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As people pay more attention to environmental monitoring, Carbon dioxide (CO 2 ) sensors are widely used. However, most of the infrared CO 2 single-channel sensors are accompanied by low calibration efficiency and low accuracy. In order to save costs while improving calibration efficiency and accuracy, we proposed a fast calibration algorithm for Non-Dispersive Infrared (NDIR) single-channel carbon dioxide sensor based on deep learning. Firstly, we establish N network models which consist of N sensors by collecting m data points from different temperatures and concentrations. Secondly, we collect six data points from a new sensor which are measured at three temperatures and two concentrations. Thirdly, we choose multiple approximate models from N network models based on the matching of the data points. At last, we regard these models as the estimation model of the new sensor to calibrate the sensor concentration. This method eliminates the individual differences of a single model to a certain extent and achieves the purpose of rapid calibration. After comparing three kinds of neural networks and conducting relevant experiments, we chose BP neural network as the model, and set the number of selected models to three. The results show that the floating up and down by industry-standard 5% plus or minus 50 ppm calculation, the qualified rate of our method is up to 91.542% between 0 °C to 45 °C, and the qualified rate even reaches 99.063% between 20 °C to 35 °C. Compared with similar products, the qualified rate of our method in the calibration of carbon dioxide increases by 12.315% and 22.732% respectively.

Background Approximately one-third of spontaneous intracerebral hemorrhage patients did not know the onset time and were excluded from studies about time-dependent treatments for hyperacute spontaneous intracerebral hemorrhage. Aims To help clinicians explore the benefit of time-dependent treatments for unclear-onset patients, we presented artificial intelligence models to identify onset time using non-contrast computed tomography (NCCT) based on weakly supervised multitask learning (WS-MTL) structure. Methods The patients with reliable symptom onset time (strong label) or repeat CT (weak label) were included and split into training set and test set (internal and external). The WS-MTL structure utilized strong and weak labels simultaneously to improve performance. The models included three binary classification models for classifying whether NCCT acquired within 6, 8 or 12 h for different treatments measured by area under curve, and a regression model for determining the exact onset time measured by mean absolute error. The generalizability of models was also explored in comprehensive analysis. Results A total of 4004 patients with 10,780 NCCT scans were included. The performance of WS-MTL classification model showed high accuracy, and that of regression model was satisfactory in ≤6 h subgroup. In comprehensive analysis, the WS-MTL showed better performance for larger hematomas and thinner scans. And the performance improved effectively as training amounts increasing and could be improved steadily through retraining. Conclusions The WS-MTL models showed good performance and generalizability. Considering the large number of unclear-onset spontaneous intracerebral hemorrhage patients, it may be worth to integrate the WS-MTL model into clinical practice to identify the onset time.

To minimize surgical complications and staged procedures halo-traction is often used during deformity corrections. But the use of halo-traction in the treatment of rigid cervical kyphosis secondary to infections with abscesses has never been reported. This study investigated the role of halo-traction in the treatment of cervical infection patients associated with refractory kyphosis. We retrospectively reviewed 48 patients with cervical infection associated with refractory kyphosis who were treated in our spine department. Patients were divided into two groups, the traction group (A) and the non-traction group (B). Group A underwent preoperative halo-traction followed by surgery, while group B underwent surgery alone. Between the two groups, we analyzed the kyphosis deformity correction, level of fusions, disease clearing rate by erythrocyte sedimentation rate (ESR) and C-reactive protein (CRP), pain improvement by Oswestry disability index (ODI), and complications. Group A had a better correction of kyphosis deformity compared to group B (10.26±3.61)0 versus（12.24± 2.4)0 (P=0.04) ). No statistically significant differences between the two groups in terms of disease-clearing rate, pain improvement, level of fusion, and complications. Preoperative halo-traction followed by surgery is superior in kyphosis correction in the treatment of patients with cervical infections with refractory kyphosis.

