The Health Information National Trends Survey (HINTS) is a biennial, cross-sectional survey of a nationally-representative sample of American adults that is used to assess the impact of the health information environment. The survey provides updates on changing patterns, needs, and information opportunities in health; Identifies changing communications trends and practices; Assesses cancer information access and usage; Provides information about how cancer risks are perceived; and Offers a testbed to researchers to test new theories in health communication.

The Health Information National Trends Survey (HINTS) collects nationally representative data about the American public's access to and use of cancer-related information. The 2007 HINTS survey is the third in an ongoing biannual series and provides information on the changing patterns, needs, and behavior in seeking and supplying cancer information and explores how cancer risks are perceived. Respondents were asked about the ways in which they obtained health information, their use of health care services, their views about medical information and research, and their beliefs about cancer. A series of questions specifically addressed cervical cancer, colon cancer, and the Human Papillomavirus (HPV). Information was also collected on physical and mental health status, diet, physical activity, sun exposure, history of cancer, tobacco use, and whether respondents had health insurance. Demographic variables include sex, age, race, education level, employment status, marital status, household income, number of people living in the household, ownership of residence, and whether respondents were born in the United States.

This dataset tracks the updates made on the dataset "Health Information National Trends Survey (HINTS)" as a repository for previous versions of the data and metadata.

This artifact accompanies the SEET@ICSE article "Assessing the impact of hints in learning formal specification", which reports on a user study to investigate the impact of different types of automated hints while learning a formal specification language, both in terms of immediate performance and learning retention, but also in the emotional response of the students. This research artifact provides all the material required to replicate this study (except for the proprietary questionnaires passed to assess the emotional response and user experience), as well as the collected data and data analysis scripts used for the discussion in the paper.

Dataset

The artifact contains the resources described below.

Experiment resources

The resources needed for replicating the experiment, namely in directory experiment:

alloy_sheet_pt.pdf: the 1-page Alloy sheet that participants had access to during the 2 sessions of the experiment. The sheet was passed in Portuguese due to the population of the experiment.

alloy_sheet_en.pdf: a version the 1-page Alloy sheet that participants had access to during the 2 sessions of the experiment translated into English.

docker-compose.yml: a Docker Compose configuration file to launch Alloy4Fun populated with the tasks in directory data/experiment for the 2 sessions of the experiment.

api and meteor: directories with source files for building and launching the Alloy4Fun platform for the study.

Experiment data

The task database used in our application of the experiment, namely in directory data/experiment:

Model.json, Instance.json, and Link.json: JSON files with to populate Alloy4Fun with the tasks for the 2 sessions of the experiment.

identifiers.txt: the list of all (104) available participant identifiers that can participate in the experiment.

Collected data

Data collected in the application of the experiment as a simple one-factor randomised experiment in 2 sessions involving 85 undergraduate students majoring in CSE. The experiment was validated by the Ethics Committee for Research in Social and Human Sciences of the Ethics Council of the University of Minho, where the experiment took place. Data is shared the shape of JSON and CSV files with a header row, namely in directory data/results:

data_sessions.json: data collected from task-solving in the 2 sessions of the experiment, used to calculate variables productivity (PROD1 and PROD2, between 0 and 12 solved tasks) and efficiency (EFF1 and EFF2, between 0 and 1).

data_socio.csv: data collected from socio-demographic questionnaire in the 1st session of the experiment, namely:

participant identification: participant's unique identifier (ID);

socio-demographic information: participant's age (AGE), sex (SEX, 1 through 4 for female, male, prefer not to disclosure, and other, respectively), and average academic grade (GRADE, from 0 to 20, NA denotes preference to not disclosure).

data_emo.csv: detailed data collected from the emotional questionnaire in the 2 sessions of the experiment, namely:

participant identification: participant's unique identifier (ID) and the assigned treatment (column HINT, either N, L, E or D);

detailed emotional response data: the differential in the 5-point Likert scale for each of the 14 measured emotions in the 2 sessions, ranging from -5 to -1 if decreased, 0 if maintained, from 1 to 5 if increased, or NA denoting failure to submit the questionnaire. Half of the emotions are positive (Admiration1 and Admiration2, Desire1 and Desire2, Hope1 and Hope2, Fascination1 and Fascination2, Joy1 and Joy2, Satisfaction1 and Satisfaction2, and Pride1 and Pride2), and half are negative (Anger1 and Anger2, Boredom1 and Boredom2, Contempt1 and Contempt2, Disgust1 and Disgust2, Fear1 and Fear2, Sadness1 and Sadness2, and Shame1 and Shame2). This detailed data was used to compute the aggregate data in data_emo_aggregate.csv and in the detailed discussion in Section 6 of the paper.

