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Ideally, the monitoring data collected by the Structural health monitoring (SHM) system should purely reflect the structure status. However, sensors deployed in the field can be very vulnerable to extreme conditions such as heavy rainfall, which results in large quantities of anomalous data and unavoidably leads to the inaccuracy of structural condition assessment and even false alarms. To automatically identify whether the collected data are abnormal or not, a novel deep learning-based data anomaly detection technique combining the time-frequency method and the Convolutional Neural Network (CNN) is proposed in this paper. First, the original time-series data of the SHM system were converted to the red green blue (RGB) images by using the wavelet scalograms. Subsequently, the GoogLeNet deep neural network is applied to construct a classification model by incorporating the generated 2D images. In order to evaluate the performance of the proposed technique, the SHM data (containing seven abnormal patterns) lasting for one month of a long-span cable-stayed bridge were utilized for experimental validation. The results indicate that compared with traditional deep neural network methods, the data anomaly identification accuracy can be improved by using the proposed technique. Different types of data anomaly patterns can be accurately identified, even in the case of small samples. The proposed technique exhibits good accuracy and can be integrated into advanced SHM systems with high fidelity and intelligence. [ABSTRACT FROM AUTHOR]

Purpose: Previous studies have discovered different neuroimaging features in anti-NMDAR encephalitis associated with cognitive dysfunction. However, it is unknown whether there is a correlation between abnormal homotopic connectivity and cognitive impairment in anti-NMDAR encephalitis. We aim to explore the homotopic connectivity patterns of patients with anti-NMDAR encephalitis and their associations with clinical characteristics. Methods: Resting-state functional magnetic resonance imaging (rs-fMRI) was performed on 29 patients with anti-NMDAR encephalitis and 26 healthy controls (HCs). Voxel-mirrored homotopic connectivity (VMHC) and multivariate pattern analysis (MVPA) were applied to analyze the imaging data. A correlation was also performed between aberrant brain regions and clinical parameters. Results: Compared to HCs, the performance of alertness in the patient group was typically worse (p < 0.05). A significant decrease in VMHC was observed in many regions of the patients in comparison to HCs, including the cerebellar 6, para-hippocampal gyrus, insula, precuneus, and middle frontal gyrus (p < 0.001). The insula and middle frontal gyrus were found to show positive correlations with alertness. The MVPA method achieved a classification accuracy of 74.55% with a sensitivity of 82.76% and a specificity of 65.38% in discriminating patients from HCs. Conclusion: Our findings indicate that interhemispheric functional imbalance may play a significant role in the pathophysiology of cognitive dysfunction in anti-NMDAR encephalitis. The MVPA results suggest that abnormal VMHC may play a crucial role in the identification of patients with anti-NMDAR encephalitis from HCs. [ABSTRACT FROM AUTHOR]

Background and purpose: Anti‐N‐methyl‐d‐aspartate receptor (NMDAR) encephalitis is characterized by a range of cognitive impairments, especially in executive function. Our study aims to identify the abnormal regional homogeneity (ReHo) in anti‐NMDAR encephalitis patients and its relationship with the executive function. Methods: Forty patients and 42 healthy volunteers undertook an Attention Network Test and a resting‐state functional magnetic resonance imaging scan. ReHo analysis was performed to investigate the neuronal activity synchronization in all subjects. Based on ReHo analysis, a multivariate pattern analysis (MVPA) was carried out to identify the brain regions that differed the most between the two groups. Results: Compared to controls, the patients had higher executive control scores (p < 0.05). The patients presented reduced ReHo values in the bilateral posterior cerebellar lobe, anterior cerebellar lobe, midbrain, bilateral caudate nucleus, right superior frontal gyrus, right middle temporal gyrus, bilateral inferior parietal lobule and the left middle frontal gyrus. The ReHo values of the bilateral inferior parietal lobule in patients were found to be negatively associated with executive control scores. The classification of patients and controls using MVPA had an accuracy of 76.83%, a sensitivity of 82.50%, a specificity of 71.43% and the area under the curve was 0.83. Conclusions: Our study provides evidence of abnormal cerebral function in anti‐NMDAR encephalitis patients, which may contribute to unveiling the neuropathological mechanisms of anti‐NMDAR encephalitis and their influences on executive dysfunction. The MVPA classifier, based on ReHo, is helpful in identifying anti‐NMDAR encephalitis patients from healthy controls. [ABSTRACT FROM AUTHOR]

