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Abstract The role for routine whole genome and transcriptome analysis (WGTA) for poor prognosis pediatric cancers remains undetermined. Here, we characterize somatic mutations, structural rearrangements, copy number variants, gene expression, immuno-profiles and germline cancer predisposition variants in children and adolescents with relapsed, refractory or poor prognosis malignancies who underwent somatic WGTA and matched germline sequencing. Seventy-nine participants with a median age at enrollment of 8.8 y (range 6 months to 21.2 y) are included. Germline pathogenic/likely pathogenic variants are identified in 12% of participants, of which 60% were not known prior. Therapeutically actionable variants are identified by targeted gene report and whole genome in 32% and 62% of participants, respectively, and increase to 96% after integrating transcriptome analyses. Thirty-two molecularly informed therapies are pursued in 28 participants with 54% achieving a clinical benefit rate; objective response or stable disease ≥6 months. Integrated WGTA identifies therapeutically actionable variants in almost all tumors and are directly translatable to clinical care of children with poor prognosis cancers.

Abstract Background Porcine cysticercosis, a serious zoonotic parasitic disease, is caused by the larvae of Taenia solium and has been acknowledged by the World Organization for Animal Health. The current detection methods of Cysticercus cellulosae cannot meet the needs of large-scale and rapid detection in the field. We hypothesized that the immunofluorescence chromatography test strip (ICS) for detecting Cysticercus cellulosae, according to optimization of a series of reaction systems was conducted, and sensitivity, specificity, and stability testing, and was finally compared with ELISA. This method utilizes Eu3+-labeled time-resolved fluorescent microspheres (TRFM) coupled with TSOL18 antigen to detect TSOL18 antibodies in infected pig sera. Results ICS and autopsy have highly consistent diagnostic results (n = 133), as determined by Cohen’s κ analysis (κ = 0.925). And the results showed that the proposed ICS are high sensitivity (0.9459) with specificity (0.9792). The ICS was unable to detect positive samples of other parasites. It can be stored for at least six months at 4℃. Conclusions In summary, we established a TRFM-ICS method with higher sensitivity and specificity than indirect ELISA. Results obtained from serum samples can be read within 10 min, indicating a rapid, user-friendly test suitable for large-scale field detection.

High-throughput total nucleic acid (TNA) purification methods based on solid-phase reversible immobilization (SPRI) beads produce TNA suitable for both genomic and transcriptomic applications. Even so, small RNA species, including miRNA, bind weakly to SPRI beads under standard TNA purification conditions, necessitating a separate workflow using column-based methods that are difficult to automate. Here, an SPRI-based high-throughput TNA purification protocol that recovers DNA, RNA and small RNA, called GSC-modified RLT+ Aline bead-based protocol (GRAB-ALL), which incorporates modifications to enhance small RNA recovery is presented. GRAB-ALL was benchmarked against existing nucleic acid purification workflows and GRAB-ALL efficiently purifies TNA, including small RNA, for next-generation sequencing applications in a plate-based format suitable for automated high-throughput sample preparation.

Accurate engine gas path component fault diagnosis methods are key to ensuring the reliability and safety of engine operations. At present, the effectiveness of the data-driven gas path component fault diagnosis methods has been widely verified in engineering applications. The deep stack neural network (DSN), as a common deep learning neural network, has been gaining more attention in gas path fault diagnosis studies. However, various gas path component faults with strong coupling effects could occur simultaneously, resulting the DSN method less effective for engine gas path fault diagnosis. In order to improve the prediction performance of the DSN handling multiple gas path component fault diagnosis, a sparse regularization and representation method was proposed. The sparse regularization term is used to expand the traditional deep stacking neural network in the sparse representation, and the predicted output tag is close to the target output tag through this term. The diagnosis performance of six different neural network methods were compared by various engine gas path component fault diagnosis types. The results show that the proposed sparse regularization method significantly improves the prediction performance of the DSN, with an accuracy rate 99.9% under various gas path component fault conditions, which is higher than other methods. The proposed engine gas path component fault diagnosis method can handle multiple coupling gas path faults, and help engine operators to develop maintenance plans for the purpose of engine health management.

Introduction The work was designed to investigate the effect of continuous positive airway pressure (CPAP) on hypertension in obstructive sleep apnea-hypopnea syndrome (OSAHS) patients and to elucidate the underlying mechanisms. Material and methods We examined the effect of CPAP on blood pressure and biomarkers reflecting inflammation and oxidative stress, and investigated the correlation between changes in blood pressure and the biomarkers. Results CPAP significantly improved clinic, ambulatory and home blood pressure (p < 0.05). The hypotensive effect of CPAP was positively correlated with the decrease of interleukin-6, C-reactive protein, NADPH oxidase and malonaldehyde. Conclusions CPAP has a significant antihypertensive effect on OSAHS patients, especially nocturnal hypertension, possibly by counteracting inflammation and oxidative stress.

