Deep learning-based fully automatic segmentation of wrist cartilage in MR images

The study objective was to investigate the performance of a dedicated convolutional neural network (CNN) optimized for wrist cartilage segmentation from 2D MR images. CNN utilized a planar architecture and patch-based (PB) training approach that ensured optimal performance in the presence of a limited amount of training data. The CNN was trained and validated in twenty multi-slice MRI datasets acquired with two different coils in eleven subjects (healthy volunteers and patients). The validation included a comparison with the alternative state-of-the-art CNN methods for the segmentation of joints from MR images and the ground-truth manual segmentation. When trained on the limited training data, the CNN outperformed significantly image-based and patch-based U-Net networks. Our PB-CNN also demonstrated a good agreement with manual segmentation (Sorensen-Dice similarity coefficient (DSC) = 0.81) in the representative (central coronal) slices with large amount of cartilage tissue. Reduced performance of the network for slices with a very limited amount of cartilage tissue suggests the need for fully 3D convolutional networks to provide uniform performance across the joint. The study also assessed inter- and intra-observer variability of the manual wrist cartilage segmentation (DSC=0.78-0.88 and 0.9, respectively). The proposed deep-learning-based segmentation of the wrist cartilage from MRI could facilitate research of novel imaging markers of wrist osteoarthritis to characterize its progression and response to therapy

Experimental Biological Protocols with Formal Semantics

Both experimental and computational biology is becoming increasingly automated. Laboratory experiments are now performed automatically on high-throughput machinery, while computational models are synthesized or inferred automatically from data. However, integration between automated tasks in the process of biological discovery is still lacking, largely due to incompatible or missing formal representations. While theories are expressed formally as computational models, existing languages for encoding and automating experimental protocols often lack formal semantics. This makes it challenging to extract novel understanding by identifying when theory and experimental evidence disagree due to errors in the models or the protocols used to validate them. To address this, we formalize the syntax of a core protocol language, which provides a unified description for the models of biochemical systems being experimented on, together with the discrete events representing the liquid-handling steps of biological protocols. We present both a deterministic and a stochastic semantics to this language, both defined in terms of hybrid processes. In particular, the stochastic semantics captures uncertainties in equipment tolerances, making it a suitable tool for both experimental and computational biologists. We illustrate how the proposed protocol language can be used for automated verification and synthesis of laboratory experiments on case studies from the fields of chemistry and molecular programming

On-the-fly Uniformization of Time-Inhomogeneous Infinite Markov Population Models

This paper presents an on-the-fly uniformization technique for the analysis of time-inhomogeneous Markov population models. This technique is applicable to models with infinite state spaces and unbounded rates, which are, for instance, encountered in the realm of biochemical reaction networks. To deal with the infinite state space, we dynamically maintain a finite subset of the states where most of the probability mass is located. This approach yields an underapproximation of the original, infinite system. We present experimental results to show the applicability of our technique

ФОРМИРОВАНИЕ ПРОДУКТИВНОСТИ ИССОПА ЛЕКАРСТВЕННОГО НА КАПЕЛЬНОМ ОРОШЕНИИ В ЮЖНОЙ СТЕПИ УКРАИНЫ

Researches conducted on lands Nikolayev state agricultural research station IIA NAAS in 2017- 2018. Soil of an experimental plot is chernozem southern weakly eroded clay loamy on loess’s, it is noted by high contents potassium, average – phosphorus and it is not enough provided by nitrogen. Climate of region – continental, is characterized sharp and repeated by fluctuations annual and month temperature of air, greater spare of heat and aridity. Agrotechnic in experiment was generally accepted for southern Steppe of Ukraine. Scheme of experience included three factors – a sowing periods: II ten-day period of October (winter), II ten-day period of November (underwinter), I ten-day period of April (spring); variants of fertilizers: without fertilizers (control), recommended dose (N60P60) and N30Р30 broadcast + N30Р30 with irrigated water; the modes of irrigation: 80-70-70% of minimum moisture-holding capacity and 90-80-70% of minimum moisture-holding capacity. It is shown that hyssop – valuable spicy-aromatic culture, which on their own biological particularity, requirements to soil-climatic conditions can be successfully grown in southern Steppe of Ukraine, providing high harvest of floral mass for use in medical pharmacology. Most productivity variety Marquis (at a rate of 28.4-28.5 c/hа dry cheese) provided in variant, where contributed 50% dose of fertilizers broadcast and 50% with irrigated water and winter sowing period of culture. Maximum contents of essential oil has fixed in same variant at mode irrigation 80-70-70% of minimum moisture-holding capacity, where it has formed 0.85 %. Contents of ascorbic acid in plant raw material varied from 100.4 before 104.9 mg%.Исследования проводили на землях Николаевской ГСХОС ИОЗ НААН в 2017-2018 годах. Почва опытного участка – чернозем южный на карбонатном лессе, который характеризуется высоким содержанием калия, средним – фосфора, и недостаточно обеспеченный азотом. Климат – континентальный, характеризуется резкими и частыми колебаниями годовых и месячных температур воздуха, большими запасами тепла и засушливостью. Схема опыта включала три фактора – сроки сева: ІІ декада октября (озимый) и ІІ декада ноября (подзимний), І декада апреля (весенний); варианты удобрения: без удобрений (контроль), рекомендованная доза (N60P60) и N30Р30 вразброс + N30Р30 с поливной водой; режимы орошения: 80-70-70 % НВ и 90-80-70 % НВ. Площадь посевной делянки – 162 м2, учетной – 5 м2. Агротехника в опыте была общепринятой для южной Степи Украины. Показано, что иссоп лекарственный – ценная пряно-ароматическая культура, которая по своим биологическим особенностям, требованиям к почвенно-климатическим условиям может успешно выращиваться в южной Степи Украины, обеспечивая высокую урожайность зеленой массы для использования во врачебной фармакологии. Наибольшую урожайность сорт Маркиз (на уровне 28,4-28,5 ц/га сухого сырья) обеспечивал в варианте, где вносили 50% дозы удобрений вразброс и 50% с поливной водой и озимом срока сева культуры. Максимальным содержание эфирного масла зафиксировали в том же варианте при режиме орошения 80-70-70% НВ, где оно составило 0,85%. Содержание аскорбиновой кислоты в растительном сырье колебалось от 100,4 до 104,9 мг%

