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Proteins have been playing a critical role in the regulation of diverse biological processes related to human life. With the increasing demand, functional proteins are sparse in this immense sequence space. Therefore, protein design has become an important task in various fields, including medicine, food, energy, materials, etc. Directed evolution has recently led to significant achievements. Molecular modification of proteins through directed evolution technology has significantly advanced the fields of enzyme engineering, metabolic engineering, medicine, and beyond. However, it is impossible to identify desirable sequences from a large number of synthetic sequences alone. As a result, computational methods, including data-driven machine learning and physics-based molecular modeling, have been introduced to protein engineering to produce more functional proteins. This review focuses on recent advances in computational protein design, highlighting the applicability of different approaches as well as their limitations., (Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.net.)

Purpose: Social media celebrities are getting popular in promotions, and more people have experienced social media to receive information on diet and health tips. This research presents a study showing how social media can influence young people's diet behavior through collaboration with social media celebrities. Design/methodology/approach: Through recruiting on various social media and online education forums, this research recruited 196 young Hongkongers to participate in an online survey developed based on the AIDA (Action, Interest, Desire, Attention) marketing communication model on how youths access diet information from social media celebrities in Hong Kong. Findings: Hong Kong youths consume diet information from social media celebrities through instant messaging systems, social networking sites and online videos, and, in particular, information on food calories and nutrition. However, sponsorship from vendors would decrease their desire to agree with the messages from social media celebrities. After receiving this information, some participants would follow tips and guides from social media celebrities. However, they seldom share such information. Interestingly, males are more willing to follow these tips and guides. Originality/value: First, this study fills the gap of prior research, which did not study much on how social media celebrities contribute to diet promotion to youths in Asia. Second, through the AIDA Model, this study shows how social media can affect the awareness and accessibility of diet information by young Hongkongers, followed by initiating their interest in this topic and retrieving more relevant information. Furthermore, the authors further understand their desire to follow and improve their behavior as promoted by social media influencers and how they practice such behavior. Based on these findings, health-related brands could consider using social media influencers helping to promote their products and services, and these brands could further use social media to secure customer engagement. [ABSTRACT FROM AUTHOR]

In recent years, deep learning techniques for pansharpening multiresolution images have gained increasing interest. Due to the lack of ground truth data, most deep learning solutions rely on synthetic reduced-resolution data for supervised training. This approach has limitations due to the statistical mismatch between real full-resolution and synthetic reduced-resolution data, which affects the models' generalization capacity. Consequently, there has been a shift towards unsupervised learning frameworks for pansharpening deep learning-based techniques. Unsupervised schemes require defining sophisticated loss functions with at least two components: one for spectral quality, ensuring consistency between the pansharpened image and the input multispectral component, and another for spatial quality, ensuring consistency between the output and the panchromatic input. Despite promising results, there has been limited investigation into the interaction and balance of these loss terms to ensure stability and accuracy. This work explores how unsupervised spatial and spectral consistency losses can be reliably combined preserving the outocome quality. By examining these interactions, we propose a general rule for balancing the two loss components to enhance the stability and performance of unsupervised pansharpening models. Experiments on three state-of-the-art algorithms using WorldView-3 images demonstrate that methods trained with the proposed framework achieve good performance in terms of visual quality and numerical indexes. [ABSTRACT FROM AUTHOR]

Simple Summary: Tumor-like lesions at the craniovertebral junction, including epidermoids, dermoids, and neurenteric cysts present significant treatment challenges. Our review of several cases, combined with a meta-analysis of numerous patients, aimed to identify recurrence predictors to improve treatment and follow-up accuracy. Neurenteric cysts were the most common, generally presenting with gradual symptom onset over several months and commonly located in the anterior part of the CVJ. Recurrence in neurenteric cyst cases was linked to factors such as older age, subtotal resection, complications, rim enhancement, and poor outcomes. Recognizing these predictors is essential for enhancing treatment strategies and patient management. By understanding the key factors influencing recurrence, healthcare professionals can better tailor their approaches to ensure optimal outcomes for patients with CVJ lesions. Background: Tumor-like lesions at the craniovertebral junction mimic tumors in clinical presentation and imaging. Our study focuses on three common developmental pathologies—epidermoids, dermoids and neurenteric cysts. Methods: We conducted a retrospective analysis of a case series and a meta-analysis of 170 patients from 119 reports. Results: Neurenteric cysts predominated (81.2%). Anterior cysts were linked to neurenteric cysts, while posterior ones correlated with dermoid/epidermoid cysts (p < 0.001). Complications occurred in 27.2% of cases, with cranial nerve paresis being the most common. Most patients had excellent outcomes (75.2%) with low recurrence rates (12%). Dermoid cysts were more associated with anomalies (p < 0.001). Among 138 neurenteric cyst cases, 15 experienced recurrence, with predictors including ages 51–60 and over 70, subtotal resection, complications, and poor outcomes (p < 0.001). Cysts with total resection were significantly less likely to adhere to surrounding brain tissue (p < 0.001). CSF diversion was correlated with older age (p = 0.010) and various complications (p < 0.001). Age affected outcomes, and the hydrocephalus was linked to poor outcomes (p = 0.002). Conclusions: This meta-analysis underscores the importance of total resection in minimizing recurrence rates and emphasizes meticulous preoperative planning and imaging. Our results indicate that rim enhancement (p = 0.047) and poor outcome (p = 0.007) are significant factors associated with recurrence. Additionally, associated anomalies, as well as the patient's age and overall health, significantly influence the surgical outcomes and the likelihood of recurrence. [ABSTRACT FROM AUTHOR]

Electric vehicles (EVs) are becoming of significant interest owing to their environmental benefits; however, energy efficiency concerns remain unsolved and require more investigation. A major issue is a lack of EV charging infrastructure, which can lead to operational difficulties. Effective infrastructure development, including well-placed charging stations (CS), is critical to enhancing connectivity. To overcome this, consumers want real-time data on charging station availability, neighboring station locations, and access times. This work leverages the Distance Vector Multicast Routing Protocol (DVMRP) to enhance the information collection process for charging stations through the Internet of Things (IoT). The evolving IoT paradigm enables the use of sensors and data transfer to give real-time information. Strategic sensor placement helps forecast server access to neighboring stations, optimize vehicle scheduling, and estimate wait times. A recommender system is designed to identify stations with more rapidly charging rates, along with uniform pricing. In addition, the routing protocol has a privacy protection strategy to prevent unauthorized access and safeguard EV data during exchanges between charging stations and user locations. The system is simulated with MATLAB 2020a, and the data are controlled and secured in the cloud. The predicted algorithm's performance is evaluated using several kinds of standards, including power costs, vehicle counts, charging costs, energy consumption, and optimization values. [ABSTRACT FROM AUTHOR]

In the field of Information Retrieval, biomedical question answering is a specialized task that focuses on answering questions related to medical and healthcare domains. The goal is to provide accurate and relevant answers to the posed queries related to medical conditions, treatments, procedures, medications, and other healthcare-related topics. Well-designed models should efficiently retrieve relevant passages. Early retrieval models can quickly retrieve passages but often with low precision. In contrast, recently developed Large Language Models can retrieve documents with high precision but at a slower pace. To tackle this issue, we propose a two-stage retrieval approach that initially utilizes BM25 for a preliminary search to identify potential candidate documents; subsequently, a Large Language Model is fine-tuned to evaluate the relevance of query–document pairs. Experimental results indicate that our approach achieves comparative performances on the BioASQ and the TREC-COVID datasets. [ABSTRACT FROM AUTHOR]

