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Particle Impact Drilling (PID) is a new technology to effectively improve the rate of penetration (ROP) for oil and gas drilling in hard and strongly abrasive formations. In this paper, numerical simulation method is used to analyse the motion characteristics and the modulation method of particle swarm in high-pressure tank for the particle injection system based on differential pressure ejection in PID. The numerical simulation results show that: when there is no modulation elements, the motion of particle swarm in the high-pressure tank follows an asymmetric funnel flow with pulsating state, which could be divided into vertical flow domain, fast flow domain, slow flow domain and stagnation domain. The unstable dynamic arching effect of the funnel flow, the viscous effect of the liquid bridge force and the collapsing effect of the particle swarm could probably lead to the blockage of the discharge port of the high-pressure tank. When the semiapex angles of the high-pressure tank decreases, the volume flow rate of particles increases and the stagnation domain becomes smaller, but it becomes easier to form arching and blockage. The modelling results indicate that the pulsation of the funnel flow is minimum when the semiapex angle is 45° without the mutilation element, which means the funnel flow of the particle swarm is relatively stable. By introducing a conical modulating element above the discharge port, the unstable funnel flow of the particle swarm could be transformed to an overall uniform flow. The modelling results indicate that the installation height of the modulation element has the greatest influence on the pulsation degree. The optimized parameters for the conical modulation element based on numerical modelling tests are 70° for the vertex angle, 35 mm for the length of the flank and 70 mm for the installation heigh.

Background: Prostate cancer (PCa) is a commonly diagnosed malignant cancer and is the second- highest cause of cancer death in men worldwide. Enzalutamide is the second-generation inhibitor of androgen receptor signaling and is the fundamental drug for the treatment of advanced PCa. However, the disease will eventually progress to metastatic castration-resistant prostate cancer (CRPC) and aggressive neuroendocrine prostate cancer (NEPC) because of androgen-deprivation therapy (ADT) resistance. The aim of the study was to investigate the role of long non-coding RNA (lncRNA) AFAP1-AS1 in ADT resistance. Methods: Quantitative real-time PCR analysis (qPCR) was used to assess the expression of AFAP1-AS1 in PCa cell lines and tissues. Cell proliferation and invasion were assessed after AFAP1-AS1 knockdown using Cell Counting Kit (CCK)-8 and Transwell assay, respectively. A dual-luciferase reporter gene assay was carried out to validate the regulatory relationship among AFAP1-AS1, microRNA (miR)-15b, and insulin-like growth factor1 receptor (IGF1R). Results: AFAP1-AS1 level was markedly increased in castration-resistant C4-2 cells and NE-like cells (PC3, DU145, and NCI-H660), compared with androgen-sensitive LNCaP cells. Enzalutamide treatment increased the expression of AFAP1-AS1 in vitro and in vivo. Functionally, AFAP1-AS1 knockdown repressed tumor cell proliferation and invasion. Mechanistically, AFAP1-AS1 functioned as an oncogene in PCa through binding to miR-15b and destroying its tumor suppressor function. Finally, we identified that AFAP1-AS1 up-regulated IGF1R expression by competitively binding to miR-15b to de-repress IGF1R. Conclusion: AFAP1-AS1 facilitates PCa progression by regulating miR-15b/IGF1R axis, indicating that AFAP1-AS1 may serve as a diagnostic biomarker and therapeutic target for PCa.

Background Gastric carcinoma (GC) ranks the fifth most common cancer worldwide, with high incidence and mortality rates. Numerous microRNAs (miRNAs), including miR-654-5p, have been implicated in the pathophysiological processes of tumorigenesis. Nevertheless, the mechanism of miR-654-5p in GC is unclear. Objectives Our study is devoted to exploring the function and molecular mechanism of miR-654-5p on the malignant cell behaviors of GC. Methods The gene expression was detected by reverse transcription quantitative polymerase chain reaction. GC cell proliferation and motion were assessed by colony formation assay and transwell assay. The binding capacity between miR-654-5p and G protein-regulated inducer of neurite outgrowth 1 (GPRIN1) was explored by luciferase reporter and RNA pulldown assays. The protein levels were detected by Western blotting. Results miR-654-5p expression was higher in GC cells and tissues than control cells and tissues. miR-654-5p promoted GC cell growth and motion. Moreover, our findings showed that miR-654-5p was bound with GPRIN1. Importantly, downregulation of GPRIN1 rescued the inhibitory influence of miR-654-5p knockdown on GC cell malignant behaviors. Additionally, miR-654-5p activated the nuclear factor kappa-B (NF-κB) pathway by regulation of GPRIN1. Conclusions miR-654-5p facilitated cell proliferation, migration, and invasion in GC via targeting the GPRIN1 to activate the NF-κB pathway.

We present the design, fabrication, and characterization of suspended arrays of small volume, high quality factor (Q) silicon nitride photonic crystal nanobeam (PCNB) cavities with lateral nanorod fin structures. By controlling the alignment position of the fins with respect to the air holes, the resonance wavelength and Q-factor of the PCNB cavities can be tuned to realize the desired performance. Measured tunable range of 2 × 104 and 10 nm is achieved for Q-factor and resonance wavelength, respectively, with the highest Q-factor measured at 2.5 × 104. Incorporating such nanorod fins into the nanobeam cavity is demonstrated to provide improved mechanical support, thermal transport, and channels of lateral carrier injection for the suspended PCNB. The proposed PCNB cavities with lateral fins are advantageous for energy efficient, ultra-compact lasers, modulators, filters, and sensors.

