NUScon: a community-driven platform for quantitative evaluation of nonuniform sampling in NMR

Although the concepts of nonuniform sampling (NUS) and non-Fourier spectral reconstruction in multidimensional NMR began to emerge 4 decades ago (Bodenhausen and Ernst, 1981; Barna and Laue, 1987), it is only relatively recently that NUS has become more commonplace. Advantages of NUS include the ability to tailor experiments to reduce data collection time and to improve spectral quality, whether through detection of closely spaced peaks (i.e., “resolution”) or peaks of weak intensity (i.e., “sensitivity”). Wider adoption of these methods is the result of improvements in computational performance, a growing abundance and flexibility of software, support from NMR spectrometer vendors, and the increased data sampling demands imposed by higher magnetic fields. However, the identification of best practices still remains a significant and unmet challenge. Unlike the discrete Fourier transform, non-Fourier methods used to reconstruct spectra from NUS data are nonlinear, depend on the complexity and nature of the signals, and lack quantitative or formal theory describing their performance. Seemingly subtle algorithmic differences may lead to significant variabilities in spectral qualities and artifacts. A community-based critical assessment of NUS challenge problems has been initiated, called the “Nonuniform Sampling Contest” (NUScon), with the objective of determining best practices for processing and analyzing NUS experiments. We address this objective by constructing challenges from NMR experiments that we inject with synthetic signals, and we process these challenges using workflows submitted by the community. In the initial rounds of NUScon our aim is to establish objective criteria for evaluating the quality of spectral reconstructions. We present here a software package for performing the quantitative analyses, and we present the results from the first two rounds of NUScon. We discuss the challenges that remain and present a roadmap for continued community-driven development with the ultimate aim of providing best practices in this rapidly evolving field. The NUScon software package and all data from evaluating the challenge problems are hosted on the NMRbox platform

The C-terminal loop of the homing endonuclease I-CreI is essential for site recognition, DNA binding and cleavage

Meganucleases are sequence-specific endonucleases with large cleavage sites that can be used to induce efficient homologous gene targeting in cultured cells and plants. These enzymes open novel perspectives for genome engineering in a wide range of fields, including gene therapy. A new crystal structure of the I-CreI dimer without DNA has allowed the comparison with the DNA-bound protein. The C-terminal loop displays a different conformation, which suggests its implication in DNA binding. A site-directed mutagenesis study in this region demonstrates that whereas the C-terminal helix is negligible for DNA binding, the final C-terminal loop is essential in DNA binding and cleavage. We have identified two regions that comprise the Ser138–Lys139 and Lys142–Thr143 pairs whose double mutation affect DNA binding in vitro and abolish cleavage in vivo. However, the mutation of only one residue in these sites allows DNA binding in vitro and cleavage in vivo. These findings demonstrate that the C-terminal loop of I-CreI endonuclease plays a fundamental role in its catalytic mechanism and suggest this novel site as a region to take into account for engineering new endonucleases with tailored specificity

Fetal Neurosurgical Interventions for Spinal Malformations, Cerebral Malformations, and Hydrocephalus: Past, Present, and Future

In this article we review the last 40 years of progress in fetal neurosurgery with special attention to current controversies and upcoming challenges in the field. We surveyed the published literature describing prenatal interventions for spinal malformations, cerebral malformations, and hydrocephalus. Even the most mature treatment paradigm, intrauterine repair of myelomeningocele, stands to benefit from advances in imaging and therapeutic modalities to improve patient selection, refine surgical techniques, validate novel biologic therapies, and streamline postoperative patient care. Other conditions under evaluation include congenital cerebral malformations, such as encephalocele, cerebrovascular malformations, and hydrocephalus. We describe cross-cutting needs for advances in fetal neuroimaging, basic disease models and new therapeutic devices to support further progress across various neurosurgical conditions affecting patients during the fetal period

A methodology for the annotation of surgical videos for supervised machine learning applications

