CFANet: Context Feature Fusion and Attention Mechanism Based Network for Small Target Segmentation in Medical Images.

Medical image segmentation plays a crucial role in clinical diagnosis, treatment planning, and disease monitoring. The automatic segmentation method based on deep learning has developed rapidly, with segmentation results comparable to clinical experts for large objects, but the segmentation accuracy for small objects is still unsatisfactory. Current segmentation methods based on deep learning find it difficult to extract multiple scale features of medical images, leading to an insufficient detection capability for smaller objects.

Suppressing Structural Relaxation in Nanoscale Antimony to Enable Ultralow-Drift Phase-Change Memory Applications.

Phase-change random-access memory (PCRAM) devices suffer from pronounced resistance drift originating from considerable structural relaxation of phase-change materials (PCMs), which hinders current developments of high-capacity memory and high-parallelism computing that both need reliable multibit programming. This work realizes that compositional simplification and geometrical miniaturization of traditional GeSbTe-like PCMs are feasible routes to suppress relaxation. While to date, the aging mechanisms of the simplest PCM, Sb, at nanoscale, have not yet been unveiled.

Focal white matter microstructural alteration after anthracycline-based systemic treatment in long-term breast cancer survivors: a structural magnetic resonance imaging study.

Understanding the neural correlates of cognitive problems in patients with breast cancer (BC) after systemic treatment have been a topic of increasing investigation. The heterogeneity of the systemic treatment regimens may undermine our ability to identify brain microstructural alterations resulting from any given regimen. We investigated the detrimental effects of the anthracycline-based systemic treatment (AST) regimen (epirubicin and cyclophosphamide + docetaxel + tamoxifen) on brain gray matter (GM) and white matter (WM) microstructural alteration in long-term BC survivors.

A rare mutant of OFD1 gene responsible for Joubert syndrome with significant phenotype variation.

Joubert syndrome (JBTS), a rare genetic disorder resulted from primary cilium defects or basal-body dysfunction, is characterized by agenesis of cerebellar vermis and abnormal brain stem. Both genotypes and phenotypes of JBTS are highly heterogeneous. The identification of pathogenic gene variation is essential for making a definite diagnosis on JBTS.

[Sequential preparation of microvlllous and basal membranes from human placenta].

Objective: To improve the technology of isolating paired fractions of the maternal-facing membranes (MVM) and fetal-facing plasma membranes (BM) from a term placenta.

Methods: The component of buffer was improved based on Illsley method. The time of Mg2+ -aggregated basal membranes was extended.

Giant laryngeal angioleiomyomas: a case report with review of literature.

We report a recently encountered case of vascular leiomyoma in the larynx, a benign tumor that is rare in the organ, in a 52-year-old man. Chief complaints were a progressive hoarseness, laryngeal pain, deglutitive uncomfortableness and dyspnea in the supine position. Laryngoscope revealed many spherical tumors interlacing together with a smooth surface, measuring approximately 2x4 cm and covered with normal mucosa, found at the side of laryngeal epiglottis from its margin to supraglottis.

Synthesis and antiviral activities of cyanoacrylate derivatives containing an alpha-aminophosphonate moiety.

Target compounds 8 were obtained by the reaction of alkyl 2-cyano-3,3-dimethylthioacrylate or cyarylamide (7a-7e) and alpha-aminobenzylphosphonate (5a-5e) under reflux condition using ethanol as solvent. Their structures were clearly verified by spectroscopic data (IR and 1H, 13C, and 31P NMR) and elemental analysis. These compounds were shown to be antivirally active in the bioassay.

An improved trilinear decomposition algorithm based on a Lagrange operator.

An improved trilinear decomposition algorithm based on a Lagrange operator (LO) is developed in this paper, which introduces a Lagrange operator and penalty terms in the loss function to improve the performance of the algorithm. Compared to the traditional parallel factor (PARAFAC) algorithm, the algorithm not only may converge much faster, but also overcome the sensibility to estimate the number of components. A set of simulated and measured excitation/emission fluorescence data were treated by both the proposed and traditional PARAFAC algorithm to compare their efficiencies.