We present a genome assembly of Caretta caretta (the Loggerhead sea turtle; Chordata, Testudines, Cheloniidae), generated from genomic data from two unrelated females. The genome sequence is 2.13 gigabases in size. The assembly has a busco completion score of 96.1% and N50 of 130.95 Mb. The majority of the assembly is scaffolded into 28 chromosomal representations with a remaining 2% of the assembly being excluded from these.

Aeroengine performance diagnosis technology is essential for ensuring flight safety and reliability. The complexity of engine performance and the strong coupling of fault characteristics make it challenging to develop accurate and efficient gas path diagnosis methods. To address these issues, this study proposes a novel digital twin framework for aeroengines that achieves the digitalization of physical systems. The mechanism model is constructed at the component level. The data-driven model is built using a particle swarm optimization–extreme gradient boosting algorithm (PSO-XGBoost). These two models are fused using the low-rank multimodal fusion method (LWF) and combined with the sparse stacked autoencoder (SSAE) to form a digital twin framework of the engine for performance diagnosis. Compared to methods that are solely based on mechanism or data, the proposed digital twin framework can effectively use mechanism and data information to improve the accuracy and reliability. The research results show that the proposed digital twin framework has an error rate of 0.125% in predicting gas path parameters and has a gas path fault diagnosis accuracy of 98.6%. Considering that the degradation cost of a typical flight mission for only one aircraft engine after 3000 flight cycles is approximately USD 209.5, the proposed method has good economic efficiency. This framework can be used to improve engine reliability, availability, and efficiency, and has significant value in engineering applications.

Abstract Radar signal deinterleaving is used to separate interleaved pulse streams in the electronic support measure (ESM) systems. The histogram methods based on the difference in time of arrival (TOA) are more mature in engineering applications than other methods because of their more effortless implementation. In the increasingly complex electronic battlefield, pulse repetition interval (PRI) jitter, and pulse missing are inevitable in the interleaved pulse stream. The TOA differences of pulse trains with PRI jitter distributes in multiple adjacent PRI bins, which results in the significant reduction of the corresponding histogram value. Furthermore, the histogram values of real PRIs vary with pulse missing, which indicates that the histogram lacks robustness to pulse missing. First‐order difference curve based on sorted TOA difference sequence, denoted as FDC‐DTOA, is proposed in this paper to overcome the shortcomings mentioned above. In this method, multi‐order TOA differences are calculated and then sorted in ascending order. Then, FDC‐DTOA is obtained after a first‐order backward difference operation. Finally, an adaptive threshold is applied to FDC‐DTOA to extract candidate PRIs. The simulation results show that the proposed method has remarkable robustness to pulse missing and excellent performance in deinterleaving the pulse stream with PRI jitter.

Mmillimeter-wave (mmWave) massive multiuser multiple-input multiple-output (MU-MIMO) system usually utilizes hybrid precoding to reduce complex hardware and high energy consumption. However, most hybrid precoding scheme consider the fully connected architecture, which may result in a significant loss of energy efficiency. In this paper, we propose a hybrid precoding scheme based on the overlapped subarray architecture (OSA), where the analog part of the MU-MIMO system is realized by a OSA network. Based on the OSA architecture, a modified generalized low rank approximation of matrices (GLRAM) based hybrid precoding scheme is designed to maximize the achievable sum-rate, where the analog precoding algorithm apply the phase alignment technology and GLRAM approach to obtain RF precoder/combiner with high array gain provided by massive MIMO system. Then, the block diagonalization (BD) technology is performed to obtain the digital precoding matrix for the multiplexing gain. Simulation results show that the modified GLRAM based hybrid precoding algorithm for OSA architecture can reduce the number of iterations. Compared with the existing hybrid precoding algorithm, it achieves higher spectral efficiency and lower complexity.

Venovenous extracorporeal membrane oxygenation is effective for maintaining gas exchange in patients with respiratory failure or severe tracheal stenosis. Perioperative anesthetic management of severe airway obstruction can be associated with ventilation or intubation difficulties. Consequently, venovenous extracorporeal membrane oxygenation could be an option for treating such patients to avoid potential risks. However, only a limited number of similar cases have been reported. Therefore, we have summarized two cases to provide theoretical and practical references for treating patients with respiratory failure or severe tracheal stenosis using extracorporeal membrane oxygenation.

To address the low location accuracy and poor robustness of existing methods, error correction to improve the Stage 2 of the original Two-Stage Weighted Least Squares (TSWLS)-based methods is proposed, which involves a robust moving source localization method with high accuracy based on Time Difference Of Arrival (TDOA) and Frequency Difference Of Arrival (FDOA) in the presence of receiver location errors. This newly proposed Stage 2 performs Taylor expansion on the nuisance variables introduced in Stage 1 to construct the error correction equation, thereby avoiding the rank deficiency problem and nonlinear mathematical operations in the original TSWLS-based methods; and improving the robustness and location accuracy of the method. Theoretical analysis indicates that the proposed method can attain the Cramer-Rao Lower Bound (CRLB) under small noise condition. Simulation results show the proposed method has stronger localization robustness and better anti-noise performance over the existing methods under the common level of receiver location and measurement error.

