Mathematical methods to analyze and interpret calcium signals of astrocytes

Astrocytes are the most numerous glial cell type in the mammalian brain and permeate the entire CNS interacting with neurons, vasculature, and other glial cells. Astrocytes display intracellular calcium signals that encode information about local synaptic function, distributed network activity, and high-level cognitive functions. Several studies have investigated the calcium dynamics of astrocytes in sensory areas and have shown that these cells can encode sensory stimuli. Nevertheless, only recently the neuro-scientific community has focused its attention on the role and functions of astrocytes in associative areas such as the hippocampus. In our first study, we used the information theory formalism to show that astrocytes in the CA1 area of the hippocampus recorded with 2-photon fluorescence microscopy during spatial navigation encode spatial information that is complementary and synergistic to information encoded by nearby "place cell" neurons. In our second study, we investigated various computational aspects of applying the information theory formalism to astrocytic calcium data. For this reason, we generated realistic simulations of calcium signals in astrocytes to determine optimal hyperparameters and procedures of information measures and applied them to real astrocytic calcium imaging data. Calcium signals of astrocytes are characterized by complex spatiotemporal dynamics occurring in subcellular parcels of the astrocytic domain which makes studying these cells in 2-photon calcium imaging recordings difficult. However, current analytical tools which identify the astrocytic subcellular regions are time consuming and extensively rely on user-defined parameters. Here, we present Rapid Astrocytic calcium Spatio-Temporal Analysis (RASTA), a novel machine learning algorithm for spatiotemporal semantic segmentation of 2-photon calcium imaging recordings of astrocytes which operates without human intervention. We found that RASTA provided fast and accurate identification of astrocytic cell somata, processes, and cellular domains, extracting calcium signals from identified regions of interest across individual cells and populations of hundreds of astrocytes recorded in awake mice.

Hydrodynamic simulations of multiple stellar populations in globular clusters

The presence of multiple stellar populations in globular clusters (GCs) is now well accepted, however, very little is known regarding their origin. In this Thesis, I study how multiple populations formed and evolved by means of customized 3D numerical simulations, in light of the most recent data from spectroscopic and photometric observations of Local and high-redshift Universe. Numerical simulations are the perfect tool to interpret these data: hydrodynamic simulations are suited to study the early phases of GCs formation, to follow in great detail the gas behavior, while N-body codes permit tracing the stellar component. First, we study the formation of second-generation stars in a rotating massive GC. We assume that second-generation stars are formed out of asymptotic giant branch stars (AGBs) ejecta, diluted by external pristine gas. We find that, for low pristine gas density, stars mainly formed out of AGBs ejecta rotate faster than stars formed out of more diluted gas, in qualitative agreement with current observations. Then, assuming a similar setup, we explored whether Type Ia supernovae affect the second- generation star formation and their chemical composition. We show that the evolution depends on the density of the infalling gas, but, in general, an iron spread is developed, which may explain the spread observed in some massive GCs. Finally, we focused on the long-term evolution of a GC, composed of two populations and orbiting the Milky Way disk. We have derived that, for an extended first population and a low-mass second one, the cluster loses almost 98 percent of its initial first population mass and the GC mass can be as much as 20 times less after a Hubble time. Under these conditions, the derived fraction of second-population stars reproduces the observed value, which is one of the strongest constraints of GC mass loss.

