Fragmentation of pooled PCR products for highly multiplexed TILLING

Improvements to massively parallel sequencing have allowed the routine recovery of natural and induced sequence variants. A broad range of biological disciplines have benefited from this, ranging from plant breeding to cancer research. The need for high sequence coverage to accurately recover single nucleotide variants and small insertions and deletions limits the applicability of whole genome approaches. This is especially true in organisms with a large genome size or for applications requiring the screening of thousands of individuals, such as the reverse-genetic technique known as TILLING. Using PCR to target and sequence chosen genomic regions provides an attractive alternative as the vast reduction in interrogated bases means that sample size can be dramatically increased through amplicon multiplexing and multidimensional sample pooling while maintaining suitable coverage for recovery of small mutations. Direct sequencing of PCR products is limited, however, due to limitations in read lengths of many next generation sequencers. In the present study we show the optimization and use of ultrasonication for the simultaneous fragmentation of multiplexed PCR amplicons for TILLING highly pooled samples. Sequencing performance was evaluated in a total of 32 pooled PCR products produced from 4096 chemically mutagenized Hordeum vulgare DNAs pooled in three dimensions. Evaluation of read coverage and base quality across amplicons suggests this approach is suitable for high-throughput TILLING and other applications employing highly pooled complex sampling schemes. Induced mutations previously identified in a traditional TILLING screen were recovered in this dataset further supporting the efficacy of the approach

Ethical implications of AI in robotic surgical training: A Delphi consensus statement

CONTEXT: As the role of AI in healthcare continues to expand there is increasing awareness of the potential pitfalls of AI and the need for guidance to avoid them. OBJECTIVES: To provide ethical guidance on developing narrow AI applications for surgical training curricula. We define standardised approaches to developing AI driven applications in surgical training that address current recognised ethical implications of utilising AI on surgical data. We aim to describe an ethical approach based on the current evidence, understanding of AI and available technologies, by seeking consensus from an expert committee. EVIDENCE ACQUISITION: The project was carried out in 3 phases: (1) A steering group was formed to review the literature and summarize current evidence. (2) A larger expert panel convened and discussed the ethical implications of AI application based on the current evidence. A survey was created, with input from panel members. (3) Thirdly, panel-based consensus findings were determined using an online Delphi process to formulate guidance. 30 experts in AI implementation and/or training including clinicians, academics and industry contributed. The Delphi process underwent 3 rounds. Additions to the second and third-round surveys were formulated based on the answers and comments from previous rounds. Consensus opinion was defined as ≥ 80% agreement. EVIDENCE SYNTHESIS: There was 100% response from all 3 rounds. The resulting formulated guidance showed good internal consistency, with a Cronbach alpha of >0.8. There was 100% consensus that there is currently a lack of guidance on the utilisation of AI in the setting of robotic surgical training. Consensus was reached in multiple areas, including: 1. Data protection and privacy; 2. Reproducibility and transparency; 3. Predictive analytics; 4. Inherent biases; 5. Areas of training most likely to benefit from AI. CONCLUSIONS: Using the Delphi methodology, we achieved international consensus among experts to develop and reach content validation for guidance on ethical implications of AI in surgical training. Providing an ethical foundation for launching narrow AI applications in surgical training. This guidance will require further validation. PATIENT SUMMARY: As the role of AI in healthcare continues to expand there is increasing awareness of the potential pitfalls of AI and the need for guidance to avoid them.In this paper we provide guidance on ethical implications of AI in surgical training

SAGES consensus recommendations on an annotation framework for surgical video

Background: The growing interest in analysis of surgical video through machine learning has led to increased research efforts; however, common methods of annotating video data are lacking. There is a need to establish recommendations on the annotation of surgical video data to enable assessment of algorithms and multi-institutional collaboration. Methods: Four working groups were formed from a pool of participants that included clinicians, engineers, and data scientists. The working groups were focused on four themes: (1) temporal models, (2) actions and tasks, (3) tissue characteristics and general anatomy, and (4) software and data structure. A modified Delphi process was utilized to create a consensus survey based on suggested recommendations from each of the working groups. Results: After three Delphi rounds, consensus was reached on recommendations for annotation within each of these domains. A hierarchy for annotation of temporal events in surgery was established. Conclusions: While additional work remains to achieve accepted standards for video annotation in surgery, the consensus recommendations on a general framework for annotation presented here lay the foundation for standardization. This type of framework is critical to enabling diverse datasets, performance benchmarks, and collaboration

A randomised trial of observational learning from 2D and 3D models in robotically assisted surgery

This is the final version of the article. Available from the publisher via the DOI in this record.BACKGROUND: Advances in 3D technology mean that both robotic surgical devices and surgical simulators can now incorporate stereoscopic viewing capabilities. While depth information may benefit robotic surgical performance, it is unclear whether 3D viewing also aids skill acquisition when learning from observing others. As observational learning plays a major role in surgical skills training, this study aimed to evaluate whether 3D viewing provides learning benefits in a robotically assisted surgical task. METHODS: 90 medical students were assigned to either (1) 2D or (2) 3D observation of a consultant surgeon performing a training task on the daVinci S robotic system, or (3) a no observation control, in a randomised parallel design. Subsequent performance and instrument movement metrics were assessed immediately following observation and at one-week retention. RESULTS: Both 2D and 3D groups outperformed no observation controls following the observation intervention (ps < 0.05), but there was no difference between 2D and 3D groups at any of the timepoints. There was also no difference in movement parameters between groups. CONCLUSIONS: While 3D viewing systems may have beneficial effects for surgical performance, these results suggest that depth information has limited utility during observational learning of surgical skills in novices. The task constraints and end goals may provide more important information for learning than the relative motion of surgical instruments in 3D space.This research was supported by an Intuitive Surgical grant awarded to Dr G Buckingha

Purification of recombinant adenovirus type 3 dodecahedric virus-like particles for biomedical applications using short monolithic columns.

