Strategic approaches for designing yeast strains as protein secretion and display platforms.

Yeast has been established as a versatile platform for expressing functional molecules, owing to its well-characterized biology and extensive genetic modification tools. Compared to prokaryotic systems, yeast possesses advanced cellular mechanisms that ensure accurate protein folding and post-translational modifications. These capabilities are particularly advantageous for the expression of human-derived functional proteins. However, designing yeast strains as an expression platform for proteins requires the integration of molecular and cellular functions. By delving into the complexities of yeast-based expression systems, this review aims to empower researchers with the knowledge to fully exploit yeast as a functional platform to produce a diverse range of proteins. This review includes an exploration of the host strains, gene cassette structures, as well as considerations for maximizing the efficiency of the expression system. Through this in-depth analysis, the review anticipates stimulating further innovation in the field of yeast biotechnology and protein engineering.

Time alignment quantum illumination based on single real-time coincidence counting.

We propose and demonstrate an improved quantum illumination protocol based on the time correlation of twin photons, for the high signal-to-noise ratio (SNR) of target detection and signal reconstruction in the strong noise environment. The Hong-Ou-Mandel (HOM) interferometer is applied after the spontaneous parametric down-conversion (SPDC) process to construct a probing twin-beam in which the photon times are precisely aligned between the beams. At the radar receiver, we put forward a single real-time coincidence counting (SRCC) method on a series of time slices to reconstruct the probe signals of pulse radar and calculate the SNR advantages against the conventional pulse radar, as well as the quantum illumination (QI) protocol. Our main achievements in this research are the realization of real-time detection of quantum information while acquiring a higher SNR than QI and classical illumination (CI) protocols, as well as its demonstration of strong robustness to noise and losses, which also proposes what we believe to be a novel way for quantum target detection.

DNA fragmentation in complicated flow fields created by micro-funnel shapes.

Micro-funnels have been widely applied to produce extensionally dominant flows for DNA manipulation, such as DNA extension for DNA mapping and DNA fragmentation for gene sequencing. However, it still lacks a systematic understanding of DNA fragmentation behaviors in complicated flow fields regulated by different funnel shapes with high flow rates. This limits the rational design and application scope of related microfluidic devices. In this study, fragmentation experiments of λ DNA were carried out in microfluidic chips with four different micro-funnel shapes, namely a sudden finish, a linear contraction, a constant acceleration, and an increasing extension rate funnel. The experimental results demonstrated a significant effect of the micro-funnel shape on the produced DNA fragment size. Then, the dynamical behaviors of DNA molecules in flow fields created by different micro-funnels were simulated using a numerical method of Brownian dynamics-computational fluid dynamics. The numerical simulation revealed that both the magnitude and distribution of the extension rate of flow fields were drastically altered by the funnel shape, and the extension rate at the micro-scale was the dominant factor of DNA fragmentation. The different DNA fragmentation behaviors in four micro-funnels were investigated from the perspectives including the fragment size distribution, fragmentation location, percentage of broken molecules, conformational type and stretched length of DNA before fragmentation. The results elucidated the significant impact of funnel shape on the dynamical behaviors of DNA fragmentation. This study offers insights into the rational design of microfluidic chips for DNA manipulation.

Investigation of doping effects of different noble metals for ethanol gas sensors based on mesoporous In2O3.

Elaborating the sensitization effects of different noble metals on In2O3has great significance in providing an optimum method to improve ethanol sensing performance. In this study, long-range ordered mesoporous In2O3has been fabricated through replicating the structure of SBA-15. Different noble metals (Au, Ag, Pt and Pd) with the same doping amount (1 at%) have been introduced by anin situdoping routine. The results of the gas sensing investigation indicate that the gas responses towards ethanol can be obviously increased by doping different noble metals. In particular, the best sensing performance towards ethanol detection can be achieved through Pd doping, and the sensors based on Pd-doped In2O3not only possess the highest response (39.0-100 ppm ethanol) but also have the shortest response and recovery times at the optimal operating temperature of 250 °C. The sensing mechanism of noble metal doped materials can be attributed to the synergetic effect combining 'catalysis' and 'electronic and chemical sensitization' of noble metals. In particular, the chemical state of the noble metal also has a great influence on the gas sensing mechanism. A detailed explanation of the enhancement of gas sensing performance through noble metal doping is presented in the gas sensing mechanism part of the manuscript.