The exhaust gas temperature (EGT) baseline of an aeroengine is key to accurately analyzing engine health, formulating maintenance decisions and ensuring flight safety. However, due to the complex performance characteristics of aeroengine and the constraints of many external factors, it is difficult to obtain accurate non-linear features between various operating factors and EGT. In order to diagnose and forecast aeroengine performance quickly and accurately, four data-driven baseline prediction frameworks for EGT are proposed. These baseline frameworks took engine operating conditions and operating state control parameters as input variables and EGT as predicted output variables. The original data were collected from CFM56-5B engine ACARS flight data. Four typical machine learning methods, including Generalized Regression Neural Network (GRNN), Radial Basis Neural Network (RBF), Support Vector Regression (SVR) and Random Forest (RF) are trained to develop the models. Four aeroengine EGT baseline models were validated by comparing the after-flight data of another engine. The results show that the developed GRNN models have the best accuracy and computational efficiency compared with other models, and their RE and CPU calculation time on the verification set are 1.132 × 10−3 and 3.512 × 10−3 s, respectively. The developed baseline prediction frameworks can meet the needs of practical engineering applications for airlines. The methodologies developed can be employed by airlines to predict the EGT baseline for the purpose of engine performance monitoring and health management.

RNA sequencing (RNAseq) has been widely used to generate bulk gene expression measurements collected from pools of cells. Only relatively recently have single-cell RNAseq (scRNAseq) methods provided opportunities for gene expression analyses at the single-cell level, allowing researchers to study heterogeneous mixtures of cells at unprecedented resolution. Tumors tend to be composed of heterogeneous cellular mixtures and are frequently the subjects of such analyses. Extensive method developments have led to several protocols for scRNAseq but, owing to the small amounts of RNA in single cells, technical constraints have required compromises. For example, the majority of scRNAseq methods are limited to sequencing only the 3′ or 5′ termini of transcripts. Other protocols that facilitate full-length transcript profiling tend to capture only polyadenylated mRNAs and are generally limited to processing only 96 cells at a time. Here, we address these limitations and present a novel protocol that allows for the high-throughput sequencing of full-length, total RNA at single-cell resolution. We demonstrate that our method produced strand-specific sequencing data for both polyadenylated and non-polyadenylated transcripts, enabled the profiling of transcript regions beyond only transcript termini, and yielded data rich enough to allow identification of cell types from heterogeneous biological samples.

Abstract To weaken the effect of receiver location error on localization accuracy and make the localization model closer to the practical scenario, this paper considers the receiver location errors, usually neglected in prior studies into the measurement model, and proposes an algebraic method for locating a moving source using time difference of arrival (TDOA), frequency difference of arrival (FDOA), and differential Doppler rate measurements. The proposed method is based on the pseudo-linear set of equations and two-step weighted least square estimator. Only noise values of receiver locations and three types of positioning measurements are available for processing. In addition, a new Cramér-Rao lower bound (CRLB) combining TDOA, FDOA, and differential Doppler rate in the presence of receiver location errors is also derived in this paper. Theoretical analysis and simulation results both indicate that the proposed method can attain CRLB at a moderate noise level, avoid the rank deficiency problem efficiently, and achieve a significant improvement over the existing methods.

The analysis of cell-free circulating tumor DNA (ctDNA) is potentially a less invasive, more dynamic assessment of cancer progression and treatment response than characterizing solid tumor biopsies. Standard isolation methods require separation of plasma by centrifugation, a time-consuming step that complicates automation. To address these limitations, we present an automatable magnetic bead-based ctDNA isolation method that eliminates centrifugation to purify ctDNA directly from peripheral blood (PB). To develop and test our method, ctDNA from cancer patients was purified from PB and plasma. We found that allelic fractions of somatic single-nucleotide variants from target gene capture libraries were comparable, indicating that the PB ctDNA purification method may be a suitable replacement for the plasma-based protocols currently in use.

Frequency hopping (FH) signal has been widely used in military and civilian fields because of its superior anti-jamming performance. Meanwhile, its natural characteristics, i.e., hopped carrier frequency, brings a great challenge to the reconnaissance of FH emitters. To locate a high-speed moving FH emitter, the estimation of range difference (RD) and range rate difference (RRD) is a key process in passive localization. This paper proposes a joint RD and RRD estimation algorithm with the consideration of range migration (RM) during observation time. In this method, a new operation named frequency reversing transform is first presented to remove the random phase of the FH signal. Then, RD and RRD can be coherently estimated after RM correction via the scaled Fourier transform. This method accomplishes the RD and RRD estimation without knowing the hopped carrier frequencies of received FH signals, and it can be fast implemented by complex multiplications, fast Fourier transform (FFT) and inverse FFT operations without any searching process. Numerical experiments indicate that compared with several representative methods, the proposed estimation method achieves a better anti-noise performance with lower computational cost.

