Vector-based Approach Research Articles

Determination of Quality Value of Cotton Fiber Using Integrated Best-Worst Method-Revised Analytic Hierarchy Process

ABSTRACT Selection of cotton fibers in terms of their quality value has created a domain of emerging interest among the researchers. In this study, a newly developed Best-Worst Method (BWM) was integrated with Revised Analytic Hierarchy Process (RAHP) to rank cotton fiber lots on the basis of six apposite fiber properties namely fiber bundle tenacity, elongation, micronaire, upper half mean length, uniformity index, and short fiber index. Ranking performance of this integrated approach closely resembles those of the other multi-criteria decision-making (MCDM) approaches. No occurrence of rank reversal during the sensitivity analyses corroborates the stability and robustness of the BWM-RAHP method. Uniqueness of the present study lies in the fact that this is the maiden application of the vector-based BWM approach, that uses fewer pairwise comparisons than other variants of MCDM, in a cotton fiber grading problem. The RAHP adds value to the decision model by overcoming the problem of ranking inconsistency. Rank correlations between the ranking based on quality value of cotton and those based on yarn tenacity are also encouraging, and further bolster the efficacy of the BWM-RAHP method.

The double-edged sword of inflammation in inherited retinal degenerations: Clinical and preclinical evidence for mechanistically and prognostically impactful but treatable complications.

We present retrospective data from our clinical research efforts of the past several years alongside a review of past and current clinical and preclinical data independently by several investigators supporting our clinical evidence for the importance of inflammation in inherited retinal degenerations (IRDs). We show how inflammation is a complicating factor in IRDs but, if recognized and managed, also a great opportunity to mitigate disease severity immediately, improve patient prognosis and quality of life, extend the treatment windows for gene-specific and agnostic therapeutic approaches, mitigate the impact of inflammatory complications on the accurate estimate of vision changes in IRD natural history studies, improve the chances of safer outcomes following cataract surgery, and potentially reduce the likelihood of inflammatory adverse events and augment the efficacy of viral vector-based treatment approaches to IRDs. Manuscript contribution to the field. Inflammation has been suspected to be at play in IRDs since the beginning of the 1900s and became a research focus through the early 1990s but was then largely abandoned in favor of genetic-focused research. Thanks to regained cognizance, better research tools, and a more holistic approach to IRDs, the recent reappraisal of the role of inflammation in IRDs has brought back to the surface its importance. A potential confounder in natural history studies and a limiting factor in clinical trials if not accounted for, inflammation can be managed and often offers an opportunity for immediately improved prognosis and outcomes for IRD patients. We present our retrospective clinical evidence for connections with a measurable secondary autoimmune component that can develop in IRDs and contribute to vision loss but is at least in part treatable. We also present ample lines of evidence from the literature corroborating our clinical observations at the preclinical level.

Pseudo Relevance Feedback with Deep Language Models and Dense Retrievers: Successes and Pitfalls

Pseudo Relevance Feedback (PRF) is known to improve the effectiveness of bag-of-words retrievers. At the same time, deep language models have been shown to outperform traditional bag-of-words rerankers. However, it is unclear how to integrate PRF directly with emergent deep language models. This article addresses this gap by investigating methods for integrating PRF signals with rerankers and dense retrievers based on deep language models. We consider text-based, vector-based and hybrid PRF approaches and investigate different ways of combining and scoring relevance signals. An extensive empirical evaluation was conducted across four different datasets and two task settings (retrieval and ranking). Text-based PRF results show that the use of PRF had a mixed effect on deep rerankers across different datasets. We found that the best effectiveness was achieved when (i) directly concatenating each PRF passage with the query, searching with the new set of queries, and then aggregating the scores; (ii) using Borda to aggregate scores from PRF runs. Vector-based PRF results show that the use of PRF enhanced the effectiveness of deep rerankers and dense retrievers over several evaluation metrics. We found that higher effectiveness was achieved when (i) the query retains either the majority or the same weight within the PRF mechanism, and (ii) a shallower PRF signal (i.e., a smaller number of top-ranked passages) was employed, rather than a deeper signal. Our vector-based PRF method is computationally efficient; thus, this represents a general PRF method others can use with deep rerankers and dense retrievers.

