Classification of schizophrenic traits in transcriptions of audio spectra from patient literature: artificial intelligence models enhanced by geometric properties

Schizophrenia is a severe mental illness that affects approximately 1% of the global population and presents significant challenges for patients, families, and healthcare professionals. Characterized by symptoms such as delusions, hallucinations, disorganized speech or behavior, and cognitive impairment, this condition has an early onset and chronic trajectory, making it a debilitating challenge. Schizophrenia also imposes a substantial burden on society, exacerbated by the stigma associated with mental disorders. Technological advancements, such as computerized semantic, linguistic, and acoustic analyses, are revolutionizing the understanding and assessment of communication alterations, a significant aspect in various severe mental illnesses. Early and accurate diagnosis is crucial for improving prognosis and implementing appropriate treatments. In this context, the advancement of Artificial Intelligence (AI) has provided new perspectives for the treatment of schizophrenia, with machine learning techniques and natural language processing allowing a more detailed analysis of clinical, neurological, and behavioral data sets. The present article aims to present a proposal for computational models for the identification of schizophrenic traits in texts.  The database used in this article was created with 139 excerpts of patients' speeches reported in the book “Memories of My Nervous Disease” by German judge Daniel Paul Schreber, classifying them into three categories: 1 - schizophrenic, 2 - with schizophrenic traits and 3 - without any relation to the disorder. Of these speeches, 104 were used for training the models and the others 35 for validation.Three classification models were implemented using features based on geometric properties of graphs (number of vertices, number of cycles, girth, vertex of maximum degree, maximum clique size) and text entropy. Promising results were observed in the classification, with the Decision Tree-based model [1] achieving 100% accuracy, the KNN- k-Nearest Neighbor model observed with 62.8% accuracy, and the 'centrality-based' model with 59% precision. The high precision rates, observed when geometric properties are incorporated into Artificial Intelligence Models, suggest that the models can be improved to the point of capturing the language deviation traits that are indicative of schizophrenic disorders. In summary, this study paves the way for significant advances in the use of geometric properties in the field of psychiatry, offering a new data-based approach to the understanding and therapy of schizophrenia

Fluorescence studies on new potential antitumoral benzothienopyran-1-ones in solution and in liposomes

Fluorescence properties of four new potential antitumoral compounds, 3-arylbenzothieno[2,3-c]pyran-1-ones, were studied in solution and in lipid membranes of dipalmitoyl phosphatidylcholine (DPPC), egg yolk phosphatidylcholine (Egg-PC) and dioctadecyldimethylammonium bromide (DODAB). The 3-(4-methoxyphenyl)benzothieno[2,3-c]pyran-1-one (1c) exhibits the higher fluorescence quantum yields in all solvents studied. All compounds present a solvent sensitive emission, with significant red shifts in polar solvents for the methoxylated compounds. The results point to an ICT character of the excited state, more pronounced for compound 1c. Fluorescence (steady-state) anisotropy measurements of the compounds incorporated in liposomes of DPPC, DODAB and Egg-PC indicate that all compounds have two different locations, one due to a deep penetration in the lipid membrane and another corresponding to a more hydrated environment. In general, the methoxylated compounds prefer hydrated environments inside the liposomes. The 3-(4- fluorophenyl)benzothieno[2,3-c]pyran-1-one (1a) clearly prefers a hydrated environment, with some molecules located at the outer part of the liposome interface. On the contrary, the preferential location of 3-(2-fluorophenyl)benzothieno[2,3-c]pyran-1-one (1b) is in the region of lipid hydrophobic tails. Compounds with a planar geometry (1a and 1c) have higher mobility in the lipid membranes when phase transition occurs.Portugal and FEDER (Fundo Europeu de Desenvolvimento Regional), for financial support through Centro de Física (CFUM) and Centro de Química (CQ-UM) of University of Minho and through the Project PTDC/QUI/81238/2006. M.S.D. Carvalho and R.C. Calhelha acknowledge FCT for their PhD grants SFRH/BD/47052/2008 and SFRH/BD/29274/2006, respectively.Fundação para a Ciência e a Tecnologia (FCT

Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time,1,2 and attempts to address it require a clear un derstanding of how ecological communities respond to environmental change across time and space.3,4 While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes,5–7 vast areas of the tropics remain understudied.8–11 In the American tropics, Amazonia stands out as the world’s most diverse rainforest and the primary source of Neotropical biodiversity,12 but it remains among the least known forests in America and is often underrepre sented in biodiversity databases.13–15 To worsen this situation, human-induced modifications16,17 may elim inate pieces of the Amazon’s biodiversity puzzle before we can use them to understand how ecological com munities are responding. To increase generalization and applicability of biodiversity knowledge,18,19 it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple or ganism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region’s vulnerability to environmental change. 15%–18% of the most ne glected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lostinfo:eu-repo/semantics/publishedVersio

