Full Spectrum Research Articles

A Review on the Intersection of Artificial Intelligence on Building Resilient Infrastructure, Promoting Inclusive and Sustainable Industrialization and Fostering Innovation

Artificial Intelligence (AI) has made significant global impacts across various domains. However, it is evident that certain areas have yet to harness the full spectrum of opportunities AI can provide. This review aims to investigate the transformative effects of AI on diverse sustainable goals, including the development of resilient infrastructure, the promotion of inclusivity, and the cultivation of innovation. By shedding light on previously unnoticed challenges within the realms of industrialization and infrastructure, this study unveils a novel perspective on the potential for an AI-driven industrial and innovative world while preserving and enhancing efficiency

Beta to Theta Power Ratio in Electroencephalogram Periodic Components to Discriminate Mild Cognitive Impairment and Alzheimer’s Dementia

AbstractBackgroundAlzheimer’s disease dementia (AD) and mild cognitive impairment (MCI) are often associated with abnormalities in full power spectrum of electroencephalogram (EEG), including the ratio of beta/theta. Full spectrum EEG consists of aperiodic and periodic components with the latter being better associated with cognition. We investigated whether the aperiodic and periodic power components of EEGs are disrupted differently in individuals with MCI vs. AD vs. healthy controls (HC), and whether a periodic based beta/theta ratio better differentiates the three groups than a ratio based on the full spectrum.MethodData were collected in 199 participants ‐ 44 with HC (mean (SD) age: 69.1 (5.3) years), 114 with MCI (72.3 (7.5)), and 41 with AD (75.6 (6.5)). We cleaned the data using a band‐pass filter with cut‐off frequencies 1 and 45 Hz and then independent component analysis. We then used the “fooof” toolbox to decompose the EEGs into their aperiodic and periodic components. We used the area under the receiver operating characteristic curve (AUCROC) of a logistic regression classifier to distinguish HC from MCI and AD participants, and MCI from AD participants, using beta/theta ratios based on the periodic power spectrum vs. full power spectrum.ResultThere was an increase in full spectrum powers for delta, theta, and gamma, and a decrease of relative power for beta in AD participants compared to HC and MCI participants. In contrast, there were no differences in aperiodic background EEG components among HC, MCI, and AD participants. Overall, the periodic and full spectrum comparisons among the three groups were comparable except for the periodic based analysis that showed a difference between MCI and HC in the occipital beta/theta ratio (Bonferroni corrected p = 0.036). Classifiers based on beta/theta power ratio in EEG periodic components distinguished AD from HC and MCI with high AUCROC values (0.94 and 0.83, respectively), and outperformed classifiers based on beta/theta power ratio in EEG all oscillations (0.078 and 0.67, respectively).ConclusionThis study supports an advantage of a periodic based analysis over a full EEG spectrum analysis and the use of occipital beta/theta power ratio based on periodic components as a screening tool for AD.

Receptive vocabulary predicts multilinguals' recognition skills in adverse listening conditions.

Adverse listening conditions are known to affect bilingual listeners' intelligibility scores more than those of monolingual listeners. To advance theoretical understanding of the mechanisms underpinning bilinguals' challenges in adverse listening conditions, vocabulary size and language entropy are compared as predictors in a sentence transcription task with a heterogeneous multilingual population representative of a speech community. Adverse listening was induced through noise type, bandwidth manipulations, and sentences varying in their semantic predictability. Overall, the results generally confirm anticipated patterns with respect to sentence type, noise masking, and bandwidth. Listeners show better comprehension of semantically coherent utterances without masking and with a full spectrum. Crucially, listeners with larger receptive vocabularies and lower language entropy, a measure of the predictability of one's language use, showed improved performance in adverse listening conditions. Vocabulary size had a substantially larger effect size, indicating that vocabulary size has more impact on performance in adverse listening conditions than bilingual language use. These results suggest that the mechanism behind the bilingual disadvantage in adverse listening conditions may be rooted in bilinguals' smaller language-specific receptive vocabularies, offering a harmonious explanation for challenges in adverse listening conditions experienced by monolinguals and multilinguals.

