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With enough advanced notice, dengue outbreaks can be mitigated. As a climate-sensitive disease, environmental conditions and past patterns of dengue can be used to make predictions about future outbreak risk. These predictions improve public health planning and decision-making to ultimately reduce the burden of disease. Past approaches to dengue forecasting have used seasonal climate forecasts, but the predictive ability of a system using different lead times in a year-round prediction system has been seldom explored. Moreover, the transition from theoretical to operational systems integrated with disease control activities is rare. We introduce an operational seasonal dengue forecasting system for Vietnam where Earth observations, seasonal climate forecasts, and lagged dengue cases are used to drive a superensemble of probabilistic dengue models to predict dengue risk up to 6 months ahead. Bayesian spatiotemporal models were fit to 19 years (2002-2020) of dengue data at the province level across Vietnam. A superensemble of these models then makes probabilistic predictions of dengue incidence at various future time points aligned with key Vietnamese decision and planning deadlines. We demonstrate that the superensemble generates more accurate predictions of dengue incidence than the individual models it incorporates across a suite of time horizons and transmission settings. Using historical data, the superensemble made slightly more accurate predictions (continuous rank probability score [CRPS] = 66.8, 95% CI 60.6-148.0) than a baseline model which forecasts the same incidence rate every month (CRPS = 79.4, 95% CI 78.5-80.5) at lead times of 1 to 3 months, albeit with larger uncertainty. The outbreak detection capability of the superensemble was considerably larger (69%) than that of the baseline model (54.5%). Predictions were most accurate in southern Vietnam, an area that experiences semi-regular seasonal dengue transmission. The system also demonstrated added value across multiple areas compared to previous practice of not using a forecast. We use the system to make a prospective prediction for dengue incidence in Vietnam for the period May to October 2020. Prospective predictions made with the superensemble were slightly more accurate (CRPS = 110, 95% CI 102-575) than those made with the baseline model (CRPS = 125, 95% CI 120-168) but had larger uncertainty. Finally, we propose a framework for the evaluation of probabilistic predictions. Despite the demonstrated value of our forecasting system, the approach is limited by the consistency of the dengue case data, as well as the lack of publicly available, continuous, and long-term data sets on mosquito control efforts and serotype-specific case data. This study shows that by combining detailed Earth observation data, seasonal climate forecasts, and state-of-the-art models, dengue outbreaks can be predicted across a broad range of settings, with enough lead time to meaningfully inform dengue control. While our system omits some important variables not currently available at a subnational scale, the majority of past outbreaks could be predicted up to 3 months ahead. Over the next 2 years, the system will be prospectively evaluated and, if successful, potentially extended to other areas and other climate-sensitive disease systems.

Abstract There is increased risk of cardiovascular disease in patients with chronic inflammatory disorders such as rheumatoid arthritis, psoriatic arthritis, and systemic lupus erythematosus. Studies have shown association between cardiovascular disease (eg, myocardial infarction, heart failure, stroke) and inflammatory bowel disease. Medications such as infliximab and adalimumab (monoclonal antibodies to tumor necrosis factor α) may help decrease the inflammatory burden and cardiovascular risk; however, there have been reports of hypertriglyceridemia and worsening of moderate to severe heart failure with these medications. Janus kinase inhibitors, such as tofacitinib, have been associated with hyperlipidemia and thromboembolism. We aim to discuss clinical and imaging modalities to assess cardiovascular risk in inflammatory bowel disease patients and review the role of various medications with respect to cardiovascular disease in this population.

Water is a significant natural resource for humans. As such, wastewater containing heavy metals is seen as a grave problem for the environment. Currently, adsorption is one of the common methods used for both water purification and wastewater treatment. Adsorption relies on the physical and chemical interactions between heavy metal ions and adsorbents. Adsorptive membranes (AMs) have demonstrated high effectiveness in heavy metal removal from wastewater owing to their exclusive structural properties. This article examines the applications of adsorptive membranes such as polymeric membranes (PMs), polymer-ceramic membranes (PCMs), electrospinning nanofiber membranes (ENMs), and nano-enhanced membranes (NEMs), which demonstrate high selectivity and adsorption capacity for heavy metal ions, as well as both advantages and disadvantages of each one all, are summarized and compared shortly. Moreover, the general theories for both adsorption isotherms and adsorption kinetics are described briefly to comprehend the adsorption process. This work will be valuable to readers in understanding the current applications of various AMs and their mechanisms in heavy metal ion adsorption, as well as the recycling methods in heavy ions desorption process are summarized and described clearly. Besides, the influences of morphological and chemical structures of AMs are presented and described in detail as well.

