Using ensemble of ensemble machine learning methods to predict outcomes of cardiac resynchronization.

INTRODUCTION: The efficacy of cardiac resynchronization therapy (CRT) has been widely studied in the medical literature; however, about 30% of candidates fail to respond to this treatment strategy. Smart computational approaches based on clinical data can help expose hidden patterns useful for identifying CRT responders. METHODS: We retrospectively analyzed the electronic health records of 1664 patients who underwent CRT procedures from January 1, 2002 to December 31, 2017. An ensemble of ensemble (EoE) machine learning (ML) system composed of a supervised and an unsupervised ML layers was developed to generate a prediction model for CRT response. RESULTS: We compared the performance of EoE against traditional ML methods and the state-of-the-art convolutional neural network (CNN) model trained on raw electrocardiographic (ECG) waveforms. We observed that the models exhibited improvement in performance as more features were incrementally used for training. Using the most comprehensive set of predictors, the performance of the EoE model in terms of the area under the receiver operating characteristic curve and F1-score were 0.76 and 0.73, respectively. Direct application of the CNN model on the raw ECG waveforms did not generate promising results. CONCLUSION: The proposed CRT risk calculator effectively discriminates which heart failure (HF) patient is likely to respond to CRT significantly better than using clinical guidelines and traditional ML methods, thus suggesting that the tool can enhanced care management of HF patients by helping to identify high-risk patients.

Use of Natural Language Processing Algorithms to Identify Common Data Elements in Operative Notes for Knee Arthroplasty.

BACKGROUND: Natural language processing (NLP) methods have the capability to process clinical free text in electronic health records, decreasing the need for costly manual chart review, and improving data quality. We developed rule-based NLP algorithms to automatically extract surgery specific data elements from knee arthroplasty operative notes. METHODS: Within a cohort of 20,000 knee arthroplasty operative notes from 2000 to 2017 at a large tertiary institution, we randomly selected independent pairs of training and test sets to develop and evaluate NLP algorithms to detect five major data elements. The size of the training and test datasets were similar and ranged between 420 to 1592 surgeries. Expert rules using keywords in operative notes were used to implement NLP algorithms capturing: (1) category of surgery (total knee arthroplasty, unicompartmental knee arthroplasty, patellofemoral arthroplasty), (2) laterality of surgery, (3) constraint type, (4) presence of patellar resurfacing, and (5) implant model (catalog numbers). We used institutional registry data as our gold standard to evaluate the NLP algorithms. RESULTS: NLP algorithms to detect the category of surgery, laterality, constraint, and patellar resurfacing achieved 98.3%, 99.5%, 99.2%, and 99.4% accuracy on test datasets, respectively. The implant model algorithm achieved an F1-score (harmonic mean of precision and recall) of 99.9%. CONCLUSIONS: NLP algorithms are a promising alternative to costly manual chart review to automate the extraction of embedded information within knee arthroplasty operative notes. Further validation in other hospital settings will enhance widespread implementation and efficiency in data capture for research and clinical purposes. LEVEL OF EVIDENCE: Level III.

Pattern of third molar impactions in north-eastern peninsular Malaysia: A 10-year retrospective study.

BACKGROUND: Third molar impaction, if left untreated, has the potential to cause several complications. The evaluation of surgical difficulty of impacted third molar extraction aids in better formulation of treatment plan by minimizing surgical complications. OBJECTIVE: This study aimed to determine the prevalence of third molar impaction and related pathologic conditions in a cohort of patients living in North-eastern Peninsular Malaysia. METHODS: In this retrospective study, 490 orthopantomograms (OPGs) of patients who were referred to the Oral and Maxillofacial Surgery department between January 2010 and December 2019 were assessed. Data including age, gender, ethnicity, frequency of third molar impactions, their angulations and levels of eruption, retromolar space, and associated pathologic conditions were collected. Statistical analysis was performed using the Statistical Package for Social Sciences (SPSS) version 24.0. The significance level was set to P < 0.05. RESULTS: A total of 490 patients with a mean age of 28.87 years (range: 20-64) demonstrated 1957 impacted third molars (1022 mandibular + 935 maxillary). Impacted third molars were more likely present in females than males (1:2.20) (p < 0.05); and in Malay-ethnic (44.49%) patients followed by Chinese (34.45%) and Indians (21.02%). Mesioangular was the most common angulation of impaction both in the maxilla (24.68%) and mandible (18.34%). The most common pattern of third molar impaction was IIA (61.67%), and the retromolar space was significantly larger in males (13.6 mm; P < 0.05) than females (11.6 mm). The most frequently occurring pathological condition associated with third molars impaction is dental caries in the second or third molar (15.38%). CONCLUSIONS: This study highlights mesioangular impaction with their occlusal plane at the same level as the occlusal plane of the adjacent tooth being the most prevalent pattern of third molar impaction in North-eastern Peninsular Malaysia.

