Intercellular protein movement: deciphering the language of development.

Development in multicellular organisms requires the coordinated production of a large number of specialized cell types through sophisticated signaling mechanisms. Non-cell-autonomous signals are one of the key mechanisms by which organisms coordinate development. In plants, intercellular movement of transcription factors and other mobile signals, such as hormones and peptides, is essential for normal development. Through a combination of different approaches, a large number of non-cell-autonomous signals that control plant development have been identified. We review some of the transcriptional regulators that traffic between cells, as well as how changes in symplasmic continuity affect and are affected by development. We also review current models for how mobile signals move via plasmodesmata and how movement is inhibited. Finally, we consider challenges in and new tools for studying protein movement.

Spectroscopic evidence for a gold-coloured metallic water solution.

Insulating materials can in principle be made metallic by applying pressure. In the case of pure water, this is estimated1 to require a pressure of 48 megabar, which is beyond current experimental capabilities and may only exist in the interior of large planets or stars2-4. Indeed, recent estimates and experiments indicate that water at pressures accessible in the laboratory will at best be superionic with high protonic conductivity5, but not metallic with conductive electrons1. Here we show that a metallic water solution can be prepared by massive doping with electrons upon reacting water with alkali metals. Although analogous metallic solutions of liquid ammonia with high concentrations of solvated electrons have long been known and characterized6-9, the explosive interaction between alkali metals and water10,11 has so far only permitted the preparation of aqueous solutions with low, submetallic electron concentrations12-14. We found that the explosive behaviour of the water-alkali metal reaction can be suppressed by adsorbing water vapour at a low pressure of about 10-4 millibar onto liquid sodium-potassium alloy drops ejected into a vacuum chamber. This set-up leads to the formation of a transient gold-coloured layer of a metallic water solution covering the metal alloy drops. The metallic character of this layer, doped with around 5 × 1021 electrons per cubic centimetre, is confirmed using optical reflection and synchrotron X-ray photoelectron spectroscopies.

How Does Mg2+(aq) Interact with ATP(aq)? Biomolecular Structure through the Lens of Liquid-Jet Photoemission Spectroscopy.

Liquid-jet photoemission spectroscopy (LJ-PES) allows for a direct probing of electronic structure in aqueous solutions. We show the applicability of the approach to biomolecules in a complex environment, exploring site-specific information on the interaction of adenosine triphosphate in the aqueous phase (ATP(aq)) with magnesium (Mg2+(aq)), highlighting the synergy brought about by the simultaneous analysis of different regions in the photoelectron spectrum. In particular, we demonstrate intermolecular Coulombic decay (ICD) spectroscopy as a new and powerful addition to the arsenal of techniques for biomolecular structure investigation. We apply LJ-PES assisted by electronic-structure calculations to study ATP(aq) solutions with and without dissolved Mg2+. Valence photoelectron data reveal spectral changes in the phosphate and adenine features of ATP(aq) due to interactions with the divalent cation. Chemical shifts in Mg 2p, Mg 2s, P 2p, and P 2s core-level spectra as a function of the Mg2+/ATP concentration ratio are correlated to the formation of [Mg(ATP) 2]6-(aq), [MgATP]2-(aq), and [Mg2ATP](aq) complexes, demonstrating the element sensitivity of the technique to Mg2+-phosphate interactions. The most direct probe of the intermolecular interactions between ATP(aq) and Mg2+(aq) is delivered by the emerging ICD electrons following ionization of Mg 1s electrons. ICD spectra are shown to sensitively probe ligand exchange in the Mg2+-ATP(aq) coordination environment. In addition, we report and compare P 2s data from ATP(aq) and adenosine mono- and diphosphate (AMP(aq) and ADP(aq), respectively) solutions, probing the electronic structure of the phosphate chain and the local environment of individual phosphate units in ATP(aq). Our results provide a comprehensive view of the electronic structure of ATP(aq) and Mg2+-ATP(aq) complexes relevant to phosphorylation and dephosphorylation reactions that are central to bioenergetics in living organisms.

