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Abstract Background An increasing proportion of lung adenocarcinoma (LUAD) occurs in patients even after they have stopped smoking. Here, we aimed to determine whether tobacco smoking induced changes across LUADs from patients who formerly smoked correspond to different biological and clinical factors. Methods Random forest models (RFs) were trained utilizing a smoking associated signature developed from differentially expressed genes between LUAD patients who had never smoked (NS) or currently smoked (CS) from TCGA (n = 193) and BCCA (n = 69) cohorts. The RFs were subsequently applied to 299 and 131 formerly smoking patients from TCGA and MSKCC cohorts, respectively. FS were RF-classified as either CS-like or NS-like and associations with patient characteristics, biological features, and clinical outcomes were determined. Results We elucidated a 123 gene signature that robustly classified NS and CS in both RNA-seq (AUC = 0.85) and microarray (AUC = 0.92) validation test sets. The RF classified 213 patients who had formerly smoked as CS-like and 86 as NS-like from the TCGA cohort. CS-like and NS-like status in formerly smoking patients correlated poorly with patient characteristics but had substantially different biological features including tumor mutational burden, number of mutations, mutagenic signatures and immune cell populations. NS-like formerly smoking patients had 17.5 months and 18.6 months longer overall survival than CS-like patients from the TCGA and MSKCC cohorts, respectively. Conclusions Patients who had formerly smoked with LUAD harbor heterogeneous tumor biology. These patients can be divided by smoking induced gene expression to inform prognosis and underlying biological characteristics for treatment selection.

Abstract Vacuum ultraviolet (VUV) light sensing shows great potential applications in the space science, materials, biophysics, and plasma physics. In this work, an all-optical detection method is proposed for VUV sensing by constructing an optical fiber-end Fabry-Pérot interferometer based on a single aluminum nitride (AlN) microwire. Compared with the traditional electrical devices, this all-optical detection method overcomes the difficulties like the fast response and electromagnetic interference immunity in detecting VUV bands at the present stage, and improves the response speed. The proposed device shows the excellent performance of VUV detection, with the static sensitivity of 1.03 nm/(W·cm−2), response rise time of down to 10 µs, and decay time of 0.64 ms. Beneficial from the excellent radiation resistance of AlN microwires and UV resistance of silica fibers, the proposed device is expected to have the good stability and potential applications in the fields of the solar physics and space exploration.

The correlation between formaldehyde (FA) exposure and prevalence of asthma has been widely reported. However, the underlying mechanism is still not fully understood. FA exposure at 2.0 mg/m3 was found to exacerbate asthma in OVA-induced murine models. IFN-γ, the cytokine produced by T helper 1 (Th1) cells, was significantly induced by FA in serum and bronchoalveolar lavage fluid (BALF) of asthmatic mice, which was different from cytokines secreted by other Th cells. The observation was also confirmed by mRNA levels of Th marker genes in CD4+ T cells isolated from BALF. In addition, increased production of IFN-γ and expression of T-bet in Jurkat T cells primed with phorbol ester and phytohaemagglutinin were also observed with 100 μM FA treatment in vitro. Upregulated STAT1 phosphorylation, T-bet expression and IFN-γ production induced by FA was found to be restrained by STAT1 inhibitor fludarabine, indicating that FA promoted Th1 commitment through the autocrine IFN-γ/STAT1/T-bet pathway in asthma. This work not only revealed that FA could bias Th lineage commitment to exacerbate allergic asthma, but also identified the signaling mechanism of FA-induced Th1 differentiation, which may be utilized as the target for development of interfering strategies against FA-induced immune disorders.

