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A CdS/reduced graphene oxide (rGO) composite aerogel with CdS nanosheets (CdS NSs) grown on an interconnected three-dimensional rGO-based porous network was prepared by one-pot hydrothermal method using L-lysine as a reducing agent and the cross-linker. The results show that its enhanced adsorption toward pollutants owing to its large Brunauer-Emmett-Teller specific surface area and spongy nature, and improved light absorption due to its extremely light weight nature. Moreover, the rGO promotes the photogenerated charge separation. Thus, the CdS/rGO composite aerogel exhibits enhanced activity for photocatalytic degradation.It can be observed that the tetracycline hydrochloride (TC) has been degraded totally after 45 min by irradiated CdS/rGO composite aerogel, and almost all TC has been mineralized after 1.5 h. Moreover, the CdS/rGO composite aerogel also shows high stability and can be easily separated from the reaction systems for recycling. The photocatalytic reduction activity of CdS/rGO composite aerogel shows no obvious decrease after 5 cycles.

C12H12N2O8Co, orthorhombic, Pbca (no. 61), a = 11.4542(10) Å, b = 9.2421(8) Å, c = 25.501(2) Å, V = 2699.6(4) Å3, Z = 2, Rgt (F) = 0.0342, wRref (F2) = 0.0888, T = 296 K.

Objective To investigate the relationship between triglyceride glucose (TyG) index and the total load of cerebral small vessel disease (CSVD) and its different imaging markers. Methods A total of 202 patients with CSVD admitted to The Third Hospital of Hebei Medical University from January 2020 to May 2022 were selected. The clinical data of all patients were collected and TyG index was calculated. The lacunes, cerebral microbleeds (CMBs), white matter hyperintensity (WMH) and enlarged perivascular space (EPVS) were evaluated by MRI, and total load of CSVD score was calculated. Results 1) TyG index and total load of CSVD score: Logistic regression analysis showed the male (OR = 2.245, 95%CI: 1.017-4.957; P = 0.045), increase of age (OR = 1.084, 95%CI: 1.046-1.124; P = 0.000), hypertension (OR = 2.457, 95%CI: 1.189-5.078; P = 0.015) and increase of TyG index (OR = 2.657, 95%CI: 1.353-5.216; P = 0.005) were risk factors for moderate and severe total load of CSVD. 2) TyG index and imaging markers of CSVD: Logistic regression analysis showed the increase of TyG index was the risk factor of moderate and severe WMH (OR = 2.313, 95%CI: 1.095-4.885; P = 0.028), CMBs (OR = 1.690, 95%CI: 1.011-2.824; P = 0.045), lacunes (OR = 2.087, 95%CI: 1.235-3.525; P = 0.006), moderate and severe EPVS in basal ganglia (OR = 2.005, 95%CI: 1.190-3.378; P = 0.009). Conclusions TyG index is associated with the total load and various imaging markers of CSVD, which can be used as a valuable biomarker to evaluate CSVD and its severity.

Abstract Synthetic electronic health records (EHRs) that are both realistic and privacy-preserving offer alternatives to real EHRs for machine learning (ML) and statistical analysis. However, generating high-fidelity EHR data in its original, high-dimensional form poses challenges for existing methods. We propose Hierarchical Autoregressive Language mOdel (HALO) for generating longitudinal, high-dimensional EHR, which preserve the statistical properties of real EHRs and can train accurate ML models without privacy concerns. HALO generates a probability density function over medical codes, clinical visits, and patient records, allowing for generating realistic EHR data without requiring variable selection or aggregation. Extensive experiments demonstrated that HALO can generate high-fidelity data with high-dimensional disease code probabilities closely mirroring (above 0.9 R 2 correlation) real EHR data. HALO also enhances the accuracy of predictive modeling and enables downstream ML models to attain similar accuracy as models trained on genuine data.

