 80 observations in X-ray, ultraviolet, optical, infrared, and sub-millimeter bands from the GRANDMA (Global Rapid Advanced Network for Multi-messenger Addicts) network of observatories and from observational partners. Adding complementary data from the literature, we then derive essential physical parameters associated with the ejecta and external properties (i.e. the geometry and environment) of the GRB and compare with other analyses of this event. We spectroscopically confirm the presence of an associated supernova, SN2023pel, and we derive a photospheric expansion velocity of v $\sim$ 17$\times10^3$ km s$^{-1}$. We analyze the photometric data first using empirical fits of the flux and then with full Bayesian Inference. We again strongly establish the presence of a supernova in the data, with a maximum (pseudo-)bolometric luminosity of $5.75 \times 10^{42}$ erg/s, at $15.76^{+0.81}_{-1.21}$ days (in the observer frame) after the trigger, with a half-max time width of 22.0 days. We compare these values with those of SN1998bw, SN2006aj, and SN2013dx. Our best-fit model favours a very low density environment ($\log_{10}({n_{\rm ISM}/{\rm cm}^{-3}}) = -2.38^{+1.45}_{-1.60}$) and small values for the jet's core angle $\theta_{\rm core} = 1.54^{+1.02}_{-0.81} \ \rm{deg}$ and viewing angle $\theta_{\rm obs} = 0.76^{+1.29}_{-0.76} \ \rm{deg}$. GRB 230812B is thus one of the best observed afterglows with a distinctive supernova bump.

Phenotypes (i.e., symptoms and clinical signs) are essential for clinical diagnosis and research related to symptom science and precision health. As clinical observational manifestations of a disease, symptoms are clinically significant because they act as direct causes for patients to seek medical care and the primary indicators for clinicians to provide diagnosis/treatments. However, a comprehensive phenotypic knowledge base and high-quality symptom–gene associations are lacking. Therefore, a thorough understanding of the relationships between symptoms and other entities is urgently needed to support scientific research and clinical health care. In this paper, we constructed a systematic, large-scale, and high-quality symptom-gene associations network system named SympGAN (accessible at http://www.sympgan.org/). We provide access to the database with millions of associations between symptoms, genes, diseases, and drugs, as well as the system for users to search, analyze, knowledge inference, and present data visualization. We utilize state-of-the-art machine learning and deep learning algorithms as the backbone to form the final dataset. In addition, we utilize the RoBERTa-PubMed neural network for name entity recognition to assist in data screening. The knowledge graph is adopted to organize the relationships between different entities. We adopt ConvE, TuckER, and HypER methods for knowledge completion experiments to validate the quality of final knowledge graph triples. Based on the results, we provide online automatic knowledge inference interfaces. The system, SympGAN, has promising value for disease diagnosis, decision support in health care, precision health, and scientific research, as researchers and practitioners can easily access information about symptoms, diseases, targets, gene ontology, and drugs.

Traumatic brain injury (TBI) is a major public health problem. Here, we developed a novel model of non-invasive TBI induced by laser irradiation in the telencephalon of adult zebrafish (Danio rerio) and assessed their behavior and neuromorphology to validate the model and evaluate potential targets for neuroreparative treatment. Overall, TBI induced hypolocomotion and anxiety-like behavior in the novel tank test, strikingly recapitulating responses in mammalian TBI models, hence supporting the face validity of our model. NeuN-positive cell staining was markedly reduced one day, but not seven days, after TBI, suggesting increased neuronal damage immediately after the injury, and its fast recovery. The brain-derived neurotrophic factor (Bdnf) level in the brain dropped immediately after the trauma, but fully recovered seven days later. A marker of microglial activation, Iba1, was elevated in the TBI brain, albeit decreasing from Day 3. The levels of hypoxia-inducible factor 1-alpha (Hif1a) increased 30 min after the injury, and recovered by Day 7, further supporting the construct validity of the model. Collectively, these findings suggest that our model of laser-induced brain injury in zebrafish reproduces mild TBI and can be a useful tool for TBI research and preclinical neuroprotective drug screening. © 2022 by the authors.

UNLABELLED: Immunotherapies aimed at alleviating the inhibitory constraints on T cells have revolutionized cancer management. To date, these have focused on the blockade of cell-surface checkpoints such as PD-1. Herein we identify protein tyrosine phosphatase 1B (PTP1B) as an intracellular checkpoint that is upregulated in T cells in tumors. We show that increased PTP1B limits T-cell expansion and cytotoxicity to contribute to tumor growth. T cell-specific PTP1B deletion increased STAT5 signaling, and this enhanced the antigen-induced expansion and cytotoxicity of CD8+ T cells to suppress tumor growth. The pharmacologic inhibition of PTP1B recapitulated the T cell-mediated repression of tumor growth and enhanced the response to PD-1 blockade. Furthermore, the deletion or inhibition of PTP1B enhanced the efficacy of adoptively transferred chimeric antigen receptor (CAR) T cells against solid tumors. Our findings identify PTP1B as an intracellular checkpoint whose inhibition can alleviate the inhibitory constraints on T cells and CAR T cells to combat cancer. SIGNIFICANCE: Tumors subvert antitumor immunity by engaging checkpoints that promote T-cell exhaustion. Here we identify PTP1B as an intracellular checkpoint and therapeutic target. We show that PTP1B is upregulated in intratumoral T cells and that its deletion or inhibition enhances T-cell antitumor activity and increases CAR T-cell effectiveness against solid tumors. This article is highlighted in the In This Issue feature, p. 587.

