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The emotion regulation network (ERN) in the brain provides a framework for understanding the neuropathology of affective disorders. Although previous neuroimaging studies have investigated the neurobiological correlates of the ERN in major depressive disorder (MDD), whether patients with MDD exhibit abnormal functional connectivity (FC) patterns in the ERN and whether the abnormal FC in the ERN can serve as a therapeutic response signature remain unclear.
Methods
A large functional magnetic resonance imaging dataset comprising 709 patients with MDD and 725 healthy controls (HCs) recruited across five sites was analyzed. Using a seed-based FC approach, we first investigated the group differences in whole-brain resting-state FC of the 14 ERN seeds between participants with and without MDD. Furthermore, an independent sample (45 MDD patients) was used to evaluate the relationship between the aforementioned abnormal FC in the ERN and symptom improvement after 8 weeks of antidepressant monotherapy.
Results
Compared to the HCs, patients with MDD exhibited aberrant FC between 7 ERN seeds and several cortical and subcortical areas, including the bilateral middle temporal gyrus, bilateral occipital gyrus, right thalamus, calcarine cortex, middle frontal gyrus, and the bilateral superior temporal gyrus. In an independent sample, these aberrant FCs in the ERN were negatively correlated with the reduction rate of the HAMD17 score among MDD patients.
Conclusions
These results might extend our understanding of the neurobiological underpinnings underlying unadaptable or inflexible emotional processing in MDD patients and help to elucidate the mechanisms of therapeutic response.
Traditional bulky and complex control devices such as remote control and ground station cannot meet the requirement of fast and flexible control of unmanned aerial vehicles (UAVs) in complex environments. Therefore, a data glove based on multi-sensor fusion is designed in this paper. In order to achieve the goal of gesture control of UAVs, the method can accurately recognize various gestures and convert them into corresponding UAV control commands. First, the wireless data glove fuses flexible fiber optic sensors and inertial sensors to construct a gesture dataset. Then, the trained neural network model is deployed to the STM32 microcontroller-based data glove for real-time gesture recognition, in which the convolutional neural network-Attention mechanism (CNN-Attention) network is used for static gesture recognition, and the convolutional neural network-bidirectional long and short-term memory (CNN-Bi-LSTM) network is used for dynamic gesture recognition. Finally, the gestures are converted into control commands and sent to the vehicle terminal to control the UAV. Through the UAV simulation test on the simulation platform, the average recognition accuracy of 32 static gestures reaches 99.7%, and the average recognition accuracy of 13 dynamic gestures reaches 99.9%, which indicates that the system’s gesture recognition effect is perfect. The task test in the scene constructed in the real environment shows that the UAV can respond to the gestures quickly, and the method proposed in this paper can realize the real-time stable control of the UAV on the terminal side.
This study aimed to demonstrate the utilization value of 1PN embryos. The 1PN zygotes collected from December 2021 to September 2022 were included in this study. The embryo development, the pronuclear characteristics, and the genetic constitutions were investigated. The overall blastocyst formation and good-quality blastocyst rates in 1PN zygotes were 22.94 and 16.24%, significantly lower than those of 2PN zygotes (63.25 and 50.23%, respectively, P = 0.000). The pronuclear characteristics were found to be correlated with the developmental potential. When comparing 1PN zygotes that developed into blastocysts to those that arrested, the former exhibited a significantly larger area (749.49 ± 142.77 vs. 634.00 ± 119.05, P = 0.000), a longer diameter of pronuclear (29.81 ± 3.08 vs. 27.30 ± 3.00, P = 0.000), and a greater number of nucleolar precursor body (NPB) (11.56 ± 3.84 vs. 7.19 ± 2.73, P = 0.000). Among the tested embryos, the diploidy euploidy rate was significantly higher in blastocysts in comparison with the arrested embryos (66.67 vs. 11.76%, P = 0.000), which was also significantly higher in IVF-1PN blastocysts than in ICSI-1PN blastocysts (75.44 vs. 25.00%, P = 0.001). However, the pronuclear characteristics were not found to be linked to the chromosomal ploidy once they formed blastocysts.
In summary, while the developmental potential of 1PN zygotes is reduced, our study shows that, in addition to the reported pronuclear area and diameter, the number of NPB is also associated with their developmental potential. The 1PN blastocysts exhibit a high diploidy euploidy rate, are recommend to be clinically used post genetic testing, especially for patients who do not have other 2PN embryos available.
