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In industrial environments, cameras are heavily influenced by light and perspectives, making traditional vision-based parallel robots struggle with object-sorting tasks. Two-dimensional vision lacks depth information, which limits parallel robots to pick up objects at various heights. This paper proposes a 3D pickup estimation method for parallel robots based on point cloud simplification and registration for multi-objective pickup tasks. Firstly, a point cloud segmentation method based on the spatial domain is proposed to separate incomplete point clouds of object from the robot's workspace. The object dataset is generated by scanning complete point clouds of object using a 3D scanner. Secondly, according to the fast point feature histogram (FPFH) and the weight locally optimal projection (WLOP) algorithms, fusing the FPFH and WLOP (FF-WLOP) method is proposed to simplify the incomplete point cloud and extract more distinctive edge features of objects. The complete point cloud from the dataset is aligned with the simplified incomplete point cloud, and the calculated barycenter's coordinate information is given to the incomplete point cloud. Next, a dynamic weight singular value decomposition (D-SVD) hand-eye calibration method and an optimal projection point strategy are proposed to transform the barycenter coordinates of the object to the optimal pickup coordinates. Experimental results show that the point cloud registration error is 0.38 mm, the pickup rate is 92%, and the robot positioning error is 4.67 mm, which meets the basic pickup requirements. [ABSTRACT FROM AUTHOR]

Coal pillar is a crucial element formed during underground coal mining, playing a significant role in ensuring coal mining operations are conducted safely. Historically, research on coal pillars has primarily focused on regular-shaped pillars, with limited attention given to special-shaped pillars. Therefore, this paper aims to investigate the special-shaped coal pillar formed after the excavation of the newly constructed 3307 headgate in Dayang Coal Mine, China. The primary object of this paper is to elucidate the mechanism behind the instability failure of special-shaped coal pillars and verify the influence of various geological conditions on their stability. To accomplish these goals, numerical simulation techniques were employed to analyze the stress distribution patterns and plastic zone failure characteristics of special-shaped coal pillar. The investigation revealed the presence of stress concentration phenomena within special-shaped coal pillar, where the stress peak values were directly correlated with the width of the pillar. Moreover, it was observed that as the width of coal pillar decreased, the stress curve gradually transitioned from a bimodal shape to a unimodal shape. Additionally, the ratio of the plastic zone within coal pillar increased as the width decreased, with pronounced tensile failures occurring at sharp corners. Building upon theoretical analysis and numerical simulation results, the interior of special-shaped coal pillar was classified into four distinct zones: damaged zone, progressive zone, stable zone, and special zone. Based on this zoning approach, a stability control technology was developed, employing a combination of "pier pillar support + flexible formwork concrete + grouting reinforcement + high-strength cable and bolt + U-steel." Subsequently, an industrial experiment was conducted to validate the efficacy of this control technology. Field monitoring results demonstrated that the newly excavated roadway exhibited minimal deformation and damage, thereby satisfying the requirements for safe and efficient mine production over the anticipated service life. Highlights: The failure characteristics and bearing mechanism of special-shaped coal pillars were analyzed, and a mechanical model was established, allowing exploration of the influence of different geological conditions on pillar stability. The stress evolution and plastic zone development characteristics of special-shaped coal pillar during the whole cycle were studied. A zoning criterion for special-shaped coal pillar was proposed, along with corresponding control measures, which were successfully applied to the site, effectively controlled the deformation of roadway. [ABSTRACT FROM AUTHOR]

Blood-brain barrier breakdown and ROS overproduction are important events during the progression of ischemic stroke aggravating brain damage. Geraniol, a natural monoterpenoid, possesses anti-apoptotic, cytoprotective, anti-oxidant, and anti-inflammatory activities. Our study aimed to investigate the effect and underlying mechanisms of geraniol in oxygen-glucose deprivation/reoxygenation (OGD/R)-induced human brain microvascular endothelial cells (HBMECs). Apoptosis, caspase-3 activity, and cytotoxicity of HBMECs were evaluated using TUNEL, caspase-3 activity, and CCK-8 assays, respectively. The permeability of HBMECs was examined using FITC-dextran assay. Reactive oxygen species (ROS) production was measured using the fluorescent probe DCFH-DA. The protein levels of zonula occludens-1 (ZO-1), occludin, claudin-5, β-catenin, nuclear factor erythroid 2-related factor 2 (Nrf2), and heme oxygenase-1 (HO-1) were determined by western blotting. Geraniol showed no cytotoxicity in HBMECs. Geraniol and ROS scavenger N-acetylcysteine (NAC) both attenuated OGD/R-induced apoptosis and increase of caspase-3 activity and the permeability to FITC-dextran in HBMECs. Geraniol relieved OGD/R-induced ROS accumulation and decrease of expression of ZO-1, occludin, claudin-5, and β-catenin in HBMECs. Furthermore, we found that geraniol activated Nrf2/HO-1 pathway to inhibit ROS in HBMECs. In conclusion, geraniol attenuated OGD/R-induced ROS-dependent apoptosis and permeability in HBMECs through activating the Nrf2/HO-1 pathway. [ABSTRACT FROM AUTHOR]

The traditional multi-person human pose estimation method has several problems including low real-time detection effect, low recognition efficiency, and a large number of calculation parameters. In this paper, we propose a lightweight network SP-YOLO based on the YOLO-Pose algorithm for real-time human pose estimation. Firstly, a lightweight ghost spatial pyramid pooling-fast (GSPPF) module is proposed by using GhostNet instead of convolution in the CSPDarkNet backbone network to make the pooling structure do layer-by-layer maxpooling operation and improve the speed of the backbone network. The ultra-lightweight attention mechanism called the shuffle attention (SA) module, is added between layers. Secondly, the neck structure is enhanced, and a more efficient slim path aggregation network (Slim-PAN) module is proposed to optimize the input relationship in the pyramid-enhanced feature structure. The loss function is refined to assign different weights to human pose detection and keypoint detection. Finally, the SP-YOLO method is evaluated through dataset evaluation and real-time detection experiments. A comparison with several other methods on the COCO2017-Keypoints dataset demonstrates that SP-YOLO improves the average precision (AP) by 1.5% compared to the original YOLO-Pose while reducing the number of parameters by 7.9 M. In the real-time detection experiment, the proposed method proves to be effective for end-to-end real-time detection of human poses. [ABSTRACT FROM AUTHOR]