The influence of vascular pathology on the generalizability of machine learning models for cognitive performance estimation

AbstractBackgroundImage‐based machine learning models are promising to aid diagnosis in dementia, e.g. by diagnostic classification or estimating cognitive performance. However, the contribution of vascular pathology is often ignored, although many patients have mixed pathologies. This may hamper model generalizability from research setting to clinical practice, as the models are generally developed on populations with lower vascular burden than is encountered clinically. This study aims to evaluate whether model generalizability is influenced by differences in vascular pathology load between training and test groups, represented by white matter hyperintensities (WMHs).MethodWe developed a convolutional neural network to estimate cognitive performance through ADAS13. We included 4791 timepoints from 1632 ADNI subjects. To mimic differences in vascular burden between research and clinical populations, we split the data into a low, middle and high WMH load group using WMH ratio tertiles (WMH volume corrected for intracranial volume). The model was applied to maps derived from T1‐weighted and FLAIR brain MRI using voxel‐based morphometry: model 1 (gray matter density map (GMDM), age, sex), model 2 (GMDM, WMH density map (WMHDM), age, sex) and model 3 (GMDM, log(WMH ratio), age, sex) (Fig. 1). First, we trained all models on the low WMH load group, meaning to test on the high WMH load group to assess generalizability. Second, we trained models 1 and 2 on all data. Mean Absolute Error (MAE) served as the performance metric.ResultThe models trained on the low WMH load group (MAE = 5.08 – 5.41) performed similar to constant value prediction (MAE = 5.63) (Fig. 2, circles). Using all data, models did train successfully (model 1: MAE = 5.64, model 2: MAE = 6.31, constant value: MAE = 7.39) (Fig. 2, crosses). Because of the suboptimal model training in the low WMH load group, especially for individuals with higher ADAS13 scores which were underrepresented in the data (Fig. 3), we could not evaluate generalizability to the high WMH load group.ConclusionThis study was unable to provide new insight into the influence of vascular pathology on the generalizability of machine learning models in dementia. More clinically representative data is needed, covering the full cognitive performance spectrum and higher vascular pathology loads.

Gene‐Based Analysis with Functional Annotation in 2680 South Asians from the LASI‐DAD Study

AbstractBackgroundThe prevalence of dementia among South Asians across India is as high as 10% in those 55 years and older, yet little is known about the risk dementia factors in this population. To date, most known genetic risk loci of Alzheimer’s disease (AD) were identified from studies conducted in European Ancestry (EA) participants. The full mutational spectrum and the functional relevance of those loci in South Asians remains unknown. Analyzing DNA sequence data poses challenges due to the large number of rare variants tested, with most variants likely not associated with dementia. These challenges can be addressed with an annotation‐informed gene‐based analysis.MethodUsing whole‐genome sequence data from 2680 participants from the Diagnostic Assessment of Dementia for the Longitudinal Aging Study of India (LASI‐DAD), we performed a gene‐based analysis of 84 genes known to be associated with AD in EA. We investigated associations with the Hindi Mental State Examination (HMSE) score, general cognitive function, and five cognitive domains. For each gene, we examined missense/loss‐of‐function (LoF) variants and brain‐specific promoter/enhancer variants, separately, both with and without incorporating additional annotation weights (e.g., deleteriousness, conservation scores) using the variant‐Set Test for Association using Annotation infoRmation (STAAR).ResultIn the missense/LoF analysis, when controlling for age, sex, state/territory, and the first 10 principal components, 16 genes were nominally associated with at least one cognitive outcome (p<0.05). Three genes (APOE, PICALM, TSPOAP1) were significant after multiple testing correction (FDR q<0.1). In the brain‐specific promoter/enhancer analysis, 21 genes were nominally significant, but none were significant at FDR q<0.1. Results remained similar after adding annotation weights. The most strongly associated variants in each gene were rs429358 (APOE), rs779406084 (PICALM), and rs9913145 (TSPOAP1). rs779406084 is a missense mutation that is more prevalent in this Indian sample (MAF = 0.075%) compared to EA (MAF = 0.0015%).ConclusionSome genes associated with AD in EA are also associated with cognitive outcomes in South Asians in India. Analyzing sequence data in South Asians provides opportunities to identify potential novel causal variants that are enriched in this population.

