Tunable Stress Relaxing Biomimetic Matrices: Hyaluronan/Hydroxyapatite Hybridization Mediates Assembly of Collagen Fibrils.

The alluring correlations of cellular behaviors with viscoelastic extracellular matrices have driven increasing endeavors directed toward the understanding of mechanical cues on cell growth and differentiation via preparing biomimetic scaffolds/gels with viscoelastic controllability. Indeed, systematic investigations, especially into calcium phosphate-containing biomimetics, are relatively rare. Here, oxidized hyaluronic acid/hydroxyapatite hybrids (OHAHs) were synthesized by hyaluronan-mediated biomimetic mineralization with confined ion diffusion and subsequent oxidization treatment. The collagen self-assembly was applied to fabricate tunable stress relaxing fibrillar matrices in the presence of OHAHs in which the incorporated hyaluronic acid with aldehyde groups acted to improve the component compatibility as well as to supplement the molecular interactions with the occurrence of a Schiff-base reaction. With the addition of varying OHAH contents, the self-assembly behavior of collagen was altered, and the obtained collagen-hybrid (CH) matrices presented a heterogeneous fibrillar structure interspersed with OHAHs, characterized by large fibrillar bundles coexisting with small fibrils. The OHAHs improved the hydrogel stability of pure collagen, and according to rheological and nanoindentation measurements, CH matrices also exhibited tunable stress relaxation rates, following an OHAH concentration-dependent fashion. The proliferation and spreading of MC3T3-E1 cells cultured onto such CH matrices were further found to increase with the stress relaxing rate of the matrices. The present study showed that the introduction of hydroxyapatite incorporated with active hyaluronic acid during collagen reconstitution was a simple and effective strategy to realize the preparation of tunable stress relaxing biomimetic matrices potentially used for further appraising the regulation of mechanical cues on cell behaviors.

First report of leaf brown blight caused by Neopestalotiopsis formicarum on jabuticaba in Taiwan.

Jabuticaba (Plinia cauliflora (Mart.) Kausel) was originated from Brazil (Lorenzi 2000). The production of jabuticaba is growing globally as its value in the food and pharmaceutical industries (Benvenutti 2021). In August 2019, jabuticaba plants with symptoms of leaf blight were observed in the field at the Meinong of Kaohsiung City, Taiwan. Disease incidence was 40%. Symptoms first presented as small, water-soaked lesions on young leaves, and then dark brown lesions of 1-3 cm in diameter on mature leaves. Six symptomatic leaves were collected from 6 jabuticaba plants for verifying the causal agents. Tissues (5 × 5 mm2) were cut from the margin of symptomatic leaf. Samples were sterilized in 1% sodium hypochlorite for 60 s, rinsed with sterile distilled water three times and then placed in 1% water agar in the dark for 5 days at room temperature. Resultant fungal colonies were purified by subculturing fungal hyphal tips on potato dextrose agar in a growth chamber (28°C, 12 h photoperiod) until fungal conidia appeared. The fungi initially produced white, cottony, aerial mycelium, after which concentric black conidiomata appeared on the plates after 7 days of incubation. The 5-celled conidia were fusiform to ellipsoid, straight to slightly curved, with sizes of 24.00-44.00 µm × 6.00-13.00 µm (avg. size, 32.00 × 9.37 µm, n = 120). The apical and basal cells were hyaline, and 3 median cells were pale brown and versicolorous. Conidia had 2-3 apical appendages and a conical basal cell with a truncate base. Based on the characteristics, which were common among isolates from diseased samples, the causal pathogen was identified as Neopestalotiopsis sp. (Solarte et al. 2018). Internal transcribed spacer (ITS), translation elongation factor 1α (TEF), ß-tubulin, and large ribosomal subunit (LSU) DNA sequences were obtained from these isolates and deposited in GenBank (MN723897, ITS; MN813055, TEF; MN813054, ß-tubulin; MN860104, LSU). Sequences demonstrated high sequence identity with those of Neopestalotiopsis formicarum ex-type cultures CBS 362.72 (Maharachchikumbura et al. 2014): 99.44% for ITS (KM199358), 99.38% for TEF (KM199517), 98.86% for ß-tubulin (KM199455), and 100.00% for LSU (KM116248). The phylogenetic relationship in Neopestalotiopsis species supported the identification of our isolates as N. formicarum. Three independent 3-isolate inoculation experiments were performed to fit Koch's postulates. Surface-sterilized leaves on live plants were punctured with a needle and inoculated with 5 µL of conidial suspension (1 × 105 conidia/mL). Inoculated plants were kept in a growth chamber (25°C, 70% relative humidity) for 7 days. Control plants were inoculated with sterile distilled water and kept under the same conditions. Inoculated leaves developed brown lesions around wounds after seven days. The pathogen was re-isolated from diseased plants, following the steps used for the original procedure, with identical characteristics as the initial isolates. This is the first report of leaf brown blight caused by N. formicarum on jabuticaba in Taiwan. N. formicarum was recently considered as a new threat to jabuticaba (Gualberto et al. 2021). In addition, it has a broad host range on many tropical crops, such as guarana and banana (Gualberto et al. 2021). Neopestalotiopsis spp. have been reported to cause important economic fruit diseases (Gualberto et al. 2021). Therefore, N. formicarum may become the potential risk for fruit production of tropical crops.

