Automated apoptosis identification in fluorescence imaging of nucleus based on histogram of oriented gradients of high-frequency wavelet coefficients

The automatic and accurate identification of apoptosis facilitates large-scale cell analysis.Most identification approaches using nucleus fluorescence imaging are based on specific morphological parameters.However,these parameters cannot completely describe nuclear morphology,thus limiting the identification accuracy of models.This paper proposes a new feature extraction method to improve the performance of the model for apoptosis identification.The proposed method uses a histogram of oriented gradient(HOG)of high-frequency wavelet coefficients to extract internal and edge texture information.The HOG vectors are classified using support vector machine.The experimental results demonstrate that the proposed feature extraction method well performs apoptosis identification,attaining 95:7% accuracy with low cost in terms of time.We confirmed that our method has potential applications to cell biology research.

An in-situ self-etching enabled high-power electrode for aqueous zinc-ion batteries

Sluggish storage kinetics is considered as the main bottleneck of cathode materials for fast-charging aqueous zinc-ion batteries(AZIBs).In this report,we propose a novel in-situ self-etching strategy to unlock the Palm tree-like vanadium oxide/carbon nanofiber membrane(P-VO/C)as a robust freestanding electrode.Comprehensive investigations including the finite element simulation,in-situ X-ray diffraction,and in-situ electrochemical impedance spectroscopy disclosed it an electrochemically induced phase transformation mechanism from VO to layered Zn_(x)V_(2)O_5·nH_(2)O,as well as superior storage kinetics with ultrahigh pseudocapacitive contribution.As demonstrated,such electrode can remain a specific capacity of 285 mA h g^(-1)after 100 cycles at 1 A g^(-1),144.4 mA h g^(-1)after 1500 cycles at 30 A g^(-1),and even 97 mA h g^(-1)after 3000 cycles at 60 A g^(-1),respectively.Unexpectedly,an impressive power density of 78.9 kW kg^(-1)at the super-high current density of 100 A g^(-1)also can be achieved.Such design concept of in-situ self-etching free-standing electrode can provide a brand-new insight into extending the pseudocapacitive storage limit,so as to promote the development of high-power energy storage devices including but not limited to AZIBs.

Online soft measurement for wastewater treatment system based on hybrid deep learning

The existing automated wastewater treatment control systems encounter challenges such as the utilization of specialized testing instruments, equipment repair complications, high operational costs, substantial operational errors, and low detection accuracy. An effective soft measure model offers a viable approach for real-time monitoring and the development of automated control in the wastewater treatment process. Consequently, a novel hybrid deep learning CNN-BNLSTM-Attention (CBNLSMA) model, which incorporates convolutional neural networks (CNN), bidirectional nested long and short-term memory neural networks (BNLSTM), attention mechanisms (AM), and Tree-structure Parzen Estimators (TPE), has been developed for monitoring effluent water quality during the wastewater treatment process. The CBNLSMA model is divided into four stages: the CNN module for feature extraction and data filtering to expedite operations;the BNLSTM module for temporal data’s temporal information extraction;the AM module for model weight reassignment;and the TPE optimization algorithm for the CBNLSMA model’s hyperparameter search optimization. In comparison with other models (TPE-CNN-BNLSTM, TPE-BNLSTM-AM, TPE-CNN-AM, PSO-CBNLSTMA), the CBNLSMA model reduced the RMSE for effluent COD prediction by 25.4%, decreased the MAPE by 32.9%, and enhanced the R2 by 14.9%. For the effluent SS prediction, the CBNLSMA model reduced the RMSE by 26.4%, the MAPE by 21.0%, and improved the R2 by 35.7% compared to other models. The simulation results demonstrate that the proposed CBNLSMA model holds significant potential for real-time effluent quality monitoring, indicating its high potential for automated control in wastewater treatment processes.

Water quality soft-sensor prediction in anaerobic process using deep neural network optimized by Tree-structured Parzen Estimator

Anaerobic process is regarded as a green and sustainable process due to low carbon emission and minimal energy consumption in wastewater treatment plants(WWTPs).However,some water quality metrics are not measurable in real time,thus influencing the judgment of the operators and may increase energy consumption and carbon emission.One of the solutions is using a soft-sensor prediction technique.This article introduces a water quality soft-sensor prediction method based on Bidirectional Gated Recurrent Unit(BiGRU)combined with Gaussian Progress Regression(GPR)optimized by Tree-structured Parzen Estimator(TPE).TPE automatically optimizes the hyperparameters of BiGRU,and BiGRU is trained to obtain the point prediction with GPR for the interval prediction.Then,a case study applying this prediction method for an actual anaerobic process(2500 m^(3)/d)is carried out.Results show that TPE effectively optimizes the hyperparameters of BiGRU.For point prediction of CODeff and biogas yield,R^(2)values of BiGRU,which are 0.973 and 0.939,respectively,are increased by 1.03%–7.61%and 1.28%–10.33%,compared with those of other models,and the valid prediction interval can be obtained.Besides,the proposed model is assessed as a reliable model for anaerobic process through the probability prediction and reliable evaluation.It is expected to provide high accuracy and reliable water quality prediction to offer basis for operators in WWTPs to control the reactor and minimize carbon emission and energy consumption.

An intravascular needle coated by ZnO nanoflowers for in vivo elimination of circulating tumor cells

Circulating tumor cells(CTCs)are recognized as the main source of tumor recurrence and metastasis.Eliminating the CTCs in peripheral blood provides a new strategy to reduce the probability of recurrence or metastasis.Here,we proposed a concept to eliminate CTCs by inserting a needle in the superficial blood vessel.Using the property of ZnO and the structure of nanoflowers,we designed a medical needle coated with ZnO nanoflowers(ZNFs),which killed about 90%of captured CTCs in vitro and prevented the injecting CTCs from spreading to lung tissue in BABL/c mouse model.Results in vitro and in vivo demonstrated that the CTCs not only were captured and killed,but also detached from the needle surface after dead,enabling the ZNFs needle continually eliminate CTCs.Furthermore,a theoretical model was presented to explain the penetration mechanism of cells by nanostructures,which indicated that nanoflowers structure can puncture CTCs more easily than vertical nanowire structure.The concept of inserting an intravascular needle provides a potential strategy to lower the concentration of CTCs in blood and reduce the probability of tumor recurrence or metastasis.

Structured illumination-based super-resolution live-cell quantitative FRET imaging

Forster resonance energy transfer(FRET)microscopy provides unique insight into the functionality of biological systems via imaging the spatiotemporal interactions and functional state of proteins.Distinguishing FRET signals from sub-diffraction regions requires super-resolution(SR)FRET imaging,yet is challenging to achieve from living cells.Here,we present an SR FRET method named SIM-FRET that combines SR structured illumination microscopy(SIM)imaging and acceptor sensitized emission FRET imaging for live-cell quantitative SR FRET imaging.Leveraging the robust co-localization prior of donor and accepter during FRET,we devised a mask filtering approach to mitigate the impact of SIM reconstruction artifacts on quantitative FRET analysis.Compared to wide-field FRET imaging,SIM-FRET provides nearly twofold spatial resolution enhancement of FRET imaging at sub-second timescales and maintains the advantages of quantitative FRET analysis in vivo.We validate the resolution enhancement and quantitative analysis fidelity of SIM-FRET signals in both simulated FRET models and live-cell FRET-standard construct samples.Our method reveals the intricate structure of FRET signals,which are commonly distorted in conventional wide-field FRET imaging.