Data-driven and data-oriented methods for materials science and technologies

The discovery of new materials and their functions has always been a fundamental component of technological progress. Nowadays, the quest for new materials is stronger than ever: sustainability, medicine, robotics and electronics are all key assets which depend on the ability to create specifically tailored materials. However, designing materials with desired properties is a difficult task, and the complexity of the discipline makes it difficult to identify general criteria. While scientists developed a set of best practices (often based on experience and expertise), this is still a trial-and-error process. This becomes even more complex when dealing with advanced functional materials. Their properties depend on structural and morphological features, which in turn depend on fabrication procedures and environment, and subtle alterations leads to dramatically different results. Because of this, materials modeling and design is one of the most prolific research fields. Many techniques and instruments are continuously developed to enable new possibilities, both in the experimental and computational realms. Scientists strive to enforce cutting-edge technologies in order to make progress. However, the field is strongly affected by unorganized file management, proliferation of custom data formats and storage procedures, both in experimental and computational research. Results are difficult to find, interpret and re-use, and a huge amount of time is spent interpreting and re-organizing data. This also strongly limit the application of data-driven and machine learning techniques. This work introduces possible solutions to the problems described above. Specifically, it talks about developing features for specific classes of advanced materials and use them to train machine learning models and accelerate computational predictions for molecular compounds; developing method for organizing non homogeneous materials data; automate the process of using devices simulations to train machine learning models; dealing with scattered experimental data and use them to discover new patterns.

Development of machine learning methods for multi-modal biomarkers detection and integration

In medicine, innovation depends on a better knowledge of the human body mechanism, which represents a complex system of multi-scale constituents. Unraveling the complexity underneath diseases proves to be challenging. A deep understanding of the inner workings comes with dealing with many heterogeneous information. Exploring the molecular status and the organization of genes, proteins, metabolites provides insights on what is driving a disease, from aggressiveness to curability. Molecular constituents, however, are only the building blocks of the human body and cannot currently tell the whole story of diseases. This is why nowadays attention is growing towards the contemporary exploitation of multi-scale information. Holistic methods are then drawing interest to address the problem of integrating heterogeneous data. The heterogeneity may derive from the diversity across data types and from the diversity within diseases. Here, four studies conducted data integration using customly designed workflows that implement novel methods and views to tackle the heterogeneous characterization of diseases. The first study devoted to determine shared gene regulatory signatures for onco-hematology and it showed partial co-regulation across blood-related diseases. The second study focused on Acute Myeloid Leukemia and refined the unsupervised integration of genomic alterations, which turned out to better resemble clinical practice. In the third study, network integration for artherosclerosis demonstrated, as a proof of concept, the impact of network intelligibility when it comes to model heterogeneous data, which showed to accelerate the identification of new potential pharmaceutical targets. Lastly, the fourth study introduced a new method to integrate multiple data types in a unique latent heterogeneous-representation that facilitated the selection of important data types to predict the tumour stage of invasive ductal carcinoma. The results of these four studies laid the groundwork to ease the detection of new biomarkers ultimately beneficial to medical practice and to the ever-growing field of Personalized Medicine.

Exploiting molecular simulations as an atomic-level microscope to elucidate clinically relevant cation chloride cotransporters

The cation chloride cotransporters (CCCs) represent a vital family of ion transporters, with several members implicated in significant neurological disorders. Specifically, conditions such as cerebrospinal fluid accumulation, epilepsy, Down’s syndrome, Asperger’s syndrome, and certain cancers have been attributed to various CCCs. This thesis delves into these pharmacological targets using advanced computational methodologies. I primarily employed GPU-accelerated all-atom molecular dynamics simulations, deep learning-based collective variables, enhanced sampling methods, and custom Python scripts for comprehensive simulation analyses. Our research predominantly centered on KCC1 and NKCC1 transporters. For KCC1, I examined its equilibrium dynamics in the presence/absence of an inhibitor and assessed the functional implications of different ion loading states. In contrast, our work on NKCC1 revealed its unique alternating access mechanism, termed the rocking-bundle mechanism. I identified a previously unobserved occluded state and demonstrated the transporter's potential for water permeability under specific conditions. Furthermore, I confirmed the actual water flow through its permeable states. In essence, this thesis leverages cutting-edge computational techniques to deepen our understanding of the CCCs, a family of ion transporters with profound clinical significance.

