CondenseNet: An Efficient DenseNet using Learned Group Convolutions

Deep neural networks are increasingly used on mobile devices, where computational resources are limited. In this paper we develop CondenseNet, a novel network architecture with unprecedented efficiency. It combines dense connectivity with a novel module called learned group convolution. The dense connectivity facilitates feature re-use in the network, whereas learned group convolutions remove connections between layers for which this feature re-use is superfluous. At test time, our model can be implemented using standard group convolutions, allowing for efficient computation in practice. Our experiments show that CondenseNets are far more efficient than state-of-the-art compact convolutional networks such as MobileNets and ShuffleNets

Role of CD34 in inflammatory bowel disease

Inflammatory bowel disease (IBD) is caused by a variety of pathogenic factors, including chronic recurrent inflammation of the ileum, rectum, and colon. Immune cells and adhesion molecules play an important role in the course of the disease, which is actually an autoimmune disease. During IBD, CD34 is involved in mediating the migration of a variety of immune cells (neutrophils, eosinophils, and mast cells) to the inflammatory site, and its interaction with various adhesion molecules is involved in the occurrence and development of IBD. Although the function of CD34 as a partial cell marker is well known, little is known on its role in IBD. Therefore, this article describes the structure and biological function of CD34, as well as on its potential mechanism in the development of IBD

Neuromorphic visual artificial synapse in-memory computing systems based on GeOx-coated MXene nanosheets

Artificial synapses with light signal perception capability offer the ability to neuromorphic visual signal processing system on demand. In light of the excellent optical and electrical characteristics, the low-dimensional materials have become one of the most favorable candidates of the key component for optoelectronic artificial synapses. Previously, our group originally proposed the synthesis of germanium oxide-coated MXene nanosheets. In this work, we further applied this technology into the optoelectronic synaptic thin-film transistors for the first time. The devices exhibited the adjustable postsynaptic current behaviors under the visible light inputs. Moreover, the potentiation and depression operation modes of the devices further improved the application potential of the devices in mimicking biological synapses. Regulated by the wavelength of incident lights, the proposed artificial synapse could effectively help detect the target area of the image. Eventually, we further showed the results of the devices in the projects of neural network computing task. The long-term potentiation/depression characteristics of the conductance were applied to the synaptic weight matrix for image identification and path recognition tasks. By adding knowledge transfer in the process of recognition, the epoch required for convergence has been greatly reduced. The result of high noise tolerance revealed the great potential of the proposed transistors in establishing high-efficiency and robustness hardware neuromorphic systems for in-memory computing

QTL mapping of yield components and kernel traits in wheat cultivars TAM 112 and Duster

In the Southern Great Plains, wheat cultivars have been selected for a combination of outstanding yield and drought tolerance as a long-term breeding goal. To understand the underlying genetic mechanisms, this study aimed to dissect the quantitative trait loci (QTL) associated with yield components and kernel traits in two wheat cultivars `TAM 112' and `Duster' under both irrigated and dryland environments. A set of 182 recombined inbred lines (RIL) derived from the cross of TAM 112/Duster were planted in 13 diverse environments for evaluation of 18 yield and kernel related traits. High-density genetic linkage map was constructed using 5,081 single nucleotide polymorphisms (SNPs) from genotyping-by-sequencing (GBS). QTL mapping analysis detected 134 QTL regions on all 21 wheat chromosomes, including 30 pleiotropic QTL regions and 21 consistent QTL regions, with 10 QTL regions in common. Three major pleiotropic QTL on the short arms of chromosomes 2B (57.5 - 61.6 Mbps), 2D (37.1 - 38.7 Mbps), and 7D (66.0 - 69.2 Mbps) colocalized with genes Ppd-B1, Ppd-D1, and FT-D1, respectively. And four consistent QTL associated with kernel length (KLEN), thousand kernel weight (TKW), plot grain yield (YLD), and kernel spike-1 (KPS) (Qklen.tamu.1A.325, Qtkw.tamu.2B.137, Qyld.tamu.2D.3, and Qkps.tamu.6A.113) explained more than 5% of the phenotypic variation. QTL Qklen.tamu.1A.325 is a novel QTL with consistent effects under all tested environments. Marker haplotype analysis indicated the QTL combinations significantly increased yield and kernel traits. QTL and the linked markers identified in this study will facilitate future marker-assisted selection (MAS) for pyramiding the favorable alleles and QTL map-based cloning.Horticulture and Landscape Architectur

Dynamic modelling of ammonia biofiltration from waste gases

A dynamic model to describe ammonia removal in a gas-phase biofilter was developed. The math-ematical model is based on discretized mass balances and detailed nitrification kinetics that includeinhibitory effects caused by free ammonia (FA) and free nitrous acid (FNA). The model was able to pre-dict experimental results operation under different loading rates (from 3.2 to 13.2 g NH3h-1m-3). In par-ticular the model was capable of reproducing inhibition caused by high inlet ammonia concentrations. Alsoelimination capacity was accurately predicted. Experimental data was also used to optimize certain modelparameters such as the concentration of ammonia- and nitrite-oxidizing biomass.Peer ReviewedPostprint (published version

Single-nucleotide polymorphism discovery by high-throughput sequencing in sorghum

<p>Abstract</p> <p>Background</p> <p>Eight diverse sorghum (<it>Sorghum bicolor </it>L. Moench) accessions were subjected to short-read genome sequencing to characterize the distribution of single-nucleotide polymorphisms (SNPs). Two strategies were used for DNA library preparation. Missing SNP genotype data were imputed by local haplotype comparison. The effect of library type and genomic diversity on SNP discovery and imputation are evaluated.</p> <p>Results</p> <p>Alignment of eight genome equivalents (6 Gb) to the public reference genome revealed 283,000 SNPs at ≥82% confirmation probability. Sequencing from libraries constructed to limit sequencing to start at defined restriction sites led to genotyping 10-fold more SNPs in all 8 accessions, and correctly imputing 11% more missing data, than from semirandom libraries. The SNP yield advantage of the reduced-representation method was less than expected, since up to one fifth of reads started at noncanonical restriction sites and up to one third of restriction sites predicted <it>in silico </it>to yield unique alignments were not sampled at near-saturation. For imputation accuracy, the availability of a genomically similar accession in the germplasm panel was more important than panel size or sequencing coverage.</p> <p>Conclusions</p> <p>A sequence quantity of 3 million 50-base reads per accession using a <it>Bsr</it>FI library would conservatively provide satisfactory genotyping of 96,000 sorghum SNPs. For most reliable SNP-genotype imputation in shallowly sequenced genomes, germplasm panels should consist of pairs or groups of genomically similar entries. These results may help in designing strategies for economical genotyping-by-sequencing of large numbers of plant accessions.</p