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TREM2 is an innate immune receptor expressed by microglia in the adult brain. Genetic variation in the TREM2 gene has been implicated in risk for Alzheimer’s disease and frontotemporal dementia, while homozygous TREM2 mutations cause a rare leukodystrophy, Nasu-Hakola disease (NHD). Despite extensive investigation, the role of TREM2 in NHD pathogenesis remains poorly understood. Here, we investigate the mechanisms by which a homozygous stop-gain TREM2 mutation (p.Q33X) contributes to NHD. Induced pluripotent stem cell (iPSC)-derived microglia (iMGLs) were generated from two NHD families: three homozygous TREM2 p.Q33X mutation carriers (termed NHD), two heterozygous mutation carriers, one related non-carrier, and two unrelated non-carriers. Transcriptomic and biochemical analyses revealed that iMGLs from NHD patients exhibited lysosomal dysfunction, downregulation of cholesterol genes, and reduced lipid droplets compared to controls. Also, NHD iMGLs displayed defective activation and HLA antigen presentation. This defective activation and lipid droplet content were restored by enhancing lysosomal biogenesis through mTOR-dependent and independent pathways. Alteration in lysosomal gene expression, such as decreased expression of genes implicated in lysosomal acidification (ATP6AP2) and chaperone mediated autophagy (LAMP2), together with reduction in lipid droplets were also observed in post-mortem brain tissues from NHD patients, thus closely recapitulating in vivo the phenotype observed in iMGLs in vitro. Our study provides the first cellular and molecular evidence that the TREM2 p.Q33X mutation in microglia leads to defects in lysosomal function and that compounds targeting lysosomal biogenesis restore a number of NHD microglial defects. A better understanding of how microglial lipid metabolism and lysosomal machinery are altered in NHD and how these defects impact microglia activation may provide new insights into mechanisms underlying NHD and other neurodegenerative diseases.

In this work, the photovoltaic performance and stability of perovskite solar cells (PSCs) based on a dopant-free hole transport layer (HTL) are efficiently improved by inserting a two-dimensional (2D) interfacial layer.

This paper addresses a competition problem in two Pharmaceutical Supply Chains (PSCs) with their exclusive retailer and manufacturer. However, the key to successful manufacturing is not just understanding a Product Life Cycle (PLC), but also proactively managing products throughout their lifetime, applying the appropriate resources and sales and marketing strategies, depending on what stage products are at in the cycle. Providing a competitive pharmaceutical supply chain under a product life cycle and offering Nash and Stackelberg games under the marketing mix strategies (i.e., price, product quality, location, and advertising) and product life cycle (i.e., introduction, growth, and maturity) is a novel idea that needs to be studied. So, a demand function depends on the marketing mix strategies. The competition between PSCs is considered based on the levels of price, quality, access, and promotion (i.e., marketing mix) across different stages of their PLC. Concerning the PLC and the position of a supply chain in a specific period of their PLC, the considered competitions are Nash and Stackelberg. The results of the level of the marketing mix, demand, and profit for each supply chain are obtained by given different games in line with the stage of their PLC. Furthermore, based on investigating the previous annual data, the sensitivity coefficients of the marketing mix of PSCs in the demand function, these coefficients are considered as stochastic fuzzy parameters. Based on the obtained results, the average total profit of the two supply chains in the product life cycle, at Nash equilibrium, is 6.5 times that of Stackelberg. The average total profit of the first supply chain in the product life cycle, at Stackelberg, is 10% higher than Nash equilibrium. Moreover, the average total profit of the second supply chain in the product life cycle, at Nash equilibrium, is 19 times that of Stackelberg.

In this work, the photovoltaic performance and stability of perovskite solar cells (PSCs) based on a dopant-free hole transport layer (HTL) are efficiently improved by inserting a two-dimensional (2D) interfacial layer. The benzyl ammonium lead iodide (BA

We investigate the effects of an in-plane magnetic field on the weak antilocalization signature of near-surface quantum wells lacking bulk and inversion symmetry. The measured magnetoconductivity exhibits a strong anisotropy with respect to the direction of the in-plane magnetic field. The two-fold symmetry of the observed magneto-anisotropy originates from the competition between Rashba and Dresselhaus spin-orbit couplings. The high sensitivity of the weak antilocalization to the spin texture produced by the combined Zeeman and spin-orbit fields results in very large anisotropy ratios, reaching 100%. Using a semiclassical universal model in quantitative agreement with the experimental data, we uniquely determine the values of the Dresselhaus and Rashba parameters as well as the effective in-plane g-factor of the electrons. Understanding these parameters provides new prospects for novel applications ranging from spintronics to topological quantum computing.