Leveraging the full potential of Raman spectroscopy for the identification of amyloid aggregation states

AbstractBackgroundRaman spectroscopy is a label‐free method often used in the characterization of the beta‐amyloid (Aβ) aggregation process. This process involves peptide misfolding, which can be tracked with Raman microscopy in a non‐invasive manner, since the technique is sensitive for conformational changes in peptide structure. However, often only the spectral bands corresponding to β‐sheets and α‐helices are used for the evaluation of the aggregation state, leaving the remaining spectral information unused. The here presented evaluation aims to explore the full potential of Raman spectroscopy for the characterization of Aβ‐42 aggregates (monomers, oligomers, fibrils) in a comprehensive analysis approach, complemented by Thioflavin T (ThT) fluorescence measurements and atomic force microscopy (AFM).MethodAβ‐42 was reconstituted in diluted ammonia. were prepared by reconstituting peptide in diluted ammonia on ice and dilution with phosphate buffered saline (PBS). Oligomers and fibrils were obtained by incubation in MilliQ water or diluted hydrochloric acid for 24 hours at 4°C or 37°C, respectively. Samples were optionally further diluted for AFM, ThT fluorescence assays and . The Raman spectra were processed using Project 4 software (WITec GmbH, Ulm, Germany), and in‐house written Matlab scripts (The Mathworks, Inc., Natick, USA).ResultUsing Raman spectroscopy, as well as established methods such as AFM and ThT fluorescence, the size and aggregation state of monomeric, oligomeric and fibrillar Aβ‐42 could be confirmed and reproducibly obtained using standardized protocols. Corresponding Raman spectra of these Aβ‐42 aggregates display distinct differences not only in the β‐sheet and α‐helix spectral band, but in the whole spectrum, indicating especially changes in the orientation of tyrosine, phenylalanine, and aliphatic side chains.ConclusionHerein, the potential of Raman spectroscopy as a label‐free, conformation‐sensitive technique for the characterization of Aβ‐42 aggregates was explored by analyzing the full Raman spectrum. Clear differences between all aggregate types were identified in several spectral bands, unveiling detailed information in the Raman spectra that is often not included in the analysis of Aβ aggregates. Thus, this study provides a guideline for extracting comprehensive knowledge from spectroscopic data on peptide aggregation, potentially serving as a non‐destructive, quick addition or alternative to more established methods.

Composites of W18O49 Nanowires with g-C3N4/RGO Nanosheets for Broadband Light-Driven Photocatalytic Wastewater Purification.

Developing a photocatalyst that can effectively utilize the full solar spectrum remains a high-priority objective in the ongoing pursuit of efficient light-to-chemical energy conversion. Herein, the ternary nanocomposite g-C3N4/RGO/W18O49 (CN/RGO/WO) was constructed and characterized by a variety of techniques. Remarkably, under the excitation of photon energies ranging from the ultraviolet (UV) to the near-infrared (NIR) region, the photocatalytic performance of the CN/RGO/WO nanocomposite exhibited a significant enhancement compared with single component g-C3N4 or W18O49 nanosheets for the degradation of methyl orange (MO). The MO photodegradation rate of the optimal CN/1.0 wt% RGO/45.0 wt% WO catalyst reached 0.816 and 0.027 min-1 under UV and visible light excitation, respectively. Even under low-energy NIR light, which is not sufficient to excite g-C3N4, the MO degradation rate can still reach 0.0367 h-1, exhibiting a significant enhancement than pure W18O49. The outstanding MO removal rate and stability were demonstrated by CN/RGO/WO nanocomposites, which arise from the synergistic effect of localized surface plasmon resonance effect induced by W18O49 under vis-NIR excitation and the Z-scheme nanoheterojunction of W18O49 and g-C3N4. In this work, we have exploited the great potential of integrating nonmetallic plasmonic nanomaterials and good conductor RGO to construct high-performance g-C3N4-based full-solar spectral broadband photocatalysts.

