Miracle-dz/UniFAR-FTUs huggingface dataset:
Identification of upregulated DOCK1 in leukemia stem cells associated with risk stratification in acute myeloid leukemia through bioinformatics analysis

Xenotransplantation studies showed that acute myeloid leukemia (AML) is organized as a hierarchy sustained by leukemia stem cells (LSCs), and the LSCs persistence influences AML clinical outcomes because of the high relapse rate and therapy resistance. This study aimed to identify differentially expressed genes between LSCs and non-LSCs, from which prognosis-related genes were extracted and confirmed in AML cohorts. Meanwhile, involved alterations of methylation pattern were analyzed. Differentially expressed genes between LSC and non-LSC fractions were screened in GSE30377 and GSE76008 datasets from the Gene Expression Omnibus (GEO). AML prognosis-related genes were identified and verified in TCGA, GEO, TARGET databases and Beat AML programme. To understand its molecular mechanisms, the altered degrees of methylation were investigated in GEO, TCGA and TARGET databases. The expression level of DOCK1 was higher in LSCs than in non-LSCs, and was also implicated with LSC-related gene through the gene set enrichment analysis. Besides, the significant differences in gene expression between AML CD34+ and CD34cells, normal CD34+ and mononuclear cells, as well as hematopoietic stem cells and granulocytes/monocytes were observed. The significantly lower methylation levels were found in the DOCK1 promoter of LSCs, and the mechanism of DOCK1 upregulation in LSCs could be mediated by hypomethylation. Furthermore, the high expression of DOCK1 was associated with a worse prognosis in six independent cohorts of de novo AML patients. Our results indicated that the high expression of DOCK1 regulated by promoter hypomethylation was a potential biomarker of LSCs and, more importantly, a predicted adverse prognosis factor for AML.
Room Layout Estimation with Pretrained Convolutional Neural Networks and Color Augmentation

With companies such as Tesla rising in value, so too does the use of computer vision. Demand for autonomous machines may be in the near future, which means models need to identify and navigate through cluttered environments. A particular subset of Computer Vision that deals with these matters is called scene understanding - the features and properties computers are able to extract from images, videos, or streams. Scene understanding itself can be divided into subsets, such as layout estimation, scene classiﬁcation, and object detection. This paper will focus on layout estimation, where I will attempt to estimate the layout of a given room given a single image. My method consists of a fully convolutional deep neural network. Using the LSUN room layout dataset, I will generate a room layout and apply an algorithm to smooth the edges. The main purpose of this paper is to compare changing image qualities (saturation, contrast, hue, brightness) to data augmentation techniques used in other papers. My model achieves state of the art results against previous benchmarks.
Stratospheric Nudging And Predictable Surface Impacts (SNAPSI): A Protocol for Investigating the Role of the Stratospheric Polar Vortex in Subseasonal to Seasonal Forecasts

Abstract. Major disruptions of the winter season, high-latitude, stratospheric polar vortices can result in stratospheric anomalies that persist for months. These sudden stratospheric warming events are recognized as an important potential source of forecast skill for surface climate on subseasonal to seasonal timescales. Realizing this skill in operational subseasonal forecast models remains a challenge, as models must capture both the evolution of the stratospheric polar vortices in addition to their coupling to the troposphere. The processes involved in this coupling remain a topic of open research. We present here the Stratospheric Nudging And Predictable Surface Impacts (SNAPSI) project. SNAPSI is a new model intercomparison protocol designed to study the role of the Arctic and Antarctic stratospheric polar vortices in sub-seasonal to seasonal forecast models. Based on a set of controlled, subseasonal, ensemble forecasts of three recent events, the protocol aims to address four main scientific goals. First, to quantify the impact of improved stratospheric forecasts on near-surface forecast skill. Second, to attribute specific extreme events to stratospheric variability. Third, to assess the mechanisms by which the stratosphere influences the troposphere in the forecast models, and fourth, to investigate the wave processes that lead to the stratospheric anomalies themselves. Although not a primary focus, the experiments are furthermore expected to shed light on coupling between the tropical stratosphere and troposphere. The output requested will allow for a more detailed, process-based community analysis than has been possible with existing databases of subseasonal forecasts.
Predicting Ground-Level Ozone Concentration from Urban Satellite and Street-Level Imagery using Multimodal CNN

