Variable Filter Research Articles

Germline NQO1c.559C>T as a biomarker of genetic susceptibility to immune checkpoint blockade resistance.

8537 Background: Tobacco is the main risk factor for developing lung cancer. Yet, some heavy smokers do not develop cancer while others do at young ages. The study of cancer immunology has led to the surge of immune checkpoint blockade (ICB), which transformed the treatment landscape of cancer pts. NQO1 has been associated with extreme phenotypes of high risk for the development of tobacco-induced NSCLC and with poor survival outcomes for xerographs. Development of biomarkers for ICB has been mainly focused on tumor-related factors while genetic susceptibility of the host has been poorly explored to date and some evidence suggests that genetic dysregulation may enables immune escape. Methods: From June 2016 to January 2023, 326 NSCLC pts from two main University Hospitals of Malaga (Spain) receiving ICB were prospectively enrolled, blood samples were gathered. We selected a representative set of 60 consecutive pts for exome sequencing to characterize the germline contribution to resistance (progression within the first 6 months of treatment). PBMC DNA was extracted and prepared for exome sequencing. Raw sequencing data was processed and aligned to a reference genome using BWA. Base recalibration, germline variant calling and variant filtering was performed through GATK. Survival probabilities were estimated with the Kaplan-Meier method and compared using the log-rank test. We explored the lung cancer related genes among our results. Results: Median age at diagnosis of 64 (54-85). 76,6% were men. 44/60 (73,3%) were adenocarcinoma (ADC), PD-L1≥1% in 55% of patients, 22% shown PD-L≥50%. 78% had advanced disease at diagnosis. 65% received ICB in combination with chemotherapy. We found NQO1c.559C>T in 46,6% of pts (28/60). The variation was not associated with survival outcomes in the multivariant analysis (p>0.05) but with poor OS for non-ADC histology (19.7m vs 6m for mutated and WT, respectively; p<0.01). In addition, we found 26 germline variants associated with OS and PFS, and 52 variants with response to ICB. The variants were missense or involved a stop gain/loss. Conclusions: Exome sequencing analysis of germline variants could be a suitable methodology to identify subrogated biomarkers of immune resistance in NSCLC patients. NQO1c.559C>T might help to identify poor prognosis non-ADC pts in the ICB setting.

Petrophysical Analyses for Supporting the Search for a Claystone-Hosted Nuclear Repository

The Bundesgesellschaft für Endlagerung mbH (BGE; “Federal Company for Radioactive Waste Disposal”) is responsible for identifying a repository site in Germany for the storage of high-level radioactive waste, ensuring the best possible safety for at least 1 million years (StandAG, 2017). The three-phase site selection process is currently in Step 2 of its Phase I. Around 90 potentially suitable sub-areas are evaluated via individual representative preliminary safety assessments. These sub-areas cover all types of potential containment rock: allochthonous and autochthonous rock salt, claystone, and crystalline rock. Such a comprehensive assessment is challenging for a host rock that covers vast areas like claystone or autochthonous salt. One of the steps towards estimating parameters for a site search process requires the analysis of numerous logs from the areas of interest and their vicinity. Unfortunately, computing these parameters is a much more complex problem than it seems. While gamma ray logs have been the petrophysicist’s workhorse for decades, they allow no quantification of clay properties. Nuclear magnetic resonance and neutron activation logs yield critical information but are scarce in old wells. Decades of research have given good algorithms for defining wet clay porosity from resistivity logs, but inverting those for porosity and tortuosity remains challenging. Another log-derived parameter is a formation’s homogeneity, which quantifies the jaggedness of a logging curve. A vertical variogram or variable filter technique can accomplish this.

