Alismatales da bacia do alto e médio rio Araguaia (Brasil)

The present study deals with a survey of the order Alismatales (except Araceae) in the upper and middle Araguaia River region located between the states of Mato Grosso and Goiás, Brazil. Field expeditions were carried out during the rainy and dry seasons. The route covered approximately 2,000 km and 41 aquatic environments were visited. Thirteen taxa, representing the families Alismataceae (nine), Hydrocharitaceae (three) and Najadaceae (one) were identified. Keys for the identification of families and species in field, brief diagnoses, schematic illustrations and relevant comments were elaborated based on field observations as well as on the analysis of the specimens collected.Realizou-se o levantamento de espécies da ordem Alismatales (exceto Araceae) ocorrentes na região do alto e médio rio Araguaia, entre os estados de Mato Grosso e Goiás, Brasil. As expedições para coleta de material, ocorridas tanto na época de chuvas quanto na seca, totalizaram cerca de 2.000 km percorridos, abrangendo 41 ambientes aquáticos. Foram identificados treze táxons pertencentes às famílias Alismataceae (nove), Hydrocharitaceae (três) e Najadaceae (uma). Foram elaboradas chaves para identificação em campo das famílias e espécies, descrições breves, ilustrações esquemáticas e comentários relevantes, baseados em dados levantados em campo e através da análise do material coletado.43945

Accurate Evaluation of Feature Contributions for Sentinel Lymph Node Status Classification in Breast Cancer

The current guidelines recommend the sentinel lymph node biopsy to evaluate the lymph node involvement for breast cancer patients with clinically negative lymph nodes on clinical or radiological examination. Machine learning (ML) models have significantly improved the prediction of lymph nodes status based on clinical features, thus avoiding expensive, time-consuming and invasive procedures. However, the classification of sentinel lymph node status represents a typical example of an unbalanced classification problem. In this work, we developed a ML framework to explore the effects of unbalanced populations on the performance and stability of feature ranking for sentinel lymph node status classification in breast cancer. Our results indicate state-of-the-art AUC (Area under the Receiver Operating Characteristic curve) values on a hold-out set (67%) while providing particularly stable features related to tumor size, histological subtype and estrogen receptor expression, which should therefore be considered as potential biomarkers

Autophagy, mitochondria and oxidative stress: cross-talk and redox signalling

Reactive oxygen and nitrogen species change cellular responses through diverse mechanisms that are now being defined. At low levels, they are signalling molecules, and at high levels, they damage organelles, particularly the mitochondria. Oxidative damage and the associated mitochondrial dysfunction may result in energy depletion, accumulation of cytotoxic mediators and cell death. Understanding the interface between stress adaptation and cell death then is important for understanding redox biology and disease pathogenesis. Recent studies have found that one major sensor of redox signalling at this switch in cellular responses is autophagy. Autophagic activities are mediated by a complex molecular machinery including more than 30 Atg (AuTophaGy-related) proteins and 50 lysosomal hydrolases. Autophagosomes form membrane structures, sequester damaged, oxidized or dysfunctional intracellular components and organelles, and direct them to the lysosomes for degradation. This autophagic process is the sole known mechanism for mitochondrial turnover. It has been speculated that dysfunction of autophagy may result in abnormal mitochondrial function and oxidative or nitrative stress. Emerging investigations have provided new understanding of how autophagy of mitochondria (also known as mitophagy) is controlled, and the impact of autophagic dysfunction on cellular oxidative stress. The present review highlights recent studies on redox signalling in the regulation of autophagy, in the context of the basic mechanisms of mitophagy. Furthermore, we discuss the impact of autophagy on mitochondrial function and accumulation of reactive species. This is particularly relevant to degenerative diseases in which oxidative stress occurs over time, and dysfunction in both the mitochondrial and autophagic pathways play a role

Proceedings of the Thirteenth International Society of Sports Nutrition (ISSN) Conference and Expo

Meeting Abstracts: Proceedings of the Thirteenth International Society of Sports Nutrition (ISSN) Conference and Expo Clearwater Beach, FL, USA. 9-11 June 201

The Impact of Female Leadership on LGBTQ-Supportive Policies

In the past two decades, gender inequality in c-suites has received a large amount of attention. Thus, the number of women in top management roles has increased substantially. However, the corporate sector has also neglected other marginalized groups, specifically, members of the LGBTQ community. These individuals are important employees and previous literature has established the benefits, both financial and otherwise, that the presence of LGBTQ supportive policies have on American corporations. In this paper, I examine if the presence of women CEOs influences the LGBTQ policies that are implemented in that firm. This will be analyzed using an OLS regression model. This paper finds that there is a positive and significant relationship between female leadership and LGBTQ-supportive policies. I further find that when a firm with a female CEO is headquartered in a state which voted democratic in the 2016 or 2020 presidential elections, the positive impact on LGBTQ-supportive policies increases. This shows that the political environment also impacts the decision making of firms

