Miracle-dz/UniFAR-FTUs huggingface dataset:
Effects of stigmata maydis on the methicillin resistant Staphylococus aureus biofilm formation

Background Mastitis is an inflammatory reaction of the mammary gland tissue, which causes huge losses to dairy farms throughout the world. Staphylococcus aureus is the most frequent agent associated with this disease. Staphylococcus aureus isolates, which have the ability to form biofilms, usually lead to chronic mastitis in dairy cows. Moreover, methicillin resistance of the bacteria further complicates the treatment of this disease. Stigmata maydis (corn silk), a traditional Chinese medicine, possess many biological activities. Methods In this study, we performed antibacterial activity assays, biofilm formation assays and real-time reverse transcription PCR experiments to investigate the effect of stigmata maydis (corn silk) on biofilm formation and vancomycin susceptibility of methicillin-resistant Staphylococcus aureus (MRSA) strains isolated from dairy cows with mastitis. Results In this study, the aqueous extracts of stigmata maydis inhibited the biofilm formation ability of MRSA strains and increased the vancomycin susceptibility of the strains under biofilm-cultured conditions. Conclusion This study proves that the aqueous extracts of stigmata maydis inhibit the biofilm formation ability of MRSA strains and increase the vancomycin susceptibility of the MRSA strains under biofilm-cultured conditions.
Nonalcoholic Fatty Liver Disease Aggravated the Severity of Acute Pancreatitis in Patients

Background and Aim The incidence of nonalcoholic fatty liver disease (NAFLD) as a metabolic disease is increasing annually. In the present study, we aimed to explore the influence of NAFLD on the severity of acute pancreatitis (AP). Methods The severity of AP was diagnosed and analyzed according to the 2012 revised Atlanta Classification. Outcome variables, including the severity of AP, organ failure (all types of organ failure), and systemic inflammatory response syndrome (SIRS), were compared for patients with and without NAFLD. Results Six hundred and fifty-six patients were enrolled in the study and were divided into two groups according to the presence or absence of NAFLD. The non-NAFLD group contained 278 patients and the main etiology in this group was gallstone. The NAFLD group consisted of 378 patients and the main etiology was hyperlipidemia. The incidence of mild AP, moderately severe AP, and severe AP was 77.30%, 18.3%, and 4.3% in the non-NAFLD group and 58.2%, 33.9%, and 7.9% in the NAFLD group, respectively. There were significant differences between the two groups according to the severity of AP (P ≤ 0.001). In addition, the Ranson and BISAP scores as well as the incidence of SIRS and organ failure in the NAFLD group were higher than those in the non-NAFLD group (all P < 0.05). The patients were further divided into non-NAFLD, mild-NAFLD, and moderate-severe NAFLD (M+S-NAFLD) groups. The results showed that the severity of AP increased gradually from the non-NAFLD group to the M+S-NAFLD group. In addition, the incidence rates of SIRS and organ failure showed an upward trend with the aggravation of fatty liver severity. Multivariate logistic analysis showed that patients with NAFLD, especially those with M+S-NAFLD, had higher risks of SIRS and organ failure. Conclusions Compared with non-NAFLD, NAFLD has a clinically relevant impact on the severity of AP and may be an early prognostic parameter for patients with AP.
Synthesis and crystal structures of a copper(ii) dinuclear complex and zinc(ii) coordination polymers as materials for efficient oxidative desulfurization of dibenzothiophene

Dinuclear copper(ii) complex [Cu2(BAc)4(QX)2], and 2D zinc(ii) coordination polymer, [Zn(TDPA)2(TMPy)2]n, presented high oxidation and adsorption thermodynamics for dibenzothiophene sulfoxide (DBTO).
Rapid Detection of SdhBP225F and SdhBH272R Mutations in Boscalid Resistant Botrytis cinerea Strains by ARMS-PCR

SdhBP225Fand SdhBH272R mutations have been found associated with boscalid resistance in Botrytis cinerea from strawberry in Shanghai, China. For rapid detection of two mutations, tetra-primers were designed and optimized to gain the relatively high accuracy and specificity based on the ARMS-PCR technique, by which resistance can be identified with different lengths of products on agarose gels. The tetra-primer ARMS-PCR systems for SdhBP225F and SdhBH272R were validated by 9 SdhB-squenced strains repeatedly. Then, sensitivity of 30 more strains were also tested by the methods, which were accordant with genotypes by sequencing and the sensitivity of conidial germination to boscalid by 100%. Thus, the methods developed in this study are proved to be rapid, inexpensive, accurate and practical for resistance detection of Botrytis cinerea caused by SdhBP225F and SdhBH272R mutations.
Intraoperative Optical Coherence Tomography of a Dislocated Intraocular Lens

