Tuberculosis-targeted next-generation sequencing and machine learning: An ultrasensitive diagnostic strategy for paucibacillary pulmonary tuberculosis and tuberculous meningitis.

BACKGROUND: Existing diagnostic approaches for paucibacillary tuberculosis (TB) are limited by the low sensitivity of testing methods and difficulty in obtaining suitable samples. METHODS: An ultrasensitive TB diagnostic strategy was established, integrating efficient and specific TB targeted next-generation sequencing and machine learning models, and validated in clinical cohorts to test plasma cfDNA, cerebrospinal fluid (CSF) DNA collected from tuberculous meningitis (TBM) and pediatric pulmonary TB (PPTB) patients. RESULTS: In the detection of 227 samples, application of the specific thresholds of CSF DNA (AUC = 0.974) and plasma cfDNA (AUC = 0.908) yielded sensitivity of 97.01 % and the specificity of 95.65 % in CSF samples and sensitivity of 82.61 % and specificity of 86.36 % in plasma samples, respectively. In the analysis of 44 paired samples from TBM patients, our strategy had a high concordance of 90.91 % (40/44) in plasma cfDNA and CSF DNA with both sensitivity of 95.45 % (42/44). In the PPTB patient, the sensitivity of the TB diagnostic strategy yielded higher sensitivity on plasma specimen than Xpert assay on gastric lavage (28.57 % VS. 15.38 %). CONCLUSIONS: Our TB diagnostic strategy provides greater detection sensitivity for paucibacillary TB, while plasma cfDNA as an easily collected specimen, could be an appropriate sample type for PTB and TBM diagnosis.

Effect of Thermophilic Microbial Agents on Crude Fiber Content, Carbohydrate-Active Enzyme Genes, and Microbial Communities during Chinese Medicine Residue Composting.

The objective of this study was to investigate the impact of thermophilic bacteria on crude fiber content, carbohydrate-active enzyme (CAZyme) genes, and associated microbial communities during Chinese medicine residues composting. The study examines changes over 15 days of composting with (T) and without (CK) thermophilic microbial agents. Results show that the group T compost temperature reached a maximum of 71.0 °C and remained above 70 °C for 2 days, while the group CK maximum temperature was only 60.9 °C. On Day 15, the seed germination index (GI) of group T reached 98.7%, while the group CK GI was only 56.7%. After composting, the degradation rates of cellulose, hemicellulose, and lignin in group T increased by 5.1, 22.5, and 18.5%, respectively, compared to those in group CK. Thermophilic microbial agents changed the microbial communities related to CAZymes, increasing unclassified_o_Myxococcales and Sphaerobacter abundance and reducing Acinetobacter and Sphingobacterium abundance. Thermophilic microbial agents also increased the abundance of the GT4, GT2_Glycos_transf_2, and AA3 gene families. These results show that thermophilic microbial agents can increase composting temperature, accelerate compost maturation, and promote crude fiber degradation. Therefore, they have broad application potential.

Integrating host transcriptomic signatures for distinguishing autoimmune encephalitis in cerebrospinal fluid by metagenomic sequencing.

BACKGROUND: The early accurate diagnoses for autoimmune encephalitis (AE) and infectious encephalitis (IE) are essential since the treatments for them are different. This study aims to discover some specific and sensitive biomarkers to distinguish AE from IE at early stage to give specific treatments for good outcomes. RESULTS: We compared the host gene expression profiles and microbial diversities of cerebrospinal fluid (CSF) from 41 patients with IE and 18 patients with AE through meta-transcriptomic sequencing. Significant differences were found in host gene expression profiles and microbial diversities in CSF between patients with AE and patients with IE. The most significantly upregulated genes in patients with IE were enriched in pathways related with immune response such as neutrophil degranulation, antigen processing and presentation and adaptive immune system. In contrast, those upregulated genes in patients with AE were mainly involved in sensory organ development such as olfactory transduction, as well as synaptic transmission and signaling. Based on the differentially expressed genes, a classifier consisting of 5 host genes showed outstanding performance with an area under the receiver operating characteristic (ROC) curve (AUC) of 0.95. CONCLUSIONS: This study provides a promising classifier and is the first to investigate transcriptomic signatures for differentiating AE from IE by using meta-transcriptomic next-generation sequencing technology.

Metasurface Holography with Multiplexing and Reconfigurability.

Metasurface holography offers significant advantages, including a broad field of view, minimal noise, and high imaging quality, making it valuable across various optical domains such as 3D displays, VR, and color displays. However, most passive pure-structured metasurface holographic devices face a limitation: once fabricated, as their functionality remains fixed. In recent developments, the introduction of multiplexed and reconfigurable metasurfaces breaks this limitation. Here, the comprehensive progress in holography from single metasurfaces to multiplexed and reconfigurable metasurfaces is reviewed. First, single metasurface holography is briefly introduced. Second, the latest progress in angular momentum multiplexed metasurface holography, including basic characteristics, design strategies, and diverse applications, is discussed. Next, a detailed overview of wavelength-sensitive, angle-sensitive, and polarization-controlled holograms is considered. The recent progress in reconfigurable metasurface holography based on lumped elements is highlighted. Its instant on-site programmability combined with machine learning provides the possibility of realizing movie-like dynamic holographic displays. Finally, we briefly summarize this rapidly growing area of research, proposing future directions and potential applications.

