A dataset for fine-grained seed recognition.

The research of plant seeds has always been a focus of agricultural and forestry research, and seed identification is an indispensable part of it. With the continuous application of artificial intelligence technology in the field of agriculture, seed identification through computer vision can effectively promote the development of agricultural and forestry wisdom. Data is the foundation of computer vision, but there is a lack of suitable datasets in the agricultural field. In this paper, a seed dataset named LZUPSD is established. A device based on mobile phones and macro lenses was established to acquire images. The dataset contains 4496 images of 88 different seeds. This dataset can not only be used as data for training deep learning models in the computer field, but also provide important data support for agricultural and forestry research. As an important resource in this field, this dataset plays a positive role in modernizing agriculture and forestry.

Habitual salt preference worsens blood pressure in hospitalized hypertensive patients with omicron infection under epidemic-related stress.

BACKGROUND: We investigated the synergistic effect of stress and habitual salt preference (SP) on blood pressure (BP) in the hospitalized Omicron-infected patients. METHODS: From 15,185 hospitalized Omicron-infected patients who reported having high BP or hypertension, we recruited 662 patients. All patients completed an electronic questionnaire on diet and stress, and were required to complete morning BP monitoring at least three times. RESULTS: The hypertensive group (n = 309) had higher habitual SP (P = 0.015) and COVID-19 related stress (P < 0.001), and had longer hospital stays (7.4 ± 1.5 days vs. 7.2 ± 0.5 days, P = 0.019) compared with controls (n = 353). After adjusting for a wide range of covariates including Omicron epidemic-related stress, habitual SP was found to increase both systolic (4.9 [95% confidence interval (CI), 2.3-7.4] mmHg, P < 0.001) and diastolic (2.1 [95%CI, 0.6-3.6] mmHg, P = 0.006) BP in hypertensive patients, and increase diastolic BP (2.0 [95%CI, 0.2-3.7] mmHg, P = 0.026) in the control group. 31 (8.8%) patients without a history of hypertension were discovered to have elevated BP during hospitalization, and stress was shown to be different in those patients (P < 0.001). In contrast, habitual SP was more common in hypertensive patients with uncontrolled BP, compared with patients with controlled BP (P = 0.002). CONCLUSIONS: Habitual SP and psychosocial stress were associated with higher BP in Omicron-infected patients both with and without hypertension. Nonpharmaceutical intervention including dietary guidance and psychiatric therapy are crucial for BP control during the long COVID-19 period.

Evolution-strengthened knowledge graph enables predicting the targetability and druggability of genes.

Identifying promising targets is a critical step in modern drug discovery, with causative genes of diseases that are an important source of successful targets. Previous studies have found that the pathogeneses of various diseases are closely related to the evolutionary events of organisms. Accordingly, evolutionary knowledge can facilitate the prediction of causative genes and further accelerate target identification. With the development of modern biotechnology, massive biomedical data have been accumulated, and knowledge graphs (KGs) have emerged as a powerful approach for integrating and utilizing vast amounts of data. In this study, we constructed an evolution-strengthened knowledge graph (ESKG) and validated applications of ESKG in the identification of causative genes. More importantly, we developed an ESKG-based machine learning model named GraphEvo, which can effectively predict the targetability and the druggability of genes. We further investigated the explainability of the ESKG in druggability prediction by dissecting the evolutionary hallmarks of successful targets. Our study highlights the importance of evolutionary knowledge in biomedical research and demonstrates the potential power of ESKG in promising target identification. The data set of ESKG and the code of GraphEvo can be downloaded from https://github.com/Zhankun-Xiong/GraphEvo.

Prognostic value of the sarcomatoid component in bladder cancer: A propensity score matching study.

