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In this paper, the design, fabrication, and characterization of a robust MEMS thermal wind sensor utilizing ceramic film packaging are proposed. The ceramic film is fabricated by means of low temperature co-fired ceramic (LTCC) technology, achieving the excellent adhesion between silicon substrate and ceramic film without any thermal adhesive. The most important is that the adverse effects of the packaging on the performance of the thermal wind sensor can be minimized by adjusting the thickness and thermal conductivity of the ceramic film during the preparation of the ceramic slurry. The influences of thickness and thermal conductivity of ceramic film on the performance of sensor have been simulated by finite element method, and the results are in reasonable agreement with theoretical analysis. The proposed sensors were tested and calibrated in a wind tunnel. Experiments results demonstrate that the sensitivity of the proposed sensor is decreased only 4.7%, compared with the sensor without ceramic film. The maximum measurement error of the proposed sensor in wind speed and direction is 0.66 m/s and 5°, respectively. Compared with the sensor packaged with $250\mu \text{m}$ ceramic carrier, the heating power of the proposed sensor is reduced by nearly 50% and the sensitivity is increased by an order of magnitude at the same time.

Modeling of the packaged wind sensor operating on constant power (CP) mode is proposed in this paper. The proposed model is based on the theory of heat conduction and forced convection. The presented model indicates that the total output dc voltage is proportional to the square root of the wind speed, and ${V}_{x}$ , as well as ${V}_{y}$ , varies with the wind direction based on cosine function. The wind sensor is fabricated by microelectromechanical systems (MEMS) technology and packaged with the glue. The test is performed in the wind tunnel and the measured results indicate an agreement with the presented model. The relative error of voltage for the wind speed is less than 14% up to 30 m/s, while that for the wind direction is no more than 10.7% from 0° to 360°.

A self-heated double Wheatstone bridge configuration for a 2-D wind sensor is presented. Without a central heating element, eight thermistors are symmetrically arranged in two orthogonal directions, and four of them in each direction make up a Wheatstone bridge. The eight thermistors simultaneously act as the heating sources and temperature sensing elements. By applying the constant current supply to the bridge, the terminal voltage across the bridge and the bridge output voltage are utilized to measure the wind speed and direction, respectively. The 2-D wind sensor has been simulated numerically and fabricated directly on the ceramic substrate. Experiments demonstrate that the measured speed varies from 0 to 40 m/s with an error less than ±2 m/s while the measured direction spans from 0° to 360° with an error less than ±3° after calibration. The double bridge configuration greatly simplifies the measurement and control circuits for the 2-D wind sensor. [2018-0171]

This paper introduces a novel sensing element scheme to improve the accuracy of 2-D micromachined thermal wind sensor. The sensing element consists of eight heaters and eight thermistors placed on ceramic substrate. The octagon-arranged sixteen resistors are divided into two wind sensing groups, cross-type group and saltire-type group. These two groups monitor wind independently, and the final measured wind speed and direction are extracted from the measurement results of the two groups. Based on this octagon-sensing design, the sensor achieves higher accuracy for wind measurement, which is verified by the experiment. The results show that for the wind speed up to 33 m/s, the sensor has a measurement error less than ±1%, and the airflow direction over the full range of 360° is determined with a maximum error of ±1.5°. [2018-0045]

This paper presents the design, fabrication, and performance of a micromachined silicon thermal wind sensor with improved sensitivity. Deep reactive ion etching (DRIE) trenches are fabricated between the heater and the thermistors to suppress the lateral heat conduction in the chip. In addition, eight thermistors symmetrically arranged in four directions around the heater form two Wheatstone full-bridges, resulting in about 50% increase of the sensitivity with respect to four thermistors. Based on these two methods, the sensitivity of the micromachined silicon thermal wind sensor is improved remarkably, which is verified by the experiment. The results show that the measurement wind speed range is up to 33 m/s in constant voltage (CV) mode with the initial heating power of 256 mW. The sensitivity is measured to be 29.37 mV/ms−1 at the wind speed 3.3 m/s, achieving improvement of about 226%, compared with that of the traditional wind sensor. Wind direction measurement results show that airflow direction over the full range of 360° is determined with an accuracy of ±5°. [2017-0052]

In precision oncology, immune check point blockade therapy has quickly emerged as novel strategy by its efficacy, where programmed death ligand 1 (PD-L1) expression is used as a clinically validated predictive biomarker of response for the therapy. Automating pathological image analysis and accelerating pathology evaluation is becoming an unmet need. Artificial Intelligence and deep learning tools in digital pathology have been studied in order to evaluate PD-L1 expression in PD-L1 immunohistochemistry image. We proposed a Dual-scale Categorization (DSC)-based deep learning method that employed 2 VGG16 neural networks, 1 network for 1 scale, to critically evaluate PD-L1 expression. The DSC-based deep learning method was tested in a cohort of 110 patients diagnosed as non-small cell lung cancer. This method showed a concordance of 88% with pathologist, which was higher than concordance of 83% of 1-scale categorization-based method. Our results show that the DSCbased method can empower the deep learning application in digital pathology and facilitate computer-aided diagnosis.