Modelling, simulations and monitoring for sustainable plant and animal production systems through precision technique

Protected crop production is a modern and innovative approach to cultivating plants in a controlled environment to optimize growth, yield, and quality. This method involves using structures such as greenhouses or tunnels to create a sheltered environment. These productive solutions are characterized by a careful regulation of variables like temperature, humidity, light, and ventilation, which collectively contribute to creating an optimal microclimate for plant growth. Heating, cooling, and ventilation systems are used to maintain optimal conditions for plant growth, regardless of external weather fluctuations. Protected crop production plays a crucial role in addressing challenges posed by climate variability, population growth, and food security. Similarly, animal husbandry involves providing adequate nutrition, housing, medical care and environmental conditions to ensure animal welfare. Then, sustainability is a critical consideration in all forms of agriculture, including protected crop and animal production. Sustainability in animal production refers to the practice of producing animal products in a way that minimizes negative impacts on the environment, promotes animal welfare, and ensures the long-term viability of the industry. Then, the research activities performed during the PhD can be inserted exactly in the field of Precision Agriculture and Livestock farming. Here the focus is on the computational fluid dynamic (CFD) approach and environmental assessment applied to improve yield, resource efficiency, environmental sustainability, and cost savings. It represents a significant shift from traditional farming methods to a more technology-driven, data-driven, and environmentally conscious approach to crop and animal production. On one side, CFD is powerful and precise techniques of computer modeling and simulation of airflows and thermo-hygrometric parameters, that has been applied to optimize the growth environment of crops and the efficiency of ventilation in pig barns. On the other side, the sustainability aspect has been investigated and researched in terms of Life Cycle Assessment analyses.

Quantum Monte Carlo study of effective masses in a polarized unitary Fermi gas

Ultracold gases provide an ideal platform for quantum simulations of many-body systems. Here we are interested in a particular system which has been the focus of most experimental and theoretical works on ultracold fermionic gases: the unitary Fermi gas. In this work we study with Quantum Monte Carlo simulations a two-component gas of fermionic atoms at zero temperature in the unitary regime. Specifically, we are interested in studying how the effective masses for the quasi-particles of the two components of the Fermi liquid evolve as the polarization is progressively reduced from full to lower values. A recent theoretical work, based on alternative diagrammatic methods, has indeed suggested that such effective masses should diverge at a critical polarization. To independently verify such predictions, we perform Variational Monte Carlo (VMC) calculations of the energy based on Jastrow-Slater wavefunctions after adding or subtracting a particle with a given momentum to a full Fermi sphere. In this way, we determine the quasi-particle dispersions, from which we extract the effective masses for different polarizations. The resulting effective masses turn out to be quite close to the non-interacting values, even though some evidence of an increase for the effective mass of the minority component appears close to the predicted value for the critical polarization. Preliminary results obtained for the majority component with the Fixed-node Diffusion Monte Carlo (DMC) method seem to indicate that DMC could lead to an increase of the effective masses in comparison with the VMC results. Finally, we point out further improvements of the trial wave-function and boundary conditions that would be necessary in future simulations to draw definite conclusions on the effective masses of the polarized unitary Fermi gas.

Intermittent Fasting Induces Hypothalamic Modifications Resulting In Low Feeding Efficiency, Low Body Mass And Overeating.

Intermittent fasting (IF) is an often-used intervention to decrease body mass. In male Sprague-Dawley rats, 24 hour cycles of IF result in light caloric restriction, reduced body mass gain, and significant decreases in the efficiency of energy conversion. Here, we study the metabolic effects of IF in order to uncover mechanisms involved in this lower energy conversion efficiency. After 3 weeks, IF animals displayed overeating during fed periods and lower body mass, accompanied by alterations in energy-related tissue mass. The lower efficiency of energy use was not due to uncoupling of muscle mitochondria. Enhanced lipid oxidation was observed during fasting days, whereas fed days were accompanied by higher metabolic rates. Furthermore, an increased expression of orexigenic neurotransmitters AGRP and NPY in the hypothalamus of IF animals was found, even on feeding days, which could explain the overeating pattern. Together, these effects provide a mechanistic explanation for the lower efficiency of energy conversion observed. Overall, we find that IF promotes changes in hypothalamic function that explain differences in body mass and caloric intake.

