A Bacterial Effector Reveals the V-ATPase-ATG16L1 Axis that Initiates Xenophagy.

Antibacterial autophagy (xenophagy) is an important host defense, but how it is initiated is unclear. Here, we performed a bacterial transposon screen and identified a T3SS effector SopF that potently blocked Salmonella autophagy. SopF was a general xenophagy inhibitor without affecting canonical autophagy. S. Typhimurium ΔsopF resembled S. flexneri ΔvirAΔicsB with the majority of intracellular bacteria targeted by autophagy, permitting a CRISPR screen that identified host V-ATPase as an essential factor. Upon bacteria-caused vacuolar damage, the V-ATPase recruited ATG16L1 onto bacteria-containing vacuole, which was blocked by SopF. Mammalian ATG16L1 bears a WD40 domain required for interacting with the V-ATPase. Inhibiting autophagy by SopF promoted S. Typhimurium proliferation in vivo. SopF targeted Gln124 of ATP6V0C in the V-ATPase for ADP-ribosylation. Mutation of Gln124 also blocked xenophagy, but not canonical autophagy. Thus, the discovery of SopF reveals the V-ATPase-ATG16L1 axis that critically mediates autophagic recognition of intracellular pathogen.

Intraepithelial mast cells drive gasdermin C-mediated type 2 immunity.

A specialized population of mast cells residing within epithelial layers, currently known as intraepithelial mast cells (IEMCs), was originally observed over a century ago, yet their physiological functions have remained enigmatic. In this study, we unveil an unexpected and crucial role of IEMCs in driving gasdermin C-mediated type 2 immunity. During helminth infection, αEß7 integrin-positive IEMCs engaged in extensive intercellular crosstalk with neighboring intestinal epithelial cells (IECs). Through the action of IEMC-derived proteases, gasdermin C proteins intrinsic to the epithelial cells underwent cleavage, leading to the release of a critical type 2 cytokine, interleukin-33 (IL-33). Notably, mast cell deficiency abolished the gasdermin C-mediated immune cascade initiated by epithelium. These findings shed light on the functions of IEMCs, uncover a previously unrecognized phase of type 2 immunity involving mast cell-epithelial cell crosstalk, and advance our understanding of the cellular mechanisms underlying gasdermin C activation.

A small-molecule degrader of TET3 as treatment for anorexia nervosa in an animal model.

Anorexia nervosa (AN) is a psychiatric illness with the highest mortality. Current treatment options have been limited to psychotherapy and nutritional support, with low efficacy and high relapse rates. Hypothalamic AgRP (agouti-related peptide) neurons that coexpress AGRP and neuropeptide Y (NPY) play a critical role in driving feeding while also modulating other complex behaviors. We have previously reported that genetic ablation of Tet3, which encodes a member of the TET family dioxygenases, specifically in AgRP neurons in mice, activates these neurons and increases the expression of AGRP, NPY, and the vesicular GABA transporter (VGAT), leading to hyperphagia and anxiolytic effects. Bobcat339 is a synthetic small molecule predicted to bind to the catalytic pockets of TET proteins. Here, we report that Bobcat339 is effective in mitigating AN and anxiety/depressive-like behaviors using a well-established mouse model of activity-based anorexia (ABA). We show that treating mice with Bobcat339 decreases TET3 expression in AgRP neurons and activates these neurons leading to increased feeding, decreased compulsive running, and diminished lethality in the ABA model. Mechanistically, Bobcat339 induces TET3 protein degradation while simultaneously stimulating the expression of AGRP, NPY, and VGAT in a TET3-dependent manner both in mouse and human neuronal cells, demonstrating a conserved, previously unsuspected mode of action of Bobcat339. Our findings suggest that Bobcat339 may potentially be a therapeutic for anorexia nervosa and stress-related disorders.

Dalbergia odorifera undergoes massive molecular shifts in response to waterlogging combined with salinity.

