Minimal Program Research Articles

PRONTO-TK: a user-friendly PROtein Neural neTwOrk tool-kit for accessible protein function prediction.

Associating one or more Gene Ontology (GO) terms to a protein means making a statement about a particular functional characteristic of the protein. This association provides scientists with a snapshot of the biological context of the protein activity. This paper introduces PRONTO-TK, a Python-based software toolkit designed to democratize access to Neural-Network based complex protein function prediction workflows. PRONTO-TK is a user-friendly graphical interface (GUI) for empowering researchers, even those with minimal programming experience, to leverage state-of-the-art Deep Learning architectures for protein function annotation using GO terms. We demonstrate PRONTO-TK's effectiveness on a running example, by showing how its intuitive configuration allows it to easily generate complex analyses while avoiding the complexities of building such a pipeline from scratch.

Prenatal Exposure to Valproic Acid Across Various Indications for Use.

Teratogenic outcomes associated with valproic acid use represent a substantial concern for persons of childbearing age. Regulatory agencies worldwide have enhanced warnings or implemented risk minimization programs to reduce exposure during pregnancy. To determine pregnancy rates during valproic acid use and concomitant contraception use across indications. This retrospective cohort study used data from the Merative MarketScan commercial claims databases from January 1, 2005, to December 31, 2020, to identify female patients aged 12 to 44 years who initiated valproic acid treatment and had continuous insurance enrollment 6 months before initiation and 9 months after treatment end. A treatment episode included consecutive prescription fills that occurred within 7 days from the end of the days' supply of the previous dispensing. Data were analyzed from March 1 to September 10, 2023. Treatment episodes were categorized by inferred indication using diagnoses preceding treatment initiation, including epilepsy, migraine or headache, mood disorders, and unknown or off-label uses. Pregnancy incidence rate ratios (IRRs) were calculated and were adjusted for age and calendar year. Contraceptive use (prescription contraceptives, intrauterine devices, and implants) during treatment was examined. The cohort included 165 772 valproic acid treatment episodes among 69 390 women (mean [SD] age, 29.8 [10.0] years). Mood disorders (42.5%) were the most common indication, followed by migraine or headache (20.1%), with epilepsy playing a minor role (14.9%). Pregnancy incidence rates during valproic acid use remained unchanged, with a rate of 1.74 (95% CI, 1.14-2.53) per 100 person-years in 2005 and a rate of 1.90 (95% CI, 1.16-3.12) per 100 person-years in 2019. Compared with epilepsy, pregnancy rates were more than double for mood disorder (IRR, 2.16 [95% CI, 1.93-2.42]) and migraine or headache (IRR, 2.01 [95% CI, 1.92-2.09]). Few treatment episodes coincided with contraceptive use (37 012 [22.3%]), and oral dosage forms were the most common (27 069 [73.1%]). In this cohort study of patients of childbearing age who used valproic acid, pregnancy rates during valproic acid use did not decrease despite enhanced US Food and Drug Administration safety communications, and contraception use remained low. Patients with migraine and mood disorders accounted for the largest proportion of valproic acid use and had the highest pregnancy rates, while patients with epilepsy had the lowest. These findings suggest a need to enhance efforts to mitigate prenatal exposure to valproic acid, especially for indications where the risk of use during pregnancy outweighs the benefit.

Ascle-A Python Natural Language Processing Toolkit for Medical Text Generation: Development and Evaluation Study.

Medical texts present significant domain-specific challenges, and manually curating these texts is a time-consuming and labor-intensive process. To address this, natural language processing (NLP) algorithms have been developed to automate text processing. In the biomedical field, various toolkits for text processing exist, which have greatly improved the efficiency of handling unstructured text. However, these existing toolkits tend to emphasize different perspectives, and none of them offer generation capabilities, leaving a significant gap in the current offerings. This study aims to describe the development and preliminary evaluation of Ascle. Ascle is tailored for biomedical researchers and clinical staff with an easy-to-use, all-in-one solution that requires minimal programming expertise. For the first time, Ascle provides 4 advanced and challenging generative functions: question-answering, text summarization, text simplification, and machine translation. In addition, Ascle integrates 12 essential NLP functions, along with query and search capabilities for clinical databases. We fine-tuned 32 domain-specific language models and evaluated them thoroughly on 27 established benchmarks. In addition, for the question-answering task, we developed a retrieval-augmented generation (RAG) framework for large language models that incorporated a medical knowledge graph with ranking techniques to enhance the reliability of generated answers. Additionally, we conducted a physician validation to assess the quality of generated content beyond automated metrics. The fine-tuned models and RAG framework consistently enhanced text generation tasks. For example, the fine-tuned models improved the machine translation task by 20.27 in terms of BLEU score. In the question-answering task, the RAG framework raised the ROUGE-L score by 18% over the vanilla models. Physician validation of generated answers showed high scores for readability (4.95/5) and relevancy (4.43/5), with a lower score for accuracy (3.90/5) and completeness (3.31/5). This study introduces the development and evaluation of Ascle, a user-friendly NLP toolkit designed for medical text generation. All code is publicly available through the Ascle GitHub repository. All fine-tuned language models can be accessed through Hugging Face.

