Ship Operational Efficiency from AIS Data Using Big Data Technology

The Energy Efficiency Design Index (EEDI) is a necessary benchmark for all new ships to prevent pollution from ships. MARPOL has also applied the Ship Energy Efficiency Management Plan (SEEMP) to all existing ships. The Energy Efficiency Operational Indicator (EEOI) provided by SEEMP is used to measure a ship's operational efficiency. The shipowner or operator can make strategic plans, such as routing, hull cleaning, decommissioning, new construction, and so on, by monitoring the EEOI. Fuel Oil Consumption is the most important factor in calculating EEOI (FOC). It is possible to measure it when a ship is in operation. This means that the EEOI of a ship can only be calculated by the shipowner or operator. Other stakeholders, such as the shipbuilding firm and Class, or those who do not have the measured FOC, can assess how efficiently their ships are working relative to other ships if the EEOI can be determined without the real FOC. We present a method to estimate the EEOI without requiring the actual FOC in this paper. The EEOI is calculated using data from the Automatic Identification System (AIS), ship static data, and publicly available environmental data. Big data technologies, notably Hadoop and Spark, are used because the public data is huge. We test the suggested method with real data, and the results show that it can predict EEOI from public data without having to use actual FOC Keywords: Ship operational efficiency, Energy Efficiency Operational Indicator (EEOI), Fuel Oil Consumption (FOC), Automatic Identification System (AIS), Big data

Denigrate Comment Detection in Low-Resource Hindi Language Using Attention-Based Residual Networks

Cyberspace has been recognized as a conducive environment for use of various hostile, direct, and indirect behavioural tactics to target individuals or groups. Denigration is one of the most frequently used cyberbullying ploys to actively damage, humiliate, and disparage the online reputation of target by sending, posting, or publishing cruel rumours, gossip, and untrue statements. Previous pertinent studies report detecting profane, vulgar, and offensive words primarily in the English language. This research puts forward a model to detect online denigration bullying in low-resource Hindi language using attention residual networks. The proposed model Hindi Denigrate Comment–Attention Residual Network (HDC-ARN) intends to uncover defamatory posts (denigrate comments) written in Hindi language which stake and vilify a person or an entity in public. Data with 942 denigrate comments and 1499 non-denigrate comments is scraped using certain hashtags from two recent trending events in India: Tablighi Jamaat spiked Covid-19 (April 2020, Event 1) and Sushant Singh Rajput Death (June 2020: Event 2). Only text-based features, that is, the actual content of the post, are considered. The pre-trained word embedding for Hindi language from fastText is used. The model has three ResNet blocks with an attention layer that generates a post vector for a single input, which is passed through a sigmoid activation function to get the final output as either denigrate (positive class) or non-denigrate (negative class). An F-1 score of 0.642 is achieved on the dataset.

No Humans Here

Many research communities routinely conduct activities that fall outside the bounds of traditional human subjects research, yet still frequently rely on the determinations of institutional review boards (IRBs) or similar regulatory bodies to scope ethical decision-making. Presented as a U.S. university-based fictional memo describing a post-hoc IRB review of a research study about social media and public health, this design fiction draws inspiration from current debates and uncertainties in the HCI and social computing communities around issues such as the use of public data, privacy, open science, and unintended consequences, in order to highlight the limitations of regulatory bodies as arbiters of ethics and the importance of forward-thinking ethical considerations from researchers and research communities.

Inhibition of EZH2 transactivation function sensitizes solid tumors to genotoxic stress

Drugs that block the activity of the methyltransferase EZH2 are in clinical development for the treatment of non-Hodgkin lymphomas harboring EZH2 gain-of-function mutations that enhance its polycomb repressive function. We have previously reported that EZH2 can act as a transcriptional activator in castration-resistant prostate cancer (CRPC). Now we show that EZH2 inhibitors can also block the transactivation activity of EZH2 and inhibit the growth of CRPC cells. Gene expression and epigenomics profiling of cells treated with EZH2 inhibitors demonstrated that in addition to derepressing gene expression, these compounds also robustly down-regulate a set of DNA damage repair (DDR) genes, especially those involved in the base excision repair (BER) pathway. Methylation of the pioneer factor FOXA1 by EZH2 contributes to the activation of these genes, and interaction with the transcriptional coactivator P300 via the transactivation domain on EZH2 directly turns on the transcription. In addition, CRISPR-Cas9–mediated knockout screens in the presence of EZH2 inhibitors identified these BER genes as the determinants that underlie the growth-inhibitory effect of EZH2 inhibitors. Interrogation of public data from diverse types of solid tumors expressing wild-type EZH2 demonstrated that expression of DDR genes is significantly correlated with EZH2 dependency and cellular sensitivity to EZH2 inhibitors. Consistent with these findings, treatment of CRPC cells with EZH2 inhibitors dramatically enhances their sensitivity to genotoxic stress. These studies reveal a previously unappreciated mechanism of action of EZH2 inhibitors and provide a mechanistic basis for potential combination cancer therapies.