Field and greenhouse studies attempting to describe the molecular responses of plant species under waterlogging (WL) combined with salinity (ST) are almost nonexistent. We integrated transcriptional, metabolic, and physiological responses involving several crucial transcripts and common differentially expressed genes and metabolites in fragrant rosewood (Dalbergia odorifera) leaflets to dissect plant-specific molecular responses and patterns under WL combined with ST (SWL). We discovered that the synergistic pattern of the transcriptional response of fragrant rosewood under SWL was exclusively characterized by the number of regulated transcripts. The response patterns under SWL based on transcriptome and metabolome regulation statuses revealed different patterns (additive, dominant, neutral, minor, unilateral, and antagonistic) of transcripts or metabolites that were commonly regulated or expressed uniquely under SWL. Under SWL, the synergistic transcriptional response of several functional gene subsets was positively associated with several metabolomic and physiological responses related to the shutdown of the photosynthetic apparatus and the extensive degradation of starch into saccharides through α-amylase, ß-amylase, and α-glucosidase or plastoglobuli accumulation. The dissimilarity between the regulation status and number of transcripts in plants under combined stresses led to nonsynergistic responses in several physiological and phytohormonal traits. As inferred from the impressive synergistic transcriptional response to morpho-physiological changes, combined stresses exhibited a gradually decreasing effect on the changes observed at the molecular level compared to those in the morphological one. Here, by characterizing the molecular responses and patterns of plant species under SWL, our study considerably improves our understanding of the molecular mechanisms underlying combined stress.

KAT8/SIRT7-mediated Fascin-K41 acetylation/deacetylation regulates tumor metastasis in esophageal squamous cell carcinoma.

Fascin actin-bundling protein 1 (Fascin) is highly expressed in a variety of cancers, including esophageal squamous cell carcinoma (ESCC), working as an important oncogenic protein and promoting the migration and invasion of cancer cells by bundling F-actin to facilitate the formation of filopodia and invadopodia. However, it is not clear how exactly the function of Fascin is regulated by acetylation in cancer cells. Here, in ESCC cells, the histone acetyltransferase KAT8 catalyzed Fascin lysine 41 (K41) acetylation, to inhibit Fascin-mediated F-actin bundling and the formation of filopodia and invadopodia. Furthermore, NAD-dependent protein deacetylase sirtuin (SIRT) 7-mediated deacetylation of Fascin-K41 enhances the formation of filopodia and invadopodia, which promotes the migration and invasion of ESCC cells. Clinically, the analysis of cancer and adjacent tissue samples from patients with ESCC showed that Fascin-K41 acetylation was lower in the cancer tissue of patients with lymph node metastasis than in that of patients without lymph node metastasis, and low levels of Fascin-K41 acetylation were associated with a poorer prognosis in patients with ESCC. Importantly, K41 acetylation significantly blocked NP-G2-044, one of the Fascin inhibitors currently being clinically evaluated, suggesting that NP-G2-044 may be more suitable for patients with low levels of Fascin-K41 acetylation, but not suitable for patients with high levels of Fascin-K41 acetylation. © 2024 The Pathological Society of Great Britain and Ireland. Published by John Wiley & Sons, Ltd.

Let-7 underlies metformin-induced inhibition of hepatic glucose production.

SignificanceA clear mechanistic understanding of metformin's antidiabetic effects is lacking. This is because suprapharmacological concentrations of metformin have been used in most studies. Using mouse models and human primary hepatocytes, we show that metformin, at clinically relevant doses, suppresses hepatic glucose production by activating a conserved regulatory pathway encompassing let-7, TET3, and a fetal isoform of hepatocyte nuclear factor 4 alpha (HNF4α). We demonstrate that metformin no longer has potent antidiabetic actions in a liver-specific let-7 loss-of-function mouse model and that hepatic delivery of let-7 ameliorates hyperglycemia and improves glucose homeostasis. Our results thus reveal an important role of the hepatic let-7/TET3/HNF4α axis in mediating the therapeutic effects of metformin and suggest that targeting this axis may be a potential therapeutic for diabetes.

