A New Practice to Monitor the Fabrication Process of Fab-Targeting Ligands from Bevacizumab by LC-MS: Preparation and Analytical Characterization

The objective of this study was to qualitatively evaluate a Fab-targeting ligand preparation containing free thiol groups in the hinge region by using bevacizumab as a model. The evaluation focused on the purification of fragments through a nonaffinity-based process using a centrifugal ultrafiltration technique and mild reduction conditions for the intact production of F(ab’) fragments with specific inter-heavy-chain disulfide bonds cleavage. Under these conditions, F(ab’) fragments with a defined chemical composition were successfully obtained via proteolytic digestion followed by a controlled reduction reaction process maintaining the integrity of the binding sites. The ultrafiltration purification technique appears to be suitable for the removal of the digestive enzyme but inefficient for the removal of Fc fragments, thus requiring additional processing. A suitable analytical strategy was developed, allowing us to demonstrate the reformation of disulfide bridges between the two reduced cysteines within F(ab’) fragments.

An Experimental Urban Case Study with Various Data Sources and a Model for Traffic Estimation

A reliable estimation of the traffic state in a network is essential, as it is the input of any traffic management strategy. The idea of using the same type of sensors along large networks is not feasible; as a result, data fusion from different sources for the same location should be performed. However, the problem of estimating the traffic state alongside combining input data from multiple sensors is complex for several reasons, such as variable specifications per sensor type, different noise levels, and heterogeneous data inputs. To assess sensor accuracy and propose a fusion methodology, we organized a video measurement campaign in an urban test area in Zurich, Switzerland. The work focuses on capturing traffic conditions regarding traffic flows and travel times. The video measurements are processed (a) manually for ground truth and (b) with an algorithm for license plate recognition. Additional processing of data from established thermal imaging cameras and the Google Distance Matrix allows for evaluating the various sensors’ accuracy and robustness. Finally, we propose an estimation baseline MLR (multiple linear regression) model (5% of ground truth) that is compared to a final MLR model that fuses the 5% sample with conventional loop detector and traffic signal data. The comparison results with the ground truth demonstrate the efficiency and robustness of the proposed assessment and estimation methodology.

Privacy-Preserving Tampering Detection in Automotive Systems

Modern auto-vehicles are built upon a vast collection of sensors that provide large amounts of data processed by dozens of Electronic Control Units (ECUs). These, in turn, monitor and control advanced technological systems providing a large palette of features to the vehicle’s end-users (e.g., automated parking, autonomous vehicles). As modern cars become more and more interconnected with external systems (e.g., cloud-based services), enforcing privacy on data originating from vehicle sensors is becoming a challenging research topic. In contrast, deliberate manipulations of vehicle components, known as tampering, require careful (and remote) monitoring of the vehicle via data transmissions and processing. In this context, this paper documents an efficient methodology for data privacy protection, which can be integrated into modern vehicles. The approach leverages the Fast Fourier Transform (FFT) as a core data transformation algorithm, accompanied by filters and additional transformations. The methodology is seconded by a Random Forest-based regression technique enriched with further statistical analysis for tampering detection in the case of anonymized data. Experimental results, conducted on a data set collected from the On-Board Diagnostics (OBD II) port of a 2015 EUR6 Skoda Rapid 1.2 L TSI passenger vehicle, demonstrate that the restored time-domain data preserves the characteristics required by additional processing algorithms (e.g., tampering detection), showing at the same time an adjustable level of privacy. Moreover, tampering detection is shown to be 100% effective in certain scenarios, even in the context of anonymized data.

