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We are concerned about a multi-period portfolio selection problem where the issue of parameter uncertainty for the distribution of risky asset returns should be addressed properly. For analysis, we first propose a novel dynamic portfolio selection model with an l∞\documentclass[12pt]{minimal}\usepackage{amsmath}\usepackage{wasysym}\usepackage{amsfonts}\usepackage{amssymb}\usepackage{amsbsy}\usepackage{mathrsfs}\usepackage{upgreek}\setlength{\oddsidemargin}{-69pt}\begin{document}$$l_{\infty }$$\end{document} risk function, instead of the classic portfolio variance, used as risk measure. The investor in our model is assumed to choose the optimal portfolio by maximizing the expected terminal wealth at a minimum level of cumulative risk, quantified by a weighted sum of the risks in subsequent periods. The proposed multi-period model has a closed-form optimal policy that can be constructed and interpreted intuitively. We introduce Bayesian learning to account for the uncertainty in estimates of unknown parameters and discuss the impact of Bayesian learning on the investor’s decision making. Under an i.i.d. normal return-generating process with unknown means and covariance matrix, we show how Bayesian learning promotes diversification and reduces sensitivity of optimal portfolios to changes in model inputs. The numerical results based on real market data further support that the model with Bayesian learning can perform much better than a plug-in model out-of-sample with the extent of performance improvement affected by the investor’s level of risk aversion and the amount of data available. [ABSTRACT FROM AUTHOR]

We study the problem of collaborative less-than-truckload (LTL) transportation in the form of a shipper consortium, which is operated by a third-party logistics provider (3PL) through a cross-dock/pooling network. The 3PL has responsibility for planning the combined loads prior to actual shipments, hiring and routing carriers to execute shipping, and allocating the cost to the shippers in the consortium. Shippers receive substantial cost savings from combined truckload shipments. However, achieving consolidation and realizing this benefit requires addressing two essential issues: (i) how to find an approximately optimal consolidation solution in a large network with many freights and (ii) how to determine a fair cost allocation rule among the shippers' consolidated freights that ensures budget balance while minimally violating coalitional stability. Our work resolves these two issues. We formulate a time-space network flow model of the problem under both incremental and all-unit discount structures of LTL rates and propose a computationally efficient algorithm based on local search heuristics. We model the problem of allocating cost to the shippers as a cooperative game. We decompose and linearize the Lagrangian dual problem by using total unimodularity and concavity. We propose an efficiently computable cost allocation rule from the linearized dual models. The dual rule ensures stable cooperation but may have underallocation equal to a duality gap. To cover the underallocation, we further develop a budget covering procedure and define an ϵ -core allocation with desirable properties. Through extensive computational experiments, we find that the shipper consortium reduces total shipping costs by more than 40% in most cases; meanwhile, the ϵ -core allocation is typically in the core for small-scale networks while violating stability by at most 5% for large-scale networks and provides consolidated freights with more than 50% individual cost savings on average. [ABSTRACT FROM AUTHOR]

This article examines a real-time parking reservation service that dynamically allocates a limited number of parking slots (which can be reused) to serve parking requests in a crowded area. The study takes into account two stochastic factors: (i) random arrivals of parking requests and (ii) driver parking unpunctuality, which are seldom simultaneously considered in the existing literature. This study formulates the dynamic parking resource allocation problem as a joint chance-constrained model to maximize the expected total revenue over a finite horizon. Due to the complex nature of the model considering the two stochastic factors, the study proposes a data-driven approach using joint chance constraint decomposition and sample average approximation to approximate the model to a deterministic mixed-integer programming model. Furthermore, a stratified ranked set sampling method is introduced to construct a high-quality sample set as the input to the deterministic model, and valid inequalities are designed to accelerate the solution process. Numerical experiments based on real-world data are conducted to validate the performance of the proposed approach under multiple parking scenarios and provide findings. [ABSTRACT FROM AUTHOR]

In this article, we discuss the optimal scheduling problem of the recently introduced model for partial loss due to machine breakdowns, which fills up a significant gap in the existing literature. More specifically, we consider the problem of processing a number of jobs with arbitrary random processing times by a machine subject to general stochastic breakdowns, where each breakdown may cause an uncertain loss of the work achieved on the job being processed. The objective is to maximize the expected weighted discounted reward of completing the jobs in the class of unrestricted dynamic policies. We obtain the optimal dynamic polices using multi-armed bandit process methodology, which are characterized by a set of Gittins indices as solutions to a system of integral equations. Optimal solutions for a number of problems with specific loss patterns are derived. Application of the theory to the classical no-loss model is also discussed which leads to new results. [ABSTRACT FROM AUTHOR]

