Study on the expression patterns of inner root sheath-specific genes in Tan sheep hair follicle during different developmental stages.

By analyzing the expression patterns of inner root sheath (IRS) specific genes during different developmental stages of hair follicle (HF) in Tan sheep embryos and at birth, this study aims to reveal the influence of the IRS on crimped wool. Skin tissues from the scapular region of male Tan sheep were collected at 85 days (E85) and 120 days (E120) of fetal development, and at 0 days (D0), 35 days (D35), and 60 days (D60) after birth, with four samples at each stage. Real-time quantitative polymerase chain reaction (RT-qPCR) was employed to determine the relative expression levels of IRS type I keratin genes (KRT25, KRT26, KRT27, KRT28), type II keratin genes (KRT71, KRT72, KRT73, KRT74), and the trichohyalin gene (TCHH) in the skin of Tan sheep at different stages. Results showed that the expression levels of all IRS-specific genes peaked at D0, with the expression of all genes significantly higher than at E85 (P < 0.01), except for KRT73 and TCHH. The expression levels of KRT25, KRT26, and KRT72 were also significantly higher than at E120 (P < 0.01). Furthermore, the expression levels of KRT27, KRT28, KRT71, and KRT74 were significantly higher than both at E120 and D35 (P < 0.01). The expression levels of other genes at different stages showed no significant difference (P > 0.05). Conclusion: The IRS-specific genes exhibit the highest expression levels in Tan sheep at the neonatal stage. The expression levels of KRT71, KRT72, and TCHH, which are consistent with the pattern of wool crimp, may influence the morphology of the IRS and thereby affect the crimp of Tan sheep wool.

Fast Learning of Signed Distance Functions From Noisy Point Clouds Via Noise to Noise Mapping.

Learning signed distance functions (SDFs) from point clouds is an important task in 3D computer vision. However, without ground truth signed distances, point normals or clean point clouds, current methods still struggle from learning SDFs from noisy point clouds. To overcome this challenge, we propose to learn SDFs via a noise to noise mapping, which does not require any clean point cloud or ground truth supervision. Our novelty lies in the noise to noise mapping which can infer a highly accurate SDF of a single object or scene from its multiple or even single noisy observations. We achieve this by a novel loss which enables statistical reasoning on point clouds and maintains geometric consistency although point clouds are irregular, unordered and have no point correspondence among noisy observations. To accelerate training, we use multi-resolution hash encodings implemented in CUDA in our framework, which reduces our training time by a factor of ten, achieving convergence within one minute. We further introduce a novel schema to improve multi-view reconstruction by estimating SDFs as a prior. Our evaluations under widely-used benchmarks demonstrate our superiority over the state-of-the-art methods in surface reconstruction from point clouds or multi-view images, point cloud denoising and upsampling.

CAP-UDF: Learning Unsigned Distance Functions Progressively from Raw Point Clouds with Consistency-Aware Field Optimization.

Surface reconstruction for point clouds is an important task in 3D computer vision. Most of the latest methods resolve this problem by learning signed distance functions from point clouds, which are limited to reconstructing closed surfaces. Some other methods tried to represent open surfaces using unsigned distance functions (UDF) which are learned from ground truth distances. However, the learned UDF is hard to provide smooth distance fields due to the discontinuous character of point clouds. In this paper, we propose CAP-UDF, a novel method to learn consistency-aware UDF from raw point clouds. We achieve this by learning to move queries onto the surface with a field consistency constraint, where we also enable to progressively estimate a more accurate surface. Specifically, we train a neural network to gradually infer the relationship between queries and the approximated surface by searching for the moving target of queries in a dynamic way. Meanwhile, we introduce a polygonization algorithm to extract surfaces using the gradients of the learned UDF. We conduct comprehensive experiments in surface reconstruction for point clouds, real scans or depth maps, and further explore our performance in unsupervised point normal estimation, which demonstrate non-trivial improvements of CAP-UDF over the state-of-the-art methods.

Single-cell metabolic fingerprints discover a cluster of circulating tumor cells with distinct metastatic potential.

Circulating tumor cells (CTCs) are recognized as direct seeds of metastasis. However, CTC count may not be the "best" indicator of metastatic risk because their heterogeneity is generally neglected. In this study, we develop a molecular typing system to predict colorectal cancer metastasis potential based on the metabolic fingerprints of single CTCs. After identification of the metabolites potentially related to metastasis using mass spectrometry-based untargeted metabolomics, setup of a home-built single-cell quantitative mass spectrometric platform for target metabolite analysis in individual CTCs and use of a machine learning method composed of non-negative matrix factorization and logistic regression, CTCs are divided into two subgroups, C1 and C2, based on a 4-metabolite fingerprint. Both in vitro and in vivo experiments demonstrate that CTC count in C2 subgroup is closely associated with metastasis incidence. This is an interesting report on the presence of a specific population of CTCs with distinct metastatic potential at the single-cell metabolite level.

