Miracle-dz/UniFAR-FTUs huggingface dataset:
Expression of MicroRNAs Is Dysregulated by HIV While Mycobacterium tuberculosis Drives Alterations of Small Nucleolar RNAs in HIV Positive Adults With Active Tuberculosis

HIV infection affects the course of tuberculosis (TB), and HIV and Mycobacterium tuberculosis (Mtb) synergize in disease progression through complex immunological interplay. To gain further understanding of these mechanisms, we compared the microRNA (miRNA) and small nucleolar RNA (snoRNA) expression patterns in whole blood of individuals with active TB, with and without HIV coinfection (HIV+/TB+ and HIV-/TB+), and HIV and TB-negative individuals (HIV-/TB-). We found that 218 miRNAs were differentially expressed between HIV+/TB+ and HIV-/TB+, while no statistically significant difference in snoRNA expression was observed between these groups. In contrast, both miRNA (n = 179) and snoRNA (n = 103) expression patterns were significantly altered in HIV+/TB+ individuals compared to those of the HIV-/TB- controls. Of note, 26 of these snoRNAs were also significantly altered between the HIV-/TB+ and HIV-/TB- groups. Normalization toward the miRNA and snoRNA expression patterns of the HIV-/TB- control group was noted during anti-TB and antiretroviral treatment in HIV+/TB+ participants. In summary, these results show that HIV coinfection influences miRNA expression in active TB. In contrast, snoRNA expression patterns differ between individuals with and without active TB, independently of HIV coinfection status. Moreover, in coinfected individuals, therapy-induced control of HIV replication and clearance of Mtb appears to normalize the expression of some small non-coding RNA (sncRNA). These findings suggest that dysregulation of miRNA is a mechanism by which HIV may modify immunity against TB, while active TB alters snoRNA expression. Improved understanding of how regulation of sncRNA expression influences the disease course in coinfected individuals may have implications for diagnostics, risk stratification, and host-directed therapy. Here, we propose a novel mechanism by which HIV alters the immune response to TB.
Immune Checkpoint FGL1 Expression of Circulating Tumor Cells Is Associated With Poor Survival in Curatively Resected Hepatocellular Carcinoma

LAG-3 is one of the common tumor immune checkpoints. LAG-3 can inhibit the activation and proliferation of T cells, and can also suppress immunity by regulating other immune-related cell functions. FGL1 was recently discovered to be the main ligand of immune checkpoint LAG-3 and play a critical role in the inhibition of T cells. However, the FGL1 expression in circulating tumor cells (CTCs) and its clinical significance in hepatocellular carcinoma (HCC) remain unclear. Therefore, this bioinformatics analysis was performed to assess the expression of FGL1 in various tumors and its association with immune infiltration. After that, CTCs from 109 HCC patients were detected and the immunofluorescence staining was performed (CD45, EpCAM, CK8/18/19, Vimentin, Twist, DAPI and FGL1). Then, we investigated FGL1 expression and EMT of CTCs and analyzed its relationship with patient survival and clinical relevance. Bioinformatic results showed that FGL1 expression was abnormal in various tumor and it was correlated with the infiltration level of several immune cells. FGL1 expression was detected in CTCs of 40 patients (36.7%). The proportion of advanced TNM stage (P<0.001) and distant metastasis(P=0.020) in FGL1 positive patients was higher than that of FGL1 negative patients. In addition, patients with FGL1 positive circulating tumor cells had worse postoperative survival than FGL1 negative patients (p=0.0297). The mixed phenotypic CTC presented a higher level of FGL1 expression than any other types, the number of which also predicted worse prognosis(p=0.0443). We also found that the expression of FGL1 on CTCs was associated with the level of FGL1 in tumor tissues. Of 12 patients receiving PD-1/PD-L1 blockade in a total of 109 cases, 8 out of 10 patients with FGL1 positive CTC showed immunotherapy resistance. It is the first study that suggested FGL1 expression in CTCs as an indicator of the poor prognosis in HCC patients. CTC detection may serve as a promising replacement for determination of tumor tissue FGL1 expression and provide evidence for the application of immunotherapy.
Performance analysis and inference of communication networks

Performance Analysis and Inference of Communication Networks Jian Ni 2008 In this thesis we design, analyze, and develop accurate and scalable algorithms and techniques for performance analysis and inference of large communication networks. In the network plan phase, stochastic network models such as loss networks and queueing networks are often used to analyze and predict the network performance. We propose a new approach to analyze stochastic networks based on probabilistic graphical models. We use factor graphs to model the stationary distribution of a network and apply the sumproduct algorithm to compute the performance measures of interest. We demonstrate via both analysis and numerical experiments that the algorithm has excellent performance for networks with a variety of topologies. In addition, the algorithm scales gracefully to large networks and can be implemented in a distributed manner. In the network operation phase, inference of the network structure and dynamics is an essential component of many network design and management tasks to achieve high network performance. We first address the network inference problem of estimating the routing topology and link performance. We propose a new, general framework for designing network inference algorithms based on additive metrics using tools from phylogenetic inference. The framework can flexibly fuse information from different measurements to improve estimation accuracy. Based on the framework we develop several computationally efficient distance-based inference algorithms with provable performance. We also design topology inference procedures which can handle dynamic node joining and leaving efficiently. Using
Grid connection of wave energy converter in heaving mode operation by supercapacitor storage technology