While carriers can collaborate to reduce empty traveling miles in truckload shipping, this is difficult to realize because each party is self-interested who may not share his private information that is necessary for the cooperation. In this paper, we propose an iterative auction scheme, which enables carriers to collaborate by exchanging their shipping requests iteratively. The auction is shown to be incentive-compatible, individually rational, budget balanced, monotonic, and convergent. Computational experiments indicate that it has small efficiency loss and can significantly improve carriers’ profits. We also develop two acceleration methods and extend the auction to more general problems. [ABSTRACT FROM AUTHOR]

This paper considers firms making collaborative production-distribution planning with shipment consolidation to reduce costs. However, the firms have private cost information which they are not willing to disclosure. We develop a computable mechanism based on a decentralized local search heuristic combined with simulated annealing, which allows for not only system optimization but also cost allocation. The mechanism is especially applicable to the firms with private cost information, due to its good incentive properties and budget balance in almost all the cases. Computational experiments indicate that the cost savings are significant for both the system and the individual firm. [ABSTRACT FROM AUTHOR]

A version of the dynamic lot-sizing (DLS) problem involving durable products with end-of-use constraints is analyzed in this paper. First, we mathematically formulate this problem, then certain properties are derived to construct the structure of the optimal solution. Next, based on these properties, a recursive optimization algorithm is proposed for a single-item problem. Moreover, an approximate algorithm is designed on the basis of the optimization algorithm, with linear computational complexity. A heuristic approach is proposed for solving the two-item DLS problem. The difficulty in solving this problem lies in its decomposition into item-level subproblems while ensuring the feasibility of the solution. The proposed technique aims to resolve this issue by combining the capabilities of Lagrangian relaxation to decompose the problem into smaller subproblems, and a genetic algorithm (GA) is used to update the Lagrangian multipliers. Further, the computational results obtained using the proposed approach are enumerated to demonstrate its effectiveness. Finally, the conclusion and remarks are given to discuss the possible future works. [ABSTRACT FROM AUTHOR]

AbstractWith the recently emerged digital platforms in logistics, shippers can easily collaborate by bundling their heavy less-than-truckload orders via multi-stop truckload shipping to reduce transportation cost. The platform has responsibility for planning the shipping routes, for bundling the orders and fairly allocating the cost to shippers. To address this challenging problem in practice, we propose a new cooperative game based on a variant of a pickup and delivery model with soft time windows for shipper collaboration. The centralized optimization model is NP-hard and the core of the game may be empty. We adopt the least-core concept and simplify the core stability constraints as route-wise conditions. Based on theoretical results, we propose an innovative route-generation joint searching algorithm that iteratively solves the centralized optimization and least-core allocation problems at the same time, where the route-generation subproblem is solved by a customized multi-start local search subroutine. Extensive computational experiments on a real-world case demonstrate that the proposed algorithm can quickly generate a near-optimal solution with minor optimality gap and a least-core allocation with small stability deviation. With our algorithm, the shippers also receive substantial cost savings from collaboration. [ABSTRACT FROM AUTHOR]

We study cost allocation problem arising from less-than-truckload collaboration among perishable product retailers. The relevant costs we consider include fixed transportation cost, variable transportation cost, and decay loss of perishable products. Cooperative game theory is applied to study this cost allocation problem. The corresponding cooperative game, called transportation facility choice game, is established. First, we show that the core of the transportation facility choice game is non-empty. Then, we identify some conditions for concavity and quasi-concavity of the transportation facility choice game with the linear decay and negative exponential decay functions, respectively. Finally, simulation is conducted to analyze how optimal solutions differ under the linear decay and exponential decay functions, and intuitive cost allocation schemes are proposed and compared with the $$\tau $$ -value and the Shapley value of the corresponding game. Simulation results show that the optimal solution under linear decay function tends to choose facilities with higher fixed cost than that under exponential decay function. Additionally, among all the cost allocation schemes compared, the simple cost allocation scheme called A-IM, the $$\tau $$ -value, and the Shapley value have better performance in terms of the percentage of allocations lying in the core. [ABSTRACT FROM AUTHOR]

Interval-valued intuitionistic preference relation (IV-IPR) is a new type of preference structure used to describe uncertain evaluation information in decision making process. In this paper, we first define the concept of IV-IPR with multiplicative transitivity, and then give a procedure to construct an IV-IPR with multiplicative transitivity from an incomplete IV-IPR with only known off-diagonal elements. We also extend this procedure to deal with more general cases with much more known evaluation information. Moreover, we develop an approach to group decision making with incomplete IV-IPRs, and finally give an actual application of our approach and compare it with the other approach in the existing literature. [ABSTRACT FROM AUTHOR]

