A rapid and bidirectional reporter of neural activity reveals neural correlates of social behaviors in Drosophila.

Neural activity is modulated over different timescales encompassing subseconds to hours, reflecting changes in external environment, internal state and behavior. Using Drosophila as a model, we developed a rapid and bidirectional reporter that provides a cellular readout of recent neural activity. This reporter uses nuclear versus cytoplasmic distribution of CREB-regulated transcriptional co-activator (CRTC). Subcellular distribution of GFP-tagged CRTC (CRTC::GFP) bidirectionally changes on the order of minutes and reflects both increases and decreases in neural activity. We established an automated machine-learning-based routine for efficient quantification of reporter signal. Using this reporter, we demonstrate mating-evoked activation and inactivation of modulatory neurons. We further investigated the functional role of the master courtship regulator gene fruitless (fru) and show that fru is necessary to ensure activation of male arousal neurons by female cues. Together, our results establish CRTC::GFP as a bidirectional reporter of recent neural activity suitable for examining neural correlates in behavioral contexts.

Facial Nerve High-Resolution Microanatomy Analysis of the Distal Intratemporal to Extracranial Pes Segment: Reconstructive Implications.

BACKGROUND: Current knowledge of facial nerve topography between the stylomastoid foramen to the pes anserinus is very limited. Elucidating this segment's intraneural microanatomy may be advantageous in certain clinical settings: the planning of nerve grafts for gaps extending from the proximal facial nerve trunk to distal branches or in determining coaptation sites for hypoglossal jump grafts to provide selective upper and lower facial tone. This study is the first to provide high-definition intraneural topography of the aforementioned segment to optimize reconstructive outcomes. METHODS: Sixteen facial nerves extending from the second genu to the pes anserinus were harvested from eight cadavers en bloc to preserve orientation. Specimens were imaged by micro-computed tomography using a serial 6-µm protocol and digitally reconstructed three-dimensionally to be analyzed using bioinformatic tools. RESULTS: No clinically significant fascicular separation was noted between 14.4 mm proximal to the stylomastoid foramen until 4.4 mm distal to the foramen. Fascicles remained separate throughout the remainder of the specimen and were found to undergo a mean rotation of 45.5 degrees ( P = 0.0002) between 8.9 and 13.7 mm distal to the stylomastoid foramen. This reliable clockwise rotation in left nerves and counterclockwise rotation in right nerves resulted in superficially oriented fascicles entering the upper division of the pes anserinus, whereas deep-oriented fascicles entered the lower division. CONCLUSION: Intraneural facial nerve topography and rotation are consistent from 4 to 14 mm distal to the stylomastoid foramen, enabling surgeons to accurately place grafts targeted to either the upper or lower face, thus optimizing functional accuracy and minimizing synkinesis.

Heterozygous Mutation of Vegfr3 Reduces Renal Lymphatics without Renal Dysfunction.

BACKGROUND: Lymphatic abnormalities are observed in several types of kidney disease, but the relationship between the renal lymphatic system and renal function is unclear. The discovery of lymphatic-specific proteins, advances in microscopy, and available genetic mouse models provide the tools to help elucidate the role of renal lymphatics in physiology and disease. METHODS: We utilized a mouse model containing a missense mutation in Vegfr3 (dubbed Chy ) that abrogates its kinase ability. Vegfr3 Chy/+ mice were examined for developmental abnormalities and kidney-specific outcomes. Control and Vegfr3 Chy/+ mice were subjected to cisplatin-mediated injury. We characterized renal lymphatics using tissue-clearing, light-sheet microscopy, and computational analyses. RESULTS: In the kidney, VEGFR3 is expressed not only in lymphatic vessels but also, in various blood capillaries. Vegfr3 Chy/+ mice had severely reduced renal lymphatics with 100% penetrance, but we found no abnormalities in BP, serum creatinine, BUN, albuminuria, and histology. There was no difference in the degree of renal injury after low-dose cisplatin (5 mg/kg), although Vegfr3 Chy/+ mice developed perivascular inflammation. Cisplatin-treated controls had no difference in total cortical lymphatic volume and length but showed increased lymphatic density due to decreased cortical volume. CONCLUSIONS: We demonstrate that VEGFR3 is required for development of renal lymphatics. Our studies reveal that reduced lymphatic density does not impair renal function at baseline and induces only modest histologic changes after mild injury. We introduce a novel quantification method to evaluate renal lymphatics in 3D and demonstrate that accurate measurement of lymphatic density in CKD requires assessment of changes to cortical volume.

