Psychiatric disorders, including depression and schizophrenia, affect millions of individuals worldwide. However, the precise neurobiology of psychiatric disorders remains unclear. Accumulating ...evidence suggests that various inflammatory processes play a key role in depression and schizophrenia, and that anti-inflammatory drugs exert a therapeutic effect in patients with psychiatric disorders. Epoxyeicosatrienoic acids (EETs) and epoxydocosapentaenoic acids (EDPs) have potent anti-inflammatory properties. These mediators are broken down into their corresponding diols by soluble epoxide hydrolase (sEH), and inhibition of sEH enhances the anti-inflammatory effects of EETs. Therefore, sEH may play a key role in inflammation, which is involved in psychiatric disorders. Recent studies have shown that abnormal levels of sEH may be involved in the pathogenesis of certain psychiatric diseases, and that sEH inhibitors exhibit antidepressant and antipsychotic activity. The present review discusses the extensive evidence supporting sEH as a therapeutic target for psychiatric diseases, and the clinical value of sEH inhibitors as therapeutic or prophylactic drugs.
Concerning the large amount of energy consumption during the cluster head selection stage and the unequal harvested energy among nodes in energy-harvesting wireless sensor networks (EH-WSNs), an ...energy- efficient cluster head selection scheme called EECHS is proposed in this paper. The scheme divides all nodes from one cluster into three types: cluster head (CH), cluster member (CM), and scheduling node (SN). The SN is designed to monitor and store real-time information about the residual energy of all nodes, including CMs and the CH, in the same cluster. In the CH selection stage, the SN specifies a corresponding CM as the new CH according to the monitored results, thereby reducing the energy consumption caused by CH selection. In this way, the task of CH selection is migrated from CHs to SNs and, thus, the CHs can preserve more energy for data forwarding. Moreover, the EECHS adjusts the transmission radius of some nodes dynamically to prevent these nodes from discarding the harvested energy if their batteries are fully charged. A series of experiments were conducted to verify the effectiveness of the proposed EECHS, and the results demonstrate that EECHS can provide an efficient CH selection scheme for EH-WSNs and is able to use the harvested energy more efficiently than corresponding competitors.
Carbon tax represents governments' approach to steering the economy towards a greener future. Our research question focuses on the impact of carbon tax policy on a firm's production decision and ...revenue and on total social welfare. In particular, we assume that a firm's decision is subject to its behavioural considerations or, in other words, its risk attitude. We model a government plan to charge a carbon tax, and a risk-averse (or risk-neutral) firm needs to plan its production and estimate its profit if the carbon tax policy is implemented. We show that it is possible for a risk-averse firm's optimal profit, in some cases, to be higher than that of a risk-neutral firm when facing a carbon tax. This implies that the risk-averse attitude is not necessarily harmful to the firm's profit. For operations management scholars, our model highlights the importance of integrating firms' behavioural responses into the carbon tax price. Our model suggests that for governments to implement carbon tax policy effectively, they should make carbon prices vary under certain conditions and not worry about price variation antagonising firms at large.
We consider a small retailer that manages its inventory under a strict cash constraint. At each period, the retailer's available cash restricts the maximum inventory level that it can replenish. ...These retailers are nanostores, which are widely present in emerging markets, or small online retailers in some Chinese e‐commerce platforms. We assume that the retailer can decide the satisfied demand quantity even if it has enough inventory, but unsatisfied demand in one period may cause customer demand to decrease in the next period considering the loss of goodwill. The retailer could adopt a credit‐based loan provided by its suppliers or e‐commerce platforms to alleviate cash shortages. A profit maximization single‐item lot‐sizing model is constructed for this problem. Numerical tests demonstrate that cash‐flow constraint is different from traditional capacity constraint, and that cash availability, as well as loan interest rate, can substantially affect a retailer's optimal lot‐sizing decisions. Our model can help a retailer decide at the beginning of the planning horizon whether it is worth applying for a credit‐based loan with a given loan policy. We prove that the zero‐inventory‐ordering property still holds for certain situations, and propose a polynomial algorithm with heuristic adjustments to solve this problem when customers' payment delay length can be neglected. If unit variable ordering costs are equal and the loss of goodwill rate is zero, the algorithm can obtain optimal solutions. Under other situations, comparisons with CPLEX 12.6.2 show that our algorithm can reach optimal performance in most cases, and it has a computation time advantage for large‐sized problems.
•Using CVaR criterion to consider a risk-averse firm under cap-and-trade regulation.•A risk-averse firm’s order quantity and emission reduction level are investigated.•The impacts of a firm's risk ...aversion and investment coefficient on optimal decisions are explored.•The impacts of risk aversion on firm's investment coefficient and government's regulation parameters are analyzed.•The ranges of the investment coefficient and government’s carbon credit trading price are given.