The majority of the biomarkers were associated with the diagnosis of epilepsy and few of them can be applied to predict the response to antiseizure medications (ASMs). In this study, we identified 26 significantly up-regulated genes and 32 down-regulated genes by comparing the gene expression profiles of patients with epilepsy that responded to valproate with those without applying any ASM. The results of gene set enrichment analysis indicated that the ferroptosis pathway was significantly impacted (p = 0.0087) in patients who responded to valproate. Interestingly, the gene NCOA4 in this pathway exhibited significantly different expression levels between the two groups, indicating that NCOA4 could serve as a potential biomarker to better understand the mechanism of valproate resistance. In addition, six up-regulated genes SF3A2, HMGN2, PABPN1, SSBP3, EFTUD2, and CREB3L2 as well as six down-regulated genes ZFP36L1, ACRC, SUB1, CALM2, TLK1, and STX2 also showed significantly different expression patterns between the two groups. Moreover, based on the gene expression profiles of the patients with the treatment of valproate, carbamazepine, and phenytoin, we proposed a strategy for predicting the response to the ASMs by using the Connectivity Map scoring method. Our findings could be helpful for better understanding the mechanisms of drug resistance of ASMs and improving the clinical treatment of epilepsy. [ABSTRACT FROM AUTHOR]

This paper proposes a body size measurement system based on deep learning for image segmentation by binocular stereovision for clothing design. A pair of binocular stereo cameras and a precise revolving platform are used to collect the stereo images of the entire human body. A more diverse human body dataset is constructed for deep learning. An optimized girth semantic segmentation model based on PSPNet network is well trained by deep learning to realize the automatic segmentation of more girth regions in human body images taken from multiple angles. Color subspaces with larger spacing in color spaces are designed to perform more effective stereo matching. The space coordinates of 3D point corresponding to each stereo matching point pair are calculated in the same coordinate system. The space coordinates are reversely revolved back to the intial coordinates of the same coordinate system according to the revolving angle of the platform. Girths of bust, waist, hip and thigh are measured by calculating the fitting curve length after girth fitting of the markers thereon. Height of bust, waist, hip, thigh and entire body are estimated according to the ratio of the 3D distance to the number of pixels between two adjacent markers on the same girth. Quantitative experiments on many human subjects demonstrate that the proposed system can measure body size intelligently and accurately for clothing design under the regulation of China national standards GB/T 2664-2017, GA258-2009, GB/T 2665-2017 and textile industry standard FZ/T 73029-2019, FZ/T 73017-2014, FZ/ T 73059-2017, FZ/T 73022-2019. [ABSTRACT FROM AUTHOR]

Titanium alloy is widely used in high-precision fields such as aerospace industries and medical equipment because of its corrosion resistance and heat resistance. In conventional micro-EDM, the machining of small deep holes of difficult-to-cut materials has always been a problem due to difficult waste debris discharge and low machining efficiency when the machined aperture is small and the aspect ratio is large. Based on the above shortcomings, the high-frequency ultrasonic vibration was introduced into micro-EDM to improve processability. And the optimal ultrasonic system was designed on analytical calculation, finite element simulation, impedance test, and error analysis. The system was applied to SE-WK008 microporous machine for titanium alloy drilling test, and the univariate control experiment based on ultrasonic parameters and electrical parameters (processing voltage, pulse width, pulse interval, and peak current) was carried out. The influence law of each parameter on processing efficiency and electrode wear was summarized and the experimental data was fitted with polynomial curves, and then the multi-objective particle swarm optimization (MOPSO) algorithm was used to find out the optimal parameter array to verify the correctness of the selected processing parameter group. Through experimental analysis, the designed ultrasonic system can be applied to the traditional microporous machine, the highest processing efficiency is increased by 82.7%, and the maximum electrode wear is reduced by 68.7%, which points out the direction for the parameter selection of actual small deep hole machining. [ABSTRACT FROM AUTHOR]

In this paper a three-dimensional model of a novel electronic package has been developed using Finite element analysis to evaluate the shear load, tensile, bending and thermal stresses. Simulations of a complete flexible flip chip electronic packaging method are performed to minimize stresses on the packaged electronic device to improve robustness and reliability. Three component under-fill adhesives (Loctite 4860, Loctite 480 and Loctite 4902) and three circuit substrate materials (Kapton, Mylar and PEEK) are compared and the optimal thickness of each is found by shear load, tensile load, bending test and thermal expansion simulations. A fixed die size of 3.5 mm × 8 mm × 0.53 mm has been simulated and evaluated experimentally under shear and bending load. The shear and bending experimental results show good agreement with the simulation results and verify the simulated optimal thickness of the adhesive layer. The Kapton substrate together with the Loctite 4902 adhesive were identified as the optimum in the simulation. The simulation of under-fill adhesive and substrate thickness identified an optimum configuration of a 0.045–0.052 mm thick substrate layer and a 0.042–0.045 mm thickness of the Loctite 4902 adhesive. The bending simulation has also been used to determine the neutral axis of the encapsulated electronic package in this paper, thus identifying the optimal material and thickness for the encapsulation layer of the package. [ABSTRACT FROM AUTHOR]

Purpose of Review: The impact of HIV infection on the natural history of COVID-19 is unknown, given the recency of the human spread of SARS-CoV-2 (CoV). We reviewed published case series/reports of CoV-HIV coinfections to clarify epidemiologic and clinical features in China, the first nation with pandemic experience. Recent Findings: Assuming that HIV-infected persons were at average risk of CoV infection in Wuhan, we estimated HIV-CoV coinfected persons to number 412 (95%CI: 381-442); our review encompassed an estimated 16.7% (69/412) of Wuhan. Men (many of whom reported sex with other men) accounted for 71.1% (54/76) of the cases reported in China. The median age was 48.0 years old (range 24-77, interquartile:37-57). The median CD4+ cell count at the last clinical visit was 421 cells/μL; 83.0% had an undetectable viral load. Among 31 patients with clinical details reported, fatigue (41.9%), respiratory distress (41.9%), and gastrointestinal symptoms (26.7%) were most common. Among the 52 cases reporting COVID-19 clinical severity, 46.2% were severe, 44.2% mild, and 9.6% asymptomatic COVID-19. Late antiretroviral therapy (ART) was reported by 30.4% (7/23) among whom 57.1% (4/7) were confirmed as severe COVID-19. The case fatality rate was 9.1% (3/33). Severe disease and death were less common among persons who took ART prior to the COVID-19 diagnosis. Of 16 reported IL-6 results, 68.7% were within the normal range. Summary: Earlier use of ART was associated with a better COVID-19 prognosis with CoV-HIV co-infection reported from China through early 2021, but small sample sizes limit definitive conclusions. [ABSTRACT FROM AUTHOR]

