Minimal Program Research Articles

Can natural language processing be effectively applied for audit data analysis in gynaecological oncology at a UK cancer centre?

BackgroundThe British Gynaecological Cancer Society (BGCS) has highlighted the disparity of ovarian cancer outcomes in the UK compared to other European countries. Therefore, cancer quality assurance audits and subspecialty training are important in improving the UK standard of care for these patients. The current workforce crisis afflicting the NHS creates difficulty in dedicating teams of clinicians to these audits. We present a single institution study to evaluate if NLP-generated code can improve the efficiency of ovarian cancer and subspeciality reaccreditations audits. We used the chat bot Google Bard to write Visual Basic Applications algorithms that utilise Excel files from electronic health records. MethodsPrimary ovarian cancer data from 2019 to 2022 was retrospectively collected from the Cambridge University Hospital electronic health records. The surgical subspecialty reaccreditation audit analysed the 2022 surgical database. A modular coding approach with Google Bard was applied to generate audit algorithms. The time to complete these current audits was compared against the 2016 ovarian cancer and 2020 subspeciality reaccreditation audits. ResultsThe previous ovarian cancer audit conducted in 2016 required 3 clinicians for the 135 cases and data collection required 1800 min. Data analysis was completed in 300 min. The current ovarian cancer audit allocated 2 clinicians to the 600 surgical cases. Data collection was completed in 3120 min, 3360 min for code development and 720 min for testing. The 2020 subspecialty reaccreditation audit was completed in 360 min. The 2022 subspecialty reaccreditation audit was completed in 1680 min, with 960 min for code development, 240 for debugging and 480 min for testing. ConclusionWe have demonstrated that NLP-generated code can significantly increase the efficiency of surgical quality assurance audits by eliminating the need for manual data analysis. With the current trajectory of NLP development, increasingly complex algorithms can be developed with minimal programming knowledge.

Data extraction and comparison for complex systematic reviews: a step-by-step guideline and an implementation example using open-source software

BackgroundData extraction (DE) is a challenging step in systematic reviews (SRs). Complex SRs can involve multiple interventions and/or outcomes and encompass multiple research questions. Attempts have been made to clarify DE aspects focusing on the subsequent meta-analysis; there are, however, no guidelines for DE in complex SRs. Comparing datasets extracted independently by pairs of reviewers to detect discrepancies is also cumbersome, especially when the number of extracted variables and/or studies is colossal. This work aims to provide a set of practical steps to help SR teams design and build DE tools and compare extracted data for complex SRs.MethodsWe provided a 10-step guideline, from determining data items and structure to data comparison, to help identify discrepancies and solve data disagreements between reviewers. The steps were organised into three phases: planning and building the database and data manipulation. Each step was described and illustrated with examples, and relevant references were provided for further guidance. A demonstration example was presented to illustrate the application of Epi Info and R in the database building and data manipulation phases. The proposed guideline was also summarised and compared with previous DE guidelines.ResultsThe steps of this guideline are described generally without focusing on a particular software application or meta-analysis technique. We emphasised determining the organisational data structure and highlighted its role in the subsequent steps of database building. In addition to the minimal programming skills needed, creating relational databases and data validation features of Epi info can be utilised to build DE tools for complex SRs. However, two R libraries are needed to facilitate data comparison and solve discrepancies.ConclusionsWe hope adopting this guideline can help review teams construct DE tools that suit their complex review projects. Although Epi Info depends on proprietary software for data storage, it can still be a potential alternative to other commercial DE software for completing complex reviews.

Using the RE‐AIM framework to help implement an educational intervention for improving the care of persons with behavioral and psychological symptoms of dementia in primary care

AbstractApproximately 90% of persons living with dementia (PLWD) experience behavioral and psychological symptoms of dementia (BPSD) over the course of their illness, including aggression, psychosis, and depression. With a growing lack of access to specialty geriatric care, most people with BPSD will receive care from their primary care providers. Many primary care practices lack knowledge, self‐efficacy, and established pathways to expert advice for managing these complex behaviors. We have developed a comprehensive educational intervention for primary care providers and clinical staff to help them better address BPSD. The educational intervention combines two complementary approaches: the academic detailing model and the DICE ApproachTM (DICE). In a recent pilot with two primary‐care integrated memory clinic teams, we noted improvements in provider knowledge and clinical staff self‐efficacy and comfort in BPSD and dementia care after 6‐months of participation in academic detailing and DICE training. In our current study, we use the RE‐AIM framework to guide the implementation planning for our educational intervention for primary care providers and clinical staff to help them better address BPSD. The RE‐AIM framework is a widely used tool for evaluating the implementation of health interventions to ensure they are both effective and sustainable. Twenty semi‐structured interviews will be completed among providers practicing in one of two primary care integrated memory clinics who participated in the pilot program (champions) and among their generalist primary care colleagues with no to minimal program experiences (novices), in one urban and one rural healthcare system. The RE‐AIM framework informed the design of our semi‐structured interviews and will help to frame content analysis for identifying potential barriers to implementation, for scale‐up to generalist primary care teams. Five interviews have been completed to date. Qualitative analyses are also underway, and results will be presented. Findings from this study will be used to develop an implementation plan for a future randomized controlled trial to measure the effectiveness of this intervention on health outcomes in PLWD and BPSD.

