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Leaf senescence is an important developmental process in the plant life cycle and has a significant impact on agriculture. When facing harsh environmental conditions, monocarpic plants often initiate early leaf senescence as an adaptive mechanism to ensure a complete life cycle. Upon initiation, the senescence process is fine-tuned through the coordination of both positive and negative regulators. Here, we report that the small secreted peptide CLAVATA3/ESR-RELATED 14 (CLE14) functions in the suppression of leaf senescence by regulating ROS homeostasis in Arabidopsis. Expression of the CLE14-encoding gene in leaves was significantly induced by age, high salinity, abscisic acid (ABA), salicylic acid, and jasmonic acid. CLE14 knockout plants displayed accelerated progression of both natural and salinity-induced leaf senescence, whereas increased CLE14 expression or treatments with synthetic CLE14 peptides delayed senescence. CLE14 peptide treatments also delayed ABA-induced senescence in detached leaves. Further analysis showed that overexpression of CLE14 led to reduced ROS levels in leaves, where higher expression of ROS scavenging genes was detected. Moreover, CLE14 signaling resulted in transcriptional activation of JUB1, a NAC family transcription factor previously identified as a negative regulator of senescence. Notably, the delay of leaf senescence, reduction in H2O2 level, and activation of ROS scavenging genes by CLE14 peptides were dependent on JUB1. Collectively, these results suggest that the small peptide CLE14 serves as a novel "brake signal" to regulate age-dependent and stress-induced leaf senescence through JUB1-mediated ROS scavenging.

The growth of digital cameras and data communication has led to an exponential increase in video production and dissemination. As a result, automatic video analysis and understanding has become a crucial research topic in the computer vision community. However, the localization problem, which involves identifying a specific event in a large volume of data, particularly in long-form videos, remains a significant challenge. While recognizing video activity has been extensively researched, localizing a specific query in a long and untrimmed video requires the AI system to have a long-term understanding of the video, up to minutes or even hours. Therefore, we focus on studying the challenging problem of query localization in long-form videos from three perspectives: temporal modeling, localization feature, and data sampling. Firstly, a graph-based method is proposed to model long-range dependencies, which are known as the semantic context of human activities. Secondly, the task gap between action recognition and action localization is identified, and two methods are proposed from pre-training and end-to-end learning perspectives to improve the video representation with a minimal gap. Lastly, a spatiotemporal localization problem is addressed in a more realistic dataset constructed from live human beings with various job positions and geometric locations. Our study finds that data sampling is more crucial than model architecture design, and proposes several ways to reduce sampling bias. In summary, the dissertation aims to advance the machine intelligence of video understanding and we hope this work has practical implications for human beings in AI assistants, recommendations, health care, security, and other applications.

Both Minsky's "society of mind" and Schmidhuber's "learning to think" inspire diverse societies of large multimodal neural networks (NNs) that solve problems by interviewing each other in a "mindstorm." Recent implementations of NN-based societies of minds consist of large language models (LLMs) and other NN-based experts communicating through a natural language interface. In doing so, they overcome the limitations of single LLMs, improving multimodal zero-shot reasoning. In these natural language-based societies of mind (NLSOMs), new agents -- all communicating through the same universal symbolic language -- are easily added in a modular fashion. To demonstrate the power of NLSOMs, we assemble and experiment with several of them (having up to 129 members), leveraging mindstorms in them to solve some practical AI tasks: visual question answering, image captioning, text-to-image synthesis, 3D generation, egocentric retrieval, embodied AI, and general language-based task solving. We view this as a starting point towards much larger NLSOMs with billions of agents-some of which may be humans. And with this emergence of great societies of heterogeneous minds, many new research questions have suddenly become paramount to the future of artificial intelligence. What should be the social structure of an NLSOM? What would be the (dis)advantages of having a monarchical rather than a democratic structure? How can principles of NN economies be used to maximize the total reward of a reinforcement learning NLSOM? In this work, we identify, discuss, and try to answer some of these questions., Comment: 9 pages in main text + 7 pages of references + 38 pages of appendices, 14 figures in main text + 13 in appendices, 7 tables in appendices