An 88‐year‐old female with pseudoexfoliation syndrome presented with sudden vision loss and was found to have a dislocated one piece intraocular lens (IOL) that was resting on the retina. She underwent 23‐gauge pars plana vitrectomy, IOL explantation, and anterior chamber IOL insertion. Intraoperative optical coherence tomography (EnFocus iOCT; Leica, Wetzlar, Germany) was used to assess the position and vault of the IOL on the retinal surface [Figure 1a]. Utilization of iOCT enabled the surgeon to safely grasp the IOL with forceps in the location with the greatest vault in order to minimize any potential trauma to the retina [Figure 1b].
Deep Reinforcement Learning for Reducing Latency in Mission Critical Services

Next-generation wireless networks will be supporting mission critical services such as safety related applications of connected autonomous vehicles, and real-time control of medical and industrial systems, as well as serving traditional mobile users. In mission critical services, high-reliability and low-latency requirements should be satisfied. In this paper, we aim to reduce the latency of uplink scheduling of a network of Mission Critical Devices (MCDs) while maintaining fairness among other users, served by a dense small cell network. We propose a Deep Reinforcement Learning algorithm, namely Delay Minimizing Deep Q-Learning (DMDQ), that combines Long Short-term Memory with Q-learning. The problem is cast as a resource block allocation for delay minimization. The proposed algorithm is compared to a tabular Q-learning approach and a simple Round Robin (RR) algorithm in terms of latency, throughput, fairness and convergence. Our performance results show that DMDQ outperforms both schemes in terms of latency and offers high fairness. The Q-learning approach achieves slightly higher throughput than DMDQ however DMDQ convergences faster.
Single Top as Window on Heavy Quarks and Other New Physics

This paper discusses what the study of the electroweak production of single top quarks can tell us about additional quark generations, and how to distinguish the new quarks hypothesis from other kinds of new physics. We also suggest some new studies that become possible for the first time thanks to the large statistics of single top quarks produced at the LHC.
Maximum Concurrent Flow Problem in MPLS-Based Software Defined Networks

Multi-protocol label switching (MPLS) is supported by OpenFlow version 1.2 or higher and widely used in software defined networking (SDN) to achieve higher performance and flexibility. Due to the shortage of the ternary content addressable memory (TCAM), the number of forwarding entries installed in a switch needs to be bounded (node path-degree constraints). Besides, the maximum concurrent flow problem, which asks to maximize the minimum fraction of the flow of a commodity to its demand, is widely studied because of a wide range of applications. In this paper, we address the maximum concurrent flow problem while ensuring the flow routed for a commodity does not exceed its demand (demand constraints) and the node path-degree constraints are imposed, termed the bounded path- degree maximum concurrent flow (BPMCF) problem. We first show the BPMCF problem is NP-hard and intractable to devise any approximation algorithm. Then, we propose an algorithm for the BPMCF problem. Finally, we evaluate the performance of the proposed algorithm through computer simulations and experiments on Global Environment for Network Innovations (GENI) testbed using the real-life traces collected from SNDlib.
Joint Optimization of Wireless Power Transfer and Collaborative Beamforming for Relay Communications

In this work, multiple relays are employed to assist data transmissions from a multi-antenna hybrid access point (HAP) to a distant receiver underlaying a cellular system. The relays are powered by energy harvesting (EH) from the HAP's signal beamforming, following a power splitting (PS) protocol. We aim to maximize the throughput subject to interference constraints at the cellular user equipments (CUEs). Each relay is allowed to adjust the PS ratio and transmit power to improve the data rate, meanwhile restrict the aggregate interference to the CUEs in a probabilistic manner. We also optimize the HAP's beamforming strategy to control the information and energy transfer to the relays. We show that the proposed throughput maximization problem, though non-convex, can be solved optimally by exploiting its monotonicity and designing a successive approximation algorithm. Numerical results verify the close coupling between the relays' power control and the HAP's energy transfer, which calls for a joint optimization to ensure the throughput-optimal performance.
Uplink Coverage of Machine-Type Communications in Ultra-Dense Networks

Extended coverage is an essential requirement of the successful deployment of Machine-Type Communication (MTC) in harsh scenarios. This would require the MTC nodes to transmit their measurement reports with a high transmit power. At the same time, it is desirable to minimize the transmit power of the MTC nodes for the sake of a longer battery lifetime. These contradicting targets make the uplink coverage one of the main limiting factors to the fruition of MTC applications. In this paper, we analyze the uplink coverage of MTC considering the distinguishing features of a dense network including the high density of cells and the short distances between the cells and the MTC nodes. We model the path loss as a stretched exponential path-loss (SEPL) to capture the short link distances. In addition, a truncated channel inversion power control is considered to satisfy the strict requirements on the power consumption of MTC nodes. The accurate and tractable results unveiled the impact of the system parameters on the network performance. Particularly, the uplink coverage significantly improved at moderate cell density, reasonable bandwidth, and low transmission power. Interestingly, our results reveal that the maximum transmit power has no impact on the uplink coverage in the considered scenario. Fortunately, this allows for a longer lifetime for the battery-powered devices of future IoT applications.
Minimax Design Of Constant Modulus Mimo Waveforms

