Leptin Receptor Overlapping Transcript (LEPROT) Is Associated with the Tumor Microenvironment and a Prognostic Predictor in Pan-Cancer

Background: Leptin receptor overlapping transcript (LEPROT) is reported to be involved in metabolism regulation and energy balance as well as molecular signaling of breast cancer and osteosarcoma. LEPROT is expressed in various tissue and is suggested to be involved in cancer developments but with contradictory roles. The comprehensive knowledge of the effects of LEPROT on cancer development and progression across pan-cancer is still missing. Methods: The expressions of LEPROT in cancers were compared with corresponding normal tissues across pan-cancer types. The relationships between expression and methylation of LEPROT were then demonstrated. The correlations of LEPROT with the tumor microenvironment (TME), including immune checkpoints, tumor immune cells infiltration (TII), and cancer-associated fibroblasts (CAFs), were also investigated. Co-expression analyses and functional enrichments were conducted to suggest the most relevant genes and the mechanisms of the effects in cancers for LEPROT. Finally, the correlations of LEPROT with patient survival and immunotherapy response were explored. Results: LEPROT expression was found to be significantly aberrant in 15/19 (78.9%) cancers compared with corresponding normal tissues; LEPROT was downregulated in 12 cancers and upregulated in three cancers. LEPROT expressions were overall negatively correlated with its methylation alterations. Moreover, LEPROT was profoundly correlated with the TME, including immune checkpoints, TIIs, and CAFs. According to co-expression analyses and functional enrichments, the interactions of LEPROT with the TME may be mediated by the interleukin six signal transducer/the Janus kinase/signal transducers and activators of the transcription signaling pathway. Prognostic values may exist for LEPROT to predict patient survival and immunotherapy response in a context-dependent way. Conclusions: LEPROT affects cancer development by interfering with the TME and regulating inflammatory or immune signals. LEPROT may also serve as a potential prognostic marker or a target in cancer therapy. This is the first study to investigate the roles of LEPROT across pan-cancer

Exploiting Record Similarity for Practical Vertical Federated Learning

As the privacy of machine learning has drawn increasing attention, federated learning is introduced to enable collaborative learning without revealing raw data. Notably, \textit{vertical federated learning} (VFL), where parties share the same set of samples but only hold partial features, has a wide range of real-world applications. However, existing studies in VFL rarely study the ``record linkage'' process. They either design algorithms assuming the data from different parties have been linked or use simple linkage methods like exact-linkage or top1-linkage. These approaches are unsuitable for many applications, such as the GPS location and noisy titles requiring fuzzy matching. In this paper, we design a novel similarity-based VFL framework, FedSim, which is suitable for more real-world applications and achieves higher performance on traditional VFL tasks. Moreover, we theoretically analyze the privacy risk caused by sharing similarities. Our experiments on three synthetic datasets and five real-world datasets with various similarity metrics show that FedSim consistently outperforms other state-of-the-art baselines

Band gap engineering of N-alloyed Ga2O3 thin films

The authors report the tuning of band gap of GaON ternary alloy in a wide range of 2.75 eV. The samples were prepared by a two-step nitridation method. First, the samples were deposited on 2-inch fused silica substrates by megnetron sputtering with NH3 and Ar gas for 60 minutes. Then they were annealed in NH3 ambience at different temperatures. The optical band gap energies are calculated from transmittance measurements. With the increase of nitridation temperature, the band gap gradually decreases from 4.8 eV to 2.05 eV. X-ray diffraction results indicate that as-deposited amorphous samples can crystallize into monoclinic and hexagonal structures after they were annealed in oxygen or ammonia ambience, respectively. The narrowing of the band gap is attributed to the enhanced repulsion of N2p -Ga3d orbits and formation of hexagonal structur

Privacy-Preserving Gradient Boosting Decision Trees

The Gradient Boosting Decision Tree (GBDT) is a popular machine learning model for various tasks in recent years. In this paper, we study how to improve model accuracy of GBDT while preserving the strong guarantee of differential privacy. Sensitivity and privacy budget are two key design aspects for the effectiveness of differential private models. Existing solutions for GBDT with differential privacy suffer from the significant accuracy loss due to too loose sensitivity bounds and ineffective privacy budget allocations (especially across different trees in the GBDT model). Loose sensitivity bounds lead to more noise to obtain a fixed privacy level. Ineffective privacy budget allocations worsen the accuracy loss especially when the number of trees is large. Therefore, we propose a new GBDT training algorithm that achieves tighter sensitivity bounds and more effective noise allocations. Specifically, by investigating the property of gradient and the contribution of each tree in GBDTs, we propose to adaptively control the gradients of training data for each iteration and leaf node clipping in order to tighten the sensitivity bounds. Furthermore, we design a novel boosting framework to allocate the privacy budget between trees so that the accuracy loss can be further reduced. Our experiments show that our approach can achieve much better model accuracy than other baselines