Improved ADRC Current Low‐Frequency Ripple Suppression Strategy Based on VFNF

AbstractIn the two‐stage inverter system, due to the space vector modulation (SVM), the midpoint potential of active neutral point clamped three‐level inverter system (ANPC‐TLIS) will have triple frequency fluctuation, under the influence of the closed‐loop control strategy, the fluctuation amplitude of the lithium battery energy storage system (LBESS) output current will increase. To reduce the low‐frequency output current fluctuation, a voltage loop strategy based on active disturbance rejection control (ADRC) with notch filter (NF)is proposed in this paper. In this scheme, the voltage outer‐loop control strategy not only maintains voltage stability, but also reduces the fluctuation amplitude of reference current. In addition, during the electromagnetic transient process, the LBESS output current fluctuation frequency is constantly changing because the power supply frequency required by linear motor is constantly changing. To solve this problem, the inverter output current frequency is feedforward to the LBESS controller, forming a variable frequency notch filter (VFNF). Finally, the effectiveness of the proposed strategy is verified by fixed‐frequency and variable frequency conditions experiments on RT‐LAB experimental platform. © 2024 Institute of Electrical Engineers of Japan and Wiley Periodicals LLC.

RCVM‐ASS‐CICSKA‐PAPT‐VDF: VLSI design of high‐speed reconfigurable compressed Vedic PAPT‐VDF filter for ECG medical application

AbstractDuring signal acquisition, the signals are impacted by multiple noise sources that must be filtered before any analysis. However, many different filter implementations in VLSI are dispersed among many studies. This study aims to give readers a systematic approach to designing a Pipelined All‐Pass Transformation based Variable digital filter (PAPT‐VDF) to eliminate the high‐frequency noise from ECG data. The modified design emphasizes first‐ and second‐order responses to obtain high‐speed filter realization with high operating frequencies. The addition of adder and multiplier designs to the hardware architecture of a filter design improves performance. The fundamental blocks of the filter design are the adder and multiplier. The adder and multiplier are employed with an Adaptable stage size‐based concatenation, incremented carry‐skip adder (ASS‐CICSKA), and Improved reconfigurable compressed Vedic multiplier (IRCVM). Utilizing the adder design diminishes the delay with enhanced performance because receiving the carry from an incrementation block is not mandatory. In the multiplier design, the compressor and the reconfigurable approach are adapted with a data detector block to detect the redundant input and lower the logic gates' switching activity with less area overhead. The proposed filter design is implemented in vertex 7 FPGA family device, and the performance measures are analyzed regarding area utilization, delay, power, and frequency. Also, by using the denoised signal, the mean square error (MSE), and signal‐to‐noise ratio (SNR) are evaluated in the MATLAB platform.

Geographical and seasonal variations of gravity wave activities in the upper mesosphere measured by space-borne imaging of molecular oxygen nightglow

Geographical and seasonal variations of gravity wave events in the upper mesosphere were investigated using the nightglow imaging data obtained by the Visible and near-Infrared Spectral Imager (VISI) on the Ionosphere, Mesosphere, upper Atmosphere and Plasmasphere (IMAP) onboard the International Space Station (ISS). The nadir-imaging data of the O2(0–0) atmospheric band (762 nm) with the typical emission peak around 95 km altitude was used to investigate small-scale waves (horizontal wavelengths less than ~ 200 km) on a global scale. To detect gravity wave events, the variance of high-pass filtered nightglow images within a local 100 km radius was evaluated, with a threshold set at three times the standard deviation from the average variance of the background level. A data screening algorithm that evaluates the variance of upwelling contamination light emission was also introduced to remove contaminated data. Applying the variance filter and data screening algorithm to a nearly 3-year data set, from November 2012 to August 2015, occurrence maps of wave events for four seasons were derived. The occurrence maps show a higher frequency of wave events in winter high latitudes (> 40° N/S), considerably attributed to gravity wave activity associated with the polar night jet. Hot spots were observed near orographic sources in winter high latitudes, including the eastern part of North America, Europe, and the southern Andes. In the summer hemisphere, hot spots were detected at mid-to-high latitudes such as North America, Europe, and the eastern side of the Eurasian continent, and at equatorial latitudes just above the intertropical convection zone (ITCZ). They are likely gravity waves from deep convection that arise from mid-latitude summertime thunderstorms and the ITCZ, respectively. During the equinox seasons, hot spots were detected near convective sources such as the Amazon Rainforest, Congo Rainforest, and the Indochina peninsula.Graphical

Functional Analysis of BRCA1 3'UTR Variants Predisposing to Breast Cancer.