Association between Grape Yeast Communities and the Vineyard Ecosystems

The grape yeast biota from several wine-producing areas, with distinct soil types and grapevine training systems, was assessed on five islands of Azores Archipelago, and differences in yeast communities composition associated with the geographic origin of the grapes were explored. Fifty-seven grape samples belonging to the Vitis vinifera grapevine cultivars Verdelho dos Acores (Verdelho), Arinto da Terceira (Arinto) and Terrantez do Pico (Terrantez) were collected in two consecutive years and 40 spontaneous fermentations were achieved. A total of 1710 yeast isolates were obtained from freshly crushed grapes and 1200 from final stage of fermentations. Twenty-eight species were identified, Hanseniaspura uvarum, Pichia terricola and Metschnikowia pulcherrima being the three most representative species isolated. Candida carpophila was encountered for the first time as an inhabitant of grape or wine-associated environments. In both sampling years, a higher proportion of H. uvarum in fresh grapes from Verdelho cultivar was observed, in comparison with Arinto cultivar. Qualitatively significant differences were found among yeast communities from several locations on five islands of the Archipelago, particularly in locations with distinctive agro-ecological compositions. Our results are in agreement with the statement that grape-associated microbial biogeography is non-randomly associated with interactions of climate, soil, cultivar, and vine training systems in vineyard ecosystems. Our observations strongly support a possible linkage between grape yeast and wine typicality, reinforcing the statement that different viti-cultural terroirs harbor distinctive yeast biota, in particular in vineyards with very distinctive environmental conditions.Joao Drumonde Neves is the recipient of a fellowship of the Azorean Government (M321/006/F/2008) and PROEMPREGO. This work was supported by the strategic programme UID/BIA/04050/2013 (POCI-01-0145-FEDER-007569) funded by national funds through the FCT I.P. and by the ERDF through the COMPETE2020 - Programa Operacional Competitividade e Internacionalizacao (POCI), and by national funds through FCT by the projects FCOMP-01-0124-008775, PTDC/AGR-ALI/103392/2008 and PTDC/AGR-ALI/121062/2010.info:eu-repo/semantics/publishedVersio

Chapter 1

Experimenting is fundamental to the training process of all scientists and engineers. While experiments have been traditionally done inside laboratories, the emergence of Information and Communication Technologies added two alter-natives accessible anytime, anywhere. These two alternatives are known as virtual and remote labs, and are sometimes indistinguishably referred as online labs. Sim-ilarly to other instructional technologies, virtual and remote labs require some ef-fort from teachers in integrating them into curricula, taking into consideration sev-eral factors that affect their adoption (i.e. cost) and their educational effectiveness (i.e. benefit). This chapter analyses these two dimensions and sustains the case where only through international cooperation it is possible to serve the large num-ber of teachers and students involved in engineering education. It presents an ex-ample in the area of Electrical and Electronics Engineering, based on a remote lab named Virtual Instruments System in Reality, and it then describes how a number of European and Latin-American institutions have been cooperating under the scope of an Erasmus+ project2, for spreading its use in Brazil and Argentina.info:eu-repo/semantics/publishedVersio

Familial hypercholesterolaemia in children and adolescents from 48 countries: a cross-sectional study

Background: Approximately 450 000 children are born with familial hypercholesterolaemia worldwide every year, yet only 2·1% of adults with familial hypercholesterolaemia were diagnosed before age 18 years via current diagnostic approaches, which are derived from observations in adults. We aimed to characterise children and adolescents with heterozygous familial hypercholesterolaemia (HeFH) and understand current approaches to the identification and management of familial hypercholesterolaemia to inform future public health strategies. Methods: For this cross-sectional study, we assessed children and adolescents younger than 18 years with a clinical or genetic diagnosis of HeFH at the time of entry into the Familial Hypercholesterolaemia Studies Collaboration (FHSC) registry between Oct 1, 2015, and Jan 31, 2021. Data in the registry were collected from 55 regional or national registries in 48 countries. Diagnoses relying on self-reported history of familial hypercholesterolaemia and suspected secondary hypercholesterolaemia were excluded from the registry; people with untreated LDL cholesterol (LDL-C) of at least 13·0 mmol/L were excluded from this study. Data were assessed overall and by WHO region, World Bank country income status, age, diagnostic criteria, and index-case status. The main outcome of this study was to assess current identification and management of children and adolescents with familial hypercholesterolaemia. Findings: Of 63 093 individuals in the FHSC registry, 11 848 (18·8%) were children or adolescents younger than 18 years with HeFH and were included in this study; 5756 (50·2%) of 11 476 included individuals were female and 5720 (49·8%) were male. Sex data were missing for 372 (3·1%) of 11 848 individuals. Median age at registry entry was 9·6 years (IQR 5·8-13·2). 10 099 (89·9%) of 11 235 included individuals had a final genetically confirmed diagnosis of familial hypercholesterolaemia and 1136 (10·1%) had a clinical diagnosis. Genetically confirmed diagnosis data or clinical diagnosis data were missing for 613 (5·2%) of 11 848 individuals. Genetic diagnosis was more common in children and adolescents from high-income countries (9427 [92·4%] of 10 202) than in children and adolescents from non-high-income countries (199 [48·0%] of 415). 3414 (31·6%) of 10 804 children or adolescents were index cases. Familial-hypercholesterolaemia-related physical signs, cardiovascular risk factors, and cardiovascular disease were uncommon, but were more common in non-high-income countries. 7557 (72·4%) of 10 428 included children or adolescents were not taking lipid-lowering medication (LLM) and had a median LDL-C of 5·00 mmol/L (IQR 4·05-6·08). Compared with genetic diagnosis, the use of unadapted clinical criteria intended for use in adults and reliant on more extreme phenotypes could result in 50-75% of children and adolescents with familial hypercholesterolaemia not being identified. Interpretation: Clinical characteristics observed in adults with familial hypercholesterolaemia are uncommon in children and adolescents with familial hypercholesterolaemia, hence detection in this age group relies on measurement of LDL-C and genetic confirmation. Where genetic testing is unavailable, increased availability and use of LDL-C measurements in the first few years of life could help reduce the current gap between prevalence and detection, enabling increased use of combination LLM to reach recommended LDL-C targets early in life