Unsupervised machine learning for the discovery of latent disease clusters and patient subgroups using electronic health records.

Machine learning has become ubiquitous and a key technology on mining electronic health records (EHRs) for facilitating clinical research and practice. Unsupervised machine learning, as opposed to supervised learning, has shown promise in identifying novel patterns and relations from EHRs without using human created labels. In this paper, we investigate the application of unsupervised machine learning models in discovering latent disease clusters and patient subgroups based on EHRs. We utilized Latent Dirichlet Allocation (LDA), a generative probabilistic model, and proposed a novel model named Poisson Dirichlet Model (PDM), which extends the LDA approach using a Poisson distribution to model patients' disease diagnoses and to alleviate age and sex factors by considering both observed and expected observations. In the empirical experiments, we evaluated LDA and PDM on three patient cohorts, namely Osteoporosis, Delirium/Dementia, and Chronic Obstructive Pulmonary Disease (COPD)/Bronchiectasis Cohorts, with their EHR data retrieved from the Rochester Epidemiology Project (REP) medical records linkage system, for the discovery of latent disease clusters and patient subgroups. We compared the effectiveness of LDA and PDM in identifying disease clusters through the visualization of disease representations. We tested the performance of LDA and PDM in differentiating patient subgroups through survival analysis, as well as statistical analysis of demographics and Elixhauser Comorbidity Index (ECI) scores in those subgroups. The experimental results show that the proposed PDM could effectively identify distinguished disease clusters based on the latent patterns hidden in the EHR data by alleviating the impact of age and sex, and that LDA could stratify patients into differentiable subgroups with larger p-values than PDM. However, those subgroups identified by LDA are highly associated with patients' age and sex. The subgroups discovered by PDM might imply the underlying patterns of diseases of greater interest in epidemiology research due to the alleviation of age and sex. Both unsupervised machine learning approaches could be leveraged to discover patient subgroups using EHRs but with different foci.

A bibliometric study of the top 100 most-cited randomized controlled trials, systematic reviews and meta-analyses published in endodontic journals.

AIM: To identify and analyse the main features of the top 100 most-cited randomized controlled trials, systematic reviews and meta-analyses published in endodontic journals from 1961 to 2018. METHODOLOGY: The Clarivate Analytics' Web of Science 'All Databases' was used to search and analyse the 100 most frequently cited randomized controlled trials, systematic reviews and meta-analyses having 'randomized', 'randomised', 'randomized controlled', 'randomised controlled', 'randomized controlled trial', 'randomized controlled trials', 'clinical trial', 'systematic', 'systematic review', 'meta-analysis', and 'meta-analyses' in the title section. The 'International Endodontic Journal', 'Journal of Endodontics', 'Oral Surgery Oral Medicine Oral Pathology Oral Radiology and Endodontology', 'Australian Endodontic Journal', 'Endodontics & Dental Traumatology', 'Endo-Endodontic Practice Today' and 'European Endodontic Journal' were included in the publication name section. After ranking the articles in a descending order based on their citation counts, each article was cross-matched with the citation counts in Elsevier's Scopus and Google Scholar. The articles were analysed, and information on citation counts, citation density, year of publication, contributing authors, institutions and countries, journal of publication, study design, topic of the article and keywords was extracted. RESULTS: The citation counts of the 100 most-cited articles varied from 235 to 20 (Web of Science), 276 to 17 (Scopus) and 696 to 1 (Google Scholar). The year in which the top 100 articles were published was 2010 (n = 13). Among 373 authors, the greatest number of articles was associated with three individuals namely Reader A (n = 5), Beck M (n = 5) and Kvist T (n = 5). Most of the articles originated from the United States (n = 24) with the greatest contribution from Ohio State University (USA) (n = 5). Randomized controlled trials were the most frequent study design (n = 45) followed by systematic reviews (n = 30) with outcome studies of root canal treatment being the major topic (n = 35). The Journal of Endodontics published the largest number of included articles (n = 70) followed by the International Endodontic Journal (n = 27). Among 259 unique keywords, meta-analysis (n = 23) and systematic review (n = 23) were the most frequently used. CONCLUSION: This study has revealed that year of publication had no obvious impact on citation count. The bibliometric analysis highlighted the quantity and quality of research, and the evolution of scientific advancements made in the field of Endodontology over time. Articles before 1996, that is prior to the CONSORT statement that encouraged authors to include specific terms in the title and keywords, may not have been included in this electronic search.