Breast Cancer (BC) is the main female cancer diagnosed worldwide, and it has been described that few genes, such as BRCA1, have a high penetrance for this type of cancer. In this manuscript, we were interested in evaluating the effect of 3'UTR variants on BRCA1 expression. To accomplish this objective, Whole Exome Sequencing (WES) data of 400 patients with unselected BC was used to filter variants located in the region of interest of BRCA1 gene, finding two of them (c.*36C>G and c.*369_373del). miRGate and miRanda in silico tools were used to predict microRNA (miRNA) interaction. The two variants (c.*36C>G, c.*369_373del) were predicted to affect miRNA interaction. After cloning of BRCA1 3'UTR into pMIR-Report vector, the construct was transfected into two BC cell lines (MDA-MB-231 and MCF-7), and the variant c.*36C>G evidenced overexpression of reporter gene luciferase, showing that the transcript was not being degraded by the miRNA in MDA-MB-231 cells. The variant seems to protect against Triple Negative BC probably due to the expression level of miRNA in this particular cell line (MDA-MB-231). This is consistent with the clinical history of the patients who harbor BC Hormone Receptors positive (HR+).

Forecasting Gas Well Classification Based on a Two-Dimensional Convolutional Neural Network Deep Learning Model

In response to the limitations of existing evaluation methods for gas well types in tight sandstone gas reservoirs, characterized by low indicator dimensions and a reliance on traditional methods with low prediction accuracy, therefore, a novel approach based on a two-dimensional convolutional neural network (2D-CNN) is proposed for predicting gas well types. First, gas well features are hierarchically selected using variance filtering, correlation coefficients, and the XGBoost algorithm. Then, gas well types are determined via spectral clustering, with each gas well labeled accordingly. Finally, the selected features are inputted, and classification labels are outputted into the 2D-CNN, where convolutional layers extract features of gas well indicators, and the pooling layer, which, trained by the backpropagation of CNN, performs secondary dimensionality reduction. A 2D-CNN gas well classification prediction model is constructed, and the softmax function is employed to determine well classifications. This methodology is applied to a specific tight gas reservoir. The study findings indicate the following: (1) Via two rounds of feature selection using the new algorithm, the number of gas well indicator dimensions is reduced from 29 to 15, thereby reducing the computational complexity of the model. (2) Gas wells are categorized into high, medium, and low types, addressing a deep learning multi-class prediction problem. (3) The new method achieves an accuracy of 0.99 and a loss value of 0.03, outperforming BP neural networks, XGBoost, LightGBM, long short-term memory networks (LSTMs), and one-dimensional convolutional neural networks (1D-CNNs). Overall, this innovative approach demonstrates superior efficacy in predicting gas well types, which is particularly valuable for tight sandstone gas reservoirs.

Investigating the role of common cis-regulatory variants in modifying penetrance of putatively damaging, inherited variants in severe neurodevelopmental disorders

