Avaliação da confiabilidade em resultados numéricos obtidos na análise matricial de estruturas / Assessment of reliability in numeric results obtained in matrix analysis of structures

O objetivo deste estudo é a análise da confiabilidade dos resultados obtidos na resolução de matrizes de ordem superior em comparação ao esforço computacional entre dois dos principais métodos utilizados na resolução de sistemas lineares. Os métodos utilizados foram a Eliminação de Gauss, tido como exato, e Decomposição de Cholesky. A justificativa deste trabalho dá-se pela alta complexidade dos estudos nessa área, fazendo-se necessária a análise do erro e do esforço computacional por diferentes métodos de resolução de sistemas lineares, obtendo-se assim um modelo eficaz de análise estrutural computacional. Os dois métodos resolvem um sistema de equações lineares da forma Ax = B, usual na formulação analítica para o método dos deslocamentos, onde A é a matriz de rigidez, B é o vetor carga e x as deslocabilidades dos nós de uma estrutura reticulada aleatória. A linguagem de programação utilizada para o experimento foi a linguagem de código aberto Python 3.6, através do console Spyder 3.2. O pórtico plano analisado é uma estrutura de apenas três pavimentos, tendo como variável o número de pilares sendo gerada a análise de erro e demonstrado o tempo de processamento do estudo, essa metodologia utilizada mostra-se bastante satisfatória para o estudo por ser gerada uma matriz de rigidez de ordem superior positiva definida. Além disso, os resultados obtidos pelo programa desenvolvido coincidem com os valores obtidos em softwares conceituados que calculam as deslocabilidades dos nós em um pórtico plano, apontando a eficácia do programa estrutural desenvolvido

ABORDAGENS INOVADORAS NO TRATAMENTO DE TRANSTORNOS NEURODEGENERATIVOS

This paper delves into innovative approaches to treating neurodegenerative disorders, with a comprehensive focus on genetic therapies and stem cell applications. Neurodegenerative diseases such as Alzheimer’s and Parkinson’s present significant challenges to contemporary medicine due to their complexity and the lack of effective curative treatments. Genetic therapy, one of the latest innovations, shows promise by targeting and correcting the genetic anomalies that underlie these conditions. This approach includes techniques such as gene editing and viral vector-based gene therapy. Concurrently, stem cell therapies are emerging as a powerful method for regenerating damaged neurons and promoting neuroplasticity. The paper reviews current advancements and clinical trials related to genetic and stem cell therapies, discussing their efficacy, ethical and technical challenges, and future prospects. It suggests that the integration of these innovative strategies could offer new hope for patients with neurodegenerative disorders, emphasizing the necessity of continued investment in research and development to address the limitations of traditional treatments.O artigo examina as abordagens inovadoras no tratamento de transtornos neurodegenerativos, com um enfoque detalhado nas terapias genéticas e no uso de células-tronco. Transtornos como Alzheimer e Parkinson continuam a desafiar a medicina devido à sua complexidade e à ausência de tratamentos curativos eficazes. A terapia genética, uma das mais recentes inovações, oferece potencial para corrigir as anomalias genéticas subjacentes que contribuem para o desenvolvimento dessas condições. Esta abordagem inclui técnicas como edição de genes e terapia gênica baseada em vetores virais. Simultaneamente, as terapias com células-tronco estão se destacando ao proporcionar a regeneração de neurônios danificados e a promoção da neuroplasticidade. O artigo analisa os avanços atuais e ensaios clínicos de terapias genéticas e de células-tronco, destacando a eficácia, os desafios éticos e técnicos, e as perspectivas futuras. O estudo sugere que a combinação dessas abordagens inovadoras poderá oferecer novas esperanças para os pacientes com transtornos neurodegenerativos, sublinhando a importância de investir em pesquisa e desenvolvimento para superar as limitações dos tratamentos tradicionais

Prognostic value of programmed cell death ligand 1 (PD-L1) expression in patients with stage III non-small cell lung cancer under different treatment types: a retrospective study

ABSTRACT Objective Currently programmed cell death protein 1 (PD-1) inhibitors in combination with other therapies are being evaluated to determine their efficacy in cancer treatment. However, the effect of PD-ligand (L) 1 expression on disease outcomes in stage III (EC III) non-small cell lung cancer is not completely understood. Therefore, this study aimed to assess the influence of PD-L1 expression on the outcomes of EC III non-small cell lung cancer. Methods This study was conducted on patients diagnosed with EC III non-small cell lung cancer who underwent treatment at a tertiary care hospital. PD-L1 expression was determined using immunohistochemical staining, all patients expressed PD-L1. Survival was estimated using the Kaplan-Meier method. Relationships between variables were assessed using Cox proportional regression models. Results A total of 49 patients (median age=69 years) with EC III non-small cell lung cancer and PD-L1 expression were evaluated. More than half of the patients were men, and most were regular smokers. The patients were treated with neoadjuvant chemotherapy, surgery, or sequential or combined chemotherapy and radiotherapy. The median progression-free survival of the entire cohort was 14.2 months, and the median overall survival was 20 months. There was no significant association between PD-L1 expression and disease progression, clinical characteristics, or overall survival. Conclusions PD-L1 expression was not correlated with EC III non-small cell lung cancer outcomes. Whether these findings differ from the association with immune checkpoint inhibitors remains to be addressed in future studies

