Short-term physiological effects of a very low-calorie ketogenic diet: Effects on adiponectin levels and inflammatory states

Adipose tissue is a multifunctional organ involved in many physiological and metabolic processes through the production of adipokines and, in particular, adiponectin. Caloric restriction is one of the most important strategies against obesity today. The very low-calorie ketogenic diet (VLCKD) represents a type of caloric restriction with very or extremely low daily food energy consumption. This study aimed to investigate the physiological effects of a VLCKD on anthropometric and biochemical parameters such as adiponectin levels, as well as analyzing oligomeric profiles and cytokine serum levels in obese subjects before and after a VLCKD. Twenty obese subjects were enrolled. At baseline and after eight weeks of intervention, anthropometric and biochemical parameters, such as adiponectin levels, were recorded. Our findings showed a significant change in the anthropometric and biochemical parameters of these obese subjects before and after a VLCKD. We found a negative correlation between adiponectin and lipid profile, visceral adipose tissue (VAT), C-reactive protein (CRP), and pro-inflammatory cytokines such as tumor necrosis factor-α (TNF-α), which confirmed the important involvement of adiponectin in metabolic and inflammatory diseases. We demonstrated the beneficial short-term effects of a VLCKD not only in the treatment of obesity but also in the establishment of obesity-correlated diseases

Variability and changes of daily climate extremes over the core crop region of Argentina

Ponencia presentada en: XI Congreso de la Asociación Española de Climatología celebrado en Cartagena entre el 17 y el 19 de octubre de 2018.[ES]La variabilidad y los cambios en extremos climáticos afectan la región núcleo de cultivos de Argentina y pueden incrementar su vulnerabilidad ocasionando desastres sin precedentes. Este estudio investiga los cambios de largo período y la variabilidad interanual de los extremos climáticos diarios de precipitación y temperatura y evalúa en qué medida los reanálisis globales reproducen la variabilidad observada en el pasado reciente. Los datos incluyen observaciones con calidad controlada (1963-2013) y los reanálisis ERA-Interim y NCEP2 (1979-2011). Los extremos climáticos se caracterizan espacial y temporalmente con 11 índices de los propuestos por el Equipo de Expertos sobre Detección e Índices de Cambio Climático. Se aplicó un Análisis Espectral Singular para detectar los modos principales de las series temporales medias areales de los índices. Se ajustaron tendencias no-paramétricas lineales a las series temporales de cada índice para estimar la distribución espacial de los cambios medios. Los extremos de temperatura están cambiando hacia condiciones más cálidas. Los días cálidos han estado aumentando desde 1990 mientras que los días fríos han ido decreciendo. Las noches cálidas y frías muestran una señal de calentamiento significativa que parece estar estabilizándose en las últimas décadas. Los eventos de precipitación intensa aumentaron constantemente en la mayor parte de la región desde 1970. La cantidad máxima anual de precipitación en un día aumentó desde la década de 1970 hasta la del 2000, estabilizándose en años recientes. El reanálisis ERA-Interim puede reconocer los extremos de temperatura en tiempo y en espacio, mientras que el antiguo NCEP2 presenta errores sistemáticos. Ambos reanálisis reproducen la máxima precipitación anual en 5 días con grandes sesgos. Aunque se esperaría que los reanálisis agreguen información para extremos climáticos en áreas de observaciones escasas, aún deben usarse con mucha precaución y solo como complemento de las observaciones.[EN]Variability and changes in climate extremes affect the core crop region of Argentina and may increase its vulnerability leading to unprecedented disasters. This study investigates the long-term changes and interannual variability of daily temperature and precipitation climate extremes and assesses to what extent global reanalyses reproduce the observed variability in the recent past. Datasets include quality-controlled observations (1963-2013) and ERA-Interim and NCEP2 reanalyses (1979-2011). Climate extremes are characterized spatially and temporally by 11 indices proposed by the Expert Team on Climate Change Detection and Indices. A Singular Spectrum Analysis was applied to detect the leading modes of the area-averaged index time series. Nonparametric linear trends were fitted to each index time series to estimate the spatial distribution of mean changes. Temperature extremes are changing towards warmer conditions. Warm days has been increasing since 1990 while cold days has been decreasing. Warm and cold nights show a significant signal of warming that seems to be stabilizing in recent decades. Intense precipitation events in most of the region increased steadily since 1970. The annual maximum amount of 1-day precipitation events increased from the 1970s to the 2000s, stabilizing in recent years. The ERA-Interim reanalysis can recognize temperature extremes in time and space, while the older NCEP2 presents systematic biases. Both reanalyses reproduce the annual maximum 5-day precipitation with large biases. Although reanalyses would be expected to add information for climate extremes in areas of scarce observations, they still need to be used with great caution and only as a complement to observations.We appreciate the grant from the PRODACT 2018 of the Science and Technical Secretariat (FICH UNL). This research was carried out with support of Projects CRN3035 and CRN3095 of the Inter-American Institute for Global Change Research (IAI), which is supported by the US National Science Foundation. UNL Project C.A.I. + D. 2016 32/180 is also acknowledged

