Find research datasets worth reusing

opencc-by-4.0Jun 2024View details →

Symptoms of Burnout

List of Symptoms of Burnout - Component of the Paper "Burnout in Software Projects: An Analysis of Stack Exchange Discussions"

opencc-by-4.0Jul 2024View details →

Data set supplementing "A Use-Case Specific Framework for Designing Representative Vignettes (RepVig) and Evaluating Triage Accuracy of Laypeople and Symptom-Assessment Applications"

This is the de-identified data set used to conduct the analyses of our study "A Use-Case Specific Framework for Designing Representative Vignettes (RepVig) and Evaluating Triage Accuracy of Laypeople and Symptom-Assessment Applications" (<a href="https://doi.org/10.1101/2024.04.02.24305193">https://doi.org/10.1101/2024.04.02.24305193</a>). The data comprises the answers to cases given by laypeople, symptom-assessment applications, and large language models and the corresponding solutions for each case. The cases were developed in the study with a focus on external validity. The dataset contains three datafiles: collected data for laypeople, for symptom-assessment applications, and for large language models.

opencc-by-4.0Jul 2024View details →

Figure 1 Symptoms seen on peanut leaves inoculated with P. arachidicola conidia suspensions.

Figure 1 Symptoms seen on peanut leaves inoculated with P. arachidicola conidia suspensions. The selected peanut leaves were spray inoculated with P. arachidicola conidia suspensions and their phenotypes were observed at 0, 7, 14, and 21dpi. Figure 2 Gene characterization and analysis for differential expressed genes. (A). Venn dialog of expressed genes from genome analysis. Venn diagrams depict the numbers of expressed genes in peanut leaves that were infected by P. arachidicola at 7 (JZ7), 14 (JZ14), 21(JZ21) dpi. The numbers indicate the counts of common or unique expressed genes. (B). Heatmap of DEGs (differentially expressed genes). Red spots mean up-regulated genes; blue indicates down-regulated genes. Blue spots indicate down-DEGs, Red spots indicate up-DEGs. In the red-blue scale, darker red colors indicate the most upregulated and the darker blue the most downregulated genes. (C). Go analysis for gene functional classifcation shows the number of genes involved in the biological process, cellular component and molecular function, respectively. (D). KEGG pathway enrichments of DEGs. The panel shows 20 enriched KEGG terms for DEGs responding to P. arachidicola infection. The legend is shown on the picture. Figure 3 Analysis of IFS protein function (A). Functional domains, phylogenetic analysis, and multiple sequence alignment of the two AhIFS proteins. (B). Functional domain schematic for the AhIFS proteins. The predicted functional domains (http://smart.embl-heidelberg.de/) indicated that both proteins contained P450 domains. (C).Phylogenetic tree analysis of the AhIFS orthologs. Close orthologs from Arachis hypogaea and other plants, including MpIFS1 (Mucuna pruriens, RDX63227.1), PsIFS1 (Pisum sativum, AAQ10282.2), GmIFS1 (Glycine max, NP_001236022.1), and GsIFS1 (Glycine soja, ACA81484.1), were used for IFS protein sequence alignment. An amino acid identity of 100% is indicated by a black background; amino acids with conservation exceeding 50% are in black on a gray background; and similar amino acids are in white on a gray background. Figure 4 (A).The flavonoids and isoflavones biosynthesis pathways. Enzymes are indicated in uppercase letters. PAL, phenylalanine ammonia-lyase; C4H, cinnamate 4-hydroxylase; 4CL, 4-coumarate: CoA ligase; CHS, chalcone synthase; CHI, chalcone isomerase; IFS, isoflavone synthase; The expression of IFS genes refers to the expression of genes at three infection time points(7,14,21day) in the transcriptome. (B, C).Transcriptional expression of AhIFS genes at the infection stage. The y- and x-axes represent the relative expression levels and time course for P. arachidicola infection, respectively. Mean values and standard deviations (SDs) were obtained from three biological and three technical replicates. Error bars are SDs; time points are 0, 7, 14, and 21hpi; and asterisks indicate that the expression level was significantly different from the control (*P< 0.05, **P< 0.01). Figure 5 Overexpression of AhIFSs boosts resistance of N. benthamiana to P. parasitica. (A). Representative photographs of N. benthamiana leaves under infection. The constructs, pBINPLUS:AhIFSs and pBINPLUS:GFP, were transiently expressed in N. benthamiana leaves. (A). tumefaciens-mediated transient expression of CK (right) and AhIFSs (left) fusion proteins in N. benthamiana leaves. The infiltrated leaves (after 48 h) were inoculated with P. parasitica zoospores, and photographed at 36 hpi and48 hpi, respectively. (B). Lesion diameters (mm) of N. benthamiana leaves. The data was calculated from three independent biological replicates using at least eight leaves each (** P < 0.01 compared with GFP; Dunnett’s test). Figure 6 Position of target sites on the AhIFS1 and AhIFS2 genes, and identifcation of the mutant lines produced using the CRISPR/Cas9 system. (A). CRISPR/Cas9 sgRNA-1 and sgRNA-2 target the first exon of AhIFS1 and AhIFS2 genes.  The ATTG is the initial recognition motif,and the protospacer adjacent motif (PAM) is underlined in red. Gray boxes, blue boxes and black lines indicate UTRs, exons and introns, respectively. (B). The edited sequences at the target site are shown in HY23 and mutation lines. The blue nucleotides indicate SNP in the mutant ahifs1-5/16, ahifs2-5/11,ahifs2-26 and the blue ‘-’ indicates the deletions between HY23 and mutant ahifs1-21. The target sequence is shown by the red underline, and nucleotides marked in red represent PAM. (C). Relative expression of AHIFS genes in the mutant. (D). Knockout of AhIFSs genes reduce its disease resistance. The symptoms of peanut mutant leaves inoculated with P. arachidicola conidia suspensions. The detached leaves of peanut were sprayed inoculation with P. arachidicola conidia suspensions and phenotypes were observed at 14 dpi.