Background: Palmoplantar warts (PWs) are a usual skin disease associated with human papillomavirus (HPV) that can affect patients’ quality of life. The traditional Chinese medicine (TCM) Weiren Xiaoyou formula (WRXYF) is a relatively gentle and effective therapy that has achieved good therapeutic effects in clinical practice, but its mechanism has not yet been studied. Methods: A meta-analysis was carried out to identify the potential advantages of topical TCM for PW treatment. Clinical cases suggested that WRXYF was an effective therapeutic agent against PWs. Network pharmacology was utilized to predict potential targets for the main bioactive compound, tanshinone IIA (Tan IIA), in WRXYF. High-performance liquid chromatography with electrospray mass spectrometry (HPLC/ESI-MS) was applied to detect major components. The bioactivity of Tan IIA against PWs was then validated with quantitative polymerase chain reaction (q-PCR), fluorescence in situ hybridization (FISH), electron microscopy and Western blotting. Results: A meta-analysis was conducted on 10 randomized clinical trials (RCTs) involving 2260 participants suggested that topical TCM could more effectively treat PWs than conventional medications. Network pharmacology identified Tan IIA as a candidate agent from 17 major compounds assessed by HPLC/ESI-MS because of its stable binding with 10 PW targets. HPV2, HPV27, and HPV57 were the main infectious strains in tissues obtained from PW patients and in HPV-infected HaCaT cells. Tan IIA treatment effectively destroyed viral particles and reduced the viral copy numbers of the three HPV subtypes. The results shown that Tan IIA has the ability to halt the cell cycle of HPV-infected HaCaT cells specifically in the G0/G1 phase. A total of 6 cell cycle-related proteins were regulated after Tan IIA treatment, demonstrating the role of Tan IIA in inhibiting the cell cycle. Conclusion: Tan IIA, the primary bioactive constituent in WRXYF, enhances PWs by halting the cell cycle in the G0/G1 phase via modulation of the p53 signaling pathway.

Genome-wide association studies (GWAS) have identified many genetic loci associated with complex traits and diseases in the past 20 years. Multiple heritable covariates may be added into GWAS regression models to estimate direct effects of genetic variants on a focal trait, or to improve the power by accounting for environmental effects and other sources of trait variations. When one or more covariates are causally affected by both genetic variants and hidden confounders, adjusting for them in GWAS will produce biased estimation of SNP effects, known as collider bias. Several approaches have been developed to correct collider bias through estimating the bias by Mendelian randomization (MR). However, these methods work for only one covariate, some of which utilize MR methods with relatively strong assumptions, both of which may not hold in practice. In this paper, we extend the bias-correction approaches in two aspects: first we derive an analytical expression for the collider bias in the presence of multiple covariates, then we propose estimating the bias using a robust multivariable MR (MVMR) method based on constrained maximum likelihood (called MVMR-cML), allowing the presence of invalid instrumental variables (IVs) and correlated pleiotropy. We also established the estimation consistency and asymptotic normality of the new bias-corrected estimator. We conducted simulations to show that all methods mitigated collider bias under various scenarios. In real data analyses, we applied the methods to two GWAS examples, the first a GWAS of waist-hip ratio with adjustment for only one covariate, body-mass index (BMI), and the second a GWAS of BMI adjusting metabolomic principle components as multiple covariates, illustrating the effectiveness of bias correction.

Abstract Lung cancer is the leading cause of cancer deaths worldwide. Despite never smokers comprising between 10 and 25% of all cases, lung cancer in never smokers (LCNS) is relatively under characterized from an etiological and biological perspective. The application of multi-omics techniques on large patient cohorts has significantly advanced the current understanding of LCNS tumor biology. By synthesizing the findings of multi-omics studies on LCNS from a clinical perspective, we can directly translate knowledge regarding tumor biology into implications for patient care. Primarily focused on never smokers with lung adenocarcinoma, this review details the predominance of driver mutations, particularly in East Asian patients, as well as the frequency and importance of germline variants in LCNS. The mutational patterns present in LCNS tumors are thoroughly explored, highlighting the high abundance of the APOBEC signature. Moreover, this review recognizes the spectrum of immune profiles present in LCNS tumors and posits how it can be translated to treatment selection. The recurring and novel insights from multi-omics studies on LCNS tumor biology have a wide range of clinical implications. Risk factors such as exposure to outdoor air pollution, second hand smoke, and potentially diet have a genomic imprint in LCNS at varying degrees, and although they do not encompass all LCNS cases, they can be leveraged to stratify risk. Germline variants similarly contribute to a notable proportion of LCNS, which warrants detailed documentation of family history of lung cancer among never smokers and demonstrates value in developing testing for pathogenic variants in never smokers for early detection in the future. Molecular driver subtypes and specific co-mutations and mutational signatures have prognostic value in LCNS and can guide treatment selection. LCNS tumors with no known driver alterations tend to be stem-like and genes contributing to this state may serve as potential therapeutic targets. Overall, the comprehensive findings of multi-omics studies exert a wide influence on clinical management and future research directions in the realm of LCNS.