Current freezing and thawing scenarios employed by North Atlantic fisheries: their potential role in Newfoundland and Labrador’s northern cod (Gadus morhua) fishery

Seafood is very perishable and can quickly spoil due to three mechanisms: autolysis, microbial degradation, and oxidation. Primary commercial sectors within the North Atlantic fisheries include demersal, pelagic, and shellfish fisheries. The preservation techniques employed across each sector can be relatively consistent; however, some key differences exist across species and regions to maintain product freshness. Freezing has long been employed as a preservation technique to maintain product quality for extended periods. Freezing allows seafood to be held until demand improves and shipped long distances using lower-cost ground transportation while maintaining organoleptic properties and product quality. Thawing is the opposite of freezing and can be applied before additional processing or the final sale point. However, all preservation techniques have limitations, and a properly frozen and thawed fish will still suffer from drip loss. This review summarizes the general introduction of spoilage and seafood spoilage mechanisms and the latest preservation techniques in the seafood industry, focusing on freezing and thawing processes and technologies. This review also considers the concept of global value chains (GVC) and the points to freeze and thaw seafood along the GVC to improve its quality with the intention of helping Newfoundland and Labrador’s emerging Northern cod (Gadus morhua) fisheries enhance product quality, meet market demands and increase stakeholder value.

Towards Optimal Parallelism-Aware Service Chaining and Embedding

<div>Emerging 5G technologies can significantly reduce end-to-end service latency for applications requiring strict quality of service (QoS). With network function virtualization (NFV), to complete a client’s request from those applications, the client’s data can sequentially go through multiple service functions (SFs) for processing/analysis but introduce additional processing delay. To reduce the processing delay from the serially-running SFs, network function parallelism (NFP) that allows multiple SFs to run in parallel is introduced. In this work, we study how to apply NFP into the SF chaining and embedding process such that the latency, including processing and propagation delays, can be jointly minimized. We introduce a novel augmented graph to address the parallel relationship constraint among the required SFs. Considering parallel relationship constraints, we propose a novel problem called parallelism-aware service function chaining and embedding (PSFCE). For this problem, we propose a near-optimal maximum parallel block gain (MPBG) first optimization algorithm when computing resources at each physical node are enough to host the required SFs. When computing resources are limited, we propose a logarithm-approximate algorithm, called parallelism-aware SFs deployment (PSFD), to jointly optimize processing and propagation delays. We conduct extensive simulations on multiple network scenarios to evaluate the performances of our schemes. Accordingly, we find that (i) MPBG is near-optimal, (ii) the optimization of end-to-end service latency largely depends on the processing delay in small networks and is impacted more by the propagation delay in large networks, and (iii) PSFD outperforms the schemes directly extended from existing works regarding end-to-end latency.</div>

Towards Optimal Parallelism-Aware Service Chaining and Embedding

<div>Emerging 5G technologies can significantly reduce end-to-end service latency for applications requiring strict quality of service (QoS). With network function virtualization (NFV), to complete a client’s request from those applications, the client’s data can sequentially go through multiple service functions (SFs) for processing/analysis but introduce additional processing delay. To reduce the processing delay from the serially-running SFs, network function parallelism (NFP) that allows multiple SFs to run in parallel is introduced. In this work, we study how to apply NFP into the SF chaining and embedding process such that the latency, including processing and propagation delays, can be jointly minimized. We introduce a novel augmented graph to address the parallel relationship constraint among the required SFs. Considering parallel relationship constraints, we propose a novel problem called parallelism-aware service function chaining and embedding (PSFCE). For this problem, we propose a near-optimal maximum parallel block gain (MPBG) first optimization algorithm when computing resources at each physical node are enough to host the required SFs. When computing resources are limited, we propose a logarithm-approximate algorithm, called parallelism-aware SFs deployment (PSFD), to jointly optimize processing and propagation delays. We conduct extensive simulations on multiple network scenarios to evaluate the performances of our schemes. Accordingly, we find that (i) MPBG is near-optimal, (ii) the optimization of end-to-end service latency largely depends on the processing delay in small networks and is impacted more by the propagation delay in large networks, and (iii) PSFD outperforms the schemes directly extended from existing works regarding end-to-end latency.</div>

Intraoperative discrimination of native meningioma and dura mater by Raman spectroscopy