Rethinking Autonomous Surgery: Focusing on Enhancement over Autonomy.

CONTEXT: As robot-assisted surgery is increasingly used in surgical care, the engineering research effort towards surgical automation has also increased significantly. Automation promises to enhance surgical outcomes, offload mundane or repetitive tasks, and improve workflow. However, we must ask an important question: should autonomous surgery be our long-term goal? OBJECTIVE: To provide an overview of the engineering requirements for automating control systems, summarize technical challenges in automated robotic surgery, and review sensing and modeling techniques to capture real-time human behaviors for integration into the robotic control loop for enhanced shared or collaborative control. EVIDENCE ACQUISITION: We performed a nonsystematic search of the English language literature up to March 25, 2021. We included original studies related to automation in robot-assisted laparoscopic surgery and human-centered sensing and modeling. EVIDENCE SYNTHESIS: We identified four comprehensive review papers that present techniques for automating portions of surgical tasks. Sixteen studies relate to human-centered sensing technologies and 23 to computer vision and/or advanced artificial intelligence or machine learning methods for skill assessment. Twenty-two studies evaluate or review the role of haptic or adaptive guidance during some learning task, with only a few applied to robotic surgery. Finally, only three studies discuss the role of some form of training in patient outcomes and none evaluated the effects of full or semi-autonomy on patient outcomes. CONCLUSIONS: Rather than focusing on autonomy, which eliminates the surgeon from the loop, research centered on more fully understanding the surgeon's behaviors, goals, and limitations could facilitate a superior class of collaborative surgical robots that could be more effective and intelligent than automation alone. PATIENT SUMMARY: We reviewed the literature for studies on automation in surgical robotics and on modeling of human behavior in human-machine interaction. The main application is to enhance the ability of surgical robotic systems to collaborate more effectively and intelligently with human surgeon operators.

Interpretable deep learning uncovers cellular properties in label-free live cell images that are predictive of highly metastatic melanoma.

Deep learning has emerged as the technique of choice for identifying hidden patterns in cell imaging data but is often criticized as "black box." Here, we employ a generative neural network in combination with supervised machine learning to classify patient-derived melanoma xenografts as "efficient" or "inefficient" metastatic, validate predictions regarding melanoma cell lines with unknown metastatic efficiency in mouse xenografts, and use the network to generate in silico cell images that amplify the critical predictive cell properties. These exaggerated images unveiled pseudopodial extensions and increased light scattering as hallmark properties of metastatic cells. We validated this interpretation using live cells spontaneously transitioning between states indicative of low and high metastatic efficiency. This study illustrates how the application of artificial intelligence can support the identification of cellular properties that are predictive of complex phenotypes and integrated cell functions but are too subtle to be identified in the raw imagery by a human expert. A record of this paper's transparent peer review process is included in the supplemental information. VIDEO ABSTRACT.

A Silver Lining? Fewer Non-Severe Acute Respiratory Syndrome Coronavirus 2 Respiratory Viruses During the Coronavirus Disease 2019 Pandemic.

Nonpharmaceutical interventions (NPIs) have "flattened the curve" of the coronavirus disease 2019 pandemic; however the effect of these interventions on other respiratory viruses is unknown. We used aggregate level case count data for 8 respiratory viruses and compared the institutional and statewide case counts before and during the period that NPIs were active. We observed a 61% decrease (incidence rate ratio, 0.39; 95% confidence interval, .37-.41; P < .001) in non-severe acute respiratory syndrome coronavirus 2 respiratory viral infections when NPIs were implemented. This finding, if further verified, should guide future public health initiatives to mitigate viral epidemics.

Robust and automated detection of subcellular morphological motifs in 3D microscopy images.

Rapid developments in live-cell three-dimensional (3D) microscopy enable imaging of cell morphology and signaling with unprecedented detail. However, tools to systematically measure and visualize the intricate relationships between intracellular signaling, cytoskeletal organization and downstream cell morphological outputs do not exist. Here, we introduce u-shape3D, a computer graphics and machine-learning pipeline to probe molecular mechanisms underlying 3D cell morphogenesis and to test the intriguing possibility that morphogenesis itself affects intracellular signaling. We demonstrate a generic morphological motif detector that automatically finds lamellipodia, filopodia, blebs and other motifs. Combining motif detection with molecular localization, we measure the differential association of PIP2 and KrasV12 with blebs. Both signals associate with bleb edges, as expected for membrane-localized proteins, but only PIP2 is enhanced on blebs. This indicates that subcellular signaling processes are differentially modulated by local morphological motifs. Overall, our computational workflow enables the objective, 3D analysis of the coupling of cell shape and signaling.