Aim: Since tissue inhibitors of matrix metalloproteinase 3 (TIMP-3) was reported to be a potential risk factor of atherosclerosis, aneurysm, hypertension, and post-ischaemic neuronal injury, it may also be a candidate risk factor of stress. Therefore, this study was designed to explore the causal role of TIMP-3 in the risk of ischaemic stroke (IS) and intracerebral haemorrhage (ICH), which are the two main causes of stress via this Mendelian Randomisation (MR) study.Methods: The summarised data of TIMP-3 level in circulation was acquired from the Cooperative Health Research in the Region of Augsburg public database and the outcome of IS and ICH was obtained from genome-wide association studies conducted by MEGASTROKE and the International Stroke Genetics Consortium, respectively. Five statistical methods including inverse-variance weighting, weighted-median analysis, MR-Egger regression, MR Pleiotropy RESidual Sum and Outlier test, and MR-Robust Adjusted Profile Score were applied to evaluate the causal role of TIMP-3 in the occurrence of IS and ICH. Inverse-variance weighting was applied for assessing causality. Furthermore, heterogeneity and pleiotropic tests were utilised to confirm the reliability of this study.Results: We found that TIMP-3 could be a positively causal relationship with the incidence of IS (OR = 1.026, 95% CI: 1.007–1.046, p = 0.0067), especially for the occurrence of small vessel stroke (SVS; OR = 1.045, 95% CI: 1.016–1.076, p = 0.0024). However, the causal effects of TIMP-3 on another IS subtype cardioembolic stroke (CES; OR = 1.049, 95% CI: 1.006–1.094, p = 0.024), large artery stroke (LAS; OR = 1.0027, 95% CI: 0.9755–1.0306, p = 0.849) and ICH (OR = 0.9900, 95% CI: 0.9403–1.0423, p = 0.701), as well as ICH subtypes were not observed after Bonferroni corrections (p = 0.00714).Conclusion: Our results revealed that high levels of circulating TIMP-3 causally increased the risk of developing IS and SVS, but not CES, LAS, ICH, and all ICH subtypes. Further investigation is required to elucidate the underlying mechanism.

Background. Proximal junction kyphosis (PJK) is the postsurgical radiographic event seen in the surgical plane after the fusion of a spinal deformity. Unfavorable health outcomes have been reported in symptomatic PJK patients compared to non-PJK patients. Methods. The data for adult scoliosis patients who underwent curve correction were extracted from the hospital database. Pelvic and spinal parameters were measured and calculated to compare four predictive formulae for occurrences of PJK. Formula 1. Restoration of hypothetical values of lumbar lordosis (LL) and thoracic kyphosis (TK) according to pelvic incidence (PI). Formula 2. Evaluation of global sagittal alignment. Formula 3. Restoration of the apex of LL to its hypothetical position according to the spine shape. Formula 4. Evaluation of positive-sum and negative-sum of (LL + TK). Results. A total number of cases were 52. There were 14 cases of PJK. The incidence of PJK was 26.9%, and the mean age for PJK cases was 63.2 ± 5.2. The excellent predictor for occurrences of PJK was formula 3. Postsurgical sagittal apexes of lumbar lordosis were located in their hypothetical position in 24 cases, and 12.5% of these cases developed PJK. While sagittal apexes were not located in their hypothetical position in 28 patients, PJK occurred in 39.3% of them ( P = 0.03 , OR: 4.53, (95% CI: 1.09–18.9)). The second good predictor for occurrences of PJK was formula 2 (GSA >45° versus GSA P = 0.17 ). The other two formulae (1 and 4) were not good predictors for occurrences of PJK. Conclusion. Among the four proposed formulae for predicting occurrences of PJK, the position of the sagittal apex of lumbar lordosis is an excellent predictor of the development of PJK, followed by GSA. Hypothetical values of LL and TK, and positive or negative-sum of (LL + TK), are weak predictors for occurrences of PJK.