The presence of transmitter and receiver location uncertainties has been known to remarkably deteriorate the target localization accuracy in multi-static passive radar (MPR) system. This paper explores the use of calibration targets, the positions and velocities of which are known to the MPR system, to counter the loss in target localization accuracy arising from the transmitter/receiver location uncertainties. We firstly evaluate the Cramér-Rao lower bound (CRLB) for bistatic range (BR) and bistatic range rate (BRR)-based target localization in the presence of calibration targets, which analytically indicates the potential of calibration targets in enhancing localization accuracy. After that, a novel closed-form solution is proposed for target localization using the BR and BRR measurements from the unknown target as well as the additional BR and BRR measurements from the calibration targets. The proposed solution includes two processing steps, referred to as calibration step and localization step respectively. The calibration step is devoted to refine the inaccurate transmitter and receiver locations using the BR and BRR measurements from the calibration targets, and then the localization step is devoted to determine the target position and velocity based on the refined transmitter/receiver locations and the BR/BRR measurements from the unknown target. The accuracy of proposed solution is shown analytically to accomplish the CRLB under sufficiently small BR/BRR measurement noises and transmitter/receiver location errors. Simulation results verify the effectiveness and superiority of the proposed solution over existing algorithms.

By employing lens antenna arrays, the number of radio frequency (RF) chains in millimeter-wave (mmWave) communications can be significantly reduced. However, most existing studies consider the phase shifters (PSs) as the main components of the analog beamformer, which may result in a significant loss of energy efficiency (EE). In this paper, we propose a switch selecting network to solve this issue, where the analog part of the beamspace MIMO system is realized by a sub-connected switch selecting network rather than the PS network. Based on the proposed architecture and inspired by the cross-entropy (CE) optimization developed in machine learning, an optimal hybrid cross-entropy (HCE)-based hybrid precoding scheme is designed to maximize the achievable sum rate, where the probability distribution of the hybrid precoder is updated by minimizing CE with unadjusted probabilities and smoothing constant. Simulation results show that the proposed HCE-based hybrid precoding can not only effectively achieve the satisfied sum-rate, but also outperform the PSs schemes concerning energy efficiency.

Degradation solutions containing atrazine need to be further purified before they are discharged into the aquatic environment. With the objectives of evaluating removal capacity of the microalga Chlorella sp. toward atrazine in degradation solutions and toxicity of the degradation products, we investigated the removal efficiency (RE) and bioaccumulation of atrazine in the microalgae after an 8 d exposure to diluted degraded solutions containing 40 μg/L and 80 μg/L of atrazine as well as degradation products in the present study. Moreover, pure atrazine solutions with similar concentrations were simultaneously inoculated with the microalgae in order to distinguish the influence of the products. The photocatalytic degradation results showed that 31.4% of atrazine was degraded after 60 min, and three degradation products, desisopropyl-atrazine (DIA), desethyl-atrazine (DEA), and desethyl-desisopropyl-atrazine (DEIA) were detected. After an 8-d exposure, 83.0% and 64.3% of atrazine were removed from the degraded solutions containing 40 μg/L and 80 μg/L of atrazine, respectively. In comparison with the control, i.e., pure atrazine solution with equal concentration, Chlorella sp. in the degraded atrazine solution showed lower RE and growth rate. The photosynthetic parameters, especially performance index (PIABS), clearly displayed the differences between treatments. The values of PIABS of Chlorella sp. cultured in degradation atrazine for 8 days were significantly lower (P

Accurate and fast identification of vibration signals detected based on the phase-sensitive optical time-domain reflectometer (Φ-OTDR) is crucial in reducing the false-alarm rate of the long-distance distributed vibration warning system. This study proposes a computer vision-based Φ-OTDR multi-vibration events detection method in real-time, which can effectively detect perimeter intrusion events and reduce personnel patrol costs. Pulse accumulation, pulse cancellers, median filter, and pseudo-color processing are employed for vibration signal feature enhancement to generate vibration spatio-temporal images and form a customized dataset. This dataset is used to train and evaluate an improved YOLO-A30 based on the YOLO target detection meta-architecture to improve system performance. Experiments show that using this method to process 8069 vibration data images generated from 5 abnormal vibration activities for two types of fiber optic laying scenarios, buried underground or hung on razor barbed wire at the perimeter of high-speed rail, the system mAP@.5 is 99.5%, 555 frames per second (FPS), and can detect a theoretical maximum distance of 135.1 km per second. It can quickly and effectively identify abnormal vibration activities, reduce the false-alarm rate of the system for long-distance multi-vibration along high-speed rail lines, and significantly reduce the computational cost while maintaining accuracy.

The globally-distributed Ranidae (true frogs) are the largest frog family. Here, Hammond et al. present a draft genome of the North American bullfrog, Rana (Lithobates) catesbeiana, as a foundation for future understanding of true frog genetics as amphibian species face difficult environmental challenges.