Design of ultra-thin underwater acoustic metasurface for broadband low-frequency diffuse reflection by deep neural networks.

Underwater acoustic metasurfaces have broad application prospects for the stealth of underwater objects. However, problems such as a narrow operating frequency band, poor operating performance, and considerable thickness at low frequencies remain. In this study a reverse design method for ultra-thin underwater acoustic metasurfaces for low-frequency broadband is proposed using a tandem fully connected deep neural network. The tandem neural network consists of a pre-trained forward neural network and a reverse neural network, based on which a set of elements with flat phase variation and an almost equal phase shift interval in the range of 700-1150 Hz is designed. A diffuse underwater acoustic metasurface with 60 elements was designed, showing that the energy loss of the metasurface in the echo direction was greater than 10 dB. Our work opens a novel pathway for realising low-frequency wideband underwater acoustic devices, which will enable various applications in the future.

Airspeed-Aided State Estimation Algorithm of Small Fixed-Wing UAVs in GNSS-Denied Environments.

Aimed at improving the navigation accuracy of the fixed-wing UAVs in GNSS-denied environments, this paper proposes an algorithm of nongravitational acceleration estimation based on airspeed and IMU sensors, which use a differential tracker (TD) model to further supplement the effect of linear acceleration for UAVs under dynamic flight. We further establish the mapping relationship between vehicle nongravitational acceleration and the vehicle attitude misalignment angle and transform it into the attitude angle rate deviation through the nonlinear complementary filtering model for real-time compensation. It can improve attitude estimation precision significantly for vehicles in dynamic conditions. Furthermore, a lightweight complementary filter is used to improve the accuracy of vehicle velocity estimation based on airspeed, and a barometer is fused on the height channel to achieve the accurate tracking of height and the lift rate. The algorithm is actually deployed on low-cost fixed-wing UAVs and is compared with ACF, EKF, and NCF by using real flight data. The position error within 30 s (about 600 m flying) in the horizontal channel flight is less than 30 m, the error within 90 s (about 1800 m flying) is less than 50 m, and the average error of the height channel is 0.5 m. The simulation and experimental tests show that this algorithm can provide UAVs with good attitude, speed, and position calculation accuracy under UAV maneuvering environments.

Decoding multiclass motor imagery EEG from the same upper limb by combining Riemannian geometry features and partial least squares regression.

OBJECTIVE: Due to low spatial resolution and poor signal-to-noise ratio of electroencephalogram (EEG), high accuracy classifications still suffer from lots of obstacles in the context of motor imagery (MI)-based brain-machine interface (BMI) systems. Particularly, it is extremely challenging to decode multiclass MI EEG from the same upper limb. This research proposes a novel feature learning approach to address the classification problem of 6-class MI tasks, including imaginary elbow flexion/extension, wrist supination/pronation, and hand close/open within the unilateral upper limb. APPROACH: Instead of the traditional common spatial pattern (CSP) or filter-bank CSP (FBCSP) manner, the Riemannian geometry (RG) framework involving Riemannian distance and Riemannian mean was directly adopted to extract tangent space (TS) features from spatial covariance matrices of the MI EEG trials. Subsequently, to reduce the dimensionality of the TS features, the algorithm of partial least squares regression was applied to obtain more separable and compact feature representations. MAIN RESULTS: The performance of the learned RG feature representations was validated by a linear discriminative analysis and support vector machine classifier, with an average accuracy of 80.50% and 79.70% on EEG dataset collected from 12 participants, respectively. SIGNIFICANCE: These results demonstrate that compared with CSP and FBCSP features, the proposed approach can significantly increase the decoding accuracy for multiclass MI tasks from the same upper limb. This approach is promising and could potentially be applied in the context of MI-based BMI control of a robotic arm or a neural prosthesis for motor disabled patients with highly impaired upper limb.

Experimental demonstration of a graphene-based hybrid plasmonic modulator: publisher's note.

This publisher's note contains corrections to Opt. Lett.44, 2586 (2019)OPLEDP0146-959210.1364/OL.44.002586.

Experimental demonstration of a graphene-based hybrid plasmonic modulator.

In this Letter, we report a graphene-based hybrid plasmonic modulator (GHPM) realized by employing the electro-absorption effect of graphene. The simulation results show that the modulation efficiency of GHPM, i.e., extinction ratio per length, can be as large as 0.417 dB/µm, which is more than twice as much as that of recently presented graphene-on-silicon modulator. It was found that the improvement in modulation efficiency is mainly due to the enhancement of the overlap between graphene and the mode field in GHPM. A prototype of GHPM was fabricated. The measurement results showed that the GHPM can work in a broadband from 1530 to 1570 nm and an improved modulation efficiency of 1.08 dB (at 30 µm). Finally, we have discussed the factors that influence the modulation efficiency. Our proof-of-concept design may promote the development of on-chip graphene-based plasmonic devices.