The Immunological Role of CDK4/6 and Potential Mechanism Exploration in Ovarian Cancer

BackgroundOvarian cancer (OC) is one of the most lethal gynecologic cancers. Growing evidence has proven that CDK4/6 plays a key role in tumor immunity and the prognosis of many cancers. However, the expression and function of CDK4/6 in OC remain unclear. Therefore, we aimed to explore the influence of CDK4/6 in OC, especially on immunity.MethodsWe analyzed CDK4/6 expression and prognosis using The Cancer Genome Atlas (TCGA), Gene Expression Omnibus (GEO) and Genotype Tissue Expression (GTEx) data. Subsequently, we used the cytoHubba plug-in of Cytoscape software and starBase to identify the noncoding RNAs (ncRNAs) regulating CDK4/6. Finally, we verified the effect of CDK4/6 on immunity in OC cell lines and animal models.ResultsCDK4/6 expression was higher in OC tissues than in normal ovarian tissues, and the high expression levels of CDK4/6 contributed to the immunosuppressive state of OC and were thus related to the poor prognosis of OC patients. This was also in general agreement with the results of OC cell line and animal experiments. Mechanistically, the CDK4/6 inhibitor palbociclib increased the secretion of interferon (IFN)-γ and the interferon-stimulated gene (ISG) response, thereby upregulating the expression of antigen-presenting molecules; this effect was partly dependent on the STING pathway and thus activated immunity in OC. Additionally, according to public data, the LRRC75A-AS1-hsa-miR-330-5p axis could inhibit the immune response of OC patients by upregulating CDK4/6, leading to a poor prognosis.ConclusionCDK4/6 affects the immune microenvironment of OC and correlates with the prognosis of OC patients.

Artificial Intelligence-Based Solution in Personalized Computer-Aided Arthroscopy of Shoulder Prostheses

Background: Early recognition of prostheses before reoperation can reduce perioperative morbidity and mortality. Because of the intricacy of the shoulder biomechanics, accurate classification of implant models before surgery is fundamental for planning the correct medical procedure and setting apparatus for personalized medicine. Expert surgeons usually use X-ray images of prostheses to set the patient-specific apparatus. However, this subjective method is time-consuming and prone to errors. Method: As an alternative, artificial intelligence has played a vital role in orthopedic surgery and clinical decision-making for accurate prosthesis placement. In this study, three different deep learning-based frameworks are proposed to identify different types of shoulder implants in X-ray scans. We mainly propose an efficient ensemble network called the Inception Mobile Fully-Connected Convolutional Network (IMFC-Net), which is comprised of our two designed convolutional neural networks and a classifier. To evaluate the performance of the IMFC-Net and state-of-the-art models, experiments were performed with a public data set of 597 de-identified patients (597 shoulder implants). Moreover, to demonstrate the generalizability of IMFC-Net, experiments were performed with two augmentation techniques and without augmentation, in which our model ranked first, with a considerable difference from the comparison models. A gradient-weighted class activation map technique was also used to find distinct implant characteristics needed for IMFC-Net classification decisions. Results: The results confirmed that the proposed IMFC-Net model yielded an average accuracy of 89.09%, a precision rate of 89.54%, a recall rate of 86.57%, and an F1.score of 87.94%, which were higher than those of the comparison models. Conclusion: The proposed model is efficient and can minimize the revision complexities of implants.