data_umux.csv: data collected from the user experience questionnaires in the 2 sessions of the experiment, namely:

participant identification: participant's unique identifier (ID);

user experience data: summarised user experience data from the UMUX surveys (UMUX1 and UMUX2, as a usability metric ranging from 0 to 100).

participants.txt: the list of participant identifiers that have registered for the experiment.

Analysis scripts

The analysis scripts required to replicate the analysis of the results of the experiment as reported in the paper, namely in directory analysis:

analysis.r: An R script to analyse the data in the provided CSV files; each performed analysis is documented within the file itself.

requirements.r: An R script to install the required libraries for the analysis script.

normalize_task.r: A Python script to normalize the task JSON data from file data_sessions.json into the CSV format required by the analysis script.

normalize_emo.r: A Python script to compute the aggregate emotional response in the CSV format required by the analysis script from the detailed emotional response data in the CSV format of data_emo.csv.

Dockerfile: Docker script to automate the analysis script from the collected data.

Setup

To replicate the experiment and the analysis of the results, only Docker is required.

If you wish to manually replicate the experiment and collect your own data, you'll need to install:

A modified version of the Alloy4Fun platform, which is built in the Meteor web framework. This version of Alloy4Fun is publicly available in branch study of its repository at https://github.com/haslab/Alloy4Fun/tree/study.

If you wish to manually replicate the analysis of the data collected in our experiment, you'll need to install:

Python to manipulate the JSON data collected in the experiment. Python is freely available for download at https://www.python.org/downloads/, with distributions for most platforms.

R software for the analysis scripts. R is freely available for download at https://cran.r-project.org/mirrors.html, with binary distributions available for Windows, Linux and Mac.

Usage

Experiment replication

This section describes how to replicate our user study experiment, and collect data about how different hints impact the performance of participants.

To launch the Alloy4Fun platform populated with tasks for each session, just run the following commands from the root directory of the artifact. The Meteor server may take a few minutes to launch, wait for the "Started your app" message to show.

cd experimentdocker-compose up

This will launch Alloy4Fun at http://localhost:3000. The tasks are accessed through permalinks assigned to each participant. The experiment allows for up to 104 participants, and the list of available identifiers is given in file identifiers.txt. The group of each participant is determined by the last character of the identifier, either N, L, E or D. The task database can be consulted in directory data/experiment, in Alloy4Fun JSON files.

In the 1st session, each participant was given one permalink that gives access to 12 sequential tasks. The permalink is simply the participant's identifier, so participant 0CAN would just access http://localhost:3000/0CAN. The next task is available after a correct submission to the current task or when a time-out occurs (5mins). Each participant was assigned to a different treatment group, so depending on the permalink different kinds of hints are provided. Below are 4 permalinks, each for each hint group:

Group N (no hints): http://localhost:3000/0CAN

Group L (error locations): http://localhost:3000/CA0L

Group E (counter-example): http://localhost:3000/350E

Group D (error description): http://localhost:3000/27AD

In the 2nd session, likewise the 1st session, each permalink gave access to 12 sequential tasks, and the next task is available after a correct submission or a time-out (5mins). The permalink is constructed by prepending the participant's identifier with P-. So participant 0CAN would just access http://localhost:3000/P-0CAN. In the 2nd sessions all participants were expected to solve the tasks without any hints provided, so the permalinks from different groups are undifferentiated.

Before the 1st session the participants should answer the socio-demographic questionnaire, that should ask the following information: unique identifier, age, sex, familiarity with the Alloy language, and average academic grade.

Before and after both sessions the participants should answer the standard PrEmo 2 questionnaire. PrEmo 2 is published under an Attribution-NonCommercial-NoDerivatives 4.0 International Creative Commons licence (CC BY-NC-ND 4.0). This means that you are free to use the tool for non-commercial purposes as long as you give appropriate credit, provide a link to the license, and do not modify the original material. The original material, namely the depictions of the diferent emotions, can be downloaded from https://diopd.org/premo/. The questionnaire should ask for the unique user identifier, and for the attachment with each of the depicted 14 emotions, expressed in a 5-point Likert scale.