In the growing field of virtual auditory display, personalized head-related transfer functions (HRTFs) play a vital role in establishing an accurate sound image. In this work, we propose an HRTF personalization method employing convolutional neural networks (CNN) to predict a subject's HRTFs for all directions from their scanned head geometry. To ease the training of the CNN models, we propose novel pre-processing methods for both the head scans and HRTF data to achieve compact representations. For the head scan, we use truncated spherical cap harmonic (SCH) coefficients to represent the pinna area, which is important in the acoustic scattering process. For the HRTF data, we use truncated spherical harmonic (SH) coefficients to represent the HRTF magnitudes and onsets. One CNN model is trained to predict the SH coefficients of the HRTF magnitudes from the SCH coefficients of the scanned ear geometry and other anthropometric measurements of the head. The other CNN model is trained to predict SH coefficients of the HRTF onsets from only the anthropometric measurements of the ear, head, and torso. Combining the magnitude and onset predictions, our method is able to predict the complete and global HRTF data. A leave-one-out validation with the log-spectral distortion (LSD) metric is used for objective evaluation. The results show a decent LSD level at both spatial \& temporal dimensions compared to the ground-truth HRTFs and a lower LSD than the boundary element method (BEM) simulation of HRTFs that the database provides. The localization simulation results with an auditory model are also consistent with the objective evaluation metrics, showing the localization responses with our predicted HRTFs are significantly better than with the BEM calculated ones., 11 pages, 14 figures

Additional file 1: Table S1. Search strategy of psoriasis and coronary artery disease. Table S2. Characteristics and methodological quality of the qualified studies of the 3 meta-analyses. Table S3. SNPs (p < 5 × 10−8) associated with psoriasis of European ancestry were previous reported. Table S4. The pleiotropic psoriasis-associated SNPs with cardiometabolic traits in European ancestry. Table S5. Heterogeneity and pleiotropy analysis of the psoriasis on CAD, MI and HF risk. Table S6. SNPs (p < 5 × 10−8) associated with psoriasis of East Asian ancestry were previous reported. Table S7. The pleiotropic psoriasis-associated SNPs with cardiometabolic traits in East Asian ancestry. Table S8. MR Steiger directionality test. Figure S1. Sensitivity analysis of the meta-analyses about psoriasis and CAD risk (a), MI risk (b) and HF risk (c). Figure S2. A plot relating the effect sizes of the SNP-psoriasis association (x-axis, log OR) and the SNP-CVD associations (y-axis, log OR) with standard error bars. Figure S3. Leave-one-out MR analysis for SNPs used as instruments MR analysis.

Additional file 1.

Additional file 1. SI. Inclusion and exclusion criteria. SII. Annotation of ultrasound images. SIII. Details of theautomated segmentation model for breast ultrasound images. SIV. Data Standardization and Feature Extraction. SV. Feature Selection and Model Construction. SVI. Consistency evaluation of features by Bland-Altman. Fig. S1. Inclusion and exclusion criteria. Fig. S2. U-net architecture. Fig. S3. Flowchart of feature assessment and SUAS construction. Fig. S4. Principal component analysis plot and linear model for evaluating batch effect correction in three institutions. Fig. S5. Relative variable contribution in Model2 and Model3. Fig. S6. Clinical usefulness evaluation of Model2 and Model3. Fig. S7. The changing trend of entropy-related features in three institutions. Table S1. The ultrasonographic images acquisition parameters of the multiple centers. Table S2. Similarity and consistency evaluation between automated segmentation and manual segmentation. Table S3. Key features selected for construction of phasal signatures based on ultrasound images at each single time point. Table S4. Multivariate analysis of clinicopathological model, Model 2 (the early-treatment model) and Model 3 (the posttreatment model). Table S5. Prediction performance of the signatures. Table S6. AUCs of early-pretreatment model (Model 2) and post-treatment model (Model 3) in breast cancer subtypes. Table S7. NRI test for prediction improvements of SUAS compared to single-time signature in multiple cohorts. Table S8. IDI test for prediction improvements of SUAS compared to single-time signature in multiple cohorts. Table S9. Prediction performance of the signatures and SUAS in randomly split datasets.

