Primary and metastatic squamous cell carcinoma of the thyroid gland: Two case reports.

This study reports two cases of squamous cell carcinoma of the thyroid (SCCT) presenting as the thyroid goiter, involving one case of primary squamous cell carcinoma originating from the thyroid (PSCCT) and the other case of secondary SCCT of the thyroid. A retrospective analysis of the clinical and pathological findings was done in this study report. In case 1, the thyroid ultrasound showed multi-hypoechoic well-defined nodules, labeled as 3 using Thyroid Imaging Reporting and Data System, measuring 34.1 mm × 28.9 mm × 30.3 mm and 26.5 mm × 22.2 mm × 23.9 mm in the left in the right lobar thyroid, respectively. The patient underwent surgery and was histologically diagnosed with PSCCT. In case 2, the thyroid ultrasound showed a 25.2 mm × 22.2 mm × 18.8 mm hypoechoic nodule in the right lobar thyroid. The patient underwent a frozen biopsy, the results of which increased suspicion of squamous cell carcinoma. A frozen biopsy was followed by an endoscopic evaluation that detected an ulcerative mass measuring 3.0 cm within the mucosa of esophagus. Due to a scarcity of cases, SCCT is a great challenge for the pathologists and the managing team to come up with the best treatment strategy for the patients.

Integrative structural dynamics probing of the conformational heterogeneity in synaptosomal-associated protein 25.

SNAP-25 (synaptosomal-associated protein of 25 kDa) is a prototypical intrinsically disordered protein (IDP) that is unstructured by itself but forms coiled-coil helices in the SNARE complex. With high conformational heterogeneity, detailed structural dynamics of unbound SNAP-25 remain elusive. Here, we report an integrative method to probe the structural dynamics of SNAP-25 by combining replica-exchange discrete molecular dynamics (rxDMD) simulations and label-based experiments at ensemble and single-molecule levels. The rxDMD simulations systematically characterize the coil-to-molten globular transition and reconstruct structural ensemble consistent with prior ensemble experiments. Label-based experiments using Förster resonance energy transfer and double electron-electron resonance further probe the conformational dynamics of SNAP-25. Agreements between simulations and experiments under both ensemble and single-molecule conditions allow us to assign specific helix-coil transitions in SNAP-25 that occur in submillisecond timescales and potentially play a vital role in forming the SNARE complex. We expect that this integrative approach may help further our understanding of IDPs.

Annual dynamic dataset of global cropping intensity from 2001 to 2019.

The cropping intensity has received growing concern in the agriculture field in applications such as harvest area research. Notwithstanding the significant amount of existing literature on local cropping intensities, research considering global datasets appears to be limited in spatial resolution and precision. In this paper, we present an annual dynamic global cropping intensity dataset covering the period from 2001 to 2019 at a 250-m resolution with an average overall accuracy of 89%, exceeding the accuracy of the current annual dynamic global cropping intensity data at a 500-m resolution. We used the enhanced vegetation index (EVI) of MOD13Q1 as the database via a sixth-order polynomial function to calculate the cropping intensity. The global cropping intensity dataset was packaged in the GeoTIFF file type, with the quality control band in the same format. The dataset fills the vacancy of medium-resolution, global-scale annual cropping intensity data and provides an improved map for further global yield estimations and food security analyses.

Structure, dynamics, and regulation of TRF1-TIN2-mediated trans- and cis-interactions on telomeric DNA.

TIN2 is a core component of the shelterin complex linking double-stranded telomeric DNA-binding proteins (TRF1 and TRF2) and single-strand overhang-binding proteins (TPP1-POT1). In vivo, the large majority of TRF1 and TRF2 exist in complexes containing TIN2 but lacking TPP1/POT1; however, the role of TRF1-TIN2 interactions in mediating interactions with telomeric DNA is unclear. Here, we investigated DNA molecular structures promoted by TRF1-TIN2 interaction using atomic force microscopy (AFM), total internal reflection fluorescence microscopy (TIRFM), and the DNA tightrope assay. We demonstrate that the short (TIN2S) and long (TIN2L) isoforms of TIN2 facilitate TRF1-mediated DNA compaction (cis-interactions) and DNA-DNA bridging (trans-interactions) in a telomeric sequence- and length-dependent manner. On the short telomeric DNA substrate (six TTAGGG repeats), the majority of TRF1-mediated telomeric DNA-DNA bridging events are transient with a lifetime of ~1.95 s. On longer DNA substrates (270 TTAGGG repeats), TIN2 forms multiprotein complexes with TRF1 and stabilizes TRF1-mediated DNA-DNA bridging events that last on the order of minutes. Preincubation of TRF1 with its regulator protein Tankyrase 1 and the cofactor NAD+ significantly reduced TRF1-TIN2 mediated DNA-DNA bridging, whereas TIN2 protected the disassembly of TRF1-TIN2 mediated DNA-DNA bridging upon Tankyrase 1 addition. Furthermore, we showed that TPP1 inhibits TRF1-TIN2L-mediated DNA-DNA bridging. Our study, together with previous findings, supports a molecular model in which protein assemblies at telomeres are heterogeneous with distinct subcomplexes and full shelterin complexes playing distinct roles in telomere protection and elongation.