Evaluation Of Different Treatment Methods Against Denture Stomatitis: A Randomized Clinical Study.

The aim of this clinical study was to determine the efficacy of Uncaria tomentosa (cat's claw) against denture stomatitis (DS). Fifty patients with DS were randomly assigned into 3 groups to receive 2% miconazole, placebo, or 2% U tomentosa gel. DS level was recorded immediately, after 1 week of treatment, and 1 week after treatment. The clinical effectiveness of each treatment was measured using Newton's criteria. Mycologic samples from palatal mucosa and prosthesis were obtained to determinate colony forming units per milliliter (CFU/mL) and fungal identification at each evaluation period. Candida species were identified with HiCrome Candida and API 20C AUX biochemical test. DS severity decreased in all groups (P < .05). A significant reduction in number of CFU/mL after 1 week (P < .05) was observed for all groups and remained after 14 days (P > .05). C albicans was the most prevalent microorganism before treatment, followed by C tropicalis, C glabrata, and C krusei, regardless of the group and time evaluated. U tomentosa gel had the same effect as 2% miconazole gel. U tomentosa gel is an effective topical adjuvant treatment for denture stomatitis.

Exploratory Study Of The Effect Of Lifestyle Counselling On Bone Mineral Density And Body Composition In Users Of The Contraceptive Depot-medroxyprogesterone Acetate.

To compare variations in bone mineral density (BMD) and body composition (BC) in depot-medroxyprogesterone acetate (DMPA) users and nonusers after providing counselling on healthy lifestyle habits. An exploratory study in which women aged 18 to 40 years participated: 29 new DMPA users and 25 new non-hormonal contraceptive users. All participants were advised on healthy lifestyle habits: sun exposure, walking and calcium intake. BMD and BC were assessed at baseline and 12 months later. Statistical analysis included the Mann-Whitney test or Student's t-test followed by multiple linear regression analysis. Compared to the controls, DMPA users had lower BMD at vertebrae L1 and L4 after 12 months of use. They also had a mean increase of 2 kg in total fat mass and an increase of 2.2% in body fat compared to the non-hormonal contraceptive users. BMD loss at L1 was less pronounced in DMPA users with a calcium intake ≥ 1 g/day compared to DMPA users with a lower calcium intake. DMPA use was apparently associated with lower BMD and an increase in fat mass at 12 months of use. Calcium intake ≥ 1 g/day attenuates BMD loss in DMPA users. Counselling on healthy lifestyle habits failed to achieve its aims.

[health Protection For Rural Workers: The Need To Standardize Techniques For Quantifying Dermal Exposure To Pesticides].

Quantification of dermal exposure to pesticides in rural workers, used in risk assessment, can be performed with different techniques such as patches or whole body evaluation. However, the wide variety of methods can jeopardize the process by producing disparate results, depending on the principles in sample collection. A critical review was thus performed on the main techniques for quantifying dermal exposure, calling attention to this issue and the need to establish a single methodology for quantification of dermal exposure in rural workers. Such harmonization of different techniques should help achieve safer and healthier working conditions. Techniques that can provide reliable exposure data are an essential first step towards avoiding harm to workers' health.

Growth And Body Composition In Brazilian Female Rhythmic Gymnastics Athletes.

