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Research data related to the ​​​Department of Materials Science of the University of Milano - Bicocca

Approximately 60% of fetal intrauterine growth restriction (FGR) cases result from placental dysfunction. This study aimed to identify differentially expressed proteins (DEPs) in placentas from 10 FGR and 10 normal participants using 4D-DIA proteomic chips, by comparing fetal- and maternal-side placental tissues in both groups. GO enrichment and KEGG pathway analyses were applied to interpret the biological functions of DEPs, and ELISA was used to validate candidate proteins in an additional 8 FGR and 10 normal placentas. In total, 249 DEPs were identified between maternal and fetal sides in the control group (CON-M vs CON-F), whereas only 158 DEPs were found in the FGR group (FGR-M vs FGR-F). Between-group comparisons of the same placental side showed 278 DEPs in FGR-F vs CON-F and 157 DEPs in FGR-M vs CON-M. Candidate proteins included PRPF38A, PRPF38B, THOC2 (mRNA splicing via spliceosome), COX6C and COX7B (oxidative phosphorylation). The fetal placental compartment may be the primary contributor to placental dysfunction, and these proteins may be crucial in FGR pathophysiology, requiring further research.

The supplementary material provides detailed supporting information for the experimental design and results. Table S1 summarizes the basic physical-chemical properties of colluvial soils from the study area. Table S2 lists the modelled start and end boundaries, transition periods, and corresponding historical climatic and related events. Fig. S1 shows the deposition depths and uncalibrated radiocarbon ages of charcoal samples. Fig. S2 demonstrates the Bayesian MCMC multiphase chronological model for the Kondinskaya Lowland and Bolshoy Yugan River Valley sites.

N. oceanica CCMP1779 was acclimated using 10% (v/v), 55% (v/v), and 100% CO2 at a flow rate of 3 ml/min. Five consecutive acclimation periods were conducted, each lasting 21 days. Following each round of domestication, algal cells were harvested by centrifugation to generate two parallel samples for whole-genome resequencing and variant detection.A robust, high-CO2-tolerant strain was obtained.

The data presented in this article describe dynamic input–output responses of an automated AMBU-bag resuscitator (AABR) operating under varying simulated respiratory mechanics. The dataset was acquired from a custom-built motor-driven resuscitator platform developed at the Key Laboratory of Digital Control and System Engineering (DCSELab), Ho Chi Minh City University of Technology, Vietnam. Excitation signals were applied to a DC motor to mechanically compress a standard adult AMBU-bag, while airway pressure, airflow rate, and tidal volume were measured using integrated pressure and flow sensors at a sampling frequency of 1000 Hz. Respiratory mechanics were emulated using a dual adult test lung with adjustable compliance and airway resistance, representing four clinically relevant scenarios: normal lung, stiff lung, obstructed airway, and an extreme combined condition. The repository contains 12 CSV files organized into 4 folders, each corresponding to a different lung configuration. Each folder includes three datasets generated using distinct excitation profiles (cosine-step, parabolic-step, and ramp-step) to ensure sufficient spectral richness for system identification. Each file provides synchronized time-series measurements of gripper position, PWM duty cycle, airway pressure, airflow rate, and derived tidal volume. All data are stored in raw format with clearly labelled columns and physical units to facilitate reproducibility. This dataset provides empirical dynamic data for modelling the nonlinear and time-varying behaviour of automated bag-valve-mask ventilators. It can be reused for developing and benchmarking system identification algorithms, constructing digital twins of low-cost ventilators, and designing advanced control strategies under diverse simulated respiratory conditions.

Data for Figure 3, Figure 6a, Figure 6b, and Figure 8 of the article "Heavy silicon isotope and mass-independent mercury isotope fractionation signals of Archean TTGs: Insights into the recycling of the silicified oceanic crust in the source region".

Supplementary Tables associated to research paper Vrcan et al., Genome-wide analyses to unravel genetic mechanisms governing udder morphology traits in dairy sheep.

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This dataset contains synchronized electro–thermal measurement data of an automotive LED module equipped with OSRAM Oslon Black Flat LEDs. The measurements were conducted to capture the transient electrical and thermal behavior of the device under dynamic excitation. The dataset is designed for research on data-driven electro–thermal modeling and time-series forecasting of LED systems. It consists of six independent measurement runs, each representing a complete transient sweep performed under different experimental settings. During each run, the LED module was excited using a pseudo-random multilevel current profile that drives the device through a wide range of operating conditions. This excitation strategy allows the electro-thermal system to naturally traverse a broad region of the current-voltage-temperature space within a few experiments. For each run, the measurement data is stored in two separate files generated by different measurement devices: - Electrical measurement files (Evaluation_*.json) contain the electrical measurements acquired by the source-measure unit. Each file includes both measurement metadata and time-series arrays. The recorded signals include: voltage (terminal voltage of the LED module), current (applied drive current), time stamps, sample indices, and some metadata describing the measurement configuration, such as excitation parameters, compliance voltage, measurement duration, and other acquisition settings. - Thermal recording files (recording_*.csv) files contain the thermal measurement data, recorded independently using a thermal imaging system. The recordings include temperature measurements of the LED chip region (R2) and the surrounding environment (R1) obtained from infrared thermography. The thermal data is stored as time-series values together with corresponding time stamps. Because the electrical and thermal measurements originate from two independent acquisition systems, their time bases are not synchronized. As a result, the timestamps of the electrical (Evaluation_*.json) and thermal (recording_*.csv) files differ slightly in both start time and sampling rate. To combine both signals for analysis or modeling, the data must therefore be temporally aligned and resampled based on detecting the current trigger and temperature change.