Site-Specific Photochemistry along a Protonated Peptide Scaffold.

We present a detailed study of the time-dependent photophysics and photochemistry of a known conformation of the two protonated pentapeptides Leu-enkephalin (Tyrosine-Glycine-Glycine-Phenylalanine-Leucine, YGGFL) and its chromophore-swapped analogue FGGYL, carried out under cryo-cooled conditions in the gas phase. Using ultraviolet-infrared (UV-IR) double resonance, we record excited state IR spectra as a function of time delay between UV and IR pulses. We identify unique Tyr OH stretch transitions due to the S1 state and the vibrationally excited triplet state(s) formed by intersystem crossing, Tn(v). Photofragment mass spectra are recorded out of the S1 origin and following UV-IR double resonance. Several competing site-specific fragmentation pathways are discovered involving peptide backbone cleavage, Tyr side chain loss, and N-terminal NH3 loss mediated by electron transfer. In YGGFL, IR excitation in the S1 state promotes electron transfer (ET) from the aromatic ring to the N-terminal R-NH3+ group leading to loss of neutral NH3. This product channel is missing in FGGYL due to the larger distance for ET from Y(4) to NH3+. Selective loss of the Tyr side chain occurs out of an excited state process following UV excitation and is further enhanced by IR excitation in S1 and Tn(v) states of both YGGFL and FGGYL. Finally, IR excitation in the S1 or Tn(v) states fragments the peptide backbone exclusively at amide(4), producing the b4 cation. We postulate that this selective fragmentation results from intersystem crossing to produce vibrationally excited triplets with enough energy to launch the proton along a proton conduit present in the known starting structure.

Interfacial Layer Materials with a Truxene Core for Dopant-Free NiOx -Based Inverted Perovskite Solar Cells.

Nickel oxide (NiOx ) is commonly used as a holetransporting material (HTM) in p-i-n perovskite solar cells. However, the weak chemical interaction between the NiOx and CH3 NH3 PbI3 (MAPbI3 ) interface results in poor crystallinity, ineffective hole extraction, and enhanced carrier recombination, which are the leading causes for the limited stability and power conversion efficiency (PCE). Herein, two HTMs, TRUX-D1 (N2 ,N7 ,N12 -tris(9,9-dimethyl-9H-fluoren-2-yl)-5,5,10,10,15,15-hexaheptyl-N2 ,N7 ,N12 -tris(4-methoxyphenyl)-10,15-dihydro-5H-diindeno[1,2-a:1',2'-c]fluorene-2,7,12-triamine) and TRUX-D2 (5,5,10,10,15,15-hexaheptyl-N2 ,N7 ,N12 -tris(4-methoxyphenyl)-N2 ,N7 ,N12 -tris(10-methyl-10H-phenothiazin-3-yl)-10,15-dihydro-5H-diindeno[1,2-a:1',2'-c]fluorene-2,7,12-triamine), are designed with a rigid planar C3 symmetry truxene core integrated with electron-donating amino groups at peripheral positions. The TRUX-D molecules are employed as effective interfacial layer (IFL) materials between the NiOx and MAPbI3 interface. The incorporation of truxene-based IFLs improves the quality of perovskite crystallinity, minimizes nonradiative recombination, and accelerates charge extraction which has been confirmed by various characterization techniques. As a result, the TRUX-D1 exhibits a maximum PCE of up to 20.8% with an impressive long-term stability. The unencapsulated device retains 98% of their initial performance following 210 days of aging in a glove box and 75.5% for the device after 80 days under ambient air condition with humidity over 40% at 25 °C.

DEFECTIVE EMBRYO AND MERISTEMS genes are required for cell division and gamete viability in Arabidopsis.

