Miracle-dz/UniFAR-FTUs huggingface dataset:
In Situ Visualization of the pKM101-Encoded Type IV Secretion System Reveals a Highly Symmetric ATPase Energy Center

Bacterial type IV secretion systems (T4SSs) play central roles in antibiotic resistance spread and virulence. By cryo-electron tomography (CryoET), we solved the structure of the plasmid pKM101-encoded T4SS in the native context of the bacterial cell envelope. ABSTRACT Bacterial conjugation systems are members of the type IV secretion system (T4SS) superfamily. T4SSs can be classified as “minimized” or “expanded” based on whether they are composed of a core set of signature subunits or additional system-specific components. Prototypical minimized systems mediating Agrobacterium tumefaciens transfer DNA (T-DNA) and pKM101 and R388 plasmid transfer are built from subunits generically named VirB1 to VirB11 and VirD4. We visualized the pKM101-encoded T4SS in its native cellular context by in situ cryo-electron tomography (CryoET). The T4SSpKM101 is composed of an outer membrane core complex (OMCC) connected by a thin stalk to an inner membrane complex (IMC). The OMCC exhibits 14-fold symmetry and resembles that of the T4SSR388 analyzed previously by single-particle electron microscopy. The IMC is highly symmetrical and exhibits 6-fold symmetry. It is dominated by a hexameric collar in the periplasm and a cytoplasmic complex composed of a hexamer of dimers of the VirB4-like TraB ATPase. The IMC closely resembles equivalent regions of three expanded T4SSs previously visualized by in situ CryoET but differs strikingly from the IMC of the purified T4SSR388, whose cytoplasmic complex instead presents as two side-by-side VirB4 hexamers. Analyses of mutant machines lacking each of the three ATPases required for T4SSpKM101 function supplied evidence that TraBB4 as well as VirB11-like TraG contribute to distinct stages of machine assembly. We propose that the VirB4-like ATPases, configured as hexamers of dimers at the T4SS entrance, orchestrate IMC assembly and recruitment of the spatially dynamic VirB11 and VirD4 ATPases to activate the T4SS for substrate transfer. IMPORTANCE Bacterial type IV secretion systems (T4SSs) play central roles in antibiotic resistance spread and virulence. By cryo-electron tomography (CryoET), we solved the structure of the plasmid pKM101-encoded T4SS in the native context of the bacterial cell envelope. The inner membrane complex (IMC) of the in situ T4SS differs remarkably from that of a closely related T4SS analyzed in vitro by single-particle electron microscopy. Our findings underscore the importance of comparative in vitro and in vivo analyses of the T4SS nanomachines and support a unified model in which the signature VirB4 ATPases of the T4SS superfamily function as a central hexamer of dimers to regulate early-stage machine biogenesis and substrate entry passage through the T4SS. The VirB4 ATPases are therefore excellent targets for the development of intervention strategies aimed at suppressing the action of T4SS nanomachines.
Identifying strengths and weaknesses of methods for computational network inference from single-cell RNA-seq data

Single-cell RNA-sequencing (scRNA-seq) offers unparalleled insight into the transcriptional programs of different cellular states by measuring the transcriptome of thousands of individual cells. An emerging problem in the analysis of scRNA-seq is the inference of transcriptional gene regulatory networks and a number of methods with different learning frameworks have been developed to address this problem. Here, we present an expanded benchmarking study of eleven recent network inference methods on six published scRNA-seq datasets in human, mouse, and yeast considering different types of gold standard networks and evaluation metrics. We evaluate methods based on their computing requirements as well as on their ability to recover the network structure. We find that, while most methods have a modest recovery of experimentally derived interactions based on global metrics such as Area Under the Precision Recall curve, methods are able to capture targets of regulators that are relevant to the system under study. Among the top performing methods that use only expression were SCENIC, PIDC, MERLIN or Correlation. Addition of prior biological knowledge and the estimation of transcription factor activities resulted in the best overall performance with the Inferelator and MERLIN methods that use prior knowledge outperforming methods that use expression alone. We found little to no benefit from imputation for network inference, which is further dataset-dependent. Comparisons of inferred networks for comparable bulk conditions showed that the networks inferred from scRNA-seq datasets are often better or at par with the networks inferred from bulk datasets. Our analysis should be beneficial in selecting methods for network inference. At the same time, this highlights the need for improved methods and better gold standards for regulatory network inference from scRNAseq datasets.
Manganese dioxide nanoclusters as a potential nano-sorbent for the removal of silver ion from water