Abstract C079: Comprehensive immunophenotypic profiling sheds light on the dynamic interplay between immune cells in the 4T1 breast cancer model upon anti-PD1 and anti-CTLA4 immunotherapy

Abstract Immuno-oncology has revolutionized cancer treatment by harnessing the immune system to target and eliminate tumor cells. In this study, we employed a combination of fluorescence-activated cell sorting (FACS) and Cytek full spectrum flow cytometry to investigate the impact of anti-PD1 (programmed cell death protein 1) and anti-CTLA4 (cytotoxic T-lymphocyte-associated protein 4) treatments on the 4T1 breast cancer model. We comprehensively analyzed 25 surface markers on immune cells to gain insights into the dynamic changes induced by immunotherapy. The 4T1 murine breast cancer model is known for its aggressive and metastatic nature, resembling triple-negative breast cancer in humans. We established a syngeneic orthotopic tumor graft of 4T1 cells in immunocompetent mice and treated them individually with anti-PD1 or anti-CTLA4 antibodies and compared the results to the respective isotype controls. Using FACS, we characterized immune cell populations within the tumor microenvironment and spleenocytes at multiple time points (before treatment, day 5, and day 17) during the treatment course. The Cytek full spectrum flow cytometry platform allowed simultaneous measurement of 25 surface markers, enabling a detailed analysis of immune cell phenotypes and functional states in a single experiment. Our findings revealed significant alterations in the immune landscape following anti-PD1 or anti-CTLA4 treatments. We observed an increase in tumor-infiltrating lymphocytes (TILs) and specifically CD4+ as well as CD8+ T cell infiltration within the tumor, accompanied by upregulation of T cell activation markers, such as CD69, CD38, CD44, PD1 and CD25. The immune cell composition within 4T1 tumors included CD4+ and CD8+ TILs, as well as increased populations of monocytes and macrophages. Additionally, a decrease in regulatory T cells (TRegs) was observed, indicating a potential restoration of immune balance. Moreover, F4/80+ CD206+ tumor-associated macrophages (TAMs) were identified in the 4T1 tumors vs. the spleen. This was associated with increased granzyme B production by CD8+ T cells and a shift towards an effector memory phenotype, suggesting improved cytotoxic function. To gain insights into spatial infiltration patterns of immune cells, we performed Immunohistochemistry (IHC) stainings of various immune cell markers in the 4T1 tumors. In line with the FACS results, we observed increased immune cell infiltration in tumors treated with anti-PD1 and anti-CTLA4 antibodies. Our comprehensive immunophenotypic analysis, utilizing Cytek full spectrum flow cytometry and IHC sheds light on the dynamic interplay between immune cells and the 4T1 breast cancer model during anti-PD1 and anti-CTLA4 immunotherapy. These findings provide valuable insights into the mechanisms underlying the therapeutic efficacy of immune checkpoint blockade in this aggressive cancer model. Citation Format: Christos Nikolaou, Simon Heller, Stefan Kaulfuss, Carlo Stresemann, Alexandra Eichten, Oliver von Ahsen, Martin Lange. Comprehensive immunophenotypic profiling sheds light on the dynamic interplay between immune cells in the 4T1 breast cancer model upon anti-PD1 and anti-CTLA4 immunotherapy [abstract]. In: Proceedings of the AACR-NCI-EORTC Virtual International Conference on Molecular Targets and Cancer Therapeutics; 2023 Oct 11-15; Boston, MA. Philadelphia (PA): AACR; Mol Cancer Ther 2023;22(12 Suppl):Abstract nr C079.

Synthesis, optical characteristics and thermal stability of Mn4+-doped Na3GaF6 red-emitting phosphor for high-performance warm WLED

Nowadays, Mn4+-activated fluoride red phosphors with high color purity, strong zero phonon line emission and relatively short decay time are crucial for improving the optical performance of WLED and emerging applications. In this work, a series of red-emitting Mn4+-activated fluoride phosphors in consideration of the non-equivalent substitution of Mn4+ for Ga3+ in the Na3GaF6 host are synthesized via the coprecipitation method at room temperature. The use of different reaction solvents successfully regulates the morphology, size and luminescence intensity of the phosphor. Under blue light excitation, Na3GaF6:Mn4+ sample shows a bright narrow-band red emission with exceptional correlated color temperature (4029 K). Moreover, the emission of strong zero phonon line induces the outstanding color purity of red light (93%). In addition, the critical concentration of Mn4+ is determined to be 0.04 and the concentration quenching mechanism is also systematically discussed. Noticeably, when Na3GaF6:Mn4+ phosphor achieves maximum fluorescence emission, its fluorescence lifetime value is only 3.48 ms. Additionally, the theoretical calculated results demonstrate that Na3GaF6 host can provide strong crystal field strength for Mn4+, and the Racah parameters (B and C) and nephelauxetic ratio (β1) are used to evaluate the nephelauxetic effect of Mn4+, which is compared with other types of Mn4+-activated phosphors. The activation energy, chromaticity shift and chromaticity coordinate variation are systematically calculated to analyze the thermal stability of Na3GaF6:Mn4+ red phosphor. Meanwhile, by employing the as-prepared phosphor Na3GaF6:Mn4+ as a red component, a high-performance warm WLED shows a full spectrum emission, and the corresponding cold white light emission is also transformed into the warm white light emission (CCT = 4224 K, Ra = 89.1 and LE = 111.72 lm/W). Moreover, under high driving current, such WLED exhibits stable output light efficiency. These results suggest that Na3GaF6:Mn4+ as red phosphor has a stupendous potential for warm WLED in indoor lighting.

