Search Results (59)

Malaria remains a significant global health challenge. Although the recent approval of the liver-stage vaccines RTS, S and R21 marks significant progress in malaria control, challenges remain in achieving long-lasting and broad protection. In this review, we provide an overview of the current landscape of malaria control, especially anti-malaria vaccine development. We first review the development of the RTS, S and R21 vaccines, highlighting their efficacy and limitations. We then examine other vaccines in development, including attenuated whole-sporozoite vaccines, as well as blood-stage-targeting vaccines and transmission-blocking vaccines targeting a variety of different immunogens. Additionally, we discuss emerging technologies, such as mRNA-based platforms, nanoparticle delivery systems, and novel adjuvants, assessing their potential to enhance the efficacy and mitigate the waning immunity concerns of most malaria vaccines. We believe that the identification of novel immunogen candidates, together with continued innovation in vaccine design and delivery, will enable us to win the fight against malaria in the future. Full article

With the increasing use of digital image compression technology, ensuring data integrity and security within the compression domain has become a crucial area of research. Absolute moment block truncation coding (AMBTC), an efficient lossy compression algorithm, is widely used for low-bitrate image storage and transmission. However, existing studies have primarily focused on tamper detection for AMBTC compressed images, often overlooking the integrity of the AMBTC compressed code itself. To address this gap, this paper introduces a novel anti-tampering scheme specifically designed for AMBTC compressed code. The proposed scheme utilizes shuffle pairing to establish a one-to-one relationship between image blocks. The hash value, calculated as verification data from the original data of each block, is then embedded into the bitmap of its corresponding block using the matrix coding algorithm. Additionally, a tampering localization mechanism is incorporated to enhance the security of the compressed code without introducing additional redundancy. The experimental results demonstrate that the proposed scheme effectively detects tampering with high accuracy, providing protection for AMBTC compressed code. Full article

Scomberomorus guttatus and Scomberomorus commerson are both important marine economic fish species worldwide, with high scientific and ecological value. In this study, the complete mitochondrial genome sequences of these two species of mackerel were obtained by using next-generation sequencing technology, with total lengths of 16,562 bp and 16,594 bp, respectively. Like most teleosts, both species possess 13 protein-coding genes, 22 tRNA genes, 2 rRNA genes, and 1 non-coding region D-loop. The base composition showed significant AT bias (55.1%, 53.4%) and anti-G bias (16.0%, 16.2%). In their control area, the terminal-associated sequence (TAS) was identified, and a total of three core sequences with repeated “---TACAT---ATGTA---” were found. There are typical CSB-E structures and CSB-D-like structures in the central conserved domain (CD), but no CSB-F structures have been found. Meanwhile, the CSB-2 and CSB-3 structures were identified in the conserved sequence block (CSB), but the CSB-1 structure was missing. To further investigate the phylogenetic relationships within the Scombridae family, this study conducted a comparative analysis of mitochondrial genomes from 30 Scombridae species. Phylogenetic trees encompassing 60% of the documented Scombridae species were constructed using the Neighbor-Joining (NJ) and Maximum Likelihood (ML) methods. The results revealed a close evolutionary relationship between the genus Scomber and Rastrelliger, while the genus Scomberomorus exhibited closer affinities to Thunnus, Euthynnus, and Katsuwonus. At the species level, Scomberomorus guttatus diverged earlier from Scomberomorus commerson. These findings refine and update the phylogenetic relationships among Scombridae species, providing critical molecular evidence and insights for deeper exploration of their evolutionary history and genetic affinities. Full article

With the advancement of technology, biometric recognition technology has gained widespread attention in identity authentication due to its high security and convenience. Finger vein recognition, as a biometric technology, utilizes near-infrared imaging to extract subcutaneous vein patterns, offering high security, stability, and anti-spoofing capabilities. Existing research primarily focuses on improving recognition accuracy; however, this often comes at the cost of increased model complexity, which, in turn, affects recognition efficiency, making it difficult to balance accuracy and speed in practical applications. To address this issue, this paper proposes a high-accuracy and high-efficiency finger vein recognition model called Faster Multi-Scale Finger Vein Recognition Network (FMFVNet), which optimizes recognition speed through the FasterNet Block module while ensuring recognition accuracy with the Multi-Scale Convolutional Attention (MSCA) module. Experimental results show that on the FV-USM and SDUMLA-HMT datasets, FMFVNet achieves recognition accuracies of 99.80% and 99.06%, respectively. Furthermore, the model’s inference time is reduced to 1.75 ms, representing a 20.8% improvement over the fastest baseline model and a 62.7% improvement over the slowest, achieving more efficient finger vein recognition. Full article

