Multi-Condition Latent Diffusion Network for Scene-Aware Neural Human Motion Prediction.

Inferring 3D human motion is fundamental in many applications, including understanding human activity and analyzing one's intention. While many fruitful efforts have been made to human motion prediction, most approaches focus on pose-driven prediction and inferring human motion in isolation from the contextual environment, thus leaving the body location movement in the scene behind. However, real-world human movements are goal-directed and highly influenced by the spatial layout of their surrounding scenes. In this paper, instead of planning future human motion in a "dark" room, we propose a Multi-Condition Latent Diffusion network (MCLD) that reformulates the human motion prediction task as a multi-condition joint inference problem based on the given historical 3D body motion and the current 3D scene contexts. Specifically, instead of directly modeling joint distribution over the raw motion sequences, MCLD performs a conditional diffusion process within the latent embedding space, characterizing the cross-modal mapping from the past body movement and current scene context condition embeddings to the future human motion embedding. Extensive experiments on large-scale human motion prediction datasets demonstrate that our MCLD achieves significant improvements over the state-of-the-art methods on both realistic and diverse predictions.

Identifying the twist factor of twisted partially coherent optical beams.

Twisted partially coherent light, characterized by its unique twist factor, offers novel control over the statistical properties of random light. However, the recognition of the twist factor remains a challenge due to the low coherence and the stochastic nature of the optical beam. This paper introduces a method for the recognition of twisted partially coherent beams by utilizing a circular aperture at the source plane. This aperture produces a characteristic hollow intensity structure due to the twist phase. A deep learning model is then trained to identify the twist factor of these beams based on this signature. The model, while simple in structure, effectively eliminates the need for complex optimization layers, streamlining the recognition process. This approach offers a promising solution for enhancing the detection of twisted light and paves the way for future research in this field.

Programmable Singlet Oxygen Battery for Automated Photodynamic Therapy Enabled by Pyridone-Pyridine Tautomer Engineering.

The efficacy of photodynamic therapy is hindered by the hypoxic environment in tumors and limited light penetration depth. The singlet oxygen battery (SOB) has emerged as a promising solution, enabling oxygen- and light-independent 1O2 release. However, conventional SOB systems typically exhibit an "always-ON" 1O2 release, leading to potential 1O2 leakage before and after treatment. This not only compromises therapeutic outcomes but also raises substantial biosafety concerns. In this work, we introduce a programmable singlet oxygen battery, engineered to address all the issues discussed above. The concept is illustrated through the development of a tumor-microenvironment-responsive pyridone-pyridine switch, PyAce, which exists in two tautomeric forms: PyAce-0 (pyridine) and PyAce (pyridone) with different 1O2 storage half-lives. In its native state, PyAce remains in the pyridone form, capable of storing 1O2 (t1/2 = 18.5 h). Upon reaching the tumor microenvironment, PyAce is switched to the pyridine form, facilitating rapid and thorough 1O2 release (t1/2 = 16 min), followed by quenched 1O2 release post-therapy. This mechanism ensures suppressed 1O2 production pre- and post-therapy with selective and rapid 1O2 release at the tumor site, maximizing therapeutic efficacy while minimizing side effects. The achieved "OFF-ON-OFF" 1O2 therapy showed high spatiotemporal selectivity and was independent of the oxygen supply and light illumination.

Efficient extraction of pectic polysaccharides from thinned unripe kiwifruits by deep eutectic solvent-based methods: Chemical structures and bioactivities.

To promote the potentially industrial applications of thinned unripe kiwifruits, two deep eutectic solvent-based methods, including deep eutectic solvent-assisted extraction (DAE) and microwave-assisted deep eutectic solvent extraction (MDE), were optimized for the extraction of polysaccharides from thinned unripe kiwifruits (YKP). Results showed that the yields of YKP-D prepared by DAE and YKP-DM prepared by MDE were extremely higher than YKP-H prepared by hot water extraction. Furthermore, YKP-H, YKP-D, and YKP-DM were mainly composed of pectic polysaccharides, including homogalacturonan (HG) and rhamnogalacturonan I (RG I) domains. Besides, both YKP-D and YKP-DM exhibited stronger antioxidant, anti-glycosylation, and immunomodulatory effects than those of YKP-H, and their higher contents of uronic acids and bound polyphenols as well as lower molecular weights could partially contribute to their bioactivities. Overall, these results revealed that the developed MDE method could be utilized as a promising method for highly efficient extraction of YKP with superior beneficial effects.