Understanding the impact of the built environment and urban populations on climate change and air quality is a growing challenge as the percentage of the world’s population that resides in urban areas continues to rise. In particular, human activity levels have been shown to be directly linked with ground-level ozone, which is responsible for several health and climate effects. We explore this relationship through the use of a multimodal learning architecture that predicts ozone concentrations (parts per billion) in urban areas from satellite and street-level imagery. The model comprises two Convolutional Neural Networks (CNN), one trained on satellite images of each location to learn higher-level features such as geographical characteristics and land use, and another trained on multiple street-level images of each location to learn ground-level features such as motor vehicle activity. The feature representations learned from each sub-model are concatenated and passed through several fully connected layers to predict the ozone level of the location. This concatenated model achieves a test RMSE of 11.70 ppb. This approach can be used to inform urban planning and policy by providing an insight into the particular urban features that aggravate ozone concentrations.
A SCALABLE CRYPTOGRAPHY POLICIES FOR SECURE DATA STORAGE AND ACCESS THE DATA IN CLOUD ENVIRONMENT

– Cloud computing security policies are applied for storing the data in cloud. It aims to control unauthorized users from data accessing and avoid destructions . When one data is taken to the cloud, many security concerns arise and ensure data security remains questionable. Cloud architecture threatens the safety of traditional technologies. Therefore, users of cloud services must know the dangers of loading data in this new environment. Therefore, this paper reviews different aspects of cryptography that pose a threat to cloud computing. This paper proposes ways to secure data in the cloud using strong cryptographic the AES algorithm can be used to store the data in the cloud and produce secure and scalable data storage elements. The efficiency and security of our proposed cryptosystem have been analyzed and reported.
Microwave Synthesis and Electroluminescent Characteristic of Phosphorescent Cyclometalated Platinum Complexes with Long Chain β-diketonate Ancillary Ligands

Microwave synthetic method has been developed for the synthesis of a series of cyclometalated platinum complexes with long chain β-diketonate ancillary igands, with which reaction time is greatly reduced from 32 h to several minutes.Moreover,the organic light-emitting diode(OLED) were fabricated by doping compounds in host(PFO and PBD) at different concentration and the electroluminescent characteristics of the compounds were researched.the max external quantum efficiency(1 %) of OLEDs was obtained when doping concentration of compound 3 is 8 percent.
Application of multi-objective fruit fly optimisation algorithm based on population Manhattan distance in distribution network reconfiguration

In order to optimise the operation state of the distribution network in the presence of distributed generation (DG), to reduce network loss, balance load and improve power quality in the distribution system, a multi-objective fruit fly optimisation algorithm based on population Manhattan distance (pmdMOFOA) is presented. Firstly, the global and local exploration abilities of a fruit fly optimisation algorithm (FOA) are balanced by combining population Manhattan distance (PMD) and the dynamic step adjustment strategy to solve the problems of its weak local exploration ability and proneness to premature convergence. At the same time, Chebyshev chaotic mapping is introduced during position update of the fruit fly population to improve ability of fruit flies to escape the local optimum and avoid premature convergence. In addition, the external archive selection strategy is introduced to select the best individual in history to save in external archives according to the dominant relationship amongst individuals. The leader selection strategy, external archive update and maintenance strategy are proposed to generate a Pareto optimal solution set iteratively. Lastly, an optimal reconstruction scheme is determined by the fuzzy decisionmethod. Compared with the standard FOA, the average convergence algebra of a pmdMOFOA is reduced by 44.58%. The distribution performance of non-dominated solutions of a pmdMOFOA, MOFOA, NSGA-III and MOPSO on the Pareto front is tested, and the results show that the pmdMOFOA has better diversity. Through the simulation and analysis of a typical IEEE 33-bus system with DG, load balance and voltage offset after reconfiguration are increased 0 © 2021. The Author(s). This is an open-access article distributed under the terms of the Creative Commons AttributionNonCommercial-NoDerivatives License (CCBY-NC-ND4.0, https://creativecommons.org/licenses/by-nc-nd/4.0/), which permits use, distribution, and reproduction in any medium, provided that the Article is properly cited, the use is non-commercial, and no modifications or adaptations are made. 308 Minan Tang et al. Arch. Elect. Eng. by 23.77% and 40.58%, respectively, and network loss is reduced by 57.22%, which verifies the effectiveness and efficiency of the proposed method.
The Effect of Father Involvement Towards the Tendency of Delinquent Behavior among Adolescents in Batak