Genomic ancestry estimation quantifies use of wild species in grape breeding.

BACKGROUND: Grapes are one of the world's most valuable crops and most are made into wine. Grapes belong to the genus Vitis, which includes over 60 inter-fertile species. The most common grape cultivars derive their entire ancestry from the species Vitis vinifera, but wild relatives have also been exploited to create hybrid cultivars, often with increased disease resistance. RESULTS: We evaluate the genetic ancestry of some of the most widely grown commercial hybrids from North America and Europe. Using genotyping-by-sequencing (GBS), we generated 2482 SNPs and 56 indels from 7 wild Vitis, 7 V. vinifera, and 64 hybrid cultivars. We used a principal component analysis (PCA) based ancestry estimation procedure and verified its accuracy with both empirical and simulated data. V. vinifera ancestry ranged from 11 % to 76 % across hybrids studied. Approximately one third (22/64) of the hybrids have ancestry estimates consistent with F1 hybridization: they derive half of their ancestry from wild Vitis and half from V. vinifera. CONCLUSIONS: Our results suggest that hybrid grape breeding is in its infancy. The distribution of V. vinifera ancestry across hybrids also suggests that backcrosses to wild Vitis species have been more frequent than backcrosses to V. vinifera during hybrid grape breeding. This pattern is unusual in crop breeding, as it is most common to repeatedly backcross to elite, or domesticated, germplasm. We anticipate our method can be extended to facilitate marker-assisted selection in order to introgress beneficial wild Vitis traits, while allowing for offspring with the highest V. vinifera content to be selected at the seedling stage.

FaRXf1: a locus conferring resistance to angular leaf spot caused by Xanthomonas fragariae in octoploid strawberry.

KEY MESSAGE: Angular leaf spot is a devastating bacterial disease of strawberry. Resistance from two wild accessions is highly heritable and controlled by a major locus on linkage group 6D. Angular leaf spot caused by Xanthomonas fragariae is the only major bacterial disease of cultivated strawberry (Fragaria ×ananassa). While this disease may cause reductions of up to 8 % of marketable yield in Florida winter annual production, no resistant cultivars have been commercialized. Wild accessions US4808 and US4809 were previously identified as resistant to the four genetic clades of X. fragariae, and introgression of the trait into commercial quality perennial-type germplasm was initiated. Previous reports indicated high heritability for the trait but proposed both single-locus and multi-locus inheritance models. The objective of this study was to determine the mode of inheritance of resistance, to identify causal loci, and to begin introgression of resistance into Florida-adapted germplasm. Resistance was observed in two years of field trials with inoculated plants that assayed four full-sib families descended from US4808 to US4809. Resistance segregated 1:1 in all families indicating control by a dominant allele at a single locus. Using a selective genotyping approach with the IStraw90 Axiom(®) SNP array and pedigree-based QTL detection, a single major-effect QTL was identified in two full-sib families, one descended from each resistant accession. High-resolution melt curve analysis validated the presence of the QTL in separate populations. The QTL was delimited to the 33.1-33.6 Mbp (F. vesca vesca v1.1 reference) and 34.8-35.3 Mbp (F. vesca bracteata v2.0 reference) regions of linkage group 6D for both resistance sources and was designated FaRXf1. Characterization of this locus will facilitate marker-assisted selection toward the development of resistant cultivars.

Anti-Inflammatory Activity of Haskap Cultivars is Polyphenols-Dependent.

Haskap (Lonicera caerulea L.) berries have long been used for their health promoting properties against chronic conditions. The current study investigated the effect of Canadian haskap berry extracts on pro-inflammatory cytokines using a human monocytic cell line THP-1 derived macrophages stimulated by lipopolysaccharide. Methanol extracts of haskap from different growing locations in Canada were prepared and characterized for their total phenolic profile using colorimetric assays and liquid chromatography-Mass spectrometry (UPLC-MS/MS). Human THP-1 monocytes were seeded in 24-well plates (5 × 105/well) and treated with phorbol 12-myristate 13-acetate (PMA, 0.1 µg/mL) for 48 h to induce macrophage differentiation. After 48 h, the differentiated macrophages were washed with Hank's buffer and treated with various concentrations of test compounds for 4 h, followed by the lipopolysaccharide (LPS)-stimulation (18 h). Borealis cultivar showed the highest phenolic content, flavonoid content and anthocyanin content (p < 0.05). A negative correlation existed between the polyphenol concentration of the extracts and pro-inflammatory cytokines: Interleukin-6 (IL-6), tumour necrosis factor-alpha (TNF-α), prostaglandin (PGE2), and cyclooxygenase-2 (COX-2) enzyme. Borealis exhibited comparable anti-inflammatory effects to COX inhibitory drug, diclofenac. The results showed that haskap berry polyphenols has the potential to act as an effective inflammation inhibitor.

