Metabolic profiling of triptophenolide in rat plasma, urine, bile and faeces after intragastric administration.

Triptophenolide, a major diterpenoid extracted from Tripterygium wilfordii Hook. f., has been reported to possess significant anti-tumour, anti-androgen and anti-inflammatory activities. However, the metabolic fate of triptophenolide remains unknown. Therefore, this study focused on the metabolic profiling of triptophenolide in rat plasma, urine, bile and faeces following intragastric administration. An ultraperformance liquid chromatography coupled to quadrupole time-of-flight mass spectrometry with combination of extracted ion chromatogram strategy based on 71 typical metabolic reactions was established to comprehensively profile the metabolites of triptophenolide. This strategy allowed for the identification of 17 metabolites from the biosamples. Reduction, oxidation, glucuronide conjugation, and hydroxylation were considered as its main metabolic pathways in vivo. The present study will be greatly helpful for the further pharmacological studies on triptophenolide and would provide valuable information for its clinical application.

Enhancing Short-Range Interactions to Broaden the Temperature Range for Coexistence of Antiferroelectricity and Ferroelasticity.

Antiferroelectric (AFE) materials, characterized by double electric hysteresis loops, can be transformed to the ferroelectric (FE) phase under an external electric field, making them promising candidates for electronic energy storage and solid-state refrigeration. Additionally, the field-induced strain in AFE materials is contingent upon the direction of the electric field, rendering it with a switching characteristic. Although AFE materials have made progress in the field of energy storage and negative electrocaloric effect, the coexistence of AFE and ferroelasticity is still rarely reported. Here, two isomorphic organic-inorganic hybrid perovskites, HDAEPbCl4 and HDAEPbBr4 (HDAE is [2-(hydroxydimethylammonio)ethan-1-aminium]), exhibiting FE-AFE-PE (PE is paraelectric) phase transitions, are presented. Remarkably, the temperature range where AFE and ferroelasticity coexist is significantly broadened from 59.9 K to 115.1 K by strengthening short-range forces via halogen substitution. This discovery extends the family of FE, AFE, and ferroelastic materials, contributing to the development of multifunctional materials and advancing multifunctional material development.

Symmetry breaking through molecular engineering to achieve "on-off-on" nonlinear optical switch in a [SnCl6]2- framework.

The impact of molecular point groups on phase transition materials has been seldom discussed. Two organic cations with different symmetries were incorporated into a [SnCl6]2- framework. By breaking the symmetry of cations, a novel hybrid material with two successive phase transitions featuring a rare "on-off-on" nonlinear optical switch was obtained.

Extending the emission peak tail of indole cyanine for deep-near-infrared bioimaging.

We propose a novel strategy for tailoring the structure of fluorescent molecules to achieve emission at the tail end of the NIR-II window. The favorable spectroscopic properties and low cytotoxicity of YNs make them powerful tools for bioimaging. Notably, YN-4 exhibits a brightness 2.5 times greater than YN-3, 6 times that of IR-783, and 5 times that of ICG. This enhanced brightness enabled high-resolution imaging of mouse thoracic and abdominal cavities, tumor vasculature, and real-time monitoring of gastrointestinal motility using YN-4. Furthermore, covalent grafting of glucose onto the YN-Glu scaffold significantly improved tumor-targeting capability and facilitated tracking of glucose metabolism. This work aims to extend the application of fluorescent molecule imaging beyond the NIR-IIa window.

GRAM: An interpretable approach for graph anomaly detection using gradient attention maps.

Detecting unusual patterns in graph data is a crucial task in data mining. However, existing methods face challenges in consistently achieving satisfactory performance and often lack interpretability, which hinders our understanding of anomaly detection decisions. In this paper, we propose a novel approach to graph anomaly detection that leverages the power of interpretability to enhance performance. Specifically, our method extracts an attention map derived from gradients of graph neural networks, which serves as a basis for scoring anomalies. Notably, our approach is flexible and can be used in various anomaly detection settings. In addition, we conduct theoretical analysis using synthetic data to validate our method and gain insights into its decision-making process. To demonstrate the effectiveness of our method, we extensively evaluate our approach against state-of-the-art graph anomaly detection techniques on real-world graph classification and wireless network datasets. The results consistently demonstrate the superior performance of our method compared to the baselines.