Automatic seizure onset detection plays an important role in epilepsy diagnosis. In this paper, a novel seizure onset detection method is proposed by combining empirical mode decomposition (EMD) of long-term scalp electroencephalogram (EEG) with common spatial pattern (CSP). First, wavelet transform (WT) and EMD are employed on EEG recordings respectively for filtering pre-processing and time-frequency decomposition. Then CSP is applied to reduce the dimension of multi-channel time-frequency representation, and the variance is extracted as the only feature. Afterwards, a support vector machine (SVM) group consisting of ten SVMs is served as a robust classifier. Finally, the post-processing is adopted to acquire a higher recognition rate and reduce the false detection rate. The results obtained from CHB-MIT database of 977 h scalp EEG recordings reveal that the proposed system can achieve a segment-based sensitivity of 97.34% with a specificity of 97.50% and an event-based sensitivity of 98.47% with a false detection rate of 0.63/h. This proposed detection system was also validated on a clinical scalp EEG database from the Second Hospital of Shandong University, and the system yielded a sensitivity of 93.67% and a specificity of 96.06%. At the event-based level, a sensitivity of 99.39% and a false detection rate of 0.64/h were obtained. Furthermore, this work showed that the CSP spatial filter was helpful to identify EEG channels involved in seizure onsets. These satisfactory results indicate that the proposed system may provide a reference for seizure onset detection in clinical applications.

Considering the compensation of thermo-optic coefficients (TOC) between GaN-based cavity and TiO2 photonic crystal (PhC) layer, we have theoretically designed a photonic crystal surface emitting laser (PCSEL) with temperature-insensitive wavelength. The structure includes a freestanding GaN membrane with embedded multiple InGaN/GaN quantum well (QW) layer and the top TiO2 PhC layer. The high refractive index (RI) of PhC and the freestanding membrane cavity structure cooperate to enable a stronger field-coupling in PhC, thus fulfill the athermal coupling condition. Interestingly, the wavelength temperature dependence is only 0.0015 nm/°C for such PCSEL with a 82% PhC confinement factor, and its athermal property is proportional to the field confinement factor in TiO2 PhC layer. To illustrate, a general formula has been developed after the investigations of thermo-optic effect and thermal expansion effect.

One of the challenges in brain-computer interface systems is obtaining motor imagery recognition from brain activities. Brain-signal decoding robustness and system performance improvement during the motor imagery process are two of the essential issues in brain-computer interface research. In conventional approaches, ineffective decoding of features and high complexity of algorithms often lead to unsatisfactory performance. A novel method for the recognition of motor imagery tasks is developed based on employing a modified S-transforms for spectro-temporal representation to characterize the behavior of electrocorticogram activities. A classifier is trained by using a support vector machine, and an optimized wrapper approach is applied to guide selection to implement the representation selection obtained. A channel selection algorithm optimizes the wrapper approach by adding a cross-validation step, which effectively improves the classification performance. The modified S-transform can accurately capture event-related desynchronization/event-related synchronization phenomena and can effectively locate sensorimotor rhythm information. The optimized wrapper approach used in this scheme can effectively reduce the feature dimension and improve algorithm efficiency. The method is evaluated on a public electrocorticogram dataset with a recognition accuracy of 98% and an information transfer rate of 0.8586 bit/trial. To verify the effect of the channel selection, both electrocorticogram and electroencephalogram data are experimentally analyzed. Furthermore, the computational efficiency of this scheme demonstrates its potential for online brain-computer interface systems in future cognitive tasks.

Background During metastasis, tumor cells metastasize from primary tumors to distant organs via the circulatory and the lymphatic systems. There is a plethora of information about metastasis through the circulatory system, however not much information is available about the tumor cells dissemination through the lymphatic system or the lymphatic microenvironment that aids in this process in breast cancer metastasis. Purpose The study designed to examine the tumor-derived secretome in lymph before reaching the draining lymph nodes. Methods Using a microsurgical technique, we have collected the lymph in transit from the primary tumor en route to the regional lymph node in animals with metastatic and non-metastatic mammary carcinoma and healthy controls. The lymph samples were subjected to LC-MS/MS analysis, bioinformatics, and pathway analysis. Results The metastatic tumor-draining lymph before its entry into the closest regional lymph node contain 26 proteins with >175-folds in abundance compared to lymph from non-metastatic tumor-bearing animals. Among these proteins were biliverdin reductase B, heat shock protein, coagulation factor XIII, lymphocytes cytosol protein 1, and aldose reductase. These proteins were not identified in the lymph from healthy animals. Pathways analysis revealed that cadherin-mediated endocytosis, acute phase response, junction signaling, gap junction, VEGF singling, and PI3K/AKT singling pathways are overrepresented in the lymph from metastatic tumor-bearing compared to the lymph from non-metastatic tumor-bearing animals. Among the significantly up-regulated proteins in the lymph from metastatic tumor-bearing animals were proteins that identified in exosomes include heat shock protein, enolase 1 alpha, S100, and biliverdin reductase B. One of the proteins significantly down-regulated in lymph from animals with metastasis is Kininogen, a known metastasis inhibitor protein. Conclusion Proteins and exosomal proteins in lymph draining a metastatic tumor are different from those in lymph draining non-metastatic tumors, and these proteins involved in pathways that regulate tumor cells migration and invasion.