PURPOSE: Surgical data science is an emerging field focused on quantitative analysis of pre-, intra-, and postoperative patient data (Maier-Hein et al. in Med Image Anal 76: 102306, 2022). Data science approaches can decompose complex procedures, train surgical novices, assess outcomes of actions, and create predictive models of surgical outcomes (Marcus et al. in Pituitary 24: 839-853, 2021; Røadsch et al. in Nat Mach Intell, 2022). Surgical videos contain powerful signals of events that may impact patient outcomes. A necessary step before the deployment of supervised machine learning methods is the development of labels for objects and anatomy. We describe a complete method for annotating videos of transsphenoidal surgery. METHODS: Endoscopic video recordings of transsphenoidal pituitary tumor removal surgeries were collected from a multicenter research collaborative. These videos were anonymized and stored in a cloud-based platform. Videos were uploaded to an online annotation platform. Annotation framework was developed based on a literature review and surgical observations to ensure proper understanding of the tools, anatomy, and steps present. A user guide was developed to trained annotators to ensure standardization. RESULTS: A fully annotated video of a transsphenoidal pituitary tumor removal surgery was produced. This annotated video included over 129,826 frames. To prevent any missing annotations, all frames were later reviewed by highly experienced annotators and a surgeon reviewer. Iterations to annotated videos allowed for the creation of an annotated video complete with labeled surgical tools, anatomy, and phases. In addition, a user guide was developed for the training of novice annotators, which provides information about the annotation software to ensure the production of standardized annotations. CONCLUSIONS: A standardized and reproducible workflow for managing surgical video data is a necessary prerequisite to surgical data science applications. We developed a standard methodology for annotating surgical videos that may facilitate the quantitative analysis of videos using machine learning applications. Future work will demonstrate the clinical relevance and impact of this workflow by developing process modeling and outcome predictors

Democratizing Access to Neurosurgical Medical Education: National Efforts in a Medical Student Training Camp During Coronavirus Disease 2019.

BackgroundNational medical student surveys amidst the coronavirus disease 2019 (COVID-19)-driven subinternship cancellations have demonstrated the need for supplemental, standardized subspecialty medical education, mentorship, and career planning nationally. We have presented the first live, cross-institutional virtual medical student subspecialty training camp to deliver standardized neurosurgical educational content to medical students during the COVID-19 pandemic, and its results on medical student anxiety and perceptions of neurosurgery.MethodsThe online training camp used a video conferencing platform that was open to all medical students. A post-training camp survey was administered.ResultsA total of 305 medical students registered for the event from 107 unique U.S. medical schools. Of the 305 medical students, 108 reported intending to apply to neurosurgery residency in 2021. The top medical student objectives for the training camp were program networking and mentorship. Of the 305 participants, 121 (39.7%) completed the post-training survey. Of the respondents, 65.0% reported improved neurosurgical knowledge, 79.8% reported decreased anxiety about subinternships and interviews, 82.5% reported increased enthusiasm about neurosurgery, and 100% desired a future annual virtual training camp because of the increased accessibility and decreased cost. This was especially important for students at institutions without home subspecialty programs and those with financial burdens.ConclusionsCOVID-19-driven innovations in medical education have accelerated changes that may have long been necessary. This virtual structure improved resource usage and scalability compared with in-person training, maintained social distancing, and democratized access to standardized, specialized content not often available through traditional medical curricula. Even as a supplement to in-person events, the virtual training camp model could be implemented by national medical societies, which might significantly increase medical students' preparedness for, and education in, neurosurgery and other subspecialties

Maximal Uniform Convergence Rates in Parametric Estimation Problems

This paper considers parametric estimation problems with independent, identically, non-regularly distributed data. It focuses on rate-efficiency, in the sense of maximal possible convergence rates of stochastically bounded estimators, as an optimality criterion, largely unexplored in parametric estimation. Under mild conditions, the Hellinger metric, defined on the space of parametric probability measures, is shown to be an essentially universally applicable tool to determine maximal possible convergence rates. These rates are shown to be attainable in general classes of parametric estimation problems. JEL Classification: C13, C1