Background Selenium-enriched mushrooms represent an important daily source of selenium in food products. Selenium is an essential micronutrient that enhances various biological activities, including the antioxidant, antitumor, and immune-regulatory properties of mushrooms. Scope and approach From the perspective of how selenium influences the growth and metabolism of mushrooms during the process of selenium biofortification, this review summarizes recent literature regarding mushroom growth phenotypes, major selenium metabolites, particularly selenium-polysaccharide, selenium-protein, and selenoamino acid, as well as other bioactive metabolites. In addition, a proposed selenium metabolic pathway is presented in well-studied mushrooms, thus providing a basis for further research on selenium metabolism. Key findings and conclusions Selenium-enriched mushroom cultivation primarily uses sodium selenite as the selenium source. However, although adding selenium at low concentrations promotes mushroom growth, high concentrations have the opposite effect. Nevertheless, selenium enrichment cultivation is a valid strategy for improving bioactivity as mushrooms can transform inorganic selenium into numerous important organic selenium metabolites, particularly selenium-polysaccharides, selenium-proteins and selenoamino acids. In addition, phenolics and flavonoids, basic amino acids, cordycepin, and adenosine are enhanced in mushrooms supplied a moderate level of selenium. This characterization of the unique aspects of the proposed selenium metabolism pathway in Ganoderma lucidum advances provides insights regarding selenium metabolism in mushrooms. Consequently, understanding the effect of selenium on mushroom growth and metabolism will improve the production of mushrooms with high bioactive Se metabolite content and good bioactivity, while contributing to the development and application of selenium-enriched mushrooms in the food industry.

Background: Aplasia cutis congenita (ACC) and terminal transverse limb defects (TTLDs) are the most common features of Adams-Oliver syndrome (AOS). ARHGAP31 is one of the causative genes for autosomal dominant forms of AOS, meanwhile its variants may only cause isolated TTLD. Here, we report a proband presented with apparent TTLD but not ACC.Methods: Whole exome sequencing (WES) and Sanger sequencing were applied to identify causative genes. Expression vectors were constructed for transfections in mammalian cell cultures followed by biochemical and functional analysis including immunoblotting, immunofluorescence staining, and cell counting kit-8 assay.Results: WES and Sanger sequencing suggested that the proband inherited rare ARHGAP31 variant [c.2623G > A (p.Glu875Lys)] and a rare FBLN1 variant [c.1649G > A (p.Arg550His)] from one of her asymptomatic parents, respectively. Given FBLN1 variation has also been linked to syndactyly, we suspected that the two genes together contributed to the TTLD phenotype and explored their possible roles in vitro. Mutant FBLN1 showed reduced expression resulted from impaired protein stability, whereas ARHGAP31 protein expression was unaltered by mutation. Functional assays showed that only in the co-transfected group of two mutants cell viability was decreased, cell proliferation was impaired, and apoptosis was activated. Cdc42 activity was declined by both ARHGAP31 mutation and FBLN1 mutation alone, and the two together. Furthermore, the MAPK/ERK pathway was only activated by two mutants co-transfected group compared with two wild-type transfections.Conclusion: We report a case carrying two rare variants of limb defects associated genes, ARHGAP31 and FBLN1, and provide in vitro evidence that synergistic disruption of cellular functions attributed by the two mutants may potentiate the penetrance of clinical manifestations, expanding our knowledge of clinical complexity of causal gene interactions in TTLD and other genetic disorders.

Study 3 is conducted to exclude possible mediation by emotional value and symbolic value from the effects of power and LOC on aesthetic product preferences, and to verify the mediating role of self-expression versus self-enhancement motivation to support H2.

The goal of the study is to examine whether LOC can moderate the effect of power on aesthetic product preferences.

Study 2 aimes to explore whether self-expression versus self-enhancement motivation mediate the interactive effects between LOC and power on aesthetic product preferences.

Additional file 1: Supplementary Figure 1. The heat map of common genes between DEGs and co-expressed genes

Few-shot (FS) and zero-shot (ZS) learning are two different approaches for scaling temporal action detection (TAD) to new classes. The former adapts a pretrained vision model to a new task represented by as few as a single video per class, whilst the latter requires no training examples by exploiting a semantic description of the new class. In this work, we introduce a new multi-modality few-shot (MMFS) TAD problem, which can be considered as a marriage of FS-TAD and ZS-TAD by leveraging few-shot support videos and new class names jointly. To tackle this problem, we further introduce a novel MUlti-modality PromPt mETa-learning (MUPPET) method. This is enabled by efficiently bridging pretrained vision and language models whilst maximally reusing already learned capacity. Concretely, we construct multi-modal prompts by mapping support videos into the textual token space of a vision-language model using a meta-learned adapter-equipped visual semantics tokenizer. To tackle large intra-class variation, we further design a query feature regulation scheme. Extensive experiments on ActivityNetv1.3 and THUMOS14 demonstrate that our MUPPET outperforms state-of-the-art alternative methods, often by a large margin. We also show that our MUPPET can be easily extended to tackle the few-shot object detection problem and again achieves the state-of-the-art performance on MS-COCO dataset. The code will be available in https://github.com/sauradip/MUPPET, Comment: Technical Report