Compound semiconductor heterojunctions have enabled various optoelectronic devices. Nonetheless, the formation of heterojunctions is limited by the lattice matching between the two materials. On th...

We report here an optically pumped deep UV edge emitting laser with AlGaN multiple quantum wells (MQWs) active region grown on AlN substrate by low pressure organometallic vapor phase epitaxy (LP-OMVPE) in a high-temperature reactor. The 21 period Al0.53Ga0.47N/Al0.7Ga0.3N MQWs laser structure was optically pumped using 193 nm deep UV excimer laser source. A laser peak was achieved from the cleaved facets at 280.3 nm with linewidth of 0.08 nm at room temperature with threshold power density of 320 kW/cm2. The emission is completely TE polarized and the side mode suppression ratio (SMSR) is measured to be around 14 dB at 450 kW/cm2.

Over the past years, wind energy has been considered as a promising solution for clean and sustainable energy development, but wind curtailment remains a challenge to wind power development. On this basis, utilization of non‐grid‐connected wind power becomes crucial and necessary as it can mitigate wind curtailment and improve energy efficiency. This paper proposes the collaboration strategy and optimization model of wind farm‐hybrid energy storage system (WF‐HESS) for non‐grid‐connected wind power based on battery and superconducting magnetic energy storage (SMES) whose combination can effectively cope with fluctuation and intermittence of wind input. The optimization problem is simultaneously investigated by the minimization of total cost, wind curtailment magnitude, and loss of power supply probability (LPSP). The multi‐objective particle swarm optimization (MOPSO) is introduced to find available solutions, and Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) is applied to determine the optimal one. Results are obtained for a case study, and the effectiveness and the feasibility of proposed model are verified by a comparative analysis and a sensitivity analysis. Results analysis and discussion show that the WF‐HESS model and the application of HESS have important influence on promoting utilization of non‐grid‐connected wind power and mitigating wind curtailment.

Automatic seizure detection is of great significance for epilepsy diagnosis and alleviating the massive burden caused by manual inspection of long-term EEG. At present, most seizure detection methods are highly patient-dependent and have poor generalization performance. In this study, a novel patient-independent approach is proposed to effectively detect seizure onsets. First, the multi-channel EEG recordings are preprocessed by wavelet decomposition. Then, the Convolutional Neural Network (CNN) with proper depth works as an EEG feature extractor. Next, the obtained features are fed into a Bidirectional Long Short-Term Memory (BiLSTM) network to further capture the temporal variation characteristics. Finally, aiming to reduce the false detection rate (FDR) and improve the sensitivity, the postprocessing, including smoothing and collar, is performed on the outputs of the model. During the training stage, a novel channel perturbation technique is introduced to enhance the model generalization ability. The proposed approach is comprehensively evaluated on the CHB-MIT public scalp EEG database as well as a more challenging SH-SDU scalp EEG database we collected. Segment-based average accuracies of 97.51% and 93.70%, event-based average sensitivities of 86.51% and 89.89%, and average AUC-ROC of 90.82% and 90.75% are yielded on the CHB-MIT database and SH-SDU database, respectively.

The feasibility and accuracy of several combination classification models, i.e., quadratic discriminant analysis (QDA), random forest (RF), Bernoulli naive Bayes (BNB), and support vector machine (SVM) classification models combined with either sequential feature selection (SFS) or dimensionality reduction methods, for classifying soil with laser-induced breakdown spectroscopy (LIBS) had been explored in this study. Each algorithm combination was compared to assess their classification performance. After eliminating the irrelevant features of the data using sequential feature selection (SFS), the performances were all improved for the studied four classification models, and the best accuracy reached 97.88% by SFS-SVM. The dimensions of the data were then reduced using variational autoencoder (VAE), truncated singular value decomposition (TSVD), and isometric mapping (Isomap), respectively. The classification accuracy improved for all combination models with dimensionality reduction, and impressive accuracies of 98.12% from TSVD-SVM and 98.24% from VAE-SVM were obtained. These results demonstrate an effective way to reduce uncorrelated features, high dimensionality, and redundant information in the LIBS dataset. In addition, coupling classification models with feature selection and dimensionality reduction techniques could significantly optimize the classification performance of LIBS.

We report the intrinsic linewidth in a photonic crystal surface-emitting laser diode. An intrinsic linewidth of 330 kHz was obtained without any optical feedback, one order smaller than the value of DFB lasers. The impact of cavity feedback on linewidth was also characterized.

Photonic crystal optical cavities were designed to achieve high sensitivity phase sensing with full $2\pi$ phase shift, with great potentials for high speed phase tuning in both transmissive and reflective configurations. Measurement of phase information was also demonstrated on single layer photonic crystal cavities.

