Miracle-dz/UniFAR-FTUs huggingface dataset:
The Regulation of Skeletal Muscle Protein Turnover During the Progression of Cancer Cachexia in the Apc Min / + Mouse

Muscle wasting that occurs with cancer cachexia is caused by an imbalance in the rates of muscle protein synthesis and degradation. The Apc mouse is a model of colorectal cancer that develops cachexia that is dependent on circulating IL6. However, the IL-6 regulation of muscle protein turnover during the initiation and progression of cachexia in the Apc mouse is not known. Cachexia progression was studied in Apc mice that were either weight stable (WS) or had initial (#5%), intermediate (6–19%), or extreme ($20%) body weight loss. The initiation of cachexia reduced %MPS 19% and a further ,50% with additional weight loss. Muscle IGF-1 mRNA expression and mTOR targets were suppressed with the progression of body weight loss, while muscle AMPK phosphorylation (Thr 172), AMPK activity, and raptor phosphorylation (Ser 792) were not increased with the initiation of weight loss, but were induced as cachexia progressed. ATP dependent protein degradation increased during the initiation and progression of cachexia. However, ATP independent protein degradation was not increased until cachexia had progressed beyond the initial phase. IL-6 receptor antibody administration prevented body weight loss and suppressed muscle protein degradation, without any effect on muscle %MPS or IGF-1 associated signaling. In summary, the %MPS reduction during the initiation of cachexia is associated with IGF-1/mTOR signaling repression, while muscle AMPK activation and activation of ATP independent protein degradation occur later in the progression of cachexia. IL-6 receptor antibody treatment blocked cachexia progression through the suppression of muscle protein degradation, while not rescuing the suppression of muscle protein synthesis. Attenuation of IL-6 signaling was effective in blocking the progression of cachexia, but not sufficient to reverse the process. Citation: White JP, Baynes JW, Welle SL, Kostek MC, Matesic LE, et al. (2011) The Regulation of Skeletal Muscle Protein Turnover during the Progression of Cancer Cachexia in the Apc Mouse. PLoS ONE 6(9): e24650. doi:10.1371/journal.pone. 0024650 Editor: Se-Jin Lee, Johns Hopkins University School of Medicine, United States of America Received February 28, 2011; Accepted August 16, 2011; Published September 19, 2011 Copyright: 2011 White et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Funding: This study was funded by grant 5R01CA121249 from the Nation Institutes of Health (NIH) and the National Cancer Institute (NCI) to Dr. Carson. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Competing Interests: The authors have declared that no competing interests exist. * E-mail: carsonj@mailbox.sc. edu
Focus on Learning: A Message From the Editors

The biggest change in higher education during the decade since the term “Scholarship of Teaching” was first used (Boyer, 1990) is the understanding that student learning is the sole measurement of educational quality. It is difficult, perhaps, to remember that institutional status was once measured in terms of inputs such as the number of books in the college library, the percentage of faculty members with advanced degrees, or the amount of federal funds awarded to the institution. Since the teaching><learning connection began to be investigated in a scholarly way (Richlin, 2001), the emphasis on learning outcomes has become the primary focus, with student satisfaction an important but secondary concern. Today, as we look at the presentations being proposed and selected for the Lilly Conferences on College and University Teaching, we see that upcoming additions to the higher education teaching and learning literature will include the connection of the physiology and psychology of learning to how we conduct teaching activities in and out of the classroom. The focus will remain on how we are best able to assist our students to learn both the processes and content of our disciplines. The articles in this issue of the Journal represent three areas of research into how teaching can better effect learning: providing structure, organizing for creativity, and incorporating technology.
Performance Evaluation of Loss Packet Percentage for Asymmetric Key Cryptography in VANET

Vehicular technology & communication systems provides road safety and driving comfort. This work is concerned with the security required in VANETs. Vehicle to infrastructure communication (V2I) is the communication between the vehicles and road side infrastructure. In this paper, we have evaluated the performance of non-grouping vehicles in V2I communication using asymmetric keys. As asymmetric keys are used, non-repudiation is achieved by the properties of asymmetric key cryptography. The simulations have been carried out to analyze the performance as Loss packet percentage (LPG). The obtained results clearly shows that the new improvements has been suggested in this research domain.
Efficient T-CONT-Agnostic Bandwidth and Wavelength Allocation for NG-PON2