Pilot study demonstrating potential association between breast cancer image-based risk phenotypes and genomic biomarkers.

PURPOSE: In this pilot study, the authors examined associations between image-based phenotypes and genomic biomarkers. The potential genetic contribution of UGT2B genes to interindividual variation in breast density and mammographic parenchymal patterns is demonstrated by performing an association study between image-based phenotypes and genomic biomarkers [single-nucleotide polymorphism (SNP) genotypes]. METHODS: This candidate-gene approach study included 179 subjects for whom both mammograms and blood DNA samples had been obtained. The full-field digital mammograms were acquired using a GE Senographe 2000D FFDM system (12-bit; 0.1 mm-pixel size). Regions-of-interest, 256 × 256 pixels in size, selected from the central breast region behind the nipple underwent computerized image analysis to yield image-based phenotypes of mammographic density and parenchymal texture patterns. SNP genotyping was performed using a Sequenom MassArray System. One hundred twenty three SNPs with minor allele frequency above 5% were genotyped for the UGT2B gene clusters, and used in the study. The association between the image-based phenotypes and genomic biomarkers was assessed with the Pearson correlation coefficient via thePLINK software, and included permutation and correction for multiple SNP comparisons. RESULTS: From the phenotype-genotype association analysis, a parenchyma texture coarseness feature was found to be correlated with SNP rs451632 after multiple test correction for the multiple SNPs (p = 0.022). The power law ß, which is used to characterize the frequency component of texture patterns, was found to be correlated with SNP rs4148298 (p = 0.035). CONCLUSIONS: The authors' results indicate that UGT2B gene variation may contribute to interindividual variation in mammographic parenchymal patterns and breast density. Understanding the relationship between image-based phenotypes and genomic biomarkers may help understand the biologic mechanism for image-based biomarkers and yield a future role in personalized medicine.

Enhancement of breast CADx with unlabeled data.

PURPOSE: Unlabeled medical image data are abundant, yet the process of converting them into a labeled ("truth-known") database is time and resource expensive and fraught with ethical and logistics issues. The authors propose a dual-stage CADx scheme in which both labeled and unlabeled (truth-known and "truth-unknown") data are used. This study is an initial exploration of the potential for leveraging unlabeled data toward enhancing breast CADx. METHODS: From a labeled ultrasound image database consisting of 1126 lesions with an empirical cancer prevalence of 14%, 200 different randomly sampled subsets were selected and the truth status of a variable number of cases was masked to the algorithm to mimic different types of labeled and unlabeled data sources. The prevalence was fixed at 50% cancerous for the labeled data and 5% cancerous for the unlabeled. In the first stage of the dual-stage CADx scheme, the authors term "transductive dimension reduction regularization" (TDR-R), both labeled and unlabeled images characterized by extracted lesion features were combined using dimension reduction (DR) techniques and mapped to a lower-dimensional representation. (The first stage ignored truth status therefore was an unsupervised algorithm.) In the second stage, the labeled data from the reduced dimension embedding were used to train a classifier toward estimating the probability of malignancy. For the first CADx stage, the authors investigated three DR approaches: Laplacian eigen-maps, t-distributed stochastic neighbor embedding (t-SNE), and principal component analysis. For the TDR-R methods, the classifier in the second stage was a supervised (i.e., utilized truth) Bayesian neural net. The dual-stage CADx schemes were compared to a single-stage scheme based on manifold regularization (MR) in a semisupervised setting via the LapSVM algorithm. Performance in terms of areas under the ROC curve (AUC) of the CADx schemes was evaluated in leave-one-out and .632+ bootstrap analyses on a by-lesion basis. Additionally, the trained algorithms were applied to an independent test data set consisting of 101 lesions with approximately 50% cancer prevalence. The difference in AUC (deltaAUC) between with and without the use of unlabeled data was computed. RESULTS: Statistically significant differences in the average AUC value (deltaAUC) were found in many instances between training with and without unlabeled data, based on the sample set distributions generated from this particular ultrasound data set during cross-validation and using independent test set. For example, when using 100 labeled and 900 unlabeled cases and testing on the independent test set, the TDR-R methods produced average deltaAUC=0.0361 with 95% intervals [0.0301; 0.0408] (p-value < 0.0001, adjusted for multiple comparisons, but considering the test set fixed) using t-SNE and average deltaAUC=.026 [0.0227, 0.0298] (adjusted p-value < 0.0001) using Laplacian eigenmaps, while the MR-based LapSVM produced an average deltaAUC=.0381 [0.0351; 0.0405] (adjusted p-value < 0.0001). The authors also found that schemes initially obtaining lower than average performance when using labeled data only showed the most prominent increase in performance when unlabeled data were added in the first CADx stage, suggesting a regularization effect due to the injection of unlabeled data. CONCLUSION: The findings reveal evidence that incorporating unlabeled data information into the overall development of CADx methods may improve classifier performance by non-negligible amounts and warrants further investigation.