Abstract Two floating treatment wetlands (FTWs) in experimental tanks were compared in terms of their effectiveness on removing nutrients. The results showed that the FTWs were dominated by emergent wetland plants and were constructed to remove nutrients from simulated urban stormwater. Iris pseudacorus and Thalia dealbata wetland systems were effective in reducing the nutrient. T. dealbata FTWs showed higher nutrient removal performance than I. pseudacorus FTWs. Nitrogen (N) and phosphorous (P) removal rates in water by T. dealbata FTWs were 3.95 ± 0.19 and 0.15 ± 0.01 g/m2/day, respectively. For I. pseudacorus FTWs, the TN and TP removal rates were 3.07 ± 0.15 and 0.14 ± 0.01 g/m2/day, respectively. The maximum absolute growth rate for T. dealbata corresponded directly with the maximum mean nutrient removal efficiency during the 5th stage. At harvest, N and P uptak of T. dealbata was 23.354 ± 1.366 g and 1.489 ± 0.077 g per plant, respectively, approximate twice as high as by I. pseudacorus.

Abstract The microalgae-based technology has been developed to reduce biogas slurry nutrients and upgrade biogas simultaneously. In this work, five microalgal strains named Chlorella vulgaris, Scenedesmus obliquus, Selenastrum capricornutum, Nitzschia palea, and Anabaena spiroides under mono- and co-cultivation were used for biogas upgrading. Optimum biogas slurry nutrient reduction could be achieved by co-cultivating microalgae (Chlorella vulgaris, Scenedesmus obliquus, and Nitzschia palea) with fungi using the pelletization technology. In addition, the effects of different ratio of mixed LED light wavelengths applying mixed light-emitting diode during algae strains and fungi co-cultivation on CO2 and biogas slurry nutrient removal efficiency were also investigated. The results showed that the COD (chemical oxygen demand), TN (total nitrogen), and TP (total phosphorus) removal efficiency were 85.82 ± 5.37%, 83.31 ± 4.72%, and 84.26 ± 5.58%, respectively at red: blue = 5:5 under the co-cultivation of S. obliquus and fungi. In terms of biogas upgrading, CH4 contents were higher than 90% (v/v) for all strains, except the co-cultivation with S. obliquus and fungi at red: blue = 3:7. The results indicated that co-cultivation of microalgae with fungi under mixed light wavelengths treatments was most successful in nutrient removal from wastewater and biogas upgrading.

Abstract Background RNA-Sequencing (RNA-seq) is now commonly used to reveal quantitative spatiotemporal snapshots of the transcriptome, the structures of transcripts (splice variants and fusions) and landscapes of expressed mutations. However, standard approaches for library construction typically require relatively high amounts of input RNA, are labor intensive, and are time consuming. Methods Here, we report the outcome of a systematic effort to optimize and streamline steps in strand-specific RNA-seq library construction. Results This work has resulted in the identification of an optimized messenger RNA isolation protocol, a potent reverse transcriptase for cDNA synthesis, and an efficient chemistry and a simplified formulation of library construction reagents. We also present an optimization of bead-based purification and size selection designed to maximize the recovery of cDNA fragments. Conclusions These developments have allowed us to assemble a rapid high throughput pipeline that produces high quality data from amounts of total RNA as low as 25 ng. While the focus of this study is on RNA-seq sample preparation, some of these developments are also relevant to other next-generation sequencing library types.

This study proposes an algebraic distributed source localisation algorithm that combines time difference of arrival (TDOA) and angle of arrival (AOA) measurements. The proposed algorithm uses AOAs to remove the unknown parameters in TDOA equations caused by the specially distributed structure. Then, the observation equations are transformed into a set of pseudo-linear equations and apply linear weighted least square to obtain the source position. The application of weighting matrix can lead to an approximate maximum likelihood estimator and produce a substantial improvement in source localisation accuracy. Both theoretical analysis and simulation results indicate the efficiency of the proposed algorithm and its performance can achieve the Cramer–Rao lower bound at a moderate noise level.

The target localisation in a multistatic passive radar system using bistatic range measurements from multiple transmitter–receiver pairs is considered. A novel closed-form solution employing multidimensional scaling (MDS) analysis technique is proposed, which is based on the optimisation of a cost function related to the scalar product matrix in the classical MDS framework. The proposed solution is robust to large measurement noise. Simulation results demonstrate the proposed solution achieves a significant performance improvement over existing spherical interpolation, spherical intersection and two-step weighted least squares methods, and attains the Cramer–Rao lower bound at a relatively higher measurement noise level before the threshold effect occurs.

Next generation RNA-sequencing (RNA-seq) is a flexible approach that can be applied to a range of applications including global quantification of transcript expression, the characterization of RNA structure such as splicing patterns and profiling of expressed mutations. Many RNA-seq protocols require up to microgram levels of total RNA input amounts to generate high quality data, and thus remain impractical for the limited starting material amounts typically obtained from rare cell populations, such as those from early developmental stages or from laser micro-dissected clinical samples. Here, we present an assessment of the contemporary ribosomal RNA depletion-based protocols, and identify those that are suitable for inputs as low as 1-10 ng of intact total RNA and 100-500 ng of partially degraded RNA from formalin-fixed paraffin-embedded tissues.

