Analysis of the encryption penalty in a QAM-based quantum noise stream cipher.

Quantum noise stream cipher based on quadrature-amplitude-modulation (QAM/QNSC) is a kind of physical layer encryption technology. However, the additional encryption penalty will significantly affect the practical deployment of QNSC, especially in the high capacity and long-haul transmission system. With our research, the encryption process of QAM/QNSC degrades the transmission performance of plaintext information. In this paper, we quantitatively analyze the encryption penalty of QAM/QNSC based on the proposed concept of effective minimum Euclidean distance. We calculate the theoretical signal-to-noise ratio sensitivity and encryption penalty of QAM/QNSC signals. A modified feedforward pilot-aided two-stage carrier phase recovery scheme is used to reduce the effect of laser phase noise and the encryption penalty. Experimental results achieve single-channel 205.9 Gbit/s 640km transmission with single carrier polarization-diversity-multiplexing 16-QAM/QNSC signal.

Integrating key generation and distribution with the quantum noise stream cipher system without compromising the transmission performance.

We propose and experimentally demonstrate a secure quantum noise stream cipher transmission system that integrates key generation and distribution. At the stage of carrier phase recovery, the estimated phase noise is used to generate randomness keys without additional equipment. Based on direct sequence spread spectrum technology, we integrate the distributed keys with quantum noise stream cipher signals. The key distribution and encryption transmission can be completed simultaneously without occupying additional bandwidth or time slots. By changing the position of distributed keys in the encryption base, the BER performance of QAM/QNSC signals cannot be affected by the keys. Experimental results demonstrate that the 54.5 Mbps key distribution and 31 Gbps encryption transmission without OSNR penalty can be achieved simultaneously over a 120 km standard single-mode fiber.

Real-time measurement of the trans-epithelial electrical resistance in an organ-on-a-chip during cell proliferation.

The trans-epithelial electrical resistance (TEER) is widely used to quantitatively evaluate cellular barrier function at the organ level in vitro. The measurement of the TEER in organ-on-chips (organ chips) plays a significant role in medical and pharmacological research. However, due to the limitation of the electrical equivalent model for organ chips, the existing TEER measurements usually neglect the changes of the TEER during cell proliferation, resulting in the low accuracy of the measurements. Here, we proposed a new whole-region model of the TEER and developed a real-time TEER measurement system that contains an organ chip with a plate electrode. A whole region circuit model considering the impedance of the non-cell covered region was also established, which enables TEER measurements to be independent of the changes in the cell covered region. The impedance of the non-cell covered region is here attributed to the resistance of the porous membrane. By combining the real-time measurement system and the whole region model, subtle changes in cellular activity during the proliferation stage were measured continuously every 6 minutes and a more sensitive TEER response was obtained. Furthermore, the TEER measurement accuracy was also verified by the real-time measurement of the TEER with stimulation using the permeability enhancer ethylene glycol-bis(2-aminoethylether)-N,N,N',N'-tetraacetic acid (EGTA). The obtained results indicated that the new proposed whole region model and the real-time measurement system have higher accuracy and greater sensitivity than the traditional model.

HearCough: Enabling continuous cough event detection on edge computing hearables.

Cough event detection is the foundation of any measurement associated with cough, one of the primary symptoms of pulmonary illnesses. This paper proposes HearCough, which enables continuous cough event detection on edge computing hearables, by leveraging always-on active noise cancellation (ANC) microphones in commodity hearables. Specifically, we proposed a lightweight end-to-end neural network model - Tiny-COUNET and its transfer learning based traning method. When evaluated on our acted cough event dataset, Tiny-COUNET achieved equivalent detection performance but required significantly less computational resources and storage space than cutting-edge cough event detection methods. Then we implemented HearCough by quantifying and deploying the pre-trained Tiny-COUNET to a popular micro-controller in consumer hearables. Lastly, we evaluated that HearCough is effective and reliable for continuous cough event detection through a field study with 8 patients. HearCough achieved 2 Hz cough event detection with an accuracy of 90.0% and an F1-score of 89.5% by consuming an additional 5.2 mW power. We envision HearCough as a low-cost add-on for future hearables to enable continuous cough detection and pulmonary health monitoring.

Enabling Real-Time On-Chip Audio Super Resolution for Bone-Conduction Microphones.

