Metazoan Nuclear Pores Provide a Scaffold for Poised Genes and Mediate Induced Enhancer-Promoter Contacts.

Nuclear pore complex components (Nups) have been implicated in transcriptional regulation, yet what regulatory steps are controlled by metazoan Nups remains unclear. We identified the presence of multiple Nups at promoters, enhancers, and insulators in the Drosophila genome. In line with this binding, we uncovered a functional role for Nup98 in mediating enhancer-promoter looping at ecdysone-inducible genes. These genes were found to be stably associated with nuclear pores before and after activation. Although changing levels of Nup98 disrupted enhancer-promoter contacts, it did not affect ongoing transcription but instead compromised subsequent transcriptional activation or transcriptional memory. In support of the enhancer-looping role, we found Nup98 to gain and retain physical interactions with architectural proteins upon stimulation with ecdysone. Together, our data identify Nups as a class of architectural proteins for enhancers and supports a model in which animal genomes use the nuclear pore as an organizing scaffold for inducible poised genes.

A Network Imaging Biomarker of X-Linked Dystonia-Parkinsonism.

OBJECTIVE: The purpose of this study was to characterize a metabolic brain network associated with X-linked dystonia-parkinsonism (XDP). METHODS: Thirty right-handed Filipino men with XDP (age = 44.4 ± 8.5 years) and 30 XDP-causing mutation negative healthy men from the same population (age = 37.4 ± 10.5 years) underwent [18 F]-fluorodeoxyglucose positron emission tomography. Scans were analyzed using spatial covariance mapping to identify a significant XDP-related metabolic pattern (XDPRP). Patients were rated clinically at the time of imaging according to the XDP-Movement Disorder Society of the Philippines (MDSP) scale. RESULTS: We identified a significant XDPRP topography from 15 randomly selected subjects with XDP and 15 control subjects. This pattern was characterized by bilateral metabolic reductions in caudate/putamen, frontal operculum, and cingulate cortex, with relative increases in the bilateral somatosensory cortex and cerebellar vermis. Age-corrected expression of XDPRP was significantly elevated (p < 0.0001) in XDP compared to controls in the derivation set and in the remaining 15 patients (testing set). We validated the XDPRP topography by identifying a similar pattern in the original testing set (r = 0.90, p < 0.0001; voxel-wise correlation between both patterns). Significant correlations between XDPRP expression and clinical ratings for parkinsonism-but not dystonia-were observed in both XDP groups. Further network analysis revealed abnormalities of information transfer through the XDPRP space, with loss of normal connectivity and gain of abnormal functional connections linking network nodes with outside brain regions. INTERPRETATION: XDP is associated with a characteristic metabolic network associated with abnormal functional connectivity among the basal ganglia, thalamus, motor regions, and cerebellum. Clinical signs may relate to faulty information transfer through the network to outside brain regions. ANN NEUROL 2023;94:684-695.

Rare coding variants as risk modifiers of the 22q11.2 deletion implicate postnatal cortical development in syndromic schizophrenia.

22q11.2 deletion is one of the strongest known genetic risk factors for schizophrenia. Recent whole-genome sequencing of schizophrenia cases and controls with this deletion provided an unprecedented opportunity to identify risk modifying genetic variants and investigate their contribution to the pathogenesis of schizophrenia in 22q11.2 deletion syndrome. Here, we apply a novel analytic framework that integrates gene network and phenotype data to investigate the aggregate effects of rare coding variants and identified modifier genes in this etiologically homogenous cohort (223 schizophrenia cases and 233 controls of European descent). Our analyses revealed significant additive genetic components of rare nonsynonymous variants in 110 modifier genes (adjusted P = 9.4E-04) that overall accounted for 4.6% of the variance in schizophrenia status in this cohort, of which 4.0% was independent of the common polygenic risk for schizophrenia. The modifier genes affected by rare coding variants were enriched with genes involved in synaptic function and developmental disorders. Spatiotemporal transcriptomic analyses identified an enrichment of coexpression between modifier and 22q11.2 genes in cortical brain regions from late infancy to young adulthood. Corresponding gene coexpression modules are enriched with brain-specific protein-protein interactions of SLC25A1, COMT, and PI4KA in the 22q11.2 deletion region. Overall, our study highlights the contribution of rare coding variants to the SCZ risk. They not only complement common variants in disease genetics but also pinpoint brain regions and developmental stages critical to the etiology of syndromic schizophrenia.