Dynamic bandwidth and wavelength allocation are used to demonstrate high quality of service (QoS) in time wavelength-division multiplexed–passive optical networks (TWDM-PONs). Both bandwidth and wavelength assignment are performed on the basis of transmission containers (T-CONTs) and therefore by means of upstream service priority traffic flows. Our medium access control (MAC) protocol therefore ensures consistency in processing alike classes of service across all optical network units (ONUs) in agreement with their QoS figures. For evaluation of the MAC protocol performance, a simulator has been implemented in OPNET featuring a 40 km, 40 Gbps TWDM-PON with four stacked wavelengths at 10 Gbps each and 256 ONUs. Simulation results have confirmed the efficiency of allocating bandwidth to each wavelength and the significant increase of network traffic flow due to adaptive polling from 9.04 to 9.74 Gbps. The benefit of T-CONT-centric allocation has also been measured with respect to packet delay and queue occupancy, achieving low packet delay across all T-CONTs. Therefore, improved NG-PON2 performance and greater efficiency are obtained in this first demonstration of T-CONTs allocated to both wavelength and time.
Efficient Cellular Base Stations Sleep Mode Control Using Image Matching

Green cellular network helps to decrease environmental pollution. In contrast, massive connectivity and demand for higher data rate promise the presence of new generation of cellular system (5G) and small cell networks. Hence, expectation on increasing the number of base stations (BSs), which leads to increase in energy usage. One way to improve energy consumption is by shutting down the redundant BSs while sustaining the Quality-of-Service (QoS) for each user. In this paper, we propose a dynamic structural algorithm based on transportation problem, to switch on/off the BSs in cellular networks without compromising its coverage, and maintain the networks load by neighboring cells. We use weighted graphs to translate our problem as a transportation problem and then use linear programming to solve it. The cost of transport, turning a BS into sleep mode, is illustrated as a function of energy usage,coverage area and load on the BSs. Running the propose method consecutively provides the maximum number of BSs whom are at sleep mode. The methodology explained in this paper reduces energy consumption to almost 40%, whereas maintaining all the existing loads in the network.
Advances in Deep Learning Techniques for Medical Image Analysis

Deep learning is contributing to the high level of services to the healthcare sector. As the digital medical data is increasing exponentially with time, early detection and prediction of diseases are becoming more efficient because of the deep learning techniques which reduce the fatality rate to a great extent. The main focus of this paper is to provide the comprehensive review of deep learning in the domain of medical image processing and analysis. We have demonstrated the use of new deep learning architectures in oncology for the prediction of different types of cancer like the brain, lung, skin, etc. The state-of-the-art architectures effectively carry out analysis of 2D and 3D medical images to make the diagnosis of patients faster and more accurate. The use of popular approaches in machine learning such as ensemble and transfer learning with fine-tuning of parameters improve the performance of the deep neural networks in medical image analysis. The existing deep networks urge the new image classification network called Capsule Network (CapsNet) to make the classification and detection comparatively better. The equivariance characteristics of CapsNet make it more influential as it discourages the effect of any structural invariance of an input image on the network.
Energy-aware Virtual Machine Selection and Allocation Strategies in Cloud Data Centers

These days, information technologies are expanding exponentially so the need for high-speed processors and huge storage space are developing quickly. As a result of increasing requests, more resources are required to satisfy the client's necessities. Thus, in a cloud environment, the large number of resources consumes a lot of energy during their operation, which is turned into a key issue nowadays and demands a critical discussion in the present scenario. This research paper investigates and explores the literature on the assignment of virtual machines to hosts in a data center according to variable workload requests of different cloud consumers application executing on the virtual machines. The choice of ideal virtual machines and their placement on host prompts to limit the energy utilization. This paper proposes an algorithm for improving virtual machine selection and allocation strategies in cloud data centers. The proposed algorithm is then compared with existing algorithms on the basis of performance metrics like energy consumption and VM migrations using different threshold values. As a result, the proposed algorithm emerged to be the optimized one in enhancing the use of cloud resources by lessening the energy utilization of datacenter.
Storing and Manipulating Simple and Complex Features in Database Management Systems

The architecture of Geographic Information Systems (GISs) is changing: more and more the systems are based on the integrated architecture [21], that is also storing geometric data in the data base management system (DBMS) together with the other administrative data. The first step is having data types and operators for the geometric primitives: point, line, and polygon. This has reached the level of standardization and is now implemented in several commercial DBMSs. The next step is also having support for the topologically structured features in the DBMS, that is complex features. The DBMS can check and guarantee consistency and complex operations can be executed within the DBMS. The rule of thumb whether something belongs to the DBMS tasks or to a specific application is: whenever it concerns general and reusable aspects then this belongs to the DBMS. Despite the fact that the topology model are well know, it still remains an open issue how to implement these models completely within a relational DBMS.
Comparative study of the classification models for prediction of bank telemarketing

