Empirical assessment of the assumptions of ComBat with diffusion tensor imaging.

Purpose: Diffusion tensor imaging (DTI) is a magnetic resonance imaging technique that provides unique information about white matter microstructure in the brain but is susceptible to confounding effects introduced by scanner or acquisition differences. ComBat is a leading approach for addressing these site biases. However, despite its frequent use for harmonization, ComBat's robustness toward site dissimilarities and overall cohort size have not yet been evaluated in terms of DTI. Approach: As a baseline, we match N=358 participants from two sites to create a "silver standard" that simulates a cohort for multi-site harmonization. Across sites, we harmonize mean fractional anisotropy and mean diffusivity, calculated using participant DTI data, for the regions of interest defined by the JHU EVE-Type III atlas. We bootstrap 10 iterations at 19 levels of total sample size, 10 levels of sample size imbalance between sites, and 6 levels of mean age difference between sites to quantify (i) ßAGE, the linear regression coefficient of the relationship between FA and age; (ii) Î³/f*, the ComBat-estimated site-shift; and (iii) Î´/f*, the ComBat-estimated site-scaling. We characterize the reliability of ComBat by evaluating the root mean squared error in these three metrics and examine if there is a correlation between the reliability of ComBat and a violation of assumptions. Results: ComBat remains well behaved for ßAGE when N>162 and when the mean age difference is less than 4 years. The assumptions of the ComBat model regarding the normality of residual distributions are not violated as the model becomes unstable. Conclusion: Prior to harmonization of DTI data with ComBat, the input cohort should be examined for size and covariate distributions of each site. Direct assessment of residual distributions is less informative on stability than bootstrap analysis. We caution use ComBat of in situations that do not conform to the above thresholds.

DeepN4: Learning N4ITK Bias Field Correction for T1-weighted Images.

T1-weighted (T1w) MRI has low frequency intensity artifacts due to magnetic field inhomogeneities. Removal of these biases in T1w MRI images is a critical preprocessing step to ensure spatially consistent image interpretation. N4ITK bias field correction, the current state-of-the-art, is implemented in such a way that makes it difficult to port between different pipelines and workflows, thus making it hard to reimplement and reproduce results across local, cloud, and edge platforms. Moreover, N4ITK is opaque to optimization before and after its application, meaning that methodological development must work around the inhomogeneity correction step. Given the importance of bias fields correction in structural preprocessing and flexible implementation, we pursue a deep learning approximation / reinterpretation of the N4ITK bias fields correction to create a method which is portable, flexible, and fully differentiable. In this paper, we trained a deep learning network "DeepN4" on eight independent cohorts from 72 different scanners and age ranges with N4ITK-corrected T1w MRI and bias field for supervision in log space. We found that we can closely approximate N4ITK bias fields correction with naïve networks. We evaluate the peak signal to noise ratio (PSNR) in test dataset against the N4ITK corrected images. The median PSNR of corrected images between N4ITK and DeepN4 was 47.96 dB. In addition, we assess the DeepN4 model on eight additional external datasets and show the generalizability of the approach. This study establishes that incompatible N4ITK preprocessing steps can be closely approximated by naïve deep neural networks, facilitating more flexibility. All code and models are released at https://github.com/MASILab/DeepN4 .

The investigation of sorption-desorption performance and mechanism of copper by surfactant-modified zeolite in aqueous solutions.