[Formulation regularity of traditional Chinese medicine in treatment of sinusitis based on data mining].

Based on the Drugdataexpy and the prescription modern application database, this study explored the formulation regularity of ancient and modern prescriptions for the treatment of sinusitis. The Chinese medicinal prescriptions for the treatment of sinusitis with various syndromes were retrieved from the above databases and the corresponding formulation regularity was investigated by frequency analysis, association rule analysis, and factor analysis. Eighty-seven Chinese medicinal prescriptions were included, involving five syndrome types of sinusitis and 160 Chinese medicine, which were mainly effective in releasing exterior, clearing heat, and tonifying deficiency, and acted on the lung meridian due to cold and warm nature and pungent and bitter flavor or on the spleen meridian due to warm nature and pungent flavor. Seventeen core Chinese medicine were screened out by topological data analysis, including Angelicae Dahuricae Radix, Magnoliae Flos, Glycyrrhizae Radix et Rhizoma, Xanthii Fructus, and Scutellariae Radix. Chinese medicine such as Magnoliae Flos, Angelicae Dahuricae Radix, and Xanthii Fructus were commonly used in the treatment of sinusitis of wind-heat in the lung meridian, while the combination of Glycyrrhizae Radix et Rhizoma, Magnoliae Flos, Angelicae Dahuricae Radix, Chuanxiong Rhizoma, etc. was the key compatibility in treating sinusitis of dampness-heat in the spleen and stomach. Six common factors were extracted from the factor analysis of the above two syndrome types. The findings indicate that the exterior-releasing, heat-clearing, and deficiency-tonifying Chinese medicine with cold and warm nature and pungent flavor are preferential options for the clinical treatment of sinusitis. Treatment should be based on syndrome differentiation and key therapeutic principles should be followed.

Effects of tourniquet use on clinical outcomes and cement penetration in TKA when tranexamic acid administrated: a randomized controlled trial.

BACKGROUND: The role of a tourniquet is still controversial for patients undergoing total knee arthroplasty (TKA). Our current study was performed to determine whether the nonuse of the tourniquet combine with tranexamic acid (TXA) application in TKA patients with end-stage osteoarthritis would accelerate the perioperative recovery rate and provide enough cement mantle thickness for implant fixation. METHODS: In this prospective, randomized controlled trial, 150 end-stage knee osteoarthritis patients receiving TKA were divided into three groups: group A (tourniquet group), group B (non-tourniquet group), and group C (tourniquet in cementation group). All enrolled patients received 3 g of intravenous TXA and 1 g topical TXA. The primary outcomes included blood loss variables and transfusion values. The secondary outcomes included VAS pain score, inflammatory factors level, range of motion, HSS score, postoperative hospital stay, and complication. Furthermore, by using a digital linear tomosynthesis technique, tibial baseplate bone cement mantle thickness was measured in four zones based on the knee society scoring system. RESULTS: No significant difference was found among the three groups with regards to total blood loss, transfusion, and complication. However, patients in group B showed lower inflammatory factors levels, shorter length of hospital stay, better range of motion, and lower postoperative pain. No significant difference was found among the three groups in four zones in terms of bone cement mantle thickness. CONCLUSIONS: For end-stage knee osteoarthritis patients, the absence of tourniquet did not appear to affect blood loss and cement penetration in TKA patients. Furthermore, less inflammation reaction and better knee function can be achieved without a tourniquet. We recommend no longer use a tourniquet in primary TKA for patients with end-stage osteoarthritis when TXA is administrated. TRIAL REGISTRATION: ChiCTR-INR-16009026 . LEVEL OF EVIDENCE: Therapeutic Level I.