After both sessions the participants should also answer the standard UMUX questionnaire. This questionnaire can be used freely, and should ask for the user unique identifier and answers for the standard 4 questions in a 7-point Likert scale. For information about the questions, how to implement the questionnaire, and how to compute the usability metric ranging from 0 to 100 score from the answers, please see the original paper:

Kraig Finstad. 2010. The usability metric for user experience. Interacting with computers 22, 5 (2010), 323–327.

Analysis of other applications of the experiment

This section describes how to replicate the analysis of the data collected in an application of the experiment described in Experiment replication.

The analysis script expects data in 4 CSV files,

The random Lorentz gas (RLG) is a minimal model for transport in disordered media. Despite the broad relevance of the model, theoretical grasp over its properties remains weak. For instance, the scaling with dimension $d$ of its localization transition at the void percolation threshold is not well controlled analytically nor computationally. A recent study [Biroli et al. Phys. Rev. E 103, L030104 (2021)] of the caging behavior of the RLG motivated by the mean-field theory of glasses has uncovered physical inconsistencies in that scaling that heighten the need for guidance. Here, we first extend analytical expectations for asymptotic high-d bounds on the void percolation threshold, and then computationally evaluate both the threshold and its criticality in various d. In high-d systems, we observe that the standard percolation physics is complemented by a dynamical slowdown of the tracer dynamics reminiscent of mean-field caging. A simple modification of the RLG is found to bring the interplay between percolation and mean-field-like caging down to d=3. ... [Read More]

Treasury Inflation-Protected Securities, also known as TIPS, are securities whose principal is tied to the Consumer Price Index. With inflation, the principal increases. With deflation, it decreases. When the security matures, the U.S. Treasury pays the original or adjusted principal, whichever is greater.

D'Amico, Kim, and Wei use a no-arbitrage term structure model to decompose TIPS inflation compensation into three components: inflation expectation, inflation risk premium, and TIPS liquidity premium over the 1983-present period. The model is also used to decompose nominal yields or forward rates into four components: expected real short rate, expected inflation, inflation risk premium, and real term premium.

The yield curve, also called the term structure of interest rates, refers to the relationship between the remaining time-to-maturity of debt securities and the yield on those securities. Yield curves have many practical uses, including pricing of various fixed-income securities, and are closely watched by market participants and policymakers alike for potential clues about the markets perception of the path of the policy rate and the macroeconomic outlook. This page provides daily estimated real yield curve parameters, smoothed yields on hypothetical TIPS, and implied inflation compensation, from 1999 to the present. Because this is a staff research product and not an official statistical release, it is subject to delay, revision, or methodological changes without advance notice.

(:unav)...........................................

This dataset is a compilation of easy tips to prevent type 2 diabetes. They were compiled from several documents produced by the National Diabetes Education Program (NDEP). NDEP is a partnership of the National Institutes of Health, the Centers for Disease Control and Prevention, and more than 200 public and private organizations.

United States TIPS Yield: Constant Maturity: Inflation Indexed: MA: 7 Years data was reported at 1.110 % pa in Nov 2018. This records an increase from the previous number of 1.030 % pa for Oct 2018. United States TIPS Yield: Constant Maturity: Inflation Indexed: MA: 7 Years data is updated monthly, averaging 0.760 % pa from Jan 2003 (Median) to Nov 2018, with 191 observations. The data reached an all-time high of 3.840 % pa in Nov 2008 and a record low of -1.180 % pa in Oct 2012. United States TIPS Yield: Constant Maturity: Inflation Indexed: MA: 7 Years data remains active status in CEIC and is reported by Federal Reserve Board. The data is categorized under Global Database’s United States – Table US.M008: Treasury Securities Yields.

Studies using databases reporting medicines use during breastfeeding (chronological order).

(:unav)...........................................