Blockchain technology has attracted global attention for its ability to protect data security and traceability. It has driven the development of practical applications in various fields, namely management science, transport and logistics, and supply chain management. Initial coin offerings (ICOs), an emerging start-up fundraising method developed with blockchain technology, are now popular among start-ups that issue initial tokens as the sole exchange medium for their planned products. In addition to the public offering stage and market stage, the pre-ICO stage is an important phase for many ICO projects. In this paper, we establish an initial coin offering (ICO) model with network effects, a common phenomenon in ICOs, to study the effect of institutional investors' participation in the pre-ICO stage of offerings made by cash-strapped entrepreneurs. We find that introducing institutional investors significantly increases the equilibrium price of tokens during the public offering stage as well as the price fluctuation of tokens over time. Meanwhile, entrepreneurs will over-issue tokens when the intensity of network externality is relatively high. Interestingly, when institutional investors' required rate of return decreases, the change in the optimal issuance is not monotonic. Under some circumstances, the lower the rate of return required by institutional investors is, the less the optimal issuance to them is, even if the network externality is high. A higher issuance in the pre-ICO stage is thus a mixed blessing that can increase the price during public offerings and reduce the equilibrium price in other stages. The negative impact is even greater when network externality is high. • The optimal issuance policy for the start-ups is proposed for ICOs with a pre-ICO stage. • The pre-ICO stages significantly improve the project feasibility and profitability. • Higher issuance in the pre-ICO stage only raises the price during public offerings. • Introducing institutional investors in ICO projects increases the price fluctuation. [ABSTRACT FROM AUTHOR]

In this article, we investigate group decision making problems with interval multiplicative preference relations (including complete interval multiplicative preference relations and incomplete interval multiplicative preference relations). On the basis of the number of judgments and the consistency degree of each interval multiplicative preference relation, we first give a combined weighting method to derive the weights of decision makers. Then, we establish two linear programming models to derive the weight intervals of alternatives from all individual consistent interval multiplicative preference relations and utilize the continuous ordered weighted averaging operator or the continuous ordered weighted geometric operator to aggregate all the values in each weight interval. In addition, we establish a more general model to check the consistency of all individual interval multiplicative preference relations. In the cases where the optimal objective value of the model is not zero, we can get the optimal weights of alternatives directly, and then utilize these optimal weights and the optimal deviation values derived from the model to construct consistent interval multiplicative preference relations. Furthermore, we discuss some special cases of the established models and illustrate our models with a practical example. [ABSTRACT FROM AUTHOR]

Abstract: A chemodosimeter-based fluorescent detection method coupled with molecularly imprinted polymers (MIPs) extraction was developed for determination of l-cysteine (l-Cys) by combining molecular imprinting technique with fluorescent chemodosimeter. The MIPs prepared by precipitation polymerization with l-Cys as template, possessed high specific surface area of 145m2/g and good thermal stability without decomposition lower than 300°C, and were successfully applied as an adsorbent with excellent selectivity for l-Cys over other amino acids, and enantioselectivity was also demonstrated. A novel chemodosimeter, rhodamine B1, was synthesized for discriminating l-Cys from its structurally similar homocysteine and glutathione as well as various possibly co-existing biospecies in aqueous solutions with notable fluorescence enhancement when adding l-Cys. As l-Cys was added with increasing concentrations, an emission band peaked at 580nm occurred and significantly increased in fluorescence intensity, by which the l-Cys could be sensed optically. High detectability up to 12.5nM was obtained. An excellent linearity was found within the wide range of 0.05–50μM (r=0.9996), and reasonable relative standard deviations ranging from 0.3% to 3.5% were attained. Such typical features as high selectivity, high sensitivity, easy operation and low cost enabled this MIPs-fluorometry to be potentially applicable for routine detection of trace l-Cys. [Copyright &y& Elsevier]

Uncertainty is a common phenomenon in our real world. Interval utility values and interval preference orderings are two of the simplest and most convenient tools to describe uncertain preferences in decision making. In this paper, we investigate consensus problems in group decision making with interval utility values and interval preference orderings. We first establish their transformation relations, and give a formula for calculating the association coefficients of individual uncertain preferences and group ones. We then develop a consensus procedure for group decision making with interval utility values and interval preference orderings, which takes interval utility values as the uniform preference representation. This procedure can be reduced to a series of processes for dealing with some special group decision making situations, such as: group decision making with utility values and preference orderings, group decision making with interval utility values, group decision making with interval preference orderings, etc. Finally, we illustrate the applications of the developed procedures with two practical examples. [ABSTRACT FROM AUTHOR]

