Alocação de recursos de Hardware em arquitetura C-RAN utilizando DPSO / Hardware resource Allocation in C-RAN Architecture Using DPSO

O aumento da demanda de dados, gerados em sua grande maioria por aplicativos de multimídia, torna o funcionamento da nova geração de redes moveis uma tarefa desafiadora. Uma das propostas para suportar esse trafego e a Cloud Radio Access Network (C-RAN), a qual centraliza o poder de processamento a fim de resolver o desbalanceamento de carga, executando a alocação recursos conforme a demanda da rede. Neste trabalho, e proposto um modelo de alocação de recursos que otimiza o balanceamento de carga a nível da BBU (Baseband Unit). O DPSO (Discrete Particle Swarm Optimization) é usado para a otimização da função objetivo. Os resultados apontam um desempenho superior desta função em comparação ao benchmarking em cenários de alta e baixa densidade de trafego na rede.  

Prognostic implications of the ID1 expression in acute myeloid leukemia patients treated in a resource-constrained setting

Introduction: The aberrant expression of the inhibitor of DNA binding (ID1) gene has been frequently associated with the leukemogenesis and prognostication acute myeloid leukemia (AML), although its clinical importance has never been investigated in patients treated outside well-controlled clinical trials. Methods: Using quantitative real-time polymerase chain reaction, we investigated the role of the ID1 expression in the clinical outcomes of non-selected patients with acute myeloid leukemia treated in a real-life setting. Results: Overall, 128 patients were enrolled. Patients with high ID1 expression had a lower 3-year overall survival (OS) rate of 9%, with the 95% confidence interval (95%CI) at 3 to 20%, compared to patients with a low ID1 expression (22%, 95%CI: 11 - 34%) (p = 0.037), although these findings did not retain significance after adjustment (hazard ratio (HR): 1.5, 95%CI: 0.98 - 2.28; p = 0.057). The ID1 expression had no impact on post-induction outcomes (disease-free survival, p = 0.648; cumulative incidence of relapse, p = 0.584). Conclusions: Although we are aware thar our data are confronted with many variables that cannot be fully controlled, including drug unavailability, risk-adapted treatment, comorbidities and the time from diagnosis to treatment initiation, we are firm believers that such an initiative can provide more realistic data on understudied populations, in particular those from low- and middle-income countries.</p

Federated learning enables big data for rare cancer boundary detection.

Although machine learning (ML) has shown promise across disciplines, out-of-sample generalizability is concerning. This is currently addressed by sharing multi-site data, but such centralization is challenging/infeasible to scale due to various limitations. Federated ML (FL) provides an alternative paradigm for accurate and generalizable ML, by only sharing numerical model updates. Here we present the largest FL study to-date, involving data from 71 sites across 6 continents, to generate an automatic tumor boundary detector for the rare disease of glioblastoma, reporting the largest such dataset in the literature (n = 6, 314). We demonstrate a 33% delineation improvement for the surgically targetable tumor, and 23% for the complete tumor extent, over a publicly trained model. We anticipate our study to: 1) enable more healthcare studies informed by large diverse data, ensuring meaningful results for rare diseases and underrepresented populations, 2) facilitate further analyses for glioblastoma by releasing our consensus model, and 3) demonstrate the FL effectiveness at such scale and task-complexity as a paradigm shift for multi-site collaborations, alleviating the need for data-sharing

Author Correction: Federated learning enables big data for rare cancer boundary detection.

10.1038/s41467-023-36188-7NATURE COMMUNICATIONS14

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

Global urban environmental change drives adaptation in white clover

Urbanization transforms environments in ways that alter biological evolution. We examined whether urban environmental change drives parallel evolution by sampling 110,019 white clover plants from 6169 populations in 160 cities globally. Plants were assayed for a Mendelian antiherbivore defense that also affects tolerance to abiotic stressors. Urban-rural gradients were associated with the evolution of clines in defense in 47% of cities throughout the world. Variation in the strength of clines was explained by environmental changes in drought stress and vegetation cover that varied among cities. Sequencing 2074 genomes from 26 cities revealed that the evolution of urban-rural clines was best explained by adaptive evolution, but the degree of parallel adaptation varied among cities. Our results demonstrate that urbanization leads to adaptation at a global scale