Given a graph $G$, a query node $q$, and an integer $k$, community search (CS) seeks a cohesive subgraph (measured by community models such as $k$-core or $k$-truss) from $G$ that contains $q$. It is difficult for ordinary users with less knowledge of graphs' complexity to set an appropriate $k$. Even if we define quite a large $k$, the community size returned by CS is often too large for users to gain much insight about it. Compared against the entire community, key-members in the community appear more valuable than others. To contend with this, we focus on a new problem, that is \textbf{C}ommunity \textbf{K}ey-members \textbf{S}earch problem (CKS). We turn our perspective to the key-members in the community containing $q$ instead of the entire community. To solve CKS problem, we first propose an exact algorithm based on truss decomposition as the baseline. Then, we present four random walk-based optimized algorithms to achieve a trade-off between effectiveness and efficiency, by carefully considering some important cohesiveness features in the design of transition matrix. We return the top-$n$ key-members according to the stationary distribution when random walk converges. Moreover, we propose a lightweight refinement method following an "expand-replace" manner to further optimize the top-$n$ result with little overhead, and we extend our solution to support CKS with multiple query nodes. We also analyze our solution's effectiveness theoretically. Comprehensive experimental studies on various real-world datasets demonstrate our method's superiority., Comment: In the current version, we only include an accuracy analysis, not a hard accuracy guarantee. This may confuse readers that we are sensitive to the dataset. So, we plan to re-design the key (i.e., transition matrix) of our random walk-based solution, thus providing an accuracy guarantee (e.g., upper/lower bound). We expect to withdraw this version and pay more time to re-consider this issue

As research interest surges, vector similarity search is applied in multiple fields, including data mining, computer vision, and information retrieval. {Given a set of objects (e.g., a set of images) and a query object, we can easily transform each object into a feature vector and apply the vector similarity search to retrieve the most similar objects. However, the original vector similarity search cannot well support \textit{hybrid queries}, where users not only input unstructured query constraint (i.e., the feature vector of query object) but also structured query constraint (i.e., the desired attributes of interest). Hybrid query processing aims at identifying these objects with similar feature vectors to query object and satisfying the given attribute constraints. Recent efforts have attempted to answer a hybrid query by performing attribute filtering and vector similarity search separately and then merging the results later, which limits efficiency and accuracy because they are not purpose-built for hybrid queries.} In this paper, we propose a native hybrid query (NHQ) framework based on proximity graph (PG), which provides the specialized \textit{composite index and joint pruning} modules for hybrid queries. We easily deploy existing various PGs on this framework to process hybrid queries efficiently. Moreover, we present two novel navigable PGs (NPGs) with optimized edge selection and routing strategies, which obtain better overall performance than existing PGs. After that, we deploy the proposed NPGs in NHQ to form two hybrid query methods, which significantly outperform the state-of-the-art competitors on all experimental datasets (10$\times$ faster under the same \textit{Recall}), including eight public and one in-house real-world datasets. Our code and datasets have been released at \url{https://github.com/AshenOn3/NHQ}., Comment: 16 pages, 10 figures

Additional file 2. The preferred reporting items for systematic review and meta-analysis (PRISMA) 2020 statement.

Jujube (Ziziphus jujuba Mill.) honey, one of the most valuable honey varieties from China with unique characteristics, is vulnerable to being the target of adulteration and deliberate mislabeling of botanical origin. This study investigated the typical protein component of jujube honey to authenticate the floral source by SDS-PAGE analysis combined with LC-MS/MS identification, and its stability to heating was also evaluated. One band and two adjacent but independent bands, both with molecular weights of ∼19 kDa, were notably observed in Coomassie brilliant blue- and silver-stained SDS-PAGE gels, respectively, for jujube honey from different geographic origins, whereas that was not present for the other five botanical honey varieties, suggesting this protein component was suitable as a marker for jujube honey. LC-MS/MS identification revealed that it was constituted by one Z. jujuba-derived protein (gene number:Zj.jz016003045) and two A. mellifera-derived proteins (an uncharacterized protein with accession number tr|A0A088AC16 and a cleavage fragment from major royal jelly protein-1), and the existence of plant-derived protein was attributed to the special neutral pH of jujube honey. Additionally, these protein markers exhibited good stability to heating below 85 °C/30 min. This study provided a simple method to characterize jujube honey and first identified a protein indicator to determine the botanical origin of honey.