Sign-Based Gradient Descent With Heterogeneous Data: Convergence and Byzantine Resilience.

Communication overhead has become one of the major bottlenecks in the distributed training of modern deep neural networks. With such consideration, various quantization-based stochastic gradient descent (SGD) solvers have been proposed and widely adopted, among which signSGD with majority vote shows a promising direction because of its communication efficiency and robustness against Byzantine attackers. However, signSGD fails to converge in the presence of data heterogeneity, which is commonly observed in the emerging federated learning (FL) paradigm. In this article, a sufficient condition for the convergence of the sign-based gradient descent method is derived, based on which a novel magnitude-driven stochastic-sign-based gradient compressor is proposed to address the non-convergence issue of signSGD. The convergence of the proposed method is established in the presence of arbitrary data heterogeneity. The Byzantine resilience of sign-based gradient descent methods is quantified, and the error-feedback mechanism is further incorporated to boost the learning performance Experimental results on the MNIST dataset, the CIFAR-10 dataset, and the Tiny-ImageNet dataset corroborate the effectiveness of the proposed methods.

A chromosome-level genome assembly for Onobrychis viciifolia reveals gene copy number gain underlying enhanced proanthocyanidin biosynthesis.

Sainfoin (Onobrychis viciifolia), which belongs to subfamily Papilionoideae of Leguminosae, is a vital perennial forage known as "holy hay" due to its high contents of crude proteins and proanthocyanidins (PAs, also called condensed tannins) that have various pharmacological properties in animal feed, such as alleviating rumen tympanic disease in ruminants. In this study, we select an autotetraploid common sainfoin (2n = 4x = 28) and report its high-quality chromosome-level genome assembly with 28 pseudochromosomes and four haplotypes (~1950.14 Mb, contig N50 = 10.91 Mb). The copy numbers of genes involved in PA biosynthesis in sainfoin are significantly greater than those in four selected Fabales species, namely, autotetraploid Medicago sativa and three other diploid species, Lotus japonicus, Medicago truncatula, and Glycine max. Furthermore, gene expansion is confirmed to be the key contributor to the increased expression of these genes and subsequent PA enhancement in sainfoin. Transcriptomic analyses reveal that the expression of genes involved in the PA biosynthesis pathway is significantly increased in the lines with high PA content compared to the lines with medium and low PA content. The sainfoin genome assembly will improve our understanding of leguminous genome evolution and biosynthesis of secondary metabolites in sainfoin.

A flavonoid salt probe for distinguishing between tumor and normal cells.

YH-2 represents an innovative, non-invasive fluorescent probe featuring a structure based on flavonoid onium salts. It is characterized by a well-suited Stokes shift and emits in the near-infrared (NIR) wavelength range. Its capacity to distinguish between HeLa cells, HepG2 cells, and LO2 cells is attributed to differential intracellular viscosity. Experimental results validate the heightened viscosity of organelles, such as the endoplasmic reticulum (ER), mitochondria and lysosomes in tumor cells compared to LO2 cells. Of paramount importance, YH-2 demonstrates the capability to swiftly image tumors within a mere 20 min following tail vein injection and this imaging ability can be sustained for an extended period of up to 5 h. This method offers a potential tumor diagnostic strategy in vivo.

Novel small molecule-based organic nanoparticles for second near-infrared photothermal tumor ablation.

Second near-infrared (NIR-II,1000 âˆ¼ 1700 nm) therapeutic window presents an increased tissue penetration and elevated maximal permissible exposure in the application of photothermal therapy (PTT). However, the lack of NIR-II photothermal conversion agents (PCAs) limit their further development. In this work, we rationally designed and successfully developed three novel indolium-like heptamethine cyanine dyes (NFs) by installing N,N-diethylamino on the terminal ends of a conjugated polyene backbone and replacing the middle chlorine atom with o-mercapto benzoic acid and p-mercapto benzoic acid. Notably, NF2 with stronger rotating group encapsulated in organic nanoparticles (NF2 NPs) exhibited high photothermal conversion efficiency (PCE), which could come up to (61.3 %). Then we conducted serial experiments to further investigate PTT capability of NF2 NPs 4 T1 cell line and nude mice bearing 4 T1 tumor. As expected, the resulting NF2 NPs presented the excellent photothermal conversion ability and superb PTT effect both in vivo and in vitro. This study will inspire more work for future design and clinical applications of NIR-II therapeutic agents.