opencc-by-4.0Nov 2019View details →

INDICATORS TO DISTINGUISH SYMPTOM ACCENTUATORS FROM SYMPTOM PRODUCERS IN INDIVIDUALS WITH A DIAGNOSED ADJUSTMENT DISORDER: A PILOT STUDY ON INCONSISTENCY SUBTYPES USING SIMS AND MMPI-2-RF

In the context of legal damage evaluations, evaluees may exaggerate or simulate symptoms in an attempt to obtain greater economic compensation. To date, practitioners and researchers have focused on detecting malingering behavior as an exclusively unitary construct. However, we argue that there are two types of inconsistent behavior that speak to possible malingering—accentuating (i.e., exaggerating symptoms that are actually experienced) and simulating (i.e., fabricating symptoms entirely)—each with its own unique attributes; thus, it is necessary to distinguish between them. The aim of the present study was to identify objective indicators to differentiate symptom accentuators from symptom producers and consistent participants.

opencc-by-4.0Aug 2024View details →

Dataset for article "Prevalence of Depressive Symptoms Among the US Population During COVID-19 (2021-2022): A Study Using the Goodville App"

This dataset contains PHQ-8 data obtained using Goodville app. The dataset was used for preparing research article   "Prevalence of Depressive Symptoms Among the US Population During COVID-19 (2021-2022): A Study Using the Goodville App". The first page of excel table includes all US PHQ-8 data. The second page includes only valid PHQ-8 patterns after Rasch analysis.

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Data from: Axial symptoms predict mortality in patients with Parkinson disease with subthalamic stimulation

Objective: To characterize how disease progression is associated with mortality in a large cohort of PD patients with long-term follow-up after STN-DBS. Methods: Motor and cognitive disabilities were assessed before, and 1, 2, 5 and 10 years after STN-DBS in 143 consecutive PD patients. We measured motor symptoms Off and On levodopa and STN-DBS, and recorded causes of death. We used linear mixed-models to characterize symptom progression, including interactions between treatment conditions and time to determine how treatments changed efficacy. We used joint models to link progression to mortality. Results: Median observation time was 12 years after surgery, during which akinesia, rigidity and axial symptoms worsened, with mean increases of 8.8 (SD 6.5), 1.8 (3.1) and 5.4 (4.1) points from year 1 to 10 after surgery (On dopamine/On STN-DBS), respectively. Responses to dopaminergic medication and STN-DBS were attenuated with time, but remained effective for all except axial symptoms, for which both treatments and their combination were predicted to be ineffective 20 years after surgery. Cognitive status significantly declined. Forty-one patients died with a median time to death of 9 years after surgery. The current level of axial disability was the only symptom that significantly predicted death (HR=4.30 [SE 1.50] per unit of square-root transformed axial score). Conclusions: We quantified long-term symptom progression and attenuation of dopaminergic medication and STN-DBS treatment efficacy in PD patients, and linked symptom progression to mortality. Axial disability significantly predicts individual risk of death after surgery, which may be useful for planning therapeutic strategies in PD.