Quantitative trait loci associated with amino acid concentration and in vitro protein digestibility in pea (Pisum sativum L.).

With the expanding interest in plant-based proteins in the food industry, increasing emphasis is being placed on breeding for protein concentration and quality. Two protein quality traits i.e., amino acid profile and protein digestibility, were assessed in replicated, multi-location field trials from 2019 to 2021 in pea recombinant inbred line population PR-25. This RIL population was targeted specifically for the research of protein related traits and its parents, CDC Amarillo and CDC Limerick, had distinct variations in the concentration of several amino acids. Amino acid profile was determined using near infrared reflectance analysis, and protein digestibility was through an in vitro method. Several essential amino acids were selected for QTL analysis, including lysine, one of the most abundant essential amino acids in pea, and methionine, cysteine, and tryptophan, the limiting amino acids in pea. Based on phenotypic data of amino acid profiles and in vitro protein digestibility of PR-25 harvested in seven location-years, three QTLs were associated with methionine + cysteine concentration, among which, one was located on chromosome 2 (R2 = 17%, indicates this QTL explained 17% phenotypic variation of methionine + cysteine concentration within PR-25), and two were located on chromosome 5 (R2 = 11% and 16%). Four QTLs were associated with tryptophan concentration and are located on chromosome 1 (R2 = 9%), chromosome 3 (R2 = 9%), and chromosome 5 (R2 = 8% and 13%). Three QTLs were associated with lysine concentration, among which, one was located on chromosome 3 (R2 = 10%), the other two were located on chromosome 4 (R2 = 15% and 21%). Two QTLs were associated with in vitro protein digestibility, one each located on chromosomes 1 (R2 = 11%) and 2 (R2 = 10%). QTLs associated with in vitro protein digestibility, and methionine + cysteine concentration on chromosome 2 were identified to be co-localized with known QTL for total seed protein concentration in PR-25. QTLs associated with tryptophan and methionine + cysteine concentration co-localized on chromosome 5. The identification of QTLs associated with pea seed quality is an important step towards marker-assisted selection of breeding lines with improved nutritional quality, which will further boost the competitiveness of pea in plant-based protein markets.

Identification of Quantitative Trait Loci Associated with Seed Protein Concentration in a Pea Recombinant Inbred Line Population.

This research aimed to identify quantitative trait loci (QTLs) associated with seed protein concentration in a recombinant inbred line (RIL) population of pea and aimed to validate the identified QTLs using chromosome segment-introgressed lines developed by recurrent backcrossing. PR-25, an RIL population consisting of 108 F7 bulked lines derived from a cross between CDC Amarillo (yellow cotyledon) and CDC Limerick (green cotyledon), was used in this research. The RIL population was genotyped using an Axiom 90K SNP array. A total of 10,553 polymorphic markers were used for linkage map construction, after filtering for segregation distortion and missing values. The linkage map represents 901 unique loci on 11 linkage groups which covered a map distance of 855.3 Centimorgans. Protein concentration was assessed using near-infrared (NIR) spectroscopy of seeds harvested from field trials in seven station-years in Saskatchewan, Canada, during the 2019-2021 field seasons. Three QTLs located on chromosomes 2, 3 and 5 were identified to be associated with seed protein concentration. These QTLs explained 22%, 11% and 17% of the variation for protein concentration, respectively. The identified QTLs were validated by introgression lines, developed by marker-assisted selection of backcross lines for introgression of corresponding chromosome segments (~1/4 chromosome) harboring the QTL regions. Introgression line PR-28-7, not carrying any protein-related QTLs identified in this study, was 4.7% lower in protein concentration than CDC Amarillo, the lower protein parent of PR-25 which carried one identified protein-related QTL. The SNP markers located at the peak of the three identified QTLs will be converted into breeder-friendly KASP assays, which will be used for the selection of high-protein lines from segregating populations.

Study on Wear Characteristics of Revolute Clearance Joints in Mechanical Systems.

The existence of clearance causes contact-impact forces in joints, which lead to surface wear and incessant material loss of the joint surface during the motion of mechanisms. In this work, the wear characteristics of dry revolute clearance joints in planar mechanisms are studied using a computational methodology. The normal contact force model and the tangential friction force model are established to describe the contact-impact in clearance joints. Then, the dynamic wear model based on the Archard's wear model is established to predict the wear characteristics of clearance joints in mechanisms. The dynamic wear depths of clearance joints are obtained in two steps. The first step is the dynamics analysis of mechanisms to obtain the contact and sliding characteristics between the bearing and journal in the clearance joint. The second step is the dynamic wear depth analysis of clearance joints based on dynamic Archard's wear model. Finally, a planar slider-crank mechanism with two revolute clearance joints between the connecting rod and its adjacent links is used as the implement example. Different case studies are performed to investigate the wear characteristics of clearance joints in mechanical systems.