A highly robust dual-frequency hierarchical temporal phase unwrapping (DHTPU) based on the novel spatial computer-generated Moiré profilometry (SCGMP) is proposed. The method requires only three patterns: a high-frequency fringe to provide robust surface information, a multi-period low-frequency fringe to eliminate the 2π-phase ambiguities, and a flat pattern to remove the average intensity of the two fringes. In decoding, different from traditional Moiré profilometries that rely on spectrum filters, SCGMP only employs spatial-domain calculations to extract the wrapped phase, thereby preserving more detailed information. Furthermore, we fully explore SCGMP's capability to significantly alleviate phase ambiguity and provide an algorithm to determine the maximum measurable height range for a fixed system, enabling the direct extraction of the continuous basic phase from the multi-period low-frequency fringe. Consequently, the proposed basic phase exhibits an enhanced signal-to-noise ratio, compared to the traditional basic phase derived from the single-period fringes, effectively releasing the high-frequency restriction in the traditional DHTPU. The experimental results verify that the proposed DHTPU method has considerable accuracy and great potential for high-speed measurements, due to there being only three shots required. [ABSTRACT FROM AUTHOR]

This study examines the impact of varying oxygen flow rates on the properties of Cu2O thin films deposited via radio frequency (RF) magnetron sputtering. X-ray diffraction (XRD) analysis showed a phase transition from cubic Cu2O to a mixed Cu2O and CuO phase, eventually forming a Cu4O3 tetragonal structure as oxygen content increased. The surface morphology and cross-sectional structure of Cu2O thin films observed through field emission scanning electron microscopy (FE-SEM) were found to vary significantly depending on the oxygen flow rate. X-ray photoelectron spectroscopy (XPS) indicated notable variations in the chemical states of copper and oxygen. The Cu 2p spectra revealed peaks around 933 eV and 953 eV for all samples, with the S01 sample (deposited with only argon gas) exhibiting the lowest intensity. The S02 sample showed the highest peak intensity, which then gradually decreased from S03 to S06. The O 1s spectra followed a trend with peak intensity being highest in S02 and decreasing with further oxygen flow rates, indicating the formation of complex oxides such as Cu4O3. UV-Vis-NIR spectroscopy results demonstrated a decrease in transmittance and optical band gap energy with increasing oxygen content, suggesting a decline in crystallinity and an increase in defects and impurities. These findings underscore the critical role of precise oxygen flow rate control in tailoring the structural, morphological, compositional, and optical properties of Cu2O thin films for specific electronic and optical applications. [ABSTRACT FROM AUTHOR]

Rice straw is an agricultural waste, the disposal of which through open burning is an emerging challenge for ecology. Green manufacturing using straw returning provides a more avant-garde technique that is not only an effective management measure to improve soil fertility in agricultural ecosystems but also nurtures environmental stewardship by reducing waste and the carbon footprint. However, fresh straw that is returned to the field cannot be quickly decomposed, and screening microorganisms with the capacity to degrade straw and understanding their mechanism of action is an efficient approach to solve such problems. This study aimed to reveal the potential mechanism of influence exerted by exogenous degradative bacteria (ZJW-6) on the degradation of straw, growth of plants, and soil bacterial community during the process of returning rice straw to the soil. The inoculation with ZJW-6 enhanced the driving force of cellulose degradation. The acceleration of the rate of decomposition of straw releases nutrients that are easily absorbed by rice (Oryza sativa L.), providing favorable conditions for its growth and promoting its growth and development; prolongs the photosynthetic functioning period of leaves; and lays the material foundation for high yields of rice. ZJW-6 not only directly participates in cellulose degradation as degrading bacteria but also induces positive interactions between bacteria and fungi and enriches the microbial taxa that were related to straw degradation, enhancing the rate of rice straw degradation. Taken together, ZJW-6 has important biological potential and should be further studied, which will provide new insights and strategies for the appropriate treatment of rice straw. In the future, this degrading bacteria may provide a better opportunity to manage straw in an ecofriendly manner. [ABSTRACT FROM AUTHOR]

Background: Metastasis is the main cause of cancer-related deaths, but efficient targeted therapies against metastasis are still missing. Major gaps exist in our understanding of the metastatic cascade, as existing methods cannot combine sensitivity, robustness, and practicality to dissect cancer progression. Addressing this issue requires improved strategies to distinguish early metastatic colonization from metastatic outgrowth. Methods: Luciferase-labelled MDA-MB-231, MCF7, and 4T1 breast cancer cells were spiked into samples from tumour-naïve mice to establish the limit of detection for disseminated tumour cells. Luciferase-labelled breast cancer cells (±unlabelled cancer-associated fibroblasts; CAFs) were orthotopically implanted in immunocompromised mice. An ex vivo luciferase assay was used to quantify tumour cell dissemination. Results: In vitro luciferase assay confirmed a linear and positive correlation between cancer cell numbers and the bioluminescence detected at single cell level in blood, brain, lung, liver, and mammary fat pad samples. Remarkably, single luciferase-labelled cancer cells were detectable in all of these sites, as the bioluminescence quantified in the analysed samples was substantially higher than background levels. Ex vivo, circulating tumour cells, metastasis, and tumour self-seeding were detected in all samples from animals implanted with highly metastatic luciferase-labelled MDA-MB-231 cells. In turn, detection of poorly metastatic luciferase-labelled MCF7 cells was scarce but significantly enhanced upon co-implantation with CAFs as early as 20 days after the experiment was initiated. Conclusions: These results demonstrate the feasibility of using an ultrasensitive luciferase-based method to dissect the mechanisms of early metastatic colonization to improving the development of antimetastatic therapies. [ABSTRACT FROM AUTHOR]

Due to its high spatial resolution, Raman microspectroscopy allows for the analysis of single microbial cells. Since Raman spectroscopy analyzes the whole cell content, this method is phenotypic and can therefore be used to evaluate cellular changes. In particular, labeling with stable isotopes (SIPs) enables the versatile use and observation of different metabolic states in microbes. Nevertheless, static measurements can only analyze the present situation and do not allow for further downstream evaluations. Therefore, a combination of Raman analysis and cell sorting is necessary to provide the possibility for further research on selected bacteria in a sample. Here, a new microfluidic approach for Raman-activated continuous-flow sorting of bacteria using an optical setup for image-based particle sorting with synchronous acquisition and analysis of Raman spectra for making the sorting decision is demonstrated, showing that active cells can be successfully sorted by means of this microfluidic chip. [ABSTRACT FROM AUTHOR]

In underground coal mining, machine operators put themselves at risk when getting close to the machine or cutting face to observe the process. To improve the safety and efficiency of machine operators, a cutting force sensor is proposed. A linear cutting machine is used to cut two separate coal samples cast in concrete with conical pick cutters to simulate mining with a continuous miner. Linear and neural network regression models are fit using 100 random 70:30 test/train splits. The normal force exceeds 60 kN during the rock-cutting tests, and it is averaged using a low pass filter with a 10 Hertz cutoff frequency. The sensor uses measurements of the resonant frequency of capacitive cells in a steel case to determine cutting forces. When used in the rock-cutting experiments, the sensor conforms to the tooling and the stiffness and sensitivity are increased compared to the initial configuration. The sensor is able to track the normal force on the conical picks with a mean absolute error less than 6 kN and an R 2 score greater than 0.60 using linear regression. A small neural network with a second-order polynomial expansion is able to improve this to a mean absolute error of less than 4 kN and an R 2 score of around 0.80. Filtering measurements before regression fitting is explored. This type of sensor could allow operators to assess tool wear and material type using objective force measurements while maintaining a greater distance from the cutting interface. [ABSTRACT FROM AUTHOR]

