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It is unclear how cells counteract the potentially harmful effects of uncoordinated DNA replication in the context of oncogenic stress. Here, we identify the WRAD (WDR5/RBBP5/ASH2L/DPY30) core as a modulator of DNA replication in pancreatic ductal adenocarcinoma (PDAC) models. Molecular analyses demonstrated that the WRAD core interacts with the replisome complex, with disruption of DPY30 resulting in DNA re-replication, DNA damage, and chromosomal instability (CIN) without affecting cancer cell proliferation. Consequently, in immunocompetent models, DPY30 loss induced T cell infiltration and immune-mediated clearance of highly proliferating cancer cells with complex karyotypes, thus improving anti-tumor efficacy upon anti-PD-1 treatment. In PDAC patients, DPY30 expression was associated with high tumor grade, worse prognosis, and limited response to immune checkpoint blockade. Together, our findings indicate that the WRAD core sustains genome stability and suggest that low intratumor DPY30 levels may identify PDAC patients who will benefit from immune checkpoint inhibitors.

The dual neural network (DNN)-based k -winner-take-all (WTA) model is able to identify the k largest numbers from its m input numbers. When there are imperfections, such as non-ideal step function and Gaussian input noise, in the realization, the model may not output the correct result. This brief analyzes the influence of the imperfections on the operational correctness of the model. Due to the imperfections, it is not efficient to use the original DNN- k WTA dynamics for analyzing the influence. In this regard, this brief first derives an equivalent model to describe the dynamics of the model under the imperfections. From the equivalent model, we derive a sufficient condition for which the model outputs the correct result. Thus, we apply the sufficient condition to design an efficiently estimation method for the probability of the model outputting the correct result. Furthermore, for the inputs with uniform distribution, a closed form expression for the probability value is derived. Finally, we extend our analysis for handling non-Gaussian input noise. Simulation results are provided to validate our theoretical results.

Inspired by sparse learning, the Markowitz mean-variance model with a sparse regularization term is popularly used in sparse portfolio optimization. However, in penalty-based portfolio optimization algorithms, the cardinality level of the resultant portfolio relies on the choice of the regularization parameter. This brief formulates the mean-variance model as a cardinality ( l 0 -norm) constrained nonconvex optimization problem, in which we can explicitly specify the number of assets in the portfolio. We then use the alternating direction method of multipliers (ADMMs) concept to develop an algorithm to solve the constrained nonconvex problem. Unlike some existing algorithms, the proposed algorithm can explicitly control the portfolio cardinality. In addition, the dynamic behavior of the proposed algorithm is derived. Numerical results on four real-world datasets demonstrate the superiority of our approach over several state-of-the-art algorithms.

Photoionization of matter is one of the fastest electronic processes in nature. Experimental measurements of photoionization dynamics have become possible through attosecond metrology. However, all experiments reported to date contain a so-far unavoidable measurement-induced contribution, known as continuum-continuum (CC) or Coulomb-laser-coupling delay. In traditional attosecond metrology, this contribution is nonadditive for most systems and nontrivial to calculate. Here, we introduce the concept of mirror symmetry-broken attosecond interferometry, which enables the direct and separate measurement of both the native one-photon ionization delays and the CC delays. Our technique solves the longstanding challenge of experimentally isolating these two contributions. This advance opens the door to the next generation of accurate measurements and precision tests that will set standards for benchmarking the accuracy of electronic structure and electron-dynamics methods.

A biosynthetic gene cluster for the bioactive fungal sesterterpenoids variecolin ( 1 ) and variecolactone ( 2 ) was identified in Aspergillus aculeatus ATCC 16872. Heterologous production of 1 and 2 was achieved in Aspergillus oryzae by expressing the sesterterpene synthase VrcA and the cytochrome P450 VrcB. Intriguingly, the replacement of VrcB with homologous P450s from other fungal terpenoid pathways yielded three new variecolin analogues ( 5 - 7 ). Analysis of the compounds' anticancer activity in vitro and in vivo revealed that although 5 and 1 had comparable activities, 5 was associated with significantly reduced toxic side effects in cancer-bearing mice, indicating its potentially broader therapeutic window. Our study describes the first tests of variecolin and its analogues in animals and demonstrates the utility of synthetic biology for creating molecules with improved biological activities., Competing Interests: There are no conflicts to declare., (© 2024 The Authors.)