Enhanced acetone sensing properties based onin situgrowth SnO2nanotube arrays.

Large-scale and well-alignedin situgrowth SnO2nanotube (NT) arrays have been synthesized directly on the surface of the Al2O3ceramic tube by a cost-effective template self-etching method. The morphology ofin situSnO2NTs can be adjusted by changing the concentration of urea. The structure and morphology characteristics of SnO2NT were examined via x-ray diffraction, BET, and scanning electron microscopy, respectively. A series of detections were carried out to evaluate the gas sensing performances. The results indicated thatin situgrowth SnO2NT arrays sensor exhibited an excellent response (S = 20.3), good linearity under the concentration range of ppm level (5-300 ppm), and outstanding selectivity to 100 ppm of acetone gas. Compared with the sensors fabricated by a slurry-coating method, the controllablein situassembled SnO2NT arrays exhibited a more stable structure and easier fabrication process. The high acetone sensing performance might due to the unique hollow structure and favorable orientation growth. The dominant sensing mechanism about thein situgrowth SnO2NT arrays sensor has been discussed in detail. It is expected thatin situgrowth SnO2NT arrays sensor with the general working principle and controllable growth strategy will become a promising functional material in monitoring and detecting acetone.

A Novel Smooth Variable Structure Smoother for Robust Estimation.

The smooth variable structure filter (SVSF) is a new-type filter based on the sliding-mode concepts and has good stability and robustness in overcoming the modeling uncertainties and errors. However, SVSF is insufficient to suppress Gaussian noise. A novel smooth variable structure smoother (SVSS) based on SVSF is presented here, which mainly focuses on this drawback and improves the SVSF estimation accuracy of the system. The estimation of the linear Gaussian system state based on SVSS is divided into two steps: Firstly, the SVSF state estimate and covariance are computed during the forward pass in time. Then, the smoothed state estimate is computed during the backward pass by using the innovation of the measured values and covariance estimate matrix. According to the simulation results with respect to the maneuvering target tracking, SVSS has a better performance compared with another smoother based on SVSF and the Kalman smoother in different tracking scenarios. Therefore, the SVSS proposed in this paper could be widely applied in the field of state estimation in dynamic system.

Adaptive Separation of Respiratory and Heartbeat Signals among Multiple People Based on Empirical Wavelet Transform Using UWB Radar.

The non-contact monitoring of vital signs by radar has great prospects in clinical monitoring. However, the accuracy of separated respiratory and heartbeat signals has not satisfied the clinical limits of agreement. This paper presents a study for automated separation of respiratory and heartbeat signals based on empirical wavelet transform (EWT) for multiple people. The initial boundary of the EWT was set according to the limited prior information of vital signs. Using the initial boundary, empirical wavelets with a tight frame were constructed to adaptively separate the respiratory signal, the heartbeat signal and interference due to unconscious body movement. To verify the validity of the proposed method, the vital signs of three volunteers were simultaneously measured by a stepped-frequency continuous wave ultra-wideband (UWB) radar and contact physiological sensors. Compared with the vital signs from contact sensors, the proposed method can separate the respiratory and heartbeat signals among multiple people and obtain the precise rate that satisfies clinical monitoring requirements using a UWB radar. The detection errors of respiratory and heartbeat rates by the proposed method were within ±0.3 bpm and ±2 bpm, respectively, which are much smaller than those obtained by the bandpass filtering, empirical mode decomposition (EMD) and wavelet transform (WT) methods. The proposed method is unsupervised and does not require reference signals. Moreover, the proposed method can obtain accurate respiratory and heartbeat signal rates even when the persons unconsciously move their bodies.

