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Figure S1: Related PSMA ligands Figure S2: Schematic illustration for the synthesis of PSMA-1-SMCC and characterization of PSMA-1-SMCC Figure S3: Schematic illustration for the synthesis of PSMA-1-Pc413 and characterization of PSMA-1-Pc413 Figure S4: Schematic illustration for the synthesis of PSMA-1-IR700 and characterization of PSMA-1-IR700 Figure S5: Spectra of light sources Figure S6: Emission and absorbance spectra of PSMA-1-PDT agents Figure S7: Phototoxicity of PSMA-1-Pc413 was reduced in the presence of excess amount of Cys-CO-Glu. Figure S8: Maestro images of mice organs

HPLC condition details and method for the determination of light spctra and radiant exposure

This article revisits reformatories set up under Nationalist China from 1928–37 to transform former Communists into loyal nationalist subjects. By examining confessions attributed to inmates and scandalous tales of Communists published by reformatories, it argues that these institutions were more than devices to suppress political dissent. Instead, reformatories played productive functions for the Guomindang state. First, reformatories’ in-house magazines conjured up an anticommunist figure of the Communist that combined the excesses of urban capitalism and the residues of China’s “superstitious” sect. Communist cadres, articles written by political converts suggested, were puerile, capricious, and alienated from traditional moral norms. At the same time, the Communist movement was attributed qualities of an evil cult preying on the ignorant and the irrational. Second, by publicizing the overcoming of the sins attributed to Communists, the reformatories created, with contributions by former Communists, a textual economy in which the Chinese populace as a whole turned away from left-wing politics and acquired a new subject position. More than converting individual Communists into “proper” nationalists, reformatories were supposed to bring about, if only in allegorical terms, mass conversion to the sobriety, obedience, hierarchical order, and organic unity that the nation was supposed to entail.

This paper examines the basis of Asianist sympathy for ‘New China’, as the People’s Republic was often styled, in the early 1950s by examining the writings of Indian diplomats, journalists and acad...

Background Accurate estimation of ischemic core on baseline imaging has treatment implications in patients with acute ischemic stroke (AIS). Machine learning (ML) algorithms have shown promising results in estimating ischemic core using routine noncontrast computed tomography (NCCT). Objective We used an ML-trained algorithm to quantify ischemic core volume on NCCT in a comparative analysis to pretreatment magnetic resonance imaging (MRI) diffusion-weighted imaging (DWI) in patients with AIS. Methods Patients with AIS who had both pretreatment NCCT and MRI were enrolled. An automatic segmentation ML approach was applied using Brainomix software (Oxford, UK) to segment the ischemic voxels and calculate ischemic core volume on NCCT. Ischemic core volume was also calculated on baseline MRI DWI. Comparative analysis was performed using Bland–Altman plots and Pearson correlation. Results A total of 72 patients were included. The time-to-stroke onset time was 134.2/89.5 minutes (mean/median). The time difference between NCCT and MRI was 64.8/44.5 minutes (mean/median). In patients who presented within 1 hour from stroke onset, the ischemic core volumes were significantly (p = 0.005) underestimated by ML-NCCT. In patients presented beyond 1 hour, the ML-NCCT estimated ischemic core volumes approximated those obtained by MRI-DWI and with significant correlation ( r = 0.56, p Conclusion The ischemic core volumes calculated by the described ML approach on NCCT approximate those obtained by MRI in patients with AIS who present beyond 1 hour from stroke onset.

Author(s): Tsui, Brian | Abstract: Watershed infarcts are a stroke subtype usually secondary to vascular stenosis with superimposed hypotension, leading to border zone hypoperfusion.n Therefore, usual implicated pathologies include atherosclerosis, congestive heart failure, hypotension, angiitis, and less commonly, sickle cell disease.n In this report, we present an extremely rare cause of diffuse watershed infarcts: hypereosinophilia.n Hypereosinophilic syndrome (HES) is an extremely rare cause of neurological disorders. We discuss the definition of HES, causes of HES, and its neuroradiological manifestations.