This study presents a novel approach for familiar and unfamiliar face classification based on electroencephalography (EEG). Firstly, the raw EEG epoch is temporally split into three overlapped segments, and each segment is decomposed into multiple sub-bands by band-pass filters. Then, differential entropy is employed to extract discriminative EEG features. Finally, the obtained features are concatenated and classified with the support vector machine (SVM). The results yielded on our database indicate that the proposed method can achieve a mean accuracy of 76.2% over five participants. This work primarily demonstrates that differential entropy is an effective feature for EEG-based familiar and unfamiliar face classification, and has the potential to be applied to other EEG-based visual task analyses.

In this paper, a tracking algorithm is proposed for zigzag maneuver target with non-Gaussian colored glint measurement noise. By Utilizing the measurement differencing approach and the state augmentation method, the above problem is transformed into the estimating problem for a non-linear system with white heavy-tailed measurement noise. And the augmentation state vector together with the intermediate variables are simultaneously inferred by introducing variational Bayesian (VB) approach. Simulation experiment indicates that the proposed algorithm in this paper has superior accuracy than current algorithms to process non-Gaussian colored glint measurement noise for the problem of zigzag maneuver target tracking.

Photonic crystal slabs are the state of the art in studies for the light confinement, optical wave modulating and guiding, as well as nonlinear optical response. Previous studies have shown abundant real-world implementations of photonic crystals in planar optics, metamaterials, sensors, and lasers. Here, we report a novel full 2{\pi} phase control method in the reflected light beam over the interaction with a photonic crystal resonant mode, verified by the temporal coupled-mode analysis and S-parameter simulations. Enhanced by the asymmetric coupling with the output ports, the 2{\pi} phase shift can be achieved with the silicon photonics platforms such as Silicon-on-Silica and Silicon-on-Insulator heterostructures. Such photonic crystal phase control method provides a general guide in the design of phase-shift metamaterials, suggesting a wide range of applications in the field of sensing, spatial light modulation, and beam steering., Comment: Including supplementary

Free-space coupled membrane photonic devices are highly desirable for 3D optical interconnects, free space communications, imaging, sensing, and ranging applications. Based on micro transfer printing (μTP) processes, heterogeneously integrated active photonic crystal devices can be built on the common silicon platform.

Here we describe a case of multidrug-resistant tuberculous meningitis associated with injury inflicted during spinal surgery. A 54-year-old man was diagnosed with multidrug-resistant spinal tuberculosis (TB). Anterior debridement and fusion with posterior fusion and instrumentation were conducted to remove damaged bone and TB-associated granulomatous tissue. After surgical decompression, a lancet wound (about 5 mm in length) was discovered in the cerebral dura mater at the L3 position of the vertebral body. At four weeks post-surgery, the patient experienced fever, hemiparesis, and aphasia. Cerebrospinal fluid (CSF) findings were consistent with TB meningitis, while in vitro susceptibility test results confirmed that the patient had multidrug-resistant TB. Our data highlight a possible TBM infection associated with a surgical lesion in a spinal TB patient. Rapid molecular diagnostics are urgently needed to formulate efficacious regimens for treating these patients.

Photonic crystal lasers can be realized either based on photonic bandgap defect mode or defect-free bandedge mode, while the bandgap is not essential for the latter. We review here defect-free bandedge mode based photonic crystal surface-emitting lasers (PCSELs) for on-chip integration. We first discuss ultra-thin membrane reflector vertical-cavity surface-emitting lasers (MR-VCSELs), where single layer photonic crystal slabs can be designed as a broadband membrane reflector. Later, we discuss another type of defect-free PCSELs where the lasing cavity is formed based on evanescent coupling of gain medium with the photonic crystal bandedge mode near bandedge. Cavity designs were carried out for the optimal modal overlap and high confinement factors. Lateral cavity size scaling was also investigated both theoretically and experimentally in PCSELs. Buried tunnel junction based InGaAsP quantum well heterostructures were also designed and incorporated into electrically injected PCSELs. Finally, discussions are given toward energy efficient lasers.

Recently, deep learning approaches, especially convolutional neural networks (CNNs), have attracted extensive attention in iris recognition. Though CNN-based approaches realize automatic feature extraction and achieve outstanding performance, they usually require more training samples and higher computational complexity than the classic methods. This work focuses on training a novel condensed 2-channel (2-ch) CNN with few training samples for efficient and accurate iris identification and verification. A multi-branch CNN with three well-designed online augmentation schemes and radial attention layers is first proposed as a high-performance basic iris classifier. Then, both branch pruning and channel pruning are achieved by analyzing the weight distribution of the model. Finally, fast finetuning is optionally applied, which can significantly improve the performance of the pruned CNN while alleviating the computational burden. In addition, we further investigate the encoding ability of 2-ch CNN and propose an efficient iris recognition scheme suitable for large database application scenarios. Moreover, the gradient-based analysis results indicate that the proposed algorithm is robust to various image contaminations. We comprehensively evaluated our algorithm on three publicly available iris databases for which the results proved satisfactory for real-time iris recognition.

