No Difference in the Pathogenic Microorganisms Among Different Types of Anorectal Abscesses: A Retrospective Study.

OBJECTIVE:  There is limited data on the pathogenic microorganisms associated with anorectal abscesses. The purpose of this study was to retrospectively analyze the types and quantities of pathogenic microorganisms in the pus cultures of patients with anorectal abscesses and to explore the correlation between pathogenic microorganisms and types of anorectal abscesses. METHODS:  A retrospective analysis was conducted on the microbiological data of 517 inpatient surgical patients with anorectal abscesses treated at a single center from January 2017 to December 2021. Chi-square tests were used to analyze whether there were differences in the types and quantities of pathogenic microorganisms among different types of anorectal abscesses. RESULTS:  Among the 517 patients, the mean age was 38.5 years, with an average duration of illness of 7.4 days. Of these, 440 (85.1%) were male and 77 (14.9%) were female. The types of anorectal abscesses included perianal abscesses (54 cases, 10.4%), intersphincteric abscesses (253 cases, 48.9%), ischiorectal abscesses (129 cases, 25.0%), deep posterior anal space (DPAS) abscesses (26 cases, 5.0%), supra-levator abscesses (10 cases, 1.9%), and horseshoe abscesses (45 cases, 8.7%). A total of 23 different microorganisms were cultured from the 517 pus specimens. The most common microorganism was Escherichia coli (323 cases, 62.5%), followed by Klebsiella pneumoniae (77 cases, 14.9%), Bacteroides fragilis (nine cases, 1.7%), Pseudomonas aeruginosa (eight cases, 1.5%), and Staphylococcus aureus (seven cases, 1.4%). Additionally, no microorganisms were cultured from 58 (11.2%) pus specimens. Nine patients (1.7%) were admitted with concomitant necrotizing fasciitis. Among the nine cases of concurrent necrotizing fasciitis, E. coli, K. pneumoniae, and S. aureus were cultured in six (66.7%), two (22.2%), and one (11.1%) case, respectively. Chi-square tests revealed no significant differences in the types and quantities of pathogenic microorganisms among different types of anorectal abscesses (P > 0.05). CONCLUSION:  This study provides a large sample of pus culture microbiological data from patients with anorectal abscesses. Regardless of whether it is a simple anorectal abscess or concurrent necrotizing fasciitis, E. coli was the most common microorganism cultured from the pus of patients with anorectal abscesses. Other common microorganisms include K. pneumoniae, B. fragilis, and S. aureus. These results provide evidence for the precise antibiotic treatment of anorectal abscesses. Additionally, there were no differences in the types and quantities of pathogenic microorganisms among different types of anorectal abscesses.

Multi-view optimization of time-frequency common spatial patterns for brain-computer interfaces.

BACKGROUND: Common spatial pattern (CSP) is a prevalent method applied to feature extraction in motor imagery (MI)-based brain-computer interfaces (BCIs) recorded by electroencephalogram (EEG). The selection of time windows and frequency bands prominently affects the performance of CSP algorithms. Concerning the joint optimization of these two parameters, several studies have utilized a unified framework based on different feature selection strategies and achieved considerable improvement. However, during the feature selection process, useful information could be discarded inevitably and the underlying internal structure of features could be neglected. NEW METHOD: In this paper, we proposed a novel framework termed time window filter bank common spatial pattern with multi-view optimization (TWFBCSP-MVO) to further boost the decoding of MI tasks. Concretely, after extracting CSP features from different time-frequency decompositions of EEG signals, a preliminary screening strategy based on variance ratio was devised to filter out the unrelated spatial patterns. We then introduced a multi-view learning strategy for the simultaneous optimization of time windows and frequency bands. A support vector machine classifier was trained to determine the output of the brain. RESULTS: An experimental study was conducted on two public datasets to verify the effectiveness of TWFBCSP-MVO. Results showed that the proposed TWFBCSP-MVO could help improve the performance of MI classification. COMPARISON WITH EXISTING METHODS: In comparison to other competing methods, the proposed method performed significantly better (p<0.01). CONCLUSIONS: The proposed method is a promising contestant to improve the performance of practical MI-based BCIs.