Recurrent mismatch binding by MutS mobile clamps on DNA localizes repair complexes nearby.

DNA mismatch repair (MMR), the guardian of the genome, commences when MutS identifies a mismatch and recruits MutL to nick the error-containing strand, allowing excision and DNA resynthesis. Dominant MMR models posit that after mismatch recognition, ATP converts MutS to a hydrolysis-independent, diffusive mobile clamp that no longer recognizes the mismatch. Little is known about the postrecognition MutS mobile clamp and its interactions with MutL. Two disparate frameworks have been proposed: One in which MutS-MutL complexes remain mobile on the DNA, and one in which MutL stops MutS movement. Here we use single-molecule FRET to follow the postrecognition states of MutS and the impact of MutL on its properties. In contrast to current thinking, we find that after the initial mobile clamp formation event, MutS undergoes frequent cycles of mismatch rebinding and mobile clamp reformation without releasing DNA. Notably, ATP hydrolysis is required to alter the conformation of MutS such that it can recognize the mismatch again instead of bypassing it; thus, ATP hydrolysis licenses the MutS mobile clamp to rebind the mismatch. Moreover, interaction with MutL can both trap MutS at the mismatch en route to mobile clamp formation and stop movement of the mobile clamp on DNA. MutS's frequent rebinding of the mismatch, which increases its residence time in the vicinity of the mismatch, coupled with MutL's ability to trap MutS, should increase the probability that MutS-MutL MMR initiation complexes localize near the mismatch.

Dynamic human MutSα-MutLα complexes compact mismatched DNA.

DNA mismatch repair (MMR) corrects errors that occur during DNA replication. In humans, mutations in the proteins MutSα and MutLα that initiate MMR cause Lynch syndrome, the most common hereditary cancer. MutSα surveilles the DNA, and upon recognition of a replication error it undergoes adenosine triphosphate-dependent conformational changes and recruits MutLα. Subsequently, proliferating cell nuclear antigen (PCNA) activates MutLα to nick the error-containing strand to allow excision and resynthesis. The structure-function properties of these obligate MutSα-MutLα complexes remain mostly unexplored in higher eukaryotes, and models are predominately based on studies of prokaryotic proteins. Here, we utilize atomic force microscopy (AFM) coupled with other methods to reveal time- and concentration-dependent stoichiometries and conformations of assembling human MutSα-MutLα-DNA complexes. We find that they assemble into multimeric complexes comprising three to eight proteins around a mismatch on DNA. On the timescale of a few minutes, these complexes rearrange, folding and compacting the DNA. These observations contrast with dominant models of MMR initiation that envision diffusive MutS-MutL complexes that move away from the mismatch. Our results suggest MutSα localizes MutLα near the mismatch and promotes DNA configurations that could enhance MMR efficiency by facilitating MutLα nicking the DNA at multiple sites around the mismatch. In addition, such complexes may also protect the mismatch region from nucleosome reassembly until repair occurs, and they could potentially remodel adjacent nucleosomes.

Transfer Learning for Crop classification with Cropland Data Layer data (CDL) as training samples.

Training samples is fundamental for crop mapping from remotely sensed images, but difficult to acquire in many regions through ground survey, causing significant challenge for crop mapping in these regions. In this paper, a transfer learning (TL) workflow is proposed to use the classification model trained in contiguous U.S.A. (CONUS) to identify crop types in other regions. The workflow is based on fact that same crop growing in different regions of world has similar temporal growth pattern. This study selected high confidence pixels across CONUS in the Cropland Data Layer (CDL) and corresponding 30-m 15-day composited NDVI time series generated from harmonized Landat-8 and Sentinel-2 (HLS) data as training samples, trained the Random Forest (RF) classification models and then applied the models to identify crop types in three test regions, namely Hengshui in China (HS), Alberta in Canada (AB), and Nebraska in USA (NE). NDVI time series with different length were used to identify crops, the effect of time-series length on classification accuracies were then evaluated. Furthermore, local training samples in the three test regions were collected and used to identify crops (LO) for comparison. Results showed that overall classification accuracies in HS, AB and NE were 97.79%, 86.45% and 94.86%, respectively, when using TL with NDVI time series of the entire growing season for classification. However, LO could achieve higher classification accuracies earlier than TL. Because the training samples were collected across USA containing multiple growth conditions, it increased the potential that the crop growth environment in test regions could be similar to those of the training samples; but also led to situation that different crops had similar NDVI time series, which caused lower TL classification accuracy in HS at early-season. Generally, this study provides new options for crop classification in regions of training samples shortage.