The adsorption mechanism of the CO molecule on Al(111) surface has been investigated systematically at the atom-molecule level by the method of periodic density functional theory. The adsorption energies, adsorption structures, charge transfer, and density of states have been calculated in a wide range of coverage. It is found that the hcp-hollow site is the energetically favorable site. A significant positive correlation has been found between the adsorption energy (Eads) and coverage. The adsorbed CO molecules are almost perpendicular on the surface with the C atom facing the surface. There is an obvious charge transfer from Al atoms to the C atom; the Al atoms that have interaction with the C atom offer the most charge. The 4σ, 1π, and 5σ molecular orbitals of CO are found to contribute to bonding with the Al. The charges filling in the 2π molecular orbital contribute to C–O bond activation. In conclusion, the passivation of aluminum surface and the activation of CO molecule occur simultaneously in the adsorption of CO on Al surface.

Mapping fault planes using seismic images is a crucial and time-consuming step in hydrocarbon prospecting. Conventionally, this requires significant manual efforts that normally go through several iterations to optimize how the different fault planes connect with each other. Many techniques have been developed to automate this process, such as seismic coherence estimation, edge detection, and ant-tracking, to name a few. However, these techniques do not take advantage of the valuable experience accumulated by the interpreters. We have developed a method that uses the convolutional neural network (CNN) to automatically detect and map fault zones using 3D seismic images in a similar fashion to the way done by interpreters. This new technique is implemented in two steps: training and prediction. In the training step, a CNN model is trained with annotated seismic image cubes of field data, where every point in the seismic image is labeled as fault or nonfault. In the prediction step, the trained model is applied to compute fault probabilities at every location in other seismic image cubes. Unlike reported methods in the literature, our technique does not require precomputed attributes to predict the faults. We verified our approach on the synthetic and field data sets. We clearly determined that the CNN-computed fault probability outperformed that obtained using the coherence technique in terms of exhibiting clearer discontinuities. With the capability of emulating human experience and evolving through training using new field data sets, deep-learning tools manifest huge potential in automating and advancing seismic fault mapping.

Additional file 6: Figure S4. Interaction network of AT1G30755 proteins in Arabidopsis.

Additional file 4: Figure S2. Conservative motifs of DUF668 gene family in Arabidopsis thaliana, rice, G. hirsutum, G. barbadense, G. arboreum and G. raimondii.

Additional file 5: Figure S3. Conservative motifs of DUF668 gene family in G. hirsutum.

Additional file 1: Figure S1. Distribution of the DUF668 domain in the DUF668 proteins of cotton.

Y2O2SO4 submicron spheres were prepared by facile solid-phase reaction between yttrium sulfate and submicron spherical yttrium oxide for the first time. The influence of the sintering temperature, sintering time on the crystallography and morphology of the Y2O2SO4 submicron spheres were carefully investigated. XRD patterns and SEM images of the as-prepared Y2O2SO4 submicron spheres demonstrated that they had a single monoclinic phase and uniform shapes with an average size of about 300 nm. The formation process of submicron spheres was also discussed. The surface composition, photoluminescence behaviour of Y2O2SO4 submicron spheres were studied by doping with Tb3+. XPS data revealed the samples were composed of Y, S, O, and Tb elements, and the presence of Tb3+ states. The PL results showed that the Y2O2SO4:Tb3+ submicron spheres exhibited the strongest green emission peak at 546 nm on the excitation of 233 nm. It was corresponding to the 5D4→7F5 transition of Tb3+ ions. It may be useful as green-emitting phosphors in the lamps and display devices.