An Accurate and Comprehensive Clinical Sequencing Assay for Cancer Targeted and Immunotherapies.

BACKGROUND: Incorporation of next-generation sequencing (NGS) technology into clinical utility in targeted and immunotherapies requires stringent validation, including the assessment of tumor mutational burden (TMB) and microsatellite instability (MSI) status by NGS as important biomarkers for response to immune checkpoint inhibitors. MATERIALS AND METHODS: We designed an NGS assay, Cancer Sequencing YS panel (CSYS), and applied algorithms to detect five classes of genomic alterations and two genomic features of TMB and MSI. RESULTS: By stringent validation, CSYS exhibited high sensitivity and predictive positive value of 99.7% and 99.9%, respectively, for single nucleotide variation; 100% and 99.9%, respectively, for short insertion and deletion (indel); and 95.5% and 100%, respectively, for copy number alteration (CNA). Moreover, CSYS achieved 100% specificity for both long indel (50-3,000 bp insertion and deletion) and gene rearrangement. Overall, we used 33 cell lines and 208 clinical samples to validate CSYS's NGS performance, and genomic alterations in clinical samples were also confirmed by fluorescence in situ hybridization, immunohistochemistry, and polymerase chain reaction (PCR). Importantly, the landscape of TMB across different cancers of Chinese patients (n = 3,309) was studied. TMB by CSYS exhibited a high correlation (Pearson correlation coefficient r = 0.98) with TMB by whole exome sequencing (WES). MSI measurement showed 98% accuracy and was confirmed by PCR. Application of CSYS in a clinical setting showed an unexpectedly high occurrence of long indel (6.3%) in a cohort of tumors from Chinese patients with cancer (n = 3,309), including TP53, RB1, FLT3, BRCA2, and other cancer driver genes with clinical impact. CONCLUSION: CSYS proves to be clinically applicable and useful in disclosing genomic alterations relevant to cancer target therapies and revealing biomarkers for immune checkpoint inhibitors. IMPLICATIONS FOR PRACTICE: The study describes a specially designed sequencing panel assay to detect genomic alterations and features of 450 cancer genes, including its overall workflow and rigorous clinical and analytical validations. The distribution of pan-cancer tumor mutational burden, microsatellite instability, gene rearrangement, and long insertion and deletion mutations was assessed for the first time by this assay in a broad array of Chinese patients with cancer. The Cancer Sequencing YS panel and its validation study could serve as a blueprint for developing next-generation sequencing-based assays, particularly for the purpose of clinical application.

Microfluidic DNA combing for parallel single-molecule analysis.

DNA combing is a widely used method for stretching and immobilising DNA molecules on a surface. Fluorescent labelling of genomic information enables high-resolution optical analysis of DNA at the single-molecule level. Despite its simplicity, the application of DNA combing in diagnostic workflows is still limited, mainly due to difficulties in analysing multiple small-volume DNA samples in parallel. Here, we report a simple and versatile microfluidic DNA combing technology (µDC), which allows manipulating, stretching and imaging of multiple, microliter scale DNA samples by employing a manifold of parallel microfluidic channels. Using DNA molecules with repetitive units as molecular rulers, we demonstrate that the µDC technology allows uniform stretching of DNA molecules. The stretching ratio remains consistent along individual molecules as well as between different molecules in the various channels, allowing simultaneous quantitative analysis of different samples loaded into parallel channels. Furthermore, we demonstrate the application of µDC to characterise UVB-induced DNA damage levels in human embryonic kidney cells and the spatial correlation between DNA damage sites. Our results point out the potential application of µDC for quantitative and comparative single-molecule studies of genomic features. The extremely simple design of µDC makes it suitable for integration into other microfluidic platforms to facilitate high-throughput DNA analysis in biological research and medical point-of-care applications.

Adaptive Segmentation of Remote Sensing Images Based on Global Spatial Information.

