Cardamomin Inhibits the Proliferation and Tumorigenesis of Bladder Cancer by ESR1 in PI3K/AKT Pathway.

Cardamomin has been widely studied in cancer, but its role in cancer bladder cancer has not been mentioned. In this study, we validated the anti-cancer effect of cardamom and whether its potential mechanism is related to the PI3K/AKT pathway. After treating with different doses of cardamomin, the cytotoxicity was studied by CCK8. Secondly, we analyzed the effect of cardamomin on the proliferation, apoptosis and cell movement. Next, we analyzed the regulation of ESR1 by western blot and its impact on the PI3K/AKT pathway. We also transfected ESR1 overexpression and silencing vectors, and verified the transfection efficiency through RT-qPCR. Further, the specific mechanism of the drug's inhibitory effect on bladder cancer was also determined. We constructed the subcutaneous tumor model in vivo. After cardamomin administration, we mainly analyzed the positive expression of KI67 in tumor tissues by immunohistochemistry, and the apoptotic cells in tumor tissues by TUNEL, and related proteins in PI3K/AKT pathway by western blot. In this paper, cardamomin inhibited cell proliferation and invasion ability, blocked the transition of G0/G1 phase to S phase, and increased apoptotic rate of 5637 and HT1376 cells, as well as raised ESR1 expression. Cardamomin exerted anti-tumor effect through PI3K/AKT pathway. In vivo animal experiments indicated the inhibitory effect of cardamomin on subcutaneous implanted tumor. Cardamomin inhibited the positive expression of KI67 and promoted the TUNEL-positive cells in tumor tissues. Consistent with in vitro assay, cardamomin increased the expression of ESR1 and downregulated the PI3K/AKT pathway. Cardamomin has a significant inhibitory effect on bladder cancer, and upregulate the expression of ESR1 in bladder cancer through PI3K/AKT.

Controlling the nutritional status score: a new tool for predicting postoperative mortality in patients with infrarenal abdominal aortic aneurysm treated with endovascular aneurysm repair.

Background: AAA is a fatal condition that commonly occurs during vascular surgery. Nutritional status exerts a significant influence on the prognosis of various pathological conditions Scores from the CONUT screening tool have been shown to predict outcomes of certain malignancies and chronic diseases. However, the ramifications of nutritional status on AAA patients undergoing EVAR have not been elucidated in prior studies. In this study, we aimed to elucidate the correlation between CONUT scores and postoperative prognostic outcomes in patients with AAA undergoing EVAR. Methods: This was a retrospective review of 177 AAA patients treated with EVAR from June 2018 to November 2019 in a single center. Patient characteristics, CONUT scores, and postoperative status were collected. These patients were stratified into groups A and B according to CONUT scores. Subsequently, a comparative analysis of the baseline characteristics between the two cohorts was conducted. Cox proportional hazards and logistic regression analyses were employed to identify the autonomous predictors of mid-term mortality and complications, respectively. Results: Compared with group A, patients in group B had higher midterm mortality (p < 0.001). Univariate analysis showed that CONUT scores; respiratory diseases; stent types; preoperative Hb, CRP, PT, and Fb levels were risk factors for death. Multivariate analysis confirmed that CONUT score [HR, 1.276; 95% CI, 1.029-1.584; p = 0.027] was an independent risk factor for mortality. Logistic regression analysis showed that prior arterial disease, smoking, and D-dimer levels were risk factors, although multivariate analysis showed smoking (OR, 3.492; 95% CI, 1.426-8.553; p = 0.006) was an independent risk factor. Kaplan-Meier curves showed that patients in group B had shorter mid-term survival than those in group A (log-rank p < 0.001). Conclusion: Malnutrition was strongly associated with mid-term mortality in patients with infrarenal AAA treated with EVAR.

Combined untargeted metabolomics and network pharmacology approaches to reveal the therapeutic role of withanolide B in psoriasis.