Molecular clock methodology provides the best means of establishing evolutionary timescales, the accuracy and precision of which remain reliant on calibration, traditionally based on fossil constraints on clade (node) ages. Tip calibration has been developed to obviate undesirable aspects of node calibration, including the need for maximum age constraints that are invariably very difficult to justify. Instead, tip calibration incorporates fossil species as dated tips alongside living relatives, potentially improving the accuracy and precision of divergence time estimates. We demonstrate that tip calibration yields node calibrations that violate fossil evidence, contributing to unjustifiably young and ancient age estimates, less precise and (presumably) accurate than conventional node calibration. However, we go on to show that node and tip calibrations are complementary, producing meaningful age estimates, with node minima enforcing realistic ages and fossil tips interacting with node calibrations to objectively define maximum age constraints on clade ages. Together, tip and node calibrations may yield evolutionary timescales that are better justified, more precise and accurate than either calibration strategy can achieve alone.

IntroductionHepatocellular carcinoma is one of the leading causes of cancer-related mortality worldwide. The actin-binding protein Girdin is overexpressed in various tumors, promoting tumorigenesis and progression. However, the exact mechanisms by which Girdin regulates liver cancer remain poorly understood.MethodsThis study comprehensively analyzed the expression level of Girdin in liver cancer and adjacent tissue, along with the correlation between Girdin expression and the clinical characteristics and prognosis of liver cancer. The analysis integrated data from The Cancer Genome Atlas (TCGA), Gene Expression Omnibus (GEO), and Clinical Proteomic Tumor Analysis Consortium (CPTAC) database. Subsequently, Girdin expression was knocked down to elucidate its role in the progression of liver cancer. Transcriptome sequencing was employed to investigate the mechanistic underpinnings of Girdin’s regulatory impact on liver cancer. Additionally, the Comparative Toxicogenomics Database (CTD) was utilized to identify potential drugs or molecules for liver cancer treatment.ResultsThe findings revealed elevated Girdin expression in liver cancer tissues, and heightened Girdin expression correlating with adverse clinical features and prognosis. Silencing of Girdin markedly impeded the proliferation and migration of hepatocellular carcinoma cells. Moreover, transcriptome sequencing demonstrated that silencing Girdin led to differential expression of 176 genes and inhibition of the PI3K/Akt signaling pathway, as well as its upstream pathways—Cytokine-cytokine receptor interaction and Chemokine signaling pathway. Ultimately, we propose that Imatinib Mesylate, Orantinib, Resveratrol, Sorafenib, and Curcumin may interact with Girdin, potentially contributing to the treatment of liver cancer.ConclusionThis study reveals the association between Girdin and hepatocellular carcinoma, providing novel clues for future research and treatment of hepatocellular carcinoma.

142 Global export shipment records of Tynee tips leaf with prices, volume & current Buyer's suppliers relationships based on actual Global export trade database.

.TIPS Whois Database, discover comprehensive ownership details, registration dates, and more for .TIPS TLD with Whois Data Center.

Human endogenous retroviruses (HERVs) are LTR retrotransposons that are present in the human genome. Among them, members of the HERV-K (HML-2) group are suspected to play a role in the development of different types of cancer, including lung, ovarian, and prostate cancer, as well as leukemia. Acute myeloid leukemia (AML) is an important disease that causes 1% of cancer deaths in the United States and has a survival rate of 28.7%. Here, we describe a method for assessing the statistical association between HERV-K (HML-2) transposable element insertion polymorphisms (or TIPs) and AML, using whole-genome sequencing and read mapping using TIP_finder software. Our results suggest that 101 polymorphisms involving HERV-K (HML-2) elements were correlated with AML, with a percentage between 44.4 to 56.6%, most of which (70) were located in the region from 8q24.13 to 8q24.21. Moreover, it was found that the TRIB1, LRATD2, POU5F1B, MYC, PCAT1, PVT1, and CCDC26 genes could be displaced or fragmented by TIPs. Furthermore, a general method was devised to facilitate analysis of the correlation between transposable element insertions and specific diseases. Finally, although the relationship between HERV-K (HML-2) TIPs and AML remains unclear, the data reported in this study indicate a statistical correlation, as supported by the χ2 test with p-values < 0.05.

2858 Global import shipment records of Pipette Tips with prices, volume & current Buyer's suppliers relationships based on actual Global export trade database.

This dataset provides information about the number of properties, residents, and average property values for Tips Street cross streets in Three Rivers, TX.