This paper addresses the elliptic interface problem involving jump conditions across the interface. We propose a hybrid mixed finite element method on the triangulation where the interfaces are aligned with the mesh. The second-order elliptic equation is initially decomposed into two equations by introducing a gradient term. Subsequently, weak formulations are applied to these equations. Scheme continuity is enforced using the Lagrange multiplier technique. Finally, we derive an explicit formula for the entries of the matrix equation representing Lagrange multiplier unknowns resulting from hybridization. The method yields approximations of all variables, including the solution and gradient, with optimal order. Furthermore, the matrix representing the final linear algebra systems is not only symmetric but also positive definite. Numerical examples convincingly demonstrate the effectiveness of the hybrid mixed finite element method in addressing elliptic interface problems.

Light pollution from the use of artificial lighting poses significant impacts on human health, traffic safety, ecological environment, astronomy, and energy use. The advancement of characteristics of light pollution assessment technology has played a significant role in shaping prevention and control policies, thereby enabling measures, such as environmental standards and legislation and product procurement guidelines, but considerable variation in the definition, control strategies, and regulatory frameworks remains. Therefore, there is a need to review the characteristics of light pollution, including the assessment technique, policy, and legislation. Through the literature review, it can be found that technical standards are required to prevent light pollution. For example, light pollution is decreased by 6% in France through the legislation of artificial light. Key approaches are suggested to control global light pollution, including implementing ambient brightness zoning, regulating lighting product usage, and establishing dark sky reserves. Technology and policy should be integrated. The precise data coming from satellite imagery, drones, and balloons could provide guidance when making the policies.

Rationale Despite its increasingly widespread use, little is known about the impact of cannabis smoking on the response to viral infections like influenza A virus (IAV). Many assume that cannabis smoking will disrupt antiviral responses in a manner similar to cigarette smoking; however, since cannabinoids exhibit anti-inflammatory effects, cannabis smoke exposure may impact viral infection in distinct ways. Methods Male and female BALB/c mice were exposed daily to cannabis smoke and concurrently intranasally instilled with IAV. Viral burden, inflammatory mediator levels (multiplex ELISA), lung immune cells populations (flow cytometry) and gene expression patterns (RNA sequencing) were assessed in the lungs. Plasma IAV-specific antibodies were measured via ELISA. Results We found that cannabis smoke exposure increased pulmonary viral burden while decreasing total leukocytes, including macrophages, monocytes and dendritic cell populations in the lungs. Furthermore, infection-induced upregulation of certain inflammatory mediators (interferon-γ and C-C motif chemokine ligand 5) was blunted by cannabis smoke exposure, which in females was linked to the transcriptional downregulation of pathways involved in innate and adaptive immune responses. Finally, plasma levels of IAV-specific IgM and IgG1 were significantly decreased in cannabis smoke-exposed, infected mice compared to infected controls, only in female mice. Conclusions Overall, cannabis smoke exposure disrupted host-defence processes, leading to increased viral burden and dampened inflammatory signalling. These results suggest that cannabis smoking is detrimental to the maintenance of pulmonary homeostasis during viral infection and highlight the need for data regarding the impact on immune competency in humans.

This paper introduces a novel approach employing the fast cosine transform to tackle the 2-D and 3-D fractional nonlinear Schrödinger equation (fNLSE). The fractional Laplace operator under homogeneous Neumann boundary conditions is first defined through spectral decomposition. The difference matrix Laplace operator is developed by the second-order central finite difference method. Then, we diagonalize the difference matrix based on the properties of Kronecker products. The time discretization employs the Crank–Nicolson method. The conservation of mass and energy is proved for the fully discrete scheme. The advantage of this method is the implementation of the Fast Discrete Cosine Transform (FDCT), which significantly improves computational efficiency. Finally, the accuracy and effectiveness of the method are verified through two-dimensional and three-dimensional numerical experiments, solitons in different dimensions are simulated, and the influence of fractional order on soliton evolution is obtained; that is, the smaller the alpha, the lower the soliton evolution.

