The Cousa objective: a long-working distance air objective for multiphoton imaging in vivo.

Multiphoton microscopy can resolve fluorescent structures and dynamics deep in scattering tissue and has transformed neural imaging, but applying this technique in vivo can be limited by the mechanical and optical constraints of conventional objectives. Short working distance objectives can collide with compact surgical windows or other instrumentation and preclude imaging. Here we present an ultra-long working distance (20 mm) air objective called the Cousa objective. It is optimized for performance across multiphoton imaging wavelengths, offers a more than 4 mm2 field of view with submicrometer lateral resolution and is compatible with commonly used multiphoton imaging systems. A novel mechanical design, wider than typical microscope objectives, enabled this combination of specifications. We share the full optical prescription, and report performance including in vivo two-photon and three-photon imaging in an array of species and preparations, including nonhuman primates. The Cousa objective can enable a range of experiments in neuroscience and beyond.

An uncommon neuronal class conveys visual signals from rods and cones to retinal ganglion cells.

Neurons in the central nervous system (CNS) are distinguished by the neurotransmitter types they release, their synaptic connections, morphology, and genetic profiles. To fully understand how the CNS works, it is critical to identify all neuronal classes and reveal their synaptic connections. The retina has been extensively used to study neuronal development and circuit formation. Here, we describe a previously unidentified interneuron in mammalian retina. This interneuron shares some morphological, physiological, and molecular features with retinal bipolar cells, such as receiving input from photoreceptors and relaying visual signals to retinal ganglion cells. It also shares some features with amacrine cells (ACs), particularly Aii-ACs, such as their neurite morphology in the inner plexiform layer, the expression of some AC-specific markers, and possibly the release of the inhibitory neurotransmitter glycine. Thus, we unveil an uncommon interneuron, which may play an atypical role in vision.

Comparison of struvite and K-struvite for Pb and Cr immobilisation in contaminated soil.

Struvite is a value-added by-product recovered from phosphorus-rich wastewater treatment by adding magnesium. Struvite is mainly used as slow-release fertilisers containing phosphate that can form insoluble salts with certain heavy metals. Hence, struvite may have potential application as a phosphate remediation agent for the immobilisation of heavy metals in contaminated soil, while the related study is limited. Similarly, an analogue compound of struvite, K-struvite, may also have this value but has not been reported elsewhere. This study investigated the effect of struvite and K-struvite on the remediation of Cr-spiked and Pb-spiked soil. To evaluate the feasibility, the agent dosage and two quality parameters (particle size and purity) of struvite and K-struvite were considered for the experimental design and statically analysed by principal component analysis (PCA) and partial least squares (PLS). The results show that the dosage significantly impacts the immobilisation process, while the effect of particle size and purity are negligible. Struvite and K-struvite have similar performance on heavy metals immobilisation, and both are significant in Pb immobilisation (up to 96% of F5, stable fraction) and are beneficial for reducing the most mobilised fractions (F1 and F2) of Cr to lesser than 3%. Struvite and K-struvite share similar performance due to their similar atomic radius, and the different performance between Cr and Pb immobilisation can be explained by the strong hydrolysis trend of chromium ion, which may inhibit the binding of the phosphate and chromium. The kinetic study finds that all three variables positively impact the free chromium ion, and the immobilisation process is fast so unlikely to be kinetically limited. These findings of this project will provide insight into how the immobilisation process changes in response to the dosage and quality of struvite compounds.

Non-permanent primary food packaging materials assessment: Identification, migration, toxicity, and consumption of substances.

