Combining stochastic density functional theory with deep potential molecular dynamics to study warm dense matter

In traditional finite-temperature Kohn–Sham density functional theory(KSDFT),the partial occupation of a large number of high-energy KS eigenstates restricts the use of first-principles molecular dynamics methods at extremely high temperatures.However,stochastic density functional theory(SDFT)can overcome this limitation.Recently,SDFT and the related mixed stochastic–deterministic density functional theory,based on a plane-wave basis set,have been implemented in the first-principles electronic structure software ABACUS[Q.Liu and M.Chen,Phys.Rev.B 106,125132(2022)].In this study,we combine SDFT with the Born–Oppenheimer molecular dynamics method to investigate systems with temperatures ranging from a few tens of eV to 1000 eV.Importantly,we train machine-learning-based interatomic models using the SDFT data and employ these deep potential models to simulate large-scale systems with long trajectories.Subsequently,we compute and analyze the structural properties,dynamic properties,and transport coefficients of warm dense matter.

Charge self-consistent dynamical mean field theory calculations incombination with linear combination of numerical atomic orbitalsframework based density functional theory

We present a formalism of charge self-consistent dynamical mean field theory(DMFT)in combination with densityfunctional theory(DFT)within the linear combination of numerical atomic orbitals(LCNAO)framework.We implementedthe charge self-consistent DFT+DMFT formalism by interfacing a full-potential all-electron DFT code with threehybridization expansion-based continuous-time quantum Monte Carlo impurity solvers.The benchmarks on several 3d,4fand 5f strongly correlated electron systems validated our formalism and implementation.Furthermore,within the LCANOframework,our formalism is general and the code architecture is extensible,so it can work as a bridge merging differentLCNAO DFT packages and impurity solvers to do charge self-consistent DFT+DMFT calculations.

Dynamic Characteristics of Functionally Graded Timoshenko Beams by Improved Differential Quadrature Method

This study proposes an effective method to enhance the accuracy of the Differential Quadrature Method(DQM)for calculating the dynamic characteristics of functionally graded beams by improving the form of discrete node distribution.Firstly,based on the first-order shear deformation theory,the governing equation of free vibration of a functionally graded beam is transformed into the eigenvalue problem of ordinary differential equations with respect to beam axial displacement,transverse displacement,and cross-sectional rotation angle by considering the effects of shear deformation and rotational inertia of the beam cross-section.Then,ignoring the shear deformation of the beam section and only considering the effect of the rotational inertia of the section,the governing equation of the beam is transformed into the eigenvalue problem of ordinary differential equations with respect to beam transverse displacement.Based on the differential quadrature method theory,the eigenvalue problem of ordinary differential equations is transformed into the eigenvalue problem of standard generalized algebraic equations.Finally,the first several natural frequencies of the beam can be calculated.The feasibility and accuracy of the improved DQM are verified using the finite element method(FEM)and combined with the results of relevant literature.

Hypidone hydrochloride(YL-0919)ameliorates functional deficits after traumatic brain injury in mice by activating the sigma-1 receptor for antioxidation

Traumatic brain injury involves complex pathophysiological mechanisms,among which oxidative stress significantly contributes to the occurrence of secondary injury.In this study,we evaluated hypidone hydrochloride(YL-0919),a self-developed antidepressant with selective sigma-1 receptor agonist properties,and its associated mechanisms and targets in traumatic brain injury.Behavioral experiments to assess functional deficits were followed by assessment of neuronal damage through histological analyses and examination of blood-brain barrier permeability and brain edema.Next,we investigated the antioxidative effects of YL-0919 by assessing the levels of traditional markers of oxidative stress in vivo in mice and in vitro in HT22 cells.Finally,the targeted action of YL-0919 was verified by employing a sigma-1 receptor antagonist(BD-1047).Our findings demonstrated that YL-0919 markedly improved deficits in motor function and spatial cognition on day 3 post traumatic brain injury,while also decreasing neuronal mortality and reversing blood-brain barrier disruption and brain edema.Furthermore,YL-0919 effectively combated oxidative stress both in vivo and in vitro.The protective effects of YL-0919 were partially inhibited by BD-1047.These results indicated that YL-0919 relieved impairments in motor and spatial cognition by restraining oxidative stress,a neuroprotective effect that was partially reversed by the sigma-1 receptor antagonist BD-1047.YL-0919 may have potential as a new treatment for traumatic brain injury.