Hydrocharitaceae from central Brazil: a new species of Egerlia and a note on Apalanthe granatensis

Volume: 11Start Page: 63End Page: 6

John Bove: Chronic Neuropathic Pain

John L. Bove (J.B.) was an 87 year-old widowed father of 6 and retired educator, who experienced severe chronic neuropathic pain. In 2013, J.B. presented at the emergency room with severe pain in his left abdomen and lower back. He was diagnosed with back pain, and imaging revealed a stable, non-dissecting aortic aneurysm. His pain exacerbated and he was given the diagnosis of postherpetic neuralgia. Although symptoms were subsiding, J.B.\u27s primary care provider prescribed antidepressants and performed two injections in the upper gluteal region, which exacerbated the pain, after which J.B. was referred to a pain clinic. Through his neuropathic pain journey, J.B. was treated at 4 pain clinics, which mostly focused on pharmaceutical pain management. He trialed many traditional and complementary treatments including massage therapy, chiropractic manipulation, meditation, TENS, two spinal cord stimulators, extensive medications, and acupuncture. Eventually, J.B.’s pain consisted of severe intermittent jolts to the left side, constant burning, and a hypersensitivity called “allodynia,” where even a slight breeze over his exposed skin caused severe pain.https://dune.une.edu/pain_videos/1021/thumbnail.jp

A CT-based transfer learning approach to predict NSCLC recurrence: The added-value of peritumoral region.

Non-small cell lung cancer (NSCLC) represents 85% of all new lung cancer diagnoses and presents a high recurrence rate after surgery. Thus, an accurate prediction of recurrence risk in NSCLC patients at diagnosis could be essential to designate risk patients to more aggressive medical treatments. In this manuscript, we apply a transfer learning approach to predict recurrence in NSCLC patients, exploiting only data acquired during its screening phase. Particularly, we used a public radiogenomic dataset of NSCLC patients having a primary tumor CT image and clinical information. Starting from the CT slice containing the tumor with maximum area, we considered three different dilatation sizes to identify three Regions of Interest (ROIs): CROP (without dilation), CROP 10 and CROP 20. Then, from each ROI, we extracted radiomic features by means of different pre-trained CNNs. The latter have been combined with clinical information; thus, we trained a Support Vector Machine classifier to predict the NSCLC recurrence. The classification performances of the devised models were finally evaluated on both the hold-out training and hold-out test sets, in which the original sample has been previously divided. The experimental results showed that the model obtained analyzing CROP 20 images, which are the ROIs containing more peritumoral area, achieved the best performances on both the hold-out training set, with an AUC of 0.73, an Accuracy of 0.61, a Sensitivity of 0.63, and a Specificity of 0.60, and on the hold-out test set, with an AUC value of 0.83, an Accuracy value of 0.79, a Sensitivity value of 0.80, and a Specificity value of 0.78. The proposed model represents a promising procedure for early predicting recurrence risk in NSCLC patients

A Proposal of Quantum-Inspired Machine Learning for Medical Purposes: An Application Case

Learning tasks are implemented via mappings of the sampled data set, including both the classical and the quantum framework. Biomedical data characterizing complex diseases such as cancer typically require an algorithmic support for clinical decisions, especially for early stage tumors that typify breast cancer patients, which are still controllable in a therapeutic and surgical way. Our case study consists of the prediction during the pre-operative stage of lymph node metastasis in breast cancer patients resulting in a negative diagnosis after clinical and radiological exams. The classifier adopted to establish a baseline is characterized by the result invariance for the order permutation of the input features, and it exploits stratifications in the training procedure. The quantum one mimics support vector machine mapping in a high-dimensional feature space, yielded by encoding into qubits, while being characterized by complexity. Feature selection is exploited to study the performances associated with a low number of features, thus implemented in a feasible time. Wide variations in sensitivity and specificity are observed in the selected optimal classifiers during cross-validations for both classification system types, with an easier detection of negative or positive cases depending on the choice between the two training schemes. Clinical practice is still far from being reached, even if the flexible structure of quantum-inspired classifier circuits guarantees further developments to rule interactions among features: this preliminary study is solely intended to provide an overview of the particular tree tensor network scheme in a simplified version adopting just product states, as well as to introduce typical machine learning procedures consisting of feature selection and classifier performance evaluation