In contemporary neuroimaging studies, it has been observed that patients with major depressive disorder (MDD) exhibit aberrant spontaneous neural activity, commonly quantified through the amplitude of low-frequency fluctuations (ALFF). However, the substantial individual heterogeneity among patients poses a challenge to reaching a unified conclusion.
Methods
To address this variability, our study adopts a novel framework to parse individualized ALFF abnormalities. We hypothesize that individualized ALFF abnormalities can be portrayed as a unique linear combination of shared differential factors. Our study involved two large multi-center datasets, comprising 2424 patients with MDD and 2183 healthy controls. In patients, individualized ALFF abnormalities were derived through normative modeling and further deconstructed into differential factors using non-negative matrix factorization.
Results
Two positive and two negative factors were identified. These factors were closely linked to clinical characteristics and explained group-level ALFF abnormalities in the two datasets. Moreover, these factors exhibited distinct associations with the distribution of neurotransmitter receptors/transporters, transcriptional profiles of inflammation-related genes, and connectome-informed epicenters, underscoring their neurobiological relevance. Additionally, factor compositions facilitated the identification of four distinct depressive subtypes, each characterized by unique abnormal ALFF patterns and clinical features. Importantly, these findings were successfully replicated in another dataset with different acquisition equipment, protocols, preprocessing strategies, and medication statuses, validating their robustness and generalizability.
Conclusions
This research identifies shared differential factors underlying individual spontaneous neural activity abnormalities in MDD and contributes novel insights into the heterogeneity of spontaneous neural activity abnormalities in MDD.
The delay-shift of the pre-pulse may mislead the determination of its origination and cause problems for the temporal contrast improvement of high-peak-power lasers, especially when the corresponding post-pulse is beyond the time window of the measurement device. In this work, an empirical formula is proposed to predict the delay-shift of pre-pulses for the first time. The empirical formula shows that the delay-shift is proportional to the square of the post-pulse’s initial delay, and also the ratio of the third-order dispersion to the group delay dispersion’s square, which intuitively reveals the main cause for the delay-shift and may provide a convenient routing for identifying the real sources of pre-pulses in both chirped-pulse amplification (CPA) and optical parametric chirped-pulse amplification (OPCPA) systems. The empirical formula agrees well with the experimental results both in the CPA and the OPCPA systems. Besides, a numerical simulation is also carried out to further verify the empirical formula.
This study aims to evaluate the impact of low-carbohydrate diet, balanced dietary guidance and pharmacotherapy on weight loss among individuals with overweight or obesity over a period of 3 months. The study involves 339 individuals with overweight or obesity and received weight loss treatment at the Department of Clinical Nutrition at the Second Affiliated Hospital of Zhejiang University, School of Medicine, between 1 January 2020 and 31 December 2023. The primary outcome is the percentage weight loss. Among the studied patients, the majority chose low-carbohydrate diet as their primary treatment (168 (49·56 %)), followed by balanced dietary guidance (139 (41·00 %)) and pharmacotherapy (32 (9·44 %)). The total percentage weight loss for patients who were followed up for 1 month, 2 months and 3 months was 4·98 (3·04, 6·29) %, 7·93 (5·42, 7·93) % and 10·71 (7·74, 13·83) %, respectively. Multivariable logistic regression analysis identified low-carbohydrate diet as an independent factor associated with percentage weight loss of ≥ 3 % and ≥ 5 % at 1 month (OR = 0·461, P < 0·05; OR = 0·349, P < 0·001). The results showed that a low-carbohydrate diet was an effective weight loss strategy in the short term. However, its long-term effects were comparable to those observed with balanced dietary guidance and pharmacotherapy.
We introduce a novel human-centric deep reinforcement learning recommender system designed to co-optimize energy consumption, thermal comfort, and air quality in commercial buildings. Existing approaches typically optimize these objectives separately or focus solely on controlling energy-consuming building resources without directly engaging occupants. We develop a deep reinforcement learning architecture based on multitask learning with humans-in-the-loop and demonstrate how it can jointly learn energy savings, comfort, and air quality improvements for different building and occupant actions. In addition to controlling typical building resources (e.g., thermostat setpoint), our system provides real-time actionable recommendations that occupants can take (e.g., move to a new location) to co-optimize energy, comfort, and air quality. Through real deployments across multiple commercial buildings, we show that our multitask deep reinforcement learning recommender system has the potential to reduce energy consumption by up to 8% in energy-focused optimization, improve all objectives by 5–10% in joint optimization, and improve thermal comfort by up to 21% in comfort and air quality-focused optimization compared to existing solutions.
