Epileptic seizure prediction based on EEG using pseudo-three-dimensional CNN.

Epileptic seizures are characterized by their sudden and unpredictable nature, posing significant risks to a patient's daily life. Accurate and reliable seizure prediction systems can provide alerts before a seizure occurs, as well as give the patient and caregivers provider enough time to take appropriate measure. This study presents an effective seizure prediction method based on deep learning that combine with handcrafted features. The handcrafted features were selected by Max-Relevance and Min-Redundancy (mRMR) to obtain the optimal set of features. To extract the epileptic features from the fused multidimensional structure, we designed a P3D-BiConvLstm3D model, which is a combination of pseudo-3D convolutional neural network (P3DCNN) and bidirectional convolutional long short-term memory 3D (BiConvLstm3D). We also converted EEG signals into a multidimensional structure that fused spatial, manual features, and temporal information. The multidimensional structure is then fed into a P3DCNN to extract spatial and manual features and feature-to-feature dependencies, followed by a BiConvLstm3D input to explore temporal dependencies while preserving the spatial features, and finally, a channel attention mechanism is implemented to emphasize the more representative information in the multichannel output. The proposed has an average accuracy of 98.13%, an average sensitivity of 98.03%, an average precision of 98.30% and an average specificity of 98.23% for the CHB-MIT scalp EEG database. A comparison of the proposed model with other baseline methods was done to confirm the better performance of features through time-space nonlinear feature fusion. The results show that the proposed P3DCNN-BiConvLstm3D-Attention3D method for epilepsy prediction by time-space nonlinear feature fusion is effective.

An entropy-based approach for assessing the directional persistence of cell migration.

Cell migration, which is primarily characterized by directional persistence, is essential for the development of normal tissues and organs, as well as for numerous pathological processes. However, there is a lack of simple and efficient tools to analyze the systematic properties of persistence based on cellular trajectory data. Here, we present a novel approach, the entropy of angular distribution , which combines cellular turning dynamics and Shannon entropy to explore the statistical and time-varying properties of persistence that strongly correlate with cellular migration modes. Our results reveal the changes in the persistence of multiple cell lines that are tightly regulated by both intra- and extracellular cues, including Arpin protein, collagen gel/substrate, and physical constraints. Significantly, some previously unreported distinctive details of persistence have also been captured, helping to elucidate how directional persistence is distributed and evolves in different cell populations. The analysis suggests that the entropy of angular distribution-based approach provides a powerful metric for evaluating directional persistence and enables us to better understand the relationships between cellular behaviors and multiscale cues, which also provides some insights into the migration dynamics of cell populations, such as collective cell invasion.

The structure and diversity of bacteria and fungi in the roots and rhizosphere soil of three different species of Geodorum.

Shepherd's crook (Geodorum) is a genus of protected orchids that are valuable both medicinally and ornamentally. Geodorum eulophioides (GE) is an endangered and narrowly distributed species, and Geodorum densiflorum (GD) and Geodorum attenuatum (GA) are widespread species. The growth of orchids depend on microorganisms. However, there are few studies on the microbial structure in Geodorum, and little is known about the roles of microorganisms in the endangered mechanism of G. eulophioides. This study analyzed the structure and composition of bacterial and fungal communities in the roots and rhizosphere soil of GE, GD, and GA. The results showed that Delftia, Bordetella and norank_f_Xanthobacteraceae were the dominant bacteria in the roots of Geodorum, while norank_f_Xanthobacteraceae, Gaiella and norank_f_norank_o_Gaiellales were the dominant bacteria in the rhizosphere soil of Geodorum. In the roots, the proportion of Mycobacterium in GD_roadside was higher than that in GD_understory, on the contrary, the proportion of Fusarium, Delftia and Bordetella in GD_roadside was lower than that in GD_understory. Compared with the GD_understory, the roots of GD_roadside had lower microbial diversity. In the endangered species GE, Russula was the primary fungus in the roots and rhizosphere soil, with fungal diversity lower than in the more widespread species. Among the widespread species, the dominant fungal genera in the roots and rhizosphere soil were Neocosmospora, Fusarium and Coprinopsis. This study enhances our understanding of microbial composition and diversity, providing fundamental information for future research on microbial contributions to plant growth and ecosystem function in Geodorum.