In this paper, the problem of elective surgery scheduling is studied, and resources like surgeons, nurses and operating rooms (ORs) are considered. The problem is to assign surgeries to operating rooms in order to meet three goals: (1) maximizing the number of surgeries that can be done using given fixed resources, (2) minimizing the total fixed costs and overtime costs of the ORs, and (3) minimizing the maximum of completion time of operating rooms. We take into account the uncertainty with the stochastic parameter for the regular operating time of OR in model and fuzzy constraint for resources and overtime. A multi-objective model is proposed to choose the operations to be scheduled on the selected day, and to assign the elective surgeries to OR sessions. In the first phase, we formulated a fuzzy robust optimization model and in the second phase, the sensitivity of the model to different values for penalties in the objective function, is analyzed. The efficiency of the proposed solution is validated by numerical results of applying the model to the case of a public hospital in Iran.

In our previous work, we introduced NASIL as an automated neural architecture search method with imitation learning. Time to discover optimal structures is a key concern in many AML solutions including NASIL. Here, we proposed an extended version called "GNASIL" to speed up the process. Similar to NASIL, GNASIL takes advantage of imitation learning to discover neural architectures for a given device specification. Unlike NASIL that used deep deterministic policy gradient method, GNASIL uses the soft-actor-critic to predict an optimal layer during its search. Furthermore, GNASIL employs a set of probing options and combines learning and planning options to sweep the search space faster. We investigated impact of such deliberative planning on decision making process on a speech recognition task. Reported results demonstrate that probing options in presence of imitation learning enables GNASIL algorithm to automatically learn suitable network structures with very competitive performance both in terms of speed of finding the optimal architectures and their accuracy while keeping computational footprint restrictions into consideration.

Automated machine learning (AML) refers to a class of techniques that, given a problem, can find an optimal set of model architectures, properties, and parameters. In recent years, AML has shown great success in finding neural network structures that are as good as the hand-designed structures and even better. AML is usually a time consuming process that needs to search a big search space. All available AML solutions start the search from scratch. Here we introduce neural architecture search with imitation learning (NASIL) method that starts the search by learning from hand designed structures by experts. NASIL is an actor-critic structure that uses hindsight experience replay(HER) and starts by training on these hand-designed structures stored in memory. We tested NASIL found structures on publicly available Google speech command dataset as well as in-house dataset. Our results show that NASIL is able to find a better structure than handdesigned structures in terms of accuracy and number of parameters. More importantly it is faster that similar approaches.

The Almeida lab has been supported by FCTJPCOFUND/0004/2015; Alzheimer’s Association Research Grant (AARG-19-618007); Maratona da Saúde; H2020 Spreading Excellence and Widening Participation, H2020-WIDESPREAD01-2016-2017-TeamingPhase2-GA739572; iNOVA4Health (UID/Multi/04462/2019), a program financially supported by Fundação para a Ciencia e Tecnologia (FCT)/Ministério da Educação e Ciencia, through national funds and co-funded by FEDER under the PT2020 Partnership Agreement. CG’s salary is supported by FCT-CEECIND/00410/2017. FM has been the recipient of an FCT doctoral fellowship (PD/BD/128344/2017). CP has been the recipient of an FCT doctoral fellowship (SFRH/BD/128374/2017). Alzheimer’s disease (AD) is the most common neurodegenerative disease characterized by progressive memory loss. Although AD neuropathological hallmarks are extracellular amyloid plaques and intracellular tau tangles, the best correlate of disease progression is synapse loss. What causes synapse loss has been the focus of several researchers in the AD field. Synapses become dysfunctional before plaques and tangles form. Studies based on early-onset familial AD (eFAD) models have supported that synaptic transmission is depressed by β-amyloid (Aβ) triggered mechanisms. Since eFAD is rare, affecting only 1% of patients, research has shifted to the study of the most common late-onset AD (LOAD). Intracellular trafficking has emerged as one of the pathways of LOAD genes. Few studies have assessed the impact of trafficking LOAD genes on synapse dysfunction. Since endocytic traffic is essential for synaptic function, we reviewed Aβ-dependent and independent mechanisms of the earliest synaptic dysfunction in AD. We have focused on the role of intraneuronal and secreted Aβ oligomers, highlighting the dysfunction of endocytic trafficking as an Aβ-dependent mechanism of synapse dysfunction in AD. Here, we reviewed the LOAD trafficking genes APOE4, ABCA7, BIN1, CD2AP, PICALM, EPH1A, and SORL1, for which there is a synaptic link. We conclude that in eFAD and LOAD, the earliest synaptic dysfunctions are characterized by disruptions of the presynaptic vesicle exo- and endocytosis and of postsynaptic glutamate receptor endocytosis. While in eFAD synapse dysfunction seems to be triggered by Aβ, in LOAD, there might be a direct synaptic disruption by LOAD trafficking genes. To identify promising therapeutic targets and biomarkers of the earliest synaptic dysfunction in AD, it will be necessary to join efforts in further dissecting the mechanisms used by Aβ and by LOAD genes to disrupt synapses. publishersversion published