ObjectiveLow-intensity transcranial ultrasound stimulation (TUS) is a non-invasive neuromodulation technique with high spatial resolution and feasible penetration depth. To date, the mechanisms of TUS modulated neural oscillations are not fully understood. This study designed a very low acoustic intensity (AI) TUS system that produces considerably reduced AI Ultrasound pulses (ISPTA < 0.5 W/cm2) when compared to previous methods used to measure regional neural oscillation patterns under different TUS parameters.MethodsWe recorded the local field potential (LFP) of five brain nuclei under TUS with three groups of simulating parameters. Spectrum estimation, time-frequency analysis (TFA), and relative power analysis methods have been applied to investigate neural oscillation patterns under different stimulation parameters.ResultsUnder PRF, 500 Hz and 1 kHz TUS, high-amplitude LFP activity with the auto-rhythmic pattern appeared in selected nuclei when ISPTA exceeded 12 mW/cm2. With TFA, high-frequency energy (slow gamma and high gamma) was significantly increased during the auto-rhythmic patterns. We observed an initial plateau in nuclei response when ISPTA reached 16.4 mW/cm2 for RPF 500 Hz and 20.8 mW/cm2 for RPF 1 kHz. The number of responding nuclei started decreasing while ISPTA continued increasing. Under 1.5 kHz TUS, no auto-rhythmic patterns have been observed, but slow frequency power was increased during TUS. TUS inhibited most of the frequency band and generated obvious slow waves (theta and delta band) when stimulated at RPF = 1.5 kHz, ISPTA = 8.8 mW/cm2.ConclusionThese results demonstrate that very low intensity Transcranial Ultrasound Stimulation (VLTUS) exerts significant neuromodulator effects under specific parameters in rat models and may be a valid tool to study neuronal physiology.

Sequential media G5 series (Vitrolife) and single-step medium Continuous Single Culture Complete (CSC-C) (Irvine Scientific) are two different culture media. We want to examine difference between culturing effects of the two media. To compare the fertilization and early embryo development, a prospective randomized controlled trial with sibling oocytes in infertile patients, aged ≤ 45 years with ≥ 8 oocytes (226 cycles) was conducted. Each half of the retrieved oocytes from the same patient were randomly allocated to two culture media separately. The remaining fresh cycles were randomly assigned to two culture media during the same period (179 cycles). We compared the clinical outcomes based on the total fresh ET cycles in this periods, in which the transferred embryos were only from one culture medium. Embryo outcomes: 226 cycles, included 176 IVF and 50 ICSI cycles, were analyzed, which correspond to 3518 inseminated or micro-injected oocytes. Clinical outcomes: 71 (CSC-C) and 71 (G5 series) fresh ET cycles were compared. There were no significant differences in clinical outcomes and general fertilization rate. However, the fertilization rate was superior in the CSC-C when compared with G5 in ICSI cycles (76.51% vs. 67.25%, P = 0.008). In addition, the compacted embryo development rate was significantly higher in CSC-C on day 3. The cycles that had compacted embryos on day 3 demonstrated better outcomes both in embryos as well as clinically. CSC-C had higher fertilization rates than G5 series in ICSI cycles. In addition, the compaction rates of day 3 embryos were significantly higher in CSC-C.

Background and Objective Electrocardiogram (ECG) quality assessment is significant for automatic diagnosis of cardiovascular disease and reducing the massive workload of reviewing continuous ECGs. Hence, how to design an appropriate algorithm for objectively evaluating the multi-lead ECG recordings is particularly important. Despite the deep learning methods performing well in many fields, as a data-driven method, it may not be entirely suitable for ECG analysis due to the difficulty in obtaining sufficient data and the low signal-to-noise ratio of ECG recordings. In this study, with the aim of providing an accurate and automatic ECG quality assessment scheme, we propose an innovative ECG quality assessment algorithm based on hand-crafted statistical features and deep-learned spectral features. Methods In this paper, a novel approach, combining the deep-learned Stockwell transform (S-Transform) spectrogram features and hand-crafted statistical features, is proposed for ECG quality assessment. Firstly, a double-input convolutional neural network (CNN) is established. Then, the S-Transform with a novel online augmentation scheme is performed on the multi-lead raw ECG signal received from one input layer to obtain proper time-frequency representation. After that, the CNN with three convolutional layers is employed to extract robust deep-learned features automatically. Simultaneously, the hand-crafted statistical features, including lead-fall, baseline drift, and R peak features, are calculated and fed into another input layer for feature fusion training. Finally, the deep-learned and hand-crafted features are concatenated and further fused by a fully connected layer for quality classification. Furthermore, a log-odds analysis scheme combining with a gradient-based method can localize the abnormal zone in time, frequency, and spatial domains. Results and Conclusion Our proposed method is evaluated on a publicly available database with 10-fold cross-validation. The experimental results demonstrate that the proposed assessment algorithm reached a mean accuracy of 93.09%, a mean F1-score of 0.8472, and a sensitivity of 0.9767. Moreover, comprehensive experiments indicate that the fusion of CNN features and statistical features has complementary advantages and ideal interpretability, achieving end-to-end multi-lead ECG assessment with satisfying performance.

