DeepIRES: a hybrid deep learning model for accurate identification of internal ribosome entry sites in cellular and viral mRNAs.

The internal ribosome entry site (IRES) is a cis-regulatory element that can initiate translation in a cap-independent manner. It is often related to cellular processes and many diseases. Thus, identifying the IRES is important for understanding its mechanism and finding potential therapeutic strategies for relevant diseases since identifying IRES elements by experimental method is time-consuming and laborious. Many bioinformatics tools have been developed to predict IRES, but all these tools are based on structure similarity or machine learning algorithms. Here, we introduced a deep learning model named DeepIRES for precisely identifying IRES elements in messenger RNA (mRNA) sequences. DeepIRES is a hybrid model incorporating dilated 1D convolutional neural network blocks, bidirectional gated recurrent units, and self-attention module. Tenfold cross-validation results suggest that DeepIRES can capture deeper relationships between sequence features and prediction results than other baseline models. Further comparison on independent test sets illustrates that DeepIRES has superior and robust prediction capability than other existing methods. Moreover, DeepIRES achieves high accuracy in predicting experimental validated IRESs that are collected in recent studies. With the application of a deep learning interpretable analysis, we discover some potential consensus motifs that are related to IRES activities. In summary, DeepIRES is a reliable tool for IRES prediction and gives insights into the mechanism of IRES elements.

A neural network with a human learning paradigm for breast fibroadenoma segmentation in sonography.

BACKGROUND: Breast fibroadenoma poses a significant health concern, particularly for young women. Computer-aided diagnosis has emerged as an effective and efficient method for the early and accurate detection of various solid tumors. Automatic segmentation of the breast fibroadenoma is important and potentially reduces unnecessary biopsies, but challenging due to the low image quality and presence of various artifacts in sonography. METHODS: Human learning involves modularizing complete information and then integrating it through dense contextual connections in an intuitive and efficient way. Here, a human learning paradigm was introduced to guide the neural network by using two consecutive phases: the feature fragmentation stage and the information aggregation stage. To optimize this paradigm, three fragmentation attention mechanisms and information aggregation mechanisms were adapted according to the characteristics of sonography. The evaluation was conducted using a local dataset comprising 600 breast ultrasound images from 30 patients at Suining Central Hospital in China. Additionally, a public dataset consisting of 246 breast ultrasound images from Dataset_BUSI and DatasetB was used to further validate the robustness of the proposed network. Segmentation performance and inference speed were assessed by Dice similarity coefficient (DSC), Hausdorff distance (HD), and training time and then compared with those of the baseline model (TransUNet) and other state-of-the-art methods. RESULTS: Most models guided by the human learning paradigm demonstrated improved segmentation on the local dataset with the best one (incorporating C3ECA and LogSparse Attention modules) outperforming the baseline model by 0.76% in DSC and 3.14 mm in HD and reducing the training time by 31.25%. Its robustness and efficiency on the public dataset are also confirmed, surpassing TransUNet by 0.42% in DSC and 5.13 mm in HD. CONCLUSIONS: Our proposed human learning paradigm has demonstrated the superiority and efficiency of ultrasound breast fibroadenoma segmentation across both public and local datasets. This intuitive and efficient learning paradigm as the core of neural networks holds immense potential in medical image processing.

Hybrid deep learning based prediction for water quality of plain watershed.

Establishing a highly reliable and accurate water quality prediction model is critical for effective water environment management. However, enhancing the performance of these predictive models continues to pose challenges, especially in the plain watershed with complex hydraulic conditions. This study aims to evaluate the efficacy of three traditional machine learning models versus three deep learning models in predicting the water quality of plain river networks and to develop a novel hybrid deep learning model to further improve prediction accuracy. The performance of the proposed model was assessed under various input feature sets and data temporal frequencies. The findings indicated that deep learning models outperformed traditional machine learning models in handling complex time series data. Long Short-Term Memory (LSTM) models improved the R2 by approximately 29% and lowered the Root Mean Square Error (RMSE) by about 48.6% on average. The hybrid Bayes-LSTM-GRU (Gated Recurrent Unit) model significantly enhanced prediction accuracy, reducing the average RMSE by 18.1% compared to the single LSTM model. Models trained on feature-selected datasets exhibited superior performance compared to those trained on original datasets. Higher temporal frequencies of input data generally provide more useful information. However, in datasets with numerous abrupt changes, increasing the temporal interval proves beneficial. Overall, the proposed hybrid deep learning model demonstrates an efficient and cost-effective method for improving water quality prediction performance, showing significant potential for application in managing water quality in plain watershed.

