Deep learning guided image-based droplet sorting for on-demand selection and analysis of single cells and 3D cell cultures.

Uncovering the heterogeneity of cellular populations and multicellular constructs is a long-standing goal in fields ranging from antimicrobial resistance to cancer research. Emerging technology platforms such as droplet microfluidics hold the promise to decipher such heterogeneities at ultra-high-throughput. However, there is a lack of methods able to rapidly identify and isolate single cells or 3D cell cultures. Here we demonstrate that deep neural networks can accurately classify single droplet images in real-time based on the presence and number of micro-objects including single mammalian cells and multicellular spheroids. This approach also enables the identification of specific objects within mixtures of objects of different types and sizes. The training sets for the neural networks consisted of a few hundred images manually picked and augmented to up to thousands of images per training class. Training required less than 10 minutes using a single GPU, and yielded accuracies of over 90% for single mammalian cell identification. Crucially, the same model could be used to classify different types of objects such as polystyrene spheres, polyacrylamide beads and MCF-7 cells. We applied the developed method for the selection of 3D cell cultures generated with Hek293FT cells encapsulated in agarose gel beads, highlighting the potential of the technology for the selection of objects with a high diversity of visual appearances. The real-time sorting of single droplets was in-line with droplet generation and occurred at rates up to 40 per second independently of image size up to 480 × 480 pixels. The presented microfluidic device also enabled storage of sorted droplets to allow for downstream analyses

Evaluation of the vector competence of a native UK mosquito Ochlerotatus detritus (Aedes detritus) for dengue, chikungunya and West Nile viruses

BACKGROUND: To date there has been no evidence of mosquito-borne virus transmission of public health concern in the UK, despite the occurrence of more than 30 species of mosquito, including putative vectors of arboviruses. The saltmarsh mosquito Ochlerotatus detritus [syn. Aedes (Ochlerotatus) detritus] is locally common in parts of the UK where it can be a voracious feeder on people. METHODS: Here, we assess the competence of O. detritus for three major arboviruses: dengue virus (DENV), chikungunya virus (CHIKV) and West Nile virus (WNV) using adult mosquitoes reared from wild, field-obtained immatures. RESULTS: We demonstrate laboratory competence for WNV at 21 °C, with viral RNA detected in the mosquito’s saliva 17 days after oral inoculation. By contrast, there was no evidence of laboratory competence of O. detritus for either DENV or CHIKV. CONCLUSIONS: To our knowledge, this is the first study to demonstrate competence of a UK mosquito for WNV and confirms that O. detritus may present a potential risk for arbovirus transmission in the UK and that further investigation of its vector role in the wild is required

Performance of Risk-Based Criteria for Targeting Acute HIV Screening in San Francisco

Federal guidelines now recommend supplemental HIV RNA testing for persons at high risk for acute HIV infection. However, many rapid HIV testing sites do not include HIV RNA or p24 antigen testing due to concerns about cost, the need for results follow-up, and the impact of expanded venipuncture on clinic flow. We developed criteria to identify patients in a municipal STD clinic in San Francisco who are asymptomatic but may still be likely to have acute infection.Data were from patients tested with serial HIV antibody and HIV RNA tests to identify acute HIV infection. BED-CEIA results were used to classify non-acute cases as recent or longstanding. Demographics and self-reported risk behaviors were collected at time of testing. Multivariate models were developed and preliminarily evaluated using predictors associated with recent infection in bivariate analyses as a proxy for acute HIV infection. Multivariate models demonstrating ≥70% sensitivity for recent infection while testing ≤60% of patients in this development dataset were then validated by determining their performance in identifying acute infections.From 2004-2007, 137 of 12,622 testers had recent and 36 had acute infections. A model limiting acute HIV screening to MSM plus any one of a series of other predictors resulted in a sensitivity of 83.3% and only 47.6% of patients requiring testing. A single-factor model testing only patients reporting any receptive anal intercourse resulted in 88.9% sensitivity with only 55.2% of patients requiring testing.In similar high risk HIV testing sites, acute screening using "supplemental" HIV p24 antigen or RNA tests can be rationally targeted to testers who report particular HIV risk behaviors. By improving the efficiency of acute HIV testing, such criteria could facilitate expanded acute case identification

It’s Not Only Rents: Explaining the Persistence and Change of Neopatrimonialism in Indonesia

Indonesia has long been associated with neopatrimonialism, corruption, collusion, and nepotism as the main modi operandi of politics, economics and public administration. Despite various measures and initiatives to fight these practises, little evidence for a significant decline can be found over the years. Rather, longitudinal analysis points to changes in the character of neopatrimonialism. Based on more than 60 in-depth interviews, focus-group discussions, and the analysis of both primary and secondary data, the aim of this article is, first, to describe the changes that have taken place, and, second, to investigate what accounts for these changes. Political economy concepts posit the amount and development of economic rents as the explanatory factor for the persistence and change of neopatrimonialism. This study's findings, however, indicate that rents alone cannot explain what has taken place in Indonesia. Democratisation and decentralisation exert a stronger impact

Prognostic model to predict postoperative acute kidney injury in patients undergoing major gastrointestinal surgery based on a national prospective observational cohort study.

