Detection of saliva on combustible and electronic cigarettes using the SERATEC Amylase Test and subsequent DNA analysis

Saliva can be detected on items including cigarette butts, glassware, clothing, human skin and condoms, and the identification of saliva on these types of evidence may be important to provide linkages or investigative leads in forensic cases. Sometimes when the presence of saliva is indicated, the item will be sent for deoxyribonucleic acid (DNA) analysis and may be used for identification of individuals involved in a crime. The detection of saliva mostly depends on the activity and the presence of amylase. The SERATEC® Amylase Test (SERATEC GmbH, Goettingen, Germany) is a lateral flow immunochromatographic test that targets the presence of human α-Amylase using two monoclonal anti-human-α-Amylase antibodies. This study investigates the effectiveness of using the SERATEC® Amylase Test to detect amylase on cigarette butts and vaping devices. In addition, the possible correlation between the SERATEC® Amylase Test result and the amount of DNA extracted from cigarette butt samples is evaluated. Results indicated that the cigarettes and vaping devices tested had no inhibitory effect on the SERATEC® Amylase Test. The SERATEC® Amylase test was able to detect amylase from various brands of cigarettes, marijuana cigarettes, JUULpods™ (JUUL Labs™ Inc., San Francisco, CA) and an additional vaping device. Negative amylase test results (22 of 114 samples) may be attributable to personal smoking habits and the texture of the cigarette butt wrap paper or vaping device. DNA quantification results indicated that the majority of cellular material was retained on the wrap paper even after submersion in the SERATEC® Amylase Test buffer. It is recommended that the wrap paper from the cigarette filter and the remaining extract from preliminary testing be combined prior to DNA extraction in order to maximize total DNA recovered from a cigarette sample. The correlation between the SERATEC® Amylase Test result and the quantity of DNA extracted from the same source was not linear. The presence of saliva and DNA concentration are controlled by different factors, thus using the detection of saliva to predict the recoverability of DNA on cigarettes may be valuable in some situations, but is not precise

Dynamic response analysis of intake tower in hydroelectric power station with high surrounding rock

This paper presents results of numerical analysis for the seismic response of hydropower station intake tower in step-like ground based on artificial boundary theory. A L topography finite element model was established to verify the correctness of the proposed method of viscous elasticity boundary by consider inconsistent reflective surface. The method was applied to an intake tower, and the acceleration of bedrock was obtained by seismic inversion method, then the equivalent load of each node was calculated. Five different models were established as follow: massless foundation, consistent input viscous elasticity boundary, inconsistent input viscous elasticity boundary and whether set contact, then displacement and stress were compared, the results show that the proposed method with contact was minimal. The base plate of intake tower and the foundation were in close adhesion state in the whole process of earthquake, both sides and rear side of intake tower without through disengagement phenomena from rock, it can conclude that the intake tower in the overall stability state

Redesigning spectroscopic sensors with programmable photonic circuits

Optical spectroscopic sensors are a powerful tool to reveal light-matter interactions in many fields, such as physics, biology, chemistry, and astronomy. Miniaturizing the currently bulky spectrometers has become imperative for the wide range of applications that demand in situ or even in vitro characterization systems, a field that is growing rapidly. Benchtop spectrometers are capable of offering superior resolution and spectral range, but at the expense of requiring a large size. In this paper, we propose a novel method that redesigns spectroscopic sensors via the use of programmable photonic circuits. Drawing from compressive sensing theory, we start by investigating the most ideal sampling matrix for a reconstructive spectrometer and reveal that a sufficiently large number of sampling channels is a prerequisite for both fine resolution and low reconstruction error. This number is, however, still considerably smaller than that of the reconstructed spectral pixels, benefitting from the nature of reconstruction algorithms. We then show that the cascading of a few engineered MZI elements can be readily programmed to create an exponentially scalable number of such sampling spectral responses over an ultra-broad bandwidth, allowing for ultra-high resolution down to single-digit picometers without incurring additional hardware costs. Experimentally, we implement an on-chip spectrometer with a fully-programmable 6-stage cascaded MZI structure and demonstrate a 200 nm bandwidth using only 729 sampling channels. This achieves a bandwidth-to-resolution ratio of over 20,000, which is, to our best knowledge, about one order of magnitude greater than any reported miniaturized spectrometers to date. We further illustrate that by employing dispersion-engineered waveguide components, the device bandwidth can be extended to over 400 nm

