Miracle-dz/UniFAR-FTUs huggingface dataset:
Sustainable bioenergy cropping concepts : optimizing biomass provision for different conversion routes

Today energy from biomass already contributes to a considerable share to the global energy consumption. In particular, certain modern bioenergy streams like biogas, biofuels for transportation etc., are of increasing relevance. However, several of the beneficial aspects, which were initially attributed to the utilization of today’s bioenergy, had to be relativized and the controversies regarding environmental and socio-economic drawbacks have, in the meanwhile, been on the rise. Alongside a shift to advanced conversion technologies, the improvement of bioenergy cropping systems towards a sustainable biomass provision is a key element of future bioenergy production. Against this background, the present thesis assessed various aspects in terms of biomass production, biomass provision and biomass conversion mainly addressing the two bioenergy streams – 2nd generation biofuels and biogas. For biogas, the thesis addresses aspects regarding the development of alternative cropping systems, the evaluation of novel crops for biogas purpose, and the assessment of regional biogas potentials using a crop growth model. The second focus of the thesis was set on biofuels while special attention was given to the production of microbial biodiesel, and the characterization and evaluation of potential feedstocks for this purpose. Die Nutzung von Biomasse zur Energieproduktion leistet bereits heute einen wesentlichen Beitrag zur globalen Energieversorgung, wobei insbesondere einige „moderne“ Formen der Bioenergienutzung zunehmende Bedeutung erfahren. Der verstarkte Ausbau der Bioenergienutzung basierte im Wesentlichen auf zwei zukunftsrelevanten Zielsetzungen – Energiesicherheit und Klimaschutz. Mittlerweile wurden jedoch einige der positiven Eigenschaften, die der Nutzung von Bioenergie zugeschrieben werden, relativiert und die Debatten uber okologische und sozio-okonomische Defizite der Bioenergienutzung haben zugenommen. Neben Konversionsverfahren, die den Ubergang zur Bioenergie der zweiten Generation darstellen, ist die Weiterentwicklung von Anbausystemen fur die Biomasse-produktion ein wesentliches Element im Hinblick auf eine nachhaltige Bioenergieproduktion. Die vorliegende Dissertation befasst sich mit verschiedenen Aspekten der Biomasseproduktion, Biomassebereitstellung sowie Biomassekonversion unter spezieller Berucksichtigung der Bioenergieformen – Biogas und Biokraftstoffe der zweiten Generation. Der Fokus im Bereich Biogasproduktion und -nutzung lag auf der Entwicklung alternativer Anbausysteme, der Untersuchung und Beurteilung neuartiger mehrjahriger Bioenergiepflanzen sowie der Abschatzung regionaler Biogaspotentiale unter Verwendung eines Pflanzenwachstumsmodells. Ein zweiter Schwerpunkt der Dissertation lag im Bereich Biokraftstoffe, insbesondere auf der Charakterisierung und Evaluierung potentieller Biomasserohstoffe fur die mikrobielle Biodieselproduktion.
Influence of Pavement Condition Data Variability on Network-Level Maintenance Decision

This study evaluated the impacts of data variability on pavement performance evaluation and maintenance planning. Three methods were used to quantify the variability of data. For pavement roughness and rut depth, the ratio of the difference between two wheelpaths over the sum value was used to quantify the data variation. For distress data, Monte Carlo simulation was used to quantify the error in distress extent, and the transition matrices were used to quantify the error in distress severity classification. An evaluation framework was proposed to investigate the influence of data variability on maintenance planning. The analysis results indicate that roughness for state routes exhibited a larger variation than that for Interstates. Pavement surface on the left wheelpath generally was smoother than that on the right path, regardless of route type. In regard to distress severity, the accuracy of distress extent at a low severity level had little influence on the overall distress index, while the accuracy of distress extent at moderate and high severity levels significantly influenced the overall distress index. The accuracy of distress severity at a moderate level had the most significant influence on the pavement distress index. For the current pavement management system used in Tennessee, the variability of roughness and distress severity level were the dominant influencing factors for maintenance planning, whereas the variability of distress extent had a slight influence on maintenance planning. There was no significant influence of variability of rut depth on maintenance planning.
A Deep Learning Approach to Predicting Bioactivity of Small Molecules Based on Molecular Structure

Computational techniques that can predict molecular properties based on chemical structure has the potential to significantly accelerate drug discovery. Herein, I explore DL approaches to predicting the bioactivity of small molecules from structural data by systematically comparing different molecular representations and model architectures. A word embedding strategy for molecule featurization combined with a 3-layer DNN model proved the most successful. Error analysis shows that improving the dataset labeling scheme may lead to better model performance
A Deep Learning Approach to Population Based COVID-19 Case Prediction in the US