Hydrous manganese dioxide nanoclusters are prepared by the oxidation of manganese (II) ions with ammonium persulfate and used as a new sorbent for Ag + ion removal from aqueous medium. The synthesized compound is characterized using scanning electron microscopy, dynamic light scattering, energy dispersive analysis of X-rays, X-ray diffraction and BET surface measurements. Scanning electron microscopy images showed that the synthesized Hydrous manganese dioxide nanoclusters include cactus-shaped nanoclusters with uniform needles of average diameter of 36 nm and length of 1000 nm. X-ray diffraction data reveal that γ-MnO 2 is formed in this method. Batch experiments are carried out to evaluate the Ag + adsorption capacity of Hydrous manganese dioxide nanoclusters. The maximum adsorption capacity estimated by the Langmuir model was 81.97 mg g -1 for Ag+. Silver ion adsorption on Hydrous manganese dioxide nanoclusters is a fast process and the kinetics followed a pseudo-second-order rate equation. The removal efficiency of Ag+ depends on Hydrous manganese dioxide nanoclusters amount, pH and temperature of the solution. The presences of Na + , Ca 2+ and Mg 2+ ions in the concentrations lower than 200 ppm have no significant influence on silver ion removal. The adsorbed ions can easily eluted by the small volume of a solution consisting thiourea and hydrochloric acid. Finally, the experimental data show that the synthesized Hydrous manganese dioxide nanoclusters can quantitatively remove silver ions from real water samples.
Analysis on the Trend of AIDS Incidence in Zhejiang, China Based on the Age-Period-Cohort Model from 2004 to 2018

Background: To predict the trend of acquired immune deficiency syndrome(AIDS) in specific age groups and to determine the objective population for AIDS screening, we explored the three transmission routes (homosexual transmission, heterosexual transmission, drug injection and others) and characterized each patients group using the Age-Period-Cohort(APC) model based on the whole, local and immigrant populations in Zhejiang, China.Methods: The data recruited in this paper was obtained from the national "Comprehensive AIDS Prevention and Control Information System - Antiviral Therapy Management" database and the Chinese Disease Prevention and Control Information System and the Statistical Yearbook of Zhejiang, China. An APC model was used to estimate the impact of age, period and cohort on the incidence rate of AIDS, and to predict the AIDS incidence rate in specific age groups based on sexes and transmission routes.Results: The AIDS incidence rate peaked in males between 20 and 35 years old; the incidence rate of males was higher than that of females due to the impact of period; obvious cohort effect was observed among the immigrants. In the whole and local populations, the incidence rates of males in all age groups and females in both the 35-year-old group and the whole age group were predicted to increase sharply in five years. In the immigrant population, the AIDS incidence rates of males and females in all age groups were expected to increase significantly in five years; influenced by age, the incidence of AIDS through homosexual transmission peaked between 20 and 35 years old in the whole and local populations; under the influence of period, the incidence of AIDS via homosexual transmission in the whole and local populations increased and remained stable after 2015, and the incidence of AIDS transmitted by homosexual and heterosexual routes in the immigrants also showed an increasing trend. As a result of the cohort effect, the incidence of AIDS transmitted through homosexual route among the whole and local populations peaked in 1990; the incidence of AIDS in all age groups through different transmission routes would show an upward trend over the next five years.Conclusions: The results elucidate that there are sex differences in AIDS incidence rate, and the incidence of AIDS through various transmission routes in all groups is predicted to exhibit an upward trend in the five years to come. Effective intervention strategies should be developed and implemented by the public health departments in Zhejiang to control the epidemic of AIDS.
The Diagnostic Feedback Supervision Model based on Information Technology as The New Strategy to Improve The Professional Competence of Academic Personnel in School Organization

The teacher is academic personnel at a school organization. The quality of education will be achieved if it is supported by professional teachers. There have been many attempts to improve the professional skills of teachers, but until now they have not achieved the expected qualifications. Therefore, it is necessary to formulate a more effective teacher supervision model. The success of supervision is not only determined by the intensity of the supervision carried out but also the strategy used. The diagnostic feedback supervision model based on information technology is one technique that is thought to be effective in improving teacher professional competence. The model emphasizes independent development, which is carried out through a cycle with planning, implementation, and evaluation steps, that are supported by information technology. The purpose of this study is to formulate and test the effectiveness of a diagnostic feedback supervision model based on information technology to improve teachers' professional competence. The research was conducted in Malang City, using a quasi-experimental design. The research sample taken was a number of 30 teachers. Data were collected by tests and questionnaires techniques, while descriptive statistics and t-test were used for analyzing it. The results showed that the diagnostic feedback supervision model based on information technology was very effective in improving the professional competence of teachers.
Automated Assessment of Lymphocytes Using Machine Learning Techniques

