Search

Introduction: Cabozantinib is one of the preferred treatment options in the latest metastatic renal cell carcinoma (mRCC) guidelines. Cabozantinib is also associated with high drug expenses irrespective of the used dose, because a flat-prizing model has been implemented. In addition, concomitant intake with a high-fat meal increases its bioavailability on average by 57%. Combined with the long terminal half-life of cabozantinib (99 h), this creates possibilities to extend the dosing interval to reduce drug expenses whilst maintaining equivalent exposure. Objectives: The primary objective was to evaluate the population pharmacokinetic (POPPK) model of cabozantinib developed for its registration using real-world patients' therapeutic drug monitoring (TDM) data. The secondary objective was to design, simulate, and evaluate alternative dose regimens with the aim to reduce drug expenses whilst maintaining comparable exposure. Methods: Retrospective TDM data from mRCC patients treated with cabozantinib were obtained. The data were evaluated using the published Food and Drug Administration (FDA) cabozantinib POPPK model, a two-compartment disposition model with a dual (fast and slow) lagged first-order absorption process derived from FDA registration documents, as a basis. Subsequently, simulations of alternative drug expenses saving regimens were evaluated. Results: Twenty-seven mRCC patients with 75 pharmacokinetic observations were included. Patients were treated for a median of 75 days with a median dose of 40 mg. Model evaluation results showed that the cabozantinib TDM concentrations were adequately predicted by the published FDA cabozantinib POPPK model, except for a slightly higher clearance (CL) of 3.11 L/h compared to the reported value (2.23 L/h). The simulation study indicated that an alternative dose regimen that consists of taking 60 mg of cabozantinib for 2 days and then skipping 1 day results in comparable average exposure when compared with a 40 mg daily dose, both without food interaction, while saving 33.3% of the total drug expenses per month. The food effect of a high-fat meal was also taken into account when simulating other alternative dose regimens; 40 mg every 72 h combined with a high-fat meal resulted in comparable exposure when compared with a 20 mg daily dose fasted, while saving 66.7% in drug expenses. Conclusions: In this study, the optimized cabozantinib POPPK model resulted in adequate prediction of real-world cabozantinib pharmacokinetic data. Alternative dosing regimens with and without using known food interactions were proposed that resulted in potential strategies to significantly reduce cabozantinib drug expenses. [ABSTRACT FROM AUTHOR]

ML applications proliferate across various sectors. Large internet firms employ ML to train intelligent models using vast datasets, including sensitive user information. However, new regulations like GDPR require data removal by businesses. Deleting data from ML models is more complex than databases. Machine Un-learning (MUL), an emerging field, garners academic interest for selectively erasing learned data from ML models. MUL benefits multiple disciplines, enhancing privacy, security, usability, and accuracy. This article reviews MUL's significance, providing a taxonomy and summarizing key MUL algorithms. We categorize modern MUL models by criteria, including model independence, data driven, and implementation considerations. We explore MUL applications in smart devices and recommendation systems. We also identify open questions and future research areas. This work advances methods for implementing regulations like GDPR and safeguarding user privacy. [ABSTRACT FROM AUTHOR]

To evaluate the impact of zinc sulfate (ZnSO4) concentration on the structural properties of the films, Cd1−xZnxS thin films were formed on glass substrates using chemical bath deposition (CBD) in this study. The effect of ZnSO4 precursor concentration on the surface morphology, optical properties, and morphological structure of the Cd1−xZnxS films was investigated. To study the impact of zinc doping content on the performance metrics of Cu(In1−xGax)Se2 (CIGS) cells in the experimental group and to improve the buffer layer thickness, simulations were run using one-dimensional solar cell capacitance simulator (SCAPS-1D) software. [ABSTRACT FROM AUTHOR]

Research in financial domain has shown that sentiment aspects of stock news have a profound impact on volume trades, volatility, stock prices and firm earnings. In-depth analysis of stock news is now sourced from financial reviews by various social networking and marketing sites to help improve decision making. Nonetheless, such reviews are in the form of unstructured text, which requires natural language processing (NLP) in order to extract the sentiments. Accordingly, in this study we investigate the use of NLP tasks in effort to improve the performance of sentiment classification in evaluating the information content of financial news as an instrument in investment decision support system. At present, feature extraction approach is mainly based on the occurrence frequency of words. Therefore low-frequency linguistic features that could be critical in sentiment classification are typically ignored. In this research, we attempt to improve current sentiment analysis approaches for financial news classification by focusing on low-frequency but informative linguistic expressions. Our proposed combination of low and high-frequency linguistic expressions contributes a novel set of features for sentiment classification. The experimental results show that an optimal Ngram feature selection (combination of optimal unigram and bigram features) enhances sentiment classification accuracy as compared to other types of feature sets. [ABSTRACT FROM AUTHOR]

