Understanding the molecular basis of resilience to Alzheimer’s disease

The cellular and molecular distinction between brain aging and neurodegenerative disease begins to blur in the oldest old. Approximately 15–25% of observations in humans do not fit predicted clinical manifestations, likely the result of suppressed damage despite usually adequate stressors and of resilience, the suppression of neurological dysfunction despite usually adequate degeneration. Factors during life may predict the clinico-pathologic state of resilience: cardiovascular health and mental health, more so than educational attainment, are predictive of a continuous measure of resilience to Alzheimer’s disease (AD) and AD-related dementias (ADRDs). In resilience to AD alone (RAD), core features include synaptic and axonal processes, especially in the hippocampus. Future focus on larger and more diverse cohorts and additional regions offer emerging opportunities to understand this counterforce to neurodegeneration. The focus of this review is the molecular basis of resilience to AD

Revealing the impact of lifestyle stressors on the risk of adverse pregnancy outcomes with multitask machine learning

Psychosocial and stress-related factors (PSFs), defined as internal or external stimuli that induce biological changes, are potentially modifiable factors and accessible targets for interventions that are associated with adverse pregnancy outcomes (APOs). Although individual APOs have been shown to be connected to PSFs, they are biologically interconnected, relatively infrequent, and therefore challenging to model. In this context, multi-task machine learning (MML) is an ideal tool for exploring the interconnectedness of APOs on the one hand and building on joint combinatorial outcomes to increase predictive power on the other hand. Additionally, by integrating single cell immunological profiling of underlying biological processes, the effects of stress-based therapeutics may be measurable, facilitating the development of precision medicine approaches.ObjectivesThe primary objectives were to jointly model multiple APOs and their connection to stress early in pregnancy, and to explore the underlying biology to guide development of accessible and measurable interventions.Materials and MethodsIn a prospective cohort study, PSFs were assessed during the first trimester with an extensive self-filled questionnaire for 200 women. We used MML to simultaneously model, and predict APOs (severe preeclampsia, superimposed preeclampsia, gestational diabetes and early gestational age) as well as several risk factors (BMI, diabetes, hypertension) for these patients based on PSFs. Strongly interrelated stressors were categorized to identify potential therapeutic targets. Furthermore, for a subset of 14 women, we modeled the connection of PSFs to the maternal immune system to APOs by building corresponding ML models based on an extensive single cell immune dataset generated by mass cytometry time of flight (CyTOF).ResultsJointly modeling APOs in a MML setting significantly increased modeling capabilities and yielded a highly predictive integrated model of APOs underscoring their interconnectedness. Most APOs were associated with mental health, life stress, and perceived health risks. Biologically, stressors were associated with specific immune characteristics revolving around CD4/CD8 T cells. Immune characteristics predicted based on stress were in turn found to be associated with APOs.ConclusionsElucidating connections among stress, multiple APOs simultaneously, and immune characteristics has the potential to facilitate the implementation of ML-based, individualized, integrative models of pregnancy in clinical decision making. The modifiable nature of stressors may enable the development of accessible interventions, with success tracked through immune characteristics

Benign Fractionation of Lignin with CO₂‑Expanded Solvents of Acetic Acid + Water

Kraft lignin was fractionated by molecular weight (MW), using CO₂-expanded solutions of acetic acid/water in a 90/10 wt/wt ratio. In particular, as CO₂ pressures were increased from 7 bar to 48 bar, expanding the liquid-solvent phase and reducing its dielectric strength, lignin fractions decreasing in MW from 15 000 to 1250, with polydispersity index (PDI) values decreasing from 3.7 to 1.6, were obtained. The recovered lignin fractions were similar, in terms of chemical functionality. With the use of gas-expanded liquids (GXL), only one solvent composition is required, and recovery and reuse of the biorenewable CO₂ + acetic acid–water solution is facilitated through pressure release and recompression. The process was demonstrated for the recovery of seven lignin fractions to demonstrate its versatility and effectiveness, but simpler operation with recovery of just a low MW lignin fraction and a high MW lignin fraction is more consistent with anticipated applications

Prediction of multiple neuropathologic changes from features available during life

Background Neuropathologic changes are central for both understanding of the patients’ brains and making the definitive diagnosis of dementia‐related diseases. However, many of them are only obtainable post‐mortem. To make this information available while an individual is still alive, this study developed machine learning models that predict neuropathologic changes based on features obtainable during life. Method The multi‐site post‐mortem data (n∼5000) was obtained from National Alzheimer’s Coordinating Center. A multitask long‐short term memory‐based neural network architecture was developed with custom loss to predict the 13 neuropathologic changes from features during life measured longitudinally. The performance of the model was evaluated using entire unseen sites as test sets. Evaluation metrics include area under receiver’s operating curve (AUROC) and area under precision recall curve (AUPRC). Result The model was able to predict Alzheimer’s Disease neuropathologic changes (ADNC) and any Alzheimer’s‐related pathologies, such as Braak score, with great sensitivity, specificity, and precision (for example AUROC = 0.85; AUPRC = 0.96 for ADNC). Apart from these, the model can also predict hippocampal sclerosis accurately (AUROC = 0.79; AUPRC = 0.80) and Lewy Body disease at higher precision than clinician’s diagnosis. Model interpretation shows patterns in neuropsychological tests that are predictive of pathologic changes. Additionally, model error analysis revealed factors, such as resilient case ratios, that explain variation in performance between sites. Conclusion Patterns of measurable features during life can be used by machine learning to predict ADNC, hippocampal sclerosis, and to lesser extent Lewy Body

Performance of three delignifying pretreatments on hardwoods: hydrolysis yields, comprehensive mass balances, and lignin properties.