As the most common filler in stormwater treatment, zeolite (NZ-Y) has good cation exchange capability and stabilization potential for the removal of heavy metal from aqueous solutions. In this study, sodium dodecyl sulfate (SDS) and NZ-Y were selected to preparing new adsorbent (SDS-NZ) by using a simple hydrothermal method. The sorption-desorption performance and mechanism of Cu(II) onto SDS-NZ were investigated. The results showed that the sorption of Cu(II) on SDS-NZ was in accordance with the pseudo-second-order kinetic model with an equilibrium time of 4 h. The sorption behavior fitted Langmuir isotherm with a saturation sorption capability of 9.03 mg/g, which was three times higher than that of NZ-Y. The modification of SDS increases the average pore size of NZ-Y by 3.96 nm, which results in a richer internal pore structure and more useful sorption sites for Cu(II) sorption. There was a positive correlation between solution pH values and sorption capability of Cu(II) in the range of 3.0-6.0. With the ionic strength increased, the sorption capability of Cu(II) onto SDS-NZ first decreased and then increased, which may be attributed to competitive sorption and compression of the electronic layer. The desorption of Cu(II) on SDS-NZ was favored by the increase in SDS concentration and ionic strength and decrease in solution pH values. The application of SDS-NZ in runoff improved the leaching risk of Cu(II). After several cycles, the ability of reused SDS-NZ to efficiently adsorb most heavy metals was verified with removal rates above 99%.

Evaluation of Mean Shift, ComBat, and CycleGAN for Harmonizing Brain Connectivity Matrices Across Sites.

Connectivity matrices derived from diffusion MRI (dMRI) provide an interpretable and generalizable way of understanding the human brain connectome. However, dMRI suffers from inter-site and between-scanner variation, which impedes analysis across datasets to improve robustness and reproducibility of results. To evaluate different harmonization approaches on connectivity matrices, we compared graph measures derived from these matrices before and after applying three harmonization techniques: mean shift, ComBat, and CycleGAN. The sample comprises 168 age-matched, sex-matched normal subjects from two studies: the Vanderbilt Memory and Aging Project (VMAP) and the Biomarkers of Cognitive Decline Among Normal Individuals (BIOCARD). First, we plotted the graph measures and used coefficient of variation (CoV) and the Mann-Whitney U test to evaluate different methods' effectiveness in removing site effects on the matrices and the derived graph measures. ComBat effectively eliminated site effects for global efficiency and modularity and outperformed the other two methods. However, all methods exhibited poor performance when harmonizing average betweenness centrality. Second, we tested whether our harmonization methods preserved correlations between age and graph measures. All methods except for CycleGAN in one direction improved correlations between age and global efficiency and between age and modularity from insignificant to significant with p-values less than 0.05.

Motor symptoms of Parkinson's disease are affected by temperature: A controlled pilot study.

PURPOSE: The motor symptoms (MS) of Parkinson's disease (PD) have been affecting the quality of life in patients. In clinical practice, most patients with PD report that MS are more severe in winter than in summer, and hyperthermic baths (HTB) could temporarily improve MS. The study aimed to evaluate the effects of seasonal variation and aquatic thermal environment of HTB on the MS of PD. PATIENTS AND METHODS: A cross-sectional study of 203 Chinese Han patients was performed. Univariate and multivariate analyses were performed to analyze seasonal variation in MS relative to baseline data (sex, age at onset, duration, season of birth, Hoehn and Yahr stage, family history, levodopa equivalent dose, and the effect of HTB on MS). Ten subjects participated in the HTB study, and one patient dropped out. The paired Wilcoxon rank test was used to assess the differences in the Movement Disorder Society-United Parkinson's disease Rating Scale (MDS-UPDRS) part III motor examination total scores and the modified Webster Symptoms Score between non-HTB and before HTB and between non-HTB and after HTB. RESULTS: The improvement of MS after HTB was an independent risk factor for seasonal variation in MS (OR, 25.203; 95% CI, 10.951-58.006; p = .000). Patients with PD had significant improvements in the MDS-UPDRS part III motor examination total scores, especially in bradykinesia (p = .043), rigidity (p = .008), posture (p = .038), and rest tremor amplitude (p = .047). CONCLUSION: Seasonal variation in temperature and water temperature of HTB may affect MS in some patients with PD. Simple HTB could be recommended as physiotherapy for patients with PD who report temperature-sensitive MS.

Characterizing patterns of DTI variance in aging brains.