Skeletal muscle TET3 promotes insulin resistance through destabilisation of PGC-1α.

AIM/HYPOTHESIS: The peroxisome proliferator-activated receptor-γ coactivator α (PGC-1α) plays a critical role in the maintenance of glucose, lipid and energy homeostasis by orchestrating metabolic programs in multiple tissues in response to environmental cues. In skeletal muscles, PGC-1α dysregulation has been associated with insulin resistance and type 2 diabetes but the underlying mechanisms have remained elusive. This research aims to understand the role of TET3, a member of the ten-eleven translocation (TET) family dioxygenases, in PGC-1α dysregulation in skeletal muscles in obesity and diabetes. METHODS: TET expression levels in skeletal muscles were analysed in humans with or without type 2 diabetes, as well as in mouse models of high-fat diet (HFD)-induced or genetically induced (ob/ob) obesity/diabetes. Muscle-specific Tet3 knockout (mKD) mice were generated to study TET3's role in muscle insulin sensitivity. Genome-wide expression profiling (RNA-seq) of muscle tissues from wild-type (WT) and mKD mice was performed to mine deeper insights into TET3-mediated regulation of muscle insulin sensitivity. The correlation between PGC-1α and TET3 expression levels was investigated using muscle tissues and in vitro-derived myotubes. PGC-1α phosphorylation and degradation were analysed using in vitro assays. RESULTS: TET3 expression was elevated in skeletal muscles of humans with type 2 diabetes and in HFD-fed and ob/ob mice compared with healthy controls. mKD mice exhibited enhanced glucose tolerance, insulin sensitivity and resilience to HFD-induced insulin resistance. Pathway analysis of RNA-seq identified 'Mitochondrial Function' and 'PPARα Pathway' to be among the top biological processes regulated by TET3. We observed higher PGC-1α levels (~25%) in muscles of mKD mice vs WT mice, and lower PGC-1α protein levels (~25-60%) in HFD-fed or ob/ob mice compared with their control counterparts. In human and murine myotubes, increased PGC-1α levels following TET3 knockdown contributed to improved mitochondrial respiration and insulin sensitivity. TET3 formed a complex with PGC-1α and interfered with its phosphorylation, leading to its destabilisation. CONCLUSIONS/INTERPRETATION: Our results demonstrate an essential role for TET3 in the regulation of skeletal muscle insulin sensitivity and suggest that TET3 may be used as a potential therapeutic target for the metabolic syndrome. DATA AVAILABILITY: Sequences are available from the Gene Expression Omnibus ( https://www.ncbi.nlm.nih.gov/geo/ ) with accession number of GSE224042.

Tuning the Flat Band in Bi2O2Se by Pressure to Induce Superconductivity.

The discovery of superconductivity in twisted bilayer graphene has reignited enthusiasm in the field of flat-band superconductivity. However, important challenges remain, such as constructing a flat-band structure and inducing a superconducting state in materials. Here, we successfully achieved superconductivity in Bi2O2Se by pressure-tuning the flat-band electronic structure. Experimental measurements combined with theoretical calculations reveal that the occurrence of pressure-induced superconductivity at 30 GPa is associated with a flat-band electronic structure near the Fermi level. Moreover, in Bi2O2Se, a van Hove singularity is observed at the Fermi level alongside pronounced Fermi surface nesting. These remarkable features play a crucial role in promoting strong electron-phonon interactions, thus potentially enhancing the superconducting properties of the material. These findings demonstrate that pressure offers a potential experimental strategy for precisely tuning the flat band and achieving superconductivity.

COLMARppm: A Web Server Tool for the Accurate and Rapid Prediction of 1H and 13C NMR Chemical Shifts of Organic Molecules and Metabolites.