Dynamic model of upper extremity and its application: a systematic review

Dynamics models are widely used in analyzing kinematics and kinetics analysis. Dynamic modeling and simulation can be useful in estimating the torques/forces produced in the limb during various motions, as well as any potential dangers. The aim of this research is to examine the method that is preferable in analyzing the dynamic model of the upper extremity and its application. Several methods have been identified and widely used in analyzing the dynamic model of the upper extremity such as Kane’s method, Lagrange method, optimization method, regression method and many more. Kane's approach, on the other hand, is most commonly used to analyze3D arm segments due to its advantages which is a vector-based approach. The Lagrange approach is suitable to use when the potential and kinetic energy of a system could be easily identified. Meanwhile, most of the researchers used statistical analysis to verify their model and calculation. However, it could be difficult to replicate the simulation work of others because some were not publicly available. Thus, it was recommended to choose a reference from prior studies that will serve as guidance for researchers to solve relevant issues in this field.

Gene Therapy of Sphingolipid Metabolic Disorders.

Sphingolipidoses are defined as a group of rare hereditary diseases resulting from mutations in the genes encoding lysosomal enzymes. This group of lysosomal storage diseases includes more than 10 genetic disorders, including GM1-gangliosidosis, Tay-Sachs disease, Sandhoff disease, the AB variant of GM2-gangliosidosis, Fabry disease, Gaucher disease, metachromatic leukodystrophy, Krabbe disease, Niemann-Pick disease, Farber disease, etc. Enzyme deficiency results in accumulation of sphingolipids in various cell types, and the nervous system is also usually affected. There are currently no known effective methods for the treatment of sphingolipidoses; however, gene therapy seems to be a promising therapeutic variant for this group of diseases. In this review, we discuss gene therapy approaches for sphingolipidoses that are currently being investigated in clinical trials, among which adeno-associated viral vector-based approaches and transplantation of hematopoietic stem cells genetically modified with lentiviral vectors seem to be the most effective.

DPBA-WGAN: A Vector-Valued Differential Private Bilateral Alternative Scheme on WGAN for Image Generation

The large amount of sensitive personal information used in deep learning models has attracted considerable attention for privacy security. Sensitive data may be memorialized or encoded into the parameters or generation of the Wasserstein Generative Adversarial Networks (WGAN), which can be prevented by implementing privacy-preserving algorithms during the parameter training process. Meanwhile, the model is also expected to obtain effective generated results. We propose a vector-valued differential private bilateral alternative (DPBA) algorithm, a novel perturbation method for the training process. The vector-valued Gaussian (VVG) noise involving functional structure information is injected into the WGAN to generate data with privacy protection, and the model is verified to satisfy differential privacy. The bilateral alternative noise can eventually randomly perturb the gradient and generates informative feature-rich samples. The dynamic noise and vector-based perturbation approach ensures privacy strength. After extensive evaluation, our algorithm outperformed state-of-the-art techniques in terms of usability metrics for all validation datasets. The downstream classification accuracy for the generated Modified National Institute of Standards and Technology (Mnist) was 97.04%, whereas that for the Fashion Modified National Institute of Standards and Technology (Fashion-Mnist) dataset was 80.91%. Mnist improved the average accuracy of the neural network classifier by at least 16.81%, and Fashion-Mnist by at least 3.55%. In the multichannel generation tasks, the binary classification accuracy improved by at least 10.4% compared to CelebFaces Attributes (CelebA), and the accuracy of the Street View House Numbers (SVHN) was as high as 86.1%. The perturbation method proved highly resilient to gradient attack recovery under simulated gradient attacks.

Optimization of Capillary-Based Western Blotting for MYO7A.

Myosin VIIA (MYO7A)-associated Usher syndrome type 1B (USH1B) is a severe disorder that impacts the auditory, vestibular, and visual systems of affected patients. Due to the large size (~7.5kb) of the MYO7A coding sequence, we have designed a dual adeno-associated virus (AAV) vector-based approach for the treatment of USH1B-related vision loss. Due to the added complexity of dual-AAV gene therapy, careful attention must be paid to the protein products expressed following vector recombination. In order to improve the sensitivity and quantifiability of our immunoassays, we adapted our traditional western blot protocol for use with the Jess™ Simple Western System. Following several rounds of testing, we optimized our protocol for the detection of MYO7A in two of our most frequently used sample types, mouse eyes, and infected HEK293 cell lysates.