In this paper, the self-made two-dimensional MoS2 nanosheets with five-layer thickness were attempted to be modified with oleic acid (OA) with a simple one-step route, denoted OA-MoS2. The OA-MoS2 nanosheets were confirmed by a series of characterizations. The results showed that OA molecules could chemically seal MoS2 nanosheets through C-S bonds, which significantly improved the dispersibility and stability of MoS2 nanosheets in base oil. The OA-MoS2 nanosheets possessed better dispersibility in base oil and could maintain the stability in oil up to 96 h. Furthermore, the tribological properties of OA-MoS2 nanosheets as an additive in base oil were completely studied through a ball-on-ball configuration four-ball testing machine. The experimental results showed that the average friction coefficient and average wear scar diameter of the 0.04 wt.% OA-MoS2-based oil were decreased by about 41.2% and 17.1% as compared to base oil, respectively. In addition, its extreme pressure performance increased by about 13.0%. These results indicated that OA-MoS2 nanosheets exhibited better antifriction, antiwear and extreme pressure abilities in oil due to the desirable solved dispersibility. [ABSTRACT FROM AUTHOR]

In this report, circular dichroism (CD) along with steady-state fluorescence spectroscopy and molecular modeling investigations were carried out to better understand the interaction of miriplatin with human serum albumin (HSA). The presence of miriplatin in solution is found to destabilize the native structure of HSA: The tertiary structure of HSA was changed and the microenvironment of Trp residue became more hydrophobic; the binding affinity of HSA with miriplatin indicating by 8-Anilino-1-naphthalenesulfonic acid (ANS) fluorescence study was 1.74 × 10 6 L/mol; miriplatin induced the denaturation and unfolding of HSA and disrupted the polar contacts and decreasing the reversibility of the unfolding process of protein. In addition, molecular modeling studies indicated miriplatin bound to domain II of HSA by hydrophobic force, hydrogen bonds, and electrostatic force interactions. HSA retained most of its esterase activity even after its binding with miriplatin. These results provide valuable insight into the binding mechanism between miriplatin and a plasma protein that is known to play an important role in the drug delivery of medicinal drugs to target organs. [ABSTRACT FROM AUTHOR]

For the first time, the effects of picoplatin on the structure and esterase-like catalytic activity of human serum albumin (HSA) have been investigated by spectroscopic approaches and molecular modeling. The circular dichroism (CD) spectral examinations indicated that the binding of picoplatin with HSA induced a slight decrease of a-helix content of protein and unfolded the constituent polypeptides of the protein. The synchronous fluorescence and three-dimensional fluorescence spectral methods were used to estimate the effect of picoplatin on the micro-environmental changes of the Trp and Tyr residues of HSA, indicating that the micro-environment around the Tyr and Trp residue is partly disturbed by picoplatin. UV–vis absorption spectral result indicated the formation of the ground state complex between picoplatin with HSA. The ANS binding assay indicated the existence of competitive combination of picoplatin and ANS with HSA. The studies on the effects of picoplatin on the binding of HSA with bilirubin and heme showed that picoplatin binding caused a change of angle between two chromophores of bound bilirubin and the binding site of picoplatin does not locate in subdomain IB in HSA that bound with heme. The molecular modeling results showed that picoplatin binds to the connection between domain I and domain II by hydrophobic, hydrogen bonds, and van der Waals forces. In addition, HSA maintains most of its esterase activity in the presence of picoplatin. The investigations on how picoplatin interacts with HSA are important for the understanding of the anticancer mechanism and toxicity of platinum-based anticancer drug. [ABSTRACT FROM AUTHOR]

