Gradual Self-Training via Confidence and Volume Based Domain Adaptation for Multi Dataset Deep Learning-Based Brain Metastases Detection Using Nonlocal Networks on MRI Images.

Background: Research suggests that treatment of multiple brain metastases (BMs) with stereotactic radiosurgery shows improvement when metastases are detected early, providing a case for BM detection capabilities on small lesions.

Purpose: To demonstrate automatic detection of BM on three MRI datasets using a deep learning-based approach. To improve the performance of the network is iteratively co-trained with datasets from different domains.

Morphological variations in a widely distributed Eastern Asian passerine cannot be consistently explained by ecogeographic rules.

Ecogeographic rules that describe quantitative relationships between morphologies and climate might help us predict how morphometrics of animals was shaped by local temperature or humidity. Although the ecogeographic rules had been widely tested in animals of Europe and North America, they had not been fully validated for species in regions that are less studied. Here, we investigate the morphometric variation of a widely distributed East Asian passerine, the vinous-throated parrotbill (), to test whether its morphological variation conforms to the prediction of Bergmann's rule, Allen's rules, and Gloger's rule.

Not out of the woods yet: Signatures of the prolonged negative genetic consequences of a population bottleneck in a rapidly re-expanding wader, the black-faced spoonbill Platalea minor.

The long-term persistence of a population which has suffered a bottleneck partly depends on how historical demographic dynamics impacted its genetic diversity and the accumulation of deleterious mutations. Here we provide genomic evidence for the genetic effect of a recent population bottleneck in the endangered black-faced spoonbill (Platalea minor) after its rapid population recovery. Our data suggest that the bird's effective population size, N , had been relatively stable (7500-9000) since 22,000 years ago; however, a recent brief yet severe bottleneck (N  = 20) which we here estimated to occur around the 1940s wiped out >99% of its historical N in roughly three generations.

CASPIANET++: A multidimensional Channel-Spatial Asymmetric attention network with Noisy Student Curriculum Learning paradigm for brain tumor segmentation.

Convolutional neural networks (CNNs) have been used quite successfully for semantic segmentation of brain tumors. However, current CNNs and attention mechanisms are stochastic in nature and neglect the morphological indicators used by radiologists to manually annotate regions of interest. In this paper, we introduce a channel and spatial wise asymmetric attention (CASPIAN) by leveraging the inherent structure of tumors to detect regions of saliency.

Evaluation of the poly(ADP-ribose) polymerase-1 gene variants in Alzheimer's disease.

Amyloid peptide is thought to play a critical role in neuronal death in Alzheimer's disease (AD), most likely through oxidative stress. Free radical-related injury leads to DNA breaks, which subsequently activates the repair enzyme poly(ADP-ribose) polymerase-1 (PARP-1). In this study, the relationship between genetic variants situated at the PARP-1 gene and AD development was investigated.