Axonal characterizations of connectomes in healthy and disease phenotypes are surprisingly incomplete and biased because unmyelinated axons, the most prevalent type of fibers in the nervous system, have largely been ignored as their quantitative assessment quickly becomes unmanageable as the number of axons increases. Herein, we introduce the first prototype of a high-throughput processing pipeline for automated segmentation of unmyelinated fibers. Our team has used transmission electron microscopy images of vagus and pelvic nerves in rats. All unmyelinated axons in these images are individually annotated and used as labeled data to train and validate a deep instance segmentation network. We investigate the effect of different training strategies on the overall segmentation accuracy of the network. We extensively validate the segmentation algorithm as a stand-alone segmentation tool as well as in an expert-in-the-loop hybrid segmentation setting with preliminary, albeit remarkably encouraging results. Our algorithm achieves an instance-level [Formula: see text] score of between 0.7 and 0.9 on various test images in the stand-alone mode and reduces expert annotation labor by 80% in the hybrid setting. We hope that this new high-throughput segmentation pipeline will enable quick and accurate characterization of unmyelinated fibers at scale and become instrumental in significantly advancing our understanding of connectomes in both the peripheral and the central nervous systems.

The HTS current leads for the ITER project will be the largest ever operated, with unprecedented currents, up to 68 kA and voltages, up to 14 kV. According to the ITER agreement they will be provided in-kind by China. After an extensive development program at the Hefei Institute of Plasma Physics (ASIPP), the ITER current leads were designed and qualified. The following discusses the main highlights of this development, with particular emphasis on the description of the design of the different types of ITER current leads and their final qualification in dedicated cold tests in nominal conditions., source:P Bauer et al 2020 IOP Conf. Ser.: Mater. Sci. Eng. 756 012032, source:https://iopscience.iop.org/article/10.1088/1757-899X/756/1/012032

BACKGROUND AND AIM: Considering the inconsistent findings of research into the associations between serum levels of liver enzymes (alanine aminotransferase [ALT], aspartate aminotransferase [AST], and gamma-glutamyltransferase [GGT]) and mortality among elderly people, we aimed to investigate the associations of ALT, AST, GGT, and De-Ritis ratio (DRR, defined as AST/ALT) and all-cause or cause-specific mortality among the US elderly people using National Health and Nutrition Examination Surveys data. METHODS: We included 6415 elderly participants (≥ 65 years). Exclusion criteria included positive test for hepatitis B virus, hepatitis C virus, and human immunodeficiency virus infection at baseline. Multivariable-adjusted Cox regression models calculating hazard ratios (HR) and 95% confidence intervals were developed for each of the liver enzyme measures. RESULTS: All-cause cumulative mortality was 33.8%, of which 23.8% were cardiovascular disease (CVD) deaths, 15.6% were cancer deaths, and 60.6% were other cause deaths. Adjusted Cox models found increased all-cause mortality risk for low ALT (HR: 1.70), low AST (HR: 1.13), high GGT (HR: 1.25), and high DRR (HR: 1.68). Low ALT and high DRR predicted CVD mortality. Low ALT (HR: 1.91), low AST (HR: 1.16), high GGT (HR: 1.40), and high DRR (HR: 1.76) predicted other cause mortality. CONCLUSIONS: Low ALT and high DRR were associated with increased CVD and cancer mortality. All serum liver enzyme measures were associated with all-cause mortality and other cause mortality in the US elderly population. Further studies may validate these findings in other elderly populations.

Minimizing energy loss is of critical importance in the pursuit of attaining high-performance organic solar cells. Interestingly, reorganization energy plays a crucial role in photoelectric conversion processes. However, the understanding of the relationship between reorganization energy and energy losses has rarely been studied. Here, two acceptors, Qx-1 and Qx-2, were developed. The reorganization energies of these two acceptors during photoelectric conversion processes are substantially smaller than the conventional Y6 acceptor, which is beneficial for improving the exciton lifetime and diffusion length, promoting charge transport, and reducing the energy loss originating from exciton dissociation and non-radiative recombination. So, a high efficiency of 18.2% with high open circuit voltage above 0.93 V in the PM6:Qx-2 blend, accompanies a significantly reduced energy loss of 0.48 eV. This work underlines the importance of the reorganization energy in achieving small energy losses and paves a way to obtain high-performance organic solar cells.