This paper considers the long-time coherent detection problem for maneuvering targets with jerk motion. A novel method based on product-scaled integrated cubic phase function (PSICPF) is proposed. The main strategy of PSICPF is to estimate target’s motion parameters along the slow time for each range frequency cell. In order to eliminate the coupling terms between range frequency and slow time, the scaled nonuniform fast Fourier transform (SNUFFT) is newly defined in the integrated cubic phase function (ICPF). Then, the product operation is employed to coherently synthesize the estimation results, improve the antinoise performance, and suppress the cross terms. Finally, coherent integration is achieved via keystone transform (KT) and fold factor searching. Analysis demonstrates that the SNUFFT has the same computational complexity with nonuniform fast Fourier transform (NUFFT), and thus the PSICPF could be efficiently implemented via complex multiplications, the fast Fourier transform (FFT), and NUFFT. Detailed comparisons with other representative methods in computational cost, motion parameter estimation performance, and detection ability indicate that the PSICPF could achieve a good balance between the computational cost and detection ability. Simulations and real data processing results are presented to verify the effectiveness of the proposed method.

Through the lenses of community development and social licence to operate, we consider the complex relationships between local communities and forest plantation and oil palm companies. We examine the practical challenges in implementing socially-sustainable community development (SSCD) by analyzing two corporate social investment community development projects located in West Kalimantan, Indonesia: Desa Makmur Peduli Api (integrated fire management) and Pertanian Ekologi Terpadu (ecological farming). Our study scrutinized: (i) What were the practice challenges faced by the companies in establishing SSCD?; Along with (ii) what should be done to improve how SSCD is undertaken, especially in Indonesia? We identified five challenges: (1) unresolved land conflict; (2) determining the right program; (3) building community capacity rather than providing irrelevant training; (4) a shortage of company field staff and government facilitators; and (5) managing community expectations. Better governance of SSCD will reduce conflict between affected communities and companies.

Curation and storage of formalin-fixed, paraffin-embedded (FFPE) samples are standard procedures in hospital pathology laboratories around the world. Many thousands of such samples exist and could be used for next generation sequencing analysis. Retrospective analyses of such samples are important for identifying molecular correlates of carcinogenesis, treatment history and disease outcomes. Two major hurdles in using FFPE material for sequencing are the damaged nature of the nucleic acids and the labor-intensive nature of nucleic acid purification. These limitations and a number of other issues that span multiple steps from nucleic acid purification to library construction are addressed here. We optimized and automated a 96-well magnetic bead-based extraction protocol that can be scaled to large cohorts and is compatible with automation. Using sets of 32 and 91 individual FFPE samples respectively, we generated libraries from 100 ng of total RNA and DNA starting amounts with 95-100% success rate. The use of the resulting RNA in micro-RNA sequencing was also demonstrated. In addition to offering the potential of scalability and rapid throughput, the yield obtained with lower input requirements makes these methods applicable to clinical samples where tissue abundance is limiting.

Summary: Cell fate acquisition is heavily influenced by direct interactions between master regulators and tissue-specific enhancers. However, it remains unclear how lineage-specifying transcription factors, which are often expressed in both progenitor and mature cell populations, influence cell differentiation. Using in vivo mouse liver development as a model, we identified thousands of enhancers that are bound by the master regulators HNF4A and FOXA2 in a differentiation-dependent manner, subject to chromatin remodeling, and associated with differentially expressed target genes. Enhancers exclusively occupied in the embryo were found to be responsive to developmentally regulated TEAD2 and coactivator YAP1. Our data suggest that Hippo signaling may affect hepatocyte differentiation by influencing HNF4A and FOXA2 interactions with temporal enhancers. In summary, transcription factor-enhancer interactions are not only tissue specific but also differentiation dependent, which is an important consideration for researchers studying cancer biology or mammalian development and/or using transformed cell lines. : It is unclear how key transcription factors are critical for both lineage specification during embryonic development and maintenance of a differentiated, adult phenotype. By profiling the enhancer occupancy of the key transcription factors HNF4A and FOXA2 during mouse liver development, Alder et al. have found that YAP1 can influence enhancer interactions and target gene expression levels. Enhancer switching enables HNF4A and FOXA2 to fulfill distinct roles during organ development.