Investigation of patients’preferences regarding therapeutic drug monitoring of mycophenolic acid in solid organ transplantation / 中国药房

OBJECTIVE To investigate the preferences of patients who underwent solid organ transplantation regarding therapeutic drug monitoring （TDM） of mycophenolic acid （MPA） and explore the factors influencing patients’ decision-making process， so as to provide support for the development of individualized medication guidelines for MPA and improvement of clinical decision-making. METHODS The cross-sectional study was used to design the questionnaire on the patients’ preferences to accept MPA TDM， and involved patients who underwent solid organ transplantation and received MPA treatment at two tertiary hospitals in Beijing from April 14， 2022， to June 27， 2022. The Likert 5-level scoring method was used to score the patients’ preferences to accept MPA TDM， the influencing factors and their correlation of the patients’ preferences to accept MPA TDM were analyzed by Pearson correlation analysis and binary Logistic regression analysis， and the nonparametric test and chi-square test were used to rank and analyze the consistency of the factors affecting patients’ preference decision. RESULTS A total of 140 questionnaires were collected， and the effective recovery rate was 77.35%. The average preference score of 140 patients to receive MPA TDM was （4.01±0.65）， and the overall preference value was high. There were 116 （82.86%） patients agreed or strongly agreed with MPA TDM. Significant differences were observed in preference scores between patients who had previously undergone MPA TDM and those who had never undergone it （[ 4.30±0.53） scores vs. （3.80±0.65） scores， P＜0.001]. Additionally， patients’ preference scores were significantly influenced by their understanding level and attention level （P＜0.001）. The ranking of factors contributing to decision-making exhibited consistency （P＜0.001）. The factors were ranked in descending order of clinical efficacy， safety， comfortability， economy and time cost. CONCLUSIONS The patients who underwent solid organ transplantation hold high preferences towards MPA TDM. The primary factors influencing their decisions are their prior experience， understanding level， and attention level.

Efficient estimation for large-scale linkage disequilibrium patterns of the human genome.

In this study, we proposed an efficient algorithm (X-LD) for estimating linkage disequilibrium (LD) patterns for a genomic grid, which can be of inter-chromosomal scale or of small segments. Compared with conventional methods, the proposed method was significantly faster, dropped from O(nm2) to O(n2m)-n the sample size and m the number of SNPs, and consequently we were permitted to explore in depth unknown or reveal long-anticipated LD features of the human genome. Having applied the algorithm for 1000 Genome Project (1KG), we found (1) the extended LD, driven by population structure, universally existed, and the strength of inter-chromosomal LD was about 10% of their respective intra-chromosomal LD in relatively homogeneous cohorts, such as FIN, and to nearly 56% in admixed cohort, such as ASW. (2) After splitting each chromosome into upmost of more than a half million grids, we elucidated the LD of the HLA region was nearly 42 folders higher than chromosome 6 in CEU and 11.58 in ASW; on chromosome 11, we observed that the LD of its centromere was nearly 94.05 folders higher than chromosome 11 in YRI and 42.73 in ASW. (3) We uncovered the long-anticipated inversely proportional linear relationship between the length of a chromosome and the strength of chromosomal LD, and their Pearson's correlation was on average over 0.80 for 26 1KG cohorts. However, this linear norm was so far perturbed by chromosome 11 given its more completely sequenced centromere region. Uniquely chromosome 8 of ASW was found most deviated from the linear norm than any other autosomes. The proposed algorithm has been realized in C++ (called X-LD) and is available at https://github.com/gc5k/gear2, and can be applied to explore LD features in any sequenced populations.

Partially coherent twisted vector vortex beam enabling manipulation of high-dimensional classical entanglement.