An organisation wishing to conduct data analytics to support day-to-day decision making often needs a system to help analysts represent and maintain knowledge about research variables, datasets or analytical models, and effectively determine the best combination to use when solving the problem at hand. Often, such knowledge is not explicitly captured by the organisation. To address this problem, this paper presents the design of an innovative Information Technology (IT) platform which enables data sharing between different analytics models and provides the ability to extend or customise models or data sources without necessarily involving the analysts who created them. It can make analytics knowledge readily available and modifiable for future use and problem-solving by analysts and other stakeholders. In the context of our work, we organise analytics knowledge around the concept of a research variable, which analysts often use when defining and proving a hypothesis. By focusing on such a concept, this platform is particularly suited to develop empirical data analytics applications in any domain. This paper presents the architecture of this platform, including the knowledge base and the Application Programming Interface (API) layer. Capabilities of this platform are illustrated through a software prototype and a use case on property price prediction across Sydney, Australia.

An organisation wishing to conduct data analytics to support day-to-day decision making often needs a system to help analysts represent and maintain knowledge about research variables, datasets or analytical models, and effectively determine the best combination to use when solving the problem at hand. Often, such knowledge is not explicitly captured by the organisation. To address this problem, this paper presents the design of an innovative Information Technology (IT) platform which enables data sharing between different analytics models and provides the ability to extend or customise models or data sources without necessarily involving the analysts who created them. It can make analytics knowledge readily available and modifiable for future use and problem-solving by analysts and other stakeholders. In the context of our work, we organise analytics knowledge around the concept of a research variable, which analysts often use when defining and proving a hypothesis. By focusing on such a concept, this platform is particularly suited to develop empirical data analytics applications in any domain. This paper presents the architecture of this platform, including the knowledge base and the Application Programming Interface (API) layer. Capabilities of this platform are illustrated through a software prototype and a use case on property price prediction across Sydney, Australia.

BACKGROUND: Risk-reducing salpingo-oophorectomy (RRSO) has been associated with approximately 50% breast cancer risk reduction among women with a pathogenic variant in BRCA1 or BRCA2 (BRCA1/2), a finding that has recently been questioned. METHODS: We estimated incidence rates of breast cancer and all cancers combined during 5 years of follow-up among participants selecting RRSO or ovarian cancer screening (OCS) among women with a BRCA1/2 pathogenic variant or strong breast and/or ovarian cancer family history. Ovarian or fallopian tube or peritoneal cancer incidence rates were estimated for the OCS group. Breast cancer hazard ratios (HRs) for time-dependent RRSO were estimated using Cox regression with age time-scale (4943 and 4990 women-years in RRSO and OCS cohorts, respectively). All statistical tests were two-sided. RESULTS: The RRSO cohort included 925 participants, and 1453 participants were in the OCS cohort (381 underwent RRSO during follow-up), with 88 incident breast cancers diagnosed. Among BRCA1/2 pathogenic variant carriers, a non-statistically significant lower breast cancer incidence was observed in the RRSO compared with the OCS cohort (HR = 0.86, 95% confidence interval = 0.45 to 1.67; P = .67). No difference was observed in the overall population or among subgroups stratified by prior breast cancer history or menopausal status. Seven fallopian tube and four ovarian cancers were prospectively diagnosed in the OCS cohort, and one primary peritoneal carcinoma occurred in the RRSO cohort. CONCLUSIONS: These data suggest that RRSO might be associated with reduced breast cancer incidence among women with a BRCA1/2 pathogenic variant, although the effect, if present, is small. This evolving evidence warrants a thorough discussion regarding the impact of RRSO on breast cancer risk with women considering this intervention.

Stringent COVID-19 control measures were imposed in Wuhan between January 23 and April 8, 2020. Estimates of the prevalence of infection following the release of restrictions could inform post-lockdown pandemic management. Here, we describe a city-wide SARS-CoV-2 nucleic acid screening programme between May 14 and June 1, 2020 in Wuhan. All city residents aged six years or older were eligible and 9,899,828 (92.9%) participated. No new symptomatic cases and 300 asymptomatic cases (detection rate 0.303/10,000, 95% CI 0.270-0.339/10,000) were identified. There were no positive tests amongst 1,174 close contacts of asymptomatic cases. 107 of 34,424 previously recovered COVID-19 patients tested positive again (re-positive rate 0.31%, 95% CI 0.423-0.574%). The prevalence of SARS-CoV-2 infection in Wuhan was therefore very low five to eight weeks after the end of lockdown.

Although adoptive T-cell therapy has shown remarkable clinical efficacy in haematological malignancies, its success in combating solid tumours has been limited. Here, we report that PTPN2 deletion in T cells enhances cancer immunosurveillance and the efficacy of adoptively transferred tumour-specific T cells. T-cell-specific PTPN2 deficiency prevented tumours forming in aged mice heterozygous for the tumour suppressor p53. Adoptive transfer of PTPN2-deficient CD8+ T cells markedly repressed tumour formation in mice bearing mammary tumours. Moreover, PTPN2 deletion in T cells expressing a chimeric antigen receptor (CAR) specific for the oncoprotein HER-2 increased the activation of the Src family kinase LCK and cytokine-induced STAT-5 signalling, thereby enhancing both CAR T-cell activation and homing to CXCL9/10-expressing tumours to eradicate HER-2+ mammary tumours in vivo. Our findings define PTPN2 as a target for bolstering T-cell-mediated anti-tumour immunity and CAR T-cell therapy against solid tumours.