Micro-/Nanomechanics Dependence of Biomimetic Matrices upon Collagen-Based Fibrillar Aggregation and Arrangement.

The mechanical and morphological cues of fibrillar extracellular matrices (ECMs) play vital roles in controlling the cellular behaviors. Understanding and regulating the correlation of the mechanics with morphologies, at the micro-/nanoscale are of great relevance to guide the growth and differentiation of stem or progenitor cells into the desired tissues. However, the investigations directed toward acquiring such a kind of correlation are very limited and far from satisfactory. Here, rheological and nanoindentation tests were employed to appraise the mechanical behaviors of biomimetic ECMs assembled from type I collagen solutions containing the equivalent content of alginate but with different molecular weights (MWs). An alginate-molecular-weight-dependent trend was found in the fibrillogenesis process and the fibril aggregation of these collagen-alginate (CA) matrices. The present study revealed that the viscoelasticity and nonlinear elasticity of the CA matrices relied upon their specific fibrillar architectures in which a heterogeneous structure formed with varying alginate MW, including the coexistence of small fibrils and larger fibrillar bundles. The correlation of the mechanical behaviors with the inhomogeneity in the fibrillar structures was further discussed in combination with those of Ca2+ ionically cross-linked CA matrices. This study not only presented the delicate mechanics of fibrillar ECM analogues but also showed that the introduction of affiliative matters such as polysaccharides (alginate with different MWs) is a simple and convenient strategy to achieve biomimetic hydrogels with tunable viscoelastic properties.

Cerebral Small Vessel Disease Biomarkers Detection on MRI-Sensor-Based Image and Deep Learning.

Magnetic resonance imaging (MRI) offers the most detailed brain structure image available today; it can identify tiny lesions or cerebral cortical abnormalities. The primary purpose of the procedure is to confirm whether there is structural variation that causes epilepsy, such as hippocampal sclerotherapy, local cerebral cortical dysplasia, and cavernous hemangioma. Cerebrovascular disease, the second most common factor of death in the world, is also the fourth leading cause of death in Taiwan, with cerebrovascular disease having the highest rate of stroke. Among the most common are large vascular atherosclerotic lesions, small vascular lesions, and cardiac emboli. The purpose of this thesis is to establish a computer-aided diagnosis system based on small blood vessel lesions in MRI images, using the method of Convolutional Neural Network and deep learning to analyze brain vascular occlusion by analyzing brain MRI images. Blocks can help clinicians more quickly determine the probability and severity of stroke in patients. We analyzed MRI data from 50 patients, including 30 patients with stroke, 17 patients with occlusion but no stroke, and 3 patients with dementia. This system mainly helps doctors find out whether there are cerebral small vessel lesions in the brain MRI images, and to output the found results into labeled images. The marked contents include the position coordinates of the small blood vessel blockage, the block range, the area size, and if it may cause a stroke. Finally, all the MRI images of the patient are synthesized, showing a 3D display of the small blood vessels in the brain to assist the doctor in making a diagnosis or to provide accurate lesion location for the patient.

An Eye-Tracking System based on Inner Corner-Pupil Center Vector and Deep Neural Network.

