Concurrent Consideration Research Articles

Opposition based red wolf algorithm for solving optimal reactive power problem

<p>This paper presents an opposition based red wolf optimization (ORWO) algorithm for solving optimal reactive power problem. Each red wolf has a flag vector in the algorithm, and length is equivalent to the whole sum of numbers which features in the dataset of the wolf optimization (WO). In this proposed algorithm, red wolf optimization algorithm has been intermingled with opposition-based learning (OBL). By this amalgamate procedure the convergence speed of the proposed algorithm will be increased. To discover an improved candidate solution, the concurrent consideration of a probable and its corresponding opposite are estimated which is closer to the global optimum than an arbitrary candidate solution. Proposed algorithm has been tested in standard IEEE 14-bus and 300-bus test systems. The simulation results show that the proposed algorithm reduced the real power loss considerably.</p>

Confinement of sulfur-doped NiO nanoparticles into N-doped carbon nanotube/nanofiber-coupled hierarchical branched superstructures: Electronic modulation by anion doping boosts oxygen evolution electrocatalysis

The search for non-precious and efficient electrocatalysts towards the oxygen evolution reaction (OER) is of vital importance for the future advancement of multifarious renewable energy conversion/storage technologies. Electronic modulation via heteroatom doping is recognized as one of the most forceful leverages to enhance the electrocatalytic activity. Herein, we demonstrate a delicate strategy for the in-situ confinement of S-doped NiO nanoparticles into N-doped carbon nanotube/nanofiber-coupled hierarchical branched superstructures (labeled as S-NiO@N-C NT/NFs). The developed strategy simultaneously combines enhanced thermodynamics via electronic regulation with accelerated kinetics via nanoarchitectonics. The S-doping into NiO lattice and the 1D/1D-integrated hierarchical branched carbon substrate confer the resultant S-NiO@N-C NT/NFs with regulated electronic configuration, enriched oxygen vacancies, convenient mass diffusion pathways and superior architectural robustness. Thereby, the S-NiO@N-C NT/NFs display outstanding OER properties with an overpotential of 277 mV at 10 mA cm−2 and impressive long-term durability in KOH medium. Density functional theory (DFT) calculations further corroborate that introducing S-dopant significantly enhances the interaction with key oxygenate intermediates and narrow the band gap. More encouragingly, a rechargeable Zn-air battery using an air–cathode of Pt/C + S-NiO@N-C NT/NFs exhibits a lower charge voltage and preferable cycling stability in comparison with the commercial Pt/C + RuO2 counterpart. This study highlighting the concurrent consideration of electronic regulation, architectural design and nanocarbon hybridization may shed light on the future exploration of economical and efficient electrocatalysts.

MULTI-CRITERIA ASSESSMENT AND PROCESS SELECTION MODEL FOR ADDITIVE MANUFACTURING IN THE CONCEPTUAL PHASE OF DESIGN

AbstractAdditive manufacturing (AM), which was first applied for rapid prototyping, is now becoming a real option for small-batch production of final products. Further expansion of AM is closely correlated to production costs. AM can only become competitive to traditional manufacturing methods if a product is designed for AM already from the beginning as it is an expensive technology that should only be applied if it adds enough value to the product. The aim of this paper is to increase cost awareness in the conceptual design phase and to support product developers in doing AM cost estimation and process selection. The proposed model integrates design for AM and costs calculation. The input data to the process is preliminary design and design requirements. The main contribution of this paper is the multi-criteria AM function, which enables concurrent consideration of different technical and economical criteria. The multi-criteria AM function helps to compare how AM processing and product design parameters influence the product cost. The holistic overview of different options increases the solution space and enables product optimization in several iterations.

Salient stakeholders: Using the salience stakeholder model to assess stakeholders’ influence in healthcare priority setting

Stakeholders play an important role in health priority setting, and their roles have been discussed in the literature, mainly in relationship to their power. An emerging body of literature is focusing on the legitimacy of the stakeholders. Using the case of the Uganda health system, the overall aim of this paper is to assess the utility of the salience stakeholder analysis framework in identifying the most salient stakeholders in health-care priority setting. MethodsThis was a qualitative case study involving 57 key informant interviews with national and district level policy makers and a review of policy documents. Interview data were analyzed using QSR NVivo10 qualitative data analysis software. Analysis was guided by the salience stakeholder analysis framework. FindingsAmong the eight groups of stakeholders identified by the respondents, the politicians were found to be the most salient stakeholders. However, stakeholders’ salience varied depending on the type of decision, the nature of health issue and how and who tabled the health issue. ConclusionThe salience stakeholder analysis framework, originating from the business management and political science disciplines, provided a more comprehensive stakeholder analysis by supporting the concurrent consideration of power, legitimacy and urgency in stakeholder analysis for health care priority setting.