Heterologous overexpression of heat shock protein 20 genes of different species of yellow Camellia in Arabidopsis thaliana reveals their roles in high calcium resistance.

Yellow Camellia (Camellia sect. chrysantha) is a rare ornamental plant and an important germplasm resource globally. Camellia nitidissima thrives in normal acidic soils, while Camellia limonia can adapt to the calcareous soils found in karst areas. Our previous study on the karst adaptation of yellow camellias revealed that the expression levels of heat shock protein 20(HSP20) were higher in Camellia limonia than in Camellia nitidissima. However, the functions of the HSP20 gene of Camellia limonia remain unclear to data. In this study, the HSP20 genes of Camellia limonia (ClHSP20-OE lines) and Camellia. nitidissima (CnHSP20-OE lines) were cloned and overexpressed heterologously in Arabidopsis thaliana. Additionally, we overexpressed the HSP20 gene of Arabidopsis (AtHSP20-OE lines) was also overexpressed, and the T-DNA inserted mutants (athspmutant lines) were also used to determine the functions of HSP20 genes. Under high calcium stress, the chlorophyll, nitrogen, water content and humidity of leaves were increased in ClHSP20-OE lines, while those of other lines were declined. The size of the stomatal apertures, stomatal conductance, and the photosynthetic efficiency of ClHSP20-OE lines were higher than those of the other lines. However, the accumulation of H2O2 and O2- in the leaves of ClHSP20-OE lines was the lowest among all the lines. Energy spectrum scanning revealed that the percentage of calcium on the surfaces of the leaves of ClHSP20-OE lines was relatively low, while that of athspmutant lines was the highest. The ClHSP20 gene can also affected soil humidity and the contents of soil nitrogen, phosphorus, and potassium. Transcriptome analysis revealed that the expressions of FBA5 and AT5G10770 in ClHSP20-OE lines was significantly up-regulated compared to that of CnHSP20-OE lines. Compared to that of athspmutant lines, the expressions of DREB1A and AT3G30460 was significantly upregulated in AtHSP20-OE lines, and the expression of POL was down-regulated. Our findings suggest that the HSP20 gene plays a crucial role in maintained photosynthetic rate and normal metabolism by regulating the expression of key genes under high-calcium stress. This study elucidates the mechanisms underlying the karst adaptation in Camellia. limonia and provides novel insights for future research on karst plants.

Use of pyridazinediones for tuneable and reversible covalent cysteine modification applied to peptides, proteins and hydrogels.

Reversible cysteine modification has been found to be a useful tool for a plethora of applications such as selective enzymatic inhibition, activity-based protein profiling and/or cargo release from a protein or a material. However, only a limited number of reagents display reliable dynamic/reversible thiol modification and, in most cases, many of these reagents suffer from issues of stability, a lack of modularity and/or poor rate tunability. In this work, we demonstrate the potential of pyridazinediones as novel reversible and tuneable covalent cysteine modifiers. We show that the electrophilicity of pyridazinediones correlates to the rates of the Michael addition and retro-Michael deconjugation reactions, demonstrating that pyridazinediones provide an enticing platform for readily tuneable and reversible thiol addition/release. We explore the regioselectivity of the novel reaction and unveil the reason for the fundamental increased reactivity of aryl bearing pyridazinediones by using DFT calculations and corroborating findings with SCXRD. We also applied this fundamental discovery to making more rapid disulfide rebridging agents in related work. We finally provide the groundwork for potential applications in various areas with exemplification using readily functionalised "clickable" pyridazinediones on clinically relevant cysteine and disulfide conjugated proteins, as well as on a hydrogel material.