The human eye is a vital sensory organ that provides us with visual information about the world around us. It can also convey such information as our emotional state to people with whom we interact. In technology, eye tracking has become a hot research topic recently, and a growing number of eye-tracking devices have been widely applied in fields such as psychology, medicine, education, and virtual reality. However, most commercially available eye trackers are prohibitively expensive and require that the user's head remain completely stationary in order to accurately estimate the direction of their gaze. To address these drawbacks, this paper proposes an inner corner-pupil center vector (ICPCV) eye-tracking system based on a deep neural network, which does not require that the user's head remain stationary or expensive hardware to operate. The performance of the proposed system is compared with those of other currently available eye-tracking estimation algorithms, and the results show that it outperforms these systems.

Alginate-Mediated Mineralization for Ultrafine Hydroxyapatite Hybrid Nanoparticles.

The good bioactivity of hydroxyapatite (HA) makes it become a popular biomaterial, and so, the synthesis of HA has attracted much attention. However, a simple method to prepare well-dispersive and ultrafine HA nanoparticles (NPs) still needs to be explored. Here, needle-like hybrid HA NPs were synthesized in the presence of alginate (ALG) and the mechanism of ALG regulation on forming HA/ALG hybrid NPs was investigated. The size and crystallinity of HA/ALG NPs could be controlled simply by varying the amount of ALG. With a higher concentration of ALG, the size of HA/ALG NPs reduced and their dispersity was further improved. The assembly with ca. 3-6 nm thick nanoneedles was discernible in HA/ALG NPs. The results collected by Ca2+ concentration, pH, and conductometric measurements further provided the supportive data for ALG inhibition of calcium phosphate compound formation and thus modulation of HA crystallization. In addition, the good biocompatibility of synthesized HA/ALG NPs was indicated from the results of CCK-8 assays and CLSM observations after cultured with L929 cells. The needle-like HA NPs are promising for varied biomedical purposes, and their potential application as drug carriers would be further studied.

Delicate Assembly of Ultrathin Hydroxyapatite Nanobelts with Nanoneedles Directed by Dissolved Cellulose.

Living organisms make use of a variety of inorganic and organic components to form biogenic minerals with hierarchical structures and fascinating properties, triggering the development of biomimetic mineralization. The introduction of organic additive is a versatile strategy, and a wide range of organics have already been adopted to mimic biosystems designing and synthesizing advanced functional minerals. Insoluble cellulose is the most abundant polysaccharide in nature, but the insolubility has limited its extensive applications. In this study, we first find that concentrated calcium chloride aqueous solution is an effective solvent for cellulose, and dissolved cellulose plays a pivotal role in directing the formation of ultrathin hydroxyapatite (HA) nanobelts of ca. 10 nm in thickness. To investigate the assembling process of the belt, samples collected at different reaction times were observed. The results indicate that nanoneedles form first, and then they assemble into the prototype of nanobelts by lateral/longitudinal aggregation and arrangement. Subsequently, the nanobelts gradually become dense, transparent, and smooth via crystallographic fusion of adjacent nanoneedles, indicating the highly elaborated evolution of morphology resulted from a time-dependent process. During the evolution of nanobelts, dissolved cellulose is supposed to participate in the mineralization of HA via the bonding of its hydrophilic groups with phosphate groups and calcium ions and the interaction of cellulose molecules with HA crystal planes. These findings provide unique insight into the application of dissolved cellulose in aqueous solution and an inspiration of a bottom-up strategy for designing delicate mineral assemblies directed by insoluble organics.

Physical mechanism of order between electric and magnetic dipoles in spoof plasmonic structures.

It has been recently shown that a solid-textured metal cylinder can support electric and magnetic dipolar resonances simultaneously [Phys. Rev. X4, 021003 (2014)PRXHAE2160-330810.1103/PhysRevX.4.021003] which are almost degenerate in a two-dimensional (2-D) structure and non-degenerate in a three-dimensional (3-D) structure, and with the magnetic dipole appearing at higher frequency. They are described as spoof localized plasmonic modes analogous to localized plasmonic resonances in optical frequencies. Here, we consider a hollow metal cylinder corrugated by periodic cut-through slits. Our results indicate that the magnetic dipole can be separated from the electric dipole in a 2-D structure, and magnetic dipolar resonance appears at lower frequency, rather than electric resonance in both 2-D and 3-D structures. In order to clarify the physical mechanism behind the abnormal phenomenon, we study the influence of the core material on the electric- and magnetic-dipole modes based on theoretical analysis and numerical simulation. It is discovered that there is a threshold of an imaginary part of permittivity for switching the order between electric and magnetic dipoles. These results may provide fundamental understanding and physical insight for spoof plasmonic modes supported in designer structures.