The development of Hi-C technology has generated terabytes of chromatin interaction data, which bring possibilities for insight study of chromatin structure. Several studies revealed that mammalian chromosomes are folded into topological associated domains (TADs), which are conserved across cell types. Accurate detection of topological associated domains is now a vital process for revealing the relationship between the structure and function of genome organization. Unfortunately, the current TAD detection methods require massive computing resources, careful parameter adjustment and/or encounter inconsistent results. In this paper, we propose a novel method, Spectral-Based TAD Detector (SBTD), and evaluate its performance with a set of widely accepted statistical methods. We treat the chromatin interaction matrix as a graph and first introduce cosine similarity as a measure of the interaction patterns between bins. The results show that SBTD identifies higher quality TADs than the popular methods (DomainCaller, TopDom and SpectralTAD) and the internal bins of TADs identified by SBTD have higher correlation. Besides, The TADs identified by SBTD show a highly similar histone modification signal enrichment pattern at the boundary as reported in the previous literature. Finally, the motif enrichment analysis shows that compared with the background region, the DNA motifs of known insulator proteins are significantly enriched in the TAD boundary region identified by our method, which proves the high performance of our proposed method. Overall, SBTD is much more effective than existing methods with only one easy-to-adjust parameter, cluster number, for which we provide optimization guidelines. [ABSTRACT FROM AUTHOR]

Cocrystal engineering have been widely applied in pharmaceutical, chemistry and material fields. However, how to effectively choose coformer has been a challenging task on experiments. Here we develop a graph neural network (GNN) based deep learning framework to quickly predict formation of the cocrystal. In order to capture main driving force to crystallization from 6819 positive and 1052 negative samples reported by experiments, a feasible GNN framework is explored to integrate important prior knowledge into end-to-end learning on the molecular graph. The model is strongly validated against seven competitive models and three challenging independent test sets involving pharmaceutical cocrystals, π–π cocrystals and energetic cocrystals, exhibiting superior performance with accuracy higher than 96%, confirming its robustness and generalization. Furthermore, one new energetic cocrystal predicted is successfully synthesized, showcasing high potential of the model in practice. All the data and source codes are available at https://github.com/Saoge123/ccgnet for aiding cocrystal community. Experimental determination of new cocrystals remains challenging due to the need of a systematic screening with a large range of coformers. Here the authors develop a flexible deep learning framework based on graph neural network demonstrated to quickly predict the formation of co-crystals. [ABSTRACT FROM AUTHOR]

Acquired immune deficiency syndrome (AIDS) is a fatal disease caused by human immunodeficiency virus (HIV). Although 23 different drugs have been available, the treatment of AIDS remains challenging because the virus mutates very quickly which can lead to drug resistance. Therefore, predicting drug resistance before treatment is crucial for individual treatments. Here, based on HIV target protein sequence information, we analyzed 21-drug resistance caused by mutated residues using machine learning (ML) methods. To transform target sequences into numeric vectors, seven physicochemical properties were used, which can well represent the interacting characteristics of target proteins. Then, principal component analysis (PCA) method was adopted to reduce the feature dimensionality. Random forest (RF) and support vector machine (SVM) based on three different kernel functions, including linear, polynomial and radial basis function (RBF), were all employed. By comparisons, we found that RBF-based SVM method gives a comparative performance with RF model. Further, we added the weight information to RBF-based SVM method by four different weight evaluation methods of RF, eXtreme Gradient Boosting (XGB), CfsSubsetEval and ReliefFAttributeEval, respectively. Results show that the RF-weighted RBF-based SVM yield the superior performance and 13 out of 21 drug models provide the correlation coefficients (R2) over 0.8 and 3 of them are higher than 0.9. Finally, position-specific importance analysis indicates that most of the mutation residues with high RF weight scores are proved to be closely related with drug resistance, which has been revealed in previous reports. Overall, we can expect that this method can be a supplementary tool for predicting HIV drug resistance for newly discovered mutations. Here, based on HIV target protein sequence information, we analyzed 21-drug resistance caused by mutated residues using machine learning (ML) methods by fusing the weight information of different mutation positions. [ABSTRACT FROM AUTHOR]

The monitoring of mechanical equipment systems contains an increasing number of complex content, expanding from traditional time, and frequency information to three-dimensional data of the time, space, and frequency information, and even higher-dimensional data containing subjects, experimental conditions. For high-dimensional data analysis, traditional decomposition methods such as Hilbert transform, fast Fourier transformation, and Gabor transformation not only lose the integrity of the data, but also increase the amount of calculation and introduce a lot of redundant information. The phenomenon of feature coupling, aliasing, and redundancy between the mechanical multi-source data signals will cause the inaccuracy of the evaluation, diagnosis, and prediction of industrial production operation status. The analysis of the three-way tensor composed of channel, frequency, and time is called parallel factor analysis (PARAFAC). The properties between the parallel factor analysis results and the input signals are studied through simulation experiments. Parallel factor analysis is used to decompose the third-order tensor composed of channel-time-frequency after continuous wavelet transformation of vibration signal into channel, time, and frequency characteristics. Multi-scale parallel factor analysis successfully extracted non-linear multi-dimensional dynamic fault characteristics by generating the spatial, spectral, time-domain signal loading value and three-dimensional fault characteristic expression. In order to verify the effectiveness of the space, frequency, and time domain signal loading values of the fault characteristic factors generated by the centrifugal pump system after parallel factor analysis, the characteristic factors obtained after parallel factor analysis are used as the SPRT test sequence for identification and verification. The results indicate that the method proposed in this article improves the measurement accuracy and intelligence of mechanical fault detection. [ABSTRACT FROM AUTHOR]