Psoriasis is a refractory inflammatory skin disorder in which keratinocyte hyperproliferation is a crucial pathogenic factor. Up to now, it is commonly acknowledged that psoriasis has a tight connection with metabolic disorders. Withanolides from Datura metel L. (DML) have been proved to possess anti-inflammatory and anti-proliferative properties in multiple diseases including psoriasis. Withanolide B (WB) is one of the abundant molecular components in DML. However, existing experimental studies regarding the potential effects and mechanisms of WB on psoriasis still remain lacking. Present study aimed to integrate network pharmacology and untargeted metabolomics strategies to investigate the therapeutic effects and mechanisms of WB on metabolic disorders in psoriasis. In our study, we observed that WB might effectively improve the symptoms of psoriasis and alleviate the epidermal hyperplasia in imiquimod (IMQ)-induced psoriasis-like mice. Both network pharmacology and untargeted metabolomics results suggested that arachidonic acid metabolism and arginine and proline metabolism pathways were linked to the treatment of psoriasis with WB. Meanwhile, we also found that WB may affect the expression of regulated enzymes 5-lipoxygenase (5-LOX), 12-LOX, ornithine decarboxylase 1 (ODC1) and arginase 1 (ARG1) in the arachidonic acid metabolism and arginine and proline metabolism pathways. In summary, this paper showed the potential metabolic mechanisms of WB against psoriasis and suggested that WB would have greater potential in psoriasis treatment.

Bioaccumulation and trophic transfer of per- and polyfluoroalkyl substances in a subtropical mangrove estuary food web.

Mangrove estuaries are an important land-sea transitional ecosystem that is currently under various pollution pressures, while there is a lack of research on per- and polyfluoroalkyl substances (PFAS) in the organisms of mangrove estuaries. In this study, we investigated the distribution and seasonal variation of PFAS in the tissues of organisms from a mangrove estuary. The PFAS concentrations in fish tissues varied from 0.45 ng/g ww to 17.67 ng/g ww and followed the order of viscera > head > carcass > muscle, with the highest tissue burden found in the fish carcass (39.59 ng). The log BAF values of PFDoDA, PFUnDA, and PFDA in the whole fish exceeded 3.70, indicating significant bioaccumulation. The trophic transfer of PFAS in the mangrove estuary food web showed a dilution effect, which was mainly influenced by the spatial heterogeneity of PFAS distribution in the estuarine environment, and demonstrated that the gradient dilution of PFAS in the estuary habitat environment can disguise the PFAS bio-magnification in estuarine organisms, and the larger the swimming ranges of organisms, the more pronounced the bio-dilution effect. The PFOA-equivalent HRs of category A and B fish were 3.48-5.17 and 2.59-4.01, respectively, indicating that mangrove estuarine residents had a high PFAS exposure risk through the intake of estuarine fish.

Towards Open Vocabulary Learning: A Survey.

In the field of visual scene understanding, deep neural networks have made impressive advancements in various core tasks like segmentation, tracking, and detection. However, most approaches operate on the close-set assumption, meaning that the model can only identify pre-defined categories that are present in the training set. Recently, open vocabulary settings were proposed due to the rapid progress of vision language pre-training. These new approaches seek to locate and recognize categories beyond the annotated label space. The open vocabulary approach is more general, practical, and effective than weakly supervised and zero-shot settings. This paper thoroughly reviews open vocabulary learning, summarizing and analyzing recent developments in the field. In particular, we begin by juxtaposing open vocabulary learning with analogous concepts such as zero-shot learning, open-set recognition, and out-of-distribution detection. Subsequently, we examine several pertinent tasks within the realms of segmentation and detection, encompassing long-tail problems, few-shot, and zero-shot settings. As a foundation for our method survey, we first elucidate the fundamental principles of detection and segmentation in close-set scenarios. Next, we examine various contexts where open vocabulary learning is employed, pinpointing recurring design elements and central themes. This is followed by a comparative analysis of recent detection and segmentation methodologies in commonly used datasets and benchmarks. Our review culminates with a synthesis of insights, challenges, and discourse on prospective research trajectories. To our knowledge, this constitutes the inaugural exhaustive literature review on open vocabulary learning.

A hemodynamic analysis of energy loss in abdominal aortic aneurysm using three-dimension idealized model.