Small interfering RNAs (siRNAs) are short, double-stranded RNA molecules that play a crucial role in the regulation of gene expression, particularly through a natural process called RNA interference (RNAi). Their discovery, about 25 years ago, paved the way for a whole series of research leading to synthetic molecules. The gene silencing potential of these siRNAs was initially oriented towards diseases resulting from genetic dysfunctions. This led to the development of the first synthetic siRNAs approved for human use in hereditary transthyretin amyloidosis. Subsequently, the field of application expanded beyond the confines of genetic diseases. The refinement of pharmacological techniques has led to the synthesis of a variety of siRNAs capable of blocking the production of individual proteins responsible for various disease conditions, thus expanding their field of therapeutic application. The kidney has also been affected by this new therapeutic tool, largely indirectly but also, with some difficulty, directly. The structural complexity of the kidney has made the search for siRNAs targeting its individual components very challenging. Nevertheless, the first results of the application of this new therapeutic technology to the kidney are beginning to be seen in experimental animals and in humans. siRNAs have been approved for the treatment of amyloidosis with patisiran and oxalosis with lumasiran and nedosiran. Studies are ongoing for the use of siRNAs as anti-complement drugs in IgA nephropathy, as angiotensinogen inhibitors in hypertension, or against some mediators of acute kidney injury. In this review, the biological mechanisms underlying the use of siRNAs are briefly exposed. The results of the therapeutic application of RNA interference to the kidney and its diseases are also analyzed and discussed. Full article

Optical wireless video transmission technology combines the advantages of high data rates, enhanced security, large bandwidth capacity, and strong anti-interference capabilities inherent in optical communication, establishing it as a pivotal technology in contemporary data transmission networks. However, video data comprises a large volume of image information, resulting in substantial data flow with significant redundant bits. To address this, we propose an adaptive block sampling compressive sensing algorithm that overcomes the limitations of sampling inflexibility in traditional compressive sensing, which often leads to either redundant or insufficient local sampling. This method significantly reduces the presence of redundant bits in video images. First, the sampling mechanism of the block-based compressive sensing algorithm was optimized. Subsequently, a wireless optical video transmission experimental system was developed using a Field-Programmable Gate Array chip. Finally, experiments were conducted to evaluate the transmission of video optical signals. The results demonstrate that the proposed algorithm improves the peak signal-to-noise ratio by over 3 dB compared to other algorithms, with an enhancement exceeding 1.5 dB even in field tests, thereby significantly optimizing video transmission quality. This research contributes essential technical insights for the enhancement of wireless optical video transmission performance. Full article

Prion diseases are 100% fatal infectious neurodegenerative diseases affecting the brains of humans and other mammals. The disease is caused by the formation and replication of prions, composed exclusively of the misfolded prion protein (PrP^Sc). We invented and developed the protein misfolding cyclic amplification (PMCA) technology for in vitro prion replication, which allow us to replicate the infectious agent and it is commonly used for ultra-sensitive prion detection in biological fluids, tissues and environmental samples. In this article, we studied whether PMCA can be used to screen for chemical compounds that block prion replication. A small set of compounds previously shown to have anti-prion activity in various systems, mostly using cells infected with murine prions, was evaluated for their ability to prevent the replication of prions. Studies were conducted simultaneously with prions derived from 4 species, including human, cattle, cervid and mouse. Our results show that only one of these compounds (methylene blue) was able to completely inhibit prion replication in all species. Estimation of the IC50 for methylene blue inhibition of human prions causing variant Creutzfeldt-Jakob disease (vCJD) was 7.7 μM. Finally, we showed that PMCA can be used for structure-activity relationship studies of anti-prion compounds. Interestingly, some of the less efficient prion inhibitors altered the replication of prions in some species and not others, suggesting that PMCA is useful for studying the differential selectivity of potential drugs. Full article

Addressing the issues of low reuse rates and high waste content of drilling fluids commonly observed in oilfields, research on reuse technology based on utilizing the same system across different sections of the same well has been conducted. Using the F oilfield as a case study, the mechanism of wellbore destabilization was investigated through X-ray diffraction and scanning electron microscopy. Corresponding inhibitory anti-collapse drilling fluids for shallow layers were formulated, and a successful deep drilling fluid formula was developed by adding and replacing chemicals in the base fluid, thereby achieving the reuse of multilayered waste drilling fluids. Indoor evaluation results indicate that the high-temperature rheology of the modified deep drilling fluid is reasonable; the high-temperature inhibitor performs excellently, with a 16-h rolling recovery rate of ≥98%; and the settlement stability is robust, with a settlement ratio of 0.50 after 2 h of resting. These findings demonstrate that the drilling fluid possesses both excellent sand-carrying capacity and strong inhibitory effects, meeting the requirements for rapid drilling and wellbore stabilization in this stratum. This technology is straightforward and easy to implement, and it is expected to reduce treatment costs and promote efficient development within the block. Full article