Real estate valuation based on big data

The paper considers the application of the web scrapping and machine learning algorithms for the assessment of the real estate price on the secondary housing market in Moscow. For this, we collect and process the data from the CIAN website and the data from “Reforma GKH”. To evaluate real estate objects, we consider such machine learning algorithms as Elastic Net, Random Forest and Gradient Boosting. We also apply Shapley vector-based approach to interpret the results of the black-box algorithms. The results suggest that the use of black-box algorithms in assessing the price of apartments on the Moscow secondary housing market allows to obtain more accurate price estimates both for different price segments and for the sample as a whole. At the same time, Gradient Boosting has demonstrated the best accuracy among other algorithms. Interpretation based on the Shapley vector shows that the total area, year of construction, ceiling height, renovation, as well as monolithic construction technology had a positive effect on the price. The price is negatively affected by the number of floors in the house, the possibility of mortgage and lack of repairs. Developed methodology can be applied in real estate insurance, mortgage, determination of cadastral value of real estate and others.

Distinct Phenotypic Consequences of Pathogenic Mutants Associated with Late-Onset Retinal Degeneration

A pathologic feature of late-onset retinal degeneration caused by the S163R mutation in C1q-tumor necrosis factor-5 (C1QTNF5) is the presence of unusually thick deposits between the retinal pigmented epithelium (RPE) and the vascular choroid, considered a hallmark of this disease. Following its specific expression in mouse RPE, the S163R mutant exhibits a reversed polarized distribution relative to the apically secreted wild-type C1QTNF5, and forms widespread, prominent deposits that gradually increase in size with aging. The current study shows that S163R deposits expand to a considerable thickness through a progressive increase in the basolateral RPE membrane, substantially raising the total RPE height, and enabling their clear imaging as a distinct hyporeflective layer by noninvasive optical coherence tomography in advanced age animals. This phenotype bears a striking resemblance to ocular pathology previously documented in patients harboring the S163R mutation. Therefore, a similar viral vector-based gene delivery approach was used to also investigate the behavior of P188T and G216C, two novel pathogenic C1QTNF5 mutants recently reported in patients for which histopathologic data are lacking. Both mutants primarily impacted the RPE/photoreceptor interface and did not generate basal laminar deposits. Distinct distribution patterns and phenotypic consequences of C1QTNF5 mutants were observed invivo, which suggested that multiple pathobiological mechanisms contribute to RPE dysfunction and vision loss in this disorder.

Gene therapy approaches for equine osteoarthritis.

With an intrinsically low ability for self-repair, articular cartilage injuries often progress to cartilage loss and joint degeneration resulting in osteoarthritis (OA). Osteoarthritis and the associated articular cartilage changes can be debilitating, resulting in lameness and functional disability both in human and equine patients. While articular cartilage damage plays a central role in the pathogenesis of OA, the contribution of other joint tissues to the pathogenesis of OA has increasingly been recognized thus prompting a whole organ approach for therapeutic strategies. Gene therapy methods have generated significant interest in OA therapy in recent years. These utilize viral or non-viral vectors to deliver therapeutic molecules directly into the joint space with the goal of reprogramming the cells' machinery to secrete high levels of the target protein at the site of injection. Several viral vector-based approaches have demonstrated successful gene transfer with persistent therapeutic levels of transgene expression in the equine joint. As an experimental model, horses represent the pathology of human OA more accurately compared to other animal models. The anatomical and biomechanical similarities between equine and human joints also allow for the use of similar imaging and diagnostic methods as used in humans. In addition, horses experience naturally occurring OA and undergo similar therapies as human patients and, therefore, are a clinically relevant patient population. Thus, further studies utilizing this equine model would not only help advance the field of human OA therapy but also benefit the clinical equine patients with naturally occurring joint disease. In this review, we discuss the advancements in gene therapeutic approaches for the treatment of OA with the horse as a relevant patient population as well as an effective and commonly utilized species as a translational model.