Hyperspectral pansharpening is crucial for the improvement of the usability of images in various applications. However, it remains underexplored due to a scarcity of data. The primary goal of pansharpening is to enhance the spatial resolution of hyperspectral images by reconstructing missing spectral information without compromising consistency with the original data. This paper addresses the data gap by presenting a new hyperspectral dataset specifically designed for pansharpening and the evaluation of several deep learning strategies using this dataset. The new dataset has two crucial features that make it invaluable for deep learning hyperspectral pansharpening research. (1) It presents the highest cardinality of images in the state of the art, making it the first statistically relevant dataset for hyperspectral pansharpening evaluation, and (2) it includes a wide variety of scenes, ensuring robust generalization capabilities for various approaches. The data, collected by the ASI PRISMA satellite, cover about 262,200 km2 and their heterogeneity is ensured by a random sampling of the Earth's surface. The analysis of the deep learning methods consists in the adaptation of these approaches to the PRISMA hyperspectral data and the quantitative and qualitative evaluation of their performance in this new scenario. The investigation included two settings: Reduced Resolution (RR) to evaluate the techniques in a controlled environment and Full Resolution (FR) for a real-world evaluation. In addition, for the sake of completeness, we have also included machine-learning-free approaches in both scenarios. Our comprehensive analysis reveals that data-driven neural network methods significantly outperform traditional approaches, demonstrating a superior adaptability and performance in hyperspectral pansharpening under both RR and FR protocols. [ABSTRACT FROM AUTHOR]

Biofouling is a great challenge for engineering material in medical-, marine-, and pharmaceutical-related applications. In this study, a novel trimethylamine N-oxide (TMAO)–analog monomer, 3-(2-methylacrylamido)-N,N-dimethylpropylamine N-oxide (MADMPAO), was synthesized and applied for the grafting of poly(MADMPAO) (pMPAO) brushes on quartz crystal microbalance (QCM) chips by the combination of bio-inspired poly-dopamine (pDA) and surface-initiated atom transfer radical polymerization technology. The result of ion adsorption exhibited that a sequential pDA and pMPAO arrangement from the chip surface had different characteristics from a simple pDA layer. Ion adsorption on pMPAO-grafted chips was greatly inhibited at low salt concentrations of 1 and 10 mmol/L due to strong surface hydration in the presence of charged N+ and O− of zwitterionic pMPAO brushes on the outer layer on the chip surface, well known as the "anti-polyelectrolyte" effect. During BSA adsorption, pMPAO grafting also led to a marked decrease in frequency shift, indicating great inhibition of protein adsorption. It was attributed to weaker BSA-pMPAO interaction. In this study, the Au@pDA-4-pMPAO chip with the highest coating concentration of DA kept stable dissipation in BSA adsorption, signifying that the chip had a good antifouling property. The research provided a novel monomer for zwitterionic polymer and demonstrated the potential of pMPAO brushes in the development and modification of antifouling materials. [ABSTRACT FROM AUTHOR]

Multiple myeloma (MM) is a hematologic malignancy caused by the clonal expansion of immunoglobulin-producing plasma cells in the bone marrow and/or extramedullary sites. Common manifestations of MM include anemia, renal dysfunction, infection, bone pain, hypercalcemia, and fatigue. Despite numerous recent advancements in the MM treatment paradigm, current therapies demonstrate limited long-term effectiveness and eventual disease relapse remains exceedingly common. Myeloma cells often develop drug resistance through clonal evolution and alterations of cellular signaling pathways. Therefore, continued research of new targets in MM is crucial to circumvent cumulative drug resistance, overcome treatment-limiting toxicities, and improve outcomes in this incurable disease. This article provides a comprehensive overview of the landscape of novel treatments and emerging therapies for MM grouped by molecular target. Molecular targets outlined include BCMA, GPRC5D, FcRH5, CD38, SLAMF7, BCL-2, kinesin spindle protein, protein disulfide isomerase 1, peptidylprolyl isomerase A, Sec61 translocon, and cyclin-dependent kinase 6. Immunomodulatory drugs, NK cell therapy, and proteolysis-targeting chimera are described as well. [ABSTRACT FROM AUTHOR]

Despite the growing capabilities of large language models, concerns exist about the biases they develop. In this paper, we propose a novel, automated mechanism for debiasing through specified dataset augmentation in the lens of bias producers that can be useful in a variety of industries, especially ones that are "restricted" and have limited data. We consider that bias can occur due to intrinsic model architecture and dataset quality. The two aspects are evaluated using two different metrics we created. We show that our dataset augmentation algorithm reduces bias as measured by our metrics. Our code can be found on an online GitHub repository. [ABSTRACT FROM AUTHOR]

We describe the current Good Manufacturing Practice (cGMP) production and subsequent characterization of eOD-GT8 60mer, a glycosylated self-assembling nanoparticle HIV-1 vaccine candidate and germline targeting priming immunogen. Production was carried out via transient expression in the human embryonic kidney 293 (HEK293) cell line followed by a combination of purification techniques. A large-scale cGMP (200 L) production run yielded 354 mg of the purified eOD-GT8 60mer drug product material, which was formulated at 1 mg/mL in 10% sucrose in phosphate-buffered saline (PBS) at pH 7.2. The clinical trial material was comprehensively characterized for purity, antigenicity, glycan composition, amino acid sequence, and aggregation and by several safety-related tests during cGMP lot release. A comparison of the purified products produced at the 1 L scale and 200 L cGMP scale demonstrated the consistency and robustness of the transient transfection upstream process and the downstream purification strategies. The cGMP clinical trial material was tested in a Phase 1 clinical trial (NCT03547245), is currently being stored at −80 °C, and is on a stability testing program as per regulatory guidelines. The methods described here illustrate the utility of transient transfection for cGMP production of complex products such as glycosylated self-assembling nanoparticles. [ABSTRACT FROM AUTHOR]

Vaccines are one of the most effective medical interventions, playing a pivotal role in treating infectious diseases. Although traditional vaccines comprise killed, inactivated, or live-attenuated pathogens that have resulted in protective immune responses, the negative consequences of their administration have been well appreciated. Modern vaccines have evolved to contain purified antigenic subunits, epitopes, or antigen-encoding mRNAs, rendering them relatively safe. However, reduced humoral and cellular responses pose major challenges to these subunit vaccines. Protein nanoparticle (PNP)-based vaccines have garnered substantial interest in recent years for their ability to present a repetitive array of antigens for improving immunogenicity and enhancing protective responses. Discovery and characterisation of naturally occurring PNPs from various living organisms such as bacteria, archaea, viruses, insects, and eukaryotes, as well as computationally designed structures and approaches to link antigens to the PNPs, have paved the way for unprecedented advances in the field of vaccine technology. In this review, we focus on some of the widely used naturally occurring and optimally designed PNPs for their suitability as promising vaccine platforms for displaying native-like antigens from human viral pathogens for protective immune responses. Such platforms hold great promise in combating emerging and re-emerging infectious viral diseases and enhancing vaccine efficacy and safety. [ABSTRACT FROM AUTHOR]

Background: Although antiretroviral therapy (ART) effectively halts disease progression in HIV infection, the complete eradication of the virus remains elusive. Additionally, challenges such as long-term ART toxicity, drug resistance, and the demanding regimen of daily and lifelong adherence required by ART highlight the imperative need for alternative therapeutic and preventative approaches. In recent years, broadly neutralizing antibodies (bNAbs) have emerged as promising candidates, offering potential for therapeutic, preventative, and possibly curative interventions against HIV infection. Objective: This review aims to provide a comprehensive overview of the current state of knowledge regarding the passive immunization of bNAbs in HIV-1-infected individuals. Main findings: Recent findings from clinical trials have highlighted the potential of bNAbs in the treatment, prevention, and quest for an HIV-1 cure. While monotherapy with a single bNAb is insufficient in maintaining viral suppression and preventing viral escape, ultimately leading to viral rebound, combination therapy with potent, non-overlapping epitope-targeting bNAbs have demonstrated prolonged viral suppression and delayed time to rebound by effectively restricting the emergence of escape mutations, albeit largely in individuals with bNAb-sensitive strains. Additionally, passive immunization with bNAb has provided a "proof of concept" for antibody-mediated prevention against HIV-1 acquisition, although complete prevention has not been obtained. Therefore, further research on the use of bNAbs in HIV-1 treatment and prevention remains imperative. [ABSTRACT FROM AUTHOR]

