Miracle-dz/UniFAR-FTUs huggingface dataset:
B1 siRNA Increases de novo DNA Methylation of B1 Elements and Promotes Wound Healing in Diabetic Rats

Alu (B1 in rodents) hypomethylation, commonly found in diabetes mellitus patients, increases DNA damage and, consequently, delays the healing process. Alu siRNA increases Alu methylation, reduces DNA damage, and promotes cell proliferation. Aim: To explore whether B1 siRNA treatment restores B1 hypomethylation, resulting in a reduction in DNA damage and acceleration of the healing process in diabetic rat wounds. Methods: We generated splinted-excisional wounds in a streptozotocin (STZ)-induced type I diabetic rat model and treated the wounds with B1 siRNA/Ca-P nanoparticles to generate de novo DNA methylation in B1 intersperse elements. After treatment, we investigated B1 methylation levels, wound closure rate, wound histopathological structure, and DNA damage markers in diabetic wounds compared to nondiabetic wounds. Results: We reported that STZ-induced diabetic rat wounds exhibited B1 hypomethylation, wound repair defects, anatomical feature defects, and greater DNA damage compared to normal rats. We also determined that B1 siRNA treatment by Ca-P nanoparticle delivery restored a decrease in B1 methylation levels, remedied delayed wound healing, and improved the histological appearance of the wounds by reducing DNA damage. Conclusion: B1 hypomethylation is inducible in an STZ-induced type I diabetes rat model. Restoration of B1 hypomethylation using B1 siRNA leads to increased genome stability and improved wound repair in diabetes. Thus, B1 siRNA intervention may be a promising strategy for reprogramming DNA methylation to treat or prevent DNA damage-related diseases.
Target Classification of Similar Spatial Characteristics in Complex Urban Areas by Using Multispectral LiDAR

With the rapid modernization, many remote-sensing sensors were developed for classifying urban land and environmental monitoring. Multispectral LiDAR, which serves as a new technology, has exhibited potential in remote-sensing monitoring due to the synchronous acquisition of three-dimension point cloud and spectral information. This study confirmed the potential of multispectral LiDAR for complex urban land cover classification through three comparative methods. Firstly, the Optech Titan LiDAR point cloud was pre-processed and ground filtered. Then, three methods were analyzed: (1) Channel 1, based on Titan data to simulate the classification of a single-band LiDAR; (2) three-channel information and the digital surface model (DSM); and (3) three-channel information and DSM combined with the calculated three normalized difference vegetation indices (NDVIs) for urban land classification. A decision tree was subsequently used in classification based on the combination of intensity information, elevation information, and spectral information. The overall classification accuracies of the point cloud using the single-channel classification and the multispectral LiDAR were 64.66% and 93.82%, respectively. The results show that multispectral LiDAR has excellent potential for classifying land use in complex urban areas due to the availability of spectral information and that the addition of elevation information to the classification process could boost classification accuracy.
Building a Dataset for Detection of Verb Coordinations with a Shared Argument

Coordinate structures represent a specific linguistic problem relating to questions of sentence boundaries and multiple sentence element [1]. A particular diﬀiculty lies in processing at the level of automatic syntactic analysis of the sentence. To deal with the outlined issue, we decided to use the machine learning classification method, for which it is necessary to prepare a suﬀiciently large amount of data. This paper presents the methods and procedures we used to build a dataset focused on the phenomenon of verb coordinations that may share an argument in context.
A Smart and Intelligent Home Automation System

The use of IoT devices is incredibly popular nowadays. IoT technology helps us to innovate more secure and smart devices. For the past few years, the concept of a safe and smart home has gotten a lot of attention. A home automation system is a collection of smart devices and sensors that enable various functions within a house or building, such as light, fan, and plug control, energy monitoring, temperature metering, air conditioning, heating, etc. These devices are also implemented with low power communication as well as better quality at a lower price using the internet or Bluetooth connection. The main goal of this research is to create a clever, safe, and intelligent system using IoT devices that will create an automatic home environment. This paper gives a reliable, secure, and decentralized way for recognizing and verifying IoT gadgets in the proposed home automation framework, where the SVM classifier is utilized to separate the situation with the gadgets into two classifications, e.g., "ON" and "OFF". The proposed system design is tailored to the needs of the user and offers great reliability.
Optimization and Evaluation of SO2 Emissions Based on WRF-Chem and 3DVAR Data Assimilation

