cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.10495705
title: Data set for "Decoupling Dopamine Synthesis from Impulsive Action, Risk-related Decision-Making, and Propensity to Cocaine Intake:  A Longitudinal [18F]-FDOPA PET Study in Roman High- and Low-avoidance Rats"

description: This dataset corresponds to the study Decoupling Dopamine Synthesis from Impulsive Action, Risk-related Decision-Making, and Propensity to Cocaine Intake: A Longitudinal [18F]-FDOPA PET Study in Roman High- and Low-avoidance Rats. In this study, we investigated the predictive value of DA synthesis for impulsive action, RDM, and the propensity to take cocaine in a rat model of vulnerability to cocaine abuse. Additionally, we assessed the effects of cocaine intake on these variables. Rats were tested sequentially in the rat Gambling Task (rGT) and were scanned with positron emission tomography and [18F]-FDOPA to respectively assess their levels of impulsive action/RDM and their striatal DA synthesis before and after cocaine or saline self-administration (SA). Our results revealed that baseline striatal levels of DA synthesis did not significantly predict impulsive action, RDM, or a greater propensity to cocaine self-administration (SA) in impulsive animals. Besides, we showed that impulsive action, but not RDM, predicted higher rates of cocaine-taking. However, chronic cocaine exposure had no impact on DA synthesis nor affected impulsive action and RDM. These findings indicate that the hyperresponsive DA system associated with impulsivity and a propensity for cocaine consumption, along with the reduction in this hyperresponsive DA state in impulsive animals with a history of cocaine use, might not be mediated by dynamic changes in DA synthesis.

 

Contributions for usage of this data in publications:

If you publish any work using these data, please cite this repository and the associated publication.
DOI: 10.5281/zenodo.10495706
title: Data set for "Decoupling Dopamine Synthesis from Impulsive Action, Risk-related Decision-Making, and Propensity to Cocaine Intake:  A Longitudinal [18F]-FDOPA PET Study in Roman High- and Low-avoidance Rats"

description: This dataset corresponds to the study Decoupling Dopamine Synthesis from Impulsive Action, Risk-related Decision-Making, and Propensity to Cocaine Intake: A Longitudinal [18F]-FDOPA PET Study in Roman High- and Low-avoidance Rats. In this study, we investigated the predictive value of DA synthesis for impulsive action, RDM, and the propensity to take cocaine in a rat model of vulnerability to cocaine abuse. Additionally, we assessed the effects of cocaine intake on these variables. Rats were tested sequentially in the rat Gambling Task (rGT) and were scanned with positron emission tomography and [18F]-FDOPA to respectively assess their levels of impulsive action/RDM and their striatal DA synthesis before and after cocaine or saline self-administration (SA). Our results revealed that baseline striatal levels of DA synthesis did not significantly predict impulsive action, RDM, or a greater propensity to cocaine self-administration (SA) in impulsive animals. Besides, we showed that impulsive action, but not RDM, predicted higher rates of cocaine-taking. However, chronic cocaine exposure had no impact on DA synthesis nor affected impulsive action and RDM. These findings indicate that the hyperresponsive DA system associated with impulsivity and a propensity for cocaine consumption, along with the reduction in this hyperresponsive DA state in impulsive animals with a history of cocaine use, might not be mediated by dynamic changes in DA synthesis.

 

Contributions for usage of this data in publications:

If you publish any work using these data, please cite this repository and the associated publication.
DOI: 10.6084/m9.figshare.14717287.v1
title: test result of the FPFQA method from Study on the file management method of data storage system for airborne radar

description: The result data of the FPFQA method are obtained from the data storage system which is as shown in Figure 4 of the manuscript.
DOI: 10.6084/m9.figshare.7377287
title: Additional file 2: of Identification of a SiCL1 gene controlling leaf curling and capsule indehiscence in sesame via cross-population association mapping and genomic variants screening

description: Table S2. Variants of the candidate interval C29 linked to the leaf curling trait in 822 sesame accessions with normal leaf shape and a cl1 mutant line ( www.sesamum.org ). (XLSX 14 kb)
DOI: 10.6084/m9.figshare.7377287.v1
title: Additional file 2: of Identification of a SiCL1 gene controlling leaf curling and capsule indehiscence in sesame via cross-population association mapping and genomic variants screening

