arXiv subjects

Gil Gonçalves

Publications and source records attributed to Gil Gonçalves.

6 recordsLinked to original sources

arXiv2026

Bioelectrical interfaces beyond excitable cells: cancer, aging, and gene expression modulation

The investigation of biological conductivity has evolved from its classical foundation based on ionic fluxes underpinning cardiac and neuronal excitability to a multifaceted regulator of cellular physiology. Traditional approaches for probing electrical events in living matter focused largely on action potentials recording. However, bioelectricity in non-excitable cells governs key phenomena, including developmental patterning, tissue homeostasis, and disease progression. Pioneering studies implicated endogenous bioelectrics in many aspects of morphogenesis, wound healing, regeneration, and cancer. Early findings laid the groundwork for viewing bioelectricity as a means to influence cell fate, cell cycle progression, differentiation, and senescence. More recently, spatial variations in membrane potential within tumor microenvironments were found to correlate with metastatic potential. In parallel, substantial breakthroughs have been achieved in designing advanced bioelectrical interfaces for the study of neuronal networks and cardiac function. This perspective bridges the engineering and biological domains by examining how such technologies might enable new insights into non-excitable cell electrical events at different scales of operation to ultimately manipulate cellular pathways in cancer reprogramming, anti-aging interventions, and gene expression modulation.

q-bio.QM↗

arXiv2026

Silicon Driven Facet Regulation Enables Tunable Micro-Diamond Architectures in Liquid Ga In

We report an ambient pressure liquid metal assisted CVD strategy that enables shape programmable growth of micro scale diamond by coupling liquid metl Ga In with ferrocene (Fe(C5H5)2) as an carbon precursor, nanodiamond seeds, and nanosilicon. Building on liquid metal diamond synthesis, this approach pushes liquid metal growth toward a low temperature (900 °C, 1 atm) while enabling single crystal diamonds to be scaled from ~10 μm to several tens of micrometers with well developed faceting. Ferrocene decomposition supplies a sustained interfacial carbon flux that is captured and redistributed by the Ga In melt toward seed rich liquid solid interfaces. Defect rich nanodiamond provides the crystallographic template required for reliable sp3 nucleation despite the intrinsically low carbon solubility of Ga In. Nanosilicon plays a distinct, complementary role by tuning interfacial kinetics and facet competition, enabling deliberate control of crystal habit: cubic (~10 μm), truncated tetrahedral, and fully faceted octahedral diamonds are reproducibly obtained by adjusting the nanosilicon:nanodiamond ratio, with octahedral crystals reaching ~50 μm. Importantly, crystal size is further scaled by regulating hydrogen flow: lowering the H2 rate increases net carbon retention at the liquid metal interface, raises effective supersaturation, and accelerates diamond deposition. Together, habit control (via nanosilicon: nanodiamond) and size scaling (via H2 flow) establish a practical route silicon driven facet regulation and size under ambient pressure, offering a pathway to tunable micro sized single crystal diamonds under mild conditions.

cond-mat.mtrl-sci↗

arXiv2026

Data-Driven Review and Machine Learning Prediction of Diamond Vacancy Center Synthesis

Diamond and diamond color centers have become prime hardware candidates for solid state-based technologies in quantum information and computing, optics, photonics and (bio)sensing. The synthesis of diamond materials with specific characteristics and the precise control of the hosted color centers is thus essential to meet the demands of advanced applications. Yet, challenges remain in improving the concentration, uniform distribution and quality of these centers. Here, we perform a review and meta-analysis of some of the main diamond synthesis methods and their parameters for the synthesis of N-, Si-, Ge- and Sn-vacancy color-centers. We extract quantitative data from over 60 experimental papers and organize it in a large database (170 data sets and 1692 entries). We then use the database to train two machine learning algorithms to make robust predictions about the fabrication of diamond materials with specific properties from careful combinations of synthesis parameters. We use traditional statistical indicators to benchmark the performance of the algorithms and show that they are powerful and resource-efficient tools for researchers and material scientists working with diamond color centers and their applications.

cond-mat.mtrl-sci↗

arXiv2025

Multilevel classification framework for breast cancer cell selection and its integration with advanced disease models

Breast cancer cell lines are indispensable tools for unraveling disease mechanisms, enabling drug discovery, and developing personalized treatments, yet their heterogeneity and inconsistent classification pose significant challenges in model selection and data reproducibility. This review aims at providing a comprehensive and user-friendly framework for broadly mapping the features of breast cancer types and commercially available human breast cancer cell lines, defining absolute criteria, i.e. objective features such as origin (e.g., MDA-MB, MCF), histological subtype (ductal, lobular), hormone receptor status (ER/PR/HER2), and genetic mutations (BRCA1, TP53), and relative criteria, which contextualize functional behaviors like metastatic potential, drug sensitivity, and genomic instability. It then examines how the proposed framework could be applied to cell line screening in advanced and emerging disease models. By supporting better informed choices, this work aims to improve experimental design and strengthen the connection between in vitro breast cancer studies and their clinical translation.

q-bio.QM↗

arXiv2019

A Zero-Shot Learning application in Deep Drawing process using Hyper-Process Model

One of the consequences of passing from mass production to mass customization paradigm in the nowadays industrialized world is the need to increase flexibility and responsiveness of manufacturing companies. The high-mix / low-volume production forces constant accommodations of unknown product variants, which ultimately leads to high periods of machine calibration. The difficulty related with machine calibration is that experience is required together with a set of experiments to meet the final product quality. Unfortunately, all possible combinations of machine parameters is so high that is difficult to build empirical knowledge. Due to this fact, normally trial and error approaches are taken making one-of-a-kind products not viable. Therefore, a Zero-Shot Learning (ZSL) based approach called hyper-process model (HPM) to learn the relation among multiple tasks is used as a way to shorten the calibration phase. Assuming each product variant is a task to solve, first, a shape analysis on data to learn common modes of deformation between tasks is made, and secondly, a mapping between these modes and task descriptions is performed. Ultimately, the present work has two main contributions: 1) Formulation of an industrial problem into a ZSL setting where new process models can be generated for process optimization and 2) the definition of a regression problem in the domain of ZSL. For that purpose, a 2-d deep drawing simulated process was used based on data collected from the Abaqus simulator, where a significant number of process models were collected to test the effectiveness of the approach. The obtained results show that is possible to learn new tasks without any available data (both labeled and unlabeled) by leveraging information about already existing tasks, allowing to speed up the calibration phase and make a quicker integration of new products into manufacturing systems.

cs.LG↗

arXiv2018

Hyper-Process Model: A Zero-Shot Learning algorithm for Regression Problems based on Shape Analysis

Zero-shot learning (ZSL) can be defined by correctly solving a task where no training data is available, based on previous acquired knowledge from different, but related tasks. So far, this area has mostly drawn the attention from computer vision community where a new unseen image needs to be correctly classified, assuming the target class was not used in the training procedure. Apart from image classification, only a couple of generic methods were proposed that are applicable to both classification and regression. These learn the relation among model coefficients so new ones can be predicted according to provided conditions. So far, up to our knowledge, no methods exist that are applicable only to regression, and take advantage from such setting. Therefore, the present work proposes a novel algorithm for regression problems that uses data drawn from trained models, instead of model coefficients. In this case, a shape analyses on the data is performed to create a statistical shape model and generate new shapes to train new models. The proposed algorithm is tested in a theoretical setting using the beta distribution where main problem to solve is to estimate a function that predicts curves, based on already learned different, but related ones.

cs.CV↗