Favorable Performance In Terms Research Articles

Simultaneous and rapid quantification of multi-compounds in Nauclea officinalis based on UPLC-DAD

Nauclea officinalis is a Li-folk medicine in China, with antimalarial, antibacterial, and anti-inflammatory properties. However, the phytochemicals of Nauclea officinalis have not yet been comprehensively researched. In this paper, a reliable and efficient approach based on ultra-performance liquid chromatography (UPLC) with a diode array detector (DAD) was firstly established for simultaneous quantification of alkaloids, flavonoids, and phenolic acids in N. officinalis resources. The samples were extracted using ultrasound with 60 % ethanol for 20 min (1:50 g/mL), and analyzed by the developed UPLC-DAD method. Under the optimized extraction and chromatographic conditions, the proposed approach showed favorable performance in terms of the linearity (R2 > 0.9998), precision (RSD < 3.75 %), repeatability (RSD < 0.86 %), stability (RSD < 2.14 %), and accuracy (with the recovery rates between 95.15 % and 105.49 %) within an extremely short running time (12 min). Moreover, the developed approach was successfully employed in the quantification of ten phytochemicals in different parts (leaves, branches and trunks) of Nauclea officinalis. Based on analytical results, the content of ten phytochemicals in different organs of Nauclea officinalis was significantly different, and the superior contents of these phytochemicals were located in the leaves compared to the branches and trunks. In summary, the current work established an effective method for the assay of multi-compounds, which could supply a basis of the quality control and comprehensive utilization for Nauclea officinalis.

A network meta-analysis of efficacy and safety for first-line and second/further-line therapies in postmenopausal women with hormone receptor-positive, HER2-negative, advanced breast cancer

BackgroundHormone receptor-positive/human epidermal growth factor receptor 2-negative (HR + /HER2 −) advanced breast cancer is a prevalent subtype among postmenopausal women. Despite the growing number of randomized clinical trials (RCTs) exploring this topic, the efficacy and safety of first-line and second/further-line treatments remain uncertain. Accordingly, our aim was to conduct a comprehensive evaluation of the efficacy and safety of these therapies through network meta-analysis.MethodsRCTs were identified by searching Pubmed, Embase, and major cancer conferences. The efficacy of interventions was assessed using the hazard ratios (HRs) of progression-free survival (PFS) and overall survival (OS), while safety was indicated by the incidence of any grade adverse events (AEs), grade 3–5 AEs, AEs leading to treatment discontinuation, and AEs leading to death. Both time-variant HRs fractional polynomial models and time-invariant HRs Cox-proportional hazards models were considered for handling time-to-event data. Safety indicators were analyzed using Bayesian network meta-analysis. Additionally, subgroup analyses were conducted based on patient characteristics.ResultsA total of 41 RCTs (first-line 17, second/further-lines 27) were included in the analysis. For first-line treatment, the addition of Cyclin-dependent kinase 4 and 6 (CDK4/6) inhibitors to endocrine therapy significantly improved therapeutic efficacy in terms of both PFS and OS, demonstrating the best performance across all mechanisms. Specifically, the combination of Abemaciclib and Letrozole demonstrated the most favorable performance in terms of PFS, while Ribociclib plus Fulvestrant yielded the best outcomes in OS. Incorporating the immune checkpoint inhibitor Avelumab into the regimen with CDK4/6 inhibitors and selective estrogen receptor degraders significantly enhanced both PFS and OS in second-line or later treatments. Regarding safety, endocrine monotherapy performed well. Regarding safety, endocrine monotherapy performed well. There is mounting evidence suggesting that most CDK4/6 inhibitors may demonstrate poorer performance with respect to hematologic AEs. However, additional evidence is required to further substantiate these findings.ConclusionsCDK4/6 inhibitors, combined with endocrine therapy, are pivotal in first-line treatment due to their superior efficacy and manageable AEs. For second/further-line treatment, adding immune checkpoint inhibitors to CDK4/6 inhibitors plus endocrine therapy may produce promising results. However, to reduce the results’ uncertainty, further trials comparing these novel treatments are warranted.Trial registrationRegistration number: PROSPERO (CRD42022377431).