Brain regulatory T cells.

The brain, long thought to be isolated from the peripheral immune system, is increasingly recognized to be integrated into a systemic immunological network. These conduits of immune-brain interaction and immunosurveillance processes necessitate the presence of complementary immunoregulatory mechanisms, of which brain regulatory T cells (Treg cells) are likely a key facet. Treg cells represent a dynamic population in the brain, with continual influx, specialization to a brain-residency phenotype and relatively rapid displacement by newly incoming cells. In addition to their functions in suppressing adaptive immunity, an emerging view is that Treg cells in the brain dampen down glial reactivity in response to a range of neurological insults, and directly assist in multiple regenerative and reparative processes during tissue pathology. The utility and malleability of the brain Treg cell population make it an attractive therapeutic target across the full spectrum of neurological conditions, ranging from neuroinflammatory to neurodegenerative and even psychiatric diseases. Therapeutic modalities currently under intense development include Treg cell therapy, IL-2 therapy to boost Treg cell numbers and multiple innovative approaches to couple these therapeutics to brain delivery mechanisms for enhanced potency. Here we review the state of the art of brain Treg cell knowledge together with the potential avenues for future integration into medical practice.

Current Status and Challenges in Delivering Comprehensive Care for Patients with Hemophilia.

The importance of comprehensive care as a treatment strategy for patients with hemophilia is recognized worldwide. Comprehensive care entails addressing full spectrum of medical and psychological aspects impacting both patients and their families. The primary objective of comprehensive care for individuals with hemophilia is to enable them to lead their daily lives just as anyone else would. To achieve this goal, it is necessary to have a positive and collaborative approach across various healthcare disciplines. This extends beyond clinical specialists, encompassing pediatricians, hematologists, orthopedic surgeons, dental and oral surgeons, gynecologists, nurses, physical therapists, clinical psychologists, and other professionals from diverse fields. This review article discusses the current status and challenges associated with comprehensive care for patients with hemophilia. We categorize these challenges as follows: hemophilic arthritis, rehabilitation, oral care, transitioning from pediatric to adult care, addressing carrier issues, and providing psychological care. There is still substantial work to be undertaken in addressing these hurdles and advancing the quality of comprehensive care for hemophilia patients.

Estimating the Burden of Clinically Significant Staphylococcus aureus Infections and Predictors for Hospitalization for Skin and Soft Tissue Infections, Fulton County, Georgia, 2017.

Incidence estimates of Staphylococcus aureus infections rarely include the full spectrum of clinically relevant disease from both community and healthcare settings. We conducted a prospective study capturing all S aureus infections in Fulton County, Georgia, during 2017. Medical records of patients with any incident infection (clinical cultures growing S aureus from any site, without prior positive culture in previous 14 days) were reviewed. Estimates of disease incidence were calculated using age-, race-, and sex-specific population denominators accounting for weighted sampling methods. Multivariable logistic regression models were used to identify risk factors for hospitalization among patients with skin and soft tissue infections (SSTIs). The overall incidence of clinically relevant S aureus infection was 405.7 cases per 100 000 people (standard error [SE], 5.62 [range, 400.1-411.3]). Overall incidence for those of Black race was 500.84 cases per 100 000 people (SE, 14.55), whereas White patients had overall incidence of 363.67 cases per 100 000 people (SE, 13.8). SSTIs were the most common infection (2351; 225.8 cases per 100 000 people [SE, 7.1]), and 30% required hospitalization. Among SSTIs, after adjusting for invasive disease, cellulitis, diabetes, and demographics, independent predictors of hospitalization included methicillin-resistant S aureus (adjusted odds ratio [aOR], 1.6 [95% confidence interval {CI}, 1.0-2.7]) and homelessness (aOR, 4.9 [95% CI, 1.1-22]). The burden of clinically relevant S aureus infections is high, particularly among the Black population, and risks for hospitalization among SSTIs include isolate factors and factors related to patients' vulnerability.