Considering environmental protection and sustainable development, this study proposes a multi-objective mixed-integer nonlinear programming model for the pharmaceutical supply chain network design to maximize the profit, improve consumer health and minimize environmental pollution. In this model, different scenarios are considered based on different product life cycle (PLC) stages (i.e., introduction, growth and maturity) based on marketing mixes (i.e., price, quality, place and promotion). Also, a fuzzy goal programming approach is used to solve the multi-objective problem with parameters of interval type 2 fuzzy numbers (IT2 FNs). A Taylor series is also used to linearize the health objective function. The model presented for two real supply chains with the same product and alternative used in the pharmaceutical industry to demonstrate an application to support better decision making for sustainable supply chain management. The results of this research can be a reference for sustainable supply chain research and practical management.

It is well-established that human decision making and instrumental control uses multiple systems, some which use habitual action selection and some which require deliberate planning. Deliberate planning systems use predictions of action-outcomes using an internal model of the agent's environment, while habitual action selection systems learn to automate by repeating previously rewarded actions. Habitual control is computationally efficient but are not very flexible in changing environments. Conversely, deliberate planning may be computationally expensive, but flexible in dynamic environments. This paper proposes a general architecture comprising both control paradigms by introducing an arbitrator that controls which subsystem is used at any time. This system is implemented for a target-reaching task with a simulated two-joint robotic arm that comprises a supervised internal model and deep reinforcement learning. Through permutation of target-reaching conditions, we demonstrate that the proposed is capable of rapidly learning kinematics of the system without a priori knowledge, and is robust to (A) changing environmental reward and kinematics, and (B) occluded vision. The arbitrator model is compared to exclusive deliberate planning with the internal model and exclusive habitual control instances of the model. The results show how such a model can harness the benefits of both systems, using fast decisions in reliable circumstances while optimizing performance in changing environments. In addition, the proposed model learns very fast. Finally, the system which includes internal models is able to reach the target under the visual occlusion, while the pure habitual system is unable to operate sufficiently under such conditions.

Droughts often evolve gradually and cover large areas, and therefore, affect many people and activities. This motivates developing techniques to integrate different satellite observations, to cover large areas, and understand spatial and temporal variability of droughts. In this study, we apply probabilistic techniques to generate satellite derived meteorological, hydrological, and hydro-meteorological drought indices for the world's 156 major river basins covering 2003–2016. The data includes Terrestrial Water Storage (TWS) estimates from the Gravity Recovery And Climate Experiment (GRACE) mission, along with soil moisture, precipitation, and evapotranspiration reanalysis. Different drought characteristics of trends, occurrences, areal-extent, and frequencies corresponding to 3-, 6-, 12-, and 24-month timescales are extracted from these indices. Drought evolution within selected basins of Africa, America, and Asia is interpreted. Canonical Correlation Analysis (CCA) is then applied to find the relationship between global hydro-meteorological droughts and satellite derived Sea Surface Temperature (SST) changes. This relationship is then used to extract regions, where droughts and teleconnections are strongly interrelated. Our numerical results indicate that the 3- to 6-month hydrological droughts occur more frequently than the other timescales. Longer memory of water storage changes (than water fluxes) has found to be the reason of detecting extended hydrological droughts in regions such as the Middle East and Northern Africa. Through CCA, we show that the El Niño Southern Oscillation (ENSO) has major impact on the magnitude and evolution of hydrological droughts in regions such as the northern parts of Asia and most parts of the Australian continent between 2006 and 2011, as well as droughts in the Amazon basin, South Asia, and North Africa between 2010 and 2012. The Indian ocean Dipole (IOD) and North Atlantic Oscillation (NAO) are found to have regional influence on the evolution of hydrological droughts.