On the minimal model program for projective varieties with pseudo-effective tangent sheaf

In this paper, we develop a theory of pseudo-effective sheaves on normal projective varieties. As an application, by running the minimal model program, we show that projective klt varieties with pseudo-effective tangent sheaf can be decomposed into Fano varieties and Q-abelian varieties.

KGWASflow: a modular, flexible, and reproducible Snakemake workflow for k-mers-based GWAS.

Genome-wide association studies (GWAS) have been widely used to identify genetic variation associated with complex traits. Despite its success and popularity, the traditional GWAS approach comes with a variety of limitations. For this reason, newer methods for GWAS have been developed, including the use of pan-genomes instead of a reference genome and the utilization of markers beyond single-nucleotide polymorphisms, such as structural variations and k-mers. The k-mers-based GWAS approach has especially gained attention from researchers in recent years. However, these new methodologies can be complicated and challenging to implement. Here, we present kGWASflow, a modular, user-friendly, and scalable workflow to perform GWAS using k-mers. We adopted an existing kmersGWAS method into an easier and more accessible workflow using management tools like Snakemake and Conda and eliminated the challenges caused by missing dependencies and version conflicts. kGWASflow increases the reproducibility of the kmersGWAS method by automating each step with Snakemake and using containerization tools like Docker. The workflow encompasses supplemental components such as quality control, read-trimming procedures, and generating summary statistics. kGWASflow also offers post-GWAS analysis options to identify the genomic location and context of trait-associated k-mers. kGWASflow can be applied to any organism and requires minimal programming skills. kGWASflow is freely available on GitHub (https://github.com/akcorut/kGWASflow) and Bioconda (https://anaconda.org/bioconda/kgwasflow).

Investigation of the effects of a short opponens splint and structured hand exercise program in computer engineers with wrist pain: a randomized controlled trial

Objectives. This study aimed to examine the effects of a short opponens splint and hand exercise program on pain, hand functionality, daily activities, and work efficacy in computer engineers with wrist pain. Methods. Forty-five engineers were randomized into three groups: group 1 (n = 15) utilized both splints and exercises, group 2 (n = 15) engaged in exercises only and group 3 (n = 15) received no treatment, across 8 weeks, thrice weekly. Only exercises were applied to group 2 (n = 15). No treatment was applied for group 3 (n = 15). The progress of subjects was controlled periodically each week. Outcome measurements including the Jamar hand dynamometer, pinchmeter, nine-hole peg test (9-NHPT), visual analog scale, Boston carpal tunnel syndrome questionnaire and Michigan hand outcome questionnaire (MHQ) were evaluated pre and post treatment during the study period. Results. Comparing the outcome measurements of the three groups showed a statistically significant difference between them except for the 9-NHPT and MHQ pain and esthetics (p < 0.05). According to post-hoc tests, groups 1 and 2 had more significant differences than group 3. Also, there was no statistically significant difference in any parameter between group 1 and group 2 (p > 0.017). Conclusion. Non-restrictive minimal orthosis or exercise programs are advisable for individuals with wrist pain.

Joint and Separate Minimization of Multilevel Representations of Boolean Function Systems

The results of experimental studies of the effectiveness of programs for minimizing multilevel representations of systems of Boolean functions performed during the synthesis of combinational circuits in the design library of custom CMOS VLSI are presented. The efficiency of joint and separate minimization programs using Shannon expansions for constructing multilevel representations of systems of fully defined Boolean functions in the form of interconnected systems of logical equations — complete or abbreviated formulas of Shannon expansion or formulas corresponding to Boolean networks is investigated. A comparison is made with the results of experiments performed for various types of joint minimization only. A comparison is also made with the results of solutions obtained by the program of joint and separate minimization of Boolean function systems in the DNF class. It is shown that the use of joint minimization of multi-level representations makes it possible to obtain circuits of a smaller area more often, and the use of a separate one — circuits with a lower delay.