Sarcomatoid carcinoma of the bladder is rare, and little is known about the prognostic impact of the proportion of sarcomatoid components of the bladder. The present study aimed to assess the prognostic value of the proportion of sarcomatoid components with regard to death and recurrence rates in patients with bladder cancer (BC), and to validate the worse survival results of sarcomatoid carcinomas of the bladder using propensity score matching. Patients with sarcomatoid carcinoma of the bladder who were treated at the Affiliated Hospital of Qingdao University between August 2010 and May 2021 were included in the study. A 1:2 propensity score matching system based on age, sex and pathological T stage was used for sarcomatoid and non-sarcomatoid carcinoma matching. Finally, 114 patients with BC were included. Patients with sarcomatoid carcinoma had worse 5-year cancer-specific survival (CSS) (69.1 vs. 86.9%; log-rank P=0.008) and recurrence-free survival (RFS) (64.1 vs. 83.6%; log-rank P=0.001) rates compared with patients with non-sarcomatoid carcinoma, as had the subgroup with muscle invasion. Multivariate analysis revealed sarcomatoid carcinoma as an independent prognostic factor. Patients with a low proportion of sarcomatoid components (1-50%) had a better prognosis than patients with a high proportion (>50%), and no significant difference was found compared with the non-sarcomatoid group. Overall, a proportion of sarcomatoid components >50% was a predictor of CSS and RFS. Sarcomatoid components markedly increased the risk of death and recurrence in muscle-invasive BC, but not in non-muscle-invasive BC. A higher proportion of sarcomatoid components was significantly associated with poorer survival.

View-Consistent Heterogeneous Network on Graphs With Few Labeled Nodes.

Performing transductive learning on graphs with very few labeled data, that is, two or three samples for each category, is challenging due to the lack of supervision. In the existing work, self-supervised learning via a single view model is widely adopted to address the problem. However, recent observation shows multiview representations of an object share the same semantic information in high-level feature space. For each sample, we generate heterogeneous representations and use view-consistency loss to make their representations consistent with each other. Multiview representation also inspires to supervise the pseudolabels generation by the aid of mutual supervision between views. In this article, we thus propose a view-consistent heterogeneous network (VCHN) to learn better representations by aligning view-agnostic semantics. Specifically, VCHN is constructed by constraining the predictions between two views so that the view pairs can supervise each other. To make the best use of cross-view information, we further propose a novel training strategy to generate more reliable pseudolabels, which thus enhances predictions of the VCHN. Extensive experimental results on three benchmark datasets demonstrate that our method achieves superior performance over state-of-the-art methods under very low label rates.

Undifferentiated carcinoma of the pancreas with osteoclast like giant cells: Literature review with CT/MR imaging findings in 3 cases.

Undifferentiated carcinoma with osteoclast-like giant cells of pancreas (UCOGCP) is a relatively rare tumor worldwide. Its accurate preoperative diagnosis is extremely difficult. Because the mass is usually large and closely related to neighboring structures, it is difficult to locate the tumor and it is often misdiagnosed as pancreatic cancer, neuroendocrine tumor or gastrointestinal stromal tumor. Combining literature to analyze UCOGCP clinical features (including age of onset, prevalent location) and imaging features (including lesion size, mass nature), to explore the value of preoperative CT and MRI in the diagnosis and differential diagnosis of UCOGCP and hope to help clinical diagnosis and treatment.

Exploration of the Function of Ginsenoside RD Attenuates Lipopolysaccharide-Induced Lung Injury: A Study of Network Pharmacology and Experimental Validation.

OBJECTIVE: Ginsenoside Rd (GSRd) displays a variety of pharmacological effects. However, the underlying role in acute lung injury (ALI) is not clear. In this study, the protective effect of GSRd on lipopolysaccharide (LPS)-induced ALI is investigated to explore the potential mechanisms. METHODS: GSRd-target-ALI-related gene set was constructed. And bioinformatics tools were used to discover the potential mechanism. We observed the survival of subjects for 72 h. In addition, male BALB/c mice were intraperitoneal injected with GSRd (25 and 50 mg/kg) after received one intratracheal instillation of LPS. Inflammatory changes, oxidative stress, and phosphorylation were assessed to study the biological effects. RESULTS: A total of 245 interaction genes were collected. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses were enriched in immune-inflammatory system. Among them, PI3K-Akt signaling pathway was the highest-ranked pathway of inflammatory response. In vivo study, it was found that GSRd improved survival in endotoxemic mice and inhibited the major characteristic of ALI. And the p-PI3K and p-Akt expression was significantly decreased by GSRd treatment. CONCLUSION: GSRd could protect mice against LPS-induced ALI effectively by inhibiting the PI3K-Akt signaling pathway.

