Development of pGEMINI, a Plant Gateway Destination Vector Allowing the Simultaneous Integration of Two cDNA via a Single LR-Clonase Reaction

Gateway technology has been used to facilitate the generation of a large number of constructs for the modification of plants for research purposes. However, many of the currently available vectors only allow the integration of a single cDNA of interest into an expression clone. The ability to over-express multiple genes in combination is essential for the study of plant development where several transcripts have a role to play in one or more metabolic processes. The tools to carry out such studies are limited, and in many cases rely on the incorporation of cDNA into expression systems via conventional cloning, which can be both time consuming and laborious. To our knowledge, this study reports on the first development of a vector allowing the simultaneous integration of two independent cDNAs via a single LR-clonase reaction. This vector “pGEMINI” represents a powerful molecular tool offering the ability to study the role of multi-cDNA constructs on plant development, and opens up the process of gene stacking and the study of gene combinations through transient or stable transformation procedures

Seasonal pattern in elderly hospitalized with acute kidney injury: a retrospective nationwide study in Italy

Purpose: Acute kidney injury (AKI) frequently complicates hospitalization and is associated with in-hospital mortality (IHM). It has been reported a seasonal trend in different clinical conditions. The aim of this study was to evaluate the possible relationship between seasons of the year and IHM in elderly hospitalized patients with AKI. Methods: We selected all admissions complicated by AKI between 2000 and 2015 recorded in the Italian National Hospital Database. ICD-9-CM code 584.xx identified subjects with age ≥ 65 years and age, sex, comorbidity burden, need of dialysis treatment and IHM were compared in hospitalizations recorded during the four seasons. Moreover, we plotted the AKI observed/expected ratio and percentage of mortality during the study period. Results: We evaluated 759,720 AKI hospitalizations (mean age 80.5 ± 7.8 years, 52.2% males). Patients hospitalized with AKI during winter months had higher age, prevalence of dialysis-dependent AKI, and number of deceased patients. In whole population IHM was higher in winter and lower in summer, while the AKI observed/expected ratio demonstrated two peaks, one in summer and one in winter. Logistic regression analysis demonstrated that parameters such as age, autumn, winter, comorbidity burden were positively associated with IHM. Conclusion: We conclude that a seasonality exists in AKI, however, relationship between seasons and AKI could vary depending on the aspects considered. Both autumn and winter months are independent risk factors for IHM in patients with AKI regardless of age, sex and comorbidity burden. On the contrary, summer time reduces the risk of death during hospitalizations with AKI

Holographic Evolution of Entanglement Entropy

We study the evolution of entanglement entropy in a 2-dimensional equilibration process that has a holographic description in terms of a Vaidya geometry. It models a unitary evolution in which the field theory starts in a pure state, its vacuum, and undergoes a perturbation that brings it far from equilibrium. The entanglement entropy in this set up provides a measurement of the quantum entanglement in the system. Using holographic techniques we recover the same result obtained before from the study of processes triggered by a sudden change in a parameter of the hamiltonian, known as quantum quenches. Namely, entanglement in 2-dimensional conformal field theories propagates with velocity v^2=1. Both in quantum quenches and in the Vaidya model equilibration is only achieved at the local level. Remarkably, the holographic derivation of this last fact requires information from behind the apparent horizon generated in the process of gravitational collapse described by the Vaidya geometry. In the early stages of the evolution the apparent horizon seems however to play no relevant role with regard to the entanglement entropy. We speculate on the possibility of deriving a thermalization time for occupation numbers from our analysis.Comment: 26 pages, 10 figure

Redefining Peptide 14D: Substitutional Analysis for Accelerated TB Diagnosis and Enhanced Activity against Mycobacterium tuberculosis.

Tuberculosis (TB) caused by Mycobacterium tuberculosis remains a predominant cause of mortality, especially in low- and middle-income nations. Recently, antimicrobial peptides have been discovered that at low concentrations could stimulate the growth of M. tuberculosis (hormetic response). In this study, such a peptide was used to investigate the effects on the time to positivity (TTP). A systematic substitution analysis of peptide 14D was synthesized using Spot synthesis technology, resulting in 171 novel peptides. Our findings revealed a spectrum of interactions, with some peptides accelerating M. tuberculosis growth, potentially aiding in faster diagnostics, while others exhibited inhibitory effects. Notably, peptide NH2-wkivfiwrr-CONH2 significantly reduced the TTP by 25 h compared to the wild-type peptide 14D, highlighting its potential in improving TB diagnostics by culture. Several peptides demonstrated potent antimycobacterial activity, with a minimum inhibitory concentration (MIC) of 20 µg/mL against H37Rv and a multidrug-resistant M. tuberculosis strain. Additionally, for two peptides, a strongly diminished formation of cord-like structures was observed, which is indicative of reduced virulence and transmission potential. This study underscores the multifaceted roles of antimicrobial peptides in TB management, from enhancing diagnostic efficiency to offering therapeutic avenues against M. tuberculosis