Conversational AI systems have emerged as key enablers of human-like interactions across diverse sectors. Nevertheless, the balance between linguistic nuance and factual accuracy has proven elusive. In this paper, we first introduce LLMXplorer, a comprehensive tool that provides an in-depth review of over 205 large language models (LLMs), elucidating their practical implications, ranging from social and ethical to regulatory, as well as their applicability across industries. Building on this foundation, we propose a novel functional architecture that seamlessly integrates the structured dynamics of knowledge graphs with the linguistic capabilities of LLMs. Validated using real-world AI news data, our architecture adeptly blends linguistic sophistication with factual rigor and further strengthens data security through role-based access control. This research provides insights into the evolving landscape of conversational AI, emphasizing the imperative for systems that are efficient, transparent, and trustworthy. [ABSTRACT FROM AUTHOR]

mRNA vaccines are entering a period of rapid development. However, their synthesis is still plagued by challenges related to mRNA impurities and fragments (incomplete mRNA). Most impurities of mRNA products transcribed in vitro are mRNA fragments. Only full-length mRNA transcripts containing both a 5′-cap and a 3′-poly(A) structure are viable for in vivo expression. Therefore, RNA fragments are the primary product-related impurities that significantly hinder mRNA efficacy and must be effectively controlled; these species are believed to originate from either mRNA hydrolysis or premature transcriptional termination. In the manufacturing of commercial mRNA vaccines, T7 RNA polymerase-catalyzed in vitro transcription (IVT) synthesis is a well-established method for synthesizing long RNA transcripts. This study identified a pivotal domain on the T7 RNA polymerase that is associated with erroneous mRNA release. By leveraging the advantageous properties of a T7 RNA polymerase mutant and precisely optimized IVT process parameters, we successfully achieved an mRNA integrity exceeding 91%, thereby further unlocking the immense potential of mRNA therapeutics. [ABSTRACT FROM AUTHOR]

Rare earth elements (REEs) are critical for our modern lifestyles and the transition to a low‐carbon economy. Recent advances in our understanding of the role of REEs in biology, particularly methylotrophy, have provided opportunities to explore biotechnological innovations to improve REE mining and recycling. In addition to bacterial accumulation and concentration of REEs, biological REE binders, including proteins (lanmodulin, lanpepsy) and small molecules (metallophores and cofactors) have been identified that enable REE concentration and separation. REE‐binding proteins have also been used in several mechanistically distinct REE biosensors, which have potential application in mining and medicine. Notably, the role of REEs in biology has only been known for a decade, suggesting their considerable scope for developing new understanding and novel applications. [ABSTRACT FROM AUTHOR]

Being a component of the Ras/Raf/MEK/ERK signaling pathway crucial for cellular responses, the VRAF murine sarcoma viral oncogene homologue B1 (BRAF) kinase has emerged as a promising target for anticancer drug discovery due to oncogenic mutations that lead to pathway hyperactivation. Despite the discovery of several small-molecule BRAF kinase inhibitors targeting oncogenic mutants, their clinical utility has been limited by challenges such as off-target effects and suboptimal pharmacological properties. This study focuses on identifying miniprotein inhibitors for the oncogenic V600E mutant BRAF, leveraging their potential as versatile drug candidates. Using a structure-based de novo design approach based on binding affinity to V600E mutant BRAF and hydration energy, 39 candidate miniprotein inhibitors comprising three helices and 69 amino acids were generated from the substructure of the endogenous ligand protein (14-3-3). Through in vitro binding and kinase inhibition assays, two miniproteins (63 and 76) were discovered as novel inhibitors of V600E mutant BRAF with low-micromolar activity, with miniprotein 76 demonstrating a specific impediment to MEK1 phosphorylation in mammalian cells. These findings highlight miniprotein 76 as a potential lead compound for developing new cancer therapeutics, and the structural features contributing to its biochemical potency against V600E mutant BRAF are discussed in detail. [ABSTRACT FROM AUTHOR]

Detecting cracks in the pavement is a vital component of ensuring road safety. Since manual identification of these cracks can be time-consuming, an automated method is needed to speed up this process. However, creating such a system is challenging due to factors including crack variability, variations in pavement materials, and the occurrence of miscellaneous objects and anomalies on the pavement. Motivated by the latest progress in deep learning applied to computer vision, we propose an effective U-Net-shaped model named DepthCrackNet. Our model employs the Double Convolution Encoder (DCE), composed of a sequence of convolution layers, for robust feature extraction while keeping parameters optimally efficient. We have incorporated the TriInput Multi-Head Spatial Attention (TMSA) module into our model; in this module, each head operates independently, capturing various spatial relationships and boosting the extraction of rich contextual information. Furthermore, DepthCrackNet employs the Spatial Depth Enhancer (SDE) module, specifically designed to augment the feature extraction capabilities of our segmentation model. The performance of the DepthCrackNet was evaluated on two public crack datasets: Crack500 and DeepCrack. In our experimental studies, the network achieved mIoU scores of 77.0% and 83.9% with the Crack500 and DeepCrack datasets, respectively. [ABSTRACT FROM AUTHOR]

Cancer, with its complexity and numerous origins, continues to provide a huge challenge in medical research. Anticancer peptides are a potential treatment option, but identifying and synthesizing them on a large scale requires accurate prediction algorithms. This study presents an intuitive classification strategy, named ACP-LSE, based on representation learning, specifically, a deep latent-space encoding scheme. ACP-LSE can demonstrate notable advancements in classification outcomes, particularly in scenarios with limited sample sizes and abundant features. ACP-LSE differs from typical black-box approaches by focusing on representation learning. Utilizing an auto-encoder-inspired network, it embeds high-dimensional features, such as the composition of g-spaced amino acid pairs, into a compressed latent space. In contrast to conventional auto-encoders, ACP-LSE ensures that the learned feature set is both small and effective for classification, giving a transparent alternative. The suggested approach is tested on benchmark datasets and demonstrates higher performance compared to the current methods. The results indicate improved Matthew's correlation coefficient and balanced accuracy, offering insights into crucial aspects for developing new ACPs. The implementation of the proposed ACP-LSE approach is accessible online, providing a valuable and reproducible resource for researchers in the field. [ABSTRACT FROM AUTHOR]

Purpose: Environmental knowledge management (EKM) has been studied mainly owing to the increasing awareness of environmental issues. Such issues have generated a warning in the tourism industry that has stimulated a new wave of research on EKM. EKM forges landscape characteristics and so destination image. In turn, EKM sounds affecting tourism destination which calls for destination personality which shows a research context less explored. From a knowledge management perspective, The present research aims to investigate on EKM to understand how it leverages tourists' and destination personality. Design/methodology/approach: With the intent of exploring EKM, the research uses a quantitative analysis on a sample of 2,222 young Chinese tourists. In this context, EKM is linked with destination's personality and tourists' personalities, their satisfaction with the destination and their behavioral intentions. Findings: By SPSS regression model, EKM and destination personality are positively linked. This positive relationship is also reflected on destination personality and destination satisfaction, behavioral intention. Originality/value: The authors' original contribution to the knowledge management literature extends the new wave of research on EKM. The research also proves the need to make a close collaboration between tourists, the local community and marketers. Marketers need to pay more attention to what tourists want to do and see in the place visited. In a nutshell, there is the need of enforcing and promoting EKM. [ABSTRACT FROM AUTHOR]