BackgroundThe correlations between cuproptosis and long noncoding RNAs (lncRNAs) with the tumor microenvironment (TME), immunotherapy, and some other characteristics of hepatocellular carcinoma (HCC) remain unclear.MethodsSixteen cuproptosis regulators and 356 cuproptosis-related lncRNAs (CRLnc) were identified from 374 HCC profiles in The Cancer Genome Atlas (TCGA) database. Six differentially expressed CRLnc were selected, and a prognostic risk model based on the CRLnc signature (CRLncSig) was constructed. The prognostic power of the model was verified. Moreover, a cuproptosis-related gene cluster (CRGC) was generated based on six lncRNAs and differentially expressed genes. The relationship between immune cell infiltration in the TME, immunotherapy, CRLncSig, and CRGC was demonstrated through various algorithms, Tumor Immune Dysfunction and Exclusion (TIDE), tumor mutational burden (TMB), etc. Potential drugs and sensitivity to those agents were evaluated for the risk model. LncRNA AL158166.1 was selected and verified in HCC tissues and cell lines, the impact of its knockdown and overexpression in HCC cells was examined, and the copper (Cu) concentration and the cuproptosis-related gene expression were detected.ResultsA CRLncSig prognostic risk model with good predictive ability was constructed. The low-risk group had a longer overall survival (OS), lower tumor purity, more extensive immune cell infiltration, higher immune score, enrichment in immune-activated pathways, and more positive response to immunotherapy versus the high-risk group. CRGC-B exhibited the best OS and the lowest tumor stage; the immune cell infiltration analysis was similar to the low-risk group in CRLncSig. CRGC-B belonged to the “immune-high” group of the TME. The low-risk group had a higher TIDE score and susceptibility to antitumor drugs. The lncRNA AL158166.1 had the highest hazard ratio. The levels of AL158166.1 were higher in HCC tissues versus healthy tissues. Knockdown of AL158166.1 could lead to an increase in intracellular Cu concentration, induce DLAT low expression, and inhibit the proliferation and migration of HCC cells, whereas overexpression of AL158166.1 exerted the reverse effect.ConclusionOverall, a new CRLncSig prognostic risk model and a cuproptosis-related molecular signature were constructed and evaluated. The model and signature were associated with the prognosis, immune infiltration, and immunotherapy of HCC. Inhibiting the lncRNA AL158166.1 may induce cuproptosis and showed potential for the inhibition of tumors. Evaluation of the CRLnc, CRLncSig, and CRGC may enhance our understanding of the TME, determine the effectiveness of immunotherapy, and act as a marker for the prognosis of HCC.

According to the United Nations trade policy review documents, notifications, comments, special trade concerns and official authoritative research reports, the Bangladesh agricultural food product quality and safety regulatory bodies, laws and regulations, standard system, and other aspects were reviewed. It was suggested that Chinese export enterprises take advantage of local agricultural food products to actively explore the import and export trade market between China and Bangladesh. Moreover, Chinese regulators should be followed Bangladesh agricultural food products regulatory system changes, to further strengthen the assistance of agricultural and food product safety system in Bangladesh. Functional advantages can better promote bilateral trade between the two countries.

Cross-media hashing has received extensive attention in cross-media searching tasks due to its superior searching efficiency and low storage cost.However,existing methods cannot adequately preserve the high-level semantic relevance and multi-label of multi-media data.In order to solve the above problems,this paper proposes a fine-grained semantic reasoning based cross-media dual-way adversarial hashing learning model(SDAH),which generates compact and consistent cross-media unified efficient hash semantic representations by maximizing fine-grained semantic associations between different medias.First,a fine-grained cross-media semantic association learning and inference method based on the cross-media collaborative attention mechanism is proposed.The cross-media attention mechanism collaboratively learns the fine-grained implicit semantic associations of images and texts,and obtains the salient semantic inference features of images and texts.Then,a cross-media dual-way adversarial hashing network is established to jointly learn the intra-modality and inter-modality semantic similarity constraints,and better to align the semantic distributions of different media hash codes through a two-way adversarial learning mechanism,which generates higher-quality and more discriminative cross-media unified hash representation,facilitates the process of cross-media semantic fusion and improves the cross-media searching performance.Experimental results compared with existing methods on two public datasets verify the performance superiority of the proposed method in various cross-media search scenarios.