Temporal action detection (TAD) is an important yet challenging task in video analysis. Most existing works draw inspiration from image object detection and tend to reformulate it as a proposal generation - classification problem. However, there are two caveats with this paradigm. First, proposals are not equipped with annotated labels, which have to be empirically compiled, thus the information in the annotations is not necessarily precisely employed in the model training process. Second, there are large variations in the temporal scale of actions, and neglecting this fact may lead to deficient representation in the video features. To address these issues and precisely model temporal action detection, we formulate the task of temporal action detection in a novel perspective of semantic segmentation. Owing to the 1-dimensional property of TAD, we are able to convert the coarse-grained detection annotations to fine-grained semantic segmentation annotations for free. We take advantage of them to provide precise supervision so as to mitigate the impact induced by the imprecise proposal labels. We propose an end-to-end framework SegTAD composed of a 1D semantic segmentation network (1D-SSN) and a proposal detection network (PDN).

This paper deals with the problem of localizing objects in image and video datasets from visual exemplars. In particular, we focus on the challenging problem of egocentric visual query localization. We first identify grave implicit biases in current query-conditioned model design and visual query datasets. Then, we directly tackle such biases at both frame and object set levels. Concretely, our method solves these issues by expanding limited annotations and dynamically dropping object proposals during training. Additionally, we propose a novel transformer-based module that allows for object-proposal set context to be considered while incorporating query information. We name our module Conditioned Contextual Transformer or CocoFormer. Our experiments show the proposed adaptations improve egocentric query detection, leading to a better visual query localization system in both 2D and 3D configurations. Thus, we are able to improve frame-level detection performance from 26.28% to 31.26 in AP, which correspondingly improves the VQ2D and VQ3D localization scores by significant margins. Our improved context-aware query object detector ranked first and second in the VQ2D and VQ3D tasks in the 2nd Ego4D challenge. In addition to this, we showcase the relevance of our proposed model in the Few-Shot Detection (FSD) task, where we also achieve SOTA results. Our code is available at https://github.com/facebookresearch/vq2d_cvpr., Comment: We ranked first and second in the VQ2D and VQ3D tasks in the 2nd Ego4D challenge

Additional file 1: Supplement Table 1. Propensity-matching

Additional file 3. Additional references.

Long-form video understanding requires designing approaches that are able to temporally localize activities or language. End-to-end training for such tasks is limited by the compute device memory constraints and lack of temporal annotations at large-scale. These limitations can be addressed by pre-training on large datasets of temporally trimmed videos supervised by class annotations. Once the video encoder is pre-trained, it is common practice to freeze it during fine-tuning. Therefore, the video encoder does not learn temporal boundaries and unseen classes, causing a domain gap with respect to the downstream tasks. Moreover, using temporally trimmed videos prevents to capture the relations between different action categories and the background context in a video clip which results in limited generalization capacity. To address these limitations, we propose a novel post-pre-training approach without freezing the video encoder which leverages language. We introduce a masked contrastive learning loss to capture visio-linguistic relations between activities, background video clips and language in the form of captions. Our experiments show that the proposed approach improves the state-of-the-art on temporal action localization, few-shot temporal action localization, and video language grounding tasks., Comment: 18 pages, 4 figures