This paper focuses on the problem of how to determine expert weights in multiple attribute group decision making. We first aggregate all the individual decision matrices into the collective decision matrix by means of the weighted arithmetic averaging operator, and then from the angle of minimizing group discordance, we establish a general nonlinear optimization model based on deviation function, and employ a genetic algorithm to solve our model so as to find the optimal expert weights. Moreover, we extend our model to uncertain multiple attribute group decision making, where the attribute values are interval numbers, and finally, apply our model to the plan evaluation of new model of cars of an investment company. [ABSTRACT FROM AUTHOR]

In this paper, we investigate group decision making problems based on interval fuzzy preference relations. We define an uncertain power weighted average (UPWA) operator and an uncertain power ordered weighted average (UPOWA) operator, on the basis of the power average operator of Yager (IEEE Trans Syst Man Cybern A 31:724-731, 1988) and the uncertain geometric mean. In the situations where the weights of experts are known, we develop a method based on the UPWA operator for group decision making with interval fuzzy preference relations; and in the situations where the weights of experts are unknown, we develop a method based on the UPOWA operator for group decision making with interval fuzzy preference relations. [ABSTRACT FROM AUTHOR]

Abstract: During early embryogenesis, the formation of the cardiac atrioventricular canal (AVC) facilitates the transition of the heart from a linear tube into a chambered organ. However, the genetic pathways underlying this developmental process are poorly understood. The T-box transcription factor Tbx20 is expressed predominantly in the AVC of early heart tube. It was shown that Tbx20 activates Nmyc1 and suppresses Tbx2 expression to promote proliferation and specification of the atrial and ventricular chambers, yet it is not known if Tbx20 is involved in early AVC development. Here, we report that mice lacking Tbx20 in the AVC myocardium fail to form the AVC constriction, and the endocardial epithelial–mesenchymal transition (EMT) is severely perturbed. Tbx20 maintains expression of a variety of genes, including Bmp2, Tbx3 and Hand1 in the AVC myocardium. Intriguingly, we found Bmp2 downstream genes involved in the EMT initiation are also downregulated. In addition, re-expression of Bmp2 in the AVC myocardium substantially rescues the EMT defects resulting from the lack of Tbx20, suggesting Bmp2 is one of the key downstream targets of Tbx20 in AVC development. Our data support a complex signaling network with Tbx20 suppressing Tbx2 in the AVC myocardium but also indirectly promoting Tbx2 expression through Bmp2. The spatiotemporal expression of Tbx2 in the AVC appears to be balanced between these two opposing signals. Overall, our study provides genetic evidence that Tbx20 has essential roles in regulating AVC development that coordinate early cardiac chamber formation. [Copyright &y& Elsevier]

In this correspondence paper, we consider a large group decision-making problem in which the decision makers provide their preferences over a number of alternatives using distinct preference structures, including the following: 1) utility values; 2) preference orderings; 3) multiplicative preference relations; 4) incomplete multiplicative preference relations; 5) fuzzy preference relations; and 6) incomplete fuzzy preference relations. We establish a nonlinear programming model to integrate all of these preference structures and then employ a genetic algorithm to find the solution to the problem. This model can be reduced to a variety of special models suitable for group decision-making situations in which the preferences are represented in one (or several) of the aforementioned preference structures. Finally, detailed numerical analysis is provided to verify the feasibility and effectiveness of the model. [ABSTRACT FROM PUBLISHER]

The aim of this paper is to investigate decision making problems with interval-valued intuitionistic fuzzy preference information, in which the preferences provided by the decision maker over alternatives are incomplete or uncertain. We define some new preference relations, including additive consistent incomplete interval-valued intuitionistic fuzzy preference relation, multiplicative consistent incomplete interval-valued intuitionistic fuzzy preference relation and acceptable incomplete interval-valued intuitionistic fuzzy preference relation. Based on the arithmetic average and the geometric mean, respectively, we give two procedures for extending the acceptable incomplete interval-valued intuitionistic fuzzy preference relations to the complete interval-valued intuitionistic fuzzy preference relations. Then, by using the interval-valued intuitionistic fuzzy averaging operator or the interval-valued intuitionistic fuzzy geometric operator, an approach is given to decision making based on the incomplete interval-valued intuitionistic fuzzy preference relation, and the developed approach is applied to a practical problem. It is worth pointing out that if the interval-valued intuitionistic fuzzy preference relation is reduced to the real-valued intuitionistic fuzzy preference relation, then all the above results are also reduced to the counterparts, which can be applied to solve the decision making problems with incomplete intuitionistic fuzzy preference information. [ABSTRACT FROM AUTHOR]

