NeRF-Texture: Synthesizing Neural Radiance Field Textures.

Texture synthesis is a fundamental problem in computer graphics that would benefit various applications. Existing methods are effective in handling 2D image textures. In contrast, many real-world textures contain meso-structure in the 3D geometry space, such as grass, leaves, and fabrics, which cannot be effectively modeled using only 2D image textures. We propose a novel texture synthesis method with Neural Radiance Fields (NeRF) to capture and synthesize textures from given multi-view images. In the proposed NeRF texture representation, a scene with fine geometric details is disentangled into the meso-structure textures and the underlying base shape. This allows textures with meso-structure to be effectively learned as latent features situated on the base shape, which are fed into a NeRF decoder trained simultaneously to represent the rich view-dependent appearance. Using this implicit representation, we can synthesize NeRF-based textures through patch matching of latent features. However, inconsistencies between the metrics of the reconstructed content space and the latent feature space may compromise the synthesis quality. To enhance matching performance, we further regularize the distribution of latent features by incorporating a clustering constraint. In addition to generating NeRF textures over a planar domain, our method can also synthesize NeRF textures over curved surfaces, which are practically useful. Experimental results and evaluations demonstrate the effectiveness of our approach.

A brief version of the Attitudes to Ageing Questionnaire for older Chinese adults: development and psychometric evaluation.

BACKGROUND: Positive attitudes toward aging are considered essential for achieving psychological well-being in later life. However, there is currently a lack of a concise and comprehensive measurement tool specifically designed to assess attitudes toward aging among the elderly population in China. To address this gap, the present study aimed to develop a brief version of the Attitudes to Ageing Questionnaire tailored to older Chinese individuals and evaluate its psychometric properties. METHODS: Initially, a sample of community-dwelling older adults (Sample 1: n = 442, aged 60-88) was utilized to establish a new scale format. Subsequently, two convenience samples (Sample 2: n = 311, aged 60-90; Sample 3: n = 164, aged 60-89) were employed to evaluate the psychometric properties of this scale, including factor structure, internal consistency, test-retest reliability, convergent validity, and discriminant validity. RESULTS: We selected 12 items from the original questionnaire to create the brief scale. The brief scale maintained the three-factor structure of the full-format version, encompassing psychosocial loss, physical change, and psychological growth, and demonstrated adequate psychometric properties. CONCLUSIONS: This development process shortens the administration time of the questionnaire while avoiding excessive loss of information. The newly developed scale serves as a reliable and valid assessment tool for measuring attitudes toward aging among older Chinese individuals and is well-suited for implementation in large-scale surveys that utilize an extensive array of questionnaires. This tool can be applied to assessing the effectiveness of interventions aimed at eliminating ageism.

Plant diversity increases spatial stability of aboveground productivity in alpine grasslands.

Plant diversity can significantly affect the grassland productivity and its stability. However, it remains unclear how plant diversity affects the spatial stability of natural grassland productivity, especially in alpine regions that are sensitive to climate change. We analyzed the interaction between plant (species richness and productivity, etc.) and climatic factors (precipitation, temperature, and moisture index, etc.) of alpine natural grassland on the Qinghai-Tibetan Plateau. In addition, we tested the relationship between plant diversity and spatial stability of grassland productivity. Results showed that an increase in plant diversity significantly enhanced community productivity and its standard deviation, while reducing the coefficient of variation in productivity. The influence of plant diversity on productivity and the reciprocal of productivity variability coefficient was not affected by vegetation types. The absolute values of the regression slopes between climate factors and productivity in alpine meadow communities with higher plant diversity were smaller than those in alpine meadow communities with lower plant diversity. In other words, alpine meadow communities with higher plant diversity exhibited a weaker response to climatic factors in terms of productivity, whereas those with lower plant diversity showed a stronger response. Our results indicate that high plant diversity buffers the impact of ambient pressure (e.g., precipitation, temperature) on alpine meadow productivity, and significantly enhanced the spatial stability of grassland productivity. This finding provides a theoretical basis for maintaining the stability of grassland ecosystems and scientifically managing alpine grasslands under the continuous climate change.

Recognition of 3D Images by Fusing Fractional-Order Chebyshev Moments and Deep Neural Networks.