Internal models are important when agents make decisions based on predictions of future states and their utilities. However, using internal models for planning can be time consuming. Therefore, it can be useful to use a habitual system for repetitive tasks that can be executed faster and with reduced algorithmic resources. Current evidence suggests that the brain uses both control systems, planning and habitual systems for behavioural control, which then requires an arbitration between these two systems. In our previous work [1], we proposed an Arbitrated Predictive Actor-Critic (APAC), which is a neural architecture demonstrating cooperative mechanisms of planning and habitual control systems for one step mapping. The present study tests the ability of such a model to control a simulated twojoints robotic arm during multiple reaching tasks with movement limitations that require multiple steps to solve the task. Our results show that APAC can learn the multi-step learning under various conditions. Interestingly, the APAC tends to shift from planning to habits by taking actions predicted by a habitual controller over the training time.

Humans can point fairly accurately to memorized states when closing their eyes despite slow or even missing sensory feedback. It is also common that the arm dynamics changes during development or from injuries. We propose a biologically motivated implementation of an arm controller that includes an adaptive observer. Our implementation is based on the neural field framework, and we show how a path integration mechanism can be trained from few examples. Our results illustrate successful generalization of path integration with a dynamic neural field by which the robotic arm can move in arbitrary directions and velocities. Also, by adapting the strength of the motor effect the observer implicitly learns to compensate an image acquisition delay in the sensory system. Our dynamic implementation of an observer successfully guides the arm toward the target in the dark, and the model produces movements with a bell-shaped velocity profile, consistent with human behavior data. We developed an internal model for arm control in the neural field frame-work.After learning the model can point to a target without sensory feedback.The robot learns to move in arbitrary directions from few examples.The model adapts to changing forces on the motors and delays in the system.All movements have a bell shaped velocity profile, consistent with human behavior.

The aim of machine learning is to obtain a good model to correctly predict unseen data. In order to train such models, one needs a sufficient number of clean examples of the ground truth. However, some applications' datasets are not guaranteed to consist entirely of pure examples and might contain mislabeled data. Handling mislabeled data is a domain of outlier statistics and have been studied to some extent in the context of machine learning. Here we ask how does mislabeled data in a training set effect classification performance in deep neural networks. More specifically, motivated by an industrial application, we consider the case where the probability of the class mislabeling in the training set varies considerably between each class. We hence contrast in this paper the case of systematic mislabeling of one class to the more commonly studied situation of a uniform mislabeling between all classes. We demonstrate that the non-uniform mislabeling is more challenging than the more commonly studied uniform case. We also explicitly explore the dependence of our findings to the size of the training data which is not only a common limiting factor in industrial applications but which also has a large effect on the results. We demonstrate that deep networks have an inherent robustness when large datasets are available.

Physical exercise has several beneficial effects, including reduced risk for Alzheimer's disease. Although several studies of potential risk factors for vascular dementia (VaD) exist, including physical activity, the studies have usually included few participants and there are no meta-analyses addressing this key topic.The MEDLINE database was searched using the key words 'physical exercise' 'activity' or 'walking' in combination with 'dementia' and 'vascular dementia'. Potentially relevant studies were assessed and summarised by two of the authors, and longitudinal studies with operationalized definition of physical activity providing risk for VaD in both groups were included in the meta-analysis using pooled estimates from a random effects model.A total of 24 longitudinal studies, including 1378 patients with VaD, were included in the review. The majority of individual studies did not report significant associations. Five studies fulfilled criteria for meta-analysis, including 10,108 non-demented control subjects and 374 individuals with VaD. The meta-analysis demonstrated a significant association between physical exercise and a reduced risk of developing VaD: OR 0.62 (95% CI 0.42-0.92).We conclude that there is evidence supporting the hypothesis that physical activity is likely to prevent the development of VaD, and should be highlighted as part of secondary prevention programmes in people at risk for cerebrovascular disease.