Almost all processed food comes packaged in either plastic, glass, metal, or paper and paperboard materials, and many packaging materials are disposed of after a single use (linear economy). Based on the concept of a circular economy, the recycling of food packaging materials has become one of the main targets for industries and regulators around the world. However, recycling presents particular challenges, mainly related to the recycled material composition, which determines its reusability, application, functionality, and chemical safety. In this latter matter, it has been demonstrated that the use of recycled food packaging materials increases the number and possible sources of substances that could be present in the packaging material, which is of concern as substances that can migrate into food and cause health hazards upon consumption. This review compiles information regarding substances detected in non-permanent food packaging materials, focusing mainly on plastics, paper, and paperboards. The compilation of literature studies (110 research articles) on the presence of intentionally added substances (IAS) and non-intentionally added substances (NIAS) in food packaging materials, their migration, toxicity, and dietary exposure has been summarized, evaluated, and discussed. In addition, current sustainable food packaging trends have been mentioned. Finally, approaches to reduce the presence, migration, and potential exposure to substances that have migrated from packaging materials into food have been reviewed.

Effect of saline water ionic strength on phosphorus recovery from synthetic swine wastewater.

Declining worldwide phosphate rock reserves has driven a growing interest in exploration of alternative phosphate supplies. This study involved phosphorus recovery from swine wastewater through precipitation of struvite, a valuable slow-release fertiliser. The economic feasibility of this process is highly dependent on the cost of magnesium source. Two different magnesium sources were used for phosphorus recovery: pure magnesium chloride and nanofiltration (NF) saline water retentate. The paper focuses on the impact of ionic strength on phosphorus recovery performance that has not been reported elsewhere. Experimental design with five numerical variables (Mg/P molar ratio, pH, PO43--P, NH4+-N, and Ca2+ levels) and one categorical variable (type of magnesium source) was used to evaluate the effect of ionic strength on phosphorus removal and struvite purity. The experimental data were analysed using analysis of variance (ANOVA) and principal component analysis (PCA). Results indicated that a magnesium source obtained from NF retentate was as effective as MgCl2 for struvite precipitation. It was also revealed that ionic strength had a more positive effect on struvite purity than on phosphorus removal. Within the range of parameters studied in this research, high ionic strength, high pH and wastewater with high phosphate, high ammonium and low calcium contents were found to be the most favourable conditions for struvite precipitation. Findings from this study will be beneficial to determine the feasibility of using high ionic strength saline water, such as NF seawater retentate, as a magnesium source for phosphorus recovery from wastewater that is rich in ammonium-nitrogen and phosphate.

Contrasting Temporal Patterns of Mercury, Niche Dynamics, and Body Fat Indices of Polar Bears and Ringed Seals in a Melting Icescape.

Polar bears (Ursus maritimus) and ringed seals (Pusa hispida) have a strong predator-prey relationship and are facing climate-associated Arctic habitat loss and harmful dietary exposure to total mercury (THg) and other pollutants. However, little is known about whether both species inhabiting the same area exhibit similar temporal patterns in Hg concentration, niche dynamics, and body fat indices. We used THg, δ13C, and δ15N values of western Hudson Bay polar bear hair (2004-2016) and ringed seal muscle samples (2003-2015) to investigate temporal trends of these variables and multidimensional niche metrics, as well as body fat indices for both species. We found a decline in THg concentration (by 3.8% per year) and δ13C (by 1.5‰) in ringed seals suggesting a change in feeding habits and carbon source use over time, whereas no significant changes occurred in polar bears. In contrast, the polar bear 3-dimensional niche size decreased by nearly half with no change in ringed seal niche size. The δ13C spacing between both species increased by approximately 1.5× suggesting different responses to annual changes in sympagic-pelagic carbon source production. Ringed seal body fat index was higher in years of earlier sea ice breakup with no change occurring in polar bears. These findings indicate that both species are responding differently to a changing environment suggesting a possible weakening of their predator-prey relationship in western Hudson Bay.

Wavelength Selection FOR Rapid Identification of Different Particle Size Fractions of Milk Powder Using Hyperspectral Imaging.