In order to achieve efficient recognition of 3D images and reduce the complexity of network parameters, we proposed a novel 3D image recognition method combining deep neural networks with fractional-order Chebyshev moments. Firstly, the fractional-order Chebyshev moment (FrCM) unit, consisting of Chebyshev moments and the three-term recurrence relation method, is calculated separately using successive integrals. Next, moment invariants based on fractional order and Chebyshev moments are utilized to achieve invariants for image scaling, rotation, and translation. This design aims to enhance computational efficiency. Finally, the fused network embedding the FrCM unit (FrCMs-DNNs) extracts depth features to analyze the effectiveness from the aspects of parameter quantity, computing resources, and identification capability. Meanwhile, the Princeton Shape Benchmark dataset and medical images dataset are used for experimental validation. Compared with other deep neural networks, FrCMs-DNNs has the highest accuracy in image recognition and classification. We used two evaluation indices, mean square error (MSE) and peak signal-to-noise ratio (PSNR), to measure the reconstruction quality of FrCMs after 3D image reconstruction. The accuracy of the FrCMs-DNNs model in 3D object recognition was assessed through an ablation experiment, considering the four evaluation indices of accuracy, precision, recall rate, and F1-score.

Engineering Heterostructured Fe-Co-P Arrays for Robust Sodium Storage.

Transition metal phosphides attract extensive concerns thanks to their high theoretical capacity in sodium ion batteries (SIBs). Nevertheless, the substantial volume fluctuation of metal phosphides during cycling leads to severe capacity decay, which largely hinders their large-scale deployment. In this regard, heterostructured Fe-Co-P (FeP/Co2P) arrays are firstly constructed in this work for SIBs. The novel self-supported construction without insulated binders favors fast charge migration and Na+ ion diffusion. In addition, the special heterostructure with abundant heterointerfaces could considerably mitigate the volume change during (de)sodiation and provide increased active sites for Na+ ions. Density functional theoretical (DFT) calculations confirm the built-in electric field in the heterointerfaces, which greatly hastens charge transfer and Na+ ion transportation, thereafter bringing about enhanced electrochemical performance. Most importantly, the FeP/Co2P heterostructure discloses higher electrical conductivity than that of bare FeP and Co2P based on the theoretical calculations. As anticipated, the heterostructured Fe-Co-P arrays demonstrate superior performance to that of Fe-P or Co-P anode, delivering high reversible capacities of 634 mAh g-1 at 0.2 A g-1 and 239 mAh g-1 at 1 A g-1 after 300 cycles.

DeepCBS: shedding light on the impact of mutations occurring at CTCF binding sites.

CTCF-mediated chromatin loops create insulated neighborhoods that constrain promoter-enhancer interactions, serving as a unit of gene regulation. Disruption of the CTCF binding sites (CBS) will lead to the destruction of insulated neighborhoods, which in turn can cause dysregulation of the contained genes. In a recent study, it is found that CTCF/cohesin binding sites are a major mutational hotspot in the cancer genome. Mutations can affect CTCF binding, causing the disruption of insulated neighborhoods. And our analysis reveals a significant enrichment of well-known proto-oncogenes in insulated neighborhoods with mutations specifically occurring in anchor regions. It can be assumed that some mutations disrupt CTCF binding, leading to the disruption of insulated neighborhoods and subsequent activation of proto-oncogenes within these insulated neighborhoods. To explore the consequences of such mutations, we develop DeepCBS, a computational tool capable of analyzing mutations at CTCF binding sites, predicting their influence on insulated neighborhoods, and investigating the potential activation of proto-oncogenes. Futhermore, DeepCBS is applied to somatic mutation data of liver cancer. As a result, 87 mutations that disrupt CTCF binding sites are identified, which leads to the identification of 237 disrupted insulated neighborhoods containing a total of 135 genes. Integrative analysis of gene expression differences in liver cancer further highlights three genes: ARHGEF39, UBE2C and DQX1. Among them, ARHGEF39 and UBE2C have been reported in the literature as potential oncogenes involved in the development of liver cancer. The results indicate that DQX1 may be a potential oncogene in liver cancer and may contribute to tumor immune escape. In conclusion, DeepCBS is a promising method to analyze impacts of mutations occurring at CTCF binding sites on the insulator function of CTCF, with potential extensions to shed light on the effects of mutations on other functions of CTCF.

Current clinical evidence is insufficient to support HMME-PDT as the first choice of treatment for young children with port wine birthmarks.