Soil moisture, temperature and nitrogen availability interactively regulate carbon exchange in a meadow steppe ecosystem

Primary productivity in terrestrial ecosystems can be significantly altered by the predicted increases in nitrogen (N) deposition, but it is still unclear how N deposition influences the carbon (C) exchange processes especially in dryland ecosystems. In this study, a 3-year experiment with two types of fertilizers and five N addition levels was conducted in a semiarid steppe of Erguna, Inner Mongolia to assess the effects of exogenous N input on net ecosystem productivity (NEP), gross ecosystem productivity (GEP) and ecosystem respiration (ER). Our results showed that enhanced N input significantly increased NEP and GEP when the volumetric soil water content (VWC) was greater than 15% (v/v%), but had trivial and inconsistent effects when VWC was less than that threshold. Moreover, the NEP and GEP increased significantly with increasing N addition but leveled off when the N input reached 10 g N m–2 year–1 and the VWC was above 15%. However, the ER showed a significant increase with N addition rates, regardless of soil water content. Our main findings are as follows. First, the ecosystem C exchange can be significantly enhanced by addition of N only when soil moisture exceeds certain threshold, suggesting co-limitation of water and N in the grassland ecosystem that we studied. Second, in addition to concurrent consideration of water, temperature and N as well as their interactions also need to be accounted for. Third, the frequency of days with the VWC being higher than 15% accounted only around one third of days during the three growing seasons. Taken together, our findings imply that the expected increases of carbon sequestration should be minimal under N deposition in the Inner Mongolia grassland ecosystems.

Concurrent consideration of cortical and cancellous bone within continuum bone remodelling

Continuum bone remodelling is an important tool for predicting the effects of mechanical stimuli on bone density evolution. While the modelling of only cancellous bone is considered in many studies based on continuum bone remodelling, this work presents an approach of modelling also cortical bone and the interaction of both bone types. The distinction between bone types is made by introducing an initial volume fraction. A simple point-wise example is used to study the behaviour of novel model options, as well as a proximal femur example, where the interaction of both bone types is demonstrated using initial density distributions. The results of the proposed model options indicate that the consideration of cortical bone remarkably changes the density evolution of cancellous bone, and should therefore not be neglected.

Multiscale optimization of specific elastic properties and microscopic frequency band-gaps of architectured microtruss lattice materials

This paper proposes the development of lattice materials with the concurrent consideration of their specific elastic mechanical properties and their corresponding phononic wave filtering capabilities. A multi-objective and multiscale design optimization problem is presented where the microscopic geometric parameters of truss-like lattice unit cells are introduced as design variables by means of beams cross-section shape transformers. The optimization problem is posed to maximize the stiffness and strength properties of lattice materials along with their frequency band-gap aspect ratios while minimizing their relative density. Floquet-Bloch theorem is employed for the frequency band-gap analysis while the homogenized mechanical properties are estimated using the Cauchy-Born hypothesis and the Hill-Mandel principle of macro-homogeneity. It is found that the band-gap aspect ratios are directly proportional to the macroscopic mechanical properties of the lattice, therefore posing a trade-off design problem. Two case studies are presented for the multi-objective optimization problem including the triangular and the Kagome patched honeycomb topologies. It is demonstrated that the design objectives are satisfied with “I” shaped, horizontally hollow circular or diamond cross-sections. Design charts are introduced which enable the definition of lattice microscopic geometrical attributes required for lattice material development with optimal static macroscopic and dynamic microscopic characteristics.

Considering evidence in the management of fetal growth restriction.

Considering evidence in the management of fetal growth restriction.

