Current computational tools for protein lysine acylation site prediction.

As a main subtype of post-translational modification (PTM), protein lysine acylations (PLAs) play crucial roles in regulating diverse functions of proteins. With recent advancements in proteomics technology, the identification of PTM is becoming a data-rich field. A large amount of experimentally verified data is urgently required to be translated into valuable biological insights. With computational approaches, PLA can be accurately detected across the whole proteome, even for organisms with small-scale datasets. Herein, a comprehensive summary of 166 in silico PLA prediction methods is presented, including a single type of PLA site and multiple types of PLA sites. This recapitulation covers important aspects that are critical for the development of a robust predictor, including data collection and preparation, sample selection, feature representation, classification algorithm design, model evaluation, and method availability. Notably, we discuss the application of protein language models and transfer learning to solve the small-sample learning issue. We also highlight the prediction methods developed for functionally relevant PLA sites and species/substrate/cell-type-specific PLA sites. In conclusion, this systematic review could potentially facilitate the development of novel PLA predictors and offer useful insights to researchers from various disciplines.

ResNetKhib: a novel cell type-specific tool for predicting lysine 2-hydroxyisobutylation sites via transfer learning.

Lysine 2-hydroxyisobutylation (Khib), which was first reported in 2014, has been shown to play vital roles in a myriad of biological processes including gene transcription, regulation of chromatin functions, purine metabolism, pentose phosphate pathway and glycolysis/gluconeogenesis. Identification of Khib sites in protein substrates represents an initial but crucial step in elucidating the molecular mechanisms underlying protein 2-hydroxyisobutylation. Experimental identification of Khib sites mainly depends on the combination of liquid chromatography and mass spectrometry. However, experimental approaches for identifying Khib sites are often time-consuming and expensive compared with computational approaches. Previous studies have shown that Khib sites may have distinct characteristics for different cell types of the same species. Several tools have been developed to identify Khib sites, which exhibit high diversity in their algorithms, encoding schemes and feature selection techniques. However, to date, there are no tools designed for predicting cell type-specific Khib sites. Therefore, it is highly desirable to develop an effective predictor for cell type-specific Khib site prediction. Inspired by the residual connection of ResNet, we develop a deep learning-based approach, termed ResNetKhib, which leverages both the one-dimensional convolution and transfer learning to enable and improve the prediction of cell type-specific 2-hydroxyisobutylation sites. ResNetKhib is capable of predicting Khib sites for four human cell types, mouse liver cell and three rice cell types. Its performance is benchmarked against the commonly used random forest (RF) predictor on both 10-fold cross-validation and independent tests. The results show that ResNetKhib achieves the area under the receiver operating characteristic curve values ranging from 0.807 to 0.901, depending on the cell type and species, which performs better than RF-based predictors and other currently available Khib site prediction tools. We also implement an online web server of the proposed ResNetKhib algorithm together with all the curated datasets and trained model for the wider research community to use, which is publicly accessible at https://resnetkhib.erc.monash.edu/.

Nonlinear Boost of Optical Angular Momentum Selectivity by Hybrid Nanolaser Circuits.

Selective control of light is essential for optical science and technology, with numerous applications. However, optical selectivity in the angular momentum of light has been quite limited, remaining constant by increasing the incident light power on previous passive optical devices. Here, we demonstrate a nonlinear boost of optical selectivity in both the spin and orbital angular momentum of light through near-field selective excitation of single-mode nanolasers. Our designed hybrid nanolaser circuits consist of plasmonic metasurfaces and individually placed perovskite nanowires, enabling subwavelength focusing of angular-momentum-distinctive plasmonic fields and further selective excitation of nanolasers in nanowires. The optically selected nanolaser with a nonlinear increase of light emission greatly enhances the baseline optical selectivity offered by the metasurface from about 0.4 up to near unity. Our demonstrated hybrid nanophotonic platform may find important applications in all-optical logic gates and nanowire networks, ultrafast optical switches, nanophotonic detectors, and on-chip optical and quantum information processing.

Optical Metasurfaces for Energy Conversion.

