Bandwidth superposition of linearly chirped microwave waveforms based on a Fourier domain mode-locked optoelectronic oscillator.

Optoelectronic oscillators (OEOs) are promising for radar, communication and electronic countermeasure systems. Among them, frequency-scanning OEOs with wide instantaneous bandwidth are needed for many advanced applications. In this work, we demonstrate a novel system to generate bandwidth-doubled linearly chirped microwave waveforms (LCMWs) based on bandwidth superposition using a Fourier domain mode-locked OEO (FDML OEO). In the proposed system, bandwidth-doubling is achieved by re-modulating the generated LCMW of the FDML OEO onto a frequency-scanning optical carrier signal with the help of an external Mach-Zehnder modulator. LCMWs with wide frequency scanning instantaneous bandwidth of 10 GHz are experimentally obtained. Meanwhile, these LCMWs are tunable in an ultra-wide frequency range from 1 to 39 GHz. Moreover, they are with high frequency sweep linearity of 0.5%. Our work presents a simple method to generate tunable wide-band LCMWs for potential microwave sources.

Laser-triggered combination therapy by iron sulfide-doxorubicin@functionalized nanozymes for breast cancer therapy.

BACKGROUND: The use of magnetic nanozymes (NZs) with the ability to synchronize gas therapy through photodynamic and chemotherapy in the treatment of breast cancer has received much attention. RESULTS: Hence, in this study, we designed a bovine lactoferrin-coated iron sulfide NZs containing doxorubicin (abbreviated as: FeS-Dox@bLf NZs) by wet-chemical synthesis method. Then, the physicochemical characteristics of synthesized NZs were explored by several methods. Also, the level of Fe2+, H2S and Dox releases from FeS-Dox@Lf NZs. Also, the cytotoxic effects of FeS-Dox@Lf NZs were investigated by cellular assays. After intravenous injections of NZs and laser irradiation, significant effects of FeS-Dox@Lf NZs on mice weight and tumor status were observed. Afterwards, not only the distribution of Dox in the body was examined by fluorescent, but also the time of Fe clearance and the amount of Dox and Fe retention in vital tissues were determined. The findings confirm that FeS-Dox@Lf NZs, in addition to targeted drug distribution in tumor tissue, resulted in superior therapeutic performance compared to free Dox due to reduced Dox side effects in vital tissues, and increased level of free radicals in 4T1 cells. CONCLUSION: Overall, FeS-Dox@Lf NZs with the ability to synchronize chemotherapy and gas therapy raised hopes for more effective treatment of breast cancer.

Xueshuantong Injection (Lyophilized) Attenuates Cerebral Ischemia/Reperfusion Injury by the Activation of Nrf2-VEGF Pathway.

Xueshuantong injection (Lyophilized, XST), extracted from the traditional Chinese medicinal herb Panax notoginseng, has neuroprotective effect on cerebral ischemia. Revascularization of ischemic tissue is good for the therapy of cerebrovascular disease. In this study, angiogenic potentiality and possible mechanism of XST for cerebral ischemia were explored. Rats were subjected to transient middle cerebral artery occlusion (MCAO), and then intraperitoneally administered with XST daily for 3 or 7 consecutive days. The neurological function deficits, and endogenous antioxidant capacity were evaluated. Post-stroke angiogenesis and vascularization were assessed by ELISA and immunohistochemical staining. Transcription levels of Nrf2, HO-1, NQO1 in brain tissues were analyzed by real-time RT-PCR. The results showed that XST could remarkably ameliorate neuronal functional deficit, promote angiogenesis and vascularization after MCAO. The mechanism of angiogenesis might be related to endogenous antioxidant capacity and Nrf2 pathway. In conclusion, administered XST for 7 days after stroke could significantly improve functional recovery and promote angiogenesis, that might be related to Nrf2 signaling pathway. These findings could provide scientific evidence for the use of XST in cerebral ischemic diseases and provide theoretical support for further studies.

Elemental sulfur mediated 2-substituted benzothiazole formation from 2-aminobenzenethiols and arylacetylenes or styrenes under metal-free conditions.

An oxidative cyclization of 2-aminothiophenols and arylacetylenes or styrenes for the synthesis of 2-alkylbenzothiazoles and 2-acylbenzothiazoles has been developed. Elemental sulfur was used as the effective oxidant to give the corresponding product in good yield under metal-free conditions.

MultiP-SChlo: multi-label protein subchloroplast localization prediction with Chou's pseudo amino acid composition and a novel multi-label classifier.

