Dynamic simulation of human motion: numerically efficient inclusion of muscle physiology by convex optimization

Determining the muscle forces that underlie some experimentally observed human motion, is a challenging biomechanical problem, both from an experimental and a computational point of view. No non-invasive method is currently available for experimentally measuring muscle forces. The alternative of computing them from the observed motion is complicated by the inherent overactuation of the human body: it has many more muscles than strictly needed for driving all the degrees of freedom of the skeleton. As a result, the skeleton’s equations of motion do not suffice to determine the muscle forces unambiguously. Therefore, muscle force determination is often reformulated as a (large-scale) optimization problem. Generally, the optimization approaches are classified according to the formalism, inverse or forward, adopted for solving the skeleton’s equations of motion. Classical inverse approaches are fast but do not take into account the constraints imposed by muscle physiology. Classical forward approaches, on the other hand, do take the muscle physiology into account but are extremely costly from a computational point of view. The present paper makes a double contribution. First, it proposes a novel inverse approach that results from including muscle physiology (both activation and contraction dynamics) in the inverse dynamic formalism. Second, the efficiency with which the corresponding optimization problem is solved is increased by using convex optimization techniques. That is, an approximate convex program is formulated and solved in order to provide a hot-start for the exact nonconvex program. The key element in this approximation is a (global) linearization of muscle physiology based on techniques from experimental system identification. This approach is applied to the study of muscle forces during gait. Although the results for gait are promising, experimental study of faster motions is needed to demonstrate the full power and advantages of the proposed methodology, and therefore is the subject of subsequent research.     

Dynamic modeling of filamentous bulking in lab-scale activated sludge processes

For years now, biological wastewater treatment plants rely on activated sludge systems in which a complex ecosystem, constituted mainly of bacteria and protozoa, (bio)degrade the incoming pollutants. Filamentous bulking, a phenomenon in which the filamentous organisms dominate the activated sludge is still a widespread problem in the operation of activated sludge processes with often severe economic and environmental consequences. Image analysis offers promising perspectives for early detection of filamentous bulking because the morphology parameters of the activated sludge respond rather fast to changing process conditions. This paper aims at exploiting this information in black box models to predict the evolution of the sludge volume index (SVI), a laboratory measurement currently exploited to quantify the sludge settleability. More specifically, dynamic ARX models are investigated as a function of organic loading and digital image analysis information (such as the total filament length per image and some representative mean floc shape parameters). The model’s performances are compared on the basis of a squared errors like quality criterion. While the identification results are very promising, the validation of the models on other independently generated data sets, depends on which data set is used for identification. The best performing models have (a combination of) the total filament length, one of the floc elongation parameters and the fractal dimension as inputs.     

Immunogenicity of the Envelope Surface Unit of Human Endogenous Retrovirus K18 in Mice

The triggers for the development of multiple sclerosis (MS) have not been fully understood to date. One hypothesis proposes a viral etiology. Interestingly, viral proteins from human endogenous retroviruses (HERVs) may play a role in the pathogenesis of MS. Allelic variants of the HERV-K18 env gene represent a genetic risk factor for MS, and the envelope protein is considered to be an Epstein–Barr virus-trans-activated superantigen. To further specify a possible role for HERV-K18 in MS, the present study examined the immunogenicity of the purified surface unit (SU). HERV-K18(SU) induced envelope-specific plasma IgG in immunized mice and triggered proliferation of T cells isolated from these mice. It did not trigger phenotypic changes in a mouse model of experimental autoimmune encephalomyelitis. Further studies are needed to investigate the underlying mechanisms of HERV-K18 interaction with immune system regulators in more detail.     

