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To assess whether children have phonological disorders, we need to make use of screening tools of the child's first language. This paper describes and evaluates one published phonological screening tool that has been developed for Cantonese-speaking children, the Cantonese Segmental Phonology Test (CSPT). Such a test is also valid for assessing children who speak Cantonese and other languages outside Hong Kong. In this review, we also report various findings on normal and disordered Cantonese phonological development based on this test. [ABSTRACT FROM AUTHOR]

The high frequency (HF) electronic ballast has been replacing the conventional magnetic ballast for operating fluorescent lamps. It has the advantage of low energy consumption, running the lamps free from stroboscopic effects, low noise production, being light weight and allowing the lamps to be dimmed. This study shows that for a cellular office at the perimeter of a building lit by a dimmable HF electronic ballast lighting, about 40% of the lighting energy could be saved under Hong Kong's daylight conditions. A saving of 40% at the perimeter amounts to an overall lighting energy saving of about 10% if the perimeter area is about one quarter of the total floor area. Lighting energy normally accounts for about 30% of the total energy consumption for office buildings. For a shallow plan office building, the amount of saving using a daylight linked system could be significant. [ABSTRACT FROM AUTHOR]

In the construction process of radial basis function (RBF) networks, two common crucial issues arise: the selection of RBF centers and the effective utilization of the given source without encountering the overfitting problem. Another important issue is the fault tolerant capability. That is, when noise or faults exist in a trained network, it is crucial that the network's performance does not undergo significant deterioration or decrease. However, without employing a fault tolerant procedure, a trained RBF network may exhibit significantly poor performance. Unfortunately, most existing algorithms are unable to simultaneously address all of the aforementioned issues. This paper proposes fault tolerant training algorithms that can simultaneously select RBF nodes and train RBF output weights. Additionally, our algorithms can directly control the number of RBF nodes in an explicit manner, eliminating the need for a time-consuming procedure to tune the regularization parameter and achieve the target RBF network size. Based on simulation results, our algorithms demonstrate improved test set performance when more RBF nodes are used, effectively utilizing the given source without encountering the overfitting problem. This paper first defines a fault tolerant objective function, which includes a term to suppress the effects of weight faults and weight noise. This term also prevents the issue of overfitting, resulting in better test set performance when more RBF nodes are utilized. With the defined objective function, the training process is designed to solve a generalized M-sparse problem by incorporating an ℓ 0 -norm constraint. The ℓ 0 -norm constraint allows us to directly and explicitly control the number of RBF nodes. To address the generalized M-sparse problem, we introduce the noise-resistant iterative hard thresholding (NR-IHT) algorithm. The convergence properties of the NR-IHT algorithm are subsequently discussed theoretically. To further enhance performance, we incorporate the momentum concept into the NR-IHT algorithm, referring to the modified version as "NR-IHT-Mom". Simulation results show that both the NR-IHT algorithm and the NR-IHT-Mom algorithm outperform several state-of-the-art comparison algorithms., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

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.

Objective: Mass Casualty Incidents (MCIs) involving high-speed passenger ferries (HSPFs) may result in the dual-wave phenomenon, in which the emergency department (ED) is overwhelmed by an initial wave of minor injuries, followed by a second wave of more seriously injured victims. This study aimed to characterize the time pattern of ED presentation of victims in such accidents in Hong Kong., Methods: All HSPF MCIs from 2005 to 2015 were reviewed retrospectively, with the time interval from accident to ED registration determined for each victim. Multivariable linear regression was used to identify independent factors associated with the time of ED presentation after the accidents., Results: Eight MCIs involving 492 victims were identified. Victims with an Injury Severity Score (ISS) ≥ 9 had a significantly shorter median time interval compared to those with minor injuries. An ISS ≥ 9 and evacuation by emergency service vessels were associated with a shorter delay in ED arrival, whereas ship sinking, accident at nighttime, and a longer linear distance between the accident and receiving ED were associated with a longer delay., Conclusion: The dual-wave phenomenon was not present in HSPF MCIs. Early communication is the key to ensure early resource mobilisation and a well-timed response.