The distribution and diversity of calanoid copepods were investigated using samples collected from 37 lakes in North-eastern (NE) China in 2019. A total of 10 calanoid copepods belonging to eight genera and three families were identified. Among them, Heterocope soldatovi Rylov, 1922 was recorded for the first time in China. Species from the family Centropagidae were more widespread than those from the families Temoridae and Diaptomidae. Sinocalanus doerrii (Brehm, 1909), previously not recorded in NE China, is now widespread alongside Boeckella triarticulata (Thomson G.M., 1883), and the latter species is also prevalent in that region. Canonical correspondence analysis (CCA) and redundancy analysis (RDA) revealed that calanoid copepods were significantly correlated with total phosphorus (TP), total nitrogen, conductivity, nitrate nitrogen, altitude, and dissolved organic carbon. TP was the most important environmental variable that impacted the distribution of calanoid copepods, including both fresh and saline–alkaline lakes. Integrating historical records, a total of 21 calanoid copepods were distributed in NE China, and we also gave comments on the ecology and distribution of these species. [ABSTRACT FROM AUTHOR]

Hepatitis C virus (HCV) is a major medical health burden and the leading cause of chronic liver disease and cancer worldwide. More than 58 million people are chronically infected with HCV, with 1.5 million new infections occurring each year. An effective HCV vaccine is a major public health and medical need as recognized by the World Health Organization. However, due to the high variability of the virus and its ability to escape the immune response, HCV rapidly accumulates mutations, making vaccine development a formidable challenge. An effective vaccine must elicit broadly neutralizing antibodies (bnAbs) in a consistent fashion. After decades of studies from basic research through clinical development, the antigen of choice is considered the E1E2 envelope glycoprotein due to conserved, broadly neutralizing antigenic domains located in the constituent subunits of E1, E2, and the E1E2 heterodimeric complex itself. The challenge has been elicitation of robust humoral and cellular responses leading to broad virus neutralization due to the relatively low immunogenicity of this antigen. In view of this challenge, structure-based vaccine design approaches to stabilize key antigenic domains have been hampered due to the lack of E1E2 atomic-level resolution structures to guide them. Another challenge has been the development of a delivery platform in which a multivalent form of the antigen can be presented in order to elicit a more robust anti-HCV immune response. Recent nanoparticle vaccines are gaining prominence in the field due to their ability to facilitate a controlled multivalent presentation and trafficking to lymph nodes, where they can interact with both the cellular and humoral components of the immune system. This review focuses on recent advances in understanding the E1E2 heterodimeric structure to facilitate a rational design approach and the potential for development of a multivalent nanoparticle-based HCV E1E2 vaccine. Both aspects are considered important in the development of an effective HCV vaccine that can effectively address viral diversity and escape. [ABSTRACT FROM AUTHOR]

Purpose: The purpose of this study is to reveal the mechanism of humble leadership inducing abusive supervision from the low-status compensation perspective, examining the mediation role of leader perceived thread to status. Besides, the moderation effect of regulatory focus on the mediation path is discussed. Design/methodology/approach: This study conducted a three-wave longitudinal survey. The data was collected from 438 leaders and their employees in five Chinese enterprises. This study used Mplus 7.4 and adopted a bootstrapping technique for data analysis. Findings: Humble leadership has a positive effect on leader perceived threat to status. Leader perceived threat to status plays a mediation role between humble leadership and leader abusive supervision. Leader regulatory focus is the "gate valve" that humble leadership fosters leader abusive supervision. That is, when the leader promotion focus is high, leader perceived threat to status bred by humble leadership is low, resulting in less abusive supervision. When the leader prevention focus is high, humble leadership brings relatively more abusive supervision through perceived threat to status. Originality/value: This study explores why humble leadership breeds abusive supervision behaviors and reveals the mechanism behind the negative effect of humble leadership based on low-status compensation theory. This study not only promotes the continuous development of the field of humble leadership research through empirical research but also provides guidance for effectively suppressing the negative effects of humble leadership, promoting strengths and avoiding weaknesses and suppressing inappropriate management behaviors in management practice. [ABSTRACT FROM AUTHOR]

Purpose: With the increasing computation and communication speed of mobile devices, their use and roles have been repositioned to act more than a communication tool. Around 0.4 million foreign domestic helpers (FDHs) form an integral part of Hong Kong society and have the highest concentrations of FDHs worldwide, but scant studies focus on their mobile technology usage. This study aims to discuss the aforementioned issue. Design/methodology/approach: This research investigated mobile technology usage by 145 FDHs in Hong Kong with a quantitative survey, covering their actual mobile technology usage, perceptions, influences and adoption barriers. Findings: Besides communication and connection with friends and family, participants used mobile technology for information seeking, productivity, utility and entertainment. Perception of usefulness was the greatest influence, and they would use any mobile technology once its basic function supports their daily needs. They considered no "very serious barrier," though data tariff was their biggest concern. Overall, there were some significant differences between genders in the usage of mobile technologies. Originality/value: Scant studies focus on the mobile technology usage of FDHs. The results reveal the usefulness of mobile technologies to FDHs for government policy-making and for employers and employment agencies to better support the FHDs' daily lives. [ABSTRACT FROM AUTHOR]

Purpose: This research investigates Hong Kong academic librarians' attitudes toward robotic process automation (RPA) and their willingness to learn this technology. Design/methodology/approach: This qualitative study collected data through one-on-one semi-structured interviews conducted with video conferencing software. After participants received basic RPA information and three existing library application cases, they answered questions based on the interview guide. This research used the inductive thematic analysis method to analyze the collected data. Findings: Regarding Hong Kong academic librarians' attitudes towards RPA, 19 themes were identified. Although all participants did not have previous knowledge of RPA, most showed positive attitudes toward implementing RPA in their libraries and some willingness to learn it. Besides, among all identified themes, negative attitudes mainly comprised "Affect" and "Cognition" factors, hindering RPA deployment in academic libraries. Originality/value: This research helps librarians and RPA vendors make better decisions or strategies for implementing RPA for libraries, which has not been explored, especially in East Asia. [ABSTRACT FROM AUTHOR]

Mild traumatic brain injury (mTBI) affects millions of people in the U.S. Approximately 20–30% of those individuals develop adverse symptoms lasting at least 3 months. In a rat mTBI study, the closed-head impact model of engineered rotational acceleration (CHIMERA) produced significant axonal injury in the optic tract (OT), indicating white-matter damage. Because retinal ganglion cells project to the lateral geniculate nucleus (LGN) in the thalamus through the OT, we hypothesized that synaptic density may be reduced in the LGN of rats following CHIMERA injury. A modified SEQUIN (synaptic evaluation and quantification by imaging nanostructure) method, combined with immunofluorescent double-labeling of pre-synaptic (synapsin) and post-synaptic (PSD-95) markers, was used to quantify synaptic density in the LGN. Microglial activation at the CHIMERA injury site was determined using Iba-1 immunohistochemistry. Additionally, the effects of ketamine, a potential neuroprotective drug, were evaluated in CHIMERA-induced mTBI. A single-session repetitive (ssr-) CHIMERA (3 impacts, 1.5 joule/impact) produced mild effects on microglial activation at the injury site, which was significantly enhanced by post-injury intravenous ketamine (10 mg/kg) infusion. However, ssr-CHIMERA did not alter synaptic density in the LGN, although ketamine produced a trend of reduction in synaptic density at post-injury day 4. Further research is necessary to characterize the effects of ssr-CHIMERA and subanesthetic doses of intravenous ketamine on different brain regions and multiple time points post-injury. The current study demonstrates the utility of the ssr-CHIMERA as a rodent model of mTBI, which researchers can use to identify biological mechanisms of mTBI and to develop improved treatment strategies for individuals suffering from head trauma. [ABSTRACT FROM AUTHOR]