Coupling physics in machine learning to predict properties of high-temperatures alloys

High-temperature alloy design requires a concurrent consideration of multiple mechanisms at different length scales. We propose a workflow that couples highly relevant physics into machine learning (ML) to predict properties of complex high-temperature alloys with an example of the 9–12 wt% Cr steels yield strength. We have incorporated synthetic alloy features that capture microstructure and phase transformations into the dataset. Identified high impact features that affect yield strength of 9Cr from correlation analysis agree well with the generally accepted strengthening mechanism. As a part of the verification process, the consistency of sub-datasets has been extensively evaluated with respect to temperature and then refined for the boundary conditions of trained ML models. The predicted yield strength of 9Cr steels using the ML models is in excellent agreement with experiments. The current approach introduces physically meaningful constraints in interrogating the trained ML models to predict properties of hypothetical alloys when applied to data-driven materials.

Electro-Thermal Codesign Methodology of an On-Board Electric Vehicle Charger

Abstract Significant advances are needed to optimize the charging speed, reliability, safety, and cost of today's conservatively designed electric vehicle (EV) charging systems. The design and optimization of these novel engineering systems require concurrent consideration of thermal and electrical phenomena, as well as component and system level dynamics and control to guarantee reliable continuous operation, scalability, and minimum footprint. This work addresses the concurrent thermal and electrical design constraints in a high-density, on-board, bidirectional charger with vehicle-to-grid (V2G), grid-to-vehicle (G2V), vehicle-to-house (V2H), and vehicle-to-vehicle (V2V) power transfer capabilities. The electrical design of this charger consists of DC–DC and DC–AC power stages connected in series. The power-stage circuits are implemented on a printed circuit board (PCB) with 16 surface-mount silicon carbide MOSFETs, three inductors, and one transformer. The main goal of this work is to investigate the interplay between the cooling architecture and the PCB layout, and the corresponding impact on the heat dissipation and parasitic inductance. This work compares the performance of three generations of this multifunctional charger that employ different design methodologies and proposes high-level design guidelines derived from multiphysics simulations and experimental tests.

Implications of a prognostic 40-gene expression profile (40-GEP) test for high-risk cutaneous squamous cell carcinoma (cSCC) on staging-based risk assessment and adjuvant therapy trial design.

e22091 Background: Deaths due to cSCC are expected to exceed melanoma-specific deaths. With the demonstration of effective therapies for advanced cSCC, and as treatment of patients in the adjuvant setting is considered, accurate prognosis is critical. For improved identification of ‘high-risk’ patients, with biologically aggressive disease capable of metastasis, a prognostic 40-gene expression profile (40-GEP) test was validated using an independent cohort of patients with high-risk cSCC and known clinical outcomes. The test identified three groups with increasing metastasis risk profiles: Class 1 (low risk), Class 2A (high risk), and Class 2B (highest risk) having metastasis rates of 8.9%, 20.4%, and 60%, respectively. Multivariable analysis demonstrated prognostic efficacy of the 40-GEP test alone and in combination with clinicopathological staging systems. This study evaluated risk stratification with concurrent consideration of the 40-GEP result and the Brigham and Women’s Hospital (BWH) stage. The primary objective was evaluation of the potential impact of the 40-GEP on adjuvant clinical trial design. Methods: To determine if a 40-GEP Class 2B result could optimize clinical trial accrual, metastasis rates of BWH high-risk T stage patients (T2b-T3) alone and in combination with 40-GEP results from the validation cohort were used for two-arm trial sample size calculations. Results: Metastasis rates for cases with T2b-T3 tumors increased from 35.1% to 71.4% when selecting for T2b-T3 cases with a 40-GEP Class 2B result. To provide 80% power to detect hazard ratio of 0.6 with 3 years of follow-up (alpha = 0.05), in line with improvement rates by addition of radiation to surgery, 434 T2b-T3 patients are required for randomization. However, sample size could be reduced by 51% to 214 patients by focusing enrollment on T2b-T3 patients with a 40-GEP Class 2B result. Conclusions: These results support the incorporation of the 40-GEP test into selection processes for patients with T2b-T3 tumors who are at the highest risk for metastasis and appropriate for adjuvant clinical trials.

Integrating the setup planning with fixture design practice by concurrent consideration of machining and fixture design principles

In the conventional setup planning methods, the required fixtures are planned in a sequential order relative to other activities of the setup planning. In the present study, a new approach has been developed and a concurrent method has been proposed for integrating the setup planning and fixture design practice, which paves the way for considering the fixture design principles in parallel to setup planning activities. This eliminates the need to revise the setup plans to remedy the possible conflicts which are common to conventional procedures. The fixture principles have been cast into mathematical relations as a means to developing the proposed method. In the present study, the locating surfaces are determined as the most important step of the fixture system design. The reference faces which cannot be used as locating surfaces are also distinguished, in order for the designer to revise the design of the workpiece.

