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Alessia Rota, PhD Thesis, Supplementary Files
No full textPhD Thesis Supplementary Files to the Chapter 3 (Version 1).
Supplementary File 1: Metadata of all the samples collected, including day and time of sampling, day/night distribution, season, geographic coordinates, distribution among macro-zones and micro-zones.
Supplementary File 2: List of annotated bony fish and elasmobranch taxa detected in the dataset.
Supplementary File 3: List of annotated invertebrate taxa for the nine selected phyla, namely: Annelida, Arthropoda, Bryozoa, Cnidaria, Ctenophora, Echinodermata, Mollusca, Chlorophyta, Haptophyta
Production and carbon footprint of microbial oil from waste lemon peel extract - supplementary material
No full textSupplementary data and underlying data supporting the finding described in "Production and carbon footprint of microbial oil from waste lemon peel extract".
Abstract
Background
The agricultural sector is one of the leading producers of agro-industrial solid organic waste. This waste is mainly disposed of by incineration or landfilled, representing a huge loss of potential resources, which could be used for the production of high-value chemicals. In this study, a fermentation process for the production of microbial oil from waste lemon extract (LE), an aqueous side stream deriving from waste lemon peel and pulp processing, was developed and assessed for impacts. Microbial oil can have many and diverse applications, from plasticisers in plastic and rubber compounds to moisturizers in cosmetic formulation.
Methods and results
Characterization of LE revealed that its autoclaving process is effective for increasing the concentration of readily available glucose and fructose, reaching 28.77 ± 0.08 g L-1 and 25.68 ± 0.27 g L-1. Nitrogen content was measured too, revealing a C/N ratio of 85, optimal for triggering lipid accumulation in the selected microbial cell factory. Therefore, the oleaginous yeast Cutaneotrichosporon oleaginosum was cultivated in an unmodified LE-based medium in 2 L bioreactors, resulting in a lipid accumulation of 0.47 ± 0.08 goil gCDW-1. Finally, a new lipid extraction method using green solvents was developed, which allowed to extract and purify 11.29 g of oils, corresponding to 35% of the cell dry weight.
The carbon footprint of this laboratory-scale production was estimated to be 71 - 434 kgCO2eq kg-1 microbial oil, with electricity consumption of the fermentation step as the main factor. Simulation of the process in a 300L fermenter suggests that the electricity consumption, and therefore the overall impact, can be drastically reduced with scale-up.
Conclusions
The proposed process is promising in terms of production and has the advantage of not being in competition with edible resources and land use. However, the microbial oil yield and the extraction process must be optimized to make the process sustainable
Wolfram Mathematica code for time-dependent Domenicali's equation, mixed BCs
No full textWolfram Mathematica code used for the numerical solution of the time-dependent Domenicali's equation under mixed BC
IN2SIGHT_2023_Data_Microlens-testing
No full textThis data set contains the data collected under the IN2SIGHT EU project (GA. 964481) for the test of different types of microlenses used for the project. These data refer to the year 2023. The microlenses have been tested with a USAF1951 target to evaluate the OSF and the MTF and have been used in imaging when implanted or on cells. A specific set of data regards the use of agarose phantoms containing fluorescent microbeads to evaluate the PSF in 3D. Each folder contains README.txt files and a "summary.pptx" graphical summary. The full list of folders' tree can be found in the file Implants_MetaFile_2023.xlsx
The file "Metafile_Ulenses_2023.xlsx" contains the description of the folders tree with comments.
The same file in the tab README file reports the type of microlenses investigated in vitro and in vivo.
Details on the work done can be found in the deliverables of WP2 and WP4 of the project available at the EU web site
https://cordis.europa.eu/project/id/964481/reporting
SegFVG: A High-Resolution Large-Scale Dataset for Building Segmentation from Aerial Imagery in Northeastern Italy
No full textAccurate building extraction from high-resolution aerial imagery is essential for numerous applications in remote sensing, urban planning, and disaster management. While AI-based methods enable fast, scalable, and cost-effective segmentation of building footprints, their development is often limited by the scarce availability of large-scale, geographically diverse datasets with reliable pixel-level annotations.
In this work, we present SegFVG, a large-scale, high-resolution, and geographically diverse dataset for building segmentation, focused on the Friuli Venezia Giulia region in northeastern Italy. The dataset includes over 15,000 true orthophoto aerial image tiles, each of size 2000 × 2000 pixels with a ground sampling distance of 0.1 meters, paired with precise pixel-level building segmentation masks. Covering approximately 616 square kilometers, SegFVG captures a broad spectrum of urban, suburban, and rural settings across varied landscapes, including mountainous, flat, and coastal areas. Alongside the dataset, we provide benchmark results using several deep learning models. These support the usability of SegFVG for the development of accurate segmentation models and serve as a baseline to accelerate future research in building segmentation
An auditory-mediated communication paradigm for evaluating individual needs and motivational states in locked-in patients.