Disordered network structure and function in dystonia: pathological connectivity vs. adaptive responses.

Primary dystonia is thought to emerge through abnormal functional relationships between basal ganglia and cerebellar motor circuits. These interactions may differ across disease subtypes and provide a novel biomarker for diagnosis and treatment. Using a network mapping algorithm based on resting-state functional MRI (rs-fMRI), a method that is readily implemented on conventional MRI scanners, we identified similar disease topographies in hereditary dystonia associated with the DYT1 or DYT6 mutations and in sporadic patients lacking these mutations. Both networks were characterized by contributions from the basal ganglia, cerebellum, thalamus, sensorimotor areas, as well as cortical association regions. Expression levels for the two networks were elevated in hereditary and sporadic dystonia, and in non-manifesting carriers of dystonia mutations. Nonetheless, the distribution of abnormal functional connections differed across groups, as did metrics of network organization and efficiency in key modules. Despite these differences, network expression correlated with dystonia motor ratings, significantly improving the accuracy of predictions based on thalamocortical tract integrity obtained with diffusion tensor MRI (DTI). Thus, in addition to providing unique information regarding the anatomy of abnormal brain circuits, rs-fMRI functional networks may provide a widely accessible method to help in the objective evaluation of new treatments for this disorder.

Adaptive and pathological connectivity responses in Parkinson's disease brain networks.

Functional imaging has been used extensively to identify and validate disease-specific networks as biomarkers in neurodegenerative disorders. It is not known, however, whether the connectivity patterns in these networks differ with disease progression compared to the beneficial adaptations that may also occur over time. To distinguish the 2 responses, we focused on assortativity, the tendency for network connections to link nodes with similar properties. High assortativity is associated with unstable, inefficient flow through the network. Low assortativity, by contrast, involves more diverse connections that are also more robust and efficient. We found that in Parkinson's disease (PD), network assortativity increased over time. Assoratitivty was high in clinically aggressive genetic variants but was low for genes associated with slow progression. Dopaminergic treatment increased assortativity despite improving motor symptoms, but subthalamic gene therapy, which remodels PD networks, reduced this measure compared to sham surgery. Stereotyped changes in connectivity patterns underlie disease progression and treatment responses in PD networks.

Disease specific and nonspecific metabolic brain networks in behavioral variant of frontotemporal dementia.

Behavioral variant of frontotemporal dementia (bvFTD) is common among young-onset dementia patients. While bvFTD-specific multivariate metabolic brain pattern (bFDRP) has been identified previously, little is known about its temporal evolution, internal structure, effect of atrophy, and its relationship with nonspecific resting-state networks such as default mode network (DMN). In this multicenter study, we explored FDG-PET brain scans of 111 bvFTD, 26 Alzheimer's disease, 16 Creutzfeldt-Jakob's disease, 24 semantic variant primary progressive aphasia (PPA), 18 nonfluent variant PPA and 77 healthy control subjects (HC) from Slovenia, USA, and Germany. bFDRP was identified in a cohort of 20 bvFTD patients and age-matched HC using scaled subprofile model/principle component analysis and validated in three independent cohorts. It was characterized by hypometabolism in frontal cortex, insula, anterior/middle cingulate, caudate, thalamus, and temporal poles. Its expression in bvFTD patients was significantly higher compared to HC and other dementia syndromes (p < .0004), correlated with cognitive decline (p = .0001), and increased over time in longitudinal cohort (p = .0007). Analysis of internal network organization by graph-theory methods revealed prominent network disruption in bvFTD patients. We have further found a specific atrophy-related pattern grossly corresponding to bFDRP; however, its contribution to the metabolic pattern was minimal. Finally, despite the overlap between bFDRP and FDG-PET-derived DMN, we demonstrated a predominant role of the specific bFDRP. Taken together, we validated the bFDRP network as a diagnostic/prognostic biomarker specific for bvFTD, provided a unique insight into its highly reproducible internal structure, and proved that bFDRP is unaffected by structural atrophy and independent of normal resting state networks loss.

Sporadic Creutzfeldt-Jakob disease is associated with reorganization of metabolic connectivity in a pathological brain network.

