Perfluorooctanesulphonicacid (PFOS), a persistent organic contaminant, has been widely detected in the environment, wildlife and humans, but few studies have assessed its effect on aquatic organisms. The present study evaluated the effect of PFOS on zebrafish embryos. Zebrafish embryos exhibited bent spine and developmental toxicity after exposure to various PFOS concentrations (0.01-16.0 μM) from 6 to 120 hour post-fertilization (hpf). The LC50 at 120 hpf was 4.39 μM and the EC50 at 120 hpf was 2.23 μM. PFOS induced apoptosis at 24 hpf was consistently located in the brain, eye, and tail region of embryos. PFOS elevated the basal rate of swimming after 4 days of exposure, and larvae exposed to PFOS (0.5-8.0μM) for only 1 h at 6 dpf swam faster with increasing PFOS concentration. Larvae exposed to 16.0 μM PFOS for 24 h periods from 1 to 121 hpf showed the highest incidence of malformations in the 97-121 hpf window. Continuous exposure to PFOS from 1 to 121 hpf resulted in a steady accumulation with no evidence of elimination. Our results further the understanding of the health risks of PFOS to aquatic organisms and identify additional research needed on PFOS toxicology.

In the past two decades, a significant number of innovative sensing and monitoring systems based on the machine vision-based technology have been exploited in the field of structural health monitoring (SHM). This technology has some inherent distinctive advantages such as noncontact, nondestructive, long distance, high precision, immunity to electromagnetic interference, and large-range and multiple-target monitoring. A lot of machine vision-based structural dynamic measurement and structural state inspection methods have been proposed. Real-world applications are also carried out to measure the structural physical parameters such as the displacement, strain/stress, rotation, vibration, crack, and spalling. The purpose of this review article is devoted to presenting a summary of the basic theories and practical applications of the machine vision-based technology employed in structural monitoring as well as its systematic error sources and integration with other modern sensing techniques.

This paper employs the random decrement technique as an output-only method to detect damage from the acceleration signals under a moving load. The random decrement technique is an especial averaging method that produces Random Decrement Signatures (RDS). For this purpose, Arias Intensity (AI) was employed to calculate the energy content of each RDS and substitute each acceleration signal by a scalar invariant value. Normalizing AIs, all RDSs were then updated so as to show a unique energy along the undamaged structure. Once the normalizing factor was computed for the intact structure, the damage was determined by the absolute difference of normalized AIs obtained from each individual RDS along the structure simultaneously. To verify the proposed method, two experimental models of a simply supported beam and a scaled arch bridge were developed under a moving load (vehicle simulation), and acceleration data were recorded. The results of laboratory models proved that the RDSs can accurately detect the damage location using the normalized AI without applying any further frequency filtering. This method needs neither the damage location nor modal parameters in advance, and could properly work in a noisy environment as well.

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This paper provides a simple and direct output-only baseline-free method to detect damage from the noisy acceleration data by using Moving Average Filter (MAF). MAF is a convolution approach based on a simple filter kernel (rectangular shape) that works as an averaging method to smooth signal and remove incorporated noise. In this paper, a method is proposed to employ MAF to smooth acceleration signals obtained from a series of accelerometers and determine the damage location along a steel beam. To verify the proposed method, a simply supported beam was modelled through a 3D numerical simulation and an experimental model under a moving vehicle load. The response acceleration data was then recorded at a sampling frequency of 500 Hz. Finally, damage location was identified by applying the proposed method. The results showed that the proposed method can accurately estimate the damage location from the acceleration signal without applying any frequency filtering or baseline correction.

Cracks are a potential threat to the safety and endurance of civil infrastructures, and therefore, careful and regular structural crack inspection is needed during their long-term service periods. Many image-processing approaches have been developed for structural crack detection. However, like traditional edge detection algorithms, these methods are easily disturbed by the environmental effect. Convolutional neural networks are newly developed methods and have excellent performances in the image-classification tasks. This study proposes a fully convolutional network called Ci-Net for structural crack identification. Pixel-level labeled image training data are obtained from the online data set. Four indices are adopted to evaluate the performance of the trained Ci-Net. Crack images from an indoor concrete beam test are adopted for validation of its structural crack recognition capacity. The recognition results are also compared with those obtained by the edge detection methods. It indicates that Ci-Net exhibits a better performance over the edge detection methods in structural damage detection.

Abstract:Objective: To discuss possible relationships between class III malocclusion and perioral forces by measuring the pressure from the lips and the tongue of children with class III malocclusion. Methods: Thirty-one children with class III malocclusion were investigated and their perioral forces were measured at rest and during swallowing under natural head position by a custom-made miniperioral force computer measuring system. Results: The resting pressures exerted on the labial side and palatine side of the upper left incisor, as well as the labial side and lingual side of the lower left incisor, were 0 g/cm 2 , 0 g/cm 2 , 0.57 g/cm 2 and 0.23 g/cm 2 , respectively. Correspondingly, the swallowing forces were 2.87 g/cm 2 , 5.97 g/cm 2 , 4.09 g/cm 2 and 7.89 g/cm 2 , respectively. No statistical difference between muscular pressure and gender existed. During swallowing, the lingual forces were significantly higher than the labial forces (P<0.01), however, at rest there was no significantly different force between these two sides. Compared to the normal occlusion patients, children with class III malocclusion had lower perioral forces. The upper labial resting forces (P<0.01), the lower labial resting forces (P<0.05) and all the swallowing pressures from the lips and the tongue (P<0.01) showed statistical differences between the two different occlusion conditions. Meanwhile, no significant difference was found for the resting pressure from the tongue between class III malocclusion and normal occlusion. Conclusion: Patients with class III malocclusion have lower perioral forces and this muscle hypofunction may be secondary to the spatial relations of the jaws. The findings support the spatial matrix hypothesis.

Background Bos taurus and Bos indicus are two main sub-species of cattle. However, the differential copy number variations (CNVs) between them are not yet well studied. Results Based on the new high-quality cattle reference genome ARS-UCD1.2, we identified 13,234 non-redundant CNV regions (CNVRs) from 73 animals of 10 cattle breeds (4 Bos taurus and 6 Bos indicus), by integrating three detection strategies. While 6990 CNVRs (52.82%) were shared by Bos taurus and Bos indicus, large CNV differences were discovered between them and these differences could be used to successfully separate animals into two subspecies. We found that 2212 and 538 genes uniquely overlapped with either indicine-specific CNVRs and or taurine-specific CNVRs, respectively. Based on FST, we detected 16 candidate lineage-differential CNV segments (top 0.1%) under selection, which overlapped with eight genes (CTNNA1, ENSBTAG00000004415, PKN2, BMPER, PDE1C, DNAJC18, MUSK, and PLCXD3). Moreover, we obtained 1.74 Mbp indicine-specific sequences, which could only be mapped on the Bos indicus reference genome UOA_Brahman_1. We found these sequences and their associated genes were related to heat resistance, lipid and ATP metabolic process, and muscle development under selection. We further analyzed and validated the top significant lineage-differential CNV. This CNV overlapped genes related to muscle cell differentiation, which might be generated from a retropseudogene of CTH but was deleted along Bos indicus lineage. Conclusions This study presents a genome wide CNV comparison between Bos taurus and Bos indicus. It supplied essential genome diversity information for understanding of adaptation and phenotype differences between the Bos taurus and Bos indicus populations.