An index of non-sampling error in area frame sampling based on remote sensing data.

Agricultural areas are often surveyed using area frame sampling. Using non-updated area sampling frame causes significant non-sampling errors when land cover and usage changes between updates. To address this problem, a novel method is proposed to estimate non-sampling errors in crop area statistics. Three parameters used in stratified sampling that are affected by land use changes were monitored using satellite remote sensing imagery: (1) the total number of sampling units; (2) the number of sampling units in each stratum; and (3) the mean value of selected sampling units in each stratum. A new index, called the non-sampling error by land use change index (NELUCI), was defined to estimate non-sampling errors. Using this method, the sizes of cropping areas in Bole, Xinjiang, China, were estimated with a coefficient of variation of 0.0237 and NELUCI of 0.0379. These are 0.0474 and 0.0994 lower, respectively, than errors calculated by traditional methods based on non-updated area sampling frame and selected sampling units.

Publisher Correction: Precision and accuracy of single-molecule FRET measurements-a multi-laboratory benchmark study.

This paper was originally published under standard Springer Nature copyright. As of the date of this correction, the Analysis is available online as an open-access paper with a CC-BY license. No other part of the paper has been changed.

Coordinated protein and DNA conformational changes govern mismatch repair initiation by MutS.

MutS homologs identify base-pairing errors made in DNA during replication and initiate their repair. In the presence of adenosine triphosphate, MutS induces DNA bending upon mismatch recognition and subsequently undergoes conformational transitions that promote its interaction with MutL to signal repair. In the absence of MutL, these transitions lead to formation of a MutS mobile clamp that can move along the DNA. Previous single-molecule FRET (smFRET) studies characterized the dynamics of MutS DNA-binding domains during these transitions. Here, we use protein-DNA and DNA-DNA smFRET to monitor DNA conformational changes, and we use kinetic analyses to correlate DNA and protein conformational changes to one another and to the steps on the pathway to mobile clamp formation. The results reveal multiple sequential structural changes in both MutS and DNA, and they suggest that DNA dynamics play a critical role in the formation of the MutS mobile clamp. Taking these findings together with data from our previous studies, we propose a unified model of coordinated MutS and DNA conformational changes wherein initiation of mismatch repair is governed by a balance of DNA bending/unbending energetics and MutS conformational changes coupled to its nucleotide binding properties.

Precision and accuracy of single-molecule FRET measurements-a multi-laboratory benchmark study.

Single-molecule Förster resonance energy transfer (smFRET) is increasingly being used to determine distances, structures, and dynamics of biomolecules in vitro and in vivo. However, generalized protocols and FRET standards to ensure the reproducibility and accuracy of measurements of FRET efficiencies are currently lacking. Here we report the results of a comparative blind study in which 20 labs determined the FRET efficiencies (E) of several dye-labeled DNA duplexes. Using a unified, straightforward method, we obtained FRET efficiencies with s.d. between ±0.02 and ±0.05. We suggest experimental and computational procedures for converting FRET efficiencies into accurate distances, and discuss potential uncertainties in the experiment and the modeling. Our quantitative assessment of the reproducibility of intensity-based smFRET measurements and a unified correction procedure represents an important step toward the validation of distance networks, with the ultimate aim of achieving reliable structural models of biomolecular systems by smFRET-based hybrid methods.

Early-season crop mapping using improved artificial immune network (IAIN) and Sentinel data.