Voice communication using an air-conduction microphone in noisy environments suffers from the degradation of speech audibility. Bone-conduction microphones (BCM) are robust against ambient noises but suffer from limited effective bandwidth due to their sensing mechanism. Although existing audio super-resolution algorithms can recover the high-frequency loss to achieve high-fidelity audio, they require considerably more computational resources than is available in low-power hearable devices. This paper proposes the first-ever real-time on-chip speech audio super-resolution system for BCM. To accomplish this, we built and compared a series of lightweight audio super-resolution deep-learning models. Among all these models, ATS-UNet was the most cost-efficient because the proposed novel Audio Temporal Shift Module (ATSM) reduces the network's dimensionality while maintaining sufficient temporal features from speech audio. Then, we quantized and deployed the ATS-UNet to low-end ARM micro-controller units for a real-time embedded prototype. The evaluation results show that our system achieved real-time inference speed on Cortex-M7 and higher quality compared with the baseline audio super-resolution method. Finally, we conducted a user study with ten experts and ten amateur listeners to evaluate our method's effectiveness to human ears. Both groups perceived a significantly higher speech quality with our method when compared to the solutions with the original BCM or air-conduction microphone with cutting-edge noise-reduction algorithms.

Microsphere mediated exosome isolation and ultra-sensitive detection on a dielectrophoresis integrated microfluidic device.

Tumor-derived exosomes have been recognized as potential biomarkers for cancer diagnosis because they are actively involved in cancer progression and metastasis. However, progress in practical exosome analysis is still slow due to the limitation in exosome isolation and detection. The development of microfluidic devices has provided a promising analytical platform compared with traditional methods. In this study, we develop an exosome isolation and detection method based on a microfluidic device (ExoDEP-chip), which realized microsphere mediated dielectrophoretic isolation and immunoaffinity detection. Exosomes were firstly isolated by binding to antibodies pre-immobilized on the polystyrene (PS) microsphere surface and were further detected using fluorescently labeled antibodies by fluorescence microscopy. Single microspheres were then trapped into single microwells under the DEP force in the ExoDEP-chip. A wide range from 1.4 × 103 to 1.4 × 108 exosomes per mL with a detection limit of 193 exosomes per mL was obtained. Through monitoring five proteins (CD81, CEA, EpCAM, CD147, and AFP) of exosomes from three different cell lines (A549, HEK293, and HepG2), a significant difference in marker expression levels was observed in different cell lines. Therefore, this method has good prospects in exosome-based tumor marker detection and cancer diagnosis.

A new species of Paraputo Laing (Hemiptera: Coccomorpha: Pseudococcidae) on Fagaceae from southern China.

A new species of mealybug (Hemiptera: Coccomorpha: Pseudococcidae), Paraputo nanlingensis Li & Wu, sp. n., is recorded on Fagaceae from Guangdong Province, China. The adult female is described and illustrated, and an identification key is provided to separate the adult females of Paraputo species known from China.

Acoustofluidics-enhanced biosensing with simultaneously high sensitivity and speed.

Simultaneously achieving high sensitivity and detection speed with traditional solid-state biosensors is usually limited since the target molecules must passively diffuse to the sensor surface before they can be detected. Microfluidic techniques have been applied to shorten the diffusion time by continuously moving molecules through the biosensing regions. However, the binding efficiencies of the biomolecules are still limited by the inherent laminar flow inside microscale channels. In this study, focused traveling surface acoustic waves were directed into an acoustic microfluidic chip, which could continuously enrich the target molecules into a constriction zone for immediate detection of the immune reactions, thus significantly improving the detection sensitivity and speed. To demonstrate the enhancement of biosensing, we first developed an acoustic microfluidic chip integrated with a focused interdigital transducer; this transducer had the ability to capture more than 91% of passed microbeads. Subsequently, polystyrene microbeads were pre-captured with human IgG molecules at different concentrations and loaded for detection on the chip. As representative results, ~0.63, 2.62, 11.78, and 19.75 seconds were needed to accumulate significant numbers of microbeads pre-captured with human IgG molecules at concentrations of 100, 10, 1, and 0.1 ng/mL (~0.7 pM), respectively; this process was faster than the other methods at the hour level and more sensitive than the other methods at the nanomolar level. Our results indicated that the proposed method could significantly improve both the sensitivity and speed, revealing the importance of selective enrichment strategies for rapid biosensing of rare molecules.

piRT-IFC: Physics-informed real-time impedance flow cytometry for the characterization of cellular intrinsic electrical properties.

