Simple but Effective Unsupervised Classification for Specified Domain Images: A Case Study on Fungi Images

High-quality labeled datasets are essential for deep learning. Traditional manual annotation methods are not only costly and inefficient but also pose challenges in specialized domains where expert knowledge is needed. Self-supervised methods, despite leveraging unlabeled data for feature extraction, still require hundreds or thousands of labeled instances to guide the model for effective specialized image classification. Current unsupervised learning methods offer automatic classification without prior annotation but often compromise on accuracy. As a result, efficiently procuring high-quality labeled datasets remains a pressing challenge for specialized domain images devoid of annotated data. Addressing this, an unsupervised classification method with three key ideas is introduced: 1) dual-step feature dimensionality reduction using a pre-trained model and manifold learning, 2) a voting mechanism from multiple clustering algorithms, and 3) post-hoc instead of prior manual annotation. This approach outperforms supervised methods in classification accuracy, as demonstrated with fungal image data, achieving 94.1% and 96.7% on public and private datasets respectively. The proposed unsupervised classification method reduces dependency on pre-annotated datasets, enabling a closed-loop for data classification. The simplicity and ease of use of this method will also bring convenience to researchers in various fields in building datasets, promoting AI applications for images in specialized domains

Tunable electronic properties of graphene through controlling bonding configurations of doped nitrogen atoms

Single–layer and mono–component doped graphene is a crucial platform for a better understanding of the relationship between its intrinsic electronic properties and atomic bonding configurations. Large–scale doped graphene films dominated with graphitic nitrogen (GG) or pyrrolic nitrogen (PG) were synthesized on Cu foils via a free radical reaction at growth temperatures of 230–300 °C and 400–600 °C, respectively. The bonding configurations of N atoms in the graphene lattices were controlled through reaction temperature, and characterized using Raman spectroscopy, X–ray photoelectron spectroscopy and scanning tunneling microscope. The GG exhibited a strong n–type doping behavior, whereas the PG showed a weak n–type doping behavior. Electron mobilities of the GG and PG were in the range of 80.1–340 cm2 V−1·s−1 and 59.3–160.6 cm2 V−1·s−1, respectively. The enhanced doping effect caused by graphitic nitrogen in the GG produced an asymmetry electron–hole transport characteristic, indicating that the long–range scattering (ionized impurities) plays an important role in determining the carrier transport behavior. Analysis of temperature dependent conductance showed that the carrier transport mechanism in the GG was thermal excitation, whereas that in the PG, was a combination of thermal excitation and variable range hopping

NF45/NF90-mediated rDNA transcription provides a novel target for immunosuppressant development

Herein, we demonstrate that NFAT, a key regulator of the immune response, translocates from cytoplasm to nucleolus and interacts with NF45/NF90 complex to collaboratively promote rDNA transcription via triggering the directly binding of NF45/NF90 to the ARRE2-like sequences in rDNA promoter upon T-cell activation in vitro. The elevated pre-rRNA level of T cells is also observed in both mouse heart or skin transplantation models and in kidney transplanted patients. Importantly, T-cell activation can be significantly suppressed by inhibiting NF45/NF90-dependent rDNA transcription. Amazingly, CX5461, a rDNA transcription-specific inhibitor, outperformed FK506, the most commonly used immunosuppressant, both in terms of potency and off-target activity (i.e., toxicity), as demonstrated by a series of skin and heart allograft models. Collectively, this reveals NF45/NF90-mediated rDNA transcription as a novel signaling pathway essential for T-cell activation and as a new target for the development of safe and effective immunosuppressants

Weld Defect Segmentation in X-ray Image with Boundary Label Smoothing

Weld defect segmentation (WDS) is widely used to detect defects from X-ray images for welds, which is of practical importance for manufacturing in all industries. The key challenge of WDS is that the labeled ground truth of defects is usually not accurate because of the similarities between the candidate defect and noisy background, making it difficult to distinguish some critical defects, such as cracks, from the weld line during the inference stage. In this paper, we propose boundary label smoothing (BLS), which uses Gaussian Blur to soften the labels near object boundaries to provide an appropriate representation of inaccuracy and uncertainty in ground truth labels. We incorporate BLS into dice loss, in combination with focal loss and weighted cross-entropy loss as a hybrid loss, to achieve improved performance on different types of segmentation datasets

Modeling and optimizing an electrochemical oxidation process using artificial neural network, genetic algorithm and particle swarm optimization

This study proposes a novel hybrid of artificial neural network (ANN), genetic algorithm (GA), and particle swarm optimization (PSO) to model and optimize the relevant parameters of an electrochemical oxidation (EO) Acid Black 2 process. The back propagation neural network (BPNN) was used as a modelling tool. To avoid over-fitting, GA was applied to improve the generalized capability of BPNN by optimizing the weights. In addition, an optimization model was developed to assess the performance of the EO process, where total organic carbon (TOC) removal, mineralization current efficiency (MCE), and the energy consumption per unit of TOC (ECTOC) were considered. The operation conditions of EO were further optimized via PSO. The validation results indicted the proposed method to be a promising method to estimate the efficiency and to optimize the parameters of the EO process