Substantial efforts have been made to identify crop types by region, but few studies have been able to classify crops in early season, particularly in regions with heterogeneous cropping patterns. This is because image time series with both high spatial and temporal resolution contain a number of irregular time series, which cannot be identified by most existing classifiers. In this study, we firstly proposed an improved artificial immune network (IAIN), and tried to identify major crops in Hengshui, China at early season using IAIN classifier and short image time series. A time series of 15-day composited images was generated from 10 m spatial resolution Sentinel-1 and Sentinel-2 data. Near-infrared (NIR) band and normalized difference vegetation index (NDVI) were selected as optimal bands by pair-wise Jeffries-Matusita distances and Gini importance scores calculated from the random forest algorithm. When using IAIN to identify irregular time series, overall accuracy of winter wheat and summer crops were 99% and 98.55%, respectively. We then used the IAIN classifier and NIR and NDVI time series to identify major crops in the study region. Results showed that winter wheat could be identified 20 days before harvest, as both the producer's accuracy (PA) and user's accuracy (UA) values were higher than 95% when an April 1-May 15 time series was used. The PA and UA of cotton and spring maize were higher than 95% with image time series longer than April 1-August 15. As spring maize and cotton mature in late August and September-October, respectively, these two crops can be accurately mapped 4-6 weeks before harvest. In addition, summer maize could be accurately identified after August 15, more than one month before harvest. This study shows the potential of IAIN classifier for dealing with irregular time series and Sentinel-1 and Sentinel-2 image time series at early-season crop type mapping, which is useful for crop management.

Estimation of different data compositions for early-season crop type classification.

Timely and accurate crop type distribution maps are an important inputs for crop yield estimation and production forecasting as multi-temporal images can observe phenological differences among crops. Therefore, time series remote sensing data are essential for crop type mapping, and image composition has commonly been used to improve the quality of the image time series. However, the optimal composition period is unclear as long composition periods (such as compositions lasting half a year) are less informative and short composition periods lead to information redundancy and missing pixels. In this study, we initially acquired daily 30 m Normalized Difference Vegetation Index (NDVI) time series by fusing MODIS, Landsat, Gaofen and Huanjing (HJ) NDVI, and then composited the NDVI time series using four strategies (daily, 8-day, 16-day, and 32-day). We used Random Forest to identify crop types and evaluated the classification performances of the NDVI time series generated from four composition strategies in two studies regions from Xinjiang, China. Results indicated that crop classification performance improved as crop separabilities and classification accuracies increased, and classification uncertainties dropped in the green-up stage of the crops. When using daily NDVI time series, overall accuracies saturated at 113-day and 116-day in Bole and Luntai, and the saturated overall accuracies (OAs) were 86.13% and 91.89%, respectively. Cotton could be identified 40∼60 days and 35∼45 days earlier than the harvest in Bole and Luntai when using daily, 8-day and 16-day composition NDVI time series since both producer's accuracies (PAs) and user's accuracies (UAs) were higher than 85%. Among the four compositions, the daily NDVI time series generated the highest classification accuracies. Although the 8-day, 16-day and 32-day compositions had similar saturated overall accuracies (around 85% in Bole and 83% in Luntai), the 8-day and 16-day compositions achieved these accuracies around 155-day in Bole and 133-day in Luntai, which were earlier than the 32-day composition (170-day in both Bole and Luntai). Therefore, when the daily NDVI time series cannot be acquired, the 16-day composition is recommended in this study.

Phosphorylation-induced conformational dynamics in an intrinsically disordered protein and potential role in phenotypic heterogeneity.

Intrinsically disordered proteins (IDPs) that lack a unique 3D structure and comprise a large fraction of the human proteome play important roles in numerous cellular functions. Prostate-Associated Gene 4 (PAGE4) is an IDP that acts as a potentiator of the Activator Protein-1 (AP-1) transcription factor. Homeodomain-Interacting Protein Kinase 1 (HIPK1) phosphorylates PAGE4 at S9 and T51, but only T51 is critical for its activity. Here, we identify a second kinase, CDC-Like Kinase 2 (CLK2), which acts on PAGE4 and hyperphosphorylates it at multiple S/T residues, including S9 and T51. We demonstrate that HIPK1 is expressed in both androgen-dependent and androgen-independent prostate cancer (PCa) cells, whereas CLK2 and PAGE4 are expressed only in androgen-dependent cells. Cell-based studies indicate that PAGE4 interaction with the two kinases leads to opposing functions. HIPK1-phosphorylated PAGE4 (HIPK1-PAGE4) potentiates c-Jun, whereas CLK2-phosphorylated PAGE4 (CLK2-PAGE4) attenuates c-Jun activity. Consistent with the cellular data, biophysical measurements (small-angle X-ray scattering, single-molecule fluorescence resonance energy transfer, and NMR) indicate that HIPK1-PAGE4 exhibits a relatively compact conformational ensemble that binds AP-1, whereas CLK2-PAGE4 is more expanded and resembles a random coil with diminished affinity for AP-1. Taken together, the results suggest that the phosphorylation-induced conformational dynamics of PAGE4 may play a role in modulating changes between PCa cell phenotypes. A mathematical model based on our experimental data demonstrates how differential phosphorylation of PAGE4 can lead to transitions between androgen-dependent and androgen-independent phenotypes by altering the AP-1/androgen receptor regulatory circuit in PCa cells.