Over the past twenty years, government advisory bodies have placed increasing emphasis on the need for adaptive measures in response to the effects of human-induced climate change. Integrated Assessment Models (IAMs), which incorporate macroeconomic and climate variables, feature prominently in advisory content, though they rarely draw on data from outside strictly constrained hypothetical systems. This has led to assertions that they are not well-suited to approximate complex systemic human-environment processes. Modular, interdisciplinary approaches have offered a way to address this shortcoming; however, beyond climate records, prehistoric data continue to be under-utilised in developing such models. In this paper we highlight the contribution that archaeology and palaeoecology can make to the development of the next generation IAMs that are expected to enhance provision for more local and pro-active adaptations to future climate change. We present data from one of Southeast Asia’s most heavily developed river deltas: the Red River (Song Hong) Delta, in Vietnam and localised analysis from the Tràng An Landscape Complex World Heritage Site, on the delta’s southern margin. Comparison is made between Shared Socio-economic Pathways (SSP) 5–8.5 and SSP2–4.5 emission projection models and the Mid-Holocene inundation of the Red River Basin. We highlight the value to taking a scientific long view of coastal evolution through an illustrative set of eight research foci where palaeo-data can bring new and localised empirical data to bear on future risk management planning. We proceed to demonstrate the applicability of palaeoenvironmental, zooarchaeological and historical evidence to management and the development of sustainable conservation strategies using Tràng An as a case study. In so doing, we further highlight the importance of knowledge exchange between scientific, corporate, non-governmental, local, and state stakeholders to achieve tangible results on the ground.

Insulin resistance (IR), a metabolic risk factor, is linked to the pathogenetic mechanism of primary hypertension. Detecting IR in the patients with hypertension will help to predict and stratify the added cardiovascular risk, institute appropriate IR management, and manage hypertension optimally. There are many methods for assessing IR, each with distinct advantages and disadvantages. The euglycemic insulin clamp and intravenous glucose tolerance test, gold standards for measuring IR, are used in research but not in clinical practice. Homeostatic model assessment (HOMA‐IR), a method for assessing β‐cell function and IR, is frequently applied presently, particularly in Asia. Besides, the triglyceride–glucose index (TyG) first published by South American authors showed a good correlation with the insulin clamp technique and HOMA‐IR index. This simple, convenient, and low‐cost TyG index is of research interest in many countries in Asia and can be used to screen for IR in the Asian hypertensive community.

The epithelial–mesenchymal transition (EMT) is a crucial morphological event that occurs during progression of epithelial tumors. We reported previously that levels of the δ‐crystallin/E2‐box factor 1 (δEF1) family proteins (Zinc finger E‐box binding homeobox 1 [ZEB1]/δEF1 and ZEB2/ Smad‐interacting protein 1), key regulators of the EMT, are positively correlated with EMT phenotypes and aggressiveness of breast cancer. Here, we show that Ets1 induces ZEB expression and activates the ZEB1 promoter, independently of its threonine 38 phosphorylation status. In the basal‐like subtype of breast cancer cells, siRNAs targeting Ets1 repressed expression of ZEBs and partially restored their epithelial phenotypes and sensitivity to antitumor drugs. Epithelium‐specific ETS transcription factor 1 (ESE1), a member of the Ets transcription factor family, was originally characterized as being expressed in an epithelial‐restricted pattern, placing it within the epithelium‐specific ETS subfamily. ESE1, highly expressed in the luminal subtype of breast cancer cells, was repressed by activation of the MEK–ERK pathway, resulting in induction of ZEBs through Ets1 upregulation. Conversely, Ets1, highly expressed in the basal‐like subtype, was repressed by inactivation of MEK–ERK pathway, resulting in reduction of ZEBs through ESE1 upregulation. These findings suggest that ESE1 and Ets1, whose expressions are reciprocally regulated by the MEK–ERK pathway, define the EMT phenotype through controlling expression of ZEBs in each subtype of breast cancer cells. ESE1 and Ets1, whose expressions are reciprocally regulated by MEK‐ERK pathway, define the EMT phenotype through controlling expression of ZEBs in each subtype of breast cancer cells.

