Prediction of Intrinsically Disordered Proteins Based on Deep Neural Network-ResNet18

Accurately,reliably and rapidly identifying intrinsically disordered(IDPs)proteins is essential as they often play important roles in various human diseases;moreover,they are related to numerous important biological activities.However,current computational methods have yet to develop a network that is sufficiently deep tomake predictions about IDPs and demonstrate an improvement in performance.During this study,we constructed a deep neural network that consisted of five identical variant models,ResNet18,combined with an MLP network,for classification.Resnet18 was applied for the first time as a deep model for predicting IDPs,which allowed the extraction of information fromIDP residues in greater detail and depth,and this information was then passed through the MLP network for the final identification process.Two well-known datasets,MXD494 and R80,were used as the blind independent datasets to compare their performance with that of our method.The simulation results showed that Matthew’s correlation coefficient obtained using our deep network model was 0.517 on the blind R80 dataset and 0.450 on the MXD494 dataset;thus,our method outperformed existing methods.

Pluggable multitask diffractive neural networks based on cascaded metasurfaces

Optical neural networks have significant advantages in terms of power consumption,parallelism,and high computing speed,which has intrigued extensive attention in both academic and engineering communities.It has been considered as one of the powerful tools in promoting the fields of imaging processing and object recognition.However,the existing optical system architecture cannot be reconstructed to the realization of multi-functional artificial intelligence systems simultaneously.To push the development of this issue,we propose the pluggable diffractive neural networks(P-DNN),a general paradigm resorting to the cascaded metasurfaces,which can be applied to recognize various tasks by switching internal plug-ins.As the proof-of-principle,the recognition functions of six types of handwritten digits and six types of fashions are numerical simulated and experimental demonstrated at near-infrared regimes.Encouragingly,the proposed paradigm not only improves the flexibility of the optical neural networks but paves the new route for achieving high-speed,low-power and versatile artificial intelligence systems.

Terahertz polarization conversion and sensing with double-layer chiral metasurface

The terahertz(THz)resonance,chirality,and polarization conversion properties of a double-layer chiral metasurface have been experimentally investigated by THz time domain spectroscopy system and polarization detection method.The special symmetric geometry of each unit cell with its adjacent cells makes a strong chiral electromagnetic response in this metasurface,which leads to a strong polarization conversion effect.Moreover,compared with the traditional THz transmission resonance sensing for film thickness,the polarization sensing characterized by polarization elliptical angle(PEA)and polarization rotation angle(PRA)shows a better Q factor and figure of merit(FoM).The results show that the Q factors of the PEA and PRA reach 43.8 and 49.1 when the interval film is 20µm,while the Q factor of THz resonance sensing is only 10.6.And these PEA and PRA can play a complementary role to obtain a double-parameter sensing method with a higher FoM,over 4 times than that of resonance sensing.This chiral metasurface and its polarization sensing method provide new ideas for the development of high-efficiency THz polarization manipulation,and open a window to the high sensitive sensing by using THz polarization spectroscopy.

Indoor Positioning Using Public FM and DTMB Signals Based on Compressive Sensing

Location-Based Services have become an indispensable part of our daily life, the sparsity of location finding makes it possible to estimate specific position by Compressive Sensing(CS). Using public Frequency Modulation(FM) broadcasting and Digital Television Terrestrial Multimedia Broadcasting(DTMB) signals, this paper presents an indoor positioning scheme, which is consisted of an offline stage and an online stage. In the offline stage, the Received Signal Strength(RSS) at the Reference Points(RPs) is measured, including the average and variance of public FM broadcasting and DTMB signals. In the online stage, the K-Weighted Nearest Neighbor algorithm is used to fulfill coarse positioning, which is able to narrow the selection scope of locations and choose partial RPs for accurate positioning. Then, the accurate positioning is implemented through CS. Experiment shows that the average positioning error of the proposed scheme is 1.63 m. What’s more, a CS-based method has been proposed in this paper to reduce the labor cost when collecting data. Experiment shows the average positioning error is 2.04 m, with the advantage of a 34% labor cost reduction. Experiment results indicate that the proposed system is a practical indoor positioning scheme.