To address the low clearance rate issue of the anti-blocking mechanism for maize no-till planters in the Huang-Huai-Hai Plain of China, experiments and simulations were conducted to analyze the individual and collective movements of straw under the action of the round roller-claw anti-blocking mechanism. A tracer-based measurement method for straw displacement was applied firstly. Experimental results showed that the straw forward displacement could be characterized by the average horizontal displacements of longitudinal and lateral tracers, while the straw side displacement could be characterized by the lateral displacement of the longitudinal tracer. The straw forward displacement was 58.95% greater than the side displacement. Forward, side, and total displacements of straw increased as the mechanism’s forward speed increased from 3 km/h to 7 km/h, with corresponding rates of increase at 233.98%, 43.20%, and 162.47%, respectively. Furthermore, a model of straw–soil–mechanism interaction was constructed in EDEM 2022 software. The relative error between experimental and simulated straw clearance rates was 11.20%, confirming the applicability of the simulation model for studying straw–soil–mechanism interaction. Based on the simulation model, three straw tracers of different lengths were selected to study the motion behavior of straw. It was inferred that despite differences in straw length, the movement behaviors of the three straw tracers under the influence of the anti-blocking mechanism were similar. Additionally, longer straws exhibited greater displacements in all directions. This paper serves as a reference for studying straw motion behavior influenced by anti-blocking mechanisms. Full article

This study focuses on real-time detection of maize crop rows using deep learning technology to meet the needs of autonomous navigation for weed removal during the maize seedling stage. Crop row recognition is affected by natural factors such as soil exposure, soil straw residue, mutual shading of plant leaves, and light conditions. To address this issue, the YOLOv5s network model is improved by replacing the backbone network with the improved MobileNetv3, establishing a combination network model YOLOv5-M3 and using the convolutional block attention module (CBAM) to enhance detection accuracy. Distance-IoU Non-Maximum Suppression (DIoU-NMS) is used to improve the identification degree of the occluded targets, and knowledge distillation is used to increase the recall rate and accuracy of the model. The improved YOLOv5s target detection model is applied to the recognition and positioning of maize seedlings, and the optimal target position for weeding is obtained by max-min optimization. Experimental results show that the YOLOv5-M3 network model achieves 92.2% mean average precision (mAP) for crop targets and the recognition speed is 39 frames per second (FPS). This method has the advantages of high detection accuracy, fast speed, and is light weight and has strong adaptability and anti-interference ability. It determines the relative position of maize seedlings and the weeding machine in real time, avoiding squeezing or damaging the seedlings. Full article

DNA methylation is a fundamental mechanism of epigenetic control in cells and its dysregulation is strongly implicated in cancer development. Cancers possess an extensively hypomethylated genome with focal regions of hypermethylation at CPG islands. Due to the highly conserved nature of cancer-specific methylation, its detection in cell-free DNA in plasma using liquid biopsies constitutes an area of interest in biomarker research. The advent of next-generation sequencing and newer computational technologies have allowed for the development of diagnostic and prognostic biomarkers that utilize methylation profiling to diagnose disease and stratify risk. Methylome-based predictive biomarkers can determine the response to anti-cancer therapy. An additional emerging application of these biomarkers is in minimal residual disease monitoring. Several key challenges need to be addressed before cfDNA-based methylation biomarkers become fully integrated into practice. The first relates to the biology and stability of cfDNA. The second concerns the clinical validity and generalizability of methylation-based assays, many of which are cancer type-specific. The third involves their practicability, which is a stumbling block for translating technologies from bench to clinic. Future work on developing pan-cancer assays with their respective validities confirmed using well-designed, prospective clinical trials is crucial in pushing for the greater use of these tools in oncology. Full article

Inflammatory skin diseases, such as psoriasis, atopic dermatitis, and alopecia areata, occur when the regulatory tolerance of the innate immune system is disrupted, resulting in the activation of the Janus kinase-signal transducer and activator of transcription (JAK-STAT) inflammatory signaling pathway by interleukin 6 (IL-6) and other key inflammatory cytokines. JAK inhibitors, such as tofacitinib, bind to these enzymes which are coupled to receptors on cell surfaces and block the transcription of inflammatory cytokine-induced genes. The first topical applications are being marketed, yet insufficient effects regarding indications, such as alopecia areata, suggest that improved delivery technologies could help increase the efficacy. In this study, we used sulfated dendritic polyglycerol with caprolactone segments integrated in its backbone (dPGS-PCL), with a molecular weight of 54 kDa, as a degradable carrier to load and solubilize the hydrophobic drug tofacitinib (TFB). TFB loaded in dPGS-PCL (dPGS-PCL@TFB), at a 11 w/w% loading capacity in aqueous solution, showed in an ex-vivo human skin model better penetration than free TFB in a 30:70 (v/v) ethanol/water mixture. We also investigated the anti-inflammatory efficacy of dPGS-PCL@TFB (0.5 w/w%), dPGS-PCL, and free TFB in the water/ethanol mixture by measuring their effects on IL-6 and IL-8 release, and STAT3 and STAT5 activation in ex vivo skin models of simulated inflamed human skin. Our results suggest that dPGS-PCL@TFB reduces the activation of STAT3 and STAT5 by increasing the penetration of the tofacitinib. However, no statistically significant differences with respect to the inhibition of IL-6 and IL-8 were observed in this short incubation time. Full article