Determination of sugars and organic acids in diverse carob genotypes using HPLC techniques

Carob fruit is widely known for their abundance of health-boosting compounds like polyphenols, ascorbic acid, organic acids, and micronutrients. These compounds offer various benefits, including antioxidative, antimicrobial, antidiabetic, liver-protective, anti-inflammatory, anticancer, and heart-protective effects. In this research, High-Performance Liquid Chromatography (HPLC) methods were employed to assess sugar and organic acid levels in mature fruit pods of three distinct carob genotypes from the Kahramanmaras region in Turkey. The findings revealed that genotype-2 had the highest concentrations of glucose (1301 mg/100g) and fructose (875 mg/100g), genotype-3 exhibited the highest level of xylose (1548 mg/100g), while genotype-1 displayed the highest levels of sucrose (9183 mg/100g) and total sugars (12457 mg/100g). Regarding organic acids, genotype-1 exhibited the highest levels of oxalic acid (17.62 mg/100g), citric acid (612.50 mg/100g), and fumaric acid (8.0 mg/100g), while genotype-3 showed the highest levels of malic acid (234.92 mg/100g) and succinic acid (1089.76 mg/100g); however, genotype-2 had the highest amount of ascorbic acid (8.17 mg/100g). In conclusion, genotype-1 demonstrated the most favorable performance in terms of having the highest levels of total sugar and organic acids compared to the other two genotypes.

TsImpute: an accurate two-step imputation method for single-cell RNA-seq data.

Single-cell RNA sequencing (scRNA-seq) technology has enabled discovering gene expression patterns at single cell resolution. However, due to technical limitations, there are usually excessive zeros, called "dropouts," in scRNA-seq data, which may mislead the downstream analysis. Therefore, it is crucial to impute these dropouts to recover the biological information. We propose a two-step imputation method called tsImpute to impute scRNA-seq data. At the first step, tsImpute adopts zero-inflated negative binomial distribution to discriminate dropouts from true zeros and performs initial imputation by calculating the expected expression level. At the second step, it conducts clustering with this modified expression matrix, based on which the final distance weighted imputation is performed. Numerical results based on both simulated and real data show that tsImpute achieves favorable performance in terms of gene expression recovery, cell clustering, and differential expression analysis. The R package of tsImpute is available at https://github.com/ZhengWeihuaYNU/tsImpute.

Application of machine learning techniques in estimating the construction cost of residential buildings in the Middle East region

The recent global economic crisis resulted in a crash in the real estate market. This has underscored the significance of accurately predicting construction costs for real estate units in the near-term economy. Over the past few decades, there has been limited research conducted on forecasting the construction costs of housing units. Effective cost forecasting, particularly during the early stages, can play a crucial role in minimizing expenses and ensuring the viability of a project. In recent years, the utilization of machine learning techniques has witnessed a significant surge. These techniques offer generalized solutions and demonstrate favorable performance in terms of effort, time, and cost. Therefore, the objective of this paper is to compare the capabilities of different machine learning methods in estimating real estate construction costs. As part of the study, multiple economic variables and indices will be used as inputs for the machine learning models. The paper will also assess and discuss the performance of each technique by comparing it against actual measurements. Ultimately, the aim is to identify the most suitable approach for such cost estimation tasks.

Evaluation of separating media for autopolymerizing acrylic resin fabricated on 3D-printed acrylate-based resin dental casts

Statement of problemLimited data are available regarding the most suitable separating medium for fabricating dental acrylic resin prostheses or appliances on 3-dimensional (3D)-printed resin casts. PurposeThe purpose of this in vitro study was to investigate and evaluate different separating media in terms of the ease of removal and detail reproduction of an autopolymerizing acrylic resin fabricated on acrylate-based 3D-printed resin casts. Material and methodsA cube-shaped cast was designed with a built-in truncated cone-shaped hole and a V-shaped groove at the base. Seventy-five casts were 3D-printed using acrylate-based resin and assigned to 5 groups according to the applied separating media: Siliform BEA (silicone-based), IMPRIMO 3D (alginate-based), 3D Modellisolierung (wax-based), TECHNOSIL (alginate-based), and none (control). After the application of the separating media, the truncated cone-shaped holes in the specimens were filled with autopolymerizing acrylic resin. The efficacy of the separating media was evaluated according to the ease of removal on a scale of 1 to 3 and accurate detail reproduction of the V-shaped groove on a scale of 1 to 3 under ×6 magnification. The nonparametric Kruskal-Wallis rank test was used to identify significant differences among the separating media (α=.05). ResultsSignificant differences were found among the groups (P<.001). Siliform BEA and 3D Modellisolierung had the best mean rank in both the ease of removal and detail reproduction scores, which were significantly different from those of the alginate-based separating media (IMPRIMO 3D and TECHNOSIL) and the control group (P<.01). ConclusionsThe dedicated silicone- and wax-based separating media for 3D-printed casts exhibited the most favorable performance in terms of ease of removal and detail reproduction.