Sparse index tracking, as one of the passive investment strategies, is to track a benchmark financial index via constructing a portfolio with a few assets in a market index. It can be considered as parameter learning in an adaptive system, in which we periodically update the selected assets and their investment percentages based on the sliding window approach. However, many existing algorithms for sparse index tracking cannot explicitly and directly control the number of assets or the tracking error. This article formulates sparse index tracking as two constrained optimization problems and then proposes two algorithms, namely, nonnegative orthogonal matching pursuit with projected gradient descent (NNOMP-PGD) and alternating direction method of multipliers for l 0 -norm (ADMM- l 0 ). The NNOMP-PGD aims at minimizing the tracking error subject to the number of selected assets less than or equal to a predefined number. With the NNOMP-PGD, investors can directly and explicitly control the number of selected assets. The ADMM- l 0 aims at minimizing the number of selected assets subject to the tracking error that is upper bounded by a preset threshold. It can directly and explicitly control the tracking error. The convergence of the two proposed algorithms is also presented. With our algorithms, investors can explicitly and directly control the number of selected assets or the tracking error of the resultant portfolio. In addition, numerical experiments demonstrate that the proposed algorithms outperform the existing approaches.

Among many k -winners-take-all ( k WTA) models, the dual-neural network (DNN- k WTA) model is with significantly less number of connections. However, for analog realization, noise is inevitable and affects the operational correctness of the k WTA process. Most existing results focus on the effect of additive noise. This brief studies the effect of time-varying multiplicative input noise. Two scenarios are considered. The first one is the bounded noise case, in which only the noise range is known. Another one is for the general noise distribution case, in which we either know the noise distribution or have noise samples. For each scenario, we first prove the convergence property of the DNN- k WTA model under multiplicative input noise and then provide an efficient method to determine whether a noise-affected DNN- k WTA network performs the correct k WTA process for a given set of inputs. With the two methods, we can efficiently measure the probability of the network performing the correct k WTA process. In addition, for the case of the inputs being uniformly distributed, we derive two closed-form expressions, one for each scenario, for estimating the probability of the model having correct operation. Finally, we conduct simulations to verify our theoretical results.

The objective of compressive sampling is to determine a sparse vector from an observation vector. This brief describes an analog neural method to achieve the objective. Unlike previous analog neural models which either resort to the l 1 -norm approximation or are with local convergence only, the proposed method avoids any approximation of the l 1 -norm term and is probably capable of leading to the optimum solution. Moreover, its computational complexity is lower than that of the other three comparison analog models. Simulation results show that the error performance of the proposed model is comparable to several state-of-the-art digital algorithms and analog models and that its convergence is faster than that of the comparison analog neural models.

Autism spectrum disorder (ASD) affects 1 in 44 children. Chromatin regulatory proteins are overrepresented among genes that contain high risk variants in ASD. Disruption of the chromatin environment leads to widespread dysregulation of gene expression, which is traditionally thought of as a mechanism of disease pathogenesis associated with ASD. Alternatively, alterations in chromatin dynamics could also lead to dysregulation of alternative splicing, which is understudied as a mechanism of ASD pathogenesis. The anticonvulsant valproic acid (VPA) is a well-known environmental risk factor for ASD that acts as a class I histone deacetylase inhibitor. However, the precise molecular mechanisms underlying defects in human neuronal development associated with exposure to VPA are understudied. To dissect how VPA exposure and subsequent chromatin hyperacetylation influence molecular signatures involved in ASD pathogenesis, we conducted RNA sequencing (RNA-seq) in human cortical neurons that were treated with VPA. We observed that differentially expressed genes (DEGs) were enriched for mRNA splicing, mRNA processing, histone modification and metabolism related gene sets. Furthermore, we observed widespread increases in the number and the type of alternative splicing events. Analysis of differential transcript usage (DTU) showed that exposure to VPA induces extensive alterations in transcript isoform usage across neurodevelopmentally important genes. Finally, we find that DEGs and genes that display DTU overlap with known ASD-risk genes. Altogether, these findings suggest that, in addition to differential gene expression, changes in alternative splicing correlated with alterations in the chromatin environment could act as an additional mechanism of disease in ASD., (© The Author(s) 2023. Published by Oxford University Press.)

For decades, adding fault/noise during training by gradient descent has been a technique for getting a neural network (NN) tolerant to persistent fault/noise or getting an NN with better generalization. In recent years, this technique has been readvocated in deep learning to avoid overfitting. Yet, the objective function of such fault/noise injection learning has been misinterpreted as the desired measure (i.e., the expected mean squared error (mse) of the training samples) of the NN with the same fault/noise. The aims of this article are: 1) to clarify the above misconception and 2) investigate the actual regularization effect of adding node fault/noise when training by gradient descent. Based on the previous works on adding fault/noise during training, we speculate the reason why the misconception appears. In the sequel, it is shown that the learning objective of adding random node fault during gradient descent learning (GDL) for a multilayer perceptron (MLP) is identical to the desired measure of the MLP with the same fault. If additive (resp. multiplicative) node noise is added during GDL for an MLP, the learning objective is not identical to the desired measure of the MLP with such noise. For radial basis function (RBF) networks, it is shown that the learning objective is identical to the corresponding desired measure for all three fault/noise conditions. Empirical evidence is presented to support the theoretical results and, hence, clarify the misconception that the objective function of a fault/noise injection learning might not be interpreted as the desired measure of the NN with the same fault/noise. Afterward, the regularization effect of adding node fault/noise during training is revealed for the case of RBF networks. Notably, it is shown that the regularization effect of adding additive or multiplicative node noise (MNN) during training an RBF is reducing network complexity. Applying dropout regularization in RBF networks, its effect is the same as adding MNN during training.