Combined analysis of metagenome and transcriptome revealed the adaptive mechanism of different golden Camellia species in karst regions.

Camellia sect. Chrysantha is an important rare and protected plant species. Some golden Camellia species grow in karst soil while others grow in acidic soil. In order to study the adaptation mechanism of golden Camellia to the karst environment, four species of golden Camellia growing in the karst soil (Camellia pubipetala, Camellia perpetua, Camellia grandis, and Camellia limonia) and four species growing in the acidic soil (Camellia nitidissima, Camellia euphlebia, Camellia tunghinensis, and Camellia parvipetala) were selected for this study. Combining the metagenome and transcriptome, the structure and function of the rhizosphere microbial communities and the gene expression in roots of golden Camellia were analyzed. The results showed that the rhizosphere microbial communities in different golden Camellia were significantly different in abundance of Acidobacteria, Actinobacteria, Candidatus_Rokubacteria, Nitrospirae, Planctomycetes, and Candidatus_Tectomicrobia. The proportion of Candidatus_Rokubacteria was significantly higher in the rhizosphere soil of four species of golden Camellia grown in karst areas, compared to C. nitidissima, C. euphlebia, and C. tunghinensis. The linear discriminant analysis Effect Size showed that C. parvipetala was similar to karst species in the enrichment of ABC transporters and quorum sensing. During the transcriptome analysis, numerous upregulated genes in four karst species, including CYP81E, CHS, F3H, C12RT1, NAS, and CAD, were found to be enriched in the secondary metabolite synthesis pathway in the KEGG library, when compared to C. tunghinensis. This study provides information for plant adaptation mechanisms on the rhizosphere soil microbial composition and gene expression in secondary metabolic pathways to karst habitats and its distribution in karst areas.

Epileptic prediction using spatiotemporal information combined with optimal features strategy on EEG.

Objective: Epilepsy is the second most common brain neurological disease after stroke, which has the characteristics of sudden and recurrence. Seizure prediction is seriously important for improving the quality of patients' lives. Methods: From the perspective of multiple dimensions including time-frequency, entropy and brain network, this paper proposed a novel approach by constructing the optimal spatiotemporal feature set to predict seizures. Based on strong independence and large information capabilities, the two-dimensional feature screening algorithm is performed to eliminate unnecessary redundant features. In order to verify the effectiveness of the optimal feature set, support vector machine (SVM) was used to classify the preictal and interictal states on both the Kaggle intracranial EEG and CHB-MIT scalp EEG dataset. Results: This model achieved an average accuracy of 98.01%, AUC of 0.96, F-Score of 98.3% and FPR of 0.0383/h on the Kaggle dataset; On the CHB-MIT dataset, the average accuracy, AUC, F-score and FPR were 95.93%, 0.92, 94.97% and 0.0473/h, respectively. Further ablation experiments have confirmed that the temporal and spatial features fusion has better performance than the individual temporal or spatial features. Conclusion: Compared to the state-of-the-art methods, our approach outperforms most of these existing techniques. The results show that our approach can effectively extract the spatiotemporal information of epileptic EEG signals to predict epileptic seizures with high performance.

Epileptic seizure prediction in intracranial EEG using critical nucleus based on phase transition.

BACKGROUND AND OBJECTIVE: Epilepsy is the second most prevalent neurological disorder of brain activity, affecting about seventy million people, or nearly 1% of the world population. Epileptic seizures prediction is extremely important for improving the epileptic patients' life. This paper proposed a novel method to predict seizures by detecting the critical transition of brain activities with intracranial EEG (iEEG) signals. METHODS: This article used three key measures of fluctuation, correlation and percolation to quantify pre-ictal states of epilepsy. Based on these measures, a ritical nucleus of iEEG signals was constructed and a composite index was introduced to detect the likelihood of impending seizures. In addition, we analyzed the dynamical mechanism of seizures at the tipping point from the perspective of spatial diffusion and temporal fluctuation. RESULTS: The empirical results supported that the seizures are self-initiated via a critical transition in pre-ictal state and showed that the proposed model can achieve a good prediction performance. The average accuracy, sensitivity, specificity and false-positive rate (FPR) attain 87.96%, 82.93%, 89.33% and 0.11/h respectively. The results also suggest that the temporal and spatial factors have strong synergistic effect on triggering seizures. For those seizures consistent with critical transition, the predictive performance was greatly improved with sensitivity up to 96.88%. CONCLUSIONS: This article proposed a critical nucleus model combined with spatial and temporal features of iEEG signals capable of seizure prediction. The proposed model brings insight from phase transition into epileptic iEEG signals analysis and quantifies the transition of the state to predict epileptic seizures with high accuracy.