The problem of image segmentation can be reduced to the clustering of pixels in the intensity space. The traditional fuzzy c-means algorithm only uses pixel membership information and does not make full use of spatial information around the pixel, so it is not ideal for noise reduction. Therefore, this paper proposes a clustering algorithm based on spatial information to improve the anti-noise and accuracy of image segmentation. Firstly, the image is roughly clustered using the improved Lévy grey wolf optimization algorithm (LGWO) to obtain the initial clustering center. Secondly, the neighborhood and non-neighborhood information around the pixel is added into the target function as spatial information, the weight between the pixel information and non-neighborhood spatial information is adjusted by information entropy, and the traditional Euclidean distance is replaced by the improved distance measure. Finally, the objective function is optimized by the gradient descent method to segment the image correctly.

An Efficient Extended Targets Detection Framework Based on Sampling and Spatio-Temporal Detection.

Excellent performance, real-time and low memory requirement are three vital requirements for target detection in high resolution marine radar system. Unfortunately, many current state-of-the-art methods merely achieve excellent performance when coping with highly complex scenes. In fact, a common problem is that real-time processing, low memory requirement and remarkable detection ability are difficult to coordinate. To address this issue, we propose a novel detection framework which bases its principle on sampling and spatiotemporal detection. The framework consists of two stages, coarse detection and fine detection. Sampling-based coarse detection is designed to guarantee the real-time processing and low memory requirements by locating the area where targets may exist in advance. Different from former detection methods, multi-scan video data are utilized. In the stage of fine detection, the candidate areas are grouped into three categories: single target, dense targets and sea clutter. Different approaches for processing the different categories are implemented to achieve excellent performance. The superiority of the proposed framework beyond state-of-the-art baselines is well substantiated in this work. Low memory requirement of the proposed framework was verified by theoretical analysis. Real-time processing capability was verified by the video data of two real scenarios. Synthetic data were tested to show the improvement in tracking performance by using the proposed detection framework.

A Three-Dimensional Hough Transform-Based Track-Before-Detect Technique for Detecting Extended Targets in Strong Clutter Backgrounds.

Hough Transform (HT), which has a low sensitivity to local faults and good ability in suppressing noise and clutters, usually applies to trajectory detection in a cluttered environment. This paper describes its application for detecting the trajectories of extended targets in three-dimensional measurements, i.e., a two-dimensional positional information and its measuring time. For taking the full merits of a multi-scan, the measuring time is regarded as a variable for the time axis. This correspondence extends the HT to 3-dimensional data. Meanwhile, a three-dimensional accumulator matrix is built for the purpose of voting. The voting process is done in an iterative way by selecting the 3D-line with the most votes and removing the corresponding measurements in each step. The three dimensional Hough Transform-based extended target track-before-detect technique (3DHT-ET-TBD), proposed here, is suitable to track the extended target and non-extended target simultaneously and few false alarm trajectories arise. Both the real data and simulated data are exploited to evaluate its performance. Compared with the Gaussian Mixture Probability Hypothesis Density (GM-PHD) filter based methods and a 4DHT-TBD algorithm, the 3DHT-ET-TBD is a more promising approach for multi-extended target tracking problems due to its high efficiency and low computation, especially in situations where the noise and false alarms are considerably high but few measurements are generated by the extended targets.

Modelling DNA extension and fragmentation in contractive microfluidic devices: a Brownian dynamics and computational fluid dynamics approach.