European energy policy guidelines recognise renewable energy sources the main mean to contrast the rapid fossil fuels depletion and the related global warming. Marine energy source represents an attractive and inexhaustible reservoir from which to draw. One of the major difficulties in integrating sea wave generation systems or equivalently wave energy converters (WECs) with existing electrical systems is the management of their generation intermittency. This is essentially due to the inherent nature of the sea wave source. Energy storage represents an effective enabling technology for mitigating such an effect. To this aim, this study proposes an efficient control strategy for embedded floating buoy generation systems with energy storage technologies in order to regularise the injected grid power while minimising the contractual power established by the distribution system operator. The control strategy has been tested numerically on a grid connected DC microgrid formed by a DC bus at which a floating buoy generation system is interfaced with an energy storage system, supercapacitors-based, having the purpose of smoothing the natural power fluctuations of the WEC.
Image Filter Using with Gaussian Curvature and Total Variation Model

Priors play an essential role in Bayesian theory in image processing. Geometric priors are very popular because of their physical explanation. The neighborhood structure of pixel can be described more accurately by its curvature. In this paper, we deals with the image restoration algorithms based on Gaussian curvature and total variation modal to achieve smooth denoising preserving the details of image. There we show that how priors should be imposed for certain types of surfaces and how they can be imposed efficiently in a variational framework. We first show a novel method that can reconstruct a closed surface from a finite point cloud then by using total variation modal we suppress the noise. We can directly extract prior information from the reconstructed surfaces. We provide parametric model and analyze its properties. The new modal can preserve more details while suppress noise. The experimental results are given to demonstrate the performance of the proposed method.
Text Representation for Machine Learning Applications

Most of the data generated today is unstructured which typically consists of text information. Understanding such text data is imperative given that it is rich in information and can be used widely across various applications. However, the key to understanding such data is its representation. In this survey, we discuss various text representation methods starting from count based methods to state of the art methods including distributed representational learning. These algorithms can transform large volumes of text into effective vector representations capturing the same semantic information. Further, such representations can be utilized by various machine learning algorithms.
Classification Of Hyperspectral Images Using Densenet-264 With Tensorflow

The DenseNet-264 uses an additive principle to merge the previous layer with the next layer and further it concatenates the previous layer output with next layer. This DenseNet-264 model improves the declined accuracy produced due to vanishing gradients in case of high-level neural network. Hence, the information is preserved in longer paths between the source layer and destination layer without getting vanished in between the layers. In this paper, we study the DenseNet-264 top-5 metric to classify the Hyper-Spectral Image (HSI) features from the input HSI. The simulation is conducted on several HSI images to test the efficacy of the model against various datasets, and the results of simulation shows that the proposed method achieves 99% accuracy than the other existing classifier.
THE RELATIONSHIP BETWEEN GENERAL AGGRESSION MODEL AND COMORBIDITY OF ANTISOCIAL PERSONALITY DISORDER AND BIPOLAR AFFECTIVE DISORDER POVEZANOST OPŠTEG MODELA AGRESIVNOSTI SA KOMORBIDITETOM ANTISOCIJALNOG POREMEĆAJA LIČNOSTI I BIPOLARNOG AFEKTIVNOG POREMEĆAJA

The General Aggression Model (GAM model) is a dynamic, social and cognitive development model that includes situational, individual and biological variables, based on the theories of social learning and social cognitive theories, as well as on a large number of theoretical and empirical arguments of many authors who are engaged in the study of aggression. The subject of our interest is the phenomenon of aggression in patients with bipolar affective disorder with the presence of antisocial personality disorder. The combined factors of aggressiveness in GAM model can be used to determine the presence, intensity or prediction of aggression in these patients. The General Aggression Model provides the division factors of aggressiveness into direct and indirect, such as personality factors, situations, environmental factors and biological factors.
COMBINING DEEP RESIDUAL NEURAL NETWORK FEATURES WITH SUPERVISED MACHINE LEARNING ALGORITHMS FOR REAL-TIME FACE RECOGNITION-BASED INTELLIGENT SYSTEMS

