Optimized design of porous dental implant structures based on triply periodic minimal surface (TPMS).

Objectives: This study aims to optimize the design of porous dental implants using triply periodic minimal surfaces (TPMS) to address stress shielding caused by elastic modulus mismatch between titanium implants and bone tissue, while enhancing osseointegration through controlled porosity.

Methods: Two TPMS architectures (D-type and G-type) were modeled via MathMod and Rhino software, with porosity controlled by parameter t. Finite element analysis (FEA) evaluated mechanical properties under porosities of 40 %, 60 %, and 80 %, and stress distribution in a patient-specific mandibular model under 200 N masticatory load.

Gender differences in resume language and gender gaps in salary expectations.

How men and women present themselves in their resumes may affect their opportunity in job seeking. To investigate gender differences in resume writing and how they are associated with gender gaps in the labour market, we analysed 6.9 million resumes of Chinese job applicants in this study.

Tumor suppressing function of SLC16A7 in bladder cancer and its pan-cancer analysis.

Background: Bladder cancer (BCa), a prevalent malignancy of the urinary tract, is associated with high recurrence and mortality rates. SLC16A7, a member of the solute carrier family 16 (SLC16), encodes monocarboxylate transporters that are involved in the proton-coupled transport of metabolites, including lactate, pyruvate, and ketone bodies, across cell membranes. Evidence suggests that SLC16A7 exhibits variable expression in cancers and may influence tumor development, progression, and immune regulation.

SRSF1-mediated alternative splicing regulates bladder cancer progression and cisplatin sensitivity through HIF1A/BNIP3/mitophagy axis.

Background: Alternative splicing (AS) is consistently linked to tumor progression. SRSF1, the first identified proto-oncogene in the serine/arginine-rich splicing factor (SRSF) protein family, plays a crucial role. However, the specific functions and potential mechanisms of SRSF1 in advancing bladder cancer (BCa) progression and influencing chemosensitivity remain largely unexplored.

Learning Temporal Features With Alternated Similarity and Proximity Attention for Time-Series Prediction.

Time-series prediction is a fundamental problem in various scientific and engineering domains. Recently, attention-based models have shown great promise in long-term time-series forecasting. However, we prove that vanilla attention is equivalent to a one-step random walk on a bipartite graph between the query and the keys, in which the limited number of walks and simplified graph structure could make it less powerful in capturing complex, high-order featural and temporal dependencies.

GDM-BC: Non-invasive body composition dataset for intelligent prediction of Gestational Diabetes Mellitus.

Gestational Diabetes Mellitus (GDM) refers to any degree of impaired glucose tolerance with onset or first recognition during pregnancy. As a high-prevalence disease, GDM damages the health of both pregnant women and fetuses in the short and long term. Accurate and cost-effective recognition of GDM is quite crucial to reduce the risk and economic pressure of this disease.

Assessing the effectiveness of test-trace-isolate interventions using a multi-layered temporal network.

In the early stage of an infectious disease outbreak, public health strategies tend to gravitate towards non-pharmaceutical interventions (NPIs) given the time required to develop targeted treatments and vaccines. One of the most common NPIs is Test-Trace-Isolate (TTI). One of the factors determining the effectiveness of TTI is the ability to identify contacts of infected individuals.

MolEM: a unified generative framework for molecular graphs and sequential orders.

Structure-based drug design aims to generate molecules that fill the cavity of the protein pocket with a high binding affinity. Many contemporary studies employ sequential generative models. Their standard training method is to sequentialize molecular graphs into ordered sequences and then maximize the likelihood of the resulting sequences.

N6-methyladenosine-modified RNF220 induces cisplatin resistance and immune escape via regulating PDE10A K48-linked ubiquitination in bladder cancer.

Bladder cancer (BCa) remains one of the most prevalent malignancies worldwide, with cisplatin-based combination chemotherapy as the cornerstone of adjuvant treatment. However, cisplatin resistance frequently arises in advanced BCa, limiting therapeutic efficacy. Comparative proteomic analysis of cisplatin-sensitive and -resistant BCa cells identified phosphodiesterase 10A (PDE10A) as significantly downregulated at the protein level in resistant cells, despite unchanged mRNA levels, indicating post-transcriptional regulation.

Partial Differential Equations Meet Deep Neural Networks: A Survey.

Many problems in science and engineering can be mathematically modeled using partial differential equations (PDEs), which are essential for fields like computational fluid dynamics (CFD), molecular dynamics, and dynamical systems. Although traditional numerical methods like the finite difference/element method are widely used, their computational inefficiency, due to the large number of iterations required, has long been a challenge. Recently, deep learning (DL) has emerged as a promising alternative for solving PDEs, offering new paradigms beyond conventional methods.

IGF2BP3 promotes the proliferation and cisplatin resistance of bladder cancer by enhancing the mRNA stability of CDK6 in an m6A dependent manner.

Cisplatin-based chemotherapy is a primary treatment for bladder cancer, yet the development of chemoresistance poses a significant therapeutic challenge. Insulin-like growth factor II mRNA binding protein 3 (IGF2BP3) is an RNA-binding protein and a key m6A reader that regulates various cancers through m6A-dependent mechanisms. However, its role in chemotherapy resistance in bladder cancer remains unclear.

E-3SFC: Communication-Efficient Federated Learning With Double-Way Features Synthesizing.

