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Fault diagnosis is essential to ensure the operation security and economic efficiency of the chemical system. Many fault diagnosis methods have been designed for the chemical process, but most of them ignore the temporal correlation in the sequential observation signals of the chemical process. A novel deep learning method based on Stacked Long Short-Term Memory (LSTM) neural network is proposed, which can effectively model sequential data and detect the abnormal values. The proposed method is also able to fully exploit the long-term dependencies information in raw data and adaptively extract the representative features. The dataset of Tennessee Eastman (TE) process is utilized to verify the practicability and superiority of the proposed method. Extensive experimental results show that the fault detection and diagnosis model we proposed has an excellent performance when compared with several state-of-the-art baseline methods.

Despite many years of improvements to it, TCP still suffers from an unsatisfactory performance. For services dominated by short flows (e.g., web search and e-commerce), TCP suffers from the flow startup problem and cannot fully utilize the available bandwidth in the modern Internet: TCP starts from a conservative and static initial window ( IW , 2–4 or 10), while most of the web flows are too short to converge to the best sending rate before the session ends. For services dominated by long flows (e.g., video streaming and file downloading), the congestion control ( CC ) scheme manually and statically configured might not offer the best performance for the latest network conditions. To address these two challenges, we propose TCP-RL , which uses reinforcement learning ( RL ) techniques to dynamically configure IW and CC in order to improve the performance of TCP flow transmission. Basing on the latest network conditions observed at the server side of a web service, TCP-RL dynamically configures a suitable IW for short flows through group-based RL , and dynamically configures a suitable CC scheme for long flows through deep RL . Our extensive experiments show that for short flows, TCP-RL can reduce the average transmission time by about 23%; and for long flows, compared with the performance of 14 CC schemes, TCP-RL ’s performance ranks top 5 for about 85% of the 288 given static network conditions, whereas for about 90% of conditions, its performance drops by less than 12% compared with that of the best-performing CC schemes for the same network conditions.

The current practice is to use reinforced concrete inclined column transfer structures (RC-ICTS) to support short-leg shear walls whose sections have height-to-thickness ratios of less than 8. In view of supporting upper traditional shear walls whose sections have height-to-thickness ratios greater than 8 and improving the seismic performance of RC-ICTS, this study focuses on the investigation of the steel-reinforced concrete inclined column transfer structure (SRC-ICTS). Firstly, tests involving the constant axial load and horizontal cyclic load were performed. In the testing of the three specimens, the transfer beam was damaged seriously due to shear failure, so that the non-elastic deformation of the section steel in the middle of the transfer beam was large, dissipating the external input energy effectively. The test and finite element model (FEM) results partly agree with each other, indicating that the bond-slip mechanism between the section steel and concrete has a minimal influence on the seismic performance of the SRC-ICTS. Then, by focusing on the engineering application, the FEMs of the SRC-ICTS specimens with different axial force ratios were established without considering the bond-slip mechanism. The findings on seismic performance indicate that the ultimate bearing capacity can be improved by increasing the axial force ratio; however, the energy dissipation capacity and ductility of the SRC-ICTS decrease when the axial force ratio is extremely high. The axial force ratio of SRC-ICTS is proposed to be less than 0.4 to ensure good seismic performance.

With the stable and safe requirement in the industrial technology, the fault detection, has attracted more and more attention from both scholars and companies. To make full use of the linear and non-linear features for fault detection, a method named deep extended principal component analysis - support vector machine (Deep EPCA-SVM) was proposed by combing the PCA and kernel PCA with the deep structure. Both the PCA and kernel PCA were iteratively implemented in the principle subspace and residual subspace for the extraction of linear and non-linear features. The offline model was built and applied to monitor the fault of Tennessee-Eastman process. The validation performances indicated that our proposed model outperformed the traditional PCA and PCA-SVM model, and showed higher fault detection rate than the deep DPCA-SVM.

Fault diagnosis is an important way to ensure the operation security in complex industrial processes. Considering the inherent multiscale characteristics and time dependency about industrial process monitoring data, a novel fault diagnosis method based on multiscale temporal convolutional network (MS-TCN) was proposed in this paper. Firstly, different from the widely used time-domain features with one single scale, the multiscale time-frequency information extracted with the discrete wavelet transform was also introduced to represent the raw data. And a temporal convolutional network was then combined to capture longer-term temporal feature from the sequential processing data. The experimental results on the Tennessee Eastman process indicated that, our proposed method outperformed these state-of-the-art fault diagnosis methods, especially for the 3 incipient faults hard to classify.