The South China Block (SCB) has been regarded by many as an integral part of Gondwana, but proposed timing and processes for its accretion to Gondwana vary and remain contentious, largely owing to the lack of reliable Pan-African age paleomagnetic data and tectono-magmatic records from the SCB. Integrated in situ U-Pb ages and Hf-O isotope analyses of detrital zircons from geochronologically well-calibrated Ediacaran-Cambrian sedimentary rocks of western SCB reveal age populations of 2.51, 1.85, 1.20, 0.80, and 0.52 Ga. Detrital zircon age spectra indicate a major tectonic transition for the SCB during 0.56–0.54 Ga, interpreted to reflect the beginning of the collision between SCB-Indochina and NW India blocks. The collisional event lasted until early Ordovician, leading to the suturing of the SCB-Indochina to the northern margin of East Gondwana.

In this paper, we report the new results of spectroscopic observations of $\gamma$-ray blazar S5 0716+714 from 2019 September to 2020 March with the 2.4 m optical telescope at Lijiang Observatory of Yunnan Observatories. The median cadence of observations is $\sim$ 1 day. During the second observation period (Epoch2), the observational data reveal an extremely bright state and a bluer-when-brighter (BWB) chromatism. The BWB trend of Epoch2 differs significantly from that of the first observation period (Epoch1). A significantly brightness-dependent BWB chromatism emerges in the total data of Epoch1 and Epoch2. The BWB trend becomes weaker towards the brighter states, and likely becomes saturated at the highest state. Based on a log-parabolic function, a power-law of synchrotron peak flux and frequency $\nu_{\rm{p}}$, and a power-law of the curvature of synchrotron spectrum and its $\nu_{\rm{p}}$, simulation well reproduces the brightness-dependent BWB trend of S5 0716+714. The BWB trend is seemingly controlled by the shift of $\nu_{\rm{p}}$ with respect to the observational window, and effectively may be dominated by the variations of electron average energy and magnetic field in emitting region., Comment: 16 pages, 5 figures, Accepted for publication in ApJ

Most graph-based clustering methods separate the graph construction and clustering into two independent processes. The manually pre-constructed graph may not be suitable for the subsequent clustering. Moreover, as real world data generally contains noises and outliers, the similarity graph directly learned from them will be unreliable and further impair the subsequent clustering performance. To tackle the problems, in this paper, we propose a novel clustering framework where a robust graph is learned with noise removal, and simultaneously, with desirable clustering structure. To this end, we first learn a discriminative representation of data samples via sparse reconstruction. Then, a robust graph is automatically constructed with adaptive neighbors to each data sample. Simultaneously, a reasonable rank constraint is imposed on the Laplacian matrix of similarity graph to pursue the ideal clustering structure, where the number of connected components in the learned graph is exactly equal to the number of clusters. We finally derive an alternate optimization algorithm guaranteed with convergence to solve the formulated unified learning framework to achieve better prediction accuracy. Experiments on both synthetic and real datasets demonstrate the superior performance of the proposed method compared with several state-of-the-art clustering techniques.

© 1986-2012 IEEE. This article investigates and proposes an efficient control design to address the degradation in the mechanical speed of a traction machine drive (TMD) in an electric vehicle (EV) caused by thermal effects during its operation. Variations in the operating as well as ambient temperature cause unexpected uncertainties in TMD parameters such as stator and rotor resistances, which results in significant degradation in EV's speed performance capability. To mitigate this problem, an output feedback robust linear parameter varying (LPV) controller-observer set is designed using H$ control theory that enhances the EV's speed performance in field-oriented control (FOC) frame. The internal stability of the closed-loop control and the $L_{2}$ gain bound are ensured by linear matrix inequalities. The performance of the proposed control technique is compared with that of conventional FOC, sliding mode control (SMC) and higher order sliding mode control (HOSMC) to validate its efficacy and advantages. The robustness of the proposed control technique is tested for an EV operation against the Worldwide Harmonised Light Vehicles Test Procedure (WLTP) Class 3 driving cycle. The nonlinear MATLAB simulation results guarantee the effectiveness of the proposed controller-observer set. These results are verified experimentally on an induction machine drive setup.

The Gynecologic Cancer InterGroup (GCIG) Fifth Ovarian Cancer Consensus Conference (OCCC) was held in Tokyo, Japan from 7 to 9 November 2015. It provided international consensus on 15 important questions in 4 topic areas, which were generated in accordance with the mission statement to establish 'International Consensus for Designing Better Clinical Trials'. The methodology for obtaining consensus was previously established and followed during the Fifth OCCC. All 29 clinical trial groups of GCIG participated in program development and deliberations. Draft consensus statements were discussed in topic groups as well as in a plenary forum. The final statements were then presented to all 29 member groups for voting and documentation of the level of consensus. Full consensus was obtained for 11 of the 15 statements with 28/29 groups agreeing to 3 statements, and 27/29 groups agreeing to 1 statement. The high acceptance rate of the statements among trial groups reflects the fact that we share common questions, and recognise important unmet needs that will guide future research in ovarian cancer.