A hybrid transmission model for Plasmodium vivax accounting for superinfection, immunity and the hypnozoite reservoir.

Malaria is a vector-borne disease that exacts a grave toll in the Global South. The epidemiology of Plasmodium vivax, the most geographically expansive agent of human malaria, is characterised by the accrual of a reservoir of dormant parasites known as hypnozoites. Relapses, arising from hypnozoite activation events, comprise the majority of the blood-stage infection burden, with implications for the acquisition of immunity and the distribution of superinfection. Here, we construct a novel model for the transmission of P. vivax that concurrently accounts for the accrual of the hypnozoite reservoir, (blood-stage) superinfection and the acquisition of immunity. We begin by using an infinite-server queueing network model to characterise the within-host dynamics as a function of mosquito-to-human transmission intensity, extending our previous model to capture a discretised immunity level. To model transmission-blocking and antidisease immunity, we allow for geometric decay in the respective probabilities of successful human-to-mosquito transmission and symptomatic blood-stage infection as a function of this immunity level. Under a hybrid approximation-whereby probabilistic within-host distributions are cast as expected population-level proportions-we couple host and vector dynamics to recover a deterministic compartmental model in line with Ross-Macdonald theory. We then perform a steady-state analysis for this compartmental model, informed by the (analytic) distributions derived at the within-host level. To characterise transient dynamics, we derive a reduced system of integrodifferential equations, likewise informed by our within-host queueing network, allowing us to recover population-level distributions for various quantities of epidemiological interest. In capturing the interplay between hypnozoite accrual, superinfection and acquired immunity-and providing, to the best of our knowledge, the most complete population-level distributions for a range of epidemiological values-our model provides insights into important, but poorly understood, epidemiological features of P. vivax.

Hybrid model of care for older persons for improvement of frailty index-a community-based interventional study in an urban setting.

BACKGROUND: An older person undergoes a 'disablement' process with aging. A comprehensive geriatric assessment centered around the functional status informs the healthcare provider of their frailty status, based on which tailored interventions may be designed to help prevent/reverse frailty. This study was conducted to assess the improvement in frailty index by training older persons for self-care practices using a multi-domain behavioural intervention, assisted by their caregivers. METHODS: It is a community-based interventional trial among older persons aged ≥ 60 years and their primary caregivers conducted in an urban community for a period of 15 months. A hybrid model, which exploits the advantages of every indigenous geriatric model of care, in providing a holistic care to old persons, was developed and adopted. Intervention was designed to incorporate all domains of frailty assessed, based upon self-efficacy and social interdependence theory. Frail-VIG scale and SPPB scores were used to measure the outcomes. RESULTS: 128 older persons and their primary caregivers were recruited. Median age was 70 and 67 years in the intervention and control group respectively, with majority being males. The median frailty index at baseline was 0.36 in both the groups, with improvement in intervention group (0.20) and worsening in control group (0.44) at end-line. From the DID analysis, a reduction of 0.19 points of frailty index was observed (even after adjustment for co-variates) in the intervention group, as compared to the control group. Also, it was observed that age and gender of the old person, their per capita income and the family support played an interactive effect in improvement of the frailty index. There was a significant difference in SPPB scores as well, between the groups [5 (1) in CG vs. 7 (2) in IG, p < 0.001]. CONCLUSION: Frailty could be reversed with appropriate interventions designed on the pillars of self-efficacy, and social interdependence among family members. The hybrid model of care delineates the role of caregivers, who reinforce the old persons to follow prescribed interventions.

Forecasting and analyzing influenza activity in Hebei Province, China, using a CNN-LSTM hybrid model.

