Phen2Disease: a phenotype-driven model for disease and gene prioritization by bidirectional maximum matching semantic similarities.

Human Phenotype Ontology (HPO)-based approaches have gained popularity in recent times as a tool for genomic diagnostics of rare diseases. However, these approaches do not make full use of the available information on disease and patient phenotypes. We present a new method called Phen2Disease, which utilizes the bidirectional maximum matching semantic similarity between two phenotype sets of patients and diseases to prioritize diseases and genes. Our comprehensive experiments have been conducted on six real data cohorts with 2051 cases (Cohort 1, n = 384; Cohort 2, n = 281; Cohort 3, n = 185; Cohort 4, n = 784; Cohort 5, n = 208; and Cohort 6, n = 209) and two simulated data cohorts with 1000 cases. The results of the experiments showed that Phen2Disease outperforms the three state-of-the-art methods when only phenotype information and HPO knowledge base are used, particularly in cohorts with fewer average numbers of HPO terms. We also observed that patients with higher information content scores have more specific information, leading to more accurate predictions. Moreover, Phen2Disease provides high interpretability with ranked diseases and patient HPO terms presented. Our method provides a novel approach to utilizing phenotype data for genomic diagnostics of rare diseases, with potential for clinical impact. Phen2Disease is freely available on GitHub at https://github.com/ZhuLab-Fudan/Phen2Disease.

NetGO 2.0: improving large-scale protein function prediction with massive sequence, text, domain, family and network information.

With the explosive growth of protein sequences, large-scale automated protein function prediction (AFP) is becoming challenging. A protein is usually associated with dozens of gene ontology (GO) terms. Therefore, AFP is regarded as a problem of large-scale multi-label classification. Under the learning to rank (LTR) framework, our previous NetGO tool integrated massive networks and multi-type information about protein sequences to achieve good performance by dealing with all possible GO terms (>44 000). In this work, we propose the updated version as NetGO 2.0, which further improves the performance of large-scale AFP. NetGO 2.0 also incorporates literature information by logistic regression and deep sequence information by recurrent neural network (RNN) into the framework. We generate datasets following the critical assessment of functional annotation (CAFA) protocol. Experiment results show that NetGO 2.0 outperformed NetGO significantly in biological process ontology (BPO) and cellular component ontology (CCO). In particular, NetGO 2.0 achieved a 12.6% improvement over NetGO in terms of area under precision-recall curve (AUPR) in BPO and around 2.6% in terms of $\mathbf {F_{max}}$ in CCO. These results demonstrate the benefits of incorporating text and deep sequence information for the functional annotation of BPO and CCO. The NetGO 2.0 web server is freely available at http://issubmission.sjtu.edu.cn/ng2/.

Bisphenol A exposure induces testicular oxidative damage via FTO/m6A/Nrf2 axis during postnatal development in mice.

Bisphenol A (BPA), a commonly used plasticizer in the production of polycarbonate plastics and epoxy resins, has been shown to induce male reproductive toxicity. However, the effects of BPA exposure on early testicular development have not been thoroughly studied, and the underlying mechanism is yet to be elucidated. In the current study, neonatal male mice were exposed to BPA at 0, 0.1, and 5 mg/kg, respectively, by daily subcutaneous injection during postnatal day (PND) 1-35 to explore its effects on testicular development at PND 36 (the end of the first round of spermatogenesis). Morphological analyses showed that BPA exposure significantly induced apoptosis of testicular cells (p < 0.01 and p < 0.001) and reduced the thickness of seminiferous epithelium (p < 0.01). In addition, BPA exposure significantly decreased the total antioxidant capacity of testes and levels of transcription factor Nrf2 as well as its downstream antioxidant molecules of NQO1 and GPx-1 (p < 0.05 and p < 0.01). Furthermore, global m6A modifications of mRNAs were upregulated accompanied by declined m6A demethylase (FTO) in the testes of BPA groups (p < 0.05 and p < 0.01). MeRIP-quantitative real-time polymerase chain reaction (qPCR) demonstrated that BPA exposure markedly increased the m6A modification of Nrf2 mRNA (p < 0.05 and p < 0.01). These findings suggest that upregulation of m6A induced by inhibited FTO may be involved in BPA-induced testicular oxidative stress and developmental injury during postnatal development, which provides a new idea to reveal the mechanism underlying BPA interfering with testicular development.