Therefore, in this paper the authors [[14]] study the role of big data component analysis for handling and protecting sensitive information from disclosure along with preserving the data utility. An implementation of the algorithm is tested on standard datasets from the UCI repository and performed comparably, if not better than, other algorithms for dealing with uncertain and fuzzy data. Big data analytics has received numerous attentions in many areas [[1]-[5]]. Jiang et al. [[6]] consider the structural information of the posts and the semantic information of users' interests and use UICD (User Interest Community Detection) model to analyze text streams from microblogging sites for detecting users' interest communities. [Extracted from the article]

The petrography, major, and trace elements concentration of the sandstones from the Zhanjin Formation in Qiwu area, south Qiangtang Basin, have been studied to determine their provenance, intensity of paleo-weathering and depositional tectonic setting. Petrography indicates that the composition and texture maturity of the sandstones are moderate. The detrital composition of Zhanjin Formation samples are dominated by quartz (70.1–80.3, average 74.1%) and feldspar (18.3–26.5, average 21.9%), but is low in lithic fragment (1.4–8.6, average 3.9%). The sandstone can be classified as arkose and litharenite arkose. The detrital model composition reflects that these sandstones are probably derived from a passive continental margin. The index of chemical variability (ICV), and Th/Sc vs. Zr/Sc discrimination diagram suggests that the compositional maturity and recycling were moderate to low. The index of alteration (CIA) and the A–CN–K diagram indicated that the intensity of weathering in the source area was low. The Al2O3/TiO2 vs. SiO2, Th/Sc, Co/Th, La/Sc, Cr/Th ratio values of the Zhanjin sandstones indicated that the sandstones were mainly derived from felsic source rocks. The Gondwana passive continental margin belt is probably the primary provenance area as evidenced by petrography and geochemistry features of the Zhanjin Formation. All kinds of tectonic discrimination diagrams based on major elements showed a rift setting for Zhanjin sandstones, which is consistent with the general views of study areas. [ABSTRACT FROM AUTHOR]

Utilising the mobilisation of a sink node in a wireless sensor network to combat the energy hole, or hotspot issue, is well referenced. However, another issue, that of energy spikes may remain. With the mobile sink node potentially communicating with some nodes more than others. In this study we propose the Mobility Aware Duty Cycling and Dynamic Preambling Algorithm (MADCaDPAL). This algorithm utilises an existing solution where a communication threshold is built between a mobile sink node using predictable mobility and static nodes on its path. MADCaDPAL bases decisions relating to node sleep function, moving to clear channel assessment and the subsequent sending of preambles on the relation between the threshold built by the static node and the position of the mobile sink node. MADCaDPAL achieves a reduction in average energy consumption of up to 80%, this when used in conjunction with a lightweight carrier-sense multiple access based MAC implementation. Maximum energy consumption amongst individual nodes is also brought closer to the average, reducing energy spikes and subsequently improving network lifetime. Additionally, frame delivery to the sink is improved overall. [ABSTRACT FROM AUTHOR]

The Parallel Coordinates Plot (PCP) is a popular technique for the exploration of high-dimensional data. In many cases, researchers apply it as an effective method to analyze and mine data. However, when today's data volume is getting larger, visual clutter and data clarity become two of the main challenges in parallel coordinates plot. Although Arc Coordinates Plot (ACP) is a popular approach to address these challenges, few optimization and improvement have been made on it. In this paper, we do three main contributions on the state-of-the-art PCP methods. One approach is the improvement of visual method itself. The other two approaches are mainly on the improvement of perceptual scalability when the scale or the dimensions of the data turn to be large in some mobile and wireless practical applications. 1) We present an improved visualization method based on ACP, termed as double arc coordinates plot (DACP). It not only reduces the visual clutter in ACP, but use a dimension-based bundling method with further optimization to deals with the issues of the conventional parallel coordinates plot (PCP). 2)To reduce the clutter caused by the order of the axes and reveal patterns that hidden in the data sets, we propose our first dimensional reordering method, a contribution-based method in DACP, which is based on the singular value decomposition (SVD) algorithm. The approach computes the importance score of attributes (dimensions) of the data using SVD and visualize the dimensions from left to right in DACP according the score in SVD. 3) Moreover, a similarity-based method, which is based on the combination of nonlinear correlation coefficient and SVD algorithm, is proposed as well in the paper. To measure the correlation between two dimensions and explains how the two dimensions interact with each other, we propose a reordering method based on non-linear correlation information measurements. We mainly use mutual information to calculate the partial similarity of dimensions in high-dimensional data visualization, and SVD is used to measure global data. Lastly, we use five case scenarios to evaluate the effectiveness of DACP, and the results show that our approaches not only do well in visualizing multivariate dataset, but also effectively alleviate the visual clutter in the conventional PCP, which bring users a better visual experience. [ABSTRACT FROM AUTHOR]