With the development of industrial Internet of Things, manual production in the factory has gradually changed to automated production, and the temperature requirements in many aspects of the production process are very strict. In order to control temperature more intelligently, reduce errors and improve production efficiency, through the study of BP neural network, this paper designs and implements an intelligent temperature prediction system based on BP neural network for wireless industrial Internet of Things. Through the selection of intelligent algorithms, the establishment of the model and the realization of the system, a temperature prediction system suitable for the actual production environment of the industrial Internet of Things and improving the accuracy according to the intelligent algorithm is finally realized. The system is relatively complete in function. Temperature prediction and error correction can accurately predict the change of temperature. It can meet the requirements of industrial production control and make the temperature control more stable., QC 20200427

Sesame (Sesamum indicum L.) is an important oil crop renowned for its high oil content and quality. Recently, genome assemblies for five sesame varieties including two landraces (S. indicum cv. Baizhima and Mishuozhima) and three modern cultivars (S. indicum var. Zhongzhi13, Yuzhi11 and Swetha), have become available providing a rich resource for comparative genomic analyses and gene discovery. Here, we employed a reference-assisted assembly approach to improve the draft assemblies of four of the sesame varieties. We then constructed a sesame pan-genome of 554.05 Mb. The pan-genome contained 26 472 orthologous gene clusters; 15 409 (58.21%) of them were core (present across all five sesame genomes), whereas the remaining 41.79% (11 063) clusters and the 15 890 variety-specific genes were dispensable. Comparisons between varieties suggest that modern cultivars from China and India display significant genomic variation. The gene families unique to the sesame modern cultivars contain genes mainly related to yield and quality, while those unique to the landraces contain genes involved in environmental adaptation. Comparative evolutionary analysis indicates that several genes involved in plant-pathogen interaction and lipid metabolism are under positive selection, which may be associated with sesame environmental adaption and selection for high seed oil content. This study of the sesame pan-genome provides insights into the evolution and genomic characteristics of this important oilseed and constitutes a resource for further sesame crop improvement.

The formation of a complex network is highly driven by multi-aspect node influences and interactions, reflected on network structures and the content embodied in network nodes. Limited work has jointly modeled all these aspects, which typically focuses on topological structures but overlooks the heterogeneous interactions behind node linkage and contributions of node content to the interactive heterogeneities. Here, we propose a multi-aspect interaction and influence-unified evolutionary coupled system (MAI-ECS) for network representation by involving node content and linkage-based network structure. MAI-ECS jointly and iteratively learns two systems: a multi-aspect interaction learning system to capture heterogeneous hidden interactions between nodes and an influence propagation system to capture multiaspect node influences and their propagation between nodes. MAI-ECS couples, unifies and optimizes the two systems toward an effective representation of explicit node content and network structure, and implicit node interactions and influences. MAI-ECS shows superior performance in node classification and link prediction in comparison with the stateof-the-art methods on two real-world datasets. Further, we demonstrate the semantic interpretability of the results generated by MAI-ECS.

© 1989-2012 IEEE. The representation of categorical data with hierarchical value coupling relationships (i.e., various value-to-value cluster interactions) is very critical yet challenging for capturing complex data characteristics in learning tasks. This paper proposes a novel and flexible coupled unsupervised categorical data representation (CURE) framework, which not only captures the hierarchical couplings but is also flexible enough to be instantiated for contrastive learning tasks. CURE first learns the value clusters of different granularities based on multiple value coupling functions and then learns the value representation from the couplings between the obtained value clusters. With two complementary value coupling functions, CURE is instantiated into two models: coupled data embedding (CDE) for clustering and coupled outlier scoring of high-dimensional data (COSH) for outlier detection. These show that CURE is flexible for value clustering and coupling learning between value clusters for different learning tasks. CDE embeds categorical data into a new space in which features are independent and semantics are rich. COSH represents data w.r.t. an outlying vector to capture complex outlying behaviors of objects in high-dimensional data. Substantial experiments show that CDE significantly outperforms three popular unsupervised encoding methods and three state-of-the-art similarity measures, and COSH performs significantly better than five state-of-the-art outlier detection methods on high-dimensional data. CDE and COSH are scalable and stable, linear to data size and quadratic to the number of features, and are insensitive to their parameters.

© 2018 IEEE. In some special oscillatory applications, especially those operated at high speeds, the eddy current loss of a linear permanent-magnet (PM) synchronous machine (LPMSM) should be fully considered because the loss might be large and concentrated in PMs during the oscillation period. This paper presents a loss analysis method based on computationally efficient finite-element analysis (CE-FEA) for a 20-Hz oscillatory tubular LPMSM. Since the mover speed varies with time, an equally divided model in 1/4 period is introduced to calculate the average PM eddy current loss. The flux density curves in PMs are calculated at 18 intervals by the CE-FEA, through which the change rate of the magnetic flux density is analyzed, considering both the entering and leaving effects and coil end effects. The calculation results show that the eddy current loss is obviously concentrated in PMs near the two ends of coils. The calculation results at a speed of 3.6 m/s obtained by the CE-FEA and two-dimensional and three-dimensional time-stepping FEAs are compared to validate the accuracy. Finally, the proposed method is validated by the experimental test results on a prototype LPMSM.