Income-generating Projects of a Philippine State University: Proposal for Strategic Decisions

This study is aimed at evaluating the productivity and financial performance of income-generating projects (IGPs) of a Philippine State University in the Davao Region from 2016-2020. Further, this study uses a descriptive evaluative research design to analyze and interpret data through the total population sampling, wherein all 22 IGPs were subjected to the evaluation. The results reveal net receipts of 28.16 million from 2016-2020; Crop Production was the highest contributor, while the loss of non-agriculture was recorded in 2020 due to the COVID-19 pandemic. The Crop Production of Campus C had the highest profitability in terms of net income earning 9.04 million for the period covered. Their Commercial Stalls Rental had the most favorable performance in terms of return on investment. The same IGP had the quickest ability to recoup its investment. The Gymnasium and Hostel of Campus C took the lowest Return on Investment and recovery rate. Moreover, the financial contribution of budget allocation and utilization of all IGPs was meager for Instruction and Support to Operations, and no budget shares for the areas of Research and Extension. Hence, eight IGPs are for restructuring and expansion; 11 IGPs are for restructuring only, one IGP is for termination, and one IGP is not subject to any decision strategy

Development and Evaluation of a Risk Prediction Model for Left Ventricular Aneurysm in Patients with Acute Myocardial Infarction in Northwest China

PurposeLeft ventricular aneurysm (LVA) is a severe and common mechanical comorbidity with acute myocardial infarction (AMI) that can present high mortality and serious adverse outcomes. Accordingly, there is a need for early identification and prevention of patients at risk of LVA. The aim of this study was to develop and validate a risk prediction model for LVA among AMI patients in Northwest China.MethodsA total of 509 patients with AMI were retrospectively collected between January 2018 and August 2021. All patients were randomly divided into a training group (n=356) and a validation group (n=153). Potential risk factors for LVA were screened for predictive modelling using least absolute shrinkage and selection operator regression, multivariate logistic regression, clinical relevance, and represented by a comprehensive nomogram. Receiver operating characteristic curve, calibration curve, and decision-curve analysis (DCA) were used to assess the discrimination capacity, calibration, and clinical validity, respectively.ResultsSeven predictors were finally identified for the establishment of prediction model, including age, cardiovascular disease history, left ventricular ejection fraction, ST-segment elevation, percutaneous coronary intervention history, mean platelet volume, and aspartate aminotransferase. The prediction model achieved acceptable areas under the curves of 0.901 (95% confidence interval [CI]=0.868–0.933) and 0.908 (95% CI=0.861–0.956) in the training and validation groups, respectively, and the calibration curves fit well in our model. The DCA result indicated that this nomogram exhibited a favorable performance in terms of clinical utility.ConclusionAn accurate prediction model for LVA development established, which can be applied to rapidly assess the risk of LVA in patients with AMI. Our findings will aid clinical decision-making to reduce the incidence of LVA in high-risk patients, and counteract adverse cardiovascular outcomes.

