Miracle-dz/UniFAR-FTUs huggingface dataset:
Xer recombination for the automatic deletion of selectable marker genes from plasmids in enteric bacteria

Abstract Antibiotic resistance genes are widely used to select bacteria transformed with plasmids and to prevent plasmid loss from cultures, yet antibiotics represent contaminants in the biopharmaceutical manufacturing process, and retaining antibiotic resistance genes in vaccines and biological therapies is discouraged by regulatory agencies. To overcome these limitations, we have developed X-mark™, a novel technology that leverages Xer recombination to generate selectable marker gene-free plasmids for downstream therapeutic applications. Using this technique, X-mark plasmids with antibiotic resistance genes flanked by XerC/D target sites are generated in Escherichia coli cytosol aminopeptidase (E. coli pepA) mutants, which are deficient in Xer recombination on plasmids, and subsequently transformed into enteric bacteria with a functional Xer system. This results in rapid deletion of the resistance gene at high resolution (100%) and stable replication of resolved plasmids for more than 40 generations in the absence of antibiotic selective pressure. This technology is effective in both Escherichia coli and Salmonella enterica bacteria due to the high degree of homology between accessory sequences, including strains that have been developed as oral vaccines for clinical use. X-mark effectively eliminates any regulatory and safety concerns around antibiotic resistance carryover in biopharmaceutical products, such as vaccines and therapeutic proteins. Graphical Abstract
Transcription Factor CREB3L1 Regulates the Expression of the Sodium/Iodide Symporter (NIS) in Rat Thyroid Follicular Cells

The transcription factor CREB3L1 is expressed in a wide variety of tissues including cartilage, pancreas, and bone. It is located in the endoplasmic reticulum and upon stimulation is transported to the Golgi where is proteolytically cleaved. Then, the N-terminal domain translocates to the nucleus to activate gene expression. In thyroid follicular cells, CREB3L1 is a downstream effector of thyrotropin (TSH), promoting the expression of proteins of the secretory pathway along with an expansion of the Golgi volume. Here, we analyzed the role of CREB3L1 as a TSH-dependent transcriptional regulator of the expression of the sodium/iodide symporter (NIS), a major thyroid protein that mediates iodide uptake. We show that overexpression and inhibition of CREB3L1 induce an increase and decrease in the NIS protein and mRNA levels, respectively. This, in turn, impacts on NIS-mediated iodide uptake. Furthermore, CREB3L1 knockdown hampers the increase the TSH-induced NIS expression levels. Finally, the ability of CREB3L1 to regulate the promoter activity of the NIS-coding gene (Slc5a5) was confirmed. Taken together, our findings highlight the role of CREB3L1 in maintaining the homeostasis of thyroid follicular cells, regulating the adaptation of the secretory pathway as well as the synthesis of thyroid-specific proteins in response to TSH stimulation.
Traffic Node Importance Evaluation Based on Clustering in Represented Transportation Networks

With the ability to acquire and process large-scale traffic big data, a cooperative intelligent transport system can be realized. Identifying important nodes in a traffic network contributes to better traffic control, which plays a more critical role in improving the traffic efficiency of a cooperative intelligent transport system. However, existing traffic node importance evaluation methods rely on manually designed metrics such as betweenness, degree, which may lead to biased results. Meanwhile, the traditional method of iteratively deleting nodes is unsuitable for a large-scale traffic network. In this paper, we propose a novel traffic node importance evaluation method based on clustering in represented transportation network. Specifically, the proposed method first construct a length-weighted network based on the geographic road network. Then, it learns the low-dimensional embeddings of nodes employing network representation learning. Finally, it clusters each nodes with a machine learning method and identifies the critical nodes through vehicle flow of different nodes. Experimental results on the real-word dataset show that our proposed method has excellent performance compared to baseline methods.
Decoupled R-CNN: Sensitivity-Specific Detector for Higher Accurate Localization

Object detection, as a fundamental problem in computer vision, has been widely used in many industrial applications, such as intelligent manufacturing and intelligent video surveillance. In this work, we find that classification and regression have different sensitivities to the object translation, from the investigation about the availability of highly overlapping proposals. More specifically, the regressor head has intrinsic characteristics of higher sensitivity to translation than the classifier. Based on it, we propose a decoupled sampling strategy for a deep detector, named Decoupled R-CNN, to decouple the proposals sampling for the two tasks, which induces two sensitivity-specific heads. Furthermore, we adopt the cascaded structure for the single regressor head of Decoupled R-CNN, which is an extremely simple but highly effective way of improving the performance of object detection. Extensive empirical analyses using real-world datasets demonstrate the value of the proposed method when compared with the state-of-the-art models. The reproducing code is available at https://github.com/shouwangzhe134/Decoupled-R-CNN.
How to Promote Social Entrepreneurship on Urban Education? An Active Learning Implementation