CCT5 induces epithelial-mesenchymal transition to promote gastric cancer lymph node metastasis by activating the Wnt/ß-catenin signalling pathway.

BACKGROUND: Lymph node (LN) metastasis confers gastric cancer (GC) progression, poor survival and cancer-related death. Aberrant activation of Wnt/ß-catenin promotes epithelial-mesenchymal transition (EMT) and LN metastasis, whereas the constitutive activation mutation of Wnt/ß-catenin is rare in GC, suggesting that the underlying mechanisms enhancing Wnt/ß-catenin activation need to be further investigated and understood. METHODS: Bioinformatics analyses and immunohistochemistry (IHC) were used to identify and detect LN metastasis-related genes in GC. Cellular functional assays and footpad inoculation mouse model illustrate the biological function of CCT5. Co-immunoprecipitation assays, western blot and qPCR elucidate the interaction between CCT5 and E-cadherin, and the regulation on ß-catenin activity. RESULTS: CCT5 is upregulated in LN metastatic GCs and correlates with poor prognosis. In vitro assays prove that CCT5 markedly promotes GC cell proliferation, anti-anoikis, invasion and lymphatic tube formation. Moreover, CCT5 enhances xenograft GC growth and popliteal lymph node metastasis in vivo. Furthermore, CCT5 binds the cytoplasmic domain of E-cadherin and abrogates the interaction between E-cadherin and ß-catenin, thereby releasing ß-catenin to the nucleus and enhancing Wnt/ß-catenin signalling activity and EMT. CONCLUSION: CCT5 promotes GC progression and LN metastasis by enhancing wnt/ß-catenin activation, suggesting a great potential of CCT5 as a biomarker for GC diagnosis and therapy.

ICH-LR2S2: a new risk score for predicting stroke-associated pneumonia from spontaneous intracerebral hemorrhage.

PURPOSE: We develop a new risk score to predict patients with stroke-associated pneumonia (SAP) who have an acute intracranial hemorrhage (ICH). METHOD: We applied logistic regression to develop a new risk score called ICH-LR2S2. It was derived from examining a dataset of 70,540 ICH patients between 2015 and 2018 from the Chinese Stroke Center Alliance (CSCA). During the training of ICH-LR2S2, patients were randomly divided into two groups - 80% for the training set and 20% for model validation. A prospective test set was developed using 12,523 patients recruited in 2019. To further verify its effectiveness, we tested ICH-LR2S2 on an external dataset of 24,860 patients from the China National Stroke Registration Management System II (CNSR II). The performance of ICH-LR2S2 was measured by the area under the receiver operating characteristic curve (AUROC). RESULTS: The incidence of SAP in the dataset was 25.52%. A 24-point ICH-LR2S2 was developed from independent predictors, including age, modified Rankin Scale, fasting blood glucose, National Institutes of Health Stroke Scale admission score, Glasgow Coma Scale score, C-reactive protein, dysphagia, Chronic Obstructive Pulmonary Disease, and current smoking. The results showed that ICH-LR2S2 achieved an AUC = 0.749 [95% CI 0.739-0.759], which outperforms the best baseline ICH-APS (AUC = 0.704) [95% CI 0.694-0.714]. Compared with the previous ICH risk scores, ICH-LR2S2 incorporates fasting blood glucose and C-reactive protein, improving its discriminative ability. Machine learning methods such as XGboost (AUC = 0.772) [95% CI 0.762-0.782] can further improve our prediction performance. It also performed well when further validated by the external independent cohort of patients (n = 24,860), ICH-LR2S2 AUC = 0.784 [95% CI 0.774-0.794]. CONCLUSION: ICH-LR2S2 accurately distinguishes SAP patients based on easily available clinical features. It can help identify high-risk patients in the early stages of diseases.

Flexible demodulation for rotation-induced phase difference based on a phase-controlled microwave photonic filter.