In this paper, we present a novel form of a partially coherent beam characterized by classical entanglement in higher dimensions. We coin the term "twisted vector vortex (TVV) beam" to describe this phenomenon. Similar to multi-partite quantum entangled states in higher dimensions, the partially coherent twisted vector vortex beam possesses distinct properties such as non-uniform polarization, vortex phase, and twist phase. Through experiments, we offer empirical evidence for these three degrees-of-freedom in the light field. The results demonstrate that the state of the light is inseparable in terms of polarization and orbital angular momentum (OAM) modes. Additionally, the twist phase introduces an additional dimension in controlling the vector vortex beam. This research reveals the possibility of new controlling dimensions in classical entanglement through the chirality of coherence within partially coherent light. Consequently, this opens up new avenues for the utilization of partially coherent light in both classical and quantum domains.

Specific gastrointestinal microbiota profiles in Chinese Tan sheep are associated with lauric acid content in muscle.

The biological mechanisms underlying meat quality remain unclear. Currently, many studies report that the gastrointestinal microbiota is essential for animal growth and performance. However, it is uncertain which bacterial species are specifically associated with the meat quality traits. In this study, 16S rDNA and metagenomic sequencing were performed to explore the composition and function of microbes in various gastrointestinal segments of Tan sheep and Dorper sheep, as well as the relationship between microbiota and meat quality (specifically, the fatty acid content of the muscle). In the ruminal, duodenal, and colonic microbiome, several bacteria were uniquely identified in respective breeds, including Agrobacterium tumefaciens, Bacteroidales bacterium CF, and several members of the family Oscillospiraceae. The annotation of GO, KEGG, and CAZYme revealed that these different bacterial species were linked to the metabolism of glucose, lipids, and amino acids. Additionally, our findings suggested that 16 microbial species may be essential to the content of fatty acids in the muscle, especially C12:0 (lauric acid). 4 bacterial species, including Achromobacter xylosoxidans, Mageeibacillus indolicus, and Mycobacterium dioxanotrophicus, were positively correlated with C12:0, while 13 bacteria, including Methanobrevibacter millerae, Bacteroidales bacterium CF, and Bacteroides coprosuis were negatively correlated with C12:0. In a word, this study provides a basic data for better understanding the interaction between ruminant gastrointestinal microorganisms and the meat quality traits of hosts.

Icaritin ameliorates RANKL-mediated osteoclastogenesis and ovariectomy-induced osteoporosis.

A rapidly aging society and longer life expectancy are causing osteoporosis to become a global epidemic. Over the last five decades, a number of drugs aimed at reducing bone resorption or restoring bone mass have been developed, but their efficacy and safety are limited. Icaritin (ICT) is a natural compound extracted from anti-osteoporosis herb Epimedium spp. and has been shown to inhibit osteoclast differentiation. However, the molecular mechanism by which ICT weaken RANKL-induced osteoclast differentiation has not been completely investigated. Here, we evaluated the anti-osteoclastogenic effect of ICT in vitro and the potential drug candidate for treating osteoporosis in vivo. In vitro study, ICT was found to inhibit osteoclast formation and bone resorption function via downregulating transcription factors activated T cell cytoplasm 1 (NFATc1) and c-fos, which further downregulate osteoclastogenesis-specific gene. In addition, the enhanced mitochondrial mass and function required for osteoclast differentiation was mitigated by ICT. The histomorphological results from an in vivo study showed that ICT attenuated the bone loss associated with ovariectomy (OVX). Based on these results, we propose ICT as a promising new drug strategy for osteoporosis that inhibits osteoclast differentiation.

Myeloid sarcoma as the only manifestation in a rare mixed lineage leukemia-fusion-driven acute myeloid leukemia: A case report.

BACKGROUND: The mixed lineage leukemia (MLL)-eleven-nineteen lysine-rich leukemia (ELL) fusion gene is a rare occurrence among the various MLL fusion genes. We present the first case in which myeloid sarcoma (MS) was the only manifestation of adult MLL-ELL-positive acute myeloid leukemia (AML). CASE SUMMARY: We report a case of a 33-year-old male patient who was admitted in June 2022 with a right occipital area mass measuring approximately 7 cm × 8 cm. Blood work was normal. The patient underwent right occipital giant subscalp mass excision and incisional flap grafting. Immunohistochemistry was positive for myeloperoxidase, CD43 and CD45 and negative for CD3, CD20, CD34, and CD56. The bone marrow aspirate showed hypercellularity with 20% myeloblasts. Flow cytometry showed that myeloblasts accounted for 27.21% of the nucleated cells, which expressed CD33, CD38, and CD117. The karyotype was 46, XY, t (11, 19) (q23; p13.1), -12, + mar/46, XY. Next-generation sequencing showed a fusion of MLL exon 7 to exon 2 of ELL. A diagnosis of MLL-ELL-positive AML (M2 subtype) with subcutaneous MS was made. CONCLUSION: MLL-ELL-positive AML with MS is a rare clinical entity. Additional research is needed to elucidate the molecular mechanisms of the pathogenesis of MS.

