On the Effectiveness of Compact Biomedical Transformers

Language models pre-trained on biomedical corpora, such as BioBERT, have recently shown promising results on downstream biomedical tasks. Many existing pre-trained models, on the other hand, are resource-intensive and computationally heavy owing to factors such as embedding size, hidden dimension, and number of layers. The natural language processing (NLP) community has developed numerous strategies to compress these models utilising techniques such as pruning, quantisation, and knowledge distillation, resulting in models that are considerably faster, smaller, and subsequently easier to use in practice. By the same token, in this paper we introduce six lightweight models, namely, BioDistilBERT, BioTinyBERT, BioMobileBERT, DistilBioBERT, TinyBioBERT, and CompactBioBERT which are obtained either by knowledge distillation from a biomedical teacher or continual learning on the Pubmed dataset via the Masked Language Modelling (MLM) objective. We evaluate all of our models on three biomedical tasks and compare them with BioBERT-v1.1 to create efficient lightweight models that perform on par with their larger counterparts. All the models will be publicly available on our Huggingface profile at https://huggingface.co/nlpie and the codes used to run the experiments will be available at https://github.com/nlpie-research/Compact-Biomedical-Transformers

Relaxations of fluorouracil tautomers by decorations of fullerene-like SiCs: DFT studies

Decorations of silicon carbide (SiC) fullerene-like nanoparticles by fluorouracil (FU) and its tautomers are investigated through density functional theory (DFT) calculations. Two models of fullerene-like particles including Si12C8 and Si8C12 are constructed to be counterparts of decorated hybrid structures, FU@Si12C8 and FU@Si8C12, respectively. The initial models including original FU and tautomeric structures and SiC nanoparticles are individually optimized and then combined for further optimizations in the hybrid forms. Covalent bonds are observed for FU@Si12C8 hybrids, whereas non-covalent interactions are seen for FU@Si8C12 ones. The obtained properties indicated that Si12C8 model could be considered as a better counterpart for interactions with FU structures than Si8C12 model. The results also showed significant effects of interactions on the properties of atoms close to the interacting regions in nanoparticles. Finally, the tautomeric structures show different behaviors in interactions with SiC nanoparticles, in which the SiC nanoparticles could be employed to detect the situations of tautomeric processes for FU structures. © 2016 Elsevier B.V

Increased risk of pre-eclampsia (PE) among women with the history of migraine

The Objective of this study was to assess possible association of history of migraine with pre-eclampsia (PE). This was a retrospective study to compare history of migraine in 90 women affected by PE with 90 women without PE as the control group. They recruited by a nonrandomized consecutive sampling method. Data were collected by a questionnaire including demographic, medical, obstetrics, and migraine assessment sections. Data were analyzed using SPSS. Results showed an increased risk of PE in women with history of migraine (odds ratio: 2.87; p < 0.05). Result demonstrated that migraine history in the case group is 144 and in control group is 56. Gestational age (GA) at delivery and weight of neonate (WN) were significantly lower compared to control (GA: 37.3 ± 2.6 vs. 38.7± 1.3 weeks T test; P < 0.01) (WN: 2930 ± 690 vs. 3330 ± 420; T test; P < 0.0). Cesarean section was more frequent in the PE group compared to the control group 37 (42) vs. 14 (15.6); chi square; p < 0.01. The association of migraine with PE is the result of some similar mechanism leading to endothelial dysfunction. Frequent reports of an association between migraine and PE in different populations suggest a history of migraine as a risk factor for PEgestational hypertension (GH). Copyright © Informa UK Ltd

A crowd of BashTheBug volunteers reproducibly and accurately measure the minimum inhibitory concentrations of 13 antitubercular drugs from photographs of 96-well broth microdilution plates