From the feature representation's point of view, the feature learning module of a convolutional neural network (CNN) is to transform an input pattern into a feature vector. This feature vector is then multiplied with a number of output weight vectors to produce softmax scores. The common training objective in CNNs is based on the softmax loss, which ignores the intra-class compactness. This brief proposes a constrained center loss (CCL)-based algorithm to extract robust features. The training objective of a CNN consists of two terms, softmax loss and CCL. The aim of the softmax loss is to push the feature vectors from different classes apart. Meanwhile, the CCL aims at clustering the feature vectors such that the feature vectors from the same classes are close together. Instead of using stochastic gradient descent (SGD) algorithms to learn all the connection weights and the cluster centers at the same time. Our CCL-based algorithm is based on the alternative learning strategy. We first fix the connection weights of the CNN and update the cluster centers based on an analytical formula, which can be implemented based on the minibatch concept. We then fix the cluster centers and update the connection weights for a number of SGD minibatch iterations. We also propose a simplified CCL (SCCL) algorithm. Experiments are performed on six commonly used benchmark datasets. The results demonstrate that the two proposed algorithms outperform several state-of-the-art approaches.

Chronic environmental stress can profoundly impact cell and body function. Although the underlying mechanisms are poorly understood, epigenetics has emerged as a key link between environment and health. The genomic effects of stress are thought to be mediated by the action of glucocorticoid stress hormones, primarily cortisol in humans, which act via the glucocorticoid receptor (GR). To dissect how chronic stress-driven GR activation influences epigenetic and cell states, human fibroblasts underwent prolonged exposure to physiological stress levels of cortisol and/or a selective GR antagonist. Cortisol was found to drive robust changes in cell proliferation, migration, and morphology, which were abrogated by concomitant GR blockade. The GR-driven cell phenotypes were accompanied by widespread, yet genomic context-dependent, changes in DNA methylation and mRNA expression, including gene loci with known roles in cell proliferation and migration. These findings provide insights into how chronic stress-driven functional epigenomic patterns become established to shape key cell phenotypes., Competing Interests: The authors declare no competing interests., (© 2022 The Author(s).)

The dual neural network-based k -winner-take-all (DNN- k WTA) is an analog neural model that is used to identify the k largest numbers from n inputs. Since threshold logic units (TLUs) are key elements in the model, offset voltage drifts in TLUs may affect the operational correctness of a DNN- k WTA network. Previous studies assume that drifts in TLUs follow some particular distributions. This brief considers that only the drift range, given by [-∆, ∆] , is available. We consider two drift cases: time-invariant and time-varying. For the time-invariant case, we show that the state of a DNN- k WTA network converges. The sufficient condition to make a network with the correct operation is given. Furthermore, for uniformly distributed inputs, we prove that the probability that a DNN- k WTA network operates properly is greater than (1-2∆) n . The aforementioned results are generalized for the time-varying case. In addition, for the time-invariant case, we derive a method to compute the exact convergence time for a given data set. For uniformly distributed inputs, we further derive the mean and variance of the convergence time. The convergence time results give us an idea about the operational speed of the DNN- k WTA model. Finally, simulation experiments have been conducted to validate those theoretical results.

Background: Due to active involvement with patients for COVID-19 treatments, nurses are susceptible to adverse psychological outcomes amid the COVID-19 pandemic. Despite the distress, studies have suggested that nurses are able to experience positive changes (i.e. posttraumatic growth; PTG) during the pandemic. Research on other populations has also indicated that COVID-19-specific worries and work-related coping resources are associated with people's positive changes during the pandemic., Objective: This study examined how socio-demographic characteristics, COVID-19-related worries, and work-related variables (satisfaction with work and workplace pandemic guidelines) were associated with PTG among nurses in Hong Kong., Methods: Nurses ( N  = 1510) working in hospitals and community settings were recruited through nursing associations in Hong Kong between 8 August 2020 and 22 September 2020. They were invited to complete a cross-sectional survey measuring their sociodemographic characteristics, COVID-19 worries, and satisfaction with work and workplace pandemic-control measures., Results: Results from hierarchical regressions found that those working non-full-time ( β  = -0.06), affiliating with a religion ( β  = 0.24), having higher COVID-19-related worries and psychological distress ( βs ranging from 0.12-0.15), and having higher work satisfaction ( β  = 0.14) were associated with higher PTG ( ps  < .05). Moreover, a significant interaction between psychological distress and satisfaction with workplace pandemic control guidelines emerged in explaining PTG ( β  = 0.08, p < .05), such that guideline satisfaction was only associated with higher PTG among those with higher distress ( β  = 0.09, p = .03), but not those with lower distress ( β  = -0.05, p > .05)., Conclusions: Nurses in Hong Kong did report positive changes amid the COVID-19 pandemic. Future studies could focus on the contributing factors of PTG to design for effective strategies to enhance resources for nurses to promote positive psychosocial outcomes., Competing Interests: No potential conflict of interest was reported by the author(s)., (© 2022 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group.)