[Application of Intelligent Optimization Algorithms to Wavelength Selection of Near-Infrared Spectroscopy].

Near infrared spectroscopy (NIRS) is a kind of indirect analysis technology, whose application depends on the setting up of relevant calibration model. In order to improve interpretability, accuracy and modeling efficiency of the prediction model, wavelength selection becomes very important and it can minimize redundant information of near infrared spectrum. Intelligent optimization algorithm is a sort of commonly wavelength selection method which establishes algorithm model by mathematical abstraction from the background of biological behavior or movement form of material, then iterative calculation to solve combinatorial optimization problems. Its core strategy is screening effective wavelength points in multivariate calibration modeling by using some objective functions as a standard with successive approximation method. In this work, five intelligent optimization algorithms, including ant colony optimization (ACO), genetic algorithm (GA), particle swarm optimization (PSO), random frog (RF) and simulated annealing (SA) algorithm, were used to select characteristic wavelength from NIR data of tobacco leaf for determination of total nitrogen and nicotine content and together with partial least squares (PLS) to construct multiple correction models. The comparative analysis results of these models showed that, the total nitrogen optimums models of dataset A and B were PSO-PLS and GA-PLS models. GA-PLS and SA-PLS models were the optimums for nicotine, respectively. Although not all predicting performance of these optimization models was superior to that of full spectrum PLS models, they were simplified greatly and their forecasting accuracy, precision, interpretability and stability were improved. Therefore, this research will have great significance and plays an important role for the practical application. Meanwhile, it could be concluded that the informative wavelength combination for total nitrogen were 4 587~4 878 and 6 700~7 200 cm(-1), and that for tobacco nicotine were 4 500~4 700 and 5 800~6 000 cm(-1). These selected wavelengths have actually physical significance.

Broad temperature range of cubic blue phase present in simple binary mixture systems containing rodlike Schiff base mesogens with tolane moiety.

Four simple rodlike Schiff base mesogens with tolane moiety were synthesized and applied to stabilize cubic blue phases (BPs) in simple binary mixture systems for the first time. When the chiral additive or was added into a chiral salicylaldimine-based compound, the temperature range of the cubic BP could be extended by more than 20 °C. However, when the chiral Schiff base mesogen was blended with chiral dopant possessing opposite handedness, , BPs could not be observed. Interestingly, the widest temperature range of the cubic BPs (∼35 °C) could be induced by adding the rodlike chiral dopant or into the rodlike racemic Schiff base mesogen with hydroxyl group. On the basis of our experimental results and molecular modeling, the appearance and temperature range of the BPs are affected by the dipole moment and the biaxiality of the molecular geometry. Accordingly, we demonstrated that the hydroxyl group and the methyl branch in this type of Schiff base mesogen play an important role in the stabilization of BPs.

Calibration transfer via an extreme learning machine auto-encoder.

In order to solve the spectra standardization problem in near-infrared (NIR) spectroscopy, a Transfer via Extreme learning machine Auto-encoder Method (TEAM) has been proposed in this study. A comparative study among TEAM, piecewise direct standardization (PDS), generalized least squares (GLS) and calibration transfer methods based on canonical correlation analysis (CCA) was conducted, and the performances of these algorithms were benchmarked with three spectral datasets: corn, tobacco and pharmaceutical tablet spectra. The results show that TEAM is a stable method and can significantly reduce prediction errors compared with PDS, GLS and CCA. TEAM can also achieve the best RMSEPs in most cases with a small number of calibration sets. TEAM is implemented in Python language and available as an open source package at https://github.com/zmzhang/TEAM.