BNT162b2-boosted immune responses six months after heterologous or homologous ChAdOx1nCoV-19/BNT162b2 vaccination against COVID-19

Heterologous prime/boost vaccination with a vector-based approach (ChAdOx-1nCov-19, ChAd) followed by an mRNA vaccine (e.g. BNT162b2, BNT) has been reported to be superior in inducing protective immunity compared to repeated application of the same vaccine. However, data comparing immunity decline after homologous and heterologous vaccination as well as effects of a third vaccine application after heterologous ChAd/BNT vaccination are lacking. Here we show longitudinal monitoring of ChAd/ChAd (n = 41) and ChAd/BNT (n = 88) vaccinated individuals and the impact of a third vaccination with BNT. The third vaccination greatly augments waning anti-spike IgG but results in only moderate increase in spike-specific CD4 + and CD8 + T cell numbers in both groups, compared to cell frequencies already present after the second vaccination in the ChAd/BNT group. More importantly, the third vaccination efficiently restores neutralizing antibody responses against the Alpha, Beta, Gamma, and Delta variants of the virus, but neutralizing activity against the B.1.1.529 (Omicron) variant remains severely impaired. In summary, inferior SARS-CoV-2 specific immune responses following homologous ChAd/ChAd vaccination can be compensated by heterologous BNT vaccination, which might influence the choice of vaccine type for subsequent vaccination boosts.

Inferring 2D Local Surface-Deformation Velocities Based on PSI Analysis of Sentinel-1 Data: A Case Study of Öræfajökull, Iceland

Two-dimensional deformation estimates derived from Persistent Scatterer Interferometric (PSI) analysis of Synthetic Aperture Radar (SAR) data can improve the characterisation of spatially and temporally varying deformation processes of Earth’s surface. In this study, we examine the applicability of Persistent Scatterer (PS) Line-Of-Sight (LOS) estimates in providing two-dimensional deformation information, focusing on the retrieval of the local surface-movement processes. Two Sentinel-1 image stacks, ascending and descending, acquired from 2015 to 2018, were analysed based on a single master interferometric approach. First, Interferometric SAR (InSAR) deformation signals were corrected for divergent plate spreading and the Glacial Isostatic Adjustment (GIA) signals. To constrain errors due to rasterisation and interpolation of the pointwise deformation estimates, we applied a vector-based decomposition approach to solve the system of linear equations, resulting in 2D vertical and horizontal surface-deformation velocities at the PSs. We propose, herein, a two-step decomposition procedure that incorporates the Projected Local Incidence Angle (PLIA) to solve for the potential slope-deformation velocity. Our derived 2D velocities reveal spatially detailed movement patterns of the active Svínafellsjökull slope, which agree well with the independent GPS time-series measurements available for this area.

Automated Process Planning for Embossing and Functionally Grading Materials via Site-Specific Control in Large-Format Metal-Based Additive Manufacturing.

The potential for site-specific, process-parameter control is an attribute of additive manufacturing (AM) that makes it highly attractive as a manufacturing process. The research interest in the functionally grading material properties of numerous AM processes has been high for years. However, one of the issues that slows developmental progress in this area is process planning. It is not uncommon for manual programming methods and bespoke solutions to be utilized for site-specific control efforts. This article presents the development of slicing software that contains a fully automated process planning approach for enabling through-thickness, process-parameter control for a range of AM processes. The technique includes the use of parent and child geometries for controlling the locations of site-specific parameters, which are overlayed onto unmodified toolpaths, i.e., a vector-based planning approach is used in which additional information, such as melt pool size for large-scale metal AM processes, is assigned to the vectors. This technique has the potential for macro- and micro-structural modifications to printed objects. A proof-of-principle experiment is highlighted in which this technique was used to generate dynamic bead geometries that were deposited to induce a novel surface embossing effect, and additional software examples are presented that highlight software support for more complex objects.

