A Study on the Impact of Distance-Based Value Loss on Transmission Network Power Flow Using Synthetic Networks

This paper presents a methodology for rapid generation of synthetic transmission networks and uses it to investigate how a transmission distance-based value loss affects the overall grid power flow. The networks are created with a graph theory-based method and compared to existing energy systems. The power production is located on these synthetic networks by solving a facility location optimization problem with variable distance-based value losses. Next, AC power flow is computed for a snapshot of each network using the Newton–Raphson method and the transmission grid power flow is analyzed. The presented method enables rapid analysis of several grid topologies and offers a way to compare the effects of production incentives and renewable energy policies in different network conditions.

Introduction to Big Data and Business Analytics

The term big data has come due to rapid generation of data in various organizations. In big data, the big is the buzzword. Here the data are so large and complex that the traditional database applications are not able to process (i.e., they are inadequate to deal with such volume of data). Usually the big data are described by 5Vs (volume, velocity, variety, variability, veracity). The big data can be structured, semi-structured, or unstructured. Big data analytics is the process to uncover hidden patterns, unknown correlations, predict the future values from large and complex data sets. In this chapter, the following topics will be covered more in detail. History of big data and business analytics, big data analytics technologies and tools, and big data analytics uses and challenges.

Adaptive Honeycomb Based on Additive Manufacturing: Research on Rapid Generation Algorithm, Manufacturing Process, and Mechanical Characteristics

In order to solve the problems of low efficiency and complex process in the current generation algorithm and process verification of hexagonal honeycomb structures for complex spatial shapes and arbitrarily curved surfaces, this paper proposes an adaptive hexagonal grid calculation method based on the intracellular splitting iteration method for the first time. This method can better adapt to the complex spatial shape and arbitrary curved surface structure in the three-dimensional space, and it can also achieve the purpose of enhancing the mechanical performance while maintaining the lightweight structure. According to the principle of the above algorithm, different structural models including honeycomb cells are calculated and generated. 316L Stainless Steel material and Selective Laser Melting additive manufacturing processes are also used for printing actual samples. The printed samples are mechanically compressed. According to the results of the compression curve, the critical yield force of the honeycomb grid parts with iteration is higher than that of the homogeneous honeycomb grid parts, and the value is basically greater than 30%-40%. Finally, the energy absorption efficiency can be increased by more than 20% according to the compression characteristics of the adaptive iterative honeycomb analyzed.

Swift Formation of Optimal Single Spheroids towards In-Vitro 3-Dimensional Tumour Models

Monolayer cell culture, while useful for basic in vitro studies, are not physiologically relevant. Spheroids, on the other hand provide a more complex 3-dimensional (3D) structure which more resemble the in vivo tumour growth thereby allowing results obtained with those on proliferation, cell death, differentiation, metabolism, and various anti-tumour therapies to be more predictive of in vivo outcomes. However, the cost associated with their generation often involve expensive, plate, media, and growth supplements, which have limited their use for high throughput experiments. The protocol herein presents a novel and rapid generation for single spheroids of various cancer cell lines, U87 MG; SEBTA-027; SF188, brain cancer cells, DU-145, TRAMP-C1, prostate cancer cells, in 96-round bottom well plates. Cells are washed with anti-adherent solution, and the homogeneous compact spheroid morphology was evidenced as early as 24 hours after 10 minutes centrifugation for the seeded cells. By using confocal microscopy, the proliferating cells were traced in the rim and the dead cells were found inside the core region of the spheroid. The H&E stain of spheroid slices and the western blotting were utilised to investigate the tightness of the cell packaging by adhesion proteins. Carnosine was used as an example of treatment for U87 single spheroids. The protocol allows the rapid generation of spheroids, which will help towards reducing the number of tests performed on animals.

Mitochondria Dysfunction in Frontotemporal Dementia/Amyotrophic Lateral Sclerosis: Lessons From Drosophila Models

Frontotemporal dementia (FTD) and amyotrophic lateral sclerosis (ALS) are neurodegenerative disorders characterized by declining motor and cognitive functions. Even though these diseases present with distinct sets of symptoms, FTD and ALS are two extremes of the same disease spectrum, as they show considerable overlap in genetic, clinical and neuropathological features. Among these overlapping features, mitochondrial dysfunction is associated with both FTD and ALS. Recent studies have shown that cells derived from patients’ induced pluripotent stem cells (iPSC)s display mitochondrial abnormalities, and similar abnormalities have been observed in a number of animal disease models. Drosophila models have been widely used to study FTD and ALS because of their rapid generation time and extensive set of genetic tools. A wide array of fly models have been developed to elucidate the molecular mechanisms of toxicity for mutations associated with FTD/ALS. Fly models have been often instrumental in understanding the role of disease associated mutations in mitochondria biology. In this review, we discuss how mutations associated with FTD/ALS disrupt mitochondrial function, and we review how the use of Drosophila models has been pivotal to our current knowledge in this field.