Prediction of Intrinsically Disordered Proteins with a Low Computational Complexity Method

The prediction of intrinsically disordered proteins is a hot research area in bio-information.Due to the high cost of experimental methods to evaluate disordered regions of protein sequences,it is becoming increasingly important to predict those regions through computational methods.In this paper,we developed a novel scheme by employing sequence complexity to calculate six features for each residue of a protein sequence,which includes the Shannon entropy,the topological entropy,the sample entropy and three amino acid preferences including Remark 465,Deleage/Roux,and Bfactor(2STD).Particularly,we introduced the sample entropy for calculating time series complexity by mapping the amino acid sequence to a time series of 0-9.To our knowledge,the sample entropy has not been previously used for predicting IDPs and hence is being used for the first time in our study.In addition,the scheme used a properly sized sliding window in every protein sequence which greatly improved the prediction performance.Finally,we used seven machine learning algorithms and tested with 10-fold cross-validation to get the results on the dataset R80 collected by Yang et al.and of the dataset DIS1556 from the Database of Protein Disorder(DisProt)(https://www.disprot.org)containing experimentally determined intrinsically disordered proteins(IDPs).The results showed that k-Nearest Neighbor was more appropriate and an overall prediction accuracy of 92%.Furthermore,our method just used six features and hence required lower computational complexity.

Growth of TlBa_2Ca_2Cu_3O_9 Epitaxial Thin Films by Two-Step Method in Argon

TlBa_2 Ca_2 Cu_3 O_9(Tl-1223) films have promising applications due to their high critical temperature and strong magnetic flux pinning. Nevertheless, the preparation of pure phase Tl-1223 film is still a challenge. We successfully fabricate Tl-1223 thin films on LaAlO_3(001) substrates using dc magnetic sputtering and a post annealing two-step method in argon atmosphere. The crystallization temperature of Tl-1223 films in argon is reduced by 100℃ compared to that in oxygen. This greatly reduces the volatilization of Tl and improves the surface morphology of films. The lower annealing temperature can effectively improve the repeatability of the Tl-1223 film preparation. In addition, pure Tl-1223 phase can be obtained in a broad temperature zone,from 790℃ to 830℃. In our study, the films show homogenous and dense surface morphology using the presented method. The best critical temperature of Tl-1223 films is characterized to be 110 K, and the critical current J_c(77 K, 0 T) is up to 2.13 × 106 A/cm^2.

Simulation and measurement of millimeter-wave radiation from Josephson junction array

We report the circuit simulations and experiments of millimeter-wave radiation from a high temperature superconducting(HTS) bicrystal Josephson junction(BJJ) array. To study the effects of junction characteristic parameters on radiation properties, new radiation circuit models are proposed in this paper. The series resistively and capacitively shunted junction(RCSJ) models are packaged into a Josephson junction array(JJA) model in the simulation. The current-voltage characteristics(IVCs) curve and radiation peaks are simulated and analyzed by circuit models, which are also observed from the experiment at liquid nitrogen temperature. The experimental radiation linewidth and power are in good agreement with simulated results. The presented circuit models clearly demonstrate that the inconsistency of the JJA will cause a broad linewidth and a low detected power. The junction radiation properties are also investigated at the optimal situation by circuit simulation. The results further confirm that the consistent JJA characteristic parameters can successfully narrow the radiation linewidth and increase the power of junction radiation.

A biosensor based on a modified S-taper fiber for target protein detection

A biosensor based on a modified S-taper fiber was proposed and experimentally demonstrated for antigen detection. Antibody was immobilized over the activated S-taper waist, where the fiber detected the interaction between the antibody and target antigen. Compared with a unmodified S-taper fiber structure, the silanized fiber had a detection limit of approximately 8.33 × 10-5 RIU when the concentration of antigen solution was changed1%. The antibody and antigen binding processing was monitored in real time by analyzing and investigating the transmission spectral characteristics of the modified S-taper fiber. The cost-effectiveness, real-time detection,and high integration properties of the fiber enable its use in a modal interferometer for biosensing applications including disease diagnosis and biological specificity recognition.

A twist sensor based on polarization-maintaining fibers with different cladding diameters

A fiber twist sensor using a Sagnac interferometer incorporating a tapered polarization-maintaining fiber(PMF)is proposed.The transmission properties of the sensor are investigated both theoretically and experimentally.Given the optoelastic effect,which depends on fiber geometry,the modal and group birefringences of the PMF can be controlled by applying different twist angles.The spectral wavelength shifts,free spectral ranges,and transmission losses of the original,microtapered,and etched PMFs were compared.Notably,the interference dips for the etched PMF move in opposite directions.As a result,the proposed PMF-based sensor could have multiparameter sensing applications.

Propagation characteristics of photonic crystal fibers selectively filled with ionic liquid

We present a numerical and experimental study of the propagation characteristics of photonic crystal fibers(PCFs)selectively filled with ionic liquid(IL;1-butyl-3-methylimidazolium iodine).Three types of IL-filled PCF are investigated:one with all air holes filled,one with an IL-filled air hole in the second ring,and one with an IL-filled air hole in the third ring.The results show that the third type of IL-filled PCF is the most sensitive to temperature;the sensitivity of resonant dips between the LP01 and LP21 modes is−2.9 nm/XC.Moreover,the intensity of the resonant dips changes with the polarization angle of the light source;the sensitivity is−0.79 dB per unit polarization angle.Based on this property,IL-filled PCFs with different utilities can be realized by changing the filling position flexibly.Consequently,IL-filled PCFs can be used under flexible conditions and controllable temperatures to create a compact polarization-angle sensor.