A new era of in vivo gene therapy: the applicability of a differentiated HSV-1 based vector platform for redosable medicines

Viral vector-based gene replacement approaches have traditionally focused on the use of adenoviruses, adeno-associated viruses, and lentiviruses for functional gene transmission. Innovation in payload delivery is critical for advancing the boundaries of genetic medicine. While underappreciated, herpes simplex virus type 1 (HSV-1) possesses a number of natural traits that make it an attractive alternative for gene therapy approaches, including episomal delivery, large payload capacity, a broad tissue tropism, and the ability to resist immune clearance via inhibition of innate and adaptive anti-viral immunity. Krystal Biotech has created a proprietary HSV-1-based gene delivery platform leveraging many of the natural properties innate to HSV-1, while engineering it to be replication-incompetent to reduce cytotoxicity. This platform has been validated clinically in dermatology, and its utility is being extended into programs across additional tissue types and organ systems, including initiation of a genetic pulmonary program in cystic fibrosis. This differentiated vector platform provides a broadly applicable, highly versatile gene delivery system for the development of direct and redosable genetically-coded medicines.

A vector-based semantic relatedness measure using multiple relations within SNOMED CT and UMLS

ObjectiveTo propose a new vector-based relatedness metric that derives word vectors from the intrinsic structure of biomedical ontologies, without consulting external resources such as large-scale biomedical corpora. Materials and MethodsSNOMED CT on the mapping layer of UMLS was used as a testbed ontology. Vectors were created for every concept at the end of all semantic relations—attribute–value relations and descendants as well as is_a relation—of the defining concept. The cosine similarity between the averages of those vectors with respect to each defining concept was computed to produce a final semantic relatedness. ResultsTwo benchmark sets that include a total of 62 biomedical term pairs were used for evaluation. Spearman’s rank coefficient of the current method was 0.655, 0.744, and 0.742 with the relatedness rated by physicians, coders, and medical experts, respectively. The proposed method was comparable to a word-embedding method and outperformed path-based, information content-based, and another multiple relation-based relatedness metrics. DiscussionThe current study demonstrated that the addition of attribute relations to the is_a hierarchy of SNOMED CT better conforms to the human sense of relatedness than models based on taxonomic relations. The current approach also showed that it is robust to the design inconsistency of ontologies. ConclusionUnlike the previous vector-based approach, the current study exploited the intrinsic semantic structure of an ontology, precluding the need for external textual resources to obtain context information of defining terms. Future research is recommended to prove the validity of the current method with other biomedical ontologies.

Exploring Language Markers of Mental Health in Psychiatric Stories

Diagnosing mental disorders is complex due to the genetic, environmental and psychological contributors and the individual risk factors. Language markers for mental disorders can help to diagnose a person. Research thus far on language markers and the associated mental disorders has been done mainly with the Linguistic Inquiry and Word Count (LIWC) program. In order to improve on this research, we employed a range of Natural Language Processing (NLP) techniques using LIWC, spaCy, fastText and RobBERT to analyse Dutch psychiatric interview transcriptions with both rule-based and vector-based approaches. Our primary objective was to predict whether a patient had been diagnosed with a mental disorder, and if so, the specific mental disorder type. Furthermore, the second goal of this research was to find out which words are language markers for which mental disorder. LIWC in combination with the random forest classification algorithm performed best in predicting whether a person had a mental disorder or not (accuracy: 0.952; Cohen’s kappa: 0.889). SpaCy in combination with random forest predicted best which particular mental disorder a patient had been diagnosed with (accuracy: 0.429; Cohen’s kappa: 0.304).

Genetic Therapies for Alzheimer’s Disease: A Scoping Review

Effective, disease modifying therapies for Alzheimer’s disease (AD) remain a quandary, following a panoply of expensive failures in human clinical trials. Given the stagnation in therapeutics, alternative approaches are needed. Recent successes of genetic therapies in other neurodegenerative diseases may highlight the way forward. This scoping review explores suggested targets of genetic therapy in AD, with a focus on vector-based approaches in pre-clinical and clinical trials. Putative targets of genetic therapies tested in pre-clinical trials include amyloid pathway intermediates and enzymes modulation, tau protein downregulation, APOE4 downregulation and APOE2 upregulation, neurotrophin expression (nerve growth factor (NGF) and brain-derived neurotrophic factor), and inflammatory cytokine alteration, among several other approaches. There have been three completed human clinical trials for genetic therapy in AD patients, all of which upregulated NGF in AD patients, showing some mixed evidence of benefit. Several impediments remain to be surpassed before genetic therapies can be successfully applied to AD, including the challenge of delivering monogenic genetic therapies for complex polygenic disorders, risks in the dominant delivery method (intracranial injection), stability of genetic therapies in vivo, poor translatability of pre-clinical AD models, and the expense of genetic therapy production. Genetic therapies represent an exciting opportunity within the world of AD therapeutics, but clinical applications likely remain a long term, rather than short term, possibility.

