Full-body motion assessment: Concurrent validation of two body tracking depth sensors versus a gold standard system during gait

RGB-D cameras provide 3-D body joint data in a low-cost, portable and non-intrusive way, when compared with reference motion capture systems used in laboratory settings. In this contribution, we evaluate the validity of both Microsoft Kinect versions (v1 and v2) for motion analysis against a Qualisys system in a simultaneous protocol. Two different walking directions in relation to the Kinect (towards – WT, and away – WA) were explored. For each gait trial, measures related with all body parts were computed: velocity of all joints, distance between symmetrical joints, and angle at some joints. For each measure, we compared each Kinect version and Qualisys by obtaining the mean true error and mean absolute error, Pearson’s correlation coefficient, and optical-to-depth ratio. Although both Kinect v1 and v2 and/or WT and WA data present similar accuracy for some measures, better results were achieved, overall, when using WT data provided by the Kinect v2, especially for velocity measures. Moreover, the velocity and distance presented better results than angle measures. Our results show that both Kinect versions can be an alternative to more expensive systems such as Qualisys, for obtaining distance and velocity measures as well as some angles metrics (namely the knee angles). This conclusion is important towards the off-lab non-intrusive assessment of motor function in different areas, including sports and healthcare.     

Development of a novel MATLAB-based framework for implementing mechanical joint stability constraints within OpenSim musculoskeletal models

The Static Optimization (SO) solver in OpenSim estimates muscle activations and forces that only equilibrate applied moments. In this study, SO was enhanced through an open-access MATLAB interface, where calculated muscle activations can additionally satisfy crucial mechanical stability requirements. This Stability-Constrained SO (SCSO) is applicable to many OpenSim models and can potentially produce more biofidelic results than SO alone, especially when antagonistic muscle co-contraction is required to stabilize body joints. This hypothesis was tested using existing models and experimental data in the literature. Muscle activations were calculated by SO and SCSO for a spine model during two series of static trials (i.e. simulation 1 and 2), and also for a lower limb model (supplementary material 2). In simulation 1, symmetric and asymmetric flexion postures were compared, while in simulation 2, various external load heights were compared, where increases in load height did not change the external lumbar flexion moment, but necessitated higher EMG activations. During the tasks in simulation 1, the predicted muscle activations by SCSO demonstrated less average deviation from the EMG data (6.8% −7.5%) compared to those from SO (10.2%). In simulation 2, SO predicts constant muscle activations and forces, while SCSO predicts increases in the average activations of back and abdominal muscles that better match experimental data. Although the SCSO results are sensitive to some parameters (e.g. musculotendon stiffness), when considering the strategy of the central nervous system in distributing muscle forces and in activating antagonistic muscles, the assigned activations by SCSO are more biofidelic than SO.     

Reconstruction of tricarboxylic acid cycle in Corynebacterium glutamicum with a genome-scale metabolic network model for trans-4-hydroxyproline production

trans-4-Hydroxy- l -proline (Hyp) is an abundant component of mammalian collagen and functions as a chiral synthon for the syntheses of anti-inflammatory drugs in the pharmaceutical industry. Proline 4-hydroxylase (P4H) can catalyze the conversion of l -proline to Hyp; however, it is still challenging for the fermentative production of Hyp from glucose using P4H due to the low yield and productivity. Here, we report the metabolic engineering of Corynebacterium glutamicum for the fermentative production of Hyp by reconstructing tricarboxylic acid (TCA) cycle together with heterologously expressing the p4h gene from Dactylosporangium sp. strain RH1. In silico model-based simulation showed that α-ketoglutarate was redirected from the TCA cycle toward Hyp synthetic pathway driven by P4H when the carbon flux from succinyl-CoA to succinate descended to zero. The interruption of the TCA cycle by the deletion of sucCD-encoding the succinyl-CoA synthetase (SUCOAS) led to a 60% increase in Hyp production and had no obvious impact on the growth rate. Fine-tuning of plasmid-borne ProB* and P4H abundances led to a significant increase in the yield of Hyp on glucose. The final engineered Hyp-7 strain produced up to 21.72 g/L Hyp with a yield of 0.27 mol/mol (Hyp/glucose) and a volumetric productivity of 0.36 g·L ⁻¹·hr ⁻¹ in the shake flask fermentation. To our knowledge, this is the highest yield and productivity achieved by microbial fermentation in a glucose-minimal medium for Hyp production. This strategy provides new insights into engineering C. glutamicum by flux coupling for the fermentative production of Hyp and related products.     