The top 50 most-cited articles published in the International Endodontic Journal.

AIM: To analyse the main characteristics of the top 50 most-cited articles published in the International Endodontic Journal from 1967 to 2018. METHODOLOGY: The Clarivate Analytics' Web of Science 'All Databases', Elsevier's Scopus, Google Scholar and PubMed Central were searched to retrieve the 50 most-cited articles in the IEJ published from April 1967 to December 2018. The articles were analysed and information including number of citations, year of publication, contributing authors, institutions and countries, study design, study topic, impact factor and keywords was extracted. RESULTS: The number of citations of the 50 selected papers varied from 575 to 130 (Web of Science), 656 to164 (Elsevier's Scopus), 1354 to 199 (Google Scholar) and 123 to 3 (PubMed). The majority of papers were published in the year 2001 (n = 7). Amongst 102 authors, the greatest contribution was made by four contributors that included Gulabivala K (n = 4), Ng YL (n = 4), Pitt Ford TR (n = 4) and Wesselink PR (n = 4). The majority of papers originated from the United Kingdom (n = 8) with most contributions from King's College London Dental Institute (UK) and Eastman Dental Hospital, London. Reviews were the most common study design (n = 19) followed by Clinical Research (n = 16) and Basic Research (n = 15). The majority of topics covered by the most-cited articles were Outcome Studies (n = 9), Intracanal medicaments (n = 8), Endodontic microbiology (n = 7) and Canal instrumentation (n = 7). Amongst 76 unique keywords, Endodontics (n = 7), Mineral Trioxide Aggregate (MTA) (n = 7) and Root Canal Treatment (n = 7) were the most frequently used. CONCLUSION: This is the first study to identify and analyse the top 50 most-cited articles in a specific professional journal within Dentistry. The analysis has revealed information regarding the development of the IEJ over time as well as scientific progress in the field of Endodontology.

Effect of Tumor Size and p16 Status on Treatment Outcomes - Achievement of Complete Remission in Prospectively Followed Patients with Oropharyngeal Tumors.

BACKGROUND: Oropharyngeal squamous cell tumors associated with human papillomavirus infection (p16 positive tumors) have better prognosis than p16 negative tumors regardless of the more advanced stage of the disease. Tumor volume (GTVt+n) is generally an important factor affecting treatment results of ionizing radiation. The aim of this prospective non-randomized study is to evaluate the effect of tumor volume on the (chemo)radiation treatment results in a group of patients with p16 negative and p16 positive oropharyngeal tumors. PATIENTS AND METHODS: Patients with confirmed squamous cell tumor of the oropharynx of stages III and IV, according to the 7th version of the TNM (tumor-nodes-metastases) classification, were eligible for this study. The main exclusion criteria were palliative treatment, neoadjuvant chemotherapy or planned concomitant therapy with cetuximab. Patients were treated according to standardized protocols with curative intent. Primary tumor volume (GTVt) and involved nodes volume (GTVn) were obtained from radiotherapy planning system for further statistical analysis. The differences in tumor volumes between the groups according to p16 expression were assessed with subsequent testing of probability to achieve complete remission (CR) of the disease in both groups. RESULTS: In total, 49 patients - 84% men, median age 60.5 years, 25 (51%) patients p16 positive, 40 (82%) underwent concomitant chemoradiotherapy. Median of GTVt in the whole patients group is 40.2 ccm, GTVn 11.78 ccm and median volume of the whole tumor burden (GTVt+n) 70.21 ccm (range 11.05-249). Median of GTVn was greater in the p16 positive cohort (p = 0.041). In the entire group, the median time to reach CR was 91 days (95% CI 86-107 days) from the end of radiotherapy. In the group of p16 negative patients, 14 achieved CR (61%) out of 23 patients, in p16 positive group 20 (80%) out of 25 patients (p = 0.111). P16 negative patients had a longer time to CR (p = 0.196, HR 1.58, 95% CI 0.79-3.18). None of the independently assessed volumetric parameters of the tumor (GTVt, GTVn, GTVt+n) affected CR in the p16 positive patients group, while there was a significant impact of the whole tumor burden (GTVt+n) in the p16 negative cohort (median 58.1 ccm in CR patients vs. 101.9 ccm, p = 0.018). CONCLUSION: We have showed less GTVt+n dependence to achieve CR in p16 positive tumors in comparison with p16 negative tumors. Thus, p16 positive oropharyngeal squamous cell cancers should not be withdrawn from the curative treatment intent based on the greater GTVt+n. Key words oropharyngeal neoplasms - p16 status - treatment outcome - tumor burden - complete remission This work was supported by grant of the Ministry of Health of the Czech Republic AZV 15-31627A and by grant of the Ministry of Health of the Czech Republic - Conceptual development of a research organization (MMCI 00209805). The authors declare they have no potential conflicts of interest concerning drugs, products, or services used in the study. The Editorial Board declares that the manuscript met the ICMJE recommendation for biomedical papers. Submitted: 2. 11. 2018 Accepted: 11. 11. 2018.