Optimization of Model Training Based on Iterative Minimum Covariance Determinant In Motor-Imagery BCI.

The common spatial patterns (CSP) algorithm is one of the most frequently used and effective spatial filtering methods for extracting relevant features for use in motor imagery brain-computer interfaces (MI-BCIs). However, the inherent defect of the traditional CSP algorithm is that it is highly sensitive to potential outliers, which adversely affects its performance in practical applications. In this work, we propose a novel feature optimization and outlier detection method for the CSP algorithm. Specifically, we use the minimum covariance determinant (MCD) to detect and remove outliers in the dataset, then we use the Fisher score to evaluate and select features. In addition, in order to prevent the emergence of new outliers, we propose an iterative minimum covariance determinant (IMCD) algorithm. We evaluate our proposed algorithm in terms of iteration times, classification accuracy and feature distribution using two BCI competition datasets. The experimental results show that the average classification performance of our proposed method is 12% and 22.9% higher than that of the traditional CSP method in two datasets ([Formula: see text]), and our proposed method obtains better performance in comparison with other competing methods. The results show that our method improves the performance of MI-BCI systems.

Learning Common Time-Frequency-Spatial Patterns for Motor Imagery Classification.

The common spatial patterns (CSP) algorithm is the most popular spatial filtering method applied to extract electroencephalogram (EEG) features for motor imagery (MI) based brain-computer interface (BCI) systems. The effectiveness of the CSP algorithm depends on optimal selection of the frequency band and time window from the EEG. Many algorithms have been designed to optimize frequency band selection for CSP, while few algorithms seek to optimize the time window. This study proposes a novel framework, termed common time-frequency-spatial patterns (CTFSP), to extract sparse CSP features from multi-band filtered EEG data in multiple time windows. Specifically, the whole MI period is first segmented into multiple subseries using a sliding time window approach. Then, sparse CSP features are extracted from multiple frequency bands in each time window. Finally, multiple support vector machine (SVM) classifiers with the Radial Basis Function (RBF) kernel are trained to identify the MI tasks and the voting result of these classifiers determines the final output of the BCI. This study applies the proposed CTFSP algorithm to three public EEG datasets (BCI competition III dataset IVa, BCI competition III dataset IIIa, and BCI competition IV dataset 1) to validate its effectiveness, compared against several other state-of-the-art methods. The experimental results demonstrate that the proposed algorithm is a promising candidate for improving the performance of MI-BCI systems.

Effects of Skin Friction on Tactile P300 Brain-Computer Interface Performance.

Tactile perception, the primary sensing channel of the tactile brain-computer interface (BCI), is a complicated process. Skin friction plays a vital role in tactile perception. This study aimed to examine the effects of skin friction on tactile P300 BCI performance. Two kinds of oddball paradigms were designed, silk-stim paradigm (SSP) and linen-stim paradigm (LSP), in which silk and linen were wrapped on target vibration motors, respectively. In both paradigms, the disturbance vibrators were wrapped in cotton. The experimental results showed that LSP could induce stronger event-related potentials (ERPs) and achieved a higher classification accuracy and information transfer rate (ITR) compared with SSP. The findings indicate that high skin friction can achieve high performance in tactile BCI. This work provides a novel research direction and constitutes a viable basis for the future tactile P300 BCI, which may benefit patients with visual impairments.

Cluster decomposing and multi-objective optimization based-ensemble learning framework for motor imagery-based brain-computer interfaces.

