Prioritising references for systematic reviews with RobotAnalyst: A user study

Screening references is a time-consuming step necessary for systematic reviews and guideline development. Previous studies have shown that human effort can be reduced by using machine learning software to prioritise large reference collections such that most of the relevant references are identified before screening is completed. We describe and evaluate RobotAnalyst, a Web-based software system that combines text-mining and machine learning algorithms for organising references by their content and actively prioritising them based on a relevancy classification model trained and updated throughout the process. We report an evaluation over 22 reference collections (most are related to public health topics) screened using RobotAnalyst with a total of 43 610 abstract-level decisions. The number of references that needed to be screened to identify 95% of the abstract-level inclusions for the evidence review was reduced on 19 of the 22 collections. Significant gains over random sampling were achieved for all reviews conducted with active prioritisation, as compared with only two of five when prioritisation was not used. RobotAnalyst's descriptive clustering and topic modelling functionalities were also evaluated by public health analysts. Descriptive clustering provided more coherent organisation than topic modelling, and the content of the clusters was apparent to the users across a varying number of clusters. This is the first large-scale study using technology-assisted screening to perform new reviews, and the positive results provide empirical evidence that RobotAnalyst can accelerate the identification of relevant studies. The results also highlight the issue of user complacency and the need for a stopping criterion to realise the work savings

Lutetium-labelled peptides for therapy of neuroendocrine tumours

Treatment with radiolabelled somatostatin analogues is a promising new tool in the management of patients with inoperable or metastasized neuroendocrine tumours. Symptomatic improvement may occur with 177Lu-labelled somatostatin analogues that have been used for peptide receptor radionuclide therapy (PRRT). The results obtained with 177Lu-[DOTA0,Tyr3]octreotate (DOTATATE) are very encouraging in terms of tumour regression. Dosimetry studies with 177Lu-DOTATATE as well as the limited side effects with additional cycles of 177Lu-DOTATATE suggest that more cycles of 177Lu-DOTATATE can be safely given. Also, if kidney-protective agents are used, the side effects of this therapy are few and mild and less than those from the use of 90Y-[DOTA0,Tyr3]octreotide (DOTATOC). Besides objective tumour responses, the median progression-free survival is more than 40 months. The patients' self-assessed quality of life increases significantly after treatment with 177Lu-DOTATATE. Lastly, compared to historical controls, there is a benefit in overall survival of several years from the time of diagnosis in patients treated with 177Lu-DOTATATE. These findings compare favourably with the limited number of alternative therapeutic approaches. If more widespread use of PRRT can be guaranteed, such therapy may well become the therapy of first choice in patients with metastasized or inoperable neuroendocrine tumours

Eye Size at Birth in Prosimian Primates: Life History Correlates and Growth Patterns

BACKGROUND: Primates have large eyes relative to head size, which profoundly influence the ontogenetic emergence of facial form. However, growth of the primate eye is only understood in a narrow taxonomic perspective, with information biased toward anthropoids.\ud \ud METHODOLOGY/PRINCIPAL FINDINGS: We measured eye and bony orbit size in perinatal prosimian primates (17 strepsirrhine taxa and Tarsius syrichta) to infer the extent of prenatal as compared to postnatal eye growth. In addition, multiple linear regression was used to detect relationships of relative eye and orbit diameter to life history variables. ANOVA was used to determine if eye size differed according to activity pattern. In most of the species, eye diameter at birth measures more than half of that for adults. Two exceptions include Nycticebus and Tarsius, in which more than half of eye diameter growth occurs postnatally. Ratios of neonate/adult eye and orbit diameters indicate prenatal growth of the eye is actually more rapid than that of the orbit. For example, mean neonatal transverse eye diameter is 57.5% of the adult value (excluding Nycticebus and Tarsius), compared to 50.8% for orbital diameter. If Nycticebus is excluded, relative gestation age has a significant positive correlation with relative eye diameter in strepsirrhines, explaining 59% of the variance in relative transverse eye diameter. No significant differences were found among species with different activity patterns.\ud \ud CONCLUSIONS/SIGNIFICANCE: The primate developmental strategy of relatively long gestations is probably tied to an extended period of neural development, and this principle appears to apply to eye growth as well. Our findings indicate that growth rates of the eye and bony orbit are disassociated, with eyes growing faster prenatally, and the growth rate of the bony orbit exceeding that of the eyes after birth. Some well-documented patterns of orbital morphology in adult primates, such as the enlarged orbits of nocturnal species, mainly emerge during postnatal development.\ud \u