BACKGROUND AND PURPOSE: Although sporadic Creutzfeldt-Jakob disease (sCJD) is a rare cause of dementia, it is critical to understand its functional networks as the prion protein spread throughout the brain may share similar mechanisms with other more common neurodegenerative disorders. In this study, the metabolic brain network associated with sCJD was investigated and its internal network organization was explored. METHODS: We explored 2-[18 F]fluoro-2-deoxy-d-glucose positron emission tomography (FDG-PET) brain scans of 29 sCJD patients, 56 normal controls (NCs) and 46 other dementia patients from two independent centers. sCJD-related pattern (CJDRP) was identified in a cohort of 16 pathologically proven sCJD patients and 16 age-matched NCs using scaled subprofile modeling/principal component analysis and was prospectively validated in an independent cohort of 13 sCJD patients and 20 NCs. The pattern's specificity was tested on other dementia patients and its clinical relevance by clinical correlations. The pattern's internal organization was further studied using graph theory methods. RESULTS: The CJDRP was characterized by relative hypometabolism in the bilateral caudate, thalami, middle and superior frontal gyri, parietal lobe and posterior cingulum in association with relative hypermetabolism in the hippocampi, parahippocampal gyri and cerebellum. The pattern's expression significantly discriminated sCJD from NCs and other dementia patients (p < 0.005; receiver operating characteristic analysis CJD vs. NCs area under the curve [AUC] 0.90-0.96, sCJD vs. Alzheimer's disease AUC 0.78, sCJD vs. behavioral variant of frontotemporal dementia AUC 0.84). The pattern's expression significantly correlated with cognitive, functional decline and disease duration. The metabolic connectivity analysis revealed inefficient information transfer with specific network reorganization. CONCLUSIONS: The CJDRP is a robust metabolic biomarker of sCJD. Due to its excellent clinical correlations it has the potential to monitor disease in emerging disease-modifying trials.

Neuropsychiatric symptoms after moderate-to-severe traumatic brain injury in Vietnam: Assessment, prevalence, and impact on caregivers.

OBJECTIVE: Neuropsychiatric symptoms (NPSs) after moderate-to-severe traumatic brain injury (TBI) have been well documented in WEIRD (Western, educated, industrialized, rich, and democratic) populations. In non-WEIRD populations, such as Vietnam, however, patients with TBI clinically remain uninvestigated with potential neuropsychiatric disorders, limiting on-time critical interventions. This study aims to (1) adapt the Vietnamese Neuropsychiatric Inventory (V-NPI), (2) examine NPSs after moderate-to-severe TBI and (3) evaluate their impact on caregiver burden and well-being in Vietnam. METHOD: Caregivers of seventy-five patients with TBI completed the V-NPI, and other behavior, mood, and caregiver burden scales. RESULTS: Our findings demonstrated good internal consistency, convergent validity, and structural validity of the V-NPI. Caregivers reported that 78.7% of patients with TBI had at least three symptoms and 16.0% had more than seven. Behavioral and mood symptoms were more prevalent (ranging from 44.00% to 82.67% and from 46.67% to 66.67%, respectively) and severe in the TBI group. Importantly, NPSs in patients with TBI uniquely predicted 55.95% and 33.98% of caregiver burden and psychological well-being, respectively. CONCLUSION: This study reveals the first evidence for the presence and severity of NPSs after TBI in Vietnam, highlighting an urgent need for greater awareness and clinical assessment of these symptoms in clinical practice. The adapted V-NPI can serve as a useful tool to facilitate such assessments and interventions. In addition, given the significant impact of NPS on caregiver burden and well-being, psychosocial support for caregivers should be established.

Effects of Cadmium and Lead on Muscle and Liver Glycogen Levels of Climbing Perch (Anabas testudineus).

The aim of this study was to assess the glycogen content in the muscle and liver tissues of the climbing perch (Anabas testudineus) exposed to sublethal concentrations of Cd and Pb over 28 days of exposure and 14 days of depuration. Muscle and liver glycogen levels in A. testudineus after Pb or Cd treatment were significantly lower (p < 0.05) than that of A. testudineus in the control group during the exposure phase. In the recovery phase, muscle, and liver glycogen levels in A. testudineus increased in all Pb treatment groups, whereas they continuously decreased in all Cd treatment groups. Fish affected by Cd had obvious difficulties recovering from the stress response. It was concluded that exposure to the tested concentrations of Pb and Cd could be a potent endocrine activity disruptor, which may lead to adverse impacts on the health of A. testudineus.

