During non-lethal stress, non-canonical cleavage of CASP7 occurs at calpain cleavage sites; related to Fig 4. حسب Gayathri Samarasekera (2610199)

Comparison of immune cell infiltration between high-risk and low-risk sample groups. حسب Changquan Shang (21647625)

Calculating the TAMs-associated score. حسب Changquan Shang (21647625)

Presentation of the DySCo framework. حسب Giuseppe de Alteriis (20846230)

"…<p>A: What is dynamic Functional Connectivity: i) We can start from any set of brain recordings, where each signal is referred to a brain location (e.g. fMRI, EEG, intracranial recordings in rodents, and more). ii) “Static” Functional Connectivity (FC) is a matrix where each entry is a time aggregated functional measure of interaction between two regions, for example, the Pearson Correlation Coefficient. iii) Dynamic Functional Connectivity (dFC) is a FC matrix (that can be calculated in different ways, see below) that changes with time, under the assumption that patterns of brain interactions are non-stationary. …"

Overview of data analysis. حسب Mehrnaz Shoushtarian (6411299)

Classification of the emergent synchronization patterns when stimulating a single brain region for three different cases. حسب Andrew Patton (10224161)

Toward effortless digital histopathology through cloud-based multi virtual staining: Proof-of-concept. حسب Mehdi Ounissi (22470332)

Pan-cancer expression characteristics of SEPN1 and its association with the TME. حسب Zisong Wang (8815727)

Overview of MINT. حسب Gabriel Matías Lorenz (21094672)

"…<p>A: List of main MINT functions. B: MINT provides multivariate information theoretic functions to quantify the amount of information that single neurons or neural populations carry about task-relevant variables (e.g., sensory stimuli or behavioral choices). …"

Correlation of <i>VDAC1</i>, <i>HSPA8</i>, <i>SOD1</i>, <i>HSPA9</i>, <i>TXN</i>, and <i>SNCA</i> with immune cell infiltration in sepsis-induced ALI. حسب Xue Fu (549661)

Mechanomics Code - JVT حسب Carlo Vittorio Cannistraci (5854046)

"…The functions were tested respectively in: MATLAB 2018a or youger, Python 3.9.4, R 4.0.3.…"

Code and data for evaluating oil spill amount from text-form incident information حسب Yiming Liu (18823387)

"…These are separately stored in the folders “description” and “posts”.</p><h2>Algorithms for Evaluating Release Amount (RA)</h2><p dir="ltr">The algorithms are split into the following three notebooks based on their functions:</p><ol><li><b>"1_RA_extraction.ipynb"</b>:</li><li><ul><li>Identifies oil spill-related incidents from raw incident data.…"

NanoDB: Research Activity Data Management System حسب Lorenci Gjurgjaj (19702207)

"…Cross-Platform Compatibility: Works on Windows, macOS, and Linux. In a Python environment or as an executable. Ease of Implementation: Using the flexibility of the Python framework all the data setup and algorithm can me modified and new functions can be easily added. …"

Code and Data for 'Fabrication and testing of lensed fiber optic probes for distance sensing using common path low coherence interferometry' حسب Radu Stancu (21165068)

"…Distance Sensing</p><p dir="ltr">Code and data to demonstrate extracting distance sensing data from A-scans and to generate Fig. 8 using the algorithm described in Fig. 7. Functions to generate distance measurements are in 'distance_sensing_utilities.py' and an example of how to use this on data in the 'data' folder is in 'distance_sensing_example.py', which generates Fig 8. …"

Software: Learning zero-cost portfolio selection with pattern matching حسب Tim Gebbie (8064947)

"…The method adaptively "learns" the most effective k and ell combinations online as the data iterates and is return weighted in the learning algorithm using performance weighted learning (PWL):</p><p dir="ltr">B(T) = SUM_K,L(SH(T-1|K,L) H(T|K,L)) / SUM_K,L SH(T-1|K,L) Eqn. (1)</p><p dir="ltr">For B the resulting portfolio weights, K and L the hyper parameters, SH the performance measure at time T as a function of K and L e.g. portfolio wealth or profit-loss, and H the agent controls as a (N,M,T) weight matrix/tensor for N agents and M objects at times T. …"

Spatiotemporal Soil Erosion Dataset for the Yarlung Tsangpo River Basin (1990–2100) حسب peng xin (21382394)

"…Bias correction was conducted using a 25-year baseline (1990–2014), with adjustments made monthly to correct for seasonal biases. The corrected bias functions were then applied to adjust the years (2020–2100) of daily rainfall data using the "ibicus" package, an open-source Python tool for bias adjustment and climate model evaluation. …"

Brain-in-the-Loop Learning for Intelligent Vehicle Decision-Making حسب Xiaofei Zhang (16483224)

"…In this paper, we utilize functional near-infrared spectroscopy (fNIRS) signals as real-time human risk-perception feedback to establish a brain-in-the-loop (BiTL) trained artificial intelligence algorithm for decision-making. …"

Landscape17 حسب Vlad Carare (22092515)

"…In particular, the explicit inclusion of transition states, which are more difficult to characterise using standard molecular dynamics, allows for assessment of global kinetics and comparison of MLIP landscapes with the DFT reference.</p><h3>Density functional theory calculations</h3><p dir="ltr">The reference potential energy landscapes were computed using density functional theory with the ωB97x hybrid-energy exchange correlation functional and a 6-31G(d) basis set within Psi4. …"

PSO-Optimized Electronic Load Controller with Intelligent Energy Recovery for Self-Excited Induction Generator Based Micro-Hydro Systems حسب MRINAL KANTI RAJAK (21838169)

"…The dataset includes: (1) <b>PSO configuration parameters</b> - complete algorithm setup with population size (N=20), adaptive inertia weights (0.9→0.4), time-varying cognitive/social coefficients (c1: 2.5→0.5, c2: 0.5→2.5), search space boundaries for all 10 optimization variables, and convergence criteria specifications; (2) <b>Multi-objective fitness function data</b> - detailed weight adaptation formulas, individual objective convergence statistics (voltage: 15.3 iter, frequency: 19.2 iter, THD: 12.8 iter, energy: 23.0 iter), and composite fitness evolution from 0.537 to 0.903 over 50 iterations; (3) <b>Particle dynamics tracking</b> - complete position and velocity trajectories for all 20 particles across optimization dimensions [Kpv, Kiv, Kdv, Kpf, Kif, Kdf, ma, θphase, fc, Ppump,ref], diversity evolution (100%→8%), and exploration/exploitation transition patterns; (4) <b>Real-time implementation metrics</b> - computational requirements (2.6 kB memory, 67% CPU utilization), execution timing (0.83 ms average, 1.2 ms worst-case), and synchronization protocols for 100 Hz optimization loops; and (5) <b>Validation datasets</b> - performance verification across six different load conditions, convergence statistics, and algorithm robustness testing results demonstrating consistent ±1.8% voltage regulation and ±0.9% frequency stability achievements, all provided in structured CSV/JSON formats with comprehensive documentation under CC-BY license.…"

CSPP instance حسب peixiang wang (19499344)

"…</b></p><p dir="ltr">Its primary function is to create structured datasets that simulate container terminal operations, which can then be used for developing, testing, and benchmarking optimization algorithms (e.g., for yard stacking strategies, vessel stowage planning).…"