BackgroundDeep learning models have shown great success in automating tasks in sleep medicine by learning from carefully annotated electroencephalogram (EEG) data. However, effectively using a large amount of raw EEG data remains a challenge. ObjectiveIn this study, we aim to learn robust vector representations from massive unlabeled EEG signals, such that the learned vectorized features (1) are expressive enough to replace the raw signals in the sleep staging task, and (2) provide better predictive performance than supervised models in scenarios involving fewer labels and noisy samples. MethodsWe propose a self-supervised model, Contrast with the World Representation (ContraWR), for EEG signal representation learning. Unlike previous models that use a set of negative samples, our model uses global statistics (ie, the average representation) from the data set to distinguish signals associated with different sleep stages. The ContraWR model is evaluated on 3 real-world EEG data sets that include both settings: at-home and in-laboratory EEG recording. ResultsContraWR outperforms 4 recently reported self-supervised learning methods on the sleep staging task across 3 large EEG data sets. ContraWR also supersedes supervised learning when fewer training labels are available (eg, 4% accuracy improvement when less than 2% of data are labeled on the Sleep EDF data set). Moreover, the model provides informative, representative feature structures in 2D projection. ConclusionsWe show that ContraWR is robust to noise and can provide high-quality EEG representations for downstream prediction tasks. The proposed model can be generalized to other unsupervised physiological signal learning tasks. Future directions include exploring task-specific data augmentations and combining self-supervised methods with supervised methods, building upon the initial success of self-supervised learning reported in this study.

Before uncovering coal in crosscut of high gas or outburst mine, it is necessary to design drainage boreholes and display the position of boreholes on the interaction surface of roadway and coal seam.In order to solve the problem that common AutoCAD and other drawing softwares can not efficiently meet the needs of a large number of calculations, combined with the relevant provisions of coal mine gas drainage and based on Matlab software programming, utilized Hungarian algorithm and simulated annealing algorithm, the automatic optimization design software of drilling parameters of crosscut uncovering coal gas drainage is developed.The results show that: ①the automatic optimization design software can be used to solve the problems of automatic optimization design and scheme comparison of drilling parameters based on any coal seam, roadway spatial position and extraction input conditions, which greatly improves the calculational efficiency and accuracy; ②The image drawing function realized by programming can realize the three-dimensional display of drilling track; ③On the basis of drilling parameters, the parameters of drilling layout are designed and verified synchronously to realize the closed-loop control of design and verification; ④The function of arbitrary hole filling parameters greatly improves the practicability of the automatic optimization design software and solves the problem of hole filling parameter design.The application of automatic optimization design of gas drainage borehole parameters in crosscut uncovering can obtain the optimal drainage borehole parameters, greatly improve the work efficiency of field technicians in coal mine, and provide important support for intelligent mining in coal mines.

Maximum margin clustering (MMC) is an effective clustering algorithm, which first extends a large margin principle into unsupervised learning. This paper revisits the MMC problem and points out the potential problems encountered by a cutting plane approach. We propose an improved MMC algorithm via the bundle method (BMMC). Specifically, the constrained convex-concave procedure algorithm is first applied to decompose the MMC problem into a series of convex sub-problems, and then, the bundle method is adopted to efficiently solve each sub-problem. Moreover, a simpler formulation for the multi-class MMC is presented. In addition to clustering problems, the BMMC is also extended to the semi-supervised case by incorporating the pairwise constraints, which reveals its high scalability. Compared with the previous works, the proposed solution is much simpler and faster. The experiments on several data sets are conducted to demonstrate the effectiveness of our proposed algorithm.

Abstract Parkinson’s disease (PD) is associated with diverse clinical manifestations including motor and non-motor signs and symptoms, and emerging biomarkers. We aimed to reveal the heterogeneity of PD to define subtypes and their progression rates using an automated deep learning algorithm on the top of longitudinal clinical records. This study utilizes the data collected from the Parkinson’s Progression Markers Initiative (PPMI), which is a longitudinal cohort study of patients with newly diagnosed Parkinson’s disease. Clinical information including motor and non-motor assessments, biospecimen examinations, and neuroimaging results were used for identification of PD subtypes. A deep learning algorithm, Long-Short Term Memory (LSTM), was used to represent each patient as a multi-dimensional time series for subtype identification. Both visualization and statistical analysis were performed for analyzing the obtained PD subtypes. As a result, 466 patients with idiopathic PD were investigated and three subtypes were identified. Subtype I (Mild Baseline, Moderate Motor Progression) is comprised of 43.1% of the participants, with average age 58.79 ± 9.53 years, and was characterized by moderate functional decay in motor ability but stable cognitive ability. Subtype II (Moderate Baseline, Mild Progression) is comprised of 22.9% of the participants, with average age 61.93 ± 6.56 years, and was characterized by mild functional decay in both motor and non-motor symptoms. Subtype III (Severe Baseline, Rapid Progression) is comprised 33.9% of the patients, with average age 65.32 ± 8.86 years, and was characterized by rapid progression of both motor and non-motor symptoms. These subtypes suggest that when comprehensive clinical and biomarker data are incorporated into a deep learning algorithm, the disease progression rates do not necessarily associate with baseline severities, and the progression rate of non-motor symptoms is not necessarily correlated with the progression rate of motor symptoms.