BACKGROUND: Influenza, an acute infectious respiratory disease, presents a significant global health challenge. Accurate prediction of influenza activity is crucial for reducing its impact. Therefore, this study seeks to develop a hybrid Convolution Neural Network-Long Short Term Memory neural network (CNN-LSTM) model to forecast the percentage of influenza-like-illness (ILI) rate in Hebei Province, China. The aim is to provide more precise guidance for influenza prevention and control measures. METHODS: Using ILI% data from 28 national sentinel hospitals in the Hebei Province, spanning from 2010 to 2022, we employed the Python deep learning framework PyTorch to develop the CNN-LSTM model. Additionally, we utilized R and Python to develop four other models commonly used for predicting infectious diseases. After constructing the models, we employed these models to make retrospective predictions, and compared each model's prediction performance using mean absolute error (MAE), root mean square error (RMSE), mean absolute percentage error (MAPE), and other evaluation metrics. RESULTS: Based on historical ILI% data from 28 national sentinel hospitals in Hebei Province, the Seasonal Auto-Regressive Indagate Moving Average (SARIMA), Extreme Gradient Boosting (XGBoost), Convolution Neural Network (CNN), Long Short Term Memory neural network (LSTM) models were constructed. On the testing set, all models effectively predicted the ILI% trends. Subsequently, these models were used to forecast over different time spans. Across various forecasting periods, the CNN-LSTM model demonstrated the best predictive performance, followed by the XGBoost model, LSTM model, CNN model, and SARIMA model, which exhibited the least favorable performance. CONCLUSION: The hybrid CNN-LSTM model had better prediction performances than the SARIMA model, CNN model, LSTM model, and XGBoost model. This hybrid model could provide more accurate influenza activity projections in the Hebei Province.

Development and Validation of a Computed Tomography-Based Model for Noninvasive Prediction of the T Stage in Gastric Cancer: Multicenter Retrospective Study.

BACKGROUND: As part of the TNM (tumor-node-metastasis) staging system, T staging based on tumor depth is crucial for developing treatment plans. Previous studies have constructed a deep learning model based on computed tomographic (CT) radiomic signatures to predict the number of lymph node metastases and survival in patients with resected gastric cancer (GC). However, few studies have reported the combination of deep learning and radiomics in predicting T staging in GC. OBJECTIVE: This study aimed to develop a CT-based model for automatic prediction of the T stage of GC via radiomics and deep learning. METHODS: A total of 771 GC patients from 3 centers were retrospectively enrolled and divided into training, validation, and testing cohorts. Patients with GC were classified into mild (stage T1 and T2), moderate (stage T3), and severe (stage T4) groups. Three predictive models based on the labeled CT images were constructed using the radiomics features (radiomics model), deep features (deep learning model), and a combination of both (hybrid model). RESULTS: The overall classification accuracy of the radiomics model was 64.3% in the internal testing data set. The deep learning model and hybrid model showed better performance than the radiomics model, with overall classification accuracies of 75.7% (P=.04) and 81.4% (P=.001), respectively. On the subtasks of binary classification of tumor severity, the areas under the curve of the radiomics, deep learning, and hybrid models were 0.875, 0.866, and 0.886 in the internal testing data set and 0.820, 0.818, and 0.972 in the external testing data set, respectively, for differentiating mild (stage T1~T2) from nonmild (stage T3~T4) patients, and were 0.815, 0.892, and 0.894 in the internal testing data set and 0.685, 0.808, and 0.897 in the external testing data set, respectively, for differentiating nonsevere (stage T1~T3) from severe (stage T4) patients. CONCLUSIONS: The hybrid model integrating radiomics features and deep features showed favorable performance in diagnosing the pathological stage of GC.

Hybrid umbilical cord blood banking: literature review.

PURPOSE: Interest gaps between public and private umbilical cord blood banks have led to the introduction of hybrid banking options. Hybrid models combine features of private and public banks as well as interests of parents, children and of patients, in order to find an optimized solution. While several different models of hybrid banks exist, there is a lack of literature about this novel model of cord blood stem cell banking. Therefore, the aim of this literature review is to assess different options of umbilical cord blood banking and whether hybrid banking could be a valuable alternative to the existing public and private cord blood banking models. METHODS: We performed a systematic literature search, using five main databases. Five hybrid models regarding their advantages as well as their challenges are discussed in this review. RESULTS: We found that a wealth of literature exists about public cord blood banking, while private and hybrid banking are understudied. Different modalities of hybrid cord blood banking are being described in several publications, providing the basis to assess different advantages and disadvantages as well as practicability. CONCLUSION: Hybrid banks, especially the sequential model, seem to have potential as an alternative to the existing banking models worldwide. A previously conducted survey among pregnant women showed a preference for hybrid banking, if such an option was available. Nevertheless, opinions among stakeholders differ and more research is needed to evaluate, if hybrid banking provides the expected benefits.