Background Oncopanel genomic testing, which identifies important somatic variants, is increasingly common in medical practice and especially in clinical trials. Currently, there is a paucity of reliable genomic reference samples having a suitably large number of pre-identified variants for properly assessing oncopanel assay analytical quality and performance. The FDA-led Sequencing and Quality Control Phase 2 (SEQC2) consortium analyze ten diverse cancer cell lines individually and their pool, termed Sample A, to develop a reference sample with suitably large numbers of coding positions with known (variant) positives and negatives for properly evaluating oncopanel analytical performance. Results In reference Sample A, we identify more than 40,000 variants down to 1% allele frequency with more than 25,000 variants having less than 20% allele frequency with 1653 variants in COSMIC-related genes. This is 5–100× more than existing commercially available samples. We also identify an unprecedented number of negative positions in coding regions, allowing statistical rigor in assessing limit-of-detection, sensitivity, and precision. Over 300 loci are randomly selected and independently verified via droplet digital PCR with 100% concordance. Agilent normal reference Sample B can be admixed with Sample A to create new samples with a similar number of known variants at much lower allele frequency than what exists in Sample A natively, including known variants having allele frequency of 0.02%, a range suitable for assessing liquid biopsy panels. Conclusion These new reference samples and their admixtures provide superior capability for performing oncopanel quality control, analytical accuracy, and validation for small to large oncopanels and liquid biopsy assays.

The concrete open-web sandwich slab is composed of the vierendeel girders intersecting each other. The vierendeel girder are composed of the upper rib, lower rib and shear key. The upper and lower rib mainly bear the axial force, and the shear keys mainly bear the shear force. A double scalar elastic damage constitutive model considering shear damage was derived to accurately simulate the mechanical property of the shear key in which one scalar reflected the volume damage, and the other one reflected the shear damage. The damage constitutive model in the study, the uniaxial tension and compression damage constitutive model (GB50010 (2015)), and the double scalar elastic damage constitutive model of tension and compression (Liu (2021)) were compared by simulating the shear key. The results showed that the elastic damage constitutive model proposed in the study can be used for the mechanical property analysis of the shear key and can better evolve the damage development of the shear key. The damage constitutive calculation model can be provided for the analysis of the vierendeel girder and the open-web sandwich slab.

Long-span prestressed double-layer composite torsional reticulated shells have beautiful shapes, which make them a solution widely applied in public buildings requiring long-span structures, such as airports and stadiums. However, once the progressive collapse occurs, this will cause serious issues. There are few studies on the damage assessment of long-span spatial structures under accidental loads. This paper proposes a new dynamic damage evaluation index that takes into account the displacement and the cumulative plastic energy dissipation to evaluate the damage of the long-span prestressed double-layer composite torsional reticulated shell. Sleeve structures were applied to the long-span space structures to study their control effect on structural damage. The equivalent load transient unloading method was used to analyze the dynamic time history of the structure. Results show that the structure suffered severe damage and progressive collapse failure after removing four nodes at different positions near the supports. In the position where the plastic hinges first appear and the position with the maximum displacement, the sleeve structures have a poor damage control effect on the structure. Arranging the two sleeve structures in the position of maximum stress, the damage of the structure can be controlled, thus reducing the severe damage and progressive collapse failure to basic intact damage.

Fault detection is an important step to ensure safe and reliable production in industrial processes. Data-driven technology is one of the most widely studied fault detection methods. This paper proposed a data-driven fault detection method named deep dynamic Principal Component Analysis-Support Vector Machine (Deep DPCA-SVM) for industrial processes. By constructing a multi-layer DPCA structure for robust feature extraction, a fault detection model with high precision could be retrieved based on the SVM classifier. The proposed Deep DPCA-SVM method was applied to the Tennessee Eastman (TE) process, and its superior performances indicated that our proposed method could extract the more efficient features for the fault detection.