Auxiliary guidance manufacture and revealing potential mechanism of perovskite solar cell using machine learning

To promote the development of global carbon neutrality,perovskite solar cells(PSCs)have become a research hotspot in related fields.How to obtain PSCs with expected performance and explore the potential factors affecting device performance are the research priorities in related fields.Although some classical computational methods can facilitate material development,they typically require complex mathematical approximations and manual feature screening processes,which have certain subjectivity and one-sidedness,limiting the performance of the model.In order to alleviate the above challenges,this paper proposes a machine learning(ML)model based on neural networks.The model can assist both PSCs design and analysis of their potential mechanism,demonstrating enhanced and comprehensive auxiliary capabilities.To make the model have higher feasibility and fit the real experimental process more closely,this paper collects the corresponding real experimental data from numerous research papers to develop the model.Compared with other classical ML methods,the proposed model achieved better overall performance.Regarding analysis of underlying mechanism,the relevant laws explored by the model are consistent with the actual experiment results of existing articles.The model exhibits great potential to discover complex laws that are difficult for humans to discover directly.In addition,we also fabricated PSCs to verify the guidance ability of the model in this paper for real experiments.Eventually,the model achieved acceptable results.This work provides new insights into integrating ML methods and PSC design techniques,as well as bridging photovoltaic power generation technology and other fields.

Erratum:Growth of TlBa2Ca2Cu3O9 Epitaxial Thin Films by Two-Step Method in Argon[Chin.Phys.Lett.36(2019)057401]

In Fig.2 of the article,[1]the figure caption should be“Rocking curve of the(006)peak of the Tl-1223 thin film”instead of“Rocking curve of the(003)peak of the Tl-1223 thin film”.The corresponding description of this figure should be“As shown in Fig.2,the full width at half maximum(FWHM)of the Tl-1223(006)reflection peak is only 0.58∘,which shows that the film possesses a good crystalline structure”,instead of“As shown in Fig.2,the FWHM of the Tl-1223(003)reflection peak is only 0.58∘,which shows that the film possesses a good crystalline structure”.

Remote sensing of trace Na element in sea salt aerosol with a sensitivity level of 10 pg/m^(3)[Invited]

We have developed a remote sea salt aerosol fluorescence spectroscopy system integrating a high-power industrial-grade femtosecond laser to enhance detection sensitivity and precision in complex environments.This system successfully detects sea salt aerosol particles,achieving a detection limit of 0.015 ng/m^(3) for neutral Na element(Na I)at 589 nm,with a detection range of 30 m.Our findings demonstrate significant improvements in remote aerosol monitoring,addressing previous challenges in long-range and high-precision sensing with a detection accuracy previously unattainable below 10 ng/m^(3).

Two-person device-free localization system based on ZigBee and transformer

Most studies on device-free localization currently focus on single-person scenarios.This paper proposes a novel method for device-free localization that utilizes ZigBee received signal strength indication(RSSI)and a Transformer network structure.The method aims to address the limited research and low accuracy of two-person device-free localization.This paper first describes the construction of the sensor network used for collecting ZigBee RSSI.It then examines the format and features of ZigBee data packages.The algorithm design of this paper is then introduced.The box plot method is used to identify abnormal data points,and a neural network is used to establish the mapping model between ZigBee RSSI matrix and localization coordinates.This neural network includes a Transformer encoder layer as the encoder and a fully connected network as the decoder.The proposed method's classification accuracy was experimentally tested in an online test stage,resulting in an accuracy rate of 98.79%.In conclusion,the proposed two-person localization system is novel and has demonstrated high accuracy.

Improved sensing performance of WO_(3) nanoparticles decorated with Ag and Pt nanoparticles

Gas sensors built with metal oxide semiconductors have attracted tremendous attention due to the growing demand for the detection of inflammable,explosive and toxic gases.Herein,to improve the sensing response,WO_(3) nanoparticles decorated with Ag and Pt bimetals(Ag and Pt/WO_(3) NPs)have been developed via combined hydrolysis and hydrothermal strategies.Such sensors prototypes show high response to acetone(Ra/Rg=250@100×10^(-6),140℃),which is 6.1 fold as high as that of the pristine WO_(3) NPs(Ra/Rg=41@100×10^(-6),140℃).Moreover,the recovery time of Ag and Pt/WO_(3) NPs was reduced from 138 to 13 s compared with that of the pristine WO_(3) NPs.The improved acetone sensing performance may be attributed to that the chemical and electronic sensitization of Ag and Pt to WO_(3) NPs increases adsorbed oxygen species,speeds up the reaction and thus boosts the sensing response.Our strategy that decoration of dual precious metals onto WO_(3) NPs improves the acetone sensing performance may be applied to the gas sensors of other sensing materials.