Hybrid physics-based and data-driven modeling for bioprocess online simulation and optimization

Model-based online optimization has not been widely applied to bioprocesses due to the challenges of modeling complex biological behaviors, low-quality industrial measurements, and lack of visualization techniques for ongoing processes. This study proposes an innovative hybrid modeling framework which takes advantages of both physics-based and data-driven modeling for bioprocess online monitoring, prediction, and optimization. The framework initially generates high-quality data by correcting raw process measurements via a physics-based noise filter (a generally available simple kinetic model with high fitting but low predictive performance); then constructs a predictive data-driven model to identify optimal control actions and predict discrete future bioprocess behaviors. Continuous future process trajectories are subsequently visualized by re-fitting the simple kinetic model (soft sensor) using the data-driven model predicted discrete future data points, enabling the accurate monitoring of ongoing processes at any operating time. This framework was tested to maximize fed-batch microalgal lutein production by combining with different online optimization schemes and compared against the conventional open-loop optimization technique. The optimal results using the proposed framework were found to be comparable to the theoretically best production, demonstrating its high predictive and flexible capabilities as well as its potential for industrial application.     

A new generation of predictive models: The added value of hybrid models for manufacturing processes of therapeutic proteins

Due to the lack of complete understanding of metabolic networks and reaction pathways, establishing a universal mechanistic model for mammalian cell culture processes remains a challenge. Contrarily, data-driven approaches for modeling these processes lack extrapolation capabilities. Hybrid modeling is a technique that exploits the synergy between the two modeling methods. Although mammalian cell cultures are among the most relevant processes in biotechnology and indeed looks ideal for hybrid modeling, their application has only been proposed but never developed in the literature. This study provides a quantitative assessment of the improvement brought by hybrid models with respect to the state-of-the-art statistical predictive models in the context of therapeutic protein production. This is illustrated using a dataset obtained from a 3.5 L fed-batch experiment. With the goal to robustly define the process design space, hybrid models reveal a superior capability to predict the time evolution of different process variables using only the initial and process conditions in comparison to the statistical models. Hybrid models not only feature more accurate prediction results but also demonstrate better robustness and extrapolation capabilities. For the future application, this study highlights the added value of hybrid modeling for model-based process optimization and design of experiments.     

Streptomycin-induced ribosome engineering complemented with fermentation optimization for enhanced production of 10-membered enediynes tiancimycin-A and tiancimycin-D

Tiancimycins (TNMs) are a group of 10-membered anthraquinone-fused enediynes, newly discovered from Streptomyces sp. CB03234. Among them, TNM-A and TNM-D have exhibited excellent antitumor performances and could be exploited as very promising warheads for the development of anticancer antibody-drug conjugates (ADCs). However, their low titers, especially TNM-D, have severely limited following progress. Therefore, the streptomycin-induced ribosome engineering was adopted in this work for strain improvement of CB03234, and a TNMs high producer S. sp. CB03234-S with the K43N mutation at 30S ribosomal protein S12 was successfully screened out. Subsequent media optimization revealed the essential effects of iodide and copper ion on the production of TNMs, while the substitution of nitrogen source could evidently promote the accumulation of TNM-D, and the ratio of produced TNM-A and TNM-D was responsive to the change of carbon and nitrogen ratio in the medium. Further amelioration of the pH control in scaled up 25 L fermentation increased the average titers of TNM-A and TNM-D up to 13.7 ± 0.3 and 19.2 ± 0.4 mg/L, respectively. The achieved over 45-fold titer improvement of TNM-A, and 109-fold total titer improvement of TNM-A and TNM-D enabled the efficient purification of over 200 mg of each target molecule from 25 L fermentation. Our efforts have demonstrated a practical strategy for titer improvement of anthraquinone-fused enediynes and set up a solid base for the pilot scale production and preclinical studies of TNMs to expedite the future development of anticancer ADC drugs.     

