Miracle-dz/UniFAR-FTUs huggingface dataset:
Genome editing in wheat microspores and haploid embryos mediated by delivery of ZFN proteins and cell‐penetrating peptide complexes

Summary Recent advances in genome engineering technologies based on designed endonucleases (DE) allow specific and predictable alterations in plant genomes to generate value‐added traits in crops of choice. The EXZACT Precision technology, based on zinc finger nucleases (ZFN), has been successfully used in the past for introduction of precise mutations and transgenes to generate novel and desired phenotypes in several crop species. Current methods for delivering ZFNs into plant cells are based on traditional genetic transformation methods that result in stable integration of the nuclease in the genome. Here, we describe for the first time, an alternative ZFN delivery method where plant cells are transfected with ZFN protein that eliminates the need for stable nuclease genomic integration and allows generation of edited, but not transgenic cells or tissues. For this study, we designed ZFNs targeting the wheat IPK1 locus, purified active ZFN protein from bacterial cultures, complexed with cell‐penetrating peptides (CPP) and directly transfected the complex into either wheat microspores or embryos. NGS analysis of ZFN‐treated material showed targeted edits at the IPK1 locus in independent experiments. This is the first description of plant microspore genome editing by a ZFN when delivered as a protein complexed with CPP.
Semi-Supervised Classification of Polarimetric SAR Images Using Markov Random Field and Two-Level Wishart Mixture Model

In this work, we propose a semi-supervised method for classification of polarimetric synthetic aperture radar (PolSAR) images. In the proposed method, a 2-level mixture model is constructed by associating each component density with a unique Wishart mixture model (instead of a single Wishart distribution as that in the conventional Wishart mixture model). This modeling scheme facilitates the accurate description of data for the categories, each of which includes multiple subcategories. The learning algorithm for the proposed model is developed based on variational inference and all the update equations are obtained in closed form. In the learning algorithm, the spatial interdependencies are incorporated by imposing a Markov random field prior on the indicator variable to alleviate the speckle effect on the classification results. The experimental results demonstrate the improved performance of the proposed method compared with the unsupervised version and supervised version of the proposed model as well as an existing method for semi-supervised classification.
Minimal genetic differentiation of the malaria vector Nyssorhynchus darlingi associated with forest cover level in Amazonian Brazil

The relationship between deforestation and malaria in Amazonian Brazil is complex, and a deeper understanding of this relationship is required to inform effective control measures in this region. Here, we are particularly interested in characterizing the impact of land use and land cover change on the genetics of the major regional vector of malaria, Nyssorhynchus darlingi (Root). We used nextera-tagmented, Reductively Amplified DNA (nextRAD) genotyping-by-sequencing to genotype 164 Ny. darlingi collected from 16 collection sites with divergent forest cover levels in seven municipalities in four municipality groups that span the state of Amazonas in northwestern Amazonian Brazil: São Gabriel da Cachoeira, Presidente Figueiredo, four municipalities in the area around Cruzeiro do Sul, and Lábrea. Using a dataset of 5,561 Single Nucleotide Polymorphisms (SNPs), we investigated the genetic structure of these Ny. darlingi populations with a combination of model- and non-model-based analyses. We identified weak to moderate genetic differentiation among the four municipality groups. There was no evidence for microgeographic genetic structure of Ny. darlingi among forest cover levels within the municipality groups, indicating that there may be gene flow across areas of these municipalities with different degrees of deforestation. Additionally, we conducted an environmental association analysis using two outlier detection methods to determine whether individual SNPs were associated with forest cover level without affecting overall population genetic structure. We identified 14 outlier SNPs, and investigated functions associated with their proximal genes, which could be further characterized in future studies.
Sodium-nickel chloride single cell battery electrical model – Discharge voltage behavior

