Multifocal multiview imaging and data compression based on angular-focal-spatial representation.

Multifocal multiview (MFMV) is an emerging high-dimensional optical data that allows to record richer scene information but yields huge volumes of data. To unveil its imaging mechanism, we present an angular-focal-spatial representation model, which decomposes high-dimensional MFMV data into angular, spatial, and focal dimensions. To construct a comprehensive MFMV dataset, we leverage representative imaging prototypes, including digital camera imaging, emerging plenoptic refocusing, and synthesized Blender 3D creation. It is believed to be the first-of-its-kind MFMV dataset in multiple acquisition ways. To efficiently compress MFMV data, we propose the first, to our knowledge, MFMV data compression scheme based on angular-focal-spatial representation. It exploits inter-view, inter-stack, and intra-frame predictions to eliminate data redundancy in angular, focal, and spatial dimensions, respectively. Experiments demonstrate the proposed scheme outperforms the standard HEVC and MV-HEVC coding methods. As high as 3.693 dB PSNR gains and 64.22% bitrate savings can be achieved.

Disturbed hippocampal histidine metabolism contributes to cognitive impairment induced by recurrent nonsevere hypoglycemia in diabetes.

Hypoglycemia is a common adverse reaction to glucose-lowering treatment. Diabetes mellitus (DM) combined with recurrent nonsevere hypoglycemia (RH) can accelerate cognitive decline. Currently, the metabolic pattern changes in cognition-related brain regions caused by this combined effect of DM and RH (DR) remain unclear. In this study, we first characterized the metabolic profiles of the hippocampus in mice exposed to DR using non-targeted metabolomic platforms. Our results showed that DR induced a unique metabolic pattern in the hippocampus, and several significant differences in metabolite levels belonging to the histidine metabolism pathway were discovered. Based on these findings, in the follow-up experiment, we found that histidine treatment could attenuate the cognitive impairment and rescue the neuronal and synaptic damage induced by DR in the hippocampus, which are closely related to ameliorated mitochondrial injury. These findings provide new insights into the metabolic mechanisms of the hippocampus in the progression of DR, and l-histidine supplementation may be a potential metabolic therapy in the future.

End-to-end varifocal multiview images coding framework from data acquisition end to vision application end.

The emerging data, varifocal multiview (VFMV) has an exciting prospect in immersive multimedia. However, the distinctive data redundancy of VFMV derived from dense arrangements and blurriness differences among views causes difficulty in data compression. In this paper, we propose an end-to-end coding scheme for VFMV images, which provides a new paradigm for VFMV compression from data acquisition (source) end to vision application end. VFMV acquisition is first conducted in three ways at the source end, including conventional imaging, plenoptic refocusing, and 3D creation. The acquired VFMV has irregular focusing distributions due to varying focal planes, which decreases the similarity among adjacent views. To improve the similarity and the consequent coding efficiency, we rearrange the irregular focusing distributions in descending order and accordingly reorder the horizontal views. Then, the reordered VFMV images are scanned and concatenated as video sequences. We propose 4-directional prediction (4DP) to compress the reordered VFMV video sequences. Four most similar adjacent views from the left, upper left, upper and upper right directions serve as reference frames to improve the prediction efficiency. Finally, the compressed VFMV is transmitted and decoded at the application end, benefiting potential vision applications. Extensive experiments demonstrate that the proposed coding scheme is superior to the comparison scheme in objective quality, subjective quality and computational complexity. Experiments on new view synthesis show that VFMV can achieve extended depth of field than conventional multiview at the application end. Validation experiments show the effectiveness of view reordering, the advantage over typical MV-HEVC, and the flexibility on other data types, respectively.

High dimensional optical data - varifocal multiview imaging, compression and evaluation.