[MicroRNAs in Prediction of Response to Radiotherapy in Head and Neck Cancer Patients - Pilot Study]. / MikroRNA v predikci odpovedi na radioterapii u pacientu s nádory hlavy a krku - pilotní studie.

BACKGROUND: Radiotherapy plays a key role in the treatment of squamous cell head and neck cancers (HNSCC). The effectivity of radiation therapy is often limited by radioresistance of these tumours. microRNAs (miRNAs) are endogenous, evolutionary conserved, small non-coding RNAs involved in regulation of cellular processes associated with radioresistance. The objective of this study was to identify miRNA profile enabling to predict the radiation treatment outcomes in HNSCC patients. MATERIAL AND METHODS: The retrospective study included HNSCC patients who underwent a definitive radiotherapy. Patients were divided into two groups according to loco-regional control (LRC) as follows - short LRC (n = 22; median 5.1 months (min. 1.3, max, 18.6)) vs. long LRC (n = 21; 60.4 (min. 46.8, max. 98.8)) group. Global miRNA expression profiles were obtained by use of Affymetrix microarray technology (GeneChip miRNA 4.0 Array). RESULTS: We identified 24 miRNAs to be significantly associated with LRC (p < 0.05), all of these miRNAs were upregulated in patients with short LRC. Out of these miRNAs, 12 miRNAs with p < 0.025 and 4 miRNAs with p < 0.01 have been identified. CONCLUSION: miRNAs seems to be promising as potential biomarkers predicting radiotherapy treatment outcomes in patients with HNSCC.Key words: microRNAs - radiotherapy - head and neck cancer The authors declare they have no potential conflicts of interest concerning drugs, products, or services used in the study. The Editorial Board declares that the manuscript met the ICMJE recommendation for biomedical papers. Supported by Ministry of Health of the Czech Republic, grant No. 15-31627A. All rights reserved.Submitted: 19. 3. 2018Accepted: 20. 3. 2018.

Development and Validation of a Mobile Phone Application for Measuring Knee Range of Motion.