Real-time transformation was important for the practical implementation of impedance flow cytometry. The major obstacle was the time-consuming step of translating raw data to cellular intrinsic electrical properties (e.g., specific membrane capacitance Csm and cytoplasm conductivity σcyto). Although optimization strategies such as neural network-aided strategies were recently reported to provide an impressive boost to the translation process, simultaneously achieving high speed, accuracy, and generalization capability is still challenging. To this end, we proposed a fast parallel physical fitting solver that could characterize single cells' Csm and σcyto within 0.62 ms/cell without any data preacquisition or pretraining requirements. We achieved the 27000-fold acceleration without loss of accuracy compared with the traditional solver. Based on the solver, we implemented physics-informed real-time impedance flow cytometry (piRT-IFC), which was able to characterize up to 100,902 cells' Csm and σcyto within 50 min in a real-time manner. Compared to the fully connected neural network (FCNN) predictor, the proposed real-time solver showed comparable processing speed but higher accuracy. Furthermore, we used a neutrophil degranulation cell model to represent tasks to test unfamiliar samples without data for pretraining. After being treated with cytochalasin B and N-Formyl-Met-Leu-Phe, HL-60 cells underwent dynamic degranulation processes, and we characterized cell's Csm and σcyto using piRT-IFC. Compared to the results from our solver, accuracy loss was observed in the results predicted by the FCNN, revealing the advantages of high speed, accuracy, and generalizability of the proposed piRT-IFC.

Dynamic Detection of Specific Membrane Capacitance and Cytoplasmic Resistance of Neutrophils After Ischemic Stroke.

Peripheral blood is the most readily available resource for stroke patient prognosis, but there is a lack of methods to detect dynamic changes of neutrophils in peripheral blood that can be used in the clinic. Herein, we developed a procedure to characterize dynamic changes of neutrophils based on their electrical properties in rats after transient middle cerebral artery occlusion (MCAO). We characterized the specific membrane capacitance (Csm) and cytoplasmic resistance (σcyto) of approximately 27,600 neutrophils from MCAO rats 24 h after ischemia/reperfusion. We found that the Csm and σcyto of neutrophils in the MCAO group were significantly higher compared to the sham group. Furthermore, we observed a monotonically upward shift in neutrophil Csm in the MCAO group during the four 5-minute test cycles. Our findings suggest that the dynamic changes of cellular electrical properties could reflect neutrophil activity and serve as a prognostic indicator for ischemic stroke in the clinical setting.

From the teapot effect to tap-triggered self-wetting: a 3D self-driving sieve for whole blood filtration.

Achieving passive microparticle filtration with micropore membranes is challenging due to the capillary pinning effect of the membranes. Inspired by the teapot effect that occurs when liquid (tea) is poured from a teapot spout, we proposed a tap-triggered self-wetting strategy and utilized the method with a 3D sieve to filter rare cells. First, a 3D-printed polymer tap-trigger microstructure was implemented. As a result, the 3 µm micropore membrane gating threshold (the pressure needed to open the micropores) was lowered from above 3000 to 80 Pa by the tap-trigger microstructure that facilated the liquid leakage and spreading to self-wet more membrane area in a positive feedback loop. Then, we implemented a 3D cone-shaped cell sieve with tap-trigger microstructures. Driven by gravity, the sieve performed at a high throughput above 20 mL/min (DPBS), while the micropore size and porosity were 3 µm and 14.1%, respectively. We further filtered leukocytes from whole blood samples with the proposed new 3D sieve, and the method was compared with the traditional method of leukocyte isolation by chemically removing red blood cells. The device exhibited comparable leukocyte purity but a higher platelet removal rate and lower leukocyte simulation level, facilitating downstream single-cell analysis. The key results indicated that the tap-triggered self-wetting strategy could significantly improve the performance of passive microparticle filtration.

A 3D Capillary-Driven Multi-Micropore Membrane-Based Trigger Valve for Multi-Step Biochemical Reaction.