This study aimed to explore the combined association between the dietary antioxidant quality score (DAQS) and leisure-time physical activity on sleep patterns in cancer survivors. Data of cancer survivors were extracted from the National Health and Nutrition Examination Surveys (NHANES) database in 2007-2014 in this cross-sectional study. Weighted multivariable logistic regression models were used to estimate odds ratios (ORs) and 95% confidence intervals (CIs) for the association of DAQS and leisure-time physical activity on sleep patterns. The combined association was also assessed in subgroups of participants based on age, and use of painkillers and antidepressants. Among the eligible participants, 1,133 had unhealthy sleep patterns. After adjusting for covariates, compared to low DAQS level combined with leisure-time physical activity level <600 MET·min/week, high DAQS level combined with leisure-time physical activity ≥600 MET·min/week was associated with lower odds of unhealthy sleep patterns (OR=0.41, 95%CI: 0.23-0.72). Additionally, the association of high DAQS level combined with high leisure-time physical activity with low odds of unhealthy sleep patterns was also significant in <65 years old (OR=0.30, 95%CI: 0.13-0.70), non-painkiller (OR=0.39, 95%CI: 0.22-0.71), non-antidepressant (OR=0.49, 95%CI: 0.26-0.91) and antidepressant (OR=0.11, 95%CI: 0.02-0.50) subgroups. DAQS and leisure-time physical activity had a combined association on sleep patterns in cancer survivors. However, the causal associations of dietary nutrient intake and physical activity with sleep patterns in cancer survivors needs further clarification.
We consider continuous-state branching processes (CB processes) which become extinct almost surely. First, we tackle the problem of describing the stationary measures on $(0,+\infty)$ for such CB processes. We give a representation of the stationary measure in terms of scale functions of related Lévy processes. Then we prove that the stationary measure can be obtained from the vague limit of the potential measure, and, in the critical case, can also be obtained from the vague limit of a normalized transition probability. Next, we prove some limit theorems for the CB process conditioned on extinction in a near future and on extinction at a fixed time. We obtain non-degenerate limit distributions which are of the size-biased type of the stationary measure in the critical case and of the Yaglom distribution in the subcritical case. Finally we explore some further properties of the limit distributions.
A fixed-time control strategy based on adaptive event-triggered communication and force estimators is proposed for a class of teleoperation systems with time-varying delays and limited bandwidth. Two force estimators are designed to estimate the operator force and environment force instead of force sensors. With the position, velocity, force estimate signals, and triggering error, an adaptive event-triggered scheme is designed, which automatically adjusts the triggering thresholds to reduce the access frequency of the communication network. With the state information transmitted at the moment of event triggering while considering the time-varying delays, a fixed-time sliding mode controller is designed to achieve the position and force tracking. The stability of the system and the convergence of tracking error within a fixed time are mathematically proved. Experimental results indicate that the control strategy can significantly reduce the information transmission, enhance the bandwidth utilization, and ensure the convergence of tracking error within a fixed time for teleoperation systems.
