Dendritic and axonal targeting patterns of a genetically-specified class of retinal ganglion cells that participate in image-forming circuits.

BackgroundThere are numerous functional types of retinal ganglion cells (RGCs), each participating in circuits that encode a specific aspect of the visual scene. This functional specificity is derived from distinct RGC morphologies and selective synapse formation with other retinal cell types; yet, how these properties are established during development remains unclear. Islet2 (Isl2) is a LIM-homeodomain transcription factor expressed in the developing retina, including approximately 40% of all RGCs, and has previously been implicated in the subtype specification of spinal motor neurons. Based on this, we hypothesized that Isl2+ RGCs represent a related subset that share a common function.ResultsWe morphologically and molecularly characterized Isl2+ RGCs using a transgenic mouse line that expresses GFP in the cell bodies, dendrites and axons of Isl2+ cells (Isl2-GFP). Isl2-GFP RGCs have distinct morphologies and dendritic stratification patterns within the inner plexiform layer and project to selective visual nuclei. Targeted filling of individual cells reveals that the majority of Isl2-GFP RGCs have dendrites that are monostratified in layer S3 of the IPL, suggesting they are not ON-OFF direction-selective ganglion cells. Molecular analysis shows that most alpha-RGCs, indicated by expression of SMI-32, are also Isl2-GFP RGCs. Isl2-GFP RGCs project to most retino-recipient nuclei during early development, but specifically innervate the dorsal lateral geniculate nucleus and superior colliculus (SC) at eye opening. Finally, we show that the segregation of Isl2+ and Isl2- RGC axons in the SC leads to the segregation of functional RGC types.ConclusionsTaken together, these data suggest that Isl2+ RGCs comprise a distinct class and support a role for Isl2 as an important component of a transcription factor code specifying functional visual circuits. Furthermore, this study describes a novel genetically-labeled mouse line that will be a valuable resource in future investigations of the molecular mechanisms of visual circuit formation

Data augmentation and semi-supervised learning for deep neural networks-based text classifier

User feedback is essential for understanding user needs. In this paper, we use free-text obtained from a survey on sleep-related issues to build a deep neural networks-based text classifier. However, to train the deep neural networks model, a lot of labelled data is needed. To reduce manual data labelling, we propose a method which is a combination of data augmentation and pseudo-labelling: data augmentation is applied to labelled data to increase the size of the initial train set and then the trained model is used to annotate unlabelled data with pseudo-labels. The result shows that the model with the data augmentation achieves macro-averaged f1 score of 65.2% while using 4,300 training data, whereas the model without data augmentation achieves macro-averaged f1 score of 68.2% with around 14,000 training data. Furthermore, with the combination of pseudo-labelling, the model achieves macro-averaged f1 score of 62.7% with only using 1,400 training data with labels. In other words, with the proposed method we can reduce the amount of labelled data for training while achieving relatively good performance

Automated detection of celiac disease on duodenal biopsy slides: a deep learning approach

Celiac disease prevalence and diagnosis have increased substantially in recent years. The current gold standard for celiac disease confirmation is visual examination of duodenal mucosal biopsies. An accurate computer-aided biopsy analysis system using deep learning can help pathologists diagnose celiac disease more efficiently. In this study, we trained a deep learning model to detect celiac disease on duodenal biopsy images. Our model uses a state-of-the-art residual convolutional neural network to evaluate patches of duodenal tissue and then aggregates those predictions for whole-slide classification. We tested the model on an independent set of 212 images and evaluated its classification results against reference standards established by pathologists. Our model identified celiac disease, normal tissue, and nonspecific duodenitis with accuracies of 95.3%, 91.0%, and 89.2%, respectively. The area under the receiver operating characteristic curve was greater than 0.95 for all classes. We have developed an automated biopsy analysis system that achieves high performance in detecting celiac disease on biopsy slides. Our system can highlight areas of interest and provide preliminary classification of duodenal biopsies prior to review by pathologists. This technology has great potential for improving the accuracy and efficiency of celiac disease diagnosis.Comment: Accepted in Journal of Pathology Informatic