Swarm electrification: Harnessing surplus energy in off-grid solar home systems for universal electricity access

Achieving universal access to electricity by 2030, as set out by the Sustainable Development Goals, presents a significant challenge given the current rate of progress. A recent promising concept is swarm electrification. Its central idea is the peer-to-peer energy sharing of surplus energy in solar home systems (SHSs) to connect additional neighbors and grow a bottom-up grid. This paper studies the surplus energy in SHSs and its underlying influencing factors as a basis for swarm electrification. An open-source multi-model-based techno-economic analysis of off-grid SHS including surplus energy as a value is presented. Three distinct household types from the tier 3 category in the Multi-tier framework are compared based on their unique ratios of peak-to-average demand and percentage of load consumption during sun hours. A statistical analysis of surplus energy for each household type is presented and energy sharing with additional households at tier 1–2 is simulated. Two economic analysis methods, including surplus energy, are presented and compared: single-objective cost minimization and multi-objective compromise programming. The study finds that a low ratio of demand during sun hours leads to higher surplus energy volumes, while a peak-to-average ratio alone cannot give such indications. Both economic methods suggest that optimizing the SHS design for tier 3 households involves a slight increase in solar power capacity when considering the expected revenue from selling surplus energy to 2–3 households in tiers 1–2. The total cost for the tier 3 households are reduced by 40%−64%, additionally to decreasing their own lost load by 4%−7%, and reducing the up-front cost to get electricity access for the tier 1–2 households by 50% compared to purchasing their own full SHS.

Analyzing Thalamocortical Tract-Tracing Experiments in a Common Reference Space

Current mesoscale connectivity atlases provide limited information about the organization of thalamocortical projections in the mouse brain. Labeling the projections of spatially restricted neuron populations in thalamus can provide a functionally relevant level of connectomic analysis, but these need to be integrated within the same common reference space. Here, we present a pipeline for the segmentation, registration, integration and analysis of multiple tract-tracing experiments. The key difference with other workflows is that the data is transformed to fit the reference template. As a test-case, we investigated the axonal projections and intranuclear arrangement of seven neuronal populations of the ventral posteromedial nucleus of the thalamus (VPM), which we labeled with an anterograde tracer. Their soma positions corresponded, from dorsal to ventral, to cortical representations of the whiskers, nose and mouth. They strongly targeted layer 4, with the majority exclusively targeting one cortical area and the ones in ventrolateral VPM branching to multiple somatosensory areas. We found that our experiments were more topographically precise than similar experiments from the Allen Institute and projections to the primary somatosensory area were in agreement with single-neuron morphological reconstructions from publicly available databases. This pilot study sets the basis for a shared virtual connectivity atlas that could be enriched with additional data for studying the topographical organization of different thalamic nuclei. The pipeline is accessible with only minimal programming skills via a Jupyter Notebook, and offers multiple visualization tools such as cortical flatmaps, subcortical plots and 3D renderings and can be used with custom anatomical delineations.

Unimodal singularities and boundary divisors in the KSBA moduli of a class of Horikawa surfaces

AbstractSmooth minimal surfaces of general type with , , and constitute a fundamental example in the geography of algebraic surfaces, and the 28‐dimensional moduli space of their canonical models admits a modular compactification via the minimal model program. We describe eight new irreducible boundary divisors in such compactification parameterizing reducible stable surfaces. Additionally, we study the relation with the GIT compactification of and the Hodge theory of the degenerate surfaces that the eight divisors parameterize.

ChatGPT in pharmacometrics? Potential opportunities and limitations.

The potential of using ChatGPT in pharmacometrics was explored in this study, with a focus on developing a population pharmacokinetic (PK) model for standard half-life factor VIII. Our results demonstrated that ChatGPT can be utilized to accurately obtain typical PK parameters from literature, generate a population PK model in R and develop an interactive Shiny application to visualize the results. ChatGPT's language generation capabilities enabled the development of R codes with minimal programming knowledge and helped to identify as well fix errors in the code. While ChatGPT presents several advantages, such as its ability to streamline the development process, its use in pharmacometrics also has limitations and challenges, including the accuracy and reliability of AI-generated data, the lack of transparency and reproducibility regarding codes generated by ChatGPT. Overall, our study demonstrates the potential of using ChatGPT in pharmacometrics, but researchers must carefully evaluate its use for their specific needs.

