Association of the CHRNA3 Locus with Lung Cancer Risk and Prognosis in Chinese Han Population

IntroductionRecent genome-wide association studies in Caucasians revealed association with lung cancer risk of single nucleotide polymorphisms (SNPs) in the locus containing two nicotine acetylcholine receptor CHRNA genes. However, the reported risk SNPs are extremely rare in Asians. This study sought to identify other variants on CHRNA3 associated with lung cancer susceptibility and to explore whether SNPs of CHRNA3 are of prognostic factors in patients with non-small cell lung cancer (NSCLC) in Chinese Han population.MethodsA case-control study of 529 cases and 567 controls was performed to study the association of three SNPs (rs3743076, rs3743078, and rs3743073) in CHRNA3 with lung cancer risk in Chinese Han population using logistic regression models. The relationship between CHRNA3 polymorphisms with overall survival among 122 patients with advanced stage (stage IIIb and IV) NSCLC were evaluated using Cox multiple model based on the International Association for the Study of Lung Cancer recommended tumor, node, metastasis new staging.ResultsPatients with genotypes TG or GG for the novel SNP rs3743073 in CHRNA3 gene, compared with those with TT, showed an increased risk of lung cancer (adjusted odds ratio = 1.91; 95% confidence interval, 1.38–2.63; p = 9.67 × 10−5) and worst survival (adjusted hazard ratio = 2.35; 95% confidence interval, 1.05–5.26; p = 0.04) in patients with advanced stage NSCLC based on International Association for the Study of Lung Cancer recommended tumor, node, metastasis new staging.ConclusionsThese results suggest that the rs3743073 polymorphism in CHRNA3 is predictive for lung cancer risk and prognostic in advanced stage NSCLC in Chinese Han population

Opening the AI black box: program synthesis via mechanistic interpretability

We present MIPS, a novel method for program synthesis based on automated mechanistic interpretability of neural networks trained to perform the desired task, auto-distilling the learned algorithm into Python code. We test MIPS on a benchmark of 62 algorithmic tasks that can be learned by an RNN and find it highly complementary to GPT-4: MIPS solves 32 of them, including 13 that are not solved by GPT-4 (which also solves 30). MIPS uses an integer autoencoder to convert the RNN into a finite state machine, then applies Boolean or integer symbolic regression to capture the learned algorithm. As opposed to large language models, this program synthesis technique makes no use of (and is therefore not limited by) human training data such as algorithms and code from GitHub. We discuss opportunities and challenges for scaling up this approach to make machine-learned models more interpretable and trustworthy.Comment: 24 page

Understanding the photoelectrochemical properties of a reduced graphene oxide-WO3 heterojunction photoanode for efficient solar-light-driven overall water splitting

WO3-reduced graphene oxide (WO3-RGO) heterojunction electrodes were prepared for photoelectrochemical (PEC) overall water splitting. The WO3 photoanode incorporated with RGO showed significantly enhanced PEC properties and, hence, photocatalytic water splitting, compared to the bare WO3 at a bias larger than 0.7 V vs. Ag/AgCl, while a decrease in the PEC properties of WO3-RGO compared to the WO3 electrode was observed at a bias smaller than 0.7 V vs. Ag/AgCl. RGO could play a favorable role in enhancing the electron-hole separation due to the presence of interface states according to the Bardeen model, but it could also provide active sites for the electron-hole recombination. A more positive applied bias is in favor of effective electron-hole separation, by means of quick collection and transport of electrons by RGO. As a result, a higher PEC performance of WO3-RGO can only be realised at a relatively more positive bias. This study gives insights into the complex nature of a RGO-semiconductor heterojunction, and its implications on the overall photoconversion efficiency

Harnessing stimuli‐responsive biomaterials for advanced biomedical applications

Cell behavior is intricately intertwined with the in vivo microenvironment and endogenous pathways. The ability to guide cellular behavior toward specific goals can be achieved by external stimuli, notably electricity, light, ultrasound, and magnetism, simultaneously harnessed through biomaterial‐mediated responses. These external triggers become focal points within the body due to interactions with biomaterials, facilitating a range of cellular pathways: electrical signal transmission, biochemical cues, drug release, cell loading, and modulation of mechanical stress. Stimulus‐responsive biomaterials hold immense potential in biomedical research, establishing themselves as a pivotal focal point in interdisciplinary pursuits. This comprehensive review systematically elucidates prevalent physical stimuli and their corresponding biomaterial response mechanisms. Moreover, it delves deeply into the application of biomaterials within the domain of biomedicine. A balanced assessment of distinct physical stimulation techniques is provided, along with a discussion of their merits and limitations. The review aims to shed light on the future trajectory of physical stimulus‐responsive biomaterials in disease treatment and outline their application prospects and potential for future development. This review is poised to spark novel concepts for advancing intelligent, stimulus‐responsive biomaterials