Hyperspectral imaging (HSI) in the spectral range of 400-1000 nm was tested to differentiate three different particle size fractions of milk powder. Partial least squares discriminant analysis (PLS-DA) was performed to observe the relationship of spectral data and particle size information for various samples of instant milk powder. The PLS-DA model on full wavelengths successfully classified the three fractions of milk powder with a coefficient of prediction 0.943. Principal component analysis (PCA) identified each of the milk powder fractions as separate clusters across the first two principal components (PC1 and PC2) and five characteristic wavelengths were recognised by the loading plot of the first three principal components. Weighted regression coefficient (WRC) analysis of the partial least squares model identified 11 important wavelengths. Simplified PLS-DA models were developed from two sets of reduced wavelengths selected by PCA and WRC and showed better performance with predictive correlation coefficients (Rp2) of 0.962 and 0.979, respectively, while PLS-DA with complete spectrum had Rp2 of 0.943. Similarly, classification accuracy of PLS-DA was improved to 92.2% for WRC based predictive model. Calculation time was also reduced to 2.1 and 2.8 s for PCA and WRC based simplified PLS-DA models in comparison to the complete spectrum model that was taking 32.2 s on average to predict the classification of milk powder samples. These results demonstrated that HSI with appropriate data analysis methods could become a potential analyser for non-invasive testing of milk powder in the future.

An improved model for the kinetics of non-oxidative hydrothermal process.

Hydrothermal processing as a post-treatment technology for sludge has attracted great interest globally as it could reduce the amount of sludge considerably. This experimental study developed a comprehensive kinetic model of cellulose degradation via non-oxidative hydrothermal processing at various temperatures (ranges 180-260 °C). Values of activation energies and pre-exponential factors were determined using chemical oxygen demand (COD)-based lumped concentrations. In this study, a new reaction pathway between solid, soluble matter and gaseous products was proposed which not only enables prediction of solid phase degradation but also can predict the formation of various types of products (in liquid and gas phase) during the reaction time. The results show that the reaction rate of cellulose to liquid products (k1=2.7×109exp(-102810RT)) were fast compared to that of for liquid products to gaseous products (k2=4.4×103exp(-64629RT)). Moreover, the model infers that the major part of solid degradation leads to the formation of the gaseous product with the reaction rate constant of k3=5.7exp(-12905RT). The proposed model can provide an opportunity to predict the performance of the non-oxidative hydrothermal processing of organic solid waste.

Phosphorus recovery through struvite crystallisation: Recent developments in the understanding of operational factors.

Phosphorus is an essential element for life and is predicted to deplete within the next 100 years. Struvite crystallization is a potential phosphorus recovery technique to mitigate this problem by producing a slow release fertilizer. However, complex wastewater composition and a large number of process variables result in process uncertainties, making the process difficult to predict and control. This paper reviews the research progress on struvite crystallization fundamentals to address this challenge. The influence of manipulated variables (e.g. seed material, magnesium dosage and pH) and sources of variation on phosphorus removal efficiency (e.g. organics and heavy metal concentration) and product purity were investigated. Recently developed models to describe, control and optimize those variables were also discussed. This review helps to identify potential challenges in different wastewater streams and provide valuable information for future phosphorus recovery unit design. It therefore paves the way for commercialization of struvite crystallization in the future.

Differential Contribution of Transmembrane Domains IV, V, VI, and VII to Human Angiotensin II Type 1 Receptor Homomer Formation.

G protein-coupled receptors (GPCRs) play an important role in drug therapy and represent one of the largest families of drug targets. The angiotensin II type 1 receptor (AT1R) is notable as it has a central role in the treatment of cardiovascular disease. Blockade of AT1R signaling has been shown to alleviate hypertension and improve outcomes in patients with heart failure. Despite this, it has become apparent that our initial understanding of AT1R signaling is oversimplified. There is considerable evidence to suggest that AT1R signaling is highly modified in the presence of receptor-receptor interactions, but there is very little structural data available to explain this phenomenon even with the recent elucidation of the AT1R crystal structure. The current study investigates the involvement of transmembrane domains in AT1R homomer assembly with the goal of identifying hydrophobic interfaces that contribute to receptor-receptor affinity. A recently published crystal structure of the AT1R was used to guide site-directed mutagenesis of outward-facing hydrophobic residues within the transmembrane region of the AT1R. Bioluminescence resonance energy transfer was employed to analyze how receptor mutation affects the assembly of AT1R homomers with a specific focus on hydrophobic residues. Mutations within transmembrane domains IV, V, VI, and VII had no effect on angiotensin-mediated ß-arrestin1 recruitment; however, they exhibited differential effects on the assembly of AT1R into oligomeric complexes. Our results demonstrate the importance of hydrophobic amino acids at the AT1R transmembrane interface and provide the first glimpse of the requirements for AT1R complex assembly.