Evaluation of Modified Calcium Removal Algorithm in dual energy CT of Internal Carotid Artery.

PURPOSE: To evaluate the performance of a dual-energy (DE) calcium removal software based on a modified three-material decomposition algorithm in assessing the stenosis of the internal carotid artery (ICA) in comparison with mixed images using digital subtraction angiography (DSA) as the reference standard. METHODS: Forty-six patients (38 men; 67±8 years old), including 154 calcified ICA segments C1-C2 (59), C3-C5 (63), C6 (24), and C7 (8), were recruited in this retrospective study. Mixed images and virtual non-calcium (VNCa) images using the modified dual-energy computed tomography (DECT) algorithm were reconstructed. The differences between VNCa and DSA images vs. mixed and DSA images of degree of stenosis were compared. The intraclass correlation coefficient (ICC) was used for assessing the agreement between VNCa, mixed images, and DSA. RESULTS: The degree of stenosis differed significantly between mixed and DSA images in the C3-C5 (30%±17.9% vs. 23.0%±16.9%, p = 0.026) and C6 (38.3%±15.4% vs. 28.5%±13.3%, p = 0.023) segments. The stenosis of VNCa images showed no significant difference with DSA images in all segments (all p > 0.05). The ICCs between VNCa and DSA images (0.86-0.97) were higher than those between the mixed and DSA images (0.68-0.96) in all segments. CONCLUSION: The performance of a modified three-material decomposition DECT algorithm for calcium removal in ICA stenosis evaluation, particularly for the C3-C5 and C6 ICA segments, was promising.

Making Historical Gyroscopes Alive-2D and 3D Preservations by Sensor Fusion and Open Data Access.

The preservation of cultural heritage assets of all kind is an important task for modern civilizations. This also includes tools and instruments that have been used in the previous decades and centuries. Along with the industrial revolution 200 years ago, mechanical and electrical technologies emerged, together with optical instruments. In the meantime, it is not only museums who showcase these developments, but also companies, universities, and private institutions. Gyroscopes are fascinating instruments with a history dating back 200 years. When J.G.F. Bohnenberger presented his machine to his students in 1810 at the University of Tuebingen, Germany, nobody could have foreseen that this fascinating development would be used for complex orientation and positioning. At the University of Stuttgart, Germany, a collection of 160 exhibits is available and in transition towards their sustainable future. Here, the systems are digitized in 2D, 2.5D, and 3D and are made available for a worldwide community using open access platforms. The technologies being used are computed tomography, computer vision, endoscopy, and photogrammetry. We present a novel workflow for combining voxel representations and colored point clouds, to create digital twins of the physical objects with 0.1 mm precision. This has not yet been investigated and is therefore pioneering work. Advantages and disadvantages are discussed and suggested work for the near future is outlined in this new and challenging field of tech heritage digitization.

A Low Temperature Drifting Acoustic Wave Pressure Sensor with an Integrated Vacuum Cavity for Absolute Pressure Sensing.

In this paper we demonstrate a novel acoustic wave pressure sensor, based on an aluminum nitride (AlN) piezoelectric thin film. It contains an integrated vacuum cavity, which is micro-fabricated using a cavity silicon-on-insulator (SOI) wafer. This sensor can directly measure the absolute pressure without the help of an external package, and the vacuum cavity gives the sensor a very accurate reference pressure. Meanwhile, the presented pressure sensor is superior to previously reported acoustic wave pressure sensors in terms of the temperature drift. With the carefully designed dual temperature compensation structure, a very low temperature coefficient of frequency (TCF) is achieved. Experimental results show the sensor can measure the absolute pressure in the range of 0 to 0.4 MPa, while the temperature range is from 20 °C to 220 °C with a TCF of -14.4 ppm/°C. Such a TCF is only about half of that of previously reported works.

Tuning of Persulfate Activation from a Free Radical to a Nonradical Pathway through the Incorporation of Non-Redox Magnesium Oxide.