Nanostructured surfaces with designed optical functionalities, such as metasurfaces, allow efficient harvesting of light at the nanoscale, enhancing light-matter interactions for a wide variety of material combinations. Exploiting light-driven matter excitations in these artificial materials opens up a new dimension in the conversion and management of energy at the nanoscale. In this review, we outline the impact, opportunities, applications, and challenges of optical metasurfaces in converting the energy of incoming photons into frequency-shifted photons, phonons, and energetic charge carriers. A myriad of opportunities await for the utilization of the converted energy. Here we cover the most pertinent aspects from a fundamental nanoscopic viewpoint all the way to applications.

Unraveling the Chirality Transfer from Circularly Polarized Light to Single Plasmonic Nanoparticles.

Due to their broken symmetry, chiral plasmonic nanostructures have unique optical properties and numerous applications. However, there is still a lack of comprehension regarding how chirality transfer occurs between circularly polarized light (CPL) and these structures. Here, we thoroughly investigate the plasmon-assisted growth of chiral nanoparticles from achiral Au nanocubes (AuNCs) via CPL without the involvement of any chiral molecule stimulators. We identify the structural chirality of our synthesized chiral plasmonic nanostructures using circular differential scattering (CDS) spectroscopy, which is correlated with scanning electron microscopy imaging at both the single-particle and ensemble levels. Theoretical simulations, including hot-electron surface maps, reveal that the plasmon-induced chirality transfer is mediated by the asymmetric distribution of hot electrons on achiral AuNCs under CPL excitation. Furthermore, we shed light on how this plasmon-induced chirality transfer can also be utilized for chiral growth in bimetallic systems, such as Ag or Pd on AuNCs. The results presented here uncover fundamental aspects of chiral light-matter interaction and have implications for the future design and optimization of chiral sensors and chiral catalysis, among others.

Disorder-Induced Topological State Transition in the Optical Skyrmion Family.

Skyrmions endowed with topological protection have been extensively investigated in various platforms including magnetics, ferroelectrics, and liquid crystals, stimulating applications such as memories, logic devices, and neuromorphic computing. While the optical counterpart has been proposed and realized recently, the study of optical skyrmions is still in its infancy. Among the unexplored questions, the investigation of the topology induced robustness against disorder is of substantial importance on both fundamental and practical sides but remains elusive. In this Letter, we manage to generate optical skyrmions numerically in real space with different topological features at will, providing a unique platform to investigate the robustness of various optical skyrmions. A disorder-induced topological state transition is observed for the first time in a family of optical skyrmions composed of six classes with different skyrmion numbers. Intriguingly, the optical skyrmions produced from a vectorial hologram are exceptionally robust against scattering from a random medium, shedding light on topological photonic devices for the generation and manipulation of robust states for applications including imaging and communication.

Sinomicrobium weinanense sp. nov., a halophilic bacterium isolated from saline-alkali soil.

A Gram-stain-negative, facultative anaerobic, non-motile, rod-shaped strain was isolated from saline-alkali soil collected in PR China, and it was designated as strain FJxsT. Its optimal growth was observed at 37-40 °C in the presence of 0-3 % (w/v) NaCl (pH 7.0). The major fatty acids of strain FJxsT were iso-C15 : 0, iso-C17 : 0 3OH, summed feature 3, C16 : 0 and iso-C15 : 1 G. The predominant respiratory quinone was menaquinone 6. The DNA G+C content of the strain was 45.18 mol%. Whole genome and 16S rRNA gene sequence analyses indicated that strain FJxsT exhibited 94.78 % sequence identity (the maximum) with Sinomicrobium soli N-1-3-6T, 94.36 % with Sinomicrobium pectinilyticum 5DNS001T, and 93.52 % with Sinomicrobium oceani SCSIO 03483T. Analyses of genotypic, phenotypic, phylogenetic and chemotaxonomic characteristics indicated that strain FJxsT represented a novel species of the genus Sinomicrobium. This novel species was named Sinomicrobium weinanense sp. nov. with its type strain as FJxsT (=CCTCC AB 2019251T=KCTC 72740T).

Trimethyltin induces apoptosis and necroptosis of mouse liver by oxidative stress through YAP phosphorylation.