description: Table S2. Variants of the candidate interval C29 linked to the leaf curling trait in 822 sesame accessions with normal leaf shape and a cl1 mutant line ( www.sesamum.org ). (XLSX 14 kb)
DOI: 10.6084/m9.figshare.20484274
title: Additional file 2 of Prediction of Back-splicing sites for CircRNA formation based on convolutional neural networks

description: Additional file 2.
DOI: 10.6084/m9.figshare.7377305
title: Additional file 3: of Identification of a SiCL1 gene controlling leaf curling and capsule indehiscence in sesame via cross-population association mapping and genomic variants screening

description: Table S3. Genomic variants screening for the curly leaf trait in sesame using BSA pool data. (XLSX 50 kb)
DOI: 10.6084/m9.figshare.20484274.v1
title: Additional file 2 of Prediction of Back-splicing sites for CircRNA formation based on convolutional neural networks

description: Additional file 2.
DOI: 10.34740/kaggle/dsv/7571018
title: Data Science Job Postings & Skills (2024)
DOI: 10.6084/m9.figshare.20484273
title: Additional file 1 of Identification of HMGB2 associated with proliferation, invasion and prognosis in lung adenocarcinoma via weighted gene co-expression network analysis

description: Additional file 1: Table S1. Clinical informations and IHC score of patients with LUAD.
DOI: 10.6084/m9.figshare.19959605.v1
title: Additional file 2 of The molecular mechanism of circRHOBTB3 inhibits the proliferation and invasion of epithelial ovarian cancer by serving as the ceRNA of miR-23a-3p

description: Additional file 2.
DOI: 10.5281/zenodo.8409815
title: Dataset for "Motor impulsivity but not risk-related impulsive choice is associated to drug intake and drug-primed relapse"

description: Introduction: Motor impulsivity and risk-related impulsive choice have been proposed as vulnerability factors for drug abuse, due to their high prevalence in drug abusers. However, how these two facets of impulsivity are associated to drug abuse remains unclear. Here, we investigated the predictive value of both motor impulsivity and risk-related impulsive choice on characteristics of drug abuse including initiation and maintenance of drug use, motivation for the drug, extinction of drug-seeking behavior following drug discontinuation and, finally, propensity to relapse.

Methods: We used the Roman High- (RHA) and Low- Avoidance (RLA) rat lines, which display innate phenotypical differences in motor impulsivity, riskrelated impulsive choice, and propensity to self-administer drugs. Individual levels of motor impulsivity and risk-related impulsive choice were measured using the rat Gambling task. Then, rats were allowed to self-administer cocaine (0.3 mg/kg/infusion; 14 days) to evaluate acquisition and maintenance of cocaine self-administration, after which motivation for cocaine was assessed using a progressive ratio schedule of reinforcement. Subsequently, rats were tested for their resistance to extinction, followed by cue-induced and drug-primed reinstatement sessions to evaluate relapse. Finally, we evaluated the effect of the dopamine stabilizer aripiprazole on reinstatement of drug-seeking behaviors.

Results: We found that motor impulsivity and risk-related impulsive choice were positively correlated at baseline. Furthermore, innate high levels of motor impulsivity were associated with higher drug use and increased vulnerability to cocaine-primed reinstatement of drug-seeking. However, no relationships were observed between motor impulsivity and the motivation for the drug, extinction or cue-induced reinstatement of drug-seeking. High levels of riskrelated impulsive choice were not associated to any aspects of drug abuse measured in our study. Additionally, aripiprazole similarly blocked cocaineprimed reinstatement of drug-seeking in both high- and low-impulsive animals, suggesting that aripiprazole acts as a D2/3R antagonist to prevent relapse independently of the levels of impulsivity and propensity to self-administer drugs.