The model adaptive space shrinkage (MASS) approach: a new method for simultaneous variable selection and outlier detection based on model population analysis.

Variable selection and outlier detection are important processes in chemical modeling. Usually, they affect each other. Their performing orders also strongly affect the modeling results. Currently, many studies perform these processes separately and in different orders. In this study, we examined the interaction between outliers and variables and compared the modeling procedures performed with different orders of variable selection and outlier detection. Because the order of outlier detection and variable selection can affect the interpretation of the model, it is difficult to decide which order is preferable when the predictabilities (prediction error) of the different orders are relatively close. To address this problem, a simultaneous variable selection and outlier detection approach called Model Adaptive Space Shrinkage (MASS) was developed. This proposed approach is based on model population analysis (MPA). Through weighted binary matrix sampling (WBMS) from model space, a large number of partial least square (PLS) regression models were built, and the elite parts of the models were selected to statistically reassign the weight of each variable and sample. Then, the whole process was repeated until the weights of the variables and samples converged. Finally, MASS adaptively found a high performance model which consisted of the optimized variable subset and sample subset. The combination of these two subsets could be considered as the cleaned dataset used for chemical modeling. In the proposed approach, the problem of the order of variable selection and outlier detection is avoided. One near infrared spectroscopy (NIR) dataset and one quantitative structure-activity relationship (QSAR) dataset were used to test this approach. The result demonstrated that MASS is a useful method for data cleaning before building a predictive model.

Clinical Results and Metal Ion Levels After Ceramic-on-Metal Total Hip Arthroplasty: A Mean 50-Month Prospective Single-Center Study.

BACKGROUND: The aim of our study was to investigate the clinical results and serum metal ion levels in a cohort of patients who received total hip arthroplasty (THA) with ceramic-on-metal (CoM) bearings. METHODS: From September 2009 to December 2011, 78 patients (89 hips) who underwent THA with CoM bearings were involved in this study. Harris Hip Score, Short-Form 12, and Western Ontario and McMaster Universities Osteoarthritis Index scores were measured and radiographs were taken for radiographic analysis. Serum metal ion levels of cobalt (Co), chromium (Cr), molybdenum (Mo), and titanium (Ti) were measured using high-resolution inductively coupled plasma-mass spectrometry. RESULTS: Severy-four patients (85 hips) were followed up at a mean of 50 months. At the end of follow-up, HSS, Short-Form 12, and Western Ontario and McMaster Universities Osteoarthritis Index scores were improved significantly compared with preoperative values. No intraoperative and postoperative complications occurred, and no radiolucency, osteolysis, and loosening was found from radiographic examination. Metal ion analysis showed that serum metal ions levels were significantly elevated compared with normal values. Spearman correlation analysis revealed that there was a correlation between 3 metal ion levels and body mass index(Co: r = 0.49, P < .01; Cr: r = 0.47, P < .01; Mo: r = 0.36, P = .04). No correlation was found between metal ion levels and age, cup abduction angle, cup anteversion angle, acetabular version, bilateral arthroplasty, cup screw used, hip stem implant type, or femoral head size. CONCLUSION: Our study concluded that the use of a CoM THA is effective clinically, but the systemic metal ion levels are significantly elevated at midterm follow-up. Whether the elevated metal ion levels will induce an adverse reaction is unknown and long-term follow-up is need.

Application of GC-MS coupled with chemometrics for scanning serum metabolic biomarkers from renal fibrosis rat.

Renal interstitial fibrosis closely relates to chronic kidney disease and is regarded as the final common pathway in most cases of end-stage renal disease. Metabolomic biomarkers can facilitate early diagnosis and allow better understanding of the pathogenesis underlying renal fibrosis. Gas chromatography-mass spectrometry (GC/MS) is one of the most promising techniques for identification of metabolites. However, the existence of the background, baseline offset, and overlapping peaks makes accurate identification of the metabolites unachievable. In this study, GC/MS coupled with chemometric methods was successfully developed to accurately identify and seek metabolic biomarkers for rats with renal fibrosis. By using these methods, seventy-six metabolites from rat serum were accurately identified and five metabolites (i.e., urea, ornithine, citric acid, galactose, and cholesterol) may be useful as potential biomarkers for renal fibrosis.