Ultrasensitive photoelectrochemical immunosensor based on Dual-Photosensitive electrodes

In the study, a photoelectrochemical (PEC) immunosensor based on dual-photosensitive electrodes was developed for cardiac troponin I (cTnI) detection. The sensing photocathode with biometric functions was prepared by CuInS2 and narrow band gap semiconductor In2S3 as the counter electrode. In this way, the separation of photoanode and biometric events was realized, and the ability of stability of the immunosensor could be effectively improved. Moreover, the attraction to the photogenerated electrons (e-) from photoanode would be increased by the abundant holes (h+) of photocathode, under the radiation of light. This tremendously improves the photoelectric response, which further improves the sensitivity of the immunosensor. The controllable-synthesis uncomplicated photoelectric material not only accords with the principle of simplicity of electrode modification but also makes the immunosensor more conducive to the practical application. Additionally, even in the case of zero bias voltage, the constructed PEC immunosensor can operate with high efficiency, namely, self-powered. The immunosensor could provide the quantitative readout photocurrent to a concentration of cTnI in the range of 0.10 pg/mL to 1.00 μg/mL and the detection limit was 0.0113 pg/mL under the optimal experimental conditions. With favorable performance in terms of anti-interference, stability, specificity and reproducibility, this immunosensor will provide new prospects for general PEC bioanalysis development.

Development and Validation of a Prediction Model for the Cure of Peritoneal Dialysis-Associated Peritonitis: A Multicenter Observational Study.

AimPeritoneal dialysis (PD)-associated peritonitis (PDAP) is a severe complication of PD. It is an important issue about whether it can be cured. At present, there is no available prediction model for peritonitis cure. Therefore, this study aimed to develop and validate a prediction model for peritonitis cure in patients with PDAP.MethodsPatients with PD who developed PDAP from four dialysis centers in Northeast China were followed up. According to the region of PD, data were divided into training and validation datasets. Initially, a nomogram for peritonitis cure was established based on the training dataset. Later, the nomogram performance was assessed by discrimination (C-statistic), calibration, and decision curves.ResultsTotally, 1,011 episodes of peritonitis were included in the final analysis containing 765 in the training dataset and 246 in the validation dataset. During the follow-up period, peritonitis cure was reported in 615 cases from the training dataset and 198 from the validation dataset. Predictors incorporated in the final nomogram included PD duration, serum albumin, antibiotics prior to admission, white cell count in peritoneal dialysate on day 5 (/μl) ≥ 100/μl, and type of causative organisms. The C-statistic values were 0.756 (95% CI: 0.713–0.799) in the training dataset and 0.756 (95% CI: 0.681–0.831) in the validation dataset. The nomogram exhibited favorable performance in terms of calibration in both the training and validation datasets.ConclusionThis study develops a practical and convenient nomogram for the prediction of peritonitis cure in patients with PDAP, which assists in clinical decision-making.

EduChain: CIA-Compliant Blockchain for Intelligent Cyber Defense of Microservices in Education Industry 4.0

Massive data handling requirement in education Industry 4.0 has attracted interests in the research of microservice architectures due to their scalability, resilience, and elasticity characteristics. This development has been challenged by extensive data exchange required by a set of independent microservices to build a complete application, which could result in increasing risks and exposure to the security and privacy breaches of the data. It is imperative to see that educational data are highly sensitive, critical for ascertaining educational attainment and facilitating credentials for qualification verifications. This article puts forward a new proposal of devising a security and privacy-preserving design mechanism of data transactions in educational microservices leveraging the blockchain technology. The design comprises three phases, namely the blockchain framework, data sending–receiving, and confidentiality-integrity-availability over a secured platform with each phase having detailed mechanisms for algorithm implementation. The proposal is shown to exhibit favorable performance in terms of time cost of publishing, throughput, and latency, and shown to have high survey acceptance in terms of confidentiality, integrity, and availability with approximately 10% improvement from prior blockchain adoption.

Photoactive Fe Catalyst for Light-Triggered Alkyd Paint Curing.