Emission inventories are important for modeling studies and policy-making, but the traditional “bottom-up” emission inventories are often outdated with a time lag, mainly due to the lack of accurate and timely statistics. In this study, we developed a “top-down” approach to optimize the emission inventory of sulfur dioxide (SO2) using the Weather Research and Forecasting model coupled with Chemistry (WRF-Chem) and a three-dimensional variational (3DVAR) system. The observed hourly surface SO2 concentrations from the China National Environmental Monitoring Center were assimilated and used to estimate the gridded concentration forecast errors of WRF-Chem. The concentration forecast errors were then converted to the emission errors by assuming a linear response from SO2 emission to concentration by grids. To eliminate the effects of modelling errors from aspects other than emissions, a strict data-screening process was conducted. Using the Multi-Resolution Emission Inventory for China (MEIC) 2010 as the a priori emission, the emission inventory for October 2015 over Mainland China was optimized. Two forecast experiments were conducted to evaluate the performance of the SO2 forecast by using the a priori (control experiment) and optimized emissions (optimized emission experiment). The results showed that the forecasts with optimized emissions typically outperformed the forecasts with 2010 a priori emissions in terms of the accuracy of the spatial and temporal distributions. Compared with the control experiment, the bias and root-mean-squared error (RMSE) of the optimized emission experiment decreased by 71.2% and 25.9%, and the correlation coefficients increased by 50.0%. The improvements in Southern China were more significant than those in Northern China. For the Sichuan Basin, Yangtze River Delta, and Pearl River Delta, the bias and RMSEs decreased by 76.4–94.2% and 29.0–45.7%, respectively, and the correlation coefficients increased by 23.5–53.4%. This SO2 emission optimization methodology is computationally cost-effective.
On the accuracy of diffusion models for life-cycle assessment of concrete structures

Abstract In durability analysis and life-cycle assessment of concrete structures transport of chlorides and other aggressive agents is generally described by using Fick’s laws of diffusion. This model is frequently applied in a simplified one-dimensional (1D) form. However, in practical applications the diffusion process is more properly described by two- or three-dimensional patterns of concentration gradients. In this paper, the accuracy of the 1D modelling of diffusion and its impact on the life-cycle assessment of concrete structures under corrosion is evaluated in deterministic and probabilistic terms with respect to more accurate two-dimensional (2D) formulations. The influence of the diffusion modelling on the time-variant corrosion damage of concrete cross-sections is studied with reference to the local damage of the reinforcing steel bars and the global deterioration of bending moment–curvature capacity curves. The results show that 2D diffusion models may be necessary for a realistic life-cycle assessment of concrete structures under corrosion, since 1D models can lead to significant inaccuracies depending on the geometrical aspect ratio of the cross-section, location of reinforcing steel bars and exposure conditions.
A Multiview Semantic Vegetation Index for Robust Estimation of Urban Vegetation Cover

Urban vegetation growth is vital for developing sustainable and liveable cities in the contemporary era since it directly helps people’s health and well-being. Estimating vegetation cover and biomass is commonly done by calculating various vegetation indices for automated urban vegetation management and monitoring. However, most of these indices fail to capture robust estimation of vegetation cover due to their inherent focus on colour attributes with limited viewpoint and ignore seasonal changes. To solve this limitation, this article proposed a novel vegetation index called the Multiview Semantic Vegetation Index (MSVI), which is robust to color, viewpoint, and seasonal variations. Moreover, it can be applied directly to RGB images. This Multiview Semantic Vegetation Index (MSVI) is based on deep semantic segmentation and multiview field coverage and can be integrated into any vegetation management platform. This index has been tested on Google Street View (GSV) imagery of Wyndham City Council, Melbourne, Australia. The experiments and training achieved an overall pixel accuracy of 89.4% and 92.4% for FCN and U-Net, respectively. Thus, the MSVI can be a helpful instrument for analysing urban forestry and vegetation biomass since it provides an accurate and reliable objective method for assessing the plant cover at street level.
Development of HAMOD: a High Agreement Multi-lingual Outlier Detection dataset

In this paper we describe further development of a High Agreement Multi-lingual Outlier Detection dataset (HAMOD) outlier that is used for the purpose of evaluation of automatic distributional thesauri. We briefly introduce the task and methodological motivation for developing such a dataset, then we present the current status of the dataset and related tools as well as results measured on the dataset so far (both in terms of agreement rates and thesauri eveluation). Finally we discuss future developments of HAMOD.
A fuzzy rule base minimization perspective in XAI

Fuzzy rule-based systems are raising great interest in the last years in eXpalianable Artificial Intelligence. These systems represents knowledge easily understood by humans but they are not interpretable per se . They, in fact, must remain simple and understandable, and the rule base must be compactness. In this work a fuzzy rule base minimization approach based on rough sets theory and a greedy algorithm is proposed. The reduction of the fuzzy rules makes the rule base simpler, and thus easier to produce explainable inference systems (e
In Vitro Susceptibility of Typhoidal, Nontyphoidal, and Extended-Spectrum-β-Lactamase-Producing Salmonella to Ceftolozane/Tazobactam