The purpose of this study is to investigate the effect of father involvement towards the tendency of delinquent behavior among adolescents in Batak Toba ethnic. This study uses a nonprobability sampling technique and the total number of samples was 161 Batak Toba adolescent in Medan city with the age ranging from 15 to 18 years old. The data were collected using father involvement scale and the tendency of delinquent behavior scale. The methodology of data analysis used in this study is a simple regression method. The results showed that father involvement has an effect towards the tendency of delinquent behavior among adolescents in Batak Toba ethnic. This research showed that higher their involvement, the lower the tendency of delinquent behavior rate of adolescents in Batak Toba ethnic and vice versa. Keywords— Adolescent, father involvement, delinquent behavior.
Design and Implementation of Single Precision FPM in FPU for Optimized Speed

: Currently, each CPU has one or additional Floating Point Units (FPUs) integrated inside it. It is usually utilized in math wide-ranging applications, such as digital signal processing. It is found in places be established in engineering, medical and military fields in adding along to in different fields requiring audio, image or video handling. A high-speed and energy-efficient floating point unit is naturally needed in the electronics diligence as an arithmetic unit in microprocessors. Most of the operations accounting 95% of conformist FPU are multiplication and addition. Many applications need the speedy execution of arithmetic operations. In the existing system, the FPM(Floating Point Multiplication) have more delay and fewer speed and fewer throughput. The demand for high speed and throughput intended to design the multiplier and adder blocks within the FPM (Floating point multiplication) in a format of 32 bit single precision floating point. This is designed with Verilog code using Xilinx ISE 14.5i software tool is employed to code and verify the ensuing waveforms of the designed code.
NOVEL GROUND MOTION PREDICTION MODEL FOR PEAK INELASTIC DISPLACEMENTS

Several previous studies have shown that the peak inelastic displacement of a single-degree-of-freedom system (SDOF) is a more efficient ground motion intensity measure (IM) for estimating drift demands than the commonly used 5%-damped pseudo-acceleration spectral ordinate. Most methods for estimating peak inelastic displacements have been based either on R--T relationships, equivalent linearization methods, or displacement modification factors. Nonetheless, these methods typically neglect the influence of event characteristics, such as earthquake magnitude or site-to-source distance. This paper proposes a ground motion prediction model (GMPM) for directly estimating medians, dispersions, and probability of exceedance, of the random horizontal component of peak inelastic displacements of 5%-damped bilinear SDOF systems on stiff soils, generated by shallow crustal earthquakes. The set of equations was developed using a mixed effects model with 1662 ground motions recorded in 63 seismic events. The predictor variables of the proposed model are the period of vibration and the normalized yield strength of the system, defined as its lateral yield strength normalized by its weight, as well as the event moment magnitude and the Joyner-Boore distance to the rupture. This model not only results in lower dispersions than the previous approaches, but also has the advantage of not requiring another GMPM for estimating inelastic displacements. The proposed GMPM allows the estimation of the probability of exceedance of a peak inelastic displacement value, as a function of the earthquake magnitude and distance to the source.
U-Net: Convolution Neural Network for Lung Image Segmentation and Classiﬁcation in Chest X-ray images