Meningiomas are among the most frequent tumors of the central nervous system. For a total resection, shown to decrease recurrences, it is paramount to reliably discriminate tumor tissue from normal dura mater intraoperatively. Raman spectroscopy (RS) is a non-destructive, label-free method for vibrational analysis of biochemical molecules. On the microscopic level, RS was already used to differentiate meningioma from dura mater. In this study we test its suitability for intraoperative macroscopic meningioma diagnostics. RS is applied to surgical specimen of intracranial meningiomas. The main purpose is the differentiation of tumor from normal dura mater, in order to potentially accelerate the diagnostic workflow. The collected meningioma and dura mater samples (n = 223 tissue samples from a total of 59 patients) are analyzed under untreated conditions using a new partially robotized RS acquisition system. Spectra (n = 1273) are combined with the according histopathological analysis for each sample. Based on this, a classifier is trained via machine learning. Our trained classifier separates meningioma and dura mater with a sensitivity of 96.06 $$\pm $$ ± 0.03% and a specificity of 95.44 $$\pm $$ ± 0.02% for internal fivefold cross validation and 100% and 93.97% if validated with an external test set. RS is an efficient method to discriminate meningioma from healthy dura mater in fresh tissue samples without additional processing or histopathological imaging. It is a quick and reliable complementary diagnostic tool to the neuropathological workflow and has potential for guided surgery. RS offers a safe way to examine unfixed surgical specimens in a perioperative setting.

Mixed magnesian binders using local silicate waste production

Today, the waste of the mining industry is more than 8 billion tons. Analysis of the literature data showed that most of the man-made waste that is generated as a result of the development of mineral deposits is suitable for use in many industries, in particular, in the production of building materials. The use of technogenic raw materials allows us to solve the following tasks: Environmental aspect - reducing the number of dumps and reducing their volumes. And this, in turn, improves the ecology of regions and territories. 2. Economic aspect - reducing the cost of construction products through the use of almost free raw materials, the release of more competitive products. Of course, it is necessary to provide that part of the costs will be spent on additional processing, revision, activation, modification of this technogenic raw material component. But today we have to think about how to clear the territory of substandard “waste rock” and use it to reduce the production and consumption of natural raw materials. A similar process can create waste-free production.

Effect of wet chemical treatment on the properties of GaAs FIB-modified surface

We present the results of studies of the effect of wet chemical treatment on the properties of a GaAs surface modified by a gallium focused ion beam. Our studies based on results of AFM, KpAFM and Raman spectroscopy measurements have shown that, during wet chemical treatment, the damaged areas disappear completely in the case of low accelerating voltages and small doses of ions. At the same time, large accelerating voltages lead to the formation of extended damaged regions, the complete removal of which requires a longer treatment or additional processing.

Composting of fine fraction after mechanical-biological treatment of municipal solid waste

One of most common types of municipal solid waste treatment is mechanical-biological treatment (MBT), which in practice has many variations depending on the method of conducting the technological process and it is possible to get different output fractions. In this paper is analysed waste generated after the MBT with biodrying, where waste after mechanical treatment undergoes process of biodrying, and then is RDF (recovery derived fuel) separated. Fine fraction remains with a high content of organic matter that without additional processing cannot be disposed of on a landfill. The aim of this research was to determine the possibility of fine fraction composting in different conditions – in the open, in the open and covered area, and indoors. In each area are formed three compost piles: 100% fine fraction (KH1, KH4, and KH7), 70% fine fraction and 30% wood chips (KH2, KH5, and KH8), 50% fine fraction and 50% wood chips (KH3, KH6, and KH9). Moisture content, temperature and dissolved organic carbon (DOC) were monitored. Results show that after 13 weeks samples KH1, KH4, and KH7 (100% content of fine fractions) did not achieve DOC value less than 3 000 mg/l. The most effective composting in terms of reducing the DOC is achieved in samples KH3, KH6, KH9. Based on results obtained, it can be concluded that by adding wood chips in fine fraction in ratio 50:50, the most effective and fastest reduction of organic matter is achieved in the analysed samples.