Washington State University Tri-Cities

Considering today’s challenges regarding climate change and rapid depletion of conventional fossil fuels, algae-based fuels seem to be promising and sustainable alternatives via various conversion methods. Despite growing interest in algal-based applications, the full potential of algal biomass as a versatile and sustainable feedstock remains underutilized as it is abundantly available when compared to terrestrial plants. The biomass from algae can be used for producing biofuels and chemicals as they are high in proteins, carbs, and lipids. Producing fuel from algae remains commercially challenging because cultivation and harvesting demand significant energy and incur high costs. At the pilot scale, these challenges often create major bottlenecks in converting algae into biofuels or biochemicals. Adopting an integrated algae biorefinery approach can help lower both energy use and expenses. Such systems enable the simultaneous recovery of biopolymers, biochemicals, biofuels, and biofertilizers with better cost-effectiveness than traditional processes. Ultimately, making algae-based production more economically feasible strengthens its potential role in advancing a green economy. This review articulates recent advances in chemical, thermochemical, and biochemical conversion processes of algae-based biomass. Chemical transesterification, thermochemical processes such as gasification, pyrolysis, and hydrothermal treatments, and biochemical conversion methods like fermentation, anaerobic digestion, and photobiological techniques are discussed in this review. For biochemical conversion methods such as fermentation, anaerobic digestion and photobiological techniques are mentioned towards the end of this review.

Abstract The lower humification efficiency during composting is an emerging challenge that attenuates compost quality. Although biochar has the potential to enhance humification, its effectiveness is limited by factors such as its alkaline pH and the absence of functional groups. Here, Ca-modified biochar was prepared via co-pyrolysis of coconut shell and oyster shell (1:1 ratio) at 600 °C and added to pig manure composting at a 10% (w/w) dose. The result of spectroscopy analysis of the developed compost revealed that carboxyl and carbonyl functional groups facilitated the adhesion of oyster shells to the biochar surface. The addition of Ca-modified biochar in compost not only increased the concentrations of humic acid (+ 8.6%) but also improved the seed germination index (GI) (+ 18.9%) compared to the control group. The excitation-emission matrix analysis indicated that the modified biochar facilitated the transformation of protein-like molecules, thereby enhancing the humification of organic matter by 4.92% compared to the control group. Microbial analysis indicated that Proteobacteria and Bacteroidetes were predominant in the modified biochar group, exhibiting a 107% higher prevalence, as compared to the control group. These enriched microbial taxa, known for their role in lignin degradation, are inferred to inhibit nitrogen volatilization, generate precursor substances, and catalyze the conversion of organic matter into stable humus. The correlation analysis further revealed a significant positive correlation between Proteobacteria and GI ( p < 0.05). These findings demonstrated that Ca-modified biochar enhances physicochemical and microbiological processes, offering a scalable solution for improving humification and supporting sustainable compost production. Graphical Abstract

This systematic review evaluated the psychometric performance of the National Institutes of Health's Toolbox Cognition Battery (NIHTB‐CB) composite scores in older adults with and without Alzheimer's disease and related dementias (ADRD). A systematic literature search was conducted using MEDLINE, Embase, PsycINFO, and CINHAL databases. The evidence quality of NIHTB‐CB measurement properties was assessed using integrated Consensus‐based Standards for the Selection of Health Measurement Instrument (COSMIN) methodology and the Interpretation/Use Argument framework. Fourteen studies met inclusion criteria for this review of the NIHTB‐CB. Evidence supporting the scoring, generalization, and extrapolation inferences of the Total, Crystallized, and Fluid composite scores in older adults ranged from developing through exemplary ratings. Findings indicate additional research is warranted on the NIHTB‐CB in older adult and ADRD populations. The present study highlights the importance of continued use and research of the NIHTB‐CB in diverse, older populations who are at risk for ADRD.HighlightsLimited research focuses on the National Institutes of Health's Toolbox Cognition Battery (NIHTB‐CB) composites in older adults.The general psychometric robustness of the NIHTB‐CB has been es.The Crystallized composite shows proficient psychometric evidence.The psychometric evidence of Fluid composite is developing due to limited data.

Abstract Bacteriocins offer a promising solution to antibiotic resistance, possessing the ability to target a wide range of bacteria with precision. Thus, there is an urgent need for a computational model to predict new bacteriocins and aid in drug development. This work centers on constructing predictive models with XGBoost machine learning algorithm, using physicochemical structural properties and sequence profiles of protein sequences. We employed correlation analyses, cross-validation, and hypergraph-based techniques to select features. Cross-validation feature evaluation (CVFE) partitions the dataset, selects features within each partition, and identifies common features, ensuring representativeness. On the contrary, hypergraph-based feature evaluation (HFE) focuses on minimizing hypergraph cut conductance, leveraging higher-order data relationships to precisely utilize information regarding feature and sample correlations. The XGBoost models were built using the selected features obtained from these two feature evaluation methods. Our HFE-based approach achieved 99.11% accuracy and an AUC of 0.9974 on the test data, overall outperforming the CVFE-based feature evaluation method and yielding results comparable to existing approaches. We also analyzed the feature contributions directly from the best model using SHapley Additive exPlanations (SHAP). Our web application, accessible at https://shiny.tricities.wsu.edu/bacteriocin-prediction/, offers prediction results, probability scores, and SHAP plots using both cross-validation- and hypergraph-based methods, along with previously implemented approaches for feature selection.