With the implementation of carbon regulations, firmes are facing pressure on emission reduction and need to consider investing in clean equipment and technology to reduce carbon emissions. However, the resulting uncertainty poses risks to operation management, and decision-makers have different risk preferences. This paper considers a risk-averse firm confronted with cap-and-trade regulation and uncertain market demand. The customers are assumed to be environment-friendly, and thus the demand is influenced by carbon emissions. The joint decisions on order quantity and emission reduction levels are investigated under the conditional value-at-risk (CVaR) framework. This research formulates a joint decision model based on the CVaR criterion and explores how a firm’s risk aversion and investment coefficient influence optimal decisions. The findings show that if the investment coefficient of carbon emission reduction is sufficiently small, firms will not trade any carbon credits. This implies that for the effectiveness of the carbon market, the government should continue decreasing the carbon quota with the development of clean technologies. Additionally, the cap-and-trade regulation encourages firms to invest in carbon emission reduction and does not necessarily deplete the risk-averse firm's profit. Particularly, it is found that in the higher trading price of carbon, the optimal emission reduction level is more sensitive to risk aversion than to the order quantity.
Schematic diagram of fisetin in LPS-induced septic AKI.
Fisetin modulated the activities of TLR4/Src-mediated NF-κB p65 and MAPK pathways, thus alleviating kidney inflammation and apoptosis in ...LPS-induced septic AKI.
Display omitted
•Fisetin is a polyphenolic flavonoid in many fruits and vegetables.•Fisetin alleviated kidney injury against LPS-induced septic AKI.•Fisetin inhibited LPS-induced kidney inflammation and apoptosis.•Fisetin inhibited renal Src-mediated NF-κB and MAPK signalling pathways in LPS-induced septic AKI.
Sepsis is defined as end-organ dysfunction resulting from the host’s inflammatory response to infection. One of the most common sepsis-injured organs is the kidneys, resulting in acute kidney injury (AKI) that contributes to the high morbidity and mortality, especially patients in the intensive care unit. Fisetin, a naturally occurring flavonoid, has been reported to protect against the rat of lipopolysaccharide (LPS)-induced acute lung injury. However, the effect of fisetin on septic AKI remains unknown.
The current study proposed to systematically investigate the renoprotective effects and the underlying mechanisms of fisetin in septic AKI mice.
The model of septic AKI was established on male C57BL/6 J mice by a single intraperitoneal injection of LPS (10 mg/kg). Fisetin was administrated by gavage at 100 mg/kg for 3 consecutive days before LPS injection and the mice were sacrificed at 16 h after LPS injection. The serum and kidney samples were evaluated for biochemical analysis, histopathological examinations as well as inflammation and apoptosis related gene/protein expression.
Pretreatment with fisetin significantly alleviated the elevated levels of serum creatinine and blood urea nitrogen in LPS-treated mice. Consistently, LPS induced renal damage as implied by histopathological score and the increased injury markers NGAL and KIM-1, which was attenuated by fisetin. Meanwhile, LPS injection triggered proinflammatory cytokine production and inflammation related proteins in the kidneys. However, fisetin inhibited renal expression of IL-6, IL-1β, TNF-α, HMGB1, iNOS and COX-2 to improve inflammatory response. Furthermore, fisetin effectively reduced the number of TUNEL positive apoptotic cells and suppressed apoptotic protein of Bcl-2, BAX and cleaved caspase-3 in the kidneys of LPS-induced septic AKI. Mechanistically, LPS stimulated the expression of TLR4 and the phosphorylation of NF-κB p65, MAPK (p38, ERK1/2 and JNK), Src and AKT in the injured kidneys, while fisetin notably suppressed the corresponding protein expression.
