SAgric-IoT: an IoT-based platform and deep learning for greenhouse monitoring

The Internet of Things (IoT) and convolutional neural networks (CNN) integration is a growing topic of interest for researchers as a technology that will contribute to transforming agriculture. IoT will enable farmers to decide and act based on data collected from sensor nodes regarding field conditions and not purely based on experience, thus minimizing the wastage of supplies (seeds, water, pesticide, and fumigants). On the other hand, CNN complements monitoring systems with tasks such as the early detection of crop diseases or predicting the number of consumable resources and supplies (water, fertilizers) needed to increase productivity. This paper proposes SAgric-IoT, a technology platform based on IoT and CNN for precision agriculture, to monitor environmental and physical variables and provide early disease detection while automatically controlling the irrigation and fertilization in greenhouses. The results show SAgric-IoT is a reliable IoT platform with a low packet loss level that considerably reduces energy consumption and has a disease identification detection accuracy and classification process of over 90%

Predicting abnormal respiratory patterns in older adults using supervised machine learning on Internet of medical things respiratory frequency data

Wearable Internet of Medical Things (IoMT) technology, designed for non-invasive respiratory monitoring, has demonstrated considerable promise in the early detection of severe diseases. This paper introduces the application of supervised machine learning techniques to predict respiratory abnormalities through frequency data analysis. The principal aim is to identify respiratory-related health risks in older adults using data collected from non-invasive wearable devices. This article presents the development, assessment, and comparison of three machine learning models, underscoring their potential for accurately predicting respiratory-related health issues in older adults. The convergence of wearable IoMT technology and machine learning holds immense potential for proactive and personalized healthcare among older adults, ultimately enhancing their quality of life

A review of deep learning applications for the next generation of cognitive networks

Intelligence capabilities will be the cornerstone in the development of next-generation cognitive networks. These capabilities allow them to observe network conditions, learn from them, and then, using prior knowledge gained, respond to its operating environment to optimize network performance. This study aims to offer an overview of the current state of the art related to the use of deep learning in applications for intelligent cognitive networks that can serve as a reference for future initiatives in this field. For this, a systematic literature review was carried out in three databases, and eligible articles were selected that focused on using deep learning to solve challenges presented by current cognitive networks. As a result, 14 articles were analyzed. The results showed that applying algorithms based on deep learning to optimize cognitive data networks has been approached from different perspectives in recent years and in an experimental way to test its technological feasibility. In addition, its implications for solving fundamental challenges in current wireless networks are discussed

The use of deep learning to improve player engagement in a video game through a dynamic difficulty adjustment based on skills classification

The balance between game difficulty and player skill in the evolving landscape of the video game industry is a significant factor in player engagement. This study introduces a deep learning (DL) approach to enhance gameplay by dynamically adjusting game difficulty based on a player’s skill level. Our methodology aims to prevent player disengagement, which can occur if the game difficulty significantly exceeds or falls short of the player’s skill level. Our evaluation indicates that such dynamic adjustment leads to improved gameplay and increased player involvement, with 90% of the players reporting high game enjoyment and immersion levels

Single-cell resolution of lineage trajectories in the Arabidopsis stomatal lineage and developing leaf

Dynamic cell identities underlie flexible developmental programs. The stomatal lineage in the Arabidopsis leaf epidermis features asynchronous and indeterminate divisions that can be modulated by environmental cues. The products of the lineage, stomatal guard cells and pavement cells, regulate plant-atmosphere exchanges, and the epidermis as a whole influences overall leaf growth. How flexibility is encoded in development of the stomatal lineage and how cell fates are coordinated in the leaf are open questions. Here, by leveraging single-cell transcriptomics and molecular genetics, we uncovered models of cell differentiation within Arabidopsis leaf tissue. Profiles across leaf tissues identified points of regulatory congruence. In the stomatal lineage, single-cell resolution resolved underlying cell heterogeneity within early stages and provided a fine-grained profile of guard cell differentiation. Through integration of genome-scale datasets and spatiotemporally precise functional manipulations, we also identified an extended role for the transcriptional regulator SPEECHLESS in reinforcing cell fate commitment.Peer reviewe