MOTIVATION: Identifying protein subchloroplast localization in chloroplast organelle is very helpful for understanding the function of chloroplast proteins. There have existed a few computational prediction methods for protein subchloroplast localization. However, these existing works have ignored proteins with multiple subchloroplast locations when constructing prediction models, so that they can predict only one of all subchloroplast locations of this kind of multilabel proteins. RESULTS: To address this problem, through utilizing label-specific features and label correlations simultaneously, a novel multilabel classifier was developed for predicting protein subchloroplast location(s) with both single and multiple location sites. As an initial study, the overall accuracy of our proposed algorithm reaches 55.52%, which is quite high to be able to become a promising tool for further studies. AVAILABILITY AND IMPLEMENTATION: An online web server for our proposed algorithm named MultiP-SChlo was developed, which are freely accessible at http://biomed.zzuli.edu.cn/bioinfo/multip-schlo/. CONTACT: pandaxiaoxi@gmail.com or gzli@tongji.edu.cn SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Multi-location gram-positive and gram-negative bacterial protein subcellular localization using gene ontology and multi-label classifier ensemble.

BACKGROUND: It has become a very important and full of challenge task to predict bacterial protein subcellular locations using computational methods. Although there exist a lot of prediction methods for bacterial proteins, the majority of these methods can only deal with single-location proteins. But unfortunately many multi-location proteins are located in the bacterial cells. Moreover, multi-location proteins have special biological functions capable of helping the development of new drugs. So it is necessary to develop new computational methods for accurately predicting subcellular locations of multi-location bacterial proteins. RESULTS: In this article, two efficient multi-label predictors, Gpos-ECC-mPLoc and Gneg-ECC-mPLoc, are developed to predict the subcellular locations of multi-label gram-positive and gram-negative bacterial proteins respectively. The two multi-label predictors construct the GO vectors by using the GO terms of homologous proteins of query proteins and then adopt a powerful multi-label ensemble classifier to make the final multi-label prediction. The two multi-label predictors have the following advantages: (1) they improve the prediction performance of multi-label proteins by taking the correlations among different labels into account; (2) they ensemble multiple CC classifiers and further generate better prediction results by ensemble learning; and (3) they construct the GO vectors by using the frequency of occurrences of GO terms in the typical homologous set instead of using 0/1 values. Experimental results show that Gpos-ECC-mPLoc and Gneg-ECC-mPLoc can efficiently predict the subcellular locations of multi-label gram-positive and gram-negative bacterial proteins respectively. CONCLUSIONS: Gpos-ECC-mPLoc and Gneg-ECC-mPLoc can efficiently improve prediction accuracy of subcellular localization of multi-location gram-positive and gram-negative bacterial proteins respectively. The online web servers for Gpos-ECC-mPLoc and Gneg-ECC-mPLoc predictors are freely accessible at http://biomed.zzuli.edu.cn/bioinfo/gpos-ecc-mploc/ and http://biomed.zzuli.edu.cn/bioinfo/gneg-ecc-mploc/ respectively.

Application of metabolomics on diagnosis and treatment of patients with psoriasis in traditional Chinese medicine.

Traditional Chinese medicine (TCM) is one of the oldest forms of medical system. With syndrome as the core of diagnosis and therapy in TCM, it has the advantage of collecting macroscopic information of patients for diagnosis. To understand the in vivo mechanism of TCM, a metabolomics approach was used to investigate the global biological characterization of the urine of psoriasis patients with Blood Stasis Syndrome and the therapeutic metabolomics mechanism of the Optimized Yinxieling formula. A total of 41 cases of psoriasis patients with Blood Stasis Syndrome and 19 healthy volunteers were enrolled in this study. Fasting urine samples from patients with consecutive Optimized Yinxieling intake after 0, 4, 8 and 12 weeks and from healthy volunteers were analyzed by Orthogonal Projection on Latent Structures Discriminant Analysis (OPLS-DA), which was utilized for High Performance Liquid Chromatography (HPLC) analysis and temporal metabolic changes identification. For psoriasis group, the scores of PASI of patients decreased after 12 weeks of Optimized Yinxieling treating. The metabolic variations visualized not only in the healthy group and psoriasis group, but also in the psoriasis group before and after Optimized Yinxieling treatment, demonstrated that the metabolic characteristics of the two groups were significantly different. The optimized complex structure of the target proteins from Protein Data Bank was analyzed by software package Discovery Studio. With docking score of original inhibitor and the receptor as the threshold values, two compounds from Chinese medicinal chemical database were predicted to have good interactions with the target proteins. The Metabolomics technique combining molecular docking analysis enhanced our current understanding of the metabolic response to Blood Stasis Syndrome of Psoriasis and the action mechanism of Optimized Yinxieling. This article is part of a Special Issue entitled: Computational Proteomics, Systems Biology & Clinical Implications. Guest Editor: Yudong Cai.