© 1989-2012 IEEE. The representation of categorical data with hierarchical value coupling relationships (i.e., various value-to-value cluster interactions) is very critical yet challenging for capturing complex data characteristics in learning tasks. This paper proposes a novel and flexible coupled unsupervised categorical data representation (CURE) framework, which not only captures the hierarchical couplings but is also flexible enough to be instantiated for contrastive learning tasks. CURE first learns the value clusters of different granularities based on multiple value coupling functions and then learns the value representation from the couplings between the obtained value clusters. With two complementary value coupling functions, CURE is instantiated into two models: coupled data embedding (CDE) for clustering and coupled outlier scoring of high-dimensional data (COSH) for outlier detection. These show that CURE is flexible for value clustering and coupling learning between value clusters for different learning tasks. CDE embeds categorical data into a new space in which features are independent and semantics are rich. COSH represents data w.r.t. an outlying vector to capture complex outlying behaviors of objects in high-dimensional data. Substantial experiments show that CDE significantly outperforms three popular unsupervised encoding methods and three state-of-the-art similarity measures, and COSH performs significantly better than five state-of-the-art outlier detection methods on high-dimensional data. CDE and COSH are scalable and stable, linear to data size and quadratic to the number of features, and are insensitive to their parameters.

© 2013 IEEE. Currently, unsupervised heterogeneous domain adaptation in a generalized setting, which is the most common scenario in real-world applications, is under insufficient exploration. Existing approaches either are limited to special cases or require labeled target samples for training. This paper aims to overcome these limitations by proposing a generalized framework, named as transfer independently together (TIT). Specifically, we learn multiple transformations, one for each domain (independently), to map data onto a shared latent space, where the domains are well aligned. The multiple transformations are jointly optimized in a unified framework (together) by an effective formulation. In addition, to learn robust transformations, we further propose a novel landmark selection algorithm to reweight samples, i.e., increase the weight of pivot samples and decrease the weight of outliers. Our landmark selection is based on graph optimization. It focuses on sample geometric relationship rather than sample features. As a result, by abstracting feature vectors to graph vertices, only a simple and fast integer arithmetic is involved in our algorithm instead of matrix operations with float point arithmetic in existing approaches. At last, we effectively optimize our objective via a dimensionality reduction procedure. TIT is applicable to arbitrary sample dimensionality and does not need labeled target samples for training. Extensive evaluations on several standard benchmarks and large-scale datasets of image classification, text categorization and text-to-image recognition verify the superiority of our approach.

The formation of a complex network is highly driven by multi-aspect node influences and interactions, reflected on network structures and the content embodied in network nodes. Limited work has jointly modeled all these aspects, which typically focuses on topological structures but overlooks the heterogeneous interactions behind node linkage and contributions of node content to the interactive heterogeneities. Here, we propose a multi-aspect interaction and influence-unified evolutionary coupled system (MAI-ECS) for network representation by involving node content and linkage-based network structure. MAI-ECS jointly and iteratively learns two systems: a multi-aspect interaction learning system to capture heterogeneous hidden interactions between nodes and an influence propagation system to capture multiaspect node influences and their propagation between nodes. MAI-ECS couples, unifies and optimizes the two systems toward an effective representation of explicit node content and network structure, and implicit node interactions and influences. MAI-ECS shows superior performance in node classification and link prediction in comparison with the stateof-the-art methods on two real-world datasets. Further, we demonstrate the semantic interpretability of the results generated by MAI-ECS.

© 2013 IEEE. Currently, unsupervised heterogeneous domain adaptation in a generalized setting, which is the most common scenario in real-world applications, is under insufficient exploration. Existing approaches either are limited to special cases or require labeled target samples for training. This paper aims to overcome these limitations by proposing a generalized framework, named as transfer independently together (TIT). Specifically, we learn multiple transformations, one for each domain (independently), to map data onto a shared latent space, where the domains are well aligned. The multiple transformations are jointly optimized in a unified framework (together) by an effective formulation. In addition, to learn robust transformations, we further propose a novel landmark selection algorithm to reweight samples, i.e., increase the weight of pivot samples and decrease the weight of outliers. Our landmark selection is based on graph optimization. It focuses on sample geometric relationship rather than sample features. As a result, by abstracting feature vectors to graph vertices, only a simple and fast integer arithmetic is involved in our algorithm instead of matrix operations with float point arithmetic in existing approaches. At last, we effectively optimize our objective via a dimensionality reduction procedure. TIT is applicable to arbitrary sample dimensionality and does not need labeled target samples for training. Extensive evaluations on several standard benchmarks and large-scale datasets of image classification, text categorization and text-to-image recognition verify the superiority of our approach.