Objective: Transitional care is important to successful hospital discharge. Providing patients with a clear and concise summary of medication-related information can help improve outcomes, in particular, among older adults. The present study aimed to propose a framework for the development of salient medication reminders (SMR), which include drug-related risks and precautions, using the Delphi process., Design: Identification of potential SMR statements for 80% of medication types used by older adult patients discharged from geriatric medicine departments, followed by a Delphi survey and expert panel discussion., Settings: Medical and geriatric departments of public hospitals in Hong Kong., Participants: A panel of 13 geriatric medical experts., Outcome Measure: A Likert scale ranging from 1 (strongly disagree) to 5 (strongly agree) points, scoring item relevance, importance and clarity. The minimum of 70% consensus was required for each statement to be included., Results: The expert panel achieved consensus through the Delphi process on 80 statements for 44 medication entities. Subsequently, the SMR steering group endorsed the inclusion of these statements in the SMR to be disseminated among older adults at the time of discharge from geriatric medicine departments., Conclusions: The Delphi process contributed to the development of SMR for older adult patients discharged from public hospitals in Hong Kong. Patient experience with and staff response to the SMR were assessed at four hospitals before implementation at all public hospitals., Competing Interests: Competing interests: None declared., (© Author(s) (or their employer(s)) 2021. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ.)

A recent crystal structure of a ribosome complex undergoing partial translocation in the absence of elongation factor EF-G showed disruption of codon-anticodon pairing and slippage of the reading frame by -1, directly implicating EF-G in preservation of the translational reading frame. Among mutations identified in a random screen for dominant-lethal mutations of EF-G were a cluster of six that map to the tip of domain IV, which has been shown to contact the codon-anticodon duplex in trapped translocation intermediates. In vitro synthesis of a full-length protein using these mutant EF-Gs revealed dramatically increased -1 frameshifting, providing new evidence for a role for domain IV of EF-G in maintaining the reading frame. These mutations also caused decreased rates of mRNA translocation and rotational movement of the head and body domains of the 30S ribosomal subunit during translocation. Our results are in general agreement with recent findings from Rodnina and coworkers based on in vitro translation of an oligopeptide using EF-Gs containing mutations at two positions in domain IV, who found an inverse correlation between the degree of frameshifting and rates of translocation. Four of our six mutations are substitutions at positions that interact with the translocating tRNA, in each case contacting the RNA backbone of the anticodon loop. We suggest that EF-G helps to preserve the translational reading frame by preventing uncoupled movement of the tRNA through these contacts; a further possibility is that these interactions may stabilize a conformation of the anticodon that favors base-pairing with its codon., (© 2021 Niblett et al.; Published by Cold Spring Harbor Laboratory Press for the RNA Society.)

Psychosocial stress, especially when chronic or excessive, can increase disease risk and accelerate biological aging. Although the underlying mechanisms are unclear, in vivo studies have associated exposure to stress and glucocorticoid stress hormones with shorter telomere length. However, the extent to which prolonged glucocorticoid exposure can shorten telomeres in controlled experimental settings remains unknown. Using a well-characterized cell line of human fibroblasts that undergo gradual telomere shortening during serial passaging in culture, we show that prolonged exposure (up to 51 days) to either naturalistic levels of the human endogenous glucocorticoid cortisol or the more potent synthetic glucocorticoid dexamethasone is not sufficient to accelerate telomere shortening. While our findings await extension in other cell types and biological contexts, they indicate that the in vivo association of psychosocial stress with telomere shortening is unlikely to be mediated by a direct and universal glucocorticoid effect on telomere length.

Epstein-Barr virus (EBV) was the first human tumor virus being discovered and remains to date the only human pathogen that can transform cells in vitro. 55 years of EBV research have now brought us to the brink of an EBV vaccine. For this purpose, recombinant viral vectors and their heterologous prime-boost vaccinations, EBV-derived virus-like particles and viral envelope glycoprotein formulations are explored and are discussed in this review. Even so, cell-mediated immune control by cytotoxic lymphocytes protects healthy virus carriers from EBV-associated malignancies, antibodies might be able to prevent symptomatic primary infection, the most likely EBV-associated pathology against which EBV vaccines will be initially tested. Thus, the variety of EBV vaccines reflects the sophisticated life cycle of this human tumor virus and only vaccination in humans will finally be able to reveal the efficacy of these candidates. Nevertheless, the recently renewed efforts to develop an EBV vaccine and the long history of safe adoptive T cell transfer to treat EBV-associated malignancies suggest that this oncogenic γ-herpesvirus can be targeted by immunotherapies. Such vaccination should ideally implement the very same immune control that protects healthy EBV carriers.

