Model-based design and control of a small-scale integrated continuous end-to-end mAb platform

A continuous integrated bioprocess available from the earliest stages of process development allows for an easier, more efficient and faster development and characterization of an integrated process as well as production of small-scale drug candidates. The process presented in this article is a proof-of-concept of a continuous end-to-end monoclonal antibody production platform at a very small scale based on a 200 ml alternating tangential flow filtration perfusion bioreactor, integrated with the purification process with a model-based design and control. The downstream process, consisting of a periodic twin-column protein A capture, a virus inactivation, a CEX column and an AEX column, was compactly implemented in a single chromatography system, with a purification time of less than 4 hr. Monoclonal antibodies were produced for 17 days in a high cell density perfusion culture of CHO cells with titers up to 1.0 mg/ml. A digital twin of the downstream process was created by modelling all the chromatography steps. These models were used for real-time decision making by the implementation of control strategies to automatize and optimize the operation of the process. A consistent glycosylation pattern of the purified product was ensured by the steady state operation of the process. Regarding the removal of impurities, at least a 4-log reduction in the HCP levels was achieved. The recovery yield was up to 60%, and a maximum productivity of 0.8 mg/ml/day of purified product was obtained.     

Raman-based dynamic feeding strategies using real-time glucose concentration monitoring system during adalimumab producing CHO cell cultivation

The use of Process Analytical Technology tools coupled with chemometrics has been shown great potential for better understanding and control of mammalian cell cultivations through real-time process monitoring. In-line Raman spectroscopy was utilized to determine the glucose concentration of the complex bioreactor culture medium ensuring real-time information for our process control system. This work demonstrates a simple and fast method to achieve a robust partial least squares calibration model under laboratory conditions in an early phase of the development utilizing shake flask and bioreactor cultures. Two types of dynamic feeding strategies were accomplished where the multi-component feed medium additions were controlled manually and automatically based on the Raman monitored glucose concentration. The impact of these dynamic feedings was also investigated and compared to the traditional bolus feeding strategy on cellular metabolism, cell growth, productivity, and binding activity of the antibody product. Both manual and automated dynamic feeding strategies were successfully applied to maintain the glucose concentration within a narrower and lower concentration range. Thus, besides glucose, the glutamate was also limited at low level leading to reduced production of inhibitory metabolites, such as lactate and ammonia. Consequently, these feeding control strategies enabled to provide beneficial cultivation environment for the cells. In both experiments, higher cell growth and prolonged viable cell cultivation were achieved which in turn led to increased antibody product concentration compared to the reference bolus feeding cultivation.     

Population modelling and genetics of a critically endangered Madagascan palm Tahina spectabilis

Madagascar is home to 208 indigenous palm species, almost all of them endemic and >80% of which are endangered. We undertook complete population census and sampling for genetic analysis of a relatively recently discovered giant fan palm, the Critically Endangered Tahina spectablis in 2008 and 2016. Our 2016 study included newly discovered populations and added to our genetic study. We incorporated these new populations into species distribution niche model (SDM) and projected these onto maps of the region. We developed population matrix models based on observed demographic data to model population change and predict the species vulnerability to extinction by undertaking population viability analysis (PVA). We investigated the potential conservation value of reintroduced planted populations within the species potential suitable habitat. We found that the population studied in 2008 had grown in size due to seedling regeneration but had declined in the number of reproductively mature plants, and we were able to estimate that the species reproduces and dies after approximately 70 years. Our models suggest that if the habitat where it resides continues to be protected the species is unlikely to go extinct due to inherent population decline and that it will likely experience significant population growth after approximately 80 years due to the reproductive and life cycle attributes of the species. The newly discovered populations contain more genetic diversity than the first discovered southern population which is genetically depauperate. The species appears to demonstrate a pattern of dispersal leading to isolated founder plants which may eventually lead to population development depending on local establishment opportunities. The conservation efforts currently put in place including the reintroduction of plants within the species potential suitable habitat if maintained are thought likely to enable the species to sustain itself but it remains vulnerable to anthropogenic impacts.     