Objective: The aim of this study is to perform specific hemodynamic simulations of idealized abdominal aortic aneurysm (AAA) models with different diameters, curvatures and eccentricities and evaluate the risk of thrombosis and aneurysm rupture. Methods: Nine idealized AAA models with different diameters (3 cm or 5 cm), curvatures (0° or 30°) and eccentricities (centered on or tangent to the aorta), as well as a normal model, were constructed using commercial software (Solidworks; Dassault Systemes S.A, Suresnes, France). Hemodynamic simulations were conducted with the same time-varying volumetric flow rate extracted from the literature and 3-element Windkessel model (3 EWM) boundary conditions were applied at the aortic outlet. Several hemodynamic parameters such as time-averaged wall shear stress (TAWSS), oscillatory shear index (OSI), relative residence time (RRT), endothelial cell activation potential (ECAP) and energy loss (EL) were obtained to evaluate the risk of thrombosis and aneurysm rupture under different conditions. Results: Simulation results showed that the proportion of low TAWSS region and high OSI region increases with the rising of aneurysm diameter, whereas decreases in the curvature and eccentric models of the corresponding diameters, with the 5 cm normal model having the largest low TAWSS region (68.5%) and high OSI region (40%). Similar to the results of TAWSS and OSI, the high ECAP and high RRT areas were largest in the 5 cm normal model, with the highest wall-averaged value (RRT: 5.18 s, ECAP: 4.36 Pa-1). Differently, the increase of aneurysm diameter, curvature, and eccentricity all lead to the increase of mean flow EL and turbulent EL, such that the highest mean flow EL (0.82 W · 10-3) and turbulent EL (1.72 W · 10-3) were observed in the eccentric 5 cm model with the bending angle of 30°. Conclusion: Collectively, increases in aneurysm diameter, curvature, and eccentricity all raise mean flow EL and turbulent flow EL, which may aggravate the damage and disturbance of flow in aneurysm. In addition, it can be inferred by conventional parameters (TAWSS, OSI, RRT and ECAP) that the increase of aneurysm diameter may raise the risk of thrombosis, whereas the curvature and eccentricity appeared to have a protective effect against thrombosis.

Efficient CsPbBr3 Perovskite Light-Emitting Diodes via Novel Multi-Step Ligand Exchange Strategy Based on Zwitterionic Molecules.

Perovskite nanocrystals have absorbed increasing interest, especially in the field of optoelectronics, owing to their unique characteristics, including their tunable luminescence range, robust solution processability, facile synthesis, and so on. However, in practice, due to the inherent instability of the traditional long-chain insulating ligands surrounding perovskite quantum dots (PeQDs), the performance of the as-fabricated QLED is relatively disappointing. Herein, the zwitterion 3-(decyldimethylammonio)propanesulfonate (DLPS) with the capability of double passivating perovskite quantum dots could effectively replace the original long-chain ligand simply through a multistep post-treatment strategy to finally inhibit the formation of defects. It was indicated from theexperimental results that the DLPS, as one type of ligand with the bimolecular ion, was very adavntageous in replacing long-chain ligands and further suppressing the formation of defects. Finally, the perovskite quantum dots with greatly enhanced PLQY as high as 98% were effectively achieved. Additionally, the colloidal stability of the corresponding PeQDs has been significantly enhanced, and a transparent colloidal solution was obtained after 45 days under ambient conditions. Finally, the as-fabricated QLEDs based on the ligand-exchanged PeQDs exhibited a maximum brightness of 9464 cd/m2 and an EQE of 12.17%.

Reinforcement Learning for Blast Furnace Ironmaking Operation With Safety and Partial Observation Considerations.