The aim was to analyse the physical growth and body composition of rhythmic gymnastics athletes relative to their level of somatic maturation. This was a cross-sectional study of 136 athletes on 23 teams from Brazil. Mass, standing height and sitting height were measured. Fat-free and fat masses, body fat percentages and ages of the predicted peak height velocity (PHV) were calculated. The z scores for mass were negative during all ages according to both WHO and Brazilian references, and that for standing height were also negative for all ages according to WHO reference but only until 12 years old according to Brazilian reference. The mean age of the predicted PHV was 12.1 years. The mean mass, standing and sitting heights, body fat percentage, fat-free mass and fat mass increased significantly until 4 to 5 years after the age of the PHV. Menarche was reached in only 26% of these athletes and mean age was 13.2 years. The mass was below the national reference standards, and the standing height was below only for the international reference, but they also had late recovery of mass and standing height during puberty. In conclusion, these athletes had a potential to gain mass and standing height several years after PHV, indicating late maturation.

Petroleomics By Electrospray Ionization Ft-icr Mass Spectrometry Coupled To Partial Least Squares With Variable Selection Methods: Prediction Of The Total Acid Number Of Crude Oils.

Negative-ion mode electrospray ionization, ESI(-), with Fourier transform ion cyclotron resonance mass spectrometry (FT-ICR MS) was coupled to a Partial Least Squares (PLS) regression and variable selection methods to estimate the total acid number (TAN) of Brazilian crude oil samples. Generally, ESI(-)-FT-ICR mass spectra present a power of resolution of ca. 500,000 and a mass accuracy less than 1 ppm, producing a data matrix containing over 5700 variables per sample. These variables correspond to heteroatom-containing species detected as deprotonated molecules, [M - H](-) ions, which are identified primarily as naphthenic acids, phenols and carbazole analog species. The TAN values for all samples ranged from 0.06 to 3.61 mg of KOH g(-1). To facilitate the spectral interpretation, three methods of variable selection were studied: variable importance in the projection (VIP), interval partial least squares (iPLS) and elimination of uninformative variables (UVE). The UVE method seems to be more appropriate for selecting important variables, reducing the dimension of the variables to 183 and producing a root mean square error of prediction of 0.32 mg of KOH g(-1). By reducing the size of the data, it was possible to relate the selected variables with their corresponding molecular formulas, thus identifying the main chemical species responsible for the TAN values.

Estimated Disability-adjusted Life Years Averted By Long-term Provision Of Long Acting Contraceptive Methods In A Brazilian Clinic.