No full textThe stimulus set was used in the ERP paper "Decoding Motivational States and Craving through Electrical Markers for Neural 'Mind Reading’ by Proverbio AM & Zanetti A (2025). The aim of this study was to identify electrical neuromarkers of 12 different motivational and physiological states (such as visceral craves, affective and somatosensory states, and secondary needs) in LIS, coma, or minimally conscious state patients.
Auditory stimuli were designed by combining a human expressive voice with a background sound to evoke a context related to the targeted needs. The stimuli included: primary or visceral needs (hunger, thirst, and sleep), homeostatic or somatosensory sensations (cold, heat, and pain), emotional or affective states (sadness, joy, and fear), and secondary needs (desire for music, movement, and play). 17 audio clips were recorded for each micro-category, each replicated twice: once with a male voice and once with a female voice, totaling 408 stimuli.
Audacity software was used to combining the vocal track with a background context coherent with the verbal content. Human voices were recorded using Microphone 202 K38 by Hompower (SNR = 80 dB). The semantic content, the prosodic intonation and the emotional tone of all voices were coherent and appropriately matched. Some of the background sounds were recorded using the same microphone, while others were sourced from the publicly accessible BBC Sound Effects library for scientific purposes (https://sound-effects.bbcrewind.co.uk/search).
Research was funded by ATE – Fondo di Ateneo No. 31159-2019-ATE-0064, University of Milano-Bicocca.
The research project, entitled “Auditory imagery in BCI mental reconstruction” was preapproved by the Research Assessment Committee of the Department of Psychology (CRIP) for minimal risk projects, under the aegis of the Ethical committee of University of Milano-Bicocca, on February 9th, 2024, protocol n: RM-2024-775)
A Nonverbal Signs Dataset for the Italian Population
No full textA Nonverbal Signs Dataset for the Italian Population
1,522 Colorful Stimuli of Spontaneous Social Communication, for experimental settings, including EEG/ERP experiments.
The provided stimuli are made available for use with the understanding that proper acknowledgment and citation of the source are required in any resulting work or publication. Proper attribution not only ensures academic integrity but also duly recognizes the effort involved in their development. Should further details regarding citation be needed, the relevant information can be provided upon request.
SOURCE and VALIDATION studies:
- Proverbio AM, Gabaro V, Orlandi A, Zani A. Semantic brain areas are involved in gesture comprehension: An electrical neuroimaging study. Brain Lang. 2015 Aug;147:30-40. doi: 10.1016/j.bandl.2015.05.002.
- Proverbio AM, Ornaghi L, Gabaro V. How face blurring affects body language processing of static gestures in women and men. Soc Cogn Affect Neurosci. 2018 Jun 1;13(6):590-603. doi: 10.1093/scan/nsy033
Knockin’ on H(e)aven’s door. Financial crises and offshore wealth
No full textReproduction Package for Knockin’ on H(e)aven’s door. Financial crises and offshore wealth by Silvia Marchesi and Giovanna Marcolongo. For further information see the README fil
High-fidelity reconstruction of single-photon emission with a wQED device based on a transmon qubit
No full textThe files are from frequency domain measurements (VNA and Spectrum Analyzer) and time domain measurements (digitizer) of a microwave single-photon source and power sensor device (superconducting transmon qubit).
Both files can be read as python dictonaries of arrays (or matrices) and they feature explanatory key names.
- ReflectionMeasurement.mat : Contains data for the reflection measurement at the emission port of the qubit. Two matrices are included: one with the qubit detuned by several GHz ("offRes" flag) and one with the qubit at the center of the bandwidth analyzed ("onRes" flag)
- SpectrumAnalyzerMeasurement.mat: Contains the SA traces measured for a VNA tone with power "Pvna_corrected".
- RabiOscillations.mat: Contains mode matched Rabi emission of the single-photon source and it is used to calibrate the qubit drive pulses.
- QuadratureDistribution.mat: Contains the mode-matched emission of the source after preparing the ground, excited and superposition states of the qubit
Snow surface parameters from PRISMA data
No full textThis dataset has been used in the manuscript titled “Snow Surface Parameters from PRISMA Data in the Alps and Their Implications for Radiative Forcing” and provides snow property parameters retrieved from PRISMA hyperspectral imagery acquired over Plateau Rosa (Aosta Valley, European Alps) following a Saharan dust deposition event in July 2024. The parameters include mineral dust concentration, snow grain size, broadband albedo, and instantaneous radiative forcing. These were estimated using a supervised learning approach based on the K-Nearest Neighbors (KNN) algorithm, which was applied to match observed spectra with a reference library generated using the BioSNICAR radiative transfer model. The dataset also includes Asymptotic Radiative Transfer (S-ART) model (S-ART) outputs and in situ measurements collected during a dedicated field campaign. These data support the investigation of the effects of light-absorbing impurities on snow radiative properties, contributing to the improvement of climate and hydrological modeling in high-altitude environments