Objective. Motor imagery (MI) is a mental representation of motor behavior and a widely used pattern in electroencephalogram (EEG) based brain-computer interface (BCI) systems. EEG is known for its non-stationary, non-linear features and sensitivity to artifacts from various sources. This study aimed to design a powerful classifier with a strong generalization capability for MI based BCIs.Approach. In this study, we proposed a cluster decomposing based ensemble learning framework (CDECL) for EEG classification of MI based BCIs. The EEG data was decomposed into sub-data sets with different distributions by clustering decomposition. Then a set of heterogeneous classifiers was trained on each sub-data set for generating a diversified classifier search space. To obtain the optimal classifier combination, the ensemble learning was formulated as a multi-objective optimization problem and a stochastic fractal based binary multi-objective fruit fly optimization algorithm was proposed for solving the ensemble learning problem.Main results.The proposed method was validated on two public EEG datasets (BCI Competition IV datasets IIb and BCI Competition IV dataset IIa) and compared with several other competing classification methods. Experimental results showed that the proposed CDECL based methods can effectively construct a diversity ensemble classifier and exhibits superior classification performance in comparison with several competing methods.Significance. The proposed method is promising for improving the performance of MI-based BCIs.

The Influence of Visual Attention on The Performance of A Novel Tactile P300 Brain-Computer Interface with Cheeks-Stim Paradigm.

Tactile P300 brain-computer interface (BCI) generally has a worse accuracy and information transfer rate (ITR) than the visual-based BCI. It may be due to the fact that human beings have a relatively poor tactile perception. This study investigated the influence of visual attention on the performance of a tactile P300 BCI. We designed our paradigms based on a novel cheeks-stim paradigm which attached the stimulators on the subject's cheeks. Two paradigms were designed as follows: a paradigm with no visual attention and another paradigm with visual attention to the target position. Eleven subjects were invited to perform the two paradigms. We also recorded and analyzed the eyeball movement data during the paradigm with visual attention to explore whether the eyeball movement would have an effect on the BCI classification. The average online accuracy was 89.09% for the paradigm with visual attention, which was significantly higher than that of the paradigm with no visual attention (70.45%). Significant difference in ITR was also found between the two paradigms ([Formula: see text]). The results demonstrated that visual attention was an effective method to improve the performance of tactile P300 BCI. Our findings suggested that it may be feasible to complete an efficient tactile BCI system by adding visual attention.

Internal Feature Selection Method of CSP Based on L1-Norm and Dempster-Shafer Theory.

The common spatial pattern (CSP) algorithm is a well-recognized spatial filtering method for feature extraction in motor imagery (MI)-based brain-computer interfaces (BCIs). However, due to the influence of nonstationary in electroencephalography (EEG) and inherent defects of the CSP objective function, the spatial filters, and their corresponding features are not necessarily optimal in the feature space used within CSP. In this work, we design a new feature selection method to address this issue by selecting features based on an improved objective function. Especially, improvements are made in suppressing outliers and discovering features with larger interclass distances. Moreover, a fusion algorithm based on the Dempster-Shafer theory is proposed, which takes into consideration the distribution of features. With two competition data sets, we first evaluate the performance of the improved objective functions in terms of classification accuracy, feature distribution, and embeddability. Then, a comparison with other feature selection methods is carried out in both accuracy and computational time. Experimental results show that the proposed methods consume less additional computational cost and result in a significant increase in the performance of MI-based BCI systems.

Bispectrum-Based Channel Selection for Motor Imagery Based Brain-Computer Interfacing.

The performance of motor imagery (MI) based Brain-computer interfacing (BCI) is easily affected by noise and redundant information that exists in the multi-channel electroencephalogram (EEG). To solve this problem, many temporal and spatial feature based channel selection methods have been proposed. However, temporal and spatial features do not accurately reflect changes in the power of the oscillatory EEG. Thus, spectral features of MI-related EEG signals may be useful for channel selection. Bispectrum analysis is a technique developed for extracting non-linear and non-Gaussian information from non-linear and non-Gaussian signals. The features extracted from bispectrum analysis can provide frequency domain information about the EEG. Therefore, in this study, we propose a bispectrum-based channel selection (BCS) method for MI-based BCI. The proposed method uses the sum of logarithmic amplitudes (SLA) and the first order spectral moment (FOSM) features extracted from bispectrum analysis to select EEG channels without redundant information. Three public BCI competition datasets (BCI competition IV dataset 1, BCI competition III dataset IVa and BCI competition III dataset IIIa) were used to validate the effectiveness of our proposed method. The results indicate that our BCS method outperforms use of all channels (83.8% vs 69.4%, 86.3% vs 82.9% and 77.8% vs 68.2%, respectively). Furthermore, compared to the other state-of-the-art methods, our BCS method also can achieve significantly better classification accuracies for MI-based BCI (Wilcoxon signed test, p < 0.05).