The sillenite compound Bi12SiO20 was synthesized via Pechini sol–gel method. Composition, crystallinity, microscopic appearance, and optical properties of samples were characterized by XRD, SEM, FTIR, and UV/Vis/NIR spectrophotometer. The photocatalytic property of pure Bi12SiO20 was also investigated by detecting the degradation rate of rhodamine B (RhB). The experimental conditions related to the preparation of pure phase bismuth silicate, such as the content of citric acid, calcination temperature schedule, and the molar ratio of Bi/Si were investigated. And when n(citric acid) = 13 mmol, n(Bi/Si) = 10, and heat preservation at 630 °C for 5 h, pure bismuth silicate powder can be successfully synthesized. And the degradation of RhB by Bi12SiO20 under visible light for 150 min can reach to 80%, which indicated that pure Bi12SiO20 is an excellent photocatalyst under the visible light. In addition, the capture experiments demonstrate that (Vo+) dominates in photocatalytic degradation. Highlights: Pure Bi12SiO20 photocatalyst can be successfully synthesized by Pechini method. Bi12SiO20 has a typical sillenite structure with the band gap of 2.4 eV. Bi12SiO20 shows a better visible-light photocatalytic performance than P25. [ABSTRACT FROM AUTHOR]

Objective: This study was designed to investigate the situation of social anxiety among left-behind children in rural areas of Hunan Province, China, and to study the effect of social anxiety on loneliness. Methodology: A total of 797 left-behind children were selected from rural areas in Hunan Province, China. The basic information questionnaire for left-behind children, Social Anxiety Scale for Children, Perceived Social Support Scale and UCLA Loneliness Scale were used for investigation. Results: (1) The social anxiety score of left-behind children in China is 6.84 ± 4.28, higher than non-left-behind children, and the score of boys is lower than that of girls, with no significant difference. The score of left-behind children in grade 5 is significantly lower than that of left-behind children in other grades. (2) The overall loneliness score is 16.30 ± 5.10, higher than non-left-behind children, and the score of boys is significantly higher than that of girls. (3) Social anxiety has a significant positive predictive effect on loneliness (P < 0.01). Regression analysis shows that social anxiety not only affects loneliness directly, but also affects loneliness indirectly through the mediating role of perceived social support. Conclusions: Social anxiety is a risk factor for loneliness of left-behind children in rural areas. In the influence of social anxiety on loneliness, social support plays a partial mediating role. [ABSTRACT FROM AUTHOR]

Background: Computational prediction of inhibition efficiency (IE) for inhibitor molecules is a crucial supplementary way to design novel molecules that can efficiently inhibit corrosion onto metallic surfaces. Purpose: Here we are dedicated to developing a new machine learning-based predictor for the inhibition efficiency (IE) of benzimidazole derivatives. Methods: First, a comprehensively numerical representation was given on inhibitor molecules from all aspects of energy, electronic, topological, physicochemical and spatial properties based on 3-D structures and 150 valid structural descriptors were obtained. Then, a thorough investigation of these structural descriptors was implemented. The multicollinearity-based clustering analysis was performed to remove the linear correlated feature variables, so 47 feature clusters were produced. Meanwhile, Gini importance by random forest (RF) was used to further measure the contributions of the descriptors in each cluster and 47 non-linear descriptors were selected with the highest Gini importance score in the corresponding cluster. Further, considering the limited number of available inhibitors, different feature subsets were constructed according to the Gini importance score ranking list of 47 descriptors. Results: Finally, support vector machine (SVM) models based on different feature subsets were tested by leave-one-out cross validation. Through comparisons, the optimal SVM model with the top 11 descriptors was achieved based on Poly kernel. This model yields a promising performance with the correlation coefficient (R) and root-mean-square error (RMSE) of 0.9589 and 4.45, respectively, which indicates that the method proposed by us gives the best performance for the current data. Conclusion: Based on our model, 6 new benzimidazole molecules were designed and their IE values predicted by this model indicate that two of them have high potential as outstanding corrosion inhibitors. [ABSTRACT FROM AUTHOR]

Histone modifications play critical roles in mammalian development, regulating chromatin structure and gene expression. Dynamic histone modifications among cell types have been shown to associate with changes in mammalian development. However, how to quantitatively measure the histone modification alterations and how histone modifications vary across cell types under different genetic contexts remain largely unexplored and whether these changes are related to the primary DNA sequence remains limited. Here, we employed an entropy-based method to measure histone modification alterations in six definite genomic regions across five cell types and identified lineage-specific histone modification genes. We observed that histone modification alterations prefer to enrich in 5′-UTR exons, and also in 3′-UTR exons and its downstream. Then we built a model to predict the histone modification patterns from the primary DNA sequence. We found that the frequencies of k-mer sequence compositions are predictive of histone modification patterns, suggesting that the primary DNA sequence correlated with the histone modification alterations among cell types. Additionally, the lineage-specific histone modification genes display a higher conservation and lower GC-content. Together, we performed a systematic analysis for histone modification alterations and demonstrated how to identify genomic region-specific elements of epigenetic and genetic regulation and histone modification patterns across different cell types. [ABSTRACT FROM AUTHOR]

A commercial textile-grade acrylic fiber was modified with guanidine hydrochloride for the preparation of low-cost carbon fibers. Thermal behavior of the modified acrylic fiber was studied by simultaneous differential scanning calorimetry and thermogravimetric analysis, and the effect of guanidine hydrochloride on the thermal stabilization of acrylic fiber was investigated by Fourier transform infrared spectrometry and densimetry. The results indicated that guanidine hydrochloride could initiate the cyclization reaction of nitrile groups at lower temperature and promote the thermal stabilization of acrylic fiber. Compared with the carbon fibers prepared from the original acrylic fiber, the tensile strength and tensile modulus of the carbon fibers prepared from the modified acrylic fiber had been increased by about 72% and 34%, respectively. The guanidine hydrochloride-modified acrylic fiber was expected to be a practical low-cost carbon fiber precursor. [ABSTRACT FROM AUTHOR]

Exercise dependence is a psychological problem that cannot be ignored and is positively related to anxiety and depression of college students. However, only a few effective intervention methods are available to deal with exercise dependence. This study aims to investigate the intervention effects of motivation interviewing Chinese modified on the mental health of college students with exercise dependence. Thirty college students with exercise dependence were selected from Hunan University of Science and Technology in Hunan province of China to participate in the experiment. The participants were divided equally into the intervention and control groups. A three-week motivation interviewing Chinese modified session was conducted in the intervention group, whereas no intervention was carried out in the control group. This strategy allowed for the vertical and the horizontal comparison of the intervention objects' situation before and after the experiment. The State-Trait Anxiety Inventory and other Scales were used to evaluate the effects of the intervention and explore the intervention effects of motivation interviewing Chinese modified on the mental health of college students with exercise dependence. After three weeks of motivation interviewing Chinese modified, differences in state anxiety, depression, self-satisfaction, negative emotion, energy, and positive emotion in the intervention and control groups before and after the intervention appear to be statistically significant (P < 0.05). Motivation interviewing Chinese modified can improve the mental health level of college students with exercise dependence. Hence, motivation interviewing Chinese modified is good for the treatment of addiction behaviors and provides a reliable intervention method for exercise dependence. [ABSTRACT FROM AUTHOR]