Over the past two years, COVID-19 has caused over 700,000 deaths with 44 million cases in the United States alone (1). In an effort to curb the spread of the illness during the peak of the first wave, 42 US states and territories issued mandatory stay-at-home orders (2). As these lockdowns and stayat-home orders became the new normal during the past two years, the US has seen unprecedentedly low rates of travel, with travel spending decreasing by 42% (nearly $500 billion) from 2019 to the end of 2020 (3). Decreasing travel became synonymous with preventing the spread of COVID; however, surveys show that there are major disparities between the percentage of people staying home in each state with some state percentages as high as 80% and others as low as 5% (4). Machine learning techniques have the potential to gain insights from these disparities and their resulting correlations with COVID cases. As such, we aim to investigate the predictive capabilities of Neural Networks and several machine learning baselines when applied to US travel data in order to predict the quantity of COVID cases at a county level.
Inequalities in the progress of multiple chronic conditions: A systematic review of longitudinal studies

The objective of this review is to assess the impact of socioeconomic factors on the progress of multiple chronic health conditions (MCC) in Adults. Two independent investigators searched three databases (MEDLINE, EMBASE and LILACS) up to August 2021 to identify longitudinal studies on inequalities in progress of MCC. Grey literature was searched using Open Grey and Google Scholar. Inclusion criteria were retrospective and prospective longitudinal studies; adult population; assessed socioeconomic inequalities in progress of MCC. Quality of included studies and risk of bias were assessed using the Newcastle Ottawa Quality Assessment Scale for longitudinal studies. Nine longitudinal studies reporting socioeconomic inequalities in progress of MCC were included. Two of the studies had poor quality. Studies varied in terms of follow-up time, sample size, included chronic conditions and socioeconomic indicators. Due to high heterogeneity meta-analysis was not possible. The studies showed positive association between lower education (five studies), lower income and wealth (two studies), area deprivation (one study), lower job categories (two studies) and belonging to ethnic minority (two study) and progress of MCC. The review demonstrated socioeconomic inequality in progress of multiple chronic conditions. trial registratiom: The review protocol was registered in the International Prospective Register of Systematic Reviews (CRD42021229564).
3D engineered human gingiva fabricated with electrospun collagen scaffolds provides a platform for in vitro analysis of gingival seal to abutment materials

In order to advance models of human oral mucosa towards routine use, these models must faithfully mimic the native tissue structure while also being scalable and cost efficient. The goal of this study was to develop a low-cost, keratinized human gingival model with high fidelity to human attached gingiva and demonstrate its utility for studying the implant-tissue interface. Primary human gingival fibroblasts (HGF) and keratinocytes (HGK) were isolated from clinically healthy gingival biopsies. Four matrices, electrospun collagen (ES), decellularized dermis (DD), type I collagen gels (Gel) and released type I collagen gels (Gel-R)) were tested to engineer lamina propria and gingiva. HGF viability was similar in all matrices except for Gel-R, which was significantly decreased. Cell penetration was largely limited to the top layers of all matrices. Histomorphometrically, engineered human gingiva was found to have similar appearance to the native normal human gingiva except absence of rete pegs. Immunohistochemical staining for cell phenotype, differentiation and extracellular matrix composition and organization within 3D engineered gingiva made with electrospun collagen was mostly in agreement with normal gingival tissue staining. Additionally, five types of dental material posts (5-mm diameter x 3-mm height) with different surface characteristics were used [machined titanium, SLA (sandblasted-acid etched) titanium, TiN-coated (titanium nitride-coated) titanium, ceramic, and PEEK (Polyetheretherketone) to investigate peri-implant soft tissue attachment studied by histology and SEM. Engineered epithelial and stromal tissue migration to the implant-gingival tissue interface was observed in machined, SLA, ceramic, and PEEK groups, while TiN was lacking attachment. Taken together, the results suggest that electrospun collagen scaffolds provide a scalable, reproducible and cost-effective lamina propria and 3D engineered gingiva that can be used to explore biomaterial-soft tissue interface.
Polypyrimidine tract binding proteins PTBP1 and PTBP2 interact with distinct proteins under splicing conditions

RNA binding proteins play an important role in regulating alternative pre-mRNA splicing and in turn cellular gene expression. Polypyrimidine tract binding proteins, PTBP1 and PTBP2, are paralogous RNA binding proteins that play a critical role in the process of neuronal differentiation and maturation; changes in the concentration of PTBP proteins during neuronal development direct splicing changes in many transcripts that code for proteins critical for neuronal differentiation. How the two related proteins regulate different sets of neuronal exons is unclear. The distinct splicing activities of PTBP1 and PTBP2 can be recapitulated in an in vitro splicing system with the differentially regulated N1 exon of the c-src pre-mRNA. Here, we conducted experiments under these in vitro splicing conditions to identify PTBP1 and PTBP2 interacting partner proteins. Our results highlight that both PTBPs interact with proteins that participate in chromatin remodeling and transcription regulation. Our data reveal that PTBP1 interacts with many proteins involved in mRNA processing including splicing regulation while PTBP2 does not. Our results also highlight enzymes that can serve as potential “writers” and “erasers” in adding chemical modifications to the PTB proteins. Overall, our study highlights important differences in protein-protein interactions between the PTBP proteins under splicing conditions and supports a role for post-translational modifications in dictating their distinct splicing activities.
Sex- and age- dependent effect of pre-gestational chronic stress and mirtazapine treatment on neurobehavioral development of Wistar rat offspring