opencc-zeroDec 2018View details →

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Use of medicinal plants for COVID-19 prevention and respiratory symptom treatment during the pandemic in Cusco, Peru: A cross-sectional survey

Background: The burden of the COVID-19 pandemic in Peru has led to people seeking alternative treatments as preventives and treatment options such as medicinal plants. This study aimed to assess factors associated with the use of medicinal plants as preventive or treatment of respiratory symptom related to COVID-19 during the pandemic in Cusco, Peru. Method: A web-based cross-sectional study was conducted on general public (20- to 70-year-old) from August 31 to September 20, 2020. Data were collected using a structured questionnaire via Google Forms, it consisted of an 11-item questionnaire that was developed and validated by expert judgment using Aiken's V (Aiken's V > 0.9). Both descriptive statistics and bivariate followed by multivariable logistic regression analyses were conducted to assess factors associated with the use of medicinal plants for COVID-19 prevention and respiratory symptom treatment during the pandemic. Prevalence ratios (PR) with 95% Confidence Interval (CI), and a P-value of 0.05 was used to determine statistical significance. Results: A total of 1,747 respondents participated in the study, 80.2% reported that they used medicinal plants as preventives, while 71% reported that they used them to treat respiratory symptoms. At least, 24% of respondents used medicinal plants when presenting with two or more respiratory symptoms, while at least 11% used plants for malaise. For treatment or prevention, the multivariate analysis showed that most respondents used eucalyptus (p < 0.001 for both), ginger (p < 0.022 for both), spiked pepper (p < 0.003 for both), garlic (p = 0.023 for prevention), and chamomile (p = 0.011 for treatment). The respondents with COVID-19 (p < 0.001), at older ages (p = 0.046), and with a family member or friend who had COVID-19 (p < 0.001) used more plants for prevention. However, the respondents with technical or higher education used less plants for treatment (p < 0.001). Conclusion: There was a significant use of medicinal plants for both prevention and treatment, which was associated with several population characteristics and whether respondents had COVID-19.

opencc-zeroSep 2021View details →

opencc-by-4.0Feb 2023View details →

Analysis of symptoms and quality of life of people with prolonged diagnosis of COVID-19, and the efficacy of an intervention in primary care using ICT

This is a ranzomized clinical study called: “Analysis of symptoms and quality of life of people with prolonged diagnosis of COVID-19, and the efficacy of an intervention in primary care using ICT”, approved in mid-2020, with reference number ISRCTN91104012. The main objetive is to analyse the overall effectiveness and cost-efficiency of a mobile application (APP) as a community health asset (HA) with recommendations and recovery exercises created bearing in mind the main symptoms presented by Long COVID patients in order to improve their quality of life, as well as other secondary variables, such as the number and severity of ongoing symptoms, physical and cognitive functions, affective state, and sleep quality. The first step was to design and develop the technologic community resource, the APP, following the steps involved in the process of recommending health assets (RHA). After this, a protocol of a randomised clinical trial for analysing its effectiveness and cost-efficiency as a HA was developed. The participants will be assigned to: (1st) usual treatment by the primary care practitioner (TAU), as a control group; and (2nd) TAU + use of the APP as a HA and adjuvant treatment in their recovery + three motivational interviews (MI), as an interventional group. An evaluation will be carried out at baseline with further assessments three and six months following the end of the intervention.

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Recurrent pain symptoms in adolescents with generalized and specific problematic Internet use: Associations analysis, confounding and mediating effects of comorbid psychosocial problems