Face Recognition is the domain of technology in Computer Vision that deals with the process of identifying faces of known and unknown per-sons-based on facial patterns. Despite all the recent researches these years on Face Recognition technology, the development of real-time face recognition has always been a challenging task. This kind of technology has its applications widespread like security, medical diagnosis, educational sectors, etc. The advancements in High-end processors and High-Definition cameras led to the design of Face recognition systems that use offline or real-time input datasets. In this paper, our main aim is to focus on real-time video feeds taken from a framed classroom environment to identify the students or the faculty members and tag their names, comparing them with the already stored face databases. Attendance marking is a daily routine that follows calling the name or passing the attendance books, which is very time-consuming, and they tend to begin proxy at-tendances. This study proposes a attendance marking system using face recognition and Deep Learning techniques on a Raspberry Pi board. The proposed system delivers an approach to make real-time face recognition-based attendance systems by extracting deep facial features using deep residual network (ResNet)-based CNNs. Then that deep facial feature dataset is combined with Machine Learning Algorithm such as SVM to perform face detection and recognize the faces. The maximum recognition accuracy of 80% is obtained using the planned system on the real-time face images provided and will be further improved.
Development Roadmap of a Deorbit Kit Based on Electrodynamic Tether

Low Earth Orbit (LEO) is an environment rich of natural resources. Earth magnetic field is used to control spacecraft attitude, Earth gravity harmonics are used to control the satellite’s orbit evolution and solar radiation is used to generate power. Nevertheless, today’s spacecraft are not designed to use another very valuable natural LEO resource: the ionospheric plasma. Among the different devices that could take advantage of it, ElectroDynamic Tether (EDT) is the most promising one due to its passive and propellant-less character. EDT consists of a long metallic tape connected with a spacecraft. It naturally captures electrons from the plasma and an active electron emitter, or a coating with a low work-function material, emits the electrons back to the plasma to achieve a steady electrical current. The action of the ambient magnetic field on the tether current gives a Lorentz force without using propellant. The EDT physics has been already demonstrated in orbit in the 1990s, but it is not until today that the technology has found a primary application in the satellite de-orbiting. EDT technology can be more efficient than conventional chemical and electrical propulsion, resulting in the best choice for passive de-orbit systems. The promising character of this green de-orbiting system has been recently acknowledged by the European Commission that granted the FET-OPEN project entitled Electrodynamic Tether technology for PAssive Consumable-less deorbit Kit (E.T.PACK). Funded with 3M€ and started in March 2019, E.T. PACK will develop a DK based on EDT technology with Technology Readiness Level equal to 4 and aligned with the needs of a future in orbit demonstration. The DK will be a fully autonomous system designed to de-orbit satellite from 200 to 1000kg in orbits up to 1200km. DK will be bolted on customer satellite before launch. Upon activation from ground, the DK will remove spacecraft residual angular velocity, acquire a stable attitude and deploy a maximum of 3km long tape. The DK mass is expected to be less than 5% of the customer satellite mass and de-orbit a 700kg satellite from 800km altitude polar orbit in less than 1.5 years. This work presents the design of the De-orbit Kit Demonstrator (DKD), simulations of the system performances and the development roadmap. Specific goals of the DKD are to test the deployment mechanism in orbit, to evaluate the de-orbit performances and assess the maneuverability of the tether for collision avoidance.
Experimental Study of Large-amplitude Wavefront Correction in Free-space Coherent Optical Communication

Qian Guo*, Shuang Cheng, and Xizheng Ke School of Mechanical and Electrical Engineering, Xi’an Polytechnic University, Xi’an, Shaanxi 710048, China Faculty of Automation and Information Engineering, Xi’an University of Technology, Xi’an, Shaanxi 710048, China Xi’an Key Laboratory of Modern Intelligent Textile Equipment, College of Mechanical and Electronic Engineering, Xi’an Polytechnic University, Xi’an, Shaanxi 710048, China Shaanxi Civil-Military Integration Key Laboratory of Intelligence Collaborative Networks, Xi’an University of Technology, Xi’an 710126, China
Bounding the Flow Time in Online Scheduling with Structured Processing Sets

Replication in distributed key-value stores makes scheduling more challenging, as it introduces processing set restrictions, which limits the number of machines that can process a given task. We focus on the online minimization of the maximum response time in such systems, that is, we aim at bounding the latency of each task. When processing sets have no structure, Anand et al. (Algorithmica, 2017) derive a strong lower bound on the competitiveness of the problem: no online scheduling algorithm can have a competitive ratio smaller than $\Omega(m)$, where $m$ is the number of machines. In practice, data replication schemes are regular, and structured processing sets may make the problem easier to solve. We derive new lower bounds for various common structures, including inclusive, nested or interval structures. In particular, we consider fixed sized intervals of machines, which mimic the standard replication strategy of key-value stores. We prove that EFT (Earliest Finish Time) scheduling is ($3-2/k$)-competitive when optimizing max-flow on disjoint intervals of size $k$. However, we show that the competitive ratio of EFT is at least $m-k+1$ when these intervals overlap, even when unit tasks are considered. We compare these two replication strategies in simulations and assess their efficiency when popularity biases are introduced, i.e., when some machines are accessed more frequently than others because they hold popular data. Even though overlapping intervals suffer from a bad worst-case in theory, they enable clusters to reach a maximum load that is up to 50% higher than with disjoint sets.
Arbuscular Mycorrhizal Association with Rattan Species of the Belum-Temengor Forest Complex, Perak, Malaysia