When less is more - Endogenous tagging with TurboID increases the sensitivity of proximity labelling-based experiments

In recent years, proximity labelling has established itself as an unbiased and powerful approach to map the interactome of specific proteins. Generally, protein fusions with labelling enzymes are transiently overexpressed to perform these experiments. Using a pipeline for the rapid generation CRISPR-Cas9 knock-ins (KIs) based on antibiotic selection, we were able to compare the performance of commonly used labelling enzymes when endogenously expressed. We found TurboID and its shorter variant miniTurboID to be superior above other labelling enzymes at physiological expression levels. Endogenous tagging of the μ subunit of the AP-1 complex increased the sensitivity for detection of interactors in a proximity labelling experiment and resulted in a more comprehensive mass spectrometry data set. We were able to identify several known interactors of the complex and cargo proteins that simple overexpression of a labelling enzyme fusion protein could not reveal. Our approach greatly simplifies the execution of proximity labelling experiments for proteins in their native cellular environment and allows going from CRISPR transfection to mass spectrometry data in just over a month.

Phage-like particle vaccines are highly immunogenic and protect against pathogenic coronavirus infection and disease

The response by vaccine developers to the COVID-19 pandemic has been extraordinary with effective vaccines authorized for emergency use in the U.S. within one year of the appearance of the first COVID-19 cases. However, the emergence of SARS-CoV-2 variants and obstacles with the global rollout of new vaccines highlight the need for platforms that are amenable to rapid tuning and stable formulation to facilitate the logistics of vaccine delivery worldwide. We developed a designer nanoparticle platform using phage-like particles (PLPs) derived from bacteriophage lambda for multivalent display of antigens in rigorously defined ratios. Here, we engineered PLPs that display the receptor binding domain (RBD) protein from SARS-CoV-2 and MERS-CoV, alone (RBD-SARS-PLPs, RBD-MERS-PLPs) and in combination (hCoV-RBD PLPs). Functionalized particles possess physiochemical properties compatible with pharmaceutical standards and retain antigenicity. Following primary immunization, BALB/c mice immunized with RBD-SARS- or RBD-MERS-PLPs display serum RBD-specific IgG endpoint and live virus neutralization titers that, in the case of SARS-CoV-2, were comparable to those detected in convalescent plasma from infected patients. Further, these antibody levels remain elevated up to 6 months post-prime. In dose response studies, immunization with as little as one microgram of RBD-SARS-PLPs elicited robust neutralizing antibody responses. Finally, animals immunized with RBD-SARS-PLPs, RBD-MERS-PLPs, and hCoV-RBD PLPs were protected against SARS-CoV-2 and/or MERS-CoV lung infection and disease. Collectively, these data suggest that the designer PLP system provides a platform for facile and rapid generation of single and multi-target vaccines.

Automated Hyperspectral Vegetation Index Derivation Using A Hyperparameter Optimization Framework for High-Throughput Plant Phenotyping

Hyperspectral vegetation indices (VIs) are widely deployed in agriculture remote sensing and plant phenotyping to estimate plant biophysical and biochemical traits. However, existing VIs consist mainly of simple 2-band indices which limits the net performance and often do not generalize well for other traits than they were originally designed for. We present an automated hyperspectral vegetation index (AutoVI) system for the rapid generation of novel 2- to 6-band trait-specific indices in a streamlined process covering model selection, optimization and evaluation driven by the tree parzen estimator algorithm. Its performance was tested in generating novel indices to estimate chlorophyll and sugar contents in wheat. Results show that AutoVI can rapidly generate complex novel VIs (≥4-band index) which correlated strongly (R2 > 0.8) with measured chlorophyll and sugar contents in wheat. AutoVI-derived indices were used as features in simple and stepwise multiple linear regression for chlorophyll and sugar content estimation, and outperformed results achieved with existing 47 VIs and those provided by partial least squares regression. The AutoVI system can deliver novel trait-specific VIs readily adoptable in high-throughput plant phenotyping platforms and should appeal to plant scientists and breeders. A graphical user interface of AutoVI is herein provided.

Type S Non Ribosomal Peptide Synthetases for the rapid generation of tailor-made peptide libraries

Bacterial natural products in general, and non-ribosomally synthesized peptides in particular, are structurally diverse and provide us with a broad range of pharmaceutically relevant bioactivities. Yet, traditional natural product research suffers from rediscovering the same scaffolds and has been stigmatised as inefficient, time-, labour-, and cost-intensive. Combinatorial chemistry, on the other hand, can produce new molecules in greater numbers, cheaper and in less time than traditional natural product discovery, but also fails to meet current medical needs due to the limited biologically relevant chemical space that can be addressed. Consequently, methods for the high throughput generation of new-to-nature natural products would offer a new approach to identifying novel bioactive chemical entities for the hit to lead phase of drug discovery programms. As a follow-up to our previously published proof-of-principle study on generating bipartite type S non-ribosomal peptide synthetases (NRPSs), we now envisaged the de novo generation of non-ribosomal peptides (NRPs) on an unreached scale. Using synthetic zippers, we split NRPS in up to three subunits and rapidly generated different bi- and tripartite NRPS libraries to produce 49 peptides, peptide derivatives, and de novo peptides at good titres up to 145 mgL-1. A further advantage of type S NRPSs not only is the possibility to easily expand the created libraries by re-using previously created type S NRPS, but that functions of individual domains as well as domain-domain interactions can be studied and assigned rapidly.