Making proper decision online in complex environment during the blast furnace (BF) operation is a key factor in achieving long-term success and profitability in the steel manufacturing industry. Regulatory lags, ore source uncertainty, and continuous decision requirement make it a challenging task. Recently, reinforcement learning (RL) has demonstrated state-of-the-art performance in various sequential decision-making problems. However, the strict safety requirements make it impossible to explore optimal decisions through online trial and error. Therefore, this article proposes a novel offline RL approach designed to ensure safety, maximize return, and address issues of partially observed states. Specifically, it utilizes an off-policy actor-critic framework to infer the optimal decision from expert operation trajectories. The "actor" in this framework is jointly trained by the supervision and evaluation signals to make decision with low risk and high return. Furthermore, we investigate a recurrent version of the actor and critic networks to better capture the complete observations, which solves the partially observed Markov decision process (POMDP) arising from sensor limitations. Verification within the BF smelting process demonstrates the improvements of the proposed algorithm in performance, i.e., safety and return.

Resveratrol Promotes Proliferation, Antioxidant Properties, and Progesterone Production in Yak (Bos grunniens) Granulosa Cells.

Resveratrol (RES) is a class of natural polyphenolic compounds known for its strong anti-apoptotic and antioxidant properties. Granulosa cells (GCs) are one of the important components of ovarian follicles and play crucial roles in follicular development of follicles in the ovary. Here, we explored the effects of RES on the proliferation and functions of yak GCs. Firstly, we evaluated the effect of RES dose and time in culture on the viability of GCs, and then the optimum treatment protocol (10 µM RES, 36 h) was selected to analyze the effects of RES on the proliferation, cell cycle, apoptosis, malondialdehyde (MDA), glutathione (GSH), reactive oxygen species (ROS) accumulation, lipid droplet content, ATP production, and steroidogenesis of GCs, as well as the expression of related genes. The results show that RES treatment significantly (1) increased cell viability and proliferation and inhibited cell apoptosis by upregulating BCL-2 and SIRT1 genes and downregulating BAX, CASP3, P53, and KU70 genes; (2) increased the proportion of GCs in the S phase and upregulated CCND1, PCNA, CDK4, and CDK5 genes; (3) reduced ROS accumulation and MDA content and increased GSH content, as well as upregulating the relative expression levels of CAT, SOD2, and GPX1 genes; (4) decreased lipid droplet content and increased ATP production; (5) promoted progesterone (P4) secretion and the expression of P4 synthesis-related genes (StAR, HSD3B1, and CYP11A1); and (6) inhibited E2 secretion and CYP19A1 expression. These findings suggest that RES at 10 µM increases the proliferation and antioxidant properties, inhibits apoptosis, and promotes ATP production, lipid droplet consumption, and P4 secretion of yak GCs.

Unlocking the Effect of Chain Length and Terminal Group on Ethylene Glycol Ether Family Toward Advanced Aqueous Electrolytes.

Constructing high-performance hybrid electrolyte is important to advanced aqueous electrochemical energy storage devices. However, due to the lack of in-depth understanding of how the molecule structures of cosolvent additives influence the properties of electrolytes significantly impeded the development of hybrid electrolytes. Herein, a series of hybrid electrolytes are prepared by using ethylene glycol ether with different chain lengths and terminal groups as additives. The optimized 2 m LiTFSI-90%DDm hybrid electrolyte prepared from diethylene glycol dimethyl ether (DDm) molecule showcases excellent comprehensive performance and significantly enhances the operating voltage of supercapacitors (SCs) to 2.5 V by suppressing the activity of water. Moreover, the SC with 2 m LiTFSI-90%DDm hybrid electrolyte supplies a long-term cycling life of 50 000 cycles at 1 A g-1 with 92.3% capacitance retention as well as excellent low temperature (-40 ºC) cycling performance (10 000 times at 0.2 A g-1). Universally, Zn//polyaniline full cell with 2 m Zn(OTf)2-90%DDm electrolyte manifests outstanding cycling performance in terms of 77.9% capacity retention after 2,000 cycles and a dendrite-free Zn anode. This work inspires new thinking of developing advanced hybrid electrolytes by cosolvent molecule design toward high-performance energy storage devices.

Animal models of heart failure with preserved ejection fraction (HFpEF): from metabolic pathobiology to drug discovery.