Rattan Calamus spp. is a high commercial value palm from the subfamily Calamoideae the primary source for cane in the well-developed rattan industry. Most studies on rattan have focused on its biodiversity, distribution, quality, strength, physical, mechanical, and morphological properties and genetics. Still, only a few have investigated the association of rattan with arbuscular mycorrhizal (AM) fungi. These mycorrhizal fungi are well known to play essential functions as promoting plant growth, maintaining plant community biodiversity and nutrient cycles in soil. This study aims to identify the established AM fungi community and their ecological interactions with Calamus spp. in the Belum-Temengor Forest Complex, Perak, Malaysia. Calamus spp. roots and their rhizospheric soil samples were collected from six sampling sites in the Belum-Temengor Forest Complex, one of the oldest rainforests in the world. The degree of mycorrhizal colonisation in Calamus spp. was evaluated using the grid lines method. At the same time, the AM fungi spore diversity in the rhizospheric soils were isolated using the wet sieving method and identified taxonomically analysed into different genera. Calamus insignis showed the highest degree of mycorrhizal colonisation amongst all the Calamus spp. present on the sampling sites. The AM fungi spores isolated from the rhizospheric soil from Belum-Temengor Forest Complex belonged to the genera Acaulospora, Entrophospora, Gigaspora, Glomus, and Scutellospora. Glomus was the most frequently found genus in all the sampling sites. This study is the first record of the AM fungal diversity found in the Belum-Temengor Forest Complex.
Decentralized Control of Aerial Manipulators based on a momentum-based estimator

Aerial Robotic Manipulators can be controlled either in a centralized or decentralized way. This Part of the book addresses the latter option, in which the aerial platform and the arm are controlled separately in a robust way to counteract the reciprocal disturbances. An estimator of such disturbances, based on the momentum of the system and acting on the unmanned aerial vehicle, is designed. Experiments validate the approach. 1 Brief introduction A decentralized approach might be suitable in those circumstances where the dynamics of the robotic arm attached to the UAV is not fast enough to compensate for the position errors of the aerial platform, and/or the arm does not allow a torque control modality. In this context, the term decentralized is used to indicate the case where the UAV and the robotic arm are seen as two separate independent entities: the effect of the movements of the arm is seen as an external disturbance by the UAV and vice versa. Therefore, control issues for the two entities are addressed separately. On the one hand, robust control of the sole robot manipulator is well-established in the literature [9], and it is out of the scope of this chapter. On the other hand, the control of the single UAV with standard techniques may be not sufficient to deal with the presence of the moving arm. As a matter of fact, in addition to the standard issues caused by neglecting relevant aerodynamics terms within the dynamic model and the underactuation of the floating platform (in single-direction thrust UAVs like the quadrotors), suitable control laws must be designed to counteract the destabilizing movements of the arm. For this reason, suitable disturbance observers/estimators and robust controllers are thus adequately designed and superimposed to the existing controllers. A short literature review reveals a multilayer architecture to control multirotor UAVs equipped with a servo robot arm [6], where the momentum-based estimator presented in [4, 5] is employed to compensate for unwanted aerodynamic
Controlling defective items in a complex multi-phase manufacturing system

In manufacturing systems, defective items are produced for machine drift and error. Usually, an imperfect production rate is random, and if the items are not reworked, these are considered trash and harm the environment. The proposed model aims to reduce waste by reworking defective products and maximizing profit. For profit maximization or overall cost minimization of the manufacturing system, setup cost has significant. A discrete investment for each phase is introduced with an inequality investment constraint for reducing the setup cost. Selling price-dependent demand is trained for more generalized applications for various industries. The proposed model is a multi-phase manufacturing system with optimum batch size, selling price, and investment with an irregular, imperfect production rate. Defects are detected at the first inspection, and the reworked items are checked if the reworked items are all non-defective in the second inspection. The model conducts a two-stage inspection. One is for detecting defective items, and another is for checking if all items are not defective after reworking. The model is solved with the Karush–Kuhn–Tucker (KKT) method, and the global maximum profit is obtained. The model shows that all investments should be assigned to maximize the profit and the optimal solution. Reducing setup cost with the investment is better than a constant setup cost.