Despite rapid progress in metabolomics research, a major bottleneck is the large number of metabolites whose chemical structures are unknown or whose spectra have not been deposited in metabolomics databases. Nuclear magnetic resonance (NMR) spectroscopy has a long history of elucidating chemical structures from experimentally measured 1H and 13C chemical shifts. One approach to characterizing the chemical structures of an unknown metabolite is to predict the 1H and 13C chemical shifts of candidate compounds (e.g., metabolites from the Human Metabolome Database (HMDB)) and compare them with chemical shifts of the unknown. However, accurate prediction of NMR chemical shifts in aqueous solution is challenging due to limitations of experimental chemical shift libraries and the high computational cost of quantum chemical methods. To improve NMR prediction accuracy and applicability, an empirical prediction strategy is introduced here to provide an accurately predicted chemical shift for organic molecules and metabolites within seconds. Unique features of COLMARppm include (i) the training library exclusively consisting of high quality NMR spectra measured under standard conditions in aqueous solution, (ii) utilization of NMR motif information, and (iii) leveraging of the improved prediction accuracy for the automated assignment of experimental chemical shifts for candidate structures. COLMARppm is demonstrated in terms of accuracy and speed for a set of 20 compounds taken from the HMDB for chemical shift prediction and resonance assignment. COLMARppm is applicable to a wide range of small molecules and can be directly incorporated into metabolomics workflows.

Holistic Prediction of AuNP Aggregation in Diverse Aqueous Suspensions Based on Machine Vision and Dark-Field Scattering Imaging.

The localized surface-plasmon resonance of the AuNP in aqueous media is extremely sensitive to environmental changes. By measuring the signal of plasmon scattering light, the dark-field microscopic (DFM) imaging technique has been used to monitor the aggregation of AuNPs, which has attracted great attention because of its simplicity, low cost, high sensitivity, and universal applicability. However, it is still challenging to interpret DFM images of AuNP aggregation due to the heterogeneous characteristics of the isolated and discontinuous color distribution. Herein, we introduce machine vision algorithms for the training of DFM images of AuNPs in different saline aqueous media. A visual deep learning framework based on AlexNet is constructed for studying the aggregation patterns of AuNPs in aqueous suspensions, which allows for rapid and accurate identification of the aggregation extent of AuNPs, with a prediction accuracy higher than 0.96. With the aid of machine learning analysis, we further demonstrate the prediction ability of various aggregation phenomena induced by both cation species and the concentration of the external saline solution. Our results suggest the great potential of machine vision frameworks in the accurate recognition of subtle pattern changes in DFM images, which can help researchers build predictive analytics based on DFM imaging data.

Physio-biochemical and metabolomic responses of the woody plant Dalbergia odorifera to salinity and waterlogging.

BACKGROUND: Trees have developed a broad spectrum of molecular mechanisms to counteract oxidative stress. Secondary metabolites via phenolic compounds emblematized the hidden bridge among plant kingdom, human health, and oxidative stress. Although studies have demonstrated that abiotic stresses can increase the production of medicinal compounds in plants, research comparing the efficiency of these stresses still needs to be explored. Thus, the present research paper provided an exhaustive comparative metabolomic study in Dalbergia odorifera under salinity (ST) and waterlogging (WL). RESULTS: High ST reduced D. odorifera's fresh biomass compared to WL. While WL only slightly affected leaf and vein size, ST had a significant negative impact. ST also caused more significant damage to water status and leaflet anatomy than WL. As a result, WL-treated seedlings exhibited better photosynthesis and an up-regulation of nonenzymatic pathways involved in scavenging reactive oxygen species. The metabolomic and physiological responses of D. odorifera under WL and salinity ST stress revealed an accumulation of secondary metabolites by the less aggressive stress (WL) to counterbalance the oxidative stress. Under WL, more metabolites were more regulated compared to ST. ST significantly altered the metabolite profile in D. odorifera leaflets, indicating its sensitivity to salinity. WL synthesized more metabolites involved in phenylpropanoid, flavone, flavonol, flavonoid, and isoflavonoid pathways than ST. Moreover, the down-regulation of L-phenylalanine correlated with increased p-coumarate, caffeate, and ferulate associated with better cell homeostasis and leaf anatomical indexes under WL. CONCLUSIONS: From a pharmacological and medicinal perspective, WL improved larger phenolics with therapeutic values compared to ST. Therefore, the data showed evidence of the crucial role of medical tree species' adaptability on ROS detoxification under environmental stresses that led to a significant accumulation of secondary metabolites with therapeutic value.