Expert recommendations on the management of patients with metastatic castration-resistant prostate cancer who progress after CHAARTED or LATITUDE

Objective: Our aim was to provide practical recommendations on the management of patients with metastatic castration-resistant prostate cancer (mCRPC) who have progressed after docetaxel plus androgen-deprivation therapy (ADT) or abiraterone plus ADT. Methods: Systematic literature review (SLR), nominal group meeting, and Delphi process. A panel of 12 experts was established who defined the scope, users, and sections of the document. We performed an SLR in order to assess the efficacy and safety of available drugs in patients with mCRPC. Abstracts from the American Society of Oncology and European Society for Medical Oncology meetings were also examined. The results were discussed during an expert meeting in which 14 recommendations were generated. The level of agreement with the recommendations was also tested by 13 additional experts following the Delphi process. Recommendations were voted by means of scores ranging from 0 (total disagreement) to 10 (total agreement). We defined agreement when at least 70% of the experts voted ¿7. Next, we assigned a level of evidence and grade to the recommendation using the Oxford Centre for Evidence-based Medicine Levels of Evidence, following which the final document was drafted. Results: The literature search did not find any articles meeting the inclusion criteria. Finally, 13 out of 14 recommendations were accepted after two Delphi rounds (two were modified after the first round). They pertain to general and individual case-based treatment recommendations. Conclusions: In mCRPC patients who have progressed after docetaxel or abiraterone plus ADT in the metastatic hormone-sensitive prostate cancer setting, these recommendations may support treatment decision-making, due to the lack of evidence or other globally accepted sequencing algorithms

A new scenario in metastatic renal cell carcinoma: a SOG‑GU consensus

[Abstract] Background This article describes and compares approved targeted therapies and the newer immunotherapy agents. Materials and methods This article especially performs an in-depth review of currently available data for tivozanib, explaining its mechanism of action, its safety profle and its role as an efcacy drug in the management of renal cancer. Results Despite the fact that the treatment of advanced RCC has been dramatically modifed in recent years, durable remissions are scarce and it remains a lethal disease. For frst- and second-line therapy, there is now growing evidence to guide the selection of the appropriate treatment. Conclusions Several TKIs are standard of care at diferent settings. Among those approved TKIs, tivozanib has similar efcacy than others with a better safety profle. The use of prognostic factors is critical to the selection of optimal therapy

Aplicação de patterns no desenvolvimento de um sistema CAD para microeletrônica

Apresenta a aplicação de técnica de orientação a objetos para resolver problemas de integração em ferramentas de CAD para microeletrônica. A abordagem enfatiza o uso de patterns e componentização para obter homogeneidade e o reuso de software. Os componentes são responsáveis por realizar tarefas básicas como a interpretação de linguagens e edição gráfica. A divisão de arquitetura em camadas garante a modularidade e provê o acoplamento de ferramentas funcionais. A arquitetura também suporta a distribuição de objetos, aumentando o potencial de processamento em redes de computadores

Neuregulin 1 type III reduces severity in a mouse model of Congenital Hypomyelinating Neuropathy

Myelin sheath thickness is precisely regulated and essential for rapid propagation of action potentials along myelinated axons. In the peripheral nervous system, extrinsic signals from the axonal protein neuregulin 1 type III regulate Schwann cell fate and myelination. Here we ask if modulating neuregulin 1 type III levels in neurons would restore myelination in a model of congenital hypomyelinating neuropathy (CHN). Using a mouse model of CHN, we rescued the myelination defects by early overexpression of neuregulin 1 type III. Surprisingly, the rescue was independent from the upregulation of Egr2 or essential myelin genes. Rather, we observed the activation of MAPK/ERK and other myelin genes such as peripheral myelin protein 2 (Pmp2) and oligodendrocyte myelin glycoprotein (Omg). We also confirmed that the permanent activation of MAPK/ERK in Schwann cells has detrimental effects on myelination. Our findings demonstrate that the modulation of axon-to-glial neuregulin 1 type III signaling has beneficial effects and restores myelination defects during development in a model of CHN