High-performance demodulation of Sagnac effect is of great importance for rotation rate measurement in inertial navigation system. In this paper, we propose a flexible measurement of rotation rate based on a phase-controlled microwave photonic filter (MPF), which incorporates an orthogonal double-sideband (ODSB) modulator, a Sagnac loop, a linearly chirped fiber Bragg grating (LCFBG), a polarizer, and a photodetector. The ODSB modulator is used to generate optical carrier (OC) and first-order sidebands with mutually orthogonal polarizations. For the MPF, its central frequency can be tuned through changing the phase difference between the OC and first-order sidebands thanks to the dispersion of the LCFBG. Therefore, if the OC and first-order sidebands are separated by a polarization beam splitter and then travel along the Sagnac loop in opposite directions, the rotation-induced phase difference between them will lead to a shift on the frequency response of the MPF. Thus, two ways can be adopted to detect the rotation rate of the Sagnac loop for different applications: monitoring the frequency response shift of the MPF and measuring the power variation at a certain frequency. Besides, the measurement sensitivity can be easily adjusted to satisfy specific requirements by tuning a polarization controller or choosing a different operating frequency. An experiment is performed to validate the proposed scheme. The results show that the maximum frequency shift of the MPF can reach 1.7 GHz at a rotation rate of 1 rad/s, and a scale factor of 0.016 mW/(rad/s) is obtained at 4 GHz.

COVID-WideNet-A capsule network for COVID-19 detection.

Ever since the outbreak of COVID-19, the entire world is grappling with panic over its rapid spread. Consequently, it is of utmost importance to detect its presence. Timely diagnostic testing leads to the quick identification, treatment and isolation of infected people. A number of deep learning classifiers have been proved to provide encouraging results with higher accuracy as compared to the conventional method of RT-PCR testing. Chest radiography, particularly using X-ray images, is a prime imaging modality for detecting the suspected COVID-19 patients. However, the performance of these approaches still needs to be improved. In this paper, we propose a capsule network called COVID-WideNet for diagnosing COVID-19 cases using Chest X-ray (CXR) images. Experimental results have demonstrated that a discriminative trained, multi-layer capsule network achieves state-of-the-art performance on the COVIDx dataset. In particular, COVID-WideNet performs better than any other CNN based approaches for diagnosis of COVID-19 infected patients. Further, the proposed COVID-WideNet has the number of trainable parameters that is 20 times less than that of other CNN based models. This results in fast and efficient diagnosing COVID-19 symptoms and with achieving the 0.95 of Area Under Curve (AUC), 91% of accuracy, sensitivity and specificity respectively. This may also assist radiologists to detect COVID and its variant like delta.

HPOLabeler: improving prediction of human protein-phenotype associations by learning to rank.

MOTIVATION: Annotating human proteins by abnormal phenotypes has become an important topic. Human Phenotype Ontology (HPO) is a standardized vocabulary of phenotypic abnormalities encountered in human diseases. As of November 2019, only <4000 proteins have been annotated with HPO. Thus, a computational approach for accurately predicting protein-HPO associations would be important, whereas no methods have outperformed a simple Naive approach in the second Critical Assessment of Functional Annotation, 2013-2014 (CAFA2). RESULTS: We present HPOLabeler, which is able to use a wide variety of evidence, such as protein-protein interaction (PPI) networks, Gene Ontology, InterPro, trigram frequency and HPO term frequency, in the framework of learning to rank (LTR). LTR has been proved to be powerful for solving large-scale, multi-label ranking problems in bioinformatics. Given an input protein, LTR outputs the ranked list of HPO terms from a series of input scores given to the candidate HPO terms by component learning models (logistic regression, nearest neighbor and a Naive method), which are trained from given multiple evidence. We empirically evaluate HPOLabeler extensively through mainly two experiments of cross validation and temporal validation, for which HPOLabeler significantly outperformed all component models and competing methods including the current state-of-the-art method. We further found that (i) PPI is most informative for prediction among diverse data sources and (ii) low prediction performance of temporal validation might be caused by incomplete annotation of new proteins. AVAILABILITY AND IMPLEMENTATION: http://issubmission.sjtu.edu.cn/hpolabeler/. CONTACT: zhusf@fudan.edu.cn. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

FullMeSH: improving large-scale MeSH indexing with full text.