Effects of a Vibro-Tactile P300 Based Brain-Computer Interface on the Coma Recovery Scale-Revised in Patients With Disorders of Consciousness.

Persons diagnosed with disorders of consciousness (DOC) typically suffer from motor and cognitive disabilities. Recent research has shown that non-invasive brain-computer interface (BCI) technology could help assess these patients' cognitive functions and command following abilities. 20 DOC patients participated in the study and performed 10 vibro-tactile P300 BCI sessions over 10 days with 8-12 runs each day. Vibrotactile tactors were placed on the each patient's left and right wrists and one foot. Patients were instructed, via earbuds, to concentrate and silently count vibrotactile pulses on either their left or right wrist that presented a target stimulus and to ignore the others. Changes of the BCI classification accuracy were investigated over the 10 days. In addition, the Coma Recovery Scale-Revised (CRS-R) score was measured before and after the 10 vibro-tactile P300 sessions. In the first run, 10 patients had a classification accuracy above chance level (>12.5%). In the best run, every patient reached an accuracy ≥60%. The grand average accuracy in the first session for all patients was 40%. In the best session, the grand average accuracy was 88% and the median accuracy across all sessions was 21%. The CRS-R scores compared before and after 10 VT3 sessions for all 20 patients, are showing significant improvement (p = 0.024). Twelve of the twenty patients showed an improvement of 1 to 7 points in the CRS-R score after the VT3 BCI sessions (mean: 2.6). Six patients did not show a change of the CRS-R and two patients showed a decline in the score by 1 point. Every patient achieved at least 60% accuracy at least once, which indicates successful command following. This shows the importance of repeated measures when DOC patients are assessed. The improvement of the CRS-R score after the 10 VT3 sessions is an important issue for future experiments to test the possible therapeutic applications of vibro-tactile and related BCIs with a larger patient group.

Temporal frequency joint sparse optimization and fuzzy fusion for motor imagery-based brain-computer interfaces.

BACKGROUND: Motor imagery (MI) related features are typically extracted from a fixed frequency band and time window of EEG signal. Meanwhile, the time when the brain activity associated with the occurring task varies from person to person and trial to trial. Thus, some of the discarded EEG data with time may contain MI-related information. NEW METHOD: This study proposes a temporal frequency joint sparse optimization and fuzzy fusion (TFSOFF) method for joint frequency band optimization and classification fusion on multiple time windows to effectively utilize the signals of all time period within the MI task. Raw EEG data are first segmented into multiple subtime windows using a sliding window approach. Then, a set of overlapping bandpass filters is performed on each time window to generate a set of overlapping subbands, and common spatial pattern is used for feature extraction at each subband. Joint frequency band optimization is conducted on multiple time windows using a joint sparse optimization model. Fuzzy integral is used to fuse each time window after joint optimization. RESULTS: The proposed TFSOFF is validated on two public EEG datasets and compared with several other competing methods. Experimental results show that the proposed TFSOFF can effectively extract MI related features of all time period EEG signals within the MI task and helps improving the classification performance of MI. COMPARISON WITH EXISTING METHODS: The proposed TFSOFF exhibits superior performance in comparison with several competing methods. CONCLUSIONS: The proposed method is a suitable method for improving the performance of MI-based BCIs.

Novel hybrid brain-computer interface system based on motor imagery and P300.