Background: As large analyses merge data across sites, a deeper understanding of variance in statistical assessment across the sources of data becomes critical for valid analyses. Diffusion tensor imaging (DTI) exhibits spatially varying and correlated noise, so care must be taken with distributional assumptions. Purpose: We characterize the role of physiology, subject compliance, and the interaction of subject with the scanner in the understanding of DTI variability, as modeled in spatial variance of derived metrics in homogeneous regions. Methods: We analyze DTI data from 1035 subjects in the Baltimore Longitudinal Study of Aging (BLSA), with ages ranging from 22.4 to 103 years old. For each subject, up to 12 longitudinal sessions were conducted. We assess variance of DTI scalars within regions of interest (ROIs) defined by four segmentation methods and investigate the relationships between the variance and covariates, including baseline age, time from the baseline (referred to as "interval"), motion, sex, and whether it is the first scan or the second scan in the session. Results: Covariate effects are heterogeneous and bilaterally symmetric across ROIs. Inter-session interval is positively related (p ≪ 0.001) to FA variance in the cuneus and occipital gyrus, but negatively (p ≪ 0.001) in the caudate nucleus. Males show significantly (p ≪ 0.001) higher FA variance in the right putamen, thalamus, body of the corpus callosum, and cingulate gyrus. In 62 out of 176 ROIs defined by the Eve type-1 atlas, an increase in motion is associated (p < 0.05) with a decrease in FA variance. Head motion increases during the rescan of DTI (Δµ = 0.045 millimeters per volume). Conclusions: The effects of each covariate on DTI variance, and their relationships across ROIs are complex. Ultimately, we encourage researchers to include estimates of variance when sharing data and consider models of heteroscedasticity in analysis. This work provides a foundation for study planning to account for regional variations in metric variance.

Predicting Age from White Matter Diffusivity with Residual Learning.

Imaging findings inconsistent with those expected at specific chronological age ranges may serve as early indicators of neurological disorders and increased mortality risk. Estimation of chronological age, and deviations from expected results, from structural magnetic resonance imaging (MRI) data has become an important proxy task for developing biomarkers that are sensitive to such deviations. Complementary to structural analysis, diffusion tensor imaging (DTI) has proven effective in identifying age-related microstructural changes within the brain white matter, thereby presenting itself as a promising additional modality for brain age prediction. Although early studies have sought to harness DTI's advantages for age estimation, there is no evidence that the success of this prediction is owed to the unique microstructural and diffusivity features that DTI provides, rather than the macrostructural features that are also available in DTI data. Therefore, we seek to develop white-matter-specific age estimation to capture deviations from normal white matter aging. Specifically, we deliberately disregard the macrostructural information when predicting age from DTI scalar images, using two distinct methods. The first method relies on extracting only microstructural features from regions of interest (ROIs). The second applies 3D residual neural networks (ResNets) to learn features directly from the images, which are non-linearly registered and warped to a template to minimize macrostructural variations. When tested on unseen data, the first method yields mean absolute error (MAE) of 6.11 ± 0.19 years for cognitively normal participants and MAE of 6.62 ± 0.30 years for cognitively impaired participants, while the second method achieves MAE of 4.69 ± 0.23 years for cognitively normal participants and MAE of 4.96 ± 0.28 years for cognitively impaired participants. We find that the ResNet model captures subtler, non-macrostructural features for brain age prediction.

Reproducibility evaluation of the effects of MRI defacing on brain segmentation.