Static/dynamic Analysis of Functionally Graded and Layered Magneto-electro-elastic Plate/pipe under Hamiltonian System

The 3-dimensional couple equations of magneto-electro-elastic structures are derived under Hamiltonian system based on the Hamilton principle. The problem of single sort of variables is converted into the problem of double sorts of variables, and the Hamilton canonical equations are established. The 3-dimensional problem of magneto-electro-elastic structure which is investigated in Euclidean space commonly is converted into symplectic system. At the same time the Lagrange system is converted into Hamiltonian system. As an example, the dynamic characteristics of the simply supported functionally graded magneto-electro-elastic material （FGMM） plate and pipe are investigated. Finally, the problem is solved by symplectic algorithm. The results show that the physical quantities of displacement, electric potential and magnetic potential etc. change continuously at the interfaces between layers under the transverse pressure while some other physical quantities such as the stress, electric and magnetic displacement are not continuous. The dynamic stiffness is increased by the piezoelectric effect while decreased by the piezomagnetic effect.

Apples to oranges:environmentally derived,dynamic regulation of serotonin neuron subpopulations in adulthood?

Traumatic brain injury(TBI)is a public health problem with an undue economic burden that impacts nearly every age,ethnic,and gender group across the globe(Capizzi et al.,2020).TBIs are often sustained during a dynamic range of exposures to energetic environmental forces and as such outcomes are typically heterogeneous regarding severity and pathology(Capizzi et al.,2020).

Chondroitinase ABC combined with Schwann cell transplantation enhances restoration of neural connection and functional recovery following acute and chronic spinal cord injury

Schwann cell transplantation is considered one of the most promising cell-based therapy to repair injured spinal cord due to its unique growth-promoting and myelin-forming properties.A the Food and Drug Administration-approved Phase I clinical trial has been conducted to evaluate the safety of transplanted human autologous Schwann cells to treat patients with spinal cord injury.A major challenge for Schwann cell transplantation is that grafted Schwann cells are confined within the lesion cavity,and they do not migrate into the host environment due to the inhibitory barrier formed by injury-induced glial scar,thus limiting axonal reentry into the host spinal cord.Here we introduce a combinatorial strategy by suppressing the inhibitory extracellular environment with injection of lentivirus-mediated transfection of chondroitinase ABC gene at the rostral and caudal borders of the lesion site and simultaneously leveraging the repair capacity of transplanted Schwann cells in adult rats following a mid-thoracic contusive spinal cord injury.We report that when the glial scar was degraded by chondroitinase ABC at the rostral and caudal lesion borders,Schwann cells migrated for considerable distances in both rostral and caudal directions.Such Schwann cell migration led to enhanced axonal regrowth,including the serotonergic and dopaminergic axons originating from supraspinal regions,and promoted recovery of locomotor and urinary bladder functions.Importantly,the Schwann cell survival and axonal regrowth persisted up to 6 months after the injury,even when treatment was delayed for 3 months to mimic chronic spinal cord injury.These findings collectively show promising evidence for a combinatorial strategy with chondroitinase ABC and Schwann cells in promoting remodeling and recovery of function following spinal cord injury.

Resting-state brain network remodeling after different nerve reconstruction surgeries:a functional magnetic resonance imaging study in brachial plexus injury rats

Distinct brain remodeling has been found after different nerve reconstruction strategies,including motor representation of the affected limb.However,differences among reconstruction strategies at the brain network level have not been elucidated.This study aimed to explore intranetwork changes related to altered peripheral neural pathways after different nerve reconstruction surgeries,including nerve repair,endto-end nerve transfer,and end-to-side nerve transfer.Sprague–Dawley rats underwent complete left brachial plexus transection and were divided into four equal groups of eight:no nerve repair,grafted nerve repair,phrenic nerve end-to-end transfer,and end-to-side transfer with a graft sutured to the anterior upper trunk.Resting-state brain functional magnetic resonance imaging was obtained 7 months after surgery.The independent component analysis algorithm was utilized to identify group-level network components of interest and extract resting-state functional connectivity values of each voxel within the component.Alterations in intra-network resting-state functional connectivity were compared among the groups.Target muscle reinnervation was assessed by behavioral observation(elbow flexion)and electromyography.The results showed that alterations in the sensorimotor and interoception networks were mostly related to changes in the peripheral neural pathway.Nerve repair was related to enhanced connectivity within the sensorimotor network,while end-to-side nerve transfer might be more beneficial for restoring control over the affected limb by the original motor representation.The thalamic-cortical pathway was enhanced within the interoception network after nerve repair and end-to-end nerve transfer.Brain areas related to cognition and emotion were enhanced after end-to-side nerve transfer.Our study revealed important brain networks related to different nerve reconstructions.These networks may be potential targets for enhancing motor recovery.