High-quality metadata annotations for data hosted in large public repositories are essential for research reproducibility and for conducting fast, powerful and scalable meta-analyses. Currently, a majority of sequencing samples in the National Center for Biotechnology Information’s Sequence Read Archive (SRA) are missing metadata across several categories. In an effort to improve the metadata coverage of these samples, we leveraged almost 44 million attribute–value pairs from SRA BioSample to train a scalable, recurrent neural network that predicts missing metadata via named entity recognition (NER). The network was first trained to classify short text phrases according to 11 metadata categories and achieved an overall accuracy and area under the receiver operating characteristic curve of 85.2% and 0.977, respectively. We then applied our classifier to predict 11 metadata categories from the longer TITLE attribute of samples, evaluating performance on a set of samples withheld from model training. Prediction accuracies were high when extracting sample Genus/Species (94.85%), Condition/Disease (95.65%) and Strain (82.03%) from TITLEs, with lower accuracies and lack of predictions for other categories highlighting multiple issues with the current metadata annotations in BioSample. These results indicate the utility of recurrent neural networks for NER-based metadata prediction and the potential for models such as the one presented here to increase metadata coverage in BioSample while minimizing the need for manual curation. Database URL: https://github.com/cartercompbio/PredictMEE

Background and Purpose: Accurate estimation of ischemic core on baseline imaging has treatment implications in patients with acute ischemic stroke (AIS). Machine learning (ML) algorithms have shown promising results in estimating ischemic core using routine non-contrast CT (NCCT). We used a ML-trained algorithm to quantify ischemic core volume on NCCT and compared the results to concurrent diffusion MRI as the reference standard in patients with AIS. Methods: We analyzed consecutive anterior circulation AIS patients who had baseline (pretreatment) NCCT and MRI (DWI). Ischemic lesion volume was calculated on MRI-DWI using an automated software (Olea Medical SAS, La Ciotat, France). An automatic segmentation approach using a combination of traditional 3D graphics and statistical methods, and ML classification techniques (Brainomix, Oxford, United Kingdom) was used to identify ischemic core voxels on NCCT. Total ischemic core volumes on ML-NCCT and DWI-MR were quantitatively compared by Bland-Altman plots and Pearson correlation. Results: A total of 50 patients (27 female, 23 male, mean age 72.6 years) were included. Baseline imaging was performed within 173 ± 143 minutes (mean ± SD) from symptom onset. The mean time difference between MRI and NCCT was 72 min. The baseline NIHSS was 14, 8-21 (Median, IQR). Algorithm-segmented ischemic core volume detected on NCCT was median 12.7 mL, IQR 3.5-26.0 mL. Ischemic core volume on DWI MRI was median 8.8 mL, IQR 3.2-34.0 mL. ML-NCCT core volumes significantly correlated with DWI MRI core volumes, r =0.61, p p =0.81. For the reperfusion treatment threshold of an ischemic core volume within 70 mL, while no patients would have been excluded using our algorithm, five patients would have been incorrectly dichotomized as having an ischemic volume of Conclusion: This ML-approach accurately quantifies ischemic core volume on NCCT compared to the reference standard of diffusion MRI in patients with AIS.

Background and Purpose: In acute ischemic stroke (AIS), perfusion imaging, while not directly visualizing collateral vessels, can provide important insight into collateral robustness, indexed by perfusion lesion volume and by perfusion lesion heterogeneity. Two proposed perfusion lesion heterogeneity measures indexing collateral status are the Perfusion Collateral Index (PCI) and Hypoperfusion Intensity Ratio (HIR), but their accuracy compared with direct collateral assessment on DSA has been incompletely characterized. Methods: Consecutive AIS patients with anterior circulation large vessel occlusion who underwent pre-endovascular thrombectomy MRI perfusion imaging were included. MRI measures analyzed were: 1) Perfusion Collateral Index ( PCI) - the volume of moderately hypoperfused tissue (arterial tissue delay time between 2 and 6 seconds: ATD 2-6sec ) multiplied by its corresponding relative cerebral blood volume using Olea software; 2) Hypoperfusion Intensity Ratio (HIR) ratio of moderate TMax >6 s lesion volume versus severe Tmax >10 s lesion volume with the RAPID software program. DSA collateral scores were evaluated by ASITN grading and dichotomized to inadequate (ASTIN Results: Among 48 patients meeting entry criteria, age (mean ± SD) was 70 (± 15.2), 54% were female, and NIHSS (median, IQR) was 15 (10-19). For HIR, there was no significant difference in score values in patients with adequate vs inadequate collaterals: 0.35 ± 0.20 vs 0.39 ± 0.25, p=0.68. ROC analysis using previously described cut-off of 0.4 resulted in an AUC of 0.52 and sensitivity/specificity of 71% / 33%. For PCI, score values were significantly higher in patients with adequate vs inadequate collaterals, 117 ± 61 vs. 57 ± 41, p=0.002. ROC analysis using previously described cut-off of 62 resulted in an AUC of 0.8 and sensitivity/specificity of 84% / 78%. Conclusion: Collateral status can be accurately assessed on perfusion MRI with the Perfusion Collateral Index, which outperformed the Hypoperfusion Intensity Ratio. MRI-PCI is an informative imaging biomarker of collateral status in patients with AIS.

