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Lipid droplets (LDs), crucial regulators of lipid metabolism, accumulate during oocyte development. However, their roles in fertility remain largely unknown. During Drosophila oogenesis, LD accumulation coincides with actin remodeling necessary for follicle development. Loss of the LD-associated Adipose Triglyceride Lipase (ATGL) disrupts both actin bundle formation and cortical actin integrity, an unusual phenotype also seen when the prostaglandin (PG) synthase Pxt is missing. Dominant genetic interactions and PG treatment of follicles indicate ATGL acts upstream of Pxt to regulate actin remodeling. Our data suggest ATGL releases arachidonic acid (AA) from LDs to serve as the substrate for PG synthesis. Lipidomic analysis detects AA-containing triglycerides in ovaries, and these are increased when ATGL is lost. High levels of exogenous AA block follicle development; this is enhanced by impairing LD formation and suppressed by reducing ATGL. Together these data support the model that AA stored in LD triglycerides is released by ATGL to drive the production of PGs, which promote actin remodeling necessary for follicle development. We speculate this pathway is conserved across organisms to regulate oocyte development and promote fertility.

Animal embryos are provided by their mothers with a diverse nutrient supply that is crucial for development. In Drosophila, the three most abundant nutrients (triglycerides, proteins and glycogen) are sequestered in distinct storage structures: lipid droplets (LDs), yolk vesicles (YVs) and glycogen granules (GGs). Using transmission electron microscopy as well as live and fixed sample fluorescence imaging, we find that all three storage structures are dispersed throughout the egg but are then spatially allocated to distinct tissues by gastrulation: LDs largely to the peripheral epithelium, YVs and GGs to the central yolk cell. To confound the embryo's ability to sort its nutrients, we employ Jabba and mauve mutants to generate LD-GG and LD-YV compound structures. In these mutants, LDs are mis-sorted to the yolk cell and their turnover is delayed. Our observations demonstrate dramatic spatial nutrient sorting in early embryos and provide the first evidence for its functional importance.

Many signals, particularly of biological origin, suffer from a signal-to-noise ratio sufficiently low that it can be difficult to classify individual examples reliably, even with relatively sophisticated machine-learning techniques such as deep learning. In some cases, the noise can be high enough that it is even difficult to achieve convergence during training. We considered this problem for one data type that often suffers from such difficulties, namely electroencephalography (EEG) data from cognitive neuroscience studies in humans. One solution to increase signal-to-noise is, of course, to perform averaging among trials, which has been employed before in other studies of human neuroscience but not, to our knowledge, investigated rigorously, particularly not in deep learning. Here, we parametrically studied the effects of different amounts of trial averaging during training and/or testing in a human EEG dataset, and compared the results to that of a related algorithm, Mixup. Broadly, we found that even a small amount of averaging could significantly improve classification, particularly when both training and testing data were subjected to averaging. Simple averaging clearly outperformed Mixup, although the benefits of averaging differed across classification categories. Overall, our results confirm the value of averaging during training and testing when single-trial classification is not strictly necessary for the application in question.HighlightsAveraging trials can dramatically improve performance in classification of EEG dataThe benefits can be seen when averaging on both training and test datasetsSimple trial averaging outperformed a popular related algorithm, MixupHowever, effects of averaging differed across different stimulus categories

Animal pigment patterns are excellent models to elucidate mechanisms of biological organization. Although theoretical simulations, such as Turing reaction-diffusion systems, recapitulate many animal patterns, they are insufficient to account for those showing a high degree of spatial organization and reproducibility. Here, we compare the coats of the African striped mouse (Rhabdomys pumilio) and the laboratory mouse (Mus musculus) to study the molecular mechanisms controlling stripe pattern formation. By combining transcriptomics, mathematical modeling, and mouse transgenics, we show thatSfrp2regulates the distribution of hair follicles and establishes an embryonic prepattern that foreshadows pigment stripes. Moreover, by developing and employingin vivogene editing experiments in striped mice, we find thatSfrp2knockout is sufficient to alter the stripe pattern. Strikingly, mutants also exhibit changes in coat color, revealing an additional function ofSfrp2in regulating hair color. Thus, a single factor controls coat pattern formation by acting both as an orienting signaling mechanism and a modulator of pigmentation. By uncovering a multifunctional regulator of stripe formation, our work provides insights into the mechanisms by which spatial patterns are established in developing embryos and the molecular basis of phenotypic novelty.