This paper investigates the performance of machine learning techniques for automated assessment of lymphocytes. A total of four algorithms, i.e., support vector machine (SVM), deep learning for java (DL4J), multi-layer perceptron (MLP) and $K^{\ast}$ are applied to the lymphocytes dataset. To ensure the robustness, all the four algorithms are evaluated with both preprocessed data and data without prior pre-processing. With the pre-processed data, MLP outperforms all the other techniques and achieve an accuracy of 98.64%. SVM, DL4J and $K^{\ast}$ achieve accuracies of 97.97%, 96.62% and 97.29%, respectively.
Classification of Arrhythmia in Time Series ECG Signals Using Image Encoding And Convolutional Neural Networks

Electrocardiograph (ECG) signal analysis has been used extensively to study a patient’s heart and detect problems like arrhythmia for decades. Manual analysis of ECG in real time is laborious and therefore not practical for doctors. Deep learning helps make this job much easier due to quicker learning of signal features and event prediction. Deep Learning classifiers can help doctors differentiate between normal and abnormal ECG signals based on the basic and advanced features of ECG signals. This paper focuses on building a Convolutional Neural Network (CNN) to classify arrhythmia in dual channel ECG signals based on images generated by time series to image encoding techniques. The ECG time series signals were converted into images using Gramian Angular Fields (GAF) and Markov Transition Fields (MTF). These images were fed as input into the deep learning classifier which further classified the signals into various types. Our model achieved an accuracy of 97% for the GAF images and 85% per cent for the MTF images.
Full-Duplex Self Cancellation Techniques Using Independent Component Analysis

Independent component analysis (ICA) has been shown as a promising means for solving the self-interference cancellation (SIC) problem for in-band full duplex systems. This paper presents a detailed analysis of the interference suppression capability and computational complexity of several different ICA algorithms, operating at either real-valued or complex-valued domain. In addition, on the basis of the setup of the full-duplex system, we show that a much simplified complex-valued ICA algorithm that only performs whitening and decorrelation processes are sufficient to separate the signal of interest from the mixed signal. Extensive simulation results are presented in this paper to illustrate the performance and complexity of various ICA approaches applying to the full-duplex system.
Parallel and Flexible 5G LDPC Decoder Architecture Targeting FPGA

The quasi-cyclic (QC) low-density parity-check (LDPC) code is a key error correction code for the fifth generation (5G) of cellular network technology. Designed to support several frame sizes and code rates, the 5G LDPC code structure allows high parallelism to deliver the high demanding data rate of 10 Gb/s. This impressive performance introduces challenging constraints on the hardware design. Particularly, allowing such high flexibility can introduce processing rate penalties on some configurations. In this context, a novel highly parallel and flexible hardware architecture for the 5G LDPC decoder is proposed, targeting field-programmable gate array (FPGA) devices. The architecture supports frame parallelism to maximize the utilization of the processing units, significantly improving the processing rate. The controller unit was carefully designed to support all 5G configurations and to avoid update conflicts. Furthermore, an efficient data scheduling is proposed to increase the processing rate. Compared to the recent related state of the art, the proposed FPGA prototype achieves a higher processing rate per hardware resource for most configurations.
A Two Phase Denoising Approach to Remove Uneven illumination From Ancient Note Book Images

Aged handwritten document binarization is a tedious process of removing a type of noise like uneven illumination. Most of the aged documents have a challenge like uneven illumination. The proposed work mainly focus on removal of uneven illumination from aged handwritten Malayalam documents. The dataset which is used for experimentation is notebook image named ‘Panchathanthram’ written in 1857 AD to 1866 AD. The proposed work deals with a two phase model which is followed by degraded notebook image as dataset, first level obtaining the threshold image, second level removing the border noise. In first level gray scale image is passed to Butterworth filter as well as Otsu thresholding to obtain the threshold image. At the second level threshold image is passed to a flood fill algorithm for creating a masked image and applies bitwise-AND followed by slicing operation obtained output image is padded to make border as white.
Covid-19 Accelerates Digital Transformation in Industries: Challenges, Issues, Barriers and Problems in Transformation