The DEFECTIVE EMBRYO AND MERISTEMS 1 (DEM1) gene encodes a protein of unknown biochemical function required for meristem formation and seedling development in tomato, but it was unclear whether DEM1's primary role was in cell division or alternatively, in defining the identity of meristematic cells. Genome sequence analysis indicates that flowering plants possess at least two DEM genes. Arabidopsis has two DEM genes, DEM1 and DEM2, which we show are expressed in developing embryos and meristems in a punctate pattern that is typical of genes involved in cell division. Homozygous dem1 dem2 double mutants were not recovered, and plants carrying a single functional DEM1 allele and no functional copies of DEM2, i.e. DEM1/dem1 dem2/dem2 plants, exhibit normal development through to the time of flowering but during male reproductive development, chromosomes fail to align on the metaphase plate at meiosis II and result in abnormal numbers of daughter cells following meiosis. Additionally, these plants show defects in both pollen and embryo sac development, and produce defective male and female gametes. In contrast, dem1/dem1 DEM2/dem2 plants showed normal levels of fertility, indicating that DEM2 plays a more important role than DEM1 in gamete viability. The increased importance of DEM2 in gamete viability correlated with higher mRNA levels of DEM2 compared to DEM1 in most tissues examined and particularly in the vegetative shoot apex, developing siliques, pollen and sperm. We also demonstrate that gamete viability depends not only on the number of functional DEM alleles inherited following meiosis, but also on the number of functional DEM alleles in the parent plant that undergoes meiosis. Furthermore, DEM1 interacts with RAS-RELATED NUCLEAR PROTEIN 1 (RAN1) in yeast two-hybrid and pull-down binding assays, and we show that fluorescent proteins fused to DEM1 and RAN1 co-localize transiently during male meiosis and pollen development. In eukaryotes, RAN is a highly conserved GTPase that plays key roles in cell cycle progression, spindle assembly during cell division, reformation of the nuclear envelope following cell division, and nucleocytoplasmic transport. Our results demonstrate that DEM proteins play an essential role in cell division in plants, most likely through an interaction with RAN1.

High incidence of cerebrovascular lesions on magnetic resonance imaging in pediatric COVID-19 during omicron outbreak - A retrospective case series.

BACKGROUND: The incidence of pediatric hospitalizations has significantly increased since the spread of the omicron variant of COVID-19. Changes of characteristics in respiratory and neurological symptoms have been reported. We performed a retrospective, cross-sectional study to characterize the MRI change in children with an emphasis on the change of cerebral vasculatures. METHODS: We retrospectively collected clinical and MRI data of 31 pediatric patients with neurological symptoms during the acute infection and abnormalities on MRI during the outbreak of omicron variant from April 2022 to June 2022 in Taiwan. The clinical manifestations and MRI abnormalities were collected and proportion of patients with vascular abnormalities was calculated. RESULTS: Among 31 pediatric patients with post-COVID-19 neurological symptoms, MRI abnormalities were observed in 15 (48.4%), predominantly encephalitis/encephalopathy (73.3%). Notable MRI findings included focal diffusion-weighted imaging (DWI) hyperintensity in cerebral cortex and thalamus, diffuse cortical T2/DWI hyperintensity, and lesions in the medulla, pons, cerebellum, and splenium of corpus callosum. Vascular abnormalities were seen in 12 (80%) patients with MRI abnormalities, mainly affecting the middle cerebral arteries. The spectrum of neurological manifestations ranged from seizures to Alice in Wonderland syndrome, underscoring the diverse impact of COVID-19 on pediatric patients. CONCLUSION: A high proportion of vascular abnormalities was observed in pediatric patients with neurological involvements, suggesting that vascular involvement is an important mechanism of neurological manifestations in omicron variant infection.

Neurological manifestations of SARS-CoV-2 infection in children in Taiwan: A cross-section, multicenter study.