Trimethyltin (TMT) is widely used as a major component of plastic stabilizers in agriculture and industry, and can accumulate in large quantities in the liver. To investigate the relationship between liver tissue damage induced by TMT exposure and YAP phosphorylation in mice, we gave the mice drinking water containing 0.01 mg/mL TMT for 14 days to establish an in vivo experimental model, and continuously treated AML12 cells with 20 µM TMT for 24 h to establish an in vitro experimental model. Transcriptomics revealed that TMT exposure altered 62,466 apparently diversely expressed genes, including 1197 upregulated and 899 downregulated genes, and that enrichment of the Hippo pathway occurred. Moreover, western blotting (WB) and quantitative real-time PCR (qRTPCR) results showed that TMT exposure triggered an increase in the expression of P-YAP, apoptosis and necroptosis-interrelated genes, and a decrease in Bcl-2 expression in mouse livers tissues and AML12 cells. The expression of P-YAP was significantly suppressed in the TRULI-treated TMT-exposed AML12 cells, while oxidative stress levels and damage were also significantly attenuated. In conclusion, TMT triggers YAP phosphorylation to induce oxidative stress inducing apoptosis and necroptosis in mouse livers tissues. Our results confirm the liver toxic effect and specific mechanism of TMT.

Automatic Time Picking for Weak Seismic Phase in the Strong Noise and Interference Environment: An Hybrid Method Based on Array Similarity.

The extraction of travel-time curve of seismic phase is very important for the subsequent inference of the structural properties of underground media in seismology. In recent years, with the increase in the amount of data, manual processing is facing significant challenges, and automatic signal processing has gradually become the mainstream. According to the similarity of array signals and considering the elimination of outliers, we propose an improved multi-channel cross-correlation method using the L1 norm measure to obtain preliminary results, which builds on a new controllable measurement mode. Then, the post-correction step is carried out in combination with the signal gain property of beamforming technique. Based on these two methods, this paper proposes a new scheme of automatic arrival time picking. We apply the scheme to actual data to verify the effects of the two methods step by step. The entire scheme achieves fine results: direct water waves, seismic waves refracted by the crust and seismic waves reflected by the upper mantle are automatically detected. In addition, compared with the two traditional methods, the scheme proposed in this paper has a better overall effect and a reasonable computation cost.

An Optimal Subspace Deconvolution Algorithm for Robust and High-Resolution Beamforming.

Utilizing the difference in phase and power spectrum between signals and noise, the estimation of direction of arrival (DOA) can be transferred to a spatial sample classification problem. The power ratio, namely signal-to-noise ratio (SNR), is highly required in most high-resolution beamforming methods so that high resolution and robustness are incompatible in a noisy background. Therefore, this paper proposes a Subspaces Deconvolution Vector (SDV) beamforming method to improve the robustness of a high-resolution DOA estimation. In a noisy environment, to handle the difficulty in separating signals from noise, we intend to initial beamforming value presets by incoherent eigenvalue in the frequency domain. The high resolution in the frequency domain guarantees the stability of the beamforming. By combining the robustness of conventional beamforming, the proposed method makes use of the subspace deconvolution vector to build a high-resolution beamforming process. The SDV method is aimed to obtain unitary frequency matrixes more stably and improve the accuracy of signal subspaces. The results of simulations and experiments show that when the input SNR is less than -27 dB, signals of decomposition differ unremarkably in the subspace while the SDV method can still obtain clear angles. In a marine background, this method works well in separating the noise and recruiting the characteristics of the signal into the DOA for subsequent processing.

Deblurring of Sound Source Orientation Recognition Based on Deep Neural Network.

Underwater target detection and identification technology are currently two of the most important research directions in the information disciplines. Traditionally, underwater target detection technology has struggled to meet the needs of current engineering. However, due to the large manifold error of the underwater sonar array and the complexity of ensuring long-term signal stability, traditional high-resolution array signal processing methods are not ideal for practical underwater applications. In conventional beamforming methods, when the signal-to-noise ratio is lower than -43.05 dB, the general direction can only be vaguely identified in the general direction. To address the above challenges, this paper proposes a beamforming method based on a deep neural network. Through preprocessing, the space-time domain of the target sound signal is converted into two-dimensional data in the angle-time domain. Subsequently, we trained the network with enough sample datasets. Finally, high-resolution recognition and prediction of two-dimensional images are realized. The results of the test dataset in this paper demonstrate the effectiveness of the proposed method, with a minimum signal-to-noise ratio of -48 dB.

