Large expert-curated database for benchmarking document similarity detection in biomedical literature search

Document recommendation systems for locating relevant literature have mostly relied on methods developed a decade ago. This is largely due to the lack of a large offline gold-standard benchmark of relevant documents that cover a variety of research fields such that newly developed literature search techniques can be compared, improved and translated into practice. To overcome this bottleneck, we have established the RElevant LIterature SearcH consortium consisting of more than 1500 scientists from 84 countries, who have collectively annotated the relevance of over 180 000 PubMed-listed articles with regard to their respective seed (input) article/s. The majority of annotations were contributed by highly experienced, original authors of the seed articles. The collected data cover 76% of all unique PubMed Medical Subject Headings descriptors. No systematic biases were observed across different experience levels, research fields or time spent on annotations. More importantly, annotations of the same document pairs contributed by different scientists were highly concordant. We further show that the three representative baseline methods used to generate recommended articles for evaluation (Okapi Best Matching 25, Term Frequency–Inverse Document Frequency and PubMed Related Articles) had similar overall performances. Additionally, we found that these methods each tend to produce distinct collections of recommended articles, suggesting that a hybrid method may be required to completely capture all relevant articles. The established database server located at https://relishdb.ict.griffith.edu.au is freely available for the downloading of annotation data and the blind testing of new methods. We expect that this benchmark will be useful for stimulating the development of new powerful techniques for title and title/abstract-based search engines for relevant articles in biomedical research

Classification of Caesarean Section and Normal Vaginal Deliveries Using Foetal Heart Rate Signals and Advanced Machine Learning Algorithms

ABSTRACT – Background: Visual inspection of Cardiotocography traces by obstetricians and midwives is the gold standard for monitoring the wellbeing of the foetus during antenatal care. However, inter- and intra-observer variability is high with only a 30% positive predictive value for the classification of pathological outcomes. This has a significant negative impact on the perinatal foetus and often results in cardio-pulmonary arrest, brain and vital organ damage, cerebral palsy, hearing, visual and cognitive defects and in severe cases, death. This paper shows that using machine learning and foetal heart rate signals provides direct information about the foetal state and helps to filter the subjective opinions of medical practitioners when used as a decision support tool. The primary aim is to provide a proof-of-concept that demonstrates how machine learning can be used to objectively determine when medical intervention, such as caesarean section, is required and help avoid preventable perinatal deaths. Methodology: This is evidenced using an open dataset that comprises 506 controls (normal virginal deliveries) and 46 cases (caesarean due to pH ≤7.05 and pathological risk). Several machine-learning algorithms are trained, and validated, using binary classifier performance measures. Results: The findings show that deep learning classification achieves Sensitivity = 94%, Specificity = 91%, Area under the Curve = 99%, F-Score = 100%, and Mean Square Error = 1%. Conclusions: The results demonstrate that machine learning significantly improves the efficiency for the detection of caesarean section and normal vaginal deliveries using foetal heart rate signals compared with obstetrician and midwife predictions and systems reported in previous studies

Large expert-curated database for benchmarking document similarity detection in biomedical literature search

Document recommendation systems for locating relevant literature have mostly relied on methods developed a decade ago. This is largely due to the lack of a large offline gold-standard benchmark of relevant documents that cover a variety of research fields such that newly developed literature search techniques can be compared, improved and translated into practice. To overcome this bottleneck, we have established the RElevant LIterature SearcH consortium consisting of more than 1500 scientists from 84 countries, who have collectively annotated the relevance of over 180 000 PubMed-listed articles with regard to their respective seed (input) article/s. The majority of annotations were contributed by highly experienced, original authors of the seed articles. The collected data cover 76% of all unique PubMed Medical Subject Headings descriptors. No systematic biases were observed across different experience levels, research fields or time spent on annotations. More importantly, annotations of the same document pairs contributed by different scientists were highly concordant. We further show that the three representative baseline methods used to generate recommended articles for evaluation (Okapi Best Matching 25, Term Frequency-Inverse Document Frequency and PubMed Related Articles) had similar overall performances. Additionally, we found that these methods each tend to produce distinct collections of recommended articles, suggesting that a hybrid method may be required to completely capture all relevant articles. The established database server located at https://relishdb.ict.griffith.edu.au is freely available for the downloading of annotation data and the blind testing of new methods. We expect that this benchmark will be useful for stimulating the development of new powerful techniques for title and title/abstract-based search engines for relevant articles in biomedical research.Peer reviewe

Bio-Monitoring of Airborne Fungi and Antifungal Activity of Clerodendrum Infortanum L. Against Dominant Fungi

The present study provides baseline information on the quantitative and qualitative estimation of aeromycoflora. Burkard personal airsampler and Anderson two stage airsampler were used to detect the quantitative and qualitative estimation of aeromycoflora. 17 non-viable fungal spores were recorded with the aid of Burkard personal airsampler and 12 viable fungal genera were detected using Anderson two stage airsampler. Higher concentration airborne fungi observed in the month of March. Aspergillus sp, Ascospore, Basidiospore, Curvularia sp, Alternaria sp were found to be Nigrospora sp most predominant nonviable fungal genera whereas dominant viable genera were Aspergillus sp, Penicillium sp, Cladosporium sp, Curvularia sp, Trichoderma sp and Fusarium sp in both the environments. The result of antifungal potential of Clerodendrum infortunatum showed highest efficacy against Aspergillus sp followed by Penicillium sp and Fusarium sp. This present study provided the baseline information about the viable and non-viable concentration in the study sites. Besides the outcomes of this study along with the insightful explanation could aptly provide basis for strategizing effective preventive measures against airborne-fungi. Those are responsible for causing different agricultural crops diseases and human respiratory ailments

Crystalline Domain Structure and Cholesterol Crystal Nucleation in Single Hydrated DPPC:Cholesterol:POPC Bilayers

Grazing incidence X-ray diffraction measurements were performed on single hydrated bilayers and monolayers of DPPC:Cholesterol:POPC at varying concentrations. There are substantial differences in the phase and structure behavior of the crystalline domains formed within the bilayers relative to the corresponding monolayers, due to interactions between the opposing leaflets. Depending on the lipid composition, these interactions led to phase separation, changes in molecular tilt angle, or formation of cholesterol crystals. In monolayers, DPPC and cholesterol form a single crystalline phase at all compositions studied. In bilayers, a second crystalline phase appears when cholesterol levels are increased: domains of cholesterol and DPPC form monolayer thick crystals where each of the lipid leaflets diffracts independently, whereas excess cholesterol forms cholesterol bilayer thick crystals at a DPPC:Chol ratio < 46:54 +/- 2 mol %. The nucleation of the cholesterol crystals occurs at concentrations relevant to the actual cell plasma membrane composition