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A novel large neutral amino acid transporter 1 (LAT1)-specific inhibitor, JPH203, is expected to cause cancer-specific starvation and possess anti-tumor effects; however, its anti-tumor mechanism for colorectal cancer (CRC) remains unclear. We analyzed LAT family gene expressions in public databases using UCSC Xena and evaluated LAT1 protein expression using immunohistochemistry in 154 cases of surgically resected CRC. We also evaluated mRNA expression using polymerase chain reaction in 10 CRC cell lines. Furthermore, JPH203 treatment experiments were conducted in vitro and in vivo using an allogeneic immune-responsive mouse model with abundant stroma created via the orthotopic transplantation of the mouse-derived CRC cell line CT26 and mesenchymal stem cells. The treatment experiments were followed by comprehensive gene expression analyses with RNA sequencing. Database analyses and immunohistochemistry research on clinical specimens revealed that LAT1 expression was cancer-dominant, and its increase was accompanied by tumor progression. In vitro, JPH203 was effective in an LAT1 expression-dependent manner. In vivo, JPH203 treatment considerably reduced tumor size and metastasis, and RNA sequencing-based pathway analysis showed that not only tumor growth and amino acid metabolism pathways, but also stromal activation-related pathways were suppressed. The results of the RNA sequencing were validated in the clinical specimens, as well as both in vitro and in vivo. LAT1 expression in CRC plays an important role in tumor progression. JPH203 may inhibit the progression of CRC and tumor stromal activity.

Venoms of solitary wasps are utilized for prey capture (insects and spiders), paralyzing them with a stinger injection to be offered as food for their larvae. Thus, the identification and characterization of the components of solitary wasp venoms can have biotechnological application. In the present study, the venom components profile of a solitary scoliid wasp, Campsomeriella annulata annulata, was investigated through a comprehensive analysis using LC-MS and -MS/MS. Online mass fingerprinting revealed that the venom extract contains 138 components, and MS/MS analysis identified 44 complete sequences of the peptide components. The peptides are broadly divided into two classes: bradykinin-related peptides, and linear α-helical peptides. Among the components of the first class, the two main peptides, α-campsomerin (PRLRRLTGLSPLR) and β-campsomerin (PRLRRLTGLSPLRAP), had their biological activities evaluated. Both peptides had no effects on metallopeptidases [human neprilysin (NEP) and angiotensin-converting enzyme (ACE)] and acetylcholinesterase (AChE), and had no cytotoxic effects. Studies with PC12 neuronal cells showed that only α-campsomerin was able to enhance cell viability, while β-campsomerin had no effect. It is noteworthy that the only difference between the primary structures from these peptides is the presence of the AP extension at the C-terminus of β-campsomerin, compared to α-campsomerin. Among the linear α-helical peptides, annulatin (ISEALKSIIVG-NH2) was evaluated for its biological activities. Annulatin showed histamine releasing activity from mast cells and low hemolytic activity, but no antimicrobial activities against all microbes tested were observed. Thus, in addition to providing unprecedented information on the whole components, the three peptides selected for the study suggest that molecules present in solitary scoliid wasp venoms may have interesting biological activities.

Presented here are the observations and interpretations from a comprehensive analysis of 16 representative particles returned from the C-type asteroid Ryugu by the Hayabusa2 mission. On average Ryugu particles consist of 50% phyllosilicate matrix, 41% porosity and 9% minor phases, including organic matter. The abundances of 70 elements from the particles are in close agreement with those of CI chondrites. Bulk Ryugu particles show higher δ18O, Δ17O, and ε54Cr values than CI chondrites. As such, Ryugu sampled the most primitive and least-thermally processed protosolar nebula reservoirs. Such a finding is consistent with multi-scale H-C-N isotopic compositions that are compatible with an origin for Ryugu organic matter within both the protosolar nebula and the interstellar medium. The analytical data obtained here, suggests that complex soluble organic matter formed during aqueous alteration on the Ryugu progenitor planetesimal (several 10’s of km), <2.6 Myr after CAI formation. Subsequently, the Ryugu progenitor planetesimal was fragmented and evolved into the current asteroid Ryugu through sublimation.