LRRK2 and GBA Variants Exert Distinct Influences on Parkinson's Disease-Specific Metabolic Networks.

The natural history of idiopathic Parkinson's disease (PD) varies considerably across patients. While PD is generally sporadic, there are known genetic influences: the two most common, mutations in the LRRK2 or GBA1 gene, are associated with slower and more aggressive progression, respectively. Here, we applied graph theory to metabolic brain imaging to understand the effects of genotype on the organization of previously established PD-specific networks. We found that closely matched PD patient groups with the LRRK2-G2019S mutation (PD-LRRK2) or GBA1 variants (PD-GBA) expressed the same disease networks as sporadic disease (sPD), but PD-LRRK2 and PD-GBA patients exhibited abnormal increases in network connectivity that were not present in sPD. Using a community detection strategy, we found that the location and modular distribution of these connections differed strikingly across genotypes. In PD-LRRK2, connections were gained within the network core, with the formation of distinct functional pathways linking the cerebellum and putamen. In PD-GBA, by contrast, the majority of functional connections were formed outside the core, involving corticocortical pathways at the network periphery. Strategically localized connections within the core in PD-LRRK2 may maintain PD network activity at lower levels than in PD-GBA, resulting in a less aggressive clinical course.

A wavelet-based method to exploit epigenomic language in the regulatory region.

MOTIVATION: Epigenetic landscapes in the regulatory regions reflect binding condition of transcription factors and their co-factors. Identifying epigenetic condition and its variation is important in understanding condition-specific gene regulation. Computational approaches to explore complex multi-dimensional landscapes are needed. RESULTS: To study epigenomic condition for gene regulation, we developed a method, AWNFR, to classify epigenomic landscapes based on the detected epigenomic landscapes. Assuming mixture of Gaussians for a nucleosome, the proposed method captures the shape of histone modification and identifies potential regulatory regions in the wavelet domain. For accuracy estimation as well as enhanced computational speed, we developed a novel algorithm based on down-sampling operation and footprint in wavelet. We showed the algorithmic advantages of AWNFR using the simulated data. AWNFR identified regulatory regions more effectively and accurately than the previous approaches with the epigenome data in mouse embryonic stem cells and human lung fibroblast cells (IMR90). Based on the detected epigenomic landscapes, AWNFR classified epigenomic status and studied epigenomic codes. We studied co-occurring histone marks and showed that AWNFR captures the epigenomic variation across time. AVAILABILITY AND IMPLEMENTATION: The source code and supplemental document of AWNFR are available at http://wonk.med.upenn.edu/AWNFR.

Longitudinal Network Changes and Phenoconversion Risk in Isolated REM Sleep Behavior Disorder.

Isolated rapid eye movement sleep behavior disorder (iRBD) is a prodromal syndrome for Parkinson's disease (PD) and related α-synucleinopathies. We conducted a longitudinal imaging study of network changes in iRBD and their relationship to phenoconversion. Expression levels for the PD-related motor and cognitive networks (PDRP and PDCP) were measured at baseline, 2 and 4 years, along with dopamine transporter (DAT) binding. PDRP and PDCP expression increased over time, with higher values in the former network. While abnormal functional connections were identified initially within the PDRP, others bridging the two networks appeared later. A model based on the rates of PDRP progression and putamen dopamine loss predicted phenoconversion within 1.2 years in individuals with iRBD. In aggregate, the data suggest that maladaptive reorganization of brain networks takes place in iRBD years before phenoconversion. Network expression and DAT binding measures can be used together to assess phenoconversion risk in these individuals.

Deep brain stimulation of the subthalamic nucleus for Parkinson's disease: A network imaging marker of the treatment response.