Within Asia, the period from 1840s to 1960s had witnessed the rise and decline of Pax Britannica, the growth of multiple and often competing anti-colonial movements, and the entrenchment of the nation-state system. Beyond Pan-Asianism seeks to demonstrate the complex interactions between China, India, and their neighbouring societies against this background of imperialism and nationalist resistance. The contributors to this volume, from India, the West, and the Chinese-speaking world, cover a tremendous breadth of figures, including novelists, soldiers, intelligence officers, archivists, among others, by deploying published and archival materials in multiple Asian and Western languages. This volume also attempts to answer the question of how China–India connectedness in the modern period should be narrated. Instead of providing one definite answer, it engages with prevailing and past frameworks—notably ‘Pan-Asianism’ and ‘China/India as Method’—with an aim to provoke further discussions on how histories of China–India and, by extension the non-Western world, can be conceptualized.

The essays in this volume describe the manifold ways in which China, India, and their respective societies were connected from the 1840s to the 1960s. This period witnessed the inexorable rise and terminal decline of Pax Britannica in Asia, the blooming of anti-colonial movements of various ideological hues, and the spread and entrenchment of the nation-state system across the world. This layered legacy looms large in the relations between Chinese and Indian societies in the twenty-first century. Euro-American imperialism figured as much more than the backdrop against which China and India interacted. Practitioners of global history (...

This chapter examines the Indian prime minister Jawaharlal Nehru’s (1889–1964) assessments on the nature of the Chinese Communist revolution and the state it created in the early 1950s. It argues that two interlocking sets of considerations informed the Nehru government’s approach to the newly founded People’s Republic of China (PRC). One consideration was pragmatic, reckoning that the government headquartered in Beijing was well-functioning and stable. India was not in a position to ignore or antagonize its giant eastern neighbour. The other consideration was explicitly ideological; it sees the PRC as the culmination of a nationalist project with which India identified. The PRC was, as far as Nehru was concerned, undergoing a period of moderate socialist reconstruction, akin to India’s postcolonial experiment. The chapter compares, homing in on New Dehli’s and Beijing’s reactions to the Korean crisis, Nehru’s ruminations on domestic socioeconomic development and Asian independence and those of Chinese leaders such as Mao Zedong (1893–1976) and Song Qingling (1893–1981). It observes that while there were significant differences between the two states, they shared an anticolonial internationalism that laid the groundwork for solidarity between the two countries and paved the way for the celebrated Bandung Conference in August 1955.

Purpose To retrospectively investigate the safety, efficacy, and complications associated with TrapEase inferior vena cava filters. Methods All patients who received a TrapEase filter at a single institution between April 2003 and January 2013 were identified, and outcomes were reviewed and analyzed. Results During the study period, 594 patients (278 women; mean age, 68.9 ± 13.6 years; range, 19.2-96.3 years) received a TrapEase filter. The duration of this study was 88 months, with a median clinical follow-up of 3.6 months (range, 0-148.3 months). During follow-up, 489 of 594 patients (82.3%) died and 105 remained alive with filters in situ. Nine cases of breakthrough pulmonary embolism occurred among the 582 patients with clinical follow-up (1.5%). Among 128 patients with imaging that contained the filter, there were 17 cases of filter fracture (13.3%). Of the 39 patients with available computed tomography scans, eight had filling defects within the filter suggestive of thrombus or embolus (20.5%), including two patients with complete caval occlusion. Recurrent deep vein thrombosis occurred in 109 out of 582 patients (18.7%) with clinical follow-up. Conclusions Most patients who received TrapEase filters died during follow-up, possibly because operators chose to implant a permanent filter in patients with known terminal illnesses. The filter fracture rate seemed to be high, but there were no instances of free fracture fragment or distant migration. Although the filter may theoretically be effective in preventing thrombus migration owing to the double basket design, pulmonary embolism breakthrough rates were comparable with rates seen with other filters.