Purpose: Recent advances in magnetic resonance (MR) scanner quality and the rapidly improving nature of facial recognition software have necessitated the introduction of MR defacing algorithms to protect patient privacy. As a result, there are a number of MR defacing algorithms available to the neuroimaging community, with several appearing in just the last 5 years. While some qualities of these defacing algorithms, such as patient identifiability, have been explored in the previous works, the potential impact of defacing on neuroimage processing has yet to be explored. Approach: We qualitatively evaluate eight MR defacing algorithms on 179 subjects from the OASIS-3 cohort and 21 subjects from the Kirby-21 dataset. We also evaluate the effects of defacing on two neuroimaging pipelines-SLANT and FreeSurfer-by comparing the segmentation consistency between the original and defaced images. Results: Defacing can alter brain segmentation and even lead to catastrophic failures, which are more frequent with some algorithms, such as Quickshear, MRI_Deface, and FSL_deface. Compared to FreeSurfer, SLANT is less affected by defacing. On outputs that pass the quality check, the effects of defacing are less pronounced than those of rescanning, as measured by the Dice similarity coefficient. Conclusions: The effects of defacing are noticeable and should not be disregarded. Extra attention, in particular, should be paid to the possibility of catastrophic failures. It is crucial to adopt a robust defacing algorithm and perform a thorough quality check before releasing defaced datasets. To improve the reliability of analysis in scenarios involving defaced MRIs, it is encouraged to include multiple brain segmentation pipelines.

DeepN4: Learning N4ITK Bias Field Correction for T1-weighted Images.

T1-weighted (T1w) MRI has low frequency intensity artifacts due to magnetic field inhomogeneities. Removal of these biases in T1w MRI images is a critical preprocessing step to ensure spatially consistent image interpretation. N4ITK bias field correction, the current state-of-the-art, is implemented in such a way that makes it difficult to port between different pipelines and workflows, thus making it hard to reimplement and reproduce results across local, cloud, and edge platforms. Moreover, N4ITK is opaque to optimization before and after its application, meaning that methodological development must work around the inhomogeneity correction step. Given the importance of bias fields correction in structural preprocessing and flexible implementation, we pursue a deep learning approximation / reinterpretation of the N4ITK bias fields correction to create a method which is portable, flexible, and fully differentiable. In this paper, we trained a deep learning network "DeepN4" on eight independent cohorts from 72 different scanners and age ranges with N4ITK-corrected T1w MRI and bias field for supervision in log space. We found that we can closely approximate N4ITK bias fields correction with naïve networks. We evaluate the peak signal to noise ratio (PSNR) in test dataset against the N4ITK corrected images. The median PSNR of corrected images between N4ITK and DeepN4 was 47.96 dB. In addition, we assess the DeepN4 model on eight additional external datasets and show the generalizability of the approach. This study establishes that incompatible N4ITK preprocessing steps can be closely approximated by naïve deep neural networks, facilitating more flexibility. All code and models are released at https://github.com/MASILab/DeepN4.

Enhanced photocatalytic activity of Ag@g-C3N4 nanotubes by regulating photogenerated carriers to bypass the recombination center under the Lorentz force.

Photocatalysis, as a form of solar energy conversion, has considerable development prospects for solving energy exhaustion and environmental pollution. Promoting the utilisation of photocarriers is the key way to enhance photocatalytic activity and quantum efficiency. The g-C3N4 with the width of the band gap responsive to visible light, which is a great concern for researchers, was prepared by thermal decomposition and the insides were stripped from the outer wall and then curled to form the nanotubes (NTs), microtubes and shorten the migration distance of the electrons and holes. To promote the separation of the photocarriers in the g-C3N4, Ag particles are deposited by photoreduction as electron "traps" with surface plasmon resonance (SPR), and an external magnetic field is introduced during the photocatalysis. Under the Lorentz force, the photocatalytic efficiency of the Ag@g-C3N4 NTs is 200% higher than that of bulk g-C3N4, as a result of being able to prolong the life of the photogenerated carriers to bypass the recombination sites.

A reproducibility evaluation of the effects of MRI defacing on brain segmentation.