Collaborative Enhancement of Consistency and Accuracy in US Diagnosis of Thyroid Nodules Using Large Language Models.

Background Large language models (LLMs) hold substantial promise for medical imaging interpretation. However, there is a lack of studies on their feasibility in handling reasoning questions associated with medical diagnosis. Purpose To investigate the viability of leveraging three publicly available LLMs to enhance consistency and diagnostic accuracy in medical imaging based on standardized reporting, with pathology as the reference standard. Materials and Methods US images of thyroid nodules with pathologic results were retrospectively collected from a tertiary referral hospital between July 2022 and December 2022 and used to evaluate malignancy diagnoses generated by three LLMs-OpenAI's ChatGPT 3.5, ChatGPT 4.0, and Google's Bard. Inter- and intra-LLM agreement of diagnosis were evaluated. Then, diagnostic performance, including accuracy, sensitivity, specificity, and area under the receiver operating characteristic curve (AUC), was evaluated and compared for the LLMs and three interactive approaches: human reader combined with LLMs, image-to-text model combined with LLMs, and an end-to-end convolutional neural network model. Results A total of 1161 US images of thyroid nodules (498 benign, 663 malignant) from 725 patients (mean age, 42.2 years ± 14.1 [SD]; 516 women) were evaluated. ChatGPT 4.0 and Bard displayed substantial to almost perfect intra-LLM agreement (κ range, 0.65-0.86 [95% CI: 0.64, 0.86]), while ChatGPT 3.5 showed fair to substantial agreement (κ range, 0.36-0.68 [95% CI: 0.36, 0.68]). ChatGPT 4.0 had an accuracy of 78%-86% (95% CI: 76%, 88%) and sensitivity of 86%-95% (95% CI: 83%, 96%), compared with 74%-86% (95% CI: 71%, 88%) and 74%-91% (95% CI: 71%, 93%), respectively, for Bard. Moreover, with ChatGPT 4.0, the image-to-text-LLM strategy exhibited an AUC (0.83 [95% CI: 0.80, 0.85]) and accuracy (84% [95% CI: 82%, 86%]) comparable to those of the human-LLM interaction strategy with two senior readers and one junior reader and exceeding those of the human-LLM interaction strategy with one junior reader. Conclusion LLMs, particularly integrated with image-to-text approaches, show potential in enhancing diagnostic medical imaging. ChatGPT 4.0 was optimal for consistency and diagnostic accuracy when compared with Bard and ChatGPT 3.5. © RSNA, 2024 Supplemental material is available for this article.

US-based Sequential Algorithm Integrating an AI Model for Advanced Liver Fibrosis Screening.