Estimation of age-stratified contact rates during the COVID-19 pandemic using a novel inference algorithm

Well parameterised epidemiological models including accurate representation of contacts, are fundamental to controlling epidemics. However, age-stratified contacts are typically estimated from pre-pandemic/peace-time surveys, even though interventions and public response likely alter contacts. Here we fit age-stratified models, including re-estimation of relative contact rates between age-classes, to public data describing the 2020-21 COVID-19 outbreak in England. This data includes age-stratified population size, cases, deaths, hospital admissions, and results from the Coronavirus Infection Survey (almost 9000 observations in all). Fitting stochastic compartmental models to such detailed data is extremely challenging, especially considering the large number of model parameters being estimated (over 150). An efficient new inference algorithm ABC-MBP combining existing Approximate Bayesian Computation (ABC) methodology with model-based proposals (MBP) is applied. Modified contact rates are inferred alongside time-varying reproduction numbers that quantify changes in overall transmission due to pandemic response, and age-stratified proportions of asymptomatic cases, hospitalisation rates and deaths. These inferences are robust to a range of assumptions including the values of parameters that cannot be estimated from available data. ABC-MBP is shown to enable reliable joint analysis of complex epidemiological data yielding consistent parametrisation of dynamic transmission models that can inform data-driven public health policy and interventions.

How Efficient are the Romanian Sports Federations? A Theoretical and Experimental Approach

Examining the links between performance and financial issues has been, and still is, subject of a great number of researches both in the business and in the non-profit environment. In this respect, the world of sports has not been bypassed either, using concepts and / or instruments from accounting, economics or statistic areas in order to analyse a firm or an NGO in the sport industry and his success. The present study represents one of the first attempt to value the Romanian sports federations’ activity which is not based solely on the scores attained following places and medals won by athletes participating in the national and international competition system. And it is intended to identify an appropriate methodology to highlight how efficient the sports federations have been in 2019 year, reporting the results obtained to the resources used, from a domestic perspective (meaning the participation in national sports events and the scores obtained). This paper addresses 12 Romanian sports federations (out of 75 federations on sport branches) and their efficiency, through the lens of several input, output and outcome indicators, using public data gathered from various sources - the Romanian Ministry of Finance, the National Institute of Statistics, the Romanian Ministry of Youth and Sports. Using statistical tools, such as Spearman rank correlation, as well as statistical methods, such as standardization, we developed an efficiency calculation methodology, which could be used to support managerial team to improve and / or reshape federations' activities, if necessary.

Molecular Characterization and Clinical Relevance of ALDH2 in Human Cancers

Background: Aldehyde dehydrogenase 2 (ALDH2) is well-known to be a key enzyme in alcohol metabolism. However, a comprehensive understanding of ALDH2 across human cancers is lacking.Methods: A systematic and comprehensive analysis of the molecular alterations and clinical relevance for ALDH2 in more than 10,000 samples from 33 cancer types was performed. qRT-PCR was performed on 60 cancer and 60 paired nontumor tissues.Results: It was observed that ALDH2 was generally downregulated in most cancers, which was mainly driven by DNA hypermethylation rather than mutations or copy number variations. Besides, ALDH2 was closely related to the inhibition and activation of tumor pathways and a variety of potential targeted agents had been discovered in our research. Last but not least, ALDH2 had the best prediction efficacy in assessing immunotherapeutic response compared with PD-L1, PD-1, CTLA4, CD8, and tumor mutation burden (TMB) in cutaneous melanoma. According to the analysis of large-scale public data and 60 pairs of clinical cancer samples, we found the downregulation of ALDH2 expression tends to suggest the malignant phenotypes and adverse prognosis, which might enhance the precise diagnosis and timely intervention of cancer patients.Conclusion: This study advanced the understanding of ALDH2 across cancers, and provided important insight into chemotherapy, immunotherapy and prognosis of patients with cancer.

United States Dietary Trends Since 1800: Lack of Association Between Saturated Fatty Acid Consumption and Non-communicable Diseases

We reviewed data on the American diet from 1800 to 2019.Methods: We examined food availability and estimated consumption data from 1808 to 2019 using historical sources from the federal government and additional public data sources.Results: Processed and ultra-processed foods increased from <5 to >60% of foods. Large increases occurred for sugar, white and whole wheat flour, rice, poultry, eggs, vegetable oils, dairy products, and fresh vegetables. Saturated fats from animal sources declined while polyunsaturated fats from vegetable oils rose. Non-communicable diseases (NCDs) rose over the twentieth century in parallel with increased consumption of processed foods, including sugar, refined flour and rice, and vegetable oils. Saturated fats from animal sources were inversely correlated with the prevalence of NCDs.Conclusions: As observed from the food availability data, processed and ultra-processed foods dramatically increased over the past two centuries, especially sugar, white flour, white rice, vegetable oils, and ready-to-eat meals. These changes paralleled the rising incidence of NCDs, while animal fat consumption was inversely correlated.