Transmitter and receiver position errors have been known to significantly deteriorate target localization accuracy in a multi-static passive radar (MPR) system. This paper explores the use of calibration targets, whose positions are known to the MPR system, to counter the loss in target localization accuracy arising from transmitter/receiver position errors. This paper firstly evaluates the Cramér–Rao lower bound (CRLB) for bistatic range (BR)-based target localization with calibration targets, which analytically indicates the potential of calibration targets in enhancing localization accuracy. After that, this paper proposes a novel closed-form solution, which includes two steps: calibration step and localization step. Firstly, the calibration step is devoted to refine the inaccurate transmitter and receiver locations using the BR measurements from the calibration targets, and then in the calibration step, the target localization can be accurately achieved by using the refined transmitter/receiver positions and the BR measurements from the unknown target. Theoretical analysis and simulation results indicate that the proposed method can attain the CRLB at moderate measurement noise level, and exhibits the superiority of localization accuracy over existing algorithms.

In practical applications, the assumption of omnidirectional elements is not effective in general, which leads to the direction-dependent mutual coupling (MC). Under this condition, the performance of traditional calibration algorithms suffers. This paper proposes a new self-calibration method based on the time-frequency distributions (TFDs) in the presence of direction-dependent MC. Firstly, the time-frequency (TF) transformation is used to calculate the space-time-frequency distributions (STFDs) matrix of received signals. After that, the estimated steering vector and corresponding noise subspace are estimated by the steps of noise removing, single-source TF points extracting and clustering. Then according to the transformation relationship between the MC coefficients, steering vector and MC matrix, we deduce a set of linear equations. Finally, with two-step alternating iteration, the equations are solved by least square method in order to estimate DOA and MC coefficients. Simulations results show that the proposed algorithm can achieve direction-dependent MC self-calibration and outperforms the existing algorithms.

Current bias compensation methods for distributed localization consider the time difference of arrival (TDOA) and frequency difference of arrival (FDOA) measurements noise, but ignore the negative influence by the sensor location uncertainties on source localization accuracy. Therefore, a new bias compensation method for distributed localization is proposed to improve the localization accuracy in this paper. This paper derives the theoretical bias of maximum likelihood estimation when the sensor location errors and positioning measurements noise both exist. Using the rough estimate result by MLE to subtract the theoretical bias can obtain a more accurate source location estimation. Theoretical analysis and simulation results indicate that the theoretical bias derived in this paper matches well with the actual bias in moderate noise level so that it can prove the correctness of the theoretical derivation. Furthermore, after bias compensation, the estimate accuracy of the proposed method achieves a certain improvement compared with existing methods.

The conventional moving source localization methods are based on centralized sensors. This paper presents a moving source localization method for distributed passive sensors using TDOA and FDOA measurements. The novel method firstly uses the steepest descent algorithm to obtain a proper initial value of source position and velocity. Then, the coarse location estimation is obtained by maximum likelihood estimation (MLE). Finally, more accurate location estimation is achieved by subtracting theoretical bias, which is approximated by the actual bias using the estimated source location and noisy data measurement. Both theoretical analysis and simulations show that the theoretical bias always meets the actual bias when the noise level is small, and the proposed method can reduce the bias effectively while keeping the same root mean square error (RMSE) with the original MLE and Taylor-series method. Meanwhile, it is less sensitive to the initial guess and attains the CRLB under Gaussian TDOA and FDOA noise at a moderate noise level before the thresholding effect occurs.

A novel low-complexity robust adaptive beamforming (RAB) technique is proposed in order to overcome the major drawbacks from which the recent reported RAB algorithms suffer, mainly the high computational cost and the requirement for optimization programs. The proposed algorithm estimates the array steering vector (ASV) using a closed-form formula obtained by a subspace-based method and reconstructs the interference-plus-noise (IPN) covariance matrix by utilizing a sampling progress and employing the covariance matrix taper (CMT) technique. Moreover, the proposed beamformer only requires knowledge of the antenna array geometry and prior information of the probable angular sector in which the actual ASV lies. Simulation results demonstrate the effectiveness and robustness of the proposed algorithm and prove that this algorithm can achieve superior performance over the existing RAB methods.

LIM and SH3 protein 1 (LASP-1) is a specific focal adhesion protein that is known to be involved in numerous biological and pathological processes. LASP-1 overexpression has been described in several types of cancers, but its expression and role in clear cell renal cell cancer (ccRCC) remains unknown.Using immunohistochemistry, we analyzed LASP-1 protein expression in 216 clinicopathologically characterized ccRCC cases. We also examined LASP-1 expression in 20 paired ccRCC tissues and in 2 cell lines by real-time PCR and Western blot. Using RNA interference, we investigated the effects of LASP-1 depletion on tumor cell behavior in vitro. Statistical analyses were used to determine the associations between LASP-1 levels, tumor features and patient outcomes.LASP-1 overexpression was observed in ccRCC tissues (P