Digitaria ciliaris var. chrysoblephara (Fig. & De Not.) R.R. Stewart is an annual xeromorphic weed that severely infests direct-seeded rice fields in China. Herbicide resistance is emerging in D. ciliaris var. chrysoblephara owing to extensive and recurrent use of the acetyl-CoA carboxylase (ACCase)-inhibiting herbicide metamifop. In this study, a total of 53 D. ciliaris var. chrysoblephara populations randomly sampled from direct-seeded rice fields across Jiangsu Province were investigated for metamifop resistance and potential resistance-endowing mutations. Single-dose assays revealed that 17 (32.1%) populations evolved resistance to metamifop and 5 (9.4%) populations were in the process of developing resistance. The resistance index (RI) of metamifop-resistant populations ranged from 2.7 to 32.1. Amino acid substitutions (Ile-1781-Leu, Trp-2027-Cys/Ser, and Ile-2041-Asn) in ACCase genes were detected in resistant D. ciliaris var. chrysoblephara plants and caused various cross-resistance patterns to ACCase-inhibiting herbicides. All of four resistant populations (YC07, YZ09, SQ03, and HA06), with different ACCase mutations, exhibited cross-resistance to the aryloxyphenoxypropionate (APP) herbicides cyhalofop-butyl (RI values: 10.0 to 19.9), fenoxaprop-P-ethyl (RI values: 53.7 to 132.8), and haloxyfop-P-methyl (RI values: 6.2 to 62.6), and the phenylpyrazoline (DEN) pinoxaden (RI values: 2.3 to 5.4), but responded differently to the cyclohexanedione (CHD) herbicides clethodim and sethoxydim. It is noteworthy that four postemergence herbicides used for rice cropping, including bispyribac-sodium, pyraclonil, quinclorac, and anilofos, showed poor control effect against D. ciliaris var. chrysoblephara, suggesting few alternations for managing this weed in rice fields except ACCase inhibitors. In conclusion, this work demonstrated that the D. ciliaris var. chrysoblephara had developed resistance to ACCase-inhibiting herbicides in rice cultivation of China, and target-site amino acid substitutions in ACCase were primarily responsible for metamifop resistance.
Helicopters are used in complex and harsh operational environments, such as search and rescue missions and firefighting, that require operating in ground proximity, tracking targets while avoiding impacting obstacles, namely a combination of point tracking (positive) and boundary avoidance (negative) objectives. A simulation task representing simplified helicopter dynamics is used to investigate point tracking and boundary avoidance tasks. The variance and regression analysis are used to study the effects of task conditions on participants’ tracking errors and input aggression. The overall tracking error shows a negative correlation with input aggression. The participants tend to have higher input aggression and lower tracking error near the boundaries, exposing the switching of manipulation input strategies under different task conditions. It also suggests a potential way of designing simulation tasks for human operators manipulating helicopters and a trigger for investigating pilots’ biodynamic feedthrough.
Previous studies have suggested that the habenula (Hb) may be involved in the mechanism of obsessive-compulsive disorder (OCD). However, the specific role of Hb in OCD remains unclear. This study aimed to explore the structural and functional abnormalities of Hb in OCD and their relationship with the clinical symptoms.
Methods
Eighty patients with OCD and 85 healthy controls (HCs) were recruited as the primary dataset. The grey matter volume, resting-state functional connectivity (FC), and effective connectivity (EC) of the Hb were calculated and compared between OCD group and HCs. An independent replication dataset was used to verify the stability and robustness of the results.
Results
Patients with OCD exhibited smaller Hb volume and increased FC of right Hb-left hippocampus than HCs. Dynamic causal model revealed an increased EC from left hippocampus to right Hb and a less inhibitory causal influence from the right Hb to left hippocampus in the OCD group compared to HCs. Similar results were found in the replication dataset.
Conclusions
This study suggested that abnormal structure of Hb and hippocampus-Hb connectivity may contribute to the pathological basis of OCD.
The association between obesity and depression may partly depend on the contextual metabolic health. The effect of change in metabolic health status over time on subsequent depression risk remains unclear. We aimed to assess the prospective association between metabolic health and its change over time and the risk of depression across body mass index (BMI) categories.
Methods
Based on a nationally representative cohort, we included participants enrolled at the wave 2 (2004–2005) of the English Longitudinal Study of Ageing and with follow-up for depression at wave 8 (2016–2017). Participants were cross-classified by BMI categories and metabolic health (defined by the absence of hypertension, diabetes, and hypercholesterolemia) at baseline or its change over time (during waves 3–6). Logistic regression model was used to calculate odds ratios (ORs) and 95% confidence intervals (CIs) for the risk of depression at follow-up stratified by BMI category and metabolic health status with adjustment for potential confounders.
Results
The risk of depression was increased for participants with metabolically healthy obesity compared with healthy nonobese participants, and the risk was highest for those with metabolically unhealthy obesity (OR 1.62, 95% CI 1.18–2.20). Particularly hypertension and diabetes contribute most to the increased risk. The majority of metabolically healthy participants converted to unhealthy metabolic phenotype (50.1% of those with obesity over 8 years), which was associated with an increased risk of depression. Participants who maintained metabolically healthy obesity were still at higher risk (1.99, 1.33–2.72), with the highest risk observed for those with stable unhealthy metabolic phenotypes.