Superoxide Dismutase 1 Regulation of CXCR4-Mediated Signaling in Prostate Cancer Cells is Dependent on Cellular Oxidative State.

BACKGROUND/AIMS: CXCL12, acting via one of its G protein-coupled receptors, CXCR4, is a chemoattractant for a broad range of cell types, including several types of cancer cells. Elevated expression of CXCR4, and its ligand CXCL12, play important roles in promoting cancer metastasis. Cancer cells have the potential for rapid and unlimited growth in an area that may have restricted blood supply, as oxidative stress is a common feature of solid tumors. Recent studies have reported that enhanced expression of cytosolic superoxide dismutase (SOD1), a critical enzyme responsible for regulation of superoxide radicals, may increase the aggressive and invasive potential of malignant cells in some cancers. METHODS: We used a variety of biochemical approaches and a prostate cancer cell line to study the effects of SOD1 on CXCR4 signaling. RESULTS: Here, we report a direct interaction between SOD1 and CXCR4. We showed that SOD1 interacts directly with the first intracellular loop (ICL1) of CXCR4 and that the CXCL12/CXCR4-mediated regulation of AKT activation, apoptosis and cell migration in prostate cancer (PCa) cells is differentially modulated under normal versus hypoxic conditions when SOD1 is present. CONCLUSIONS: This study highlights a potential new regulatory mechanism by which a sensor of the oxidative environment could directly regulate signal transduction of a receptor involved in cancer cell survival and migration.

Mature Turkey Breeder Hens Exposed to Pandemic Influenza H1N1: Resultant Effects on Morbidity, Mortality, and Fecundity.

During the artificial insemination process, turkey breeder hens may become infected with influenza virus acquired from humans. The virus has been shown, through experimental infection, to localize in the reproductive tract, with limited dissemination in other tissues. A limited number of hens were used during these studies, and the overall flock morbidity, mortality, and fecundity were not able to be determined. The current case follows the progression of clinical signs in a flock of commercial breeder hens from onset of egg production losses in one house through the subsequent drops in four remaining houses. Each house contained approximately 3000 hens and followed a sequential loss of shell quality, reduced numbers of eggs, and fertility, while mild clinical signs were observed and mortality was slightly increased in a house with concurrent fowl cholera (Pasturella multocida) infection.

COVERT: A classless approach to generating balanced datasets for process modelling.

In this work, a classless oversampling technique, Covert, was developed to improve historical datasets from industrial processing plants to aid process modelling. Using kernel density estimation and nearest neighbour algorithms, sparse regions are identified and resampled, developing a more balanced dataset. When applied to a real dataset from a geothermal power plant, Covert outperforms current best practice (Smote) in uniformly populating the input feature space and generating credible data in the output variable. When used to develop a data-driven model Covert improved model accuracy by 20% when predicting outside the original data's feature space. Smote, however, reduced model accuracy by 6% in the same feature space. Developing reliable models of industrial processes continues to be a significant hurdle in developing a digital twin. Using Covert, existing imbalanced historical data can be used to extend the range of applicability of any process model.

Integrating Image Analysis and Machine Learning for Moisture Prediction and Appearance Quality Evaluation: A Case Study of Kiwifruit Drying Pretreatment.