We consider a variant of the resource constrained scheduling problem, which originated from a machine scheduling problem. A number of jobs are to be processed on a single machine with a prespecified capacity. Each job has a size and a processing time, and the machine can process a set of jobs simultaneously as long as the total size of the jobs being processed does not exceed the capacity of the machine. The objective is to find a schedule to minimize the total time (i.e., the makespan) to complete all jobs, subject to the machine capacity constraint. For the problem where the machine is completely available at the starting time, we analyze a previously published algorithm and prove that the worst-case ratio of this algorithm is no more than 7/4, which significantly improves the previous result and thus answers an open question about this algorithm. For the problem where the machine is partially available at the beginning, an algorithm is shown to have a worst-case ratio of no more than two. An on-line algorithm is also presented. [ABSTRACT FROM AUTHOR]

• New and realistic operation model for shared parking based on multi-unit exchange. • Novel application of trading network mechanism to market design for shared parking. • A rebalance procedure for exchange with uncertain cancelations over time. • Computational experiments with a real-world case and interesting managerial insights. Shared parking is recognized as a promising solution to tackle the parking space shortage problem in population-dense cities. However, peer-to-peer shared parking by individuals is not easy to implement in such cities, because it requires the consent of the majority of residents and the facilitation of the property management agency. In this paper we propose a new model for shared parking driven by morning commute. We consider the agencies of residential parking lots as the players to make collective decisions on behalf of the property owners, and study the market design problem for this new shared parking model. Specifically, we investigate how to assign to each agency a quota of parking spaces from every other agency that its commuters can book in advance through an e-platform and how to specify transfer prices for exchanges, with the objective to maximize agencies' welfare. Our problem is a new discrete many-to-many exchange economy with multi-unit valuation. We design a stable exchange scheme based on the trading network theory, and show that this new scheme is efficient and in the core. In the real environment with random cancelations over time, we further develop a rolling-horizon rebalance procedure that minimizes the welfare loss. Computational experiments using real-world data demonstrate that our stable exchange scheme substantially outperforms the commonly adopted solutions based on the first-come-first-serve protocol and also reveal a variety of interesting insights for practice. [ABSTRACT FROM AUTHOR]

Background: Nicolaides–Baraitser syndrome (NCBRS) is a severe neurodevelopmental disorder with multiple abnormalities. To date, all pathogenic variants in SMARCA2 causing NCBRS are de novo and most are missense variants located in the ATPase domain of SMARCA2 protein. Methods: In this study, a familial trio whole‐exome sequencing was performed on the proband presenting with intellectual disability, early‐onset epilepsy, and autistic features. A novel missense variant c.553C>G (p.Gln185Glu) in SMARCA2 was identified, which is located in the QLQ domain. The same variant was subsequently also found in the mother's ongoing pregnancy. Samples from accessible tissues such as saliva and sperm other than blood were collected from the parents, and the detection of the target variant was performed by amplicon‐based deep sequencing. Results: Low‐level mosaicism of the target variant c.553C>G (p.Gln185Glu) was detected in the father's sperm with allele fraction of 2.8% by amplicon‐based deep sequencing, which was not detected in either parents' blood or saliva specimens. Heterozygosity of this variant was confirmed in the proband. Conclusion: This is the first report of paternal germline mosaicism for a SMARCA2 disease‐causing variant. In addition, the missense variant c.553C>G (p.Gln185Glu) in the QLQ domain causes mainly neurological and developmental phenotypes with unremarkable characteristic facial features and limb abnormalities. Our findings expand the phenotypic spectrum and mode of genetic transmission associated with the SMARCA2 variants. [ABSTRACT FROM AUTHOR]

Abstract: We are concerned with a problem where a manufacturer has to complete a number of orders received from the customers. Due to the capacity limit, the manufacturer has to subcontract a certain set of jobs to a third party for processing. We investigate the model that involves two machines, with one being a machine owned by the manufacturer and the other by the third party. The available time slot (i.e., the shift) on the third-party machine each day is extendable, at an extra cost. Transportation time to have a job processed by the third-party machine is also considered. The objective is to minimize the total cost, including the lateness cost, the cost to use the third-party machine, and the overtime cost. This is shown to be an integer nonlinear optimization problem, which is NP-hard. Properties on this model are obtained. Algorithms to find optimal solutions are developed. [Copyright &y& Elsevier]