Discussion: Altogether, our study highlights motor impulsivity as an important predictive factor for drug abuse and drug-primed relapse. On the other hand, the involvement of risk-related impulsive choice as a risk factor for drug abuse appears to be limited.
DOI: 10.5281/zenodo.8409816
title: Dataset for "Motor impulsivity but not risk-related impulsive choice is associated to drug intake and drug-primed relapse"

description: Introduction: Motor impulsivity and risk-related impulsive choice have been proposed as vulnerability factors for drug abuse, due to their high prevalence in drug abusers. However, how these two facets of impulsivity are associated to drug abuse remains unclear. Here, we investigated the predictive value of both motor impulsivity and risk-related impulsive choice on characteristics of drug abuse including initiation and maintenance of drug use, motivation for the drug, extinction of drug-seeking behavior following drug discontinuation and, finally, propensity to relapse.

Methods: We used the Roman High- (RHA) and Low- Avoidance (RLA) rat lines, which display innate phenotypical differences in motor impulsivity, riskrelated impulsive choice, and propensity to self-administer drugs. Individual levels of motor impulsivity and risk-related impulsive choice were measured using the rat Gambling task. Then, rats were allowed to self-administer cocaine (0.3 mg/kg/infusion; 14 days) to evaluate acquisition and maintenance of cocaine self-administration, after which motivation for cocaine was assessed using a progressive ratio schedule of reinforcement. Subsequently, rats were tested for their resistance to extinction, followed by cue-induced and drug-primed reinstatement sessions to evaluate relapse. Finally, we evaluated the effect of the dopamine stabilizer aripiprazole on reinstatement of drug-seeking behaviors.

Results: We found that motor impulsivity and risk-related impulsive choice were positively correlated at baseline. Furthermore, innate high levels of motor impulsivity were associated with higher drug use and increased vulnerability to cocaine-primed reinstatement of drug-seeking. However, no relationships were observed between motor impulsivity and the motivation for the drug, extinction or cue-induced reinstatement of drug-seeking. High levels of riskrelated impulsive choice were not associated to any aspects of drug abuse measured in our study. Additionally, aripiprazole similarly blocked cocaineprimed reinstatement of drug-seeking in both high- and low-impulsive animals, suggesting that aripiprazole acts as a D2/3R antagonist to prevent relapse independently of the levels of impulsivity and propensity to self-administer drugs.

Discussion: Altogether, our study highlights motor impulsivity as an important predictive factor for drug abuse and drug-primed relapse. On the other hand, the involvement of risk-related impulsive choice as a risk factor for drug abuse appears to be limited.
DOI: 10.6084/m9.figshare.14717188.v1
title: The effects of sample size and sample prevalence on cellular automata simulation of urban growth

description: This study investigates the effects of sample size and sample prevalence on cellular automata (CA) simulation of urban growth. We take the CA models based on an artificial neural network (ANN), logistic regression (LR), and support vector machine (SVM) as examples, to simulate the urban growth of Wuhan city in China and the Wuhan Metropolitan Area under different sampling schemes. The results of the CA models based on the ANN, LR, and SVM methods are generally consistent. The sampling scheme with a small sample size and a low sample prevalence should be discarded because of the high uncertainty. Sample size determines the robustness of a CA model, whereas sample prevalence affects the performance of a CA model when there are sufficient samples. In particular, the closer the sample prevalence is to the population prevalence, the higher the simulation accuracy and the lower the shape complexity and fragmentation of the simulated urban patterns. We suggest that the optimal sampling scheme has a sample rate of 1% and a sample prevalence that is the same as the population prevalence. The selection of the optimal sampling scheme is independent of the population sizes represented by different study areas.
DOI: 10.6084/m9.figshare.13668122
title: Additional file 1 of Polysaccharide from Angelica sinensis attenuates SNP-induced apoptosis in osteoarthritis chondrocytes by inducing autophagy via the ERK1/2 pathway

description: Additional file 1: SNP viability.
DOI: 10.6084/m9.figshare.19959605
title: Additional file 2 of The molecular mechanism of circRHOBTB3 inhibits the proliferation and invasion of epithelial ovarian cancer by serving as the ceRNA of miR-23a-3p

description: Additional file 2.
DOI: 10.6084/m9.figshare.5279983.v1
title: Estimation of Optimal Treatment Regimes Using Lists