Nanoparticles (NPs) have been surfacing as a pivotal platform for vaccine development. In our previous work, we developed a cholera toxin B subunit (CTB)-based self-assembled nanoparticle (CNP) and produced highly promising bioconjugate nanovaccines by loading bacterial polysaccharide (OPS) in vivo. In particular, the Klebsiella pneumoniae O2 serotype vaccine showcased a potent immune response and protection against infection. However, extremely low yields limited its further application. In this study, we prepared an efficient Klebsiella pneumoniae bioconjugate nanovaccine in Escherichia coli with a very high yield. By modifying the 33rd glycine (G) in the CNP to aspartate (D), we were able to observe a dramatically increased expression of glycoprotein. Subsequently, through a series of mutations, we determined that G33D was essential to increasing production. In addition, this increase only occurred in engineered E. coli but not in the natural host K. pneumoniae strain 355 (Kp355) expressing OPSKpO2. Next, T-cell epitopes were fused at the end of the CNP(G33D), and animal experiments showed that fusion of the M51 peptide induced high antibody titers, consistent with the levels of the original nanovaccine, CNP-OPSKpO2. Hence, we provide an effective approach for the high-yield production of K. pneumoniae bioconjugate nanovaccines and guidance for uncovering glycosylation mechanisms and refining glycosylation systems. [ABSTRACT FROM AUTHOR]

Purpose: The purpose of this paper is to study the electronic transport performance of Ag-ZnO film under dark and UV light conditions. Design/methodology/approach: Ag-doped ZnO thin films were prepared on fluorine thin oxide (FTO) substrates by sol-gel method. The crystal structure of ZnO and Ag-ZnO powders was tested by X-ray diffraction with Cu Kα radiation. The absorption spectra of ZnO and Ag-ZnO films were recorded by a UV–visible spectrophotometer. The micro electrical transport performance of Ag-ZnO thin films in dark and light state was investigated by photoassisted conductive atomic force microscope (PC-AFM). Findings: The results show that the dark reverse current of Ag-ZnO films does not increase, but the reverse current increases significantly under illumination, indicating that the response of Ag-ZnO films to light is greatly improved, owing to the formation of Ohmic contact. Originality/value: To the best of the author's knowledge, the micro electrical transport performance of Ag-ZnO thin films in dark and light state was firstly investigated by PC-AFM. [ABSTRACT FROM AUTHOR]

With the vast amount of social media posts available online, topic modeling and sentiment analysis have become central methods to better understand and analyze online behavior and opinion. However, semantic and sentiment analysis have rarely been combined for joint topic-sentiment modeling which yields semantic topics associated with sentiments. Recent breakthroughs in natural language processing have also not been leveraged for joint topic-sentiment modeling so far. Inspired by these advancements, this paper presents a novel framework for joint topic-sentiment modeling of short texts based on pre-trained language models and a clustering approach. The method leverages techniques from dimensionality reduction and clustering for which multiple algorithms were considered. All configurations were experimentally compared against existing joint topic-sentiment models and an independent sequential baseline. Our framework produced clusters with semantic topic quality scores of up to 0.23 while the best score among the previous approaches was 0.12 . The sentiment classification accuracy increased from 0.35 to 0.72 and the uniformity of sentiments within the clusters reached up to 0.9 in contrast to the baseline of 0.56 . The presented approach can benefit various research areas such as disaster management where sentiments associated with topics can provide practical useful information. [ABSTRACT FROM AUTHOR]

In the fringe projection profilometry (FPP), the traditional phase-shifting (TPS) algorithm and the Fourier transform (FT) algorithm are beset with a conundrum where measurement efficiency and conflicts with measurement accuracy, thereby limiting their application in dynamic three-dimensional (3D) measurements. Here, we propose a phase shift generation (PSG) method, which acquires the sinusoidal fringes by sparse sampling and reconstructs the complete phase-shifting sequence by generating the missing fringes with superimposed coupling of adjacent fringes. According to our proposed PSG method in which the sinusoidal fringe sequence size is about half of the TPS method, meaning that the PSG method will be timesaving in the phase-shifting sequence sampling process. Moreover, because of the utilization of multiframe fringes, our PSG method allows for a more accurate measurement than the FT method. Both simulation and experimental results demonstrate that our proposed PSG method can well balance the measurement accuracy and efficiency with a lower sampling rate, bearing a great potential to be applied in both scientific and industrial areas. [ABSTRACT FROM AUTHOR]

In recent years, the emergence of large-scale language models, such as ChatGPT, has presented significant challenges to research on knowledge graphs and knowledge-based reasoning. As a result, the direction of research on knowledge reasoning has shifted. Two critical issues in knowledge reasoning research are the algorithm of the model itself and the selection of paths. Most studies utilize LSTM as the path encoder and memory module. However, when processing long sequence data, LSTM models may encounter the problem of long-term dependencies, where memory units of the model may decay gradually with an increase in time steps, leading to forgetting earlier input information. This can result in a decline in the performance of the LSTM model in long sequence data. Additionally, as the data volume and network depth increase, there is a risk of gradient disappearance. This study improved and optimized the LSTM model to effectively address the problems of gradient explosion and gradient disappearance. An attention layer was employed to alleviate the issue of long-term dependencies, and ConvR embedding was used to guide path selection and action pruning in the reinforcement learning inference model. The overall model achieved excellent reasoning results. [ABSTRACT FROM AUTHOR]

Nanoparticles (NPs) have been widely utilized in vaccine design. Although numerous NPs have been explored, NPs with adjuvant effects on their own have rarely been reported. We produce a promising self-assembled NP by integrating the pentameric Escherichia coli heat-labile enterotoxin B subunit (LTB) (studied as a vaccine adjuvant) with a trimer-forming peptide. This fusion protein can self-assemble into the NP during expression, and polysaccharide antigens (OPS) are then loaded in vivo using glycosylation. We initially produced two Salmonella paratyphi A conjugate nanovaccines using two LTB subfamilies (LTIB and LTIIbB). After confirming their biosafety in mice, the data showed that both nanovaccines (NP(LTIB)-OPSSPA and NP(LTIIbB)-OPSSPA) elicited strong polysaccharide-specific antibody responses, and NP(LTIB)-OPS resulted in better protection. Furthermore, polysaccharides derived from Shigella or Klebsiella pneumoniae were loaded onto NP(LTIB) and NP(LTIIbB). The animal experimental results indicated that LTIB, as a pentamer module, exhibited excellent protection against lethal infections. This effect was also consistent with that of the reported cholera toxin B subunit (CTB) modular NP in all three models. For the first time, we prepared a novel promising self-assembled NP based on LTIB. In summary, these results indicated that the LTB-based nanocarriers have the potential for broad applications, further expanding the library of self-assembled nanocarriers. [ABSTRACT FROM AUTHOR]

Despite notable advancements achieved on Hyperspectral (HS) pansharpening tasks through deep learning techniques, previous methods are inherently constrained by convolution or self-attention intrinsic defects, leading to limited performance. In this paper, we proposed an Attention-Interactive Dual-Branch Convolutional Neural Network (AIDB-Net) for HS pansharpening. Our model purely consists of convolutional layers and simultaneously inherits the strengths of both convolution and self-attention, especially the modeling of short- and long-range dependencies. Specially, we first extract, tokenize, and align the hyperspectral image (HSI) and panchromatic image (PAN) by Overlapping Patch Embedding Blocks. Then, we specialize a novel Spectral-Spatial Interactive Attention which is able to globally interact and fuse the cross-modality features. The resultant token-global similarity scores can guide the refinement and renewal of the textural details and spectral characteristics within HSI features. By deeply combined these two paradigms, our AIDB-Net significantly improve the pansharpening performance. Moreover, with the acceleration by the convolution inductive bias, our interactive attention can be trained without large scale dataset and achieves competitive time cost with its counterparts. Compared with the state-of-the-art methods, our AIDB-Net makes 5.2%, 3.1%, and 2.2% improvement on PSNR metric on three public datasets, respectively. Comprehensive experiments quantitatively and qualitatively demonstrate the effectiveness and superiority of our AIDB-Net. [ABSTRACT FROM AUTHOR]

