Mass spectrometer apparatus for analyzing multiple fluid samples concurrently

A mass spectrometer utilizing an inlet nozzle having multiple atmospheric pressure inlets to provide multiple streams of different fluid samples such that their chemical contents can be analyzed simultaneously within a single mass spectrometer with limited or no interaction between the individual streams of sample. This capability is made possible by positioning the nozzle within a nozzle housing wherein the nozzle defines a plurality of orifices extending therethrough from the atmospheric pressure environment of the orifice inlets to the reduced air pressure environment of the nozzle outlets without allowing any mixing between the samples as they pass through the nozzle. Samples are provided to the nozzle by an electrospray ionization needle which simultaneously ionizes the fluid and supplies it to one individual nozzle orifice. Multiple electrospray ionization spray means are provided with one for each fluid sample to prevent mixing therebetween and to facilitate simultaneous analysis of different fluid samples within a mass spectrometer particularly as used within a time-of-flight mass spectrometer. The nozzle allows the samples to pass into a quadrupole ion guide which carries the sample into the detector apparatus for analysis thereof.Board of Regents, University of Texas Syste

Novel phenanthrene-degrading bacteria identified by DNA-stable isotope probing

Microorganisms responsible for the degradation of phenanthrene in a clean forest soil sample were identified by DNA-based stable isotope probing (SIP). The soil was artificially amended with either 12C- or 13C-labeled phenanthrene, and soil DNA was extracted on days 3, 6 and 9. Terminal restriction fragment length polymorphism (TRFLP) results revealed that the fragments of 219- And 241-bp in HaeIII digests were distributed throughout the gradient profile at three different sampling time points, and both fragments were more dominant in the heavy fractions of the samples exposed to the 13C-labeled contaminant. 16S rRNA sequencing of the 13C-enriched fraction suggested that Acidobacterium spp. within the class Acidobacteria, and Collimonas spp. within the class Betaproteobacteria, were directly involved in the uptake and degradation of phenanthrene at different times. To our knowledge, this is the first report that the genus Collimonas has the ability to degrade PAHs. Two PAH-RHDα genes were identified in 13C-labeled DNA. However, isolation of pure cultures indicated that strains of Staphylococcus sp. PHE-3, Pseudomonas sp. PHE- 1, and Pseudomonas sp. PHE-2 in the soil had high phenanthrene-degrading ability. This emphasizes the role of a culture-independent method in the functional understanding of microbial communities in situ

Modeling, Simulation and Implementation of a Bird-Inspired Morphing Wing Aircraft

We present a design of a bird-inspired morphing wing aircraft, including bionic research, modeling, simulation and flight experiments. Inspired by birds and activated by a planar linkage, our proposed aircraft has three key states: gliding, descending and high-maneuverability. We build the aerodynamic model of the aircraft and analyze its mechanisms to find out a group of optimized parameters. Furthermore, we validate our design by Computational Fluid Dynamics (CFD) simulation based on Lattice-Boltzmann technology and determine three phases of the planar linkage for the three states. Lastly, we manufacture a prototype and conduct flight experiments to test the performance of the aircraft.Comment: 2019 3rd International Conference on Robotics and Automation Sciences (ICRAS

Identification of Benzo[a]pyrene-metabolizing bacteria in forest soils by using DNA-based stable-isotope probing