Depression recognition using functional connectivity based on dynamic causal model

Dynamic casual modeling of functional magnetic resonance imaging（fMRI） signals is employed to explore critical emotional neurocircuitry under sad stimuli. The intrinsic model of emotional loops is built on the basis of Papez＇s circuit and related prior knowledge, and then three modulatory connection models are established. In these models, stimuli are placed at different points, which represents they affect the neural activities between brain regions, and these activities are modulated in different ways. Then, the optimal model is selected by Bayesian model comparison. From group analysis, patients＇ intrinsic and modulatory connections from the anterior cingulate cortex （ACC） to the right inferior frontal gyrus （rlFG） are significantly higher than those of the control group. Then the functional connection parameters of the model are selected as classifier features. The classification accuracy rate from the support vector machine（SVM） classifier is 80.73%, which, to some extent, validates the effectiveness of the regional connectivity parameters for depression recognition and provides a new approach for the clinical diagnosis of depression.

Flow behavior and dynamic softening mechanism of Pt−10Ir precious alloy

The hot deformation behavior of Pt−10Ir alloy was studied under a wide range of deformation parameters.At a low deformation temperature(950−1150℃),the softening mechanism is primarily dynamic recovery.In addition,dynamic recrystallization by progressive lattice rotation near grain boundaries(DRX by LRGBs)and microshear bands assisted dynamic recrystallization(MSBs assisted DRX)coordinate the deformation.However,it is difficult for the dynamic softening to offset the stain hardening due to a limited amount of DRXed grains.At a high deformation temperature(1250−1350℃),three main DRX mechanisms associated with strain rates occur:DRX by LRGBs,DRX by a homogeneous increase in misorientation(HIM)and geometric DRX(GDRX).With increasing strain,DRX by LRGBs is enhanced gradually under high strain rates;the“pinch-off”effect is enhanced at low strain rates,which was conducive to the formation of a uniform and fine microstructure.

Preparation and applications of calcium ferrite as a functional material:A review

Calcium ferrite(CF)is recognized as a potential green and efficient functional material because of its advantages of magnetism,electrochemistry,catalysis,and biocompatibility in the fields of materials chemistry,environmental engineering,and biomedicine.There-fore,the obtained research results need to be systematically summarized,and new perspectives on CF and its composite materials need to be analyzed.Based on the presented studies of CF and its composite materials,the types and structures of the crystal are summarized.In addition,the current application technologies and theoretical mechanisms with various properties in different fields are elucidated.Moreover,the various preparation methods of CF and its composite materials are elaborated in detail.Most importantly,the advantages and disadvantages of the synthesis methods of CF and its composite materials are discussed,and the existing problems and emerging challenges in practical production are identified.Furthermore,the key future research directions of CF and its composite materials have been prospected from the potential application technologies to provide references for its synthesis and efficient utilization.

Nanograting‑Based Dynamic Structural Colors Using Heterogeneous Materials

Dynamic structuralcolors can change in response todifferent environmental stimuli.This ability remains effectiveeven when the size of the speciesresponsible for the structural coloris reduced to a few micrometers,providing a promising sensingmechanism for solving microenvironmentalsensing problems inmicro-robotics and microfluidics.However, the lack of dynamicstructural colors that can encoderapidly, easily integrate, and accuratelyreflect changes in physical quantities hinders their use in microscale sensing applications. Herein, we present a 2.5-dimensionaldynamic structural color based on nanogratings of heterogeneous materials, which were obtained by interweaving a pH-responsive hydrogelwith an IP-L photoresist. Transverse gratings printed with pH-responsive hydrogels elongated the period of longitudinal grating in the swollenstate, resulting in pH-tuned structural colors at a 45° incidence. Moreover, the patterned encoding and array printing of dynamic structuralcolors were achieved using grayscale stripe images to accurately encode the periods and heights of the nanogrid structures. Overall, dynamicstructural color networks exhibit promising potential for applications in information encryption and in situ sensing for microfluidic chips.