The manufacturer who is a supplier of trade credit may face non-payment risk from customers and a capital shortage problem simultaneously. Trade credit insurance, as one of the most important risk management tools, has been widely used in companies' daily operation. In this study, the manufacturer who allows customers to delay payment for goods already delivered purchases trade credit insurance to transfer and reduce non-payment risk and borrows money from a bank to accommodate the capital constraint problem. The Stackelberg game and loss-averse theory are used to establish a newsboy model including trade credit insurance, and the optimal insurance coverage and total sales of the manufacturer are thereby investigated. Subsequently, the interest rate decision of the bank under different risk-averse situations is also characterized. We find that the interest rate set by a loss-averse bank is equal to or greater than that given by a risk-neutral bank. The use of trade credit insurance can help the manufacturer expand sales and dramatically reduce its default risk. Both the bank and the manufacturer are better off due to the use of trade credit insurance, but contrary to what one might expect, the bank prefers giving a higher interest rate to the manufacturer when the premium rate is in a reasonable region, which indicates that the manufacturer cannot use the insurance to negotiate better financing terms. [ABSTRACT FROM AUTHOR]

Abstract: Tbx18 has been shown to be essential for ureteral development. However, it remains unclear whether it plays a direct role in kidney development. Here we addressed this by focusing on examining the pattern and contribution of Tbx18+ cells in the kidney and its role in kidney vascular development. Expression studies and genetic lineage tracing revealed that Tbx18 is expressed in renal capsule, vascular smooth muscle cells and pericytes and glomerular mesangial cells in the kidney and that Tbx18-expressing progenitors contribute to these cell types. Examination of Tbx18 −/− kidneys revealed large reduction in vasculature density and dilation of glomerular capillary loops. While SMA+ cells were reduced in the mutant, PDGFRβ+ cells were seen in early capillary loop renal corpuscles in the mutant, but fewer than in the controls, and further development of the mesangium failed. Analysis of kidney explants cultured from E12.5 excluded the possibility that the defects observed in the mutant were caused by ureter obstruction. Reduced proliferation in glomerular tuft and increased apoptosis in perivascular mesenchyme were observed in Tbx18 −/− kidneys. Thus, our analyses have identified a novel role of Tbx18 in kidney vasculature development. [Copyright &y& Elsevier]

How cell fate decisions of stem and progenitor cells are regulated by their microenvironment or niche is a central question in stem cell and regenerative biology. Although functional analysis of hair follicle epithelial stem cells by gene targeting is well established, the molecular and genetic characterization of the dermal counterpart during embryonic morphogenesis has been lacking because of the absence of cell type-specific drivers. Here, we report that T-box transcription factor Tbx18 specifically marks dermal papilla (DP) precursor cells during embryonic hair follicle morphogenesis. With Tbx18LacZ, Tbx18H2BGFP, and Tbx18Cre knock-in mouse models, we demonstrate LacZ and H2BGFP (nuclear green fluorescent protein) expression and Cre activity in dermal condensates of nascent first-wave hair follicles at E14.5. As Tbx18 expression becomes more widespread throughout the dermis at later developmental stages, we use tamoxifen-inducible Cre-expressing mice, Tbx18MerCreMer, to exclusively target DP precursor cells and their progeny. Finally, we ablate Tbx18 in full knockout mice, but find no perturbations in hair follicle formation, suggesting that Tbx18 is dispensable for normal DP function. In summary, our study establishes Tbx18 as a genetic driver to target for the first time embryonic DP precursors for labeling, isolation, and gene ablation that will greatly enhance investigations into their molecular functions during hair follicle morphogenesis. [ABSTRACT FROM AUTHOR]