Point-of-care testing (POCT) techniques based on microfluidic devices enabled rapid and accurate tests on-site, playing an increasingly important role in public health. As the critical component of capillary-driven microfluidic devices for POCT use, the capillary microfluidic valve could schedule multi-step biochemical operations, potentially being used for broader complex POCT tasks. However, owing to the reciprocal relationship between the capillary force and aperture in single-pore microchannels, it was challenging to achieve a high gating threshold and high operable liquid volume simultaneously with existing 2D capillary trigger valves. This paper proposed a 3D capillary-driven multi-microporous membrane-based trigger valve to address the issue. Taking advantage of the high gating threshold determined by micropores and the self-driven capillary channel, a 3D trigger valve composed of a microporous membrane for valving and a wedge-shaped capillary channel for flow pumping was implemented. Utilizing the capillary pinning effect of the multi-micropore membrane, the liquid above the membrane could be triggered by putting the drainage agent into the wedge-shaped capillary channel to wet the underside of the membrane, and it could also be cut off by taking away the agent. After theoretical analysis and performance characterizations, the 3D trigger valve performed a high gating threshold (above 1000 Pa) and high trigger efficiency with an operable liquid volume above 150 µL and a trigger-to-drain time below 6 s. Furthermore, the retention and trigger states of the valve could be switched for repeatable triggering for three cycles within 5 min. Finally, the microbead-based immunoreaction and live cell staining applications verified the valve's ability to perform multi-step operations. The above results showed that the proposed 3D trigger valve could be expected to play a part in wide-ranging POCT application scenarios.

A Novel Single-Cell RNA Sequencing Data Feature Extraction Method Based on Gene Function Analysis and Its Applications in Glioma Study.

Critical in revealing cell heterogeneity and identifying new cell subtypes, cell clustering based on single-cell RNA sequencing (scRNA-seq) is challenging. Due to the high noise, sparsity, and poor annotation of scRNA-seq data, existing state-of-the-art cell clustering methods usually ignore gene functions and gene interactions. In this study, we propose a feature extraction method, named FEGFS, to analyze scRNA-seq data, taking advantage of known gene functions. Specifically, we first derive the functional gene sets based on Gene Ontology (GO) terms and reduce their redundancy by semantic similarity analysis and gene repetitive rate reduction. Then, we apply the kernel principal component analysis to select features on each non-redundant functional gene set, and we combine the selected features (for each functional gene set) together for subsequent clustering analysis. To test the performance of FEGFS, we apply agglomerative hierarchical clustering based on FEGFS and compared it with seven state-of-the-art clustering methods on six real scRNA-seq datasets. For small datasets like Pollen and Goolam, FEGFS outperforms all methods on all four evaluation metrics including adjusted Rand index (ARI), normalized mutual information (NMI), homogeneity score (HOM), and completeness score (COM). For example, the ARIs of FEGFS are 0.955 and 0.910, respectively, on Pollen and Goolam; and those of the second-best method are only 0.938 and 0.910, respectively. For large datasets, FEGFS also outperforms most methods. For example, the ARIs of FEGFS are 0.781 on both Klein and Zeisel, which are higher than those of all other methods but slight lower than those of SC3 (0.798 and 0.807, respectively). Moreover, we demonstrate that CMF-Impute is powerful in reconstructing cell-to-cell and gene-to-gene correlation and in inferring cell lineage trajectories. As for application, take glioma as an example; we demonstrated that our clustering methods could identify important cell clusters related to glioma and also inferred key marker genes related to these cell clusters.

Discovery of Potential Anti-infective Therapy Targeting Glutamine Synthetase in Staphylococcus xylosus.

Glutamine synthetase (GS), which catalyzes the production of glutamine, plays essential roles in most biological growth and biofilm formation, suggesting that GS may be used as a promising target for antibacterial therapy. We asked whether a GS inhibitor could be found as an anti-infective agent of Staphylococcus xylosus (S. xylosus). Here, computational prediction followed by experimental testing was used to characterize GS. Sorafenib was finally determined through computational prediction. In vitro experiments showed that sorafenib has an inhibitory effect on the growth of S. xylosus by competitively occupying the active site of GS, and the minimum inhibitory concentration was 4 mg/L. In vivo experiments also proved that treatment with sorafenib significantly reduced the levels of TNF-α and IL-6 in breast tissue from mice mastitis, which was further confirmed by histopathology examination. These findings indicated that sorafenib could be utilized as an anti-infective agent for the treatment of infections caused by S. xylosus.