Recently, the research of Internet of Things (IoT) and Multimedia Big Data (MBD) has been growing tremendously. Both IoT and MBD have a lot of multimedia data, which can be tampered easily. Therefore, the research of multimedia forensics is necessary. Copy-move is an important branch of multimedia forensics. In this paper, a novel copy-move forgery detection scheme using combined features and transitive matching is proposed. First, SIFT and LIOP are extracted as combined features from the input image. Second, transitive matching is used to improve the matching relationship. Third, a filtering approach using image segmentation is proposed to filter out false matches. Fourth, affine transformations are estimated between these image patches. Finally, duplicated regions are located based on those affine transformations. The experimental results demonstrate that the proposed scheme can achieve much better detection results on the public database under various attacks. [ABSTRACT FROM AUTHOR]

A nitrogen-fixing, endospore-forming bacterium, designated strain L201T was isolated from the leaves of Bryophyllum pinnatum growing in South China Agricultural University. Phylogenetic analysis of the 16S rRNA gene sequence indicated that strain L201T is affiliated with the genus Paenibacillus, and closely related to Paenibacillus albidus Q4-3T (97.4%), Paenibacillus odorifer DSM 15391T (97.3%) and Paenibacillus borealis DSM 13188T (97.2%). The main fatty acids components was anteiso-C15:0 (48.1%). The predominant isoprenoid quinone was MK-7. The major polar lipids were found to be diphosphatidylglycerol, phosphatidylethanolamine and phosphatidylglycerol. The G+C content of strain L201T was 43.9%. DNA-DNA relatedness between L201T and the reference strain was 29.8%. Biological and biochemical tests, protein patterns, genomic DNA fingerprinting and comparison of cellular fatty acids distinguished strain L201T from the closely related Paenibacillus species. Based on these data, the novel species Paenibacillus bryophyllum sp. nov. is proposed, with the type strain L201T(= KCTC 33951 T = GDMCC 1.1251 T). [ABSTRACT FROM AUTHOR]

The enormous developments in the field of wireless communication technologies have made the unlicensed spectrum bands crowded, resulting uncontrolled interference to the traditional wireless network applications. On the other hand, licensed spectrum bands are almost completely allocated to the licensed users also known as Primary users (PUs). This dilemma became a blackhole for the upcoming innovative wireless network applications. To mitigate this problem, the cognitive radio (CR) concept emerges as a promising solution for reducing the spectrum scarcity issue. The CR network is a low cost solution for efficient utilization of the spectrum by allowing secondary users (SUs) to exploit the unoccupied licensed spectrum. In this paper, we model the PU’s utilization activity by a two-state Discrete-Time-Markov Chain (DTMC) (i.e., Free and busy states), for identifying the temporarily unoccupied spectrum bands,. Furthermore, we propose a Cognitive Radio Sense-and-Wait assisted HARQ scheme, which enables the Cluster Head (CH) to perform sensing operation for the sake of determining the PU’s activity. Once the channel is found in free state, the CH advertise control signals to the member nodes for data transmission relying on Stop-and-Wait Hybrid- Automatic Repeat-Request (SW-HARQ). By contrast, when the channel is occupied by the PU, the CH waits and start sensing again. Additionally, the proposed CRSW assisted HARQ scheme is analytical modeled, based on which the closed-form expressions are derived both for average block delay and throughput. Finally, the correctness of the closed-form expressions are confirmed by the simulation results. It is also clear from the performance results that the level of PU utilization and the reliability of the PU channel have great influence on the delay and throughput of CRSW assisted HARQ model. [ABSTRACT FROM AUTHOR]

The North Qilian orogenic belt is the key to figure out the evolution and assembly of Asia. The Upper Silurian Hanxia Formation which is deposited in the north area of North Qilian Orogenic Belt is of utmost important to reveal the architecture and orogenic process of the North Qilian belt. So provenance analysis of the Hanxia Formation is of significance to reveal not only that the tectonic evolution of the central orogenic belt China, but also that Paleozoic Asia plate reconstruction. The ratios of elements and some discrimination diagrams based on geochemistry indicate that felsic rocks constitute their main source rocks. The chemical index of alteration is less than 80, indicating that the source rocks are relatively fresh and of low maturity. On the Th/Sc versus Zr/Sc scatter plot, samples from Hanxia Formation occur along the magmatic compositional variation trend of rocks, indicating that the rocks did not undergo obvious sedimentary sorting and recycling. The major and trace elements discrimination diagrams imply that Hanxia Formation rocks were derived from continental island arc. Recent studies show that the North China plate subducted southwards and produced subduction-related arc magmatism along the southern margin of the North Qilian Terrane during the Silurian. Therefore, we infer that in the late Silurian period the subduction-related arc became accreted to the Central Qilian terrane to the south, forming a composite continental arc, and the North Qilian belt accumulated in a fore-arc basin. [ABSTRACT FROM AUTHOR]