This research paper has evaluated various classification models for prediction of bank telemarketing campaign results regarding the probability of the subscription of the customer to the deposit. The effectiveness of these algorithms has been evaluated by Receiving Operator Characteristic (ROC) and Cumulative Accuracy Profile (CAP) curve analysis, the accuracy of the algorithm and variance of the predictions. According to the results of the research, the best model for bank telemarketing effectiveness prediction are Random Forest and Deep Artificial Neural Network. The Logistic Regression and Naive Bayes are not as suitable as the other classification methods for this kind of problems due to the poor accuracy and overfitting issues.
Comparison and Evaluation of Real Time Reservation Technologies in the Intelligent Public Transport System

The demand of Public Transport System (PTS) is increasing day by day because it is quite easy, frequently available and cheap in cost as compared to the private transport system. It attracts attentions both from academia and Industry. But at the same time have many issues such as-travel time estimation, route selection, real-time tracking of vehicles, and efficient way of reservation. The solution of these problems helps the drivers as well as to users to use PTS in timely and efficient manner. Although many state-of-the-art proposals exist in the literature to address these issues there is scope available to identify the parameters responsible for an efficient way of real-time PTS reservation. Keeping the above-mentioned points into consideration, this paper provides the detailed taxonomy of PTS and also give proper categorization of the existing approaches. This gives the much-needed clarity with regards to the issues related to Intelligent PTS and their solutions available in the literature. The parameters used for framing the main taxonomy are such as-quality & fare, route selection, seat selection, and seat availability. We also compared all standard algorithm and approaches available for PTS. We also highlight the open issues and challenges encountered by the researchers. These help the beginners who need to begin the work in this.
Highly Accurate Prediction of Radio Propagation Using Model Classifier

In this paper, we propose a measurement-based spectrum database using model classifier. In the radio propagation, path loss is the fundamental factor to recognize the coverage area. However, it is difficult to accurately estimate the radio environment only path loss estimation because of the shadowing deviation. Therefore, we estimate the propagation model including shadowing, and by determining the usage range of the estimated model, we reduce the registered data size while accurately estimating the radio environment. The database firstly accumulates the received signal strength indicator (RSSI) related to the locations of receivers and we construct the model classifier. Then, the database assigns the propagation model in each mesh so that Root Mean Squared Error (RMSE) between datasets and the models is minimized. We used measurement datasets of a 3GPP cellular band in the real environment to construct the model classifier. Our results show that the proposed method can accurately estimate the radio propagation while the registered data size is significantly reduced. Additionally, we discuss a method of power control based on the proposed method for improving the communication efficiency.
Passivity-Based Controller for Nonideal DC-to-DC Boost Converter

This paper presents the design of a passivity-based controller for a nonideal DC-DC boost converter containing parasitic terms (NPBC). The PBC for the ideal case converter circuit leads to current and voltage offsets which are not tolerated in high-performance systems. All results have been obtained by simulation on both state-space averaging model and using the more realistic hybrid system model. The NPBC functions by keeping the prescribed inductor current at the desired value irrespective of the output load. With an appropriate computation of the equilibrium point in an adaptive manner, also shown in the paper, the capacitor voltage can be robustly controlled.
Random Access Preamble for High Doppler in Millimeter-Wave Cellular Systems

In this paper, a random-access preamble (RAP) for the user equipment (UE) experiencing high Doppler in millimeter-wave (mmWave) cellular systems is proposed. After demonstrating the inefficiency of the conventional long-term evolution (LTE)-based RAP for fast-moving UEs (FUEs) in mmWave systems, linear-frequency-modulation-based RAP (LFM RAP) is proposed for the detection of FUEs. The correlation of LTE-based RAP, and ambiguity and extended-ambiguity functions of LFM RAP are analyzed to examine their properties affected by the high Doppler. The performance of RAPs is evaluated through detection and false alarm probabilities in a high Doppler and multiuser environment. The results show that the LFM RAP is appropriate for FUEs in mmWave cellular systems because of its high detection probability in a high-Doppler environment.
The Hybrid EPILE + PO Combined with the Adaptive Cross Approximation to Study the Electromagnetic Scattering from two 3D Scatterers

Calculating the RCS (Radar Cross Section) of two 3D scatterers (target above a rough surface) needs to numerically solve a set of integral equations involving a large number of unknowns. Such large applications in remote sensing cannot be solved easily with a conventional Method of Moments (MoM) by using a direct LU inversion. Thus, hybridization between the E-PILE (Extended Propagation-Inside-Layer Expansion) and the Physical Optics approximation (PO) reduces significantly the memory requirements and CPU time. The resulting method called E-PILE + PO. In this work, we take advantage of the rank-deficient nature of the coupling matrices, corresponding to scatterer 1 and scatterer 2 interactions, to further reduce the complexity of the method by using the ACA (Adaptive Cross Approximation). The ACA algorithm permits to strongly compress the coupling matrices without a loss of accuracy and the memory requirement is then strongly reduced. The proposed method is then applied for a scenario of a square plate located above sea-like two-dimensional rough surface.
Data Analytics in the 5G Radio Access Network and Its Applicability to Fixed Wireless Access