Herein, we show that the photoactive complexes [(Cp)Fe(arene)]+ (Cp = cyclopentadienyl; arene = C6H6, C6H5Me) act as latent catalysts that allow for photochemical control over the onset of alkyd paint curing, without the need for antiskinning agents such as the volatile 2-butanone oxime normally used to prevent curing during paint storage. The highly soluble neutral complexes [(Cp)Fe(Ch)] and [(Cp)Fe(Ch′)] (Ch = cyclohexadienyl, Ch′ = methylcyclohexadienyl) readily convert to the photoactive complexes [(Cp)Fe(arene)]+ upon oxidation in alkyd, allowing the latter to be dosed in a wide range of concentrations. Infrared and Raman studies show similar spectral changes of the alkyd paint matrix as have been observed in alkyd curing mediated by well-known, industrially applied cobalt- and manganese-based catalyst Co(neodecanoate)2 and [(Me3TACN)2Mn2(μ-OOCR)3](OOCR). The [(Cp)Fe(Ch)]/[(Cp)Fe(arene)]+ system performs equally well as these cobalt- and manganese-based catalysts in terms of drying time and outperform the manganese catalyst by showing a hardness development (increase) similar to that of the cobalt-based catalyst. Based on electron paramagnetic resonance and light–activity studies, we propose that photolysis of [(Cp)Fe(arene)]+ generates short-lived active FeII species, explaining the desired latency. The [(Cp)Fe(Ch)]/[(Cp)Fe(arene)]+ alkyd curing systems presented herein are unique examples of intrinsically latent paint curing catalysts that (1) are based on an abundant and harmless transition metal (Fe), (2) do not require any antiskinning agents, and (3) show favorable performance in terms of drying times and hardness development.

Distributed fiber optic radiation sensors

Abstract. The international research efforts focused on the development of radiation sensors based on optic fibers have their origins in the 1970s (Evans et al., 1978). Generally, the lightweight fiber optic sensors are immune to electromagnetic field interference and high voltages making them deployable in harsh environments at hard to reach areas where conventional sensors usually will not work at all. A further advantage of such radiation sensors is the possibility of remote and real-time monitoring (Huston et al., 2001). In this work, we present our results achieved in several research activities for development of fiber optic dosimeters. The findings show that both the measurement of the radiation-induced attenuation (RIA) along the entire sensing fiber and the accompanying change in the refractive index of the fiber core can be used for distributed radiation monitoring in the kGy and MGy range, respectively. Depending on the fiber type and material the RIA shows varying response to dose rates, environmental temperatures and the wavelength of the laser source used. Thereby, an operation with visible laser light provides most favorable performance in terms of high radiation sensitivity. Operating at these wavelengths, RIA monitoring could yield high-sensitivity dose measurement with sub-gray resolution and accuracy (Stajanca and Krebber, 2017b); however, conventional optical time-domain reflectometry (OTDR) systems for RIA measurements operating in the visible range suffer from low-spatial resolution, long measurement times and poor signal-to-noise (SNR) ratio. The limitations of the OTDR performance can be overcome by the incoherent optical frequency domain reflectometry (I-OFDR) developed by the Federal Institute of Materials Research and Testing (BAM, Liehr et al., 2009) with potential for dynamic real-time measurement. Over the years, several highly radiation sensitive fibers, such as perfluorinated polymer optical fibers (PF-POF, Stajanca and Krebber, 2017a), phosphorous-doped silica optical fibers (SOF, Paul et al., 2009), aluminium-doped SOF (Faustov et al., 2013) and erbium-doped SOF (Wosniok et al., 2016) have been identified and are commercially available. As mentioned before, the radiation-induced RIA increase is associated with an increase in the refractive index leading also to material compaction in the fiber core. The latter two effects can be used for measuring radiation distribution based on Brillouin scattering in the range of high radiation doses of several MGy (Phéron et al., 2012; Wosniok et al., 2016). When using fiber optic sensors for radiation monitoring, the existing post-irradiation annealing behavior of the optical fiber sensors must also be considered.

A Data Aggregation Privacy Protection Algorithm Based on Fat Tree in Wireless Sensor Networks

Wireless sensor network is the momentous part of the Internet of Things. Data aggregation technology is the most practical method to reduce the amount of communication among nodes. Adding a privacy protection mechanism to data aggregation is one of the important means for privacy protection and security in wireless sensor networks. Aiming at certain performance defects of the existing SMART (Slice-Mix-AggRegaTe) privacy protection algorithms, a fat tree-based data aggregation privacy protection algorithm is proposed in this paper, which is referred to as the FTSMART (Fat Tree Slice-Mix-AggRegaTe) algorithm. Concerning the innovative algorithm, the fat tree (FT) is introduced, the fat tree structure is adopted to optimize the data slicing scheme and the aggregation tree generation scheme, and the allocation of fixed time intervals is employed for nodes to reduce the transmission collision in the data aggregation process and to guarantee the completion of the data transmission. The simulation experiment results demonstrate that the FTSMART algorithm has presented the favorable performance in terms of the privacy protection, the network communication overhead, and the data aggregation accuracy.