One size does not fit all: Customizing MCMC methods for hierarchical models using NIMBLE

Improved efficiency of Markov chain Monte Carlo facilitates all aspects of statistical analysis with Bayesian hierarchical models. Identifying strategies to improve MCMC performance is becoming increasingly crucial as the complexity of models, and the run times to fit them, increases. We evaluate different strategies for improving MCMC efficiency using the open‐source software NIMBLE (R package nimble) using common ecological models of species occurrence and abundance as examples. We ask how MCMC efficiency depends on model formulation, model size, data, and sampling strategy. For multiseason and/or multispecies occupancy models and for N‐mixture models, we compare the efficiency of sampling discrete latent states vs. integrating over them, including more vs. fewer hierarchical model components, and univariate vs. block‐sampling methods. We include the common MCMC tool JAGS in comparisons. For simple models, there is little practical difference between computational approaches. As model complexity increases, there are strong interactions between model formulation and sampling strategy on MCMC efficiency. There is no one‐size‐fits‐all best strategy, but rather problem‐specific best strategies related to model structure and type. In all but the simplest cases, NIMBLE's default or customized performance achieves much higher efficiency than JAGS. In the two most complex examples, NIMBLE was 10–12 times more efficient than JAGS. We find NIMBLE is a valuable tool for many ecologists utilizing Bayesian inference, particularly for complex models where JAGS is prohibitively slow. Our results highlight the need for more guidelines and customizable approaches to fit hierarchical models to ensure practitioners can make the most of occupancy and other hierarchical models. By implementing model‐generic MCMC procedures in open‐source software, including the NIMBLE extensions for integrating over latent states (implemented in the R package nimbleEcology), we have made progress toward this aim.     

成年雄性林麝消化道寄生虫群落 对不同扰动策略的响应

人工繁育是当前我国保护野生林麝（Moschus berezovskii）资源的主要手段之一,但在林麝种群复壮的过程中,消化道寄生虫病始终威胁着林麝的健康。为探究林麝消化道寄生虫群落对不同扰动策略的响应,本研究分别使用复合药阿苯达唑伊维菌素粉和单一成分药阿维菌素粉去除林麝体内线虫和绦虫,监测林麝其他寄生虫和群落动态变化。选取60只雄性林麝,随机分为三组,分别为阿苯达唑伊维菌素用药组（20只）、阿维菌素用药组（20只）和未做任何处理的对照组（20只）,并连续采集8周用药组和对照组林麝的新鲜粪便。基于改良的Wisconsin粪便虫卵漂浮计数法检测粪便中的虫卵和卵囊,对实验结果进行Kruskal-Wallis检验、Wilcoxon秩检验、Mann Whitney检验和双因素方差分析。研究结果显示,对照组和阿苯达唑伊维菌素用药组林麝的寄生虫感染均为混合感染,球虫为优势物种,其负载量显著高于线虫和绦虫（P < 0.05),阿维菌素用药组林麝仅感染球虫。药物扰动后,两组用药组的林麝球虫的流行率、平均感染强度均高于对照组,但线虫流行率均低于对照组（27.15%,42.15%）。此外,用药后,阿苯达唑伊维菌素用药组林麝先于阿维菌素用药组再次感染线虫和绦虫。通过比较用药组和对照组林麝寄生虫的群落动态变化,表明林麝消化道寄生虫感染现象较为普遍,多重感染中球虫和蠕虫存在竞争关系,复合药物阿苯达唑伊维菌素粉对林麝寄生虫群落的扰动程度更大,林麝寄生虫群落恢复能力与扰动程度成正比。建议林麝人工繁育基地加强科学性和计划性驱虫,并持续性开展林麝寄生虫感染的监测工作。     

New advances on the Aurignacian in the central Iberian Mediterranean basin

This study aims to obtain a chronological and cultural framework of the Evolved Aurignacian in the central Iberian Mediterranean basin and find agreement between this framework and other sequences of the Iberian southeast. Over the last few years, there has been remarkable progress in the research of the Evolved Aurignacian sites in the Valencian area, making a review of the main characteristics of the technocomplex on a regional scale necessary. The recent fieldwork carried out in Cova de les Malladetes (Valencia) and in Cova de les Cendres (Alicante) have been key to understanding the lithic, osseous and ornament assemblages ascribed to the Evolved Aurignacian. Several Bayesian modelled ages have been constructed from the large dataset of chronological dates obtained at Malladetes and Cendres, as well as in other sites. The Bayesian models have allowed us to chronologically place the characteristics of the analysed assemblages. The present research supports the importance of the Aurignacian as the first technocomplex of the Upper Palaeolithic in this area of the Iberian Peninsula.     