In-Vitro Classification of Saliva Samples of COPD Patients and Healthy Controls Using Machine Learning Tools

Chronic Obstructive Pulmonary Disease (COPD) is a life-threatening lung disease and a major cause of morbidity and mortality worldwide. Although a curative therapy has yet to be found, permanent monitoring of biomarkers that reflect the disease progression plays a pivotal role for the effective management of COPD. The accurate examination of respiratory tract fluids like saliva is a promising approach for staging disease and predicting its upcoming exacerbations in a Point-of-Care (PoC) environment. However, the concurrent consideration of patients' demographic and medical parameters is necessary for achieving accurate outcomes. Therefore, Machine Learning (ML) tools can play an important role for analyzing patient data and providing comprehensive results for the recognition of COPD in a PoC setting. As a result, the objective of this research work was to implement ML tools on data acquired from characterizing saliva samples of COPD patients and healthy controls as well as their demographic information for PoC recognition of the disease. For this purpose, a permittivity biosensor was used to characterize dielectric properties of saliva samples and, subsequently, ML tools were applied on the acquired data for classification. The XGBoost gradient boosting algorithm provided a high classification accuracy and sensitivity of 91.25% and 100%, respectively, making it a promising model for COPD evaluation. Integration of this model on a neuromorphic chip, in the future, will enable the real-time assessment of COPD in PoC, with low cost, low energy consumption, and high patient privacy. In addition, constant monitoring of COPD in a near-patient setup will enable the better management of the disease exacerbations.

An integrated fuzzy-AHP and TOPSIS approach for maintenance policy selection

PurposeThe purpose of this paper is to select the best maintenance policy for different types of equipment of a manufacturer integrating the fuzzy analytic hierarchy process (FAHP) and the technique for order of preference by similarity to ideal solution (TOPSIS) models.Design/methodology/approachThe decision hierarchy of this research includes three levels. The first level aims to choose the best maintenance policy for different types of equipment of an acid manufacturer. These equipment pieces include molten sulfur ponds, boiler, absorption tower, cooling towers, converter, heat exchanger and sulfur fuel furnace. The second level includes decision criteria of added-value, risk level and the cost. Lastly, the third level comprises time-based maintenance (TBM), corrective maintenance (CM), shutdown maintenance and condition-based maintenance (CBM) as four maintenance policies.FindingsThe best maintenance policy for different types of equipment of a manufacturer is the main finding of this research. Based on the obtained results, CBM policy is suggested for absorption tower, boiler, cooling tower and molten sulfur ponds, TBM policy is suggested for converters and heat exchanger and CM policy is suggested for a sulfur fuel furnace.Originality/valueThis research develops a novel model by integrating FAHP and an interval TOPSIS with concurrent consideration of added-value, risk level and cost to select the best maintenance policy. According to the highlights of the previous studies conducted on maintenance policy selection and related tools and techniques, an operative integrated approach to combine risk, added-value and cost with integrated fuzzy models is not developed yet. The majority of the previous studies have considered classic fuzzy approaches such as FAHP, FANP, Fuzzy TOPSIS, etc., which are not completely capable to reflect the decision makers’ viewpoints.

A slow independent component analysis algorithm for time series feature extraction with the concurrent consideration of high-order statistic and slowness

For the process data analytics, numerous statistical methods are designed to extract informative features to reveal latent characteristics and correlation patterns among the variables. As a classic statistical method, independent component analysis extracts mutually independent components by searching for the maximum direction of non-Gaussianity from the mixed signals. However, the algorithm does not work for the latent Gaussian components, and the disorder of extracted features also brings trouble to the practical applications. In the present work, a slow independent component analysis algorithm is proposed to tackle the problems by concurrently considering the high-order statistic and slowness. It constructs a dual-objective optimization criterion function, simultaneously incorporating independence and slow varying characteristics into the feature extraction procedure, which ties together the ideas of independent component analysis and slow feature analysis under the same mathematical umbrella. Through adjusting the controllable sub-optimization objective weight, the proposed slow independent component analysis adds insight into the different roles of independence and slow varying characteristics in calibration modeling, and, thus, provides possibilities to combine the advantages of independent component analysis and slow feature analysis. Compared with the traditional method, the new algorithm no longer relies solely on higher-order statistic but also utilizes the slow varying characteristic to extract latent Gaussian or non-Gaussian components from mixed signals. Besides the unsupervised paradigm, the proposed algorithm is extended to the supervised form for classification task by utilizing the temporal structure as well as the label information. The effectiveness of the proposed algorithm is illustrated by blind source separation task and industrial fault diagnosis task using real-world datasets including speech signals and multiphase flow facility.