Knee range of motion (ROM) is an important indicator of knee function. Outside the clinical setting, patients may not be able to accurately assess knee ROM, which may impair recovery following trauma or surgery. This study aims to validate a smartphone mobile application developed to measure knee ROM compared to visual and goniometer ROM measurements. A knee ROM Android mobile application was developed to measure knee ROM. Patients ≥ 18 years old presenting to an orthopaedic clinic with native knee complaints were approached to participate. Knee ROM was measured bilaterally by an arthroplasty-trained surgeon using (1) vision, (2) goniometer, and (3) the mobile application. Measurements were compared in flexion and extension using a one-way analysis of variance with post hoc Tukey test (alpha = 0.05). Eighty-four knee ROM measurements (40 left, 44 right) were obtained in 47 patients. Median Kellgren-Lawrence grade from available radiographs was grade 3. In flexion, mobile application (117.6 ± 14.7 degrees) measurements were not significantly different from visual (116.1 ± 13.6 degrees) or goniometer (116.2 ± 13.6 degrees) measurements. In extension, mobile application (4.8 ± 7.3 degrees) measurements were significantly different from visual (1.9 ± 4.1 degrees) measurements on post hoc analysis (p < 0.01), while no differences were present compared to goniometer (3.1 ± 5.8 degrees) measurements. Our study found that a mobile application for evaluating knee ROM was noninferior to goniometer-based measurements performed by an arthroplasty-trained surgeon. Future studies will investigate this application's utility in (1) remote patient care, (2) accelerating recovery during rehabilitation, (3) detecting early postoperative complications including arthrofibrosis, and (4) adding additional functionalities to the application to provide more detail-oriented descriptive analyses of patient knee function.

Fair AI-powered orthopedic image segmentation: addressing bias and promoting equitable healthcare.

AI-powered segmentation of hip and knee bony anatomy has revolutionized orthopedics, transforming pre-operative planning and post-operative assessment. Despite the remarkable advancements in AI algorithms for medical imaging, the potential for biases inherent within these models remains largely unexplored. This study tackles these concerns by thoroughly re-examining AI-driven segmentation for hip and knee bony anatomy. While advanced imaging modalities like CT and MRI offer comprehensive views, plain radiographs (X-rays) predominate the standard initial clinical assessment due to their widespread availability, low cost, and rapid acquisition. Hence, we focused on plain radiographs to ensure the utilization of our contribution in diverse healthcare settings, including those with limited access to advanced imaging technologies. This work provides insights into the underlying causes of biases in AI-based knee and hip image segmentation through an extensive evaluation, presenting targeted mitigation strategies to alleviate biases related to sex, race, and age, using an automatic segmentation that is fair, impartial, and safe in the context of AI. Our contribution can enhance inclusivity, ethical practices, equity, and an unbiased healthcare environment with advanced clinical outcomes, aiding decision-making and osteoarthritis research. Furthermore, we have made all the codes and datasets publicly and freely accessible to promote open scientific research.

HexAI-TJAtxt: A textual dataset to advance open scientific research in total joint arthroplasty.

Total joint arthroplasty (TJA) is the most common and fastest inpatient surgical procedure in the elderly, nationwide. Due to the increasing number of TJA patients and advancements in healthcare, there is a growing number of scientific articles being published in a daily basis. These articles offer important insights into TJA, covering aspects like diagnosis, prevention, treatment strategies, and epidemiological factors. However, there has been limited effort to compile a large-scale text dataset from these articles and make it publicly available for open scientific research in TJA. Rapid yet, utilizing computational text analysis on these large columns of scientific literatures holds great potential for uncovering new knowledge to enhance our understanding of joint diseases and improve the quality of TJA care and clinical outcomes. This work aims to build a dataset entitled HexAI-TJAtxt, which includes more than 61,936 scientific abstracts collected from PubMed using MeSH (Medical Subject Headings) terms within "MeSH Subheading" and "MeSH Major Topic," and Publication Date from 01/01/2000 to 12/31/2022. The current dataset is freely and publicly available at https://github.com/pitthexai/HexAI-TJAtxt, and it will be updated frequently in bi-monthly manner from new abstracts published at PubMed.

Probing Patient Messages Enhanced by Natural Language Processing: A Top-Down Message Corpus Analysis.

Background. Patients increasingly use asynchronous communication platforms to converse with care teams. Natural language processing (NLP) to classify content and automate triage of these messages has great potential to enhance clinical efficiency. We characterize the contents of a corpus of portal messages generated by patients using NLP methods. We aim to demonstrate descriptive analyses of patient text that can contribute to the development of future sophisticated NLP applications. Methods. We collected approximately 3,000 portal messages from the cardiology, dermatology, and gastroenterology departments at Mayo Clinic. After labeling these messages as either Active Symptom, Logistical, Prescription, or Update, we used NER (named entity recognition) to identify medical concepts based on the UMLS library. We hierarchically analyzed the distribution of these messages in terms of departments, message types, medical concepts, and keywords therewithin. Results. Active Symptom and Logistical content types comprised approximately 67% of the message cohort. The "Findings" medical concept had the largest number of keywords across all groupings of content types and departments. "Anatomical Sites" and "Disorders" keywords were more prevalent in Active Symptom messages, while "Drugs" keywords were most prevalent in Prescription messages. Logistical messages tended to have the lower proportions of "Anatomical Sites,", "Disorders,", "Drugs,", and "Findings" keywords when compared to other message content types. Conclusions. This descriptive corpus analysis sheds light on the content and foci of portal messages. The insight into the content and differences among message themes can inform the development of more robust NLP models.