Tuberculosis is a respiratory disease that is treatable with antibiotics. An increasing prevalence of resistance means that to ensure a good treatment outcome it is desirable to test the susceptibility of each infection to different antibiotics. Conventionally, this is done by culturing a clinical sample and then exposing aliquots to a panel of antibiotics, each being present at a pre-determined concentration, thereby determining if the sample isresistant or susceptible to each sample. The minimum inhibitory concentration (MIC) of a drug is the lowestconcentration that inhibits growth and is a more useful quantity but requires each sample to be tested at a range ofconcentrations for each drug. Using 96-well broth micro dilution plates with each well containing a lyophilised pre-determined amount of an antibiotic is a convenient and cost-effective way to measure the MICs of several drugs at once for a clinical sample. Although accurate, this is still an expensive and slow process that requires highly-skilled and experienced laboratory scientists. Here we show that, through the BashTheBug project hosted on the Zooniverse citizen science platform, a crowd of volunteers can reproducibly and accurately determine the MICs for 13 drugs and that simply taking the median or mode of 11-17 independent classifications is sufficient. There is therefore a potential role for crowds to support (but not supplant) the role of experts in antibiotic susceptibility testing

Multiview classification and dimensionality reduction of scalp and intracranial EEG data through tensor factorisation

Electroencephalography (EEG) signals arise as a mixture of various neural processes that occur in different spatial, frequency and temporal locations. In classification paradigms, algorithms are developed that can distinguish between these processes. In this work, we apply tensor factorisation to a set of EEG data from a group of epileptic patients and factorise the data into three modes; space, time and frequency with each mode containing a number of components or signatures. We train separate classifiers on various feature sets corresponding to complementary combinations of those modes and components and test the classification accuracy of each set. The relative influence on the classification accuracy of the respective spatial, temporal or frequency signatures can then be analysed and useful interpretations can be made. Additionaly, we show that through tensor factorisation we can perform dimensionality reduction by evaluating the classification performance with regards to the number mode components and by rejecting components with insignificant contribution to the classification accuracy

Entrepreneurs’ mental health and well-being:A review and research agenda

Interest in entrepreneurs’ mental health and well-being (MWB) is growing in recognition of the role of MWB in entrepreneurs’ decision making, motivation, and action. Yet relevant knowledge is dispersed across disciplines, which makes what we currently understand about entrepreneurs’ MWB unclear. In this systematic review I integrate insights from 144 empirical studies. These studies show that research is focused on three research questions: (1) Do different types of entrepreneurs differ in their MWB? What are the (2) antecedents and (3) consequences of entrepreneurs’ MWB? The review systematizes evidence on known antecedents and consequences of entrepreneurs’ MWB but also reveals overlooked and undertheorized sources and outcomes of entrepreneurs’ MWB. The review provides a mapping and framework that advance research on entrepreneurs’ MWB and help to position entrepreneurs’ MWB more centrally in management and entrepreneurship research. It calls for researchers to go beyond applying models developed for employees to understand entrepreneurs. Instead, the findings point the way to developing a dedicated theory of entrepreneurial work and MWB that is dynamic, socialized, and open to considering context and acknowledges variability and fluidity across entrepreneurs’ life domains, as well as the centrality of work for entrepreneurs’ identity

‘Midwives Overboard!’ Inside their hearts are breaking, their makeup may be flaking but their smile still stays on

PROBLEM: Midwifery practice is emotional and, at times, traumatic work. Cumulative exposure to this, in an unsupportive environment can result in the development of psychological and behavioural symptoms of distress. BACKGROUND: As there is a clear link between the wellbeing of staff and the quality of patient care, the issue of midwife wellbeing is gathering significant attention. Despite this, it can be rare to find a midwife who will publically admit to how much they are struggling. They soldier on, often in silence. AIM: This paper aims to present a narrative review of the literature in relation to work-related psychological distress in midwifery populations. Opportunities for change are presented with the intention of generating further conversations within the academic and healthcare communities. METHODS: A narrative literature review was conducted. FINDINGS: Internationally, midwives experience various types of work-related psychological distress. These include both organisational and occupational sources of stress. DISCUSSION: Dysfunctional working cultures and inadequate support are not conducive to safe patient care or the sustained progressive development of the midwifery profession. New research, revised international strategies and new evidence based interventions of support are required to support midwives in psychological distress. This will in turn maximise patient, public and staff safety. CONCLUSIONS: Ethically, midwives are entitled to a psychologically safe professional journey. This paper offers the principal conclusion that when maternity services invest in the mental health and wellbeing of midwives, they may reap the rewards of improved patient care, improved staff experience and safer maternity services