A hybrid imputation approach for microarray missing value estimation.

BACKGROUND: Missing data is an inevitable phenomenon in gene expression microarray experiments due to instrument failure or human error. It has a negative impact on performance of downstream analysis. Technically, most existing approaches suffer from this prevalent problem. Imputation is one of the frequently used methods for processing missing data. Actually many developments have been achieved in the research on estimating missing values. The challenging task is how to improve imputation accuracy for data with a large missing rate. METHODS: In this paper, induced by the thought of collaborative training, we propose a novel hybrid imputation method, called Recursive Mutual Imputation (RMI). Specifically, RMI exploits global correlation information and local structure in the data, captured by two popular methods, Bayesian Principal Component Analysis (BPCA) and Local Least Squares (LLS), respectively. Mutual strategy is implemented by sharing the estimated data sequences at each recursive process. Meanwhile, we consider the imputation sequence based on the number of missing entries in the target gene. Furthermore, a weight based integrated method is utilized in the final assembling step. RESULTS: We evaluate RMI with three state-of-art algorithms (BPCA, LLS, Iterated Local Least Squares imputation (ItrLLS)) on four publicly available microarray datasets. Experimental results clearly demonstrate that RMI significantly outperforms comparative methods in terms of Normalized Root Mean Square Error (NRMSE), especially for datasets with large missing rates and less complete genes. CONCLUSIONS: It is noted that our proposed hybrid imputation approach incorporates both global and local information of microarray genes, which achieves lower NRMSE values against to any single approach only. Besides, this study highlights the need for considering the imputing sequence of missing entries for imputation methods.

Cough event classification by pretrained deep neural network.

BACKGROUND: Cough is an essential symptom in respiratory diseases. In the measurement of cough severity, an accurate and objective cough monitor is expected by respiratory disease society. This paper aims to introduce a better performed algorithm, pretrained deep neural network (DNN), to the cough classification problem, which is a key step in the cough monitor. METHOD: The deep neural network models are built from two steps, pretrain and fine-tuning, followed by a Hidden Markov Model (HMM) decoder to capture tamporal information of the audio signals. By unsupervised pretraining a deep belief network, a good initialization for a deep neural network is learned. Then the fine-tuning step is a back propogation tuning the neural network so that it can predict the observation probability associated with each HMM states, where the HMM states are originally achieved by force-alignment with a Gaussian Mixture Model Hidden Markov Model (GMM-HMM) on the training samples. Three cough HMMs and one noncough HMM are employed to model coughs and noncoughs respectively. The final decision is made based on viterbi decoding algorihtm that generates the most likely HMM sequence for each sample. A sample is labeled as cough if a cough HMM is found in the sequence. RESULTS: The experiments were conducted on a dataset that was collected from 22 patients with respiratory diseases. Patient dependent (PD) and patient independent (PI) experimental settings were used to evaluate the models. Five criteria, sensitivity, specificity, F1, macro average and micro average are shown to depict different aspects of the models. From overall evaluation criteria, the DNN based methods are superior to traditional GMM-HMM based method on F1 and micro average with maximal 14% and 11% error reduction in PD and 7% and 10% in PI, meanwhile keep similar performances on macro average. They also surpass GMM-HMM model on specificity with maximal 14% error reduction on both PD and PI. CONCLUSIONS: In this paper, we tried pretrained deep neural network in cough classification problem. Our results showed that comparing with the conventional GMM-HMM framework, the HMM-DNN could get better overall performance on cough classification task.

ISMAC: An Intelligent System for Customized Clinical Case Management and Analysis.