The exponential growth in model sizes has significantly increased the communication burden in federated learning (FL). Existing methods to alleviate this burden by transmitting compressed gradients often face high compression errors, which slow down the model's convergence. To simultaneously achieve high compression effectiveness and lower compression errors, we study the gradient compression problem from a novel perspective.

STSF: Spiking Time Sparse Feedback Learning for Spiking Neural Networks.

Spiking neural networks (SNNs) are biologically plausible models known for their computational efficiency. A significant advantage of SNNs lies in the binary information transmission through spike trains, eliminating the need for multiplication operations. However, due to the spatio-temporal nature of SNNs, direct application of traditional backpropagation (BP) training still results in significant computational costs.

Combining pelvic floor ultrasonography with deep learning to diagnose anterior compartment organ prolapse.

Background: Anterior compartment prolapse is a common pelvic organ prolapse (POP), which occurs frequently among middle-aged and elderly women and can cause urinary incontinence, perineal pain and swelling, and seriously affect their physical and mental health. At present, pelvic floor ultrasound is the primary examination method, but it is not carried out by many primary medical institutions due to the significant shortcomings of training in the early stage and the variable image quality. There has been great progress in the application of deep learning (DL) in image-based diagnosis in various clinical contexts.

Inhibition of SIRT4 promotes bladder cancer progression and immune escape via attenuating CD8 T cells function.

Background: Bladder cancer (BCa) is one of the most common malignancies of the urinary system and is characterized by a high recurrence rate and significant mortality. Sirtuin 4 (SIRT4), a member of the NAD-dependent deacetylase and ADP-ribosyltransferase family, is involved in regulating cellular metabolism, DNA repair, and longevity, potentially influencing tumor progression and immune escape. This study aimed to elucidate the role of SIRT4 in BCa.

Membrane palmitoylated protein MPP1 inhibits immune escape by regulating the USP12/ CCL5 axis in urothelial carcinoma.

Background: The response rate to immunotherapy in patients with urothelial carcinoma remains limited. Studies have shown that membrane palmitoylated proteins (MPPs) play key roles in tumor progression. However, the mechanisms by which MPP1 regulates immune escape in urothelial carcinoma are not well understood.

Dual-modality visual feature flow for medical report generation.

Medical report generation, a cross-modal task of generating medical text information, aiming to provide professional descriptions of medical images in clinical language. Despite some methods have made progress, there are still some limitations, including insufficient focus on lesion areas, omission of internal edge features, and difficulty in aligning cross-modal data. To address these issues, we propose Dual-Modality Visual Feature Flow (DMVF) for medical report generation.

TargetSA: adaptive simulated annealing for target-specific drug design.

Motivation: The burgeoning field of target-specific drug design has attracted considerable attention, focusing on identifying compounds with high binding affinity toward specific target pockets. Nevertheless, existing target-specific deep generative models encounter notable challenges. Some models heavily rely on elaborate datasets and complicated training methodologies, while others neglect the multi-constraint optimization problem inherent in drug design, resulting in generated molecules with irrational structures or chemical properties.

CircFAM64A(3) promoted bladder cancer proliferation and inhibited CD8 + T cell via sponging to miR-149-5p and activated IL-6/JAK/STAT pathway.

Background: The significance of circular RNA in tumour biology is increasingly recognized. This study aims to explore the value of circFAM64A(3) in the proliferation and immune evasion of bladder cancer.

Methods: Bioinformatics were used to identify the differentially expressed circular RNAs in bladder cancer.

EU-Net: Automatic U-Net neural architecture search with differential evolutionary algorithm for medical image segmentation.

Medical images are crucial in clinical practice, providing essential information for patient assessment and treatment planning. However, manual extraction of information from images is both time-consuming and prone to errors. The emergence of U-Net addresses this challenge by automating the segmentation of anatomical structures and pathological lesions in medical images, thereby significantly enhancing the accuracy of image interpretation and diagnosis.

CircZNF609 inhibited bladder cancer immunotherapy sensitivity via enhancing fatty acid uptake through IGF2BP2/CD36 pathway.

Circular RNAs (circRNAs) are gaining attention for their involvement in immune escape and immunotherapy sensitivity regulation. CircZNF609 is a well-known oncogene in various solid tumours. Our previous research revealed its role in reducing the chemosensitivity of bladder cancer (BCa) to cisplatin.

CGFTrans: Cross-Modal Global Feature Fusion Transformer for Medical Report Generation.

Medical report generation, as a cross-modal automatic text generation task, can be highly significant both in research and clinical fields. The core is to generate diagnosis reports in clinical language from medical images. However, several limitations persist, including a lack of global information, inadequate cross-modal fusion capabilities, and high computational demands.

Cross-Modal Retrieval With Noisy Correspondence via Consistency Refining and Mining.

The success of existing cross-modal retrieval (CMR) methods heavily rely on the assumption that the annotated cross-modal correspondence is faultless. In practice, however, the correspondence of some pairs would be inevitably contaminated during data collection or annotation, thus leading to the so-called Noisy Correspondence (NC) problem. To alleviate the influence of NC, we propose a novel method termed Consistency REfining And Mining (CREAM) by revealing and exploiting the difference between correspondence and consistency.

METTL16 suppressed the proliferation and cisplatin-chemoresistance of bladder cancer by degrading PMEPA1 mRNA in a m6A manner through autophagy pathway.

N6-methyladenosine (m6A) is important in the physiological processes of many species. Methyltransferase-like 16 (METTL16) is a novel discovered m6A methylase, regulating various tumors in an m6A-dependent manner. However, its function in bladder cancer (BLCA) remains largely unclear.