Both typhoidal and nontyphoidal salmonellae are included in the top 15 drug-resistant threats described by the U.S. Centers for Disease Control and Prevention. ABSTRACT Both typhoidal and nontyphoidal salmonellae are included in the top 15 drug-resistant threats described by the U.S. Centers for Disease Control and Prevention. There is an urgent need to look for alternative antibiotics for the treatment of Salmonella infections. We used the broth microdilution test to examine the in vitro susceptibilities of typhoidal and nontyphoidal salmonellae, including isolates positive for extended-spectrum β-lactamase (ESBL), to ceftolozane/tazobactam and six other antibiotics. Of the 313 (52 typhoidal and 261 nontyphoidal) Salmonella isolates tested, 98.7% were susceptible to ceftolozane/tazobactam. Based on the overall MIC50/90 values, Salmonella isolates were more susceptible to ceftolozane/tazobactam (0.25/0.5 mg/L) than all the comparator agents: ampicillin (≥64/≥64 mg/L), levofloxacin (0.25/1 mg/L), azithromycin (4/16 mg/L), ceftriaxone (≤0.25/4 mg/L), chloramphenicol (8/≥64 mg/L), and trimethoprim/sulfamethoxazole (1/≥8 mg/L). Comparison of the activities of the antimicrobial agents against nontyphoidal Salmonella isolates according to their serogroups showed that ceftolozane/tazobactam had the highest activity (100%) against Salmonella serogroup D, G, I, and Q isolates, whereas the lowest activity (85.7%) was observed against serogroup E isolates. All 10 ESBL-producing Salmonella isolates (all nontyphoidal), of which 8 were CTX-M-55 producers and 2 were CTX-M-65 producers, were sensitive to ceftolozane/tazobactam, albeit with MIC50/90 values higher (1/2 mg/L) than those for non-ESBL producers (0.25/0.5 mg/L). In summary, our data indicate that ceftolozane/tazobactam is active against most strains of both typhoidal and nontyphoidal salmonellae and also against ESBL-producing salmonellae.
Development of Bridge Girder Movement Criteria for Accelerated Bridge Construction

End diaphragms connect multiple girders to form a bridge superstructure system for effective resistance to earthquake loads. Concrete girder bridges that include end diaphragms consistently proved to perform well during previous earthquake events. However, whether concrete bridges without end diaphragms are definitively inadequate in seismic performance is yet to be answered. The 2010 Chile Earthquake indicated that properly designed bridge girders and their lateral movement stoppers (shear keys) may perform equally well with those with end diaphragms. In this report, a feasibility study on the design of girder bridges without end diaphragms is presented. This study is particularly significant in the context of accelerated bridge construction since concrete diaphragms are often cast in place and eliminating them can save field erection time and cost. The key to make the no-diaphragm concept work is to understand how multiple girders can work together during a transverse earthquake excitation without breaking the girders and bridge deck. Specifically, a three-dimensional finite element model of a representative concrete girder bridge with and without end diaphragms is established and analyzed to understand the effects of various design parameters (e.g. diaphragm height, diaphragm thickness, the coefficient of friction between girders and their supporting elements, the number and size of shear keys) on transverse girder movement capacity. Numerical results indicate that properly designed end diaphragms can increase the transverse capacity of a bridge by making individual girders work together but can be substituted by shear keys placed between the strengthened girders. In doing so, both transverse capacity and stiffness of the bridge superstructure can be significantly increased. Shear keys are more reliable than the friction mechanism. In particular, movable shear keys are more effective in distributing loads among multiple girders.
In memory of Gary Bauchan: Utilizing an integrated taxonomy approach for the description of a new species of Gamasellodes (Mesostigmata: Ascidae)

Abstract Gamasellodes garybauchani sp. nov. Rueda-Ramírez & Santos is described based on specimens collected at the Beltsville Agricultural Research Center (BARC) (Maryland, USA). Twenty-one specimens were used for the description, of which five were subjected to low temperature scanning electron microscopy (LTSEM), followed by DNA extraction for genetic analysis and subsequent slide mounting. The remaining 16 specimens were subjected to only DNA extraction for genetic analysis and subsequent slide mounting. Morphological characters of the new species observed both in the images obtained in LTSEM and in the observation of the mounted specimens are detailed. Molecular information available on the Barcode of Life Datasysetm (BOLD) is presented. An update to the recent key to the Gamasellodes species is provided.
Analytical Study on Building a Comprehensive Big Data Management Maturity Framework

Harnessing big data in organizations today realizes benefits for competitive advantage. Generated profound insights are reflected in informed decision making, creating better business plans, and improved service delivery. Yet, organizations are still not recognizing how mature their big data management capabilities are. However, there is no structured approach to assess and build necessary capabilities for valuable big data utilizing, which draws a clear improvement pathway. Existing solutions lack a consistent perception of big data management capabilities, a reliable assessment, and a rigid improvement scheme. This paper contributes in building an analytical study on existing key works in assessing and building big data management capabilities. Drawing upon the results and gaps revealed from this analytical study, the main requirements for building a comprehensive big data management maturity framework are defined. This framework will enable organizations to assess and improve their current capabilities towards effective big data management.
The cytoplasmic incompatibility Cif proteins from prophage WO modify sperm genome integrity

