Search Results (12,767)

Nanosatellites increasingly produce more data than can be downlinked within a reasonable time due to their limited bandwidth and power. Therefore, an on-board system is required to prioritize scientifically significant data for downlinking, as described by scientists. This paper determines whether natural language processing can be used to prioritize remote sensing images on CubeSats with more flexibility compared to existing methods. Two approaches implementing the same conceptual prioritization pipeline are compared. The first uses YOLOv8 and Llama2 to extract image features and compare them with text descriptions via cosine similarity. The second approach employs CLIP, fine-tuned on remote sensing data, to achieve the same. Both approaches are evaluated on real nanosatellite hardware, the VERTECS Camera Control Board. The CLIP approach, particularly the ResNet50-based model, shows the best performance in prioritizing and sequencing remote sensing images. This paper demonstrates that on-orbit prioritization using natural language descriptions is viable and allows for more flexibility than existing methods. Full article

Background and Objectives: Neisseria gonorrhoeae and Chlamydia trachomatis can cause similar clinical syndromes and may coexist in infections. In emergency medicine, empirical treatment targeting both pathogens is often employed, potentially contributing to antibiotic resistance. Gonococcal resistance has emerged against first-line antimicrobials, necessitating prior testing for fluoroquinolone susceptibility. Certest Biotec developed two molecular diagnostic products for simultaneous detection: VIASURE C. trachomatis & N. gonorrhoeae Real-Time PCR Detection Kit and VIASURE Neisseria gonorrhoeae Ciprofloxacin-Resistant Real-Time PCR Detection Kit. To evaluate these products, clinical performance assessments were conducted at the Clinical Microbiology Laboratory of Miguel Servet University Hospital in Zaragoza, Spain. Results and Conclusions: Both VIASURE assays under study demonstrated high clinical sensitivity and specificity compared to reference molecular assays and Sanger sequencing. These kits offer an accurate diagnosis, facilitating appropriate treatment choices while addressing concerns about emerging antibiotic resistance. Methods: A total of 540 clinical samples from 540 patients already characterized as positive or negative for CT and NG by a molecular assay and by antibiotic susceptibility testing for ciprofloxacin using a concentration gradient diffusion method were used for the clinical evaluation. In cases where sensitivity results were unavailable, conventional PCR and Sanger sequencing were employed. Full article

As distributed photovoltaic (PV) technology rapidly develops and is widely applied, the methods of cyberattacks are continuously evolving, posing increasingly severe threats to the communication networks of distributed PV systems. Recent studies have shown that the Transformer model, which effectively integrates global information and handles long-distance dependencies, has garnered significant attention. Based on this, our research proposes a model named STformer, which is applied to the task of attack detection in distributed PV communication. Specifically, we propose a temporal attention mechanism and a variable attention mechanism. The temporal attention mechanism focuses on capturing subtle changes and trends in data sequences over time, ensuring a highly sensitive recognition of patterns inherent in time-series data. In contrast, the variable attention mechanism analyzes the intrinsic relationships and interactions between different variables, uncovering critical correlations that may indicate abnormal behavior or potential attacks. Additionally, we incorporate the Uniform Manifold Approximation and Projection (UMAP) dimensionality reduction technique. This technique not only helps reduce computational complexity but, in certain cases, can enhance anomaly detection performance. Finally, compared to classical and advanced methods, STformer demonstrates satisfactory performance in simulation experiments. Full article