Objective and Impact Statement . Real-time monitoring of the temperatures of regional tissue microenvironments can serve as the diagnostic basis for treating various health conditions and diseases. Introduction . Traditional thermal sensors allow measurements at surfaces or at near-surface regions of the skin or of certain body cavities. Evaluations at depth require implanted devices connected to external readout electronics via physical interfaces that lead to risks for infection and movement constraints for the patient. Also, surgical extraction procedures after a period of need can introduce additional risks and costs. Methods . Here, we report a wireless, bioresorbable class of temperature sensor that exploits multilayer photonic cavities, for continuous optical measurements of regional, deep-tissue microenvironments over a timeframe of interest followed by complete clearance via natural body processes. Results . The designs decouple the influence of detection angle from temperature on the reflection spectra, to enable high accuracy in sensing, as supported by in vitro experiments and optical simulations. Studies with devices implanted into subcutaneous tissues of both awake, freely moving and asleep animal models illustrate the applicability of this technology for in vivo measurements. Conclusion . The results demonstrate the use of bioresorbable materials in advanced photonic structures with unique capabilities in tracking of thermal signatures of tissue microenvironments, with potential relevance to human healthcare.

Integrated optical E-field sensor based on nanobeam cavity is reported. Theoretical Emin is 0.15 V/m. Temperature-induced resonant wavelength variation is within 3 pm/°C in the range of -40 to 40 °C.

We show that by using a perturbed photonic Dirac-cone, one can realize ultra-narrow and finite Q-factor peak in the wavevector space, with both the peak value and the width separately tunable. We also discuss a lower bound in the minimal viable width given a peak Q-value while maintaining sufficient Q differentiation among modes. The strong angular and frequency Q-selection finds applications in optical devices where strong angle- and frequency-selection is needed., Comment: 6 pages, 4 figures

Epitaxial regrowth is investigated as a method of PCSEL fabrication and is compared to non-regrowth methods. The influence of regrowth to photonic crystal morphology is explored and optically pumped and electrically injected devices are demonstrated.

To identify precursors of epileptic seizures, an EEG characteristic analysis is carried out based on a roughness-length method, where fractal dimensions and intercept values are extracted to measure the structure complexity and the amplitude roughness of EEG signals in different phases. Using the significant changes of the fractal dimension and intercept in the preictal phase with respect to those in the interictal phase, a patient-specific seizure prediction algorithm is then proposed by combining with a gradient boosting classifier. The probabilistic outputs of the trained gradient boosting classifier are further processed by threshold comparison and rule-based judgment to distinguish preictal EEG from interictal EEG and to generate seizure alerts. The prediction algorithm was evaluated on 20 patients’ intracranial EEG recordings from the Freiburg EEG database, which contains the preictal periods of 65 seizures and 499[Formula: see text]h interictal EEG. Setting the seizure prediction horizon as 2[Formula: see text]min, averaged sensitivity values of 90.42% and 91.67% with averaged false prediction rates of 0.12/h and 0.10/h were achieved for seizure occurrence periods of 30 and 50[Formula: see text]min, respectively. These results demonstrate the ability of fractal dimension and intercept metrics in predicting the occurrence of seizures.

Static and dynamic characteristics of quantum well lasers are simulated and their dependence on volume of active region and quality factor of cavity are obtained using a three-rate equation model. Energy cost of 0.06 fJ/bit is obtained from a proposed nanobeam cavity Photonic crystal laser.

One-dimensional grating couplers based on hybrid silicon-LNOI platform for polarization-independent and high-efficient single-polarization coupling efficiencies are reported. Over 70% coupling efficiency for single polarization and 51% for polarization independent coupling designs can be achieved based on the simple uniform silicon grating design.

Background: We conducted this retrospective study to reveal the accuracy of metagenomic next-generation sequencing (mNGS) for diagnosing osteoarticular infections from fresh abscess specimens obtained from patients in an HIV-naive population.Methods: We retrospectively analyzed hospital records at three participating TB-specialized hospitals for patients admitted with suggestive diagnoses of osteoarticular tuberculosis between January 2018 and August 2019. Abscess specimens obtained from each patient were tested via pathogen culture, GeneXpert Mycobacterium tuberculosis (MTB)/rifampicin (RIF), and mNGS assay.Results: A total of 82 abscess samples were collected from patients with osteoarticular infections, including 53 cases with (64.6%) bacterial, 21 (25.6%) with mycobacterial, 7 (8.5%) with fungal, and 1 (1.2%) with actinomycetal organisms detected. Analysis of mNGS assay results identified potential pathogens in all cases, with M. tuberculosis complex (MTBC) most frequently isolated, followed by Staphylococcus aureus and Brucella melitensis. Conventional culture testing identified causative pathogens in only 48.4% of samples, a significantly lower rate than the mNGS pathogen identification rate (100%, p < 0.01). Culture-positive group specimens yielded significantly greater numbers of sequence reads than did culture-negative group specimens (p < 0.01). Of patients receiving surgical interventions and mNGS-guided treatment, 76 (92.7%) experienced favorable outcomes by the time of follow-up assessment at 3 months post-treatment. Notably, MTBC detection in two patients experiencing treatment failure suggests that they had mixed infections with MTBC and other pathogens.Conclusion: Results presented here demonstrate that mNGS has a greater pathogen detection rate in osteoarticular infections than conventional culture-based methods.