Comparison of Hybrid Classifiers for Crop Classification Using Normalized Difference Vegetation Index Time Series: A Case Study for Major Crops in North Xinjiang, China.

A range of single classifiers have been proposed to classify crop types using time series vegetation indices, and hybrid classifiers are used to improve discriminatory power. Traditional fusion rules use the product of multi-single classifiers, but that strategy cannot integrate the classification output of machine learning classifiers. In this research, the performance of two hybrid strategies, multiple voting (M-voting) and probabilistic fusion (P-fusion), for crop classification using NDVI time series were tested with different training sample sizes at both pixel and object levels, and two representative counties in north Xinjiang were selected as study area. The single classifiers employed in this research included Random Forest (RF), Support Vector Machine (SVM), and See 5 (C 5.0). The results indicated that classification performance improved (increased the mean overall accuracy by 5%~10%, and reduced standard deviation of overall accuracy by around 1%) substantially with the training sample number, and when the training sample size was small (50 or 100 training samples), hybrid classifiers substantially outperformed single classifiers with higher mean overall accuracy (1%~2%). However, when abundant training samples (4,000) were employed, single classifiers could achieve good classification accuracy, and all classifiers obtained similar performances. Additionally, although object-based classification did not improve accuracy, it resulted in greater visual appeal, especially in study areas with a heterogeneous cropping pattern.

Crop Classification Based on Time Series MODIS EVI and Ground Observation for Three Adjoining Years in Xinjiang.

There is a regular use of Moderate Resolution Imaging Spectroradiometer (MODIS) 250 meter EVI to classify the crops on a regional level throughout the world. A rapid agricultural land use change attributed to new Chinese agriculture policy is attracting many researchers to focus. The objective of this study is to present a more straightforward multiyear classification methodology using time series MODIS EVI with 250 meters spatial resolution and subsequent field data in Xinjiang, China. An extensive polygon based ground reference annual crop data were collected for the years 2011, 2012 and 2013 throughout the study area. The most pure pixel within each polygon was selected which eases crop differentiation. Artificial Immune Network (ABNet) was used to classify cotton, maize, wheat/others, rice and grapes, dominating most of the study area. The data of two different years were used together to classify the crop of next year, as 2011 and 2012 were used to classify crops of 2013. Classification results were validated using the same year ground data. Results showed the classification accuracy above 80% for each year with kappa coefficient of 0. 7 and above. However more research and additional ground reference data are needed to classify a range of crops in the study area which will give a more detailed view of the land use land cover change strengthening agriculture decisions practices in the future.

Modeling spatial patterns of soil respiration in maize fields from vegetation and soil property factors with the use of remote sensing and geographical information system.

To examine the method for estimating the spatial patterns of soil respiration (Rs) in agricultural ecosystems using remote sensing and geographical information system (GIS), Rs rates were measured at 53 sites during the peak growing season of maize in three counties in North China. Through Pearson's correlation analysis, leaf area index (LAI), canopy chlorophyll content, aboveground biomass, soil organic carbon (SOC) content, and soil total nitrogen content were selected as the factors that affected spatial variability in Rs during the peak growing season of maize. The use of a structural equation modeling approach revealed that only LAI and SOC content directly affected Rs. Meanwhile, other factors indirectly affected Rs through LAI and SOC content. When three greenness vegetation indices were extracted from an optical image of an environmental and disaster mitigation satellite in China, enhanced vegetation index (EVI) showed the best correlation with LAI and was thus used as a proxy for LAI to estimate Rs at the regional scale. The spatial distribution of SOC content was obtained by extrapolating the SOC content at the plot scale based on the kriging interpolation method in GIS. When data were pooled for 38 plots, a first-order exponential analysis indicated that approximately 73% of the spatial variability in Rs during the peak growing season of maize can be explained by EVI and SOC content. Further test analysis based on independent data from 15 plots showed that the simple exponential model had acceptable accuracy in estimating the spatial patterns of Rs in maize fields on the basis of remotely sensed EVI and GIS-interpolated SOC content, with R2 of 0.69 and root-mean-square error of 0.51 µmol CO2 m(-2) s(-1). The conclusions from this study provide valuable information for estimates of Rs during the peak growing season of maize in three counties in North China.