Clinical cases are primary and vital evidence for Traditional Chinese Medicine (TCM) clinical research. A great deal of medical knowledge is hidden in the clinical cases of the highly experienced TCM practitioner. With a deep Chinese culture background and years of clinical experience, an experienced TCM specialist usually has his or her unique clinical pattern and diagnosis idea. Preserving huge clinical cases of experienced TCM practitioners as well as exploring the inherent knowledge is then an important but arduous task. The novel system ISMAC (Intelligent System for Management and Analysis of Clinical Cases in TCM) is designed and implemented for customized management and intelligent analysis of TCM clinical data. Customized templates with standard and expert-standard symptoms, diseases, syndromes, and Chinese Medince Formula (CMF) are constructed in ISMAC, according to the clinical diagnosis and treatment characteristic of each TCM specialist. With these templates, clinical cases are archived in order to maintain their original characteristics. Varying data analysis and mining methods, grouped as Basic Analysis, Association Rule, Feature Reduction, Cluster, Pattern Classification, and Pattern Prediction, are implemented in the system. With a flexible dataset retrieval mechanism, ISMAC is a powerful and convenient system for clinical case analysis and clinical knowledge discovery.

Syndrome Differentiation Analysis on Mars500 Data of Traditional Chinese Medicine.

Mars500 study was a psychological and physiological isolation experiment conducted by Russia, the European Space Agency, and China, in preparation for an unspecified future manned spaceflight to the planet Mars. Its intention was to yield valuable psychological and medical data on the effects of the planned long-term deep space mission. In this paper, we present data mining methods to mine medical data collected from the crew consisting of six spaceman volunteers. The synthesis of the four diagnostic methods of TCM, inspection, listening, inquiry, and palpation, is used in our syndrome differentiation. We adopt statistics method to describe the syndrome factor regular pattern of spaceman volunteers. Hybrid optimization based multilabel (HOML) is used as feature selection method and multilabel k-nearest neighbors (ML-KNN) is applied. According to the syndrome factor statistical result, we find that qi deficiency is a base syndrome pattern throughout the entire experiment process and, at the same time, there are different associated syndromes such as liver depression, spleen deficiency, dampness stagnancy, and yin deficiency, due to differences of individual situation. With feature selection, we screen out ten key factors which are essential to syndrome differentiation in TCM. The average precision of multilabel classification model reaches 80%.

Development and psychometric validation of the Chinese version of Skindex-29 and Skindex-16.

BACKGROUND: Dermatological disease significantly affects patient's health-related quality of life (HrQoL). Skindex is one of the most frequently used dermatology-specific HrQoL measures. Currently no Chinese version of Skindex is available. The aim of this study was to translate and culturally adapt Skindex-29 and Skindex-16 into Chinese, and to evaluate their reliability and validity. METHODS: Translation and cultural adaption were performed following guidelines for cross-cultural adaption of health-related quality of life measures. Subsequently, a cross-sectional study was conducted in which patients with dermatological disease (n = 225) were enrolled. The Chinese version of Skindex-29 and Skindex-16 and Dermatology Life Quality Index (DLQI) were completed. Reliability was evaluated with internal consistency using Cronbach's alpha. Validity was evaluated using known-groups validity, convergent validity and factor structure validity. RESULTS: There were both seven items of Skindex-29 and Skindex-16 requiring a second forward- and backward- translation to achieve the final satisfactory Chinese version. The internal consistency reliability was high (range of Cronbach's alpha for the scales of Skindex-29 0.85-0.97, Skindex-16 0.86-0.96). Known-group validity was demonstrated by higher scores from patients with inflammatory dermatosis than from patients with isolated skin lesions (P < 0.05). Evidence of factor structure validity of the Skindex-29 and Skindex-16 was demonstrated by both exploratory factor analysis that accounted for 68.66% and 77.78% of the total variance, respectively, and confirmatory factor analysis with acceptable fitness into the expected three-factor structure. CONCLUSION: This study has developed semantically equivalent translations of Skindex-29 and Skindex-16 into Chinese. The evaluation of the instruments' psychometric properties shows they have substantial evidence of reliability and validity for use as HrQoL instruments in Chinese patients with dermatological disease.

Factors influencing the quality of preconception healthcare in China: applying a preconceptional instrument to assess healthcare needs.