Novel therapies such as CAR-T, BTK inhibitors and PD-1 inhibitors have changed the management of aggressive B-cell lymphomas. Nonetheless, these novel therapies have their own risk of late toxicities including second malignancies. They also create a subgroup of patients with relapse, treatment failure, or indefinite maintenance. We discuss the current role of autologous and allogeneic stem cell transplantation in this context. In patients with recurrent diffuse large B-cell lymphoma, CAR-T cell treatment has largely replaced autologous transplant. Autologous transplant should be considered in patients with late relapses and in selected patients with T-cell-rich B-cell lymphoma, where CAR-T cell therapy may be less effective. It also remains the treatment of choice for consolidation of patients with primary CNS lymphoma. In mantle cell lymphoma, intensive chemotherapy combined with BTK inhibitors and rituximab results in excellent outcomes, and the role of autologous transplantation is declining. In Hodgkin’s lymphoma, autologous transplant consolidation remains the standard of care for patients who failed initial chemotherapy. Allogeneic transplantation has lower relapse rates but more complications and higher non-relapse mortality than autologous transplantation. It is usually reserved for patients who fail autologous transplantation or in whom autologous stem cells cannot be collected. It may also have an important role in patients who fail CAR-T therapies. The increasing complexity of care and evolving sequencing of therapies for patients with aggressive B-cell lymphomas only emphasizes the importance of appropriate patient selection and optimal timing of stem cell transplantation. Full article

Abstract: Chlorophyll prediction facilitates the comprehension of red tide characteristics and enables early warning. In practice, it is formulated as a multivariate time series forecasting problem aimed at forecasting future chlorophyll concentrations by considering both exogenous factors and chlorophyll. However, the multi-step prediction of chlorophyll concentration poses a formidable challenge due to the intricate interaction between factors and the long temporal dependence between input sequences. In this work, we propose a Multi-attention Recurrent Neural Network (MaRNN) for the multi-step prediction of chlorophyll concentration. The MaRNN comprises an encoder incorporating two-stage spatial attention and a decoder employing temporal attention. The encoder first learns the significance of exogenous factors for prediction in the first phase, and subsequently captures the spatial correlation between the exogenous sequence and chlorophyll sequence in the second phase. The decoder further excavates input sequences that exhibit a strong correlation with the task through temporal attention module, thereby enhancing the prediction accuracy of the model. Experiments conducted on two real-world datasets reveal that MaRNN not only surpasses state-of-the-art methods in performance, but also offers interpretability for chlorophyll prediction. Full article

Grapevine (Vitis vinifera L.) is a globally significant economic crop. However, its widely cultivated varieties are highly susceptible to white rot disease. To elucidate the mechanisms of resistance in grapevine against this disease, we utilized time-ordered gene co-expression network (TO-GCN) analysis to investigate the molecular responses in the grapevine varieties ‘Guifeimeigui’ (GF) and ‘Red Globe’ (RG). An assessment of their resistance demonstrated that GF is highly resistant to white rot, whereas RG is highly susceptible. We conducted transcriptome sequencing and a TO-GCN analysis on leaf samples from GF and RG at seven time points post-infection. Although a significant portion of the differentially expressed genes related to disease resistance were shared between GF and RG, the GF variety rapidly activated its defense mechanisms through the regulation of transcription factors during the early stages of infection. Notably, the gene VvLOX3, which is a key enzyme in the jasmonic acid biosynthetic pathway, was significantly upregulated in GF. Its upstream regulator, Vitvi08g01752, encoding a HD-ZIP family transcription factor, was identified through TO-GCN and yeast one-hybrid analyses. This study provides new molecular insights into the mechanisms of grapevine disease resistance and offers a foundation for breeding strategies aimed at enhancing resistance. Full article

Various types of numerical control (NC) machine tools can be standardly operated and controlled based on NC data that can be easily generated using widespread CAD/CAM systems. On the other hand, the operation environments of industrial robots still depend on conventional teaching and playback systems provided by the makers, so it seems that they have not been standardized and unified like NC machine tools yet. Additionally, robotic functional extensions, e.g., the easy implementation of a machine learning model, such as a convolutional neural network (CNN), a visual feedback controller, cooperative control for multiple robots, and so on, has not been sufficiently realized yet. In this paper, a hyper cutter location source (HCLS)-data-based robotic interface is proposed to cope with the issues. Due to the HCLS-data-based robot interface, the robotic control sequence can be visually and unifiedly described as NC codes. In addition, a VGG19-based CNN model for defect detection, whose classification accuracy is over 99% and average time for forward calculation is 70 ms, can be systematically incorporated into a robotic control application that handles multiple robots. The effectiveness and validity of the proposed system are demonstrated through a cooperative pick and place task using three small-sized industrial robot MG400s and a peg-in-hole task while checking undesirable defects in workpieces with a CNN model without using any programmable logic controller (PLC). The specifications of the PC used for the experiments are CPU: Intel(R) Core(TM) i9-10850K CPU 3.60 GHz, GPU: NVIDIA GeForce RTX 3090, Main memory: 64 GB. Full article