Fisetin alleviated kidney inflammation and apoptosis to protect against LPS-induced septic AKI mice via inhibiting Src-mediated NF-κB p65 and MAPK signaling pathways
Limited data are available on metabolic responses of plants to copper (Cu)-toxicity. Firstly, we investigated Cu-toxic effects on metabolomics, the levels of free amino acids, NH4+-N, NO3--N, total ...nitrogen, total soluble proteins, total phenolics, lignin, reduced glutathione (GSH) and malondialdehyde, and the activities of nitrogen-assimilatory enzymes in ‘Shatian’ pummelo (Citrus grandis) leaves. Then, a conjoint analysis of metabolomics, physiology and transcriptomics was performed. Herein, 59 upregulated 30 primary metabolites (PMs) and 29 secondary metabolites (SMs) and 52 downregulated (31 PMs and 21 SMs) metabolites were identified in Cu-toxic leaves. The toxicity of Cu to leaves was related to the Cu-induced accumulation of NH4+ and decrease of nitrogen assimilation. Metabolomics combined with physiology and transcriptomics revealed some adaptive responses of C. grandis leaves to Cu-toxicity, including (a) enhancing tryptophan metabolism and the levels of some amino acids and derivatives (tryptophan, phenylalanine, 5-hydroxy-l-tryptophan, 5-oxoproline and GSH); (b) increasing the accumulation of carbohydrates and alcohols and upregulating tricarboxylic acid cycle and the levels of some organic acids and derivatives (chlorogenic acid, quinic acid, d-tartaric acid and gallic acid o-hexoside); (c) reducing phospholipid (lysophosphatidylcholine and lysophosphatidylethanolamine) levels, increasing non-phosphate containing lipid monoacylglycerol ester (acyl 18:2) isomer 1 levels, and inducing low-phosphate-responsive gene expression; and (d) triggering the biosynthesis of some chelators (total phenolics, lignin, l-trytamine, indole, eriodictyol C-hexoside, quercetin 5-O-malonylhexosyl-hexoside, N-caffeoyl agmatine, N′-p-coumaroyl agmatine, hydroxy-methoxycinnamate and protocatechuic acid o-glucoside) and vitamins and derivatives (nicotinic acid-hexoside, B1 and methyl nicotinate). Cu-induced upregulation of many antioxidants could not protect Cu-toxic leaves from oxidative damage. To conclude, our findings corroborated the hypothesis that extensive reprogramming of metabolites was carried out in Cu-toxic C. grandis leaves in order to cope with Cu-toxicity.
Display omitted
•Citrus leaves underwent extensive metabolic reprograming under Cu-toxicity.•Cu impaired N assimilation in leaves, thus lowering Cu-tolerance.•Cu increased carbohydrates and alcohols levels and upregulated TCA cycle in leaves.•Cu triggered the biosynthesis of some chelators, vitamins and derivatives in leaves.•MDA accumulated in Cu-toxic leaves despite increased levels of some antioxidants.
•New model of storage location assignment (SLA) considering demand association pattern.•Demand amount is considered in SLA based on support count of itemset.•A heuristic and a simulated annealing ...algorithm are developed for the solution.•The new SLA model outperforms existing methods in terms of travel distance.
Order-picking is the most time- and labor-consuming operation in a warehouse and significantly influences supply chain efficiency. One of the basic methods for improving order-picking efficiency involves assigning storage locations to appropriate items, i.e., the storage location assignment problem (SLAP). In existing studies, most storage assignment methods only consider the properties of individual item rather than the item groups that are usually collectively required. This paper introduces the concept of the demand correlation pattern (DCP) to describe the correlation among items, based on which a new model is constructed to address the SLAP. The model is subsequently reduced using the S-shape routing strategy, and a method for determining DCPs from historical data is proposed. To solve the model, a heuristic and a simulated annealing method are developed. The proposed methods are examined and compared extant methods using both real data collected from an online retailer and numerical instances that are randomly generated. The computational results are discussed.
Navigation accuracy of an inertial navigation system can be significantly enhanced by rotating inertial measurement unit with gimbals. Therefore, nonorthogonal angles of gimbals, which are coupled ...into the navigation error during rotation, should be calibrated and compensated effectively. In this paper, the relationship model of nonorthogonal angles and navigation error is established. Then, the calibration scheme and observation equation during gimbals rotation is proposed. Proved by a piecewise constant system method, all of the error parameters are observable and can be estimated by an extended Kalman filter. Experimental results show that compared with the traditional method, the proposed method can substantially reduce velocity error on static base. Moreover, the position accuracy of long-term navigation under moving base is also significantly increased.
•Order-picking efficiency is improved via efficient storage assignment.•New concept is introduced for better assignment by exploiting order characteristics.•A data-based approach (DBA) is developed ...for the solution.•DBA outputs competitive results on real data with short running time.•DBA is extended to the case of high-level warehouses.
Fast development of online retail industry requires customer orders to be fulfilled within tight windows, where order-picking, the most time-consuming and labor-intensive activity in warehouses, plays an important role. One of the basic ways to improve order-picking operation is assigning storage locations to appropriate items. The storage location assignment problem is in general NP-hard and is mainly solved by heuristics which usually suffer from limited solution quality or high computational effort, especially for large scale problems. In literature, most studies make the storage assignment decisions according to item properties, such as turnover or correlation, which are statistically extracted from item orders. These storage methods follow a data→concept→assign decision mechanism and may ignore useful data characteristics that are not conceptualized. This paper presents a new approach to improve the order-picking operation, which directly uses item orders to make the decisions without any statistical treatments, i.e., following a data→assign mechanism. The concept of good move pair is introduced to quickly find a better assignment through directly exploiting data characteristics of item orders, and an iterative algorithm is developed to minimize the total travel distance. We evaluate the algorithm on real data and numerical instances, and compare its performance with extant methods in the literature. The results show that the proposed method significantly outperforms other methods in most cases. We also extend the algorithm to the case of high-level warehouses and examine its effectiveness.
PDF