通过优化表达元件及培养基组分提高地衣芽胞杆菌中碱性丝氨酸蛋白酶产量

【背景】碱性丝氨酸蛋白酶(Subtilisin)是一种具有广泛用途的工业酶制剂。【目的】旨在通过优化启动子、信号肽及培养基组分来提高地衣芽胞杆菌中碱性丝氨酸蛋白酶产量。【方法】以地衣芽胞杆菌BL10为出发菌株,构建了含有4种不同类型启动子(PbacA、P43、PaprE和PsrfA)及4种不同类型信号肽(SPVpr、SPSacB、SPSacC和SPAprE)的碱性丝氨酸蛋白酶表达菌株,并在获得高产菌株的基础上进行培养基优化。【结果】4种启动子的表达水平为PbacAPaprEP43PsrfA,4种信号肽的分泌效率为SPAprESPSacCSPSacBSPVpr。其中,菌株BL10/pPbacA-aprE产生最高的碱性丝氨酸蛋白酶酶活(275.21 U/mL),相比于出发菌株BL10/pHY-aprE (167.98 U/mL)提高了64%。随后,通过对发酵培养基成分进行优化并结合正交优化,获得了一种高产碱性丝氨酸蛋白酶的培养基(g/L):玉米淀粉40.0,豆粕50.0,(NH4)2SO4 4.0,K2HPO4 3.0,CaCO3 1.0。最后,碱性丝氨酸蛋白酶酶活提高到747.37 U/mL,是初始酶活的4.45倍。【结论】为工业化高产碱性丝氨酸蛋白酶提供了一种有效策略。     

Recent advances in plasmid-based tools for establishing novel microbial chassis

A key challenge for domesticating alternative cultivable microorganisms with biotechnological potential lies in the development of innovative technologies. Within this framework, a myriad of genetic tools has flourished, allowing the design and manipulation of complex synthetic circuits and genomes to become the general rule in many laboratories rather than the exception. More recently, with the development of novel technologies such as DNA automated synthesis/sequencing and powerful computational tools, molecular biology has entered the synthetic biology era. In the beginning, most of these technologies were established in traditional microbial models (known as chassis in the synthetic biology framework) such as Escherichia coli and Saccharomyces cerevisiae, enabling fast advances in the field and the validation of fundamental proofs of concept. However, it soon became clear that these organisms, although extremely useful for prototyping many genetic tools, were not ideal for a wide range of biotechnological tasks due to intrinsic limitations in their molecular/physiological properties. Over the last decade, researchers have been facing the great challenge of shifting from these model systems to non-conventional chassis with endogenous capacities for dealing with specific tasks. The key to address these issues includes the generation of narrow and broad host plasmid-based molecular tools and the development of novel methods for engineering genomes through homologous recombination systems, CRISPR/Cas9 and other alternative methods. Here, we address the most recent advances in plasmid-based tools for the construction of novel cell factories, including a guide for helping with “build-your-own” microbial host.     

Glucose-based optimization of CHO-cell perfusion cultures

Perfusion cultures of CHO cells producing t-PA were performed using acoustic filter cell retention. A robust off-line glucose analysis and predictive control protocol was developed to maintain the process within approximately 0.5 mM of the glucose set point, without the need for a more fallible on-line sensor. Glucose usage (the difference between the inlet and reactor glucose concentrations) provided an easily measured indicator of overall medium utilization for mapping acceptable ranges of operation, including the edge of failure. Earlier onset of perfusion with a ramping glucose set point (1.5 mM/d) resulted in improved growth and consistency during the perfusion culture start-up. At steady state, the t-PA concentration variability increased gradually with increasing glucose usage up to approximately 22 mM, then up to 24 mM the variability increased threefold. Peak t-PA concentrations of over 90 mg/L were obtained by controlling at a glucose usage of approximately 24 mM, but these t-PA levels were not sustainable for more than 3 days. A consistent t-PA concentration of 40 mg/L was obtained at a glucose usage of 21.5 mM.     

Application of statistically-based experimental designs for the optimization of eicosapentaenoic acid production by the diatom Nitzschia laevis

Statistically based experimental designs were applied to the optimization of medium components and environmental factors for eicosapentaenoic acid (EPA) production by the diatom Nitzschia laevis in heterotrophic conditions. First, the Plackett-Burman design was used to evaluate the effects of variables including medium components and environmental factors on cell growth and EPA production. Among these variables, NaCl, CaCl(2), PI metal solution, pH, and temperature were identified to have the significant effects (with confidence level > 90 %). Subsequently, the concentrations of NaCl, CaCl(2), PI metal solution as well as the values of pH and temperature were optimized using central composite design. The cell growth and EPA production were found to respectively correlate to NaCl, CaCl(2), pH, and temperature that could be represented by second-order polynomial models. The optimal values of the four parameters were determined by response surface and numerical analyses as 8 g/L NaCl, 0.10 g/L CaCl(2), pH 8.5 and 19.8 degrees C for cell dry weight (DW), and 14 g/L NaCl, 0.10 g/L CaCl(2), pH 8.5 and 18 degrees C for EPA production, respectively. The subsequent verification experiments confirmed the validity of the models. This optimization strategy led to a DW of 9 g/L, an EPA yield of 280 mg/L and an EPA productivity of 28 mg/L/d, respectively, which were considerably higher than those obtained in the previous studies.