Transgenic expression of Cre recombinase driven by a specific promoter is normally used to conditionally knockout a gene in a tissue- or cell-type-specific manner. In αMHC-Cre transgenic mouse model, expression of Cre recombinase is controlled by the myocardial-specific α-myosin heavy chain (αMHC) promoter, which is commonly used to edit myocardial-specific genes. Toxic effects of Cre expression have been reported, including intro-chromosome rearrangements, micronuclei formation and other forms of DNA damage, and cardiomyopathy was observed in cardiac-specific Cre transgenic mice. However, mechanisms associated with Cardiotoxicity of Cre remain poorly understood. In our study, our data unveiled that αMHC-Cre mice developed arrhythmias and died after six months progressively, and none of them survived more than one year. Histopathological examination showed that αMHC-Cre mice had aberrant proliferation of tumor-like tissue in the atrial chamber extended from and vacuolation of ventricular myocytes. Furthermore, the αMHC-Cre mice developed severe cardiac interstitial and perivascular fibrosis, accompanied by significant increase of expression levels of MMP-2 and MMP-9 in the cardiac atrium and ventricular. Moreover, cardiac-specific expression of Cre led to disintegration of the intercalated disc, along with altered proteins expression of the disc and calcium-handling abnormality. Comprehensively, we identified that the ferroptosis signaling pathway is involved in heart failure caused by cardiac-specific expression of Cre, on which oxidative stress results in cytoplasmic vacuole accumulation of lipid peroxidation on the myocardial cell membrane. Taken together, these results revealed that cardiac-specific expression of Cre recombinase can lead to atrial mesenchymal tumor-like growth in the mice, which causes cardiac dysfunction, including cardiac fibrosis, reduction of the intercalated disc and cardiomyocytes ferroptosis at the age older than six months in mice. Our study suggests that αMHC-Cre mouse models are effective in young mice, but not in old mice. Researchers need to be particularly careful when using αMHC-Cre mouse model to interpret those phenotypic impacts of gene responses. As the Cre-associated cardiac pathology matched mostly to that of the patients, the model could also be employed for investigating age-related cardiac dysfunction.

Many video analysis tasks require temporal localization thus detection of content changes. However, most existing models developed for these tasks are pre-trained on general video action classification tasks. This is because large scale annotation of temporal boundaries in untrimmed videos is expensive. Therefore no suitable datasets exist for temporal boundary-sensitive pre-training. In this paper for the first time, we investigate model pre-training for temporal localization by introducing a novel boundary-sensitive pretext (BSP) task. Instead of relying on costly manual annotations of temporal boundaries, we propose to synthesize temporal boundaries in existing video action classification datasets. With the synthesized boundaries, BSP can be simply conducted via classifying the boundary types. This enables the learning of video representations that are much more transferable to downstream temporal localization tasks. Extensive experiments show that the proposed BSP is superior and complementary to the existing action classification based pre-training counterpart, and achieves new state-of-the-art performance on several temporal localization tasks., 11 pages, 4 figures

Resistance to maize rough dwarf disease (MRDD), a major cause of crop losses, depends on external conditions such as the virus transmission period and the rate of viruliferous small brown planthoppers, Laodelphax striatellus. The precise identification of MRDD contributes to the utilization of resistant germplasm and the cloning of resistant genes. In this study, eight maize varieties were artificially inoculated in a greenhouse with viruliferous planthoppers. The viral titers in maize seedlings were detected by quantitative fluorescence RT-PCR, and the viral replication curves were analyzed by regression. A logistic model fit the Rice black-streaked dwarf virus (RBSDV) replication data for five susceptible varieties well, whereas a linear model fit the data for three resistant varieties. Among the five susceptible varieties, the time points with the maximum replication rates (tIP) of the highly susceptible Ye478 and XH6 were significantly earlier than those of the three susceptible varieties, Mo17, Zheng58, and Zhengdan958. P138, the most highly resistant variety, had the lowest slope of the best fit line, followed by moderately resistant Chang7-2 and Dan 340. The RBSDV replication curve model developed in this study can accurately identify the resistance of maize germplasm to MRDD at the molecular level. Our results also suggested that tIP and the slope of the RBSDV replication curve can be considered new criteria to evaluate maize resistance to MRDD.

Aiming at the problem that existing monitoring and early warning methods can not effectively realize gas turbine state monitoring and early warning under complex operating conditions, a monitoring and early warning technology based on nonlinear fitting and mixed Gaussian distribution is proposed, and a detailed analysis is carried out by taking the average gas temperature as an example. Firstly, based on the least squares nonlinear fitting method, the nonlinear relationship between the average gas temperature and power is obtained as the baseline of the normal state. Then through the statistical analysis of the probability distribution of the residual after fitting, it is found that it meets the two Weighted Gaussian mixture distribution model. Finally, the EM algorithm is used to estimate the parameters of the two-weighted mixed Gaussian distribution of the residuals, and the upper and lower thresholds are set according to the three principles of the two-weighted Gaussian distribution. The verification results of the normal data set and the abnormal data set showed that the set threshold value of the sub-operating conditions is reasonable and credible, and can effectively identify the abnormal state of the average gas temperature under variable operating conditions. The comparison and analysis of abnormal data sets with the existing monitoring and warning methods highlights the superiority of the monitoring and warning methods mentioned in this article, and also has a certain reference value for the monitoring and warning of other equipment under complex working conditions.