MOTIVATION: With the rapidly growing biomedical literature, automatically indexing biomedical articles by Medical Subject Heading (MeSH), namely MeSH indexing, has become increasingly important for facilitating hypothesis generation and knowledge discovery. Over the past years, many large-scale MeSH indexing approaches have been proposed, such as Medical Text Indexer, MeSHLabeler, DeepMeSH and MeSHProbeNet. However, the performance of these methods is hampered by using limited information, i.e. only the title and abstract of biomedical articles. RESULTS: We propose FullMeSH, a large-scale MeSH indexing method taking advantage of the recent increase in the availability of full text articles. Compared to DeepMeSH and other state-of-the-art methods, FullMeSH has three novelties: (i) Instead of using a full text as a whole, FullMeSH segments it into several sections with their normalized titles in order to distinguish their contributions to the overall performance. (ii) FullMeSH integrates the evidence from different sections in a 'learning to rank' framework by combining the sparse and deep semantic representations. (iii) FullMeSH trains an Attention-based Convolutional Neural Network for each section, which achieves better performance on infrequent MeSH headings. FullMeSH has been developed and empirically trained on the entire set of 1.4 million full-text articles in the PubMed Central Open Access subset. It achieved a Micro F-measure of 66.76% on a test set of 10 000 articles, which was 3.3% and 6.4% higher than DeepMeSH and MeSHLabeler, respectively. Furthermore, FullMeSH demonstrated an average improvement of 4.7% over DeepMeSH for indexing Check Tags, a set of most frequently indexed MeSH headings. AVAILABILITY AND IMPLEMENTATION: The software is available upon request. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

NetGO: improving large-scale protein function prediction with massive network information.

Automated function prediction (AFP) of proteins is of great significance in biology. AFP can be regarded as a problem of the large-scale multi-label classification where a protein can be associated with multiple gene ontology terms as its labels. Based on our GOLabeler-a state-of-the-art method for the third critical assessment of functional annotation (CAFA3), in this paper we propose NetGO, a web server that is able to further improve the performance of the large-scale AFP by incorporating massive protein-protein network information. Specifically, the advantages of NetGO are threefold in using network information: (i) NetGO relies on a powerful learning to rank framework from machine learning to effectively integrate both sequence and network information of proteins; (ii) NetGO uses the massive network information of all species (>2000) in STRING (other than only some specific species) and (iii) NetGO still can use network information to annotate a protein by homology transfer, even if it is not contained in STRING. Separating training and testing data with the same time-delayed settings of CAFA, we comprehensively examined the performance of NetGO. Experimental results have clearly demonstrated that NetGO significantly outperforms GOLabeler and other competing methods. The NetGO web server is freely available at http://issubmission.sjtu.edu.cn/netgo/.

Enhancing Removal of Cr(VI), Pb2+, and Cu2+ from Aqueous Solutions Using Amino-Functionalized Cellulose Nanocrystal.

In this work, the amino-functionalized cellulose nanocrystal (ACNC) was prepared using a green route and applied as a biosorbent for adsorption of Cr(VI), Pb2+, and Cu2+ from aqueous solutions. CNC was firstly oxidized by sodium periodate to yield the dialdehyde nanocellulose (DACNC). Then, DACNC reacted with diethylenetriamine (DETA) to obtain amino-functionalized nanocellulose (ACNC) through a Schiff base reaction. The properties of DACNC and ACNC were characterized by using elemental analysis, Fourier transform infrared spectroscopy (FT-IR), Kaiser test, atomic force microscopy (AFM), X-ray diffraction (XRD), and zeta potential measurement. The presence of free amino groups was evidenced by the FT-IR results and Kaiser test. ACNCs exhibited an amphoteric nature with isoelectric points between pH 8 and 9. After the chemical modification, the cellulose I polymorph of nanocellulose remained, while the crystallinity decreased. The adsorption behavior of ACNC was investigated for the removal of Cr(VI), Pb2+, and Cu2+ in aqueous solutions. The maximum adsorption capacities were obtained at pH 2 for Cr(VI) and pH 6 for Cu2+ and Pb2+, respectively. The adsorption all followed pseudo second-order kinetics and Sips adsorption isotherms. The estimated adsorption capacities for Cr(VI), Pb2+, and Cu2+ were 70.503, 54.115, and 49.600 mg/g, respectively.

High sensitivity demodulation of a reflective interferometer-based optical current sensor using an optoelectronic oscillator.