Contains fulltext : 195643.pdf (publisher's version ) (Open Access), Thymidylate synthase (TYMS) is a crucial enzyme for DNA synthesis. TYMS expression is regulated by its antisense mRNA, ENOSF1. Disrupted regulation may promote uncontrolled DNA synthesis and tumor growth. We sought to replicate our previously reported association between rs495139 in the TYMS-ENOSF1 3' gene region and increased risk of mucinous ovarian carcinoma (MOC) in an independent sample. Genotypes from 24,351 controls to 15,000 women with invasive OC, including 665 MOC, were available. We estimated per-allele odds ratios (OR) and 95% confidence intervals (CI) using unconditional logistic regression, and meta-analysis when combining these data with our previous report. The association between rs495139 and MOC was not significant in the independent sample (OR = 1.09; 95% CI = 0.97(-)1.22; p = 0.15; N = 665 cases). Meta-analysis suggested a weak association (OR = 1.13; 95% CI = 1.03(-)1.24; p = 0.01; N = 1019 cases). No significant association with risk of other OC histologic types was observed (p = 0.05 for tumor heterogeneity). In expression quantitative trait locus (eQTL) analysis, the rs495139 allele was positively associated with ENOSF1 mRNA expression in normal tissues of the gastrointestinal system, particularly esophageal mucosa (r = 0.51, p = 1.7 x 10(-28)), and nonsignificantly in five MOC tumors. The association results, along with inconclusive tumor eQTL findings, suggest that a true effect of rs495139 might be small.

Innovative technology has been revolutionizing the educational experience in tertiary institutions over the past decades. Using novel iBeacon Technology, a mobile application called “iClassPolyU” was developed for collecting and disseminating information to and from students. The application was employed in four medium to large classrooms and the user experience was evaluated through a survey. Usage of the application was examined through quiz completion and usage rates. Results showed that each class had a quiz completion rate of over 80%. Moreover, students believed that their physical participation could be enhanced by the application and that it was effective, easy to use, and flexible. Students proposed the inclusion of mixed reality, virtual reality, and augmented reality in the future. This type of technology provides another pathway for student learning, an opportunity to give students more control over their learning, and educators with more information regarding their students.

BACKGROUND: While their incidence is on the rise, twin pregnancies are associated with risks to the mothers and their babies. This study aims to investigate the likelihood of adverse neonatal outcomes of twins following assisted reproductive technology (ART) compared to non-ART twins. METHODS: A retrospective population study using the Australian National Perinatal Data Collections (NPDC) which included 19,662 twins of ≥20 weeks gestational age or ≥ 400 g birthweight in Australia. Maternal outcomes and neonatal outcomes (preterm birth, low birth weight, resuscitation and neonatal death) were compared. Generalized Estimating Equations were used to assess the likelihood of any neonatal outcomes, with adjusted odds ratio (AOR) and 95% confidence intervals (CI) presented. Weinberg's differential rule was used to estimate monozygotic twin rate. RESULTS: ART mothers were 3.3 years older than non-ART mothers. The rates of pregnancy-induced hypertension and gestational diabetes were significantly higher for ART mothers than non-ART mothers (12.2% vs. 8.4%, p < 0.01) and (9.7% vs. 7.5%, p < 0.01) respectively. The incidence of monozygotic twins was 2.0% for ART twins and 1.1% for non-ART twins. Compared with non-ART twins, ART twins had higher rates of preterm birth (AOR 1.13, 95% CI: 1.05-1.22), low birth weight (AOR 1.13, 95% CI: 1.05-1.22), and resuscitation (AOR 1.26, 95% CI: 1.17-1.36). Liveborn ART twins had 28% (AOR 1.28, 95% CI 1.09-1.50) increased odds of having any adverse neonatal outcome compared to liveborn non-ART twins, especially for opposite-sex ART twins (AOR 1.42, 95% CI 1.11-1.82). CONCLUSION: As ART twins had higher rates of adverse outcome, special prenatal care is recommended. Couples accessing ART should be fully informed of the risk of adverse outcome of twin pregnancies.

Thymidylate synthase (TYMS) is a crucial enzyme for DNA synthesis. TYMS expression is regulated by its antisense mRNA, ENOSF1. Disrupted regulation may promote uncontrolled DNA synthesis and tumor growth. We sought to replicate our previously reported association between rs495139 in the TYMS-ENOSF1 3' gene region and increased risk of mucinous ovarian carcinoma (MOC) in an independent sample. Genotypes from 24,351 controls to 15,000 women with invasive OC, including 665 MOC, were available. We estimated per-allele odds ratios (OR) and 95% confidence intervals (CI) using unconditional logistic regression, and meta-analysis when combining these data with our previous report. The association between rs495139 and MOC was not significant in the independent sample (OR = 1.09; 95% CI = 0.97⁻1.22; p = 0.15; N = 665 cases). Meta-analysis suggested a weak association (OR = 1.13; 95% CI = 1.03⁻1.24; p = 0.01; N = 1019 cases). No significant association with risk of other OC histologic types was observed (p = 0.05 for tumor heterogeneity). In expression quantitative trait locus (eQTL) analysis, the rs495139 allele was positively associated with ENOSF1 mRNA expression in normal tissues of the gastrointestinal system, particularly esophageal mucosa (r = 0.51, p = 1.7 × 10-28), and nonsignificantly in five MOC tumors. The association results, along with inconclusive tumor eQTL findings, suggest that a true effect of rs495139 might be small.