Varifocal multiview (VFMV) is an emerging high-dimensional optical data in computational imaging and displays. It describes scenes in angular, spatial, and focal dimensions, whose complex imaging conditions involve dense viewpoints, high spatial resolutions, and variable focal planes, resulting in difficulties in data compression. In this paper, we propose an efficient VFMV compression scheme based on view mountain-shape rearrangement (VMSR) and all-directional prediction structure (ADPS). The VMSR rearranges the irregular VFMV to form a new regular VFMV with mountain-shape focusing distributions. This special rearrangement features prominently in enhancing inter-view correlations by smoothing focusing status changes and moderating view displacements. Then, the ADPS efficiently compresses the rearranged VFMV by exploiting the enhanced correlations. It conducts row-wise hierarchy divisions and creates prediction dependencies among views. The closest adjacent views from all directions serve as reference frames to improve the prediction efficiency. Extensive experiments demonstrate the proposed scheme outperforms comparison schemes by quantitative, qualitative, complexity, and forgery protection evaluations. As high as 3.17 dB gains of peak signal-to-noise ratio (PSNR) and 61.1% bitrate savings can be obtained, achieving the state-of-the-art compression performance. VFMV is also validated could serve as a novel secure imaging format protecting optical data against the forgery of large models.

Enhancing Molecular Representations Via Graph Transformation Layers.

Molecular representation learning is an essential component of many molecule-oriented tasks, such as molecular property prediction and molecule generation. In recent years, graph neural networks (GNNs) have shown great promise in this area, representing a molecule as a graph composed of nodes and edges. There are increasing studies showing that coarse-grained or multiview molecular graphs are important for molecular representation learning. Most of their models, however, are too complex and lack flexibility in learning different granular information for different tasks. Here, we proposed a flexible and simple graph transformation layer (i.e., LineEvo), a plug-and-use module for GNNs, which enables molecular representation learning from multiple perspectives. The LineEvo layer transforms fine-grained molecular graphs into coarse-grained ones based on the line graph transformation strategy. Especially, it treats the edges as nodes and generates the new connected edges, atom features, and atom positions. By stacking LineEvo layers, GNNs can learn multilevel information, from atom-level to triple-atoms level and coarser level. Experimental results show that the LineEvo layers can improve the performance of traditional GNNs on molecular property prediction benchmarks on average by 7%. Additionally, we show that the LineEvo layers can help GNNs have more expressive power than the Weisfeiler-Lehman graph isomorphism test.

Gradient Boosted Machine Learning Model to Predict H2, CH4, and CO2 Uptake in Metal-Organic Frameworks Using Experimental Data.

Predictive screening of metal-organic framework (MOF) materials for their gas uptake properties has been previously limited by using data from a range of simulated sources, meaning the final predictions are dependent on the performance of these original models. In this work, experimental gas uptake data has been used to create a Gradient Boosted Tree model for the prediction of H2, CH4, and CO2 uptake over a range of temperatures and pressures in MOF materials. The descriptors used in this database were obtained from the literature, with no computational modeling needed. This model was repeated 10 times, showing an average R2 of 0.86 and a mean absolute error (MAE) of ±2.88 wt % across the runs. This model will provide gas uptake predictions for a range of gases, temperatures, and pressures as a one-stop solution, with the data provided being based on previous experimental observations in the literature, rather than simulations, which may differ from their real-world results. The objective of this work is to create a machine learning model for the inference of gas uptake in MOFs. The basis of model development is experimental as opposed to simulated data to realize its applications by practitioners. The real-world nature of this research materializes in a focus on the application of algorithms as opposed to the detailed assessment of the algorithms.

Lifestyle is associated with thyroid function in subclinical hypothyroidism: a cross-sectional study.