Global food production depends upon successful pollination, a process that relies on natural and managed pollinators. However, natural pollinators are declining due to factors such as climate change, habitat loss, and pesticide use. This paper presents an integrated robotic system for precision pollination in apples. The system consisted of a machine vision system to identify target flower clusters and estimate their positions and orientations, and a manipulator motion planning and actuation system to guide the sprayer to apply charged pollen suspension to the target flower clusters. The system was tested in the lab, followed by field evaluation in Honeycrisp and Fuji orchards. In the Honeycrisp variety, the robotic pollination system achieved a fruit set of 34.8% of sprayed flowers with 87.5% of flower clusters having at least one fruit when a 2 gm/l pollen suspension was used. In comparison, the natural pollination technique achieved a fruit set of 43.1% with 94.9% of clusters with at least one fruit. In Fuji apples, the robotic system with same pollen concentration achieved lower pollination success, with 7.2% of sprayed flowers setting fruit and 20.6% of clusters having at least one fruit, compared to 33.1% and 80.6%, respectively, with natural pollination. Fruit quality analysis showed that robotically pollinated fruits were comparable to naturally pollinated fruits in terms of color, weight, diameter, firmness, soluble solids, and starch content. Additionally, the system cycle time was 6.5 s per cluster. The results showed a promise for robotic pollination in apple orchards. However, further research and development is needed to improve the system and assess its suitability across diverse orchard environments and apple cultivars. • Novel robotic system: machine vision, 6-DOF manipulator, and electrostatic sprayer. • Field-tested in orchards; fruit set and quality were evaluated. • Achieved fruit set in 87.5% Honeycrisp clusters vs 94.9% natural pollination. • Fruit quality was comparable to natural across multiple parameters. • Pollen concentration, cultivar, and environment may affect pollination efficacy.

Abstract In recent years, there has been a widespread focus on utilizing bioenergy derived from forests as an effective and sustainable renewable energy alternative. The utilization of wood wastes and forest residues to generate heat and produce energy has gained significant importance. However, there are certain challenges associated with using these materials in their original form. Biomass pelletizing presents a promising solution by converting raw biomass into fuel pellets that possess high energy density. Furthermore, the resulting biochar plays a vital role in enhancing soil quality. This chapter delves into the process of manufacturing wood fuel pellets and explores their diverse applications across residential and industrial sectors. It also examines technologies for producing biochar from this waste, highlighting potential benefits such as carbon sequestration and soil enrichment they offer. Moreover, this chapter also discusses various heat generation technologies that can be used in conjunction with biomass pellet production.

False data injection (FDI) attacks against power system state estimation through manipulating measurements can result in economic losses and grid operating security issues. FDI attacks are stealthy to the traditional bad data detector. However, existing FDI construction methods fail to consider the stealthiness of attacks against machine-learning (ML) detectors. Since the historical measurement patterns are generally utilized by ML detectors, we apply the tensor completion (TC) technique in the FDI construction to manipulate compromised measurements matching the historical measurement patterns. We propose a novel convex TC-based FDI (TC-FDI) attack algorithm that 1) minimizes the nuclear norm of the compromised measurement tensor to make the compromised measurements consistent with the historical ones and 2) maximizes the L1-norm of the incremental voltage to ensure a sufficient negative impact on the power system operation. Further, the reactance perturbation strategy (RPS) is utilized to detect the TC-FDI attacks by breaking the spatial and temporal correlation of the compromised measurements. Numerical results on the IEEE 14-bus system show the stealthiness of the proposed attacks to the statistic-based detectors and ML detectors. The efficacy of the RPS in detecting TC-FDI attacks is also demonstrated.

The study of One Million U.S. Radiation Workers and Veterans, the Million Person Study (MPS), examines the health consequences, both cancer and non-cancer, of exposure to ionizing radiation received gradually over time. Recently the MPS has focused on mortality patterns from neurological and behavioral conditions, e.g., Parkinson's disease, Alzheimer's disease, dementia, and motor neuron disease such as amyotrophic lateral sclerosis. A fuller picture of radiation-related late effects comes from studying both mortality and the occurrence (incidence) of conditions not leading to death. Accordingly, the MPS is identifying neurocognitive diagnoses from fee-for-service insurance claims from the Centers for Medicare and Medicaid Services (CMS), among Medicare beneficiaries beginning in 1999 (the earliest date claims data are available). Linkages to date have identified ∼540,000 workers with available health information. Such linkages provide individual information on important co-factor and confounding variables such as smoking, alcohol consumption, blood pressure, obesity, diabetes and many other health and demographic characteristics. The total person-level set of time-dependent variables, outcomes, organ-specific dose measures, co-factors, and demographics will be massive and much too large to be evaluated with standard software. Thus, development of specialized open-source software designed for large datasets (Colossus) is nearly complete. The wealth of information available from CMS claims data, coupled with individual dose reconstructions, will thus greatly enhance the quality and precision of health evaluations for this new field of low-dose radiation and neurocognitive effects.

This systematic review evaluated the effect of speech interventions with the addition of speech generating devices on vocalizations of children with autism spectrum disorder (ASD) and complex communication needs. This review synthesized information across 18 studies published in the past three decades with the primary focus on the facilitative effect of SGDs on the speech production of individuals with ASD, and further highlighted the role of SGDs on the development of natural speech when combined with appropriate intervention strategies. These studies involved a total of 108 participants aged 3 to 13 and were thoroughly coded according to participant demographics, design, pre-and post-speech measure, intervention strategy, methodological rigor, and type of verbal operant. SGD-infused interventions combined with evidence-based practices can lead to increases in children's expressive language and social communication utterances. Despite the promising outcomes, optimal outcomes are associated with some of the participants’ prelinguistic predictors as measured before the interventions.