Conclusions
Obesity remains a risk factor for depression, independent of whether other metabolic risk factors are present or whether participants convert to unhealthy metabolic phenotypes over time. Long-term maintenance of metabolic health and healthy body weight may be beneficial for the population mental well-being.
COVID-19 carriers experience psychological stresses and mental health issues such as varying degrees of stigma. The Social Impact Scale (SIS) can be used to measure the stigmatisation of COVID-19 carriers who experience such problems.
Aims
To evaluate the reliability and validity of the Chinese version of the SIS, and the association between stigma and depression among asymptomatic COVID-19 carriers in Shanghai, China.
Method
A total of 1283 asymptomatic COVID-19 carriers from Shanghai Ruijin Jiahe Fangcang Shelter Hospital were recruited, with a mean age of 39.64 ± 11.14 years (59.6% male). Participants completed questionnaires, including baseline information and psychological measurements, the SIS and Self-Rating Depression Scale. The psychometrics of the SIS and its association with depression were examined through exploratory factor analysis, confirmatory factor analysis and receiver operating characteristic analysis.
Results
The average participant SIS score was 42.66 ± 14.61 (range: 24–96) years. Analyses suggested the model had four factors: social rejection, financial insecurity, internalised shame and social isolation. The model fit statistics of the four-factor SIS were 0.913 for the comparative fit index, 0.902 for the Tucker–Lewis index and 0.088 for root-mean-square error of approximation. Standard estimated factor loadings ranged from 0.509 to 0.836. After controlling for demographic characteristics, the total score of the 23-item SIS predicted depression (odds ratio: 1.087, 95% CI 1.061–1.115; area under the curve: 0.84, 95% CI 0.788–0.892).
Conclusions
The Chinese version of the SIS showed good psychometric properties and can be used to assess the level of perceived stigma experienced by asymptomatic COVID-19 carriers.
Childhood maltreatment is an established risk factor for psychopathology. However, it remains unclear how childhood traumatic events relate to mental health problems and how the brain is involved. This study examined the serial mediation effect of brain morphological alterations and emotion-/reward-related functions on linking the relationship from maltreatment to depression. We recruited 156 healthy adolescents and young adults and an additional sample of 31 adolescents with major depressive disorder for assessment of childhood maltreatment, depressive symptoms, cognitive reappraisal and anticipatory/consummatory pleasure. Structural MRI data were acquired to identify maltreatment-related cortical and subcortical morphological differences. The mediation models suggested that emotional maltreatment of abuse and neglect, was respectively associated with increased gray matter volume in the ventral striatum and greater thickness in the middle cingulate cortex. These structural alterations were further related to reduced anticipatory pleasure and disrupted cognitive reappraisal, which contributed to more severe depressive symptoms among healthy individuals. The above mediating effects were not replicated in our clinical group partly due to the small sample size. Preventative interventions can target emotional and reward systems to foster resilience and reduce the likelihood of future psychiatric disorders among individuals with a history of maltreatment.
To compare how clinical researchers generate data-driven hypotheses with a visual interactive analytic tool (VIADS, a visual interactive analysis tool for filtering and summarizing large datasets coded with hierarchical terminologies) or other tools.
Methods:
We recruited clinical researchers and separated them into “experienced” and “inexperienced” groups. Participants were randomly assigned to a VIADS or control group within the groups. Each participant conducted a remote 2-hour study session for hypothesis generation with the same study facilitator on the same datasets by following a think-aloud protocol. Screen activities and audio were recorded, transcribed, coded, and analyzed. Hypotheses were evaluated by seven experts on their validity, significance, and feasibility. We conducted multilevel random effect modeling for statistical tests.
Results:
Eighteen participants generated 227 hypotheses, of which 147 (65%) were valid. The VIADS and control groups generated a similar number of hypotheses. The VIADS group took a significantly shorter time to generate one hypothesis (e.g., among inexperienced clinical researchers, 258 s versus 379 s, p = 0.046, power = 0.437, ICC = 0.15). The VIADS group received significantly lower ratings than the control group on feasibility and the combination rating of validity, significance, and feasibility.
Conclusion:
The role of VIADS in hypothesis generation seems inconclusive. The VIADS group took a significantly shorter time to generate each hypothesis. However, the combined validity, significance, and feasibility ratings of their hypotheses were significantly lower. Further characterization of hypotheses, including specifics on how they might be improved, could guide future tool development.