KNOTTED1-like homeobox (KNOX) transcription factors - Hubs in a plethora of networks: A review.

KNOX (KNOTTED1-like HOMEOBOX) belongs to a class of important homeobox genes, which encode the homeodomain proteins binding to the specific element of target genes, and widely participate in plant development. Advancements in genetics and molecular biology research generate a large amount of information about KNOX genes in model and non-model plants, and their functions in different developmental backgrounds are gradually becoming clear. In this review, we summarize the known and presumed functions of the KNOX gene in plants, focusing on horticultural plants and crops. The classification and structural characteristics, expression characteristics and regulation, interacting protein factors, functions, and mechanisms of KNOX genes are systematically described. Further, the current research gaps and perspectives were discussed. These comprehensive data can provide a reference for the directional improvement of agronomic traits through KNOX gene regulation.

Exploring the Robustness of Human Parsers Toward Common Corruptions.

Human parsing aims to segment each pixel of the human image with fine-grained semantic categories. However, current human parsers trained with clean data are easily confused by numerous image corruptions such as blur and noise. To improve the robustness of human parsers, in this paper, we construct three corruption robustness benchmarks, termed LIP-C, ATR-C, and Pascal-Person-Part-C, to assist us in evaluating the risk tolerance of human parsing models. Inspired by the data augmentation strategy, we propose a novel heterogeneous augmentation-enhanced mechanism to bolster robustness under commonly corrupted conditions. Specifically, two types of data augmentations from different views, i.e., image-aware augmentation and model-aware image-to-image transformation, are integrated in a sequential manner for adapting to unforeseen image corruptions. The image-aware augmentation can enrich the high diversity of training images with the help of common image operations. The model-aware augmentation strategy that improves the diversity of input data by considering the model's randomness. The proposed method is model-agnostic, and it can plug and play into arbitrary state-of-the-art human parsing frameworks. The experimental results show that the proposed method demonstrates good universality which can improve the robustness of the human parsing models and even the semantic segmentation models when facing various image common corruptions. Meanwhile, it can still obtain approximate performance on clean data.

Adversarial Dense Contrastive Learning for Semi-Supervised Semantic Segmentation.

Semi-supervised dense prediction tasks, such as semantic segmentation, can be greatly improved through the use of contrastive learning. However, this approach presents two key challenges: selecting informative negative samples from a highly redundant pool and implementing effective data augmentation. To address these challenges, we present an adversarial contrastive learning method specifically for semi-supervised semantic segmentation. Direct learning of adversarial negatives is adopted to retain discriminative information from the past, leading to higher learning efficiency. Our approach also leverages an advanced data augmentation strategy called AdverseMix, which combines information from under-performing classes to generate more diverse and challenging samples. Additionally, we use auxiliary labels and classifiers to prevent over-adversarial negatives from affecting the learning process. Our experiments on the Pascal VOC and Cityscapes datasets demonstrate that our method outperforms the state-of-the-art by a significant margin, even when using a small fraction of labeled data.

Additional cytogenetic abnormalities in patients with newly diagnosed acute promyelocytic leukemia predict inferior event-free survival.