Market emphasizes the need for a strategic view of the digital transformation of business, where transformation signifies a trend that allows changes in the core business processes and contributes to the development of sustainable business models. Complexity of the business transformation process affects the emergence of certain challenges and problems that need to be overcome on the way to creating innovative business models that will enable the use of full organizational potential. Purpose of this research is to delineate the various difficulties in digital transformation and determine what are the challenges, issues, barriers, and problems that organizations face in the desire to transform business. Paper presents a review of the literature of relevant research bases, where a qualitative and quantitative analysis of the results was made. Results show the frequency of occurrence of certain difficulties in DT within four categories - challenges, issues, barriers and problems, but also the occurrence of difficulties in the inevitable transformation due to the Covid-19 pandemic. It can be said that the pandemic has affected rapid adjustments, but also changes in the business models of organizations and that it has indirectly initiated digital transformation projects within organizations accompanied by various challenges, issues, barriers, and problems.
A Novel Optimization Technique for Integrated Supply Chain Network in Industries - A Technical Perspective

Supply chain (SC) management is an integral part of the technological advancement of industries nowadays. This study designed and presents an optimization model for the inventory-location technical problems in an SC network. The model considers carbon emission and perishable products and formulated a novel model based on the mixed-integer linear programming concept. The proposed model aimed at minimizing the total costs in the network. The solution of the model obtained using the GAMS optimization package. The results show that an optimal solution is reachable within a reasonable time with the presented mathematical model.
Automated Defect Detection in Physical Components using Machine Learning

It is a crucial part of any manufacturing process, either using manual inspection or using today's modern approaches, to detect the defects at the earlier stages to minimise the risks of failure at later stages. In the early days, manual inspection was prone to many errors, leading to a loss of resources and was very time-consuming. Among the other research areas, it is also an active field of research to achieve the perfect balance between high performance and accuracy in defect detection. ResNet, AlexNet, GoogLeNet, and VGGNet has shown remarkable improvement over old traditional designs in this regard. Image processing and deep learning-based object detection model adopted by Google Cloud Machine Learning Engine were widely used for defect detection and had shown somewhat satisfactory results. In this paper, we proposed a model which is successfully trained on the Google Cloud ML Engine. The results have shown that MobileNet-SSD can automatically detect surface defects more frequently, accurately, and precisely compared to conventional deep learning methods. We have used the pre-trained model of MobileNet V2, which is already trained on lakhs of images and is resource-efficient because it needs small memory setup and lower processing power of the CPU.
Radiation Hardened 12T SRAM With Crossbar-Based Peripheral Circuit in 28nm CMOS Technology

Conventional hardened cells are not robust enough to single event upset (SEU) in 28nm technology due to the scaling of the transistors. High soft error rate is caused by particle striking at cells and logic circuit in SRAM. This work proposes an SEU robust dual access 12T (DA-12T) SRAM with a radiation hardened crossbar-based peripheral circuit (CBPC). The proposed cell with 209% area penalty is more SEU robust than most cells. The CBPC can reduce the read failure rate of SRAMs. The new sense amplifier ensures the correct and rapid reading operation speed when suffering read disturbance. The experiment results show that the SEU cross-section of proposed cell is 60% of standard cell with dummy. Almost no read failure is observed in SRAM with CPBC when operational frequency exceeds 40MHz. Further investigation indicated that DA-12T cell and well isolation technique can reduce the read failure rate.
AnSMart: A SVM-based anomaly detection scheme via system profiling in Smart Grids

Anomaly detection techniques analyze consumer spend patterns and grid load profiles to predict possible deviations from normal behavior. However, as the measured data is time-varying, profiling the measured drifts becomes complex owing to the amount of raw generated data. Motivated from the aforementioned discussions, in this paper, we propose a scheme, AnSMart, to predict deviations in smart grid (SG) data through obtained profiling operations. The scheme operates in two phases. In the first phase, valuable grid features are extracted from internal system call lists made by grid kernels after load profiling operations are completed over a day. Then, in the second phase, based on data logs, vector differences are computed for call vectors and malign scenarios are identified. The data is fed to the support vector machine (SVM) model for training, and compromised grid behavior is classified. SVM predicts metrics deviation from normal grids. The deviation is measured depending on parameters like-call vector signal, call-sizes, call-list deviations, and call-return values collected from the open-source libiec61850 library that consists of resource-rich (RR) and resource-limited (RL) libraries for both compromised and uncompromised grids. Based on different cases, a total of 50 experiments were conducted. The obtained F-score is 0.926 and the accuracy of 92.5% is obtained based on system-call stacks and grid operating system (OS) behavior that outperforms the conventional anomaly-based approaches on SG.