Bioprocess applications of model-based estimation techniques

The last decade has seen the development of a number of approaches for estimating those variables which are difficult to measure on-line in industrial process situations. Whilst a range of techniques is available, a common element is the use of process knowledge in the form of a system model. In the case of bioprocess systems, although a large range of models has been presented in the literature, their use in estimation schemes on an industrial scale has been limited. A number of reasons can be identified for their low level of utilisation. Of particular significance is the uncertainty which exists in quantifying system performance and the process-model mismatch which inevitably results. The level of ‘pre-defined model’ uncertainty, together with the knowledge gained during the course of the fermentation, serves to dictate estimator structure. The paper considers a range of estimation strategies and contrasts, through industrial applications, their performance characteristics and utility.     

Preparation and characterization of comb-shaped polyesters from 2,2-dioctadecyl-1,3-propanediol and phthalic acids

Summary New types of comb-shaped polyesters have been prepared by transesterification of 2,2-dioctadecyl-1,3-propanediol and the three isomeric diphenyl phthalates. Intrinsic viscosity and SEC analyses allowed determination of DPs (60–89) and molar masses; results compare well with absolute average weight molar masses (56,000–118,000) determined by low-angle laser light scattering. High-resolution ¹³C NMR reveals the characteristic aromatic and glycol polyester patterns; it resolves many side-chain methylene carbons, but shows no evidence of end-groups. All the polyesters are crystalline as shown by X-ray diffractometry and DSC, the crystallinity being exclusively due to the octadecyl side-group crystallization in the hexagonal form. The results indicate that the crystallinity decreases substantially when going from the ortho- to the terephthalate polyester.     

Serial measurements of body composition in obese subjects during a very-low-energy diet (VLED) comparing bioelectrical impedance with hydrodensitometry

Bioelectrical impedance analysis (BIA) is a convenient, inexpensive, and noninvasive technique for measuring body composition. BIA has been strongly correlated with total body water (TBW) and also has been validated against hydrodensitometry (HD). The accuracy and clinical utility of BIA and HD during periods of substantial weight loss remain controversial. We measured body composition in moderately and severely obese patients serially using both methods during a very-low-energy diet (VLED). Mean initial weight in these patients was 116 (+/-30) kg (range, 74-196 kg). Mean weight loss was 24 (+/-13) kg with a decrease in fat mass (FM) by HD of kg (p < 0.001) and a decrease in fat-free mass (FFM) of 3.6 kg (p < 0.05). Loss of FFM is best predicted by the rate (kg/wk) of weight loss (r2 = 0.86, p < 0.0001). FFM, as predicted from BIA equations, was highly correlated with FFM as estimated by HD during all testing sessions (r = 0.92-0.98). Although highly correlated, BIA overestimated FFM relative to HD and this difference appeared to be more pronounced for taller patients with greater truncal obesity. Although the discrepancy was no greater during weight-loss treatment, the level of disagreement was considerable. Therefore, the two methods cannot be used interchangeably to monitor relative changes in body composition in patients with obesity during treatment with VLED. The discrepancy between BIA and HD may be caused by body mass distribution considerations and by perturbations in TBW which affect the hydration quotient for FFM (BIA) and/or which affect the density constants for FFM and FM (HD).     

A Novel Human Neutralizing mAb Recognizes Delta,Gamma and Omicron Variants of SARS-CoV-2 and Can Be Used in Combination with Sotrovimab