Engineered functional doped hydroxyapatite coating on titanium implants for osseointegration

Titanium (Ti) and its alloys are commonly used implant materials in orthopedics due to their good corrosion resistance, mechanical properties, and biocompatibility. However, the inherent biological inertness of the Ti surface leads to insufficient osseointegration and antibacterial ability, which may result in implant failure. Hydroxyapatite (HA) is currently the most widely used material in the biomedical field. It is one of the bioactive coating materials because of its chemical and structural similarity to natural bone. At present, many techniques are used to deposit HA as a coating material on Ti implants. The stability of the HA coating is the most important factor in determining the success of the implant. In addition, biofunctional ions have been introduced into HA coatings to enhance functional performance. This article aims to present the crystal structure and characteristics of HA and the principle of doping ions into HA. The preparation methods for the deposition of HA functional coatings on Ti and its alloys are introduced and discuss its advantages and limitations. In addition, the coating of doped HA on the surface of Ti and its alloys to improve their surface properties for bone integration is also reviewed

Distribution Prediction of Strategic Flight Delays via Machine Learning Methods

Predicting flight delays has been a major research topic in the past few decades. Various machine learning algorithms have been used to predict flight delays in short-range horizons (e.g., a few hours or days prior to operation). Airlines have to develop flight schedules several months in advance; thus, predicting flight delays at the strategic stage is critical for airport slot allocation and airlines’ operation. However, less work has been dedicated to predicting flight delays at the strategic phase. This paper proposes machine learning methods to predict the distributions of delays. Three metrics are developed to evaluate the performance of the algorithms. Empirical data from Guangzhou Baiyun International Airport are used to validate the methods. Computational results show that the prediction accuracy of departure delay at the 0.65 confidence level and the arrival delay at the 0.50 confidence level can reach 0.80 without the input of ATFM delay. Our work provides an alternative tool for airports and airlines managers for estimating flight delays at the strategic phase

Attenuation Characterization of Terahertz Waves in Foggy and Rainy Conditions at 0.1–1 THz Frequencies

Investigating the absorption and scattering effects of atmospheric particles, i.e., raindrops and fog droplets, is required to establish a comprehensive and accurate channel model. However, for long-distance communication in outdoor scenarios, research on the propagation characterization of fog and rain attenuation in the terahertz (THz) band is insufficient. In this study, fog and rain attenuation characterization with different conditions are characterized. First, fog attenuation at different temperatures and diverse visibility is explored using Rayleigh approximation theory and Mie theory. The results demonstrate that visibility and frequency have a stronger effect than temperature on fog attenuation. Then, rain attenuation as a function of rainfall rate is theoretically determined using Mie theory and the Joss, M-P, and Weibull distribution. The results show that rainfall rate and frequency have greater impact than raindrop distribution on rain attenuation. There are large differences in rainfall attenuation under diverse distributions. Accurate fog and rainfall attenuation information can be used to better estimate path loss and the link budget for terahertz communication in outdoor scenarios

Interaction between Fungal Communities, Soil Properties, and the Survival of Invading E. coli O157:H7 in Soils

Pathogens that invade into the soil cancontaminate food and water, andinfect animals and human beings. It is well documented that individual bacterial phyla are well correlated with the survival of E. coliO157 (EcO157), while the interaction betweenthe fungal communities and EcO157 survival remains largely unknown. In this study, soil samples from Tongliao, Siping, and Yanji in northeast China were collected and characterized. Total DNA was extracted for fungal and bacterial community characterization. EcO157 cells were spiked into the soils, and their survival behavior was investigated. Results showed that both fungal and bacterial communities were significantly correlated (p < 0.01) with the survival of EcO157 in soils, and the relative abundances of fungal groups (Dothideomycetes and Sordariomycetes) and some bacterial phyla (Acidobacteria, Firmicutes, gamma- and delta-Proteobacteria)weresignificantly correlated with ttds (p < 0.01). Soil pH, EC (electric conductance) salinity, and water-soluble nitrate nitrogen were significantly correlated with survival time (time to reach the detection limit, ttd) (p < 0.05). The structural equation model indicated that fungal communities could directly influence ttds, and soil properties could indirectly influence the ttds through fungal communities. The first log reduction time (δ) was mainly correlated with soil properties, while the shape parameter (p) was largely correlated with fungal communities. Our data indicated that both fungal and bacterial communities were closely correlated (p < 0.05)with the survival of EcO157 in soils, and different fungal and bacterial groups might play different roles. Fungal communities and bacterial communities explained 5.87% and 17.32% of the overall variation of survival parameters, respectively. Soil properties explained about one-third of the overall variation of survival parameters. These findings expand our current understanding of the environmental behavior of human pathogens in soils