Developing Disability-Focused Pre-Health and Health Professions Curricula

People with disabilities (PWD) comprise a significant part of the population yet experience some of the most profound health disparities. Among the greatest barriers to quality care are inadequate health professions education related to caring for PWD. Drawing upon the expertise of health professions educators in medicine, public health, nursing, social work, and physician assistant programs, this forum showcases innovative methods for teaching core disability skills and concepts grounded in disability studies and the health humanities. Each of the essays offers practical guidance for developing curricular interventions appropriate for students at various levels of training and familiarity with disability to be implemented in classroom discussions, case-based learning, lectures, panels, and clinical simulations across the full spectrum of pre-health and health professions education.

Health‐related quality of life in full spectrum of Alzheimer’s disease: a longitudinal analysis

AbstractBackgroundUnderstanding the health‐related quality of life (HR‐QoL) in subjective cognitive decline (SCD), mild cognitive impairment (MCI) and demented patients is essential from the health economic perspective for evaluating new disease‐modifying treatments and preventive interventions. This study aims to assess HR‐QoL and the factors associated with longitudinal change in HR‐QoL in the full spectrum of Alzheimer’s disease.MethodLongitudinal data from 918 SCD and 1,346 MCI patients from the MEMENTO cohort were included in the study. MCI was defined as performing one standard deviation (SD) worse than the age, sex and education‐level thresholds in one or more cognitive domains identified within six months of the study enrollment. SCD was defined as having isolated subjective cognitive complaints but without any objective cognitive deficit and aged 60 years or older. HR‐QoL was measured by EuroQol EQ‐5D‐3L patient‐reported version. Linear mixed‐effect models with disease stage as a time‐varying covariate were used to (1) assess the utility changes over time across the dementia spectrum and (2) compare the utility changes between amyloid positive and negative SCD and MCI patients.ResultAt baseline, mean utilities were 0.84 ± 0.16 and 0.81 ± 0.18 in SCD and MCI patients. Linear mixed effect models estimated that the utilities were ‐0.016 (SE 0.003), ‐0.07 (SE 0.009), ‐0.18 (SE 0.014) and ‐0.36 (SE 0.034) lower than SCD in patients with MCI, mild, moderate and severe dementia over the study period. Time is associated with a significant decrease in utility with ‐0.008 (SE 0.001) per year. Baseline age, BMI, female, diabetes, cardiovascular history, neuropsychiatric symptoms and inability to perform Instrumental activity of daily living (IADL) were negatively associated with utilities. Amyloid‐positive SCD patients showed a faster decline in utility than amyloid‐negative SCD patients (p < 0.05). The decrease was not statistically significant in amyloid‐positive MCI patients compared to amyloid‐negative MCI.ConclusionHealth utilities decrease over time in all stages of dementia, and the decline is steeper in moderate and severe stages. Positive amyloid accelerates health utility decline in SCD but not MCI individuals. The ability to perform IADL is the major factor affecting HR‐QoL in all phases of dementia.

Toward a Unified Classification System for Brain-Mind Disorders: Putting Calls for Integrated Clinical Neuroscience Into Action.

Dividing the brain-mind into the specialized fields of neurology and psychiatry has produced many granular advantages, but these silos have imposed barriers to comprehensively understanding and contextualizing the fundamentals governing mental life and its maladies. Scientific inquiry into these fundamentals cannot reach its full potential without interdigitating the boundaries of two specialties of the same organ for both scholarship and clinical practice. We propose that to truly integrate disorders of the brain and the mind for research and clinical care, we must carefully reexamine the classification of its disorders (nosology) as an instrument to develop a coherent pathological and psychological framework. We call on professional organizations from neurology, psychiatry, behavioral neurology, neuropsychiatry, neuropsychology, and other relevant subspecialties (eg, geriatric psychiatry) to convene a multidisciplinary task force to define the current classification principles of their subspecialties and work toward developing an integrated nosology. The effect of a shared classification system, which we acknowledge is a difficult proposition philosophically and politically, would have transformative potential across educational, clinical, scientific, programmatic, and sociocultural realms. If accomplished, this initiative would provide a definitive step toward reducing stigma (and promoting reimbursement parity) for the full spectrum of complex brain disorders (regardless of traditional neurologic vs psychiatric conceptualizations).