Hospital readmission costs a lot of money every year. Many hospital readmissions are avoidable, and excessive hospital readmissions could also be harmful to the patients. Accurate prediction of hospital readmission can effectively help reduce the readmission risk. However, the complex relationship between readmission and potential risk factors makes readmission prediction a difficult task. The main goal of this paper is to explore deep learning models to distill such complex relationships and make accurate predictions.We propose CONTENT, a deep model that predicts hospital readmissions via learning interpretable patient representations by capturing both local and global contexts from patient Electronic Health Records (EHR) through a hybrid Topic Recurrent Neural Network (TopicRNN) model. The experiment was conducted using the EHR of a real world Congestive Heart Failure (CHF) cohort of 5,393 patients.The proposed model outperforms state-of-the-art methods in readmission prediction (e.g. 0.6103 ± 0.0130 vs. second best 0.5998 ± 0.0124 in terms of ROC-AUC). The derived patient representations were further utilized for patient phenotyping. The learned phenotypes provide more precise understanding of readmission risks.Embedding both local and global context in patient representation not only improves prediction performance, but also brings interpretable insights of understanding readmission risks for heterogeneous chronic clinical conditions.This is the first of its kind model that integrates the power of both conventional deep neural network and the probabilistic generative models for highly interpretable deep patient representation learning. Experimental results and case studies demonstrate the improved performance and interpretability of the model.

Yb:YAG transparent ceramic nano-powder was prepared by chemical co-precipitation method, with ammonium bicarbonate as the precipitant and polyethylene glycol as surfactant. The addition of polyethylene glycol can reduce the agglomeration and particle size of the prepared Yb:YAG powder. The morphology, thermal stability and phase structure of Yb:YAG nano-powder were charactered by scanning electron microscopy (SEM), thermogravimetry and differential thermal analysis (TG-DTA), X-ray diffraction (XRD), and Fourier transform infrared spectroscopy. The results show that well-crystallized nano-powder was obtained by calcining the precursors at 900 °C for 3 h. The average particle size of Yb:YAG powder is about 100–200 nm. When the volume amount of polyethylene glycol is 2.0%, well-dispersed Yb:YAG powder with spherical particles of 100 nm diameter was obtained.

Beamforming of Ultra Wide Band (UWB) signals is a crucial technology in UWB radar. Conventional beamforming methods have limitations in instantaneous bandwidth, scanning range, beam pattern deviation and a mainlobe distortion. Direct Time Delay (DTD) compensation is an effective way to avoid above problems. In this paper, a DTD compensation based on Hermite interpolation filters is presented to implement beamforming of UWB signals. Theoretical analysis and simulation results show that the magnitude responses and group delay of the proposed filters are better than those based on Lagrange and Radial basis methods. The demonstration of the UWB Linear-Frequency modulated (LFM) signal also shows excellent performances on the UWB beamforming of the proposed method.

Different modes of electron impact (EI), positive chemical ionization (PCI) and negative chemical ionization (NCI) in Gas Chromatography-Mass Spectrometry (GC-MS) determination of organochlorine pesticides (OCPs) and polychlorinated biphenyls (PCBs) in food are summarized and compared in this paper. Both PCI-MS/MS and EI-MS/MS have high selectivity and sensitivity. PCI-MS/MS has obvious advantages in the analysis of compounds containing nitro or carbonyl groups, while EI-MS/MS has slightly better performance when dealing with dieldrin, endrin, endosulfan and its metabolites. NCI-MS has the highest sensitivity, but weaker anti-interference ability, and is not suitable for the analysis of DDTs and PCBs. In summary, these three mass spectrometry methods, which are complementary and alternative to the routine analysis, can satisfy the requirements of pesticide residue analysis in food by providing good accuracy, good precision and low detection limits.Also it points out that Gas Chromatography showed a superiority in organochlorine compound analysis, but for traditional GC-MS, especially in the EI ionization mode, the spectrum is more complicated because of matrix interference. New ionization modes including the highly selective chemical ionization, will be one of future developments and trends of Gas Chromatography applications in the field of food safety analysis.