Humans automatically and unintentionally detect and remember regularities in the visual environment−a type of learning termed visual statistical learning (VSL). Many aspects of learning from reward resemble statistical learning in some respects, yet whether and how reward learning impacts VSL is largely unexamined. In two studies, we investigated the impact of reward on VSL and examined the neural basis of this interaction using fMRI. Subjects completed a risky choice task, in which they learned the values (high or low) of fractal images through a trial-and-error binary-choice task. Unbeknownst to subjects, we paired images so that some images always predicted other images on the following trial. This led to four types of pairings (High-High, High-Low, Low-High, and Low-Low). In a subsequent recognition task and reward memory task, we asked them to choose the more familiar of two pairs (a target and a foil) and to recall the value of images (high or low). We found better recognition when the first image of a pair was a high-value image, with High-High pairs showing the highest recognition rate. To investigate the neural basis of this effect, we measured brain responses to visual images that were associated with both varying levels of reward and sequential contingencies with event-related fMRI. Subjects completed the same risky choice task and then passively viewed a stream of the images with pairwise relationships intact. Brain responses to images during the risky choice task were affected by both value and statistical contingencies. When we compared responses between the first image of a pair that was high-value and the first image of a pair that was low-value, we found greater activation in regions that included inferior frontal gyrus, left anterior cingulate gyrus, middle temporal gyrus, superior temporal gyrus, hippocampus, orbitofrontal cortex, caudate, nucleus accumbens, hippocampus, and lateral occipital cortex. These findings are not driven solely by the value difference, but rather the interaction between statistically structured information and reward − the same value contrast yielded no regions for either second-image contrasts or for singletons. Our results suggest that the first images of pairs that were associated with high-value, in comparison to those associated with low-value, were involved in greater attentional engagement, potentially enabling better memory for statistically learned pairs and reward information. Additionally, we found neural evidence that when an image contains both statistical structure and reward information, the reward learning may be predicted by the type of the statistical structure it is associated with. We conclude that reward contingencies affect VSL, with high-value associated with stronger behavioral and neural signatures of such learning.

Animal embryos are provisioned by their mothers with a diverse nutrient supply critical for development. In Drosophila, the three most abundant nutrients (triglycerides, proteins, and glycogen) are sequestered in distinct storage structures, lipid droplets (LDs), yolk vesicles (YVs) and glycogen granules (GGs). Using transmission electron microscopy as well as live and fixed-sample fluorescence imaging, we find that all three storage structures are dispersed throughout the egg but are then spatially allocated to distinct tissues by gastrulation: LDs largely to the peripheral epithelium, YVs and GGs to the central yolk cell. To confound the embryo’s ability to sort its nutrients, we employ mutants in Jabba and Mauve to generate LD:GG or LD:YV compound structures. In these mutants, LDs are missorted to the yolk cell and their turnover is delayed. Our observations demonstrate dramatic spatial nutrient sorting in early embryos and provide the first evidence for its functional importance.

Federal Regulation 40 CFR §63.670 requires flare operators to specify smokeless design capacity for flares with no visible emissions. Alternatively, 96.5% combustion efficiency (CE) or 98% destruct...

Populations of wild spring-run Chinook salmon in California’s Central Valley, once numbering in the millions, have dramatically declined to record low numbers. Dam construction, habitat degradation, and altered flow regimes have all contributed to depress populations, which currently persist in only a few tributaries to the Sacramento River. Mill Creek (Tehama County) continues to support these threatened fish, and contains some of the most pristine spawning and rearing habitat available in the Central Valley. Despite this pristine habitat, the number of Chinook salmon returning to spawn has declined to record low numbers, likely due to poor outmigration survival rates. From 2013 to 2017, 334 smolts were captured and acoustic tagged while out-migrating from Mill Creek, allowing for movement and survival rates to be tracked over 250 km through the Sacramento River. During this study California experienced both a historic drought and record rainfall, resulting in dramatic fluctuations in year-to-year river flow and water temperature. Cumulative survival of tagged smolts from Mill Creek through the Sacramento River was 9.5% (±1.6) during the study, with relatively low survival during historic drought conditions in 2015 (4.9% ± 1.6) followed by increased survival during high flows in 2017 (42.3% ± 9.1). Survival in Mill Creek and the Sacramento River was modeled over a range of flow values, which indicated that higher flows in each region result in increased survival rates. Survival estimates gathered in this study can help focus management and restoration actions over a relatively long migration corridor to specific regions of low survival, and provide guidance for management actions in the Sacramento River aimed at restoring populations of threatened Central Valley spring-run Chinook salmon.