Background: The ubiquitination process plays a crucial role in neuronal differentiation and function. Numerous studies have focused on the expression and functions of E3 ligases during these different stages, far fewer on E2 conjugating enzymes. In mice, as in humans, these E2s belong to 17 conjugating enzyme families. Objectives: We analyzed by real-time PCR the expression dynamics of all known E2 genes during an in vitro differentiation of mouse hippocampal neuronal cultures, and after, we analyzed their stimulation with N-methyl-D-aspartate (NMDA). Results: We found that 36 of the 38 E2 genes were expressed in hippocampal neurons. Many were up-regulated during neuritogenesis and/or synaptogenesis stages, such as Ube2h, Ube2b, and Aktip. Rapid and delayed responses to NMDA stimulation were associated with the increased expression of several E2 genes, such as Ube2i, the SUMO-conjugating E2 enzyme. We also observed similar expression profiles within the same E2 gene family, consistent with the presence of similar transcription factor binding sites in their respective promoter sequences. Conclusions: Our study indicates that specific expression profiles of E2 genes are correlated with changes in neuronal differentiation and activity. A better understanding of the regulation and function of E2s is needed to better understand the role played by the ubiquitination process in physiological mechanisms and pathophysiological alterations involved in neurodevelopmental or neurodegenerative diseases. Full article

Heavy metals like arsenic, mercury, cadmium, and lead are harmful pollutants that can change how our genes are regulated without altering the DNA sequence, specifically through a process called DNA methylation (DNAm) at 5-methylcytosine, an epigenetic mark that we will focus on in this review. These changes in DNAm are most sensitive during pregnancy, a critical time for development when these modifications can affect how traits are expressed. Historically, most research on these environmental effects has focused on adults, but now there is more emphasis on studying the impacts during early development and childhood. The placenta acts as a protective barrier between the mother and the baby, and by examining it, scientists can identify changes in key genes that might affect long-term health. This review looks at how exposure to heavy metals during pregnancy can cause changes in the gene regulation by DNAm in newborns, as seen in their umbilical cord blood. These changes reflect the baby’s genetic state during pregnancy and can be influenced by the mother’s environment and genetics, as well as the baby’s own genetics. Full article

This article investigates the design of time-accurate input signals in the angle-of-attack and pitch rate space to identify the aerodynamic characteristics of a generic triple-delta wing configuration at subsonic speeds. Regression models were created from the time history of signal simulations in DoD HPCMP CREATE^TM-AV/Kestrel software. The input signals included chirp, Schroeder, pseudorandom binary sequence (PRBS), random, and sinusoidal signals. Although similar in structure, the coefficients of these regression models were estimated based on the specific input signals. The signals covered a wide range of angle-of-attack and pitch rate space, resulting in varying regression coefficients for each signal. After creating and validating the models, they were used to predict static aerodynamic data at a wide range of angles of attack but with zero pitch rate. Next, slope coefficients and dynamic derivatives in the pitch direction were estimated from each signal. These predictions were compared with each other as well as with the ONERA wind tunnel data and some CFD calculations from the DLR TAU code provided by the NATO Science and Technology Organization research task group AVT-351. Subsequently, the models were used to predict different pitch oscillations at various mean angles of attack with given amplitudes and frequencies. Again, the model predictions were compared with wind tunnel data. Final predictions involved responses to new signals from different models. A feed-forward neural network was then used to model pressure coefficients on the upper surface of the vehicle at different spanwise sections for each signal and the validated models were used to predict pressure data at different angles of attack. Overall, the models predict similar integrated forces and moments, with the main discrepancies appearing at higher angles of attack. All models failed to predict the stall behavior observed in the measurements and CFD data. Regarding the pressure data, the PRBS signal provided the best accuracy among all the models. Full article