Heterogeneous integration of dissimilar materials are essential in achieving advanced functionalities in integrated photonics and electronics integrated chips. Over the years, transfer printing technique has emerged to be a viable and practical process for heterogeneous integration of functional nanomembranes of different crystalline semiconductor materials. Here we report the development of transfer printing automation tools and processes based on commercial Ficontec optical characterization and assemble tool. Custom-made polydimethylsiloxane (PDMS) stamp printing head was developed along with the automatic pick and printing processes. A wide range of materials were also investigated including thin film silicon and monolayer 2D materials. Improved yield and device performance were achieved based on the characterizations of optical and light-matter interactions.

Background Albumin-globulin ratio (AGR), a variable based on serum albumin and non-albumin proteins, has been demonstrated as a predictor of mortality in patients with malignant neoplasm. The aim of this study was to evaluate the prognostic value of AGR on peritoneal dialysis (PD) patients. Methods We retrospectively analyzed 602 incident PD patients from January 1st, 2008, to December 31st, 2017, at our center and followed them until December 31st, 2018. Kaplan-Meier curves and multivariate Cox regression models were applied to analyze the association between AGR and all-cause of mortality and cardiovascular mortality. Results The median follow-up time was 32.17 (interquartile range = 32.80) months. During follow-up, 131 (21.8%) patients died, including 57 patients (43.5%) who died due to cardiovascular diseases. Kaplan-Meier curves showed that patients with AGR > 1.26 had better rates of survival than those with AGR ≤ 1.25 (p p = 0.022 and HR: 2.01, 95% CI: 1.10–3.69, p = 0.023 respectively]. Conclusions Patients with a low AGR level had an increased all-cause and cardiovascular mortality. AGR may be a useful index in identifying patients on PD at risk for CVD and all-cause of mortality.

Background The lack of specificity and high degree of false positive and false negative rates when using mammographic screening for detecting early-stage breast cancer is a critical issue. Blood-based molecular assays that could be used in adjunct with mammography for increased specificity and sensitivity could have profound clinical impact. Our objective was to discover and independently verify a panel of candidate blood-based biomarkers that could identify the earliest stages of breast cancer and complement current mammographic screening approaches. Methods We used affinity hydrogel nanoparticles coupled with LC-MS/MS analysis to enrich and analyze low-abundance proteins in serum samples from 20 patients with invasive ductal carcinoma (IDC) breast cancer and 20 female control individuals with positive mammograms and benign pathology at biopsy. We compared these results to those obtained from five cohorts of individuals diagnosed with cancer in organs other than breast (ovarian, lung, prostate, and colon cancer, as well as melanoma) to establish IDC-specific protein signatures. Twenty-four IDC candidate biomarkers were then verified by multiple reaction monitoring (LC-MRM) in an independent validation cohort of 60 serum samples specifically including earliest-stage breast cancer and benign controls (19 early-stage (T1a) IDC and 41 controls). Results In our discovery set, 56 proteins were increased in the serum samples from IDC patients, and 32 of these proteins were specific to IDC. Verification of a subset of these proteins in an independent cohort of early-stage T1a breast cancer yielded a panel of 4 proteins, ITGA2B (integrin subunit alpha IIb), FLNA (Filamin A), RAP1A (Ras-associated protein-1A), and TLN-1 (Talin-1), which classified breast cancer patients with 100% sensitivity and 85% specificity (AUC of 0.93). Conclusions Using a nanoparticle-based protein enrichment technology, we identified and verified a highly specific and sensitive protein signature indicative of early-stage breast cancer with no false positives when assessing benign and inflammatory controls. These markers have been previously reported in cell-ECM interaction and tumor microenvironment biology. Further studies with larger cohorts are needed to evaluate whether this biomarker panel improves the positive predictive value of mammography for breast cancer detection.

Automatic seizure detection plays a significant role in monitoring and diagnosis of epilepsy. This paper presents an efficient automatic seizure detection method based on Stockwell transform (S-transform) and bidirectional long short-term memory (BiLSTM) neural networks for intracranial EEG recordings. First, S-transform is applied to raw EEG segments, and the obtained matrix is grouped into time-frequency blocks as the inputs fed into BiLSTM for feature selecting and classification. Afterwards, postprocessing is adopted to improve detection performance, which includes moving average filter, threshold judgment, multichannel fusion, and collar technique. A total of 689 h intracranial EEG recordings from 20 patients are used for evaluation of the proposed system. Segment-based assessment results show that our system achieves a sensitivity of 98.09% and specificity of 98.69%. For the event-based evaluation, a sensitivity of 96.3% and a false detection rate of 0.24/h are yielded. The satisfactory results indicate that this seizure detection approach possess promising potential for clinical practice.

A reliable seizure prediction system has important implications for improving the quality of epileptic patients’ life and opening new therapeutic possibilities for human health. In this paper, a new method combining local mean decomposition (LMD) and convolutional neural network (CNN) is proposed for seizure prediction. Firstly, the LMD is employed to decompose the raw EEG signals into a string of product functions (PFs). Subsequently, three PFs (PF2–PF4) are selected to learn the EEG features automatically using the deep CNN. In order to obtain the most important information from the features extracted by the CNN, the principal components analysis is applied to remove the redundant features. After that, these features are fed into the Bayesian linear discriminant analysis for classifying the cerebral state as interictal or preictal. The proposed method achieves a sensitivity of 87.7% with the false prediction rate of 0.25/h using intracranial EEG signals of 21 patients from a publicly available EEG dataset. The experimental results suggest that the proposed method can become a potential approach for predicting the impending seizures in clinical application.