Temporal and spatial dynamic propagation of electroencephalogram by combining power spectral and synchronization in childhood absence epilepsy.

Objective: During the transition from normal to seizure and then to termination, electroencephalography (EEG) signals have complex changes in time-frequency-spatial characteristics. The quantitative analysis of EEG characteristics and the exploration of their dynamic propagation in this paper would help to provide new biomarkers for distinguishing between pre-ictal and inter-ictal states and to better understand the seizure mechanisms. Methods: Thirty-three children with absence epilepsy were investigated with EEG signals. Power spectral and synchronization were combined to provide the time-frequency-spatial characteristics of EEG and analyze the spatial distribution and propagation of EEG in the brain with topographic maps. To understand the mechanism of spatial-temporal evolution, we compared inter-ictal, pre-ictal, and ictal states in EEG power spectral and synchronization network and its rhythms in each frequency band. Results: Power, frequency, and spatial synchronization are all enhanced during the absence seizures to jointly dominate the epilepsy process. We confirmed that a rapid diffusion at the onset accompanied by the frontal region predominance exists. The EEG power rapidly bursts in 2-4 Hz through the whole brain within a few seconds after the onset. This spatiotemporal evolution is associated with spatial diffusion and brain regions interaction, with a similar pattern, increasing first and then decreasing, in both the diffusion of the EEG power and the connectivity of the brain network during the childhood absence epilepsy (CAE) seizures. Compared with the inter-ictal group, we observed increases in power of delta and theta rhythms in the pre-ictal group (P < 0.05). Meanwhile, the synchronization of delta rhythm decreased while that of alpha rhythm enhanced. Conclusion: The initiation and propagation of CAE seizures are related to the abnormal discharge diffusion and the synchronization network. During the seizures, brain activity is completely changed with the main component delta rhythm. Furthermore, this article demonstrated for the first time that alpha inhibition, which is consistent with the brain's feedback regulation mechanism, is caused by the enhancement of the network connection. Temporal and spatial evolution of EEG is of great significance for the transmission mechanism, clinical diagnosis and automatic detection of absence epilepsy seizures.

Quantitative analysis of heart rate variability parameter and mental stress index.

Background: Cardiovascular disease not only occurs in the elderly but also tends to become a common social health problem. Considering the fast pace of modern life, quantified heart rate variability (HRV) indicators combined with the convenience of wearable devices are of great significance for intelligent telemedicine. To quantify the changes in human mental state, this article proposes an improved differential threshold algorithm for R-wave detection and recognition of electrocardiogram (ECG) signals. Methods: HRV is a specific quantitative indicator of autonomic nerve regulation of the heart. The recognition rate is increased by improving the starting position of R wave and the time-window function of the traditional differential threshold method. The experimental platform is a wearable sign monitoring system constructed based on body area networks (BAN) technology. Analytic hierarchy process (AHP) is used to construct the mental stress assessment model, the weight judgment matrix is constructed according to the influence degree of HRV analysis parameters on mental stress, and the consistency check is carried out to obtain the weight value of the corresponding HRV analysis parameters. Results: Experimental results show that the recognition rate of R wave of real-time 5 min ECG data collected by this algorithm is >99%. The comprehensive index of HRV based on weight matrix can greatly reduce the deviation caused by the measurement error of each parameter. Compared with traditional characteristic wave recognition algorithms, the proposed algorithm simplifies the process, has high real-time performance, and is suitable for wearable analysis devices with low-configuration requirements. Conclusion: Our algorithm can describe the mental stress of the body quantitatively and meet the requirements of application demonstration.