This paper analyzes the effects of active learning (AL) on the social entrepreneurship (SE) of physical education teacher education students (n = 158) from an Urban School. AL participants applied several strategies including but not limited to flipped classroom, problem solving, team projects, or case studies. The topic was approached using mixed methods with methodological triangulation. Quantitative evidence was gathered through a quasi-experimental design of six non-equivalent groups implementing the Social Entrepreneurship Competency Scale (SECS). Meanwhile, qualitative analysis was undertaken analyzing 119 reflective journals. Quantitative results provide significant evidence regarding the positive effect of AL on SE. Qualitative analysis complements this view describing how SE was developed (e.g., providing-receiving feedback among peers). Additionally, data transformation displays an alternative analysis of the benefit provided. Our results display how AL improves SE of physical education teacher education students, enhancing new research into this direction to meet current education demands.
Water‐resistant conductive organogels with sensation and actuation functions for artificial neuro‐sensory muscular systems

The development of functional flexible conductive materials can significantly contribute to the improvement of intelligent human–computer integration. However, it is a challenge to endow human–machine interface with perception and response actuation simultaneously. Herein, a customizable and multifunctional electronic conductive organogel is proposed by combining conductive carbon nanotube (CNT) clusters and flexible adhesive organogels. The conductive CNT cluster layers generated on the surface of organogels equip the resulting organogel‐based conductors with considerable quasi‐superhydrophobicity and increase their potential applicability as highly sensitive stress and strain sensors. In particular, this quasi‐superhydrophobicity is insensitive to tensile strain. Based on customizable conductive networks and entropy‐driven organogel actuation, the conductive organogels can sense various strain and stress signals and imitate natural organisms with muscle actuation and neurofeedback. This strategy for preparing electronic conductors can enhance the rational design of soft robotics and artificial intelligence devices, facilitating further progress of human‐like intelligent systems.
Augmentation of Human Action Datasets with Suboptimal Warping and Representative Data Samples

The popularity of action recognition (AR) approaches and the need for improvement of their effectiveness require the generation of artificial samples addressing the nonlinearity of the time-space, scarcity of data points, or their variability. Therefore, in this paper, a novel approach to time series augmentation is proposed. The method improves the suboptimal warped time series generator algorithm (SPAWNER), introducing constraints based on identified AR-related problems with generated data points. Specifically, the proposed ARSPAWNER removes potential new time series that do not offer additional knowledge to the examples of a class or are created far from the occupied area. The constraints are based on statistics of time series of AR classes and their representative examples inferred with dynamic time warping barycentric averaging technique (DBA). The extensive experiments performed on eight AR datasets using three popular time series classifiers reveal the superiority of the introduced method over related approaches.
Detection of COVID-19 Lung Lesions in Computed Tomography Images Using Deep Learning

The novel coronavirus (COVID-19) is a disease that mainly affects the lung tissue. The detection of lesions caused by this disease can help to provide an adequate treatment and monitoring its evolution. This research focuses on the bi- nary classification of lung lesions caused by COVID-19 in images of computed tomography (CT) using deep learning. The database used in the experiments comes from two independent repositories, which contains tomographic scans of patients with a positive diagnosis of COVID-19. The output layers of four pre-trained convolutional networks were adapted to the proposed task and re-trained using the fine-tuning technique. The models were validated with test images from the two database’s repositories. The model VGG19, considering one of the repositories, showed the best performance with 88% and 90.2% of accuracy and recall, respectively. The model combination using the soft voting technique presented the highest accuracy (84.4%), with a recall of 94.4% employing the data from the other repository. The area under the receiver operating characteristic curve was 0.92 at best. The proposed method based on deep learning represents a valuable tool to automatically classify COVID-19 lesions on CT images and could also be used to assess the extent of lung infection.
The Fourth Industrial Revolution and Multimedia Converging Technology: Pervasive AR Platform Construction using a Mobile Robot based Projection Technology

The fourth industrial revolution is expected to show technological innovation that develops among different fields beyond boundaries through the convergence and integration of fields. With the development and convergence of digital technology, users can receive information anywhere in the world. In this paper, we propose an adaptive interaction concept in a various environment by using a mobile robot based on projection augmented reality (AR). Most previous studies have aimed fixed projector or projection for a pre-designed environment. Thus, they provide only limited information. To overcome the abovementioned problem, we provide the adaptive information by implementing a projection AR system that can be mounted on the mobile robot. For that, the mobile robot based on the projection system was defined as Pervasive AR. Pervasive AR is configured with a pervasive display, a pervasive interface, and seamless interaction. The Pervasive AR technology enables the user to access information immediately by expanding the display area into real space, which implies an environment of intuitive and convenient interaction by expanding the user interface. This system can be applied to various areas, such as a home environment and a public space.
Investigation of Source Code Mining using Novel Code Mining Parameter Matrix: Recent State of Art