Contains fulltext : 195643.pdf (publisher's version ) (Open Access), Thymidylate synthase (TYMS) is a crucial enzyme for DNA synthesis. TYMS expression is regulated by its antisense mRNA, ENOSF1. Disrupted regulation may promote uncontrolled DNA synthesis and tumor growth. We sought to replicate our previously reported association between rs495139 in the TYMS-ENOSF1 3' gene region and increased risk of mucinous ovarian carcinoma (MOC) in an independent sample. Genotypes from 24,351 controls to 15,000 women with invasive OC, including 665 MOC, were available. We estimated per-allele odds ratios (OR) and 95% confidence intervals (CI) using unconditional logistic regression, and meta-analysis when combining these data with our previous report. The association between rs495139 and MOC was not significant in the independent sample (OR = 1.09; 95% CI = 0.97(-)1.22; p = 0.15; N = 665 cases). Meta-analysis suggested a weak association (OR = 1.13; 95% CI = 1.03(-)1.24; p = 0.01; N = 1019 cases). No significant association with risk of other OC histologic types was observed (p = 0.05 for tumor heterogeneity). In expression quantitative trait locus (eQTL) analysis, the rs495139 allele was positively associated with ENOSF1 mRNA expression in normal tissues of the gastrointestinal system, particularly esophageal mucosa (r = 0.51, p = 1.7 x 10(-28)), and nonsignificantly in five MOC tumors. The association results, along with inconclusive tumor eQTL findings, suggest that a true effect of rs495139 might be small.

Contains fulltext : 195643.pdf (publisher's version ) (Open Access), Thymidylate synthase (TYMS) is a crucial enzyme for DNA synthesis. TYMS expression is regulated by its antisense mRNA, ENOSF1. Disrupted regulation may promote uncontrolled DNA synthesis and tumor growth. We sought to replicate our previously reported association between rs495139 in the TYMS-ENOSF1 3' gene region and increased risk of mucinous ovarian carcinoma (MOC) in an independent sample. Genotypes from 24,351 controls to 15,000 women with invasive OC, including 665 MOC, were available. We estimated per-allele odds ratios (OR) and 95% confidence intervals (CI) using unconditional logistic regression, and meta-analysis when combining these data with our previous report. The association between rs495139 and MOC was not significant in the independent sample (OR = 1.09; 95% CI = 0.97(-)1.22; p = 0.15; N = 665 cases). Meta-analysis suggested a weak association (OR = 1.13; 95% CI = 1.03(-)1.24; p = 0.01; N = 1019 cases). No significant association with risk of other OC histologic types was observed (p = 0.05 for tumor heterogeneity). In expression quantitative trait locus (eQTL) analysis, the rs495139 allele was positively associated with ENOSF1 mRNA expression in normal tissues of the gastrointestinal system, particularly esophageal mucosa (r = 0.51, p = 1.7 x 10(-28)), and nonsignificantly in five MOC tumors. The association results, along with inconclusive tumor eQTL findings, suggest that a true effect of rs495139 might be small.

Innovative technology has been revolutionizing the educational experience in tertiary institutions over the past decades. Using novel iBeacon Technology, a mobile application called “iClassPolyU” was developed for collecting and disseminating information to and from students. The application was employed in four medium to large classrooms and the user experience was evaluated through a survey. Usage of the application was examined through quiz completion and usage rates. Results showed that each class had a quiz completion rate of over 80%. Moreover, students believed that their physical participation could be enhanced by the application and that it was effective, easy to use, and flexible. Students proposed the inclusion of mixed reality, virtual reality, and augmented reality in the future. This type of technology provides another pathway for student learning, an opportunity to give students more control over their learning, and educators with more information regarding their students.

Copyright © 2018, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved. Mixed data with both categorical and continuous features are ubiquitous in real-world applications. Learning a good representation of mixed data is critical yet challenging for further learning tasks. Existing methods for representing mixed data often overlook the heterogeneous coupling relationships between categorical and continuous features as well as the discrimination between objects. To address these issues, we propose an auto-instructive representation learning scheme to enable margin-enhanced distance metric learning for a discrimination-enhanced representation. Accordingly, we design a metric-based auto-instructor (MAI) model which consists of two collaborative instructors. Each instructor captures the feature-level couplings in mixed data with fully connected networks, and guides the infinite-margin metric learning for the peer instructor with a contrastive order. By feeding the learned representation into both partition-based and density-based clustering methods, our experiments on eight UCI datasets show highly significant learning performance improvement and much more distinguishable visualization outcomes over the baseline methods.

Copyright © 2018, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved. Mixed data with both categorical and continuous features are ubiquitous in real-world applications. Learning a good representation of mixed data is critical yet challenging for further learning tasks. Existing methods for representing mixed data often overlook the heterogeneous coupling relationships between categorical and continuous features as well as the discrimination between objects. To address these issues, we propose an auto-instructive representation learning scheme to enable margin-enhanced distance metric learning for a discrimination-enhanced representation. Accordingly, we design a metric-based auto-instructor (MAI) model which consists of two collaborative instructors. Each instructor captures the feature-level couplings in mixed data with fully connected networks, and guides the infinite-margin metric learning for the peer instructor with a contrastive order. By feeding the learned representation into both partition-based and density-based clustering methods, our experiments on eight UCI datasets show highly significant learning performance improvement and much more distinguishable visualization outcomes over the baseline methods.