210. Comparing and contrasting the clinical utility of sagittal spine alignment classification frameworks: Roussouly vs SRS-Schwab

BACKGROUND CONTEXT For surgical adult spinal deformity (ASD) patients, incorporating restoration of appropriate Roussouly Classificaion shape into surgical planning may improve outcomes. With the introduction and use of the SRS-Schwab ASD classification system, it is important to compare the utility of both classification frameworks as they relate to surgical outcomes. PURPOSE Compare outcomes of surgical ASD patients classified by both Roussouly and Schwab systems. STUDY DESIGN/SETTING Retrospective review of prospective ASD database. PATIENT SAMPLE A total of 515 ASD patients. OUTCOME MEASURES Complications; Health-related quality of life (HRQL): ODI, SF-36 PCS, SRS-22r. METHODS ASD patients with pre- and 2-year postop (2Y) radiographic/HRQL data were grouped by “theoretical” Roussouly type (Type 1: PI 60°) and “current” Roussouly type (1: SS 45°), as published. Means comparison tests assessed outcomes of those that mismatched “theoretical” and “current” types at both pre- and 2Y intervals (mismatched) to those of preoperative mismatched patients that matched at 2-years (matched). Subanalysis assessed outcomes of patients that improved in Schwab modifiers, and patients that both improved in both Schwab modifiers and matched Roussouly type by 2Y. RESULTS Included 515 ASD patients (59±14yrs, 80%F). The overall cohort showed moderate preoperative global and lumbo-pelvic deformity: SVA(64mm±73), PT(25°±11), PI-LL(16°±21). Preoperative breakdown of “current” Roussouly types: Type 1 (10%), Type 2 (54%), Type 3 (24%), Type 4 (12%). Preoperative mismatch between “current” and “theoretical” types was 60%. By 2Y, 16% of patients matched Roussouly types. Matched and Mismatched groups did not differ in rates of reaching MCID for any HRQL metrics by 2Y(all p>0.1). Reop, PJK and complications did not differ between matched and mismatched(all p>0.1), though mismatched patients showed a trend of increased instrumentation failure(17% vs 26%, p=0.080). By 2Y, 28% of patients improved in PT Schwab modifier, 37% in SVA, and 46% in PI-LL. Patients that improved in PT modifier had lower instrumentation failure rates (16% vs 24%) and higher rates of reaching 2Y MCID for ODI (58% vs 48%) and SRS Activity (75% vs 64%) than patients that did not improve. Patients that both Matched Roussouly at 2Y and improved in all Schwab modifiers met MCID for ODI and SRS Activity at higher rates than patients that did not. Roussouly Match patients that improved in Schwab PT met SRS Activity MCID at a higher rate, and had less renal, infection, and neurologic complications than patients that did not. Roussouly Match patients that improved in Schwab SVA also had superior SF-36 scores and fewer cardiopulmonary complications(all p CONCLUSIONS For surgical ASD patients, isolated restoration of sagittal spinal shape per the Roussouly system was not associated with superior clinical outcomes or complications by 2 years. In contrast, improvement in PT, per the SRS-Schwab system, was associated with substantial improvement in low-back disability by 2 years. Collectively, patients that both matched Roussouly sagittal type and improved in Schwab modifiers had superior complication and patient-reported outcomes by 2 years. These results suggest that concurrent consideration of both existing systems may offer utility in establishing optimal realignment targets. FDA DEVICE/DRUG STATUS This abstract does not discuss or include any applicable devices or drugs.