GWAS identifies genetic factors associated with severity of cerebral amyloid angiopathy pathology

AbstractBackgroundCerebral amyloid angiopathy (CAA) commonly co‐occurs with Alzheimer’s disease (AD) pathologies, leading to an increased risk and severity of cognitive decline. While CAA is known to impact cognitive impairment, the genetics of CAA are not well understood, and most previous sample sizes have been small. To address this gap, we conducted a genome‐wide association study (GWAS) of CAA pathology to identify genetic factors that are associated with this understudied AD‐related amyloidosis.MethodWe utilized data from 6,038 autopsied and genotyped individuals from the National Alzheimer’s Coordinating Centers (NACC) who were evaluated for CAA pathology upon autopsy. Participants were age 50+ at death and non‐Hispanic white. Genetic data were available from SNP arrays imputed to TOPMed and analyses were conducted only on common variants (MAF >0.01). GWAS performed ordinal logistic regression to determine the effects of genetic variants on CAA pathology (coded as 0, none; 1, mild; 2, moderate; 3, severe). Models adjusted for sex, age at death, and the first three genetic principal components (PC‐AiR; GENESIS; R v4.2). We then meta‐analyzed across the cohorts (METAL).ResultWe observed genome‐wide significant associations with CAA severity at established AD loci, including APOE (top SNP: rs429358, MAF = 0.155, P = 1.6E−86) and BIN1 (top SNP: rs6733839, MAF = 0.397, P = 3.9E−08). We observed a novel association with CAA pathology on chromosome 9 (top SNP: rs10908023, MAF = 0.258, P = 4.3E−11).ConclusionThis is the largest study to date aimed at identifying genetics factors associated with CAA pathology. Our findings offer new insights into the genetics of CAA, a common player in the AD landscape. We acknowledge the need for a larger dataset and are now expanding the study by sample size and by including additional ADRD phenotypes to gain a better understanding of the full spectrum AD/ADRD. Continuing to uncover the genetic underpinnings of CAA could potentially help us think about AD treatment in new ways and mitigate the negative side‐effects of brain swelling and bleeding that have been observed in recent clinical trials.

Sun-induced fluorescence spectrum as a tool for assessing peatland vegetation productivity in the framework of warming and reduced precipitation experiment

Northern peatlands store a large amount of carbon in the form of partially decomposed organic matter. Because the majority of northern peatlands are located in remote areas, remote sensing serves as a suitable alternative to traditional surveys, enabling to enhance our understanding of peatland vegetation. Among various optical remote sensing signals, sun-induced fluorescence (SIF) is the most directly connected to carbon assimilation by plants, making it a promising early-response indicator for assessing the impact of climate change on natural ecosystems. However, the behavior of SIF throughout the season and the strength of the relationship between SIF and carbon assimilation for peatland vegetation, which consists of peat mosses and vascular plants with diverse anatomy, morphology, physiology, and phenology, have not yet been studied. Therefore, we conducted the first comprehensive assessment of the full spectrum SIF, reflectance, and gross primary production (GPP) for two distinct peatland vegetation communities under control (C), warming (W), and warming with reduced precipitation (WP) conditions throughout an entire season. While we could detect clear differences in SIF and reflectance between the two vegetation communities for original, C, vegetation during the main growing season, these differences diminished when W and WP were applied. The W and WP conditions caused a more pronounced change in plant biomass for vegetation characterized by a higher proportion of peat mosses and low creeping shrubs, which resulted in significant changes in the SIF and reflectance spectrum. Our findings demonstrate that the domination of peatland by vascular plants that is expected due to future warmer conditions causes stronger seasonal variation of SIF, reflectance, and GPP. We observed that far-red SIF and the spectrally integrated full SIF spectrum strongly correlate (r2 > 0.85) with GPP regardless of vegetation community, temperature, and precipitation regime. However, the use of a novel multiple wavelength regression model using ten bands from the full SIF spectrum allowed for higher accuracy in the estimation of GPP compared to the use of single bands or integrated total SIF value. Moreover, such a model has a more stable performance when transferring from one vegetation community to another. Conversely, the correlation strength of traditional vegetation indices like the Normalized Difference Vegetation Index or MERIS Terrestrial Chlorophyll Index depends on the peatland vegetation community. While the SIF:GPP relationship exhibits similar r2 values for vegetation communities with different ratios of planophile and electrophile leaves, the slope of the linear model depends on this ratio. This study performed at the ground level shows for the first time the importance of full spectrum SIF for the monitoring of a heterogeneous ecosystem like peatland, which will help to better utilize the SIF products obtained through current and future satellite missions.