Key message: Using a combination of phenotypic screening, genetic and statistical analyses, and high-throughput genome-wide sequencing, we have finely mapped a dominant Phytophthora resistance gene in soybean cultivar Wayao. Abstract: Phytophthora root rot (PRR) caused by Phytophthora sojae is one of the most important soil-borne diseases in many soybean-production regions in the world. Identification of resistant gene(s) and incorporating them into elite varieties are an effective way for breeding to prevent soybean from being harmed by this disease. Two soybean populations of 191 F individuals and 196 F recombinant inbred lines (RILs) were developed to map Rps gene by crossing a susceptible cultivar Huachun 2 with the resistant cultivar Wayao. Genetic analysis of the F population indicated that PRR resistance in Wayao was controlled by a single dominant gene, temporarily named RpsWY, which was mapped on chromosome 3. A high-density genetic linkage bin map was constructed using 3469 recombination bins of the RILs to explore the candidate genes by the high-throughput genome-wide sequencing. The results of genotypic analysis showed that the RpsWY gene was located in bin 401 between 4466230 and 4502773 bp on chromosome 3 through line 71 and 100 of the RILs. Four predicted genes ( Glyma03g04350, Glyma03g04360, Glyma03g04370, and Glyma03g04380) were found at the narrowed region of 36.5 kb in bin 401. These results suggest that the high-throughput genome-wide resequencing is an effective method to fine map PRR candidate genes. [ABSTRACT FROM AUTHOR]

Soil and water quality is greatly affected by environmental pollution due to the increasing trend of urbanization and industrialization. In many developing countries, including Pakistan, the situation is more alarming as no preventive measures are still taken to tackle the problem. Although in developed countries, many techniques are used to remediate the environment including phytoremediation. It is the most eco-friendly technique in which plants are used to remove pollutants from the environment. Pakistan has also a great diversity of plants which could be used for the remediation of environmental pollutants. To our knowledge, few studies from Pakistan were reported about the use of flora for phytoremediation. According to recent literature, 50 plant species from Pakistan are studied for remediation purposes. In this review, the potential of different plant species for phytoremediation from Pakistan has been discussed along with their comparison to other countries to relate future perspectives. [ABSTRACT FROM AUTHOR]

Thirty-three endophytic diazotrophs were isolated from surface-sterilized leaves, stem, and roots of wild rice Oryza alta. The SDS-PAGE profile of total protein and insertion sequence-based polymerase chain reaction (IS-PCR) fingerprinting grouped the isolates into four clusters (I-IV). The 16S rRNA gene sequence homology of the representative strains B21, B31, B1, and B23 of clusters I, II, III, and IV were assigned to Pseudomonas oleovorans (99.2% similarity), Burkholderia fungorum (99.4% similarity), Enterobacter cloacae (98.9% similarity), and Acinetobacter johnsonii (98.4% similarity), respectively. The results showed wide genetic diversity of the putative diazotrophic strains of the wild rice, O. alta, and the strains of cluster IV are the first report of nitrogen-fixing Acinetobacter species. The cell size, phenotypic characters, total protein profile, genomic DNA fingerprinting, DNA-DNA hybridization, and antibiotic resistance differentiated strain B23 from its closest relatives A. johnsonii LMG999 and Acinetobacter haemolyticus LMG996. The DNA-DNA hybridization also distinguished the strain B23 from the closely related Acinetobacter species. Based on these data, a novel species, Acinetobacter oryzae sp. nov., and strain B23 (=LMG25575 = CGMCC1.10689) as the type strain were proposed. [ABSTRACT FROM AUTHOR]

Organizations’ own personnel now have a greater ability than ever before to misuse their access to critical organizational assets. Insider threat detection is a key component in identifying rare anomalies in context, which is a growing concern for many organizations. Existing perimeter security mechanisms are proving to be ineffective against insider threats. As a prospective filter for the human analysts, a new deep learning based insider threat detection method that uses the Dempster-Shafer theory is proposed to handle both accidental as well as intentional insider threats via organization’s channels of communication in real time. The long short-term memory (LSTM) architecture together with multi-head attention mechanism is applied in this work to detect anomalous network behavior patterns. Furthermore, belief is updated with Dempster’s conditional rule and utilized to fuse evidence to achieve enhanced prediction. The CERT Insider Threat Dataset v6.2 is used to train the behavior model. Through performance evaluation, our proposed method is proven to be effective as an insider threat detection technique. [ABSTRACT FROM AUTHOR]