Dear ChatGPT – can you teach me how to program an app for laboratory medicine?

Abstract Objectives The multifaceted potential of ChatGPT in the medical domain remains underexplored, particularly regarding its application in software development by individuals with a medical background but limited information technology expertise. Methods This study investigates ChatGPT’s utility in creating a laboratory medicine application. Results Despite minimal programming skills, the authors successfully developed an automated intra-assay, inter-device precision test for immunophenotyping with a shiny user interface, facilitated by ChatGPT. While the coding process was expedited, meticulous oversight and error correction by the authors were imperative. Conclusions These findings highlight the value of large language models such as ChatGPT in code-based application development for automating work processes in a medical context. Particularly noteworthy is the facilitation of these tasks for non-technically trained medical professionals and its potential for digital medical education.

Identification of potential landfill site suitability for urban solid waste disposal of Balurghat Municipality of Dakshin Dinajpur District using GIS and multi-criteria decision-making approach

The production of urban solid waste is one of the most dangerous environmental health risks on the planet. By selecting suitable landfill sites, it is possible to resolve the issue of health risks and environmental pollution driven by the unscientific disposal of solid waste. The current research is centered on determining suitable landfill locations for solid waste disposal within the Balurghat municipality through the integration of Geographic Information System (GIS) and Multi-Criteria Decision-Making (MCDM) approach. Ten factors were taken into consideration according to their weightage including elevation (0.022), Lithology (0.027), Slope (0.032), Land use and land cover LULC (0.040), Depth of water (0.057), Population density (0.075), Distance from sensitive places (0.107), Distance from road (0.154), Distance from river (0.205) and Land value (0.282). After conducting thorough field visits, an analytical hierarchy process (AHP) under the MCDM approach using GIS reveals acceptable sites that were noted and referenced. Final results are validated through a receiver operating characteristic (ROC) curve using randomly selected 80 potential landfill-suitable site location points with higher accuracy, and a total of 93% of the area under the curve (AUC) has been found worthy to validate the spatial result. The present investigation revealed that only 0.65% of land is very highly suitable, 31.06% of land is highly suitable, 52.54% of land is moderately suitable and 14.74% of land is not suitable for landfilling in Balurghat Municipal area and surroundings respectively. The highly suitable site belongs to the Mamna, Katna, Kuaran, Amtali, Noksha, Bannapara, and Malancha villages of the study area which is a very negligible area. So, the planners should adopt alternative strategies like source minimization, composting, sanitary landfills and awareness programmes for sustainable solid waste management. Hence, the Balurghat Municipality administration, urban planners, and administrative authorities stand to gain valuable insights from the findings of this study as they endeavour to enhance the management of urban solid waste in the area.

Successive upper approximation methods for generalized fractional programs

The majorization approximation procedure consists in replacing the resolution of a nonlinear optimization problem by solving a sequence of simpler ones, whose objective and constraint functions upper estimate those of the original problem. For generalized fractional programming, i.e., constrained minimization programs whose objective functions are maximums of finite ratios of functions, we propose an adapted scheme that simultaneously upper approximates parametric functions formed by the objective and constraint functions. For directionally convex functions, that is, functions whose directional derivatives are convex with respect to directions, we will establish that every cluster point of the generated sequence satisfies Karush-Kuhn-Tucker type conditions expressed in terms of directional derivatives. The proposed procedure unifies several existing methods and gives rise to new ones. Numerical problems are solved to test the efficiency of our methods, and comparisons with different approaches are given.

Programming the Minimal Model Program: a proposal

AbstractThe aim of this paper is to propose a strategy to implement the Minimal Model Program in modern computer algebra systems.

The structure of DP and IZP in central Kurdish (Minimal approach)

The stages of Generative grammar complement each other and several different theories and hypotheses have been presented. This research aims to present the structure of Determiner phrase according to the stages of Generative grammar until the last stage of the grammar, which is the Minimal program we wanted to apply the assumptions in the MP in Kurdish also we tried to explain the structure of Dp and Izp in Kurdish move and checking its feature because in MP every Grammatical feature in the Derivation become a head of functional phrase so DP and IZP has a different feature so they are two Different phrase. The research consists of an introduction and five parts with results and sources.