Waveform optimization is a crucial step in the design of a multiple-input multiple-output (MIMO) system. This paper considers the joint optimization of constant modulus waveforms and mismatched (or matched) receive filters to suppress the auto- and cross-correlations using the minimax $(\ell_{\infty})$ design criterion. For practical waveform length and system size, the waveform design problem becomes quite challenging due to the large problem size (more than $10 ^{5}$ unimodular complex variables and $10 ^{6}$ nonlinear constraints). In addition to the large size, this problem is nonconvex, nonsmooth, and as such, can not be handled effectively by the existing waveform design algorithms or off-the-shelve optimization tools. This paper develops an efficient primal-dual type algorithm with low per-iteration complexity to solve this problem. Numerical comparison shows that the waveforms based on the minimax design outperform those obtained from the existing $\ell_{2}$ norm design by 4-5 dBs in terms of peak sidelobe levels (PSL).
BHEEM: A Blockchain-Based Framework for Securing Electronic Health Records

In the present era of smart cities and homes, the private information of the patients such as their names, address, and disease is being breached on a regular basis, which indirectly related to the security of electronic health records (EHRs). The existing state-of-the-art schemes handling the security of EHRs have resulted in data being generally inaccessible to patients. These schemes struggle in providing the efficient balance between the data privacy, need for patients, and providers to regularly interact with data. Blockchain technology resolves the aforementioned issues because it shares the data in a decentralized and transactional fashion. This can be leveraged in the healthcare sector to maintain the balance between privacy and accessibility of EHRs. In this paper, we propose a Blockchain-based framework for efficient storage and maintenance of EHRs. This further provides the secure and efficient access to medical data by patients, providers, and third parties, while preserving the patient private information. The goals of this paper are to analyze how our proposed framework fulfills the needs of patients, providers, and third parties, and to understand how the framework maintains the privacy and security concerns in the healthcare 4.0.
E-RPL: A Routing Protocol for IoT Networks

Internet of things is the new era of networking and smart communication. Recent researches treat some issues and challenges of IoT. QoS routing protocols for IoT have been a rising research topic for years. In this paper we present a new approach called E-RPL; it is an enhancement of the Routing Protocol for Low power and lossy networks (RPL). Comparing to RPL, E-RPL decreases the number of control messages. The new protocol proposes also a new flexible multi-constrained objective function (OF) that can integrate several metrics including energy, delay and bandwidth to define the end-to-end path between the sink and a given node. The simulation results show a remarkable improvement of energy consumption, routing overhead, and end-to-end delay.
Userspace Hypervisor Data Characterization in Virtualized Environment

Memory-intensive applications have grown rapidly in order to adapt to the changing needs of businesses. Memory errors become even more concerning, since configuration at extended operating points makes hardware more susceptible to system failures. Some data structures may be more sensitive to errors and may cause system crashes more easily than others. A failure in a critical data structure can cause a complete system crash. In this paper, we propose a sensitivity characterization to the hypervisor structures. First, we implement an error-injection framework using Syscall ptrace. We profile both static and dynamic data structures through our framework. Then we provide the detailed analysis and data characterization to QEMU hypervisor. Finally, we discuss the checkpointing and selective mechanism in case the QEMU hypervisor crash.
Coverage Analysis for Ultra-Dense Networks with Dynamic TDD

In recent years, the dramatically rising mobile data traffic has required an ever-increasing data rates. Ultra-dense network (UDN) is a promising technique to significantly enhance the network capacity by densely deploying small cells. The large amount of traffic also has been characterized by asymmetry and variations in both time and space. Dynamic time-division duplex (DTDD) has been taken into account to accommodate the traffic due to its advantage in the dynamic adjustment of UL/DL configuration. In this work, we propose an analytical framework to investigate the coverage performance of a UDN operating DTDD scheme, where impacts of line-of-sight (LOS)/non-line-of-sight (NLOS) propagation in large-scale fading and small-scale fading are both incorporated. Our results show that the LOS propagation in small-scale fading can substantially improve the DL and UL coverage probabilities in the low-to-middle density region, while the enhancement vanishes in the ultra-dense region where the network coverage is dominated by the LOS propagation in large-scale fading.
Identification Rates for Block-correlated Gaussian Sources