In this article, with the aim of better understanding the development of Vietnamese Buddhist nuns, the period of the Buddhist revival movement is investigated. This event is considered a turning point for Vietnamese Buddhism. In addition, it will help to shed light on the status of Vietnamese nuns. In this article—which is mainly based on archival documents kept in the National Overseas Archives (the French colonial archives held at the Archives Nationales d’Outre-Mer) and the National Archives Center I, Buddhism periodicals, and memoirs—the status of Vietnamese women during the French colonial period is clarified, as well as the positive effects of the colonial regime in regard to the change in women’s perceptions. Then, the differences in the nuns’ situation in three regions are analyzed. Finally, an exploration is conducted into the rise of nuns during the revival movement and the emergence of reformist nuns. Indeed, it is reformist nuns that have shaped the image of modern Vietnamese nuns. Moreover, they also created a direction by which the following generations could continue along, as well as playing an important role in the Vietnam Buddhist Sangha.

With the rapid advancement of modern technology, flexible and wearable electronics, particularly electromechanical sensors, have gained considerable attention for motion‐based applications in medical health monitoring and artificial intelligence. Correspondingly, extensive efforts have been dedicated to enhancing their performance and practicality. Electromechanical sensors based on functional composites and structures accurately detect and monitor the body, generating the corresponding output signals. However, there are several limitations in manufacturing composite sensors, such as the selection and combination of functional materials, geometrical structures, constructed conductive pathways, stability of output signals, and operational lifetime. Thus, this review summarizes notable trends in the electromechanical sensors using functional composites and structures. This also provides an overview of different types of electromechanical pressure and strain sensors, exploring their operational mechanisms regarding triboelectricity, piezoelectricity, piezocapacitance, and piezoresistivity. The unique characteristics of functional materials, including conductive polymers, nanostructured metals, and carbon nanomaterials and composites, are analyzed alongside various design concepts for highly flexible and stretchable sensors. Furthermore, potential applications concerning human motion and human–machine interfaces are also recommended. Additionally, several future outlooks are reviewed for insights into future prospects and strategies. Thus, this review can assist readers in understanding current electromechanical devices more accurately. This review summarizes recent trends of electromechanical sensors designed from functional composites and structures, in which the scientific keys and the unique characteristics of functional materials are analyzed alongside various design concepts for highly flexible and stretchable sensors. Concomitantly, potential applications and several outlooks concerning human motion and human–machine interfaces are also recommended for future prospects and strategies.

PurposeTo provide better services to customers, especially immediate responses and 24/7 availability, businesses are implementing text-based automated conversational agents, i.e. chatbots on their social platforms and websites. Chatbots are required to not only provide customers with necessary consultancy and guidance but also communicate friendly and socially. Based on the cognitive fit theory, this study attempts to examine the role of chatbot as a decision aid and how the match between information presentation in forms of decisional guidance and communication style and the shopping task influences consumers' perceived cognitive fit and decision performance outcomes.Design/methodology/approachA 2 x 2 x 2 between subject online experiment was conducted to identify which kind of decisional guidance (suggestive and informative guidance) and communication style (task-oriented vs social-oriented style) are the most appropriate for each type of shopping task (searching vs browsing task).FindingsThe findings show that when customers interact with chatbots, they will perceive higher cognitive fit if the chatbots provide them with suggestive guidance and communicate in a friendly style especially when they perform a searching task.Originality/valueThis study is the first attempt to understand the role of chatbots as a decision aid to customers using the communicative language. This study also tries to explore the cognitive fit theory in a novel way, and we propose the information presentation in forms of communicative language rather than matrices, tables and graphs.