The software projects without a proper detection technique for defects in developed modules may lead to software full of problems and not generating desired output mentioned in the required specification by customer. The requirements are generated during the mutual discussion between client and the development companies in the initial phases. The software development companies are always liable to deliver the software with agreed performance. Hence the software companies put lot of efforts in detecting and resolving the defects in the software. However the detecting and fixing process of detects is time consuming process and ignoring the defects may lead to malfunctions ranging from losing a small penny to million dollar loss. Software development companies deal with defects which are known and predictable and sometimes unknown and unpredictable defects. The known defects can be deal with pre-planned development strategies and are generally less time consuming. The known defects will not disturb the cost and time estimations for the project. However the unknown defects are unpredictable, hence the resolution of those defects also cannot be pre-defined. Hence the development industries keep huge efforts to deploy multiple prediction techniques to detect unknown defects for the next modules from the existing defect matrix generated during the development phases. The early documents have demonstrated the use of software defect matrix to demonstrate the time complexity, memory requirements and development cost in terms of time to market. An in depth calculation steps are to be executed to determine the number of defects in the software module or the complete program. However the recent researches have demonstrated the use of software defect matrix to form the guidelines for defect detection. The parallel researches have also demonstrated good classification techniques for defects matrices to focus on one specific objective and ignore the rest of the matrices for the same objective. The rest of the matrices can also
Evidential Nearest Neighbours in Active Learning

Active learning is a subfield of machine learning which allows to reduce the amount of data necessary to train a classifier. The training set is built in an iterative way such that only the most significant and informative data are used and labeled by an external person called oracle. It is furthermore possible to use active learning with the theory of belief functions in order to take erroneous labels due to the oracle’s uncertainty and imprecision into account in order to limit their influence on the classifier’s performance. In this article, we compare the classifier of the k nearest neighbours (kNN) to a variant based on belief functions from the theory of belief functions (EkNN), in a situation where some labels have been noised in order to model uncertain labels. We show that although the superiority of EkNN over kNN is not systematic, there are some interesting and modest results supporting the relevance of belief functions in active learning.
Human+AI Crowd Task Assignment Considering Result Quality Requirements

This paper addresses the problem of dynamically assigning tasks to a crowd consisting of AI and human workers. Currently, crowdsourcing the creation of AI programs is a common practice. To apply such kinds of AI programs to the set of tasks, we often take the ``all-or-nothing'' approach that waits for the AI to be good enough. However, this approach may prevent us from exploiting the answers provided by the AI until the process is completed, and also prevents the exploration of different AI candidates. Therefore, integrating the created AI, both with other AIs and human computation, to obtain a more efficient human-AI team is not trivial. In this paper, we propose a method that addresses these issues by adopting a ``divide-and-conquer'' strategy for AI worker evaluation. Here, the assignment is optimal when the number of task assignments to humans is minimal, as long as the final results satisfy a given quality requirement. This paper presents some theoretical analyses of the proposed method and an extensive set of experiments conducted with open benchmarks and real-world datasets. The results show that the algorithm can assign many more tasks than the baselines to AI when it is difficult for AIs to satisfy the quality requirement for the whole set of tasks. They also show that it can flexibly change the number of tasks assigned to multiple AI workers in accordance with the performance of the available AI workers.
Montecarlo Methods to Efficently Compute Volume of Solids

In this work we present and compare chosen Monte Carlo methods, used to calculate solid’s volume. Such solids are defined with a certain surface 𝜚 ( 𝜙,𝜃 ) wich results in classical methods being hard or impossible to apply. In designating those volume, due to the desire to shorten calculation time, we will apply parallel calculations.
Short-term Predictive Models for Influenza-like Illness in Korea: Using Weekly ILI Surveillance Data and Web Search Queries

Since Google launched a prediction service for influenza-like illness(ILI), studies on ILI prediction based on web search data have proliferated worldwide. In this regard, this study aims to build short-term predictive models for ILI in Korea using ILI and web search data and measure the performance of the said models. In these proposed ILI predictive models specific to Korea, ILI surveillance data of Korea CDC and Korean web search data of Google and Naver were used along with the ARIMA model. Model 1 used only ILI data. Models 2 and 3 added Google and Naver search data to the data of Model 1, respectively. Model 4 included a common query used in Models 2 and 3 in addition to the data used in Model 1. In the training period, the goodness of fit of all predictive models was higher than 95% (R 2 ). In predictive periods 1 and 2, Model 1 yielded the best predictions (99.98% and 96.94%, respectively). Models 3(a), 4(b), and 4(c) achieved stable predictability higher than 90% in all predictive periods, but their performances were not better than that of Model 1. The proposed models that yielded accurate and stable predictions can be applied to early warning systems for the influenza pandemic in Korea, with supplementary studies on improving their performance.
Near-Field Coupling and Mode Competition in Multiple Anapole Systems