A Rapid Adaptation Approach for Dynamic Air‑Writing Recognition Using Wearable Wristbands with Self‑Supervised Contrastive Learning

Wearable wristband systems leverage deep learning to revolutionize hand gesture recognition in daily activities.Unlike existing approaches that often focus on static gestures and require extensive labeled data,the proposed wearable wristband with selfsupervised contrastive learning excels at dynamic motion tracking and adapts rapidly across multiple scenarios.It features a four-channel sensing array composed of an ionic hydrogel with hierarchical microcone structures and ultrathin flexible electrodes,resulting in high-sensitivity capacitance output.Through wireless transmission from a Wi-Fi module,the proposed algorithm learns latent features from the unlabeled signals of random wrist movements.Remarkably,only few-shot labeled data are sufficient for fine-tuning the model,enabling rapid adaptation to various tasks.The system achieves a high accuracy of 94.9%in different scenarios,including the prediction of eight-direction commands,and air-writing of all numbers and letters.The proposed method facilitates smooth transitions between multiple tasks without the need for modifying the structure or undergoing extensive task-specific training.Its utility has been further extended to enhance human–machine interaction over digital platforms,such as game controls,calculators,and three-language login systems,offering users a natural and intuitive way of communication.

Efficient and Stable Perovskite Solar Cells and Modules Enabled by Tailoring Additive Distribution According to the Film Growth Dynamics

Gas quenching and vacuum quenching process are widely applied to accelerate solvent volatilization to induce nucleation of perovskites in blade-coating method.In this work,we found these two pre-crystallization processes lead to different order of crystallization dynamics within the perovskite thin film,resulting in the differences of additive distribution.We then tailor-designed an additive molecule named 1,3-bis(4-methoxyphenyl)thiourea to obtain films with fewer defects and holes at the buried interface,and prepared perovskite solar cells with a certified efficiency of 23.75%.Furthermore,this work also demonstrates an efficiency of 20.18%for the large-area perovskite solar module(PSM)with an aperture area of 60.84 cm^(2).The PSM possesses remarkable continuous operation stability for maximum power point tracking of T_(90)>1000 h in ambient air.

Dynamic Multi-Graph Spatio-Temporal Graph Traffic Flow Prediction in Bangkok:An Application of a Continuous Convolutional Neural Network

The ability to accurately predict urban traffic flows is crucial for optimising city operations.Consequently,various methods for forecasting urban traffic have been developed,focusing on analysing historical data to understand complex mobility patterns.Deep learning techniques,such as graph neural networks(GNNs),are popular for their ability to capture spatio-temporal dependencies.However,these models often become overly complex due to the large number of hyper-parameters involved.In this study,we introduce Dynamic Multi-Graph Spatial-Temporal Graph Neural Ordinary Differential Equation Networks(DMST-GNODE),a framework based on ordinary differential equations(ODEs)that autonomously discovers effective spatial-temporal graph neural network(STGNN)architectures for traffic prediction tasks.The comparative analysis of DMST-GNODE and baseline models indicates that DMST-GNODE model demonstrates superior performance across multiple datasets,consistently achieving the lowest Root Mean Square Error(RMSE)and Mean Absolute Error(MAE)values,alongside the highest accuracy.On the BKK(Bangkok)dataset,it outperformed other models with an RMSE of 3.3165 and an accuracy of 0.9367 for a 20-min interval,maintaining this trend across 40 and 60 min.Similarly,on the PeMS08 dataset,DMST-GNODE achieved the best performance with an RMSE of 19.4863 and an accuracy of 0.9377 at 20 min,demonstrating its effectiveness over longer periods.The Los_Loop dataset results further emphasise this model’s advantage,with an RMSE of 3.3422 and an accuracy of 0.7643 at 20 min,consistently maintaining superiority across all time intervals.These numerical highlights indicate that DMST-GNODE not only outperforms baseline models but also achieves higher accuracy and lower errors across different time intervals and datasets.