A Hybrid Concept Analysis of Frailty Among Older Adults Living in the Community.

AIM: To analyse the concept of frailty through a literature review and in-depth interviews. DESIGN: A hybrid model of concept analysis. METHODS: The theoretical phase identified 43 articles for reviewing the definition and measurement of frailty. Seven frail older adults were invited in the fieldwork phase for in-depth interviews. In the final analysis phase, results from the fieldwork and theoretical phases were integrated to obtain a final definition of frailty. RESULTS: Attributes of frailty were heterogeneous, involving dynamic/bidirectional, multidimensional and multiple systems. The antecedents of the concept were exposure to various stimuli and challenges in responding to these stimuli. Consequences included losing autonomy and adverse health outcomes. Four themes of frailty were identified based on the fieldwork data: 'accumulation of functional decline', 'powerlessness of coping with', 'vicissitudes of lived experience' and 'loss of autonomy and positivity'. CONCLUSIONS: The final definition of frailty was 'a dynamic and fluctuating process of powerlessness to manage biopsychosocial and environmental stimuli, involving functional decline and vicissitudes of life, which results in losing autonomy and positivity or adverse health outcomes'. IMPLICATIONS FOR THE PROFESSION AND/OR PATIENT CARE: Characterising the definition of frailty is essential for nurses to address the lived experiences of older adults when providing person-centred care and for developing interventions that meet the needs of frail older adults. IMPACT: Since some discrepancies existed in the definition of frailty from individual perception of older adults, combined in-depth interviews with a theoretical literature review were used to provide comprehensive insight. This concept analysis provides guidelines of training for nurses and opportunities to improving quality of life for community dwelling older adults. REPORTING METHOD: N/A. PATIENT OR PUBLIC CONTRIBUTION: No Patient or Public Contribution.

Hybrid modeling of an ultracentrifugation process for separation of full and empty adeno-associated virus particles.

Ultracentrifugation is an attractive method for separating full and empty capsids, exploiting their density difference. Changes of the serotype/capsid, density of loading material, or the genetic information contained in the adeno-associated viruses (AAVs) require the adaptation of the harvesting parameters and the density gradient loaded onto the centrifuge. To streamline these adaptations, a mathematical model could support the design and testing of operating conditions.Here, hybrid models, which combine empirical functions with artificial neural networks, are proposed to describe the separation of full and empty capsids as a function of material and operational parameters, i.e., the harvest model. In addition, critical quality attributes are estimated by a quality model which is operating on top of the harvest model. The performance of these models was evaluated using test data and two additional blind runs. Also, a "what-if" analysis was conducted to investigate whether the models' predictions align with expectations.It is concluded that the models are sufficiently accurate to support the design of operating conditions, though the accuracy and applicability of the models can further be increased by training them on more specific data with higher variability.

Photovoltaic Power Prediction Based on Hybrid Deep Learning Networks and Meteorological Data.

Conventional point prediction methods encounter challenges in accurately capturing the inherent uncertainty associated with photovoltaic power due to its stochastic and volatile nature. To address this challenge, we developed a robust prediction model called QRKDDN (quantile regression and kernel density estimation deep learning network) by leveraging historical meteorological data in conjunction with photovoltaic power data. Our aim is to enhance the accuracy of deterministic predictions, interval predictions, and probabilistic predictions by incorporating quantile regression (QR) and kernel density estimation (KDE) techniques. The proposed method utilizes the Pearson correlation coefficient for selecting relevant meteorological factors, employs a Gaussian Mixture Model (GMM) for clustering similar days, and constructs a deep learning prediction model based on a convolutional neural network (CNN) combined with a bidirectional gated recurrent unit (BiGRU) and attention mechanism. The experimental results obtained using the dataset from the Australian DKASC Research Centre unequivocally demonstrate the exceptional performance of QRKDDN in deterministic, interval, and probabilistic predictions for photovoltaic (PV) power generation. The effectiveness of QRKDDN was further validated through ablation experiments and comparisons with classical machine learning models.

An Optimal Spatio-Temporal Hybrid Model Based on Wavelet Transform for Early Fault Detection.