100 Years of the Journal of Dental Research: A Bibliometric Analysis.

Since its inception in 1919, the Journal of Dental Research has continually published high-quality articles that span the breadth of research topics relevant to dentistry, oral surgery, and medicine. As part of the journal's centennial celebration, we conducted an electronic search on Scopus to identify and analyze the top 100 most cited articles from 1919 to 2018. Since Scopus does not capture older citations, we conducted an additional analysis by Google Scholar to identify key articles published in the first 50 y of the journal. Based on Scopus, the articles were ranked in descending order per their citation counts. The citation counts of the 100 most cited articles varied from 262 to 1,503. The year in which the largest number of top 100 articles were published was 2004 (n = 6). Within the top 100, the majority of articles originated from the United States (n = 52). Research Reports-Biomaterials & Bioengineering was the most frequent category of cited articles (n = 35). There was no significant association between total citation count and time since publication (correlation coefficient = -0.051, P = 0.656). However, there was a significant negative association of citation density (correlation coefficient = -0.610, P < 0.01) with time since publication. Our analyses demonstrate the broad reach of the journal and the dynamics in citation patterns and research agenda over its 100-y history. There is considerable evidence of the high variance in research output, when measured via citations, across the globe. Moreover, it remains unclear how patients' priorities and dental health care needs are aligned with the perceived influence of single research pieces identified by our search. Our findings may help to inspire future research in tackling these inequalities and highlight the need for conceptualizing research priorities.

Relationship Between Very Cold Outside Weather and Surgical Outcome: Integrating Shallow and Deep Artificial Neural Nets.

Patients' hospital length of stay (LOS) as a surgical outcome is important indicator of quality of care. We used EMR data to build artificial neural network models to better understand the impact of cold weather on outcome of first surgeries in a day in comparison to a matched cohort receiving surgical treatment in warm days. We found that LOS for first-in-a-day cardiac and orthopedic surgical cases are longer in very cold days.

The stem cell transplant program in Pakistan--the first decade.

Stem cell transplantation is curative in a number of otherwise fatal hematological diseases. In Pakistan, SCT was started in October 1995 at Dr Ziauddin Hospital by Dr Tahir Shamsi and his team. The first case was of a young man suffering from AML. In 1999, allogeneic BMT was started at Bismillah Taqee Institute of Health Sciences and Blood Diseases Centre, Karachi. In 2001, the Armed Forces Bone Marrow Transplant Centre, started functioning. Since then, over 350 allogeneic stem cell transplants have been carried out in these latter two centers. Another 50 autologous procedures were carried out in all centers. In 2004, a third center started transplants at the Aga Khan Hospital. The main indications for transplant are aplastic anemia, beta-thalassemia major and hematological malignancies. HLA-identical sibling donors provide stem cells for the recipient. In 70% of cases, a matched donor is identified. In sharp contrast to the rest of the world, the majority of transplants are allogeneic, donor-recipient pairs are CMV positive and fungal infection, tuberculosis and malaria are particular problems. The early results are promising, with transplant-related mortality reported to be 10-20%, whereas long-term survival is reported to be 78, 72 and 49% in aplastic anemia, beta-thalassemia major and leukemia, respectively. Financial constraints, poor socioeconomic status, poor transfusion services, trained human resources and difficulty in keeping pace with technological advances are major hurdles in the growth of transplant medicine. Government support is badly needed to strengthen existing facilities and to develop more centers.

MCIndoor20000: A fully-labeled image dataset to advance indoor objects detection.