BACKGROUND: Few studies have focused on the association between lifestyle and subclinical hypothyroidism (SCH). The purpose of this study was to investigate the association between lifestyle and thyroid function in SCH. METHODS: This study was a part of a community-based and cross-sectional study, the Epidemiological Survey of Thyroid Diseases in Fujian Province, China. A total of 159 participants with SCH (81 males and 78 females) and 159 euthyroid (87 males and 72 females) participants without any missing data were included in the analysis. General information and lifestyle information including sleep, exercise, diet and smoking habits of the participants was collected by questionnaire and Pittsburgh sleep quality index scale (PSQI) was collected. Thyroid stimulating hormone (TSH), free thyroxine (FT4), thyroid peroxidase antibody (TPOAb), thyroid globulin antibody (TgAb) and urine iodine concentration (UIC) were tested. Thyroid homeostasis parameter thyroid' s secretory capacity (SPINA-GT), Jostel's TSH index (TSHI), thyrotroph T4 sensitivity index (TTSI) were calculated. Logistic regression and multiple linear regression were performed to assess associations. RESULTS: Compared with euthyroid subjects, patients with SCH were more likely to have poor overall sleep quality (15.1 vs.25.8 %, P = 0.018) and l less likely to stay up late on weekdays (54.7 vs. 23.9 % P < 0.001). In SCH group, exercise was the influencing factor of TSH (ß= -0.224, P = 0.004), thyroid secretory capacity (ß = 0.244, P = 0.006) and thyrotropin resistance (ß = 0.206, P = 0.009). Iodine excess was the influencing factor of thyroid secretory capacity (ß = 0.209, P = 0.001) and pituitary thyroid stimulating function (ß = 0.167, P = 0.034). Smoking was the influencing factor of pituitary thyroid stimulating function (ß = 0.161, P = 0.040). Staying up late on weekends was the influencing factor of thyroid secretory capacity (ß = 0.151, P = 0.047). After adjusting for possible confounders, logistic regression showed that those with poor overall sleep quality assessed by PSQI and iodine excess had an increased risk of SCH (OR 2.159, 95 %CI 1.186-3.928, P = 0.012 and OR 2.119, 95 %CI 1.008-4.456, P = 0.048, respectively). CONCLUSIONS: Lifestyle including sleep, smoking, diet and exercise was closely related to thyroid function especially thyroid homeostasis in SCH.

Contrasting impacts of mobilisation and immobilisation amendments on soil health and heavy metal transfer to food chain.

Heavy metal mobilisation or immobilisation have been widely applied in situ for soil remediation. However, the consequences of the mobilisation or immobilisation amendments on soil health and heavy metal transfer are rarely compared. In this study, four mobilisation additives (EDTA, humic acid, oxalic acid and citric acid) and four immobilisation additives (calcium silicate, lime, biochar and pig manure) were applied in soils contaminated with Cd, Zn, and Pb to investigate their effects on soil microbial and nematode communities, chemical speciation of metals in Amaranthus tricolour L., and metal food chain transfer in soil-plant-insect system. We found that mobilisation amendments inhibited plant growth and EDTA reduced microbial biomass indicated by phospholipid fatty acids. In contrast, immobilisation amendments promoted plant growth. However, abundances of microbe and nematode were reduced by calcium silicate and lime, while they were substantially increased by biochar and pig manure. We also realised that the immobilisation amendments shifted the water-soluble and pectate-/protein-associated fractions to phosphate-/oxalate-associated fractions of metals in plant leaves, enhanced detoxification ability of Prodenia litura larvae, and reduced metal transfer along food chain. However, opposite changes were observed in mobilisation treatments. According to redundancy analysis, we found that the addition of biochar or pig manure improved soil health and function by reducing metal availability and increasing soil available N and P concentrations. Our results indicate that organic immobilisation amendments most effectively improve soil health and reduce metal transfer, and should be recommended for remediation of heavy metal-contaminated soils.

Block-wise focal stack image representation for end-to-end applications.

In optical imaging systems, the depth of field (DoF) is generally constricted due to the nature of optical lens. The limited DoF produces partially focused images of the scene. Focal stack images (FoSIs) are a sequence of images that focused on serial depths of a scene. FoSIs are capable of extending DoF of optical systems and provide practical solutions for computational photography, macroscopic and microscopic imaging, interactive and immersive media. However, high volumes of data remains one of the biggest obstacles to the development of end-to-end applications. In order to solve this challenge, we propose a block-wise Gaussian based representation model for FoSIs and utilize this model to solve the problem of coding, reconstruction and rendering for end-to-end applications. Experimental results demonstrate the high efficiency of proposed representation model and the superior performance of proposed schemes.

Enantioseparation of racemic amlodipine using immobilized ionic liquid by solid-phase extraction.

In this paper, a novel l-glutamate based immobilized chiral ionic liquid (SBA-IL (Glu)) was prepared by chemical bonding method and applied as a solid sorbent for chiral separation of amlodipine. The performance of SBA-IL (Glu) was investigated for the absorption of (S)-amlodipine and separation of amlodipine enantiomer. The static experiment showed that equilibrium adsorption was achieved within 80 minutes, and the saturation adsorptions capacity was 12 mg/g. The complex was then packed in a glass chromatographic column for the separation of amlodipine and the enantiomeric excess (%ee) of (S)-amlodipine reached 24.67%. The immobilized ionic liquids exhibit good reusability, and the separation efficiency remains 18.24% after reused five times, which allows potential scale-up for the chiral separation of amlodipine.