Weld Defect Segmentation in X-ray Image with Boundary Label Smoothing

Weld defect segmentation (WDS) is widely used to detect defects from X-ray images for welds, which is of practical importance for manufacturing in all industries. The key challenge of WDS is that the labeled ground truth of defects is usually not accurate because of the similarities between the candidate defect and noisy background, making it difficult to distinguish some critical defects, such as cracks, from the weld line during the inference stage. In this paper, we propose boundary label smoothing (BLS), which uses Gaussian Blur to soften the labels near object boundaries to provide an appropriate representation of inaccuracy and uncertainty in ground truth labels. We incorporate BLS into dice loss, in combination with focal loss and weighted cross-entropy loss as a hybrid loss, to achieve improved performance on different types of segmentation datasets

Effects of Physical Activity on Quality of Life, Anxiety and Depression in Breast Cancer Survivors: A Systematic Review and Meta-analysis

Summary: Purpose: Anxiety, depression, and poor quality of life (QOL) were considered important concerns that hindered the rehabilitation of breast cancer survivors. A number of studies have investigated the effects of physical activity, but they have not reached the same conclusions. This review aimed to identify the effects of physical activity on QOL, anxiety, and depression in breast cancer survivors. Methods: PubMed, Embase, Web of Science, Cumulative Index to Nursing and Allied Health Literature (CINAHL), PsycINFO, SinoMed, CNKI, Vip, and WanFang databases were searched for the time period between January 1, 2012, and April 30, 2022. Studies were included if they were randomized controlled trials of the effects of physical activity on QOL, anxiety, or depression in breast cancer survivors. The tools of the Joanna Briggs Institute were used to assess the quality of the included studies. R software version 4.3.1 was used for meta-analysis. Results: A total of 26 studies, involving 2105 participants, were included in the systematic review. Among these, 20 studies involving 1228 participants were included in the meta-analysis. Compared with the control group, the results indicated that physical activity can significantly improve QOL(Hedges' g = 0.67; 95% CI 0.41–0.92) and reduce anxiety (Hedges' g = −0.28; 95% CI −0.46 to −0.10) in breast cancer survivors. However, the effect of physical activity on depression (Hedges' g = −0.46; 95% CI −0.99 to 0.06) was not statistically significant. Conclusions: Physical activity was an effective intervention to improve QOL and reduce anxiety in breast cancer survivors, as well as showed positive trends in depression, although without statistical significance. More well-designed studies are required to clarify the effects of different types of physical activities on the QOL, anxiety, and depression among breast cancer survivors. Registered number on PROSPERO: CRD42022363094.https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=363094

The retrospective data analysis on the pedigree of nervous system diseases in children

Abstract Nowadays, the development of diagnosis and treatment technology is constantly changing the pedigree and classification of nervous system diseases. Analyzing changes in earlier disease pedigrees can help us understand the changes involved in disease diagnosis from a macro perspective, as well as predict changes in later disease pedigrees and the direction of diagnosis and treatment. The inpatients of the neurology department from January 2012 to December 2020 in Hunan Children's Hospital were retrospectively analyzed. There were 36,777 patients enrolled in this study. The next analysis was based on factors like age, gender, length of stay (LoS), number of patients per month and per year (MNoP and ANoP, respectively), and average daily hospital cost (ADHE). To evaluate the characteristics of neurological diseases, we applied a series of statistical tools such as numerical characteristics, boxplots, density charts, one-way ANOVA, Kruskal–Wallis tests, time-series plots, and seasonally adjusted indices. The statistical analysis of neurological diseases led to the following conclusions: First, children having neurological illnesses are most likely to develop them between the ages of 4 and 8 years. Benign intracranial hypertension was the youngest mean age of onset among the various neurologic diseases, and most patients with bacterial intracranial infection were young children. Some diseases have a similar mean age of onset, such as seizures (gastroenteritis/diarrhea) and febrile convulsions. Second, women made up most patients with autoimmune diseases of the central nervous system. Treatment options for inherited metabolic encephalopathy and epilepsy are similar, but they differ significantly for viral intracranial infection. Some neurologic diseases were found to have seasonal variations; for example, infectious diseases of the central nervous system were shown to occur more commonly in the warm season, whereas, autoimmune diseases primarily appeared in the autumn and winter months. Additionally, the number of patients admitted to hospitals with intracranial infections and encephalopathy has dramatically dropped recently, but the number of patients with autoimmune diseases of the central nervous system and hereditary metabolic encephalopathy has been rising year over year. Finally, we discovered apparent polycentric distributions in various illnesses’ density distributions. The study offered an epidemiological basis for common nervous system diseases, including evidence from age of onset, number of cases, and so on. The pedigree of nervous system diseases has significantly changed. The proportion of patients with neuroimmune diseases and genetic metabolic diseases is rising while the number of patients with infection-related diseases and uncertain diagnoses is decreasing. The existence of a disease multimodal model suggests that there is still a lack of understanding of many diseases' diagnosis and treatment, which needs to be improved further because accurate diagnosis aids in the formulation of individualized treatment plans and the allocation of medical resources; additionally, there is still a lack of effective treatment for most genetic diseases. The seasonal characteristics of nervous system diseases suggest the need for the improvement of sanitation, living conditions, and awareness of daily health care