Recent work has revealed an important role for rare, incompletely penetrant inherited coding variants in neurodevelopmental disorders (NDDs). Additionally, we have previously shown that common variants contribute to risk for rare NDDs. Here, we investigate whether common variants exert their effects by modifying gene expression, using multi-cis-expression quantitative trait loci (cis-eQTL) prediction models. We first performed a transcriptome-wide association study for NDDs using 6987 probands from the Deciphering Developmental Disorders (DDD) study and 9720 controls, and found one gene, RAB2A, that passed multiple testing correction (p = 6.7 × 10–7). We then investigated whether cis-eQTLs modify the penetrance of putatively damaging, rare coding variants inherited by NDD probands from their unaffected parents in a set of 1700 trios. We found no evidence that unaffected parents transmitting putatively damaging coding variants had higher genetically-predicted expression of the variant-harboring gene than their child. In probands carrying putatively damaging variants in constrained genes, the genetically-predicted expression of these genes in blood was lower than in controls (p = 2.7 × 10–3). However, results for proband-control comparisons were inconsistent across different sets of genes, variant filters and tissues. We find limited evidence that common cis-eQTLs modify penetrance of rare coding variants in a large cohort of NDD probands.

Evaluation of the mean glandular dose (MGD) for phantoms with different glandularity/adipose tissue ratios using MgB4O7:Dy,Na dosimeters in mammography

This study focuses on the use of MgB4O7:Dy,Na dosimeters in mammography measurements. The reproducibility measurement and dose-response curves were studied for the characterization of dosimeters. Next, variation in entrance surface dose (ESD) and mean glandular dose (MGD) was investigated according to different glandularity/adipose tissue ratios of 30%/70%, 50%/50% and 70%/30% for fixed anode and variable filter combinations (Mo/Mo, Mo/Rh) using BR-12 phantoms with thicknesses from 2 to 6 cm. The ESD and MGD values increased at varying rates with increasing phantom thicknesses and glandularity ratios. The increase rate in MGD is ∼30% for both Mo/Mo and Mo/Rh for the difference between (70%/30%) and (50%/50%), considering the thickness of 6 cm. On the other hand, the MGD value obtained for a glandular ratio of 70%/30% at 6 cm for Mo/Mo is ∼40% higher than that for Mo/Rh. A proportional comparison of MGD differences obtained according to glandularity and filter changes may provide valuable contributions to the literature. Within this study, it was shown that MgB4O7:Dy,Na dosimeters can be used as new alternative dosimeters in the field of mammography to perform ESD and MGD measurements. In further studies, MgB4O7:Dy,Na dosimeters can be used to determine the various organ doses, such as eye and thyroid, resulting from the scattered radiation dose using rando phantoms.

Differential optical transfer function (dOTF) broad-spectrum wavefront sensing using integral field unit spectroscopy

Context. Detecting and characterizing Earth-like exoplanets is a critical scientific goal for the next generation of telescopes. However, current direct imaging instruments are hindered by evolving noncommon path aberrations (NCPAs), which lead to persistent speckles in the images. The differential optical transfer function (dOTF) is an image-based, noniterative, and model-independent wavefront sensor that combines differential images to estimate the complex field of the pupil. Aims. Given the objective of spectrally characterizing exoplanets to discern their composition and potentially detect biosignatures, we aim to explore the integration of the dOTF sensing method with an integral field unit (IFU) spectrograph. An IFU spectrograph generates a spectral data cube that contains diverse spectral information within a single dataset. Combining these two concepts is expected to improve the efficiency with which NCPAs can be spectrally measured and characterized. Methods. This Letter presents an implementation strategy for integrating dOTF and IFU spectroscopy data, enabling estimations of the electric field at various wavelengths simultaneously. Results. This method is specifically optimized to provide the most effective combination with minimal hardware requirements. It involves the use of multiple simultaneous deformable mirror actuator displacements in conjunction with an adapted Lyot stop employing a circular variable filter. Conclusions. The dOTF wavefront sensor coupled with an IFU spectrograph promises to be rapid and efficient, capable of measuring NCPAs across the entire spectrum provided by the spectrograph. Because the combination of dOTF and IFU spectroscopy is immediately applicable, it is of potential interest for various present-day on-sky instruments, as well as future IFU-based instruments coupled to the next generation of extremely large telescopes (ELTs).