Motor imagery (MI) is a mental representation of motor behavior and has been widely used in electroencephalogram based brain-computer interfaces (BCIs). Several studies have demonstrated the efficacy of MI-based BCI-feedback training in post-stroke rehabilitation. However, in the earliest stage of the training, calibration data typically contain insufficient discriminability, resulting in unreliable feedback, which may decrease subjects' motivation and even hinder their training. To improve the performance in the early stages of MI training, a novel hybrid BCI paradigm based on MI and P300 is proposed in this study. In this paradigm, subjects are instructed to imagine writing the Chinese character following the flash order of the desired Chinese character displayed on the screen. The event-related desynchronization/synchronization (ERD/ERS) phenomenon is produced with writing based on one's imagination. Simultaneously, the P300 potential is evoked by the flash of each stroke. Moreover, a fusion method of P300 and MI classification is proposed, in which unreliable P300 classifications are corrected by reliable MI classifications. Twelve healthy naïve MI subjects participated in this study. Results demonstrated that the proposed hybrid BCI paradigm yielded significantly better performance than the single-modality BCI paradigm. The recognition accuracy of the fusion method is significantly higher than that of P300 (p < 0.05) and MI (p < 0.01). Moreover, the training data size can be reduced through fusion of these two modalities.

An ERP-based BCI with peripheral stimuli: validation with ALS patients.

Many studies reported that ERP-based BCIs can provide communication for some people with amyotrophic lateral sclerosis (ALS). ERP-based BCIs often present characters within a matrix that occupies the center of the visual field. However, several studies have identified some concerns with the matrix-based approach. This approach may lead to fatigue and errors resulting from flashing adjacent stimuli, and is impractical for users who might want to use the BCI in tandem with other software or feedback in the center of the monitor. In this paper, we introduce and validate an alternate ERP-based BCI display approach. By presenting stimuli near the periphery of the display, we reduce the adjacency problem and leave the center of the display available for feedback or other applications. Two ERP-based display approaches were tested on 18 ALS patients to: (1) compare performance between a conventional matrix speller paradigm (Matrix-P, mean visual angle 6°) and a new speller paradigm with peripherally distributed stimuli (Peripheral-P, mean visual angle 8.8°); and (2) assess performance while spelling 42 characters online continuously, without a break. In the Peripheral-P condition, 12 subjects attained higher than 80% feedback accuracy during online performance, and 7 of these subjects obtained higher than 90% accuracy. The experimental results showed that the Peripheral-P condition yielded performance comparable to the conventional Matrix-P condition (p > 0.05) in accuracy and information transfer rate. This paper introduces a new display approach that leaves the center of the monitor open for feedback and/or other display elements, such as movies, games, art, or displays from other AAC software or conventional software tools.

Developing a Novel Tactile P300 Brain-Computer Interface With a Cheeks-Stim Paradigm.

OBJECTIVE: Tactile brain-computer interface (BCI) systems can provide new communication and control options for patients with impairments of eye movements or vision. One of the most common modalities used in these BCIs is the P300 potential. Until now, tactile P300 BCIs have been successfully constructed by situating tactile stimuli at various parts of the human body. This article proposed a novel tactile P300 BCI paradigm for further expanding the tactile stimulation methods. METHODS: In our proposed paradigm, the spatial target vibrotactile stimuli were delivered to subject's left and right cheeks. To validate the feasibility of our proposed paradigm, a traditional tactile P300 BCI paradigm employing spatial target vibrotactile stimuli to subject's left and right wrists was used for comparison. RESULTS: The experimental results of nine healthy subjects demonstrated that the proposed paradigm could obtain significantly higher classification accuracy and information transfer rate than the traditional paradigm (both for p < 0.05). Furthermore, the subjective feedback showed that our proposed paradigm was more favored by the subjects compared to the traditional paradigm, and most subjects reported that the new paradigm helped them easily distinguish between targets and non-targets. CONCLUSION: The proposed tactile P300 BCI paradigm is feasible, and can bring about superior performance and use-evaluation. SIGNIFICANCE: The new paradigm might lead to many promising applications of such BCIs.