Objectives: Little is known about the value of routine clinical assessment in identifying patients with coronavirus disease 2019 (COVID-19) in the emergency department (ED). We aimed to compare the exposure history, signs and symptoms, laboratory, and radiographic features of ED patients who tested positive and negative for COVID-19., Methods: This was a case-control study in 7 EDs in Hong Kong from 20 January to 29 February 2020. Thirty-seven patients with laboratory-confirmed COVID-19 were age- and sex-matched to 111 controls. We compared the groups with univariate analysis and calculated the odds ratio (OR) of having COVID-19 for each characteristic that was significantly different between the groups with adjustment for age and presumed location of acquiring the infection., Results: There were no significant differences in patient characteristics and reported symptoms between the groups. A positive contact history within 14 days (adjusted OR 37.61, 95% CI: 10.86-130.19), bilateral chest radiograph shadow (adjusted OR 13.19, 95% CI: 4.66-37.35), having prior medical consultation (adjusted OR 7.43, 95% 2.89-19.09), a lower white blood cell count (adjusted OR 1.30, 95% CI: 1.11-1.51), and a lower platelet count (adjusted OR 1.07, 95% CI: 1.01-1.12) were associated with a higher odds of COVID-19 separately. A higher neutrophil count was associated with a lower odds of COVID-19 (adjusted OR 0.77, 95% CI: 0.65-0.91)., Conclusion: This study highlights a number of clinical features that may be useful in identifying high-risk patients for early testing and isolation while waiting for the test result. Further studies are warranted to verify the findings., Competing Interests: The authors have no conflicts of interest to declare., (© 2020 The Authors. JACEP Open published by Wiley Periodicals LLC on behalf of the American College of Emergency Physicians.)

Over decades, gradient descent has been applied to develop learning algorithm to train a neural network (NN). In this brief, a limitation of applying such algorithm to train an NN with persistent weight noise is revealed. Let V(w) be the performance measure of an ideal NN. V(w) is applied to develop the gradient descent learning (GDL). With weight noise, the desired performance measure (denoted as J(w) ) is E[V(~w)|w] , where ~w is the noisy weight vector. Applying GDL to train an NN with weight noise, the actual learning objective is clearly not V(w) but another scalar function L(w) . For decades, there is a misconception that L(w) = J(w) , and hence, the actual model attained by the GDL is the desired model. However, we show that it might not: 1) with persistent additive weight noise, the actual model attained is the desired model as L(w) = J(w) ; and 2) with persistent multiplicative weight noise, the actual model attained is unlikely the desired model as L(w) ≠ J(w) . Accordingly, the properties of the models attained as compared with the desired models are analyzed and the learning curves are sketched. Simulation results on 1) a simple regression problem and 2) the MNIST handwritten digit recognition are presented to support our claims.

Purpose: Recent studies demonstrate the role of the tumor microenvironment in tumor progression. However, strategies used to overcome the malignant phenotypes of cancer cells modulated by the microenvironment have not been thoroughly explored. In this study, we evaluated the therapeutic efficacy of a newly developed mAb targeting microfibril-associated protein 5 (MFAP5), which is secreted predominately by cancer-associated fibroblast (CAF), in ovarian and pancreatic cancer models. Experimental Design: MAbs were developed using human MFAP5 recombinant protein as an antigen in mice, and antibodies from hybridoma clones were evaluated for their specificity to human and murine MFAP5. An Octet RED384 system was used to determine the kinetics of binding affinity and the specificity of the antibody clones, which were followed by epitope mapping and functional characterization by in vitro assays. The therapeutic efficacy of a lead anti-MFAP5 antibody clone 130A in tumor suppression was evaluated by ovarian tumor- and pancreatic tumor-bearing mouse models., Results: Three hybridoma clones, which produced antibodies with high affinity and specificity to MFAP5, were selected for functional studies. Antibody clone 130A, which recognizes a common epitope shared between human and murine MFAP5 protein, was further selected for in vivo studies. Results showed that clone 130A downregulated MFAP5-induced collagen production in CAFs, suppressed intratumoral microvessel leakiness, and enhanced paclitaxel bioavailability in both ovarian and pancreatic cancer mouse models., Conclusions: These data suggest that MFAP5 blockade using an immunologic approach inhibits fibrosis, induces tumor vessel normalization, and enhances chemosensitivity in ovarian and pancreatic cancer, and can be used as a novel therapeutic agent., (©2019 American Association for Cancer Research.)