Transient Analysis of a Selective Partial-Update LMS Algorithm

In applications where large-order filters are needed, the computational load of adaptive filtering algorithms can become prohibitively expensive. In this paper, a comprehensive analysis of a selective partial-update least mean squares, named SPU-LMS-M-min, is developed. By employing the partial-update strategy for a non-normalized adaptive scheme, the designer can choose an appropriate number of update blocks considering a trade-off between convergence rate and computational complexity, which can result in a more than 40% reduction in the number of multiplications in some configurations compared to the traditional LMS algorithm. Based on the principle of minimum distortion, a selection criterion is proposed that is based on the input signal’s blocks with the lowest energy, whereas typical Selective Partial Update (SPU) algorithms use a selection criterion based on blocks with highest energy. Stochastic models are developed for the mean weights and mean and mean squared behaviour of the proposed algorithm, which are further extended to accommodate scenarios involving time-varying dynamics and suboptimal filter lengths. Simulation results show that the theoretical predictions are in good agreement with the experimental outcomes. Furthermore, it is demonstrated that the proposed selection criterion can be easily extended to active noise cancellation algorithms as well as algorithms utilizing variable filter length. This allows for the reduction of computational costs for these algorithms without compromising their asymptotic performance.

Abstract 3954: Increased mosaic chromosomal alterations among survivors of childhood acute myeloid leukemia

Abstract Background: We previously characterized point mutations of clonal hematopoiesis (CH) including single nucleotide variants or small insertion/deletions residing in a set of cancer-related genes (e.g., DNMT3A and TET2), and found that CH point mutations occurred at a much higher prevalence in childhood cancer survivors (CCS) than in age-sex-race matched community controls. However, mosaic chromosomal alterations (mCAs), another form of CH with megabase-scale structural alterations (copy gain, copy loss and copy-neutral loss of heterozygosity), have not been examined. Methods: Detection of mCAs was performed on whole-genome sequencing (WGS, 30X) data from 5-year CCS with the Mosaic Chromosomal Alterations (MoChA) caller which utilizes phased genotypes, coverage, and B allele frequency (BAF) at heterozygous sites (HETS). We implemented the pipeline established by the TOPMed consortium to detect low mutant cell fractions (CF ≥1%). Variant filtration criteria: minor allele frequency <1%, the read depth of either allele <5, and only one variant retained in each 1Kb window. Called mCA filtration criteria: <100 HETS; lod score <5; relative coverage >2.9 or BAF deviation >0.16 and relative coverage >2.5. Autosomal mCAs (CF ≥1%, HETS >100 and length >1Mb) retained. Multivariable Cox models evaluated the association between mCAs and specific late-effects including all-cause mortality, obesity, myocardial infarction (MI) or cardiomyopathy (CMP); multivariable logistic models evaluated the association between mCAs and childhood cancer diagnoses. All models were adjusted for age, sex and therapeutic exposures. Results: In our evaluation of 4,330 CCS (median age at blood draw: 26.8 years) and 433 controls (33.6 years) with WGS data from the St. Jude Lifetime Cohort, the prevalence of mCAs was slightly higher among CCS than community controls (4.83% vs. 4.16%). Among CCS, mCAs were associated with therapeutic exposures (alkylating agents: odds ratio [OR]=1.48, 95% CI=1.09-2.00; antimetabolites: 1.53, 1.02-2.31; corticosteroids: 0.60, 0.40-0.91). In contrast to recent mCA studies in the general population, we observed no co-occurrence with CH point mutations, and no associations with late-effects including all-cause mortality, obesity, MI or CMP. Interestingly, mCAs were significantly associated with the primary diagnosis of acute myeloid leukemia (AML, OR=2.86, 95% CI=1.63-5.01). Furthermore, this finding was replicated in 2898 5-year survivors from the Childhood Cancer Survivor Study (OR=3.77, 95% CI=1.94-7.33). Conclusion: mCAs were not associated with age-related morbidities and mortality among CCS. However, we identified a striking association between mCAs and previous diagnoses of AML independent of treatment exposures. If validated among AML patients at the time of diagnosis, mCAs, which reflect the underlying genome instability, may serve as a diagnostic biomarker for AML. Citation Format: Xijun Zhang, Na Qin, Qian Dong, Cheng Chen, John Easton, Heather Mulder, Xiang Chen, Kyla Shelton, Cindy Im, Yutaka Yasui, Greg Armstrong, Kirsten K. Ness, Melissa M. Hudson, Paul Auer, Jinghui Zhang, Zhaoming Wang. Increased mosaic chromosomal alterations among survivors of childhood acute myeloid leukemia [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 3954.