DNA-based stable-isotope probing (DNA-SIP) was used in this study to investigate the uncultivated bacteria with benzo[a]pyrene (BaP) metabolism capacities in two Chinese forest soils (Mt. Maoer in Heilongjiang Province and Mt. Baicaowa in Hubei Province). We characterized three different phylotypes with responsibility for BaP degradation, none of which were previously reported as BaP-degrading microorganisms by SIP. In Mt. Maoer soil microcosms, the putative BaP degraders were classified as belonging to the genus Terrimonas (family Chitinophagaceae, order Sphingobacteriales), whereas Burkholderia spp. were the key BaP degraders in Mt. Baicaowa soils. The addition of metabolic salicylate significantly increased BaP degradation efficiency in Mt. Maoer soils, and the BaP-metabolizing bacteria shifted to the microorganisms in the family Oxalobacteraceae (genus unclassified). Meanwhile, salicylate addition did not change either BaP degradation or putative BaP degraders in Mt. Baicaowa. Polycyclic aromatic hydrocarbon ring-hydroxylating dioxygenase (PAH-RHD) genes were amplified, sequenced, and quantified in the DNA-SIP (13)C heavy fraction to further confirm the BaP metabolism. By illuminating the microbial diversity and salicylate additive effects on BaP degradation across different soils, the results increased our understanding of BaP natural attenuation and provided a possible approach to enhance the bioremediation of BaP-contaminated soils

Saiyan: Design and Implementation of a Low-power Demodulator for LoRa Backscatter Systems

The radio range of backscatter systems continues growing as new wireless communication primitives are continuously invented. Nevertheless, both the bit error rate and the packet loss rate of backscatter signals increase rapidly with the radio range, thereby necessitating the cooperation between the access point and the backscatter tags through a feedback loop. Unfortunately, the low-power nature of backscatter tags limits their ability to demodulate feedback signals from a remote access point and scales down to such circumstances. This paper presents Saiyan, an ultra-low-power demodulator for long-range LoRa backscatter systems. With Saiyan, a backscatter tag can demodulate feedback signals from a remote access point with moderate power consumption and then perform an immediate packet retransmission in the presence of packet loss. Moreover, Saiyan enables rate adaption and channel hopping-two PHY-layer operations that are important to channel efficiency yet unavailable on long-range backscatter systems. We prototype Saiyan on a two-layer PCB board and evaluate its performance in different environments. Results show that Saiyan achieves 5 gain on the demodulation range, compared with state-of-the-art systems. Our ASIC simulation shows that the power consumption of Saiyan is around 93.2 uW. Code and hardware schematics can be found at: https://github.com/ZangJac/Saiyan

Efficient Ambient LoRa Backscatter with On-Off Keying Modulation

Backscatter communication holds potential for ubiquitous and low-cost connectivity among low-power IoT devices. To avoid interference between the carrier signal and the backscatter signal, recent works propose a frequency-shifting technique to separate these two signals in the frequency domain. Such proposals, however, have to occupy the precious wireless spectrum that is already overcrowded, and increase the power, cost, and complexity of the backscatter tag. In this paper, we revisit the classic ON-OFF Keying (OOK) modulation and propose Aloba, a backscatter system that takes the ambient LoRa transmissions as the excitation and piggybacks the in-band OOK modulated signals over the LoRa transmissions. Our design enables the backsactter signal to work in the same frequency band of the carrier signal, meanwhile achieving flexible data rate at different transmission range. The key contributions of Aloba include: (1) the design of a low-power backscatter tag that can pick up the ambient LoRa signals from other signals. (2) a novel decoding algorithm to demodulate both the carrier signal and the backscatter signal from their superposition. We further adopt link coding mechanism and interleave operation to enhance the reliability of backscatter signal decoding. We implement Aloba and conduct head-to-head comparison with the state-of-the-art LoRa backscatter system PLoRa in various settings. The experiment results show Aloba can achieve 199.4 Kbps data rate at various distances, 52.4 times higher than PLoRa

Bacteria capable of degrading anthracene, phenanthrene, and fluoranthene as revealed by DNA based stable-isotope probing in a forest soil