• An improved YOLOv4 network adopting the pruning technique and the EvoNorm-S0 structure was presented. • The pruning technique is used to light-weight the network structure, and the EvoNorm-S0 could improve the detection accuracy. • Compared with the original network, the mAP50 of the improved network is increased from 91.69% to 92.54% (after 100 epochs of training for both the original and the improved YOLOv4), and the weight of the proposed network is just 54.9% of the original ones. • Compared with three other leading algorithms in this field, the proposed network can identify the largest number of target species in the image, and has the highest mAP50. oncrete is a widely used material in the infrastructure system. However, this material is susceptible to several factors that eventually create concrete cracks. Thus, accurately identifying the cracks' size and location in concrete structures is crucial for structural safety evaluation. In this research, an improved YOLOv4 network adopting the pruning technique and the EvoNorm-S0 structure was put forward to better identify concrete cracks from many misleading targets. The pruning technique is used to light-weight the network structure, and the EvoNorm-S0 could improve the detection accuracy. The results indicate that compared with the original YOLOv4, the mAP50 of the improved network is increased from 91.69% to 92.54% when both models are trained for 100 epochs, and the 1-Batch inference time is reduced by 15.9%. Moreover, the weight of the proposed network is just 54.9% of the original ones. The proposed network was also compared with three other leading algorithms in this field (i.e., SSD300, YOLOv3, and YOLO X-L) using the same dataset. The results show that the proposed network can not only correctly classify the largest number of objects with a fast calculation speed, but also has the highest mAP50. Thus, this proposed network exhibits several advantages for detecting concrete cracks and is a desirable tool for practical engineering. [ABSTRACT FROM AUTHOR]

In this article, an attempt is made to analysis the binding mechanism of γ-Fe 2 O 3 nanoparticles with fibrinogen by using a combination of circular dichroism, UV–vis, fluorescence spectroscopic and computational methods. The multi-spectroscopic data revealed that the complex easily formed between γ-Fe 2 O 3 nanoparticles and fibrinogen by mainly hydrogen bonding forces. The binding constants of fibrinogen with γ-Fe 2 O 3 nanoparticles were 2.24 × 10 7 , 1.15 × 10 7 and 0.72 × 10 7 L mol −1 at 298, 304, and 310 K, respectively. Furthermore, the results from circular dichroism, UV–vis, synchronous fluorescence, and three-dimensional fluorescence studies showed that the strong binding interaction of γ-Fe 2 O 3 nanoparticles with fibrinogen induced an obvious perturbation in the protein secondary and tertiary structure. Moreover, the results of molecular modeling indicated the existence of the preferable binding site on fibrinogen for γ-Fe 2 O 3 NPs model. [ABSTRACT FROM AUTHOR]

Objective: Cognitive deficit is common in patients with temporal lobe epilepsy (TLE). Here, we aimed to investigate the modular architecture of functional networks associated with distinct cognitive states in TLE patients together with the role of the thalamus in modular networks. Methods: Resting‐state functional magnetic resonance imaging scans were acquired from 53 TLE patients and 37 matched healthy controls. All patients received the Montreal Cognitive Assessment test and accordingly were divided into TLE patients with normal cognition (TLE‐CN, n = 35) and TLE patients with cognitive impairment (TLE‐CI, n = 18) groups. The modular properties of functional networks were calculated and compared including global modularity Q, modular segregation index, intramodular connections, and intermodular connections. Thalamic subdivisions corresponding to the modular networks were generated by applying a 'winner‐take‐all' strategy before analyzing the modular properties (participation coefficient and within‐module degree z‐score) of each thalamic subdivision to assess the contribution of the thalamus to modular functional networks. Relationships between network properties and cognitive performance were then further explored. Results: Both TLE‐CN and TLE‐CI patients showed lower global modularity, as well as lower modular segregation index values for the ventral attention network and the default mode network. However, different patterns of intramodular and intermodular connections existed for different cognitive states. In addition, both TLE‐CN and TLE‐CI patients exhibited anomalous modular properties of functional thalamic subdivisions, with TLE‐CI patients presenting a broader range of abnormalities. Cognitive performance in TLE‐CI patients was not related to the modular properties of functional network but rather to the modular properties of functional thalamic subdivisions. Conclusions: The thalamus plays a prominent role in modular networks and potentially represents a key neural mechanism underlying cognitive impairment in TLE. [ABSTRACT FROM AUTHOR]