A fully-labeled image dataset provides a unique resource for reproducible research inquiries and data analyses in several computational fields, such as computer vision, machine learning and deep learning machine intelligence. With the present contribution, a large-scale fully-labeled image dataset is provided, and made publicly and freely available to the research community. The current dataset entitled MCIndoor20000 includes more than 20,000 digital images from three different indoor object categories, including doors, stairs, and hospital signs. To make a comprehensive dataset addressing current challenges that exist in indoor objects modeling, we cover a multiple set of variations in images, such as rotation, intra-class variation plus various noise models. The current dataset is freely and publicly available at https://github.com/bircatmcri/MCIndoor20000.

SD-SEM: sparse-dense correspondence for 3D reconstruction of microscopic samples.

Scanning electron microscopy (SEM) imaging has been a principal component of many studies in biomedical, mechanical, and materials sciences since its emergence. Despite the high resolution of captured images, they remain two-dimensional (2D). In this work, a novel framework using sparse-dense correspondence is introduced and investigated for 3D reconstruction of stereo SEM images. SEM micrographs from microscopic samples are captured by tilting the specimen stage by a known angle. The pair of SEM micrographs is then rectified using sparse scale invariant feature transform (SIFT) features/descriptors and a contrario RANSAC for matching outlier removal to ensure a gross horizontal displacement between corresponding points. This is followed by dense correspondence estimation using dense SIFT descriptors and employing a factor graph representation of the energy minimization functional and loopy belief propagation (LBP) as means of optimization. Given the pixel-by-pixel correspondence and the tilt angle of the specimen stage during the acquisition of micrographs, depth can be recovered. Extensive tests reveal the strength of the proposed method for high-quality reconstruction of microscopic samples.

Development and validation of a simple high performance thin layer chromatography method combined with direct 1,1-diphenyl-2-picrylhydrazyl assay to quantify free radical scavenging activity in wine.

The aim of this study was to: (a) develop a simple, high performance thin layer chromatographic (HPTLC) method combined with direct 1,1-diphenyl-2-picrylhydrazyl (DPPH) assay to rapidly assess and compare free radical scavenging activity or anti-oxidant activity for major classes of polyphenolics present in wines; and (b) to investigate relationship between free radical scavenging activity to the total polyphenolic content (TPC) and total antioxidant capacity (TAC) in the wine samples. The most potent free radical scavengers that we tested for in the wine samples were found to be resveratrol (polyphenolic non-flavonoid) and rutin (flavonoid), while polyphenolic acids (caffeic acid and gallic acid) although present in all wine samples were found to be less potent free radical scavengers. Therefore, the total antioxidant capacity was mostly affected by the presence of resveratrol and rutin, while total polyphenolic content was mostly influenced by the presence of the less potent free radical scavengers gallic and caffeic acids.

3DSEM++: Adaptive and intelligent 3D SEM surface reconstruction.

Structural analysis of microscopic objects is a longstanding topic in several scientific disciplines, such as biological, mechanical, and materials sciences. The scanning electron microscope (SEM), as a promising imaging equipment has been around for decades to determine the surface properties (e.g., compositions or geometries) of specimens by achieving increased magnification, contrast, and resolution greater than one nanometer. Whereas SEM micrographs still remain two-dimensional (2D), many research and educational questions truly require knowledge and facts about their three-dimensional (3D) structures. 3D surface reconstruction from SEM images leads to remarkable understanding of microscopic surfaces, allowing informative and qualitative visualization of the samples being investigated. In this contribution, we integrate several computational technologies including machine learning, contrario methodology, and epipolar geometry to design and develop a novel and efficient method called 3DSEM++ for multi-view 3D SEM surface reconstruction in an adaptive and intelligent fashion. The experiments which have been performed on real and synthetic data assert the approach is able to reach a significant precision to both SEM extrinsic calibration and its 3D surface modeling.

3DSEM: A 3D microscopy dataset.

The Scanning Electron Microscope (SEM) as a 2D imaging instrument has been widely used in many scientific disciplines including biological, mechanical, and materials sciences to determine the surface attributes of microscopic objects. However the SEM micrographs still remain 2D images. To effectively measure and visualize the surface properties, we need to truly restore the 3D shape model from 2D SEM images. Having 3D surfaces would provide anatomic shape of micro-samples which allows for quantitative measurements and informative visualization of the specimens being investigated. The 3DSEM is a dataset for 3D microscopy vision which is freely available at [1] for any academic, educational, and research purposes. The dataset includes both 2D images and 3D reconstructed surfaces of several real microscopic samples.