A challenge for mobile measurement of fugitive methane emissions is the availability of portable sensors that feature high sensitivity and fast response times, simultaneously. A methane gas sensor to measure fugitive emissions was developed using a continuous-wave, thermoelectrically cooled, GaSb-based distributed feedback diode laser emitting at a wavelength of 3.27 μm to probe methane in its strong ν3 vibrational band. Direct absorption spectra (DAS) as well as wavelength-modulated spectra (WMS) of pressure-broadened R(3) manifold lines of methane were recorded through a custom-developed open-path multipass cell with an effective optical path length of 6.8 m. A novel metrological approach was taken to characterize the sensor response in terms of the linearity of different WMS metrics, namely, the peak-to-peak amplitude of the X2f component and the peak and/or the integrated area of the background-subtracted quadrature signal (i.e., Q(2f – 2f0)) and the background-subtracted 1f-normalized quadrature signal (i.e., Q(2f/1f – 2f0/1f0)). Comparison with calibration gas concentrations spanning 1.5 to 40 ppmv indicated that the latter WMS metric showed the most linear response, while fitting DAS provides a traceable reference. In the WMS mode, a sensitivity better than 1 ppbv was achieved at a 1 s integration time. The sensitivity and response time are well-suited to measure enhancements in ambient methane levels caused by fugitive emissions.

The remarkable success of SARS CoV-2 mRNA-based vaccines and the ensuing interest in mRNA vaccines and therapeutics have highlighted the need for a scalable clinical-enabling manufacturing process to produce such products, and robust analytical methods to demonstrate safety, potency, and purity. To date, production processes have either not been disclosed or are bench-scale in nature and cannot be readily adapted to clinical and commercial scale production. To address these needs, we have advanced an aqueous-based scalable process that is readily adaptable to GMP-compliant manufacturing, and developed the required analytical methods for product characterization, quality control release, and stability testing. We also have demonstrated the products produced at manufacturing scale under such approaches display good potency and protection in relevant animal models with mRNA products encoding both vaccine immunogens and antibodies. Finally, we discuss continued challenges in raw material identification, sourcing and supply, and the cold chain requirements for mRNA therapeutic and vaccine products. While ultimate solutions have yet to be elucidated, we discuss approaches that can be taken that are aligned with regulatory guidance.

Previous attempts to classify task from eye movement data have relied on model architectures designed to emulate theoretically defined cognitive processes and/or data that have been processed into aggregate (e.g., fixations, saccades) or statistical (e.g., fixation density) features. Black box convolutional neural networks (CNNs) are capable of identifying relevant features in raw and minimally processed data and images, but difficulty interpreting these model architectures has contributed to challenges in generalizing lab-trained CNNs to applied contexts. In the current study, a CNN classifier was used to classify task from two eye movement datasets (Exploratory and Confirmatory) in which participants searched, memorized, or rated indoor and outdoor scene images. The Exploratory dataset was used to tune the hyperparameters of the model, and the resulting model architecture was retrained, validated, and tested on the Confirmatory dataset. The data were formatted into timelines (i.e., x-coordinate, y-coordinate, pupil size) and minimally processed images. To further understand the informational value of each component of the eye movement data, the timeline and image datasets were broken down into subsets with one or more components systematically removed. Classification of the timeline data consistently outperformed the image data. The Memorize condition was most often confused with Search and Rate. Pupil size was the least uniquely informative component when compared with the x- and y-coordinates. The general pattern of results for the Exploratory dataset was replicated in the Confirmatory dataset. Overall, the present study provides a practical and reliable black box solution to classifying task from eye movement data.

Global flaring volume exceeds 140 billion m³ annually and flares are a key source of particulate air pollution. During flowback operations subsequent to fracturing of a well, droplets of flowback water-with varying levels of dissolved salts-can be entrained in the flared gas. Despite the widespread prevalence of fracturing, very little is known about the properties of particle emissions from such flares. To study these properties, we used a laboratory pipe flare producing a turbulent diffusion flame without and with entrained droplets. Entrained droplets of deionized water, sodium chloride solution, and solutions representing two typical flowback waters in Canada (Cardium and Duvernay) were used. Three different gas compositions (consisting of C1 to C7 alkanes, carbon dioxide, and nitrogen) representative of flares in the upstream oil and gas sector in Alberta, Canada were studied. The results showed that the salt in the entrained flowback droplets increased the particle concentration by about one order of magnitude by forming freshly nucleated salt particles. Moreover, soot concentration increased as a result of entrained salt. Effective density results showed that small particles (300 nm) were mostly soot—a result also confirmed by transmission electron microscopy (TEM). Electron micrographs showed that the majority of particles were either individual salt particles or internally-mixed soot-salt particles. The inorganic salt particles mainly consisted of Na and Cl, the two most abundant elements in flowback water. Raman spectroscopy indicated that the salt had much less (or no) impact on graphitic nanostructure of soot, while the fuel blend had a significant effect. The results of this study are significant as they reveal that current emission inventories based on flaring of gases only may underestimate soot emissions from flares with entrained droplets.