An inter-subject model to reduce the calibration time for motion imagination-based brain-computer interface.

A major factor blocking the practical application of brain-computer interfaces (BCI) is the long calibration time. To obtain enough training trials, participants must spend a long time in the calibration stage. In this paper, we propose a new framework to reduce the calibration time through knowledge transferred from the electroencephalogram (EEG) of other subjects. We trained the motor recognition model for the target subject using both the target's EEG signal and the EEG signals of other subjects. To reduce the individual variation of different datasets, we proposed two data mapping methods. These two methods separately diminished the variation caused by dissimilarities in the brain activation region and the strength of the brain activation in different subjects. After these data mapping stages, we adopted an ensemble method to aggregate the EEG signals from all subjects into a final model. We compared our method with other methods that reduce the calibration time. The results showed that our method achieves a satisfactory recognition accuracy using very few training trials (32 samples). Compared with existing methods using few training trials, our method achieved much greater accuracy. Graphical abstract The framework of the proposed method. The workflow of the framework have three steps: 1, process each subjects EEG signals according to the target subject's EEG signal. 2, generate models from each subjects' processed signals. 3, ensemble these models to a final model, the final model is a model for the target subject.

A Decoding Scheme for Incomplete Motor Imagery EEG With Deep Belief Network.

High accuracy decoding of electroencephalogram (EEG) signal is still a major challenge that can hardly be solved in the design of an effective motor imagery-based brain-computer interface (BCI), especially when the signal contains various extreme artifacts and outliers arose from data loss. The conventional process to avoid such cases is to directly reject the entire severely contaminated EEG segments, which leads to a drawback that the BCI has no decoding results during that certain period. In this study, a novel decoding scheme based on the combination of Lomb-Scargle periodogram (LSP) and deep belief network (DBN) was proposed to recognize the incomplete motor imagery EEG. Particularly, instead of discarding the entire segment, two forms of data removal were adopted to eliminate the EEG portions with extreme artifacts and data loss. The LSP was utilized to steadily extract the power spectral density (PSD) features from the incomplete EEG constructed by the remaining portions. A DBN structure based on the restricted Boltzmann machine (RBM) was exploited and optimized to perform the classification task. Various comparative experiments were conducted and evaluated on simulated signal and real incomplete motor imagery EEG, including the comparison of three PSD extraction methods (fast Fourier transform, Welch and LSP) and two classifiers (DBN and support vector machine, SVM). The results demonstrate that the LSP can estimate relative robust PSD features and the proposed scheme can significantly improve the decoding performance for the incomplete motor imagery EEG. This scheme can provide an alternative decoding solution for the motor imagery EEG contaminated by extreme artifacts and data loss. It can be beneficial to promote the stability, smoothness and maintain consecutive outputs without interruption for a BCI system that is suitable for the online and long-term application.

Enhanced Photocatalytic Activities of g-C3N4 via Hybridization with a Bi-Fe-Nb-Containing Ferroelectric Pyrochlore.

Ferroelectricity may promote photocatalytic performance because the carrier-separation efficiency can be effectively improved by the internal electrostatic field caused by spontaneous polarization. Heterostructures that combine ferroelectric materials with other semiconductor materials can be further advantageous to the photocatalysis process. In this work, Bi1.65Fe1.16Nb1.12O7 was hybridized with g-C3N4 via a facile low-temperature method. The results of high-resolution transmission electron microscopy confirmed that a tight interface was formed between g-C3N4 and Bi1.65Fe1.16Nb1.12O7, which gave the (g-C3N4)-(Bi1.65Fe1.16Nb1.12O7) heterojunction a more superior visible light photocatalytic performance. The degradation of rhodamine B by optimized (g-C3N4)0.5-(Bi1.65Fe1.16Nb1.12O7)0.5 under visible light was almost 3.3 times higher than that by monomer Bi1.65Fe1.16Nb1.12O7 and 7.4 times higher than that by g-C3N4. The (g-C3N4)0.5-(Bi1.65Fe1.16Nb1.12O7)0.5 sample also showed the highest photocurrent in the photoelectrochemical tests. To further verify the benefit of the built-in electric field in terms of the photocatalytic performance, Bi2FeNbO7, with a higher spontaneous polarization, was also synthesized and hybridized with g-C3N4. Both Bi2FeNbO7 and (g-C3N4)0.5-(Bi2FeNbO7)0.5 exhibited better photocatalytic activities than those of Bi1.65Fe1.16Nb1.12O7 and (g-C3N4)0.5-(Bi1.65Fe1.16Nb1.12O7)0.5, although the latter ones had a stronger visible-light absorbance. This implies the very promising prospects of applying ferroelectric materials for solar energy harvest.