Subthalamic nucleus deep brain stimulation (STN-DBS) alleviates motor symptoms of Parkinson's disease (PD), thereby improving quality of life. However, quantitative brain markers to evaluate DBS responses and select suitable patients for surgery are lacking. Here, we used metabolic brain imaging to identify a reproducible STN-DBS network for which individual expression levels increased with stimulation in proportion to motor benefit. Of note, measurements of network expression from metabolic and BOLD imaging obtained preoperatively predicted motor outcomes determined after DBS surgery. Based on these findings, we computed network expression in 175 PD patients, with time from diagnosis ranging from 0 to 21 years, and used the resulting data to predict the outcome of a potential STN-DBS procedure. While minimal benefit was predicted for patients with early disease, the proportion of potential responders increased after 4 years. Clinically meaningful improvement with stimulation was predicted in 18.9 - 27.3% of patients depending on disease duration.

The combination of CYP2C19 polymorphism and inflammatory cell ratios in prognosis cardiac adverse events after acute coronary syndrome.

Background: CYP2C19 gene polymorphism combination with inflammatory cell ratios was significant in the prognosis of coronary heart disease. Materials and methods: A cross-sectional analysis study, with 6 months follow-up on 142 patients with acute coronary syndrome. Patients were analyzed for CYP2C19 gene polymorphisms by real-time polymerase chain reaction (PCR) and complete blood count to determine inflammatory cell ratios and recorded cardiovascular events (CEs) after following up to 6 months. Results: For 90-day CEs, CYP2C19 gene polymorphism (Hazard Ratio (HR): 1.965, 95 % Confidence Interval (CI): 1.012-3.814), the combination of a neutrophil and lymphocyte ratio (NLR) ≥ 2.982 (HR: 13.001, 95 % CI: 1.37-97.304) or a platelet to lymphocyte ratio (PLR) ≥ 162.42 (HR: 2.878, 95 % CI: 1.212-6.835) was independent predictors of CEs. For 180-day CEs, CYP2C19 gene polymorphism combination with NLR ≥3.02 (HR: 13.946, 95 % CI: 1.833-106.121) or PLR ≥160.38 (HR: 5.349, 95 % CI: 1.379-20.745) or monocyte to lymphocyte ratio (MLR) ≥ 0.3 (HR: 4.699, 95 % CI: 1.032-31.393) were independent predictors of CEs. Conclusion: NLR, PLR or MLR combined with CYP2C19 gene polymorphism were stronger independent predictors of cardiovascular events in patients with acute coronary syndromes compared to CYP2C19 gene polymorphism and inflammatory cell ratios separately. CYP2C19 polymorphism and high NLR was the strongest predictor of both CEs at 90 days and 180 days.

Polygenic prediction of human longevity on the supposition of pervasive pleiotropy.

The highly polygenic nature of human longevity renders cross-trait pleiotropy an indispensable feature of its genetic architecture. Leveraging the genetic correlation between the aging-related traits (ARTs), we sought to model the additive variance in lifespan as a function of cumulative liability from pleiotropic segregating variants. We tracked allele frequency changes as a function of viability across different age bins and prioritized 34 variants with an immediate implication on lipid metabolism, body mass index (BMI), and cognitive performance, among other traits, revealed by PheWAS analysis in the UK Biobank. Given the highly complex and non-linear interactions between the genetic determinants of longevity, we reasoned that a composite polygenic score would approximate a substantial portion of the variance in lifespan and developed the integrated longevity genetic scores (iLGSs) for distinguishing exceptional survival. We showed that coefficients derived from our ensemble model could potentially reveal an interesting pattern of genomic pleiotropy specific to lifespan. We assessed the predictive performance of our model for distinguishing the enrichment of exceptional longevity among long-lived individuals in two replication cohorts and showed that the median lifespan in the highest decile of our composite prognostic index is up to 4.8 years longer. Finally, using the proteomic correlates of iLGS, we identified protein markers associated with exceptional longevity irrespective of chronological age and prioritized drugs with repurposing potentials for gerotherapeutics. Together, our approach demonstrates a promising framework for polygenic modeling of additive liability conferred by ARTs in defining exceptional longevity and assisting the identification of individuals at higher risk of mortality for targeted lifestyle modifications earlier in life. Furthermore, the proteomic signature associated with iLGS highlights the functional pathway upstream of the PI3K-Akt that can be effectively targeted to slow down aging and extend lifespan.

Straightforward smartphone assay for quantifying tannic acid in beverages based on colour change of Eu3+/polyethyleneimine complex.