We designed a compact and efficient high-power laser that generates chaotic green light by perturbing a 520 nm laser diode with optical feedback. This achieved a continuous output power of 30 mW with a bandwidth of more than 1 GHz and an electrical-to-green conversion efficiency of 6.25%. All possible chaotic states were demonstrated for different diode-driving currents and feedback intensities. The FWHM and the peak side-lobe level (PSL) are affected by the injection current and feedback intensity. The FWHM increases with increasing feedback intensity, and the PSL increases with both injection current and feedback intensity. The compact chaotic laser could be used in underwater detection to suppress backscattering because of its intrinsic intensity modulation at high frequency. We finally found the optimum range of chaotic states for underwater detection, for which the ratio of input current to threshold current is 1.5 : 2.1. [ABSTRACT FROM AUTHOR]

To explore the influence of exercise motivation and basic psychological needs on exercise dependence among Chinese college students. 1689 college students engaged in long-term exercise were selected from three universities in Hunan province in China as the objects of the survey. 1601 valid copies of the questionnaire were collected. Exercise Addiction Inventory, Motivation for Physical Activity Measure-Revised, Psychological Need Satisfaction in Exercise Scale and other questionnaires were used in the correlation analysis and structural equation model to investigate the influence of psychological needs and exercise motivation on exercise dependence among Chinese college students. Appearance motivation and ability motivation directly affect exercise dependence positively (r = 0.303, r = 0.269), while competence need directly affects exercise dependence positively (r = 0.309), and indirectly affects exercise dependence through appearance motivation and ability motivation. Appearance motivation and ability motivation directly affect exercise dependence positively; competence need not only directly affects exercise dependence but also indirectly affects it through appearance and ability. [ABSTRACT FROM AUTHOR]

Identifying protein-RNA binding residues is essential for understanding the mechanism of protein-RNA interactions. So far, rigid distance thresholds are commonly used to define protein-RNA binding residues. However, after investigating 182 non-redundant protein-RNA complexes, we find that it would be unsuitable for a certain amount of complexes since the distances between proteins and RNAs vary widely. In this work, a novel definition method was proposed based on a flexible distance cutoff. This method can fully consider the individual differences among complexes by setting a variable tolerance limit of protein-RNA interactions, i.e. the double minimum-distance by which different distance thresholds are achieved for different complexes. In order to validate our method, a comprehensive comparison between our flexible method and traditional rigid methods was implemented in terms of interface structure, amino acid composition, interface area and interaction force, etc. The results indicate that this method is more reasonable because it incorporates the specificity of different complexes by extracting the important residues lost by rigid distance methods and discarding some redundant residues. Finally, to further test our double minimum-distance definition strategy, we developed a classifier to predict those binding sites derived from our new method by using structural features and a random forest machine learning algorithm. The model achieved a satisfactory prediction performance and the accuracy on independent data sets reaches to 85.0%. To the best of our knowledge, it is the first prediction model to define positive and negative samples using a flexible cutoff. So the comparison analysis and modeling results have demonstrated that our method would be a very promising strategy for more precisely defining protein-RNA binding sites. [ABSTRACT FROM AUTHOR]

Novel Bi12SiO20–Bi2SiO5 heterostructure photocatalysts were prepared by controlling the molar ratio of Bi/Si in the reactants via Pechini method. With the increasement of the molar ratio of Bi/Si, the samples tend to be well-crystallized Bi12SiO20 and the content of Bi2SiO5 decreases in composites. Experiments reveal that the heterojunction shows higher visible light photocatalytic activity than pure Bi12SiO20 and Bi2SiO5 due to an increase in light harvesting efficiency and an efficient electron–hole separation. And the heterostructure photocatalyst is stable in the photocatalytic reaction process. The influence of some trapping agents on the photodegradation of Rhodamine B was investigated, and h+ and ·O2− were verified to play more important roles than ·OH−. [ABSTRACT FROM AUTHOR]

Single-nucleotide polymorphism (SNP) is a basical variation in genome. When SNPs occur at the binding sites of microRNA, they can influence the binding efficiency, cause a fluctuation of the mRNA in vivo, and thus arouse posttranscriptional level abnormality. Therefore, SNP has a strong correlation with diseases. Although enormous SNPs have been experimentally identified, only a tiny proportion of them are truly disease-associated SNPs (dSNPs) that relate to microRNA modification and then are involved in disease causing process. Therefore, it is important to distinguish dSNPs from the usual SNPs. Analysis here shows that composition is different between sequence segments centered by dSNP and SNP. Inspired by the composition, transition and distribution features which are meaningful and effective in characterizing proteins' sequence information, we improved and applied it to represent the frequency and physicochemical properties of a gene sequence. Binary encoding scheme was also used for further labelling four nucleic acids (A, T, C, and G). First, clustering analysis was performed to gain reasonable negative samples. Then, optimization tests were implemented on different ratios of positive vs negative samples and different feature subsets retrieved by evaluation method of F score. The optimal model constructed by random forest achieves an accuracy of more than 90% on the testing data set. Moreover, the promising results of the external validation also demonstrate the practical applicability of our method. Finally, principal component analysis on the features indicates that all features in our method gain the gross contribution to the prediction model. [ABSTRACT FROM AUTHOR]

Various bacterial pathogens can deliver their secreted substrates also called as effectors through type IV secretion systems (T4SSs) into host cells and cause diseases. Since T4SS secreted effectors (T4SEs) play important roles in pathogen-host interactions, identifying them is crucial to our understanding of the pathogenic mechanisms of T4SSs. A few computational methods using machine learning algorithms for T4SEs prediction have been developed by using features of C-terminal residues. However, recent studies have shown that targeting information can also be encoded in the N-terminal region of at least some T4SEs. In this study, we present an effective method for T4SEs prediction by novelly integrating both N-terminal and C-terminal sequence information. First, we collected a comprehensive dataset across multiple bacterial species of known T4SEs and non-T4SEs from literatures. Then, three types of distinctive features, namely amino acid composition, composition, transition and distribution and position-specific scoring matrices were calculated for 50 N-terminal and 100 C-terminal residues. After that, we employed information gain represent to rank the importance score of the 150 different position residues for T4SE secretion signaling. At last, 125 distinctive position residues were singled out for the prediction model to classify T4SEs and non-T4SEs. The support vector machine model yields a high receiver operating curve of 0.916 in the fivefold cross-validation and an accuracy of 85.29% for the independent test set. [ABSTRACT FROM AUTHOR]