The concept of randomized neural networks (RNNs), such as the random vector functional link network (RVFL) and extreme learning machine (ELM), is a widely accepted and efficient network method for constructing single-hidden layer feedforward networks (SLFNs). Due to its exceptional approximation capabilities, RNN is being extensively used in various fields. While the RNN concept has shown great promise, its performance can be unpredictable in imperfect conditions, such as weight noises and outliers. Thus, there is a need to develop more reliable and robust RNN algorithms. To address this issue, this paper proposes a new objective function that addresses the combined effect of weight noise and training data outliers for RVFL networks. Based on the half-quadratic optimization method, we then propose a novel algorithm, named noise-aware RNN (NARNN), to optimize the proposed objective function. The convergence of the NARNN is also theoretically validated. We also discuss the way to use the NARNN for ensemble deep RVFL (edRVFL) networks. Finally, we present an extension of the NARNN to concurrently address weight noise, stuck-at-fault, and outliers. The experimental results demonstrate that the proposed algorithm outperforms a number of state-of-the-art robust RNN algorithms., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

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.

Recent methods based on deep learning have shown promise in converting grayscale images to colored ones. However, most of them only allow limited user inputs (no inputs, only global inputs, or only local inputs), to control the output colorful images. The possible difficulty lies in how to differentiate the influences of different inputs. To solve this problem, we propose a two-stage deep colorization method allowing users to control the results by flexibly setting global inputs and local inputs. The key steps include enabling color themes as global inputs by extracting K mean colors and generating K-color maps to define a global theme loss, and designing a loss function to differentiate the influences of different inputs without causing artifacts. We also propose a color theme recommendation method to help users choose color themes. Based on the colorization model, we further propose an image compression scheme, which supports variable compression ratios in a single network. Experiments on colorization show that our method can flexibly control the colorized results with only a few inputs and generate state-of-the-art results. Experiments on compression show that our method achieves much higher image quality at the same compression ratio when compared to the state-of-the-art methods.

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.

Background: Accidents involving high-speed passenger ferries have the potential to cause mass-casualty incidents (MCIs), yet there is a lack of relevant studies available to inform hospital disaster preparedness planning., Objective: The objective was to study the injury patterns and outcomes of MCI victims involved in high-speed passenger ferry accidents in Hong Kong waters., Methods: A retrospective study was conducted from 1 January 2005 to 31 December 2015. All MCIs involving high-speed passenger ferries were captured from the Marine Department of Hong Kong. Victims of all age who were sent to the accident and emergency departments (A&Es) of seven public hospitals around Victoria Harbour, including three trauma centres, were identified from electronic disaster registries of the study hospitals. Data on injury patterns and outcomes were extracted from medical records with the Injury Severity Score (ISS) calculated for each victim. The Kruskal-Wallis test was used to compare medians of the ISS across different mechanisms of injury. Multivariable logistic regression was performed to identify independent predictors for major trauma (ISS≥16)., Results: During the study period, eight MCIs involving high-speed passenger ferries were reported and 512 victims (median age: 44 years, age range: 2-85 years) were sent to the study hospitals. The A&E triage categories were Cat 1, 3.1%; Cat 2, 4.3%; Cat 3, 19.3%; Cat 4, 72.9%; and Cat 5, 0.4%, respectively. The median ISS was 1.0 (interquartile range: 1.0-2.0). Fourteen victims (2.7%) had an ISS≥16 and age was the only independent predictor for major trauma (OR 1.06, p = 0.025, 95% CI 1.01-1.11). Trauma call was activated at A&E for 11 victims. In total, 100 victims (19.5%) were admitted to the study hospitals, including 19 (3.5%) and 15 (2.9%) who required surgery and intensive care unit stay, respectively. Eleven victims (2.1%) died, mostly due to drowning., Conclusion: MCIs involving high-speed passenger ferries can result in a sudden surge in demand for both A&E and in-patient care, though the majority of victims may have minor injuries. Better access to lifejackets and mandatory seatbelt use may help to reduce injuries and deaths., Competing Interests: Declaration of Competing Interest The authors have no conflicts of interest to declare., (Copyright © 2019. Published by Elsevier Ltd.)

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.)

Systematic bonding analysis has been carried out for transition metal complexes containing more than one redox-active/non-innocent ligand. These complexes include tetralithio spiroaromatic palladole, dicupra[10]annulene, highly reduced nickel cyclooctadiene complex and tris(dithiolene)vanadium(iv). Our detailed bonding analysis reveals a common inter-ligand delocalisation pattern in these cases.