Adenovirus type 3 dodecahedric virus-like particles (Ad3 VLP) are an interesting delivery vector. They penetrate animal cells in culture very efficiently and up to 300,000 Ad3 VLP can be observed in one cell. The purification of such particles usually consists of several steps. In these work we describe the method development and optimization for the purification of Ad3 VLP using the Convective Interaction Media analytical columns (CIMac). Results obtained with the CIMac were compared to the already established two-step purification protocol for Ad3 VLP based on sucrose density gradient ultracentifugation and the Q-Sepharose ion-exchange column. Pure, concentrated and bioactive VLP were obtained and characterized by several analytical methods. The recovery of the Ad3 VLP was more than 50% and the purified fraction was almost completely depleted of DNA; less than 1% of DNA was present. The purification protocol was shortened from five days to one day and remarkably high penetration efficacy of the CIMac-purified vector was retained. Additionally, CIMac QA analytical column has proven to be applicable for the final and in-process control of various Ad3 VLP sample

Spectrum and Density of Gamma and X-ray Induced Mutations in a Non-Model Rice Cultivar

Physical mutagens are a powerful tool used for genetic research and breeding for over eight decades. Yet, when compared to chemical mutagens, data sets on the effect of different mutagens and dosages on the spectrum and density of induced mutations remain lacking. To address this, we investigated the landscape of mutations induced by gamma and X-ray radiation in the most widely cultivated crop species: rice. A mutant population of a tropical upland rice, Oryza sativa L., was generated and propagated via self-fertilization for seven generations. Five dosages ranging from 75 Gy to 600 Gy in both X-ray and gamma-irradiated material were applied. In the process of a forward genetic screens, 11 unique rice mutant lines showing phenotypic variation were selected for mutation analysis via whole-genome sequencing. Thousands of candidate mutations were recovered in each mutant with single base substitutions being the most common, followed by small indels and structural variants. Higher dosages resulted in a higher accumulation of mutations in gamma-irradiated material, but not in X-ray-treated plants. The in vivo role of all annotated rice genes is yet to be directly investigated. The ability to induce a high density of single nucleotide and structural variants through mutagenesis will likely remain an important approach for functional genomics and breeding

Metabolic and non-metabolic peripheral neuropathy: Is there a place for therapeutic apheresis?

As the rate of obesity and the incidence of diabetes mellitus have been increasing, diabetic neuropathy has become the most common cause of peripheral neuropathy in developed countries. In addition, a variety of pathogenetically heterogeneous disorders can lead to impairment of the peripheral nervous system including amyloidosis, vitamin deficiencies, uremia and lipid disorders, alcohol abuse, autoimmune and infectious diseases as well as exposure to environmental toxins. We have noted that a combination of these disorders may aggravate the manifestations of peripheral diabetic neuropathy, an effect, which is most pronounced when metabolic and non-metabolic pathologies lead to cumulative damage. Current treatment options are limited and generally have unsatisfactory results in most patients. Therapeutic apheresis (INUSpherese®) allows the removal of metabolic, inflammatory, immunologic and environmental contributors to the disease process and may be an effective treatment option. We reviewed the developments in therapeutic apheresis for metabolic and non-metabolic peripheral neuropathy, including the current literature as well as data from our university diabetes center. © 2019 American Institute of Physics Inc.. All rights reserved

A Multistage Registration Method Using Texture Features

We present a novel, multistage registration method based on Laws’ texture features. In general, a large number of texture features may be extracted from the original intensity images. For each of the texture features, a criterion function that measures the similarity between the images may be derived. The proposed registration method consists of two major steps. In the first step, a dataset of images with the corresponding gold standard is used. In this step, the selection and ranking of the texture features for registration is made. The selection and ranking of the features is based on their robustness, accuracy, and capture range. The selected features are then entered in the second step, where the actual registration is performed using a sequence of registration stages. Our method is based on the selection of the most robust feature for the first registration stage and the selection of accurate feature(s) for the subsequent stages. The texture features are daisy-chained so that the accuracy of the previous feature is sufficient for the capture range of the next feature. We tested our method on 11 2D image pairs containing digital reconstructed radiographs and electron portal imaging modalities, which were difficult to register using intensity features alone. With our method, we have successfully registered 75% of the initial displacements, ranging from 5 to 7.5 mm, with the target-registration error below 3 mm, whereas the traditional intensity-based approach delivered only 15% successfully registered cases