BiSiO is a metastable phase compound and one of the important Bi-based Aurivillius phase oxide semiconductors. Pure BiSiO powders were synthesized successfully by Pechini sol-gel method. The samples were characterized by X-ray diffraction, X-ray photoelectron spectroscopy, field-emission scanning electron microscopy, transmission electron microscopy, thermal analyzer, FTIR spectrometer, Raman spectrometer, fluorescence spectrophotometer and UV/Vis/NIR spectrophotometer. The mechanism of the reaction process was studied. The structure, the optical and photocatalytic properties of pure samples were investigated. The results show that citric acid, polyethylene glycol, solvents and calcination temperature have important effects on the reaction, and pure BiSiO can be obtained at 650 °C for 1 h. The as-prepared BiSiO has a typical Aurivillius-like structure and its bang gap energy is 3.5 eV. The results of degradation of Rhodamine B reveal that pure BiSiO powders prepared by Pechini sol-gel method possess a synergistic effect of adsorption and photocatalysis, which indicates that the BiSiO powders can be used as an efficient material for wastewater treatment. [ABSTRACT FROM AUTHOR]

Using Bi(NO)·5HO and Si(OCH) as the starting materials, citric acid and polyethylene glycol (PEG)-6000 as the complexing agents, pure BiSiO powders with good photocatalytic activity were first prepared at low temperature via Pechini sol-gel method. The effect of calcination temperature on the preparation of pure phase as well as the photocatalytic properties was investigated. The thermal analysis and X-ray diffraction results showed that the crystallization temperature of BiSiO is about 400 °C and pure sample can be obtained at 550 °C. The infrared spectra indicated the pure BiSiO samples have an Aurivillius-like structure. Furthermore, the as-prepared BiSiO exhibited efficient photocatalytic activity in the decomposition of Rhodamine B due to a synergistic effect of adsorption and photocatalysis. [ABSTRACT FROM AUTHOR]

The assessment of binding affinity between ligands and the target proteins plays an essential role in drug discovery and design process. As an alternative to widely used scoring approaches, machine learning methods have also been proposed for fast prediction of the binding affinity with promising results, but most of them were developed as all-purpose models despite of the specific functions of different protein families, since proteins from different function families always have different structures and physicochemical features. In this study, we proposed a random forest method to predict the protein-ligand binding affinity based on a comprehensive feature set covering protein sequence, binding pocket, ligand structure and intermolecular interaction. Feature processing and compression was respectively implemented for different protein family datasets, which indicates that different features contribute to different models, so individual representation for each protein family is necessary. Three family-specific models were constructed for three important protein target families of HIV-1 protease, trypsin and carbonic anhydrase respectively. As a comparison, two generic models including diverse protein families were also built. The evaluation results show that models on family-specific datasets have the superior performance to those on the generic datasets and the Pearson and Spearman correlation coefficients ( R and Rs) on the test sets are 0.740, 0.874, 0.735 and 0.697, 0.853, 0.723 for HIV-1 protease, trypsin and carbonic anhydrase respectively. Comparisons with the other methods further demonstrate that individual representation and model construction for each protein family is a more reasonable way in predicting the affinity of one particular protein family. [ABSTRACT FROM AUTHOR]

We designed a compact diode-end-close-pumped passively Q-switched composite Nd:YAG/Cr:YAG laser. By a single F-mount packaged fast-collimated laser diode without any optical pump coupling components, we achieved a 1.7 ns pulse width output with an 11.4 kHz pulse repetition rate at a 2 W continuous wave pump power in the resonator formed by the coated end surfaces of the crystal, and this agrees with numerical simulations. The corresponding pulse energy and average output power is 21 μJ and 240 mW at 1,064 nm, and the beam is near-diffraction-limited with M∼1.5. To the best of our knowledge, these are the highest pulse energy and average power in directly close-pumped passive lasers. The laser overall size without the forced air cooling system is smaller than a one-pound coin, and such miniaturization will lead to more widespread applications. [ABSTRACT FROM AUTHOR]

We present an efficient high-power quasicontinuous-wave (QCW) all-solid-state laser at 532 nm with a simple and compact symmetrical resonator. We design a stable green laser at 532 nm with an average power of 123 W and a repetition rate of 10 kHz by combining a single side-pumped Nd:YAG laser module with a type II LBO crystal for intracavity frequency doubling. The corresponding electrical-to-green conversion efficiency is 12.5%. To the best of our knowledge, this is the highest electrical-to-green efficiency in 532 nm lasers with above 100 W output power ever reported. The pulse width is 62 ns at 123 W, so the peak power is calculated up to 198.4 kW. Such a compact and efficient resonator is promising for integration into laser-application systems. [ABSTRACT FROM AUTHOR]

Single-nucleotide polymorphisms (SNPs) are the most frequent form of genetic variations. Non-synonymous SNPs (nsSNPs) occurring in coding region result in single amino acid substitutions that associate with human hereditary diseases. Plenty of approaches were designed for distinguishing deleterious from neutral nsSNPs based on sequence level information. Novel in this work, combinations of protein-protein interaction (PPI) network topological features were introduced in predicting disease-related nsSNPs. Based on a dataset that was compiled from Swiss-Prot, a random forest model was constructed with an average accuracy value of 80.43 % and an MCC value of 0.60 in a rigorous tenfold crossvalidation test. For an independent dataset, our model achieved an accuracy of 88.05 % and an MCC of 0.67. Compared with previous studies, our approach presented superior prediction ability. Results showed that the incorporated PPI network topological features outperform conventional features. Our further analysis indicated that disease-related proteins are topologically different from other proteins. This study suggested that nsSNPs may share some topological information of proteins and the change of topological attributes could provide clues in illustrating functional shift due to nsSNPs. [ABSTRACT FROM AUTHOR]