Hormonal fluctuations, such as the perinatal period, may increase susceptibility of women to depression, which in turn exert a negative impact on child’s neurodevelopment, becoming a risk factor in development of neuropsychiatric disorders. Moreover, the use of antidepressants during this critical period presents a serious health concern for both the mother and the child, due to the consequences of treatment in terms of the reliability and safety for the proper neurodevelopment of the organism being not well known. Atypical antidepressants, such as mirtazapine, that targets both serotonergic and noradrenergic systems in the central nervous system (CNS), represent a novel focus of research due to its unique pharmacological profile. The aim of this work was to study the effects of maternal depression and/or perinatal antidepressant mirtazapine treatment on the neurobehavioral development of the offspring. Pre-gestationally chronically stressed or non-stressed Wistar rat dams were treated with either mirtazapine (10 mg/kg/day) or vehicle during pregnancy and lactation followed by analysis of offspring’s behavior at juvenile and adolescent age. We found mirtazapine induced significant alterations of nursing behavior. In offspring, pregestational stress (PS) had an anxiogenic effect on adolescent males (p≤0.05) and increased their active behavior in forced swim test (p≤0.01). Interaction between pregestational stress and mirtazapine treatment variously induced anxiolytic changes of juvenile (p≤0.05) and adolescent (p≤0.05) females and impairment of spatial memory (p≤0.01) in adolescent females as well. Hippocampal density of synaptophysin, pre-synaptic protein marker, was decreased mainly by mirtazapine treatment. In conclusion, our results show mirtazapine induced significant alterations in maternal behavior and several sex- and age-dependent changes in neurobehavioral development of offspring caused by both prenatal mirtazapine treatment and/or chronic pregestational stress.
Burkholderia pseudomallei pathogenesis in human skin fibroblasts: A Bsa type III secretion system is involved in the invasion, multinucleated giant cell formation, and cellular damage

Burkholderia pseudomallei—a causative agent of melioidosis that is endemic in Southeast Asia and Northern Australia—is a Gram-negative bacterium transmitted to humans via inhalation, inoculation through skin abrasions, and ingestion. Melioidosis causes a range of clinical presentations including skin infection, pneumonia, and septicemia. Despite skin infection being one of the clinical symptoms of melioidosis, the pathogenesis of B. pseudomallei in skin fibroblasts has not yet been elucidated. In this study, we investigated B. pseudomallei pathogenesis in the HFF-1 human skin fibroblasts. On the basis of co-culture assays between different B. pseudomallei clinical strains and the HFF-1 human skin fibroblasts, we found that all B. pseudomallei strains have the ability to mediate invasion, intracellular replication, and multinucleated giant cell (MNGC) formation. Furthermore, all strains showed a significant increase in cytotoxicity in human fibroblasts, which coincides with the augmented expression of matrix metalloproteinase-2. Using B. pseudomallei mutants, we showed that the B. pseudomallei Bsa type III secretion system (T3SS) contributes to skin fibroblast pathogenesis, but O-polysaccharide, capsular polysaccharide, and short-chain dehydrogenase metabolism do not play a role in this process. Taken together, our findings reveal a probable connection for the Bsa T3SS in B. pseudomallei infection of skin fibroblasts, and this may be linked to the pathogenesis of cutaneous melioidosis.
Adapting to change: How has COVID-19 affected people’s work and personal goals?

COVID-19 has had a devastating impact on the global economy and affected millions of people’s work and personal lives across the world. The purpose of the present study was to better understand how individuals’ work and personal goals have been affected by the pandemic and how they have adapted to these changes. We conducted qualitative semi-structured interviews (n = 48) and surveyed participants (n = 200) weekly for 5 weeks. Both methods revealed similar themes regarding the adaptation and pursuit of goals (social support, handling unpredictable situations, logistics, solving problems creatively, goal postponement, and no changes). Survey responses also showed that most individuals experienced their goals as more difficult (79%; 13% easier; 9% no change) and found that many had had to adapt or postpone their work and personal goals, often due to logistical difficulties. Businesses and governments should do more to help individuals adapt their goals to the new circumstances.
Neoantigens and their potential applications in tumor immunotherapy