Recent deep learning models for information retrieval typically aim to learn features either about the contents of the document and the query, or about the interactions between them. However, the existing literature shows that document ranking depends simultaneously on many factors, including both content and interaction features. The integration of both types of neural features has not been extensively studied. In addition, many studies have also shown that the deep neural features cannot replace completely the traditional features, but are complementary. It is thus reasonable to combine deep neural features with traditional features. In this paper, we propose an integrated end-to-end learning framework based on learning-to-rank (L2R) to learn both neural features and the L2R ranking function simultaneously. The framework also has the flexibility to integrate arbitrary traditional features. Our experiments on public datasets confirm that such an integrated learning strategy is better than separate learning of features and ranking function, and integrating traditional features can further improve the results.

The logging system records the logs generated by the software so that the administrator can handle the problems that occur. However, the traditional log system is not secure enough and the stored logs are easily falsified. As a decentralized distributed storage technology, the blockchain can ensure that the blockchain network works normally in the presence of a few malicious nodes or failed nodes. So we use the blockchain to store the logs, which improves the security of the log system. In order to improve the performance of the blockchain, we use a voting-based consensus algorithm as a blockchain consistency protocol. This article introduces the architecture and implementation of the log system and verifies the feasibility of the system through experiments.

HLA-DR-molecules are highly expressed in synovial tissue (ST), the target of the immune response in chronic inflammatory forms of arthritis. Here, we used LC-MS/MS to identify HLA-DR-presented self-peptides in cells taken directly from clinical samples: ST, synovial fluid mononuclear cells (SFMC), or peripheral blood mononuclear cells (PBMC) from five patients with rheumatoid arthritis (RA) and eight with Lyme arthritis (LA). We identified 1,593 non-redundant HLA-DR-presented peptides, derived from 870 source proteins. Sixty-seven percent of the peptides identified in SFMC and 55% of those found in PBMC were found in ST, but analysis of SFMC/PBMC also revealed new antigen-presented peptides. Peptides were synthesized and examined for reactivity with the patients’ PBMC. To date, three autoantigens in RA and four novel autoantigens in LA, presented in ST and/or PBMC, were shown to be targets of T- and B-cell responses in these diseases; ongoing analyses may add to this list. Thus, immunoprecipitation and LC-MS/MS can now identify hundreds of HLA-DR-presented self-peptides from individual patients’ tissues or fluids with mixed cell populations. Importantly, identification of HLA-DR-presented peptides from SFMC or PBMC allows testing of more patients, including those early in the disease. Direct analysis of clinical samples facilitates identification of novel immunogenic T-cell epitopes. Data are available via ProteomeXchange (identifier PXD003051).

Semi-supervised learning has attracted significant attention in pattern recognition and machine learning. Among these methods, a very popular type is semi-supervised support vector machines. However, parameter selection in heat kernel function during the learning process is troublesome and harms the performance improvement of the hypothesis. To solve this problem, a novel local behavioral searching strategy is proposed for semi-supervised learning in this paper. In detail, based on human behavioral learning theory, the support vector machine is regularized with the un-normalized graph Laplacian. After building local distribution of feature space, local behavioral paradigm considers the form of the underlying probability distribution in the neighborhood of a point. Validation of the proposed method is performed with toy and real-life data sets. Results demonstrate that compared with traditional method, our method can more effectively and stably enhance the learning performance.

As a novel aircraft, the tilt-rotor UAV (TRUAV) has potential applications with the merits of high efficiency cruise of fixed-wing aircraft and vertical takeoff and landing of multi-rotors aircraft. A reasonable and accurate flight dynamic model of TRUAV is the basis of performance analysis and controller design. Due to the existence of three different flight modes and other aerodynamic factors, dynamic modeling of TRUAV is more complicated than a fixed-wing or multi-rotors aircraft. Until now, many researches have paid a lot attention on this field. In this paper, we review the art of dynamic modeling techniques of TRUAVs in low speed flight. The features and research states of theoretic, numerical, experimental and hybrid methods are summarized in this paper. Finally, the further research tendencies are discussed.

