Zim1, a maternally expressed mouse Kruppel-type zinc-finger gene located in proximal chromosome 7

In analysis of a conserved region of proximal mouse chromosome 7 and human chromosome 19q, we have isolated a novel mouse gene, Zim1 (imprinted zinc-finger gene 1), encoding a typical Kruppel-type (C2H2) zinc-finger protein, located within 30 kb of a known imprinted gene, Peg3 (paternally expressed gene 3). Our studies demonstrate that Zim1 is also imprinted; the gene is expressed mainly from the maternal allele and at high levels only during embryonic and neonatal stages. In contrast to most tissues, Zim1 is expressed biallelically in neonatal and adult brain with slightly more input from the maternal allele. Zim1 produces multiple transcripts that range in size from 7.5 to 15 kb. The 7.5 kb transcript is expressed at highest levels and appears to be embryo specific. Whole mount in situ hybridization analysis indicates that Zim1 is expressed at significant levels in the apical ectodermal ridge of the limb buds during embryogenesis, suggesting a potential role of Zim1 in limb formation. We have identified the potential human ortholog of Zim1 near PEG3 in a conserved, gene-rich region of human chromosome 19q13.4. The close juxtaposition of reciprocally imprinted genes has also been seen in other imprinted regions, such as human 11p15.5/Mmu7 (H19/Igf2) and suggests that the two genes may be co-regulated. These and other data suggest the presence of an unexplored, conserved imprinted domain in human chromosome 19q13.4 and proximal Mmu7

Parsing Semantic Parts of Cars Using Graphical Models and Segment Appearance Consistency

This paper addresses the problem of semantic part parsing (segmentation) of cars, i.e.assigning every pixel within the car to one of the parts (e.g.body, window, lights, license plates and wheels). We formulate this as a landmark identification problem, where a set of landmarks specifies the boundaries of the parts. A novel mixture of graphical models is proposed, which dynamically couples the landmarks to a hierarchy of segments. When modeling pairwise relation between landmarks, this coupling enables our model to exploit the local image contents in addition to spatial deformation, an aspect that most existing graphical models ignore. In particular, our model enforces appearance consistency between segments within the same part. Parsing the car, including finding the optimal coupling between landmarks and segments in the hierarchy, is performed by dynamic programming. We evaluate our method on a subset of PASCAL VOC 2010 car images and on the car subset of 3D Object Category dataset (CAR3D). We show good results and, in particular, quantify the effectiveness of using the segment appearance consistency in terms of accuracy of part localization and segmentation.This work was supported by the Center for Brains, Minds and Machines (CBMM), funded by NSF STC award CCF - 1231216

Expression Of Carbonic Anhydrase I In Motor Neurons And Alterations in ALS

Carbonic anhydrase I (CA1) is the cytosolic isoform of mammalian α-CA family members which are responsible for maintaining pH homeostasis in the physiology and pathology of organisms. A subset of CA isoforms are known to be expressed and function in the central nervous system (CNS). CA1 has not been extensively characterized in the CNS. In this study, we demonstrate that CA1 is expressed in the motor neurons in human spinal cord. Unexpectedly, a subpopulation of CA1 appears to be associated with endoplasmic reticulum (ER) membranes. In addition, the membrane-associated CA1s are preferentially upregulated in amyotrophic lateral sclerosis (ALS) and exhibit altered distribution in motor neurons. Furthermore, long-term expression of CA1 in mammalian cells activates apoptosis. Our results suggest a previously unknown role for CA1 function in the CNS and its potential involvement in motor neuron degeneration in ALS

Rapid evolution of a recently retroposed transcription factor YY2 in mammalian genomes

YY2 was originally identified due to its unusual similarity to the evolutionarily well-conserved zinc finger gene YY1. In this study, we have determined the evolutionary origin and conservation of YY2 using comparative genomic approaches. Our results indicate that YY2 is a retroposed copy of YY1 that has been inserted into another gene locus named Mbtps2 (membrane-bound transcription factor protease site 2). This retroposition is estimated to have occurred after the divergence of placental mammals from other vertebrates based on the detection of YY2 only in the placental mammals. The N- and C-terminal regions of YY2 have evolved under different selection pressures. The N-terminal region has evolved at a very fast pace with very limited functional constraints, whereas the DNA-binding, C-terminal region still maintains a sequence structure very similar to that of YY1 and is also well conserved among placental mammals. In situ hybridizations using different adult mouse tissues indicate that mouse YY2 is expressed at relatively low levels in Purkinje and granular cells of cerebellum and in neuronal cells of cerebrum, but at very high levels in testis. The expression levels of YY2 are much lower than those of YY1, but the overall spatial expression patterns are similar to those of Mbtps2, suggesting a possible shared transcriptional control between YY2 and Mbtps2. Taken together, the formation and evolution of YY2 represent a very unusual case where a transcription factor was first retroposed into another gene locus encoding a protease and survived with different selection schemes and expression patterns. © 2005 Elsevier Inc. All rights reserved

Advances in circulating tumor cells for early detection, prognosis and metastasis reduction in lung cancer

Globally, lung cancer stands as the leading type of cancer in terms of incidence and is the major source of mortality attributed to cancer. We have outlined the molecular biomarkers for lung cancer that are available clinically. Circulating tumor cells (CTCs) spread from the original location, circulate in the bloodstream, extravasate, and metastasize, forming secondary tumors by invading and establishing a favorable environment. CTC analysis is considered a common liquid biopsy method for lung cancer. We have enumerated both in vivo and ex vivo techniques for CTC separation and enrichment, examined the advantages and limitations of these methods, and also discussed the detection of CTCs in other bodily fluids. We have evaluated the value of CTCs, as well as CTCs in conjunction with other biomarkers, for their utility in the early detection and prognostic assessment of patients with lung cancer. CTCs engage with diverse cells of the metastatic process, interfering with the interaction between CTCs and various cells in metastasis, potentially halting metastasis and enhancing patient prognosis

Boosting Few-Shot Segmentation via Instance-Aware Data Augmentation and Local Consensus Guided Cross Attention

Few-shot segmentation aims to train a segmentation model that can fast adapt to a novel task for which only a few annotated images are provided. Most recent models have adopted a prototype-based paradigm for few-shot inference. These approaches may have limited generalization capacity beyond the standard 1- or 5-shot settings. In this paper, we closely examine and reevaluate the fine-tuning based learning scheme that fine-tunes the classification layer of a deep segmentation network pre-trained on diverse base classes. To improve the generalizability of the classification layer optimized with sparsely annotated samples, we introduce an instance-aware data augmentation (IDA) strategy that augments the support images based on the relative sizes of the target objects. The proposed IDA effectively increases the support set's diversity and promotes the distribution consistency between support and query images. On the other hand, the large visual difference between query and support images may hinder knowledge transfer and cripple the segmentation performance. To cope with this challenge, we introduce the local consensus guided cross attention (LCCA) to align the query feature with support features based on their dense correlation, further improving the model's generalizability to the query image. The significant performance improvements on the standard few-shot segmentation benchmarks PASCAL-$5^i$ and COCO-$20^i$ verify the efficacy of our proposed method