Neurostimulation Combined With Cognitive Intervention in Alzheimer’s Disease (NeuroAD): Study Protocol of Double-Blind, Randomized, Factorial Clinical Trial

Despite advances in the treatment of Alzheimer’s disease (AD), there is currently no prospect of a cure, and evidence shows that multifactorial interventions can benefit patients. A promising therapeutic alternative is the use of transcranial direct current stimulation (tDCS) simultaneously with cognitive intervention. The combination of these non-pharmacological techniques is apparently a safe and accessible approach. This study protocol aims to compare the efficacy of tDCS and cognitive intervention in a double-blind, randomized and factorial clinical trial. One hundred participants diagnosed with mild-stage AD will be randomized to receive both tDCS and cognitive intervention, tDCS, cognitive intervention, or placebo. The treatment will last 8 weeks, with a 12-month follow-up. The primary outcome will be the improvement of global cognitive functions, evaluated by the AD Assessment Scale, cognitive subscale (ADAS-Cog). The secondary outcomes will include measures of functional, affective, and behavioral components, as well as a neurophysiological marker (Brain-derived neurotrophic factor, BDNF). This study will enable us to assess, both in the short and long term, whether tDCS is more effective than the placebo and to examine the effects of combined therapy (tDCS and cognitive intervention) and isolated treatments (tDCS vs. cognitive intervention) on patients with AD.Clinical Trial Registration: www.ClinicalTrials.gov, identifier NCT02772185—May 5, 2016

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

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 understanding 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,5,6,7 vast areas of the tropics remain understudied.8,9,10,11 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 underrepresented in biodiversity databases.13,14,15 To worsen this situation, human-induced modifications16,17 may eliminate pieces of the Amazon's biodiversity puzzle before we can use them to understand how ecological communities 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 organism 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 neglected 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 lost

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 understanding 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,5,6,7 vast areas of the tropics remain understudied.8,9,10,11 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 underrepresented in biodiversity databases.13,14,15 To worsen this situation, human-induced modifications16,17 may eliminate pieces of the Amazon's biodiversity puzzle before we can use them to understand how ecological communities 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 organism 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 neglected 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 lost

ATLANTIC-PRIMATES: a dataset of communities and occurrences of primates in the Atlantic Forests of South America

Primates play an important role in ecosystem functioning and offer critical insights into human evolution, biology, behavior, and emerging infectious diseases. There are 26 primate species in the Atlantic Forests of South America, 19 of them endemic. We compiled a dataset of 5,472 georeferenced locations of 26 native and 1 introduced primate species, as hybrids in the genera Callithrix and Alouatta. The dataset includes 700 primate communities, 8,121 single species occurrences and 714 estimates of primate population sizes, covering most natural forest types of the tropical and subtropical Atlantic Forest of Brazil, Paraguay and Argentina and some other biomes. On average, primate communities of the Atlantic Forest harbor 2 ± 1 species (range = 1–6). However, about 40% of primate communities contain only one species. Alouatta guariba (N = 2,188 records) and Sapajus nigritus (N = 1,127) were the species with the most records. Callicebus barbarabrownae (N = 35), Leontopithecus caissara (N = 38), and Sapajus libidinosus (N = 41) were the species with the least records. Recorded primate densities varied from 0.004 individuals/km 2 (Alouatta guariba at Fragmento do Bugre, Paraná, Brazil) to 400 individuals/km 2 (Alouatta caraya in Santiago, Rio Grande do Sul, Brazil). Our dataset reflects disparity between the numerous primate census conducted in the Atlantic Forest, in contrast to the scarcity of estimates of population sizes and densities. With these data, researchers can develop different macroecological and regional level studies, focusing on communities, populations, species co-occurrence and distribution patterns. Moreover, the data can also be used to assess the consequences of fragmentation, defaunation, and disease outbreaks on different ecological processes, such as trophic cascades, species invasion or extinction, and community dynamics. There are no copyright restrictions. Please cite this Data Paper when the data are used in publications. We also request that researchers and teachers inform us of how they are using the data. © 2018 by the The Authors. Ecology © 2018 The Ecological Society of Americ