BACKGROUND: The SARS-CoV-2 virus has been a global public health threat since December 2019. This study aims to investigate the neurological characteristics and risk factors of coronavirus disease 2019 (COVID-19) in Taiwanese children, using data from a collaborative registry. METHODS: A retrospective, cross-sectional, multi-center study was done using an online network of pediatric neurological COVID-19 cohort collaborative registry. RESULTS: A total of 11160 COVID-19-associated emergency department (ED) visits and 1079 hospitalizations were analyzed. Seizures were the most common specific neurological symptom, while encephalitis and acute disseminated encephalomyelitis (ADEM) was the most prevalent severe involvement. In ED patients with neurological manifestations, severe neurological diagnosis was associated with visual hallucination, seizure with/without fever, behavior change, decreased GCS, myoclonic jerk, decreased activity/fatigue, and lethargy. In hospitalized patients with neurological manifestations, severe neurological diagnosis was associated with behavior change, visual hallucination, decreased GCS, seizure with/without fever, myoclonic jerk, fatigue, and hypoglycemia at admission. Encephalitis/ADEM was the only risk factor for poor neurological outcomes at discharge in hospitalized patients. CONCLUSION: Neurological complications are common in pediatric COVID-19. Visual hallucination, seizure, behavior change, myoclonic jerk, decreased GCS, and hypoglycemia at admission are the most important warning signs of severe neurological involvement such as encephalitis/ADEM.

MESMERIC: Machine Learning-Based Trust Management Mechanism for the Internet of Vehicles.

The emerging yet promising paradigm of the Internet of Vehicles (IoV) has recently gained considerable attention from researchers from academia and industry. As an indispensable constituent of the futuristic smart cities, the underlying essence of the IoV is to facilitate vehicles to exchange safety-critical information with the other vehicles in their neighborhood, vulnerable pedestrians, supporting infrastructure, and the backbone network via vehicle-to-everything communication in a bid to enhance the road safety by mitigating the unwarranted road accidents via ensuring safer navigation together with guaranteeing the intelligent traffic flows. This requires that the safety-critical messages exchanged within an IoV network and the vehicles that disseminate the same are highly reliable (i.e., trustworthy); otherwise, the entire IoV network could be jeopardized. A state-of-the-art trust-based mechanism is, therefore, highly imperative for identifying and removing malicious vehicles from an IoV network. Accordingly, in this paper, a machine learning-based trust management mechanism, MESMERIC, has been proposed that takes into account the notions of direct trust (encompassing the trust attributes of interaction success rate, similarity, familiarity, and reward and punishment), indirect trust (involving confidence of a particular trustor on the neighboring nodes of a trustee, and the direct trust between the said neighboring nodes and the trustee), and context (comprising vehicle types and operating scenarios) in order to not only ascertain the trust of vehicles in an IoV network but to segregate the trustworthy vehicles from the untrustworthy ones by means of an optimal decision boundary. A comprehensive evaluation of the envisaged trust management mechanism has been carried out which demonstrates that it outperforms other state-of-the-art trust management mechanisms.

Voltage-dependent anion channel proteins associate with dynamic Bamboo mosaic virus-induced complexes.

Infection cycles of viruses are highly dependent on membrane-associated host factors. To uncover the infection cycle of Bamboo mosaic virus (BaMV) in detail, we purified the membrane-associated viral complexes from infected Nicotiana benthamiana plants and analyzed the involved host factors. Four isoforms of voltage-dependent anion channel (VDAC) proteins on the outer membrane of mitochondria were identified due to their upregulated expression in the BaMV complex-enriched membranous fraction. Results from loss- and gain-of-function experiments indicated that NbVDAC2, -3, and -4 are essential for efficient BaMV accumulation. During BaMV infection, all NbVDACs concentrated into larger aggregates, which overlapped and trafficked with BaMV virions to the structure designated as the "dynamic BaMV-induced complex." Besides the endoplasmic reticulum and mitochondria, BaMV replicase and double-stranded RNAs were also found in this complex, suggesting the dynamic BaMV-induced complex is a replication complex. Yeast two-hybrid and pull-down assays confirmed that BaMV triple gene block protein 1 (TGBp1) could interact with NbVDACs. Confocal microscopy revealed that TGBp1 is sufficient to induce NbVDAC aggregates, which suggests that TGBp1 may play a pivotal role in the NbVDAC-virion complex. Collectively, these findings indicate that NbVDACs may associate with the dynamic BaMV-induced complex via TGBp1 and NbVDAC2, -3, or -4 and can promote BaMV accumulation. This study reveals the involvement of mitochondrial proteins in a viral complex and virus infection.