BACKGROUND: The innovative combination of all-trans retinoic acid (ATRA) and arsenic trioxide (ATO) has established a new chapter of curative approach in acute promyelocytic leukemia (APL). The disease characteristics and prognostic influence of additional cytogenetic abnormalities (ACA) in APL with modern therapeutic strategy need to be elucidated. METHODS: In the present study, we retrospectively investigated disease features and prognostic power of ACA in 171 APL patients treated with ATRA-ATO-containing regimens. RESULTS: Patients with ACA had markedly decreased hemoglobin levels than that without ACA (p = 0.021). Risk stratification in the ACA group was significantly worse than that in the non-ACA group (p = 0.032). With a median follow-up period of 62.0 months, worse event-free survival (EFS) was demonstrated in patients harboring ACA. Multivariate analysis showed that ACA was an independent adverse factor for EFS (p = 0.033). By further subgroup analysis, in CD34 and CD56 negative APL, patients harboring ACA had inferior EFS (p = 0.017; p = 0.037). CONCLUSIONS: To sum up, ACA remains the independent prognostic value for EFS, we should build risk-adapted therapeutic strategies in the long-term management of APL when such abnormalities are detected.

[Neuroprotective effect of tetramethylpyrazine on mice after spinal cord injury].

This study aims to investigate the neuroprotective effect of tetramethylpyrazine on mice after spinal cord injury and its mechanism. Seventy-five female C57BL/6 mice were randomly divided into 5 groups, namely, a sham operation group, a model group, a tetramethylpyrazine low-dose group(25 mg·kg~(-1)), a tetramethylpyrazine medium-dose group(50 mg·kg~(-1)), and a tetramethylpyrazine high-dose group(100 mg·kg~(-1)), with 15 mice in each group. Modified Rivlin method was used to establish the mouse model of acute spinal cord injury. After 14 d of tetramethylpyrazine intervention, the motor function of hind limbs of mice was evaluated by basso mouse scale(BMS) and inclined plate test. The levels of inflammatory cytokines tumor necrosis factor-α(TNF-α), interleukin-6(IL-6), and interleukin-1ß(IL-1ß) in the spinal cord homogenate were determined by enzyme-linked immunosorbent assay(ELISA). Hematoxylin-eosin(HE) staining was used to observe the histology of the spinal cord, and Nissl's staining was used to observe the changes in the number of neurons. Western blot and immunofluorescence method were used to detect the expression of glial fibrillary acidic protein(GFAP) and C3 protein. Tetramethylpyrazine significantly improved the motor function of the hind limbs of mice after spinal cord injury, and the BMS score and inclined plate test score of the tetramethylpyrazine high-dose group were significantly higher than those of the model group(P<0.01). The levels of TNF-α, IL-6, and IL-1ß in spinal cord homogenate of the tetramethylpyrazine high-dose group were significantly decreased(P<0.01). After tetramethylpyrazine treatment, the spinal cord morphology recovered, the number of Nissl bodies increased obviously with regular shape, and the loss of neurons decreased. As compared with the model group, the expression of GFAP and C3 protein was significantly decreased(P<0.05,P<0.01) in tetramethylpyrazine high-dose group. In conclusion, tetramethylpyrazine can promote the improvement of motor function and play a neuroprotective role in mice after spinal cord injury, and its mechanism may be related to inhibiting inflammatory response and improving the hyperplasia of glial scar.

Structural Design of Intelligent Reversible Two-Way Structural Color Films.

Structural color always shows a reversible switch between reflection and transmission states when viewed from different angles, attracting increasing attention in display applications. However, this switching between reflection and transmission states of structural color suffers from the inherent lack of autonomous regulation, which is unmanageable in the case of different application scenarios. Here, we design an intelligent two-way structural color film which can reversibly change its color when applied with an extra stimulation such as voltage, heat signal, or light. A special structural feature contains a traditional photonic crystal film of polystyrene (PS) microspheres assembled by smart windows. Remarkably, our structural color film shows a prominent polarization sensitivity, and the angle dependence of the structural color broadens the gamut of display color demonstrated by both finite element theoretical analysis and experimental observation. Prospectively, this hierarchically designed film provides a promising pathway toward next-generation multicolor displays and smart windows.

Nanowire-based smart windows combining electro- and thermochromics for dynamic regulation of solar radiation.