Heart failure (HF) with preserved ejection fraction (HFpEF) is currently a preeminent challenge for cardiovascular medicine. It has a poor prognosis, increasing mortality, and is escalating in prevalence worldwide. Despite accounting for over 50% of all HF patients, the mechanistic underpinnings driving HFpEF are poorly understood, thus impeding the discovery and development of mechanism-based therapies. HFpEF is a disease syndrome driven by diverse comorbidities, including hypertension, diabetes and obesity, pulmonary hypertension, aging, and atrial fibrillation. There is a lack of high-fidelity animal models that faithfully recapitulate the HFpEF phenotype, owing primarily to the disease heterogeneity, which has hampered our understanding of the complex pathophysiology of HFpEF. This review provides an updated overview of the currently available animal models of HFpEF and discusses their characteristics from the perspective of energy metabolism. Interventional strategies for efficiently utilizing energy substrates in preclinical HFpEF models are also discussed.

Multiple Complementary Priors for Multispectral Image Compressive Sensing Reconstruction.

Compressive sensing (CS) techniques using a few compressed measurements have drawn considerable interest in reconstructing multispectral imagery (MSI). Nonlocal-based tensor methods have been widely used for MSI-CS reconstruction, which employ the nonlocal self-similarity (NSS) property of MSI to obtain satisfactory results. However, such methods only consider the internal priors of MSI while ignoring important external image information, for example deep-driven priors learned from a corpus of natural image datasets. Meanwhile, they usually suffer from annoying ringing artifacts due to the aggregation of overlapping patches. In this article, we propose a novel approach for highly effective MSI-CS reconstruction using multiple complementary priors (MCPs). The proposed MCP jointly exploits nonlocal low-rank and deep image priors under a hybrid plug-and-play framework, which contains multiple pairs of complementary priors, namely, internal and external, shallow and deep, and NSS and local spatial priors. To make the optimization tractable, a well-known alternating direction method of multiplier (ADMM) algorithm based on the alternating minimization framework is developed to solve the proposed MCP-based MSI-CS reconstruction problem. Extensive experimental results demonstrate that the proposed MCP algorithm outperforms many state-of-the-art CS techniques in MSI reconstruction. The source code of the proposed MCP-based MSI-CS reconstruction algorithm is available at: https://github.com/zhazhiyuan/MCP_MSI_CS_Demo.git.

VGSG: Vision-Guided Semantic-Group Network for Text-Based Person Search.

Text-based Person Search (TBPS) aims to retrieve images of target pedestrian indicated by textual descriptions. It is essential for TBPS to extract fine-grained local features and align them crossing modality. Existing methods utilize external tools or heavy cross-modal interaction to achieve explicit alignment of cross-modal fine-grained features, which is inefficient and time-consuming. In this work, we propose a Vision-Guided Semantic-Group Network (VGSG) for text-based person search to extract well-aligned fine-grained visual and textual features. In the proposed VGSG, we develop a Semantic-Group Textual Learning (SGTL) module and a Vision-guided Knowledge Transfer (VGKT) module to extract textual local features under the guidance of visual local clues. In SGTL, in order to obtain the local textual representation, we group textual features from the channel dimension based on the semantic cues of language expression, which encourages similar semantic patterns to be grouped implicitly without external tools. In VGKT, a vision-guided attention is employed to extract visual-related textual features, which are inherently aligned with visual cues and termed vision-guided textual features. Furthermore, we design a relational knowledge transfer, including a vision-language similarity transfer and a class probability transfer, to adaptively propagate information of the vision-guided textual features to semantic-group textual features. With the help of relational knowledge transfer, VGKT is capable of aligning semantic-group textual features with corresponding visual features without external tools and complex pairwise interaction. Experimental results on two challenging benchmarks demonstrate its superiority over state-of-the-art methods.

A hemodynamic analysis of fenestrated physician-modified endograft repair for complicated aortic dissections involving the visceral arteries.