description: Precision medicine is currently a topic of great interest in clinical and intervention science.  A key component of precision medicine is that it is evidence-based, that is, data-driven, and consequently there has been tremendous interest in estimation of precision medicine strategies using observational or randomized study data. One way to formalize precision medicine is through a treatment regime, which is a sequence of decision rules, one per stage of clinical intervention, that map up-to-date patient information to a recommended treatment. An optimal treatment regime is defined as maximizing the mean of some cumulative clinical outcome if applied to a population of interest. It is well-known that even under simple generative models an optimal treatment regime can be a highly nonlinear function of patient information. Consequently, a focal point of recent methodological research has been the development of flexible models for estimating optimal treatment regimes. However, in many settings, estimation of an optimal treatment regime is an exploratory analysis intended to generate new hypotheses for subsequent research and not to directly dictate treatment to new patients. In such settings, an estimated treatment regime that is interpretable in a domain context may be of greater value than an unintelligible treatment regime built using “black-box” estimation methods. We propose an estimator of an optimal treatment regime composed of a sequence of decision rules, each expressible as a list of “if-then” statements that can be presented as either a paragraph or as a simple flowchart that is immediately interpretable to domain experts. The discreteness of these lists precludes smooth, that is, gradient-based, methods of estimation and leads to nonstandard asymptotics. Nevertheless, we provide a computationally efficient estimation algorithm, prove consistency of the proposed estimator, and derive rates of convergence. We illustrate the proposed methods using a series of simulation examples and application to data from a sequential clinical trial on bipolar disorder. Supplementary materials for this article are available online.
DOI: 10.6084/m9.figshare.5279983.v2
title: Interpretable Dynamic Treatment Regimes

description: Precision medicine is currently a topic of great interest in clinical and intervention science.  A key component of precision medicine is that it is evidence-based, that is, data-driven, and consequently there has been tremendous interest in estimation of precision medicine strategies using observational or randomized study data. One way to formalize precision medicine is through a treatment regime, which is a sequence of decision rules, one per stage of clinical intervention, that map up-to-date patient information to a recommended treatment. An optimal treatment regime is defined as maximizing the mean of some cumulative clinical outcome if applied to a population of interest. It is well-known that even under simple generative models an optimal treatment regime can be a highly nonlinear function of patient information. Consequently, a focal point of recent methodological research has been the development of flexible models for estimating optimal treatment regimes. However, in many settings, estimation of an optimal treatment regime is an exploratory analysis intended to generate new hypotheses for subsequent research and not to directly dictate treatment to new patients. In such settings, an estimated treatment regime that is interpretable in a domain context may be of greater value than an unintelligible treatment regime built using “black-box” estimation methods. We propose an estimator of an optimal treatment regime composed of a sequence of decision rules, each expressible as a list of “if-then” statements that can be presented as either a paragraph or as a simple flowchart that is immediately interpretable to domain experts. The discreteness of these lists precludes smooth, that is, gradient-based, methods of estimation and leads to nonstandard asymptotics. Nevertheless, we provide a computationally efficient estimation algorithm, prove consistency of the proposed estimator, and derive rates of convergence. We illustrate the proposed methods using a series of simulation examples and application to data from a sequential clinical trial on bipolar disorder. Supplementary materials for this article are available online.
DOI: 10.6084/m9.figshare.5279989
title: A Tailored Multivariate Mixture Model for Detecting Proteins of Concordant Change Among Virulent Strains of <i>Clostridium Perfringens</i>

description: Necrotic enteritis (NE) is a serious disease of poultry caused by the bacterium <i>C. perfringens</i>. To identify proteins of <i>C. perfringens</i> that confer virulence with respect to NE, the protein secretions of four NE disease-producing strains and one baseline nondisease-producing strain of <i>C. perfringens</i> were examined. The problem then becomes a clustering task, for the identification of two extreme groups of proteins that were produced at either concordantly higher or concordantly lower levels across all four disease-producing strains compared to the baseline, when most of the proteins do not exhibit significant change across all strains. However, the existence of some nuisance proteins of discordant change may severely distort any biologically meaningful cluster pattern. We develop a tailored multivariate clustering approach to robustly identify the proteins of concordant change. Using a three-component normal mixture model as the skeleton, our approach incorporates several constraints to account for biological expectations and data characteristics. More importantly, we adopt a sparse mean-shift parameterization in the reference distribution, coupled with a regularized estimation approach, to flexibly accommodate proteins of discordant change. We explore the connections and differences between our approach and other robust clustering methods, and resolve the issue of unbounded likelihood under an eigenvalue-ratio condition. Simulation studies demonstrate the superior performance of our method compared with a number of alternative approaches. Our protein analysis along with further biological investigations may shed light on the discovery of the complete set of virulence factors in NE. Supplementary materials for this article are available online.
DOI: 10.6084/m9.figshare.14717290.v1
title: test result of the ACPA method from Study on the file management method of data storage system for airborne radar

