Kinetochore life histories reveal an Aurora-B-dependent error correction mechanism in anaphase

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Focus: A Multifaceted Battle Against Cancer: Extracting Knowledge from Chemical Imaging Data Using Computational Algorithms for Digital Cancer Diagnosis

Fourier transform infrared (FTIR) spectroscopic imaging is an emerging microscopy modality for clinical histopathologic diagnoses as well as for biomedical research. Spectral data recorded in this modality are indicative of the underlying, spatially resolved biochemical composition but need computerized algorithms to digitally recognize and transform this information to a diagnostic tool to identify cancer or other physiologic conditions. Statistical pattern recognition forms the backbone of these recognition protocols and can be used for highly accurate results. Aided by biochemical correlations with normal and diseased states and the power of modern computer-aided pattern recognition, this approach is capable of combating many standing questions of traditional histology-based diagnosis models. For example, a simple diagnostic test can be developed to determine cell types in tissue. As a more advanced application, IR spectral data can be integrated with patient information to predict risk of cancer, providing a potential road to precision medicine and personalized care in cancer treatment. The IR imaging approach can be implemented to complement conventional diagnoses, as the samples remain unperturbed and are not destroyed. Despite high potential and utility of this approach, clinical implementation has not yet been achieved due to practical hurdles like speed of data acquisition and lack of optimized computational procedures for extracting clinically actionable information rapidly. The latter problem has been addressed by developing highly efficient ways to process IR imaging data but remains one that has considerable scope for progress. Here, we summarize the major issues and provide practical considerations in implementing a modified Bayesian classification protocol for digital molecular pathology. We hope to familiarize readers with analysis methods in IR imaging data and enable researchers to develop methods that can lead to the use of this promising technique for digital diagnosis of cancer.     

Effects of a participatory approach,with systematic impact matrix analysis in herd health planning in organic dairy cattle herds

The animal health and welfare status in European organic dairy production does not in all aspects meet the organic principles and consumers’ expectations and needs to be improved. To achieve this, tailored herd health planning, targeted to the specific situation of individual farms could be of use. The aim of this study was to apply herd health planning in a structured participatory approach, with impact matrix analysis, not previously used in this context, in European organic dairy farms and to assess changes in animal health and welfare. Herd health planning farm visits were conducted on 122 organic dairy farms in France, Germany and Sweden. The farmer, the herd veterinarian and/or an advisor took part in the farm discussions. The researcher served as facilitator. Baseline data on the animal health status of the individual farm, collected from national milk recording schemes, were presented as an input for the discussion. Thereafter a systematic impact matrix analysis was performed. This was to capture the complexity of individual farms with the aim to identify the farm-specific factors that could have a strong impact on animal health. The participants (i.e. farmer, veterinarian and advisor) jointly identified areas in need of improvement, taking the health status and the interconnected farm system components into account, and appropriate actions were jointly identified. The researcher took minutes during the discussions, and these were shared with the participants. No intervention was made by the researcher, and further actions were left with the participants. The number of actions per farm ranged from 0 to 22. The change in mortality, metabolic diseases, reproductive performance and udder health was assessed at two time points, and potential determinators of the change were evaluated with linear regression models. A significant association was seen between change in udder health, as measured by the somatic cell count, and country. At the first follow-up, a significant association was also found between change in the proportion of prolonged calving interval and the farmers’ desire to improve reproductive health as well as with an increase in herd size, but this was not seen at the second follow-up. The degree of implementation of the actions was good (median 67%, lower quartile 40%, upper quartile 83%). To conclude, the degree of implementation was quite high, improvement of animal health could not be linked to the herd health planning approach. However, the approach was highly appreciated by the participants and deserves further study.     

Theory of chaperonin action: inertial model for enhancement of prokaryotic Rubisco assembly.

We have performed a computational simulation of the aggregation and chaperonin-dependent reconstitution of dimeric prokaryotic ribulose bisphosphate carboxylase/oxygenase (Rubisco), based on the data of P. Goloubinoff et al. (1989, Nature 342, 884-889) and P. V. Viitanen et al. (1990, Biochemistry 29, 5665-5671). The aggregation is simulated by a set of 12 differential equations representing the aggregation of the Rubisco folding intermediate, Rubisco-I, with itself and with aggregates of Rubisco-I, leading up to dodecamers. Four rate constants, applying to forward or reverse steps in the aggregation process, were included. Optimal values for these constants were determined using the ellipsoid algorithm as implemented by one of us (Ecker, J.G. & Kupferschmid, M., 1988, Introduction to Operations Research, Wiley, New York, pp. 315-322). Intensive exploration of simpler aggregation models did not identify an alternative that could simulate the data as well as this one. The activity of the chaperonin in this system was simulated by using this aggregation model, combined with a model similar to that proposed by Goloubinoff et al. (1989). The model assumes that the chaperonin can bind the folding intermediate rapidly, and that the chaperonin complex releases the Rubisco molecule slowly, permitting time for its spontaneous folding while interacting with the chaperonin. This is followed by self-association of the folded Rubisco monomer to yield the active dimeric Rubisco. A modification of the model that simulates temperature effects was also constructed. The most important results we obtained indicate that the chaperonin-dependent reconstitution of Rubisco can be simulated adequately without invoking any catalysis of folding by the chaperonin. In addition, the simulations predict values for the association rate constant of Rubisco-I with the chaperonin, and other variables, that are subject to experimental verification.     