Orbital-Angular-Momentum-Controlled Hybrid Nanowire Circuit.

Plasmonic nanostructures can enable compact multiplexing of the orbital angular momentum (OAM) of light; however, strong dissipation of the highly localized OAM-distinct plasmonic fields in the near-field region hinders on-chip OAM transmission and processing. Superior transmission efficiency is offered by semiconductor nanowires sustaining highly confined optical modes, but only the polarization degree of freedom has been utilized for their selective excitation. Here we demonstrate that incident OAM beams can selectively excite single-crystalline cadmium sulfide (CdS) nanowires through coupling OAM-distinct plasmonic fields into nanowire waveguides for long-distance transportation. This allows us to build an OAM-controlled hybrid nanowire circuit for optical logic operations including AND and OR gates. In addition, this circuit enables the on-chip photoluminescence readout of OAM-encrypted information. Our results open exciting new avenues not only for nanowire photonics to develop OAM-controlled optical switches, logic gates, and modulators but also for OAM photonics to build ultracompact photonic circuits for information processing.

RcMYB84 and RcMYB123 mediate jasmonate-induced defense responses against Botrytis cinerea in rose (Rosa chinensis).

Jasmonates (JAs) are important for pathogen resistance in many plants, but the role of these phytohormones in fungal pathogen resistance in rose is unclear. Here, we determined that exogenous application of methyl jasmonate increased resistance to the important fungal pathogen Botrytis cinerea in Rosa chinensis 'Old blush', whereas silencing the JA biosynthetic pathway gene Allene Oxide Synthase (AOS) and JA co-receptor gene CORONATINE INSENSITIVE 1 (COI1) suppressed this response. Transcriptome profiling identified various MYB transcription factor genes that responded to both JA and B. cinerea treatment. Silencing Ri-RcMYB84/Ri-RcMYB123 increased the susceptibility of rose plants to B. cinerea and inhibited the protective effects of JA treatment, confirming the crucial roles of these genes in JA-induced responses to B. cinerea. JAZ1, a key repressor of JA signaling, directly interacts with RcMYB84 and RcMYB123 to deplete their free pools. The JAZ1-RcMYB84 complex binds to the RcMYB123 promoter via the CAACTG motifs to block its transcription. Upon JA treatment, the expression of RcMYB123 is de-repressed, and free forms of RcMYB84 and RcMYB123 are released due to JAZ1 degradation, thereby activating the defense responses of plants to B. cinerea. These findings shed light on the molecular mechanisms underlying JA-induced pathogen resistance in roses.

The B-box protein BBX19 suppresses seed germination via induction of ABI5.

Seed germination is a fundamental process in the plant life cycle and is regulated by functionally opposing internal and external inputs. Here we explored the role of a negative regulator of photomorphogenesis, a B-box-containing protein (BBX19), as a molecular link between the inhibitory action of the phytohormone abscisic acid (ABA) and the promoting role of light in germination. We show that seeds of BBX19-overexpressing lines, in contrast to those of BBX19 RNA interference lines, display ABA hypersensitivity, albeit independently of elongated hypocotyl 5 (HY5). Moreover, we establish that BBX19 functions neither via perturbation of GA signaling, the ABA antagonistic phytohormone, nor through interference with the DELLA protein germination repressors. Rather, BBX19 functions as an inducer of ABA INSENSITIVE5 (ABI5) by binding to the light-responsive GT1 motifs in the gene promoter. In summary, we identify BBX19 as a regulatory checkpoint, directing diverse developmental processes and tailoring adaptive responses to distinct endogenous and exogenous signals.

Caffeine Effect on Cognitive Function during a Stroop Task: fNIRS Study.

Acting as a brain stimulant, coffee resulted in heightening alertness, keeping arousal, improving executive speed, maintaining vigilance, and promoting memory, which are associated with attention, mood, and cognitive function. Functional near-infrared spectroscopy (fNIRS) is a noninvasive optical method to monitor brain activity by measuring the absorption of the near-infrared light through the intact skull. This study is aimed at acquiring brain activation during executing task performance. The aim is to explore the effect of coffee on cognitive function by the fNIRS neuroimaging method, particularly on the prefrontal cortex regions. The behavioral experimental results on 31 healthy subjects with a Stroop task indicate that coffee can easily and effectively modulate the execute task performance by feedback information of the response time and accuracy rate. The findings of fNIRS showed that apparent hemodynamic changes were detected in the bilateral VLPFC regions and the brain activation regions varied with different coffee conditions.