A novel, to the best of our knowledge, interrogation scheme based on an optoelectronic oscillator (OEO) with high sensitivity and high speed response for a fiber optical current sensor utilizing a reflective interferometer is proposed and experimentally demonstrated. Due to the Faraday effect, a magneto-optic phase shift induced by current variation is generated between two orthogonal light waves. The polarization-dependent properties of the Mach-Zehnder modulator are used to convert the magneto-optic phase shift into the phase difference between the optical carrier and sideband, which is then mapped to the oscillating frequency shift by closing an OEO loop. A high current sensitivity of 152.5 kHz/A with a range of 0-2.5 A is obtained in the experiment.

Multimodal Data Fusion in Learning Analytics: A Systematic Review.

Multimodal learning analytics (MMLA), which has become increasingly popular, can help provide an accurate understanding of learning processes. However, it is still unclear how multimodal data is integrated into MMLA. By following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, this paper systematically surveys 346 articles on MMLA published during the past three years. For this purpose, we first present a conceptual model for reviewing these articles from three dimensions: data types, learning indicators, and data fusion. Based on this model, we then answer the following questions: 1. What types of data and learning indicators are used in MMLA, together with their relationships; and 2. What are the classifications of the data fusion methods in MMLA. Finally, we point out the key stages in data fusion and the future research direction in MMLA. Our main findings from this review are (a) The data in MMLA are classified into digital data, physical data, physiological data, psychometric data, and environment data; (b) The learning indicators are behavior, cognition, emotion, collaboration, and engagement; (c) The relationships between multimodal data and learning indicators are one-to-one, one-to-any, and many-to-one. The complex relationships between multimodal data and learning indicators are the key for data fusion; (d) The main data fusion methods in MMLA are many-to-one, many-to-many and multiple validations among multimodal data; and (e) Multimodal data fusion can be characterized by the multimodality of data, multi-dimension of indicators, and diversity of methods.

DeepText2GO: Improving large-scale protein function prediction with deep semantic text representation.

As of April 2018, UniProtKB has collected more than 115 million protein sequences. Less than 0.15% of these proteins, however, have been associated with experimental GO annotations. As such, the use of automatic protein function prediction (AFP) to reduce this huge gap becomes increasingly important. The previous studies conclude that sequence homology based methods are highly effective in AFP. In addition, mining motif, domain, and functional information from protein sequences has been found very helpful for AFP. Other than sequences, alternative information sources such as text, however, may be useful for AFP as well. Instead of using BOW (bag of words) representation in traditional text-based AFP, we propose a new method called DeepText2GO that relies on deep semantic text representation, together with different kinds of available protein information such as sequence homology, families, domains, and motifs, to improve large-scale AFP. Furthermore, DeepText2GO integrates text-based methods with sequence-based ones by means of a consensus approach. Extensive experiments on the benchmark dataset extracted from UniProt/SwissProt have demonstrated that DeepText2GO significantly outperformed both text-based and sequence-based methods, validating its superiority.

Use of facebook to engage water customers: A comprehensive study of current U.K. and Australian practices and trends.

Given regulatory developments, it is imperative that water businesses implement effective customer engagement strategies. Among other options, Facebook offers enormous potential given the ability to connect with customers, involve customers in the co-creation of content, obtain real-time feedback on customer preferences and promote water conversation behaviours. This paper examines how effectively 20 large Australian and U.K. water businesses are using Facebook to engage customers. It also identifies how these firms can improve Facebook engagement by optimising posts type, timing, content, frequency and other factors. The total sample included more than 300,000 responses to nearly 17,000 posts between 2010 and 2017. Rapid growth in the utilisation of Facebook by water businesses was observed given the number of posts and customers engaging with this content. The results of the analysis of popular posts identified innovative ways some water businesses are using Facebook posts to promote the health benefits of tap water consumption, water conservation behaviours and responsible wastewater practices. Despite the trends, most firms still make less than one post per day and of those customers who have engaged, most have done with a single response. Further analysis revealed that few posts, and only a relatively small number of customer comments, pertained to water pricing matters. To promote engagement, water businesses must improve post regularity, the degree to which they moderate Facebook discussion, the utilisation of videos and photos, and further consider the underlying content of posts.

The effect of nursing participation in the design of a critical care information system: a case study in a Chinese hospital.