Background Noninvasive tests can be used to screen patients with chronic liver disease for advanced liver fibrosis; however, the use of single tests may not be adequate. Purpose To construct sequential clinical algorithms that include a US deep learning (DL) model and compare their ability to predict advanced liver fibrosis with that of other noninvasive tests. Materials and Methods This retrospective study included adult patients with a history of chronic liver disease or unexplained abnormal liver function test results who underwent B-mode US of the liver between January 2014 and September 2022 at three health care facilities. A US-based DL network (FIB-Net) was trained on US images to predict whether the shear-wave elastography (SWE) value was 8.7 kPa or higher, indicative of advanced fibrosis. In the internal and external test sets, a two-step algorithm (Two-step#1) using the Fibrosis-4 Index (FIB-4) followed by FIB-Net and a three-step algorithm (Three-step#1) using FIB-4 followed by FIB-Net and SWE were used to simulate screening scenarios where liver stiffness measurements were not or were available, respectively. Measures of diagnostic accuracy were calculated using liver biopsy as the reference standard and compared between FIB-4, SWE, FIB-Net, and European Association for the Study of the Liver guidelines (ie, FIB-4 followed by SWE), along with sequential algorithms. Results The training, validation, and test data sets included 3067 (median age, 42 years [IQR, 33-53 years]; 2083 male), 1599 (median age, 41 years [IQR, 33-51 years]; 1124 male), and 1228 (median age, 44 years [IQR, 33-55 years]; 741 male) patients, respectively. FIB-Net obtained a noninferior specificity with a margin of 5% (P < .001) compared with SWE (80% vs 82%). The Two-step#1 algorithm showed higher specificity and positive predictive value (PPV) than FIB-4 (specificity, 79% vs 57%; PPV, 44% vs 32%) while reducing unnecessary referrals by 42%. The Three-step#1 algorithm had higher specificity and PPV compared with European Association for the Study of the Liver guidelines (specificity, 94% vs 88%; PPV, 73% vs 64%) while reducing unnecessary referrals by 35%. Conclusion A sequential algorithm combining FIB-4 and a US DL model showed higher diagnostic accuracy and improved referral management for all-cause advanced liver fibrosis compared with FIB-4 or the DL model alone. © RSNA, 2024 Supplemental material is available for this article. See also the editorial by Ghosh in this issue.

Boosting Efficient Alkaline Hydrogen Evolution Reaction of CoFe-Layered Double Hydroxides Nanosheets via Co-Coordination Mechanism of W-Doping and Oxygen Defect Engineering.

While surface defects and heteroatom doping exhibit promising potential in augmenting the electrocatalytic hydrogen evolution reaction (HER), their performance remains unable to rival that of the costly Pt-based catalysts. Yet, the concurrent modification of catalysts by integrating both approaches stands as a promising strategy to effectively address the aforementioned limitation. In this work, tungsten dopants are introduced into self-supported CoFe-layered double hydroxides (LDH) on nickel foam using a hydrothermal method, and oxygen vacancies (Ov) are further introduced through calcination. The analysis results demonstrated that tungsten doping reduces the Ov formation energy of CoFeW-LDH. The Ov acted as oxophilic sites, facilitating water adsorption and dissociation, and reducing the barrier for cleaving HO─H bonds from 0.64 to 0.14 eV. Additionally, Ov regulated the electronic structure of CoFeW-LDH to endow optimized hydrogen binding ability on tungsten atoms, thereby accelerating alkaline Volmer and Heyrovsky reaction kinetics. Specifically, the abundance of Ov induced a transition of tungsten from a six-coordinated to highly active four-coordinated structure, which becomes the active site for HER. Consequently, an ultra-low overpotential of 41 mV at 10 mA cm-2, and a low Tafel slope of 35 mV dec-1 are achieved. These findings offer crucial insights for the design of efficient HER electrocatalysts.

DSEATM: drug set enrichment analysis uncovering disease mechanisms by biomedical text mining.

Disease pathogenesis is always a major topic in biomedical research. With the exponential growth of biomedical information, drug effect analysis for specific phenotypes has shown great promise in uncovering disease-associated pathways. However, this method has only been applied to a limited number of drugs. Here, we extracted the data of 4634 diseases, 3671 drugs, 112 809 disease-drug associations and 81 527 drug-gene associations by text mining of 29 168 919 publications. On this basis, we proposed a 'Drug Set Enrichment Analysis by Text Mining (DSEATM)' pipeline and applied it to 3250 diseases, which outperformed the state-of-the-art method. Furthermore, diseases pathways enriched by DSEATM were similar to those obtained using the TCGA cancer RNA-seq differentially expressed genes. In addition, the drug number, which showed a remarkable positive correlation of 0.73 with the AUC, plays a determining role in the performance of DSEATM. Taken together, DSEATM is an auspicious and accurate disease research tool that offers fresh insights.