Angle measurement is an essential component of precision measurement and serves as a crucial prerequisite for high-end manufacturing. It guides the implementation of precision manufacturing and assembly. The current angle measurement methods mainly focus on multiple axes, high precision, and large measurement ranges. This article introduces the technology of angle measurement from the perspectives of single-axis and multi-axis measurement schemes. Firstly, the single-axis measurement scheme is primarily achieved through optical methods, such as encoder discs that measure energy changes and interferometric phase changes, as well as mechanical, electromagnetic, and inertial angle measurement methods, among which interferometric methods offer the highest accuracy, with high cost, and encoder discs provide the largest measurement range with an ordinary price. Secondly, in the multi-axis measurement scheme, autocollimation instruments, including plane mirrors, gratings, and self-designed targets, are the main options. Although grating encoders can achieve three degrees of freedom in angle measurement with an ordinary price, they are limited in terms of measurement range and sensitivity compared to self-designed targets. Lastly, artificial intelligence assistance precision measurement is increasingly being embraced due to significant advancements in computer performance, making it more convenient to identify the relationship between measured values and detection values. In conclusion, angle measurement plays a crucial role in precision manufacturing, and the evolving and improving technologies provide the manufacturing industry with greater choices. The purpose of this review is to help readers quickly find more suitable technical solutions according to current application requirements, such as single/multiple axes, accuracy level, measuring range, budget, etc. [ABSTRACT FROM AUTHOR]

Particle aggregation is essential in many industrial processes, spanning the pharmaceutical and food industries, polymer production, and the environment, among others. However, aggregation can also occur, in some processes, as a non-desired side effect. Thus, to be able to monitor aggregation in industrial processes is of high importance to guarantee that the final, required product characteristics are obtained. In this paper, we present an extensive review of the different techniques available for monitoring particle characteristics in industrial processes involving particulate materials, with special emphasis on aggregation processes. These methods include both off-line and on-line techniques, based either on image acquisition techniques or different radiation scattering techniques (light-scattering and ultrasound spectroscopy). The principles behind each technique are addressed, together with their relevant applications, advantages, and disadvantages. [ABSTRACT FROM AUTHOR]

Ensuring robustness of image classifiers against adversarial attacks and spurious correlation has been challenging. One of the most effective methods for adversarial robustness is a type of data augmentation that uses adversarial examples during training. Here, inspired by computational models of human vision, we explore a synthesis of this approach by leveraging a structured prior over image formation: the 3D geometry of objects and how it projects to images. We combine adversarial training with a weight initialization that implicitly encodes such a prior about 3D objects via 3D reconstruction pre-training. We evaluate our approach using two different datasets and compare it to alternative pre-training protocols that do not encode a prior about 3D shape. To systematically explore the effect of 3D pre-training, we introduce a novel dataset called Geon3D, which consists of simple shapes that nevertheless capture variation in multiple distinct dimensions of geometry. We find that while 3D reconstruction pre-training does not improve robustness for the simplest dataset setting, we consider (Geon3D on a clean background) that it improves upon adversarial training in more realistic (Geon3D with textured background and ShapeNet) conditions. We also find that 3D pre-training coupled with adversarial training improves the robustness to spurious correlations between shape and background textures. Furthermore, we show that the benefit of using 3D-based pre-training outperforms 2D-based pre-training on ShapeNet. We hope that these results encourage further investigation of the benefits of structured, 3D-based models of vision for adversarial robustness. [ABSTRACT FROM AUTHOR]

Sudden cardiac death due to ventricular fibrillation (VF) during ST-elevation acute myocardial infarction (STEAMI) significantly contributes to cardiovascular-related deaths. Although VF has been linked to genetic factors, variations in copy number variation (CNV), a significant source of genetic variation, have remained largely unexplored in this context. To address this knowledge gap, this study performed whole exome sequencing analysis on a cohort of 39 patients with STEAMI who experienced VF, aiming to elucidate the role of CNVs in this pathology. The analysis revealed CNVs in the form of duplications in the PARP2 and TTC5 genes as well as CNVs in the form of deletions in the MUC15 and PPP6R1 genes, which could potentially serve as risk indicators for VF during STEAMI. The analysis also underscores notable CNVs with an average gene copy number equal to or greater than four in DEFB134, FCGR2C, GREM1, PARM1, SCG5, and UNC79 genes. These findings provide further insight into the role of CNVs in VF in the context of STEAMI. [ABSTRACT FROM AUTHOR]

Simple Summary: Protein structure prediction using computer algorithms has long been a challenge; however, the recent introduction of algorithms like AlphaFold2 and ESMFold to predict protein structure has raised the hope for in silico drug discovery, a long sought-after breakthrough. Since the release of these algorithms, it has not been realized whether these algorithms apply if the structure is already reported to be available to the algorithms. Still, the confidence in the predicted structure varies from very low to very high, which is an observation that is unrelated to any physicochemical or biological property of the protein. Any amino acid chain sequence change fails to predict the structure, limiting the utility of these algorithms to an academic exercise. Still, researchers continue to search for the utility of the confidence scores and, despite failing, continue to suggest possible applications, resulting from the logical belief that if the confidence scores are different and reproducible, this must relate to the protein structure. To end this misconception, we predicted the structures of 204 FDA-approved therapeutic proteins, with a wishful thought that the confidence scores, if correlated on this large database, can assist in rank-ordering these proteins for their possible batch-to-batch variability, which could help to reduce testing when these molecules are developed as biosimilars. We also studied modified structures that were not predicted since no reference structure was available for the algorithms to function. This conclusion applies to the two tested algorithms, which showed comparable and proportional confidence intervals. This conclusion is controversial but deserves the attention of researchers who continue to hope to find any drug discovery utility for these algorithms. The three-dimensional protein structure is pivotal in comprehending biological phenomena. It directly governs protein function and hence aids in drug discovery. The development of protein prediction algorithms, such as AlphaFold2, ESMFold, and trRosetta, has given much hope in expediting protein-based therapeutic discovery. Though no study has reported a conclusive application of these algorithms, the efforts continue with much optimism. We intended to test the application of these algorithms in rank-ordering therapeutic proteins for their instability during the pre-translational modification stages, as may be predicted according to the confidence of the structure predicted by these algorithms. The selected molecules were based on a harmonized category of licensed therapeutic proteins; out of the 204 licensed products, 188 that were not conjugated were chosen for analysis, resulting in a lack of correlation between the confidence scores and structural or protein properties. It is crucial to note here that the predictive accuracy of these algorithms is contingent upon the presence of the known structure of the protein in the accessible database. Consequently, our conclusion emphasizes that these algorithms primarily replicate information derived from existing structures. While our findings caution against relying on these algorithms for drug discovery purposes, we acknowledge the need for a nuanced interpretation. Considering their limitations and recognizing that their utility may be constrained to scenarios where known structures are available is important. Hence, caution is advised when applying these algorithms to characterize various attributes of therapeutic proteins without the support of adequate structural information. It is worth noting that the two main algorithms, AlfphaFold2 and ESMFold, also showed a 72% correlation in their scores, pointing to similar limitations. While much progress has been made in computational sciences, the Levinthal paradox remains unsolved. [ABSTRACT FROM AUTHOR]