Polarization-multiplexed multifocal arrays by a π-phase-step-modulated azimuthally polarized beam.

We demonstrate a polarization-multiplexed multifocal array capable of individually manipulating the focal polarization state in each focal spot. Breaking the rotational phase symmetry through adding a π-phase-step to an azimuthally polarized beam leads to a linear focal polarization state with high polarization purity. Through the superposition of such modulated azimuthal polarization fields at the back aperture of the objective, multifocal arrays with individually controllable and non-identical polarization states can be achieved. In addition, this approach exhibits a sub-diffraction-limited feature with an improved lateral resolution of 10% reduced full width at half-maximums in each linearly polarized focal spot. Consequently, applying this technique to parallel polarization-multiplexed optical recording is demonstrated.

Three-dimensional parallel recording with a Debye diffraction-limited and aberration-free volumetric multifocal array.

In this Letter, we report on the generation of high-quality Debye diffraction-limited volumetric multifocal arrays. The multifocal arrays with a uniformity of 0.99 over the entire focal region of a high numerical-aperture objective are volumetrically generated by using the vectorial Debye-based three-dimensional (3D) Fourier-transform method, through the accurate phase modulation on an Ewald cap. Thus, this method is capable of dynamic spherical aberration compensation. Applying this feature into 3D parallel aberration-free optical recording reveals a significant increase in the throughput by two orders of magnitude.

Super-resolved pure-transverse focal fields with an enhanced energy density through focus of an azimuthally polarized first-order vortex beam.

We report on the experimental demonstration of super-resolved pure-transverse focal fields through focusing an azimuthally polarized first-order vortex (FOV) beam. The optimized confinement of focal fields by creating constructive interference through the superposition of the FOV on an azimuthally polarized beam is observed by both a scanning near-field microscope and a two-photon fluorescence microscope. An enhanced peak intensity of the focal spot by a factor of 1.8 has been observed compared with that of the unmodulated azimuthally polarized beam. The super-resolved and pure-transverse focal fields with a 31% reduced focal area determined by the full-width at half-maximum compared to that of tightly focused circular polarization is experimentally corroborated. This superiority over the circular polarization stands for any numerical aperture greater than 0.4. This technique holds the potential for applications requiring subwavelength resolution and pure-transverse fields such as high-density optical data storage and high-resolution microscopy.

Beyond the lab: a nanoimprint metalens array-based augmented reality.

A see-through augmented reality prototype has been developed based on an ultrathin nanoimprint metalens array, opening up a full-colour, video-rate, and low-cost 3D near-eye display.

SLAM: Structure-aware lysine ß-hydroxybutyrylation prediction with protein language model.

Post-translational modifications (PTMs) diversify protein functions by adding/removing chemical groups to certain amino acid. As a newly-reported PTM, lysine ß-hydroxybutyrylation (Kbhb) presents a new avenue to functional proteomics. Therefore, accurate and efficient prediction of Kbhb sites is imperative. However, the current experimental methods for identifying PTM sites are often expensive and time-consuming. Up to now, there is no computational method proposed for Kbhb sites detection. To this end, we present the first deep learning-based method, termed SLAM, to in silico identify lysine ß-hydroxybutyrylation sites. The performance of SLAM is evaluated on both 5-fold cross-validation and independent test, achieving 0.890, 0.899, 0.907 and 0.923 in terms of AUROC values, on the general and species-specific independent test sets, respectively. As one example, we predicted the potential Kbhb sites in human S-adenosyl-L-homocysteine hydrolase, which is in agreement with experimentally-verified Kbhb sites. In summary, our method could enable accurate and efficient characterization of novel Kbhb sites that are crucial for the function and stability of proteins and could be applied in the structure-guided identification of other important PTM sites. The SLAM online service and source code is available at https://ai4bio.online/SLAM and https://github.com/Gabriel-QIN/SLAM, respectively.