Nonradical-based advanced oxidation processes for pollutant removal have attracted much attention due to their inherent advantages. Herein we report that magnesium oxides (MgO) in CuOMgO/Fe3O4 not only enhanced the catalytic properties but also switched the free radical peroxymonosulfate (PMS)-activated process into the 1O2 based nonradical process. CuOMgO/Fe3O4 catalyst exhibited consistent performance in a wide pH range from 5.0 to 10.0, and the degradation kinetics were not inhibited by the common free radical scavengers, anions, or natural organic matter. Quantitative structure-activity relationships (QSARs) revealed the relationship between the degradation rate constant of 14 substituted phenols and their conventional descriptor variables (i.e., Hammett constants σ, σ-, σ+), half-wave oxidation potential (E1/2), and pKa values. QSARs together with the kinetic isotopic effect (KIE) recognized the electron transfer as the dominant oxidation process. Characterizations and DFT calculation indicated that the incorporated MgO alters the copper sites to highly oxidized metal centers, offering a more suitable platform for PMS to generate metastable copper intermediates. These highly oxidized metals centers of copper played the key role in producing O2•- after accepting an electron from another PMS molecule, and finally 1O2 as sole reactive species was generated from the direct oxidation of O2•- through thermodynamically feasible reactions.

Comparison of image quality between split-filter twin beam dual energy and single energy images in abdominal CT.

PURPOSE: To compare the objective and subjective image quality between composed images from split-filter twin beam dual energy (TBDE) and single-energy computed tomography (SECT) in abdominal CT. METHODS: In this prospective study, 103 patients were imaged using TBDE (n = 51) or SECT (n = 52). The CT number and noise were measured for the following six abdominal structures: liver, spleen, fat, muscle, aorta and portal vein. The normalised noise level for the liver was separately measured and compared. The consistency of the SNR and CT number was compared between the two groups. The subjective image quality was evaluated using six aspects in a blinded manner. Cohen's Kappa statistic was used to determine the level of agreement between the two radiologists. RESULTS: For the objective image quality comparison, the SNR of all structures was higher using TBDE compared to SECT (p < 0.05). The CT value for different structures were comparable between the two groups (p > 0.05). Among all patient sizes, the noise level for TBDE images was significantly lower (7-17% reduction) compared to the SECT images (p < 0.01). Furthermore, noise reduction's magnitude increases with body size. For image quality's subjective evaluation, TBDE images are superior for certain aspects. Cohen's Kappa values (0.7634-0.8460) suggest an adequate level of agreement between the two observers. CONCLUSIONS: TBDE scan mode can yield similar or even better objective and subjective image quality at the same level of radiation than conventional SECT. Quantitatively, TBDE images have a 7-17% reduction in noise, depending on the size of the scanned body regions.

Preparation of Yolk-Shell-Structured Cox Fe1-x P with Enhanced OER Performance.

The design and development of low-cost, highly efficient, and stable electrocatalysts to take the place of noble-metal catalysts for the oxygen evolution reaction (OER) remain a significant challenge. Herein, the synthesis of yolk-shell-structured binary transition metal phosphide Cox Fe1-x P with different Co/Fe ratios by phosphidation of a cobalt ferrite precursor is reported. The as-synthesized Cox Fe1-x P catalysts were used for the OER. All yolk-shell Cox Fe1-x P catalysts with different Co/Fe ratios showed much better performance than the corresponding solid catalyst. The formation of Co oxides on the catalyst surface during OER and the optimal Co/Fe ratio were found to be critical to their activity. Among the as-prepared Cox Fe1-x P catalysts, that with a Co/Fe ratio of 0.47/0.53 (Co0.47 Fe0.53 P) exhibited the best performance. Co0.47 Fe0.53 P has an overpotential of 277 mV at a current density of 10 mA cm-2 , a Tafel slope of 37 mV dec-1 , and superior stability in alkaline medium. The outstanding performance is partly ascribed to the transfer of valence electrons from Co to P and Fe. The Co0.47 Fe0.53 P matrix with excellent conductivity and Fe phosphate that is stable on the surface of the catalyst are also helpful for the OER performance. In addition, the yolk-shell structure of Co0.47 Fe0.53 P increases the contact area between electrolyte and catalyst. These characteristics of Co0.47 Fe0.53 P greatly improve its OER performance. This optimized binary transition metal phosphide provides a new approach for the design of nonprecious-metal electrocatalysts.