The purpose of this paper is to propose a novel aircraft wing loads calculation model, called long short-term memory residual network (LSTM-ResNet), which can evaluate the loads based on the strain distribution. To achieve this goal, firstly, the data acquisition experiment is designed and performed with a real aircraft wing. In this experiment, we used the Fiber Bragg Grating (FBG) technology as the measurement method to collect strain-load data from the aircraft wing. Then, we propose the LSTM-ResNet model with the one-dimensional convolutional(1D-CNN) architecture. This model is capable of extracting the temporal and spatial representational information from the strain-load data of the aircraft wing. Experimental results demonstrate that the proposed method effectively evaluate the loads of the aircraft wing. To prove the superiority of LSTM-ResNet model, we compared the proposed model with existing loads calculation methods on our experimental dataset. The results show it has a competitive average relative error (0.08%). Moreover, those promising results may pave the way to use the deep learning algorithm in aircraft wing loads calculation.

Polymer systems induced by the reaction between monomers and photo-initiators play a crucial role in the formation of volume-phase gratings. In this paper, we fabricated a dual-photo-initiator photopolymer by doping EY (Eosin Yellow) molecules into a TI (Titanocene, Irgacure 784@BASF) dispersed PMMA (poly-[methyl methacrylate]) substrate system, with the aim of promoting the diffusion and polymerization processes in volume holographic storage. The two-wave interference system is adopted to record a permanent grating structure in our materials. The temporal diffraction variations of photopolymerization (during the interference exposure) and dark diffusion (after the interference exposure) processes have been investigated and analyzed. Aiming to analyze the influence of EY doping ratios on holographic performances, some key parameters were examined in the experiment. We first measured the temporal evolution of diffraction efficiency, then an exponential fitting was adopted to obtain the response time. Finally, the angular selectivity was evaluated by the Bragg condition after holographic recording. Also, the temporal evolution of each component is described by the nonlocal polymerization-driven diffusion model with a dual-photo-initiator composition, theoretically. Furthermore, we experimentally achieved the holographic grating enhancement in both the dark diffusion and photopolymerization processes by doping appropriate EY concentrations, respectively. This work provides a foundation for the acceptability of TI&EY/PMMA polymers in further holographic storage research.

Abstract As a promising cathode material of sodium‐ion batteries, Na3V2(PO4)3 (NVP) has attracted extensive attention in recent years due to its high stability and fast Na+ ion diffusion. However, the reversible capacity based on the two‐electron reaction mechanism is not satisfactory limited by the inactive M1 lattice sites during the insertion/extraction process. Herein, self‐supporting 3D porous NVP materials with different crystallinity are fabricated on carbon foam substrates by a facile electrostatic spray deposition method. The V5+/V4+ redox couple is effectively activated and the three‐electron reactions are realized in NVP for sodium storage by a proper crystallinity tuning. In a disordered NVP sample, an ultra‐high specific capacity of 179.6 mAh g−1 at 0.2 C is achieved due to the coexistence of redox reactions of the V4+/V3+ and V5+/V4+ couples. Moreover, a pseudocapacitive charge storage mechanism induced by the disordered structure is first observed in the NVP electrode. An innovative model is given to understand the disorder‐induced‐pseudocapacitance phenomenon in this polyanion cathode material.

D-mannose is a natural and biologically active monosaccharide. It is the C-2 epimer of glucose and a component of a variety of polysaccharides in plants. In addition, D-mannose also naturally exists in some cells of the human body and participates in the immune regulation of cells as a prebiotic. Its good physiological benefits to human health and wide application in the food and pharmaceutical industries have attracted widespread attention. Therefore, in-depth research on preparation methods of D-mannose has been widely developed. This article summarizes the main production methods of D-mannose in recent years, especially the in-depth excavation from biomass raw materials such as coffee grounds, konjac flour, acai berry, etc., to provide new ideas for the green manufacture of D-mannose.