Medical Image analysis and diagnosis is an indispensable part of today’s unpredictable and insanitary environment. Analysis takes long procedure to ﬁnd disease and it needs screening of internal organs of human body. Clinical analysis takes more time and sometimes may result in failure. Chest X-ray images are fundamental visual mechanism for medical world. Lung chest X-ray images need more support from Medical Diagnosis and identiﬁcation. Automatic segmentation in supervised learning needs training data and unsupervised learning approaches needs labelled images in addition to shape, size and texture variation in patients lung chest X-ray images require more assistance and support from medical radiologist. Some machine learning approaches failed to handle natural form of raw data visualisation in chest X-ray images. Deep learning models accept and process data in their own nature. Due to this nature deep learning approaches plays major role in medical image segmentation. It accesses large set of data and executes in little time and produce expected result effectively. Central impact of this paper is to deliver appropriate segmentation architecture for lung chest X-ray images. In this paper U-NET based convolution deep neural network is used for lung chest X-ray image segmentation and classiﬁcation. 512 X 512 image sizes are used for training and testing. The intention of this method is to extract lung region from chest X-ray image and classify as nodule or non-nodule images and also cancerous or non-cancerous. The result indicates that the proposed method achieve high accuracy as 89.76 % for segmentation and 98.40 % for nodule classiﬁcation and 98.79% for cancer classiﬁcation.
Sensors & Transducers

: Human-robot interaction (HRI) is the extensive research topic which aims at the complementary combination between the robot capabilities and human skills. The robots assist humans in terms of precision, speed, and force. The humans contribute in terms of the experience, knowledge of executing the task, intuition, and easy adaptation and learning, and understanding of control strategies. In this manuscript, a survey on the applications of HRI is presented. These applications could be industrial, medical, agricultural, servical, and educational. HRI can be found in industrial applications in handling of the workpieces in the production lines, co-manipulation tasks, welding processes, parts assembly, and painting. Assistive robotics are one from the highest profile areas in HRI. For people with the physical and the mental challenges, the robots can provide the opportunity of interaction and therapy. Furthermore, HRI can be widely applied in hospitals. Nowadays, HRI is very important to fight against the new coronavirus (COVID-19) pandemic. In agriculture, the robot is able to help humans with many tasks such as harvesting, seeding, fertilizing, spraying, weed detection, hauling, and mowing. HRI is quite new in the field of education. However, the robot can help children in classrooms for learning processes. In addition, the robots can help young children empathy and social skills. HRI can be found in other applications such as home use, inventory management, mining, Space exploration, and UAVs.
In Situ Cavitation of Phenolic Supramolecules with PEO–PPO–PEO Block Copolymers by Friedel–Crafts Alkylation toward Ordered Nanoporous Polymers

The release of hydrogen halides in Friedel–Crafts alkylation has long been considered as troublesome and detrimental. Here we demonstrate that the released HCl in alkylation is beneficial and highly demanded to remove poly(ethylene oxide)-b-poly(propylene oxide)-b-poly(ethylene oxide) templates from the phenolic supramolecules for the preparation of ordered nanoporous phenolic polymers as additional treatment for template removal is no longer necessary. Friedel–Crafts alkylation using bifunctional alkylation agent, 1,4-bis(chloromethyl)benzene, leads to the hyper-cross-linking of phenolic polymers, thus generating micropores in the skeleton of the polymers. HCl in situ generated as the byproduct during alkylation facilely extracts the copolymer because of its enhanced diffusion through the micropores. The prepared phenolic polymers exhibit high surface areas and well-ordered porosities in a hexagonal or gyriodal structure. The in situ cavitation by alkylation takes place at much milder conditions and has .
This information is current as Cell Antigen Presentation in Murine Lupus Reveals Multiple Nonredundant Roles for B Specific MHC Class II Deletion −