The equivalent circuit battery models have been used in many applications, being used mainly in applications involving battery management systems. In this bstudy, the first, second and third order Thevenin models for a single cell sodium-nickel chloride battery were implemented. A pulse discharge test was conducted, and the parameters were estimated using the non-linear least squares method. In the results a comparison of the accuracy between the three models at the pulse discharge test is presented. In general, the direct ratio between the degree of the model and the decrease of the mean error is verified. However, an analysis evaluating the balance between accuracy and computational effort may indicate models with lower grade as the best option for some applications.
Classification and predictive analysis of educational data to improve the quality of distance learning courses

Educational Data Mining (EDM) is the research area concerned with the use of data mining methods and its development in order to explore data sets collected in educational platforms. EDM has a considerable potential to improve the education quality. In this paper, we first model the Moodle variables of the Brazilian National School of Public Administration (Enap) database. Next we apply EDM to understand the standard Moodle variables influence and the proposed variables in the attendees performance and dropout rates. Our framework includes the following data mining approaches: decision tree, Adaboost based decision tree, random forest and k-means. The obtained Adaboost based decision tree outperforms the other approaches and the state-of-the-art framework with 91 % of Precision, 89 % of Recall and 90 % of F1 score in terms of the attendees performance evaluation.
Road Network Extraction from Satellite Images Using CNN Based Segmentation and Tracing

Drawing and updating road networks are both time-consuming and labor-intensive. Deep learning technology and high-resolution remote sensing images have provided opportunities for automatic road extraction. However, recent convolutional neural network (CNN) based segmentation methods have shown serious problems on connectivity; road tracing methods with single starting point perform well in connectivity but often result in part areas unreached. We propose a multiple starting points tracer which benefits from both segmentation and tracing methods. We compare our approach with most recent tracing methods on satellite images of global cities and find that our method achieves 8% improvement on IoU.
Comparing Phenometrics Extracted From Dense Landsat-Like Image Time Series for Crop Classification

In this research, we compared two different sets of land surface phenological metrics (phenometrics) derived from dense satellite image time series to classify agricultural land in the Cerrado biome. We derived phenometrics from a dense Enhanced Vegetation Index (EVI) data cube with an 8-day temporal resolution and subjected them to classification using the Random Forest (RF) algorithm. We used a hierarchical classification with four levels, from land cover to crop rotation classes. We then evaluated the classification results comparing the use of phenometrics extracted using TIMESAT software [1], those obtained by polar representation, proposed by Körting et al. (2013) and the combination of both. We concluded that the accuracies of semi-perennial and winter crop classes increase substantially when using TIMESAT metrics combined with Polar features, and the misclassifications between single crops with non commercial crops are reduced.
Sensitivity of Vegetation Shortwave Albedo to Topography

Rugged terrain significantly complicates the land surface albedo modeling and retrievals in remote sensing. Neglecting the topographic effects may lead to large uncertainties when estimating land surface albedo over rugged terrain. In this study’ the sensitivities of snow-free vegetation shortwave albedo to topography are quantitatively investigated and analyzed based on the Moderate Resolution Imaging Spectroradiometer (MODIS) land surface albedo data and the global sensitivity analysis of a mechanistically-based land surface albedo parameterized model over rugged terrain. The results reveal that the topography can account for more than 30% of the total variabilities of the land surface albedo and the topographic effects on land surface albedo cannot be neglected even for the terrain with the mean slope of 10°.
Hurricane Building Damage Assessment using Post-Disaster UAV Data

Unmanned aerial vehicle (UAV) systems are becoming increasingly important since fine spatial and high temporal resolution data have been unobtainable from traditional remote sensing platforms. Advanced UAV data can provide a great opportunity for disaster monitoring, including building damage assessment. Hurricane Harvey struck southern Texas, U.S.A, on August 26, 2017, causing catastrophic flooding and storm damage. We visited Holiday Beach, which suffered severe building damage, and conducted UAV surveying for building damage assessment. In this study, a region growing scheme was proposed to estimate the building damage, considering building elevation change and the spectral difference of debris. In addition, different weight coefficients of a digital elevation model (DEM) were investigated. The results showed that the proposed method can be used for high-definition building damage assessment through accurate detection of the intact building regions.
Triplet Decoders Neural Network Ensemble System and T-Conversion for Traffic Speed Sequence Prediction