Stability Prediction in Smart Grid Using PSO Optimized XGBoost Algorithm with Dynamic Inertia Weight Updation

Prediction of stability in SG(Smart Grid)is essential in maintaining consistency and reliability of power supply in grid infrastructure.Analyzing the fluctuations in power generation and consumption patterns of smart cities assists in effectively managing continuous power supply in the grid.It also possesses a better impact on averting overloading and permitting effective energy storage.Even though many traditional techniques have predicted the consumption rate for preserving stability,enhancement is required in prediction measures with minimized loss.To overcome the complications in existing studies,this paper intends to predict stability from the smart grid stability prediction dataset using machine learning algorithms.To accomplish this,pre-processing is performed initially to handle missing values since it develops biased models when missing values are mishandled and performs feature scaling to normalize independent data features.Then,the pre-processed data are taken for training and testing.Following that,the regression process is performed using Modified PSO(Particle Swarm Optimization)optimized XGBoost Technique with dynamic inertia weight update,which analyses variables like gamma(G),reaction time(tau1–tau4),and power balance(p1–p4)for providing effective future stability in SG.Since PSO attains optimal solution by adjusting position through dynamic inertial weights,it is integrated with XGBoost due to its scalability and faster computational speed characteristics.The hyperparameters of XGBoost are fine-tuned in the training process for achieving promising outcomes on prediction.Regression results are measured through evaluation metrics such as MSE(Mean Square Error)of 0.011312781,MAE(Mean Absolute Error)of 0.008596322,and RMSE(Root Mean Square Error)of 0.010636156 and MAPE(Mean Absolute Percentage Error)value of 0.0052 which determine the efficacy of the system.

3D Printing of Tough Hydrogel Scaffolds with Functional Surface Structures for Tissue Regeneration

Hydrogel scaffolds have numerous potential applications in the tissue engineering field.However,tough hydrogel scaffolds implanted in vivo are seldom reported because it is difficult to balance biocompatibility and high mechanical properties.Inspired by Chinese ramen,we propose a universal fabricating method(printing-P,training-T,cross-linking-C,PTC&PCT)for tough hydrogel scaffolds to fill this gap.First,3D printing fabricates a hydrogel scaffold with desired structures(P).Then,the scaffold could have extraordinarily high mechanical properties and functional surface structure by cycle mechanical training with salting-out assistance(T).Finally,the training results are fixed by photo-cross-linking processing(C).The tough gelatin hydrogel scaffolds exhibit excellent tensile strength of 6.66 MPa(622-fold untreated)and have excellent biocompatibility.Furthermore,this scaffold possesses functional surface structures from nanometer to micron to millimeter,which can efficiently induce directional cell growth.Interestingly,this strategy can produce bionic human tissue with mechanical properties of 10 kPa-10 MPa by changing the type of salt,and many hydrogels,such as gelatin and silk,could be improved with PTC or PCT strategies.Animal experiments show that this scaffold can effectively promote the new generation of muscle fibers,blood vessels,and nerves within 4 weeks,prompting the rapid regeneration of large-volume muscle loss injuries.

Efficient and Stable Photoassisted Lithium‑Ion Battery Enabled by Photocathode with Synergistically Boosted Carriers Dynamics

Efficient and stable photocathodes with versatility are of significance in photoassisted lithium-ion batteries(PLIBs),while there is always a request on fast carrier transport in electrochemical active photocathodes.Present work proposes a general approach of creating bulk heterojunction to boost the carrier mobility of photocathodes by simply laser assisted embedding of plasmonic nanocrystals.When employed in PLIBs,it was found effective for synchronously enhanced photocharge separation and transport in light charging process.Additionally,experimental photon spectroscopy,finite difference time domain method simulation and theoretical analyses demonstrate that the improved carrier dynamics are driven by the plasmonic-induced hot electron injection from metal to TiO_(2),as well as the enhanced conductivity in TiO2 matrix due to the formation of oxygen vacancies after Schottky contact.Benefiting from these merits,several benchmark values in performance of TiO2-based photocathode applied in PLIBs are set,including the capacity of 276 mAh g^(−1) at 0.2 A g^(−1) under illumination,photoconversion efficiency of 1.276%at 3 A g^(−1),less capacity and Columbic efficiency loss even through 200 cycles.These results exemplify the potential of the bulk heterojunction strategy in developing highly efficient and stable photoassisted energy storage systems.