Introduction

Short introduction to the topics of the volume Objects of Memory, Memory of Objects

Phenolic composition of red grapes grown in Southern Italy

The phenolic composition of red grapes native to Southern Italy (Aglianico, Carignano, Frappato, Gaglioppo, Negro Amaro, Nero d'Avola, Primitivo, Tintilia, and Uva di Troia) and an "international" grape (Cabernet Sauvignon) introduced into the Apulia region were investigated. Results showed that these cultivars could be divided into two groups on the basis of both their anthocyanin content and the presence of ortho-hydroxylated groups. Further differences regarded the ratio between flavans reacting with vanillin and proanthocyanidins. The anthocyanin profile of the skin of Negro Amaro, Primitivo and Uva di Troia grapes was found to be a specific characteristic of the grape variety which was affected only slightly by the place of growing. The different phenolic composition of the cultivars determines a different aptitude to wine production. The Cabernet Sauvignon grapes, due to their high concentration in polyphenolic substances, could be added to the native grape varieties in order to produce wines with a more complex aroma

Interannual-to-multidecadal hydroclimate variability and its sectoral impacts in northeastern Argentina

This study examines the joint variability of precipitation, river streamflow and temperature over northeastern Argentina; advances the understanding of their links with global SST forcing; and discusses their impacts on water resources, agriculture and human settlements. The leading patterns of variability, and their nonlinear trends and cycles are identified by means of a principal component analysis (PCA) complemented with a singular spectrum analysis (SSA). Interannual hydroclimatic variability centers on two broad frequency bands: one of 2.5–6.5 years corresponding to El Niño Southern Oscillation (ENSO) periodicities and the second of about 9 years. The higher frequencies of the precipitation variability (2.5–4 years) favored extreme events after 2000, even during moderate extreme phases of the ENSO. Minimum temperature is correlated with ENSO with a main frequency close to 3 years. Maximum temperature time series correlate well with SST variability over the South Atlantic, Indian and Pacific oceans with a 9-year frequency. Interdecadal variability is characterized by low-frequency trends and multidecadal oscillations that have induced a transition from dryer and cooler climate to wetter and warmer decades starting in the mid-twentieth century. The Paraná River streamflow is influenced by North and South Atlantic SSTs with bidecadal periodicities. The hydroclimate variability at all timescales had significant sectoral impacts. Frequent wet events between 1970 and 2005 favored floods that affected agricultural and livestock productivity and forced population displacements. On the other hand, agricultural droughts resulted in soil moisture deficits that affected crops at critical growth stages. Hydrological droughts affected surface water resources, causing water and food scarcity and stressing the capacity for hydropower generation. Lastly, increases in minimum temperature reduced wheat and barley yields

Enhancing PFI Prediction with GDS-MIL: A Graph-Based Dual Stream MIL Approach

Whole-Slide Images (WSI) are emerging as a promising resource for studying biological tissues, demonstrating a great potential in aiding cancer diagnosis and improving patient treatment. However, the manual pixel-level annotation of WSIs is extremely time-consuming and practically unfeasible in real-world scenarios. Multi-Instance Learning (MIL) have gained attention as a weakly supervised approach able to address lack of annotation tasks. MIL models aggregate patches (e.g., cropping of a WSI) into bag-level representations (e.g., WSI label), but neglect spatial information of the WSIs, crucial for histological analysis. In the High-Grade Serous Ovarian Cancer (HGSOC) context, spatial information is essential to predict a prognosis indicator (the Platinum-Free Interval, PFI) from WSIs. Such a prediction would bring highly valuable insights both for patient treatment and prognosis of chemotherapy resistance. Indeed, NeoAdjuvant ChemoTherapy (NACT) induces changes in tumor tissue morphology and composition, making the prediction of PFI from WSIs extremely challenging. In this paper, we propose GDS-MIL, a method that integrates a state-of-the-art MIL model with a Graph ATtention layer (GAT in short) to inject a local context into each instance before MIL aggregation. Our approach achieves a significant improvement in accuracy on the “Ome18” PFI dataset. In summary, this paper presents a novel solution for enhancing PFI prediction in HGSOC, with the potential of significantly improving treatment decisions and patient outcomes