This brief presents analytical results on the effect of additive weight/bias noise on a Boltzmann machine (BM), in which the unit output is in {-1, 1} instead of {0, 1}. With such noise, it is found that the state distribution is yet another Boltzmann distribution but the temperature factor is elevated. Thus, the desired gradient ascent learning algorithm is derived, and the corresponding learning procedure is developed. This learning procedure is compared with the learning procedure applied to train a BM with noise. It is found that these two procedures are identical. Therefore, the learning algorithm for noise-free BMs is suitable for implementing as an online learning algorithm for an analog circuit-implemented BM, even if the variances of the additive weight noise and bias noise are unknown.

In the eukaryotic cell, spliceosomes assemble onto pre-mRNA cotranscriptionally. Spliceosome assembly takes place in the context of the chromatin environment, suggesting that the state of the chromatin may affect splicing. The molecular details and mechanisms through which chromatin affects splicing, however, are still unclear. Here, we show a role for the histone methyltransferase Set2 and its histone modification, H3K36 methylation, in pre-mRNA splicing through high-throughput sequencing. Moreover, the effect of H3K36 methylation on pre-mRNA splicing is mediated through the chromodomain protein Eaf3. We find that Eaf3 is recruited to intron-containing genes and that Eaf3 interacts with the splicing factor Prp45. Eaf3 acts with Prp45 and Prp19 after formation of the precatalytic B complex around the time of splicing activation, thus revealing the step in splicing that is regulated by H3K36 methylation. These studies support a model whereby H3K36 facilitates recruitment of an "adapter protein" to support efficient, constitutive splicing., (Copyright © 2019 The Authors. Published by Elsevier Inc. All rights reserved.)

The Epstein-Barr virus (EBV) is one of the predominant tumor viruses in humans, but so far no therapeutic or prophylactic vaccination against this transforming pathogen is available. We demonstrated that heterologous prime-boost vaccination with the nuclear antigen 1 of EBV (EBNA1), either targeted to the DEC205 receptor on DCs or expressed from a recombinant modified vaccinia virus Ankara (MVA) vector, improved priming of antigen-specific CD4+ T cell help. This help supported the expansion and maintenance of EBNA1-specific CD8+ T cells that are most efficiently primed by recombinant adenoviruses that encode EBNA1. These combined CD4+ and CD8+ T cell responses protected against EBNA1-expressing T and B cell lymphomas, including lymphoproliferations that emerged spontaneously after EBNA1 expression. In particular, the heterologous EBNA1-expressing adenovirus, boosted by EBNA1-encoding MVA vaccination, demonstrated protection as a prophylactic and therapeutic treatment for the respective lymphoma challenges. Our study shows that such heterologous prime-boost vaccinations against EBV-associated malignancies as well as symptomatic primary EBV infection should be further explored for clinical development.

Background: Bulk tumor tissue samples are used for generating gene expression profiles in most research studies, making it difficult to decipher the stroma-cancer crosstalk networks. In the present study, we describe the use of microdissected transcriptome profiles for the identification of cancer-stroma crosstalk networks with prognostic value, which presents a unique opportunity for developing new treatment strategies for ovarian cancer., Methods: Transcriptome profiles from microdissected ovarian cancer-associated fibroblasts (CAFs) and ovarian cancer cells from patients with high-grade serous ovarian cancer (n = 70) were used as input data for the computational systems biology program CCCExplorer to uncover crosstalk networks between various cell types within the tumor microenvironment. The crosstalk analysis results were subsequently used for discovery of new indications for old drugs in ovarian cancer by computational ranking of candidate agents. Survival analysis was performed on ovarian tumor-bearing Dicer/Pten double-knockout mice treated with calcitriol, a US Food and Drug Administration-approved agent that suppresses the Smad signaling cascade, or vehicle control (9-11 mice per group). All statistical tests were two-sided., Results: Activation of TGF-β-dependent and TGF-β-independent Smad signaling was identified in a particular subtype of CAFs and was associated with poor patient survival (patients with higher levels of Smad-regulated gene expression by CAFs: median overall survival = 15 months, 95% confidence interval [CI] = 12.7 to 17.3 months; vs patients with lower levels of Smad-regulated gene expression: median overall survival = 26 months, 95% CI = 15.9 to 36.1 months, P = .02). In addition, the activated Smad signaling identified in CAFs was found to be targeted by repositioning calcitriol. Calcitriol suppressed Smad signaling in CAFs, inhibited tumor progression in mice, and prolonged the median survival duration of ovarian cancer-bearing mice from 36 to 48 weeks (P = .04)., Conclusions: Our findings suggest the feasibility of using novel multicellular systems biology modeling to identify and repurpose known drugs targeting cancer-stroma crosstalk networks, potentially leading to faster and more effective cures for cancers., (© The Author(s) 2018. Published by Oxford University Press.)

In a recent issue of Molecular Cell, Boehm et al. (2018), Blazquez et al. (2018), and Gonatopoulos-Pournatzis et al. (2018) uncover novel mechanisms by which the cell regulates splicing of cryptic splice sites and microexons., (Copyright © 2018 Elsevier Inc. All rights reserved.)