Ti–6Al–4V (TC4) and Ti–6.5Al–3.5Mo–1.5Zr–0.3Si (TC11) joints were achieved via Ti–37.5Zr–15Ni–10Cu (wt%) filler metal when the brazing temperature was in the range from 950 °C (below β-transus) to 1040 °C (above β-transus) for 10–60 min. The role of brazing parameters in the microstructure evolution as well as mechanical properties of both base alloys and brazed joints was studied. The research analysis suggested that the typical interfacial microstructure was divided into five characteristic zones including reaction phases of α-Ti, β-Ti and (Ti, Zr)2CuNi. With the holding time prolonged, the sectionalized structure transformed into lamellar Widmanstätten structure in the brazing seam at the temperature of 950 °C, and the optimized shear strength reached 616 MPa at 950 °C for 60 min. Nevertheless, when increasing the brazing temperature to 1000 °C, the joint shear strength tended to be stable as the holding time exceeded 20 min due to the elemental homogenization, and the shear strength reached 627 MPa for the holding time of 20 min. Tensile test results showed that the mechanical properties of both TC4 and TC11 alloys were dramatically degraded at the heat treatment temperature of 1000 °C owning to the drastic grain coarsening and phase transition. Additionally, the plastic strain of TC4/TC11 joint brazed at 1000 °C for 20 min was 1.66%, while that of joint brazed at 950 °C for 60 min reached 2.01%. The variation in mechanical properties of base alloys as well as brazed joints under different thermal conditions revealed that the optimized temperature for brazing of titanium alloys was lower than β-transus with a long time. [ABSTRACT FROM AUTHOR]

The digital economy has greatly impacted the traditional parking industry. This study focuses on a real-time parking reservation system that allocates available parking slots (supplies) to serve the parking requests (demands) of drivers within a certain area. The challenge lies in dealing with the randomness of requests with varying parking durations and the reusability of parking resources, which have not been well researched. To address this problem, we propose a stochastic optimization model and a data-driven two-stage approximation approach. The first stage generates a pre-allocation scheme as a basis, while the second stage dynamically adjusts the scheme based on observed requests and available parking slots to improve the system's performance throughout a finite operational horizon. The study has three main contributions. Firstly, this paper proposes a data-driven nonparametric approach based on sample average approximation (SAA) to solve the stochastic optimization model. Secondly, it develops a stratified ranked set sampling (SRSS) method to ensure the quality of the sample set and proves that the sample set sampled by the SRSS method is unbiased. Finally, an evaluation method is proposed to establish a confidence interval for the expected revenue obtained by the data-driven method. Experimental results based on real parking demand data show that our approach outperforms both a commonly used heuristic allocation policy in the parking industry and the standard SAA approach with two popular sampling methods and an evaluation method based on Student's T-test in the literature. [ABSTRACT FROM AUTHOR]

We consider a decentralized assembly system in which $$m+n$$ unreliable suppliers sell complementary components to an assembler, who faces a random demand. We assume that $$m$$ of these suppliers are more powerful than the assembler and sell components via push contracts, while the remaining $$n$$ suppliers are less powerful than the assembler and sell components via pull contracts. The supply chain operations are modelled by a three-stage game. We first characterize the equilibrium decisions of all chain members, and find that supply chain efficiency depends heavily on system parameters. We then develop a mechanism to coordinate the supply chain. The mechanism consists of two policies, including a buyback policy between push suppliers and the assembler, and a subsidy policy between the assembler and pull suppliers. We show that the mechanism can eliminate the two sources of 'double marginalization' that exist in the chain with unreliable supply and uncertain demand, and achieve Pareto improvements. [ABSTRACT FROM AUTHOR]

Proper formation of ureteral smooth muscle cells (SMCs) during embryogenesis is essential for ureter peristalsis that propels urine from the kidney to the bladder in mammals. Currently the molecular factors that regulate differentiation of ureteral mesenchymal cells into SMCs are incompletely understood. A recent study has reported that Smad4 deficiency reduces the number of ureteral SMCs. However, its precise role in the ureteral smooth muscle development remains largely unknown. Here, we used Tbx18:Cre knock-in mouse line to delete Smad4 to examine its requirement in the development of ureteral mesenchyme and SMC differentiation. We found that mice with specific deletion of Smad4 in Tbx18-expressing ureteral mesenchyme exhibited hydroureter and hydronephrosis at embryonic day (E) 16.5, and the mutant mesenchymal cells failed to differentiate into SMCs with increased apoptosis and decreased proliferation. Molecular markers for SMCs including alpha smooth muscle actin (α-SMA) and smooth muscle myosin heavy chain (SM-MHC) were absent in the mutant ureters. Moreover, disruption of Smad4 significantly reduced the expression of genes, including Sox9, Tbx18 and Myocardin associated with SMC differentiation. These findings suggest that Smad4 is essential for initiating the SMC differentiation program during ureter development. [ABSTRACT FROM AUTHOR]