Latency, which directly affects the user experience and revenue of web services, is far from ideal in reality, due to the well-known TCP flow startup problem. Specifically, since TCP starts from a conservative and static initial window (IW, 2∼4 or 10), most of the web flows are too short to have enough time to find its best congestion window before the session ends. As a result, TCP cannot fully utilize the available bandwidth in the modern Internet. In this paper, we propose to use group-based reinforcement learning (RL) to enable a web server, through trial-and-error, to dynamically set a suitable IW for a web flow before its transmission starts. Our proposed system, SmartIW, collects TCP flow performance metrics (e.g., transmission time, loss rate, RTT) in real-time without any client assistance. Then these metrics are aggregated into groups with similar features (subnet, ISP, province, etc.) to satisfy RL's requirement. SmartIW has been deployed in one of the top global search engines for more than a year. Our online and testbed experiments show that, compared to the common practice of $\boldsymbol{IW}=10$ , SmartIW can reduce the average transmission time by 23% to 29%.

Current TCP is very inefficient for web services. Web transactions are often very short-lived. TCP flow starts with a conservative initial congestion window (IW), which causes multiple round-trip times to finish the transmission even if the end-to-end bandwidth is sufficient for the transaction to be finished in one round-trip time. Previous research efforts have been focusing on finding the overall best IW for the entire Internet or a service company. However, we observe that one-IW-fits-all is suboptimal after one year of online measurement in Baidu, one of the top global search engine companies. To reduce the TCP latency, we propose TCP WISE, which dynamically assigns suitable IWs for different user cluster at different times on the server side. The values of users' IWs are proactively learned based on the historical experience on the server-side. Our testbed experiments show that our learning algorithm can handle the network changes and converge to the best IW. We have deployed TCP WISE in one of Baidu's production data center, and results shows that the 80th percentile latency of the HTTP responses has been reduced by 10.4% compared with current TCP with a fixed IW of 10.

The tilt tri-rotor UAV which combines the advantages of both rotary wing and fixed wing vehicle, offers the capability of hover, vertical take-off and landing and fast forward movement. Because of the complex structure and aerodynamics, the modeling and control of tilt tri-rotor UAV reamians a problem. In this paper, precise model of a tilt tri-rotor UAV is developed by combing system identification and physical measurement. An attitude controller is designed based on control allocation of attitude and throttle on the powerplant. The detailed mathematical model is obtained by the equations of Newton-Euler, which parameters are acquired from designed experiments. To improve the stability of helicopter fight, a new control allocation strategy is developed in the framework of PID control.

Reaction wheel assembly (RWA) is an attitude control actuator for spacecraft. Its induced micro vibration will have a harsh effect on the performance of sensitive payloads. In this paper, a compact four-legged vibration isolation system for RWA is designed and investigated. Piezoelectric stack actuators (PSA) are chosen and dynamic equations are derived by Newton-Euler approach. Moreover, to identify low-frequency modal excitation of synthesis body composed by RWA and isolator, we propose one solution based on blind source separation (BSS). Simulation studies are conducted to verify the solution. The result shows that modal excitation within 10Hz–30Hz could be extracted effectively.

A novel tilt tri-rotor unmanned aerial vehicle (UAV) which combines the vertical flight capability of a helicopter and forward flight performance of an airplane is proposed. The two front rotors are able to tilt from the vertical to the horizontal position, however, the third rotor is fixed in the aft fuselage with a small angle. The nonlinear dynamic model of tilt tri-rotor UAV is given by the equations of Newton-Euler. By banlancing flight conditions in transition mode where there are redundant controls, the transition corridor is obtained. To resolve the attitude control problem of tilt tri-rotor UAV, we proposed an improved control allocation based on the rotational speeds of three propellers and the tilt angles of two front rotors. Besides, the structure of PID controller including inter loop and outer loop is developted. Simulation results during full envelope flight are presented, and the success of the modeling carried out as well as the control strategy designed are demonstrated.

Vertical take-off and landing (VTOL) aerial vehicles have been a hot topic in the aerospace field, since they possess joint advantages of the multi-rotor drone and the fixed-wing unmanned aerial vehicle (UAV). In this paper, a novel propulsion scheme is proposed for a micro triple tilt-rotor VTOL prototype. Furthermore, the design, implementation and analysis of a gasoline-electric hybrid propulsion system are respectively presented. Hybrid power architecture is designed with constraints on the characteristics of the power-plants and the energy and is based on three fundamental hybrid-electric configurations. The dynamic model and attitude control principle is also given for this triple tilt-rotor VTOL UAV. Dynamics performance testing is conducted by flight experiments of the developed micro triple tilt-rotor VTOL prototype.

