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Atmospheric particulates can be produced by emissions or form de novo. New particle formation usually occurs in relatively clean air. This is because preexisting particles in the atmosphere will scavenge the precursors of new particles and suppress their formation. However, observations in some heavily polluted megacities have revealed substantial rates of new particle formation despite the heavy loads of ambient aerosols. Yao et al. investigated new particle formation in Shanghai and describe the conditions that make this process possible. The findings will help inform policy decisions about how to reduce air pollution in these types of environments. Science , this issue p. 278 Atmospheric new particle formation in heavily polluted cities can occur in certain chemical environments. Atmospheric new particle formation (NPF) is an important global phenomenon that is nevertheless sensitive to ambient conditions. According to both observation and theoretical arguments, NPF usually requires a relatively high sulfuric acid (H 2 SO 4 ) concentration to promote the formation of new particles and a low preexisting aerosol loading to minimize the sink of new particles. We investigated NPF in Shanghai and were able to observe both precursor vapors (H 2 SO 4 ) and initial clusters at a molecular level in a megacity. High NPF rates were observed to coincide with several familiar markers suggestive of H 2 SO 4 –dimethylamine (DMA)–water (H 2 O) nucleation, including sulfuric acid dimers and H 2 SO 4 -DMA clusters. In a cluster kinetics simulation, the observed concentration of sulfuric acid was high enough to explain the particle growth to ~3 nanometers under the very high condensation sink, whereas the subsequent higher growth rate beyond this size is believed to result from the added contribution of condensing organic species. These findings will help in understanding urban NPF and its air quality and climate effects, as well as in formulating policies to mitigate secondary particle formation in China.

Background Identification of potential drug-target interactions (DTIs) with high accuracy is a key step in drug discovery and repositioning, especially concerning specific drug targets. Traditional experimental methods for identifying the DTIs are arduous, time-intensive, and financially burdensome. In addition, robust computational methods have been developed for predicting the DTIs and are widely applied in drug discovery research. However, advancing more precise algorithms for predicting DTIs is essential to meet the stringent standards demanded by drug discovery. Results We proposed a novel method called GSRF-DTI, which integrates networks with a deep learning algorithm to identify DTIs. Firstly, GSRF-DTI learned the embedding representation of drugs and targets by integrating multiple drug association information and target association information, respectively. Then, GSRF-DTI considered the influence of drug-target pair (DTP) association on DTI prediction to construct a drug-target pair network (DTP-NET). Next, we utilized GraphSAGE on DTP-NET to learn the potential features of the network and applied random forest (RF) to predict the DTIs. Furthermore, we conducted ablation experiments to validate the necessity of integrating different types of network features for identifying DTIs. It is worth noting that GSRF-DTI proposed three novel DTIs. Conclusions GSRF-DTI not only considered the influence of the interaction relationship between drug and target but also considered the impact of DTP association relationship on DTI prediction. We initially use GraphSAGE to aggregate the neighbor information of nodes for better identification. Experimental analysis on Luo’s dataset and the newly constructed dataset revealed that the GSRF-DTI framework outperformed several state-of-the-art methods significantly.

Although adaptive cancer therapy shows promise in integrating evolutionary dynamics into treatment scheduling, the stochastic nature of cancer evolution has seldom been taken into account. Various sources of random perturbations can impact the evolution of heterogeneous tumors, making performance metrics of any treatment policy random as well. In this paper, we propose an efficient method for selecting optimal adaptive treatment policies under randomly evolving tumor dynamics. The goal is to improve the cumulative “cost” of treatment, a combination of the total amount of drugs used and the total treatment time. As this cost also becomes random in any stochastic setting, we maximize the probability of reaching the treatment goals (tumor stabilization or eradication) without exceeding a pre-specified cost threshold (or a “budget”). We use a novel Stochastic Optimal Control formulation and Dynamic Programming to find such “threshold-aware” optimal treatment policies. Our approach enables an efficient algorithm to compute these policies for a range of threshold values simultaneously. Compared to treatment plans shown to be optimal in a deterministic setting, the new “threshold-aware” policies significantly improve the chances of the therapy succeeding under the budget, which is correlated with a lower general drug usage. We illustrate this method using two specific examples, but our approach is far more general and provides a new tool for optimizing adaptive therapies based on a broad range of stochastic cancer models.