A Unified Model of the GABA(A) Receptor Comprising Agonist and Benzodiazepine Binding Sites

We present a full-length α(1)β(2)γ(2) GABA receptor model optimized for agonists and benzodiazepine (BZD) allosteric modulators. We propose binding hypotheses for the agonists GABA, muscimol and THIP and for the allosteric modulator diazepam (DZP). The receptor model is primarily based on the glutamate-gated chloride channel (GluCl) from C. elegans and includes additional structural information from the prokaryotic ligand-gated ion channel ELIC in a few regions. Available mutational data of the binding sites are well explained by the model and the proposed ligand binding poses. We suggest a GABA binding mode similar to the binding mode of glutamate in the GluCl X-ray structure. Key interactions are predicted with residues α(1)R66, β(2)T202, α(1)T129, β(2)E155, β(2)Y205 and the backbone of β(2)S156. Muscimol is predicted to bind similarly, however, with minor differences rationalized with quantum mechanical energy calculations. Muscimol key interactions are predicted to be α(1)R66, β(2)T202, α(1)T129, β(2)E155, β(2)Y205 and β(2)F200. Furthermore, we argue that a water molecule could mediate further interactions between muscimol and the backbone of β(2)S156 and β(2)Y157. DZP is predicted to bind with interactions comparable to those of the agonists in the orthosteric site. The carbonyl group of DZP is predicted to interact with two threonines α(1)T206 and γ(2)T142, similar to the acidic moiety of GABA. The chlorine atom of DZP is placed near the important α(1)H101 and the N-methyl group near α(1)Y159, α(1)T206, and α(1)Y209. We present a binding mode of DZP in which the pending phenyl moiety of DZP is buried in the binding pocket and thus shielded from solvent exposure. Our full length GABA(A) receptor is made available as Model S1

Prolonged rote learning produces delayed memory facilitation and metabolic changes in the hippocampus of the ageing human brain

Background: Repeated rehearsal is one method by which verbal material may be transferred from short- to long-term memory. We hypothesised that extended engagement of memory structures through prolonged rehearsal would result in enhanced efficacy of recall and also of brain structures implicated in new learning. Twenty-four normal participants aged 55-70 (mean = 60.1) engaged in six weeks of rote learning, during which they learned 500 words per week every week (prose, poetry etc.). An extensive battery of memory tests was administered on three occasions, each six weeks apart. In addition, proton magnetic resonance spectroscopy (H-1-MRS) was used to measure metabolite levels in seven voxels of interest (VOIs) (including hippocampus) before and after learning.Results: Results indicate a facilitation of new learning that was evident six weeks after rote learning ceased. This facilitation occurred for verbal/episodic material only, and was mirrored by a metabolic change in left posterior hippocampus, specifically an increase in NAA/(Cr+Cho) ratio.Conclusion: Results suggest that repeated activation of memory structures facilitates anamnesis and may promote neuronal plasticity in the ageing brain, and that compliance is a key factor in such facilitation as the effect was confined to those who engaged fully with the training

Mapping and linking supply- and demand-side measures in climate-smart agriculture. A review

Climate change and food security are two of humanity’s greatest challenges and are highly interlinked. On the one hand, climate change puts pressure on food security. On the other hand, farming significantly contributes to anthropogenic greenhouse gas emissions. This calls for climate-smart agriculture—agriculture that helps to mitigate and adapt to climate change. Climate-smart agriculture measures are diverse and include emission reductions, sink enhancements, and fossil fuel offsets for mitigation. Adaptation measures include technological advancements, adaptive farming practices, and financial management. Here, we review the potentials and trade-offs of climate-smart agricultural measures by producers and consumers. Our two main findings are as follows: (1) The benefits of measures are often site-dependent and differ according to agricultural practices (e.g., fertilizer use), environmental conditions (e.g., carbon sequestration potential), or the production and consumption of specific products (e.g., rice and meat). (2) Climate-smart agricultural measures on the supply side are likely to be insufficient or ineffective if not accompanied by changes in consumer behavior, as climate-smart agriculture will affect the supply of agricultural commodities and require changes on the demand side in response. Such linkages between demand and supply require simultaneous policy and market incentives. It, therefore, requires interdisciplinary cooperation to meet the twin challenge of climate change and food security. The link to consumer behavior is often neglected in research but regarded as an essential component of climate-smart agriculture. We argue for not solely focusing research and implementation on one-sided measures but designing good, site-specific combinations of both demand- and supply-side measures to use the potential of agriculture more effectively to mitigate and adapt to climate change