Humans automatically detect and remember regularities in the visual environment-a type of learning termed visual statistical learning (VSL). Many aspects of learning from reward resemble VSL in certain respects, yet whether and how reward learning impacts VSL is largely unexamined. In two studies, we found that reward contingencies affect VSL, with high-value associated with stronger behavioral and neural signatures of such learning than low-value images. In Experiment 1, participants learned values (high or low) of images through a trial-and-error risky choice task. Unbeknownst to them, images were paired as four types-High-High, High-Low, Low-High, and Low-Low. In subsequent recognition and reward memory tests, participants chose the more familiar of two pairs (a target and a foil) and recalled the value of images. We found better recognition when the first images of pairs have high-values, with High-High pairs showing the highest recognition rate. In Experiment 2, we provided evidence that both value and statistical contingencies affected brain responses. When we compared responses between the high-value first image and the low-value first image, greater activation in regions that included inferior frontal gyrus, anterior cingulate gyrus, hippocampus, among other regions, were found. These findings were driven by the interaction between statistically structured information and reward-the same value contrast yielded no regions for second-image contrasts and for singletons. Our results suggest that when reward information is embedded in stimulus-stimulus associations, it may alter the learning process; specifically, the higher-value first image potentially enables better memory for statistically learned pairs and reward information.

Working memory (WM) is critical to many aspects of cognition, but it frequently fails. Much WM research has focused on capacity limits, but even for single, simple features, the fidelity of individual representations is limited. Why is this? One possibility is that, because of neural noise and interference, neural representations do not remain stable across a WM delay, nor do they simply decay, but instead, they may “drift” over time to a new, less accurate state. We tested this hypothesis in a functional magnetic resonance imaging study of a match/nonmatch WM recognition task for a single item with a single critical feature: orientation. We developed a novel pattern-based index of “representational drift” to characterize ongoing changes in brain activity patterns throughout the WM maintenance period, and we were successfully able to predict performance on the match/nonmatch recognition task using this representational drift index. Specifically, in trials where the target and probe stimuli matched, participants incorrectly reported more nonmatches when their activity patterns drifted away from the target. In trials where the target and probe did not match, participants incorrectly reported more matches when their activity patterns drifted toward the probe. On the basis of these results, we contend that neural noise does not cause WM errors merely by degrading representations and increasing random guessing; instead, one means by which noise introduces errors is by pushing WM representations away from the target and toward other meaningful (yet incorrect) configurations. Thus, we demonstrate that behaviorally meaningful drift within representation space can be indexed by neuroimaging.

Global gas flaring is an important source of black carbon (BC) emissions with uncertain climate impacts. The link between atmospheric concentration and direct radiative forcing (DRF) by BC is its mass absorption cross-section (MAC). MAC data for flare-generated BC are lacking in the literature and the only known data conflict with generally-accepted BC MAC values, which are assumed to be source-independent. This paper presents the first measurements of BC MAC for large-scale flares, burning globally-representative, industry-relevant flare gas compositions in a controlled facility. BC MAC was calculated with precisely-quantified uncertainties using photoacoustic and thermal-optical instruments. Flare-generated carbon was found to be primarily elemental in composition (typically >92%), and most probably externally-mixed based on detailed analysis of attenuation vs. evolved carbon data and consideration of flare-specific mechanisms for organic carbon emissions. Flare BC MAC was generally larger than well-cited literature values and had statistically significant variations with fuel and operating conditions. Variability in BC MAC was well-predicted by a novel phenomenological model based on flame radiative characteristics and relative BC production. The derived model consolidates previously-unreconciled disparate data from different sources and suggests that flare BC MAC is likely >1.3–2 times standard values, implying an underestimation of DRF by flare-generated BC.

Mammalian periodic pigment patterns, such as spots and stripes, have long interested mathematicians and biologists because they arise from non-random developmental processes that are programmed to be spatially constrained, and can therefore be used as a model to understand how organized morphological structures develop. Despite such interest, the developmental and molecular processes underlying their formation remain poorly understood. Here, we argue that Arvicanthines, a clade of African rodents that naturally evolved a remarkable array of coat patterns, represent a tractable model system in which to dissect the mechanistic basis of pigment pattern formation. Indeed, we review recent insights into the process of stripe formation that were obtained using an Arvicanthine species, the African striped mouse (Rhabdomys pumilio), and discuss how these rodents can be used to probe deeply into our understanding of the factors that specify and implement positional information in the skin. By combining naturally evolved pigment pattern variation in rodents with classic and novel experimental approaches, we can substantially advance our understanding of the processes by which spatial patterns of cell differentiation are established during embryogenesis, a fundamental question in developmental biology.