Water droplets containing the SARS-CoV-2 virus, responsible for coronavirus 2019 transmission, were introduced into a controlled-temperature and -humidity chamber. The SARS-CoV-2 virus with green fluorescent protein tag in droplets was used to infect Caco-2 cells, with viability assessed through flow cytometry and microscopic counting. Whereas temperature fluctuations within typical indoor ranges (20°C–30°C) had minimal impact, we observed a notable decrease in infection rate as the surrounding air’s relative humidity increased. By investigating humidity levels between 20% and 70%, we identified a threshold of ≥40% relative humidity as most effective in diminishing SARS-CoV-2 infectivity. We also found that damage of the viral proteins under high relative humidity may be responsible for the decrease in their activity. This outcome supports previous research demonstrating a rise in the concentration of reactive oxygen species within water droplets with elevated relative humidity.
A low-energy proton accelerator named pulsed synchronous linear accelerator (PSLA) is proposed and developed at the Institute of Fluid Physics, which is driven by unipolar-pulsed high voltages. Pulsed-accelerating electric fields and low-energy ion beams are precisely synchronized on temporal and spatial positions for continuous acceleration. The operating mode and the features of the PSLA are introduced. At present, the feasibility of a low-energy proton PSLA has been verified in principle. An average accelerating gradient up to 3 MV/m for protons is achieved.
Craft story recall test in the National Alzheimer’s Coordinating Center Uniformed Data Set 3 (NACC UDS3) neuropsychological battery has been employed to assess verbal memory and assist clinical diagnosis of mild cognitive impairment (MCI) and dementia. While a Chinese version of the test was adapted, no existing literature has examined the diagnostic validity of the test in Chinese Americans. This study aimed to evaluate the predictive validity of both immediate and delayed recall.
Participants and Methods:
The Chinese version of Craft Story was administered in to 78 Chinese participants per their language preference of Mandarin or Cantonese. Outcome measures were verbatim and paraphrase recall of the story immediately and after a 20-minute delay. A multiple linear regression was performed to investigate the association of each outcome measure with age, education, gender, age when moved to the U.S., years in the U.S., and testing language. To assess its diagnostic value, cutoff standard deviation scores of -1.5 and -2.0 from the mean of the clinically cognitive normal participants were generated for MCI and dementia diagnoses, respectively. Due to the small sample size, a normative group fitting the mean age (73 years), years of education (12 years), and the majority gender (female) of the current sample were used to identify standard cut points. A receiver-operating characteristic analysis was used to compare predicted diagnosis with actual clinical diagnosis obtained through patients’ overall performance and a consensus meeting by licensed clinicians.
Results:
Younger age (p < 0.05) and being tested in Mandarin (p < .01) were positively associated with immediate and delayed recall. Strong positive correlations between each measure were observed (all p < .001), indicating a significant relationship between information encoded and retained. Among all the participants, 15 (19.2%) were diagnosed with MCI and 22 (28.2%) with dementia. For MCI diagnosis, the standard cutoff scores demonstrated adequate sensitivity (verbatim=82%, paraphrase=91%) but low specificity (verbatim=44%, paraphrase=67%) in all outcome measures. For dementia diagnosis, delayed recall showed strong sensitivity (100%) and adequate specificity (75%) in both verbatim and paraphrasing scores. Immediate recall paraphrase (sensitivity = 95%, specificity = 50%) showed a better sensitivity but lower specificity than verbatim scoring (sensitivity = 86%, specificity = 58%). The accuracy was higher in delayed recall for both MCI and dementia diagnosis. A preliminary analysis on the optimal cut points indicated higher cutoff scores to distinguish MCI and dementia from clinically cognitive normal population, and from each other (e.g., the optimal cut point for delayed verbatim in distinguishing MCI from normal is 8.0 (sensitivity=89%, specificity=73%, AUC=84.3%)).
Conclusions:
Consistent with previous literature, Craft Story delayed recall served as a more accurate diagnostic tool for both MCI and dementia compared to immediate recall in older Chinese Americans. However, poor specificity might increase the chance of following false positive subjects in clinical trials. In addition, testing language appeared to impact performance on verbal memory recall of constructed information. Thus, future studies should focus on developing normative scores that address both the overall cultural differences of Chinese Americans and the heterogeneity within this population.