High-accuracy optical vector network analyzer for optical notch and bandpass responses.

A high-accuracy optical vector network analyzer (OVNA) based on optical carrier-suppressed double sideband (CS-DSB) modulation is proposed and experimentally demonstrated. The ±1st-order sideband signals are generated by CS-DSB modulation and then pass through the symmetric optical device under test (DUT). The band-stop or band-pass responses can be realized by detecting and processing the double frequency of the driven RF signal. Compared with the conventional symmetrical DSB-based OVNA, the measurement accuracy is improved by eliminating the errors caused by the even-order sidebands, and the complexity is reduced as the proposed method with only one step measurement can avoid the complex postprocessing. In addition, the optical carrier is aligned to the center frequency of the DUT by employing the Pound-Drever-Hall (PDH) technique, which provides stable measurement. At the same time, the limitation that the band-pass responses cannot be measured by the traditional single-sideband (SSB)-based OVNA is overcome. Additionally, accurate magnitude and phase responses of the DUT near the optical carrier can be also achieved since the proposed OVNA is optical filter-immune. The proposed method is theoretically analyzed and verified by experiment. A Fabry-Perot (FP) interferometer serves as the symmetric DUT; the band-stop responses in a frequency range of 6 GHz are obtained with a resolution of 1.2 MHz; and the band-pass responses with the range from 0 to 13 GHz offsetting the optical carrier are also obtained. The measurement time can reach up to 30 min with high stability. The proposed OVNA offers enhanced accuracy and a stable approach for applications in photonic systems and other innovations.

Maize tassels detection: a benchmark of the state of the art.

BACKGROUND: The population of plants is a crucial indicator in plant phenotyping and agricultural production, such as growth status monitoring, yield estimation, and grain depot management. To enhance the production efficiency and liberate labor force, many automated counting methods have been proposed, in which computer vision-based approaches show great potentials due to the feasibility of high-throughput processing and low cost. In particular, with the success of deep learning, more and more deeper learning-based approaches are introduced to deal with agriculture automation. Since different detection- and regression-based counting models have distinct characteristics, how to choose an appropriate model given the target task at hand remains unexplored and is important for practitioners. RESULTS: Targeting in-field maize tassels as a representative case study, the goal of this work is to present a comprehensive benchmark of state-of-the-art object detection and object counting methods, including Faster R-CNN, YOLOv3, FaceBoxes, RetinaNet, and the leading counting model of maize tassels-TasselNet. We create a Maize Tassel Detection Counting (MTDC) dataset by supplementing bounding box annotations to the Maize Tassels Counting (MTC) dataset to allow the training of detection models. We investigate key factors effecting the practical applications of the models, such as convergence behavior, scale robustness, speed-accuracy trade-off, as well as parameter sensitivity. Based on our benchmark, we summarise the advantages and limitations of each method and suggest several possible directions to improve current detection- and regression-based counting approaches to benefit next-generation intelligent agriculture. CONCLUSIONS: Current state-of-the-art detection- and regression-based counting approaches can all achieve a relatively high degree of accuracy when dealing with in-field maize tassels, with at least 0.85 R 2 values and 28.2% rRMSE error. While detection-based methods are more robust than regression-based methods in scale variations and can infer extra information (e.g., object positions and sizes), the latter ones have significantly faster convergence behaviors and inference speed. To choose an appropriate in-filed plant counting method, accuracy, robustness, speed and some other algorithm-specific factors should be taken into account with the same priority. This work sheds light on different aspects of existing detection and counting approaches and provides guidance on how to tackle in-field plant counting. The MTDC dataset is made available at https://git.io/MTDC.

Enhanced Carrier Screening for Spinal Muscular Atrophy: Detection of Silent (SMN1: 2 + 0) Carriers Utilizing a Novel TaqMan Genotyping Method.

BACKGROUND: Individuals whose copies of the survival motor neuron 1 (SMN1) gene exist on the same chromosome are considered silent carriers for spinal muscular atrophy (SMA). Conventional screening for SMA only determines SMN1 copy number without any information regarding how those copies are arranged. A single nucleotide variant (SNV) rs143838139 is highly linked with the silent carrier genotype, so testing for this SNV can more accurately assess risk to a patient of having an affected child. METHODS: Using a custom-designed SNV-specific Taqman genotyping assay, we determined and validated a model for silent-carrier detection in the laboratory. RESULTS: An initial cohort of 21 pilot specimens demonstrated results that were 100% concordant with a reference laboratory method; this cohort was utilized to define the reportable range. An additional 177 specimens were utilized for a broader evaluation of clinical validity and reproducibility. Allelic-discrimination analysis demonstrated tight clustering of genotype groupings and excellent reproducibility, with a coefficient of variation for all genotypes ranging from 1% to 4%. CONCLUSION: The custom-developed Taqman SNV genotyping assay we tested provides a rapid, accurate, and cost-effective method for routine SMA silent-carrier screening and considerably improves detection rates of residual risk for SMA carriers.

Design and Fabrication of High Activity Retention Al-Based Composite Powders for Mild Hydrogen Generation.

Al-Bi-Sn-Cu composite powders for hydrogen generation were designed from the calculated phase diagram and prepared by the gas atomization process. The morphologies and structures of the composite powders were investigated using X-ray diffraction (XRD) and a scanning electron microscope (SEM) equipped with energy-dispersive X-ray (EDX) spectroscopy, and the results indicate that the Cu additive enhanced the phase separation between the Al-rich phase and the (Bi, Sn)-rich phase. The hydrogen generation performances were investigated by reacting the materials with distilled water. The Al-Bi-Sn-Cu powders reveal a stable hydrogen generation rate, and the Al-10Bi-7Sn-3Cu (wt%) powder exhibits the best hydrogen generation performance in 50 °C distilled water which reaches 856 mL/g in 800 min. In addition, the antioxidation properties of the powders were also studied. The Al-10Bi-7Sn-3Cu (wt%) powder has a good resistance to oxidation and moisture, which shows great potential for being the hydrogen source for fuel cell applications.

No evidence for association between 19 cholinergic genes and bipolar disorder.

Cholinergic dysfunction has been proposed for the pathogenesis of bipolar disorder (BD), and we have therefore performed a systematic association study of cholinergic system genes in BD (including schizoaffective disorder bipolar type). We genotyped 93 single nucleotide polymorphisms (SNPs) in 19 genes (CHAT, CHRM1-5, CHRNA1-7, CHRNA9, CHRNA10, and CHRNB1-4) in two series of samples: the National Institute of Mental Health (NIMH) Genetics Initiative pedigrees with 474 samples from 152 families, and the Clinical Neurogenetics (CNG) pedigrees with 83 samples from 22 multiplex families. Sib-transmission/disequilibrium test (sib_TDT) analysis showed nominally significant transmission bias for four SNPs (CHRNA2: rs7017417, P = 0.024; CHRNA5: rs514743, P = 0.031; CHRNB1: rs2302762, P = 0.049; CHRNB4: rs1948, P = 0.031). Haploview analyses showed nominally significant transmission bias of several haplotypes in CHRNA2, CHRNA7, CHRNB1, and CHRNB4, respectively. However, none of these associations reached gene-wide significance after correction by permutation. Alcohol dependence (including alcohol abuse) was not a significant covariate in the present genetic association analysis. Thus, it is unlikely that these 19 cholinergic genes play a major role in the pre-disposition to BD in these pedigrees.