A new method for wavelength interval selection that intelligently optimizes the locations, widths and combinations of the intervals.

In this study, a new algorithm for wavelength interval selection, known as interval variable iterative space shrinkage approach (iVISSA), is proposed based on the VISSA algorithm. It combines global and local searches to iteratively and intelligently optimize the locations, widths and combinations of the spectral intervals. In the global search procedure, it inherits the merit of soft shrinkage from VISSA to search the locations and combinations of informative wavelengths, whereas in the local search procedure, it utilizes the information of continuity in spectroscopic data to determine the widths of wavelength intervals. The global and local search procedures are carried out alternatively to realize wavelength interval selection. This method was tested using three near infrared (NIR) datasets. Some high-performing wavelength selection methods, such as synergy interval partial least squares (siPLS), moving window partial least squares (MW-PLS), competitive adaptive reweighted sampling (CARS), genetic algorithm PLS (GA-PLS) and interval random frog (iRF), were used for comparison. The results show that the proposed method is very promising with good results both on prediction capability and stability. The MATLAB codes for implementing iVISSA are freely available on the website: .

Multiscale peak detection in wavelet space.

Accurate peak detection is essential for analyzing high-throughput datasets generated by analytical instruments. Derivatives with noise reduction and matched filtration are frequently used, but they are sensitive to baseline variations, random noise and deviations in the peak shape. A continuous wavelet transform (CWT)-based method is more practical and popular in this situation, which can increase the accuracy and reliability by identifying peaks across scales in wavelet space and implicitly removing noise as well as the baseline. However, its computational load is relatively high and the estimated features of peaks may not be accurate in the case of peaks that are overlapping, dense or weak. In this study, we present multi-scale peak detection (MSPD) by taking full advantage of additional information in wavelet space including ridges, valleys, and zero-crossings. It can achieve a high accuracy by thresholding each detected peak with the maximum of its ridge. It has been comprehensively evaluated with MALDI-TOF spectra in proteomics, the CAMDA 2006 SELDI dataset as well as the Romanian database of Raman spectra, which is particularly suitable for detecting peaks in high-throughput analytical signals. Receiver operating characteristic (ROC) curves show that MSPD can detect more true peaks while keeping the false discovery rate lower than MassSpecWavelet and MALDIquant methods. Superior results in Raman spectra suggest that MSPD seems to be a more universal method for peak detection. MSPD has been designed and implemented efficiently in Python and Cython. It is available as an open source package at .

Depth-Sensor-Based Monitoring of Therapeutic Exercises.

In this paper, we propose a self-organizing feature map-based (SOM) monitoring system which is able to evaluate whether the physiotherapeutic exercise performed by a patient matches the corresponding assigned exercise. It allows patients to be able to perform their physiotherapeutic exercises on their own, but their progress during exercises can be monitored. The performance of the proposed the SOM-based monitoring system is tested on a database consisting of 12 different types of physiotherapeutic exercises. An average 98.8% correct rate was achieved.

Prediction of peptide fragment ion mass spectra by data mining techniques.

Accurate prediction of peptide fragment ion mass spectra is one of the critical factors to guarantee confident peptide identification by protein sequence database search in bottom-up proteomics. In an attempt to accurately and comprehensively predict this type of mass spectra, a framework named MS(2)PBPI is proposed. MS(2)PBPI first extracts fragment ions from large-scale MS/MS spectra data sets according to the peptide fragmentation pathways and uses binary trees to divide the obtained bulky data into tens to more than 1000 regions. For each adequate region, stochastic gradient boosting tree regression model is constructed. By constructing hundreds of these models, MS(2)PBPI is able to predict MS/MS spectra for unmodified and modified peptides with reasonable accuracy. Moreover, high consistency between predicted and experimental MS/MS spectra derived from different ion trap instruments with low and high resolving power is achieved. MS(2)PBPI outperforms existing algorithms MassAnalyzer and PeptideART.