Software defined networking agent demonstration to enable configuration and management of XGS-PON architectures

This paper describes the design and implementation of an OpenFlow software defined network (SDN) agent that manages and configures 10-gigabit-capable symmetric passive optical network (XGS-PON) architectures. Acting as an OpenFlow switch, the SDN agent communicates with an SDN controller using OpenFlow, while holding direct communication with the optical line terminal (OLT) through the chipset manufacturer-specific application programming interface, eliminating the need for emulating SDN layers in hardware devices. The proposal was evaluated through experiments conducted on a White Box XGS-PON OLT using the Open Network Operating System. The results demonstrate that the proposal facilitates a real-time SDN configuration of various Internet services, successfully fulfilling different quality of service requirements. Due to its ease of deployment, low complexity, smooth learning curve, scalability, and flexibility in integrating services, the proposal has significant potential. As a result, it offers a rapid SDN solution for configuring and testing new functionalities with minimal programming changes required in specific layers of the developed SDN agent.

Healthcare staff's experiences of using manual physical restraint: A meta-synthesis review.

Manual restraint is a hands-on type of physical restraint used to prevent harm to service users and staff, and to administer necessary treatments. This article reports on a review and meta-synthesis of the qualitative literature on healthcare staff's experiences of using manual restraint. Three electronic databases (CINAHL Complete, MEDLINE, and PsycINFO) were systematically searched, and 19 studies were included. Thematic synthesis was used to synthesize the findings. The Critical Appraisal Skills Programme (CASP) checklist was used to appraise study quality. The synthesis generated one overarching interpretive theme, "unpleasant but necessary," and five subthemes: "maintaining safety triumphs all," "emotional distress," "significance of coping," "feeling conflicted," and "depletion." Seven studies indicated that, from staff perspectives, manual restraint was not always used as a last resort. Healthcare staff experience manual restraint as a psychologically and physically unpleasant practice, yet paradoxically deem its use to be sometimes necessary to keep themselves and service users safe from harm. The findings indicate a need for healthcare staff support, post-restraint debriefing meetings with service users, and the implementation of manual restraint minimization programs in healthcare settings.

Beyond mathematics, statistics, and programming: data science, machine learning, and artificial intelligence competencies and curricula for clinicians, informaticians, science journalists, and researchers

ABSTRACT Data science, machine learning and artificial intelligence applications impact clinicians, informaticians, science journalists, and researchers. Most biomedical data science training focuses on learning a programming language in addition to higher mathematics and advanced statistics. This approach is appropriate for graduate students but greatly reduces the number of individuals in healthcare who can be involved in data science. To serve these four stakeholder audiences, we describe several curricular strategies focusing on solving real problems of interest to these audiences. Relevant competencies for these audiences include using intuitive programming tools that facilitate data exploration with minimal programming background, creating data models, evaluating results of data analyses, and assessing data science research reports, among others. Offering the curricula described here more broadly could broaden the stakeholder groups knowledgeable about and engaged in data science.

Optimizing Patient Record Linkage in a Master Patient Index Using Machine Learning: Algorithm Development and Validation

Background To provide quality care, modern health care systems must match and link data about the same patient from multiple sources, a function often served by master patient index (MPI) software. Record linkage in the MPI is typically performed manually by health care providers, guided by automated matching algorithms. These matching algorithms must be configured in advance, such as by setting the weights of patient attributes, usually by someone with knowledge of both the matching algorithm and the patient population being served. Objective We aimed to develop and evaluate a machine learning–based software tool, which automatically configures a patient matching algorithm by learning from pairs of patient records previously linked by humans already present in the database. Methods We built a free and open-source software tool to optimize record linkage algorithm parameters based on historical record linkages. The tool uses Bayesian optimization to identify the set of configuration parameters that lead to optimal matching performance in a given patient population, by learning from prior record linkages by humans. The tool is written assuming only the existence of a minimal HTTP application programming interface (API), and so is agnostic to the choice of MPI software, record linkage algorithm, and patient population. As a proof of concept, we integrated our tool with SantéMPI, an open-source MPI. We validated the tool using several synthetic patient populations in SantéMPI by comparing the performance of the optimized configuration in held-out data to SantéMPI’s default matching configuration using sensitivity and specificity. Results The machine learning–optimized configurations correctly detect over 90% of true record linkages as definite matches in all data sets, with 100% specificity and positive predictive value in all data sets, whereas the baseline detects none. In the largest data set examined, the baseline matching configuration detects possible record linkages with a sensitivity of 90.2% (95% CI 88.4%-92.0%) and specificity of 100%. By comparison, the machine learning–optimized matching configuration attains a sensitivity of 100%, with a decreased specificity of 95.9% (95% CI 95.9%-96.0%). We report significant gains in sensitivity in all data sets examined, at the cost of only marginally decreased specificity. The configuration optimization tool, data, and data set generator have been made freely available. Conclusions Our machine learning software tool can be used to significantly improve the performance of existing record linkage algorithms, without knowledge of the algorithm being used or specific details of the patient population being served.