Which data assimilation method to use and when: unlocking the potential of observations in shoreline modelling

Shoreline predictions are essential for coastal management. In this era of increasing amounts of data from different sources, it is imperative to use observations to ensure the reliability of shoreline forecasts. Data assimilation has emerged as a powerful tool to bridge the gap between episodic and imprecise spatiotemporal observations and the incomplete mathematical equations describing the physics of coastal dynamics. This research seeks to maximize this potential by assessing the effectiveness of different data assimilation algorithms considering different observational data characteristics and initial system knowledge to guide shoreline models towards delivering results as close as possible to the real world. Two statistical algorithms (stochastic ensemble and extended Kalman filters) and one variational algorithm (4D-Var) are incorporated into an equilibrium cross-shore model and a one-line longshore model. A twin experimental procedure is conducted to determine the observation requirements for these assimilation algorithms in terms of accuracy, length of the data collection campaign and sampling frequency. Similarly, the initial system knowledge needed and the ability of the assimilation methods to track the system nonstationarity are evaluated under synthetic scenarios. The results indicate that with noisy observations, the Kalman filter variants outperform 4D-Var. However, 4D-Var is less restrictive in terms of initial system knowledge and tracks nonstationary parametrizations more accurately for cross-shore processes. The findings are demonstrated at two real beaches governed by different processes with different data sources used for calibration. In this contribution, the coastal processes assimilated thus far in shoreline modelling are extended, the 4D-Var algorithm is applied for the first time in the field of shoreline modelling, and guidelines on which assimilation method can be most beneficial in terms of the available observational data and system knowledge are provided.

Model-based impending lithium battery terminal voltage collapse detection via data-driven and machine learning approaches

Lithium battery is an important power source of an electrical vehicle (EV). Practically, when a battery is about to fail, it needs to be removed from the load because keeping it connected can lead to permanent damage. Hence, it is important to detect battery failure to sustain the lifespan of the battery and avoid safety issues which will contribute to the sustainability of the EV. However, determining the effective disconnecting or discharging moment of the battery remains a challenging issue in EV applications. In order to solve the abovementioned problem, a control framework and maximum likelihood estimation are proposed to estimate the parameters of a battery model. In addition, both the sparse representation and dynamic mode decomposition (DMD) approaches are applied to protect the physical battery unit from a failure scenario. For comparison purposes, the proposed framework is compared to a Lyapunov-based detection approach along with neural network (NN) and linear discriminant analysis (LDA). Moreover, several state estimation algorithms are applied to estimate battery state of charge (SOC) at each time step: extended Kalman filter, extended Kalman smooth variable structure filter, and cubature Kalman filter. Finally, the experimental results showed that the proposed DMD approach outperforms the sparse representation, NN, LDA, and Lyapunov-based approaches in terms of detection accuracy. Moreover, the proposed DMD strategy is more robust towards the inaccuracy of the estimated SOC. Besides that, the proposed battery model parameter estimation approach exhibited fast processing time; therefore, it is a reliable choice for recomputing the model parameters from time to time to compensate aging effect. An autonomous unmanned aerial vehicle (UAV) was simulated as a proof-of-concept in a MATLAB environment to verify the detection performance of the proposed methods. Both actual and UAV results showed that the DMD demonstrated more robust detection performance than other methods in terms of processing time and detection accuracy.