OBJECTIVE: The aim of this study is to perform patient-specific hemodynamic simulations of the patients with complicated aortic dissection underwent Physician-modified endograft (PMEG) and evaluate the treatment outcome. METHOD: 12 patient-specific models were reconstructed from computed tomography angiography (CTA) data of 6 patients with complicated aortic dissection before and after the PMEG. Hemodynamic simulations were conducted with the same time-varying volumetric flow rate extracted from the literature and 3-element Windkessel model (3 EWM) boundary conditions were applied at the aortic outlet. Hemodynamic indicators such as time-averaged wall shear stress (TAWSS), relative residence time (RRT) and endothelial cell activation potential (ECAP) were obtained to evaluate the postoperative effect of PMEG. RESULTS: Comparing with the preoperative models, the flow rates of most visceral arteries were increased in the postoperative models (PSMA = 0.012, PRRA = 0.013, and PLRA = 0.005). Pressure and TAWSS in visceral regions were significantly reduced (PP = 0.003 and PTAWSS = 0.017). With the false lumens (FL) covered by the stent grafts, the average TAWSS level increased in the regions of postoperative abdominal aorta (P = 0.002), and the average RRT and ECAP values decreased significantly (PRRT = 0.02 and PECAP = 0.003). CONCLUSION: This study shows that PMEG, as a new technique for the treatment of complicated aortic dissection involving the distal tears in the visceral region, can effectively restore the abnormal blood supply of the visceral arteries, reduce the risk of aortic rupture, the formation of aortic dissection aneurysm (ADA), and thrombosis. This corresponds well with clinical retrospective studies and 1-year follow-up outcomes. The findings of this study are of great significance for the development of PMEG.

Fast-SNN: Fast Spiking Neural Network by Converting Quantized ANN.

Spiking neural networks (SNNs) have shown advantages in computation and energy efficiency over traditional artificial neural networks (ANNs) thanks to their event-driven representations. SNNs also replace weight multiplications in ANNs with additions, which are more energy-efficient and less computationally intensive. However, it remains a challenge to train deep SNNs due to the discrete spiking function. A popular approach to circumvent this challenge is ANN-to-SNN conversion. However, due to the quantization error and accumulating error, it often requires lots of time steps (high inference latency) to achieve high performance, which negates SNN's advantages. To this end, this paper proposes Fast-SNN that achieves high performance with low latency. We demonstrate the equivalent mapping between temporal quantization in SNNs and spatial quantization in ANNs, based on which the minimization of the quantization error is transferred to quantized ANN training. With the minimization of the quantization error, we show that the sequential error is the primary cause of the accumulating error, which is addressed by introducing a signed IF neuron model and a layer-wise fine-tuning mechanism. Our method achieves state-of-the-art performance and low latency on various computer vision tasks, including image classification, object detection, and semantic segmentation. Codes are available at: https://github.com/yangfan-hu/Fast-SNN.

Robust Reflection Removal With Flash-Only Cues in the Wild.

We propose a simple yet effective reflection-free cue for robust reflection removal from a pair of flash and ambient (no-flash) images. The reflection-free cue exploits a flash-only image obtained by subtracting the ambient image from the corresponding flash image in raw data space. The flash-only image is equivalent to an image taken in a dark environment with only a flash on. This flash-only image is visually reflection-free and thus can provide robust cues to infer the reflection in the ambient image. Since the flash-only image usually has artifacts, we further propose a dedicated model that not only utilizes the reflection-free cue but also avoids introducing artifacts, which helps accurately estimate reflection and transmission. Our experiments on real-world images with various types of reflection demonstrate the effectiveness of our model with reflection-free flash-only cues: our model outperforms state-of-the-art reflection removal approaches by more than 5.23 dB in PSNR. We extend our approach to handheld photography to address the misalignment between the flash and no-flash pair. With misaligned training data and the alignment module, our aligned model outperforms our previous version by more than 3.19 dB in PSNR on a misaligned dataset. We also study using linear RGB images as training data.

Molecular characterization and effects of the TGIF1 gene on proliferation and steroidogenesis in yak (Bos grunniens) granulosa cells.