Palladium-catalyzed synthesis of 2,3-disubstituted indoles via arylation of ortho-alkynylanilines with arylsiloxanes.

In this study, we report the electrophilic cyclization of N,N-dimethyl-o-alkynylanilines with arylsiloxanes in the presence of [Pd(OAc)2] and Ag2O catalytic system, which leads to the efficient synthesis of indoles, similar to the one that is obtained through Larock indole synthesis. A range of aryl(trimethoxy)silanes with EDGs and EWGs were successfully utilized for the synthesis of a diverse variety of substituted indoles via the cleavage of the C-Si bond. This protocol exhibits good functional group tolerance and wide substrate scope to provide 2,3-diaryl-N-methylindoles in 26-88% yields.

Base-mediated chalcogenoaminative annulation of 2-alkynylanilines for direct access to 3-sulfenyl/selenyl-1H-indoles.

An efficient and transition metal-free synthesis of 3-sulfenyl/selenyl-1H-indoles via a base-assisted chalcogenoaminative annulation of 2-alkynyl aniline with disulfides/diselenides is described. A series of 2-alkynylanilines were found compatible with dichalcogenides in this transformation providing 3-sulfenyl/selenyl-1H-indoles in good to excellent yields. The presented methodology has the advantages of easily available raw materials, functional group tolerance, and a wide range of substrates that provide access to 3-sulfenylindoles and 3-selenylindoles.

Elucidating the Motif for CpG Oligonucleotide Binding to the Dendritic Cell Receptor DEC-205 Leads to Improved Adjuvants for Liver-Resident Memory.

DEC-205 is a cell-surface receptor that transports bound ligands into the endocytic pathway for degradation or release within lysosomal endosomes. This receptor has been reported to bind a number of ligands, including keratin, and some classes of CpG oligodeoxynucleotides (ODN). In this study, we explore in detail the requirements for binding ODNs, revealing that DEC-205 efficiently binds single-stranded, phosphorothioated ODN of ≥14 bases, with preference for the DNA base thymidine, but with no requirement for a CpG motif. DEC-205 fails to bind double-stranded phosphodiester ODN, and thus does not bind the natural type of DNA found in mammals. The ODN binding preferences of DEC-205 result in strong binding of B class ODN, moderate binding to C class ODN, minimal binding to P class ODN, and no binding to A class ODN. Consistent with DEC-205 binding capacity, induction of serum IL-12p70 or activation of B cells by each class of ODN correlated with DEC-205 dependence in mice. Thus, the greater the DEC-205 binding capacity, the greater the dependence on DEC-205 for optimal responses. Finally, by covalently linking a B class ODN that efficiently binds DEC-205, to a P class ODN that shows poor binding, we improved DEC-205 binding and increased adjuvancy of the hybrid ODN. The hybrid ODN efficiently enhanced induction of effector CD8 T cells in a DEC-205-dependent manner. Furthermore, the hybrid ODN induced robust memory responses, and was particularly effective at promoting the development of liver tissue-resident memory T cells.

Molecular Cage Reports on Its Contents: Spectroscopic Signatures of Cryo-Cooled K+- and Ba2+-Benzocryptand Complexes.