Semi-supervised learning has attracted a significant amount of attention in pattern recognition and machine learning. Among these methods, a very popular type is semi-supervised support vector machines. However, parameter selection in heat kernel function during the learning process is troublesome and harms the performance improvement of the hypothesis. To solve this problem, a novel local behavioral searching strategy is proposed for semi-supervise d learning in this paper. In detail, based on human behavioral learning theory, the support vector machine is regularized with the un-normalized graph Laplacian. After building local distribution of feature space, local behavioral paradigm considers the form of the underlying probability distribution in the neighborhood of a point. Validation of the proposed method is performed with extensive experiments. Results demonstrate that compared with traditional method, our method can more effectively and stably enhance the learning performance.

Single-gimbal control moment gyroscope (SGCMG) is a key attitude control actuator of Tiaqngong-1 target spacecraft. Since the successful launch of Tiangong-1 in September, 2011, the SGCMGs in it have been working smoothly in-orbit with excellent performance, which have accomplished the mission of attitude control of Tiangong-1 as well as the rendezvous docking. Now, the SGCMGs are working in the extension mission phase. In this paper, the mission requirement, design proposal, and the key technology-design of long life rotor bearing system and its test result are introduced. The SGCMGs in Tiangong-1 are developed independently by our country and utilized in-orbit for the first time. The design of long life rotor bearing system provides an important reference for SGCMGs in other spacecraft.

Advances in proteomics research involve the use of high-precision and high-resolution mass spectrometry instruments. Although hardware improvements are the main impetus for the acquisition of high-quality data, enhancements in software tools are also needed. In this study, recalibration was verified as an important way to improve data accuracy. A new version tool, known as FTDR 2.0, was developed to recalibrate the mass-to-charge ratio error of most observed parent ions to the sub part per million level in routine experiments. First, many new parameters were introduced and screened as features online to reduce systematic error and to adapt to various data sets. Second, a support vector regression model was trained to characterize the complex nonlinear maps from features to mass-to-charge ratio measurement errors. Third, a specific mass-to-charge ratio error tolerance for each parent ion was estimated by considering the impact of signal intensity. FTDR 2.0 is a user-friendly tool that supports most commonly used data standards and formats. A C++ library and the source code are provided to support the redevelopment and integration into other mass spectrometry data processing tools. The performance of FTDR 2.0 was verified using several experimental data sets from different research programs. Recalibration with FTDR 2.0 has been proved to improve the peptide identification in qualitative, quantitative, and post-translational modification analyses.

The high complexity of the proteome has brought great challenges to mass spectrometry-based protein identification. The technical requirements continuously forward the development of mass spectrometry. The advance in hardware and software of instrument platform provides more choices and supports for protein identification. However, it is necessary to design high-quality data acquisition strategy, which is heavily dependent on the specific biological problem and the sample, to make the best use of the performance of the instrument. Here, the data acquisition strategies that have been developed for mass spectrometers in high throughput protein identification were reviewed. The simple repetitions, ion exclusion, ion inclusion, online intelligent data acquisition and segmented scanning technology for Bottom-up strategy were highlighted, and the impact of these strategies on the protein identification was concerned. Finally, the advantages and disadvantages of various strategies were summarized, and the future directions of developing the data acquisition strategy for mass spectrometers were discussed.

Database searching based methods for label-free quantification aim to reconstruct the peptide extracted ion chromatogram based on the identification information, which can limit the search space and thus make the data processing much faster. The random effect of the MS/MS sampling can be remedied by cross-assignment among different runs. Here, we present a new label-free fast quantitative analysis tool, LFQuant, for high-resolution LC-MS/MS proteomics data based on database searching. It is designed to accept raw data in two common formats (mzXML and Thermo RAW), and database search results from mainstream tools (MASCOT, SEQUEST, and X!Tandem), as input data. LFQuant can handle large-scale label-free data with fractionation such as SDS-PAGE and 2D LC. It is easy to use and provides handy user interfaces for data loading, parameter setting, quantitative analysis, and quantitative data visualization. LFQuant was compared with two common quantification software packages, MaxQuant and IDEAL-Q, on the replication data set and the UPS1 standard data set. The results show that LFQuant performs better than them in terms of both precision and accuracy, and consumes significantly less processing time. LFQuant is freely available under the GNU General Public License v3.0 at http://sourceforge.net/projects/lfquant/.

