Single-crystal quality data from polycrystalline samples: finding the needle in the haystack

Multi-grain crystallography, traditionally performed at synchrotron sources in association with high-pressure studies, has new relevance with respect to laboratory single-crystal X-ray diffraction, in which crystals can be grown rapidly in situ, and a preliminary dataset analysed and solved in a matter of minutes. Subsequently, a full-sphere of IUCr-quality data can then be collected in a few hours. To demonstrate the applicability of laboratory multi-grain crystallography with Cu Kα X-rays, co-crystals of hexafluorobenzene and pyrrole were grown rapidly by cooling a 1:1 liquid mixture in an X-ray capillary on the diffractometer. The software is able to identify a single unit cell from as few as 10% of the diffraction spots from a small number of diffraction frames. Once a unit cell is identified, a full crystal structure solution is rapidly obtained by collecting a small amount of data to a resolution of ca 1 Å. The co-crystal obtained from the 1:1 mixture showed that hexafluorobenzene and pyrrole crystallize in a 3:4 ratio, in contrast to the columnar 1:1 adduct structures typified by hexafluorobenzene and benzene. The generality of our multi-grain approach for samples that are liquid at room temperature (and form a polycrystalline solid mass on cooling) is further demonstrated by investigating and solving the 1:1 co-crystal formed between hexafluorobenzene and pyridine

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

Teaching and learning protein crystallography over the internet

The Internet, information technology, and the increased need for lifelong learning are changing higher education. We describe the structure of a one-year, protein crystallography course delivered completely over the Internet, and the teaching and learning experience of thr first two years

Synthesis, characterization, X-ray structure, DNA binding, antioxidant and docking study of new organotin(IV) complexes

Four new diorganotin(IV) complexes, including Me₂SnL(1), Bu₂SnL(2), Ph₂SnL(3) and Oct₂SnL(4), were synthesized by reacting R₂SnCl₂ (where R = Me, Bu, Ph, Oct) with N′-(3,5-dichloro-2-hydroxybenzylidene)-2-phenylacetohydrazide H₂L. The synthesized ligand and complexes were structurally characterized by various techniques, including Fourier transform infrared (FT-IR), ¹H and ¹³C nuclear magnetic resonance (NMR) and mass spectrometry. The data suggested that tin–oxygen and tin–nitrogen bonds are formed during complex formation, confirming the coordination of ligand with dialkyltin(IV) moieties and the presence of penta-coordinated structures. The single-crystal X-ray study was performed to confirm the molecular structure of complex 1, which revealed that the molecular structure is distorted towards square pyramidal geometry. The compound–DNA interaction was investigated via spectroscopic, electrochemical and molecular docking studies; the mode of interaction found in all cases was intercalative in nature. The complex 3 showed the highest binding ability with sodium salt of salmon sperm DNA (SS-DNA) (1.93 × 10⁵ M⁻¹). The data obtained for DNA interaction studies from theoretical calculation via docking studies match well with those observed from spectroscopic and electrochemical analyses. Electrochemical and thermodynamic parameters, including diffusion coefficient, ∆H, ∆G and ∆S, were also calculated. The DPPH antioxidant results showed that complex 2 is an active antioxidant

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 science. © The Author(s) 2019. Published by Oxford University Press