Smart window is an attractive option for efficient heat management to minimize energy consumption and improve indoor living comfort owing to their optical properties of adjusting sunlight. To effectively improve the sunlight modulation and heat management capability of smart windows, here, we propose a co-assembly strategy to fabricate the electrochromic and thermochromic smart windows with tunable components and ordered structures for the dynamic regulation of solar radiation. Firstly, to enhance both illumination and cooling efficiency in electrochromic windows, the aspect ratio and mixed type of Au nanorods are tuned to selectively absorb the near-infrared wavelength range of 760 to 1360 nm. Furthermore, when assembled with electrochromic W18O49 nanowires in the colored state, the Au nanorods exhibit a synergistic effect, resulting in a 90% reduction of near-infrared light and a corresponding 5 °C cooling effect under 1-sun irradiation. Secondly, to extend the fixed response temperature value to a wider range of 30-50 °C in thermochromic windows, the doping amount and mixed type of W-VO2 nanowires are carefully regulated. Last but not the least, the ordered assembly structure of the nanowires can greatly reduce the level of haze and enhance visibility in the windows.

Natural products for treatment of premature ovarian failure: a narrative review.

Premature ovarian failure (POF) is a female reproductive system disease caused by many factors and systems, which has seriously affected the quality of life of women of childbearing age. Clinically, the disease is difficult to treat while its incidence rate shows an increasing trend. In recent years, natural products used as multi-pathway, multi-target and efficient drugs, have become the focus of many research and clinical studies in China and abroad, and the effect of phytochemicals derived from edible plants and Chinese medicine herbs on POF were investigated in several papers. Using "premature ovarian failure" or "ovary" and related natural products as keywords, we retrieved and reviewed research articles from China National Knowledge Infrastructure Database, Wanfang, PubMed, Web of Science and other literature databases. Up to October, 2021, natural compounds with prophylactic or interference inhibition effects on POF mainly included flavonoids, polysaccharides, saponins, and polyphenols. Their effect on POF and ovarian function was closely related to their antioxidant, antiapoptotic, antiaging, immunoregulatory and estrogen-like activities.

CTCNet: A CNN-Transformer Cooperation Network for Face Image Super-Resolution.

Recently, deep convolution neural networks (CNNs) steered face super-resolution methods have achieved great progress in restoring degraded facial details by joint training with facial priors. However, these methods have some obvious limitations. On the one hand, multi-task joint learning requires additional marking on the dataset, and the introduced prior network will significantly increase the computational cost of the model. On the other hand, the limited receptive field of CNN will reduce the fidelity and naturalness of the reconstructed facial images, resulting in suboptimal reconstructed images. In this work, we propose an efficient CNN-Transformer Cooperation Network (CTCNet) for face super-resolution tasks, which uses the multi-scale connected encoder-decoder architecture as the backbone. Specifically, we first devise a novel Local-Global Feature Cooperation Module (LGCM), which is composed of a Facial Structure Attention Unit (FSAU) and a Transformer block, to promote the consistency of local facial detail and global facial structure restoration simultaneously. Then, we design an efficient Feature Refinement Module (FRM) to enhance the encoded features. Finally, to further improve the restoration of fine facial details, we present a Multi-scale Feature Fusion Unit (MFFU) to adaptively fuse the features from different stages in the encoder procedure. Extensive evaluations on various datasets have assessed that the proposed CTCNet can outperform other state-of-the-art methods significantly. Source code will be available at https://github.com/IVIPLab/CTCNet.

CaCo: Both Positive and Negative Samples are Directly Learnable via Cooperative-Adversarial Contrastive Learning.

As a representative self-supervised method, contrastive learning has achieved great successes in unsupervised training of representations. It trains an encoder by distinguishing positive samples from negative ones given query anchors. These positive and negative samples play critical roles in defining the objective to learn the discriminative encoder, avoiding it from learning trivial features. While existing methods heuristically choose these samples, we present a principled method where both positive and negative samples are directly learnable end-to-end with the encoder. We show that the positive and negative samples can be cooperatively and adversarially learned by minimizing and maximizing the contrastive loss, respectively. This yields cooperative positives and adversarial negatives with respect to the encoder, which are updated to continuously track the learned representation of the query anchors over mini-batches. The proposed method achieves 71.3% and 75.3% in top-1 accuracy respectively over 200 and 800 epochs of pre-training ResNet-50 backbone on ImageNet1K without tricks such as multi-crop or stronger augmentations. With Multi-Crop, it can be further boosted into 75.7%. The source code and pre-trained model are released in https://github.com/maple-research-lab/caco.