Print ISSN: 0022-1767 Online ISSN: 1550-6606. Immunologists, Inc. All rights reserved. Copyright © 2015 by The American Association of 9650 Rockville Pike, Bethesda, MD 20814-3994. The American Association of Immunologists, Inc., is published twice each month by The Journal of Immunology at N ew Y rk M ed C olege H ealth Scences L irary on Sptem er 4, 2015 hp://w w w jim m unl.org/ D ow nladed from
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1 Baskin Center for Computer S ien e and Engineering, University of California, Santa Cruz, CA 95064, U.S.A. E-mail: {jagota,rlyngsoe} se.u s . edu 2 BRICS⋆ ⋆ ⋆, Department of Computer S ien e, University of Aarhus, Ny Munkegade, DK-8000 Århus C, Denmark. E-mail: storm bri s.dk. Abstra t Sto hasti models are ommonly used in bioinformati s, e.g. hidden Markov models for modeling sequen e families or sto hasti ontext-free grammars for modeling RNA se ondary stru ture formation. Comparing data is a ommon task in bioinformati s, and it is thus natural to onsider how to ompare sto hasti models. In this paper we present the rst study of the problem of omparing a hidden Markov model and a sto hasti ontext-free grammar. We des ribe how to ompute their o-emission or ollision probability, i.e. the probability that they independently generate the same sequen e. We also onsider the related problem of nding a run through a hidden Markov model and derivation in a grammar that generate the same sequen e and have maximal joint probability by a generalization of the CYK algorithm for parsing a sequen e by a sto hasti ontext-free grammar. We illustrate the methods by an experiment on RNA se ondary stru tures. 1 Introdu tion The basi hain-like stru ture of the key biomole ules, DNA, RNA, and proteins, allows an abstra t view of these as strings, or sequen es, over nite alphabets, obviously of nite length. Furthermore, these sequen es are not ompletely random, but exhibit various kinds of stru tures in di erent ontexts. E.g. a family of homologous proteins is likely to have similar amino a id residues in equivalent positions; an RNA sequen e will have pairs of omplementary subsequen es to form base pairing heli es. Hen e, it is natural to onsider applying models from formal language theory to model di erent lasses of biologi al sequen es. Though not ompletely random, biologi al sequen es an still possess inherent sto hasti traits, e.g. due to mutations in a family of homologous sequen es or a la k of knowledge (and omputing power) to orre tly model all aspe ts of RNA se ondary stru ture formation. Thus, it is often better to use sto hasti models giving a probability distribution over all sequen es, where a high
Industrial Engineering and Computer Sciences Division (G2I) RETRIEVAL STATUS VALUES IN INFORMATION EVALUATION

Evaluation is one major step of an IR process. It helps to classify and compare Information Retrieval (IR) systems according to their e ectiveness or their e ciency. Many IR works carried di erent kinds of studies about evaluation methods and many evaluation measures have been proposed. Most of these measures are based on the ranking of documents retrieved by IR systems in response to queries. The retrieved documents are ranked according to their retrieval status values if these are monotonously increasing with the probability of relevance of documents. However, few IR works tried to know more about these RSV and their possible use for IR evaluation. In this work, we analyze di erent RSV computations and investigate the links between the RSVs and the IR systems evaluation.
Lattice dynamics and phase transitions in KAl(MoO4)2, RbAl(MoO4)2 and CsAl(MoO4)2 layered crystals

Raman and IR studies of KAl(MoO4)2, RbAl(MoO4)2 and CsAl(MoO4)2 are reported. The assignments of modes are given on the basis of lattice dynamics calculations. The temperature dependence of the KAl(MoO4)2 vibrational modes shows that this compound exhibits a second-order phase transition around 90 K from the to most probably a monoclinic and ferroelastic phase.