Exploiting nonsystematic covariate monitoring to broaden the scope of evidence about the causal effects of adaptive treatment strategies

In studies based on electronic health records (EHR), the frequency of covariate monitoring can vary by covariate type, across patients, and over time, which can limit the generalizability of inferences about the effects of adaptive treatment strategies. In addition, monitoring is a health intervention in itself with costs and benefits, and stakeholders may be interested in the effect of monitoring when adopting adaptive treatment strategies. This paper demonstrates how to exploit nonsystematic covariate monitoring in EHR‐based studies to both improve the generalizability of causal inferences and to evaluate the health impact of monitoring when evaluating adaptive treatment strategies. Using a real world, EHR‐based, comparative effectiveness research (CER) study of patients with type II diabetes mellitus, we illustrate how the evaluation of joint dynamic treatment and static monitoring interventions can improve CER evidence and describe two alternate estimation approaches based on inverse probability weighting (IPW). First, we demonstrate the poor performance of the standard estimator of the effects of joint treatment‐monitoring interventions, due to a large decrease in data support and concerns over finite‐sample bias from near‐violations of the positivity assumption (PA) for the monitoring process. Second, we detail an alternate IPW estimator using a no direct effect assumption. We demonstrate that this estimator can improve efficiency but at the potential cost of increase in bias from violations of the PA for the treatment process.     

Efficient screening of predictive biomarkers for individual treatment selection

The development of molecular diagnostic tools to achieve individualized medicine requires identifying predictive biomarkers associated with subgroups of individuals who might receive beneficial or harmful effects from different available treatments. However, due to the large number of candidate biomarkers in the large‐scale genetic and molecular studies, and complex relationships among clinical outcome, biomarkers, and treatments, the ordinary statistical tests for the interactions between treatments and covariates have difficulties from their limited statistical powers. In this paper, we propose an efficient method for detecting predictive biomarkers. We employ weighted loss functions of Chen et al. to directly estimate individual treatment scores and propose synthetic posterior inference for effect sizes of biomarkers. We develop an empirical Bayes approach, namely, we estimate unknown hyperparameters in the prior distribution based on data. We then provide efficient screening methods for the candidate biomarkers via optimal discovery procedure with adequate control of false discovery rate. The proposed method is demonstrated in simulation studies and an application to a breast cancer clinical study in which the proposed method was shown to detect the much larger numbers of significant biomarkers than existing standard methods.     

青藏高原草地产量与草畜平衡变化

深入了解青藏高原草地生产力与草畜平衡状态的变化,是青藏高原生态屏障建设和生态环境保护的基础。利用遥感植被指数和叶面积指数产品,基于VIP生态水文过程模型,模拟分析了青藏高原2000—2018年间草地生产力的时空格局,并结合同期农牧业统计资料,分析了青藏高原县域尺度草畜平衡状态的变化。结果表明:青藏高原草地多年平均净初级生产力(NPP)为158.4 g C·m^-2·a^-1;近20年来草地生产力上升趋势明显,显著上升面积的比例为44.7%。气候变暖、降水量增加、草本植物生长期延长和大气CO₂浓度升高是青藏高原草地生产力提高的主要驱动因素。基于草场产量估算的青藏高原平均理论载畜量为1.17 SU·hm^-2,年增长率为0.011 SU·hm^-2。2000年以来青藏高原草地超载情况总体趋于好转,严重超载县的面积比例已降至20%以下,其中超载程度较严重的地区,畜牧业的维持和发展主要依靠作物秸秆补饲。研究结果可为区域农牧业结构调整和生态环境保护提供科学依据。