The incidence of malignant tumors is increasing, the majority of which are associated with high morbidity and mortality rates worldwide. The traditional treatment method for malignant tumors is surgery, coupled with radiotherapy or chemotherapy. However, these therapeutic strategies are frequently accompanied with adverse side effects. Over recent decades, tumor immunotherapy shown promise in demonstrating notable efficacy for the treatment of cancer. With the development of sequencing technology and bioinformatics algorithms, neoantigens have become compelling targets for cancer immunotherapy due to high levels of immunogenicity. In addition, neoantigen-based vaccines have demonstrated potential for cancer therapy, primarily by augmenting T-cell responses. Neoantigens have also been shown to be effective in immune checkpoint blockade therapy. Therefore, neoantigens may serve to be predictive biomarkers and synergistic treatment targets in cancer immunotherapy. The aim of the present review was to provide an overview of the recent progress in the classification, screening and clinical application of neoantigens for cancer therapy.
People’s desire to be in nature and how they experience it are partially heritable

Nature experiences have been linked to mental and physical health. Despite the importance of understanding what determines individual variation in nature experience, the role of genes has been overlooked. Here, using a twin design (TwinsUK, number of individuals = 2,306), we investigate the genetic and environmental contributions to a person’s nature orientation, opportunity (living in less urbanized areas), and different dimensions of nature experience (frequency and duration of public nature space visits and frequency and duration of garden visits). We estimate moderate heritability of nature orientation (46%) and nature experiences (48% for frequency of public nature space visits, 34% for frequency of garden visits, and 38% for duration of garden visits) and show their genetic components partially overlap. We also find that the environmental influences on nature experiences are moderated by the level of urbanization of the home district. Our study demonstrates genetic contributions to individuals’ nature experiences, opening a new dimension for the study of human–nature interactions.
The spike-and-slab elastic net as a classification tool in Alzheimer’s disease

Alzheimer’s disease (AD) is the leading cause of dementia and has received considerable research attention, including using neuroimaging biomarkers to classify patients and/or predict disease progression. Generalized linear models, e.g., logistic regression, can be used as classifiers, but since the spatial measurements are correlated and often outnumber subjects, penalized and/or Bayesian models will be identifiable, while classical models often will not. Many useful models, e.g., the elastic net and spike-and-slab lasso, perform automatic variable selection, which removes extraneous predictors and reduces model variance, but neither model exploits spatial information in selecting variables. Spatial information can be incorporated into variable selection by placing intrinsic autoregressive priors on the logit probabilities of inclusion within a spike-and-slab elastic net framework. We demonstrate the ability of this framework to improve classification performance by using cortical thickness and tau-PET images from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) to classify subjects as cognitively normal or having dementia, and by using a simulation study to examine model performance using finer resolution images.
A callosal biomarker of behavioral intervention outcomes for autism spectrum disorder? A case-control feasibility study with diffusion tensor imaging

Tentative results from feasibility analyses are critical for planning future randomized control trials (RCTs) in the emerging field of neural biomarkers of behavioral interventions. The current feasibility study used MRI-derived diffusion imaging data to investigate whether it would be possible to identify neural biomarkers of a behavioral intervention among people diagnosed with autism spectrum disorder (ASD). The corpus callosum has been linked to cognitive processing and callosal abnormalities have been previously found in people diagnosed with ASD. We used a case-control design to evaluate the association between the type of intervention people diagnosed with ASD had previously received and their current white matter integrity in the corpus callosum. Twenty-six children and adolescents with ASD, with and without a history of parent-managed behavioral intervention, underwent an MRI scan with a diffusion data acquisition sequence. We conducted tract-based spatial statistics and a region of interest analysis. The fractional anisotropy values (believed to indicate white matter integrity) in the posterior corpus callosum was significantly different across cases (exposed to parent-managed behavioral intervention) and controls (not exposed to parent-managed behavioral intervention). The effect was modulated by the intensity of the behavioral intervention according to a dose-response relationship. The current feasibility case-control study provides the basis for estimating the statistical power required for future RCTs in this field. In addition, the study demonstrated the effectiveness of purposely-developed motion control protocols and helped to identify regions of interest candidates. Potential clinical applications of diffusion tensor imaging in the evaluation of treatment outcomes in ASD are discussed.
ACTA Background Paper: A Brief Description of the National Accelerated Literacy Program

This article discusses the theoretical underpinnings and teaching sequences and strategies of the National Accelerated Literacy Program, which is being introduced into Northern Territory Department of Employment, Education and Training schools and is already in place in some schools in other states. The Program is based on the understanding that learning to be a literate member of a society requires a student to learn the discourse or tacit ground-rules that apply to literacy lessons in schools. The implications for teaching that come out of this understanding include using texts that provide literate models for teaching how literate discourse works, and using supportive teaching or scaffolding around these literate texts.