Traffic flow prediction is essential in Intelligent Transportation System (ITS). Due to the complexity of the traffic environment, predicting future traffic speed is difficult. A lot of methods have been proposed to forecast traffic flow, such as support vector regression (SVR), recurrent neural networks (RNN), and so on. However, there are few ensemble models methods for predicting traffic flow, especially for predicting multi-step traffic speed. Meanwhile, these ensemble methods often use many base models for regression. Thus, the performances of these existing methods are worth exploring for multi-step traffic forecasts with few base models. Besides, this paper proposes a model based on ensemble models, called “Triplet Decoders Neural Network” (TDNN), to compare with these methods. By analyzing the characteristic of traffic speed data, this paper also proposes a data preprocessing model called “T-Conversion” to help RNN capturing long-term dependencies. Our experiments are implemented based on real traffic speed data from Baidu in Beijing, China because the prediction for complex traffic is more valuable. With these new novel ideas, our experiments show the superior performances of TDNN, and the significant effect of T-Conversion for predicting traffic speed sequences.
HIV testing and linkage to care in North Carolina: Early diagnosis, late diagnosis, and delayed presentation to care

Objective: Our objective was to describe the frequency and types of STI co-infections among patients with acute HIV infection (AHI). Methods: We examined surveillance data from publicly-funded HIV testing sites in North Carolina. Antibody negative specimens are retested for HIV RNA using specimen pooling. Demographics, symptoms, and STI infections are collected. We calculated the prevalence of co-infection and determined risk factors for co-infection with prevalence ratios (PR). Results: Between 11/1/2002 and 10/31/2006, 75 clients with AHI were identified. Of these, 23 (31%) had STI co-infections at the time of diagnosis — nine (39%) had gonorrhea and five had trichomoniasis (22%). The prevalence was highest in women (53%) compared to 35% in heterosexual men (PR=0.67, 95% CI 0.31, 1.45) and 18% in men who have sex with men (PR=0.34, 95% CI 0.15, 0.76). The type of co-infection differed by gender with women more likely to be diagnosed with trichomoniasis (p<.01). Non-Whites were 3.9 times as likely to report a co-infection as Whites (95% CI 1.00, 15.10). Conclusions: STI co-infection is common among patients with AHI. Differences in the prevalence of co-infection by gender, risk and race suggest that STIs may have a role in the heterosexual HIV epidemic in North Carolina.
Three-Dimensional Urban Characterization Using Polarimetric SAR Correlation Tomographic Techniques and TSX/TDX Images

Polarimetric synthetic aperture radar tomography (Pol-TomoSAR) allows to achieve a 3-D characterization over urban areas using multiple polarimetric acquisitions. However, using spaceborne datasets, such as TerraSAR-X, it is difficult to localize the distributed or uncorrelated scattering patterns along elevation due to the temporal decorrelation. In order to overcome this limitation, this paper proposes polarimetric correlation tomographic techniques based on Tandem-mode images. The key of this technique is to build a covariance matrix from the observed Tandem coherence pairs, and then apply conventional covariance-based tomographic techniques. This processing allows to extract both coherent and distributed scatterers. The resulting 3-D reconstruction is more refined and detailed, compared to the one derived from TerraSAR-X data. Seven TSX/TDX pairs in fully polarimetric mode over a small county in Yunnan province, China, are used to demonstrate the effectiveness of this technique for the characterization of urban environments.
A Brief Review Regarding Sensor Reduction and Faults in Shunt Active Power Filter