Wavelength continuously tunable and wavelength spacing variable comb filter based on a High-Birefringence fiber loop mirror

We demonstrated an all-fiber comb filter that can continuously tune the wavelength for four switchable wavelength spacings. The comb filter consists of a fiber loop mirror (FLM) that is inserted with three polarization maintaining fibers (PMFs) and three polarization controllers (PCs). The transmission characteristics of the comb filter was theoretically analyzed. The output characteristics of the comb filters shows a dependence of the orientation angles of the PCs. With the angles being optimally set, the comb filter with four different wavelength spacings can be achieved. The wavelength continuously tunable and wavelength spacing variable mechanisms are elaborated. The three PCs are employed to enable the tunable and switchable functions. The two PCs between the PMFs serve as a polarization rotator to align the birefringent axes of the PMFs for varying the wavelength spacing while the PC adjacent to the optical coupler can be used for wavelength tuning function. When the two quarter-waveplates (QWPs) of the PC are optimally set at π/4 and - π/4, the half-waveplate (HWP) acts as a wavelength tuning element, and when the QWPs are set at zero degrees, the HWP serves as an amplitude tuning element. The center wavelength of the comb filter could be linearly tuned with the rotation angle of the HWP, and a π/2 change in the rotation angle leads to a wavelength change in one free-spectral-range (FSR). A π/4 change in the rotation angle could bring the normalized peak transmission from its maximum at 1 to minimum at 0. The proposed tunable technique has been verified by our experimental results. We experimentally demonstrated the comb filters can switch among FSRs of 0.75 nm, 1.01 nm, 1.51 nm, and 3.1 nm with tunable functions.

A general-purpose spectroscopic camera for single-board computers

A low-cost consumer-grade spectroscopic system offers potential applications in many fields. Linear variable optical filters (LVOFs) placed in front of imaging sensors are one of the most promising avenues to realize this goal. In this work, we built a spectroscopic system by depositing an LVOF directly onto the protective window of OV5647 color and OV9281 monochrome imaging sensors for the Raspberry Pi. The ¼” sensors cost $15-$80 CAD at the time of writing and have linear dimensions of only 2.7 – 3.6 mm. To achieve a suitable gradient over such small sensors, an ultra-slow shutting system was built for the physical vapor deposition of an SiO2 gradient layer between two partially transparent silver mirrors to form a miniature LVOF. The resulting devices operated as effective low-cost spectrometers that were single-moded in the visible spectrum. These simple systems were finally used to measure spectra from sample light sources, clearly illustrating the advantages and limitations of this simple and economical approach towards everyday consumer spectroscopy.

Establishment of a machine learning predictive model for non-alcoholic fatty liver disease: A longitudinal cohort study

Background and aimsNon-alcoholic fatty liver disease (NAFLD) is a common chronic liver disease, which lacks effective drug treatments. This study aimed to construct an eXtreme Gradient Boosting (XGBoost) prediction model to identify or evaluate potential NAFLD patients. Methods and resultsWe conducted a longitudinal study of 22,140 individuals from the Beijing Health Management Cohort. Variable filtering was performed using the least absolute shrinkage and selection operator. Random Over Sampling Examples was used to address imbalanced data. Next, the XGBoost model and the other three machine learning (ML) models were built using balanced data. Finally, the variable importance of the XGBoost model was ranked. Among four ML algorithms, we got that the XGBoost model outperformed the other models with the following results: accuracy of 0.835, sensitivity of 0.835, specificity of 0.834, Youden index of 0.669, precision of 0.831, recall of 0.835, F-1 score of 0.833, and an area under the curve of 0.914. The top five variables with the greatest impact on the onset of NAFLD were aspartate aminotransferase, cardiometabolic index, body mass index, alanine aminotransferase, and triglyceride-glucose index. ConclusionThe predictive model based on the XGBoost algorithm enables early prediction of the onset of NAFLD. Additionally, assessing variable importance provides valuable insights into the prevention and treatment of NAFLD.