Purpose: Recent advances in magnetic resonance (MR) scanner quality and the rapidly improving nature of facial recognition software have necessitated the introduction of MR defacing algorithms to protect patient privacy. As a result, there are a number of MR defacing algorithms available to the neuroimaging community, with several appearing in just the last five years. While some qualities of these defacing algorithms, such as patient identifiability, have been explored in previous works, the potential impact of defacing on neuroimage processing has yet to be explored. Approach: We qualitatively evaluate eight MR defacing algorithms on 179 subjects from the OASIS-3 cohort and the 21 subjects from the Kirby-21 dataset. We also evaluate the effects of defacing on two neuroimaging pipelines-SLANT and FreeSurfer-by comparing the segmentation consistency between the original and defaced images. Results: Defacing can alter brain segmentation and even lead to catastrophic failures, which are more frequent with some algorithms such as Quickshear, MRI_Deface, and FSL_deface. Compared to FreeSurfer, SLANT is less affected by defacing. On outputs that pass the quality check, the effects of defacing are less pronounced than those of rescanning, as measured by the Dice similarity coefficient. Conclusions: The effects of defacing are noticeable and should not be disregarded. Extra attention, in particular, should be paid to the possibility of catastrophic failures. It is crucial to adopt a robust defacing algorithm and perform a thorough quality check before releasing defaced datasets. To improve the reliability of analysis in scenarios involving defaced MRIs, it's encouraged to include multiple brain segmentation pipelines.

Wavelength-tunable noise-like fiber laser based on PbS quantum dot saturable absorber film.

A wavelength-tunable noise-like pulse (NLP) erbium-doped fiber laser incorporating PbS quantum dot (QD) polystyrene (PS) composite film as a saturable absorber (SA) is experimentally demonstrated. The wavelength tuning is implemented via a Lyot filter consisting of a segment of polarization-maintaining fiber (PMF) and a 45° tilted fiber grating. By adjusting the polarization state of the ring cavity, the laser can deliver NLP with a continuous wavelength-tunable range from 1550.21 to 1560.64 nm. During continuous wavelength tuning, the output power varies between a range of 30.88-48.8 mW. Worthwhile noting is that the output power of 48.8 mW is the reported highest output power for wavelength-tunable NLP operation in an erbium-doped fiber laser using composite film as a saturable absorber.

Micro-physical parameter dynamic evolution behaviour of a natural ester molecular chain under a changing electric field and its correlation mechanism with lightning impulse discharge: theoretical analysis.

The lightning impulse breakdown properties of natural esters are very important for their further applications. This paper focuses on the discharge mechanism investigation of a natural ester insulating liquid under a lightning impulse electric field. Based on density functional theory (DFT), the configuration, electron structure, ionization and electron affinity process, excitation process and molecular orbital of natural ester molecules were calculated under different electric field strengths. A correlation mechanism between the micro-physical parameters of ester insulating liquid molecules and discharge was proposed. The molecular electrostatic potential was used to predict the active point of discharge. The results show that the molecular structure of triglycerides shows yield behaviour under electric field action. The electrons are redistributed in the direction of the source of the electric field. Among the four triglycerides, the ionization and electron affinity process, excitation process and molecular orbital of glycerol tripalmitate were least affected by the electric field. The microscopic properties of other triglycerides were significantly affected by the electric field. According to the electrostatic potential (ESP) result of natural ester molecules, it can be predicted in the experiment that the surface of H atoms of the triglyceride ester group easily forms electron traps to bind electrons, while the surface of an O atom at the ester of a triglyceride undergoes electron collisions resulting in an electrical discharge. The proportion of palmitic acid in natural esters could be increased or pure glycerol tripalmitate could be used as an insulating oil to solve the problem of the low lightning impulse breakdown voltage of natural esters.

Can the carbon emission trading scheme promote corporate environmental protection investment in China?

Since 2013, a pilot market of carbon emission trading scheme (ETS) has operated in China, with results showing a reduction in the carbon intensity of the economy. How does ETS affect enterprises' environmental protection behaviors? We conducted a quasinatural experiment by using A-share-listed companies from 2010 to 2019 as research samples and matching them using the propensity score matching method. The difference in differences model was then used to empirically assess the effect and influencing mechanism of the ETS on corporate environmental investment. Finally, the robustness and heterogeneity of the empirical results were checked. ETS seems to have significantly increased the levels of capitalized and expense-based environmental protection investment. Among them, low-carbon technological innovation plays an intermediary role in the impact of ETS on capitalized environmental protection investment. State-owned enterprises preferred capitalized environmental protection investment, whereas private enterprises preferred expense-based environmental protection investment. Moreover, having a political connection could compromise the role of ETS in promoting environmental protection investment. Our study provides some suggestions, such as accelerating the rollout of low-carbon technologies, increasing financial support for private enterprises, establishing an official reputation mechanism, and strengthening the transparency of carbon emission information disclosure.