Global, regional and national temporal trends in prevalence for musculoskeletal disorders in women of childbearing age, 1990-2019: an age-period-cohort analysis based on the Global Burden of Disease Study 2019.

OBJECTIVES: To provide an overview and in-depth analysis of temporal trends in prevalence of musculoskeletal (MSK) disorders in women of childbearing age (WCBA) at global, regional and national levels over the last 30 years, with a special focus on their associations with age, period and birth cohort. METHODS: Estimates and 95% uncertainty intervals (UIs) for MSK disorders prevalence in WCBA were extracted from the Global Burden of Diseases, Injuries and Risk Factors Study 2019. An age-period-cohort model was adopted to estimate the overall annual percentage change of prevalence (net drift, % per year), annual percentage change of prevalence within each age group (local drift, % per year), fitted longitudinal age-specific rates adjusted for period deviations (age effects) and period/cohort relative risks (period/cohort effects) from 1990 to 2019. RESULTS: In 2019, the global number of MSK disorders prevalence in WCBA was 354.57 million (95% UI: 322.64 to 387.68). Fifty countries had at least one million prevalence, with India, China, the USA, Indonesia and Brazil being the highest accounting for 51.03% of global prevalence. From 1990 to 2019, a global net drift of MSK disorders prevalence in WCBA was -0.06% (95% CI: -0.07% to -0.05%) per year, ranging from -0.09% (95% CI: -0.10% to -0.07%) in low-middle sociodemographic index (SDI) region to 0.10% (95% CI: 0.08% to 0.12%) in high-middle SDI region, with 138 countries presenting increasing trends, 24 presenting decreasing trends and 42 presenting relatively flat trends. As reflected by local drift, higher SDI regions had more age groups showing rising prevalence whereas lower SDI regions had more declining prevalence. Globally, an increasing occurrence of MSK disorders prevalence in WCBA beyond adolescent and towards the adult stage has been prominent. Age effects illustrated similar patterns across different SDI regions, with risk increasing with age. High SDI region showed generally lower period risks over time, whereas others showed more unfavourable period risks. High, high-middle and middle SDI regions presented unfavourable prevalence deteriorations, whereas others presented favourable prevalence improvements in successively birth cohorts. CONCLUSIONS: Although a favourable overall temporal trend (net drift) of MSK disorders prevalence in WCBA was observed over the last 30 years globally, there were 138 countries showing unfavourable rising trends, coupled with deteriorations in period/cohort risks in many countries, collectively raising concerns about timely realisation of the Targets of Sustainable Development Goal. Improvements in the MSK disorders-related prevention, management and treatment programmes in WCBA could decline the relative risk for successively younger birth cohorts and for all age groups over period progressing.

Quantitative real-time phase microscopy for extended depth-of-field imaging based on the 3D single-shot differential phase contrast (ssDPC) imaging method.

Optical diffraction tomography (ODT) is a promising label-free imaging method capable of quantitatively measuring the three-dimensional (3D) refractive index distribution of transparent samples. In recent years, partially coherent ODT (PC-ODT) has attracted increasing attention due to its system simplicity and absence of laser speckle noise. Quantitative phase imaging (QPI) technologies represented by Fourier ptychographic microscopy (FPM), differential phase contrast (DPC) imaging and intensity diffraction tomography (IDT) need to collect several or hundreds of intensity images, which usually introduce motion artifacts when shooting fast-moving targets, leading to a decrease in image quality. Hence, a quantitative real-time phase microscopy (qRPM) for extended depth of field (DOF) imaging based on 3D single-shot differential phase contrast (ssDPC) imaging method is proposed in this research study. qRPM incorporates a microlens array (MLA) to simultaneously collect spatial information and angular information. In subsequent optical information processing, a deconvolution method is used to obtain intensity stacks under different illumination angles in a raw light field image. Importing the obtained intensity stack into the 3D DPC imaging model is able to finally obtain the 3D refractive index distribution. The captured four-dimensional light field information enables the reconstruction of 3D information in a single snapshot and extending the DOF of qRPM. The imaging capability of the proposed qRPM system is experimental verified on different samples, achieve single-exposure 3D label-free imaging with an extended DOF for 160 µm which is nearly 30 times higher than the traditional microscope system.