Continuously variable Fourier filters fabricated using varying angle glancing angle deposition for chip-scale spectroscopy.

Low-cost spectroscopy has received a great deal of attention in recent years in applications such as food inspection, disease detection, and manufacturing. Current spectroscopic systems rely on multiple optical components, making them mechanically fragile systems. In our previous work, we demonstrated the use of Fourier filtering using thin dielectric films. The sampling effect from the cavity resonances can be used to decompose a signal into its Fourier components. Although the thin films were deposited directly on the face of the detectors, filters of varying thicknesses were needed, which required multiple lithographic processes. To overcome this challenge, in this work, we use a continuously variable filters deposited by a single-step electron-beam evaporation technique. We demonstrate a novel, to our knowledge, method that utilizes the glancing angle deposition technique with a continuously varying angle in order to produce tens of variable Fourier filters in a single deposition run. To prove this technique, we deposit this variable filter on a 38-channel linear detector and show the results from this device.

A new smooth variable structure Tobit filter for systems with censored measurements and model parameters uncertainty

The smooth variable structure filter (SVSF) is an effective state estimation strategy for systems with model parameters uncertainty. The superiority of SVSF, however, depends on accurate sensor measurements. In practice, the accuracy of traditional SVSF algorithm will be degraded or even diverge for censored measurements. To solve this problem, in this paper, firstly, the Tobit censored model is introduced to describe censored phenomenon for systems with model parameters uncertainty and censored measurements. The innovation, residual, innovation covariance and state prediction error cross-covariance are revised based on cumulative probability density. Then, the near optimal smooth boundary layer (SBL) is recalculated based on the minimum trace principle of estimation error covariance. A robust filter gain is redesigned based on the convergence of estimation error absolute, and the necessary conditions for the convergence of the proposed filter are proved. In the end, oscillators, distributed target tracking and the Udacity self-driving data-set are used to verify that the proposed algorithm has higher accuracy and better robustness under model parameters uncertainty and censored measurements.

Genetic blueprint of congenital muscular dystrophies with brain malformations in Egypt: A report of 11 families

Congenital muscular dystrophies (CMDs) are a group of rare muscle disorders characterized by early onset hypotonia and motor developmental delay associated with brain malformations with or without eye anomalies in the most severe cases. In this study, we aimed to uncover the genetic basis of severe CMD in Egypt and to determine the efficacy of whole exome sequencing (WES)-based genetic diagnosis in this population. We recruited twelve individuals from eleven families with a clinical diagnosis of CMD with brain malformations that fell into two groups: seven patients with suspected dystroglycanopathy and five patients with suspected merosin-deficient CMD. WES was analyzed by variant filtering using multiple approaches including splicing and copy number variant (CNV) analysis. We identified likely pathogenic variants in FKRP in two cases and variants in POMT1, POMK, and B3GALNT2 in three individuals. All individuals with merosin-deficient CMD had truncating variants in LAMA2. Further analysis in one of the two unsolved cases showed a homozygous protein-truncating variant in Feline Leukemia Virus subgroup C Receptor 1 (FLVCR1). FLVCR1 loss of function has never been previously reported. Yet, loss of function of its paralog, FLVCR2, causes lethal hydranencephaly-hydrocephaly syndrome (Fowler Syndrome) which should be considered in the differential diagnosis for dystroglycanopathy. Overall, we reached a diagnostic rate of 86% (6/7) for dystroglycanopathies and 100% (5/5) for merosinopathy. In conclusion, our results provide further evidence that WES is an important diagnostic method in CMD in developing countries to improve the diagnostic rate, management plan, and genetic counseling for these disorders.