All-dielectric metamaterials are a promising platform for the development of integrated photonics applications. In this work, we investigate the mutual coupling and interaction of an ensemble of anapole states in silicon nanoparticles. Anapoles are intriguing non-radiating states originated by the superposition of internal multipole components which cancel each other in the far-field. While the properties of anapole states in single nanoparticles have been extensively studied, the mutual interaction and coupling of several anapole states have not been characterized. By combining first-principles simulations and analytical results, we demonstrate the transferring of anapole states across an ensemble of nanoparticles, opening to the development of advanced integrated devices and robust waveguides relying on non-radiating modes.
An update of the Angiosperm Phylogeny Group classiﬁcation for the orders and families of ﬂowering plants:

A revised and updated classiﬁcation for the families of ﬂowering plants is provided. Many recent studies have yielded increasingly detailed evidence for the positions of formerly unplaced families, resulting in a number of newly adopted orders, including Amborellales, Berberidopsidales, Bruniales, Buxales, Chloranthales, Escalloniales, Huerteales, Nymphaeales, Paracryphiales, Petrosaviales, Picramniales, Trochodendrales, Vitales and Zygophylla-les. A number of previously unplaced genera and families are included here in orders, greatly reducing the number of unplaced taxa; these include Hydatellaceae (Nymphaeales), Haptanthaceae (Buxales), Peridiscaceae (Saxifragales), Huaceae (Oxalidales), Centroplacaceae and Rafflesiaceae (both Malpighiales), Aphloiaceae, Geisso-lomataceae and Strasburgeriaceae (all Crossosomatales), Picramniaceae (Picramniales), Dipentodontaceae and Gerrardinaceae (both Huerteales), Cytinaceae (Malvales), Balanophoraceae (Santalales), Mitrastemonaceae (Ericales) and Boraginaceae (now at least known to be a member of lamiid clade). Newly segregated families for genera previously understood to be in other APG-recognized families include Petermanniaceae (Liliales), Calophyllaceae (Malpighiales), Capparaceae and Cleomaceae (both Brassicales), Schoepﬁaceae (Santalales), Anacampserotaceae, Limeaceae, Lophiocarpaceae, Montiaceae and Talinaceae (all Caryophyllales) and Linder-niaceae and Thomandersiaceae (both Lamiales). Use of bracketed families is abandoned because of its unpopu-larity, and in most cases the broader circumscriptions are retained; these include Amaryllidaceae, Asparagaceace and Xanthorrheaceae (all Asparagales), Passiﬂoraceae (Malpighiales), Primulaceae (Ericales) and several other smaller families. linear order APG, subfamilial
LONDON Crowdfunding to Finance R&D of Treatments for Neglected Tropical Diseases: Current Trends in and Future Potential

A serious lack of funding exists for the research and development (R&D) of therapeutics, diagnostics and preventive measures for neglected tropical diseases (NTDs). Crowdfunding to finance R&D for NTDs has high promise as it is a new and alternate source of capital. This study aims to explore current trends of crowdfunding for R&D for NTDs. Our study uncovers that while the number of crowdfunding campaigns for NTDs has been increasing since 2010, crowdfunding overall has not reached its full potential. Several factors contributing positively to the success of crowdfunding campaigns were identified, These and the promotion of the crowdfunding ecosystem could aid the unlocking of its potential as a complementary financing source to conventional funding practices of R&D for NTDs.
A Novel Approach for Fake Comments and Reviews Detection on the Online Social Networks

As the primary source of information dissemination, social media networks have surpassed traditional news organisations for the first time. Nonetheless, as the number of people who use social media web-sites grows, they become more susceptible to the spread of misinformation, making it increasingly difficult to distinguish between real news and false news in real time. In this paper, we proposed a machine learning technique for the detection of fake comments in social networks. According to the results of the experiment, it is clear that the machine learning technique efficiently detects the fake comments.
Design of Multi-Function Electrical Stimulator

A new electrical stimulator is proposed and designed.The equipment consists of four test channels which work independently or by turns.And desired test parameters,such as stimulating voltage,time delay in each channel and training circles,can be set or changed by user in the beginning or training procedure.The stimulating signal is generated by a power amplifier driven by DAC of controller.As the flexibility of DAC,the stimulator can generate desired stimulating signal of any form by updating the stimulator's firmware.Therefore the stimulator can satisfy many applications by updating the firmware,and especially fit the case that stimulating parameters remain unknown in some science research fields.