Based on a volume-averaged two-phase approach, a coupled concentration, temperature, and velocity fields model has been established to predict the formation of macrosegregation during solidification. Because of the significant influence of velocity field on solute transfer and distribution during solidification process, the density of liquid steel was set as a function of temperature and concentration to accurately calculate the velocity field. Therefore, the influence of gravity, temperature gradient, concentration gradient, and volume shrinkage on velocity field distribution was comprehensively considered. The calculation result showed good agreement with previous reports. Thereafter, the current model was applied to simulate the solidification of 12Cr2Mo1R (ASTM standard 2.25Cr1Mo) heavy ingot, and the influence of surface cooling intensity on the final carbon macrosegregation was investigated. The results showed that with the increase of cooling intensity, the solidification time, flow velocity, and mushy zone width decrease, and as a result, macrosegregation is alleviated. When the heat-transfer coefficient is less than 1000 W m K, macrosegregation dramatically decreases with the rise of cooling intensity. In contrast, when heat-transfer coefficient is greater than 1000 W m K, the effect of reducing the central carbon segregation by increasing cooling is weakened. [ABSTRACT FROM AUTHOR]

Protein-protein interactions (PPIs) play crucial roles in diverse cellular processes. There are different types of PPIs based on the composition, affinity and whether the association is permanent or transient. Analyzing the diversity of PPIs at the atomic level is crucial for uncovering the key features governing the interactions involved in PPI. A systematic physico-chemical and conformational studies were implemented on interfaces involved in different PPIs, including crystal packing, weak transient heterodimers, weak transient homodimers, strong transient heterodimers and homodimers. The comparative analysis shows that the interfaces tend to be larger, less planar, and more tightly packed with the increase of the interaction strength. Meanwhile the strong interactions undergo greater conformational changes than the weak ones involving main chains as well as side chains. Finally, using 18 features derived from our analysis, we developed a support vector regression model to predict the binding affinity with a promising result, which further demonstrate the reliability of our studies. We believe this study will provide great help in more thorough understanding the mechanism of diverse PPIs. [ABSTRACT FROM AUTHOR]

In this work, we used molecular dynamic (MD) simulation to study trypsin with and without a six-amino-acid peptide bound in three different solvents (water, acetonitrile and hexane) in order to provide molecular information for well understanding the structure and function of enzymes in non-aqueous media. The results show that the enzyme is more compact and less native-like in hexane than in the other two polar solvents. The substrate could stabilize the native protein structure in the two polar media, but not in the non-polar hexane. There are no significant differences in the conformation of the S1 pocket upon the substrate binding in water and acetonitrile media while a reverse behavior is observed in hexane media, implying a possible induced fit binding mechanism in the non-polar media. The substrate binding enhances the stability of catalytic H-bond network since it could expel the solvent molecules from the active site. The enzyme and the substrate appear to be more appropriate to the reactive conformation in the organic solvents compared with aqueous solution. There is much greater substrate binding strength in hexane media than the water and acetonitrile ones since the polar solvent significantly weakens electrostatic interactions, which are observed to be the main driving force to the binding. In addition, some residues of the S1 pocket could remain favorable contribution to the binding despite the solvent change, but with differences in the contribution extent, the number and the type of residues between the three media. [Figure not available: see fulltext.] [ABSTRACT FROM AUTHOR]

The work uses MD simulation to study effects of five water contents (3 %, 10 %, 20 %, 50 %, 100 % v/v) on the tetrahedral intermediate of chymotrypsin - trifluoromethyl ketone in polar acetonitrile and non-polar hexane media. The water content induced changes in the structure of the intermediate, solvent distribution and H-bonding are analyzed in the two organic media. Our results show that the changes in overall structure of the protein almost display a clear correlation with the water content in hexane media while to some extent U-shaped/bell-shaped dependence on the water content is observed in acetonitrile media with a minimum/maximum at 10-20 % water content. In contrast, the water content change in the two organic solvents does not play an observable role in the stability of catalytic hydrogen-bond network, which still exhibits high stability in all hydration levels, different from observations on the free enzyme system [Zhu L, Yang W, Meng YY, Xiao X, Guo Y, Pu X, Li M () J Phys Chem B 116(10):3292-3304]. In low hydration levels, most water molecules mainly distribute near the protein surface and an increase in the water content could not fully exclude the organic solvent from the protein surface. However, the acetonitrile solvent displays a stronger ability to strip off water molecules from the protein than the hexane. In a summary, the difference in the calculated properties between the two organic solvents is almost significant in low water content (<10 %) and become to be small with increasing water content. In addition, some structural properties at 10 ~ 20 % v/v hydration zone, to large extent, approach to those in aqueous solution. [Figure not available: see fulltext.] [ABSTRACT FROM AUTHOR]

Internal motions and flexibility are essential for biological functions in proteins. To assess the internal fluctuations and conformational flexibility of proteins, reliable computational methods are needed. In this study, wavelet transformation was used to filter out the noise and facilitate investigating the internal positional fluctuations of enzymes within nuclear magnetic resonance (NMR) structure ensembles. Moreover, potential active sites were identified by combining with positional fluctuation score, sequence conservation, and solvent accessible surface area. Among the total 107 catalytic residues in 44 examined enzymes, 69 residues were identified correctly. Our results suggest that wavelet transform analysis of structure ensemble is applicable to extract essential fluctuation information of proteins; furthermore, analysis of positional fluctuations is helpful for the identification of catalytic residues. [ABSTRACT FROM AUTHOR]

This work mainly studies the effects of the position (there are two possible hydrated sites) and the manner (i.e., whether water acts as a proton donor or acceptor) of hydration by various numbers of water molecules on the stability of 14 solvated N-methylacetamide structures, NMA-(HO) ( n = 1-3), as well as the binding strength between the NMA and the water cluster, using molecular dynamics (MD) and B3LYP methods. Natural bond orbital (NBO) analysis is used to explore the origin of these effects. Some novel observations are obtained from the work. Our results show that monohydration at the carbonyl site favors stability and binding strength compared to monohydration at the amino site. Similarly, the preferred hydration at the carbonyl site is observed for dihydrated NMAs when the second water is added as a proton donor to the C=O group or the first water is H-bonded to the C=O group. However, unfavorable hydration at the C=O site occurs if the second water acts as a proton acceptor. Trihydration by a ring cluster of three water molecules at either the carbonyl site or the amino one yields relatively stable complexes, but significantly disfavors binding strength. The other trihydrated NMAs show similar behavior to dihydrated NMAs. In addition, our results show that the C=O and N-H frequencies can still be utilized to examine the H-bond effects of the water cluster. [Figure not available: see fulltext.] [ABSTRACT FROM AUTHOR]