Background:The adaptive immune response intrinsically depends on hyper variable human leukocyte antigen (HLA) genes. Correct HLA typing is crucial for successful donor-patient matching in organ transplantation and recently is also an emerging biomarker for immune checkpoint inhibitor. With the great progress on next generation sequencing (NGS) technology, sequencing accuracy and throughput have increased while the cost for data have decreased faster than Moore's Law, thus HLA tying by NGS has been widely used from laboratory to clinic. Method:We tested three NGS assay for HLA typing and two recently published HLA typing software from 24 samples. Three assays involve two target capture methods (WES and HLA target sequencing) and one amplification sequencing method. Two software involve Xhla and HLAHD, respectively. First, we compared 3 assay performance based on PE150 read length and xHLA software by both 4 alleles and 8 alleles. Second, we compared the performance of two widespread HLA typing software xHLA and HLA-HD according to different assay type, read depth, read length and speed. Results:First of all, for PE150 datasets on three assay, the accuracy rate of HLAHD is all 100%, while xhla in WES is 99%, amplicom 98.6%, target HLA sequencing 100%. On reads length evaluation, the accuracy of HLAHD decreases gradually from PE150 to PE100 and PE75, and the capture method is more sensitive to reads length than the amplification assay, while Xhla is very robust on read length. On coverage evaluation, when the amplicom sequencing data was down-sampled 500X to 10X, the accuracy of HLAHHD and XHLA is both 100% in 50X and above and decrease gradually when less than 50X. In terms of time, with the increase in sequencing depth, running time for both software increase. HLAHD prediction from fastq file is longer than that from bam file, especially when the panel data is relatively large, the time will increase exponentially. Conclusions:Our benchmarking dataset show that current HLA assay and computational methods for HLA typing generally provide satisfactory results, while different assay type matched with different software, read depth and read length do affect the performance. Citation Format: Rui Meng, Tao Zhu, Yu Gong, Yanan Chen, Hua Dong, Ye Yizhou, Fugen Li, Ping Liu, Minya Yao. Benchmarking the HLA typing performance of three HLA assay and two software [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 5116.

The brain's ability to change in response to experience is essential for healthy brain function, and abnormalities in this process contribute to a variety of brain disorders. To better understand the mechanisms by which brain circuits react to an animal's experience requires the ability to monitor the experience-dependent molecular changes in a given set of neurons, over a prolonged period of time, in the live animal. While experience and associated neural activity is known to trigger gene expression changes in neurons most of the methods to detect such changes do not allow repeated observation of the same neurons over multiple days or do not have sufficient resolution to observe individual neurons. Here, we describe a method that combines in vivo two-photon microscopy with a genetically encoded fluorescent reporter to track experience-dependent gene expression changes in individual cortical neurons over the course of day-to-day experience. One of the well-established experience-dependent genes is Activity-regulated cytoskeletal associated protein (Arc). The transcription of Arc is rapidly and highly induced by intensified neuronal activity and its protein product regulates the endocytosis of glutamate receptors and long-term synaptic plasticity. The expression of Arc has been widely used as a molecular marker to map neuronal circuits involved in specific behaviors. In most of those studies, Arc expression was detected by in situ hybridization or immunohistochemistry in fixed brain sections. Although those methods revealed that the expression of Arc was localized to a subset of excitatory neurons after behavioral experience, how the cellular patterns of Arc expression might change with multiple episodes of repeated or distinctive experiences over days was not investigated. In vivo two-photon microscopy offers a powerful way to examine experience-dependent cellular changes in the living brain. To enable the examination of Arc expression in live neurons by two-photon microscopy, we previously generated a knock-in mouse line in which a GFP reporter is placed under the control of the endogenous Arc promoter. This protocol describes the surgical preparations and imaging procedures for tracking experience-dependent Arc-GFP expression patterns in neuronal ensembles in the live animal. In this method, chronic cranial windows were first implanted in Arc-GFP mice over the cortical regions of interest. Those animals were then repeatedly imaged by two-photon microscopy after desired behavioral paradigms over the course of several days. This method may be generally applicable to animals carrying other fluorescent reporters of experience-dependent molecular changes.