In the process of object tracking, because of the light, the angle of view as well as the target shape change causes the goal which is easy to lose. In this paper, we use the SVM based on Laplacian Kernel Function. Under the framework of Struck, it is applied to the object tracking, and comparing with other kernel functions, such as Linear Kernel Function, Gauss Kernel Function and so on, which can achieve the object tracking with high accuracy and stability.

Nowadays, quadrotor is playing a growing number of critical roles in a great many of aspects. A crucial ability is the autonomous landing ability onto a target such as a fixed platform, moving platform, or ship deck platform, employing on-board vision. Researching autonomous landing technologies employing vision for quadrotor have grown up to be the second active area of research over the past years. This article primarily gives the foremost research groups involved in the development of on-board vision autonomous landing technologies for quadrotor, then it discusses the specifics of two factors are taken into account for a smooth landing: sensing and control. The aim of this article is to examine the state-of-the art autonomous landing technologies employing on-board vision that capture entire milestones and seminal works. Last but not least, the article stands out three challenging parts in this research field.

This paper presents the design and modeling of a tri-rotor unmanned aerial vehicle (UAV) system with double front tilt rotors for the flight dynamic control. The aerodynamic model of the tilt-rotor UAV is obtained based on the force and moment equations and its parameters are calculated by several measurements. For the flight dynamics control, the equilibrium point is calculated through a numerical analysis.

In recent years, the tiltrotor UAV (Unmanned Aerial Vehicle) attracts increasing concern. As progressively requirements are being imposed on its control systems, accurate dynamics modeling is becoming increasingly important. However most commercial off-the-shelf UAVs have no accurate dynamics model. In this paper, the system identification method is adopted to model a home-made tiltrotor UAV attitude dynamics and then design the model-based optimal controller with LQR (Linear-Quadratic Regulator) method. Finally, a hovering flight experiment is carried out. Result shows that with the controller based on the identification, the tiltrotor UAV could realize stable hovering.

Because of its simple experiment design and low experiment cost, label-free quantitative technology based on mass spectrometry analysis is being used more and more widely. Aiming at peptide identification and quantification, which are pivotal step of the label-free quantitative analysis, we develop an efficient algorithm named XIC Finder based on C++ platform. In term of the peptides which failed to be identified by MS/MS spectrum, we u tilize ESP model trained by 20 optimized peptide features with Random Forest method, to predict those peptides detectability and chose the highest scored peptide as the correct peptide. Compared with MaxQuant and IDEAL-Q, other algorithms developed for quantitative MS data, XIC Finder improves the performance of the peptide identification and qu antification significantly. Furthermore, we evaluated the reproducibility and precision of XIC Finder by using the replication dataset and the UPS1 standard data set respectively and prove the result is better than other algorithms.

This paper describes our work on formation flight control of unmanned aerial vehicles (UAVs). A formation flight test system from theory to actual flight is developed to narrow the gap between the theory and the practice. Firstly, a leader-follower based 3D formation control algorithm is designed and validated via numerical simulation. Secondly, the method is embedded realized in our autopilot, then a hardware-in-the-loop simulation subsystem is constructed to further test the algorithm by combining the autopilot with a realistic flight simulator X-Plane. The interface and protocol between the autopilot and X-plane are designed same with that of the formation flight test platform. Consequently, the autopilot passed the validation in the hardware-in-the-loop simulation can be directly moved on to the formation flight test platform. Finally, results of the numerical simulation, hardware-in-the-loop simulation and actual flight tests are presented to illustrate the performance of the entire formation flight test system and the proposed methodology.

In this study, we presented a quality control tool named PepDistiller to facilitate the validation of MASCOT search results. By including the number of tryptic termini, and integrating a refined false discovery rate (FDR) calculation method, we demonstrated the improved sensitivity of peptide identifications obtained from semitryptic search results. Based on the analysis of a complex data set, approximately 7% more peptide identifications were obtained using PepDistiller than using MASCOT Percolator. Moreover, the refined method generated lower FDR estimations than the percentage of incorrect target (PIT) fixed method applied in Percolator. Using a standard data set, we further demonstrated the increased accuracy of the refined FDR estimations relative to the PIT-fixed FDR estimations. PepDistiller is fast and convenient to use, and is freely available for academic access. The software can be downloaded from http://www.bprc.ac.cn/pepdistiller.