Fragmenting DNA into short pieces is an essential manipulation in many biological studies, ranging from genome sequencing to molecular diagnosis. Among various DNA fragmentation methods, microfluidic hydrodynamic DNA fragmentation has huge advantages especially in terms of handling small-volume samples and being integrated into automatic and all-in-one DNA analysis equipment. Despite the fast progress in experimental studies and applications, a systematic understanding of how DNA molecules are distributed, stretched and fragmented in a confined microfluidic field is still lacking. In this work, we investigate the extension and fragmentation of DNA in a typical contractive microfluidic field, which consists of a shear flow-dominated area and an elongational flow-dominated area, using the Brownian dynamics-computational fluid dynamics method. Our results show that the shear flow at the straight part of the microfluidic channel and the elongational flow at the contractive bottleneck together determine the performance of DNA fragmentation. The average fragment size of DNA decreases with the increase of the strain rate of the elongational flow, and the upstream shear flow can significantly precondition the conformation of DNA to produce shorter and more uniform fragments. A systematic study of the dynamics of DNA fragmentation shows that DNA tends to break at the mid-point when the strain rate of elongational flow is small, and the breakage point largely deviates from the midpoint as the strain rate increases. Our simulation of the thorough DNA fragmentation process in a realistic microfluidic field agrees well with experimental results. We expect that our study can shed new light on the development of future microfluidic devices for DNA fragmentation and integrated DNA analysis devices.

A Carrier Estimation Method Based on MLE and KF for Weak GNSS Signals.

Maximum likelihood estimation (MLE) has been researched for some acquisition and tracking applications of global navigation satellite system (GNSS) receivers and shows high performance. However, all current methods are derived and operated based on the sampling data, which results in a large computation burden. This paper proposes a low-complexity MLE carrier tracking loop for weak GNSS signals which processes the coherent integration results instead of the sampling data. First, the cost function of the MLE of signal parameters such as signal amplitude, carrier phase, and Doppler frequency are used to derive a MLE discriminator function. The optimal value of the cost function is searched by an efficient Levenberg-Marquardt (LM) method iteratively. Its performance including Cramér-Rao bound (CRB), dynamic characteristics and computation burden are analyzed by numerical techniques. Second, an adaptive Kalman filter is designed for the MLE discriminator to obtain smooth estimates of carrier phase and frequency. The performance of the proposed loop, in terms of sensitivity, accuracy and bit error rate, is compared with conventional methods by Monte Carlo (MC) simulations both in pedestrian-level and vehicle-level dynamic circumstances. Finally, an optimal loop which combines the proposed method and conventional method is designed to achieve the optimal performance both in weak and strong signal circumstances.

Clustered Multi-Task Learning for Automatic Radar Target Recognition.

Model training is a key technique for radar target recognition. Traditional model training algorithms in the framework of single task leaning ignore the relationships among multiple tasks, which degrades the recognition performance. In this paper, we propose a clustered multi-task learning, which can reveal and share the multi-task relationships for radar target recognition. To further make full use of these relationships, the latent multi-task relationships in the projection space are taken into consideration. Specifically, a constraint term in the projection space is proposed, the main idea of which is that multiple tasks within a close cluster should be close to each other in the projection space. In the proposed method, the cluster structures and multi-task relationships can be autonomously learned and utilized in both of the original and projected space. In view of the nonlinear characteristics of radar targets, the proposed method is extended to a non-linear kernel version and the corresponding non-linear multi-task solving method is proposed. Comprehensive experimental studies on simulated high-resolution range profile dataset and MSTAR SAR public database verify the superiority of the proposed method to some related algorithms.

Doppler shift and ambiguity velocity caused by relative motion in quantum-enhanced measurement.

We study the effect of relative motion on a frequency-entangled-based ranging scheme. Two major puzzles arise, i.e., Doppler shift and ambiguity velocity. During condition of rapid relative motion, Doppler shift invalidates the measurement result of this scheme; while during condition of slow relative motion, the ambiguity velocity turns into a major limitation. If relative speed between targets and measurement platform exceeds the ambiguity velocity, an accumulated profile obtained by the coincidence measurement will be distorted, which causes a lower ranging accuracy. Theoretical analysis shows a time-varying delay can be introduced to solve the two major puzzles.

Prognostic value of microRNA-10b overexpression in peripheral blood mononuclear cells of nonsmall-cell lung cancer patients.