Shunt Active Power Filter (SAPF) works basically with the help of two key operations i.e. Reference current generation technique and current controller. Both these operations require measurement of currents and voltages of the system which is done with the help of sensors. Failure due to faults in these sensors causes SAPF to aggravate power quality problems instead of compensating it. Also, the reduction in the number of sensors will make SAPF more economical and reliable. Therefore, sensorless and fault tolerant control of SAPF is a very attractive solution for its robust operation. The aim of this paper is to present and discuss details about the different solutions and development in sensor reduction and faults in SAPF. A literature review has been done in a classified manner based on the type of faults and the type of sensor reduced.
Lupus, Silica, and Dietary Omega-3 Fatty Acid Interventions

Two environmental factors, crystalline silica (cSiO2), a toxic airborne particle encountered occupationally, and docosahexaenoic acid (DHA), a dietary omega-3 highly unsaturated fatty acid (HUFA), have the potential to influence the development of systemic lupus erythematosus (lupus). Using the NZBWF1 mouse, which spontaneously develops lupus, we found that intranasal exposure to cSiO2 significantly decreases latency and promotes rapid progression of the disease. Specifically, cSiO2 induces the development of ectopic lymphoid structures (ELS) containing germinal centers in the lungs that yield vigorous and diverse autoantibody responses locally and systemically. Transcriptomic analysis revealed that cSiO2 promotes a robust type I interferon gene signature that likely precipitates ELS neogenesis. Intriguingly, dietary supplementation with human-relevant doses of DHA impedes cSiO2-induced gene expression, ELS neogenesis, autoantibody elevation, and glomerulonephritis in this lupus-prone mouse model. Together, our findings point to the feasibility of enhancing tissue omega-3 HUFAs as a personalized nutritional intervention to impede onset and progression of environment-triggered autoimmune disease.
Cooperative Control of UAV Cluster Formation Based on Distributed Consensus

Aiming at the main problems of assembly, formation maintenance and formation transformation in UAV cluster formation control, the distributed consistency method is used to design and simulate the control algorithm. Firstly, under the determined communication topology, the assembly control, formation route and speed tracking control of UAV formation based on distributed consensus control algorithm are implemented. Then the convergence conditions of distributed consensus control algorithm under the condition of communication topology transformation is analyzed, and a cluster formation transformation based on consensus control algorithm is designed and implemented. The simulation results show that the distributed consensus-based control algorithm can complete the operation of the UAV cluster formation, maintenance, and formation transformation.
Durability of Hearing Preservation Following Microsurgical Resection of Vestibular Schwannoma.

OBJECTIVE To ascertain long-term hearing outcomes in patients with serviceable hearing following microsurgical resection of sporadic vestibular schwannoma (VS). STUDY DESIGN Retrospective cohort. SETTING Tertiary academic referral center. PATIENTS Forty-three adult subjects with unilateral sporadic VS who had serviceable hearing (American Academy of Otolaryngology-Head and Neck Surgery [AAO-HNS] class A or B) on initial postoperative audiogram following microsurgical resection between 2003 and 2016 with a minimum of two postoperative audiograms available for review. INTERVENTION Surgical treatment with a retrosigmoid or middle cranial fossa approach. MAIN OUTCOME MEASURE Rate of maintaining serviceable hearing, as estimated using the Kaplan-Meier method, in accordance with the 1995 and 2012 AAO-HNS guidelines on reporting hearing outcomes. RESULTS The median immediate postoperative pure-tone average (PTA) and word recognition score (WRS) were 31 dB and 95%, respectively. At last follow-up, the median PTA was 38 dB with a median change of 5 dB from initial postoperative audiogram, and the median WRS was 90% with a median change of 0% from initial postoperative audiogram. Eight patients developed non-serviceable hearing at a median of 4.1 years following microsurgical resection (interquartile range, 2.9-7.0). The median duration of hearing follow-up for the 35 patients who maintained serviceable hearing was 3.1 years (interquartile range, 2.2-7.5). Tumor control was achieved in 41 (95%) patients. The rate of maintaining serviceable hearing at 5 years was 81%. CONCLUSION Microsurgical resection provides excellent tumor control and durable long-term hearing in those with AAO-HNS class A or B hearing postoperatively. The paradigm of proactive microsurgical resection-when the tumor is small and hearing is good-hinges on the surgeon's ability to preserve residual hearing in a very high percentage of cases at or near preoperative hearing levels to maintain an advantage over conservative observation with regard to long-term hearing preservation.
Analysis of the Impact of Google Maps’ Level on Object Detection