Adaptive Structure Discovery for Multimedia Analysis Using Multiple Features.

Multifeature learning has been a fundamental research problem in multimedia analysis. Most existing multifeature learning methods exploit graph, which must be computed beforehand, as input to uncover data distribution. These methods have two major problems confronted. First, graph construction requires calculating similarity based on nearby data pairs by a fixed function, e.g., the RBF kernel, but the intrinsic correlation among different data pairs varies constantly. Therefore, feature learning based on such predefined graphs may degrade, especially when there is dramatic correlation variation between nearby data pairs. Second, in most existing algorithms, each single-feature graph is computed independently and then combine them for learning, which ignores the correlation between multiple features. In this paper, a new unsupervised multifeature learning method is proposed to make the best utilization of the correlation among different features by jointly optimizing data correlation from multiple features in an adaptive way. As opposed to computing the affinity weight of data pairs by a fixed function, the weight of affinity graph is learned by a well-designed optimization problem. Additionally, the affinity graph of data pairs from different features is optimized in a global level to better leverage the correlation among different channels. In this way, the adaptive approach correlates the features of all features for a better learning process. Experimental results on real-world datasets demonstrate that our approach outperforms the state-of-the-art algorithms on leveraging multiple features for multimedia analysis.

Multiview Consensus Graph Clustering.

A graph is usually formed to reveal the relationship between data points and graph structure is encoded by the affinity matrix. Most graph-based multiview clustering methods use predefined affinity matrices and the clustering performance highly depends on the quality of graph. We learn a consensus graph with minimizing disagreement between different views and constraining the rank of the Laplacian matrix. Since diverse views admit the same underlying cluster structure across multiple views, we use a new disagreement cost function for regularizing graphs from different views toward a common consensus. Simultaneously, we impose a rank constraint on the Laplacian matrix to learn the consensus graph with exactly connected components where is the number of clusters, which is different from using fixed affinity matrices in most existing graph-based methods. With the learned consensus graph, we can directly obtain the cluster labels without performing any post-processing, such as -means clustering algorithm in spectral clustering-based methods. A multiview consensus clustering method is proposed to learn such a graph. An efficient iterative updating algorithm is derived to optimize the proposed challenging optimization problem. Experiments on several benchmark datasets have demonstrated the effectiveness of the proposed method in terms of seven metrics.

Adaptive Structure Concept Factorization for Multiview Clustering.

Most existing multiview clustering methods require that graph matrices in different views are computed beforehand and that each graph is obtained independently. However, this requirement ignores the correlation between multiple views. In this letter, we tackle the problem of multiview clustering by jointly optimizing the graph matrix to make full use of the data correlation between views. With the interview correlation, a concept factorization-based multiview clustering method is developed for data integration, and the adaptive method correlates the affinity weights of all views. This method differs from nonnegative matrix factorization-based clustering methods in that it can be applicable to data sets containing negative values. Experiments are conducted to demonstrate the effectiveness of the proposed method in comparison with state-of-the-art approaches in terms of accuracy, normalized mutual information, and purity.

Diverse Non-Negative Matrix Factorization for Multiview Data Representation.

Non-negative matrix factorization (NMF), a method for finding parts-based representation of non-negative data, has shown remarkable competitiveness in data analysis. Given that real-world datasets are often comprised of multiple features or views which describe data from various perspectives, it is important to exploit diversity from multiple views for comprehensive and accurate data representations. Moreover, real-world datasets often come with high-dimensional features, which demands the efficiency of low-dimensional representation learning approaches. To address these needs, we propose a diverse NMF (DiNMF) approach. It enhances the diversity, reduces the redundancy among multiview representations with a novel defined diversity term and enables the learning process in linear execution time. We further propose a locality preserved DiNMF (LP-DiNMF) for more accurate learning, which ensures diversity from multiple views while preserving the local geometry structure of data in each view. Efficient iterative updating algorithms are derived for both DiNMF and LP-DiNMF, along with proofs of convergence. Experiments on synthetic and real-world datasets have demonstrated the efficiency and accuracy of the proposed methods against the state-of-the-art approaches, proving the advantages of incorporating the proposed diversity term into NMF.