BACKGROUND: Intensive care information systems (ICIS) are continuously evolving to meet the ever changing information needs of intensive care units (ICUs), providing the backbone for a safe, intelligent and efficient patient care environment. Although beneficial for the international advancement in building smart environments to transform ICU services, knowledge about the contemporary development of ICIS worldwide, their usage and impacts is limited. This study aimed to fill this knowledge gap by researching the development and implementation of an ICIS in a Chinese hospital, nurses' use of the system, and the impact of system use on critical care nursing processes and outcomes. METHODS: This descriptive case study was conducted in a 14-bed Respiratory ICU in a tertiary hospital in Beijing. Participative design was the method used for ICU nurses, hospital IT department and a software company to collaboratively research and develop the ICIS. Focus group discussions were conducted to understand the subjective perceptions of the nurses toward the ICIS. Nursing documentation time and quality were compared before and after system implementation. ICU nursing performance was extracted from the annual nursing performance data collected by the hospital. RESULTS: A participative design process was followed by the nurses in the ICU, the hospital IT staff and the software engineers in the company to develop and implement a highly useful ICIS. Nursing documentation was fully digitized and was significantly improved in quality and efficiency. The wrong data, missing data items and calculation errors were significantly reduced. Nurses spent more time on direct patient care after the introduction of the ICIS. The accuracy and efficiency of medication administration was also improved. The outcome was improvement in ward nursing performance as measured by ward management, routine nursing practices, disinfection and isolation, infection rate and mortality rate. CONCLUSIONS: Nurses in this ICU unit in China actively participated in the ICIS development and fully used the system to document care. Introduction of the ICIS led to significant improvement in quality and efficiency in nursing documentation, medication order transcription and administration. It allowed nurses to spend more time with patients to improve quality of care. These led to improvement in overall nursing performance. Further study should investigate how the ICIS system contributes to the improvement in decision making of ICU nurses and intensivists.

MHC2MIL: a novel multiple instance learning based method for MHC-II peptide binding prediction by considering peptide flanking region and residue positions.

BACKGROUND: Computational prediction of major histocompatibility complex class II (MHC-II) binding peptides can assist researchers in understanding the mechanism of immune systems and developing peptide based vaccines. Although many computational methods have been proposed, the performance of these methods are far from satisfactory. The difficulty of MHC-II peptide binding prediction comes mainly from the large length variation of binding peptides. METHODS: We develop a novel multiple instance learning based method called MHC2MIL, in order to predict MHC-II binding peptides. We deem each peptide in MHC2MIL as a bag, and some substrings of the peptide as the instances in the bag. Unlike previous multiple instance learning based methods that consider only instances of fixed length 9 (9 amino acids), MHC2MIL is able to deal with instances of both lengths of 9 and 11 (11 amino acids), simultaneously. As such, MHC2MIL incorporates important information in the peptide flanking region. For measuring the distances between different instances, furthermore, MHC2MIL explicitly highlights the amino acids in some important positions. RESULTS: Experimental results on a benchmark dataset have shown that, the performance of MHC2MIL is significantly improved by considering the instances of both 9 and 11 amino acids, as well as by emphasizing amino acids at key positions in the instance. The results are consistent with those reported in the literature on MHC-II peptide binding. In addition to five important positions (1, 4, 6, 7 and 9) for HLA(human leukocyte antigen, the name of MHC in Humans) DR peptide binding, we also find that position 2 may play some roles in the binding process. By using 5-fold cross validation on the benchmark dataset, MHC2MIL outperforms two state-of-the-art methods of MHC2SK and NN-align with being statistically significant, on 12 HLA DP and DQ molecules. In addition, it achieves comparable performance with MHC2SK and NN-align on 14 HLA DR molecules. MHC2MIL is freely available at http://datamining-iip.fudan.edu.cn/service/MHC2MIL/index.html.

Flow2GNN: Flexible Two-Way Flow Message Passing for Enhancing GNNs Beyond Homophily.