Background: Acute illness, existing co-morbidities and surgical stress response can all contribute to postoperative acute kidney injury (AKI) in patients undergoing major gastrointestinal surgery. The aim of this study was prospectively to develop a pragmatic prognostic model to stratify patients according to risk of developing AKI after major gastrointestinal surgery. Methods: This prospective multicentre cohort study included consecutive adults undergoing elective or emergency gastrointestinal resection, liver resection or stoma reversal in 2-week blocks over a continuous 3-month period. The primary outcome was the rate of AKI within 7 days of surgery. Bootstrap stability was used to select clinically plausible risk factors into the model. Internal model validation was carried out by bootstrap validation. Results: A total of 4544 patients were included across 173 centres in the UK and Ireland. The overall rate of AKI was 14·2 per cent (646 of 4544) and the 30-day mortality rate was 1·8 per cent (84 of 4544). Stage 1 AKI was significantly associated with 30-day mortality (unadjusted odds ratio 7·61, 95 per cent c.i. 4·49 to 12·90; P < 0·001), with increasing odds of death with each AKI stage. Six variables were selected for inclusion in the prognostic model: age, sex, ASA grade, preoperative estimated glomerular filtration rate, planned open surgery and preoperative use of either an angiotensin-converting enzyme inhibitor or an angiotensin receptor blocker. Internal validation demonstrated good model discrimination (c-statistic 0·65). Discussion: Following major gastrointestinal surgery, AKI occurred in one in seven patients. This preoperative prognostic model identified patients at high risk of postoperative AKI. Validation in an independent data set is required to ensure generalizability

The Man in the web, and other folk tales

75 p.; 18 cm

Deep learning guided image-based droplet sorting for on-demand selection and analysis of single cells and 3D cell cultures.

Uncovering the heterogeneity of cellular populations and multicellular constructs is a long-standing goal in fields ranging from antimicrobial resistance to cancer research. Emerging technology platforms such as droplet microfluidics hold the promise to decipher such heterogeneities at ultra-high-throughput. However, there is a lack of methods able to rapidly identify and isolate single cells or 3D cell cultures. Here we demonstrate that deep neural networks can accurately classify single droplet images in real-time based on the presence and number of micro-objects including single mammalian cells and multicellular spheroids. This approach also enables the identification of specific objects within mixtures of objects of different types and sizes. The training sets for the neural networks consisted of a few hundred images manually picked and augmented to up to thousands of images per training class. Training required less than 10 minutes using a single GPU, and yielded accuracies of over 90% for single mammalian cell identification. Crucially, the same model could be used to classify different types of objects such as polystyrene spheres, polyacrylamide beads and MCF-7 cells. We applied the developed method for the selection of 3D cell cultures generated with Hek293FT cells encapsulated in agarose gel beads, highlighting the potential of the technology for the selection of objects with a high diversity of visual appearances. The real-time sorting of single droplets was in-line with droplet generation and occurred at rates up to 40 per second independently of image size up to 480 × 480 pixels. The presented microfluidic device also enabled storage of sorted droplets to allow for downstream analyses

Evidence for memorized site-fidelity in Anopheles arabiensis

A mark-recapture experiment was carried out in northern Tanzania to determine whether Anopheles arabiensis exhibits memory, by investigating if bloodfed individuals would return to either the location or the host where or on which they had obtained a previous bloodmeal, behaviours termed site-fidelity and host-fidelity respectively. Over 4300 mosquitoes were collected from 2 houses, marked with different fluorescent colours according to whether they were caught in cattle sheds, 'cattle-fed', or within human bednets, 'human-fed', at either location, then released from a third location. Over 17 000 mosquitoes were collected and examined over the next 8 days. In total, 1% of released mosquitoes were recaptured. Of these, 68% had returned to the house where they were first caught, demonstrating site-fidelity (P=0.007). However, 86% of recaptured mosquitoes were caught on cattle regardless of where they were initially caught (P=0.185). Bloodmeal identification showed that a high proportion of mosquitoes classed as human-fed contained bovine blood, thereby confounding the investigation into host-fidelity. Notably, the proportion of mosquitoes with mixed bloodmeals depended on the proximity of cattle and humans, with significantly higher proportions of mixed bloodmeals occurring when cattle and humans slept in close proximity. The effects of the observed behaviours on malaria epidemiology are discussed