LRFNet: A deep learning model for the assessment of liver reserve function based on Child-Pugh score and CT image.

BACKGROUND AND OBJECTIVE: Liver reserve function should be accurately evaluated in patients with hepatic cellular cancer before surgery to evaluate the degree of liver tolerance to surgical methods. Meanwhile, liver reserve function is also an important indicator for disease analysis and prognosis of patients. Child-Pugh score is the most widely used liver reserve function evaluation and scoring system. However, this method also has many shortcomings such as poor accuracy and subjective factors. To achieve comprehensive evaluation of liver reserve function, we developed a deep learning model to fuse bimodal features of Child-Pugh score and computed tomography (CT) image. METHODS: 1022 enhanced abdomen CT images of 121 patients with hepatocellular carcinoma and impaired liver reserve function were retrospectively collected. Firstly, CT images were pre-processed by de-noising, data amplification and normalization. Then, new branches were added between the dense blocks of the DenseNet structure, and the center clipping operation was introduced to obtain a lightweight deep learning model liver reserve function network (LRFNet) with rich liver scale features. LRFNet extracted depth features related to liver reserve function from CT images. Finally, the extracted features are input into a deep learning classifier composed of fully connected layers to classify CT images into Child-Pugh A, B and C. Precision, Specificity, Sensitivity, and Area Under Curve are used to evaluate the performance of the model. RESULTS: The AUC by our LRFNet model based on CT image for Child-Pugh A, B and C classification of liver reserve function was 0.834, 0.649 and 0.876, respectively, and with an average AUC of 0.774, which was better than the traditional clinical subjective Child-Pugh classification method. CONCLUSION: Deep learning model based on CT images can accurately classify Child-Pugh grade of liver reserve function in hepatocellular carcinoma patients, provide a comprehensive method for clinicians to assess liver reserve function before surgery.

Technological Requirements and Challenges in Wireless Body Area Networks for Health Monitoring: A Comprehensive Survey.

With the rapid growth in healthcare demand, an emergent, novel technology called wireless body area networks (WBANs) have become promising and have been widely used in the field of human health monitoring. A WBAN can collect human physical parameters through the medical sensors in or around the patient's body to realize real-time continuous remote monitoring. Compared to other wireless transmission technologies, a WBAN has more stringent technical requirements and challenges in terms of power efficiency, security and privacy, quality of service and other specifications. In this paper, we review the recent WBAN medical applications, existing requirements and challenges and their solutions. We conducted a comprehensive investigation of WBANs, from the sensor technology for the collection to the wireless transmission technology for the transmission process, such as frequency bands, channel models, medium access control (MAC) and networking protocols. Then we reviewed its unique safety and energy consumption issues. In particular, an application-specific integrated circuit (ASIC)-based WBAN scheme is presented to improve its security and privacy and achieve ultra-low energy consumption.

Multi-modal feature-fusion for CT metal artifact reduction using edge-enhanced generative adversarial networks.

Computed Tomography (CT) imaging is one of the most widely-used and cost-effective technology for organ screening and diseases diagnosis. Because of existence of metallic implants in some patients, the CT images acquired from these patients are often corrupted by undesirable metal artifacts, which causes severe problem of metal artifact. Although there have been proposed many methods to reduce metal artifact, reduction is still challenging and inadequate, and results are suffering from symptom variance, second artifact and poor subjective evaluation. To address these problems, we propose a novel metal artifact reduction method based on generative adversarial networks to simultaneously reduce metal artifacts and enhance texture structure of corrected CT images. Specifically, we firstly incorporate interactive information (text) and imaging CT (image) into a comprehensive feature to yield multi-modal feature-fusion representation, which overcomes the representative ability limitation of single-modal data. The incorporation of interaction information constrains the feature generation to ensure symptom consistency between corrected and target CT. Then, we design an edge-enhance sub-network to avoid second artifact and suppress noise. Besides, we invite three professional physicians to evaluate corrected CT image subjectively. In this paper, We achieved average increment of 11.3% PSNR and 12.1% SSIM on DeepLesion dataset. The subjective evaluations by physicians show that ours outperforms over 6.3%, 7.1%, 5.50% and 6.9% in term of sharpness, resolution, invariance and acceptability, respectively. Our proposed method can achieve high-quality metal artifact reduction results.