Localization and Registration of 2D Histological Mouse Brain Images in 3D Atlas Space.

To accurately explore the anatomical organization of neural circuits in the brain, it is crucial to map the experimental brain data onto a standardized system of coordinates. Studying 2D histological mouse brain slices remains the standard procedure in many laboratories. Mapping these 2D brain slices is challenging; due to deformations, artifacts, and tilted angles introduced during the standard preparation and slicing process. In addition, analysis of experimental mouse brain slices can be highly dependent on the level of expertise of the human operator. Here we propose a computational tool for Accurate Mouse Brain Image Analysis (AMBIA), to map 2D mouse brain slices on the 3D brain model with minimal human intervention. AMBIA has a modular design that comprises a localization module and a registration module. The localization module is a deep learning-based pipeline that localizes a single 2D slice in the 3D Allen Brain Atlas and generates a corresponding atlas plane. The registration module is built upon the Ardent python package that performs deformable 2D registration between the brain slice to its corresponding atlas. By comparing AMBIA's performance in localization and registration to human ratings, we demonstrate that it performs at a human expert level. AMBIA provides an intuitive and highly efficient way for accurate registration of experimental 2D mouse brain images to 3D digital mouse brain atlas. Our tool provides a graphical user interface and it is designed to be used by researchers with minimal programming knowledge.

Analytical Approximation-Based Approach for Passenger Flow Control Strategy in Oversaturated Urban Rail Transit Systems

Focusing on a heavily congested urban rail corridor, this study investigates the passenger flow control strategy optimization problem from a mesoscopic perspective to reduce platform congestion and enhance service quality. Based on a quadratic functional approximation for passenger arrival rates, an analytical formula for calculating passenger waiting time is derived based on the classic deterministic queueing theory. We formulate the problem as a continuous nonlinear programming model to minimize the total passenger waiting time within transportation capacity constraints. A Lagrangian relaxation approach effectively transforms the original complex problem into an unconstrained minimization program. The analytical solution relating to optimal flow control strategy is derived by directly solving the unconstrained program. To further provide an integrated optimization framework from both the supply and demand sides, we extend the abovementioned passenger flow control optimization model into an integrated mixed-integer nonlinear programming model to jointly optimize the passenger-flow control strategy and train frequency setting. Numerical examples are presented to demonstrate the applicability and effectiveness of the proposed models. The computational results show that the produced high-quality passenger flow control strategy significantly reduces total passenger delay.

Toric foliated minimal model program

Using the theory of Klyachko filtrations for reflexive sheaves on toric varieties, we give a description of toric foliations and their singularities in terms of combinatorial data. We extend Spicer's results about co-rank one toric foliations to those of any ranks on Q-factorial toric varieties and prove that the toric foliated Minimal Model Program exists.

RWCVP: a companion R package for the World Checklist of Vascular Plants.

The World Checklist of Vascular Plants (WCVP) is an extremely valuable resource that is being used to address many fundamental and applied questions in plant science, conservation, ecology and evolution. However, databases of this size require data manipulation skills that pose a barrier to many potential users. Here, we present rWCVP, an open-source R package that aims to facilitate the use of the WCVP by providing clear, intuitive functions to execute many common tasks. These functions include taxonomic name reconciliation, geospatial integration, mapping and generation of multiple different summaries of the WCVP in both data and report format. We have included extensive documentation and tutorials, providing step-by-step guides that are accessible even to users with minimal programming experience. rWCVP is available on cran and GitHub.

Globally pmb{+}-regular varieties and the minimal model program for threefolds in mixed characteristic

We establish the Minimal Model Program for arithmetic threefolds whose residue characteristics are greater than five. In doing this, we generalize the theory of global F-regularity to mixed characteristic and identify certain stable sections of adjoint line bundles. Finally, by passing to graded rings, we generalize a special case of Fujita’s conjecture to mixed characteristic.