Image clustering is a complex procedure that is significantly affected by the choice of the image representation. Generally speaking, image representations are generated by using handcraft features or trained neural networks. When dealing with high dimension data, these two representation methods cause two problems: i) the representation ability of the manually designed features is limited; ii) the non-representative and meaningless feature of a trained deep network may hurt the clustering performance. To overcome these problems, we propose a new clustering method which efficiently builds an image representation and precisely discovers the cluster assignments. Our main tools are an unsupervised representation learning method based on Deep Mutual Information Maximization (DMIM) system, and a clustering method based on self-training algorithm. Specifically speaking, to extract the informative representation of image data, we derive the maximum mutual information theory and propose a system to learn the maximum mutual information between the input images and the latent representations. To discover the clusters and assign each image a clustering label, a self-training mechanism is applied to cluster the learned representations. The superiority and validity of our algorithm are verified in a series of real-world image clustering experiments.

Because of poor surface hydrophilicity, meta-aramid fibers readily form flocs by intertwining or interlacing, and this severely affects the uniformity of meta-aramid paper. To investigate the flocculation mechanism of meta-aramid fiber suspensions, the critical flocculant concentration, shear, and compressive network strength of meta-aramid fiber suspensions were examined. A hand sheet former was used to study the influence of the yielding properties of suspensions on the uniformity of meta-aramid paper, and the relationship between the formation index and rheological properties was determined. The results showed that the critical gel concentration ranged from 0.37 to 0.68 g/L, which was much lower than that of plant fiber suspensions. In addition, the compressive yield stress (Py) and shear yield stress (τy) of the meta-aramid fiber suspensions were found to increase linearly and exponentially, respectively, with an increasing concentration, and the uniformity index of the paper sheets was found to depend on a power of τy⋅Py. This provides an effective method for predicting paper sheet uniformity.

In the context of the coronavirus disease-2019 (COVID-19) pandemic, mental health problems of parents and children have become a public issue. Herein, we explored the association between parental well-being index and child mental health problems during the pandemic and the mediating role of harsh parenting and child sleep disturbances. An online survey was conducted among 16,398 parents of children aged 3–6 years (48.1% girls, Mage = 4.69 years, SDage = 0.75 years) from March 15 to 29, 2020. Child mental health (Strengths and Difficulties Questionnaire, SDQ), sleep problems (Children's Sleep Habits Questionnaire, CSHQ), and parental well-being index (World Health Organization-Five Well-Being Index, WHO-5), and harsh parenting were reported by parents. The results revealed that a higher parental well-being index was associated with lower child mental health problems. Harsh parenting and child sleep problems were significant mediators within the association. This study indicates the association between parental well-being index and child mental health during the pandemic and underlying mechanism, and has important implications for reducing parental and child mental health problems.

Fluorescent hollow organic molecular cocrystals comprised of (E)-4-(2(anthracen-9-yl)vinyl)pyridine-1,2,4,5-tetracyanobenzene (APE-TCNB) were prepared via a surfactant-mediated co-precipitation method. The size and morphology of these cocrystals could be easily tuned by varying the type and concentration of the surfactant, incubation time, and temperature. Moreover, optical fluorescence and scanning electron microscopy characterization indicated that the APE-TCNB microcrystals contained two symmetric empty cavities when 3-(N, N-dimethyldodecylammonio)propane sulfonate (BS12) was used as the surfactant. The cross-polarized microscope and powder X-ray diffraction (PXRD) measurements both showed that the prepared microcrystals exhibited high crystallinity. APE and TCNB molecules were found to align parallelly along the crystallographic a-axis in the crystal lattice, and the strong π–π intermolecular interactions facilitated the formation of unique crystal chambers. A series of measurements and characterization, including UV-Vis absorption spectroscopy, infrared spectroscopy, steady-state, and time-resolved fluorescence spectroscopy, also verified that strong charge–transfer (CT) interactions had been established in the APE-TCNB microcrystals. Moreover, these APE-TCNB microcrystals could emit bright red luminescence, which extended to the near-infrared region (~800 nm), displaying a strong charge-transfer property. Here, we have shown a general facile way to make organic cocrystals with complex structures and topologies using a self-assembly method.

