Research on labor education of college students in the new era from the perspective of “three complete education”

This paper discusses the value of Strengthening College Students’ labor education from the perspective of “three complete education”, focuses on the fundamental task of “Building Morality and cultivating people”, and explores the feasible path of the implementation of College Students’ labor education in the new era, so as to strengthen college students’ ideological and political education, deepen college students’ labor consciousness and ability, improve their ideological and political consciousness, and build a solid ideological foundation, Cultivate good conduct and ethics, promote the all-round development of college students, and become qualifi ed successors of socialism with ideals, morality, culture and discipline

Gene Expression Data for DLBCL Cancer Survival Prediction with a Combination of Machine Learning Technologies

Gene expression profiles have become an important and promising way for cancer prognosis and treatment. In addition to their application in cancer class prediction and discovery, gene expression data can be used for the prediction of patient survival. Here, we use particle swarm optimization (PSO) to address one of the major challenges in gene expression data analysis, the curse of dimensionality, in order to discriminate high risk patients from low risk patients. A discrete binary version of PSO is used for gene selection and dimensionality reduction, and a probabilistic neural network (PNN) is implemented as the classifier. The experimental results on the diffuse large B-cell lymphoma data set demonstrate the effectiveness of PSO/PNN system in survival prediction

A Statistical Solution to a Text Decoding Challenge Problem

Given an encoded unknown text message in the form of a three dimensional spatial series generated by the use of four smooth nonlinear functions, we use a method based on simple statistical reasoning to pick up samples for rebuilding the four functions. The estimated functions are then used to decode the sequence. The experimental results show that our method gives a nearly perfect decoding, enabling us to submit a 100% accurate solution to the IJCNN challenge proble

Periodontal Regeneration of Teeth with Radicular Developmental Groove after Intentional Replantation: Two Case Reports

Our case reports probe whether intentional replantation is a feasible and successful treatment for teeth with radicular developmental groove. Radicular developmental groove is an anatomical malformation that often leads to combined periodontal-endodontic lesion. Treatment of complex radicular groove presents a great challenge to the operator. Two cases of periodontal compromised teeth with this developmental anomaly were treated with intentional replantation and followed up for 2 years. The teeth were asymptomatic and functional. The periodontal probing depths decreased from original 10 mm to 2-3 mm. The receded gingival papillae associated with the teeth was regenerated two years after intentional replantation. With careful case selection and treatment planning, intentional replantation may be a predictable alternative treatment modality for the combined endodontic‐periodontal lesion accompanied with radicular developmental groove

Deep learning models for cancer stem cell detection: a brief review

Cancer stem cells (CSCs), also known as tumor-initiating cells (TICs), are a subset of tumor cells that persist within tumors as a distinct population. They drive tumor initiation, relapse, and metastasis through self-renewal and differentiation into multiple cell types, similar to typical stem cell processes. Despite their importance, the morphological features of CSCs have been poorly understood. Recent advances in artificial intelligence (AI) technology have provided automated recognition of biological images of various stem cells, including CSCs, leading to a surge in deep learning research in this field. This mini-review explores the emerging trend of deep learning research in the field of CSCs. It introduces diverse convolutional neural network (CNN)-based deep learning models for stem cell research and discusses the application of deep learning for CSC research. Finally, it provides perspectives and limitations in the field of deep learning-based stem cell research

Cloth2Tex: A Customized Cloth Texture Generation Pipeline for 3D Virtual Try-On

Fabricating and designing 3D garments has become extremely demanding with the increasing need for synthesizing realistic dressed persons for a variety of applications, e.g. 3D virtual try-on, digitalization of 2D clothes into 3D apparel, and cloth animation. It thus necessitates a simple and straightforward pipeline to obtain high-quality texture from simple input, such as 2D reference images. Since traditional warping-based texture generation methods require a significant number of control points to be manually selected for each type of garment, which can be a time-consuming and tedious process. We propose a novel method, called Cloth2Tex, which eliminates the human burden in this process. Cloth2Tex is a self-supervised method that generates texture maps with reasonable layout and structural consistency. Another key feature of Cloth2Tex is that it can be used to support high-fidelity texture inpainting. This is done by combining Cloth2Tex with a prevailing latent diffusion model. We evaluate our approach both qualitatively and quantitatively and demonstrate that Cloth2Tex can generate high-quality texture maps and achieve the best visual effects in comparison to other methods. Project page: tomguluson92.github.io/projects/cloth2tex/Comment: 15 pages, 15 figure

Co-existence of Anaerobic Ammonium Oxidation Bacteria and Denitrifying Anaerobic Methane Oxidation Bacteria in Sewage Sludge: Community Diversity and Seasonal Dynamics