MiR429 expression level in renal cell cancer and its correlation with the prognosis of patients.

PURPOSE: To test the hypothesis that miR429 expression in renal cancer patients is increased and plays a role in the pathogenesis of the disease. METHODS: Twenty-seven renal cancer patients admitted to our hospital from May 2014 to May 2015 were enrolled as the study group, and 28 non-cancer patients were selected during the same period as the control group. Renal biopsy and serum samples were used to detect miR429 expression levels, and the patient histories were obtained to make relevant associations to clinical outcomes. In addition, the renal cancer cell line SK458 was used for overexpressing or knocking out miR429 in in vitro experiments to observe changes in proliferation and apoptosis rates. RESULTS: The expression levels of miR429 in renal tissues and serum of renal cancer patients were significantly higher compared with control patients (p<0.05). In addition, a correlation was found between the levels of miR429 in the serum of renal cell cancer patients and their clinical outcome after conventional treatment, with patients expressing lower miR429 levels showing better clinical outcomes. Finally, experiments with renal cancer cells revealed that the proliferation of cells overexpressing miR429 was increased and their apoptosis rate was significantly reduced, while the opposite was true in miR429-knockout cells. CONCLUSIONS: It seems that miR429 can inhibit normal apoptosis rates and lead to high proliferation rates. Accordingly, the higher serum miR429 level in renal cancer patients suggests that it plays a role in the pathogenesis of the disease, while the differential miR429 levels according to the patients' clinical outcomes after treatment suggest that miR429 may be useful as a marker for prognosis.

Feature-Linking Model for Image Enhancement.

Inspired by gamma-band oscillations and other neurobiological discoveries, neural networks research shifts the emphasis toward temporal coding, which uses explicit times at which spikes occur as an essential dimension in neural representations. We present a feature-linking model (FLM) that uses the timing of spikes to encode information. The first spiking time of FLM is applied to image enhancement, and the processing mechanisms are consistent with the human visual system. The enhancement algorithm achieves boosting the details while preserving the information of the input image. Experiments are conducted to demonstrate the effectiveness of the proposed method. Results show that the proposed method is effective.

Local sparsity enhanced compressed sensing magnetic resonance imaging in uniform discrete curvelet domain.

BACKGROUND: Compressed sensing(CS) has been well applied to speed up imaging by exploring image sparsity over predefined basis functions or learnt dictionary. Firstly, the sparse representation is generally obtained in a single transform domain by using wavelet-like methods, which cannot produce optimal sparsity considering sparsity, data adaptivity and computational complexity. Secondly, most state-of-the-art reconstruction models seldom consider composite regularization upon the various structural features of images and transform coefficients sub-bands. Therefore, these two points lead to high sampling rates for reconstructing high-quality images. METHODS: In this paper, an efficient composite sparsity structure is proposed. It learns adaptive dictionary from lowpass uniform discrete curvelet transform sub-band coefficients patches. Consistent with the sparsity structure, a novel composite regularization reconstruction model is developed to improve reconstruction results from highly undersampled k-space data. It is established via minimizing spatial image and lowpass sub-band coefficients total variation regularization, transform sub-bands coefficients l 1 sparse regularization and constraining k-space measurements fidelity. A new augmented Lagrangian method is then introduced to optimize the reconstruction model. It updates representation coefficients of lowpass sub-band coefficients over dictionary, transform sub-bands coefficients and k-space measurements upon the ideas of constrained split augmented Lagrangian shrinkage algorithm. RESULTS: Experimental results on in vivo data show that the proposed method obtains high-quality reconstructed images. The reconstructed images exhibit the least aliasing artifacts and reconstruction error among current CS MRI methods. CONCLUSIONS: The proposed sparsity structure can fit and provide hierarchical sparsity for magnetic resonance images simultaneously, bridging the gap between predefined sparse representation methods and explicit dictionary. The new augmented Lagrangian method provides solutions fully complying to the composite regularization reconstruction model with fast convergence speed.