BCI-Based Rehabilitation on the Stroke in Sequela Stage.

Background: Stroke is the leading cause of serious and long-term disability worldwide. Survivors may recover some motor functions after rehabilitation therapy. However, many stroke patients missed the best time period for recovery and entered into the sequela stage of chronic stroke. Method: Studies have shown that motor imagery- (MI-) based brain-computer interface (BCI) has a positive effect on poststroke rehabilitation. This study used both virtual limbs and functional electrical stimulation (FES) as feedback to provide patients with a closed-loop sensorimotor integration for motor rehabilitation. An MI-based BCI system acquired, analyzed, and classified motor attempts from electroencephalogram (EEG) signals. The FES system would be activated if the BCI detected that the user was imagining wrist dorsiflexion on the instructed side of the body. Sixteen stroke patients in the sequela stage were randomly assigned to a BCI group and a control group. All of them participated in rehabilitation training for four weeks and were assessed by the Fugl-Meyer Assessment (FMA) of motor function. Results: The average improvement score of the BCI group was 3.5, which was higher than that of the control group (0.9). The active EEG patterns of the four patients in the BCI group whose FMA scores increased gradually became centralized and shifted to sensorimotor areas and premotor areas throughout the study. Conclusions: Study results showed evidence that patients in the BCI group achieved larger functional improvements than those in the control group and that the BCI-FES system is effective in restoring motor function to upper extremities in stroke patients. This study provides a more autonomous approach than traditional treatments used in stroke rehabilitation.

The Study of Generic Model Set for Reducing Calibration Time in P300-Based Brain-Computer Interface.

P300-based brain-computer interfaces (BCIs) provide an additional communication channel for individuals with communication disabilities. In general, P300-based BCIs need to be trained, offline, for a considerable period of time, which causes users to become fatigued. This reduces the efficiency and performance of the system. In order to shorten calibration time and improve system performance, we introduce the concept of a generic model set. We used ERP data from 116 participants to train the generic model set. The resulting set consists of ten models, which are trained by weighted linear discriminant analysis (WLDA). Twelve new participants were then invited to test the validity of the generic model set. The results demonstrated that all new participants matched the best generic model. The resulting mean classification accuracy equaled 80% after online training, an accuracy that was broadly equivalent to the typical training model method. Moreover, the calibration time was shortened by 70.7% of the calibration time of the typical model method. In other words, the best matching model method only took 81s to calibrate, while the typical model method took 276s. There were also significant differences in both accuracy and raw bit rate between the best and the worst matching model methods. We conclude that the strategy of combining the generic models with online training is easily accepted and achieves higher levels of user satisfaction (as measured by subjective reports). Thus, we provide a valuable new strategy for improving the performance of P300-based BCI.

Correlation-based channel selection and regularized feature optimization for MI-based BCI.

Multi-channel EEG data are usually necessary for spatial pattern identification in motor imagery (MI)-based brain computer interfaces (BCIs). To some extent, signals from some channels containing redundant information and noise may degrade BCI performance. We assume that the channels related to MI should contain common information when participants are executing the MI tasks. Based on this hypothesis, a correlation-based channel selection (CCS) method is proposed to select the channels that contained more correlated information in this study. The aim is to improve the classification performance of MI-based BCIs. Furthermore, a novel regularized common spatial pattern (RCSP) method is used to extract effective features. Finally, a support vector machine (SVM) classifier with the Radial Basis Function (RBF) kernel is trained to accurately identify the MI tasks. An experimental study is implemented on three public EEG datasets (BCI competition IV dataset 1, BCI competition III dataset IVa and BCI competition III dataset IIIa) to validate the effectiveness of the proposed methods. The results show that the CCS algorithm obtained superior classification accuracy (78% versus 56.4% for dataset1, 86.6% versus 76.5% for dataset 2 and 91.3% versus 85.1% for dataset 3) compared to the algorithm using all channels (AC), when CSP is used to extract the features. Furthermore, RCSP could further improve the classification accuracy (81.6% for dataset1, 87.4% for dataset2 and 91.9% for dataset 3), when CCS is used to select the channels.