BACKGROUND: Port wine birthmark (PWB) is a congenital vascular malformation of the skin. Pulsed dye laser (PDL) is the "gold standard" for the treatment of PWB globally. Hematoporphyrin monomethyl ether (HMME or hemoporfin)-mediated photodynamic therapy (HMME-PDT) has emerged as the first choice for PWB treatment, particularly for young children, in many major hospitals in China during the past several decades. AIM: To evaluate whether HMME-PDT is superior to PDL by comparing the clinical efficacies of both modalities. METHOD: PubMed records were searched for all relevant studies of PWB treatment using PDL (1988-2023) or HMME-PDT (2007-2023). Patient characteristics and clinical efficacies were extracted. Studies with a quartile percentage clearance or similar scale were included. A mean color clearance index (CI) per study was calculated and compared among groups. An overall CI (C0), with data weighted by cohort size, was used to evaluate the final efficacy for each modality. RESULT: A total of 18 HMME-PDT studies with 3910 patients in China were eligible for inclusion in this analysis. Similarly, 40 PDL studies with 5094 patients from nine different countries were eligible for inclusion in this analysis. Over 58% of patients in the HMME-PDT studies were minors (<18 years old). A significant portion (21.3%) were young children (<3 years old). Similarly, 33.2% of patients in the PDL studies were minors. A small proportion (9.3%) was young children. The overall clearance rates for PDL were slightly, but not significantly, higher than those for HMME-PDT in cohorts with patients of all ages (C0, 0.54 vs. 0.48, p = 0.733), subpopulations with only minors (C0, 0.54 vs. 0.46, p = 0.714), and young children (C0, 0.67 vs. 0.50, p = 0.081). Regrettably, there was a lack of long-term data on follow-up evaluations for efficacy and impact of HMME-PDT on young children in general, and central nervous system development in particular, because their blood-brain barriers have a greater permeability as compared to adults. CONCLUSION: PDL shows overall albeit insignificantly higher clearance rates than HMME-PDT in patients of all ages; particularly statistical significance is nearly achieved in young children. Collectively, current evidence is insufficient to support HMME-PDT as the first choice of treatment of PWBs in young children given: (1) overall inferior efficacy as compared to PDL; (2) risk of off-target exposure to meningeal vasculature during the procedure; (3) administration of steriods for mitigation of side effects; -and (4) lack of long-term data on the potential impact of HMME on central nervous system development in young children.

Adjustment of scRNA-seq data to improve cell-type decomposition of spatial transcriptomics.

Most sequencing-based spatial transcriptomics (ST) technologies do not achieve single-cell resolution where each captured location (spot) may contain a mixture of cells from heterogeneous cell types, and several cell-type decomposition methods have been proposed to estimate cell type proportions of each spot by integrating with single-cell RNA sequencing (scRNA-seq) data. However, these existing methods did not fully consider the effect of distribution difference between scRNA-seq and ST data for decomposition, leading to biased cell-type-specific genes derived from scRNA-seq for ST data. To address this issue, we develop an instance-based transfer learning framework to adjust scRNA-seq data by ST data to correctly match cell-type-specific gene expression. We evaluate the effect of raw and adjusted scRNA-seq data on cell-type decomposition by eight leading decomposition methods using both simulated and real datasets. Experimental results show that data adjustment can effectively reduce distribution difference and improve decomposition, thus enabling for a more precise depiction on spatial organization of cell types. We highlight the importance of data adjustment in integrative analysis of scRNA-seq with ST data and provide guidance for improved cell-type decomposition.

Sulfur-bridged bonds enabled structure modulation and space confinement of MnS for superior sodium-ion capacitors.