An optimal spatio-temporal hybrid model (STHM) based on wavelet transform (WT) is proposed to improve the sensitivity and accuracy of detecting slowly evolving faults that occur in the early stage and easily submerge with noise in complex industrial production systems. Specifically, a WT is performed to denoise the original data, thus reducing the influence of background noise. Then, a principal component analysis (PCA) and the sliding window algorithm are used to acquire the nearest neighbors in both spatial and time dimensions. Subsequently, the cumulative sum (CUSUM) and the mahalanobis distance (MD) are used to reconstruct the hybrid statistic with spatial and temporal sequences. It helps to enhance the correlation between high-frequency temporal dynamics and space and improves fault detection precision. Moreover, the kernel density estimation (KDE) method is used to estimate the upper threshold of the hybrid statistic so as to optimize the fault detection process. Finally, simulations are conducted by applying the WT-based optimal STHM in the early fault detection of the Tennessee Eastman (TE) process, with the aim of proving that the fault detection method proposed has a high fault detection rate (FDR) and a low false alarm rate (FAR), and it can improve both production safety and product quality.

A lightweight hybrid model for the automatic recognition of uterine fibroid ultrasound images based on deep learning.

PURPOSE: Uterine fibroids (UF) are the most frequent tumors in ladies and can pose an enormous threat to complications, such as miscarriage. The accuracy of prognosis may also be affected by way of doctor inexperience and fatigue, underscoring the want for automatic classification fashions that can analyze UF from a giant wide variety of images. METHODS: A hybrid model has been proposed that combines the MobileNetV2 community and deep convolutional generative adversarial networks (DCGAN) into useful resources for medical practitioners in figuring out UF and evaluating its characteristics. Real-time automated classification of UF can aid in diagnosing the circumstance and minimizing subjective errors. The DCGAN science is utilized for superior statistics augmentation to create first-rate UF images, which are labeled into UF and non-uterine-fibroid (NUF) classes. The MobileNetV2 model then precisely classifies the photos based totally on this data. RESULTS: The overall performance of the hybrid model contrasts with different models. The hybrid model achieves a real-time classification velocity of 40 frames per second (FPS), an accuracy of 97.45%, and an F1 rating of 0.9741. CONCLUSION: By using this deep learning hybrid approach, we address the shortcomings of the current classification methods of uterine fibroid.

Designing a deep hybridized residual and SE model for MRI image-based brain tumor prediction.

Deep learning techniques have become crucial in the detection of brain tumors but classifying numerous images is time-consuming and error-prone, impacting timely diagnosis. This can hinder the effectiveness of these techniques in detecting brain tumors in a timely manner. To address this limitation, this study introduces a novel brain tumor detection system. The main objective is to overcome the challenges associated with acquiring a large and well-classified dataset. The proposed approach involves generating synthetic Magnetic Resonance Imaging (MRI) images that mimic the patterns commonly found in brain MRI images. The system utilizes a dataset consisting of small images that are unbalanced in terms of class distribution. To enhance the accuracy of tumor detection, two deep learning models are employed. Using a hybrid ResNet+SE model, we capture feature distributions within unbalanced classes, creating a more balanced dataset. The second model, a tailored classifier identifies brain tumors in MRI images. The proposed method has shown promising results, achieving a high detection accuracy of 98.79%. This highlights the potential of the model as an efficient and cost-effective system for brain tumor detection.

Integrating temporal decomposition and data-driven approaches for predicting coastal harmful algal blooms.

Frequent coastal harmful algal blooms (HABs) threaten the ecological environment and human health. Biscayne Bay in southeastern Florida also faces algal bloom issues; however, the mechanisms driving these blooms are not fully understood, emphasizing the importance of HAB prediction for effective environmental management. The overarching goal of this study is to offer a robust HAB predictive framework and try to enhance the understanding of HAB dynamics. This study established three scenarios to predict chlorophyll-a concentrations, a recognized representative of HABs: Scenario 1 (S1) using single nonlinear machine learning (ML) algorithms, hybrid Scenario 2 (S2) combining linear models and nonlinear ML algorithms, and hybrid Scenario 3 (S3) combining temporal decomposition and ML (TD-ML) algorithms. The novel-developed S3 TD-ML hybrid models demonstrated superior predictive capabilities, achieving all R2 values above 0.9 and MAPE under 30% in tests, significantly outperforming the S1 with an average R2 of 0.16 and the S2 with an R2 of -0.06. S3 models effectively captured the algal dynamics, successfully predicting complex time series with extremes and noise. In addition, we unveiled the relationship between environmental variables and chlorophyll-a through correlation analysis and found that climate change might intensify the HABs in Biscayne Bay. This research developed a precise predictive framework for early warning and proactive management of HABs, offering potential global applicability and improved prediction accuracy to address HAB challenges.