Structural Reorganization in Two Alfalfa Mitochondrial Genome Assemblies and Mitochondrial Evolution in Medicago Species.

Plant mitochondria are crucial for species evolution, phylogenetics, classification, and identification as maternal genetic material. However, the presence of numerous repetitive sequences, complex structures, and a low number of genes in the mitochondrial genome has hindered its complete assembly and related research endeavors. In this study, we assembled two mitochondrial genomes of alfalfa varieties of Zhongmu No.1 (299,123 bp) and Zhongmu No.4 (306,983 bp), based on a combination of PacBio, Illumina, and Hi-C sequences. The comparison of genome assemblies revealed that the same number of mitochondrial genes, including thirty-three protein-coding genes, sixteen tRNA genes, and three rRNA genes existed in the two varieties. Additionally, large fragments of repetitive sequences were found underlying frequent mitochondrial recombination events. We observed extensive transfer of mitochondrial fragments into the nuclear genome of Zhongmu No.4. Analysis of the cox1 and rrn18s genes in 35 Medicago accessions revealed the presence of population-level deletions and substitutions in the rrn18s gene. We propose that mitochondrial structural reorganizations may contribute to alfalfa evolution.

Graph Neural Network-Based Node Deployment for Throughput Enhancement.

The recent rapid growth in mobile data traffic entails a pressing demand for improving the throughput of the underlying wireless communication networks. Network node deployment has been considered as an effective approach for throughput enhancement which, however, often leads to highly nontrivial nonconvex optimizations. Although convex-approximation-based solutions are considered in the literature, their approximation to the actual throughput may be loose and sometimes lead to unsatisfactory performance. With this consideration, in this article, we propose a novel graph neural network (GNN) method for the network node deployment problem. Specifically, we fit a GNN to the network throughput and use the gradients of this GNN to iteratively update the locations of the network nodes. Besides, we show that an expressive GNN has the capacity to approximate both the function value and the gradients of a multivariate permutation-invariant function, as a theoretic support to the proposed method. To further improve the throughput, we also study a hybrid node deployment method based on this approach. To train the desired GNN, we adopt a policy gradient algorithm to create datasets containing good training samples. Numerical experiments show that the proposed methods produce competitive results compared with the baselines.

Tumor-Targeting Near-Infrared Dimeric Heptamethine Cyanine Photosensitizers with an Aromatic Diphenol Linker for Imaging-Guided Cancer Phototherapy.

Phototherapy is considered a promising alternative to conventional tumor treatments due to its noninvasive modality and effective therapeutic effect. However, designing a photosensitizer with satisfactory therapeutic effect and high security remains a considerable challenge. Herein, a series of dimeric heptamethine cyanine photosensitizers with an aromatic diphenol linker at the meso position is developed to improve the photothermal conversion efficiency (PCE). Thanks to the extended conjugate system and high steric hindrance, the screened 26NA-NIR and 44BP-NIR exhibit high PCE (≈35%), bright near-infrared (NIR) fluorescence, excellent reactive oxygen species (ROS) generation capability, and improved photostability. Furthermore, their outstanding performance on imaging-guided PDT-PTT synergistic therapy is demonstrated by in vivo and in vitro experiments. In conclusion, this study designs a series of dimeric heptamethine cyanine photosensitizers and presents two compounds for potential clinical applications. The strategy provides a new method to design NIR photosensitizers for imaging-guided cancer treatment.

Three near-infrared and lysosome-targeting probes for photodynamic therapy (PDT).