Research on Finger Vein Image Segmentation and Blood Sampling Point Location in Automatic Blood Collection.

In the fingertip blood automatic sampling process, when the blood sampling point in the fingertip venous area, it will greatly increase the amount of bleeding without being squeezed. In order to accurately locate the blood sampling point in the venous area, we propose a new finger vein image segmentation approach basing on Gabor transform and Gaussian mixed model (GMM). Firstly, Gabor filter parameter can be set adaptively according to the differential excitation of image and we use the local binary pattern (LBP) to fuse the same-scale and multi-orientation Gabor features of the image. Then, finger vein image segmentation is achieved by Gabor-GMM system and optimized by the max flow min cut method which is based on the relative entropy of the foreground and the background. Finally, the blood sampling point can be localized with corner detection. The experimental results show that the proposed approach has significant performance in segmenting finger vein images which the average accuracy of segmentation images reach 91.6%.

Exogenous melatonin promotes growth and sucrose metabolism of grape seedlings.

Melatonin (MT) has many important functions in plants. In this study, different concentrations of MT (0, 50, 100, 150, and 200 µmol/L) were sprayed on grape seedlings, and its effects on plant growth and sucrose metabolism were determined. The results show that there was a mutual influence and promotional relationship between growth and sugar metabolism in grape seedlings. The MT treatments promoted the development and growth of grape seedlings by increasing their biomass and promoting the photosynthetic performance of leaves. This resulted in increased nutrient absorption and a greater ability to compete for resources. The increase in photosynthesis resulted in greater sucrose production. The MT treatments increased the activities of enzymes related to sucrose metabolism, so that a large amount of sucrose was hydrolysed into glucose and fructose to meet the rapid growth requirements of grape seedlings. The increased total soluble sugars contents and increased activities of antioxidant enzymes resulted in greater resistance of grape seedlings, and greater adaptability to environmental changes. In general, MT treatments had beneficial effects on grape seedling growth, glucose metabolism, and resistance. Under these conditions, foliar spraying with MT at 150 µmol/L had the best effects.

Phytoremediation potential of Pterocypsela laciniata as a cadmium hyperaccumulator.

To identify new cadmium (Cd) hyperaccumulators, the artificially high soil Cd concentration method was used to screen six common farmland weeds. Among them, only Pterocypsela laciniata (Houtt.) C. Shih showed characteristics of a Cd hyperaccumulator and was selected for further studies. In pot experiments, soil Cd concentrations of 5, 10, and 25 mg kg-1 increased the biomass and photosynthetic pigment concentrations in P. laciniata when compared with the control, whereas 75 and 100 mg kg-1 decreased them (the maxima were at 10 mg kg-1 soil Cd). The antioxidant enzyme activities and the soluble protein concentrations of P. laciniata showed similar trends as biomass. The Cd concentrations in roots and shoots of P. laciniata increased as soil Cd concentration increased. When the soil Cd concentration was 50 mg kg-1, the Cd concentration in the shoots of P. laciniata was 116 mg kg-1 (the critical value for Cd hyperaccumulators is 100 mg kg-1). Both the root and shoot bioconcentration factors of P. laciniata were larger than 1.0, and the translocation factor exceeded 1.0 in almost all treatments. The Cd extractions by the shoots and whole plants of P. laciniata reached maxima at 208 and 375 µg plant-1, respectively. The Cd extractions by P. laciniata were different between two ecotypes. Therefore, P. laciniata is a Cd hyperaccumulator that could remediate Cd-contaminated soils, but the ecotypes should be considered when using P. laciniata for phytoremediation.