Background: There have been numerous articles as to whether the staining index (SI) of astrocyte elevated gene-1 (AEG-1) adversely affects clinical progression and prognosis of gastrointestinal cancers. Nevertheless, controversy still exists in terms of correlations between AEG-1 SI and clinicopathological parameters including survival data. Consequently, we conducted a comprehensive meta-analysis to confirm the role of AEG-1 in clinical outcomes of gastrointestinal carcinoma patients. Methods: We performed a comprehensive search in PubMed, ISI Web of Science, Cochrane Central Register of Controlled Trials, EMBASE, Science Direct, Wiley Online Library, China National Knowledge Infrastructure (CNKI), WanFang and Chinese VIP databases. STATA 12.0 (STATA Corp., College, TX) was used to analyze the data extracted from suitable studies and Newcastle-Ottawa Scale was applied to assess the quality of included articles. Results: The current meta-analysis included 2999 patients and our results suggested that strong associations emerged between AEG-1 SI and histological differentiation (OR = 2.129, 95%CI: 1.377–3.290, P = 0.001), tumor (T) classification (OR = 2.272, 95%CI: 1.147–4.502, P = 0.019), lymph node (N) classification (OR = 2.696, 95%CI: 2.178–3.337, P<0.001) and metastasis (M) classification (OR = 3.731, 95%CI: 2.167–6.426, P<0.001). Furthermore, high AEG-1 SI was significantly associated with poor overall survival (OS) (HR = 2.369, 95%CI: 2.005–2.800, P<0.001) and deteriorated disease-free survival (DFS) (HR = 1.538, 95%CI: 1.171–2.020, P = 0.002). For disease-specific survival (DSS) and relapse-free survival (RFS), no statistically significant results were observed (HR = 1.573, 95%CI: 0.761–3.250, P = 0.222; HR = 1.432, 95%CI: 0.108–19.085, P = 0.786). Subgroup analysis demonstrated that high AEG-1 SI was significantly related to poor prognosis in esophageal squamous cell carcinoma (ESCC) (HR = 1.715, 95%CI: 1.211–2.410, P = 0.002), gastric carcinoma (GC) (HR = 2.255, 95%CI: 1.547–3.288, P<0.001), colorectal carcinoma (CRC) (HR = 2.922, 95%CI: 1.921–4.444, P<0.001), gallbladder carcinoma (GBC) (HR = 3.047, 95%CI: 1.685–5.509, P<0.001), hepatocellular carcinoma (HCC) (HR = 2.245, 95%CI: 1.620–3.113, P<0.001), pancreatic adenocarcinoma (PAC) (HR = 2.408, 95%CI: 1.625–3.568, P<0.001). Conclusions: The current meta-analysis indicated that high AEG-1 SI might be associated with tumor progression and poor survival status in patients with gastrointestinal cancer. AEG-1 might play a vital role in promoting tumor aggression and could serve as a potential target for molecular treatments. Further clinical trials are needed to validate whether AEG-1 SI provides valuable insights into improving treatment decisions. [ABSTRACT FROM AUTHOR]