Lysosome, an organelle which contains a number of hydrolases and hydrogen ions, plays a crucial role in cellular survival and apoptosis. If selectively destroy lysosomes membrane, inner hydrolases and hydrogen ions will leak and induce cell death. In this work, three lysosome-targeting fluorescent probes (HCL 1-3, heptamethine cyanine lysosomal-targeting probe) were designed, synthesized and developed for photodynamic therapy. Piperazine and N, N-dimethyl structures made HCL 1-3 have good lysosome targeting ability while Pearson's correlation coefficients reached 0.85, 0.87 and 0.78. It can be concluded from MTT test, HCL 1-3 have high photo cytotoxicity and low dark cytotoxicity from MTT test. Calcein/PI staining assays also supported cytotoxicity of HCL 1-3 under light conditions. In vivo experiments, HCL 2 accumulated in tumor and a strong fluorescence signal was observed at 12 h post injection. All results showed that our experiments provide help and new ideas for cyanine dyes in cancer treatment.

Accuracy of genomic selection for alfalfa biomass yield in two full-sib populations.

Alfalfa (Medicago sativa) is one of the most important leguminous forages, widely planted in temperate and subtropical regions. As a homozygous tetraploid, its complex genetic background limits genetic improvement of biomass yield attributes through conventional breeding methods. Genomic selection (GS) could improve breeding efficiency by using high-density molecular markers that cover the whole genome to assess genomic breeding values. In this study, two full-sib F1 populations, consisting of 149 and 392 individual plants (P149 and P392), were constructed using parents with differences in yield traits, and the yield traits of the F1 populations were measured for several years in multiple environments. Comparisons of individual yields were greatly affected by environments, and the best linear unbiased prediction (BLUP) could accurately represent the original yield data. The two hybrid F1 populations were genotyped using GBS and RAD-seq techniques, respectively, and 47,367 and 161,170 SNP markers were identified. To develop yield prediction models for a single location and across locations, genotypic and phenotypic data from alfalfa yields in multiple environments were combined with various prediction models. The prediction accuracies of the F1 population, including 149 individuals, were 0.11 to 0.70, and those of the F1 population, consisting of 392 individuals, were 0.14 to 0.67. The BayesC and RF models had the highest average prediction accuracy of 0.60 for two F1 populations. The accuracy of the prediction models for P392 was higher than that of P149. By analyzing multiple prediction models, moderate prediction accuracies are obtained, although accuracies will likely decline across multiple locations. Our study provided evidence that GS can accelerate the improvement of alfalfa yield traits.

The complete chloroplast genome of Medicago arabica (Fabaceae).

Medicago arabica (Linnaeus, 1762) Huds. is an important annual legume forage that grows in a wide range of climates, from subtropical to temperate. This study aimed to sequence the chloroplast genome of M. arabica and compare it with other legumes. In this study, we sequenced the entire chloroplast genome of M. arabica, which has 125,056 base pairs. The total GC content of the chloroplast genome of M. arabica was 34.4%. From the 110 unique genes of the circular genome, 30 tRNA genes, four rRNA genes, and 76 protein-coding genes were successfully annotated. A maximum likelihood (ML) tree was constructed using the model species and 17 species of the Medicago genus. M. arabica was shown to be phylogenetically closely related to M. polymorpha. The nucleotide diversity of the chloroplast genome may provide valuable molecular markers to study chloroplast, genetic breeding, and plant molecular evolution. These findings provide a solid foundation for future research on the molecular biology of the chloroplast.

The complete chloroplast genome of Medicago scutellata (Fabaceae).

Medicago scutellata (Linnaeus, 1753) is one of the essential leguminous forages distributed across tropical and subtropical regions of the world. In our study, we obtained the complete chloroplast genome of M. scutellata with a length of 124,082 bp. The total GC content of the entire chloroplast genome of M. scutellata was 33.9%. Among the 110 unique genes in the circular genome, 30 tRNA, 4 rRNA, and 76 protein-coding genes were successfully annotated. A phylogenetic tree constructed using common protein-coding genes revealed that M. scutellata is closely related to M. truncatula from the Fabaceae family.

Numerical investigation on the enhanced damping behavior of bio-inspired nacreous composites by introducing interlocked structure.