Problematic Internet use (PIU) in adolescents is a global public health issue especially exacerbated by the restrictions caused by the COVID-19 epidemic. The authors assume a negative impact of PIU not only in relation to adolescents' mental health and social behaviour but also relative to somatic components of their health, particularly concerning the occurrence of pain symptoms – recurrent headache (RH), recurrent abdominal pain (RAP), and recurrent back pain (RBP). The present study aims to identify the associations between different types of PIU (generalized PIU – PIUgen, problematic computer game use – PUgame, and problematic social media use – PUsocial), and pain symptoms (RH, RAP and RBP), and to determine the role of psychosocial factors in the emergence of these associations. Methods: The research represents a one-stage observational study of 4,411 adolescents (53.6% girls; Mage = 14.53±1.52) in an unbiased school sample, held in three large cities of Central Siberia. The frequency and intensity of pain symptoms over the past three months were ascertained by completing a number of questionnaires. The following questionnaires were used to identify PIU and its types: the Chen Internet Addiction Scale (CIAS for PIUgen), the Game Addiction Scale for Adolescents (GASA for PUgame), and The Social Media Disorder Scale (SMDS for PUsocial). The psychological problems were assessed using The Strengths and Difficulties Questionnaire (SDQ). Logistic and linear regression, as well as mediation analysis, were used. Results: Considering both the frequency and intensity of pain, along with the presence of psychosocial problems as a confounder, a direct association of RH with PUsocial only was revealed (OR = 1.68, CI = 1.33–2.11). Using the same criteria, RAP in this sample was associated with PIUgen and PUsocial (OR = 1.99, CI = 1.51–2.62 and 2.10, CI = 1.62–2.72, respectively). RBP, according to the obtained data, was also associated with PIUgen and PUsocial both in terms of pain frequency (OR = 1.99, CI = 1.53–2.57 and OR = 2.20, CI = 1.44–3.38, respectively) and intensity. PUgame was weakly associated only with RAP and showed no significant association with RH and RBP. The mediation analysis indicated that the revealed positive associations were largely mediated by psychosocial problems comorbid for PIUgen and PUsocial (but not PUgame). Conclusions: The frequently reported association of recurrent pain symptoms with PIU in adolescents is largely mediated by comorbid psychosocial problems. The direct effect of excessive Internet use on somatic pain symptoms in adolescents is mild-to-moderate and depends on the content predominantly consumed.

opencc-zeroMay 2024View details →

opencc-by-4.0Oct 2023View details →

Replication Package for the Paper: Towards Automated Identification of Violation Symptoms of Architecture Erosion

Abstract of this Study <div> <div>Architecture erosion has a detrimental effect on maintenance and evolution, as the implementation deviates from the intended architecture. To prevent this, development teams need to understand early enough the symptoms of erosion, and particularly violations of the intended architecture. One feasible way is through the automated identification of architecture violations from textual artifacts, and particularly code reviews. In this paper, we developed 15 machine learning-based and 4 deep learning-based classifiers with three pre-trained word embeddings to identify violation symptoms of architecture erosion from developer discussions in code reviews. Specifically, we looked at code review comments from four large open-source projects from the OpenStack (Nova and Neutron) and Qt (Qt Base and Qt Creator) communities. We then conducted a survey and semi-structured interviews to acquire feedback from the involved participants who discussed architecture violations in code reviews, to validate the usefulness of our trained classifiers. Moreover, we conducted additional comparative experiments by evaluating Large Language Model (LLM)-based classifiers, including GPT-4o, Qwen-2.5, and DeepSeek-R1. The results show that, for ML/DL-based classifiers, the SVM classifier based on word2vec pre-trained word embedding performs the best with an F1-score of 0.779. In most cases, classifiers with the fastText pre-trained word embedding model can achieve relatively good performance. Moreover, 200-dimensional pre-trained word embedding models outperform classifiers that use 100- and 300-dimensional models. For ML/DL-based classifiers, ensemble classifiers based on the majority voting strategy can enhance the classifier and outperform the individual classifiers. The findings derived from the online survey and interviews conducted with the involved developers reveal that the violation symptoms identified by our approaches have practical value and can provide early warnings for impending architecture erosion. Furthermore, LLM-based classifiers consistently outperform traditional ML/DL models, with GPT-4o yielding the highest F1-score of 0.851, though ensemble strategies offered no further performance gains for ensemble LLM-based classifiers in our case. We investigate the automated identification of violation symptoms from code reviews using both traditional ML/DL and state-of-the-art LLM techniques. Our contributions include the automated approach for identifying violation symptoms in code reviews, a systematic comparison of ML/DL and LLM approaches, and practitioner-centered insights into their practical usefulness, ultimately contributing to better architectural conformance and sustainability in software systems.</div> </div> Structure of the Replication Package <ul> <li>data.zip includes: (1) extracted features (extracted_features) as inputs of classifiers, that is, word vectors of the extracted violation symptoms based on the three pre-trained word embedding models (i.e., word2vec, fastText, GloVe). (2) word_embedding includes pre-trained word embedding models. Due to the large size, we listed the download URL Download_url.txt, and we used embedding_dim.py to change the dimensions of the fastText models. (3) Violation symptoms.xlsx and Randomly_selected_comments.xlsx represent the review comments labeled as violations and non-violations, respectively.</li> <li>scripts.zip includes the Python scripts used to run the experiments, including data preprocessing and classifier training.</li> <li>survey and interview.zip include the survey form, interview protocol and questions, and the template of customized emails that we sent to participants.</li> </ul> Experiment Steps 1. Preprocessing and feature extraction:  <ul> <li>Run feature_extraction.py to conduct preprocessing and feature extraction after adjusting appropriate parameters.</li> <li>It includes five steps: (1) Tokenization (2) Noise Removal (3) Stop words Removal (4) Capitalization Conversion (5) Stemming.</li> <li>Feature selection methods: word2vec, fastText, and Glove.</li> </ul> 2. Training classifiers:  <ul> <li>Run Classifiers_ML.py to train machine learning-based classifiers.</li> <li>Run Classifiers_DL_classifiers.py to train deep learning-based classifiers.</li> <li> <div> <div>Run LLM.py to generate LLM-based classifiers.</div> <div>Run LLM_performance.py to evaluate the performance of LLM-based classifiers.</div> <div> <div> <div>Run LLM_voting.py to conduct voting strategy for ensemble LLM-based classifiers.</div> </div> </div> </div> </li> <li>Machine learning algorithms: Support Vector Machine (SVM), Logistic Regression (LR), Decision Tree (DT), Bernoulli Naive Bayes (NB), and k-Nearest Neighbor (kNN).</li> <li>Deep learning algorithm: TextCNN.</li> <li> <div> <div>Large language models: GPT-4o, Qwen-2.5, DeepSeek-R1.</div> </div> </li> </ul> Experiment Environment Required packages and their versions: <ul> <li>torch==1.11.0</li> <li>numpy==1.22.3</li> <li>gensim==4.1.2</li> <li>fasttext==0.9.2</li> <li>pandas==1.4.1</li> <li>torchtext==0.12.0</li> <li>sklearn==0.0</li> <li>scikit-learn==1.0.2</li> <li>w2vembeddings==0.1.2</li> <li>matplotlib==3.5.1</li> <li>tqdm==4.62.3</li> <li>nltk==3.7 </li> <li> <div> <div>openai==1.95.1</div> </div> </li> </ul>