UV photofragment spectroscopy and IR-UV double resonance methods are used to determine the structure and spectroscopic responses of a three-dimensional [2.2.2]-benzocryptand cage to the incorporation of a single K+ or Ba2+ imbedded inside it (labeled as K+-BzCrypt, Ba2+-BzCrypt). We studied the isolated ion-cryptand complex under cryo-cooled conditions, brought into the gas phase by nano-electrospray ionization. Incorporation of a phenyl ring in place of the central ethyl group in one of the three N-CH2-CH2-O-CH2-CH2-O-CH2-CH2-N chains provides a UV chromophore whose S0-S1 transition we probe. K+-BzCrypt and Ba2+-BzCrypt have their S0-S1 origin transitions at 35,925 and 36,446 cm-1, respectively, blue-shifted by 174 and 695 cm-1 from that of 1,2-dimethoxybenzene. These origins are used to excite a single conformation of each complex selectively and record their IR spectra using IR-UV dip spectroscopy. The alkyl CH stretch region (2800-3000 cm-1) is surprisingly sensitive to the presence and nature of the encapsulated ion. We carried out an exhaustive conformational search of cage conformations for K+-BzCrypt and Ba2+-BzCrypt, identifying two conformations (A and B) that lie below all others in energy. We extend our local mode anharmonic model of the CH stretch region to these strongly bound ion-cage complexes to predict conformation-specific alkyl CH stretch spectra, obtaining quantitative agreement with experiment for conformer A, the gas-phase global minimum. The large electrostatic effect of the charge on the O- and N-lone pairs affects the local mode frequencies of the CH2 groups adjacent to these atoms. The localized CH2 scissors modes are pushed up in frequency by the adjacent O/N-atoms so that their overtones have little effect on the alkyl CH stretch region. However, the localized CH2 wags are nearly degenerate and strongly coupled to one another, producing an array of delocalized wag normal modes, whose highest frequency members reach up above 1400 cm-1. As such, their overtones mix significantly with the CH stretch modes, most notably involving the CH2 symmetric stretch fundamentals of the central ethyl groups in the all-alkyl chains and the CH stretches adjacent to the N-atoms and antiperiplanar to the nitrogen lone pair.

Evaporation and scattering of neon, methane, and water from a dodecane flat liquid jet.

The evaporation and scattering of Ne, CD4, and D2O from a dodecane flat liquid jet are investigated in a molecular beam apparatus. The experiment yields translational energy distributions as a function of scattering angle by means of a rotatable mass spectrometer. In the evaporation experiments, one observes a Maxwell-Boltzmann distribution with a cos θ angular distribution superimposed on a weak, isotropic background. The scattering experiments show contributions from impulsive scattering and thermal desorption. At select incident angles for the three systems, angular distributions show super-specular scattering for the impulsive scattering channel, an effect attributed to anisotropic momentum transfer to the liquid surface. The impulsive scattering channel is analyzed with a soft-sphere model to explore energy transfer between the scatterer and liquid as a function of deflection angle. Compared to Ne scattering, the polyatomic gases exhibit more thermal desorption and, in the impulsive scattering channel, a higher degree of internal excitation.

Fine-Tuned Temporal Dense Sampling with 1D Convolutional Neural Network for Human Action Recognition.

Human action recognition is a constantly evolving field that is driven by numerous applications. In recent years, significant progress has been made in this area due to the development of advanced representation learning techniques. Despite this progress, human action recognition still poses significant challenges, particularly due to the unpredictable variations in the visual appearance of an image sequence. To address these challenges, we propose the fine-tuned temporal dense sampling with 1D convolutional neural network (FTDS-1DConvNet). Our method involves the use of temporal segmentation and temporal dense sampling, which help to capture the most important features of a human action video. First, the human action video is partitioned into segments through temporal segmentation. Each segment is then processed through a fine-tuned Inception-ResNet-V2 model, where max pooling is performed along the temporal axis to encode the most significant features as a fixed-length representation. This representation is then fed into a 1DConvNet for further representation learning and classification. The experiments on UCF101 and HMDB51 demonstrate that the proposed FTDS-1DConvNet outperforms the state-of-the-art methods, with a classification accuracy of 88.43% on the UCF101 dataset and 56.23% on the HMDB51 dataset.

EnViTSA: Ensemble of Vision Transformer with SpecAugment for Acoustic Event Classification.