The effects of loving-kindness and compassion meditation on life satisfaction: A systematic review and meta-analysis.

Loving-kindness and compassion meditation (LKCM) was a promising intervention for improving life satisfaction, but previous findings have been inconsistent. The current study provides a systematic review and meta-analysis, including 23 empirical studies on LKCM with life satisfaction as an outcome variable. The primary meta-analysis indicated that LKCM significantly enhanced life satisfaction in pre-post design (g = 0.312, k = 15, n = 451), but the significance disappeared in the additional meta-analysis based on randomized controlled trials (g = 0.106, k = 6, n = 404). Moderator analyses found significant effects for type of control (i.e., the effects of LKCM were inferior to active control group, but superior to waitlist condition), but not for other moderators (i.e., participant type, previous meditation experience, specific protocol, components of LKCM, combination with mindfulness mediation, and intervention length). Narrative review identified self-compassion and positive emotions as important mediators. The practice time of LKCM had indirect but not direct association with life satisfaction. The findings supported that LKCM is promising in increasing life satisfaction, but more studies are needed to investigate the effects with more rigorous designs. Future studies should investigate other potential mechanisms and clarify whether LKCM change the reality or the perception of life.

Structured Multimodal Attentions for TextVQA.

Text based Visual Question Answering (TextVQA) is a recently raised challenge requiring models to read text in images and answer natural language questions by jointly reasoning over the question, textual information and visual content. Introduction of this new modality - Optical Character Recognition (OCR) tokens ushers in demanding reasoning requirements. Most of the state-of-the-art (SoTA) VQA methods fail when answer these questions because of three reasons: (1) poor text reading ability; (2) lack of textual-visual reasoning capacity; and (3) choosing discriminative answering mechanism over generative couterpart (although this has been further addressed by M4C). In this paper, we propose an end-to-end structured multimodal attention (SMA) neural network to mainly solve the first two issues above. SMA first uses a structural graph representation to encode the object-object, object-text and text-text relationships appearing in the image, and then designs a multimodal graph attention network to reason over it. Finally, the outputs from the above modules are processed by a global-local attentional answering module to produce an answer splicing together tokens from both OCR and general vocabulary iteratively by following M4C. Our proposed model outperforms the SoTA models on TextVQA dataset and two tasks of ST-VQA dataset among all models except pre-training based TAP. Demonstrating strong reasoning ability, it also won first place in TextVQA Challenge 2020. We extensively test different OCR methods on several reasoning models and investigate the impact of gradually increased OCR performance on TextVQA benchmark. With better OCR results, different models share dramatic improvement over the VQA accuracy, but our model benefits most blessed by strong textual-visual reasoning ability. To grant our method an upper bound and make a fair testing base available for further works, we also provide human-annotated ground-truth OCR annotations for the TextVQA dataset, which were not given in the original release. The code and ground-truth OCR annotations for the TextVQA dataset are available at https://github.com/ChenyuGAO-CS/SMA.

Alisol A Alleviates Arterial Plaque by Activating AMPK/SIRT1 Signaling Pathway in apoE-Deficient Mice.