Targeting cancers with amplified or abnormally activated c-Met (hepatocyte growth factor receptor) may have therapeutic benefit based on nonclinical and emerging clinical findings. However, the eventual emergence of drug resistant tumors motivates the pre-emptive identification of potential mechanisms of clinical resistance. We rendered a MET amplified gastric cancer cell line, GTL16, resistant to c-Met inhibition with prolonged exposure to a c-Met inhibitor, PF-04217903 (METi). Characterization of surviving cells identified an amplified chromosomal rearrangement between 7q32 and 7q34 which overexpresses a constitutively active SND1-BRAF fusion protein. In the resistant clones, hyperactivation of the downstream MAPK pathway via SND1-BRAF conferred resistance to c-Met receptor tyrosine kinase inhibition. Combination treatment with METi and a RAF inhibitor, PF-04880594 (RAFi) inhibited ERK activation and circumvented resistance to either single agent. Alternatively, treatment with a MEK inhibitor, PD-0325901 (MEKi) alone effectively blocked ERK phosphorylation and inhibited cell growth. Our results suggest that combination of a c-Met tyrosine kinase inhibitor with a BRAF or a MEK inhibitor may be effective in treating resistant tumors that use activated BRAF to escape suppression of c-Met signaling.

The H3K9me3 histone modification is often found at promoter regions, where it functions to repress transcription. However, we have previously shown that 3' exons of zinc finger genes (ZNFs) are marked by high levels of H3K9me3. We have now further investigated this unusual location for H3K9me3 in ZNF genes. Neither bioinformatic nor experimental approaches support the hypothesis that the 3' exons of ZNFs are promoters. We further characterized the histone modifications at the 3' ZNF exons and found that these regions also contain H3K36me3, a mark of transcriptional elongation. A genome-wide analysis of ChIP-seq data revealed that ZNFs constitute the majority of genes that have high levels of both H3K9me3 and H3K36me3. These results suggested the possibility that the ZNF genes may be imprinted, with one allele transcribed and one allele repressed. To test the hypothesis that the contradictory modifications are due to imprinting, we used a SNP analysis of RNA-seq data to demonstrate that both alleles of certain ZNF genes having H3K9me3 and H3K36me3 are transcribed. We next analyzed isolated ZNF 3' exons using stably integrated episomes. We found that although the H3K36me3 mark was lost when the 3' ZNF exon was removed from its natural genomic location, the isolated ZNF 3' exons retained the H3K9me3 mark. Thus, the H3K9me3 mark at ZNF 3' exons does not impede transcription and it is regulated independently of the H3K36me3 mark. Finally, we demonstrate a strong relationship between the number of tandemly repeated domains in the 3' exons and the H3K9me3 mark. We suggest that the H3K9me3 at ZNF 3' exons may function to protect the genome from inappropriate recombination rather than to regulate transcription.

Massively parallel sequencing technology now provides the opportunity to sample the transcriptome of a given tissue comprehensively. Transcripts at only a few copies per cell are readily detectable, allowing the discovery of low abundance viral and bacterial transcripts in human tissue samples. Here we describe an approach for mining large sequence data sets for the presence of microbial sequences. Further, we demonstrate the sensitivity of this approach by sequencing human RNA-seq libraries spiked with decreasing amounts of an RNA-virus. At a modest depth of sequencing, viral transcripts can be detected at frequencies less than 1 in 1,000,000. With current sequencing platforms approaching outputs of one billion reads per run, this is a highly sensitive method for detecting putative infectious agents associated with human tissues.

China’s farmland tenure, characterised by a household responsibility system (HRS) and collective landownership, has contributed to the continuation of poverty and natural resource deterioration in semiarid regions. Incongruent with local ecological, social and political conditions, the HRS has been linked to rising social and political tensions. Drawing on ethnographic research in Guyuan County, North China, this paper provides peasants’ experiences of and views on the land issue and examines the linkages between land tenure, poverty and the governance of natural resources—grassland, forest land, farmland and water. It shows that an appropriate land tenure system can only be achieved if the fragmentation and individualization of the HRS are reformed through an innovative institutional design. The paper also contributes to a critical understanding of China’s agrarian reform by articulating the need for land tenure diversity serving the overall goal of sustainable land use and management and shaping sound statepeasant relations.

The eukaryotic genome is packaged as chromatin with nucleosomes comprising its basic structural unit, but the detailed structure of chromatin and its dynamic remodeling in terms of individual nucleosome positions has not been completely defined experimentally for any genome. We used ultra-high-throughput sequencing to map the remodeling of individual nucleosomes throughout the yeast genome before and after a physiological perturbation that causes genome-wide transcriptional changes. Nearly 80% of the genome is covered by positioned nucleosomes occurring in a limited number of stereotypical patterns in relation to transcribed regions and transcription factor binding sites. Chromatin remodeling in response to physiological perturbation was typically associated with the eviction, appearance, or repositioning of one or two nucleosomes in the promoter, rather than broader region-wide changes. Dynamic nucleosome remodeling tends to increase the accessibility of binding sites for transcription factors that mediate transcriptional changes. However, specific nucleosomal rearrangements were also evident at promoters even when there was no apparent transcriptional change, indicating that there is no simple, globally applicable relationship between chromatin remodeling and transcriptional activity. Our study provides a detailed, high-resolution, dynamic map of single-nucleosome remodeling across the yeast genome and its relation to global transcriptional changes.