Tannic acid (TA)-a natural product-is a polyphenol derivative that occurs in certain kinds of beverages. A large amount of TA could give rise to an unpleasant flavour and could negatively affect the human body by causing stomach irritation, abdominal pain, nausea, vomiting, and even death. Thus, the need exists for a simple TA detection procedure that meets specific criteria such as on-site analysis, portability, and affordability. Herein, we present a new TA assay, which is based on the fluorescent quenching effect of an efficient fluorophore, and which comprises a smartphone-integrated homemade reader system. The fluorescent polyethyleneimine-derivatised polymer (FP), a strong emitter at 510 nm, was synthesised with the aid of a facile sonication method. In the presence of Eu3+ ions, TA quenches the fluorescence of the FP via electrostatic interaction. A smartphone was used to capture an image of the FP undergoing fluorescence for conversion to RGB values. The blue channel was chosen for further analysis because it offered the highest R2-value compared to the red and green channels. We verified these results using a commercial spectrofluorometer and calculated the limit of detection of this assay as 87 nM and 20 nM for the homemade reader and spectrofluorometer, respectively. The detection range for TA with the proposed assay is 0.16-66.66 µM. The application of the proposed method to real beverage samples for TA detection demonstrates its analytical applicability.

Automated differential diagnosis of dementia syndromes using FDG PET and machine learning.

Background: Metabolic brain imaging with 2-[18F]fluoro-2-deoxy-D-glucose positron emission tomography (FDG PET) is a supportive diagnostic and differential diagnostic tool for neurodegenerative dementias. In the clinic, scans are usually visually interpreted. However, computer-aided approaches can improve diagnostic accuracy. We aimed to build two machine learning classifiers, based on two sets of FDG PET-derived features, for differential diagnosis of common dementia syndromes. Methods: We analyzed FDG PET scans from three dementia cohorts [63 dementia due to Alzheimer's disease (AD), 79 dementia with Lewy bodies (DLB) and 23 frontotemporal dementia (FTD)], and 41 normal controls (NCs). Patients' clinical diagnosis at follow-up (25 ± 20 months after scanning) or cerebrospinal fluid biomarkers for Alzheimer's disease was considered a gold standard. FDG PET scans were first visually evaluated. Scans were pre-processed, and two sets of features extracted: (1) the expressions of previously identified metabolic brain patterns, and (2) the mean uptake value in 95 regions of interest (ROIs). Two multi-class support vector machine (SVM) classifiers were tested and their diagnostic performance assessed and compared to visual reading. Class-specific regional feature importance was assessed with Shapley Additive Explanations. Results: Pattern- and ROI-based classifier achieved higher overall accuracy than expert readers (78% and 80% respectively, vs. 71%). Both SVM classifiers performed similarly to one another and to expert readers in AD (F1 = 0.74, 0.78, and 0.78) and DLB (F1 = 0.81, 0.81, and 0.78). SVM classifiers outperformed expert readers in FTD (F1 = 0.87, 0.83, and 0.63), but not in NC (F1 = 0.71, 0.75, and 0.92). Visualization of the SVM model showed bilateral temporal cortices and cerebellum to be the most important features for AD; occipital cortices, hippocampi and parahippocampi, amygdala, and middle temporal lobes for DLB; bilateral frontal cortices, middle and anterior cingulum for FTD; and bilateral angular gyri, pons, and vermis for NC. Conclusion: Multi-class SVM classifiers based on the expression of characteristic metabolic brain patterns or ROI glucose uptake, performed better than experts in the differential diagnosis of common dementias using FDG PET scans. Experts performed better in the recognition of normal scans and a combined approach may yield optimal results in the clinical setting.

Apathy in a high prevalence population of moderate to severe traumatic brain injury: An investigation in Vietnam.