The appearance of dried fruit clearly influences the consumer's perception of the quality of the product but is a subtle and nuanced characteristic that is difficult to quantitatively measure, especially online. This paper describes a method that combines several simple strategies to assess a suitable surrogate for the elusive quality using imaging, combined with multivariate statistics and machine learning. With such a convenient tool, this study also shows how one can vary the pretreatments and drying conditions to optimize the resultant product quality. Specifically, an image batch processing method was developed to extract color (hue, saturation, and value) and morphological (area, perimeter, and compactness) features. The accuracy of this method was verified using data from a case study experiment on the pretreatment of hot-air-dried kiwifruit slices. Based on the extracted image features, partial least squares and random forest models were developed to satisfactorily predict the moisture ratio (MR) during drying process. The MR of kiwifruit slices during drying could be accurately predicted from changes in appearance without using any weighing device. This study also explored determining the optimal drying strategy based on appearance quality using principal component analysis. Optimal drying was achieved at 60 °C with 4 mm thick slices under ultrasonic pretreatment. For the 70 °C, 6 mm sample groups, citric acid showed decent performance.

BDNF and cAMP are neuroprotective in a porcine model of traumatic optic neuropathy.

Traumatic optic neuropathy (TON) is a devastating condition that can occur after blunt or penetrating trauma to the head, leading to visual impairment or blindness. Despite these debilitating effects, no clinically available therapeutic targets neuroprotection or promotes axon regeneration in this or any optic neuropathy. Limited data in large-animal models are a major obstacle to advancing treatments toward clinical therapeutics. To address this issue, we refined a surgical model of TON in Yucatan minipigs. First, we validated the model by demonstrating visual impairment by flash visual-evoked potential and retinal ganglion cell degeneration and death. Next, we developed and optimized a delivery method and nontoxic dosing of intravitreal brain-derived neurotrophic factor (BDNF) and cAMP. Finally, we showed that intravitreal injection of BDNF and cAMP rescued visual function and protected against retinal ganglion cell death and optic nerve axon degeneration. Together these data in a preclinical large-animal model advance our understanding of and ability to model TON and further identify and develop candidate clinical therapeutics.

New insight into thermal hydrolysis of sewage sludge from solubilisation analysis.

Thermal hydrolysis, a sludge pre-treatment process prior to anaerobic digestion, is increasing in popularity in academia and industry due to the potential of biogas production enhancement. However, there is a limited understanding of the solubilisation mechanism, which significantly influences the biogas yield. This study evaluated the influence of flashing, reaction time, and temperature to understand the mechanism. It was found that while hydrolysis is the primary process (responsible for approximately 76-87% of sludge solubilisation), the sudden decompression via flashing at the end of the process, creating shear force to break the cell membrane, contributes a considerable percentage (approximately 24-13% depended on the treatment conditions) to the solubilisation of treated sludge. More importantly, the decompression helps significantly shorten the reaction time from 30 min to 10 min, which in turn reduces the sludge's colour, minimises energy consumption, and eliminates the formation of inhibitory compounds for anaerobic digestion. However, a considerable loss in volatile fatty acids (650 mg L⁻1 of acetic acid at 160 °C) during flash decompression should be considered.

Application of Three-Dimensional Digital Photogrammetry to Quantify the Surface Roughness of Milk Powder.

The surface appearance of milk powders is a crucial quality property since the roughness of the milk powder determines its functional properties, and especially the purchaser perception of the milk powder. Unfortunately, powder produced from similar spray dryers, or even the same dryer but in different seasons, produces powder with a wide variety of surface roughness. To date, professional panelists are used to quantify this subtle visual metric, which is time-consuming and subjective. Consequently, developing a fast, robust, and repeatable surface appearance classification method is essential. This study proposes a three-dimensional digital photogrammetry technique for quantifying the surface roughness of milk powders. A contour slice analysis and frequency analysis of the deviations were performed on the three-dimensional models to classify the surface roughness of milk powder samples. The result shows that the contours for smooth-surface samples are more circular than those for rough-surface samples, and the smooth-surface samples had a low standard deviation; thus, milk powder samples with the smoother surface have lower Q (the energy of the signal) values. Lastly, the performance of the nonlinear support vector machine (SVM) model demonstrated that the technique proposed in this study is a practicable alternative technique for classifying the surface roughness of milk powders.