Increased number of tumor-infiltrating CD8+ lymphocytes is associated with improved survival in patients with advanced stage high grade serous ovarian cancer (HGSOC) but the underlying molecular mechanism has not been thoroughly explored. Using transcriptome profiling of microdissected HGSOC tissue with high and low CD8+ lymphocyte count and subsequent validation studies, we demonstrated that significantly increased ISG15 (Interferon-stimulated gene 15) expression in HGSOC was associated with high CD8+ lymphocyte count and with the improvement in median overall survival in both univariate and multivariate analyses. Further functional studies showed that endogenous and exogenous ISG15 suppressed ovarian cancer progression through ISGylation of ERK in HGSOC, and activation of NK cells and CD8+ T lymphocytes. These data suggest that the development of treatment strategies based on up-regulating ISG15 in ovarian cancer cells or increased circulating ISG15 in ovarian cancer patients is warranted.

Background: We have previously reported an antigen-specific protocol to induce transplant tolerance and linked suppression to human embryonic stem cell (hESC)-derived tissues in immunocompetent mice through coreceptor and costimulation blockade. However, the exact mechanisms of acquired immune tolerance in this model have remained unclear., Methods: We utilize the NOD.Foxp3 hCD2 reporter mouse line and an ablative anti-hCD2 antibody to ask if CD4 + FOXP3 + regulatory T cells (Treg) are required for coreceptor and costimulation blockade-induced immune tolerance. We also perform genome-wide single-cell RNA-sequencing to interrogate Treg during immune rejection and tolerance and to indicate possible mechanisms involved in sustaining Treg function., Results: We show that Treg are indispensable for tolerance induced by coreceptor and costimulation blockade as depletion of which with an anti-hCD2 antibody resulted in rejection of hESC-derived pancreatic islets in NOD.Foxp3 hCD2 mice. Single-cell transcriptomic profiling of 12,964 intragraft CD4 + T cells derived from rejecting and tolerated grafts reveals that Treg are heterogeneous and functionally distinct in the two outcomes of transplant rejection and tolerance. Treg appear to mainly promote chemotactic and ubiquitin-dependent protein catabolism during transplant rejection while seeming to harness proliferative and immunosuppressive function during tolerance. We also demonstrate that this form of acquired transplant tolerance is associated with increased proliferation and PD-1 expression by Treg. Blocking PD-1 signaling with a neutralizing anti-PD-1 antibody leads to reduced Treg proliferation and graft rejection., Conclusions: Our results suggest that short-term coreceptor and costimulation blockade mediates immune tolerance to hESC-derived pancreatic islets by promoting Treg proliferation through engagement of PD-1. Our findings could give new insights into clinical development of hESC-derived pancreatic tissues, combined with immunotherapies that expand intragraft Treg, as a potentially sustainable alternative treatment for T1D.

In this paper, the effect of input noise, output node stochastic, and recurrent state noise on the Wang $k$ WTA is analyzed. Here, we assume that noise exists at the recurrent state $y(t)$ and it can either be additive or multiplicative. Besides, its dynamical change (i.e., $dy/dt$ ) is corrupted by noise as well. In sequel, we model the dynamics of $y(t)$ as a stochastic differential equation and show that the stochastic behavior of $y(t)$ is equivalent to an Ito diffusion. Its stationary distribution is a Gibbs distribution, whose modality depends on the noise condition. With moderate input noise and very small recurrent state noise, the distribution is single modal and hence $y(\infty )$ has high probability varying within the input values of the $k$ and $k+1$ winners (i.e., correct output). With small input noise and large recurrent state noise, the distribution could be multimodal and hence $y(\infty )$ could have probability varying outside the input values of the $k$ and $k+1$ winners (i.e., incorrect output). In this regard, we further derive the conditions that the $k$ WTA has high probability giving correct output. Our results reveal that recurrent state noise could have severe effect on Wang $k$ WTA. But, input noise and output node stochastic could alleviate such an effect.

In the training stage of radial basis function (RBF) networks, we need to select some suitable RBF centers first. However, many existing center selection algorithms were designed for the fault-free situation. This brief develops a fault tolerant algorithm that trains an RBF network and selects the RBF centers simultaneously. We first select all the input vectors from the training set as the RBF centers. Afterward, we define the corresponding fault tolerant objective function. We then add an -norm term into the objective function. As the -norm term is able to force some unimportant weights to zero, center selection can be achieved at the training stage. Since the -norm term is nondifferentiable, we formulate the original problem as a constrained optimization problem. Based on the alternating direction method of multipliers framework, we then develop an algorithm to solve the constrained optimization problem. The convergence proof of the proposed algorithm is provided. Simulation results show that the proposed algorithm is superior to many existing center selection algorithms.