Symptom Burden and Unmet Supportive Care Needs in Lung Cancer Patients Undergoing First or Second Line Immunotherapy

ClinicalTrials.gov study NCT03741868. IPD Sharing: NO. Countries: 1. Publications: 2.

Use of a Diary to Assess and Monitor Behavioral and Psychological Symptoms of Dementia ( BPSDiary )

ClinicalTrials.gov study NCT05977855. IPD Sharing: NO. Countries: 1. Publications: 1.

Effect of tDCS on Cognition, Symptoms in Chronic Schizophrenia Patients With Tardive Dyskinesia

ClinicalTrials.gov study NCT03497013. IPD Sharing: NO. Countries: 1. Publications: 1.

restrictedIPD-UNDECIDEDFeb 2026View details →

Efficacy Study of Acupuncture to Relieve Symptoms of Chronic Prostatitis

ClinicalTrials.gov study NCT00260637. IPD Sharing: Not stated. Countries: 1. Publications: 2.

restrictedIPD-UNDECIDEDFeb 2026View details →

Open-labelled Study to Evaluate the Effect of SE5-OH Tablets on Healthy Women With Menopausal Symptoms

ClinicalTrials.gov study NCT06398236. IPD Sharing: Not stated. Countries: 1. Publications: 41.

The Effect of Mindfulness on Psychophysiological Stress in a Population With Elevated Stress Symptoms

ClinicalTrials.gov study NCT05832632. IPD Sharing: NO. Countries: 1. Publications: 6.

Impact of the Routine Collection of Symptoms on Quality of Life in Dialysis Patients

ClinicalTrials.gov study NCT05154708. IPD Sharing: NO. Countries: 1. Publications: 12.

Cognitive Behavioral Group Therapy for the Management of Menopause Symptoms in Mood Disorders

ClinicalTrials.gov study NCT02860910. IPD Sharing: NO. Countries: 1. Publications: 2.

Effects of High-Intensity Interval Training on Depressive Symptoms in Hong Kong Older Adults

ClinicalTrials.gov study NCT06014294. IPD Sharing: NO. Countries: 1. Publications: 12.