Near-infrared brain functional characteristics of mild cognitive impairment with sleep disorders

BACKGROUND Mild cognitive impairment(MCI)has a high risk of progression to Alzheimer’s disease.The disease is often accompanied by sleep disorders,and whether sleep disorders have an effect on brain function in patients with MCI is unclear.AIM To explore the near-infrared brain function characteristics of MCI with sleep disorders.METHODS A total of 120 patients with MCI(MCI group)and 50 healthy subjects(control group)were selected.All subjects underwent the functional near-infrared spec-troscopy test.Collect baseline data,Mini-Mental State Examination,Montreal Cognitive Assessment scale,fatigue severity scale(FSS)score,sleep parameter,and oxyhemoglobin(Oxy-Hb)concentration and peak time of functional near-infrared spectroscopy test during the task period.The relationship between Oxy-RESULTS Compared with the control group,the FSS score of the MCI group was higher(t=11.310),and the scores of Pittsburgh sleep quality index,sleep time,sleep efficiency,nocturnal sleep disturbance,and daytime dysfunction were higher(Z=-10.518,-10.368,-9.035,-10.661,-10.088).Subjective sleep quality and total sleep time scores were lower(Z=-11.592,-9.924).The sleep efficiency of the MCI group was lower,and the awakening frequency,rem sleep latency period,total sleep time,and oxygen desaturation index were higher(t=5.969,5.829,2.887,3.003,5.937).The Oxy-Hb concentration at T0,T1,and T2 in the MCI group was lower(t=14.940,11.280,5.721),and the peak time was higher(t=18.800,13.350,9.827).In MCI patients,the concentration of Oxy-Hb during T0 was negatively correlated with the scores of Pittsburgh sleep quality index,sleep time,total sleep time,and sleep efficiency(r=-0.611,-0.388,-0.563,-0.356).It was positively correlated with sleep efficiency and total sleep time(r=0.754,0.650),and negatively correlated with oxygen desaturation index(r=-0.561)and FSS score(r=-0.526).All comparisons were P<0.05.CONCLUSION Patients with MCI and sleep disorders have lower near-infrared brain function than normal people,which is related to sleep quality.Clinically,a comprehensive assessment of the near-infrared brain function of patients should be carried out to guide targeted treatment and improve curative effect.

Emerging structures and dynamic mechanisms ofγ-secretase for Alzheimer’s disease

γ-Secretase,called“the proteasome of the membrane,”is a membrane-embedded protease complex that cleaves 150+peptide substrates with central roles in biology and medicine,including amyloid precursor protein and the Notch family of cell-surface receptors.Mutations inγ-secretase and amyloid precursor protein lead to early-onset familial Alzheimer’s disease.γ-Secretase has thus served as a critical drug target for treating familial Alzheimer’s disease and the more common late-onset Alzheimer’s disease as well.However,critical gaps remain in understanding the mechanisms of processive proteolysis of substrates,the effects of familial Alzheimer’s disease mutations,and allosteric modulation of substrate cleavage byγ-secretase.In this review,we focus on recent studies of structural dynamic mechanisms ofγ-secretase.Different mechanisms,including the“Fit-Stay-Trim,”“Sliding-Unwinding,”and“Tilting-Unwinding,”have been proposed for substrate proteolysis of amyloid precursor protein byγ-secretase based on all-atom molecular dynamics simulations.While an incorrect registry of the Notch1 substrate was identified in the cryo-electron microscopy structure of Notch1-boundγ-secretase,molecular dynamics simulations on a resolved model of Notch1-boundγ-secretase that was reconstructed using the amyloid precursor protein-boundγ-secretase as a template successfully capturedγ-secretase activation for proper cleavages of both wildtype and mutant Notch,being consistent with biochemical experimental findings.The approach could be potentially applied to decipher the processing mechanisms of various substrates byγ-secretase.In addition,controversy over the effects of familial Alzheimer’s disease mutations,particularly the issue of whether they stabilize or destabilizeγ-secretase-substrate complexes,is discussed.Finally,an outlook is provided for future studies ofγ-secretase,including pathways of substrate binding and product release,effects of modulators on familial Alzheimer’s disease mutations of theγ-secretase-substrate complexes.Comprehensive understanding of the functional mechanisms ofγ-secretase will greatly facilitate the rational design of effective drug molecules for treating familial Alzheimer’s disease and perhaps Alzheimer’s disease in general.