Facilitating standardized COVID-19 suspicion prediction based on computed tomography radiomics in a multi-demographic setting

Objective: To develop an automatic COVID-19 Reporting and Data System (CO-RADS)–based classification in a multi-demographic setting. Methods: This multi-institutional review boards–approved retrospective study included 2720 chest CT scans (mean age, 58 years [range 18–100 years]) from Italian and Russian patients. Three board-certified radiologists from three countries assessed randomly selected subcohorts from each population and provided CO-RADS–based annotations. CT radiomic features were extracted from the selected subcohorts after preprocessing steps like lung lobe segmentation and automatic noise reduction. We compared three machine learning models, logistic regression (LR), multilayer perceptron (MLP), and random forest (RF) for the automated CO-RADS classification. Model evaluation was carried out in two scenarios, first, training on a mixed multi-demographic subcohort and testing on an independent hold-out dataset. In the second scenario, training was done on a single demography and externally validated on the other demography. Results: The overall inter-observer agreement for the CO-RADS scoring between the radiologists was substantial (k = 0.80). Irrespective of the type of validation test scenario, suspected COVID-19 CT scans were identified with an accuracy of 84%. SHapley Additive exPlanations (SHAP) interpretation showed that the “wavelet_(LH)_GLCM_Imc1” feature had a positive impact on COVID prediction both with and without noise reduction. The application of noise reduction improved the overall performance between the classifiers for all types. Conclusion: Using an automated model based on the COVID-19 Reporting and Data System (CO-RADS), we achieved clinically acceptable performance in a multi-demographic setting. This approach can serve as a standardized tool for automated COVID-19 assessment. Keypoints: • Automatic CO-RADS scoring of large-scale multi-demographic chest CTs with mean AUC of 0.93 ± 0.04. • Validation procedure resembles TRIPOD 2b and 3 categories, enhancing the quality of experimental design to test the cross-dataset domain shift between institutions aiding clinical integration. • Identification of COVID-19 pneumonia in the presence of community-acquired pneumonia and other comorbidities with an AUC of 0.92

STAMINA: Stochastic Approximate Model-Checker for Infinite-State Analysis

Stochastic model checking is a technique for analyzing systems that possess probabilistic characteristics. However, its scalability is limited as probabilistic models of real-world applications typically have very large or infinite state space. This paper presents a new infinite state CTMC model checker, STAMINA, with improved scalability. It uses a novel state space approximation method to reduce large and possibly infinite state CTMC models to finite state representations that are amenable to existing stochastic model checkers. It is integrated with a new property-guided state expansion approach that improves the analysis accuracy. Demonstration of the tool on several benchmark examples shows promising results in terms of analysis efficiency and accuracy compared with a state-of-the-art CTMC model checker that deploys a similar approximation method

Bounding Mean First Passage Times in Population Continuous-Time Markov Chains

We consider the problem of bounding mean first passage times and reachability probabilities for the class of population continuous-time Markov chains, which capture stochastic interactions between groups of identical agents. The quantitative analysis of such models is notoriously difficult since typically neither state-based numerical approaches nor methods based on stochastic sampling give efficient and accurate results. Here, we propose a novel approach that leverages techniques from martingale theory and stochastic processes to generate constraints on the statistical moments of first passage time distributions. These constraints induce a semi-definite program that can be used to compute exact bounds on reachability probabilities and mean first passage times without numerically solving the transient probability distribution of the process or sampling from it. We showcase the method on some test examples and tailor it to models exhibiting multimodality, a class of particularly challenging scenarios from biology