Data and Knowledge Co-driving for Cancer Subtype Classification on Multi-Scale Histopathological Slides

Artificial intelligence-enabled histopathological data analysis has become a valuable assistant to the pathologist. However, existing models lack representation and inference abilities compared with those of pathologists, especially in cancer subtype diagnosis, which is unconvincing in clinical practice. For instance, pathologists typically observe the lesions of a slide from global to local, and then can give a diagnosis based on their knowledge and experience. In this paper, we propose a Data and Knowledge Co-driving (D&K) model to replicate the process of cancer subtype classification on a histopathological slide like a pathologist. Specifically, in the data-driven module, the bagging mechanism in ensemble learning is leveraged to integrate the histological features from various bags extracted by the embedding representation unit. Furthermore, a knowledge-driven module is established based on the Gestalt principle in psychology to build the three-dimensional (3D) expert knowledge space and map histological features into this space for metric. Then, the diagnosis can be made according to the Euclidean distance between them. Extensive experimental results on both public and in-house datasets demonstrate that the D&K model has a high performance and credible results compared with the state-of-the-art methods for diagnosing histopathological subtypes. Code: https://github.com/Dennis-YB/Data-and-Knowledge-Co-driving-for-Cancer-Subtypes-Classificatio

Kinetic Study of DNA Topoisomerases by Supercoiling-Dependent Fluorescence Quenching

DNA topoisomerases are essential enzymes for all living organisms and important targets for anticancer drugs and antibiotics. Although DNA topoisomerases have been studied extensively, steady-state kinetics has not been systematically investigated because of the lack of an appropriate assay. Previously, we demonstrated that newly synthesized, fluorescently labeled plasmids pAB1_FL905 and pAB1_FL924 can be used to study DNA topoisomerase-catalyzed reactions by fluorescence resonance energy transfer (FRET) or supercoiling-dependent fluorescence quenching (SDFQ). With the FRET or SDFQ method, we performed steady-state kinetic studies for six different DNA topoisomerases including two type IA enzymes ( and DNA topoisomerase I), two type IB enzymes (human and variola DNA topoisomerase I), and two type IIA enzymes ( DNA gyrase and human DNA topoisomerase IIα). Our results show that all DNA topoisomerases follow the classical Michaelis-Menten kinetics and have unique steady-state kinetic parameters, , , and . We found that for all topoisomerases are rather low and that such low values may stem from the tight binding of topoisomerases to DNA. Additionally, we confirmed that novobiocin is a competitive inhibitor for adenosine 5\u27-triphosphate binding to DNA gyrase, demonstrating the utility of our assay for studying topoisomerase inhibitors

Temporal dynamics of competitive fertilization in social groups of red junglefowl (Gallus gallus) shed new light on avian sperm competition

Studies of birds have made a fundamental contribution to elucidating sperm competition processes, experimentally demonstrating the role of individual mechanisms in competitive fertilization. However, the relative importance of these mechanisms and the way in which they interact under natural conditions remain largely unexplored. Here, we conduct a detailed behavioural study of freely mating replicate groups of red junglefowl, Gallus gallus, to predict the probability that competing males fertilize individual eggs over the course of 10-day trials. Remating frequently with a female and mating last increased a male's probability of fertilization, but only for eggs ovulated in the last days of a trial. Conversely, older males, and those mating with more polyandrous females, had consistently lower fertilization success. Similarly, resistance to a male's mating attempts, particularly by younger females, reduced fertilization probability. After considering these factors, male social status, partner relatedness and the estimated state of male extragonadal sperm reserves did not predict sperm competition outcomes. These results shed new light on sperm competition dynamics in taxa such as birds, with prolonged female sperm storage and staggered fertilizations. This article is part of the theme issue 'Fifty years of sperm competition'

Gut microbiome-based noninvasive diagnostic model to predict acute coronary syndromes

BackgroundPrevious studies have shown that alterations in the gut microbiota are closely associated with Acute Coronary Syndrome (ACS) development. However, the value of gut microbiota for early diagnosis of ACS remains understudied.MethodsWe recruited 66 volunteers, including 29 patients with a first diagnosis of ACS and 37 healthy volunteers during the same period, collected their fecal samples, and sequenced the V4 region of the 16S rRNA gene. Functional prediction of the microbiota was performed using PICRUSt2. Subsequently, we constructed a nomogram and corresponding webpage based on microbial markers to assist in the diagnosis of ACS. The diagnostic performance and usefulness of the model were analyzed using boostrap internal validation, calibration curves, and decision curve analysis (DCA).ResultsCompared to that of healthy controls, the diversity and composition of microbial community of patients with ACS was markedly abnormal. Potentially pathogenic genera such as Streptococcus and Acinetobacter were significantly increased in the ACS group, whereas certain SCFA-producing genera such as Blautia and Agathobacter were depleted. In addition, in the correlation analysis with clinical indicators, the microbiota was observed to be associated with the level of inflammation and severity of coronary atherosclerosis. Finally, a diagnostic model for ACS based on gut microbiota and clinical variables was developed with an area under the receiver operating characteristic (ROC) curve (AUC) of 0.963 (95% CI: 0.925–1) and an AUC value of 0.948 (95% CI: 0.549–0.641) for bootstrap internal validation. The calibration curves of the model show good consistency between the actual and predicted probabilities. The DCA showed that the model had a high net clinical benefit for clinical applications.ConclusionOur study is the first to characterize the composition and function of the gut microbiota in patients with ACS and healthy populations in Southwest China and demonstrates the potential effect of the microbiota as a non-invasive marker for the early diagnosis of ACS