Among many current data processing systems, the objectives are often not the reproduction of data, but to compute some answers based on the data responding to some queries. The similarity identification task is to identify the items in a database which are similar to a given query item regarding to a certain metric. The problem of compression for similarity identification has been studied in [1]. Unlike classic compression problems, the focus is not on reconstructing the original data. Instead, the compression rate is determined by the desired reliability of the answers. Specifically, the information measure identification rate of a compression scheme characterizes the minimum compression rate that can be achieved which guarantees reliable answers with respect to a given similarity threshold. In this paper, we study the component-based quadratic similarity identification for correlated sources. The blocks are first decorrelated by Karhunen-Loève transform. Then, the decorrelated data is processed by a distinct D-admissible system for each component. We derive the identification rate of component-based scheme for block-correlated Gaussian sources. In addition, we characterize the identification rate of a special setting where any information regarding to the component similarity thresholds is unknown while only the similarity threshold of the whole scheme is given. Furthermore, we prove that block-correlated Gaussian sources are the “most difficult” to code under the special setting.
A Privacy-Preserving Thin-Client Scheme in Blockchain-Based PKI

Traditional centralized PKIs are vulnerable due to the single point of failure. A feasible solution is to build a decentralized PKI without certificate authority (CA). Web of Trust is the first step toward realizing a decentralized PKI, but it still has some limitations such as missing incentive and leaking user's privacy. Blockchain's numerous desirable properties, such as cryptographical security, decentralized nature and unalterable transaction record, make it a suitable tool to implement a decentralized PKI. However, the latest research findings about blockchain-based PKI are still incompatible with the thin-clients which have limited storage ability to download the entire blockchain. To combat that, we firstly present a Privacy-preserving Thin-client Scheme (PTS) utilizing the idea of k-anonymity, which enables thin-clients to run normally as full node users and protect user's privacy simultaneously. After that, in order to reduce cost, we further propose an Efficient Privacy preserving Thin-client Scheme (EPTS) employing the method of PIR (private information retrieval). Then security analysis and functional comparison are performed to demonstrate the high security and comprehensive functionality of EPTS compared with existing schemes. Finally, extensive experiments are undertaken to confirm that EPTS can reduce computational cost and communication cost impressively.
Outage Performance of Cooperative NOMA Networks with Hardware Impairments

We investigate the outage performance of cooperative non-orthogonal multiple access (NOMA) networks with transceiver hardware impairments, where the estimated channel state information and &#945;-&#956; fading channels are considered. Closed-form expressions for the outage probability of cooperative NOMA networks are derived for two representative scenarios. The first scenario is the base station (BS) directly communicates with NOMA users. The second scenario is BS communicates with NOMA users via the AF relaying. The diversity orders are presented for the two scenarios. It reveals that there is an error floor for the outage probability due to the distortion noise and estimation error.
MDP-Based Resource Allocation Scheme Towards a Vehicular Fog Computing with Energy Constraints

As mobile applications deliver increasingly complex functionalities, the demands for even more intensive computation would quickly transcend energy capability of mobile devices. On one hand and in an attempt to address such issues, fog computing paradigm is introduced to mitigate the limited energy and computation resources available within constrained mobile devices, by moving computation resources closer to their users at the edge of the access network. On another hand, most of electric vehicles (EVs), with increasing computation, storage and energy capabilities, spend more than 90% of time on parking lots. In this paper, we conceive the basic idea of using the underutilized computation resources of parked EVs as fog nodes in order to provide on-demand computation at the vicinity of the access network. The proposed Vehicular Fog Computing (VFC) architecture aggregates the abundant unused resources of parked vehicles, and uses it to serve mobile users' demands. The resource allocation problem is formulated as a Markov Decision Process (MDP) and dynamic programming is used to solve the underling decision problem. Extensive simulation results show the effectiveness of the proposed approach by improving the global reward value by 51% and scoring an energy gain of 66% compared to two other models.
MIRA!: An SDN-Based Framework for Cross-Domain Fast Migration of Ultra-Low Latency 5G Services

Given the constantly growing demand for inter- data-center services that 5G networks are bringing, live migration has become a covet and very challenging technology. Meanwhile, the emergence of Software Defined Networking (SDN) and Network Function Virtualization (NFV) technologies has completely transformed modern networks by offering more flexibility and at the same time more complexity. So far, investigations have been confined to integrating the live migration process with SDN&#x002F;NFV paradigms in order to ensure the desired Quality of Experience (QoE). However, the simple integration is not sufficient to handle unexpected cases such as resources' unavailability, networking issues, and system control. For this purpose, we present MIRA! , a novel framework for managing reliable live migrations of virtual resources across different Infrastructure as a Service (IaaS), handling unexpected cases, while ensuring high QoS and a very low downtime without human intervention using an SDN aware solution. To validate our proposed framework, we performed a set of experimental evaluations under different configurations. The obtained results of our proposed framework show a 21% time reduction compared to a prior work and an interesting behavior while modifying the number of allocated CPU cores.