DNA Fingerprinting of Mycobacterium leprae Strains Using Variable Number Tandem Repeat (VNTR) - Fragment Length Analysis (FLA)

The study of the transmission of leprosy is particularly difficult since the causative agent, Mycobacterium leprae, cannot be cultured in the laboratory. The only sources of the bacteria are leprosy patients, and experimentally infected armadillos and nude mice. Thus, many of the methods used in modern epidemiology are not available for the study of leprosy. Despite an extensive global drug treatment program for leprosy implemented by the WHO1, leprosy remains endemic in many countries with approximately 250,000 new cases each year.2 The entire M. leprae genome has been mapped3,4 and many loci have been identified that have repeated segments of 2 or more base pairs (called micro- and minisatellites).5 Clinical strains of M. leprae may vary in the number of tandem repeated segments (short tandem repeats, STR) at many of these loci.5,6,7 Variable number tandem repeat (VNTR)5 analysis has been used to distinguish different strains of the leprosy bacilli. Some of the loci appear to be more stable than others, showing less variation in repeat numbers, while others seem to change more rapidly, sometimes in the same patient. While the variability of certain VNTRs has brought up questions regarding their suitability for strain typing7,8,9, the emerging data suggest that analyzing multiple loci, which are diverse in their stability, can be used as a valuable epidemiological tool. Multiple locus VNTR analysis (MLVA)10 has been used to study leprosy evolution and transmission in several countries including China11,12, Malawi8, the Philippines10,13, and Brazil14. MLVA involves multiple steps. First, bacterial DNA is extracted along with host tissue DNA from clinical biopsies or slit skin smears (SSS).10 The desired loci are then amplified from the extracted DNA via polymerase chain reaction (PCR). Fluorescently-labeled primers for 4-5 different loci are used per reaction, with 18 loci being amplified in a total of four reactions.10 The PCR products may be subjected to agarose gel electrophoresis to verify the presence of the desired DNA segments, and then submitted for fluorescent fragment length analysis (FLA) using capillary electrophoresis. DNA from armadillo passaged bacteria with a known number of repeat copies for each locus is used as a positive control. The FLA chromatograms are then examined using Peak Scanner software and fragment length is converted to number of VNTR copies (allele). Finally, the VNTR haplotypes are analyzed for patterns, and when combined with patient clinical data can be used to track distribution of strain types

c-Src links a RANK/αvβ3 integrin complex to the osteoclast cytoskeleton

RANK ligand (RANKL), by mechanisms unknown, directly activates osteoclasts to resorb bone. Because c-Src is key to organizing the cell's cytoskeleton, we asked if the tyrosine kinase also mediates RANKL-stimulated osteoclast activity. RANKL induces c-Src to associate with RANK(369–373) in an αvβ3-dependent manner. Furthermore, RANK(369–373) is the only one of six putative TRAF binding motifs sufficient to generate actin rings and activate the same cytoskeleton-organizing proteins as the integrin. While c-Src organizes the cell's cytoskeleton in response to the cytokine, it does not participate in RANKL-stimulated osteoclast formation. Attesting to their collaboration, αvβ3 and activated RANK coprecipitate, but only in the presence of c-Src. c-Src binds activated RANK via its Src homology 2 (SH2) domain and αvβ3 via its SH3 domain, suggesting the kinase links the two receptors. Supporting this hypothesis, deletion or inactivating point mutation of either the c-Src SH2 or SH3 domain obviates the RANK/αvβ3 association. Thus, activated RANK prompts two distinct signaling pathways; one promotes osteoclast formation, and the other, in collaboration with c-Src-mediated linkage to αvβ3, organizes the cell's cytoskeleton