Reconciliation in palliative care: A concept analysis.

OBJECTIVES: The aim was to gain a deeper understanding of the meaning of reconciliation as a concept in palliative care. Terminal illnesses affect almost all aspects of life and being close to death may lead to a need for reconciliation. The end of life is stressful on an existential level for both patients and relatives. It can therefore be of relevance for palliative care nurses to understand the meaning of reconciliation. METHODS: This study used a design for a literature study in accordance with a hybrid model. A deductive qualitative content analysis of autobiographies about being seriously ill and in a palliative stage in life was used to test the meaning of reconciliation. Ethical aspects concerning the use of autobiographies and the ethical principles of the Helsinki Declaration were considered. The theoretical perspective was the caritative theory of caring. RESULTS: The result showed that for patients in palliative care, reconciliation can be described as a strive for acceptance, to live in a truthful way, to forgive and be forgiven. People wish to create meaning in their existence and reconcile as a whole in body, spirit, and soul. By striving to unite suffering, life, and death as well as a peaceful relationship with relatives, people can achieve reconciliation at the end of life. Reconciliation is something ongoing and can be a force in what has been, what is, and what will be. SIGNIFICANCE OF RESULTS: We conclude that reconciliation is a concept of importance when caring for patients in end-of-life care. However, reconciliation can be expressed in different ways without necessarily using the concept itself. A broader and deeper understanding of the concept facilitates conversations about the meaning of reconciliation in palliative care and can enable patients who strive to achieve reconciliation to be more easily identified and supported.

Artificial intelligence methods for modeling gasification of waste biomass: a review.

Gasification is a highly promising thermochemical process that shows considerable potential for the efficient conversion of waste biomass into syngas. The assessment of the feasibility and comparative advantages of different biomass and waste gasification schemes is contingent upon a multifaceted combination of interrelated criteria. Conventional analytical approaches employed to facilitate decision-making rely on a multitude of inadequately defined parameters. Consequently, substantial efforts have been directed toward enhancing the efficiency and productivity of thermochemical conversion processes. In recent times, artificial intelligence (AI)-based models and algorithms have gained prominence, serving as indispensable tools for expediting these processes and formulating strategies to address the growing demand for energy. Notably, machine learning (ML) and deep learning (DL) have emerged as cutting-edge AI models, demonstrating exceptional effectiveness and profound relevance in the realm of thermochemical conversion systems. This study provides an overview of the machine learning (ML) and deep learning (DL) approaches utilized during gasification and evaluates their benefits and drawbacks. Many industries and applications related to energy conversion systems use AI algorithms. Predicting the output of conversion systems and subjects linked to optimization are two of this science's critical applications. This review sheds light on the burgeoning utility of AI, particularly ML and DL, which have garnered significant attention due to their applications in productivity prediction, process optimization, real-time process monitoring, and control. Furthermore, the integration of hybrid models has become commonplace, primarily owing to their demonstrated success in modeling and optimization tasks. Importantly, the adoption of these algorithms significantly enhances the model's capability to tackle intricate challenges, as DL methodologies have evolved to offer heightened accuracy and reduced susceptibility to errors. Within the scope of this study, an exhaustive exploration of ML and DL techniques and their applications has been conducted, uncovering existing research knowledge gaps. Based on a comprehensive critical analysis, this review offers recommendations for future research directions, accentuating the pivotal findings and conclusions derived from the study.

PIS-Net: Efficient Medical Image Segmentation Network with Multivariate Downsampling for Point-of-Care.