Due to the unique "Brick-and-Mortar" structure, nacre exhibits extraordinary mechanical properties such as high strength and toughness, which are naturally exclusive in traditional engineering materials. The main threat to the shell is the impact load along the direction perpendicular to the lamellar structure. However, how it attenuates stress wave and dissipates kinetic energy during impact events remains unclear, especially along different loading directions (the directions perpendicular and parallel to the lamellar structure). In this paper, damping performance of nacreous bio-inspired composites is investigated to evaluate the energy dissipation from the perspective of dynamic modulus using theoretical and numerical methods. It is found that the stress states and Poisson's ratio of the "mortar" exert remarkable influence on composites' loss modulus. Moreover, the predicted optimal aspect ratio in this work is consistent with the previously reported experimental observation. Additionally, by introducing interlocked structure, the composites show strong direction-dependent damping behaviors, and the enhanced loss modulus is observed both in longitudinal and normal direction. The findings are not only expected to achieve a deep understanding of the dynamic energy dissipation mechanism of nacre, but also to provide a guideline for design of bio-inspired composites responding to shock loads.

Real-Time Compact Environment Representation for UAV Navigation.

Recently, unmanned aerial vehicles (UAVs) have attracted much attention due to their on-demand deployment, high mobility, and low cost. For UAVs navigating in an unknown environment, efficient environment representation is needed due to the storage limitation of the UAVs. Nonetheless, building an accurate and compact environment representation model is highly non-trivial because of the unknown shape of the obstacles and the time-consuming operations such as finding and eliminating the environmental details. To overcome these challenges, a novel vertical strip extraction algorithm is proposed to analyze the probability density function characteristics of the normalized disparity value and segment the obstacles through an adaptive size sliding window. In addition, a plane adjustment algorithm is proposed to represent the obstacle surfaces as polygonal prism profiles while minimizing the redundant obstacle information. By combining these two proposed algorithms, the depth sensor data can be converted into the multi-layer polygonal prism models in real time. Besides, a drone platform equipped with a depth sensor is developed to build the compact environment representation models in the real world. Experimental results demonstrate that the proposed scheme achieves better performance in terms of precision and storage as compared to the baseline.

Is Disposal of Unused Pharmaceuticals as Municipal Solid Waste by Landfilling a Good Option? A Case Study in China.

Properly disposing of unused pharmaceuticals is essential to minimize emissions of active pharmaceutical ingredients (APIs). The aim of this study was to determine whether disposing of unused pharmaceuticals in household solid waste is a cost-effective way of attenuating pharmaceutical emissions. We calculated attenuation rates (ARs) for unused pharmaceuticals by performing mass balance calculations for disposal to landfill. The results indicated that the average ARs for disposal as household solid waste reached 63% to 100% for our investigated pharmaceuticals at the worst scenario, indicating that disposal as household solid waste strongly attenuated emissions of APIs. Disposing of unused pharmaceuticals as household solid waste could be a cost-effective disposal method from the view of reducing APIs emission, but should be used with caution.

Identifying targets of potential concern by a screening level ecological risk assessment of human use pharmaceuticals in China.

The pollution of pharmaceuticals has attracted a lot of concerns during recent years. The goal of this study was to identify targets of ecological concern considering human use pharmaceuticals marketed in China. We constructed a database for 593 active pharmaceutical ingredients (APIs) by collecting their information on use and emission (e.g. production, human excretion, and removal by wastewater treatment) to calculated predicted environmental concentrations (PECs) by using an adapted European Medicines Agency method. PECs were comparable to the reported measurements for most APIs, demonstrating that the adapted method is reliable for the prioritization practice. Then PECs were compared to toxicity thresholds of three aquatic taxa (algae, daphnia, and fish). As a result, a total of 31 APIs, which were potentially risky and should be taken into consideration in future studies, were identified. Three APIs would pose a high risk with risk quotient (RQ) greater than 10. Six APIs were identified with moderate risks (1 < RQ < 10), and four of them were not reported before: rifaximin, griseofulvin, amikacin, and niclosamide. Of the 22 APIs with low risks (0.1 < RQ < 1), 17 have never been monitored previously in China and even worldwide. This study has yielded some probable antibiotics that should be considered as monitoring targets in China in the future.