Recent successes in deep learning have inspired researchers to apply deep neural networks to Acoustic Event Classification (AEC). While deep learning methods can train effective AEC models, they are susceptible to overfitting due to the models' high complexity. In this paper, we introduce EnViTSA, an innovative approach that tackles key challenges in AEC. EnViTSA combines an ensemble of Vision Transformers with SpecAugment, a novel data augmentation technique, to significantly enhance AEC performance. Raw acoustic signals are transformed into Log Mel-spectrograms using Short-Time Fourier Transform, resulting in a fixed-size spectrogram representation. To address data scarcity and overfitting issues, we employ SpecAugment to generate additional training samples through time masking and frequency masking. The core of EnViTSA resides in its ensemble of pre-trained Vision Transformers, harnessing the unique strengths of the Vision Transformer architecture. This ensemble approach not only reduces inductive biases but also effectively mitigates overfitting. In this study, we evaluate the EnViTSA method on three benchmark datasets: ESC-10, ESC-50, and UrbanSound8K. The experimental results underscore the efficacy of our approach, achieving impressive accuracy scores of 93.50%, 85.85%, and 83.20% on ESC-10, ESC-50, and UrbanSound8K, respectively. EnViTSA represents a substantial advancement in AEC, demonstrating the potential of Vision Transformers and SpecAugment in the acoustic domain.

Gait-CNN-ViT: Multi-Model Gait Recognition with Convolutional Neural Networks and Vision Transformer.

Gait recognition, the task of identifying an individual based on their unique walking style, can be difficult because walking styles can be influenced by external factors such as clothing, viewing angle, and carrying conditions. To address these challenges, this paper proposes a multi-model gait recognition system that integrates Convolutional Neural Networks (CNNs) and Vision Transformer. The first step in the process is to obtain a gait energy image, which is achieved by applying an averaging technique to a gait cycle. The gait energy image is then fed into three different models, DenseNet-201, VGG-16, and a Vision Transformer. These models are pre-trained and fine-tuned to encode the salient gait features that are specific to an individual's walking style. Each model provides prediction scores for the classes based on the encoded features, and these scores are then summed and averaged to produce the final class label. The performance of this multi-model gait recognition system was evaluated on three datasets, CASIA-B, OU-ISIR dataset D, and OU-ISIR Large Population dataset. The experimental results showed substantial improvement compared to existing methods on all three datasets. The integration of CNNs and ViT allows the system to learn both the pre-defined and distinct features, providing a robust solution for gait recognition even under the influence of covariates.

Recent Advances in Traffic Sign Recognition: Approaches and Datasets.

Autonomous vehicles have become a topic of interest in recent times due to the rapid advancement of automobile and computer vision technology. The ability of autonomous vehicles to drive safely and efficiently relies heavily on their ability to accurately recognize traffic signs. This makes traffic sign recognition a critical component of autonomous driving systems. To address this challenge, researchers have been exploring various approaches to traffic sign recognition, including machine learning and deep learning. Despite these efforts, the variability of traffic signs across different geographical regions, complex background scenes, and changes in illumination still poses significant challenges to the development of reliable traffic sign recognition systems. This paper provides a comprehensive overview of the latest advancements in the field of traffic sign recognition, covering various key areas, including preprocessing techniques, feature extraction methods, classification techniques, datasets, and performance evaluation. The paper also delves into the commonly used traffic sign recognition datasets and their associated challenges. Additionally, this paper sheds light on the limitations and future research prospects of traffic sign recognition.

HGR-ViT: Hand Gesture Recognition with Vision Transformer.

Hand gesture recognition (HGR) is a crucial area of research that enhances communication by overcoming language barriers and facilitating human-computer interaction. Although previous works in HGR have employed deep neural networks, they fail to encode the orientation and position of the hand in the image. To address this issue, this paper proposes HGR-ViT, a Vision Transformer (ViT) model with an attention mechanism for hand gesture recognition. Given a hand gesture image, it is first split into fixed size patches. Positional embedding is added to these embeddings to form learnable vectors that capture the positional information of the hand patches. The resulting sequence of vectors are then served as the input to a standard Transformer encoder to obtain the hand gesture representation. A multilayer perceptron head is added to the output of the encoder to classify the hand gesture to the correct class. The proposed HGR-ViT obtains an accuracy of 99.98%, 99.36% and 99.85% for the American Sign Language (ASL) dataset, ASL with Digits dataset, and National University of Singapore (NUS) hand gesture dataset, respectively.