Semi-supervised learning (SSL) models, integrating labeled and unlabeled data, have gained prominence in vision-based tasks, yet their susceptibility to adversarial attacks remains underexplored. This paper unveils the vulnerability of SSL models to gray-box adversarial attacks—a scenario where the attacker has partial knowledge of the model. We introduce an efficient attack method, Gray-box Adversarial Attack on Semi-supervised learning (GAAS), which exploits the dependency of SSL models on publicly available labeled data. Our analysis demonstrates that even with limited knowledge, GAAS can significantly undermine the integrity of SSL models across various tasks, including image classification, object detection, and semantic segmentation, with minimal access to labeled data. Through extensive experiments, we exhibit the effectiveness of GAAS, comparing it to white-box attack scenarios and underscoring the critical need for robust defense mechanisms. Our findings highlight the potential risks of relying on public datasets for SSL model training and advocate for the integration of adversarial training and other defense strategies to safeguard against such vulnerabilities. [ABSTRACT FROM AUTHOR]

The discovery of the link between microRNAs (miRNAs) and a myriad of human diseases, particularly various cancer types, has generated significant interest in exploring their potential as a novel class of drugs. This has led to substantial investments in interdisciplinary research fields such as biology, chemistry, and medical science for the development of miRNA-based therapies. Furthermore, the recent global success of SARS-CoV-2 mRNA vaccines against the COVID-19 pandemic has further revitalized interest in RNA-based immunotherapies, including miRNA-based approaches to cancer treatment. Consequently, RNA therapeutics have emerged as highly adaptable and modular options for cancer therapy. Moreover, advancements in RNA chemistry and delivery methods have been pivotal in shaping the landscape of RNA-based immunotherapy, including miRNA-based approaches. Consequently, the biotechnology and pharmaceutical industry has witnessed a resurgence of interest in incorporating RNA-based immunotherapies and miRNA therapeutics into their development programs. Despite substantial progress in preclinical research, the field of miRNA-based therapeutics remains in its early stages, with only a few progressing to clinical development, none reaching phase III clinical trials or being approved by the US Food and Drug Administration (FDA), and several facing termination due to toxicity issues. These setbacks highlight existing challenges that must be addressed for the broad clinical application of miRNA-based therapeutics. Key challenges include establishing miRNA sensitivity, specificity, and selectivity towards their intended targets, mitigating immunogenic reactions and off-target effects, developing enhanced methods for targeted delivery, and determining optimal dosing for therapeutic efficacy while minimizing side effects. Additionally, the limited understanding of the precise functions of miRNAs limits their clinical utilization. Moreover, for miRNAs to be viable for cancer treatment, they must be technically and economically feasible for the widespread adoption of RNA therapies. As a result, a thorough risk evaluation of miRNA therapeutics is crucial to minimize off-target effects, prevent overdosing, and address various other issues. Nevertheless, the therapeutic potential of miRNAs for various diseases is evident, and future investigations are essential to determine their applicability in clinical settings. [ABSTRACT FROM AUTHOR]

In this study, the influence of U-bends on the flow and pressure propagation characteristics of a gas–liquid two-phase flow in upstream and downstream straight pipes was investigated experimentally. The superficial velocities of the gas and liquid are 0.18–25.11 m/s and 0.20–1.98 m/s, respectively, covering plug flow, slug flow, and annular flow. The experiments were conducted in U-tubes with inner diameters of 9 mm and 12 mm and with a curvature ratio of 8.33. The U-tube was C-shaped. The pressure fluctuations at the axial measurement points of the straight tubes were measured. Flow images of the distal straight tubes and U-bends were obtained. The disturbance from U-bends in the two-phase flow in the vicinity of the bend is very obvious. The perturbation from U-bends in the fluid in the adjacent straight tubes is highly related to the incoming flow pattern. The slug flow has the most significant influence, whereas the effects of the plug and annular flows are small. Fundamentally, it mainly depends on the weight relationship between the gravity, centrifugal force, and inertial force of the gas–liquid two-phase fluid. The pressure fluctuation propagates in the form of a wave with the same dominant frequency in the straight pipes of the U-tube. The pressure pulsation energy in the straight tubes strengthens with decreasing distance from the 180° return bend. In addition, the pressure fluctuation energy downstream of the U-bend is greater than that upstream of the return bend. [ABSTRACT FROM AUTHOR]

This perspective sheds light on the transformative impact of recent computational advancements in the field of protein therapeutics, with a particular focus on the design and development of antibodies. Cutting-edge computational methods have revolutionized our understanding of protein–protein interactions (PPIs), enhancing the efficacy of protein therapeutics in preclinical and clinical settings. Central to these advancements is the application of machine learning and deep learning, which offers unprecedented insights into the intricate mechanisms of PPIs and facilitates precise control over protein functions. Despite these advancements, the complex structural nuances of antibodies pose ongoing challenges in their design and optimization. Our review provides a comprehensive exploration of the latest deep learning approaches, including language models and diffusion techniques, and their role in surmounting these challenges. We also present a critical analysis of these methods, offering insights to drive further progress in this rapidly evolving field. The paper includes practical recommendations for the application of these computational techniques, supplemented with independent benchmark studies. These studies focus on key performance metrics such as accuracy and the ease of program execution, providing a valuable resource for researchers engaged in antibody design and development. Through this detailed perspective, we aim to contribute to the advancement of antibody design, equipping researchers with the tools and knowledge to navigate the complexities of this field. [ABSTRACT FROM AUTHOR]

In this study, we propose a meticulous method for the three-dimensional modeling of slope models using structured light, a swift and cost-effective technique. Our approach aims to enhance the understanding of slope behavior during landslides by capturing and analyzing surface deformations. The methodology involves the initial capture of images at various stages of landslides, followed by the application of the structured light method for precise three-dimensional reconstructions at each stage. The system's low-cost nature and operational convenience make it accessible for widespread use. Subsequently, a comparative analysis is conducted to identify regions susceptible to severe landslide disasters, providing valuable insights for risk assessment. Our findings underscore the efficacy of this system in facilitating a qualitative analysis of landslide-prone areas, offering a swift and cost-efficient solution for the three-dimensional reconstruction of slope models. [ABSTRACT FROM AUTHOR]

The Sustainable Development Science Satellite 1 (SDGSAT-1) satellite, launched in November 2021, is dedicated to providing data detailing the "traces of human activities" for the implementation of the United Union's 2030 Agenda for Sustainable Development and global scientific research. The glimmer imager (GI) that is equipped on SDGSAT-1 can provide nighttime light (NL) data with a 10 m panchromatic (PAN) band and red, green, and blue (RGB) bands of 40 m resolution, which can be used for a wide range of applications, such as in urban expansion, population studies of cities, and economics of cities, as well as nighttime aerosol thickness monitoring. The 10 m PAN band can be fused with the 40 m RGB bands to obtain a 10 m RGB NL image, which can be used to identify the intensity and type of night lights and the spatial distribution of road networks and to improve the monitoring accuracy of sustainable development goal (SDG) indicators related to city developments. Existing remote sensing image fusion algorithms are mainly developed for daytime optical remote sensing images. Compared with daytime optical remote sensing images, NL images are characterized by a large amount of dark (low-value) pixels and high background noises. To investigate whether daytime optical image fusion algorithms are suitable for the fusion of GI NL images and which image fusion algorithms are the best choice for GI images, this study conducted a comprehensive evaluation of thirteen state-of-the-art pansharpening algorithms in terms of quantitative indicators and visual inspection using four GI NL datasets. The results showed that PanNet, GLP_HPM, GSA, and HR outperformed the other methods and provided stable performances among the four datasets. Specifically, PanNet offered UIQI values ranging from 0.907 to 0.952 for the four datasets, whereas GSA, HR, and GLP_HPM provided UIQI values ranging from 0.770 to 0.856. The three methods based on convolutional neural networks achieved more robust and better visual effects than the methods using multiresolution analysis at the original scale. According to the experimental results, PanNet shows great potential in the fusion of SDGSAT-1 GI imagery due to its robust performance and relatively short training time. The quality metrics generated at the degraded scale were highly consistent with visual inspection, but those used at the original scale were inconsistent with visual inspection. [ABSTRACT FROM AUTHOR]