High performance few-mode fiber-based light field direction sensing system using deep convolutional neural network: fiber speckle demodulation network (FSDNET).

Precisely sensing the light field direction information plays the essential role in the fields of three-dimensional (3D) imaging, light field sensing, target positioning and tracking, remote sensing, etc. It is thrilling to find that the optical fiber can be used as a sensing component due to its high sensitivity, compact size, and strong resistance to electromagnetic interference. According to the core principle that the few-mode fiber output speckle pattern is sensitive to the change of incident light field direction, the variation characteristics is further investigated in this research study. Based on the simulation and analysis of the fiber transmission characteristics, the output speckle corresponding to the incident light field with the direction in the range of ±6° horizontally and vertically are calculated. Furthermore, a deep convolutional neural network (CNN): fiber speckle demodulation network (FSDNET) is proposed and constructed to establish what we believe to be a novel way to reveal and identify the mapping relationship between the light field direction and the output speckle. The theoretical simulation shows that the mean absolute error (MAE) between the perceived light field directions and the true directions is 0.01°. Then, a light field direction sensing system based on the few-mode fiber is developed. Regarding to the performance of the sensing system, the MAE of the FSDNET for the light field directions that have appeared in the training set is 0.0389°, and for testing set of the unknown directions that have not appeared in the training set, the MAE is 0.0570°. Therefore, the simulation and experimental results prove that high performance sensing of light field direction can be achieved by the proposed few-mode fiber sensing system and the FSDNET.

High-precision analysis of aberration contribution of Zernike freeform surface terms for non-zero field of view.

Clarifying the aberrations arising from freeform surfaces is of great significance for maximizing the potential of freeform surfaces in the design of optical systems. However, the current precision in calculating aberration contribution of freeform surface terms for non-zero field of view is insufficient, impeding the development of freeform imaging systems with larger field of view. This paper proposes a high-precision analysis of aberration contribution of freeform surface terms based on nodal aberration theory, particularly for non-zero field points. Accurate calculation formulas of aberrations generated by Zernike terms on freeform surface are presented. Design examples illustrate that the calculation error of the provided formulas is 78% less than that of conventional theoretical values. Building upon high-precision analysis, we propose an optimization method for off-axis freeform surface systems and illustrate its effectiveness through the optimization of an off-axis three-mirror system. This research extends the applicability of nodal aberration theory in aberration analysis, offering valuable insights for the optimal design and alignment of optical freeform systems.

Semi-supervised angular super-resolution method for autostereoscopic 3D surface measurement.

Autostereoscopic 3D measuring systems are an accurate, rapid, and portable method for in situ measurements. These systems use a micro-lens array to record 3D information based on the light-field theory. However, the spatial-angular-resolution trade-off curtails their performance. Although learning models were developed for super-resolution, the scarcity of data hinders efficient training. To address this issue, a novel, to the best of our knowledge, semi-supervised learning paradigm for angular super-resolution is proposed for data-efficient training, benefiting both autostereoscopic and light-field devices. A convolutional neural network using motion estimation is developed for a view synthesis. Subsequently, a high-angular-resolution autostereoscopic system is presented for an accurate profile reconstruction. Experiments show that the semi-supervision enhances view reconstruction quality, while the amount of training data required is reduced by over 69%.