Chordoma is a rare bone tumor with an unknown etiology and high recurrence rate. Here we conduct whole genome sequencing of 80 skull-base chordomas and identify PBRM1 , a SWI/SNF (SWItch/Sucrose Non-Fermentable) complex subunit gene, as a significantly mutated driver gene. Genomic alterations in PBRM1 (12.5%) and homozygous deletions of the CDKN2A/2B locus are the most prevalent events. The combination of PBRM1 alterations and the chromosome 22q deletion, which involves another SWI/SNF gene ( SMARCB1 ), shows strong associations with poor chordoma-specific survival (Hazard ratio [HR] = 10.55, 95% confidence interval [CI] = 2.81-39.64, p = 0.001) and recurrence-free survival (HR = 4.30, 95% CI = 2.34-7.91, p = 2.77 × 10 −6 ). Despite the low mutation rate, extensive somatic copy number alterations frequently occur, most of which are clonal and showed highly concordant profiles between paired primary and recurrence/metastasis samples, indicating their importance in chordoma initiation. In this work, our findings provide important biological and clinical insights into skull-base chordoma. Skull base chordomas are treated with surgery and chemotherapy but often recur due to incomplete resection, understanding the molecular underpinnings of the tumours may provide additional therapeutic strategies. Here, the authors carry out whole genome sequencing of 80 skull base chordoma tumours and identify the SWI/SNF component—PBRM1—as a frequently mutated gene.

Background Due to its enormous biomass, Antarctic krill ( Euphausia superba ) plays a crucial role in the Antarctic Ocean ecosystem. In recent years, Antarctic krill has found extensive application in aquaculture, emerging as a sustainable source of aquafeed with ideal nutritional profiles. However, a comprehensive study focused on the detailed effects of dietary Antarctic krill on aquaculture animals, especially farmed marine fishes, is yet to be demonstrated. Results In this study, a comparative experiment was performed using juvenile P. leopardus , fed with diets supplemented with Antarctic krill (the krill group) or without Antarctic krill (the control group). Histological observation revealed that dietary Antarctic krill could reduce lipid accumulation in the liver while the intestine exhibited no obvious changes. Enzyme activity measurements demonstrated that dietary Antarctic krill had an inhibitory effect on oxidative stress in both the intestine and the liver. By comparative transcriptome analysis, a total of 1,597 and 1,161 differentially expressed genes (DEGs) were identified in the intestine and liver, respectively. Functional analysis of the DEGs showed multiple enriched terms significantly related to cholesterol metabolism, antioxidants, and immunity. Furthermore, the expression profiles of representative DEGs, such as dhcr7 , apoa4 , sc5d , and scarf1 , were validated by qRT-PCR and fluorescence in situ hybridization. Finally, a comparative transcriptome analysis was performed to demonstrate the biased effects of dietary Antarctic krill and astaxanthin on the liver of P. leopardus . Conclusions Our study demonstrated that dietary Antarctic krill could reduce lipid accumulation in the liver of P. leopardus , enhance antioxidant capacities in both the intestine and liver, and exhibit molecular-level improvements in lipid metabolism, immunity, and antioxidants. It will contribute to understanding the protective effects of Antarctic krill in P. leopardus and provide insights into aquaculture nutritional strategies.

Intratumor heterogeneity (ITH) of genomic alterations may impact prognosis of lung adenocarcinoma (LUAD). Here, we investigate ITH of somatic copy number alterations (SCNAs), DNA methylation, and point mutations in lung cancer driver genes in 292 tumor samples from 84 patients with LUAD. LUAD samples show substantial SCNA and methylation ITH, and clonal architecture analyses present congruent evolutionary trajectories for SCNAs and DNA methylation aberrations. Methylation ITH mapping to gene promoter areas or tumor suppressor genes is low. Moreover, ITH composed of genetic and epigenetic mechanisms altering the same cancer driver genes is shown in several tumors. To quantify ITH for valid statistical association analyses, we develope an average pairwise ITH index (APITH), which does not depend on the number of samples per tumor. Both APITH indexes for SCNAs and methylation aberrations show significant associations with poor prognosis. This study further establishes the important clinical implications of genetic and epigenetic ITH in LUAD. Many tumors are known to be heterogeneous. Here, the authors examined multiple samples from 84 patients with lung adenocarcinoma and demonstrate that the intratumor heterogeneity of methylation and copy number associates with poor prognosis.

Full-field optical angiography (FFOA) has considerable potential for clinical applications in the prevention and diagnosis of various diseases. However, owing to the limited depth of focus attainable using optical lenses, only information about blood flow in the plane within the depth of field can be acquired using existing FFOA imaging techniques, resulting in partially unclear images. To produce fully focused FFOA images, an FFOA image fusion method based on the nonsubsampled contourlet transform and contrast spatial frequency is proposed. Firstly, an imaging system is constructed, and the FFOA images are acquired by intensity-fluctuation modulation effect. Secondly, we decompose the source images into low-pass and bandpass images by performing nonsubsampled contourlet transform. A sparse representation-based rule is introduced to fuse the lowpass images to effectively retain the useful energy information. Meanwhile, a contrast spatial frequency rule is proposed to fuse bandpass images, which considers the neighborhood correlation and gradient relationships of pixels. Finally, the fully focused image is produced by reconstruction. The proposed method significantly expands the range of focus of optical angiography and can be effectively extended to public multi-focused datasets. Experimental results confirm that the proposed method outperformed some state-of-the-art methods in both qualitative and quantitative evaluations.