Crushing rate is one of the key indicators to evaluate combine harvester operating quality. During the harvesting, crushing rate could be affected by factors like crop status, operation parameters, working condition and so on. Aiming at identifying the influencing factors and minimizing the crushing rate effectively, a mathematical model of the crushing rate was established through four-factor and three-level field experiments in this paper. The optimum operation parameters of combine harvester were identified through range and variance analysis. The field experiment results showed that the effect level of the four factors was: moisture content > threshing cylinder rotation speed > operation speed > concave clearance. Through simulating the optimization equation, the best parameter combination under the field experiment condition was obtained: 6 km/h - operation speed, 900 r/min - threshing cylinder rotation speed, 15 mm - concave clearance, 14.4 % - moisture content and 0.72 % - crushing rate as the minimum value.

Sewage sludge is the major by-product of wastewater treatment plants, and about 30% readily biodegradable organic matters might be reused through the mass reduction process, which could be also reduced the disposal fee. In this study, the microbubble ozonation (MB-O3) was employed to improve the oxidation efficiency for sludge solubilization. At 160 mgO3/gSS, the maximum mixed liquor suspended solids (MLSS) reduction ratio was 37.5% and the protein and polysaccharide contents increased to 31.6 and 138.6 mg/L, respectively. It was proposed that aromatic protein and soluble microbial in sludge were oxidized preferentially by MB-O3, and the dissolved organic matter (DOM) fractions (mainly humic-acid-like substances) exhibited low degradability according to the variations of fluorescence excitation-emission spectrum coupled with fluorescence regional integration. MB-O3 could enhance the settleability, but deteriorate sludge dewaterability at low dosage (< 160 mgO3/gSS) due to a reduction in particle size from 61.7 to 47.5 μm. MB-O3 has a good performance on the mass reduction of sludge through the improvement of the radical generated.

Researches in surface defect classification and segmentation technology have been seen significant progress in recent years. However, there are few works on One-Class learning in this direction by a single model. In previous researches, some problems remain unsolved in the surface defect detection methods, e.g. the training needs a large number of samples and these models cannot classify and locate the surface defect accurately, etc. The main contribution in this work is that we summarize the overall ideas of previous research in network design and propose a multi-task model which could be trained only using a few of positive samples. Meanwhile, the experiments on AITEX detection datasets[1] which get 84.4% DR, 4.4% FAR and 34.2% MIOU, and conduct an ablation experiment in real industrial product dataset to validate the effect of different backbones on DCSNet. It’s worth mentioning that DCSNet provides a solution to the task of surface defect classification and segmentation based on One-Class learning. The code will be open source in ext-link-type="uri" xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="https://agit.ai/wyxxx/zhengtu">https://agit.ai/wyxxx/zhengtu.

A roadblock for supramolecular hydrogels is their poor mechanical properties. Herein, to enhance the mechanical strength of supramolecular hydrogels, agarose(AG) was incorporated into the low molecular weight hydrogelator( G1). The results of scanning electron microscopy(SEM), circular dichroism(CD) and Fourier transform infrared spectroscopy(FTIR) prove that G1 gelators can self-assemble into cross-linked network together with AG. The mechanical properties of the gels are characterized by a rotary rheometer and the mechanical properties of the hybrid hydrogels(Hgel) can be significantly improved and may be further tuned by changing the ratio of the two components. For example, the elastic modulus of Hgel II[m(G1):m(AG)=7:3] is about 2 times higher than that of G1 hydrogel. The results demonstrate that the mechanical property of hybrid supramolecular hydrogels can be adjusted through the formation of a cross-linked network.

Greenhouse horticulture helps agricultural products to grow more efficiently and keeps them high quality. Based on analysis of available equipment and control system, this paper summarized the advantages and disadvantages of various equipment used in greenhouse and puts forward future development trend of intelligent greenhouses. The intelligent control system of heating, cooling and irrigation is analyzed. Then, intelligent equipment of each system is summarized. Finally, the existing problems was put forward and development trend was looked forward to.