Manganese sulfide (MnS) is a promising converion-type anode for sodium storage, owing to the virtues of high theoretical capacity, coupled with it crustal abundance and cost-effectiveness. Nevertheless, MnS suffers from inadequate electronic conductivity, sluggish Na+ reaction kinetics and considerable volume variation during discharge/charge process, thereby impeding its rate capability and capacity retention. Herein, a novel lamellar heterostructured composite of Fe-doped MnS nanoparticles/positively charged reduced graphene oxide (Fe-MnS/PG) was synthesized to overcome these issues. The Fe-doping can accelerate the ion/electron transfer, endowing fast electrochemical kinetics of MnS. Meanwhile, the graphene space confinement with strong MnSC bond interactions can facilite the interfacial electron transfer, hamper volume expansion and aggregation of MnS nanoparticles, stabilizing the structural integrity, thus improving the Na+ storage reversibility and cyclic stability. Combining the synergistic effect of Fe-doping and space confinement with strong MnSC bond interactions, the as-produced Fe-MnS/PG anode presents a remarkable capacity of 567 mAh/g at 0.1 A/g and outstanding rate performance (192 mAh/g at 10 A/g). Meanwhile, the as-assembled sodium-ion capacitor (SIC) can yield a high energy density of 119 Wh kg-1 and a maximum power density of 17500 W kg-1, with capacity retention of 77 % at 1 A/g after 5000 cycles. This work offers a promising strategy to develop MnS-based practical SICs with high energy and long lifespan, and paves the way for fabricating advanced anode materials.

Contrastive pre-training and 3D convolution neural network for RNA and small molecule binding affinity prediction.

MOTIVATION: The diverse structures and functions inherent in RNAs present a wealth of potential drug targets. Some small molecules are anticipated to serve as leading compounds, providing guidance for the development of novel RNA-targeted therapeutics. Consequently, the determination of RNA-small molecule binding affinity is a critical undertaking in the landscape of RNA-targeted drug discovery and development. Nevertheless, to date, only one computational method for RNA-small molecule binding affinity prediction has been proposed. The prediction of RNA-small molecule binding affinity remains a significant challenge. The development of a computational model is deemed essential to effectively extract relevant features and predict RNA-small molecule binding affinity accurately. RESULTS: In this study, we introduced RLaffinity, a novel deep learning model designed for the prediction of RNA-small molecule binding affinity based on 3D structures. RLaffinity integrated information from RNA pockets and small molecules, utilizing a 3D convolutional neural network (3D-CNN) coupled with a contrastive learning-based self-supervised pre-training model. To the best of our knowledge, RLaffinity was the first deep learning based method for the prediction of RNA-small molecule binding affinity. Our experimental results exhibited RLaffinity's superior performance compared to baseline methods, revealed by all metrics. The efficacy of RLaffinity underscores the capability of 3D-CNN to accurately extract both global pocket information and local neighbor nucleotide information within RNAs. Notably, the integration of a self-supervised pre-training model significantly enhanced predictive performance. Ultimately, RLaffinity was also proved as a potential tool for RNA-targeted drugs virtual screening. AVAILABILITY AND IMPLEMENTATION: https://github.com/SaisaiSun/RLaffinity.

Treatment outcome, toxicity, and quality of life of patients with bronchus-associated lymphoid tissue lymphoma.

The disease failure patterns and optimal treatment of bronchus-associated lymphoid tissue (BALT) lymphoma are unknown. This retrospective study involved 71 patients with primary BALT lymphoma who had received radiotherapy (RT), surgery, immunochemotherapy (IC), or observation. The median follow-up time was 66 months. The 5-year overall survival and lymphoma-specific survival were 91.2% and 96.1%, respectively, and were not significantly different among treatments. The 5-year cumulative incidence of overall failure for RT, surgery, IC, and observation was 0%, 9.7% (p = .160), 30.8% (p = .017), and 31.3% (p = .039). There was no grade ≥3 toxicity in RT group according to the CTCAE 5.0 reporting system. Quality of life (QoL) was at similarly good levels among the treatment groups. BALT lymphoma had a favorable prognosis but persistent risk of relapse after IC or observation. Given the very low disease failure risk and good QoL, RT remains an effective initial treatment for BALT lymphoma.Key PointsBALT lymphoma has a favorable prognosis but a persistent progression and relapse risk.Radiotherapy is associated with lower failure of disease progression and relapse, low toxicity and good quality of life.

Protective effect of liver X receptor on cigarette smoke and lipopolysaccharide induced airway inflammation and emphysema in mice.