The effects of exogenous organic acids on the growth, photosynthesis and cellular ultrastructure of Salix variegata Franch. Under Cd stress.

We studied the effects of three organic acids (citric acid, tartaric acid and malic acid) on the biomass, photosynthetic pigment content and photosynthetic parameters of Salix variegata under Cd stress and observed the ultrastructure of mesophyll cells in each treatment. Cd stress significantly reduced photosynthesis by reducing the content of pigments and disrupting chloroplast structure, which consequently decreased the biomass. However, respective addition of three organic acids greatly increased the biomass of S. variegata under Cd stress. Among them, the effect of malic acid or tartaric acid on shoot and total biomass accumulation was greater than that of citric acid. The alleviation of biomass probably related with the photosynthetic process. Results revealed that treatment with each organic acid enhanced the net photosynthesis rate under Cd stress. Malic acid promoted plant growth and biomass by increasing the chlorophyll content and mitigating damage to the photosynthetic apparatus resulting from Cd stress. Tartaric acid had little impact on the photosynthetic pigment content, but it was important in mitigating the ultrastructural damage of plants caused by Cd. Addition of citric acid significantly increased the carotenoid as well as the number and volume of chloroplasts in mesophyll cells, while the mitigation of structural damage in the photosynthetic apparatus was weaker than that in tartaric acid or malic acid treatment. It is concluded that application of tartaric acid or malic acid is effective in increasing the growth potential of S. variegata under Cd stress and thus can be a promising approach for the phytoremediation of Cd-contaminated soil.

Enantioseparation of flurbiprofen enantiomers using chiral ionic liquids by liquid-liquid extraction.

Flurbiprofen is a kind of nonsteroidal anti-inflammatory drug, which has been widely used in clinic for treatment of rheumatoid arthritis and osteoarthritis. It has been reported that S-flurbiprofen shows good performance on clinic anti-inflammatory treatment, while R-enantiomer almost has no pharmacological activities. It has important practical values to obtain optically pure S-flurbiprofen. In this work, chiral ionic liquids, which have good structural designability and chiral recognize ability, were selected as the extraction selector by the assistance of quantum chemistry calculations. The distribution behaviors of flurbiprofen enantiomers were investigated in the extraction system, which was composed of organic solvent and aqueous phase containing chiral ionic liquid. The results show that maximum enantioselectivity up to 1.20 was attained at pH 2.0, 25°C using 1,2-dichloroethane as organic solvent, 1-butyl-3-methylimidazole L-tryptophan ([Bmim][L-trp]) as chiral selector. The racemic flurbiprofen initial concentration was 0.2 mmol L-1 , and [Bmim][L-trp] concentration was 0.02 mol L-1 . Furthermore, the recycle of chiral ionic liquids has been achieved by reverse extraction process of the aqueous phase with chiral selector, which is significant for industrial application of chiral ionic liquids and scale-up of the extraction process.

Responses of soil microbial community and enzymes during plant-assisted biodegradation of di-(2-ethylhexyl) phthalate and pyrene.

A pot experiment was conducted to explore the plant-assisted degradation efficiency of di-(2-ethylhexyl) phthalate (DEHP) and pyrene. Three plant species: Ceylon spinach, sunflower, and leaf mustard were cultivated in co-contaminated soils under three contamination levels: control (T0), 20 mg kg-1 (T20), and 50 mg kg-1 (T50). The results showed that a higher DEHP and pyrene degradation efficiency was observed evidently in planted cases, increasing from 42 to 53-59% (T0), 61 to 65-76% (T20) and 52 to 68-78% (T50) for DEHP, and from 22 to 30-49% (T0), 58 to 62-72% (T20), and 54 to 57-70% (T50) for pyrene. Under T20 contamination level, soil phospholipid fatty-acid analysis depicted the increased microbial biomass in rhizosphere, especially the arbuscular mycorrhizal fungus that is effective for the degradation of organic pollutants. The study also revealed that the activities of dehydrogenase, acid phosphomonoesterase, urease, and phenol oxidase negatively correlated with pollutant concentration. In general, the removal rate of DEHP and pyrene was highest in the soil planted with leaf mustard for each contamination level considered. For soils at T20 level, sunflower and leaf mustard appeared as interesting phytoremediation plants due to the improved removal rates of organic pollutants and the soil microbial activity.