MNL-Network: A Multi-Scale Non-local Network for Epilepsy Detection From EEG Signals.

Epilepsy is a prevalent neurological disorder that threatens human health in the world. The most commonly used method to detect epilepsy is using the electroencephalogram (EEG). However, epilepsy detection from the EEG is time-consuming and error-prone work because of the varying levels of experience we find in physicians. To tackle this challenge, in this paper, we propose a multi-scale non-local (MNL) network to achieve automatic EEG signal detection. Our MNL-Network is based on 1D convolution neural network involving two specific layers to improve the classification performance. One layer is named the signal pooling layer which incorporates three different sizes of 1D max-pooling layers to learn the multi-scale features from the EEG signal. The other one is called a multi-scale non-local layer, which calculates the correlation of different multi-scale extracted features and outputs the correlative encoded features to further enhance the classification performance. To evaluate the effectiveness of our model, we conduct experiments on the Bonn dataset. The experimental results demonstrate that our MNL-Network could achieve competitive results in the EEG classification task.

Arsenical keratosis caused by medication: a case report and literature.

Medication-induced arsenical keratosis is a rare type of arsenical keratosis. We describe here a case of 70-year-old man to explore the clinical characters, diagnosis and treatment of medication-induced arsenical keratosis in order to improve the understanding of this disease and reduce the misdiagnosis rate. The clinical characters, signs, lab findings as well as progression, diagnosis and treatment in the case of arsenical keratosis were analyzed. The patient of medication-induced arsenical keratosis suffered from chronic eczema. He has taken realgar during the treatment. His medication caused arsenical keratosis. Medication-induced arsenical keratosis is rare. Making the medication history clear and using urine arsenic detection if necessary are of significance to understand the patients' condition. It is quite effective that using Sodium Dimercaptosulphonate during the treatment without delay.

Effects of basic fibroblast growth factor and cyclin D1 on cigarette smoke-induced pulmonary vascular remodeling in rats.

Cigarette smoking may contribute to pulmonary hypertension in chronic obstructive pulmonary disease by resulting in pulmonary vascular remodeling that involves pulmonary artery smooth muscle cell proliferation. This study investigated the effects of basic fibroblast growth factor (bFGF) and cyclin D1 on the pulmonary vascular remodeling in smoking-exposed rats. Twenty-four male Wistar rats were randomly divided into four groups. Three tobacco-exposed groups were exposed to the smoke produced by 20 cigarettes for 60 min, twice a day for two, four or eight weeks, and the control group were exposed to fresh air. The expression of bFGF and cyclin D1 in the pulmonary arterial smooth muscle cells were determined using immunohistochemistry. Quantitative polymerase chain reaction was conducted to determine the expression levels of bFGF and cyclin D1 mRNA. In addition, the expression of bFGF and cyclin D1 proteins was evaluated by western blotting. The expression of bFGF and cyclin D1 at the mRNA and protein levels was shown to increase with the duration of smoke exposure (P<0.05). The correlation analysis indicated the expression of bFGF and cyclin D1 was positively associated with the pulmonary vessel wall thickness. The expression of bFGF was positively associated with that of cyclin D1. Collectively, the data demonstrated that the upregulation of bFGF and cyclin D1 occurred in rats subjected to smoke exposure, which may be associated with the abnormal proliferation of the smooth muscle cells in the pulmonary arteries.

[Association of GSTM1 and GSTT1 polymorphisms with noise-induced hearing loss: a meta-analysis].

OBJECTIVE: To evaluate the association of glutathione S-transferase M1 (GSTM1) and glutathione S-transferase T1 (GSTT1) polymorphisms with noise-induced hearing loss. METHODS: The Cochrane library, PubMed, OVID, EMBASE, Springer, Wanfang Data, VIP, CNKI, and CBM were searched to collect case-control studies on GSTM1 or GSTT1 polymorphism and noise-induced hearing loss. The articles meeting the inclusion criteria were reviewed systematically, and the reported data were aggregated using Revman 5.0. RESULTS: Five studies were included in the meta-analysis. The meta-analysis and subgroup analysis showed that the persons with GSTM1 null genotype had an increased risk of noise-induced hearing loss compared with those with GSTM1 wild genotype (OR = 1.37, 95%CI: 1.13∼1.66); in the Chinese population, the risk of noise-induced hearing loss was higher in persons with GSTM1 null genotype than in those with GSTM1 wild genotype (OR = 1.5, 95%CI: 1.2∼1.86); there was no significant difference in the risk of noise-induced hearing loss between persons with GSTT1 null and wild genotypes. CONCLUSION: GSTM1 polymorphism is related to noise-induced hearing loss, but GSTT1 polymorphism is unrelated to this condition.