Intracellular ATP delivery to in vitro expanded mouse CD27− γδ T cells

Summary: Intracellular ATP supports the function of γδT17 cells in mice. Here, we present a protocol for intracellular ATP delivery to in vitro expanded mouse CD27− γδ T cells. We describe steps for pre-coating well plates, preparing lymphocytes, culturing CD27− γδ T cells, and ATP delivery. We then detail functional evaluation of γδ T cells by flow cytometry. Appropriate concentrations of control and ATP vesicles are detailed for intracellular ATP delivery, which can also be applied to other immune cells.For complete details on the use and execution of this protocol, please refer to Wang et al. (2023).1 : Publisher’s note: Undertaking any experimental protocol requires adherence to local institutional guidelines for laboratory safety and ethics

Unlabeled Imperfect Demonstrations in Adversarial Imitation Learning

Adversarial imitation learning has become a widely used imitation learning framework. The discriminator is often trained by taking expert demonstrations and policy trajectories as examples respectively from two categories (positive vs. negative) and the policy is then expected to produce trajectories that are indistinguishable from the expert demonstrations. But in the real world, the collected expert demonstrations are more likely to be imperfect, where only an unknown fraction of the demonstrations are optimal. Instead of treating imperfect expert demonstrations as absolutely positive or negative, we investigate unlabeled imperfect expert demonstrations as they are. A positive-unlabeled adversarial imitation learning algorithm is developed to dynamically sample expert demonstrations that can well match the trajectories from the constantly optimized agent policy. The trajectories of an initial agent policy could be closer to those non-optimal expert demonstrations, but within the framework of adversarial imitation learning, agent policy will be optimized to cheat the discriminator and produce trajectories that are similar to those optimal expert demonstrations. Theoretical analysis shows that our method learns from the imperfect demonstrations via a self-paced way. Experimental results on MuJoCo and RoboSuite platforms demonstrate the effectiveness of our method from different aspects

Effects of Irrigation Regime and Nitrogen Fertilizer Management on CH4, N2O and CO2 Emissions from Saline–Alkaline Paddy Fields in Northeast China

Irrigation regime and fertilizer nitrogen (N) are considered as the most effective agricultural management systems to mitigate greenhouse gas (GHG) emissions from crop fields, but few studies have involved saline–alkaline paddy soil. Gas emitted from saline–alkaline paddy fields (1-year-old and 57-year-old) was collected during rice growing seasons by the closed chamber method. Compared to continuous flooding irrigation, lower average CH4 flux (by 22.81% and 23.62%), but higher CO2 flux (by 24.84% and 32.39%) was observed from intermittent irrigation fields. No significant differences of N2O flux were detected. Application rates of N fertilizer were as follows: (1) No N (N0); (2) 60 kg ha−1 (N60); (3) 150 kg ha−1 (N150); and (4) 250 kg ha−1 (N250). The cumulative emissions of GHG and N fertilizer additions have positive correlation, and the largest emission was detected at the rate of 250 kg N ha−1 (N250). Global warming potential (GWP, CH4 + N2O + CO2) of the 57-year-old field under the N250 treatment was up to 4549 ± 296 g CO2-eq m−2, approximately 1.5-fold that of N0 (no N application). In summary, the results suggest that intermittent irrigation would be a better regime to weaken the combined GWP of CH4 and N2O, but N fertilizer contributed positively to the GWP

Mechanism of Balanced Strength and Ductility in High-Strength Low-Alloy Steel

A high-strength low-alloy steel with balanced strength and ductility was reported. A product of the strength and elongation (PSE) at a break of ~19 GPa% was obtained. The microstructure of the material was investigated by scanning electron microscopy, electron backscattered diffraction, and transmission electron microscopy methods. Phase transformation follows the K–S orientation relationships. Interconnecting structures generate due to the variant interactions within one prior austenite grain. The multi-phase microstructure containing both soft and hard phases contributes to good plasticity. The homogeneously distributed NbC nanoparticles make up the loss of strength ascribed to the soft retained austenite and keep the strength at an extremely high level

Mechanism of Balanced Strength and Ductility in High-Strength Low-Alloy Steel

A high-strength low-alloy steel with balanced strength and ductility was reported. A product of the strength and elongation (PSE) at a break of ~19 GPa% was obtained. The microstructure of the material was investigated by scanning electron microscopy, electron backscattered diffraction, and transmission electron microscopy methods. Phase transformation follows the K–S orientation relationships. Interconnecting structures generate due to the variant interactions within one prior austenite grain. The multi-phase microstructure containing both soft and hard phases contributes to good plasticity. The homogeneously distributed NbC nanoparticles make up the loss of strength ascribed to the soft retained austenite and keep the strength at an extremely high level