OBJECTIVE: The aim of this study is to assess the impact of Liver X receptors (LXRs) on airway inflammation, airway remodeling, and lipid deposition induced by cigarette smoke and lipopolysaccharide (LPS) exposure in the lung. METHODS: Wild mice and LXR-deficient mice were exposed to cigarette smoke and LPS to induce airway inflammation and remodeling. In addition, some wild mice received intraperitoneal treatment with the LXR agonist GW3965 before exposure to cigarette smoke and LPS. Lung tissue and bronchoalveolar lavage fluid were collected to evaluate airway inflammation, airway remodeling and lipid deposition. RESULTS: Exposure to cigarette smoke and LPS resulted in airway inflammation, emphysema and lipid accumulation in wild mice. These mice also exhibited downregulated LXRα and ABCA1 in the lung. Treatment with GW3965 mitigated inflammation, remodeling and lipid deposition, while the deletion of LXRs exacerbated these effects. Furthermore, GW3965 treatment following exposure to cigarette smoke and LPS increased LXRα and ABCA1 expression and attenuated MyD88 expression in wild mice. CONCLUSION: LXRs demonstrate the potential to mitigate cigarette smoke and LPS- induced airway inflammation, emphysema and lipid disposition in mice.

Inhibition of RhoGEF/RhoA alleviates regorafenib resistance and cancer stemness via Hippo signaling pathway in hepatocellular carcinoma.

Patients with hepatocellular carcinoma (HCC) are vulnerable to drug resistance. Although drug resistance has been taken much attention to HCC therapy, little is known of regorafenib and regorafenib resistance (RR). This study aimed to determine the drug resistance pattern and the role of RhoA in RR. Two regorafenib-resistant cell lines were constructed based on Huh7 and Hep3B cell lines. In vitro and in vivo assays were conducted to study RhoA expression, the activity of Hippo signaling pathway and cancer stem cell (CSC) traits. The data showed that RhoA was highly expressed, Hippo signaling was hypoactivated and CSC traits were more prominent in RR cells. Inhibiting RhoA could reverse RR, and the alliance of RhoA inhibition and regorafenib synergistically attenuated CSC phenotype. Furthermore, inhibiting LARG/RhoA increased Kibra/NF2 complex formation, prevented YAP from shuttling into the nucleus and repressed CD44 mRNA expression. Clinically, the high expression of RhoA correlated with poor prognosis. LARG, RhoA, YAP1 and CD44 show positive correlation with each other. Thus, inhibition of RhoGEF/RhoA has the potential to reverse RR and repress CSC phenotype in HCC.

Nutrition therapy in critically ill patients with severe acute pancreatitis.

A significant proportion of patients (10%-20%) with acute pancreatitis develop severe acute pancreatitis characterized by pancreatic necrosis, systemic inflammation, and organ failure, commonly requiring intensive care unit (ICU) admission. In this specific population, nutrition therapy is more challenging than that in the general ICU population, primarily because of inevitable gastrointestinal involvement by pancreatic inflammation. In this review, we discussed several key aspects of nutrition therapy in this population, including key pathophysiology that may impede nutrition therapy, the timing and implementation of enteral nutrition and parenteral nutrition, the importance of specific nutrient supplements, and the long-term outcomes that may be addressed by nutrition therapy.

The hedonic overdrive model best explains high-fat diet-induced obesity in C57BL/6 mice.

OBJECTIVE: High-fat diets cause obesity in male mice; however, the underlying mechanisms remain controversial. Here, three contrasting ideas were assessed: hedonic overdrive, reverse causality, and passive overconsumption models. METHODS: A total of 12 groups of 20 individually housed 12-week-old C57BL/6 male mice were exposed to 12 high-fat diets with varying fat content from 40% to 80% (by calories), protein content from 5% to 30%, and carbohydrate content from 8.4% to 40%. Body weight and food intake were monitored for 30 days after 7 days at baseline on a standard low-fat diet. RESULTS: After exposure to the diets, energy intake increased first, and body weight followed later. Intake then declined. The peak energy intake was dependent on both dietary protein and carbohydrate, but not the dietary fat and energy density, whereas the rate of decrease in intake was only related to dietary protein. On high-fat diets, the weight of food intake declined, but despite this average reduction of 14.4 g in food intake, they consumed, on average, 357 kJ more energy than at baseline. CONCLUSIONS: The hedonic overdrive model fit the data best. The other two models were not supported.

MuNeRF: Robust Makeup Transfer in Neural Radiance Fields.