RNA-protein interactions play a pivotal role in various biological processes, such as mRNA processing, protein synthesis, assembly, and function of ribosome. In this work, we have introduced a computational method for predicting RNA-binding sites in proteins based on support vector machines by using a variety of features from amino acid sequence information including position-specific scoring matrix (PSSM) profiles, physicochemical properties and predicted solvent accessibility. Considering the influence of the surrounding residues of an amino acid and the dependency effect from the neighboring amino acids, a sliding window and a smoothing window are used to encode the PSSM profiles. The outer fivefold cross-validation method is evaluated on the data set of 77 RNA-binding proteins (RBP77). It achieves an overall accuracy of 88.66% with the Matthew's correlation coefficient (MCC) of 0.69. Furthermore, an independent data set of 39 RNA-binding proteins (RBP39) is employed to further evaluate the performance and achieves an overall accuracy of 82.36% with the MCC of 0.44. The result shows that our method has good generalization abilities in predicting RNA-binding sites for novel proteins. Compared with other previous methods, our method performs well on the same data set. The prediction results suggest that the used features are effective in predicting RNA-binding sites in proteins. The code and all data sets used in this article are freely available at . [ABSTRACT FROM AUTHOR]

Lipid–protein interactions play a vital role in various biological processes, which are involved in cellular functions and can affect the stability, folding and the function of peptides and proteins. In this study, a sequence-based method by using support vector machine and position specific scoring matrix (PSSM) was proposed to predict lipid-binding sites. Considering the influence of surrounding residues of one amino acid, a sliding window was chosen to encode the PSSM profiles. By incorporating the evolutionary information and the local features of residues surrounding one lipid-binding site, the method yielded a high accuracy of 80.86% and the Matthew’s Correlation Coefficient of 0.58 by using fivefold cross validation test. The good result indicates the applicability of the method. [ABSTRACT FROM AUTHOR]

Genome-wide association studies (GWAS) have identified more than sixty single nucleotide polymorphisms (SNPs) associated with increased risk for type 2 diabetes (T2D). However, the identification of causal risk SNPs for T2D pathogenesis was complicated by the factor that each risk SNP is a surrogate for the hundreds of SNPs, most of which reside in non-coding regions. Here we provide a comprehensive annotation of 65 known T2D related SNPs and inspect putative functional SNPs probably causing protein dysfunction, response element disruptions of known transcription factors related to T2D genes and regulatory response element disruption of four histone marks in pancreas and pancreas islet. In new identified risk SNPs, some of them were reported as T2D related SNPs in recent studies. Further, we found that accumulation of modest effects of single sites markedly enhanced the risk prediction based on 1989 T2D samples and 3000 healthy controls. The AROC value increased from 0.58 to 0.62 by only using genotype score when putative risk SNPs were added. Besides, the net reclassification improvement is 10.03% on the addition of new risk SNPs. Taken together, functional annotation could provide a list of prioritized potential risk SNPs for the further estimation on the T2D susceptibility of individuals. [ABSTRACT FROM AUTHOR]

Accumulating evidence suggests that noncoding RNAs (ncRNAs) have important regulatory functions. However, lacking of functional annotations for ncRNAs hampered us from carrying out the subsequent functional or predictive research. Here we dissected the expression profiles of 3,458 rat noncoding genes using rat bodymap RNA-sequencing data consisting of 11 solid organs over four developmental stages (juvenile, adolescent, adult and aged) from both sexes, and conducted a comprehensive analysis of differentially expressed noncoding genes (DEnGs) between various conditions. We then constructed a co-expression network between protein-coding and noncoding genes to infer biological functions of noncoding genes. Modules of interest were linked to online databases including DAVID for functional annotation and pathway analysis. Our results indicated that noncoding genes are functionally enriched through pathways similar to those of protein-coding genes. Terms about development of the immune system were enriched with genes from age-related modules, whereas terms about sexual reproduction were enriched with genes in sex-related modules. We also built connection networks on some significant modules to visualize the interactions and regulatory relationship between protein-coding and noncoding genes. Our study could improve our understanding and facilitate a deeper investigation on organ/age/sex-related regulatory events of noncoding genes, which may lead to a superior preclinical model for drug development and translational medicine. [ABSTRACT FROM AUTHOR]

In the work, we mainly used molecular dynamics (MD) simulation and protein structure network (PSN) to study subtilisin Carlsberg (SC) immobilized onto carbon nanotube (CNT) in water, acetonitrile and heptane solvents, in order to explore activation mechanism of enzymes in non-aqueous media. The result indicates that the affinity of SC with CNT follows the decreasing order of water > acetonitrile > heptane. The overall structure of SC and the catalytic triad display strong robustness to the change of environments, responsible for the activity retaining. However, the distances between two β-strands of substrate-binding pocket are significantly expanded by the immobilization in the increasing order of water < acetonitrile < heptane, contributing to the highest substrate-binding energy in heptane media. PSN analysis further reveals that the immobilization enhances structural communication paths to the substrate-binding pocket, leading to its larger change than the free-enzymes. Interestingly, the increase in the number of the pathways upon immobilization is not dependent on the absorbed extent but the desorbed one, indicating significant role of shifting process of experimental operations in influencing the functional region. In addition, some conserved and important hot-residues in the paths are identified, providing molecular information for functional modification. [ABSTRACT FROM AUTHOR]

With the rapid increase in crystal structures of protein-protein complexes deposited in the Protein Data Bank (PDB), more and more crystal contacts have been shown to have similar or even larger interface areas than biological interfaces. However, little attention has been paid to these large crystal packing contacts and their structural principles remain unknown. To address this issue, we used a comparative feature analysis to analyze the geometric and physicochemical properties of large crystal packing contacts by comparing two types of specific protein-protein interactions (PPIs), weak transient complexes and permanent homodimers. Our results show that although large crystal packing contacts have a similar interface area and contact size as permanent homodimers, they tend to be more planar, loosely packed and less hydrophobic than permanent homodimers and cannot form a central core region that is fully buried during interaction. However, the properties of large crystal packing contacts, except for the interface area and contact size, more closely resemble those of weak transient complexes. The large overlap between biological and large crystal packing contacts indicates that interface properties are not efficient indicators for classification of biological interfaces from large crystal packing contacts and finding other specific features urgently needed. [ABSTRACT FROM AUTHOR]