Remote sensing images have different levels based on spatial resolution, which will affect the object detection performance seriously. This paper quantitatively analyses the impact of Google Maps’ level on object detection, taking the transmission tower as an example. The object area proportion (OAP) index is defined to help choose the data used for rapid detections and is capable of obtaining the optimal results under the particular requirements of speed and accuracy when observing a specific object in remote sensing images.
Parameter Estimation of SPMSM Using Adaptive Filter and Extended Sliding-Mode Observer

We propose a parameter estimation algorithm for surface-mounted permanent magnet synchronous motors (SPMSMs) using normalized least mean square (NLMS) adaptive filter and extended sliding-mode mechanical parameter observer (ESMMPO). The adaptive filter estimates electrical parameters, stator inductance, resistance, and rotor linkage flux. The ESMMPO estimates the system disturbance, from which mechanical parameters are extracted. The key feature of the proposed algorithm is that the two estimation algorithms for electrical and mechanical parameters are effectively integrated into a single framework, and the estimated rotor linkage flux is used for the system disturbance estimation of the ESMMPO in real time, which results in the accuracy improvement of the mechanical parameters. Performing extensive simulation experiments for the proposed algorithm, we verify the system parameter estimation performance for SPMSMs.
A Deep Architecture Based on a Two-Stage Learning for Semantic Segmentation of Large-Size Remote Sensing Images

Remote sensing images (RSIs) usually have much larger size compared to typical natural images used in computer vision applications. This makes the computational cost of training convolutional neural networks with full-size images unaffordable. Commonly used methodologies for semantic segmentation of RSIs perform training and prediction on cropped local image patches. Thus they fail to model the potential dependencies between ground objects at a higher level of abstraction. In order to better exploit global context information in RSIs, a deep architecture based on a two-stage training approach that is specially tailored to training large-size RSIs is proposed. In the first training stage, down-scaled images are used as input to learn high-level features from a large image area. In the second training stage, a local feature extraction network is designed to extract low-level information from cropped image patches. The complementary information learned from different levels is fused to make the prediction. As a result, the proposed two-stage training approach is able to exploit the context information of RSIs from a larger perspective without losing spatial details. Experimental results on a benchmark remote sensing dataset demonstrate the effectiveness of the proposed approach.
Estimation of Air Velocity by Fusing Plant Model and Measurement of Inertial Measurement Unit

A set of algorithms are developed to estimate the air velocity of the quadrotors by fusing the plant model of the three-dimensional aerodynamic force and the IMU (inertial measurement unit) measurement. The main ideas are to compute out the air velocity of the quadrotors at first with the measured load acceleration and the model of the aerodynamic force, and then to smooth the computed results with the extended Kalman filtering against the model uncertainties. The developed algorithms are verified in the closed-loop flight control system. The simulation results demonstrate that the air velocity of the quadrotors are estimated well in the large airspeed and the resulting closed-loop system with the estimated air velocity can achieve almost the same flight performances as that with the real air velocity.
Morphological characteristics, cytogenetic profile, and outcome of RUNX1‐RUNX1T1‐positive acute myeloid leukemia: Experience of an Indian tertiary care center

A prototype of good prognosis, t(8;21)‐positive AML, has diverse clinical and genetic features which affect its outcome. This study aimed at evaluating the clinico‐pathological spectrum of t(8;21)‐positive AML and ascertaining prognostic factors influencing its outcome in the Indian subcontinent.