The topographical organization of collagen within the tumor microenvironment has been implicated in modulating cancer cell migration and independently predicts progression to metastasis. Here, we show that collagen matrices with small pores and short fibers, but not Matrigel, trigger a conserved transcriptional response and subsequent motility switch in cancer cells resulting in the formation of multicellular network structures. The response is not mediated by hypoxia, matrix stiffness, or bulk matrix density, but rather by matrix architecture-induced β1-integrin upregulation. The transcriptional module associated with network formation is enriched for migration and vasculogenesis-associated genes that predict survival in patient data across nine distinct tumor types. Evidence of this gene module at the protein level is found in patient tumor slices displaying a vasculogenic mimicry (VM) phenotype. Our findings link a collagen-induced migration program to VM and suggest that this process may be broadly relevant to metastatic progression in solid human cancers., Extracellular matrix plays a central role in driving cancer development. Here the authors using an in vitro approach show that confining collagen architectures induce fast and persistent cell migration and the formation of multicellular network structures linked to vascular mimicry observed in tumours from patients.

This essay reveals how Pan-Asianism was transformed from a potentially subversive ideal opposed to capitalism and Western colonialism during the Republican period into a movement championing capitalist accumulation under US domination after 1949. Focusing on Zhang Junmai’s career as an anti-Communist social democrat, the paper argues that whereas Asia’s supposed cultural commonality and superiority facilitated a critique of Western political and economic norms in the first half of the twentieth century, the Cold War imperative of containing Communist influence rendered this culturalism complicit in US strategic designs regarding Asia. Furthermore, the hope for an egalitarian economy and an international order based on altruistic reciprocity gave way to state-led developmentalism and market relations between Asian nation-states. Without a vision of overcoming the West’s political and economic hegemony, Zhang’s Pan-Asianism was reduced to hollow celebration of Oriental spirituality and of other anti-Communist Asian leaders.

SUMMARYMammals progress through similar physiological stages during life, from early development to puberty, aging, and death. Yet, the extent to which this conserved physiology reflects conserved molecular events is unclear. Here, we map common epigenetic changes experienced by mammalian genomes as they age, focusing on evolutionary comparisons of humans to dogs, an emerging model of aging. Using targeted sequencing, we characterize the methylomes of 104 Labrador retrievers spanning a 16 year age range, achieving >150X coverage within mammalian syntenic blocks. Comparison with human methylomes reveals a nonlinear relationship which translates dog to human years, aligns the timing of major physiological milestones between the two species, and extends to mice. Conserved changes center on specific developmental gene networks which are sufficient to capture the effects of anti-aging interventions in multiple mammals. These results establish methylation not only as a diagnostic age readout but as a cross-species translator of physiological aging milestones.

The successful implementation of miRNA (miR) therapies in humans will ultimately rely on the use of vehicles with improved cellular delivery capability. Here we tested a new system that leverages extracellular vesicles (EVs) laden with a tumor suppressor miRNA (miR-335) produced in B cells by plasmid DNA induction (iEVs). We demonstrate that iEVs-335 efficiently and durably restored the endogenous miR-335 pool in human triple negative breast cancer cells, downregulated the expression of the miR-335 target gene SOX4 transcription factor, and markedly inhibited tumor growth in vivo. Remarkably, iEVs-335 mediated transcriptional effects that persisted in tumors after 60 days post orthotopic implantation. Genome-wide RNASeq analysis of cancer cells treated in vitro with iEVs-335 showed the regulation of a discrete number of genes only, without broad transcriptome perturbations. This new technology may be ideally suited for therapies aimed to restore tumor suppressor miRNAs in cancer cells, disrupting the oncogenic program established after escape from miRNA control.

Prostatectomy has been the mainstay treatment for men with localized prostate cancer. Surgery, however, often can result in major side effects, which are caused from damage and removal of nerves and muscles surrounding the prostate. A technology that can help surgeons more precisely identify and remove prostate cancer resulting in a more complete prostatectomy is needed. Prostate-specific membrane antigen (PSMA), a type II membrane antigen highly expressed in prostate cancer, has been an attractive target for imaging and therapy. The objective of this study is to develop low molecular weight PSMA-targeted photodynamic therapy (PDT) agents, which would provide image guidance for prostate tumor resection and allow for subsequent PDT to eliminate unresectable or remaining cancer cells. On the basis of our highly negatively charged, urea-based PSMA ligand PSMA-1, we synthesized two PSMA-targeting PDT conjugates named PSMA-1-Pc413 and PSMA-1-IR700. In in vitro cellular uptake experiments and in vivo animal imaging experiments, the two conjugates demonstrated selective and specific uptake in PSMA-positive PC3pip cells/tumors, but not in PSMA-negative PC3flu cells/tumors. Further in vivo photodynamic treatment proved that the two PSMA-1–PDT conjugates can effectively inhibit PC3pip tumor progression. The two PSMA-1–PDT conjugates reported here may have the potential to aid in the detection and resection of prostate cancers. It may also allow for the identification of unresectable cancer tissue and PDT ablation of such tissue after surgical resection with potentially less damage to surrounding tissues. Mol Cancer Ther; 15(8); 1834–44. ©2016 AACR.