Polaryzacja: polemika z artykułem „Fanatyzm jako problem wychowawczy”

The criticism of the article “Fanaticism as an Educational Problem” focuses on the selective treatment of fanaticism symptoms, ignoring the role of political polarization and age in analysing this phenomenon. Gałkowski and Gałkowski’s article (2023) presents a valuable proposal for a minimal educational program, but it requires considering a broader social, cultural, and individual context in diagnosing fanaticism and applying effective tools to counteract this phenomenon. The starting point of the debate is the observation that fanaticism remains a negatively perceived label that excludes individuals or social groups, hence the need for clarification on how cultural diversity and intercultural communication can contribute to it. Fanaticism is associated with an existential crisis, but there are also specific developmental phenomena in children that can be mistaken for fanaticism. Literary works can help in understanding this phenomenon. The proposed examples indicate that fanaticism is primarily a group phenomenon characterized by action rather than solely an attitude to the truth. In preventing and dismantling fanaticism, it is important to consider emotions related to uncertainty and develop skills to cope with it. Finding effective alternatives to psychoactive substances in emotional regulation and violence prevention poses a challenge. Fanaticism can be present in the inertia of customs and can be recognized by sensitive young people in society’s reactions to demands for social change. The situation of individuals expelled at the Polish-Belarusian border highlights the difference between uncompromising commitment and fanaticism. Human rights should be respected without exceptions, and the willingness to accept external judgment distinguishes activists from fanatics. Building relationships free from fanaticism in communities, especially in the case of refugees, requires considering the atmosphere and pedagogy of the place, which play a crucial role in combating fanaticism.

Learning MDL Logic Programs from Noisy Data

Many inductive logic programming approaches struggle to learn programs from noisy data. To overcome this limitation, we introduce an approach that learns minimal description length programs from noisy data, including recursive programs. Our experiments on several domains, including drug design, game playing, and program synthesis, show that our approach can outperform existing approaches in terms of predictive accuracies and scale to moderate amounts of noise.

The W(E6)$W(E_6)$‐invariant birational geometry of the moduli space of marked cubic surfaces

AbstractThe moduli space of marked cubic surfaces is one of the most classical moduli spaces in algebraic geometry, dating back to the nineteenth‐century work of Cayley and Salmon. Modern interest in was restored in the 1980s by Naruki's explicit construction of a ‐equivariant smooth projective compactification of , and in the 2000s by Hacking, Keel, and Tevelev's construction of the Kollár–Shepherd‐Barron–Alexeev (KSBA) stable pair compactification of as a natural sequence of blowups of . We describe generators for the cones of ‐invariant effective divisors and curves of both and . For Naruki's compactification , we further obtain a complete stable base locus decomposition of the ‐invariant effective cone, and as a consequence find several new ‐equivariant birational models of . Furthermore, we fully describe the log minimal model program for the KSBA compactification , with respect to the divisor , where is the boundary and is the sum of the divisors parameterizing marked cubic surfaces with Eckardt points.

On the 4-dimensional minimal model program for Kähler varieties

In this article we establish the following results: Let (X,B) be a dlt pair, where X is a Q-factorial Kähler 4-fold – (i) if X is compact and KX+B∼QD≥0 for some effective Q-divisor, then (X,B) has a log minimal model, (ii) if (X/T,B) is a semi-stable klt pair, W⊂T a compact subset and KX+B is effective over W (resp. not effective over W), then we can run a (KX+B)-MMP over T (in a neighborhood of W) which ends with a minimal model over T (resp. a Mori fiber space over T). We also give a proof of the existence of flips for analytic varieties in all dimensions and the relative MMP for projective morphisms between analytic varieties.

The Reporting Recommendations Intended for Pharmaceutical Risk Minimization Evaluation Studies: Standards for Reporting of Implementation Studies Extension (RIMES-SE).

The Reporting recommendations Intended for pharmaceutical risk Minimization Evaluation Studies (RIMES) was developed to improve the quality of reporting of risk minimization program evaluations. In light of continued inadequacies in study reporting, and high-profile program implementation failures, we updated the RIMES Checklist to incorporate additional concepts from the Standards for Reporting of Implementation studies (StaRI). The development of the updated checklist, the RIMES-StaRI Extension (RIMES-SE), entailed developing a study protocol and drafting an initial pool of items based on a mapping of the RIMES against the StaRI checklist. A modified e-Delphi exercise was then conducted to determine the importance and understandability of items for checklist inclusion. An expert workshop and an online commentary period for additional feedback followed. The RIMES-SE contains 27 items. It includes two signature features of the StaRI Checklist: 1) a dual strand of items (represented in two columns) describing the risk minimization program (the 'intervention') and the corresponding implementation strategy; and 2) applicable to an array of different research methodologies. The RIMES-SE Statement and Checklist extends the reporting guidelines set forth in the original RIMES Checklist via inclusion of key implementation science concepts. It is intended to improve the quality and transparency of reporting of risk minimization evaluation studies so as to advance drug safety science.

A toolbox for decoding BCI commands based on event-related potentials.