Predicting Endoplasmic Reticulum Resident Proteins Using Auto-Cross Covariance Transformation With a U-Shaped Residue Weight-Transfer Function.

Background: The endoplasmic reticulum (ER) is an important organelle in eukaryotic cells. It is involved in many important biological processes, such as cell metabolism, protein synthesis, and post-translational modification. The proteins that reside within the ER are called ER-resident proteins. These proteins are closely related to the biological functions of the ER. The difference between the ER-resident proteins and other non-resident proteins should be carefully studied. Methods: We developed a support vector machine (SVM)-based method. We developed a U-shaped weight-transfer function and used it, along with the positional-specific physiochemical properties (PSPCP), to integrate together sequence order information, signaling peptides information, and evolutionary information. Result: Our method achieved over 86% accuracy in a jackknife test. We also achieved roughly 86% sensitivity and 67% specificity in an independent dataset test. Our method is capable of identifying ER-resident proteins.

UltraPse: A Universal and Extensible Software Platform for Representing Biological Sequences.

With the avalanche of biological sequences in public databases, one of the most challenging problems in computational biology is to predict their biological functions and cellular attributes. Most of the existing prediction algorithms can only handle fixed-length numerical vectors. Therefore, it is important to be able to represent biological sequences with various lengths using fixed-length numerical vectors. Although several algorithms, as well as software implementations, have been developed to address this problem, these existing programs can only provide a fixed number of representation modes. Every time a new sequence representation mode is developed, a new program will be needed. In this paper, we propose the UltraPse as a universal software platform for this problem. The function of the UltraPse is not only to generate various existing sequence representation modes, but also to simplify all future programming works in developing novel representation modes. The extensibility of UltraPse is particularly enhanced. It allows the users to define their own representation mode, their own physicochemical properties, or even their own types of biological sequences. Moreover, UltraPse is also the fastest software of its kind. The source code package, as well as the executables for both Linux and Windows platforms, can be downloaded from the GitHub repository.

Antimony(V) removal from water by hydrated ferric oxides supported by calcite sand and polymeric anion exchanger.

We fabricated and characterized two hybrid adsorbents originated from hydrated ferric oxides (HFOs) using a polymeric anion exchanger D201 and calcite as host. The resultant adsorbents (denoted as HFO-201 and IOCCS) were employed for Sb(V) removal from water. Increasing solution pH from 3 to 9 apparently weakened Sb(V) removal by both composites, while increasing temperature from 293 to 313 K only improved Sb(V) uptake by IOCCS. HFO-201 exhibited much higher capacity for Sb(V) than for IOCCS in the absence of other anions in solution. Increasing ionic strength from 0.01 to 0.1 mol/L NaNO3 would result in a significant drop of the capacity of HFO-201 in the studied pH ranges; however, negligible effect was observed for IOCCS under similar conditions. Similarly, the competing chloride and sulfate pose more negative effect on Sb(V) adsorption by HFO-201 than by IOCCS, and the presence of silicate greatly decreased their adsorption simultaneously, while calcium ions were found to promote the adsorption of both adsorbents. XPS analysis further demonstrated that preferable Sb(V) adsorption by both hybrids was attributed to the inner sphere complexation of Sb(V) and HFO, and Ca(II) induced adsorption enhancement possibly resulted from the formation of HFO-Ca-Sb complexes. Column adsorption runs proved that Sb(V) in the synthetic water could be effectively removed from 30 microg/L to below 5 microg/L (the drinking water standard regulated by China), and the effective treatable volume of IOCCS was around 6 times as that of HFO-201, implying that HFO coatings onto calcite might be a more effective approach than immobilization inside D201.