Ocean exploration is playing an increasingly significant role in the development of each country because of the huge material resources therein. A terrain-aided strapdown inertial navigation system based on Kalman Filter (KF) is proposed in this paper in order to improve the navigation precision of autonomous underwater vehicles. The characteristics of strapdown inertial integrated navigation system and terrain-aided navigation system are described, and improved ICCP method is applied to the terrain aided navigation system. Simulation experiments of novel integrated navigation system proposed in the paper were carried out comparing to the traditional ICCP method. The simulation experiments suggest that the improved method is able to improve the long-time navigation precision relative to the traditional method.

The handoff mechanism is a key technology to support the user seamless mobile application services in wireless communication system, which has a great impact on users’ services experiences and application. This paper presents the handoff mechanism based on mobile location prediction, using the modified Dempster-Shafer (D-S) theory together with a variety of context information is adopted to predict the location of mobile users. The method proposed in this paper can effectively avoid the “ping-pong” handoff, shorten the handoff delay, and reserve the resources in advance. It can provide fast, accurate and reliable basis for decisions, thus significantly improve the QoS of network. Theoretical analysis and simulation results show that location prediction is closer to the real trajectory of the user. Also, the RMSE of position is smaller, which indicates the success rate of handoff has been greatly improved.

The Flory–Huggins theory of polymer solutions combined with an evaporation process was employed to interpret the preparation mechanism of a super-hydrophobic surface by phase separation. The ternary phase diagram of a polymer/solvent/nonsolvent was calculated by using the solubility parameters of the components in the system. It indicated that with the increase of the nonsolvent fraction, the initial volume fraction of the polymer or the evaporation rate of the solvent, the evaporation time for phase separation would shorten and the phase separation rate would speed up. The polymer volume fraction in the dense phase would increase and the precipitating particles tended to collide and aggregate, resulting in a rough, super-hydrophobic surface. Experimental results were in agreement with this principle of phase separation for preparing super-hydrophobic surfaces. Membranes of acrylic resin and fluorinated acrylic resin with rough surfaces were successfully obtained by the phase separation method. Especially...

Ru-B/γ-Al2O3 catalyst was prepared by reductant impregnation method, which was applied in the selective hydrogenation of ethyl 1H-indole-2-carboxylate for producing ethyl 2,3,3a,7a-tetrahydro-1H-indole-2-carboxylate with hydrogen as reductant. Furthermore, we discussed the influences of substrate concentration, reaction solvent, hydrogenation temperature, initial hydrogen pressure and reaction time on the catalytic performance of the as-prepared catalyst. The obtained Ru-B/γ-Al2O3 catalyst showed a high-efficiency for the selective hydrogenation of ethyl 1H-indole-2-carboxylate(>99% conversion and selectivity) in i-propanol used as solvent at a concentration of 10%(mass fraction) of ethyl 1H-indole-2-carboxylate, a pressure of hydrogen of 6 MPa and a reaction temperature of 373 K.

Mutational inactivation of ATRX (a-thalassemia mental retardation X-linked) represents a defining molecular alteration in large subsets of malignant glioma. ATRX encodes a chromatin binding protein widely implicated in epigenetic regulation and remodeling. However, multiple studies have also associated its loss in cancer with replication stress, DNA damage, and copy number alterations (CNAs). The mechanisms by which ATRX deficiency drives this global genomic instability remain unclear. Here we report that ATRX inactivation in isogenic glioma model systems induces replication stress and DNA damage by promoting the formation of deleterious G-quadruplex (G4) secondary structure in DNA. Moreover, these effects are associated with the acquisition of disease-relevant CNAs over time. Prior work has linked G4s with genomic instability as well as CNAs in cancer. We then demonstrate, both in vitro and in vivo, that chemical G4 stabilization with CX-3543 (Quarfloxin) selectively enhances cell death in ATRX deficient isogenic cell lines as well as ATRX-mutant primary glioma stem cells derived from patients. Finally, we show that G4 stabilization synergizes with other DNA-damaging therapies, including ionizing radiation, in the ATRX-deficient context. Our findings clarify distinct mechanisms by which DNA secondary structure influences ATRX-deficient glioma pathogenesis and indicate that G4-stabilization may represent and attractive therapeutic strategy for the selective targeting of ATRX-mutant cancers. Opportunities for the development of radiosensitization approaches based on G4-stabilization are particularly intriguing, as ionizing radiation remains among the most effective non-surgical treatments for malignant glioma.