A lismatis Rhizoma (zexie), an herb used in traditional Chinese medicine, exhibits hypolipemic, anti-inflammation and anti-atherosclerotic activities. Alisol A is one of the main active ingredients in Alismatis Rhizoma extract. In this study, we investigate the role of alisol A in anti-atherosclerosis (AS). Our study demonstrated that alisol A can effectively inhibit the formation of arterial plaques and blocked the progression of AS in ApoE-/- mice fed with high-fat diet and significantly reduced the expression of inflammatory cytokins in aorta, including ICAM-1, IL-6, and MMP-9. In addition, we found that alisol A increased the expression of PPARα and PPARδ proteins in HepG2 cells and in liver tissue from ApoE-/- mice. Alisol A activated the AMPK/SIRT1 signaling pathway and NF-κB inhibitor IκBα in HepG2 cells. Our results suggested that alisol A is a multi-targeted agent that exerts anti-atherosclerotic action by regulating lipid metabolism and inhibiting inflammatory cytokine production. Therefore, alisol could be a promising lead compound to develop drugs for the treatment of AS.

A Han Chinese Family With Early-Onset Parkinson's Disease Carrying Novel Frameshift Mutation and Compound Heterozygous Mutation of PRKN Appearing Incompatible With MDS Clinical Diagnostic Criteria.

Around 15% of patients with Parkinson's disease (PD) have a family history, and 5-10% have confirmed genetic causes. PRKN is the most common gene responsible for early-onset Parkinson's disease (EOPD), while rare variants of PLA2G6 likely raise PD susceptibility in the Chinese population. We investigated the genetic information of 13 members of a Han Chinese family with known EOPD by whole-exome sequencing and Sanger sequencing, and analyzed the clinical history, physical examination, blood laboratory test, and brain imaging data of the patients. Two members, including the proband, were suspected of having EOPD. A novel homozygous frameshift mutation, c.856delT, and a compound heterozygous mutation, c.1321T>C/c.856delT of PRKN, were identified, as well as two single nucleotide variants of PLA2G6 and TENM4. The proband exhibited a rare symmetrical resting tremor limited to her lower limbs and never exhibited signs of rigidity. 18F-DOPA PET/CT scan indicated a symmetrical reduced signaling in the striatum. The novel frameshift mutation and compound heterozygous mutation of PRKN are likely to be the genetic causes of EOPD in this family.

Han Chinese family with early-onset Parkinson's disease carries novel compound heterozygous mutations in the PARK2 gene.

PURPOSE: To identify deletions, duplications, and point mutations in 55 previously reported genes associated with Parkinson's disease (PD) and certain genes associated with tremor, spinocerebellar ataxia, and dystonia in a Han Chinese pedigree with early-onset Parkinson's disease (EOPD). PATIENTS AND METHODS: Clinical examinations and genomic analyses were performed on six subjects belonging to three generations of a Han Chinese family. Target region capture and high-throughput sequencing were used to screen these genes associated with PD, tremor, spinocerebellar ataxia, and dystonia. The multiplex ligation-dependent probe amplification (MLPA) method was applied to detect rearrangements in PARK2 exons. Direct Sanger sequencing of samples from all subjects further verified the detected abnormal PRKRA, SPTBN2, and ATXN2 gene fragments. RESULTS: Two family members were diagnosed with PD based on the clinical manifestations, imaging analyses. PARK2 gene heterozygous deletion of exon 3 and heterozygous duplication of exon 6 were identified in them (II-3 and 4). A single heterozygous deletion of exon 3 in PARK2 was detected in II-5 and III-10. A single duplication of exon 6 in PARK2 was detected in I1. Both the heterozygous mutation c.2834G>A (p. R945H) in exon 16 and the heterozygous mutation c.1924 C>T (p. R642W) in exon 14 of the SPTBN2 gene were identified in II-3, II-4, and III-10. The heterozygous mutation c.2989 C>T (p. R997X) in exon 24 of the ATXN2 gene was detected in II-4 and II-5, and the heterozygous mutation c.170 C>A (p. S57Y) in exon 2 of the PRKRA gene was detected in II-3, II-4, and III-10. Other mutations in some genes associated with PD, tremor, spinocerebellar ataxia, and dystonia were not detected. CONCLUSIONS: Novel compound heterozygous mutations were identified in a Han Chinese pedigree and might represent a cause of EOPD.