Inherited microorganisms can selfishly manipulate host reproduction to drive through populations. In Drosophila melanogaster, germline expression of the native prophage WO proteins CifA and CifB cause cytoplasmic incompatibility (CI) in which sperms fertilize uninfected embryos that suffer catastrophic mitotic defects and lethality; however in infected females, CifA rescues the embryonic lethality and thus imparts a fitness advantage to Wolbachia. Despite widespread relevance to sex determination, evolution, and vector control, the mechanisms underlying when and how CI impairs male reproduction remain unknown and a topic of debate. Here we use cytochemical, microscopic, and transgenic assays in D. melanogaster to demonstrate that CifA and CifB proteins of wMel localize to nuclear DNA throughout the process of spermatogenesis. Cif proteins cause abnormal histone retention in elongating spermatids and protamine deficiency in mature sperms of CI-causing males. Protamine-deficient sperms travel to the female reproductive tract together with Cif proteins. In female ovaries, CifA localizes to germ cell nuclei and overlaps with Wolbachia in the nurse cell cytoplasm and the oocyte, however Cifs are not present in late-stage oocytes and the embryo. Moreover, CI and rescue are contingent upon a newly annotated CifA bipartite nuclear localization sequence. Our results reveal a previously unrecognized phenomena in which prophage proteins invade animal gametic nuclei and modify the histone-protamine transition of spermatogenesis.
Multispectral Deep Neural Network for Low Light Object Detection

x Chapter 1: Introduction . 1 1.1 Contributions . 7 Chapter 2: Background . 9 2.1 Image Registration . 9 2.2 Image Fusion . 12 2.3 Convolutional Neural Network . 16 2.4 Faster R-CNN . 18 2.5 Feature Pyramid Networks (FPN) . 20 2.6 Squeeze-and-Excitation Networks (SENets) . 21 2.7 Related Work. 23 2.7. 1 Multimodal Thermal Objection Detection . 23 2.7. 2 CNN Based Color and Thermal Image Fusion for Object Detection in Automated Driving . 24 Chapter 3: Datasets and Methodology . 25 3.1 Datasets . 25 3.2 Baseline Experiments . 26
Scalable parametric encoding of multiple modalities

A flexible model is introduced which shares ideas with the Autoencoder, Canonical Correlation Analysis, Singular Value Decomposition, and Procrustes Analysis. It is proposed to find relevant maps to transform multiple datasets of various types from one modality to another. Here, the Generative Encoder is used to transform spatial gene expression from breast tissue, to the images of histology tissue measured with Spatial Transcriptomics. The model is directly interpretable given all parameters are linked to the data space. It is scalable on Big Data, training reasonably on several thousand RGB images of 100 by 100 pixels in under an hour, which equates to 30,000 pixel features per sample image.
Evaluation of the financial and technical impacts of changing commercial-scale pharmaceutical manufacturing processes.

Growing pressures in the pharmaceutical industry are driving the need to optimise processes used for the manufacture of drugs at commercial-scale, in order to improve cost of goods, product throughput and production times. Evaluating the impacts of process optimisation upon these metrics presents a challenge due to complexities and trade-offs that are often encountered when developing a typical bioprocess. Such factors have resulted in a range of novel simulation- and experimental- based techniques being developed which enable rapid, accurate and cost effective assessment of manufacturing options for commercial-scale production. This thesis proposes a combination of modelling and experimental methods for evaluating the business- and process-related impacts of implementing changes to pre-existing commercial-scale pharmaceutical manufacturing processes. The approaches are illustrated through an industrial case study, focusing upon a process operated by Protherics U.K. Limited for the manufacture of the FDA-approved rattlesnake anti-venom CroFab (Crotalidae Polyvalent Immune Fab (Ovine)). The novel methods developed and illustrated in this thesis include: Investigating the effects of process changes upon calculated yields and processing times within the production framework for a pre-existing FDA-approved bio-manufacturing process Evaluating the impacts of both developing and implementing process changes, combining output metrics into a single value to simplify the assessment Developing a multi-layered simulation methodology for the rapid and efficient evaluation of bio- manufacturing process options Applying advanced sensitivity analysis techniques to identify the most critical factors that influence product yield and throughput Evaluating a novel synthetic Protein A matrix for the recovery and purification of polyclonal antibodies from hyperimmunised ovine serum Developing decision-support software to aid the design of chromatography steps for antibody purification at industrial scale Demonstrating the utility of such models by application to data and constraints derived from a full-scale industrial facility.