Commands in brain-computer interface (BCI) applications often rely on the decoding of event-related potentials (ERP). For instance, the P300 potential is frequently used as a marker of attention to an oddball event. Error-related potentials and the N2pc signal are further examples of ERPs used for BCI control. One challenge in decoding brain activity from the electroencephalogram (EEG) is the selection of the most suitable channels and appropriate features for a particular classification approach. Here we introduce a toolbox that enables ERP-based decoding using the full set of channels, while automatically extracting informative components from relevant channels. The strength of our approach is that it handles sequences of stimuli that encode multiple items using binary classification, such as target vs. nontarget events typically used in ERP-based spellers. We demonstrate examples of application scenarios and evaluate the performance of four openly available datasets: a P300-based matrix speller, a P300-based rapid serial visual presentation (RSVP) speller, a binary BCI based on the N2pc, and a dataset capturing error potentials. We show that our approach achieves performances comparable to those in the original papers, with the advantage that only conventional preprocessing is required by the user, while channel weighting and decoding algorithms are internally performed. Thus, we provide a tool to reliably decode ERPs for BCI use with minimal programming requirements.

Effectiveness and cost-effectiveness of a web-based cardiac rehabilitation programme for people with chronic stable angina: protocol for the ACTIVATE (Angina Controlled Trial Investigating the Value of the ‘Activate your heart’ Therapeutic E-intervention) randomised controlled trial

IntroductionChronic stable angina is common and disabling. Cardiac rehabilitation is routinely offered to people following myocardial infarction or revascularisation procedures and has the potential to help people with chronic stable...

Innovation in Telesupervision: Application, Evaluation, and Policy Implications

The use of technology in health service delivery (e.g., telehealth) and subsequently in training and education in preparation for health service careers (e.g., telesupervision) provides both challenges and opportunities. Embracing the opportunities afforded by telehealth and telesupervision will help equip the next generation of health professionals for the realities of the healthcare system of the future as well as promote greater equity for trainees and patients. To ensure maximum gain and minimal negative impact, training programs, researchers, and policymakers must work together. The field can move forward through application of innovative use cases, thoughtful design and implementation of evaluation, and the development of constructive policies. This column aims to inform these different stakeholders by providing applied examples of telesupervision in a telehealth training clinic, discussing the evaluation of telesupervision programs and competencies, and discussing policy implications for telesupervision.

Study on the influence of in-mold sequential injection molding process parameters on mechanical properties of self-reinforced single composites

Abstract In this paper, the self-reinforced single polymer composites (SR-SPCs) with different mechanical properties were obtained by compound injection molding technology, and the micro-morphology of these samples was observed. Then, using structured statistical methods, analysis of variance, and response surface methodology, study the effects of various molding variables on material morphology and properties and determine the most important molding variables and their interactions. Finally, the associated experimental data are fitted by the least squares minimization program, and the relevant dimensionless equations are obtained. The purpose is to objectively analyze the influence mechanism of molding parameters on SR-SPCs and establish a mechanism model. It was found that temperature change was the most important factor affecting the morphology and mechanical properties. The degree of molecular orientation is the most important factor to determine the tensile strength and elastic modulus of the sample. The change of crystallinity is the most important factor related to the elongation at break. By establishment relevant dimensionless equations, the influence of molding parameters on the mechanical properties of SR-SPCs, such as tensile strength and elastic modulus, was preliminarily studied.

PyF2F: a robust and simplified fluorophore-to-fluorophore distance measurement tool for Protein interactions from Imaging Complexes after Translocation experiments.

Structural knowledge of protein assemblies in their physiological environment is paramount to understand cellular functions at the molecular level. Protein interactions from Imaging Complexes after Translocation (PICT) is a live-cell imaging technique for the structural characterization of macromolecular assemblies in living cells. PICT relies on the measurement of the separation between labelled molecules using fluorescence microscopy and cell engineering. Unfortunately, the required computational tools to extract molecular distances involve a variety of sophisticated software programs that challenge reproducibility and limit their implementation to highly specialized researchers. Here we introduce PyF2F, a Python-based software that provides a workflow for measuring molecular distances from PICT data, with minimal user programming expertise. We used a published dataset to validate PyF2F's performance.

Abundance for uniruled pairs which are not rationally connected

One of the central aims of the Minimal Model Program is to show that a projective log canonical pair (X,\Delta) with K_X+\nobreak\Delta pseudoeffective has a good model, i.e. a minimal model (Y,\Delta_Y) such that K_Y+\Delta_Y is semiample. The goal of this paper is to show that this holds if X is uniruled but not rationally connected, assuming the Minimal Model Program in dimension \dim X-\nobreak1 . Moreover, if X is rationally connected, then we show that the existence of a good minimal model for (X,\Delta) follows from a nonexistence conjecture for a very specific class of rationally connected pairs of Calabi–Yau type.

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.