An adaptive window-based hybrid artifact removal method for Ultra-Wide Band imaging enhancement of early breast cancer detection

At present, many studies have shown that breast cancer is the primary factor affecting women's health all over the world. In the past decades, ultra-wide band (UWB) microwave imaging has been widely studied as a non-invasive breast cancer detection tool through abundant researches and engineering applications. Generally, the artifact caused by skin and other factors will significantly affect the accuracy of target detection in this technology. Therefore, how to eliminate them is crucial. In this paper, an adaptive window-based hybrid artifact removal (AW-HAR) approach is proposed. It consists of adaptive time window division, revised two-stage filtering, and Savitzky-Golay (S-G) smoothing. This method pre-analyzes the specificity of each channel and divides them into several time windows adaptively. Then adjusted two-stage filtering is carried out on each window to extract partial tumor information. Finally, the fragmented time sequences are integrated and S-G smoothing is applied. The simulation and phantom experiments demonstrate that the AW-HAR has favorable performance in terms of clutter suppression and image clarity. It effectively suppresses surface disturbance and irrelevant response, enhancing the region of interest (ROI) in microwave imaging.

Influence on Polyurethane Synthesis Parameters Upon the Performance of Base Asphalt

The objective of this study aims to investigate the performance of thermoplastic polyurethane elastomers (TPU)-modified asphalt, with special focus on the influence of polyurethane synthesis parameters upon the performance of base asphalt. In this study, the TPU modifier was incorporated into base asphalt to prepare TPU-modified asphalt by using a self-determined laboratory process. The properties of polyester-based TPU and polyether-based TPU modified asphalt with different contents was analyzed by the base performance tests. The Fourier transform infrared spectroscopy (FTIR), fluorescent microscope, scanning electron microscopy (SEM), differential scanning calorimetry (DSC), thermal gravity (TG), dynamic shear rheology (DSR), and bending beam rheology (BBR) tests were conducted to expose chemical, microstructure, and rheological properties of the asphalt binders, respectively. The results indicated that the modified asphalt with 5% TPU modifier exhibited a favorable performance in terms of the penetration, ductility, softening point, and rotational viscosity, respectively. The isocyanate and polyol reacted to form carbamate in the TPU-modified asphalt, and moreover the isocyanate and aromatic compounds reacted to form halohydrins in the asphalt. Large particles contained in the polyester-based TPU-modified asphalt increased obviously with the increase of Ch (Hard segment content) and r (Isocyanate root index, molar ratio between NCO and OH) value, the fracture mode of it subjected to external load indicated a typical brittle fracture. The content of MDI in the synthesized polyurethane modifier was an important factor affecting the high-temperature stability of the modified asphalt. The polyester-based TPU modifier had a better high temperature performance than the polyether-based TPU modifier. The polyether polyol chain segment showed a greater flexibility as compared to polyester polyol; the addition of polyether polyols can be effectively offset of MDI chain rigidity as r ≤ 1 and Ch &amp;lt; 40% leading to the improvement of the viscoelastic property of asphalt. When Ch = 40% and r = 0.95, the polyether-based TPU-modified asphalt exhibited the same high-temperature grade as the polyester-based TPU-modified asphalt, whereas the polyether-based TPU-modified asphalt showed obviously higher low temperature grades. The polyester-based TPU with Ch = 40% and r = 1 can be used as a modifier to increase the durability of asphalt binders.

Exploring competition efficiency of automobile brands: An extensional data envelopment analysis‐metafrontier framework

This study integrates metafrontier with bootstrapping to investigate automobile vehicle efficiency. The results are as follows: (1) general cars exhibited the most favorable performance in terms of price, costs, and mileage. (2) Metafrontier efficiency was the highest for general cars, which may be because this automobile type has the highest prevalence. Sports cars had the most favorable group frontier. (3) General cars had the most favorable performance in terms of miles‐per‐gallon. The conclusions could enhance consumers' green cognition and serve as a reference in purchase decision‐making; they can provide manufacturers with directions regarding future product designs to mitigate technology gaps.