Introduction: For acute ischaemic stroke patients, treatment with intravenous tissue plasminogen activator within a 4.5-hour therapeutic window is essential. We aimed to assess the time delays experienced by stroke patients arriving at the emergency department and to compare ambulance users and non-ambulance users., Methods: We performed a prospective cohort study in a tertiary hospital in Hong Kong. All acute stroke patients attending the emergency department from January to June 2017 were recruited. Patients who were in hospital at the time of stroke onset and those who transferred from other hospitals were excluded. Three phases were compared between ambulance users and non-ambulance users: phase I, between stroke onset and calling for help; phase II, between calling for help and arriving at the emergency department; and phase III, between arriving and receiving medical assessment., Results: Of 102 consecutive patients recruited, 48 (47%) patients arrived at the emergency department by ambulance. The percentage of stroke patients attending emergency department within the therapeutic window was significantly higher for ambulance users than for non-ambulance users (64.6% vs 29.6%; P<0.001). For phases I, II and III, the median times were significantly shorter for ambulance users (77.5, 32 and 8 min, respectively) than for non-ambulance users (720, 44.5 and 15 min, respectively; all P<0.001)., Conclusion: Transport of patients to the emergency department by ambulance is important for timely and effective stroke treatment.

A commonly used measurement model for locating a mobile source is time-difference-of-arrival (TDOA). As each TDOA measurement defines a hyperbola, it is not straightforward to compute the mobile source position due to the nonlinear relationship in the measurements. This brief exploits the Lagrange programming neural network (LPNN), which provides a general framework to solve nonlinear constrained optimization problems, for the TDOA-based localization. The local stability of the proposed LPNN solution is also analyzed. Simulation results are included to evaluate the localization accuracy of the LPNN scheme by comparing with the state-of-the-art methods and the optimality benchmark of Cramér-Rao lower bound.

Receptor tyrosine kinase-like orphan receptor 1 (ROR1) is an oncofetal antigen expressed on multiple tumors and has no significant expression on normal human tissues. ROR1 is highly upregulated in chronic lymphocytic leukemia (CLL) B cells. NOD-scid IL2rg -/- (NSG) mice engrafted with human CD34 + hematopoietic progenitor cells (huNSG) achieved multilineage human immune cell reconstitution including B cells, T cells, NK cells, and DCs. Like the CLL patients, huNSG mice have abnormally high percentage of CD5-expressing B cells in the periphery. In light of this, we aim to determine whether ROR1 is expressed on huNSG B cells. Using flow cytometry analysis, we found that ROR1 was highly expressed in a proportion of bone marrow, spleen, and blood B cells, which were mostly immature B cells. Transplantation of the oncogene TCL-1-transduced CD34 + cells in neonatal NSG mice did not increase the frequency of ROR1-expressing B cells, but the mouse with the highest engraftment of transduced cells developed a tumor-like lump consisting of a high percentage of ROR1-expressing B cells. This study highlights the potential use of huNSG mice to study B cell malignant diseases and to evaluate immunotherapeutics targeting ROR1.

This paper studies the effects of uniform input noise and Gaussian input noise on the dual neural network-based WTA (DNN- WTA) model. We show that the state of the network (under either uniform input noise or Gaussian input noise) converges to one of the equilibrium points. We then derive a formula to check if the network produce correct outputs or not. Furthermore, for the uniformly distributed inputs, two lower bounds (one for each type of input noise) on the probability that the network produces the correct outputs are presented. Besides, when the minimum separation amongst inputs is given, we derive the condition for the network producing the correct outputs. Finally, experimental results are presented to verify our theoretical results. Since random drift in the comparators can be considered as input noise, our results can be applied to the random drift situation.

The molecular mechanism by which cancer-associated fibroblasts (CAFs) confer chemoresistance in ovarian cancer is poorly understood. The purpose of the present study was to evaluate the roles of CAFs in modulating tumor vasculature, chemoresistance, and disease progression. Here, we found that CAFs upregulated the lipoma-preferred partner (LPP) gene in microvascular endothelial cells (MECs) and that LPP expression levels in intratumoral MECs correlated with survival and chemoresistance in patients with ovarian cancer. Mechanistically, LPP increased focal adhesion and stress fiber formation to promote endothelial cell motility and permeability. siRNA-mediated LPP silencing in ovarian tumor-bearing mice improved paclitaxel delivery to cancer cells by decreasing intratumoral microvessel leakiness. Further studies showed that CAFs regulate endothelial LPP via a calcium-dependent signaling pathway involving microfibrillar-associated protein 5 (MFAP5), focal adhesion kinase (FAK), ERK, and LPP. Thus, our findings suggest that targeting endothelial LPP enhances the efficacy of chemotherapy in ovarian cancer. Our data highlight the importance of CAF-endothelial cell crosstalk signaling in cancer chemoresistance and demonstrate the improved efficacy of using LPP-targeting siRNA in combination with cytotoxic drugs.