Mendeley Data Showcase

Targeted genomic integration of gene-sized cassettes into hematopoietic stem and progenitor cells (HSPCs) for genetic disease treatment is constrained by the low efficiency of homology-directed repair (HDR) and frequent unintended genetic changes at the editing site. To overcome these challenges, we introduce Selection by Means of Artificial Transactivators (SMArT), which transiently implements AND reporter gates to achieve templated integration of a functional cassette at the target site. HDR-edited HSPCs were enriched to very high purity through transient selector expression, whereas cells carrying undesired and potentially genotoxic on-target edits were preferentially depleted. Xenotransplantation of SMArT-enriched HSPCs in immunodeficient mice resulted in fully HDR-edited human grafts with the selector no longer detectable. SMArT strategies were implemented through clinically compliant manufacturing and selectors. They support both safe harbor integration and gene correction, can preserve physiological transcriptional regulation, and are portable across loci also with polyfunctional editors. Overall, SMArT strategies may broaden therapeutic applicability of gene-sized editing while reducing its genotoxic burden.

The original microbial data of composting soil

Daily, monthly and annual rainfall or precipitation data from the 93 stations (65 for the rainfall products) and the 7 products considered (ERA5-Land, GSMaP, IMERG, NASAPOWER, TERRACLIMATE, CHIRPS and SM2RAIN-ASCAT) in the study titled "Spatiotemporal evaluation of seven gridded rainfall and precipitation products across Argentina"

This study investigated the effect of thermomechanical aging on force generation by 3D-printed Tera Harz TC-85 and thermoformed Zendura FLX (ZF) aligners and evaluated the thickness homogeneity of both systems. Ten aligners were fabricated from each material and subjected to thermomechanical aging for up to 30 days. Forces delivered to the maxillary right central incisor (Tooth 11) were assessed at baseline and after 2, 7, and 30 days using Fuji pressure-sensitive films to quantify normal contact forces and an orthodontic measurement and simulation system (OMSS) to determine resultant three-dimensional forces. Aligner thickness was measured using a digital caliper and micro-computed tomography (µCT). TC-85 aligners generated significantly greater initial contact forces than ZF aligners (149.7 ± 25.6 N vs. 69.8 ± 9.0 N). Both materials exhibited significant force degradation following aging. OMSS analysis demonstrated comparable baseline resultant forces between groups (0.1–0.5 N), with no significant differences in facial force components. While facial forces remained relatively stable throughout the aging period, lingual resultant forces decreased significantly over time. Thickness analysis revealed marked thinning in ZF aligners (0.5 ± 0.05 mm) and localized thickening in TC-85 aligners (1.0 ± 0.09 mm). Within the limitations of this in vitro study, TC-85 aligners produced higher initial contact forces; however, both materials showed significant force decay over time, potentially affecting the predictability of orthodontic tooth movement.

Accurate national-scale rice yield forecasting is essential for food security, import planning, strategic reserve management, and climate adaptation in rice-dependent countries such as Bangladesh. Despite recent advances in deep learning and remote sensing, many existing studies rely on locally constrained datasets or overlook the challenge of limited historical records. This study proposes a hybrid framework based on transfer learning and an Attention-LSTM architecture for national rice yield forecasting in Bangladesh by integrating climatic variables, remote sensing indicators, and agricultural economic data. The proposed model was trained and validated using a walk-forward evaluation strategy over the 2006–2022 period to simulate realistic operational forecasting conditions while preventing temporal data leakage.

Anonymous dataset used for the article "Identification of risk factors for postpartum hemorrhage after manual removal of the placenta subsequent to vaginal delivery: a retrospective cohort study."

Unprocessed microscopy images

The replication crisis in psychology and the interpretability gap in neuroscience converge on a structural weakness that methodological reforms aimed at experimental design and statistical transparency have largely left untouched: hypothesis generation remains an informal, cognitively constrained process that is rarely subjected to systematic methodological scrutiny. This paper proposes a four-phase computational framework in which artificial intelligence agents—goal-directed systems capable of extended planning, structured memory retrieval, and iterative self-correction—participate as active components of the scientific reasoning process at the stage of hypothesis formation and preliminary evaluation. The framework positions these systems upstream of the experimental process—as instruments for expanding the hypothesis space explored before experimental resources are committed. Each candidate hypothesis produced by the system is accompanied by an explicit mechanistic account, a falsification condition, and an auditable reasoning chain. The framework is developed through a case study in working memory research, in which a multi-agent system navigates conflicting empirical findings on capacity limits and attentional modulation to produce three candidate hypotheses with distinct mechanistic commitments. A computational simulation implementing the biased competition architecture (Miller & Cohen, 2001) subjects all three hypotheses to identical experimental conditions and confirms that only H3 produces the predicted differential pattern of conjunction and feature errors across parametrically varied attentional load levels, providing the computational grounding for empirical follow-up that a framework paper of this kind requires. The paper argues that systematic, documented exploration of the hypothesis space is epistemologically valuable in its own right, and that agentic architectures provide tractable means for achieving it.

The data were collected to adapt and validate the 12-item Self-Rated Creativity Scale (SRCS-12) in Polish among 406 Polish university students (72.4% women), with an average age of 22, divided into artistic (n = 232) and non-artistic (n = 174) fields. In a cross-sectional online survey, participants completed the Polish SRCS, the Openness subscale of the IPIP-NEO-PI-R, and the Cognitive Flexibility Scale. An exploratory factor analysis indicated a refined two-factor, 6-item solution for Innovation Behavior (Useful) and Creative Self-Concept (Novelty). Construct validity of the 6-item Brief Self-Rated Creativity Scale (BSRCS-6) was assessed with respect to openness to experience, cognitive flexibility, gender, and the distinction between artistic and non-artistic majors.

This dataset contains 718 structured Color Doppler Echocardiogram (Echo) reports collected from cardiac patients at the Diabetic Association Medical College, Faridpur, Bangladesh. The original Electronic Health Record (EHR) reports were generated as semi-structured Microsoft Word (.doc) documents by attending cardiologists. Each report has been parsed, structured into 55 clinically meaningful fields, validated through a reverse-engineering pipeline, and enriched with two layers of clinical labels: Overview : • 718 Color Doppler Echocardiogram reports from cardiac patients • Source: Diabetic Association Medical College, Faridpur, Bangladesh • Original format: semi-structured Microsoft Word (.doc) files by attending cardiologists • Each report parsed into 55 clinically meaningful fields • Two annotation layers: 11-class pathology label + 4-class severity label • All reports manually de-identified; no PHI retained Processing Pipeline: • Step 1: MS Word COM automation extracts raw tables and narrative from each .doc file • Step 2: Azure OpenAI (GPT-4o) populates the 55-field schema per report • Step 3: Reverse-engineering validation checks field-by-field consistency • Step 4: 11-class pathology annotation via priority keyword rules • Step 5: 4-class severity mapping applied • Step 6: Structured fields concatenated into NLP-ready free-text strings Structured Fields (55 total) • M-mode & 2-D measurements: EF, FS, IVST, LVIDd, LVIDs, LA, AO, RVGWT, MVA, and more • Chamber & valve descriptions: LV, RV, MV, AV, TV, PV • Structural findings: ASD, VSD, PDA, thrombus, vegetation, pericardium • Color Flow Doppler observations across all four valves • Free-text cardiologist Impression per report Files Provided: Extracted Medical Reports.csv — 718 rows × 57 columns (55 clinical fields + Cardiac_Class + label) Extracted Data Free Form NLP Ready.csv — each report as a single patient_data free-text string + 4-class label; ready for BERT-style transformer input