All mammals progress through similar physiological stages throughout life, from early development to puberty, aging, and death. Yet, the extent to which this conserved physiology reflects underlying genomic events is unclear. Here, we map the common methylation changes experienced by mammalian genomes as they age, focusing on comparison of humans with dogs, an emerging model of aging. Using oligo-capture sequencing, we characterize methylomes of 104 Labrador retrievers spanning a 16-year age range, achieving >150× coverage within mammalian syntenic blocks. Comparison with human methylomes reveals a nonlinear relationship that translates dog-to-human years and aligns the timing of major physiological milestones between the two species, with extension to mice. Conserved changes center on developmental gene networks, which are sufficient to translate age and the effects of anti-aging interventions across multiple mammals. These results establish methylation not only as a diagnostic age readout but also as a cross-species translator of physiological aging milestones.

Cancer genomics has enabled the exhaustive molecular characterization of tumors and exposed hepatocellular carcinoma (HCC) as among the most complex cancers. This complexity is paralleled by dozens of mouse models that generate histologically similar tumors but have not been systematically validated at the molecular level. Accurate models of the molecular pathogenesis of HCC are essential for biomedical progress; therefore we compared genomic and transcriptomic profiles of four separate mouse models [MUP transgenic, TAK1-knockout, carcinogen-driven diethylnitrosamine (DEN), and Stelic Animal Model (STAM)] with those of 987 HCC patients with distinct etiologies. These four models differed substantially in their mutational load, mutational signatures, affected genes and pathways, and transcriptomes. STAM tumors were most molecularly similar to human HCC, with frequent mutations in Ctnnb1 , similar pathway alterations, and high transcriptomic similarity to high-grade, proliferative human tumors with poor prognosis. In contrast, TAK1 tumors better reflected the mutational signature of human HCC and were transcriptionally similar to low-grade human tumors. DEN tumors were least similar to human disease and almost universally carried the Braf V637E mutation, which is rarely found in human HCC. Immune analysis revealed that strain-specific MHC-I genotype can influence the molecular makeup of murine tumors. Thus, different mouse models of HCC recapitulate distinct aspects of HCC biology, and their use should be adapted to specific questions based on the molecular features provided here.

High quality metadata annotations for data hosted in large public repositories are essential for research reproducibility, and for conducting fast, powerful and scalable meta-analyses. Currently, a majority of sequencing samples in the National Center for Biotechnology Information’s (NCBI’s) Sequence Read Archive (SRA) are missing metadata across several categories. In an effort to improve the metadata coverage of these samples, we leveraged almost 44 million attribute-value pairs from SRA BioSample to train a scalable, recurrent neural network that predicts missing metadata via Named Entity Recognition (NER). The network was first trained to classify short text phrases according to 11 metadata categories and achieved an overall accuracy and area under the receiver operating characteristic (AUROC) curve of 85.2% and 0.977 respectively. We then applied our classifier to predict 11 metadata categories from the longer TITLE attribute of samples, evaluating performance on a set of samples withheld from model training. Prediction accuracies were high when extracting sample Species (94.85%), Condition/Disease (95.65%) and Strain (82.03%) from TITLEs, with lower accuracies and lack of predictions for other categories highlighting multiple issues with the current metadata annotations in BioSample. These results indicate the utility of recurrent neural networks for NER-based metadata prediction and the potential for models such as the one presented here to increase metadata coverage in BioSample while minimizing the need for manual curation.AvailabilityAll the analyses, environments, and Jupyter notebooks pertaining to this manuscript are available on Github: https://github.com/cartercompbio/PredictMEE.