Abstract: The vertebrate heart develops from mesoderm and requires inductive signals secreted from early endoderm. During embryogenesis, Nkx2.5 acts as a key transcription factor and plays essential roles for heart formation from Drosophila to human. In mice, Nkx2.5 is expressed in the early first heart field, second heart field pharyngeal mesoderm, as well as pharyngeal endodermal cells underlying the second heart field. Currently, the specific requirements for Nkx2.5 in the endoderm versus mesoderm with regard to early heart formation are incompletely understood. Here, we performed tissue-specific deletion in mice to dissect the roles of Nkx2.5 in the pharyngeal endoderm and mesoderm. We found that heart development appeared normal after endodermal deletion of Nkx2.5 whereas mesodermal deletion engendered cardiac defects almost identical to those observed on Nkx2.5 null embryos (Nkx2.5 −/− ). Furthermore, re-expression of Nkx2.5 in the mesoderm rescued Nkx2.5 −/− heart defects. Our findings reveal that Nkx2.5 in the mesoderm is essential while endodermal expression is dispensable for early heart formation in mammals. [Copyright &y& Elsevier]

Summary: How dermal papilla (DP) niche cells regulate hair follicle progenitors to control hair growth remains unclear. Using Tbx18 Cre to target embryonic DP precursors, we ablate the transcription factor Sox2 early and efficiently, resulting in diminished hair shaft outgrowth. We find that DP niche expression of Sox2 controls the migration speed of differentiating hair shaft progenitors. Transcriptional profiling of Sox2 null DPs reveals increased Bmp6 and decreased BMP inhibitor Sostdc1, a direct Sox2 transcriptional target. Subsequently, we identify upregulated BMP signaling in knockout hair shaft progenitors and demonstrate that Bmp6 inhibits cell migration, an effect that can be attenuated by Sostdc1. A shorter and Sox2-negative hair type lacks Sostdc1 in the DP and shows reduced migration and increased BMP activity of hair shaft progenitors. Collectively, our data identify Sox2 as a key regulator of hair growth that controls progenitor migration by fine-tuning BMP-mediated mesenchymal-epithelial crosstalk. [ABSTRACT FROM AUTHOR]

Abstract: The leaf essential oil of Photinia serrulata was obtained by hydrodistillation and analyzed by GC and GC-MS. Seventy-one components were identified in the essential oil and the main components of the oil were 10-epi-γ-eudesmol (12.72%), pinene (6.85%), sabinene (5.93%), α-humulene (5.87%) and α-thujene (5.47%). The in vitro cytotoxicity of the oil on human cancer cell lines Hela, A-549 and Bel-7402 was examined. The oil was found to be very active against all the three human tumor cell lines tested with low IC50 of 0.0427μl/ml (Hela), 0.0219μl/ml (A-549) and 0.0301μl/ml (Bel-7402). The oil was also found to possess antioxidant activity as demonstrated by the 1,1-diphenyl-2-picrylhydrazyl (DPPH) radical method. [Copyright &y& Elsevier]

The cover image is based on the Original Article I Lessons learned from expanded reproductive carrier screening in self-reported Ashkenazi, Sephardi, and Mizrahi Jewish patients i by Gidon Akler et al., https://doi.org/10.1002/mgg3.1053. GLO:FU4V/01feb20:mgg31169-toc-0001.jpg PHOTO (COLOR): . gl. [Extracted from the article]

Background: Next‐generation sequencing (NGS)‐based panels have gained traction as a strategy for reproductive carrier screening. Their value for screening Ashkenazi Jewish (AJ) individuals, who have benefited greatly from population‐wide targeted testing, as well as Sephardi/Mizrahi Jewish (SMJ) individuals (an underserved population), has not been fully explored. Methods: The clinical utilization by 6,805 self‐reported Jewish individuals of an expanded NGS panel, along with several ancillary assays, was assessed retrospectively. Data were extracted for a subset of 96 diseases that, during the panel design phase, were classified as being AJ‐, SMJ‐, or pan‐Jewish/pan‐ethnic‐relevant. Results: 64.6% of individuals were identified as carriers of one or more of these 96 diseases. Over 80% of the reported variants would have been missed by following recommended AJ screening guidelines. 10.7% of variants reported for AJs were in "SMJ‐relevant genes," and 31.2% reported for SMJs were in "AJ‐relevant genes." Roughly 2.5% of individuals carried a novel, likely pathogenic variant. One in 16 linked cohort couples was identified as a carrier couple for at least one of these 96 diseases. Conclusion: For maximal carrier identification, this study supports using expanded NGS panels for individuals of all Jewish backgrounds. This approach can better empower at‐risk couples for reproductive decision making. [ABSTRACT FROM AUTHOR]