Cram\'er-Rao bound-informed training of neural networks for quantitative MRI

Neural networks are increasingly used to estimate parameters in quantitative MRI, in particular in magnetic resonance fingerprinting. Their advantages over the gold standard non-linear least square fitting are their superior speed and their immunity to the non-convexity of many fitting problems. We find, however, that in heterogeneous parameter spaces, i.e. in spaces in which the variance of the estimated parameters varies considerably, good performance is hard to achieve and requires arduous tweaking of the loss function, hyper parameters, and the distribution of the training data in parameter space. Here, we address these issues with a theoretically well-founded loss function: the Cram\'er-Rao bound (CRB) provides a theoretical lower bound for the variance of an unbiased estimator and we propose to normalize the squared error with respective CRB. With this normalization, we balance the contributions of hard-to-estimate and not-so-hard-to-estimate parameters and areas in parameter space, and avoid a dominance of the former in the overall training loss. Further, the CRB-based loss function equals one for a maximally-efficient unbiased estimator, which we consider the ideal estimator. Hence, the proposed CRB-based loss function provides an absolute evaluation metric. We compare a network trained with the CRB-based loss with a network trained with the commonly used means squared error loss and demonstrate the advantages of the former in numerical, phantom, and in vivo experiments.Comment: Xiaoxia Zhang, Quentin Duchemin, and Kangning Liu contributed equally to this wor

The impact of multiple driving factors on forest ecosystem services in karst desertification control

In the fragile karst desertification ecosystem, forests are the providers of eco-multifunctionality. And the ecosystem service (ES) supply capacity of forests is directly or indirectly affected by various driving factors. The aim of this study is to explore the driving role of forest spatial structure, species diversity, and functional diversity on ecosystem services. In this study, four forest types, namely, broad-leaved monoculture forest (planted economic forest) (F1), broad-leaved mixed forest (F2), coniferous and broad-leaved mixed forest (F3), and coniferous mixed forest (F4), were investigated in karst plateau mountain (KPM), karst plateau canyon (KPC), and karst mountain canyon (KMC) landforms. Variance analysis, correlation analysis and redundancy analysis were used to compare the differences of spatial structure, species diversity, functional diversity, and ES of different forest types and to clarify the driving role of spatial structure, species diversity, and functional diversity on ES. The results showed that the wood supply service of F3 was at least 4.27% higher than that of other forest types; carbon sequestration and oxygen release are at least 4.57 and 3.89% higher; the water holding capacity of litter and soil is higher by 6.24 and 2.26%, respectively; the soil OC, TN, TP, and TK were higher than 6.01, 1.22, 25.55, and 13.34%, respectively. The coniferous mixed forest and broadleaved mixed forest with a more complete spatial structure has a higher level of diversity, which can generate more wood and provide more soil nutrient sources, as well as stronger regulation capacity. Spatial structure affects plant productivity through interspecific relationships; soil fertility is restricted by the level of diversity; gas and water regulation are influenced by both spatial structure and diversity levels. There is a progressive driving relationship among spatial structure, diversity, and ES. In forest management, it is helpful to improve the forest ecosystem’s functioning by adjusting the forest structure using close-to-natural management measures

Rapid quantitative magnetization transfer imaging: utilizing the hybrid state and the generalized Bloch model

Purpose: To improve spatial resolution and scan time of quantitative magnetization transfer (qMT) imaging without constraints on model parameters. Theory and Methods: We combine two recently-proposed models in a Bloch-McConnell equation: the dynamics of the free spin pool is confined to the hybrid state and the dynamics of the semi-solid spin pool is described by the generalized Bloch model. We numerically optimize the flip angles and durations of a train of radio frequency pulses to enhance the encoding of three marked qMT parameters while accounting for an 8-parameter model. We sparsely sample each time frame along this spin dynamics with a 3D radial koosh-ball trajectory, reconstruct the data with sub-space modeling, and fit the qMT model with a neural network for computational efficiency. Results: We were able to extract qMT parameter maps of the whole brain with a nominal resolution of 1mm isotropic and high SNR from a 12.6 minute scan. In lesions of multiple sclerosis subjects, we observe a decreased size of the semi-solid spin pool and slower relaxation, consistent with previous reports. Conclusion: The encoding power of the hybrid state, combined with regularized image reconstruction, and the accuracy of the generalized Bloch model provide an excellent basis for highly efficient quantitative magnetization transfer imaging