Genomic Analyses Reveal Mutational Signatures and Frequently Altered Genes in Esophageal Squamous Cell Carcinoma

Esophageal squamous cell carcinoma (ESCC) is one of the most common cancers worldwide and the fourth most lethal cancer in China. However, although genomic studies have identified some mutations associated with ESCC, we know little of the mutational processes responsible. To identify genome-wide mutational signatures, we performed either whole-genome sequencing (WGS) or whole-exome sequencing (WES) on 104 ESCC individuals and combined our data with those of 88 previously reported samples. An APOBEC-mediated mutational signature in 47% of 192 tumors suggests that APOBEC-catalyzed deamination provides a source of DNA damage in ESCC. Moreover, PIK3CA hotspot mutations (c.1624G>A [p.Glu542Lys] and c.1633G>A [p.Glu545Lys]) were enriched in APOBEC-signature tumors, and no smoking-associated signature was observed in ESCC. In the samples analyzed by WGS, we identified focal (<100 kb) amplifications of CBX4 and CBX8. In our combined cohort, we identified frequent inactivating mutations in AJUBA, ZNF750, and PTCH1 and the chromatin-remodeling genes CREBBP and BAP1, in addition to known mutations. Functional analyses suggest roles for several genes (CBX4, CBX8, AJUBA, and ZNF750) in ESCC. Notably, high activity of hedgehog signaling and the PI3K pathway in approximately 60% of 104 ESCC tumors indicates that therapies targeting these pathways might be particularly promising strategies for ESCC. Collectively, our data provide comprehensive insights into the mutational signatures of ESCC and identify markers for early diagnosis and potential therapeutic targets

Multi-year mapping of cropping systems in regions with smallholder farms from Sentinel-2 images in Google Earth engine

ABSTRACTAccurate acquisition of spatial and temporal distribution information for cropping systems is important for agricultural production and food security. The challenges of extracting information about cropping systems in regions with smallholder farms are considerable, given the varied crops, complex cropping patterns, and the fragmentation of cropland with frequent reclamation and abandonment. This study presents a specialized workflow to solve this problem for regions with smallholder farms, which utilizes field samples and Sentinel-2 data to extract cropping system information over multiple years. The workflow involves four steps: 1) processing Sentinel-2 data to simulate crop growth curves with the Savitzky‒Golay filter and computing feature variables for classification, including phenology indices, spectral bands, and time series of vegetation indices; 2) mapping annual croplands with one-class support vector machine; 3) mapping various cropping patterns, including single cropping, intercropping, double cropping, multiple harvest, and fallow by decision tree and K-means clustering; and 4) mapping crops with random forest where Jeffries-Matusita distance was used to select appropriate vegetation indices. The workflow was applied in the Hetao irrigation district in Inner Mongolia Autonomous Region, China from 2018 to 2021. The overall accuracies were 0.98, 0.96, and 0.97 for cropland, cropping patterns, and crop type mapping, respectively. The mapping results indicated that the study area has low cropping continuity and is dominated by single cropping patterns. Furthermore, the area of wheat cultivation has decreased, and vegetable cultivation has expanded. Overall, the proposed workflow facilitated the accurate acquisition of cropping system information in regions with smallholder farms and demonstrated the effectiveness of available Sentinel-2 imagery in classifying complex cropping patterns. The workflow is available on Google Earth Engine

Multi-year mapping of flood autumn irrigation extent and timing in harvested croplands of arid irrigation district

Flood irrigation after crop harvest, e.g. autumn irrigation (AI), is a common irrigation practice in arid and semi-arid regions like Hetao Irrigation District (HID) in Northwest China to increase soil moisture and leach soil salt. Detailed information about the extent, timing, and amount of AI is imperative for modeling agro-hydrological processes and irrigation management. However, little attention is given to the identification of the above AI factors. There are basically three major difficulties in estimating the annual changes in AI, including a suitable index to identify AI, temporal instability of thresholds, and an effective validation method for irrigation timing. Therefore, this study proposes a simple and effective threshold-based method to extract the extent and timing of AI in the HID using MODIS water indices at a daily timescale. The Multi-Band Water Index (MBWI) time series is first reconstructed using an adaptive weighted Savitzky-Golay filter and then used to identify the AI extent and time. The proposed model has a stronger generalization capability both in time and space due to robust thresholds selected from the Z-score normalized feature variable. The model is validated both at pixels generated by the segmentation of Sentinel-derived MBWI using a threshold-based model and at sampling points from the field survey. Results show that the model performed well with an overall accuracy of more than 90.0% for the irrigation area. The overall accuracies of irrigation timing are 76.4% and 91.7% based on the middle-to-late and whole irrigation periods, respectively. We found a decreasing trend in the AI area and a gradual delay in the starting time of AI in the HID, mainly due to changes in cropping patterns, climate, and irrigation fees. Overall, the model is promising in identifying flood irrigation extent and timing in large irrigation districts and is helpful for irrigation scheduling

Multi-year mapping of cropping systems in regions with smallholder farms from Sentinel-2 images in Google Earth engine