The importance of outlier rejection and significant explanatory variable selection for pinot noir wine soft sensor development.

Sensory attributes are essential factors in determining the quality of wines. However, it can be challenging for consumers, even experts, to differentiate and quantify wines' sensory attributes for quality control. Soft sensors based on rapid chemical analysis offer a potential solution to overcome this challenge. However, the current limitation in developing soft sensors for wines is the need for a significant number of input parameters, at least 12, necessitating costly and time-consuming analyses. While such a comprehensive approach provides high accuracy in sensory quality mapping, the expensive and time-consuming studies required do not lend themselves to the industry's routine quality control activities. In this work, Box plots, Tucker-1 plots, and Principal Component Analysis (PCA) score plots were used to deal with output data (sensory attributes) to improve the model quality. More importantly, this work has identified that the number of analyses required to fully quantify by regression models and qualify by classification models can be significantly reduced. Based on regression models, only four key chemical parameters (total flavanols, total tannins, A520nmHCl, and pH) were required to accurately predict 35 sensory attributes of a wine with R2 values above 0.6 simultaneously. In addition, for classification models to accurately predict 35 sensory attributes of a wine at once with prediction accuracy above 70%, only four key chemical parameters (A280nmHCl, A520nmHCl, chemical age and pH) were required. These models with reduced chemical parameters complement each other in sensory quality mapping and provide acceptable accuracy. The application of the soft sensor based on these reduced sets of key chemical parameters translated to a potential reduction in analytical cost and labour cost of 56% for the regression model and 83% for the classification model, respectively, making these models suitable for routine quality control use.

Revisiting China's domestic greenhouse gas emission from wastewater treatment: A quantitative process life-cycle assessment.

The wastewater treatment industry could alleviate water pollution but consume a large amount of energy and resources. China has over 5000 centralized domestic wastewater treatment plants and produces an unignorable amount of greenhouse gases (GHG). By considering the wastewater treatment, wastewater discharge, and sludge disposal processes, and employing the modified process-based quantification method, this study quantifies wastewater treatment's on-site and off-site GHG emissions across China. Results showed that the total GHG emission was 67.07 Mt CO2-eq in 2017, with approximately 57% of on-site emissions. The top seven cosmopolis and metropolis (top 1%) emitted nearly 20% of the total GHG emission, while their emission intensity was relatively low due to the huge population. This means that a high urbanization rate may be a feasible way to mitigate GHG emissions in the wastewater treatment industry in the future. Furthermore, GHG reduction strategies can also focus on process optimization and improvement at WWTPs as well as the nationwide promotion of onsite thermal conversion technologies for sludge management.

The Application of Artificial Intelligence and Big Data in the Food Industry.

Over the past few decades, the food industry has undergone revolutionary changes due to the impacts of globalization, technological advancements, and ever-evolving consumer demands. Artificial intelligence (AI) and big data have become pivotal in strengthening food safety, production, and marketing. With the continuous evolution of AI technology and big data analytics, the food industry is poised to embrace further changes and developmental opportunities. An increasing number of food enterprises will leverage AI and big data to enhance product quality, meet consumer needs, and propel the industry toward a more intelligent and sustainable future. This review delves into the applications of AI and big data in the food sector, examining their impacts on production, quality, safety, risk management, and consumer insights. Furthermore, the advent of Industry 4.0 applied to the food industry has brought to the fore technologies such as smart agriculture, robotic farming, drones, 3D printing, and digital twins; the food industry also faces challenges in smart production and sustainable development going forward. This review articulates the current state of AI and big data applications in the food industry, analyses the challenges encountered, and discusses viable solutions. Lastly, it outlines the future development trends in the food industry.