Evidence for dark matter in the inner Milky Way

The ubiquitous presence of dark matter in the universe is today a central tenet in modern cosmology and astrophysics. Ranging from the smallest galaxies to the observable universe, the evidence for dark matter is compelling in dwarfs, spiral galaxies, galaxy clusters as well as at cosmological scales. However, it has been historically difficult to pin down the dark matter contribution to the total mass density in the Milky Way, particularly in the innermost regions of the Galaxy and in the solar neighbourhood. Here we present an up-to-date compilation of Milky Way rotation curve measurements, and compare it with state-of-the-art baryonic mass distribution models. We show that current data strongly disfavour baryons as the sole contribution to the galactic mass budget, even inside the solar circle. Our findings demonstrate the existence of dark matter in the inner Galaxy while making no assumptions on its distribution. We anticipate that this result will compel new model-independent constraints on the dark matter local density and profile, thus reducing uncertainties on direct and indirect dark matter searches, and will shed new light on the structure and evolution of the Galaxy.Comment: First submitted version of letter published in Nature Physics on Febuary 9, 2015: http://www.nature.com/nphys/journal/vaop/ncurrent/full/nphys3237.htm

Integrative analyses identify modulators of response to neoadjuvant aromatase inhibitors in patients with early breast cancer

Introduction Aromatase inhibitors (AIs) are a vital component of estrogen receptor positive (ER+) breast cancer treatment. De novo and acquired resistance, however, is common. The aims of this study were to relate patterns of copy number aberrations to molecular and proliferative response to AIs, to study differences in the patterns of copy number aberrations between breast cancer samples pre- and post-AI neoadjuvant therapy, and to identify putative biomarkers for resistance to neoadjuvant AI therapy using an integrative analysis approach. Methods Samples from 84 patients derived from two neoadjuvant AI therapy trials were subjected to copy number profiling by microarray-based comparative genomic hybridisation (aCGH, n = 84), gene expression profiling (n = 47), matched pre- and post-AI aCGH (n = 19 pairs) and Ki67-based AI-response analysis (n = 39). Results Integrative analysis of these datasets identified a set of nine genes that, when amplified, were associated with a poor response to AIs, and were significantly overexpressed when amplified, including CHKA, LRP5 and SAPS3. Functional validation in vitro, using cell lines with and without amplification of these genes (SUM44, MDA-MB134-VI, T47D and MCF7) and a model of acquired AI-resistance (MCF7-LTED) identified CHKA as a gene that when amplified modulates estrogen receptor (ER)-driven proliferation, ER/estrogen response element (ERE) transactivation, expression of ER-regulated genes and phosphorylation of V-AKT murine thymoma viral oncogene homolog 1 (AKT1). Conclusions These data provide a rationale for investigation of the role of CHKA in further models of de novo and acquired resistance to AIs, and provide proof of concept that integrative genomic analyses can identify biologically relevant modulators of AI response

Beyond Volume: The Impact of Complex Healthcare Data on the Machine Learning Pipeline

From medical charts to national census, healthcare has traditionally operated under a paper-based paradigm. However, the past decade has marked a long and arduous transformation bringing healthcare into the digital age. Ranging from electronic health records, to digitized imaging and laboratory reports, to public health datasets, today, healthcare now generates an incredible amount of digital information. Such a wealth of data presents an exciting opportunity for integrated machine learning solutions to address problems across multiple facets of healthcare practice and administration. Unfortunately, the ability to derive accurate and informative insights requires more than the ability to execute machine learning models. Rather, a deeper understanding of the data on which the models are run is imperative for their success. While a significant effort has been undertaken to develop models able to process the volume of data obtained during the analysis of millions of digitalized patient records, it is important to remember that volume represents only one aspect of the data. In fact, drawing on data from an increasingly diverse set of sources, healthcare data presents an incredibly complex set of attributes that must be accounted for throughout the machine learning pipeline. This chapter focuses on highlighting such challenges, and is broken down into three distinct components, each representing a phase of the pipeline. We begin with attributes of the data accounted for during preprocessing, then move to considerations during model building, and end with challenges to the interpretation of model output. For each component, we present a discussion around data as it relates to the healthcare domain and offer insight into the challenges each may impose on the efficiency of machine learning techniques.Comment: Healthcare Informatics, Machine Learning, Knowledge Discovery: 20 Pages, 1 Figur

Ferritins: furnishing proteins with iron

Ferritins are a superfamily of iron oxidation, storage and mineralization proteins found throughout the animal, plant, and microbial kingdoms. The majority of ferritins consist of 24 subunits that individually fold into 4-α-helix bundles and assemble in a highly symmetric manner to form an approximately spherical protein coat around a central cavity into which an iron-containing mineral can be formed. Channels through the coat at inter-subunit contact points facilitate passage of iron ions to and from the central cavity, and intrasubunit catalytic sites, called ferroxidase centers, drive Fe2+ oxidation and O2 reduction. Though the different members of the superfamily share a common structure, there is often little amino acid sequence identity between them. Even where there is a high degree of sequence identity between two ferritins there can be major differences in how the proteins handle iron. In this review we describe some of the important structural features of ferritins and their mineralized iron cores and examine in detail how three selected ferritins oxidise Fe2+ in order to explore the mechanistic variations that exist amongst ferritins. We suggest that the mechanistic differences reflect differing evolutionary pressures on amino acid sequences, and that these differing pressures are a consequence of different primary functions for different ferritins

Evolutionary Analysis of Mitogenomes from Parasitic and Free-Living Flatworms

Copyright: © 2015 Solà et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. The attached file is the published version of the article