TG interaction factor 1 (TGIF1) plays a major role in transcriptional inhibition and suppression of TGF-ß signaling, but its functional roles in granulosa cells (GCs) have not been elucidated; in particular, there is no information about the yak (Bos grunniens) TGIF1 gene. Therefore, the objectives of this study were to clone yak TGIF1 and investigate TGIF1 functions in yak GCs. RT‒PCR results showed that the coding region of yak TGIF1 is 759 bp and encodes 252 amino acids. Its nucleotide sequence showed 85.24-99.74% similarity to mouse, human, pig, goat and cattle homologous genes. To explore the functional roles of TGIF1, we studied proliferation, apoptosis, cell cycle progression, steroidogenesis and the expression levels of related genes in yak GCs transfected with small interfering RNA specific to TGIF1. The results showed that TGIF1 knockdown promoted proliferation and cell cycle progression and inhibited apoptosis and estradiol (E2) and progesterone (P4) production in cultured yak GCs. Conversely, TGIF1 overexpression inhibited proliferation and cell cycle progression and stimulated apoptosis and E2 and P4 production. In addition, these functional changes in yak GCs were observed parallel to the expression changes in genes involved in the cell cycle (PCNA, CDK2, CCND1, CCNE1, CDK4 and P53), apoptosis (BCL2, BAX and CASPASE3), and steroidogenesis (CYP11A1, 3ß-HSD and StAR). In conclusion, TGIF1 was relatively conserved in the course of animal evolution. TGIF1 inhibited GC viability and stimulated apoptosis and the secretion of E2 and P4 by yak GCs. Our results will help to reveal the mechanism underlying yak follicular development and improve the reproductive efficiency of female yaks.

Image-based real-time feedback control of magnetic digital microfluidics by artificial intelligence-empowered rapid object detector for automated in vitro diagnostics.

In vitro diagnostics (IVD) plays a critical role in healthcare and public health management. Magnetic digital microfluidics (MDM) perform IVD assays by manipulating droplets on an open substrate with magnetic particles. Automated IVD based on MDM could reduce the risk of accidental exposure to contagious pathogens among healthcare workers. However, it remains challenging to create a fully automated IVD platform based on the MDM technology because of a lack of effective feedback control system to ensure the successful execution of various droplet operations required for IVD. In this work, an artificial intelligence (AI)-empowered MDM platform with image-based real-time feedback control is presented. The AI is trained to recognize droplets and magnetic particles, measure their size, and determine their location and relationship in real time; it shows the ability to rectify failed droplet operations based on the feedback information, a function that is unattainable by conventional MDM platforms, thereby ensuring that the entire IVD process is not interrupted due to the failure of liquid handling. We demonstrate fundamental droplet operations, which include droplet transport, particle extraction, droplet merging and droplet mixing, on the MDM platform and show how the AI rectify failed droplet operations by acting upon the feedback information. Protein quantification and antibiotic resistance detection are performed on this AI-empowered MDM platform, and the results obtained agree well with the benchmarks. We envision that this AI-based feedback approach will be widely adopted not only by MDM but also by other types of digital microfluidic platforms to offer precise and error-free droplet operations for a wide range of automated IVD applications.

Chromosome Detection in Metaphase Cell Images Using Morphological Priors.

Reliable chromosome detection in metaphase cell (MC) images can greatly alleviate the workload of cytogeneticists for karyotype analysis and the diagnosis of chromosomal disorders. However, it is still an extremely challenging task due to the complicated characteristics of chromosomes, e.g., dense distributions, arbitrary orientations, and various morphologies. In this article, we propose a novel rotated-anchor-based detection framework, named DeepCHM, for fast and accurate chromosome detection in MC images. Our framework has three main innovations: 1) A deep saliency map representing chromosomal morphological features is learned end-to-end with semantic features. This not only enhances the feature representations for anchor classification and regression but also guides the anchor setting to significantly reduce redundant anchors. This accelerates the detection and improves the performance; 2) A hardness-aware loss weights the contribution of positive anchors, which effectively reinforces the model to identify hard chromosomes; 3) A model-driven sampling strategy addresses the anchor imbalance issue by adaptively selecting hard negative anchors for model training. In addition, a large-scale benchmark dataset with a total of 624 images and 27,763 chromosome instances was built for chromosome detection and segmentation. Extensive experimental results demonstrate that our method outperforms most state-of-the-art (SOTA) approaches and successfully handles chromosome detection, with an AP score of 93.53%.