Objectives The association between mean platelet volume/platelet count ratio (MPV/PC) and mortality has been demonstrated in various populations but not in peritoneal dialysis (PD) patients. This study investigated the association between MPV/PC and all-cause and cardiovascular mortality in PD patients. Materials and methods In this single-center retrospective cohort study, 338 patients who underwent incident PD from January 2006 to June 2014 and had baseline MPV/PC were enrolled and followed until June 30, 2015. Results The mean patient age was 50.5 ± 14.9 years (58.2% men, 22.8% diabetic). During a median follow-up of 25 (IQR 17 - 39) months, 58 patients died; 30 deaths were due to cardiovascular disease. The patients were stratified into two groups according to the PC, MPV, and MPV/PC quartiles. The all-cause and cardiovascular mortality were compared between both groups. MPV and MPV/PC and all-cause and cardiovascular mortality were significantly associated, even after adjusting for age, gender, Davies score, hemoglobin, albumin, and usage of platelet inhibitors. Only the association between baseline MPV/PC and all-cause and cardiovascular mortality was similar when we examined PC, MPV, and MPV/PC without stratification. In the multivariable adjusted model, each 0.01 increase in MPV/PC was associated with an adjusted hazard ratio (AHR) of 0.67 (95% CI, 0.51 - 0.89) for all-cause mortality and an AHR of 0.63 (95% CI, 0.42 - 0.96) for cardiovascular mortality. Conclusion MPV/PC may be a more reliable and accurate risk factor compared to MPV or PC alone. .

Visual inspection of electroencephalogram (EEG) recordings for epilepsy diagnosis is very time-consuming. Therefore, much research is devoted to developing a computer-assisted diagnostic system to relieve the workload of neurologists. In this study, a kernel version of the robust probabilistic collaborative representation-based classifier (R-ProCRC) is proposed for the detection of epileptic EEG signals. The kernel R-ProCRC jointly maximizes the likelihood that a test EEG sample belongs to each of the two classes (seizure and non-seizure), and uses the kernel function method to map the EEG samples into the higher dimensional space to relieve the problem that they are linearly non-separable in the original space. The wavelet transform with five scales is first employed to process the raw EEG signals. Next, the test EEG samples are collaboratively represented on the training sets by the kernel R-ProCRC and they are categorized by checking which class has the maximum likelihood. Finally, post-processing is deployed to reduce misjudgment and acquire more stable results. This method is evaluated on two EEG databases and yields an accuracy of 99.3% for interictal and ictal EEGs on the Bonn database. In addition, the average sensitivity of 97.48% and specificity of 96.81% are achieved from the Freiburg database. Graphical abstract Visual inspection of EEG recordings for epilepsy diagnosis is very time-consuming. Therefore, many researchers are devoted to developing a computer-assisted diagnostic system to relieve the workload of neurologists. In this paper, a kernel version of the robust probabilistic collaborative representation based classifier (R-ProCRC) is proposed for the detection of epileptic EEG signals. The kernel R-ProCRC jointly maximizes the likelihood that a test EEG sample belongs to each of the two classes, i.e., seizure and non-seizure, and uses the kernel function method to map the EEG samples into the higher dimensional space to relieve the problem that they are linearly non-separable in the original space. The main procedures of the proposed method are exhibited in the two figures as following, Fig. 1 The main procedures of the proposed method. (a) The schematic diagram of EEG classification based on the Freiburg database. (b) The detailed procedures of the kernel R-ProCRC This method has been evaluated on two different types of EEG databases and shows superior performance.

We report here the lateral cavity size scaling and confinement effects on the lasing performance in defect-free photonic crystal surface-emitting lasers (PCSEL) on silicon substrates. Hybrid PCSELs with different lateral cavity sizes and different lateral confinement geometries have been fabricated using transfer printing technology and controlled selective etching. The measured lasing properties show a strong dependence on the lateral cavity size below 100 μm. In particular, the finite lateral dimension significantly affects the laser threshold and side mode suppression ratio (SMSR) of the PCSEL devices. On the other hand, by controlling the lateral confinement using vertical etching, a reduction of the laser threshold is observed. The experimental results agree well with theoretical predictions. The work presented here can lead to ultra-compact PCSELs for on-chip integration with excellent energy efficiency.

To solve the problem of direction-of-arrival (DOA) estimation for partly calibrated array, a new gain-phase error matrix estimation scheme and a smoothed sparse signal reconstruction method tailored for the complex-valued covariance matrix are proposed. In the proposed method, DOA estimation is achieved by employing the structure of the covariance matrix for the error matrix estimation and the complex-valued gradient matrix based fast non-convexity data reconstruction. The proposed method has much faster computational speed than other sparse DOA estimation methods with partly calibrated array. In addition, simulation results show that it performs well and is independent of the errors.

Hybrid photonic integration and interfaces allow different optical materials and components to be combined on a single platform and thus are crucial to future integrated photonic systems. This feature issue covers frontier research, technologies, and perspectives in this rapidly-evolving area and aims to address the key challenges and requirements across a broad range of photonic technologies.