The dramatic experience with SARS-CoV-2 has alerted the scientific community to be ready to face new epidemics/pandemics caused by new variants. Among the therapies against the pandemic SARS-CoV-2 virus, monoclonal Antibodies (mAbs) targeting the Spike glycoprotein have represented good drugs to interfere in the Spike/ Angiotensin Converting Enzyme-2 (ACE-2) interaction, preventing virus cell entry and subsequent infection, especially in patients with a defective immune system. We obtained, by an innovative phage display selection strategy, specific binders recognizing different epitopes of Spike. The novel human antibodies specifically bind to Spike-Receptor Binding Domain (RBD) in a nanomolar range and interfere in the interaction of Spike with the ACE-2 receptor. We report here that one of these mAbs, named D3, shows neutralizing activity for virus infection in cell cultures by different SARS-CoV-2 variants and retains the ability to recognize the Omicron-derived recombinant RBD differently from the antibodies Casirivimab or Imdevimab. Since anti-Spike mAbs, used individually, might be unable to block the virus cell entry especially in the case of resistant variants, we investigated the possibility to combine D3 with the antibody in clinical use Sotrovimab, and we found that they recognize distinct epitopes and show additive inhibitory effects on the interaction of Omicron-RBD with ACE-2 receptor. Thus, we propose to exploit these mAbs in combinatorial treatments to enhance their potential for both diagnostic and therapeutic applications in the current and future pandemic waves of coronavirus.     

Cultural ‘demons’ as future builders

Usually, the shape of the future is seen as the result of a cultural flow that, according to some privileged cultural variable, like technology, goes undisturbed towards its own outcome. This is a quite naive attitude that has been very rarely successful. Both conventional technology and technology of the artificial show that, within culture, ‘demons’ are always active trying to exploit or even bypass standards in order to give birth to unexpected novelties. This is true within the pure technology area and even more in its artistic extensions. Therefore, no deterministic process is going on but, rather, a permanent remixing of chances that makes any prophecy impossible.     

Disaster-survivable cloud-network mapping

Cloud-computing services are provided to consumers through a network of servers and network equipment. Cloud-network (CN) providers virtualize resources [e.g., virtual machine (VM) and virtual network (VN)] for efficient and secure resource allocation. Disasters are one of the worst threats for CNs as they can cause massive disruptions and CN disconnection. A disaster may also induce post-disaster correlated, cascading failures which can disconnect more CNs. Survivable virtual-network embedding (SVNE) approaches have been studied to protect VNs against single physical-link/-node and dual physical-link failures in communication infrastructure, but massive disruptions due to a disaster and their consequences can make SVNE approaches insufficient to guarantee cloud-computing survivability. In this work, we study the problem of survivable CN mapping from disaster. We consider risk assessment, VM backup location, and post-disaster survivability to reduce the risk of failure and probability of CN disconnection and the penalty paid by operators due to loss of capacity. We formulate the proposed approach as an integer linear program and study two scenarios: a natural disaster, e.g., earthquake and a human-made disaster, e.g., weapons-of-mass-destruction attack. Our illustrative examples show that our approach reduces the risk of CN disconnection and penalty up to 90 % compared with a baseline CN mapping approach and increases the CN survivability up to 100 % in both scenarios.     

Provisioning of dynamic traffic in mixed-line-rate optical networks with launch power determination

In mixed-line-rate (MLR) networks, different line rates on different wavelengths can coexist on the same fiber. MLR architectures can be built over transparent optical networks, where the transmitted signals remain in the optical domain along the entire path. Along the transparent optical path, a signal experiences various physical layer impairments (PLIs), and its quality degrades as it travels through each optical component. One of the major factors that affect the transmission quality is the launch power of the optical signal. The power must be large enough to ensure noise resiliency at the receiver, but it must be below the limit where fiber nonlinearities distort the signal. Moreover, high launch power is disruptive not only for the actual lightpath itself but also for neighboring lightpaths, and this effect is particularly critical in MLR networks since advanced modulation techniques used for high line rates are highly susceptible to PLIs. In this study, we investigate the problem of determining the appropriate launch power for provisioning of dynamic connection requests in MLR networks. By setting the appropriate launch power for each connection, we aim to maximize the number of established connections. We propose two different heuristics to determine the appropriate launch power of a lightpath. Worst-case best-case average (WBA) is based on optical reach of signal in a transparent optical network. In impairment-aware launch power determination (I-ALPD), current state of the network and impairments are evaluated to determine the launch power. The proposed approaches are practical and can adapt to the needs of network operators. Simulation results show that the performances of the proposed approaches show better results than the existing schemes in terms of blocking probability and bandwidth blocking ratio.