OBJECTIVE: The reduction of goal-directed behavior, termed apathy, is a pervasive and debilitating syndrome after traumatic brain injury (TBI). However, understanding of apathy as a multifaceted construct is limited, especially in Southeast Asian nations. This study aimed to investigate the severity, insight, and psychosocial influences of apathy in executive, emotional, and initiation dimensions in Vietnam-a country with high prevalence of TBI. METHOD: One hundred and eleven Vietnamese participants (61 individuals with moderate to severe TBI and 50 healthy controls) and their informants completed the self-rated and informant-rated Dimensional Apathy Scale (DAS) for the assessment of executive, emotional, and initiation apathy severity. Insight of apathy was calculated by subtracting DAS self-ratings from informant ratings. Additionally, carers completed measures assessing psychosocial factors of overall family health and overprotective behavior, while participants rated their own self-efficacy. RESULTS: Our results showed greater informant-rated apathy for all three dimensions in individuals with TBI relative to controls. However, while people with TBI had greater self-rated initiation apathy, they regarded their executive apathy as lower and their emotional apathy as similar compared with controls. Reduced insight in patients was seen for executive and initiation apathy. Across participants, executive apathy was predicted by family functioning and overprotectiveness, emotional apathy was predicted by family functioning, and initiation apathy was predicted by self-efficacy. CONCLUSIONS: These findings support the multidimensional characterizations and socio-cultural considerations of apathy after TBI, which will potentially develop both individual-specific and symptom-specific approaches in clinical practice. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

Design of novel p-n heterojunction ZnBi2O4-ZnS photocatalysts with impressive photocatalytic and antibacterial activities under visible light.

Heterojunction structures have attracted considerable attention for enhancing electron migration across interfaces. In this report, ZnBi2O4-ZnS(12%) heterojunction photocatalysts was found to be capable of degrading over 94% of indigo carmine in a 15 mg/L solution within 90 min of visible light irradiation at a catalytic dose of 1.0 g/L and pH 4. Furthermore, more than 82% of the total organic carbon (TOC) was removed, confirming the almost complete mineralization of the indigo carmine by ZnBi2O4-ZnS(12%). Moreover, the photocatalyst exhibited high stability and retained its photocatalytic activity up to the 5th cycle of operation without photocorrosion. The dramatic enhancement in the visible-light photocatalytic performance of the ZnBi2O4-ZnS heterojunctions over pristine ZnBi2O4 and ZnS was due to the formation of a superior heterojunction between the n-type semiconductor, ZnS, and the p-type semiconductor, ZnBi2O4. This heterojunction facilitated the separation and transfer of the photoinduced electron at the interfaces of the two semiconductors. Furthermore, the ZnBi2O4-ZnS(12%) exhibited an inhibition zone of 15 mm against fecal Escherichia coli (ATCC 8739), with a minimum inhibitory concentration (MIC) of 150 µg/mL. These results demonstrated that the novel ZnBi2O4-ZnS p-n-type heterojunction is a promising visible-light active photo-catalyst for the degradation of organic pollutants and inhibition of fecal E. coli.

Solenoid and non-solenoid protein recognition using stationary wavelet packet transform.

MOTIVATION: Solenoid proteins are emerging as a protein class with properties intermediate between structured and intrinsically unstructured proteins. Containing repeating structural units, solenoid proteins are expected to share sequence similarities. However, in many cases, the sequence similarities are weak and non-detectable. Moreover, solenoids can be degenerated and widely vary in the number of units. So that it is difficult to detect them. Recently, several solenoid repeats detection methods have been proposed, such as self-alignment of the sequence, spectral analysis and discrete Fourier transform of sequence. Although these methods have shown good performance on certain data sets, they often fail to detect repeats with weak similarities. In this article, we propose a new approach to recognize solenoid repeats and non-solenoid proteins using stationary wavelet packet transform (SWPT). Our method associates with three advantages: (i) naturally representing five main factors of protein structure and properties by wavelet analysis technique; (ii) extracting novel wavelet features that can capture hidden components from solenoid sequence similarities and distinguish them from global proteins; (iii) obtaining statistics features that capture repeating motifs of solenoid proteins. RESULTS: Our method analyzes the characteristics of amino acid sequence in both spectral and temporal domains using SWPT. Both global and local information of proteins are captured by SWPT coefficients. We obtain and integrate wavelet-based features and statistics-based features of amino acid sequence to improve the classification task. Our proposed method is evaluated by comparing to state-of-the-art methods such as HHrepID and REPETITA. The experimental results show that our algorithm consistently outperforms them in areas under ROC curve. At the same false positive rate, the sensitivity of our WAVELET method is higher than other methods. AVAILABILITY: http://www.naaan.org/anvo/Software/Software.htm.