Toward more accurate variant calling for “personal genomes”

To date, researchers and clinicians use widely different methods for detecting and reporting human genetic variation. As the size of academic and private databases grow and as the use of the existing genomic techniques expand, researchers and clinicians stand to greatly benefit from the standardization of data generating approaches and analysis methodologies. To successfully implement genomic analyses in the clinic, it will be critically important to optimize the existing pipelines for attaining a higher sensitivity and specificity for more accurate and consistent variant calling

Low concordance of multiple variant-calling pipelines: practical implications for exome and genome sequencing

BACKGROUND: To facilitate the clinical implementation of genomic medicine by next-generation sequencing, it will be critically important to obtain accurate and consistent variant calls on personal genomes. Multiple software tools for variant calling are available, but it is unclear how comparable these tools are or what their relative merits in real-world scenarios might be. METHODS: We sequenced 15 exomes from four families using commercial kits (Illumina HiSeq 2000 platform and Agilent SureSelect version 2 capture kit), with approximately 120X mean coverage. We analyzed the raw data using near-default parameters with five different alignment and variant-calling pipelines (SOAP, BWA-GATK, BWA-SNVer, GNUMAP, and BWA-SAMtools). We additionally sequenced a single whole genome using the sequencing and analysis pipeline from Complete Genomics (CG), with 95% of the exome region being covered by 20 or more reads per base. Finally, we validated 919 single-nucleotide variations (SNVs) and 841 insertions and deletions (indels), including similar fractions of GATK-only, SOAP-only, and shared calls, on the MiSeq platform by amplicon sequencing with approximately 5000X mean coverage. RESULTS: SNV concordance between five Illumina pipelines across all 15 exomes was 57.4%, while 0.5 to 5.1% of variants were called as unique to each pipeline. Indel concordance was only 26.8% between three indel-calling pipelines, even after left-normalizing and intervalizing genomic coordinates by 20 base pairs. There were 11% of CG variants falling within targeted regions in exome sequencing that were not called by any of the Illumina-based exome analysis pipelines. Based on targeted amplicon sequencing on the MiSeq platform, 97.1%, 60.2%, and 99.1% of the GATK-only, SOAP-only and shared SNVs could be validated, but only 54.0%, 44.6%, and 78.1% of the GATK-only, SOAP-only and shared indels could be validated. Additionally, our analysis of two families (one with four individuals and the other with seven), demonstrated additional accuracy gained in variant discovery by having access to genetic data from a multi-generational family. CONCLUSIONS: Our results suggest that more caution should be exercised in genomic medicine settings when analyzing individual genomes, including interpreting positive and negative findings with scrutiny, especially for indels. We advocate for renewed collection and sequencing of multi-generational families to increase the overall accuracy of whole genomes

Fluorescence Correlation Spectroscopic Study of Serpin Depolymerization by Computationally Designed Peptides

Members of the serine proteinase inhibitor (serpin) family play important roles in the inflammatory and coagulation cascades. Interaction of a serpin with its target proteinase induces a large conformational change, resulting in insertion of its reactive center loop (RCL) into the main body of the protein as a new strand within beta-sheet A. Intermolecular insertion of the RCL of one serpin molecule into the beta-sheet A of another leads to polymerization, a widespread phenomenon associated with a general class of diseases known as serpinopathies. Small peptides are known to modulate the polymerization process by binding within beta-sheet A. Here, we use fluorescence correlation spectroscopy (FCS) to probe the mechanism of peptide modulation of alpha(1)-antitrypsin (alpha(1)-AT) polymerization and depolymerization, and employ a statistical computationally-assisted design strategy (SCADS) to identify new tetrapeptides that modulate polymerization. Our results demonstrate that peptide-induced depolymerization takes place via a heterogeneous, multi-step process that begins with internal fragmentation of the polymer chain. One of the designed tetrapeptides is the most potent antitrypsin depolymerizer yet found