RationaleThe accumulation of macrophages in the airways and the pulmonary interstitium is a hallmark of cigarette smoke-associated inflammation. Notably, pulmonary macrophages are not a homogenous population but consist of several subpopulations. To date, the manner in which cigarette smoke exposure affects the relative composition and functional capacity of macrophage subpopulations has not been elucidated.MethodsUsing a whole-body cigarette smoke exposure system, we investigated the impact of cigarette smoke on macrophage subpopulations in C57BL/6 mice using flow cytometry-based approaches. Moreover, we used bromodeoxyuridine labelling plus Il1a-/- and Il1r1-/- mice to assess the relative contribution of local proliferation and monocyte recruitment to macrophage accumulation. To assess the functional consequences of altered macrophage subpopulations, we used a model of concurrent bleomycin-induced lung injury and cigarette smoke exposure to examine tissue remodelling processes.Main ResultsCigarette smoke exposure altered the composition of pulmonary macrophages increasing CD11b+ subpopulations including monocyte-derived alveolar macrophages (Mo-AM) as well as interstitial macrophages (IM)1, -2 and -3. The increase in CD11b+ subpopulations was observed at multiple cigarette smoke exposure timepoints. Bromodeoxyuridine labelling and studies in Il1a-/- mice demonstrated that increased Mo-AM and IM3 turnover in the lungs of cigarette smoke-exposed mice was IL-1α dependent. Compositional changes in macrophage subpopulations were associated with impaired induction of fibrogenesis including decreased α-smooth muscle actin positive cells following intratracheal bleomycin treatment. Mechanistically, in vivo and ex vivo assays demonstrated predominant macrophage M1 polarisation and reduced matrix metallopeptidase 9 activity in cigarette smoke-exposed mice.ConclusionCigarette smoke exposure modified the composition of pulmonary macrophage by expanding CD11b+ subpopulations. These compositional changes were associated with attenuated fibrogenesis, as well as predominant M1 polarisation and decreased fibrotic activity. Overall, these data suggest that cigarette smoke exposure altered the composition of pulmonary macrophage subpopulations contributing to impaired tissue remodelling.

Cannabis is widely used for both recreational and medicinal purposes. Inhalation of combusted cannabis smoke is the most common mode of drug consumption, exposing the lungs to the pharmacologically active ingredients, including tetrahydrocannabinol (THC) and cannabidiol (CBD). While the relationship between cannabis smoke exposure and compromised respiratory health has yet to be sufficiently defined, previous investigations suggest that cannabis smoke may dysregulate pulmonary immunity. Presently, there exist few preclinical animal models that have been extensively validated for contemporary cannabis smoke exposure. To address this need, we developed a mouse model with readouts of total particulate matter, serum cannabinoid and carboxyhaemoglobin levels, lung cellular responses, and immune-mediator production. Using a commercially available smoke exposure system and a cannabis source material of documented THC/CBD composition, we exposed mice to a mean±sd total particulate matter of 698.89±66.09 µg·L−1 and demonstrate increases in serum cannabinoids and carboxyhaemoglobin. We demonstrate that cannabis smoke modulates immune cell populations and mediators in both male and female BALB/c mice. This modulation is highlighted by increases in airway and lung tissue macrophage populations, including tissue-resident alveolar macrophages, monocyte-derived alveolar macrophages, and interstitial macrophage subpopulations. No changes in airway or lung tissue infiltration of neutrophils were observed. Immune-mediator analysis indicated significant upregulation of macrophage-derived chemokine, thymus and activation-regulated chemokine, and vascular endothelial growth factor within the lung tissue of cannabis smoke-exposed mice. This accessible and reproducible smoke-exposure model provides a foundation to explore the impact of chronic cannabis exposures and/or co-exposures with pathogens of clinical relevance, such as influenza.