In 2019, Candida auris became the first fungal pathogen included in the list of the urgent antimicrobial threats by the Centers for Disease Control (CDC). Short tandem repeat (STR) analysis and whole-genome sequencing (WGS) are considered the gold standard, and can be complemented by other molecular methods, for the genomic surveillance and clade classification of this multidrug-resistant yeast. However, these methods can be expensive and require time and expertise that are not always available. The long turnaround time is especially not compatible with the speed needed to manage clonal transmission in healthcare settings. Fourier transform infrared (FTIR) spectroscopy, a biochemical fingerprint approach, has been applied in this study to a set of 74 C. auris isolates belonging to the five clades of C. auris (I-V) in combination with an artificial neural network (ANN) algorithm to create and validate “CLADE-FINDER”, a tool for C. auris clade determination. The CLADE-FINDER classifier allowed us to discriminate the four primary C. auris clades (I-IV) with a correct classification for 96% of the samples in the validation set. This newly developed genotyping scheme can be reasonably applied for the effective epidemiological monitoring and management of C. auris cases in real time. Full article

Addressing the issue of inadequate convergence and suboptimal accuracy in classical data-driven algorithms for coherent polarization–direction-of-arrival (DOA) estimation, a novel high-precision two-dimensional coherent polarization–DOA estimation method utilizing a sequence-embedding fusion (SEF) transformer is proposed for the first time. Drawing inspiration from natural language processing (NLP), this approach employs transformer-based multitasking text inference to facilitate joint estimation of polarization and DOA. This method leverages the multi-head self-attention mechanism of the transformer to effectively capture the multi-dimensional features within the spatial-polarization domain of the covariance matrix data. Additionally, an SEF module was proposed to fuse the spatial-polarization domain features from different dimensions. The module is a combination of a convolutional neural network (CNN) with local information extraction capabilities and a feature dimension transformation function, serving to improve the model’s ability to fuse information about features in the spatial-polarization domain. Moreover, to enhance the model’s expressive capacity, we designed a multi-task parallel output mode and a multi-task weighted loss function. Simulation results demonstrate that our method outperforms classical data-driven approaches in both accuracy and generalization, and the estimation accuracy of our method is improved relative to the traditional model-driven algorithm. Full article

Traditional antiviral peptide (AVP) discovery is a time-consuming and expensive process. This study introduces AVP-GPT, a novel deep learning method utilizing transformer-based language models and multimodal architectures specifically designed for AVP design. AVP-GPT demonstrated exceptional efficiency, generating 10,000 unique peptides and identifying potential AVPs within two days on a GPU system. Pre-trained on a respiratory syncytial virus (RSV) dataset, AVP-GPT successfully adapted to influenza A virus (INFVA) and other respiratory viruses. Compared to state-of-the-art models like LSTM and SVM, AVP-GPT achieved significantly lower perplexity (2.09 vs. 16.13) and higher AUC (0.90 vs. 0.82), indicating superior peptide sequence prediction and AVP classification. AVP-GPT generated a diverse set of peptides with excellent novelty and identified candidates with remarkably higher antiviral success rates than conventional design methods. Notably, AVP-GPT generated novel peptides against RSV and INFVA with exceptional potency, including four peptides exhibiting EC50 values around 0.02 uM—the strongest anti-RSV activity reported to date. These findings highlight AVP-GPT’s potential to revolutionize AVP discovery and development, accelerating the creation of novel antiviral drugs. Future studies could explore the application of AVP-GPT to other viral targets and investigate alternative AVP design strategies. Full article