0.35%THz pulse conversion efficiency achieved by Ti:sapphire femtosecond laser filamentation in argon at 1 kHz repetition rate

In this study,an optical setup for generating terahertz(THz)pulses through a two-color femtosecond laser filament was carefully designed to achieve a precise overlap of two-color laser pulses in space and time.β-barium borate(BBO),α-BBO,and a dual-wavelength plate were used to compensate the phase delay of the two-color lasers.Tilting ofα-BBO could further realize the precise spatial overlap of the two beams by counteracting the walk-off effect.The maximum out-put THz pulse energy reached 21μJ in argon gas when using a commercial Ti:sapphire laser with a pulse energy of 6 mJ at a 1 kHz repetition rate.The corresponding conversion efficiency exceeded 0.35%.

Circular cladding waveguides in Pr:YAG fabricated by femtosecond laser inscription:Raman,luminescence properties and guiding performance

We report on the fabrication of circular cladding waveguides with cross-section diameters of 60−120μm in Pr:YAG crystal by applying femtosecond laser inscription.The fabricated waveguides present 2D guidance on the cross-section and fairly low propagation losses.Multiple high-order guiding modes are observed in waveguides with different diameters.Corresponding simulation results reveal the origin of a specific kind of guiding modes.Confocal micro-Raman(μ-Raman)experiments demonstrate the modification effects in femtosecond laser affected areas and ascertain the refractive index induced guiding mechanism.In addition,luminescence emission properties of Pr3+ions at waveguide volume are well preserved during the femtosecond laser inscription process,which may result in a potential high-power visible waveguide laser.

Terahertz polarization sensing,chirality enhancement,and specific binding based on metasurface sensors for biochemical detection:a review[Invited]

Specific and highly-sensitive biochemical detection technology is particularly important in global epidemics and has critical applications in life science,medical diagnosis,and pharmaceutics.As a newly developed technology,the THz metamaterialbased sensing method is a promising technique for extremely sensitive biomolecular detection.However,due to the significant resonant peaks generated by THz metamaterials,the characteristic absorption peaks of the analyte are usually masked,making it difficult to distinguish enantiomers and specifically identify target biomolecules.Recently,new ways to overcome this limitation have become possible thanks to the emergence of chiral metasurfaces and the polarization sensing method.Additionally,functionalized metasurfaces modified by antibodies or other nanomaterials are also expected to achieve specific sensing with high sensitivity.In this review,we summarize the main advances in THz metamaterials-based sensing from a historical perspective as well as application in chiral recognition and specific detection.Specifically,we introduce the basic theory and key technology of THz polarization spectrum and chiral sensing for biochemical detection,and immune sensing based on biomolecular interaction is also discussed.We mainly focus on chiral recognition and specific sensing using THz metasurface sensors to cover the most recent advances in the topic,which is expected to break through the limitations of traditional THz absorption spectroscopy and chiral spectroscopy in the visible-infrared band and develop into an irreplaceable method for the characterization of biochemical substances.

Characteristic extraction of soliton dynamics based on convolutional autoencoder neural network

In this article,we use a convolutional autoencoder neural network to reduce data dimensioning and rebuild soliton dynamics in a passively mode-locked fiber laser.Based on the particle characteristic in double solitons and triple solitons interactions,we found that there is a strict correspondence between the number of minimum compression parameters and the number of independent parameters of soliton interaction.This shows that our network effectively coarsens the high-dimensional data in nonlinear systems.Our work not only introduces new prospects for the laser self-optimization algorithm,but also brings new insights into the modeling of nonlinear systems and description of soliton interactions.

Performance of differential phase shift keying maritime laser communication over log-normal distribution turbulence channel

Laser communication is essential part of maritime-terrestrial-air intelligent communication/sensor network. Among them, different modulation formats would play a unique role in specific applications. Based on Rytov theory, we discussed system performance of the maritime laser communication with repeated coding technology in several modulation schemes. The closed-form expression of average bit error rate(BER) from weak to moderate atmospheric turbulence described by log-normal distribution is given. Differential phase shift keying(DPSK) modulation, as a potential solution for future maritime laser communication, has attracted a lot of attention. We analyzed the effects of atmospheric turbulence parameters(visibility, refractive index structure coefficient, non-Kolmogorov spectral power-law exponent, turbulence inner scale) and DPSK system parameters(receiver aperture diameter, repeat time) on average BER in detail. Compared with the aperture-averaging effects, the system BER can be well suppressed through increasing repeat time. This work is anticipated to provide a theoretical reference for maritime laser communication systems.