Unmanned aerial vehicle (UAV)-based visual object tracking has enabled a wide range of applications and attracted increasing attention in the field of artificial intelligence (AI) because of its versatility and effectiveness. As an emerging force in the revolutionary trend of deep learning, Siamese networks shine in UAV-based object tracking with their promising balance of accuracy, robustness, and speed. Thanks to the development of embedded processors and the gradual optimization of deep neural networks, Siamese trackers receive extensive research and realize preliminary combinations with UAVs. However, due to the UAV’s limited onboard computational resources and the complex real-world circumstances, aerial tracking with Siamese networks still faces severe obstacles in many aspects. To further explore the deployment of Siamese networks in UAV-based tracking, this work presents a comprehensive review of leading-edge Siamese trackers, along with an exhaustive UAV-specific analysis based on the evaluation using a typical UAV onboard processor. Then, the onboard tests are conducted to validate the feasibility and efficacy of representative Siamese trackers in real-world UAV deployment. Furthermore, to better promote the development of the tracking community, this work analyzes the limitations of existing Siamese trackers and conducts additional experiments represented by low-illumination evaluations. In the end, prospects for the development of Siamese tracking for UAV-based AI systems are deeply discussed. The unified framework of leading-edge Siamese trackers, i.e., code library, and the results of their experimental evaluations are available at https://github.com/vision4robotics/SiameseTracking4UAV.

[This corrects the article DOI: 10.3389/fimmu.2025.1622959.].

Alzheimer's disease (AD) is a common, refractory, progressive neurodegenerative disorder in which cognitive and memory deficits are highly correlated with abnormalities in hippocampal brain regions. There is still a lack of hippocampus-related markers for AD diagnosis and prevention. Differently expressed genes were identified in the gene expression profile GSE293789 in the hippocampal brain region. Enrichment analyses GO, KEGG, and GSEA were used to identify biological pathways involved in the DEGs and AD-related group. WGCNA was used to identify the gene modules that are highly associated with AD in the samples. The intersecting genes of the genes in DEGs and modules were extracted and the top ten ranked hub genes were identified. Finally GES48350 was used as a validation cohort to predict the diagnostic efficacy of hub genes. From GSE293789, 225 DEGs were identified, which were mainly associated with calcium response, glutamatergic synapses, and calcium-dependent phospholipid-binding response. WGCNA analysis yielded dark green and bright yellow modular genes as the most relevant to AD. From these two modules, 176 genes were extracted, which were taken to be intersected with DEGs, yielding 51 intersecting genes. Then 10 hub genes were identified in them: HSPA1B, HSPB1, HSPA1A, DNAJB1, HSPB8, ANXA2, ANXA1, SOX9, YAP1, and AHNAK. Validation of these genes was found to have excellent diagnostic performance. Ten AD-related hub genes in the hippocampus were identified, contributing to further understanding of AD development in the hippocampus and development of targets for therapeutic prevention.

Deep neural networks (DNNs) have made significant achievements in a wide variety of domains. For the deep learning tasks, multiple excellent hardware platforms provide efficient solutions, including graphics processing units (GPUs), central processing units (CPUs), field programmable gate arrays (FPGAs), and application-specific integrated circuit (ASIC). Nonetheless, CPUs outperform other solutions including GPUs in many cases for the inference workload of DNNs with the support of various techniques, such as the high-performance libraries being the basic building blocks for DNNs. Thus, CPUs have been a preferred choice for DNN inference applications, particularly in the low-latency demand scenarios. However, the DNN inference efficiency remains a critical issue, especially when low latency is required under conditions with limited hardware resources, such as embedded systems. At the same time, the hardware features have not been fully exploited for DNNs and there is much room for improvement. To this end, this paper conducts a series of experiments to make a thorough study for the inference workload of prominent state-of-the-art DNN architectures on a single-instruction-multiple-data (SIMD) CPU platform, as well as with widely applicable scopes for multiple hardware platforms. The study goes into depth in DNNs: the CPU kernel-instruction level performance characteristics of DNNs including branches, branch prediction misses, cache misses, etc, and the underlying convolutional computing mechanism at the SIMD level; The thorough layer-wise time consumption details with potential time-cost bottlenecks; And the exhaustive dynamic activation sparsity with exact details on the redundancy of DNNs. The research provides researchers with comprehensive and insightful details, as well as crucial target areas for optimising and improving the efficiency of DNNs at both the hardware and software levels.