Gaillardia pulchella is an important plant species with pharmacological and ornamental applications. It contains a wide array of phytocompounds which play roles against diseases. In vitro propagation requires callogenesis and differentiation of plant organs, which offers a sustainable, alternative synthesis of compounds. The morphogenetic processes and the underlying mechanisms are, however, known to be under genetic regulation and are little understood. The present study investigated these events by generating transcriptome data, with de novo assembly of sequences to describe shoot morphogenesis molecularly in G. pulchella. The RNA was extracted from the callus of pre- and post-shoot organogenesis time. The callus induction was optimal using leaf segments cultured onto MS medium containing α-naphthalene acetic acid (NAA; 2.0 mg/L) and 6-benzylaminopurine (BAP; 0.5 mg/L) and further exhibited a high shoot regeneration/caulogenesis ability. A total of 68,366 coding sequences were obtained using Illumina150bpPE sequencing and transcriptome assembly. Differences in gene expression patterns were noted in the studied samples, showing opposite morphogenetic responses. Out of 10,108 genes, 5374 (53%) were downregulated, and there were 4734 upregulated genes, representing 47% of the total genes. Through the heatmap, the top 100 up- and downregulating genes’ names were identified and presented. The up- and downregulated genes were identified using the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway. Important pathways, operative during G. pulchella shoot organogenesis, were signal transduction (13.55%), carbohydrate metabolism (8.68%), amino acid metabolism (5.11%), lipid metabolism (3.75%), and energy metabolism (3.39%). The synthesized proteins displayed phosphorylation, defense response, translation, regulation of DNA-templated transcription, carbohydrate metabolic processes, and methylation activities. The genes’ product also exhibited ATP binding, DNA binding, metal ion binding, protein serine/threonine kinase -, ATP hydrolysis activity, RNA binding, protein kinase, heme and GTP binding, and DNA binding transcription factor activity. The most abundant proteins were located in the membrane, nucleus, cytoplasm, ribosome, ribonucleoprotein complex, chloroplast, endoplasmic reticulum membrane, mitochondrion, nucleosome, Golgi membrane, and other organellar membranes. These findings provide information for the concept of molecular triggers, regulating programming, differentiation and reprogramming of cells, and their uses. Full article

Studies on the phenotypic traits and their associations in Chinese cabbage lack precise and objective digital evaluation metrics. Traditional assessment methods often rely on subjective evaluations and experience, compromising accuracy and reliability. This study develops an innovative, comprehensive trait evaluation method based on 3D point cloud technology, with the aim of enhancing the precision, reliability, and standardization of the comprehensive phenotypic traits of Chinese cabbage. By using multi-view image sequences and structure-from-motion algorithms, 3D point clouds of 50 plants from each of the 17 Chinese cabbage varieties were reconstructed. Color-based region growing and 3D convex hull techniques were employed to measure 30 agronomic traits. Comparisons between 3D point cloud-based measurements of the plant spread, plant height, leaf area, and leaf ball volume and traditional methods yielded R² values greater than 0.97, with root mean square errors of 1.27 cm, 1.16 cm, 839.77 cm³, and 59.15 cm², respectively. Based on the plant spread and plant height, a linear regression prediction of Chinese cabbage weights was conducted, yielding an R² value of 0.76. Integrated optimization algorithms were used to test the parameters, reducing the measurement time from 55 min when using traditional methods to 3.2 min. Furthermore, in-depth analyses including variation, correlation, principal component analysis, and clustering analyses were conducted. Variation analysis revealed significant trait variability, with correlation analysis indicating 21 pairs of traits with highly significant positive correlations and 2 pairs with highly significant negative correlations. The top six principal components accounted for 90% of the total variance. Using the elbow method, k-means clustering determined that the optimal number of clusters was four, thus classifying the 17 cabbage varieties into four distinct groups. This study provides new theoretical and methodological insights for exploring phenotypic trait associations in Chinese cabbage and facilitates the breeding and identification of high-quality varieties. Compared with traditional methods, this system provides significant advantages in terms of accuracy, speed, and comprehensiveness, with its low cost and ease of use making it an ideal replacement for manual methods, being particularly suited for large-scale monitoring and high-throughput phenotyping. Full article