We rely on our sense of touch to obtain information from the external environment with which we interact. Upon touching an object, the distal mechanical stimulus propagates through the skin’s layers toward the end organs of cutaneous afferents that initiate neural responses. While the encoding of various stimuli has been thoroughly investigated in prior studies, the impact of the skin’s mechanics remains vastly understudied. In particular, while we know that the skin undergoes natural cycles of remodeling, we do not know how remodeling impacts the skin’s thickness and elasticity and what impacts those might have on the neural response and neural sensitivity between afferents. In the first aim, we performed uniaxial compression tests to measure the viscoelastic properties of 139 mouse skin specimens while also varying stretch level and rate. This is the first effort to do such a detailed characterization at different stages of the hair cycle. Over the population measured, we observed the skin’s thickness and viscoelasticity to be highly variable, yet found systematic correlations between the viscoelastic parameters and skin thickness and applied stretch. Specifically, residual stress ratio positively correlates with skin thickness and stretch, and relaxation time constants negative correlates with strain rates. In the second aim, we used the population of measurements to build finite element models to closely examine the effect of thickness-dependent viscoelasticity on the propagation of internal deformation toward the end orangs of slowly adapting type I (SAI) afferents. In simulating the observed changes to the skin’s mechanics, we find that there can be large variance in stresses and strains near the locations of end organs, which might lead to large variance in firing sensitivity. However, variance in internal deformation is significantly reduced when the stimulus tip is controlled by surface pressure and compressive stress is measured near the end organs. The combined results of the two aims indicate that the skin can reliably convey surface stimulus information to locations of tactile receptors even amidst changes in skin’s structure. During such changes, stimulus control by surface pressure may be more naturalistic and optimal and underlie how animals actively explore the tactile environment.

In order to accelerate implementation of hyperelastic materials for finite element analysis, we developed an automatic numerical algorithm that only requires the strain energy function. This saves the effort on analytical derivation and coding of stress and tangent modulus, which is time-consuming and prone to human errors. Using the one-sided Newton difference quotients, the proposed algorithm first perturbs deformation gradients and calculate the difference on strain energy to approximate stress. Then, we perturb again to get difference in stress to approximate tangent modulus. Accuracy of the approximations were evaluated across the perturbation parameter space, where we find the optimal amount of perturbation being $10^{-6}$ to obtain stress and $10^{-4}$ to obtain tangent modulus. Single element verification in ABAQUS with Neo-Hookean material resulted in a small stress error of only $7\times10^{-5}$ on average across uniaxial compression and tension, biaxial tension and simple shear situations. A full 3D model with Holzapfel anisotropic material for artery inflation generated a small relative error of $4\times10^{-6}$ for inflated radius at $25 kPa$ pressure. Results of the verification tests suggest that the proposed numerical method has good accuracy and convergence performance, therefore a good material implementation algorithm in small scale models and a useful debugging tool for large scale models., Comment: 19 pages, 3 figures, and 2 tables. Was presented as a podium presentation at the Computer Methods in Biomechanics and Biomedical Engineering 2015, September 3rd, Montreal, Quebec, Canada

This paper explores a new approach towards the distributed e-map service with CORBA. The architecture of a distributed e-map service model is described. This model mainly contains a distributed map database, a database connection layer, an application service layer and a client layer. For the sake of convenient transmission of map data, a combination of CORBA and GML method is introduced. Furthermore, in order to keep the loading balance among distributed servers, object migration is implemented among servers and security is considered.