Chasing long-range evolutionary couplings in the AlphaFold era

Coevolution between protein residues is normally interpreted as direct contact. However, the evolutionary record of a protein sequence contains rich information that may include long-range functional couplings, couplings that report on homo-oligomeric states or even conformational changes. Due to the complexity of the sequence space and the lack of structural information on various members of a protein family, it has been difficult to effectively mine the additional information encoded in a multiple sequence alignment (MSA). Here, taking advantage of the recent release of the AlphaFold (AF) database we attempt to identify coevolutionary couplings that cannot be explained simply by spatial proximity. We propose a simple computational method that performs direct coupling analysis on a MSA and searches for couplings that are not satisfied in any of the AF models of members of the identified protein family. Application of this method on 2012 protein families suggests that ~12% of the total identified coevolving residue pairs are spatially distant and more likely to be disordered than their contacting counterparts. We expect that this analysis will help improve the quality of coevolutionary distance restraints used for structure determination and will be useful in identifying potentially functional/allosteric cross-talk between distant residues.     

Modeling an immune response to influenza A virus infection in alveolar epithelial cells

Influenza A viruses (IAV) have been the cause of several influenza pandemics in history and are a significant threat for the next global pandemic. Hospitalized influenza patients often have excess interferon production and a dysregulated immune response to the IAV infection. Obtaining a better understanding of the mechanisms of IAV infection that induce these harmful effects would help drug developers and health professionals create more effective treatments for IAV infection and improve patient outcomes. IAV stimulates viral sensors and receptors expressed by alveolar epithelial cells, like RIG-I and toll-like receptor 3 (TLR3). These two pathways coordinate with one another to induce expression of type III interferons to combat the infection. Presented here is a queuing theory-based model of these pathways that was designed to analyze the timing and amount of interferons produced in response to IAV single stranded RNA and double-stranded RNA detection. The model accurately represents biological data showing the necessary coordination of the RIG-I and TLR3 pathways for effective interferon production. This model can serve as the framework for future studies of IAV infection and identify new targets for potential treatments.     

Bayesian estimation of two-part joint models for a longitudinal semicontinuous biomarker and a terminal event with INLA: Interests for cancer clinical trial evaluation

Two-part joint models for a longitudinal semicontinuous biomarker and a terminal event have been recently introduced based on frequentist estimation. The biomarker distribution is decomposed into a probability of positive value and the expected value among positive values. Shared random effects can represent the association structure between the biomarker and the terminal event. The computational burden increases compared to standard joint models with a single regression model for the biomarker. In this context, the frequentist estimation implemented in the R package frailtypack can be challenging for complex models (i.e., a large number of parameters and dimension of the random effects). As an alternative, we propose a Bayesian estimation of two-part joint models based on the Integrated Nested Laplace Approximation (INLA) algorithm to alleviate the computational burden and fit more complex models. Our simulation studies confirm that INLA provides accurate approximation of posterior estimates and to reduced computation time and variability of estimates compared to frailtypack in the situations considered. We contrast the Bayesian and frequentist approaches in the analysis of two randomized cancer clinical trials (GERCOR and PRIME studies), where INLA has a reduced variability for the association between the biomarker and the risk of event. Moreover, the Bayesian approach was able to characterize subgroups of patients associated with different responses to treatment in the PRIME study. Our study suggests that the Bayesian approach using the INLA algorithm enables to fit complex joint models that might be of interest in a wide range of clinical applications.     

东亚和东南亚旧石器初期重型工具的类型学：评Movius的分类体系

实践证明,Ｍｏｖｉｕｓ在本世纪４０年代建立的东亚,东南亚旧石器分类体系有严重缺陷。经过厘定,目前在欧洲和非洲通用的吹斫器,手斧,薄刃斧,手镐,球状器和刮削器等分类概念,也适用于东亚,东南亚旧石器初期的重型工具工业。     

计算机虚拟筛选技术在害虫行为调节剂研究中的应用

害虫行为调节剂是一种以嗅觉系统为靶标的绿色农药,在害虫的田间管理中发挥着重要的作用。然而,其先导化合物的发现通常依赖一系列生物测定的方法,不仅费时费力,且发现效率低。近年来,随着昆虫嗅觉功能数据的积累和结构生物学的飞速发展,以机器学习技术和分子对接为代表的2种基于计算机的药物虚拟筛选方法在害虫行为调节剂的先导化合物研究中发挥着重要的作用,极大地促进了先导化合物的发现效率,减少了筛选的盲目性。本文系统综述了2种虚拟筛选方法及其在害虫行为调节剂先导化合物研究中的应用,并对2种筛选策略在实际应用中存在的问题及应用前景进行了讨论。