基于Viseme的连续语音识别系统及Talking Head

为实现听觉/视觉驱动的说话人头部动画,该文给出了一个基于viseme(说话时的基本嘴形单位)的连续语音识别系统。它训练viseme隐马尔可夫模型(HMM),识别语音为viseme图像序列。建模采用triseme的概念来考虑viseme的上下文相关性,但它需要超大量的训练数据。该文根据viseme图像及其相似度权值(VSW)定义视觉问题集,用来建立triseme决策树,以实现triseme的状态捆绑及HMM参数共享。为比较系统性能,基于phoneme(听觉领域的语音基本单位)的语音识别结果也被映射为viseme序列。在评价准则上,定义viseme图像相似度加权识别精度,更全面地考虑输出和参考图像序列的差别,并用嘴形圆度和VSW曲线中的突变点来评估所得viseme序列的平滑性。结果表明,基于viseme的语音识别系统能给出更平滑和合理的嘴形图像序列。     

一种用于说话人头部动画的Viseme语音识别系统

Viseme是在语音驱动说话人头部动画中一种常用的为口形建立的音频-视频模型。本文尝试建立viseme隐马尔可夫模型(HMM),用于驱动说话人头部的语音识别系统,称为前映射系统。为了得到更精确的模型以提高识别率,引入考虑发音口形上下文的Triseme模型。但是引入Triseme模型后,随着模型数量的急剧增加将导致训练数据的严重不足。本文使用决策树状态捆绑方法来缓解这一问题,同时引入了一种以口形相似度为基础的决策树视频问题设计方法。为了比较viseme系统的性能,本文也建立了一个以phoneme为基本HMM模型的语音识别系统。在评价准则上,使用了一种客观评价说话人头部动画的加权识别率。实验表明,以viseme为基本HMM模型的前映射系统可以为说话人头部提供更加合理的口形。     

Multicellular Modelling of Difficult-to-Treat Gastrointestinal Cancers: Current Possibilities and Challenges

Cancers affecting the gastrointestinal system are highly prevalent and their incidence is still increasing. Among them, gastric and pancreatic cancers have a dismal prognosis (survival of 5–20%) and are defined as difficult-to-treat cancers. This reflects the urge for novel therapeutic targets and aims for personalised therapies. As a prerequisite for identifying targets and test therapeutic interventions, the development of well-established, translational and reliable preclinical research models is instrumental. This review discusses the development, advantages and limitations of both patient-derived organoids (PDO) and patient-derived xenografts (PDX) for gastric and pancreatic ductal adenocarcinoma (PDAC). First and next generation multicellular PDO/PDX models are believed to faithfully generate a patient-specific avatar in a preclinical setting, opening novel therapeutic directions for these difficult-to-treat cancers. Excitingly, future opportunities such as PDO co-cultures with immune or stromal cells, organoid-on-a-chip models and humanised PDXs are the basis of a completely new area, offering close-to-human models. These tools can be exploited to understand cancer heterogeneity, which is indispensable to pave the way towards more tumour-specific therapies and, with that, better survival for patients.     

Assessment of the N-nitrosopiperidine formation risk from piperine and piperidine contained in spices used as meat product additives

The N-nitrosopiperidine (NPIP) formation in blends of spices and nitrite curing salt was investigated in relation with the piperine and piperidine contents in spices. Firstly, two analytical methods were developed. Piperine was extracted with dichloromethane by means of accelerated solvent extraction (ASE) and determined by high-performance liquid chromatography (HPLC)–diode array detector (λ = 343 nm). A selective hydroextraction of piperidine using ASE and its quantification by HPLC–ELSD was applied. Both methods were sufficiently sensitive and accurate (limit of detection, limit of quantification, and recovery: 0.28, 0.84 μg, and 98.9 ± 2.6 % for piperine, and 5.76, 17.45 μg, and 95.9 ± 2.9 % for piperidine, respectively). Secondly, both compounds were quantified in commercial samples (black and white pepper, paprika, chili pepper, allspice, and nutmeg). The maximum amount of piperine (21.12 mg g^?1) was found in pepper, while the other spices contained only traces. Piperidine was detected mainly in the pepper samples, whereby the highest concentration was found in the white pepper extract (11.42 mg g^?1). Thirdly, during the storage of spices blended with nitrite curing salt, the NPIP content was determined, using a gas chromatograph coupled with a thermal energy analyzer. Against our expectations, no NPIP formation was observed in the curing mixture which contained white pepper extract. This result remains in contrast with the white pepper mixture, in which the NPIP content significantly increased from not detected to 9.80 ± 0.41 ng g^?1 after the 2 months storage period. In conclusion, high amounts of piperine or piperidine in spices do not systematically result in the formation of NPIP, when blended with nitrite curing salt .     