BACKGROUND: Preconception care is defined as the promotion of the health and well-being of a woman and her partner before pregnancy. Improving preconception health can result in improved reproductive health outcomes. China has issued latest version official guideline for preconception care in 2011. The objective of this cross-sectional study is to determine whether there is a variation in the quality of preconception healthcare services in distinct eastern and northern populations of China, and what factors are associated with such variation. METHODS: A cross-sectional survey using our previously developed preconception instrument was conducted. Women at reproductive age planning for pregnancy were surveyed along with their partners at hospitals during their pre-pregnancy health examination. Data collected include general health/life profiles, pregnancy history, alcohol/tobacco/drug exposures, immunizations, micronutrient supplements and the demands in preconception care. After quality assessment, statistical analysis were applied to evaluate the variations in preconception factors between people from Hebei and Jiangsu Provinces. RESULTS: 3202 women of reproductive age in from eastern province, Jiangsu, and in a northern province, Hebei, participated this study. 2806 of them and their partners have completed the questionnaire, at a rate of 87.6%, 1011 were from Jiangsu and 1795 were from Hebei. Statistical significance was obtained for maternal age (P < 0.001), body mass index (u =13.590, P <0.001), education (χ2 = 916.33, P < 0.001), occupation (χ2 = 901.78, P < 0.001), health status/common disease, immunization status, and need for preconception care. CONCLUSIONS: For a country as large as China, the centralized guideline for standardized preconception healthcare does have a very crucial positive role in reproductive healthcare, but it may not be suited for all populations. Regional authorities should consider the demographics and healthcare needs of the local population and modify the centralized guideline accordingly, as well as provide a better education and professional services for the public, to improve the quality of preconception services at both the regional and the national level.

[Localization of cAMP-response element binding protein target gene Staufen in mouse hippocampus by situ hybridization].

OBJECTIVE: To search for an appropriate animal model that is more closely related to human to study cAMP-response element binding protein target gene Staufen and to identify its location. METHODS: The phylogenetic tree was constructed with Staufen protein (STAUFEN) sequences of different species, and the most suitable animal model was selected by analyzing relativity among them. The Staufen fragments were amplified with reverse transcription-PCR and inserted into a vector and then the sub-clone was transformed into bacteria, selected, amplified, extracted and sequenced. Staufen probes were in vitro transcribed and hybridized in situ on the cryosections of the mouse brain. The cryosections were stained and observed. RESULTS: The clustering patterns of the phylogenetic tree indicated that the mouse and human Staufen1 had 99.7% protein sequences similarity. The mRNA of Staufen was located in CA1, CA2, CA3 and DG hippocampus regions shown by in situ hybridization. CONCLUSION: The mouse is a preferable animal model for research on Staufen transcription in hippocampus.

Intraarticular botulinum toxin type A versus corticosteroid or hyaluronic acid for painful knee osteoarthritis: A meta-analysis of head-to-head randomized controlled trials.

Intraarticular botulinum toxin type A (BTA) has been shown to be effective for painful knee osteoarthritis (KOA), while the efficacy and safety of intraarticular BTA compared to corticosteroid and hyaluronic acid (HA) remains unknown. A meta-analysis was performed to compare. A search was conducted in Medline (PubMed), CENTER (Cochrane Library), Embase (Ovid), Web of Science, Wanfang, and CNKI to find head-to-head randomized controlled trials (RCTs) directly compare the efficacy and safety between intraarticular BTA and intraarticular corticosteroid or HA for patients with painful KOA. The Cochrane Q test and estimation of I2 were used to assess heterogeneity among studies. After incorporating heterogeneity, a random-effects model was employed for data pooling. Overall, six RCTs involving 348 adults with KOA were included. Intraarticular BTA showed similar efficacy with corticosteroid as evidenced by the changes of pain visual analog scale (VAS: -0.35 [-0.97, 0.28]), total Western Ontario McMaster Universities Arthritis Index (WOMAC: 0.28 [-4.13, 4.69]), and WOMAC for pain (0.64 [-0.42, 1.70]), stiffness (-0.02 [-0.54, 0.50]), and function (0.00 [-2.99, 3.00]). Intraarticular BTA was shown to be more effective than HA in improving pain VAS (-1.31 [-1.97, -0.64]) and WOMAC for pain (-4.81 [-8.73, -0.89]), while the influence on WOMAC for knee stiffness (-1.01 [-4.43, 2.41]) and knee function (-1.86 [-6.71, 2.99]) were similar between groups. No serious adverse events were reported. Evidence from pilot RCTs suggests that intraarticular BTA may confer similar efficacy to corticosteroid for KOA, while BTA may be superior to HA for improving knee pain.

Sticky Links: Encoding Quantitative Data of Graph Edges.

Visually encoding quantitative information associated with graph links is an important problem in graph visualization. A conventional approach is to vary the thickness of lines to encode the strength of connections in node-link diagrams. In this paper, we present Sticky Links, a novel visual encoding method that draws graph links with stickiness. Taking the metaphor of links with glues, sticky links represent connection strength using spiky shapes, ranging from two broken spikes for weak connections to connected lines for strong connections. We conducted a controlled user study to compare the efficiency and aesthetic appeal of stickiness with conventional thickness encoding. Our results show that stickiness enables more effective and expressive quantitative encoding while maintaining the perception of node connectivity. Participants also found sticky links to be more aesthetic and less visually cluttering than conventional thickness encoding. Overall, our findings suggest that sticky links offer a promising alternative to conventional methods for encoding quantitative information in graphs.