A new stereospecific synthesis of phospholipid derivatives of 1,2-diacyl-sn-glycerols is reported. The synthesis is based on (1) the use of l-glyceric acid as the stereocenter for construction of the optically active phospholipid molecule, (2) preparation of 3-triphenylmethyl-sn-glycerol as the key intermediate for sequential introduction of the primary and secondary acyl functions leading to the chiral diglycerides, and (3) elaboration of the sn-3-phosphodiester headgroup via phosphorylation using 2-chloro-2-oxo-1,3,2-dioxaphospholane, followed by ring opening of the five-membered phosphorus heterocycle with trimethylamine, ammonia, as well as oxygen and sulfur nucleophiles. The sequence has been shown to be suitable for the preparation of both symmetric and mixed-chain diacylglycerols with saturated and unsaturated acyl substituents. Phospholipid headgroups including phosphocholine, phosphoethanolamine, phosphoethanol, and phosphoethylthioacetate functions have been prepared. Application of the method t...

Man has always tried to make new systems, which can do his difficult tasks. Manage and control of such new complex systems is new challenge in our life. Today, one solution to deal with this challenge is using advantage of features of autonomy. It means that instead of managing and controlling the entire system, each component or at least part of the system can manage or control itself even in unpredictable situations. In this paper, we proposed a new algorithm, is named “self-configurable particle swarm optimization algorithm (SCPSO)”. This method can control a system without outside observers in decentralize fashion. Each particle can makes decision to find optimum position for itself even when there is not enough information from whole system. By this method, particles can work autonomously even in unpredictable situations. We examine our proposed algorithm in both static and dynamic environments. This algorithm is a good method for using in disaster management or crisis management. Results show this method is a successful, especially in communication less environments.

Background: Physical exercise has several beneficial effects, including reduced risk for Alzheimer's disease. Although several studies of potential risk factors for vascular dementia (VaD) exist, including physical activity, the studies have usually included few participants and there are no meta-analyses addressing this key topic. Methods: The MEDLINE database was searched using the key words 'physical exercise' 'activity' or 'walking' in combination with 'dementia' and 'vascular dementia'. Potentially relevant studies were assessed and summarised by two of the authors, and longitudinal studies with operationalised definition of physical activity providing risk for VaD in both groups were included in the meta-analysis using pooled estimates from a random effects model. Results: A total of 24 longitudinal studies, including 1378 patients with VaD, were included in the review. The majority of individual studies did not report significant associations. Five studies fulfilled criteria for meta-analysis, including 10,108 non-demented control subjects and 374 individuals with VaD. The meta-analysis demonstrated a significant association between physical exercise and a reduced risk of developing VaD: OR 0.62 (95% CI 0.42-0.92). Conclusions: We conclude that there is evidence supporting the hypothesis that physical activity is likely to prevent the development of VaD, and should be highlighted as part of secondary prevention programmes in people at risk for cerebrovascular disease.

Quantifying local aortic stiffness properties in vivo is acknowledged as essential to assess the severity of an ascending thoracic aortic aneurysm (ATAA). Recently, the LESI (local extensional stiffness identification) methodology has been established to quantify non-invasively local stiffness properties of ATAAs using electrocardiographic-gated computed tomography (ECG-gated CT) scans. The aim of the current study was to determine the most sensitive markers of local ATAA stiffness estimation with the hypothesis that direct measures of local ATAA stiffness could better detect the high-risk patients. A cohort of 30 patients (12 BAV and 18 TAV) referred for aortic size evaluation by ECG-gated CT were recruited. For each patient, the extensional stiffness Q was evaluated by the LESI methodology whilst computational flow analyses were also performed to derive hemodynamics markers such as the wall shear stress (WSS). A strong positive correlation was found between the extensional stiffness and the aortic pulse pressure (R = 0.644 and p < 0.001). Interestingly, a significant positive correlation was also found between the extensional stiffness and patients age for BAV ATAAs (R = 0.619 and p = 0.032), but not for TAV ATAAs (R = −0.117 and p = 0.645). No significant correlation was found between the extensional stiffness and WSS evaluated locally. There was no significant difference either in the extensional stiffness between BAV ATAAs and TAV ATAAs (Q = 3.6 ± 2.5 MPa.mm for BAV ATAAs vs Q = 5.3 ± 3.1 MPa.mm for TAV ATAAs, p = 0.094). Future work will focus on relating the extensional stiffness to the patient-specific rupture risk of ATAAs on larger cohorts to confirm the promising interest of the LESI methodology.