Peptide sequence tagging (PST) is one of the basic problems in computational proteomics. It is a flexible method which has been widely used in protein identification as well as mapping post-translational modifications (PTM) to peptides. In this work, we present a new scoring scheme for peptide sequence tagging via doubly charged tandem mass spectra. Four public datasets from different types of mass spectrometers (Thermo LTQ-FT, LTQ and LCQ, Waters/Micromass QTOF) are applied to test our scoring scheme and the results are compared with the widely used PST algorithm InsPecT. The results show that our method achieves higher accuracy and better performance than InsPecT. Based on the scoring scheme, we have developed a software tool named TVNovoTag, which is available at http://www.bprc.ac.cn/TVNovoTag/.

Magnetorquers and thrusters are usually employed independently to dump the accumulated momentum of reaction wheels installed in a satellite to control the spacecraft attitude. Momentum dumping using magnetorquers can be slow and restricted by Earth magnetic field strength. Hybrid use of thrusters and magnetorquers brings the benefit of minimizing the thruster fuel dissipation and maintaining the required dumping cycle. This paper focuses on investigating and analyzing hybrid control strategies of onboard thrusters and magnetorquers for dumping reaction wheel momentum on a 3-axis stabilized Earthpointing satellite. Both one-phase and two phase combined algorithms are established. Based on optimal dumping torque profile from an optimal dumping controller by using PWM thrusters only, two sub-optimal combined dumping algorithms based on instantaneous cost function minimization on energy and fuel at each sampling interval are explored and derived. In addition, the boundary constraints due to actuator control limits for practical application concerns are analyzed and their impacts on control algorithms are discussed. Extensive simulation demonstrations revealed that a large mount of thruster propellant can be saved through hybrid control for momentum dumping. Those hybrid dumping methods do not rely on complex geomagnetic field model and can be practically implemented onboard satellites.

The purpose of this study was to describe radiologic anatomy of the left atrium diverticulum. There were 20 patients with 27 left atrium diverticulums in 120 consecutive patients who underwent CT of coronary angiography. The presence probability of left atrium diverticulum was 16.7%, male of it was 13.0%, female was 17.6%. There was no difference on gender (P0.05). There were four patients accompanying with variation of pulmonary vein at one time. The diverticulum might be single or multiple, cystiform or tubiform. It could locate anterior wall or posterior wall or superior wall of left atrium. Left atrium diverticulums which was single, cystiform, and located in anterior wall were common. The cervix width of diverticulum was 4.9 +/- 3.2 mm, the body height of them was 5.4 +/- 2.0 mm. The ratio of body height to cervix width was from 0.47 to 4.08 (median 1.16). Ten patients of them undertook cardiac ultrasound examination at same time. There were five patients who left atrial diastolic function decreased, four patients who left ventricular systolic function decreased. Three of them both existed left atrial diastolic function decreasing and left ventricular systolic function decreasing, accompanied with mitral or aortic regurgitation. No patient was found that left atrium pressure or left ventricle diastolic pressure was increasing. The left atrium diverticulums of ten patients were probably congenital because their hemodynamical status cannot lead to diverticulum formation. It can be proved by reexamination after therapy or autopsy at last. In conclusion, multi-detector row computed tomography could provide anatomy details of left atrium diverticulum to help to finish heart and chest surgery successfully.

Based on the randomized database method and a linear discriminant function (LDF) model, a new strategy to filter out false positive matches in SEQUEST database search results is proposed. Given an experiment MS/MS dataset and a protein sequence database, a randomized database is constructed and merged with the original database. Then, all MS/MS spectra are searched against the combined database. For each expected false positive rate (FPR), LDFs are constructed for different charge states and used to filter out the false positive matches from the normal database. In order to investigate the error of FPR estimation, the new strategy was applied to a reference dataset. As a result, the estimated FPR was very close to the actual FPR. While applied to a human K562 cell line dataset, which is a complicated dataset from real sample, more matches could be confirmed than the traditional cutoff-based methods at the same estimated FPR. Also, though most of the results confirmed by the LDF model were consistent with those of PeptideProphet, the LDF model could still provide complementary information. These results indicate that the new method can reliably control the FPR of peptide identifications and is more sensitive than traditional cutoff-based methods.