Accurate acquisition of spatial and temporal distribution information for cropping systems is important for agricultural production and food security. The challenges of extracting information about cropping systems in regions with smallholder farms are considerable, given the varied crops, complex cropping patterns, and the fragmentation of cropland with frequent reclamation and abandonment. This study presents a specialized workflow to solve this problem for regions with smallholder farms, which utilizes field samples and Sentinel-2 data to extract cropping system information over multiple years. The workflow involves four steps: 1) processing Sentinel-2 data to simulate crop growth curves with the Savitzky‒Golay filter and computing feature variables for classification, including phenology indices, spectral bands, and time series of vegetation indices; 2) mapping annual croplands with one-class support vector machine; 3) mapping various cropping patterns, including single cropping, intercropping, double cropping, multiple harvest, and fallow by decision tree and K-means clustering; and 4) mapping crops with random forest where Jeffries-Matusita distance was used to select appropriate vegetation indices. The workflow was applied in the Hetao irrigation district in Inner Mongolia Autonomous Region, China from 2018 to 2021. The overall accuracies were 0.98, 0.96, and 0.97 for cropland, cropping patterns, and crop type mapping, respectively. The mapping results indicated that the study area has low cropping continuity and is dominated by single cropping patterns. Furthermore, the area of wheat cultivation has decreased, and vegetable cultivation has expanded. Overall, the proposed workflow facilitated the accurate acquisition of cropping system information in regions with smallholder farms and demonstrated the effectiveness of available Sentinel-2 imagery in classifying complex cropping patterns. The workflow is available on Google Earth Engine. We proposed an integrated method to map cropping systems into smallholder regions. Annual cropland mapping is necessary in regions with complex cropping pattern. The method requires only crop samples as input and is completed on the GEE. Sentinel-2 data can effectively classify cropland, cropping patterns, and crops. The 10-day interval performs better on phenology curves based on Sentinel-2.</p

Impact of Various Dopants on Thermoelectric Transport Properties of Polycrystalline GeSb2Te4

GeSb2Te4 (GST124), one of the well‐known phase‐change materials for nonvolatile memory and rewritable optical storage, has been recently found to be promising thermoelectric materials with low lattice thermal conductivity and high electrical conductivity. However, its thermoelectric performance is greatly restricted by the excessively high hole concentration. Herein, the impact of a series of group IIIA (Al, Ga, In) and group VIA (S, Se) dopants on the electrical transport properties of polycrystalline GST124 has been studied. It is found that element sulfur (S) has the best doping efficiency because the GeS bonds are very strong and ionic that are beneficial for suppressing Ge vacancies to reduce the carrier concentration. Meanwhile, element indium (In) also shows decent doping efficiency because its ionic radius is close to the Ge ion and the InTe bonds have moderate bonding strength. Moreover, In doping introduces a resonant level in the valence band, leading to enhanced Seebeck coefficient and power factor. A high figure of merit (zT)of 0.73 at 700 K and an average zT of 0.48 over 300–750 K are obtained in Ge0.92In0.08Sb2Te4, which are 26% and 66% higher than pristine GST124. This study will advance the understanding and development of high‐performance GeSbTe‐based thermoelectric materials

Shape-Mediated Oriented Assembly of Concave Nanoparticles under Cylindrical Confinement

This contribution describes the self-assembly of colloidal nanodumbbells (NDs) with tunable shapes within cylindrical channels. We present that the intrinsic concave geometry of NDs endows them with peculiar packing and interlocking behaviors, which, in conjunction with the adjustable confinement constraint, leads to a variety of superstructures such as tilted-ladder chains and crossed-chain superlattices. A mechanistic investigation, corroborated by geometric calculations, reveals that the phase behavior of NDs under strong confinement can be rationalized by the entropy-driven maximization of the packing efficiency. Based on the experimental results, an empirical phase diagram is generated, which could provide general guidance in the design of intended superstructures from NDs. This study provides essential insight into how the interplay between the particle shape and confinement conditions can be exploited to direct the orientationally ordered assembly of concave nanoparticles into unusual superlattices

Shape-Mediated Oriented Assembly of Concave Nanoparticles under Cylindrical Confinement

This contribution describes the self-assembly of colloidal nanodumbbells (NDs) with tunable shapes within cylindrical channels. We present that the intrinsic concave geometry of NDs endows them with peculiar packing and interlocking behaviors, which, in conjunction with the adjustable confinement constraint, leads to a variety of superstructures such as tilted-ladder chains and crossed-chain superlattices. A mechanistic investigation, corroborated by geometric calculations, reveals that the phase behavior of NDs under strong confinement can be rationalized by the entropy-driven maximization of the packing efficiency. Based on the experimental results, an empirical phase diagram is generated, which could provide general guidance in the design of intended superstructures from NDs. This study provides essential insight into how the interplay between the particle shape and confinement conditions can be exploited to direct the orientationally ordered assembly of concave nanoparticles into unusual superlattices

Genomic analyses reveal mutational signatures and frequently altered genes in esophageal squamous cell carcinoma

Esophageal squamous cell carcinoma (ESCC) is one of the most common cancers world wide and the fourth most lethal cancer in China. However, although genomic studies have identified some mutations associated with ESCC, we know little of the mutational processes responsible. To identify genome-wide mutational signatures, we performed either whole-genome sequencing (WGS) or whole-exome sequencing (WES) on 104 ESCC individuals and combined our data with those of 88 previously reported samples. An APOBEC-mediated mutational signature in 47% of 192 tumors suggests that APOBEC-catalyzed deamination provides a source of DNA damage in ESCC. Moreover, PIK3CA hotspot mutations (c.1624G> A [p.Glu542Lys] and c.1633G>A [p.Glu545Lys]) were enriched in APOBEC-signature tumors, and no smoking associated signature was observed in ESCC. In the samples analyzed by WGS, we identified focal