Spectroscopic characterisation of centropolyindanes.

A highly promising class of three-dimensional polyaromatic hydrocarbons comprises the centropolyindanes. The characteristic feature of these compounds is the mutual fusion of several molecules of indane along the saturated C-C bonds of their cyclopentane rings. Among the polycyclic aromatic hydrocarbons, the centropolyindanes are special because of the saturated core of sp3-hybridised carbon atoms embedded in a three-dimensional environment of aromatic building blocks. While the centropolyindanes and their numerous derivatives have been studied in detail by NMR spectroscopy, mass spectrometry and X-ray diffraction, investigation of their vibrational features, and especially those of the neopentane core present in most cases, have not been performed so far. In the present paper, we report the first systematic study of a set of centropolyindanes by vibrational spectroscopy, using inelastic neutron scattering (INS), infrared and Raman spectroscopies.

Vibrational spectroscopy of metal methanesulfonates: M = Na, Cs, Cu, Ag, Cd.

In this work, we have used a combination of vibrational spectroscopy (infrared, Raman and inelastic neutron scattering) and periodic density functional theory to investigate six metal methanesulfonate compounds that exhibit four different modes of complexation of the methanesulfonate ion: ionic, monodentate, bidentate and pentadentate. We found that the transition energies of the modes associated with the methyl group (C-H stretches and deformations, methyl rock and torsion) are essentially independent of the mode of coordination. The SO3 modes in the Raman spectra also show little variation. In the infrared spectra, there is a clear distinction between ionic (i.e. not coordinated) and coordinated forms of the methanesulfonate ion. This is manifested as a splitting of the asymmetric S-O stretch modes of the SO3 moiety. Unfortunately, no further differentiation between the various modes of coordination: unidentate, bidentate etc … is possible with the compounds examined. While it is likely that such a distinction could be made, this will require a much larger dataset of compounds for which both structural and spectroscopic data are available than that available here.

Structure and vibrational spectroscopy of methanesulfonic acid.

In this work, we have used a combination of vibrational spectroscopy (infrared, Raman and inelastic neutron scattering) and periodic density functional theory to investigate the structure of methanesulfonic acid (MSA) in the liquid and solid states. The spectra clearly show that the hydrogen bonding is much stronger in the solid than the liquid state. The structure of MSA is not known; however, mineral acids typically adopt a chain structure in condensed phases. A periodic density functional theory (CASTEP) calculation based on the linear chain structure found in the closely related molecule trifluoromethanesulfonic acid gave good agreement between the observed and calculated spectra, particularly with regard to the methyl and sulfonate groups. The model accounts for the large widths of the asymmetric S-O stretch modes; however, the external mode region is not well described. Together, these observations suggest that the basic model of four molecules in the primitive unit cell, linked by hydrogen bonding into chains, is correct, but that MSA crystallizes in a different space group than that of trifluoromethanesulfonic acid.

[Research on biometric method of heart sound signal based on GMM].

OBJECTIVE: Extraction of cepstral coefficients combined with Gaussian Mixture Model (GMM) is used to propose a biometric method based on heart sound signal. METHODS: Firstly, the original heart sounds signal was preprocessed by wavelet denoising. Then, Linear Prediction Cepstral Coefficients (LPCC) and Mel Frequency Cepstral Coefficients (MFCC) are compared to extract representative features and develops hidden Markov model (HMM) for signal classification. At last, the experiment collects 100 heart sounds from 50 people to test the proposed algorithm. RESULTS: The comparative experiments prove that LPCC is more suitable than MFCC for heart sound biometric, and by wavelet denoising in each piece of heart sound signal, the system achieves higher recognition rate than traditional GMM. CONCLUSION: Those results show that this method can effectively improve the recognition performance of the system and achieve a satisfactory effect.