The transition from juvenility through maturation to senescence is a complex process that involves the regulation of longevity. Here, we identify JUNGBRUNNEN1 (JUB1), a hydrogen peroxide (H₂O₂)-induced NAC transcription factor, as a central longevity regulator in Arabidopsis thaliana. JUB1 overexpression strongly delays senescence, dampens intracellular H₂O₂ levels, and enhances tolerance to various abiotic stresses, whereas in jub1-1 knockdown plants, precocious senescence and lowered abiotic stress tolerance are observed. A JUB1 binding site containing a RRYGCCGT core sequence is present in the promoter of DREB2A, which plays an important role in abiotic stress responses. JUB1 transactivates DREB2A expression in mesophyll cell protoplasts and transgenic plants and binds directly to the DREB2A promoter. Transcriptome profiling of JUB1 overexpressors revealed elevated expression of several reactive oxygen species-responsive genes, including heat shock protein and glutathione S-transferase genes, whose expression is further induced by H₂O₂ treatment. Metabolite profiling identified elevated Pro and trehalose levels in JUB1 overexpressors, in accordance with their enhanced abiotic stress tolerance. We suggest that JUB1 constitutes a central regulator of a finely tuned control system that modulates cellular H₂O₂ level and primes the plants for upcoming stress through a gene regulatory network that involves DREB2A.

Individual treatment outcomes to antidepressants varies widely, yet the determinants to this difference remain elusive. MicroRNA (miRNA) gene expression regulation in major depressive disorder (MDD) has attracted interest as a biomarker. This 4-week randomized controlled trial examined changes in the plasma miRNAs that correlated with the treatment outcomes of mirtazapine (MIR) and selective serotonin reuptake inhibitor (SSRI) monotherapy. Pre- and post- treatment, we comprehensively analyzed the miRNA levels in MDD patients, and identified the gene pathways linked to these miRNAs in 46 patients. Overall, 141 miRNA levels significantly demonstrated correlations with treatment remission after 4 weeks of MIR, with miR-1237-5p showing the most robust and significant correlation after Bonferroni correction. These 141 miRNAs displayed a negative correlation with remission, indicating a decreasing trend. These miRNAs were associated with 15 pathways, including TGF-β and MAPK. Through database searches, the genes targeted by these miRNAs with the identified pathways were compared, and it was found that MAPK1, IGF1, IGF1R, and BRAF matched. Alterations in specific miRNAs levels before and after MIR treatment correlated with remission. The miRNAs mentioned in this study have not been previously reported. No other studies have investigated treatment with MIR. The identified miRNAs also correlated with depression-related genes and pathways.

Most methodological areas assume common serious reflections to certify difficult study and publication practices, and, therefore, approval in their area. Interestingly, relatively little attention has been paid to reviewing the application of Structural Equation Modeling (SEM) in environmental sustainability problems despite the growing number of publications in the past two decades. Therefore, the main objective of this study is to fill this gap by conducting a wide search in two main databases including Web of Science and Scopus to identify the studies which used SEM techniques in the period from 2005 to 2016. A critical analysis of these articles addresses some important key issues. On the basis of our results, we present comprehensive guidelines to help researchers avoid general pitfalls in using SEM. The results of this review are important and will help researchers to better develop research models based on SEM in the area of environmental sustainability.