There has been a high demand for facial makeup transfer tools in fashion e-commerce and virtual avatar generation. Most of the existing makeup transfer methods are based on the generative adversarial networks. Despite their success in makeup transfer for a single image, they struggle to maintain the consistency of makeup under different poses and expressions of the same person. In this paper, we propose a robust makeup transfer method which consistently transfers the makeup style of a reference image to facial images in any poses and expressions. Our method introduces the implicit 3D representation, neural radiance fields (NeRFs), to ensure the geometric and appearance consistency. It has two separate stages, including one basic NeRF module to reconstruct the geometry from the input facial image sequence, and a makeup module to learn how to transfer the reference makeup style consistently. We propose a novel hybrid makeup loss which is specially designed based on the makeup characteristics to supervise the training of the makeup module. The proposed loss significantly improves the visual quality and faithfulness of the makeup transfer effects. To better align the distribution between the transferred makeup and the reference makeup, a patch-based discriminator that works in the pose-independent UV texture space is proposed to provide more accurate control of the synthesized makeup. Extensive experiments and a user study demonstrate the superiority of our network for a variety of different makeup styles.

Isolation and Characterization of Yunnan Variants of the Pseudorabies Virus and Their Pathogenicity in Rats.

Porcine pseudorabies has long existed in China and is a serious threat to the Chinese farming industry. To understand the prevalence and genetic variation of the porcine pseudorabies virus (PRV) and its pathogenicity in Yunnan Province, China, we collected 560 serum samples across seven Yunnan Province regions from 2020 to 2021 and detected anti-gE antibodies in these samples. Sixty-one clinical tissue samples were also collected from pigs with suspected PRV that were vaccinated with Bartha-K61. PRV-gE antibodies were found in 29.6% (166/560) of the serum samples. The PRV positivity rate in clinical tissue samples was 13.1% (8/61). Two isolates, PRV-KM and PRV-QJ, were obtained. The identity of the gB, gD, and gE genes between these isolates and the Chinese mutants exceeded 99.5%. These isolates and the classical Fa strain were used to infect 4-week-old rats intranasally to assess their pathogenicity. All infected rats showed the typical clinical and pathological features of PRV two days post-infection. The viral loads in the organs differed significantly among the infected groups. Viruses were detected in the saliva and feces at 12 h. Significant dynamic changes in total white blood cell counts (WBC), lymphocyte counts (Lym), and neutrophil counts (Gran) occurred in the blood of the infected groups at 24 and 48 h. These results show that mutant PRV strains are prevalent in Bartha-K61-vaccinated pigs in Yunnan Province, China. Moreover, rats shed PRV in their saliva and feces during early infection, indicating the need for rodent control in combatting PRV infections in Yunnan Province, China.

MKK3 K329 Mutation Attenuates Diabetes-Associated Cognitive Dysfunction by Blocking the MKK3-RAGE Interaction and Inhibiting Neuroinflammation.

The receptor for advanced glycation end products (RAGE) contributes to diabetes-associated cognitive dysfunction (DACD) through the interaction of its C-terminal AAs 2-5 with mitogen-activated protein kinase kinase 3 (MKK3). However, the associated MKK3 binding site is unknown. Here, db/db mice were used as a model for type 2 diabetes. GST pull-down assays and AutoDock Vina simulations were conducted to identify the key RAGE binding site in MKK3. This binding site was mutated to investigate its effects on DACD and to elucidate the underlying mechanisms. The interaction of MKK3 and RAGE, the levels of inflammatory factors, and the activation of microglia and astrocytes were tested. Synaptic morphology and plasticity in hippocampal neurons were assessed via electrophysiological recordings and Golgi staining. Behavioral tests were used to assess cognitive function. In this study, MKK3 bound directly to RAGE via its lysine 329 (K329), leading to the activation of the nuclear factor-κB (NF-κB) signaling pathway, which in turn triggered neuroinflammation and synaptic dysfunction, and ultimately contributed to DACD. MKK3 mutation at K329 reversed synaptic dysfunction and cognitive deficits by downregulating the NF-κB signaling pathway and inhibiting neuroinflammation. These results confirm that neuroinflammation and synaptic dysfunction in the hippocampus rely on the direct binding of MKK3 and RAGE. We conclude that MKK3 K329 binding to C-terminal RAGE (ct-RAGE) is a key mechanism by which neuroinflammation and synaptic dysfunction are induced in the hippocampus. This study presents a novel mechanism for DACD and proposes a novel therapeutic avenue for neuroprotection in DACD.