- Abnormal serum BDNF and p-mTOR in MDD with childhood trauma
Zhao, Xinling
Published 19 July 2024 | Mendeley Data
Serum BDNF and p-mTOR levels were measured by ELISA in adolescents with major depressive episode and matched healthy controls
- Development and Validation of Yogic Personal Excellence Inventory
Bhandari, Rudra B., Chaudhry, Nidhi
Published 19 July 2024 | Mendeley Data
The data files are related to content, construct and criterion validity of yogic personal excellence inventory (YPEI) derived from the most quoted yogic classical text Patanjali Yoga Darshan. The developed YPEI is a novel and significant psychometric tool for mapping personal excellence level of an individual before prescribing personalised bio-psycho-socio-spiritual protocol for health promotion/healing/spiritual advancement. This research adds notable value in health psychology, complementary and alternative medicine and self-management. 1. Content Validity Data Excel sheet containing ten experts’ ratings on the relevance of the items of the original pool of YPEI. 2. EFA Data Excel sheet containing responses of 721 participants on 111 items retained post content validity analysis. 3. CFA Data Excel sheet containing responses of 364 participants on 71 items retained post exploratory factor analysis. 4. Criterion Validity Excel sheet containing responses of 146 participants on the final 43 items of the Yogic Personal Excellence Inventory along with Vikruti Subdosha Questionnaire, Vedic Personality Inventory, and Personal Efficacy Scale for determining convergent and discriminant validity.
- Building Resilience and Sustainable Advantage: A Case Study of Strategic Technology Adoption in Creative Media Company During Crisis
Pratama, Febby, Saraswati, Maudi
Published 19 July 2024 | Mendeley Data
Transcription data from interviews with sources relating to the role of digital technology in the creative media industry in Indonesia.
- Raw immunofluorescent data for CCL19-producing fibroblasts promote tertiary lymphoid structure formation augmenting anti-tumor IgG response in colorectal cancer liver metastasis. Zhang et al.
Zhang, Yifan
Published 19 July 2024 | Mendeley Data
- Embodied Carbon, Fire Rating, Acoustic, and Vibration Data for Eight Concrete Floor Systems
Broyles, Jonathan, Solnosky, Ryan, Brown, Nathan
Published 19 July 2024 | Mendeley Data
This dataset provides the geometric information, embodied carbon emissions, and analytically-obtained performance of three secondary design objectives (fire-resistance rating, air-borne sound insulation, and walking vibration criteria) for eight concrete floor systems when parametrically assessing different structural design parameters (i.e., span, concrete compressive strength, live load, dead load, and deflection limit). The corresponding Excel spreadsheet provides the information when considering: 1) only structural limit states, 2) structural limit states and fire rating, 3) structural limit states and acoustics, and 4) structural limit states and vibrations. The results and interpretation can be read in the corresponding journal paper (currently in review).
- Loftus et al 2024 Dev Cell: Uncropped Western Blot images
Loftus, Alex
Published 19 July 2024 | Mendeley Data
Uncropped Western Blot images for Loftus et al. 2024 Dev Cell.
- Agar dilution test and its Merits and Drawbacks in evolving dynamics of colistin susceptibility
Padmini Radhakrishnan, swathykrishna
Published 19 July 2024 | Mendeley Data
The study was designed to evaluate the agar dilution method for colistin susceptibility in CRE isolates compared to CBDE. In the study, 108 carbapenem-resistant isolates were tested for Colistin susceptibility by Microbroth Dilution, agar dilution, and colistin broth disc elution. The comparisons were made using various statistical parameters. For all isolates, all three methods of colistin susceptibility were performed.MICs and interpretive categories were documented.
- Personality Traits and Technology Adoption among a University’s Digital Technology Entrepreneurs
Pratama, Febby, Margiono, Ari
Published 19 July 2024 | Mendeley Data
Big Five Personality test results for students at the University and business income data from student businesses.
- Synthetic Reaction Wheel Datasets for Nominal and Faulty Conditions as Used for Training the One-Dimensional Sliding Window Residual Network Proposed
Hedayati, MohammadSaleh, Rahimi, Afshin
Published 19 July 2024 | Mendeley Data
All of the datasets provided here have been generated in MATLAB. The datasets describe the measurement outputs of the continuous operation of a reaction wheel based on Bialke's high fidelitiy RW model onboard a satellite that undergoes different faults. If you import any of these datasets into MATLAB, a cell-type variable called "dataset" will show up. You can also load them for use in Python and other usages. Datasets #1-#3, #4-#6, and #7-#9 are grouped together and are describing the same signals from different perspectives, therefore, they have the same number of cells. For example, the 10th cells of datasets #1-#3 describe the same signal. More information about the datasets is available in Table III in the referenced paper. For any of the MJAPF and SPF datasets, each cell is of the form 2x1001 doubles. For these datasets, the first row is the descriptor vector of the signal and the second row corresponds to the signal as perceived by MJAPF or SPF. Both signals have 1001 time steps. The time steps are 0.1 seconds apart, meaning that 1001 encompasses operational details of the reaction wheel over about 100 seconds. The raw datasets' cells are of the form 5x1001 doubles. The first row is, again, the descriptor row. The second row is the reaction wheel's motor current as received by sensors (with sensor noise). The third row is the reaction wheel's angular velocity as received by sensors (with sensor noise). The fourth row is the reaction wheel's motor current with process noise but without the measurement noise (true signals). The fifth row is the reaction wheel's angular velocity with process noise but without the measurement noise (true signals). Therefore, the datasets are structured as follows: 1) For MJAPF- or SPF-treated datasets: First row: Descriptors that describe the health state of the reaction wheel at each time step and have no units since it is a categorical variable. Second row: The reaction wheel motor's electrical current (I_m), as estimated by MJAPF or SPF, has the unit of Amperes (A). 2) For raw datasets: First row: Descriptors that describe the health state of the reaction wheel at each time step and have no units since it is a categorical variable. Second row: The reaction wheel motor measures the electrical current (I_m) without undergoing any treatments and has the unit of Amperes (A). Third row: The reaction wheel measures angular velocity (omega_m) without undergoing any treatments and has the unit of Radians per second (rad/s). Fourth row: Reaction wheel motor's real or true process electrical current (I_m) without undergoing any treatments and has the unit of Amperes (A). Fifth row: Reaction wheel's real or true process angular velocity (omega_m) without undergoing any treatments and has the unit of Radians per second (rad/s).
- Replication of "The Credit Supply Channel of Monetary Policy Tightening and its Distributional Impacts"
Bosshardt, Joshua, Di Maggio, Marco, Kakhbod, Ali, Kermani, Amir
Published 19 July 2024 | Mendeley Data
This is a replication packet for "The Credit Supply Channel of Monetary Policy Tightening and its Distributional Impacts" published in the Journal of Financial Economics. See README for further details.