CoInsight: Visual Storytelling for Hierarchical Tables With Connected Insights.

Extracting data insights and generating visual data stories from tabular data are critical parts of data analysis. However, most existing studies primarily focus on tabular data stored as flat tables, typically without leveraging the relations between cells in the headers of hierarchical tables. When properly used, rich table headers can enable the extraction of many additional data stories. To assist analysts in visual data storytelling, an approach is needed to organize these data insights efficiently. In this work, we propose CoInsight, a system to facilitate visual storytelling for hierarchical tables by connecting insights. CoInsight extracts data insights from hierarchical tables and builds insight relations according to the structure of table headers. It further visualizes related data insights using a nested graph with edge bundling. We evaluate the CoInsight system through a usage scenario and a user experiment. The results demonstrate the utility and usability of CoInsight for converting data insights in hierarchical tables into visual data stories.

InsigHTable: Insight-driven Hierarchical Table Visualization with Reinforcement Learning.

Embedding visual representations within original hierarchical tables can mitigate additional cognitive load stemming from the division of users' attention. The created hierarchical table visualizations can help users understand and explore complex data with multi-level attributes. However, because of many options available for transforming hierarchical tables and selecting subsets for embedding, the design space of hierarchical table visualizations becomes vast, and the construction process turns out to be tedious, hindering users from constructing hierarchical table visualizations with many data insights efficiently. We propose InsigHTable, a mixed-initiative and insight-driven hierarchical table transformation and visualization system. We first define data insights within hierarchical tables, which consider the hierarchical structure in the table headers. Since hierarchical table visualization construction is a sequential decision-making process, InsigHTable integrates a deep reinforcement learning framework incorporating an auxiliary rewards mechanism. This mechanism addresses the challenge of sparse rewards in constructing hierarchical table visualizations. Within the deep reinforcement learning framework, the agent continuously optimizes its decision-making process to create hierarchical table visualizations to uncover more insights by collaborating with analysts. We demonstrate the usability and effectiveness of InsigHTable through two case studies and sets of experiments. The results validate the effectiveness of the deep reinforcement learning framework and show that InsigHTable can facilitate users to construct hierarchical table visualizations and understand underlying data insights.

GoTreeScape: Navigate and Explore the Tree Visualization Design Space.

Declarative grammar is becoming an increasingly important technique for understanding visualization design spaces. The GoTreeScape system presented in the paper allows users to navigate and explore the vast design space implied by GoTree, a declarative grammar for visualizing tree structures. To provide an overview of the design space, GoTreeScape, which is based on an encoder-decoder architecture, projects the tree visualizations onto a 2D landscape. Significantly, this landscape takes the relationships between different design features into account. GoTreeScape also includes an exploratory framework that allows top-down, bottom-up, and hybrid modes of exploration to support the inherently undirected nature of exploratory searches. Two case studies demonstrate the diversity with which GoTreeScape expands the universe of designed tree visualizations for users. The source code associated with GoTreeScape is available at https://github.com/bitvis2021/gotreescape.

HiTailor: Interactive Transformation and Visualization for Hierarchical Tabular Data.

Tabular visualization techniques integrate visual representations with tabular data to avoid additional cognitive load caused by splitting users' attention. However, most of the existing studies focus on simple flat tables instead of hierarchical tables, whose complex structure limits the expressiveness of visualization results and affects users' efficiency in visualization construction. We present HiTailor, a technique for presenting and exploring hierarchical tables. HiTailor constructs an abstract model, which defines row/column headings as biclustering and hierarchical structures. Based on our abstract model, we identify three pairs of operators, Swap/Transpose, ToStacked/ToLinear, Fold/Unfold, for transformations of hierarchical tables to support users' comprehensive explorations. After transformation, users can specify a cell or block of interest in hierarchical tables as a TableUnit for visualization, and HiTailor recommends other related TableUnits according to the abstract model using different mechanisms. We demonstrate the usability of the HiTailor system through a comparative study and a case study with domain experts, showing that HiTailor can present and explore hierarchical tables from different viewpoints. HiTailor is available at https://github.com/bitvis2021/HiTailor.