This paper discusses the exploitation of data analytics for supporting the operation of Radio Resource Management (RRM) in the Next Generation Radio Access Network (NG-RAN), analysing the need for standardised and open frameworks and the specific characteristics of the NG-RAN that impact on the design of data analytics solutions. As a practical example of this concept, a simple but illustrative use case is presented, which intends to enhance the Radio Admission Control (RAC) function with data analytics information in scenarios with both Fixed Wireless Access (FWA) users and mobile users. A functional framework for the realization of the proposed solution is described and the performance of the RAC algorithm is evaluated under different scenario conditions to better delimitate its potential.
Millimeter-Wave V2V Communications with Cooperative Perception for Automated Driving

The combination of automated driving based on onboard sensors and decisions and external interactions with the environments through wireless communication has great advantages over the conventional driving systems in terms of safety and reliability. High resolution dynamic maps, which contain the real time information of the driving environment obtained by the collection and fusion of external sensors, can improve the driving safety and reliability dramatically thanks to the additional information beyond the perceptions of the onboard sensors. In this paper, we analyzed the required amount of dynamic information to be exchanged for the cooperative perception in order to enable a new level of safe and reliable automated driving. The results confirm the indispensability of millimeter-wave communications in automated driving systems. In this paper, we focus on an analysis of the required amount of the dynamic information exchange from cooperative sensor perception to enable a new level of safe and automated driving. The results show the necessity of millimeter wave communications for automated driving systems.
Polar Coding with CRC-Aided List Decoding

Abstract : This report describes some results of the project ? More reliable wireless communications through polar codes, funded in Fiscal Year 2015 by the Naval Innovative Science and Engineering (NISE) program at SSC Pacific. The purpose of the project is to determine if polar codes can outperform the forward error correction (FEC) currently used in Navy wireless communication systems. The project team has implemented an advanced decoding method called cyclic redundancy check (CRC)-aided list decoding. Our simulation results show that polar coding can produce results very similar to the FEC used in the Digital Video Broadcasting - Satellite - Second Generation (DVB-S2) standard, and can provide up to 15 percent higher throughput by using code rates not provided in the DVB-S2 standard. In any application for which the DVB-S2 FEC is considered, polar coding with CRC-aided list decoding with N = 65536 should also be considered.
A Hybrid Biometric Method for Authentication of User in Internet of Things

In near future, the world will be completely associated and the quantity of smart gadgets will develop to an extensive number, we can state ten times the human populace. IoT shall bring plethora of advantages and ease in our habitual life. But what about all the data including personal information, location monitoring, daily routine etc. be shared and stored over internet. In direction to maintain privacy of a user, we designed a hybrid biometric validation scheme for users access to a smart object. Our proposed system is the combination of users fingerprint and speech and it shows feasible results and guaranteed privacy of a user. In other words, our system will help building trust among the user who will be using intellectual body and making them feel secure on going away from the objects as only the validated user will be able to access the object.
Botnet Detection using Machine Learning

The small program to perform any type of malicious activity that may damage the system of the legal user automatically without legal users' knowledge is called a bot (bad bot). The network of bots under the control of a botmaster is called a botnet. It is a serious threat to information, communication, and economy etc. The interaction of devices to form a botnet are smartphones, computers, IoT systems whose vulnerabilities are exploited and so the security is breached to relinquish the control to bot controllers or third-party. On the basis of C2 (command and control) structure the botnet is considered as a centralized, decentralized or hybrid type, however, the architecture of botnet includes botmaster, C2 (low false positive rates) respectively. The machine learning plays an essential role in the detection and recognition of Botnets and is therefore explored in thispaper. In this paper, the mean of the accuracy of k-means clustering and j48 classification approach (hybrid approach) is calculated while using the two random dataset partitions whose sum are always equal to the original dataset to compute the accuracy of low positive rates and high positive rates based on the percentages of “correctly and incorrectly instances”. The comparative analysis of the three techniques i.e. clustering, classification, and hybrid approach suggest that the results of classification and clustering are constant on one end i.e. lower in case of classification and higher in case of clustering and on the contrary, results of our approach are varying in nature and gives the approximation in both the processes.