The Sequence Read Archive (SRA) contains over one million publicly available sequencing runs from various studies using a variety of sequencing library strategies. These data inherently contain information about underlying genomic sequence variants which we exploit to extract allelic read counts on an unprecedented scale. We reprocessed over 250,000 human sequencing runs (>1000 TB data worth of raw sequence data) into a single unified dataset of allelic read counts for nearly 300,000 variants of biomedical relevance curated by NCBI dbSNP, where germline variants were detected in a median of 912 sequencing runs, and somatic variants were detected in a median of 4,876 sequencing runs, suggesting that this dataset facilitates identification of sequencing runs that harbor variants of interest. Allelic read counts obtained using a targeted alignment were very similar to read counts obtained from whole-genome alignment. Analyzing allelic read count data for matched DNA and RNA samples from tumors, we find that RNA-seq can also recover variants identified by Whole Exome Sequencing (WXS), suggesting that reprocessed allelic read counts can support variant detection across different library strategies in SRA. This study provides a rich database of known human variants across SRA samples that can support future meta-analyses of human sequence variation.

Artificial intelligence (AI)-based methods have emerged as powerful tools to transform medical care. Although machine learning classifiers (MLCs) have already demonstrated strong performance in image-based diagnoses, analysis of diverse and massive electronic health record (EHR) data remains challenging. Here, we show that MLCs can query EHRs in a manner similar to the hypothetico-deductive reasoning used by physicians and unearth associations that previous statistical methods have not found. Our model applies an automated natural language processing system using deep learning techniques to extract clinically relevant information from EHRs. In total, 101.6 million data points from 1,362,559 pediatric patient visits presenting to a major referral center were analyzed to train and validate the framework. Our model demonstrates high diagnostic accuracy across multiple organ systems and is comparable to experienced pediatricians in diagnosing common childhood diseases. Our study provides a proof of concept for implementing an AI-based system as a means to aid physicians in tackling large amounts of data, augmenting diagnostic evaluations, and to provide clinical decision support in cases of diagnostic uncertainty or complexity. Although this impact may be most evident in areas where healthcare providers are in relative shortage, the benefits of such an AI system are likely to be universal.

This article revisits Chinese intellectual discourse on the Indian nationalist movement during the Republican period and argues that interest in the Indian National Congress cut across ideological divides. By examining a range of published sources from the 1920s to the 1940s, this article shows that leading intellectuals took seriously the political movement Mohandas Gandhi led as a distinct model of revolutionary politics, spiritual resistance against Western industrial modernity, and an uneasy alliance between the national bourgeoisie and emerging subaltern groups. It demonstrates that unfolding events in India facilitated articulation of competing views on what decolonization entailed for China and other colonized societies. The Republican period was a unique moment when, in contrast to both the late imperial and postsocialist periods, the Chinese elite considered India as a potential exemplar of a new form of revolutionary politics that held not only national but also global significance.

This article examines the politics of time and space in Nationalist China in connection with the regime's compromised anti-imperialism. It demonstrates that the Guomindang's nation-building project, which failed to confront capitalist social relations and the imperialist powers that underpinned them, overdetermined the party-state's ambivalent attitude toward modernity. The school campuses on which the Guomindang projected its social vision were, on the one hand, constructed as efficient, mechanical-clock-timed training grounds of wage laborers and, on the other hand, spaces of authenticity and beauty free from social alienation. This search for aestheticized spaces outside capitalism, in combination with a rejection of fundamental social changes, put the Guomindang in ideological league with radical right-wing movements active around the world between the two world wars. My exposition of the Guomindang's ideological import serves as a critique of recent scholarship that tends to subsume the Guomindang and its peculiar approach to capitalism under a blanket denunciation of modern political movements. Adopting a poststructuralist suspicion of post-Enlightenment rationality, some scholars of China view twentieth-century history as a unilinear succession of nation-states that, regardless of ideological affiliation, destroyed pluralistic local particularities and everyday idiosyncrasies in favor of national unity and industrial productivity. The communist movement is, according to this view, equally guilty as the Guomindang in perpetuating this process. I argue that this one-dimensional reading of political modernity not only trivializes the politics of competing revolutions but abstracts everyday experiences as basic as those of space and time from their embeddedness in larger social relations.