With solid tumor cancer survivorship increasing, the number of patients requiring post-treatment surveillance also continues to increase. This highlights the need for evidence-based cancer surveillance guidelines. Ideally, these guidelines would be based on combined high-quality data from randomized controlled trials (RCTs). We present a systematic review of published cancer surveillance RCTs in which we sought to determine the feasibility of data pooling for guideline development. We carried out a systematic search of medical databases for RCTs in which adult patients with solid tumors that had undergone surgical resection with curative intent and had no metastatic disease at presentation, were randomized to different surveillance regimens that assessed effectiveness on overall survival (OS). We extracted study characteristics and primary and secondary outcomes, and assessed risk of bias and validity of evidence with standardized checklist tools. Our search yielded 32,216 articles for review and 18 distinct RCTs were included in the systematic review. The 18 trials resulted in 23 comparisons of surveillance regimens. There was a highlevel of variation between RCTs, including the study populations evaluated, interventions assessed and follow-up periods for the primary outcome. Most studies evaluated colorectal cancer patients (11/18, [61%]). The risk of bias and validity of evidence were variable and inconsistent across studies. This review demonstrated that there is tremendous heterogeneity among RCTs that evaluate effectiveness of different postoperative surveillance regimens in cancer patients, rendering the consolidation of data to inform high-quality cancer surveillance guidelines unfeasible. Future RCTs in the field should focus on consistent methodology and primary outcome definition.

While biological fluoride ion channels display excellent F− conductivity and selectivity, designing synthetic analogues remains highly challenging. Here the authors show that zirconium-based metal–organic frameworks with F− binding sites and sub-1-nanometer channels exhibit ultrahigh F− conductivity and selectivity.

The development of LiFePO4 (LFP) in high-power energy storage devices is hampered by its slow Li-ion diffusion kinetics. Constructing the composite electrode materials with vanadium substitution is a scientific endeavor to boost LFP’s power capacity. Herein, a series of xLiFePO4·yLi3V2(PO4)3 (xLFP·yLVP) composites were fabricated using a simple spray-drying approach. We propose that 5LFP·LVP is the optimal choice for Li-ion battery promotion, owning to its excellent Li-ion storage capacity (material energy density of 413.6 W·h·kg−1), strong machining capability (compacted density of 1.82 g·cm−3) and lower raw material cost consumption. Furthermore, the 5LFP·LVP||LTO Li-ion pouch cell also presents prominent energy storage capability. After 300 cycles of a constant current test at 400 mA, 75% of the initial capacity (379.1 mA·h) is achieved, with around 100% of Coulombic efficiency. A capacity retention of 60.3% is displayed for the 300th cycle when discharging at 1200 mA, with the capacity fading by 0.15% per cycle. This prototype provides a valid and scientific attempt to accelerate the development of xLFP·yLVP composites in application-oriented Li-ion batteries.

Brown algae (Phaeophyceae) have been consumed by humans for hundreds of years. Current studies have shown that brown algae are rich sources of bioactive compounds with excellent nutritional value, and are considered functional foods with health benefits. Polysaccharides are the main constituents of brown algae; their diverse structures allow many unique physical and chemical properties that help to moderate a wide range of biological activities, including immunomodulation, antibacterial, antioxidant, prebiotic, antihypertensive, antidiabetic, antitumor, and anticoagulant activities. In this review, we focus on the major polysaccharide components in brown algae: the alginate, laminarin, and fucoidan. We explore how their structure leads to their health benefits, and their application prospects in functional foods and pharmaceuticals. Finally, we summarize the latest developments in applied research on brown algae polysaccharides.

Electrocatalysis offers important opportunities for clean fuel production, but uncovering the chemistry at the electrode surface remains a challenge. Here, the authors exploit a single-nanosheet electrode to perform in-situ measurements of water oxidation electrocatalysis and reveal a crucial interaction with oxygen.