Performance investigation on an agricultural photovoltaic thermal system based on spectral separation

Based on cascade utilization of full spectrum solar energy, a spectral splitting agriculture photovoltaic thermal hybrid system has been proposed in this study. The red and blue light is assigned to farmland for plant photosynthesis, while the photovoltaic active light is absorbed by solar cell for electricity generation, and the rest to thermal receiver for high temperature production. To maximize the power output of proposed system, redundant waste heat is recovered through ejector and converted into electricity in organic Rankine cycle subsequently. By such incorporation, the proposed system can not only increase the overall utilization efficiency of solar energy, but also achieve the cogeneration of crops and electricity on the same land. Energy, exergy, and economic analyses are performed to evaluate the performance of proposed system. Results show that the overall utilization efficiency of solar energy reaches 39.67 % with the highest losses of 79.65 MW in organic Rankine cycle. From the point view of exergy, photoelectric conversion in solar cell is responsible for the largest exergy destruction and finally the overall exergy efficiency of proposed system reaches 40.65 %, which is 16.13 % higher than that of equivalent agricultural and photovoltaic system with separate land use.

Benchmarking of deep neural networks for predicting personal gene expression from DNA sequence highlights shortcomings.

Deep learning methods have recently become the state of the art in a variety of regulatory genomic tasks1-6, including the prediction of gene expression from genomic DNA. As such, these methods promise to serve as important tools in interpreting the full spectrum of genetic variation observed in personal genomes. Previous evaluation strategies have assessed their predictions of gene expression across genomic regions; however, systematic benchmarking is lacking to assess their predictions across individuals, which would directly evaluate their utility as personal DNA interpreters. We used paired whole genome sequencing and gene expression from 839 individuals in the ROSMAP study7 to evaluate the ability of current methods to predict gene expression variation across individuals at varied loci. Our approach identifies a limitation of current methods to correctly predict the direction of variant effects. We show that this limitation stems from insufficiently learned sequence motif grammar and suggest new model training strategies to improve performance.

Simultaneous Estimates of Star-cluster Age, Metallicity, Mass, and Extinction (SESAMME). I. Presenting an MCMC Approach to Spectral Stellar Population Fitting

We present the first version release of SESAMME, a public, Python-based full spectrum fitting tool for Simultaneous Estimates of Star-cluster Age, Metallicity, Mass, and Extinction. SESAMME compares an input spectrum of a star cluster to a grid of stellar population models with an added nebular continuum component, using Markov Chain Monte Carlo methods to sample the posterior probability distribution in four dimensions: cluster age, stellar metallicity Z, reddening E(B − V), and a normalization parameter equivalent to a cluster mass. SESAMME is highly flexible in the stellar population models that it can use to model a spectrum; our testing and initial science applications use both BPASS and Starburst99. We illustrate the ability of SESAMME to recover accurate ages and metallicities even at a moderate signal-to-noise ratio (S/N ∼ 3–5 per wavelength bin) using synthetic, noise-added model spectra of young star clusters. Finally, we test the consistency of SESAMME with other age and metallicity estimates from the literature using a sample of Hubble Space Telescope/Cosmic Origins Spectrograph far-UV spectra toward young, massive clusters in M83 and NGC 1313. We find that, on the whole, SESAMME infers star cluster properties that are consistent with the literature in both low- and high-metallicity environments.