Previously uncultured β-Proteobacteria dominate in biologically active granular activated carbon (BAC) filters

Bacteria colonizing BAC filters used in drinking water purification from lake water were characterized by morphology, physiological tests, whole cell protein profiles and PLFA (phospholipid fatty acid) composition, and identified by partial 16S rRNA gene sequencing. Epifluorescence revealed prothecate bacteria to dominate in BAC. The majority of the isolates belonged to order Burkholderiales of β-Proteobacteria, a few to Comamonadaceae but the majority to an undescribed family and the related sequences belonged mainly to uncultured bacteria. Among the less common α-Proteobacteria the genus Sphingomonas and the genera Afipia, Bosea or Bradyrhizobium of the Bradyrhizobiaceae family were detected. The majority of cultured bacteria persisting in the BAC biofilter were Burkholderiales, which according to ecological information are efficient in the mineralisation of dissolved organic matter in BAC. The biotechnical potential of the previously uncultured dominant bacteria warrants to be further studied.     

Accountability and Control Over Autonomous Weapon Systems: A Framework for Comprehensive Human Oversight

Accountability and responsibility are key concepts in the academic and societal debate on Autonomous Weapon Systems, but these notions are often used as high-level overarching constructs and are not operationalised to be useful in practice. “Meaningful Human Control” is often mentioned as a requirement for the deployment of Autonomous Weapon Systems, but a common definition of what this notion means in practice, and a clear understanding of its relation with responsibility and accountability is also lacking. In this paper, we present a definition of these concepts and describe the relations between accountability, responsibility, control and oversight in order to show how these notions are distinct but also connected. We focus on accountability as a particular form of responsibility—the obligation to explain one’s action to a forum—and we present three ways in which the introduction of Autonomous Weapon Systems may create “accountability gaps”. We propose a Framework for Comprehensive Human Oversight based on an engineering, socio-technical and governance perspective on control. Our main claim is that combining the control mechanisms at technical, socio-technical and governance levels will lead to comprehensive human oversight over Autonomous Weapon Systems which may ensure solid controllability and accountability for the behaviour of Autonomous Weapon Systems. Finally, we give an overview of the military control instruments that are currently used in the Netherlands and show the applicability of the comprehensive human oversight Framework to Autonomous Weapon Systems. Our analysis reveals two main gaps in the current control mechanisms as applied to Autonomous Weapon Systems. We have identified three first options as future work for the design of a control mechanism, one in the technological layer, one in the socio-technical layer and one the governance layer, in order to achieve comprehensive human oversight and ensure accountability over Autonomous Weapon Systems.

     

Optimal adaptive control of (bio)chemical reactors: past, present and future

In this paper an overview of optimal adaptive control of (bio)chemical reactors is presented. Following the paradigm of the Minimum Principle of Pontryagin the derivation of optimal control sequences for fed-batch production processes is briefly revisited. Next, it is illustrated how the obtained optimal profiles can be exploited in the characterization of nearly optimal control sequences in terms of the qualitative behavior of the specific growth and production rates as function of the limiting substrates. Implementing this knowledge leads in a natural way to the design of (nearly) optimal adaptive feedback controllers. Special emphasis will lie on the potential of on-line biomass measurements (obtained with the Biomass Monitor) in the estimation algorithm of the growth kinetics being the adaptive component of the controller. Extensions towards fermentation processes with (i) multiple substrates and (ii) non-monotonic kinetics are also included. Finally, perspectives towards optimal adaptive control of not perfectly mixed (bio)chemical reactor systems, such as chemical tubular reactors, are outlined.