Continuous synthesis of hollow silver-palladium nanoparticles for catalytic applications.

Hollow bimetallic nanoparticles exhibit unique surface plasmonic properties, enhanced catalytic activities and high photo-thermal conversion efficiencies amongst other properties, however, their research and further deployment are currently limited by their complicated multi-step syntheses. This paper presents a novel approach for their continuous synthesis with controllable and tuneable sizes and compositions. This robust manufacturing tool, consisting of coiled flow inverter (CFI) reactors connected in series, allows for the first time the temporal and spatial separation of the initial formation of silver seeds and their subsequent galvanic displacement reaction in the presence of a palladium precursor, leading to the full control of both steps separately. We have also demonstrated that coupling the galvanic replacement and co-reduction leads to a great kinetic enhancement of the system leading to a high yield process of hollow bimetallic nanoparticles, directly applicable to other metal combinations.

MiR-302c-3p regulates autophagy and apoptosis in ovarian granulosa cells via the LATS2/YAP axis in chickens.

The degenerative process of follicular atresia in hens naturally commences in granulosa cells, significantly impacting laying hens' reproductive performance. Past studies suggested that granulosa cell autophagy and apoptosis work together to cause follicular atresia. Recent research indicates that miRNA regulates granulosa autophagy and apoptosis, which contributes to the development of follicular atresia. However, the role of miR-302c-3p in follicular atresia and development remains unclear. In this study with the RNA-seq approach, we found that miR-302c-3p expression was significantly decreased in atrophic follicles, suggesting its involvement in the follicular atresia process. Following this, we performed in vitro studies to confirm that miR-302c-3p inhibits autophagy and apoptosis in chicken granulosa cells. Mechanistically, LATS2 is considered as the putative target gene of miR-302c-3p, and it has been demonstrated that LATS2 exerts a positive regulatory role in the modulation of autophagy and apoptosis in chicken granulosa cells. Furthermore, we verified the regulatory function of miR-302c-3p in chicken granulosa cells via the LATS2-YAP signaling pathway. Our results collectively demonstrates that miR-302c-3p targets LATS2 to modulate the YAP signaling pathway, impacting autophagy and apoptosis in granulosa cells leading to follicular atresia.

Mitophagy activation by rapamycin enhances mitochondrial function and cognition in 5×FAD mice.

Alzheimer's disease (AD) is the most prevalent form of dementia, characterized by severe mitochondrial dysfunction, which is an intracellular process that is significantly compromised in the early stages of AD. Mitophagy, the selective removal of damaged mitochondria, is a potential therapeutic strategy for AD. Rapamycin, a mammalian target of rapamycin (mTOR) inhibitor, augmented autophagy and mitigated cognitive impairment. Our study revealed that rapamycin enhances cognitive function by activating mitophagy, alleviating neuronal loss, and improving mitochondrial dysfunction in 5 ×FAD mice. Interestingly, the neuroprotective effect of rapamycin in AD were negated by treatment with 3-MA, a mitophagy inhibitor. Overall, our findings suggest that rapamycin ameliorates cognitive impairment in 5 ×FAD mice via mitophagy activation and its downstream PINK1-Parkin pathway, which aids in the clearance of amyloid-ß (Aß) and damaged mitochondria. This study reveals a novel mechanism involving mitophagy regulation underlying the therapeutic effect of rapamycin in AD. This study provides new insights and therapeutic targets for rapamycin in the treatment of AD. However, there are still some shortcomings in this topic; if we can further knock out the PINK1/Parkin gene in animals or use siRNA technology, we can further confirm the experimental results.

Potential risk factors for mild cognitive impairment among patients with type 2 diabetes experiencing hypoglycemia.

