Distinct function of Chlamydomonas CTRA-CTR transporters in Cu assimilation and intracellular mobilization.

Unlabelled: Successful acclimation to copper (Cu) deficiency involves a fine balance between Cu import and export. In the green alga Chlamydomonas reinhardtii, Cu import is dependent on a transcription factor, Copper Response Regulator 1 (CRR1), responsible for activating genes in Cu-deficient cells. Among CRR1 target genes are two Cu transporters belonging to the CTR/COPT gene family (CTR1 and CTR2) and a related soluble protein (CTR3).

Distinct function of Chlamydomonas CTRA-CTR transporters in Cu assimilation and intracellular mobilization.

Successful acclimation to copper (Cu) deficiency involves a fine balance between Cu import and export. In the unicellular green alga , Cu import is dependent on C opper R esponse R egulator 1 (CRR1), the master regulator of Cu homeostasis. Among CRR1 target genes are two Cu transporters belonging to the CTR/COPT gene family ( and ) and a related soluble cysteine-rich protein (CTR3).

Sub-Bandgap Sensitization of Perovskite Semiconductors via Colloidal Quantum Dots Incorporation.

By taking advantage of the outstanding intrinsic optoelectronic properties of perovskite-based photovoltaic materials, together with the strong near-infrared (NIR) absorption and electronic confinement in PbS quantum dots (QDs), sub-bandgap photocurrent generation is possible, opening the way for solar cell efficiencies surpassing the classical limits. The present study shows an effective methodology for the inclusion of high densities of colloidal PbS QDs in a MAPbI (methylammonium lead iodide) perovskite matrix as a means to enhance the spectral window of photon absorption of the perovskite host film and allow photocurrent production below its bandgap. The QDs were introduced in the perovskite matrix in different sizes and concentrations to study the formation of quantum-confined levels within the host bandgap and the potential formation of a delocalized intermediate mini-band (IB).

MR-Class: A Python Tool for Brain MR Image Classification Utilizing One-vs-All DCNNs to Deal with the Open-Set Recognition Problem.

Background: MR image classification in datasets collected from multiple sources is complicated by inconsistent and missing DICOM metadata. Therefore, we aimed to establish a method for the efficient automatic classification of MR brain sequences.

Methods: Deep convolutional neural networks (DCNN) were trained as one-vs-all classifiers to differentiate between six classes: T1 weighted (w), contrast-enhanced T1w, T2w, T2w-FLAIR, ADC, and SWI.

MR Intensity Normalization Methods Impact Sequence Specific Radiomics Prognostic Model Performance in Primary and Recurrent High-Grade Glioma.

Purpose: This study investigates the impact of different intensity normalization (IN) methods on the overall survival (OS) radiomics models' performance of MR sequences in primary (pHGG) and recurrent high-grade glioma (rHGG).

Methods: MR scans acquired before radiotherapy were retrieved from two independent cohorts (rHGG C1: 197, pHGG C2: 141) from multiple scanners (15, 14). The sequences are T1 weighted (w), contrast-enhanced T1w (T1wce), T2w, and T2w-FLAIR.