The National High-tech Zone(NHTZs) is an important strategic platform for cultivating high-tech industries and realizing high-quality economic development in China.Based on the combined data from 2006 to 2014 of the industrial enterprise database, the customs database, and the China Development Zones Audit and Announcement Catalogue(abbreviated asthe Catalogue), this paper systematically investigates the influence of the construction of NHTZs on enterprise's total factor productivity(TFP). Results show that NHTZs have a positive impact on the TFP of enterprises in the zone, and this conclusion is still valid after considering endogeneity problems. Furthermore, the above productivity effects of NHTZs are heterogeneous in terms of enterprise ownership, external environment and establishment time, and NHTZs have greater stimulation effects on enterprise productivity after comparing with other types of functional zones. An investigation of the specific mechanisms at play shows that NHTZs promote the TFP of enterprises in the zone through the release of preferential policies, strengthening the \"technology spillover effects\" of imported intermediate goods, enhancing enterprise's innovation ability and attracting talent. In addition, based on the decomposition of industry productivity, this paper also investigates the impact of NHTZs on changes in industry productivity and finds that NHTZs promote the overall productivity of specific industries mainly by stimulating the productivity improvement of incumbent enterprises and expanding the market share of high-productivity enterprises. Moreover, the preferential policies of NHTZs do not significantly stimulate highproductivity enterprises to enter the zones, nor do they cause low-productivity enterprises to exit. This research is helpful in objectively evaluating the economic effects of the NHTZs in China and in providing a theoretical basis for its further adjustment.

Lung adenocarcinoma (LUAD) is the most common histologic subtype of lung cancer and has a high risk of distant metastasis at every disease stage. We aimed to characterize the genomic landscape of LUAD and identify mutation signatures associated with tumor progression. We performed an integrative genomic analysis, incorporating whole exome sequencing (WES), determination of DNA copy number and DNA methylation, and transcriptome sequencing for 101 LUAD samples from the Environment And Genetics in Lung cancer Etiology (EAGLE) study. We detected driver genes by testing whether the nonsynonymous mutation rate was significantly higher than the background mutation rate and replicated our findings in public datasets with 724 samples. We performed subclonality analysis for mutations based on mutant allele data and copy number alteration data. We also tested the association between mutation signatures and clinical outcomes, including distant metastasis, survival, and tumor grade. We identified and replicated two novel candidate driver genes, POU class 4 homeobox 2 (POU4F2) (mutated in 9 [8.9%] samples) and ZKSCAN1 (mutated in 6 [5.9%] samples), and characterized their major deleterious mutations. ZKSCAN1 was part of a mutually exclusive gene set that included the RTK/RAS/RAF pathway genes BRAF, EGFR, KRAS, MET, and NF1, indicating an important driver role for this gene. Moreover, we observed strong associations between methylation in specific genomic regions and somatic mutation patterns. In the tumor evolution analysis, four driver genes had a significantly lower fraction of subclonal mutations (FSM), including TP53 (p = 0.007), KEAP1 (p = 0.012), STK11 (p = 0.0076), and EGFR (p = 0.0078), suggesting a tumor initiation role for these genes. Subclonal mutations were significantly enriched in APOBEC-related signatures (p < 2.5×10-50). The total number of somatic mutations (p = 0.0039) and the fraction of transitions (p = 5.5×10-4) were associated with increased risk of distant metastasis. Our study's limitations include a small number of LUAD patients for subgroup analyses and a single-sample design for investigation of subclonality. These data provide a genomic characterization of LUAD pathogenesis and progression. The distinct clonal and subclonal mutation signatures suggest possible diverse carcinogenesis pathways for endogenous and exogenous exposures, and may serve as a foundation for more effective treatments for this lethal disease. LUAD's high heterogeneity emphasizes the need to further study this tumor type and to associate genomic findings with clinical outcomes.

Cancer development is driven by an accumulation of a small number of driver genetic mutations that confer the selective growth advantage to the cell, while most passenger mutations do not contribute to tumor progression. The identification of these driver genes responsible for tumorigenesis is a crucial step in designing effective cancer treatments. Although many computational methods have been developed with this purpose, the majority of existing methods solely provided a single driver gene list for the entire cohort of patients, ignoring the high heterogeneity of driver events across patients. It remains challenging to identify the personalized driver genes. Here, we propose a novel method (PDRWH), which aims to prioritize the mutated genes of a single patient based on their impact on the abnormal expression of downstream genes across a group of patients who share the co-mutation genes and similar gene expression profiles. The wide experimental results on 16 cancer datasets from TCGA showed that PDRWH excels in identifying known general driver genes and tumor-specific drivers. In the comparative testing across five cancer types, PDRWH outperformed existing individual-level methods as well as cohort-level methods. Our results also demonstrated that PDRWH could identify both common and rare drivers. The personalized driver profiles could improve tumor stratification, providing new insights into understanding tumor heterogeneity and taking a further step toward personalized treatment. We also validated one of our predicted novel personalized driver genes on tumor cell proliferation by vitro cell-based assays, the promoting effect of the high expression of Low-density lipoprotein receptor-related protein 1 ( LRP1 ) on tumor cell proliferation.