Anaerobic ammonium oxidation (ANAMMOX) and denitrifying anaerobic methane oxidation (DAMO) have been recently discovered as relevant processes in the carbon and nitrogen cycles of wastewater treatment plants. In this study, the seasonal dynamics of ANAMMOX and DAMO bacterial community structures and their abundance in sewage sludge collected from wastewater treatment plants were analysed. Results indicated that ANAMMOX and DAMO bacteria co-existed in sewage sludge in different seasons and their abundance was positively correlated (P < 0.05). The high abundance of ANAMMOX and DAMO bacteria in autumn and winter indicated that these seasons were the preferred time to favour the growth of ANAMMOX and DAMO bacteria. The community structure of ANNAMOX and DAMO bacteria could also shift with seasonal changes. The "Candidatus Brocadia" genus of ANAMMOX bacteria was mainly recovered in spring and summer, and an unknown cluster was primarily detected in autumn and winter. Similar patterns of seasonal variation in the community structure of DAMO bacteria were also observed. Group B was the dominant in spring and summer, whereas in autumn and winter, group A and group B presented almost the same proportion. The redundancy analysis revealed that pH and nitrate were the most significant factors affecting community structures of these two groups (P < 0.01). This study reported the diversity of ANAMMOX and DAMO in wastewater treatment plants that may be the basis for new nitrogen removal technologies

Cloud-cloud collision and star formation in G323.18+0.15

We studied the cloud-cloud collision candidate G323.18+0.15 based on signatures of induced filaments, clumps, and star formation. We used archival molecular spectrum line data from the SEDIGISM $^{13}$CO($J$\,=\,2--1) survey, from the Mopra southern Galactic plane CO survey, and infrared to radio data from the GLIMPSE, MIPS, Hi-GAL, and SGPS surveys. Our new result shows that the G323.18+0.15 complex is 3.55kpc away from us and consists of three cloud components, G323.18a, G323.18b, and G323.18c. G323.18b shows a perfect U-shape structure, which can be fully complemented by G323.18a, suggesting a collision between G323.18a and the combined G323.18bc filamentary structure. One dense compressed layer (filament) is formed at the bottom of G323.18b, where we detect a greatly increased velocity dispersion. The bridge with an intermediate velocity in a position-velocity diagram appears between G323.18a and G323.18b, which corresponds to the compressed layer. G323.18a plus G323.18b as a whole are probably not gravitationally bound. This indicates that high-mass star formation in the compressed layer may have been caused by an accidental event. The column density in the compressed layer of about $1.36 \times 10^{22}$cm$^{-2}$ and most of the dense clumps and high-mass stars are located there. The average surface density of classI and classII young stellar objects (YSOs) inside the G323.18+0.15 complex is much higher than the density in the surroundings. The timescale of the collision between G323.18a and G323.18b is $1.59$Myr. This is longer than the typical lifetime of classI YSOs and is comparable to the lifetime of classII YSOs

A voice recognition-based digital cognitive screener for dementia detection in the community: Development and validation study

IntroductionTo facilitate community-based dementia screening, we developed a voice recognition-based digital cognitive screener (digital cognitive screener, DCS). This proof-of-concept study aimed to investigate the reliability, validity as well as the feasibility of the DCS among community-dwelling older adults in China.MethodsEligible participants completed demographic, clinical, and the DCS. Diagnosis of mild cognitive impairment (MCI) and dementia was made based on the Montreal Cognitive Assessment (MoCA) (MCI: MoCA &lt; 23, dementia: MoCA &lt; 14). Time and venue for test administration were recorded and reported. Internal consistency, test-retest reliability and inter-rater reliability were examined. Receiver operating characteristic (ROC) analyses were conducted to examine the discriminate validity of the DCS in detecting MCI and dementia.ResultsA total of 103 participants completed all investigations and were included in the analysis. Administration time of the DCS was between 5.1–7.3 min. No significant difference (p &gt; 0.05) in test scores or administration time was found between 2 assessment settings (polyclinic or community center). The DCS showed good internal consistency (Cronbach’s alpha = 0.73), test-retest reliability (Pearson r = 0.69, p &lt; 0.001) and inter-rater reliability (ICC = 0.84). Area under the curves (AUCs) of the DCS were 0.95 (0.90, 0.99) and 0.77 (0.67, 086) for dementia and MCI detection, respectively. At the optimal cut-off (7/8), the DCS showed excellent sensitivity (100%) and good specificity (80%) for dementia detection.ConclusionThe DCS is a feasible, reliable and valid digital dementia screening tool for older adults. The applicability of the DCS in a larger-scale community-based screening stratified by age and education levels warrants further investigation