Due to its wide monitoring range and low cost, visual-based fire detection technology is commonly used for fire detection in open spaces. However, traditional fire detection algorithms have limitations in terms of accuracy and speed, making it challenging to detect fires in real time. These algorithms have poor anti-interference ability against fire-like objects, such as emissions from factory chimneys, clouds, etc. In this study, we developed a fire detection approach based on an improved YOLOv5 algorithm and a fire detection dataset with fire-like objects. We added three Convolutional Block Attention Modules (CBAMs) to the head network of YOLOv5 to improve its feature extraction ability. Meanwhile, we used the C2f module to replace the original C2 module to capture rich gradient flow information. Our experimental results show that the proposed algorithm achieved a mAP@50 of 82.36% for fire detection. In addition, we also conducted a comparison test between datasets with and without labeling information for fire-like objects. Our results show that labeling information significantly reduced the false-positive detection proportion of fire-like objects incorrectly detected as fire objects. Our experimental results show that the CBAM and C2f modules enhanced the network’s feature extraction ability to differentiate fire objects from fire-like objects. Hence, our approach has the potential to improve fire detection accuracy, reduce false alarms, and be more cost-effective than traditional fire detection methods. This method can be applied to camera monitoring systems for automatic fire detection with resistance to fire-like objects. Full article

(1) Background: Understanding how advanced cancers evade host innate and adaptive immune opponents has led to cancer immunotherapy. Among several immunotherapeutic strategies, the reversal of immunosuppression mediated by regulatory T cells in the tumor microenvironment (TME) using blockers of immune-checkpoint signaling in effector T cells is the most successful treatment measure. Furthermore, agonists of T cell costimulatory molecules (CD40, 4-1BB, OX40) play an additional anti-cancer role to that of checkpoint blocking in combined therapy and serve also as adjuvant/neoadjuvant/induction therapy to conventional cancer treatments, such as tumor resection and radio- and chemo- therapies. (2) Methods and Results: In this study, novel agonistic antibodies to the OX40/CD134 ectodomain (EcOX40), i.e., fully human bivalent single-chain variable fragments (HuscFvs) linked to IgG Fc (bivalent HuscFv-Fcγ fusion antibodies) were generated by using phage-display technology and genetic engineering. The HuscFvs in the fusion antibodies bound to the cysteine-rich domain-2 of the EcOX40, which is known to be involved in OX40-OX40L signaling for NF-κB activation in T cells. The fusion antibodies caused proliferation, and increased the survival and cytokine production of CD3-CD28-activated human T cells. They showed enhancement trends for other effector T cell activities like granzyme B production and lysis of ovarian cancer cells when added to the activated T cells. (3) Conclusions: The novel OX40 agonistic fusion antibodies should be further tested step-by-step toward their safe use as an adjunctive non-immunogenic cancer immunotherapeutic agent. Full article

With the advent of Industry 4.0, blockchain is one of the emerging technologies that can be used in multiple fields, especially in supply chain networks, where it is considered a sustainable concept technology. Because of its decentralization, reliability, transparency, consistency, and traceability, blockchain can challenge existing conventional models in supply chain scenarios. Therefore, this study proposes a framework that integrates the technology of Blockchain 3.0 (Hyperledger Fabric) into the tea supply chain. The concept of data traceability driven by blockchain technology consists of documenting the tea supply chain system, in which the cultivation, processing, and retailing of tea leaves are documented, and optimizing the existing anti-counterfeiting mechanisms. The ECDSA algorithm is used to confirm the identity among the entities located in the Hyperledger Fabric channel, and IPFS technology is introduced to store tea traceability data to solve the problem of data increment in the tea production process. Only the hash value and traceability code of the traceability data are stored in the block. We analyze the relationship between the number of transactions and latency, and we can see the latency time of both write and read transactions. The system tested write transactions and read transactions with a minimum write speed of 0.97 s and a maximum of 6.26 s and a minimum read speed of 0.1 s and a maximum of 4.62 s via Caliper analysis. The experimental results show that the performance analysis sounds good. This study uses distributed theory and service-oriented thinking in its specific application to the tea supply chain system model and takes each entity through a B2B connection to achieve anti-counterfeit traceability in the tea supply chain of a regulatory nature. Full article