Fe isotopes have been applied to the petrogenesis of ore deposits. However, the behavior of iron isotopes in the mineralization of porphyry-skarn deposits is still poorly understood. In this study, we report the Fe isotopes of ore mineral separations (magnetite, pyrite, chalcopyrite and pyrrhotite) from two different skarn deposits, i.e., the Tonglvshan Cu-Fe skarn deposit developed in an oxidized hydrothermal system and the Anqing Cu skarn deposit developed in a reduced hydrothermal system. In both deposits, the Fe isotopes of calculated equilibrium fluids are lighter than those of the intrusions responsible for the skarn and porphyry mineralization, corroborating the “light-Fe fluid” hypothesis. Interestingly, chalcopyrite in the oxidized-Tonglvshan skarn deposit has lighter Fe than chalcopyrite in the reduced-Anqing skarn deposit, which is best understood as the result of the prior precipitation of magnetite (heavy Fe) from the ore fluid in the oxidized-Tonglvshan systems and the prior precipitation of pyrrhotite (light Fe) from the ore fluid in the reduced-Anqing system. The δ56Fe for pyrite shows an inverse correlation with δ56Fe of magnetite in the Tonglvshan. In both deposits, the Fe isotope fractionation between chalcopyrite and pyrite is offset from the equilibrium line at 350 °C and lies between the FeS-chalcopyrite equilibrium line and pyrite-chalcopyrite equilibrium line at 350 °C. These observations are consistent with the FeS pathway towards pyrite formation. That is, Fe isotopes fractionation during pyrite formation depends on a path from the initial FeS-fluid equilibrium towards the pyrite-fluid equilibrium due to the increasing extent of Fe isotopic exchange with fluids. This finding, together with the data from other deposits, allows us to propose that the pathway effect of pyrite formation in the Porphyry-skarn deposit mineralization is the dominant mechanism that controls Fe isotope characteristics.

Many studies have shown that the average iron (Fe) isotope compositions of mantle-derived rocks, mantle peridotite and model mantle are close to those of chondrites. Therefore, it is considered that chondrite values represent the bulk Earth Fe isotope composition. However, this is a brave assumption because nearly 90% of Fe of the Earth is in the core, where its Fe isotope composition is unknown, but it is required to construct bulk Earth Fe isotope composition. We approach the problem by assuming that the Earth’s core separation can be approximated in terms of the Sudbury-type Ni-Cu sulfide mineralization, where sulfide-saturated mafic magmas segregate into immiscible sulfide liquid and silicate liquid. Their density/buoyancy controlled stratification and solidification produced net-textured ores above massive ores and below disseminated ores. The coexisting sulfide minerals (pyrrhotite (Po) > pentlandite (Pn) > chalcopyrite (Cp)) and silicate minerals (olivine (Ol) > orthopyroxene (Opx) > clinopyroxene (Cpx)) are expected to hold messages on Fe isotope fractionation between the two liquids before their solidification. We studied the net-textured ores of the Sudbury-type Jinchuan Ni-Cu sulfide deposit. The sulfide minerals show varying δ56Fe values (−1.37–−0.74‰ (Po) < 0.09–0.56‰ (Cp) < 0.53–1.05‰ (Pn)), but silicate minerals (Ol, Opx, and Cpx) have δ56Fe values close to chondrites (δ56Fe = −0.01 ± 0.01‰). The heavy δ56Fe value (0.52–0.60‰) of serpentines may reflect Fe isotopes exchange with the coexisting pyrrhotite with light δ56Fe. We obtained an equilibrium fractionation factor of Δ56Fesilicate-sulfide ≈ 0.51‰ between reconstructed silicate liquid (δ56Fe ≈ 0.21‰) and sulfide liquid (δ56Fe ≈ −0.30‰), or Δ56Fesilicate-sulfide ≈ 0.36‰ between the weighted mean bulk-silicate minerals (δ56Fe[0.70ol,0.25opx,0.05cpx] = 0.06‰) with weighted mean bulk-sulfide minerals (δ56Fe ≈ −0.30‰). Our study indicates that significant Fe isotope fractionation does take place between silicate and sulfide liquids during the Sudbury-type sulfide mineralization. We hypothesize that significant iron isotope fractionation must have taken place during core–mantle segregation, and the bulk Earth may have lighter Fe isotope composition than chondrites although Fe isotope analysis on experimental sulfide-silicate liquids produced under the varying mantle depth conditions is needed to test our results. We advocate the importance of further research on the subject. Given the close Fe-Ni association in the magmatic mineralization and the majority of the Earth’s Ni is also in the core, we infer that Ni isotope fractionation must also have taken place during the core separation that needs attention.