Message passing (MP) is crucial for effective graph neural networks (GNNs). Most local message-passing schemes have been shown to underperform on heterophily graphs due to the perturbation of updated representations caused by local redundant heterophily information. However, our experiment findings indicate that the distribution of heterophily information during MP can be disrupted by disentangling local neighborhoods. This finding can be applied to other GNNs, improving their performance on heterophily graphs in a more flexible manner compared to most heterophily GNNs with complex designs. This article proposes a new type of simple message-passing neural network called Flow2GNN. It uses a two-way flow message-passing scheme to enhance the ability of GNNs by disentangling and redistributing heterophily information in the topology space and the attribute space. Our proposed message-passing scheme consists of two steps in topology space and attribute space. First, we introduce a new disentangled operator with binary elements that disentangle topology information in-flow and out-flow between connected nodes. Second, we use an adaptive aggregation model that adjusts the flow amount between homophily and heterophily attribute information. Furthermore, we rigorously prove that disentangling in message-passing can reduce the generalization gap, offering a deeper understanding of how our model enhances other GNNs. The extensive experiment results show that the proposed model, Flow2GNN, not only outperforms state-of-the-art GNNs, but also helps improve the performance of other commonly used GNNs on heterophily graphs, including GCN, GAT, GCNII, and H 2 GCN, specifically for GCN, with up to a 25.88% improvement on the Wisconsin dataset.

Bone metastasis scintigram generation using generative adversarial learning with multi-receptive field learning and two-stage training.

BACKGROUND: Deep learning is the primary method for conducting automated analysis of SPECT bone scintigrams. The lack of available large-scale data significantly hinders the development of well-performing deep learning models, as the performance of a deep learning model is positively correlated with the size of the dataset used. Therefore, there is an urgent demand for an automated data generation method to enlarge the dataset of SPECT bone scintigrams. PURPOSE: We introduce a deep learning-based generation model that can generate realistic but not identical samples from the original SPECT bone scintigrams. METHODS: Following the generative adversarial learning architecture, a bone metastasis scintigram generation model christened BMS-Gen is proposed. First, BMS-Gen takes multiple input conditions and employs multi-receptive field learning to ensure that the generated samples are as realistic as possible. Second, BMS-Gen adopts generative adversarial learning to retain the diversity of the generated samples. Last, BMS-Gen uses a two-stage training strategy to improve the quality of the generated samples. RESULTS: Experimental evaluation conducted on a set of clinical data of SPECT BM scintigrams has shown the performance of the proposed BMS-Gen, achieving the best overall scores of 1678.0, 69.33, and 19.51 for FID (Fréchet Inception Distance), MSE (Mean Square Error), and PSNR (Peak Signal-to-Noise Ratio) metrics. The introduction of samples generated by BMS-Gen contributes a maximum (minimum) increase of 3.01% (0.15%) on the F-1 score and a maximum (minimum) increase of 6.83% (2.21%) on the DSC score for the image classification and segmentation tasks, respectively. CONCLUSIONS: The proposed BMS-Gen model can be used as a promising tool for augmenting the data of bone scintigrams, greatly facilitating the development of deep learning-based automated analysis of SPECT bone scintigrams.

Implication of Amyloid Precursor-like Protein 2 Expression in Cutaneous Squamous Cell Carcinoma Pathogenesis.

BACKGROUND/AIM: Regulatory functions of amyloid precursor-like protein 2 (APLP2) expression in intracellular trafficking of major histocompatibility complex class I (MHC-I) and biological behavior of tumor cells have been reported in various types of malignancies but not in cutaneous squamous cell carcinoma (CSCC). This study aimed to investigate the role of APLP2 expression in the pathogenesis of CSCC. PATIENTS AND METHODS: The expression of APLP2 and a key modulator of cancer immune escape, MHC-I, were determined in CSCC tissue samples obtained from 141 patients using immunohistochemistry. The regulatory effects of APLP2 expression on the biological behavior and surface expression of MHC-I in CSCC cells were investigated by trypan blue assay, Matrigel invasion assay, and in vivo xenograft analysis. RESULTS: APLP2 immunoreactivity was high in 73 (51.8%) tissue samples from patients with CSCC and was significantly related to subcutaneous fat invasion and poor prognosis in our cohort. Moreover, proliferation of and invasion by CSCC cells were significantly reduced after APLP2 knockdown in CSCC cells both in vitro and in vivo. A significant association was found between APLP2 and membrane MHC-I expression in patients with CSCC. In vivo xenograft analysis showed that APLP2 knockdown increased membrane MHC-I expression in CSCC cells. CONCLUSION: APLP2 not only acts as an oncogene in CSCC progression but also as a possible modulator of cancer immune escape by influencing MHC-I expression on the cell surface. APLP2 may serve as a novel molecular biomarker and therapeutic target for patients with CSCC.