AIMS: This study examined the association between hypoglycemia and mild cognitive impairment (MCI) among patients with type 2 diabetes mellitus (T2DM) and identified risk factors for MCI in patients with hypoglycemia. METHODS: In this retrospective study, 328 patients with T2DM were screened in 2019 and followed up in 2022. Cognitive performance was assessed using the Montreal Cognitive Assessment (MoCA). The diagnosis of MCI was based on established criteria. Risk ratio (RR) with 95 % confidence intervals (CI) was calculated to estimate the risk of MCI. Univariate and multivariate logistic regression analyses were conducted to identify risk factors for MCI in those with hypoglycemia. RESULTS: Patients with hypoglycemia had lower cognitive performance 3 years later. The RR of MCI was 2.221 (95 % CI 1.269-3.885). Multivariate logistic analysis showed that low grip strength, existing diabetic retinopathy (DR), and multiple hypoglycemia episodes were associated with higher odds of MCI in patients with hypoglycemia (adjusted odds ratio [OR] 0.909 [95 % CI 0.859-0.963]), 3.078 [95 % CI 1.158-12.358], and 4.642 [95 % CI 1.284-16.776], respectively, all P < 0.05). CONCLUSIONS: Hypoglycemia increased MCI risk among patients with T2DM. Low grip strength, DR, and multiple hypoglycemia episodes may be potential risk factors for hypoglycemia-associated MCI.

Activation of mitophagy improves cognitive dysfunction in diabetic mice with recurrent non-severe hypoglycemia.

Recurrent non-severe hypoglycemia (RH) in patients with diabetes might be associated with cognitive impairment. Previously, we found that mitochondrial dysfunction plays an important role in this pathological process; however, the mechanism remains unclear. The objective of this study was to determine the molecular mechanisms of mitochondrial damage associated with RH in diabetes mellitus (DM). We found that RH is associated with reduced hippocampal mitophagy in diabetic mice, mainly manifested by reduced autophagosome formation and impaired recognition of impaired mitochondria, mediated by the PINK1/Parkin pathway. The same impaired mitophagy initiation was observed in an in vitro high-glucose cultured astrocyte model with recurrent low-glucose interventions. Promoting autophagosome formation and activating PINK1/Parkin-mediated mitophagy protected mitochondrial function and cognitive function in mice. The results showed that impaired mitophagy is involved in the occurrence of mitochondrial dysfunction, mediating the neurological impairment associated with recurrent low glucose under high glucose conditions.

Force field-inspired molecular representation learning for property prediction.

Molecular representation learning is a crucial task to accelerate drug discovery and materials design. Graph neural networks (GNNs) have emerged as a promising approach to tackle this task. However, most of them do not fully consider the intramolecular interactions, i.e. bond stretching, angle bending, torsion, and nonbonded interactions, which are critical for determining molecular property. Recently, a growing number of 3D-aware GNNs have been proposed to cope with the issue, while these models usually need large datasets and accurate spatial information. In this work, we aim to design a GNN which is less dependent on the quantity and quality of datasets. To this end, we propose a force field-inspired neural network (FFiNet), which can include all the interactions by incorporating the functional form of the potential energy of molecules. Experiments show that FFiNet achieves state-of-the-art performance on various molecular property datasets including both small molecules and large protein-ligand complexes, even on those datasets which are relatively small and without accurate spatial information. Moreover, the visualization for FFiNet indicates that it automatically learns the relationship between property and structure, which can promote an in-depth understanding of molecular structure.

Force field-inspired transformer network assisted crystal density prediction for energetic materials.

Machine learning has great potential in predicting chemical information with greater precision than traditional methods. Graph neural networks (GNNs) have become increasingly popular in recent years, as they can automatically learn the features of the molecule from the graph, significantly reducing the time needed to find and build molecular descriptors. However, the application of machine learning to energetic materials property prediction is still in the initial stage due to insufficient data. In this work, we first curated a dataset of 12,072 compounds containing CHON elements, which are traditionally regarded as main composition elements of energetic materials, from the Cambridge Structural Database, then we implemented a refinement to our force field-inspired neural network (FFiNet), through the adoption of a Transformer encoder, resulting in force field-inspired Transformer network (FFiTrNet). After the improvement, our model outperforms other machine learning-based and GNNs-based models and shows its powerful predictive capabilities especially for high-density materials. Our model also shows its capability in predicting the crystal density of potential energetic materials dataset (i.e. Huang & Massa dataset), which will be helpful in practical high-throughput screening of energetic materials.