동시접속 라이선스 사건 (대법원 2018.11.15. 선고 2016다20916 판결) 저작권법 제35조의2 면책규정의 적용 여부에 대한 고찰

동시접속 라이선스 사건 (대법원 2018.11.15. 선고 2016다20916 판결) 저작권법 제35조의2 면책규정의 적용 여부에 대한 고찰

Dynamic modeling of flexible cooperative mobile manipulator with revolute-prismatic joints for the purpose of moving common object with closed kinematic chain using the recursive Gibbs–Appell formulation

In this paper, the dynamic equations of flexible cooperative manipulators that are kinematically and dynamically constrained to an object and are placed on a non-holonomic mobile platform are explored. The main objective is to develop the dynamics of closed kinematic cooperative manipulators by defining constraints in mobile form while considering the geometric dimensions of the common object instead of assuming a concentrated mass in the gripper as well as the application of manipulator with flexible links and revolute-prismatic joints in the constrained cooperative mobile form. Unlike robots with independent manipulators, the dynamic constraint relates the dynamic equations of object and arms, initiating the motion and introducing interaction forces from the object to arms. In addition, the kinematic constraint keeps the distance between the two arms constant by constraining the relative motion velocity of the arms’ gripper. The dynamic equations of manipulators are derived using the recursive Gibbs–Appell formulation while the object equations are obtained using the Newton-Euler approach. Finally, the motion equations for a cooperative mobile robot with two 2-flexible-link arms are simulated. The results are evaluated at two stages by incorporating dynamic constraints as well as concurrent consideration of kinematic and dynamic constraints. Also, each manipulator's dynamic model weighted by using the experimental test setup with single link and revolute-prismatic joint.

A new soft-sensor algorithm with concurrent consideration of slowness and quality interpretation for dynamic chemical process

Slowly varying process variations represented by slow features, which reflect the inherent dynamics of chemical processes, are revealed to be advantageous for quality prediction. However, if slow features are extracted from process variables as predictors without the supervision of quality indices, some obvious disadvantages are observed: (1) low quality interpretation and redundant slow features since quality information is not considered for feature extraction; (2) a lack of analysis to investigate the relationship between slow features and quality interpretation, especially for different types of quality indices. To solve the above-mentioned problems, a new soft-sensor algorithm, quality-relevant slow feature regression (QSFR), is proposed in the present work. It defines a new objective function by concurrent consideration of slowness and quality interpretation, yielding more meaningful features as predictors to interpret quality index. On the basis of this, a critical feature selection strategy is proposed based on quality interpretation to determine the retained features for regression. Moreover, an in-depth analysis of the properties of retained features is provided to reveal the hidden mechanism and how the slow time-varying process variations influence the quality interpretation. This algorithm can extract more powerful features as predictors and enhance understanding of inherent nature of slow features. Finally, the feasibility and performance of the proposed method are well illustrated for a well-known benchmark process and a real chemical process. The developed QSFR algorithm performs better than traditional slow feature regression method, in which the values of RMSE of three specific quality indices have been reduced by 8.87%, 16.60%, and 3.40%, respectively.

Genetic Interaction of APOE and FGF1 is Associated with Memory Impairment and Hippocampal Atrophy in Alzheimer's Disease.

The APOE and fibroblast growth factor 1 (FGF1) have both been associated with amyloid β accumulation and neurodegeneration. Investigation the effect of APOE-FGF1 interactions on episodic memory (EM) deficits and hippocampus atrophy (HA) might elucidate the complex clinical-pathological relationship in Alzheimer’s disease (AD). EM performance and hippocampal volume (HV) were characterized in patients with mild AD based on APOE-ε4 carrier status (APOE-ε4 carriers versus non-carriers) and FGF1 single nucleotide polymorphism (FGF1-rs34011-GG versus FGF1-rs34011-A-allele carriers). The clinical-pathological relationships within each genotypic group (ε4+/GG-carrier, ε4+/A-allele-carrier, ε4-/GG-carrier and ε4-/A-allele-carrier) were analyzed. There were no significant differences between the FGF1-rs34011-GG and FGF1-rs34011-A-allele carriers for the level of EM performance or HV (p> 0.05). The bilateral HV was significantly smaller and EM impairment was significantly worse in ε4+/GG-carrier than in ε4-/A-allele-carrier, and an interaction effect of APOE (APOE-ε4 carriers versus non-carriers) with FGF1 (FGF1-rs34011-GG versus FGF1-rs34011-A-allele carriers) predicted EM impairment (F4,92= 3.516, p= 0.018) and structural changes in voxel-based morphometry. Our data shows that concurrent consideration of APOE and FGF1 polymorphisms might be required to understand the clinical-pathological relationship in AD.