Recently, with more portable diagnostic devices being moved to people anywhere, point-of-care (PoC) imaging has become more convenient and more popular than the traditional "bed imaging". Instant image segmentation, as an important technology of computer vision, is receiving more and more attention in PoC diagnosis. However, the image distortion caused by image preprocessing and the low resolution of medical images extracted by PoC devices are urgent problems that need to be solved. Moreover, more efficient feature representation is necessary in the design of instant image segmentation. In this paper, a new feature representation considering the relationships among local features with minimal parameters and a lower computational complexity is proposed. Since a feature window sliding along a diagonal can capture more pluralistic features, a Diagonal-Axial Multi-Layer Perceptron is designed to obtain the global correlation among local features for a more comprehensive feature representation. Additionally, a new multi-scale feature fusion is proposed to integrate nonlinear features with linear ones to obtain a more precise feature representation. Richer features are figured out. In order to improve the generalization of the models, a dynamic residual spatial pyramid pooling based on various receptive fields is constructed according to different sizes of images, which alleviates the influence of image distortion. The experimental results show that the proposed strategy has better performance on instant image segmentation. Notably, it yields an average improvement of 1.31% in Dice than existing strategies on the BUSI, ISIC2018 and MoNuSeg datasets.

Effects of cropland reclamation on soil organic carbon in China's black soil region over the past 35 years.

The long-term use of cropland and cropland reclamation from natural ecosystems led to soil degradation. This study investigated the effect of the long-term use of cropland and cropland reclamation from natural ecosystems on soil organic carbon (SOC) content and density over the past 35 years. Altogether, 2140 topsoil samples (0-20 cm) were collected across Northeast China. Landsat images were acquired from 1985 to 2020 through Google Earth Engine, and the reflectance of each soil sample was extracted from the Landsat image that its time was consistent with sampling. The hybrid model that included two individual SOC prediction models for two clustering regions was built for accurate estimation after k-means clustering. The probability hybrid model, a combination between the hybrid model and classification probabilities of pixels, was introduced to enhance the accuracy of SOC mapping. Cropland reclamation results were extracted from the land cover time-series dataset at a 5-year interval. Our study indicated that: (1) Long-term use of cropland led to a 3.07 g kg-1 and 6.71 Mg C ha-1 decrease in SOC content and density, respectively, and the decrease of SOC stock was 0.32 Pg over the past 35 years; (2) nearly 64% of cropland had a negative change in terms of SOC content from 1985 to 2020; (3) cropland reclamation track changed from high to low SOC content, and almost no cropland was reclaimed on the "Black soils" after 2005; (4) cropland reclamation from wetlands resulted in the highest decrease, and reclamation period of years 31-35 decreased when SOC density and SOC stock were 16.05 Mg C ha-1 and 0.005 Pg, respectively, while reclamation period of years 26-30 from forest witnessed SOC density and stock decreases of 8.33 Mg C ha-1 and 0.01 Pg, respectively. Our research results provide a reference for SOC change in the black soil region of Northeast China and can attract more attention to the area of the protection of "Black soils" and natural ecosystems.

Changes in active commuting and changes in health: Within- and between-individual analyses among 16 881 Finnish public sector employees.

BACKGROUND: Active commuting, such as walking or cycling to work, can be beneficial for health. However, because within-individual studies on the association between change in active commuting and change in health are scarce, the previous results may have been biased due to unmeasured confounding. Additionally, prior studies have often lacked information about commuting distance. METHODS: We used two waves (2020, T1 and 2022, T2) of self-report data from the Finnish Public Sector study (N = 16,881; 80% female) to examine the within- and between associations (in a hybrid model) between active commuting and health. Exposure was measured by actively commuted kilometers per week, that is, by multiplying the number of walking or cycling days per week with the daily commuting distance. The primary outcome, self-rated health, was measured at T1 and T2. The secondary outcomes, psychological distress, and sleep problems were measured only at T2 and were therefore analyzed only in a between-individual design. RESULTS: After adjustment for potential time-varying confounders such as socioeconomic factors, body mass index, and health behaviors, an increase equivalent to 10 additional active commuting kilometers per week was associated with a small improvement in self-rated health (within-individual unstandardized beta = 0.01, 95% CI 0.01-0.02; between-individual unstandardized beta = 0.03, 95% CI 0.02-0.04). No associations were observed between changes in active commuting and psychological distress or sleep problems. CONCLUSIONS: An increase in active commuting may promote self-rated health. However, increase of tens of additional kilometers in commuting every day may be required to produce even a small effect on health.