Background Conotruncal heart defects (CTDs) are a group of congenital heart malformations that cause anomalies of cardiac outflow tracts. In the past few decades, many genes related to CTDs have been reported. Serum response factor (SRF) is a ubiquitous nuclear protein that acts as transcription factor, and SRF was found to be a critical factor in heart development and to be strongly expressed in the myocardium of the developing mouse and chicken hearts. The targeted inactivation of SRF during heart development leads to embryonic lethality and myocardial defects in mice. Methods To illustrate the relationship between SRF and human heart defects, we screened SRF mutations in 527 CTD patients, a cross sectional study. DNA was extracted from peripheral leukocyte cells for target sequencing. The mutations of SRF were detected and validated by Sanger sequencing. The affection of the mutations on wild-type protein was analyzed by in silico softwares. Western blot and real time PCR were used to analyze the changes of the expression of the mutant mRNA and protein. In addition, we carried out dual luciferase reporter assay to explore the transcriptional activity of the mutant SRF. Results Among the target sequencing results of 527 patients, two novel mutations (Mut1: c.821A > G p.G274D, the adenine(A) was mutated to guanine(G) at position 821 of the SRF gene coding sequences (CDS), lead to the Glycine(G) mutated to Asparticacid(D) at position 274 of the SRF protein amino acid sequences; Mut2: c.880G > T p.G294C, the guanine(G) was mutated to thymine (T) at position 880 of the SRF CDS, lead to the Glycine(G) mutated to Cysteine (C) at position 294 of the SRF protein amino acid sequences.) of SRF (NM_003131.4) were identified. Western blotting and real-time PCR showed that there were no obvious differences between the protein expression and mRNA transcription of mutants and wild-type SRF. A dual luciferase reporter assay showed that both SRF mutants (G274D and G294C) impaired SRF transcriptional activity at the SRF promoter and atrial natriuretic factor (ANF) promoter (p Conclusion These results suggest that SRF-p.G274D and SRF-p.G294C may have potential pathogenic effects.

Networks are abundant in the life sciences. Outstanding challenges include how to characterize similarities between networks, and in extension how to integrate information across networks. Yet, network alignment remains a core algorithmic problem. Here, we present a novel learning algorithm called evolutionary heat diffusion-based network alignment (EDNA) to address this challenge. EDNA uses the diffusion signal as a proxy for computing node similarities between networks. Comparing EDNA with state-of-the-art algorithms on a popular protein-protein interaction network dataset, using four different evaluation metrics, we achieve (i) the most accurate alignments, (ii) increased robustness against noise, and (iii) superior scaling capacity. The EDNA algorithm is versatile in that other available network alignments/embeddings can be used as an initial baseline alignment, and then EDNA works as a wrapper around them by running the evolutionary diffusion on top of them. In conclusion, EDNA outperforms state-of-the-art methods for network alignment, thus setting the stage for large-scale comparison and integration of networks., Comment: 4 Pages, 2 figures

Selenium biofortification of edible and medicinal mushrooms is an effective way to produce selenium-enriched food supplements. Ganoderma lucidum is the typical one with excellent biological activity. This study investigated G. lucidum growth and bioactive metabolites alterations during liquid culture with different concentrations of selenite. Low selenium levels did not affect growth and mycelia morphology, whereas high selenium levels negatively influenced growth, dramatically decreased biomass, caused nucleic acid and protein leakage, damaged cell walls and membranes, and resulted in indicators such as degraded cells, a red color, and an unpleasant odor. Through headspace-solid phase microextraction-gas chromatography-mass spectrometry (HS-SPME-GC-MS) analysis, ten volatile Se compounds were identified in G. lucidum with 200 ppm selenite, which led to an odor change, whereas only three with 50 ppm selenite. SeMet was the major selenoamino acid in the 50 ppm selenite group by high-performance liquid chromatography-inductively coupled plasma mass spectrometry (HPLC-ICP-MS), but more MeSeCys was produced with 200 ppm selenite. Polysaccharide yields were promoted and inhibited with 50 and 200 ppm selenite, respectively. These results provide comprehensive insights into the effects of selenite on G. lucidum in liquid culture and are beneficial for functional selenium-enriched mushroom production and improving nutritive values.