Information on microorganisms possessing the ability to metabolize different polycyclic aromatic hydrocarbons (PAHs) in complex environments helps in understanding PAHs behavior in natural environment and developing bioremediation strategies. In the present study, stable-isotope probing (SIP) was applied to investigate degraders of PAHs in a forest soil with the addition of individually 13C-labeled phenanthrene, anthracene, and fluoranthene. Three distinct phylotypes were identified as the active phenanthrene-, anthracene- and fluoranthene-degrading bacteria. The putative phenanthrene degraders were classified as belonging to the genus Sphingomona. For anthracene, bacteria of the genus Rhodanobacter were the putative degraders, and in the microcosm amended with fluoranthene, the putative degraders were identified as belonging to the phylum Acidobacteria. Our results from DNA-SIP are the first to directly link Rhodanobacter- and Acidobacteria-related bacteria with anthracene and fluoranthene degradation, respectively. The results also illustrate the specificity and diversity of three- and four-ring PAHs degraders in forest soil, contributes to our understanding on natural PAHs biodegradation processes, and also proves the feasibility and practicality of DNA-based SIP for linking functions with identity especially uncultured microorganisms in complex microbial biota

Occ3D: A Large-Scale 3D Occupancy Prediction Benchmark for Autonomous Driving

Robotic perception requires the modeling of both 3D geometry and semantics. Existing methods typically focus on estimating 3D bounding boxes, neglecting finer geometric details and struggling to handle general, out-of-vocabulary objects. 3D occupancy prediction, which estimates the detailed occupancy states and semantics of a scene, is an emerging task to overcome these limitations. To support 3D occupancy prediction, we develop a label generation pipeline that produces dense, visibility-aware labels for any given scene. This pipeline comprises three stages: voxel densification, occlusion reasoning, and image-guided voxel refinement. We establish two benchmarks, derived from the Waymo Open Dataset and the nuScenes Dataset, namely Occ3D-Waymo and Occ3D-nuScenes benchmarks. Furthermore, we provide an extensive analysis of the proposed dataset with various baseline models. Lastly, we propose a new model, dubbed Coarse-to-Fine Occupancy (CTF-Occ) network, which demonstrates superior performance on the Occ3D benchmarks. The code, data, and benchmarks are released at https://tsinghua-mars-lab.github.io/Occ3D/.Comment: Accepted to NeurIPS 202

Application of Machine Learning Algorithms to Predict Central Lymph Node Metastasis in T1-T2, Non-invasive, and Clinically Node Negative Papillary Thyroid Carcinoma

Purpose: While there are no clear indications of whether central lymph node dissection is necessary in patients with T1-T2, non-invasive, clinically uninvolved central neck lymph nodes papillary thyroid carcinoma (PTC), this study seeks to develop and validate models for predicting the risk of central lymph node metastasis (CLNM) in these patients based on machine learning algorithms. Methods: This is a retrospective study comprising 1,271 patients with T1-T2 stage, non-invasive, and clinically node negative (cN0) PTC who underwent surgery at the Department of Endocrine and Breast Surgery of The First Affiliated Hospital of Chongqing Medical University from February 1, 2016, to December 31, 2018. We applied six machine learning (ML) algorithms, including Logistic Regression (LR), Gradient Boosting Machine (GBM), Extreme Gradient Boosting (XGBoost), Random Forest (RF), Decision Tree (DT), and Neural Network (NNET), coupled with preoperative clinical characteristics and intraoperative information to develop prediction models for CLNM. Among all the samples, 70% were randomly selected to train the models while the remaining 30% were used for validation. Indices like the area under the receiver operating characteristic (AUROC), sensitivity, specificity, and accuracy were calculated to test the models' performance. Results: The results showed that ~51.3% (652 out of 1,271) of the patients had pN1 disease. In multivariate logistic regression analyses, gender, tumor size and location, multifocality, age, and Delphian lymph node status were all independent predictors of CLNM. In predicting CLNM, six ML algorithms posted AUROC of 0.70–0.75, with the extreme gradient boosting (XGBoost) model standing out, registering 0.75. Thus, we employed the best-performing ML algorithm model and uploaded the results to a self-made online risk calculator to estimate an individual's probability of CLNM (https://jin63.shinyapps.io/ML_CLNM/). Conclusions: With the incorporation of preoperative and intraoperative risk factors, ML algorithms can achieve acceptable prediction of CLNM with Xgboost model performing the best. Our online risk calculator based on ML algorithm may help determine the optimal extent of initial surgical treatment for patients with T1-T2 stage, non-invasive, and clinically node negative PTC