Background:In this work, three pretreatments under investigation at the DOE Bioenergy Research Centers (BRCs) were subjected to a side-by-side comparison to assess their performance on model bioenergy hardwoods (a eucalyptus and a hybrid poplar). These include co-solvent-enhanced lignocellulosic fractionation (CELF), pretreatment with an ionic liquid using potentially biomass-derived components (cholinium lysinate or [Ch][Lys]), and two-stage Cu-catalyzed alkaline hydrogen peroxide pretreatment (Cu-AHP). For each of the feedstocks, the pretreatments were assessed for their impact on lignin and xylan solubilization and enzymatic hydrolysis yields as a function of enzyme loading. Lignins recovered from the pretreatments were characterized for polysaccharide content, molar mass distributions, β-aryl ether content, and response to depolymerization by thioacidolysis. Results:All three pretreatments resulted in significant solubilization of lignin and xylan, with the CELF pretreatment solubilizing the majority of both biopolymer categories. Enzymatic hydrolysis yields were shown to exhibit a strong, positive correlation with the lignin solubilized for the low enzyme loadings. The pretreatment-derived solubles in the [Ch][Lys]-pretreated biomass were presumed to contribute to inhibition of enzymatic hydrolysis in the eucalyptus as a substantial fraction of the pretreatment liquor was carried forward into hydrolysis for this pretreatment. The pretreatment-solubilized lignins exhibited significant differences in polysaccharide content, molar mass distributions, aromatic monomer yield by thioacidolysis, and β-aryl ether content. Key trends include a substantially higher polysaccharide content in the lignins recovered from the [Ch][Lys] pretreatment and high β-aryl ether contents and aromatic monomer yields from the Cu-AHP pretreatment. For all lignins, the 13C NMR-determined β-aryl ether content was shown to be correlated with the monomer yield with a second-order functionality. Conclusions:Overall, it was demonstrated that the three pretreatments highlighted in this study demonstrated uniquely different functionalities in reducing biomass recalcitrance and achieving higher enzymatic hydrolysis yields for the hybrid poplar while yielding a lignin-rich stream that may be suitable for valorization. Furthermore, modification of lignin during pretreatment, particularly cleavage of β-aryl ether bonds, is shown to be detrimental to subsequent depolymerization

Performance of three delignifying pretreatments on hardwoods: hydrolysis yields, comprehensive mass balances, and lignin properties.

Background:In this work, three pretreatments under investigation at the DOE Bioenergy Research Centers (BRCs) were subjected to a side-by-side comparison to assess their performance on model bioenergy hardwoods (a eucalyptus and a hybrid poplar). These include co-solvent-enhanced lignocellulosic fractionation (CELF), pretreatment with an ionic liquid using potentially biomass-derived components (cholinium lysinate or [Ch][Lys]), and two-stage Cu-catalyzed alkaline hydrogen peroxide pretreatment (Cu-AHP). For each of the feedstocks, the pretreatments were assessed for their impact on lignin and xylan solubilization and enzymatic hydrolysis yields as a function of enzyme loading. Lignins recovered from the pretreatments were characterized for polysaccharide content, molar mass distributions, β-aryl ether content, and response to depolymerization by thioacidolysis. Results:All three pretreatments resulted in significant solubilization of lignin and xylan, with the CELF pretreatment solubilizing the majority of both biopolymer categories. Enzymatic hydrolysis yields were shown to exhibit a strong, positive correlation with the lignin solubilized for the low enzyme loadings. The pretreatment-derived solubles in the [Ch][Lys]-pretreated biomass were presumed to contribute to inhibition of enzymatic hydrolysis in the eucalyptus as a substantial fraction of the pretreatment liquor was carried forward into hydrolysis for this pretreatment. The pretreatment-solubilized lignins exhibited significant differences in polysaccharide content, molar mass distributions, aromatic monomer yield by thioacidolysis, and β-aryl ether content. Key trends include a substantially higher polysaccharide content in the lignins recovered from the [Ch][Lys] pretreatment and high β-aryl ether contents and aromatic monomer yields from the Cu-AHP pretreatment. For all lignins, the 13C NMR-determined β-aryl ether content was shown to be correlated with the monomer yield with a second-order functionality. Conclusions:Overall, it was demonstrated that the three pretreatments highlighted in this study demonstrated uniquely different functionalities in reducing biomass recalcitrance and achieving higher enzymatic hydrolysis yields for the hybrid poplar while yielding a lignin-rich stream that may be suitable for valorization. Furthermore, modification of lignin during pretreatment, particularly cleavage of β-aryl ether bonds, is shown to be detrimental to subsequent depolymerization