In 1927, the Buddhist scholar, Tan Yunshan, travelled to Santiniketan on the invitation of Rabindranath Tagore to teach Chinese at Visva Bharati University. Subsequent years would see him develop close ties with the Guomindang and Congress leaders, secure Chinese state funding for the first sinological institute in India and mediate between the nationalist movements during the Second World War. That a relatively marginal academic, who participated in neither the May Fourth Movement nor any major political party, and who had little prior experience of India, could have played such an important role in twentieth century Sino-Indian relations raises questions over the conditions that made possible Tan’s illustrious career. This article argues that Tan’s success as an institution builder and diplomatic intermediary was attributable to his ideological affinity with the increasing disillusionment with capitalist modernity in both China and India, the shifting dynamics of the Pan-Asianist movement and the conservative turn of China’s nationalist movement after its split with the communists in 1927. While Nationalist China and the Congress both tapped into the civilizational discourse that was supposed to bind the two societies together, the idealism Tan embodied was unable to withstand the conflict of priorities between nation-states in the emerging Cold War order.

An initial step in solid tumor metastasis involves the migration of tumor cells through extracellular matrix. Several cancer cell migration strategies exist in vivo, and the local properties of collagen fibers are implicated in modulating migration behaviors. Yet, individual tumor cells also display heterogeneity in their intrinsic ability to migrate and metastasize. It remains unclear to what extent intrinsic and extrinsic heterogeneity contribute to the emergence of distinct migration phenotypes and whether certain migration phenotypes contribute more to metastasis than others. To study this, we generated 3D collagen matrices of varying densities and monitored single cancer cell migration in these matrices with time-lapse microscopy. We observed a collagen density threshold at 2.5mg/ml, above which 86% of MDA-MB-231 breast cancer cells transition from single mesenchymal migration to collective cell migration, with a 50% increase in persistence after cell division. After seven days, these collectively migrating cells created networks coated with basement membrane molecules resembling a clinical phenotype known as vascular mimicry (VM).The remaining 14% of cells migrated randomly and eventually formed spheroids. HT-1080 fibrosarcoma cells also responded similarly, migrating persistently and forming cellular networks. Next we sought to identify the physical feature of high-density collagen driving VM. Neither hypoxia or matrix stiffness was sufficient to induce VM. However, PEG-induced matrix confinement triggered VM network formation. RNA sequencing revealed collectively migrating cells up-regulated a conserved transcriptional program significantly enriched for annotations of vascular development and motility regulation processes. This gene module predicted survival in human tumor transcriptome datasets. Our results suggest that the VM phenotype arises in a subpopulation of cells from a conserved transcriptional and migratory response to confinement in 3D collagen. Note: This abstract was not presented at the meeting. Citation Format: Daniel Ortiz, Brian Tsui, Tyler Goshia, Colleen Ricker, Hannah Carter, Stephanie I. Fraley. 3D matrix confinement triggers vascular mimicry through a conserved migratory and transcriptional response [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2017; 2017 Apr 1-5; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2017;77(13 Suppl):Abstract nr 1888. doi:10.1158/1538-7445.AM2017-1888

Advances in sequencing technology have resulted in an exponential increase in the availability of protein sequence information. In order to fully utilize information, it is important to translate the primary sequences into high-resolution tertiary protein structures. MODELLER is a leading homology modeling method that produces high quality protein structures. In this study, the function of MODELLER was expanded by configuring and deploying it on a parallel grid computing platform using a custom four-step workflow. The workflow consisted of template selection through a protein BLAST algorithm, target-template protein sequence alignment, distribution of model generation jobs among the compute clusters, and final protein model optimization. To test the validity of this workflow, we used the Dual Specificity Phosphatase (DSP) protein family, which shares high homology among each other. Comparison of the DSP member SSH-2 with its model counterpart revealed a minimal 1.3% difference in output energy scores. Furthermore, the Dali Pair wise Comparison Program demonstrated a 98% match among amino acid features and a Z-score of 26.6 indicating very significant similarities between the model and actual protein structure. After confirming the accuracy of our workflow, we generated 23 previously unknown DSP family protein structure models. Over 40,000 models were generated 30 times faster than conventional computing. Virtual receptor-ligand screening results of modeled protein DSP21 were compared with two known structures that had either higher or lower structural homology to DSP21. There was a significant difference (p!0.001) between the average ligand ranking discrepancy of a more homologous protein pair and a less homologous protein pair, suggesting that the protein models generated were sufficiently accurate for virtual screening. These results demonstrate the accuracy and usability of a grid-enabled MODELLER program and the increased efficiency of processing protein structure models. This workflow will help increase the speed of future drug development pipelines.