What is the contribution of the provision, at no cost for users, of long acting reversible contraceptive methods (LARC; copper intrauterine device [IUD], the levonorgestrel-releasing intrauterine system [LNG-IUS], contraceptive implants and depot-medroxyprogesterone [DMPA] injection) towards the disability-adjusted life years (DALY) averted through a Brazilian university-based clinic established over 30 years ago. Over the last 10 years of evaluation, provision of LARC methods and DMPA by the clinic are estimated to have contributed to DALY averted by between 37 and 60 maternal deaths, 315-424 child mortalities, 634-853 combined maternal morbidity and mortality and child mortality, and 1056-1412 unsafe abortions averted. LARC methods are associated with a high contraceptive effectiveness when compared with contraceptive methods which need frequent attention; perhaps because LARC methods are independent of individual or couple compliance. However, in general previous studies have evaluated contraceptive methods during clinical studies over a short period of time, or not more than 10 years. Furthermore, information regarding the estimation of the DALY averted is scarce. We reviewed 50 004 medical charts from women who consulted for the first time looking for a contraceptive method over the period from 2 January 1980 through 31 December 2012. Women who consulted at the Department of Obstetrics and Gynaecology, University of Campinas, Brazil were new users and users switching contraceptive, including the copper IUD (n = 13 826), the LNG-IUS (n = 1525), implants (n = 277) and DMPA (n = 9387). Estimation of the DALY averted included maternal morbidity and mortality, child mortality and unsafe abortions averted. We obtained 29 416 contraceptive segments of use including 25 009 contraceptive segments of use from 20 821 new users or switchers to any LARC method or DMPA with at least 1 year of follow-up. The mean (± SD) age of the women at first consultation ranged from 25.3 ± 5.7 (range 12-47) years in the 1980s, to 31.9 ± 7.4 (range 16-50) years in 2010-2011. The most common contraceptive chosen at the first consultation was copper IUD (48.3, 74.5 and 64.7% in the 1980s, 1990s and 2000s, respectively). For an evaluation over 20 years, the cumulative pregnancy rates (SEM) were 0.4 (0.2), 2.8 (2.1), 4.0 (0.4) and 1.3 (0.4) for the LNG-IUS, the implants, copper IUD and DMPA, respectively and cumulative continuation rates (SEM) were 15.1 (3.7), 3.9 (1.4), 14.1 (0.6) and 7.3 (1.7) for the LNG-IUS, implants, copper IUD and DMPA, respectively (P < 0.001). Over the last 10 years of evaluation, the estimation of the contribution of the clinic through the provision of LARC methods and DMPA to DALY averted was 37-60 maternal deaths; between 315 and 424 child mortalities; combined maternal morbidity and mortality and child mortality of between 634 and 853, and 1056-1412 unsafe abortions averted. The main limitations are the number of women who never returned to the clinic (overall 14% among the four methods under evaluation); consequently the pregnancy rate could be different. Other limitations include the analysis of two kinds of copper IUD and two kinds of contraceptive implants as the same IUD or implant, and the low number of users of implants. In addition, the DALY calculation relies on a number of estimates, which may vary in different parts of the world. LARC methods and DMPA are highly effective and women who were well-counselled used these methods for a long time. The benefit of averting maternal morbidity and mortality, child mortality, and unsafe abortions is an example to health policy makers to implement more family planning programmes and to offer contraceptive methods, mainly LARC and DMPA, at no cost or at affordable cost for the underprivileged population. This study received partial financial support from the Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP), grant # 2012/12810-4 and from the National Research Council (CNPq), grant #573747/2008-3. B.F.B., M.P.G., and V.M.C. were fellows from the scientific initiation programme from FAPESP. Since the year 2001, all the TCu380A IUD were donated by Injeflex, São Paulo, Brazil, and from the year 2006 all the LNG-IUS were donated by the International Contraceptive Access Foundation (ICA), Turku, Finland. Both donations are as unrestricted grants. The authors declare that there are no conflicts of interest associated with this study.

Evaluation Of The Effect Of Different Methods Of Microabrasion And Polishing On Surface Roughness Of Dental Enamel.

The microabrasion technique of enamel consists of selectively abrading the discolored areas or causing superficial structural changes in a selective way. In microabrasion technique, abrasive products associated with acids are used, and the evaluation of enamel roughness after this treatment, as well as surface polishing, is necessary. This in-vitro study evaluated the enamel roughness after microabrasion, followed by different polishing techniques. Roughness analyses were performed before microabrasion (L1), after microabrasion (L2), and after polishing (L3).Thus, 60 bovine incisive teeth divided into two groups were selected (n=30): G1- 37% phosphoric acid (37%) (Dentsply) and pumice; G2- hydrochloric acid (6.6%) associated with silicon carbide (Opalustre - Ultradent). Thereafter, the groups were divided into three sub-groups (n=10), according to the system of polishing: A - Fine and superfine granulation aluminum oxide discs (SofLex 3M); B - Diamond Paste (FGM) associated with felt discs (FGM); C - Silicone tips (Enhance - Dentsply). A PROC MIXED procedure was applied after data exploratory analysis, as well as the Tukey-Kramer test (5%). No statistical differences were found between G1 and G2 groups. L2 differed statistically from L1 and showed superior amounts of roughness. Differences in the amounts of post-polishing roughness for specific groups (1A, 2B, and 1C) arose, which demonstrated less roughness in L3 and differed statistically from L2 in the polishing system. All products increased enamel roughness, and the effectiveness of the polishing systems was dependent upon the abrasive used.