Hierarchical Energy Efficient Hybrid Precoding for Configurable Sub-Connected MIMO Systems

This paper proposed a configurable sub-connected architecture with a framework that dynamically activates the near-optimal subset of antennas and RF chains to implement energy-efficient hybrid precoding in millimeter wave multiple-input multiple-output system. Since the exhaust search is computational intractable, we propose a two-stage hybrid precoding algorithm, where the digital precoder is designed to eliminate the inter-user interference by zero-forcing rule. Specifically, in the first stage, we introduce an extended cross-entropy algorithm that adaptively updates the probability distribution of potential states in the analog precoder matrix, which can generate a solution that is close to the optimal with a sufficiently high probability. In the second stage, a QR-based subset selection algorithm is proposed to pick the near-optimal subset of the RF chains to further cut down on the energy cost. Simulation results show that proposed extend-CE algorithm gets favorable performance in terms of energy efficiency, and QR-based RF chain selection can achieve a near-optimal performance. Other hybrid precoding algorithms can also be incorporated into the proposed RF chain selection algorithm.

MethylStar: A fast and robust pre-processing pipeline for bulk or single-cell whole-genome bisulfite sequencing data

BackgroundWhole-Genome Bisulfite Sequencing (WGBS) is a Next Generation Sequencing (NGS) technique for measuring DNA methylation at base resolution. Continuing drops in sequencing costs are beginning to enable high-throughput surveys of DNA methylation in large samples of individuals and/or single cells. These surveys can easily generate hundreds or even thousands of WGBS datasets in a single study. The efficient pre-processing of these large amounts of data poses major computational challenges and creates unnecessary bottlenecks for downstream analysis and biological interpretation.ResultsTo offer an efficient analysis solution, we present MethylStar, a fast, stable and flexible pre-processing pipeline for WGBS data. MethylStar integrates well-established tools for read trimming, alignment and methylation state calling in a highly parallelized environment, manages computational resources and performs automatic error detection. MethylStar offers easy installation through a dockerized container with all preloaded dependencies and also features a user-friendly interface designed for experts/non-experts. Application of MethylStar to WGBS from Human, Maize and A. thaliana shows favorable performance in terms of speed and memory requirements compared with existing pipelines.ConclusionsMethylStar is a fast, stable and flexible pipeline for high-throughput pre-processing of bulk or single-cell WGBS data. Its easy installation and user-friendly interface should make it a useful resource for the wider epigenomics community. MethylStar is distributed under GPL-3.0 license and source code is publicly available for download from github https://github.com/jlab-code/MethylStar. Installation through a docker image is available from http://jlabdata.org/methylstar.tar.gz

Rheological properties and micro-characteristics of polyurethane composite modified asphalt

The objective of this study aims to investigate the rheological properties and micro-characteristics of polyurethane (PU) composite modified asphalt. In this study, the mixture of PU and rock asphalt (RA) was incorporated into base asphalt to prepare PU composite modified asphalt through a self-determined laboratory process. The properties of PU composite asphalt with different PU and RA contents was analyzed by the base performance tests. The dynamic shear rheology (DSR), bending beam rheology (BBR), X-ray diffraction (XRD), Fourier transform infrared spectroscopy (FTIR), scanning electron microscopy (SEM), differential scanning calorimetry (DSC) and thermal gravity (TG) were used to clarify rheological properties, chemical and microstructure of the binders, respectively. The results indicated that the PU composite modified asphalt with 5% PU and 5% RA exhibited a favorable performance in terms of the penetration, ductility, softening point and rotational viscosity, respectively. The PU composite modified asphalt with 3%, 5% PU and 15% RA showed the best high temperature performance grade (PG). Both the PU and RA indicated a preferable compatibility with the base asphalt. The unsaturated bond in PU crosslinked with the S-S bond in asphalt, which is helpful to improve the resistance to low temperature deformation. It can be found that the composite asphalt containing 5% PU and 5% RA performed a preferable laboratory performance investigated based on rheological properties tests and the microstructure observation. Also, the isocyanate respectively reacted with the phenol and carboxylic acid in the PU composite modified asphalt leading to the performance improvement. This study also showed that the PU has promising properties, and the PU and RA can be used as composite modification to increase the durability and service life of asphalt pavement.