We examined microRNA (miRNA)-10b expression in peripheral blood mononuclear cells (PBMCs) of nonsmall-cell lung cancer (NSCLC) patients for its clinical value. A group of 74 patients confirmed with NSCLC were recruited as case group and 52 healthy volunteers as control group. PBMCs were isolated from all subjects, and miRNA-10b expression level in these cells was measured by reverse transcription quantitative real-time polymerase chain reaction (qRT-PCR). The correlation between miRNA-10b expression levels and the clinical and pathological characteristics of NSCLC was obtained. The miRNA-10b expression level in NSCLS patients is markedly higher than control subjects (P < 0.01). Analysis of receiver operating characteristic (ROC) curve estimated the peak diagnostic sensitivity of miRNA-10b at 86.5 % and specificity at 76.9 %. NSCLC patients were divided into high expression group (64 patients) and low expression group (10 patients). Further analysis showed that miRNA-10b expression levels in PBMCs correlated with lymph node metastasis, distant metastasis, and TNM classification (all P < 0.05). The 5-year survival rate in high expression group was significantly lower than low expression group (P = 0.017). Multivariate analysis by Cox regression model showed that high miRNA-10b expression, age >60 years, lymph node and distant metastases, and stage III-IV carcinoma were risk factors for poor prognosis in NSCLC patients (all P < 0.05). MiRNA-10b expression levels in PBMCs can distinguish NSCLC patients from cancer-free subjects with a high sensitivity and specificity, suggesting that miRNA-10b expression in PBMCs is a valuable diagnostic and prognostic marker in NSCLC.

An Autonomous Star Identification Algorithm Based on One-Dimensional Vector Pattern for Star Sensors.

In order to enhance the robustness and accelerate the recognition speed of star identification, an autonomous star identification algorithm for star sensors is proposed based on the one-dimensional vector pattern (one_DVP). In the proposed algorithm, the space geometry information of the observed stars is used to form the one-dimensional vector pattern of the observed star. The one-dimensional vector pattern of the same observed star remains unchanged when the stellar image rotates, so the problem of star identification is simplified as the comparison of the two feature vectors. The one-dimensional vector pattern is adopted to build the feature vector of the star pattern, which makes it possible to identify the observed stars robustly. The characteristics of the feature vector and the proposed search strategy for the matching pattern make it possible to achieve the recognition result as quickly as possible. The simulation results demonstrate that the proposed algorithm can effectively accelerate the star identification. Moreover, the recognition accuracy and robustness by the proposed algorithm are better than those by the pyramid algorithm, the modified grid algorithm, and the LPT algorithm. The theoretical analysis and experimental results show that the proposed algorithm outperforms the other three star identification algorithms.

Novel X-ray Communication Based XNAV Augmentation Method Using X-ray Detectors.

The further development of X-ray pulsar-based NAVigation (XNAV) is hindered by its lack of accuracy, so accuracy improvement has become a critical issue for XNAV. In this paper, an XNAV augmentation method which utilizes both pulsar observation and X-ray ranging observation for navigation filtering is proposed to deal with this issue. As a newly emerged concept, X-ray communication (XCOM) shows great potential in space exploration. X-ray ranging, derived from XCOM, could achieve high accuracy in range measurement, which could provide accurate information for XNAV. For the proposed method, the measurement models of pulsar observation and range measurement observation are established, and a Kalman filtering algorithm based on the observations and orbit dynamics is proposed to estimate the position and velocity of a spacecraft. A performance comparison of the proposed method with the traditional pulsar observation method is conducted by numerical experiments. Besides, the parameters that influence the performance of the proposed method, such as the pulsar observation time, the SNR of the ranging signal, etc., are analyzed and evaluated by numerical experiments.

Nanoscale probing the kinetics of oriented bacterial cell growth using atomic force microscopy.

Probing oriented bacterial cell growth on the nanoscale: A novel open-top micro-channel is developed to facilitate the AFM imaging of physically trapped but freely growing bacteria. The growth curves of individual Escherichia coli cells with nanometer resolution and their kinetic nano-mechanical properties are quantitatively measured.