Object detection is a fundamental problem in computer vision. Impressive results have been achieved on large-scale detection benchmarks by fully-supervised object detection (FSOD) methods. However, FSOD approaches require tremendous instance-level annotations, which are time-consuming to collect. In contrast, weakly supervised object detection (WSOD) exploits easily-collected image-level labels while it suffers from relatively inferior detection performance. This thesis studies hybrid learning methods on the object detection problems. We intend to train an object detector from a dataset where both instance-level and image-level labels are employed. Extensive experiments on the challenging PASCAL VOC 2007 and 2012 benchmarks strongly demonstrate the effectiveness of our method, which gives a trade-off between collecting fewer annotations and building a more accurate object detector. Our method is also a strong baseline bridging the wide gap between FSOD and WSOD performances. Based on the hybrid learning framework, we further study the problem of object detection from a novel perspective in which the annotation budget constraints are taken into consideration. When provided with a fixed budget, we propose a strategy for building a diverse and informative dataset that can be used to optimally train a robust detector. We investigate both optimization and learning-based methods to sample which images to annotate and which level of annotations (strongly or weakly supervised) to annotate them with. By combining an optimal image/annotation selection scheme with the hybrid supervised learning, we show that one can achieve the performance of a strongly supervised detector on PASCAL-VOC 2007 while saving 12:8% of its original annotation budget. Furthermore, when 100% of the budget is used, it surpasses this performance by 2:0 mAP percentage points.

Dissertation, The incidence of thyroid cancer, and in particular papillary thyroid cancer (PTC), is rising faster than that of any other malignancy in the United States. Thyroid cancer is now the most common endocrine cancer and the fifth most common cancer in women. While most thyroid cancers are treated effectively with surgical resection and radioiodine therapy, survival drops precipitously in metastatic or radioiodine-resistant disease. 60% of papillary thyroid cancers (PTC) have an oncogenic V600E mutation in the kinase BRAF, which leads to a constitutively active and oncogenic kinase. This mutation has been associated with as well as a 2.14-fold increase in recurrent/persistent disease. Excitingly, inhibitors against this mutant kinase or its substrates, the MEK1/2 kinases, can prolong progression free survival or stabilize disease in radioiodine-refractory thyroid cancer patients. However, the indolent nature of PTC may be a challenge to the clinical adaption of these inhibitors, as the financial and physical toxicities of these treatments may be amplified over the prolonged time-course typical of PTC. MEK1/2 require copper (Cu) for kinase activity and can be inhibited with the well-tolerated and economical Cu chelator tetrathiomolybdate (TM). Cu chelators have been in use for decades in patients with Wilson’s Disease, a disease of Cu accumulation. Unlike current BRAF and MEK inhibitors, Cu chelators can be well tolerated for decades with few side effects, and thus may find use in long-term inhibition of BRAFV600E signaling in PTC. Here I test the ability of Cu chelator TM to inhibit tumor growth in BRAFV600E-mutant PTC. TM inhibited MEK1/2 kinase activity and transformed growth of human BRAFV600E-mutant PTC cells as well as or more potently than standard-of-care drugs. Consistent with TM deriving its antineoplastic activity by inhibiting MEK1/2, expression of activated ERK2, a substrate of MEK1/2, overcame the ability of TM to suppress growth of BRAFV600E-mutant PTC cells. TM was also effective in a genetically engineered mouse model of BrafV600E-mutant PTC; oral TM reduced tumor burden as well as a clinical BRAF inhibitor. This in vivo effect was attributed to a reduction of phospho-Erk1/2 signaling in the tumors. Additionally, long-term maintenance therapy using TM after cessation of a clinical BRAF inhibitor reduced tumor volume in the same mouse model. Genetic reduction of the Cu transporter CTR1 in developing tumors also trended towards a survival advantage in mice with BrafV600E-mutant PTC. Finally, TM also enhanced the antineoplastic activity of standard of care clinical BBRAF inhibitor drugs. These results support the clinical evaluation of Cu chelation as targeted therapy for BrafV600E-mutant PTC and suggests three possible avenues for clinical exploration: i) as a less toxic monotherapy for BrafV600E-mutant PTC, ii) as long-term maintenance therapy after initial treatment, and iii) as a combination-therapy amplifying the antineoplastic effects of other treatment modalities.

Additional file 1. Additional information.

Under the background of the new urbanization, this paper performs an empirical analysis of requisition-compensation balance indicators in Chuzhou City, and points out the problems in the land transaction. At the national level, it is necessary to introduce the measures to regulate the market transactions of requisition-compensation balance indicators as soon as possible, put safeguarding the interests of the majority of farmers in the first place during market transactions, and establish the a unified market platform for the transaction of land-related indicators, so as to boost the urban construction, promote the integration of urban and rural areas, and speed up the new urbanization process.