A framework for calculating orthogonal selectivities in multimodal systems directly from cell culture fluid.

This paper presents a straightforward approach for measuring and quantifying orthogonality directly in complex cell culture fluids (CCFs) without the requirement for tracking the retention behaviors of large sets of proteins. Null-producing CCFs were fractionated using linear salt gradients at constant pH on a set of multimodal resins. Fractions were then analyzed by ultraperformance-reversed phase liquid chromatography and the resulting chromatograms provided host cell protein (HCP) "fingerprints."Using these fingerprints, an inner product vector-based approach was employed to quantify the degree of orthogonality between pairs of resins and operating conditions for these large HCP protein sets. To compare resin orthogonality behavior in different expression systems, the Chinese hamster ovary and Pichia pastoris null-producing CCFs were examined. Orthogonality in multimodal systems was found to strongly depend on the expression system and the HCPs being screened. We also identified several unexpected pairs of multimodal resins within the same family that exhibited significant orthogonality. Furthermore, "self-orthogonality" was evaluated between resins operated at different pHs, and important operating regimes were identified for maximizing orthogonal selectivities. The framework developed in this paper for calculating orthogonality without the need for labor-intensive HCP tracking has important implications for efficient process development and resin/operating condition selection for both monoclonal antibody (mAb) polishing steps and non-mAb processes. In addition, this study provides a tool to unlock the untapped potential of multimodal resins by aiding in their rational selection and incorporation. Finally, the orthogonality framework here can facilitate the development of sets of next-generation multimodal resins specifically designed to provide highly orthogonal and efficient separations tailored for different expression systems.

Dosing extracellular vesicles.

Extracellular vesicles (EVs) are natural nanoparticles containing biologically active molecules. They are important mediators of intercellular communication and can be exploited therapeutically by various bioengineering approaches. To accurately determine the therapeutic potential of EVs in pre-clinical and clinical settings, dependable dosing strategies are of utmost importance. However, the field suffers from inconsistencies comprising all areas of EV production and characterisation. Therefore, a standardised and well-defined process in EV quantification, key to reliable therapeutic EV dosing, remains to be established. Here, we examined 64 pre-clinical studies for EV-based therapeutics with respect to their applied EV dosing strategies. We identified variations in effective dosing strategies irrespective of the applied EV purification method and cell source. Moreover, we found dose discrepancies depending on the disease model, where EV doses were selected without accounting for published EV pharmacokinetics or biodistribution patterns. We therefore propose to focus on qualitative aspects when dosing EV-based therapeutics, such as the potency of the therapeutic cargo entity. This will ensure batch-to-batch reliability and enhance reproducibility between applications. Furthermore, it will allow for the successful benchmarking of EV-based therapeutics compared to other nanoparticle drug delivery systems, such as viral vector-based or lipid-based nanoparticle approaches.

Vector-based three-dimensional (3D) well-path optimization assisted by geological modelling and borehole-log extraction

This paper describes a novel strategy to optimize the drilling time of three-dimensional (3D) directional wellbore trajectories using a vector-based approach subject to drilling and geological constraints. Many existing well-path models require manual entry for certain geological constraints such as formation dip or kick-off limit. In contrast, this vector-based approach ensures that geological constraints are automatically satisfied by building a geological model, and extracting a borehole log of key points along the well-path. The presented approach applies and compares a deterministic optimization technique known as Constrained Optimization by Linear Approximation (COBYLA) with a Genetic-Algorithm (GA) global optimization to determine the optimum 3D well path to drill the target. While optimizing the path, the model determines the optimum kick-off point based on the subsurface-formation strength and depth subject to predetermined doglog severity, inclination and azimuth angles. The methodology is applied to well paths with different number of build-up and drop sections in unconstrained and constrained geological settings. Results show that COBYLA and GA are comparable when not using geological modelling while GA is superior for complex well-path geology-assisted optimization problems. The technique is applicable for a single well path planning, and can be expanded to a set of wells being optimized during Field Development Planning (FDP).