description: The result data of the ACPA method are obtained from the data storage system which is as shown in Figure 4 of the manuscript.
DOI: 10.6084/m9.figshare.14717299.v1
title: test result of the FAT file system from Study on the file management method of data storage system for airborne radar

description: The result data of the FAT file system are obtained from the data storage system which is as shown in Figure 4 of the manuscript.
DOI: 10.6084/m9.figshare.19959608.v1
title: Additional file 3 of The molecular mechanism of circRHOBTB3 inhibits the proliferation and invasion of epithelial ovarian cancer by serving as the ceRNA of miR-23a-3p

description: Additional file 3.
DOI: 10.6084/m9.figshare.7377275
title: Additional file 1: of Identification of a SiCL1 gene controlling leaf curling and capsule indehiscence in sesame via cross-population association mapping and genomic variants screening

description: Table S1 Genomic variants screening for the curly leaf trait using genome variants data of sesame accessions. (XLSX 14 kb)
DOI: 10.5281/zenodo.10624390
title: Example 01 - Input data for Hydrotrack
DOI: 10.5281/zenodo.10624391
title: Example 01 - Input data for Hydrotrack
DOI: 10.6084/m9.figshare.7377317
title: Additional file 4: of Identification of a SiCL1 gene controlling leaf curling and capsule indehiscence in sesame via cross-population association mapping and genomic variants screening

description: Table S4 Genome information of the six candidate variants for the curly leaf trait. *F refers to forward direction. R refers to reverse direction. - indicates that no genes are found in the 10 Kb flanking region of the variant site. **KEGG refers to Kyoto Encyclopedia of Genes and Genomes. C29.460 is the retained gene linked to the curly leaf trait in the interval C29 in this study. (XLSX 11 kb)
DOI: 10.6084/m9.figshare.14717293.v1
title: test result of our proposed method from Study on the file management method of data storage system for airborne radar

description: The result data of our proposed method are obtained from the data storage system which is as shown in Figure 4 of the manuscript.
DOI: 10.6084/m9.figshare.7377326
title: Additional file 5: of Identification of a SiCL1 gene controlling leaf curling and capsule indehiscence in sesame via cross-population association mapping and genomic variants screening

description: Table S5. Information of primer pairs of the candidate markers for curly leaf trait. (XLSX 10 kb)
DOI: 10.6084/m9.figshare.6853103.v1
title: MOESM4 of Integrative visual omics of the white-rot fungus Polyporus brumalis exposes the biotechnological potential of its oxidative enzymes for delignifying raw plant biomass

description: Additional file 4: Dataset S2. Mean transcript read counts and numbers of proteins detected in the secretomes for each node of the Tatami map.
DOI: 10.6084/m9.figshare.7377275.v1
title: Additional file 1: of Identification of a SiCL1 gene controlling leaf curling and capsule indehiscence in sesame via cross-population association mapping and genomic variants screening

description: Table S1 Genomic variants screening for the curly leaf trait using genome variants data of sesame accessions. (XLSX 14 kb)
DOI: 10.6084/m9.figshare.7377350
title: Additional file 7: of Identification of a SiCL1 gene controlling leaf curling and capsule indehiscence in sesame via cross-population association mapping and genomic variants screening

description: Table S6. Information of primer pairs of the cDNA sequences of SiCL1 alleles in sesame. Genome information of the six candidate variants for the curly leaf trait. Note: The italic letters in the sequences are the 5â ˛ and 3â ˛ UTR regions of SiCL1 alleles. (XLSX 13 kb)