Exploring nonlinear feature space dimension reduction and data representation in breast Cadx with Laplacian eigenmaps and t-SNE.

PURPOSE: In this preliminary study, recently developed unsupervised nonlinear dimension reduction (DR) and data representation techniques were applied to computer-extracted breast lesion feature spaces across three separate imaging modalities: Ultrasound (U.S.) with 1126 cases, dynamic contrast enhanced magnetic resonance imaging with 356 cases, and full-field digital mammography with 245 cases. Two methods for nonlinear DR were explored: Laplacian eigenmaps [M. Belkin and P. Niyogi, "Laplacian eigenmaps for dimensionality reduction and data representation," Neural Comput. 15, 1373-1396 (2003)] and t-distributed stochastic neighbor embedding (t-SNE) [L. van der Maaten and G. Hinton, "Visualizing data using t-SNE," J. Mach. Learn. Res. 9, 2579-2605 (2008)]. METHODS: These methods attempt to map originally high dimensional feature spaces to more human interpretable lower dimensional spaces while preserving both local and global information. The properties of these methods as applied to breast computer-aided diagnosis (CADx) were evaluated in the context of malignancy classification performance as well as in the visual inspection of the sparseness within the two-dimensional and three-dimensional mappings. Classification performance was estimated by using the reduced dimension mapped feature output as input into both linear and nonlinear classifiers: Markov chain Monte Carlo based Bayesian artificial neural network (MCMC-BANN) and linear discriminant analysis. The new techniques were compared to previously developed breast CADx methodologies, including automatic relevance determination and linear stepwise (LSW) feature selection, as well as a linear DR method based on principal component analysis. Using ROC analysis and 0.632+bootstrap validation, 95% empirical confidence intervals were computed for the each classifier's AUC performance. RESULTS: In the large U.S. data set, sample high performance results include, AUC0.632+ = 0.88 with 95% empirical bootstrap interval [0.787;0.895] for 13 ARD selected features and AUC0.632+ = 0.87 with interval [0.817;0.906] for four LSW selected features compared to 4D t-SNE mapping (from the original 81D feature space) giving AUC0.632+ = 0.90 with interval [0.847;0.919], all using the MCMC-BANN. CONCLUSIONS: Preliminary results appear to indicate capability for the new methods to match or exceed classification performance of current advanced breast lesion CADx algorithms. While not appropriate as a complete replacement of feature selection in CADx problems, DR techniques offer a complementary approach, which can aid elucidation of additional properties associated with the data. Specifically, the new techniques were shown to possess the added benefit of delivering sparse lower dimensional representations for visual interpretation, revealing intricate data structure of the feature space.

Evaluation of computer-aided diagnosis on a large clinical full-field digital mammographic dataset.

RATIONALE AND OBJECTIVES: To convert and optimize our previously developed computerized analysis methods for use with images from full-field digital mammography (FFDM) for breast mass classification to aid in the diagnosis of breast cancer. MATERIALS AND METHODS: An institutional review board approved protocol was obtained, with waiver of consent for retrospective use of mammograms and pathology data. Seven hundred thirty-nine FFDM images, which contained 287 biopsy-proven breast mass lesions, of which 148 lesions were malignant and 139 lesions were benign, were retrospectively collected. Lesion margins were delineated by an expert breast radiologist and were used as the truth for lesion-segmentation evaluation. Our computerized image analysis method consisted of several steps: 1) identified lesions were automatically extracted from the parenchymal background using computerized segmentation methods; 2) a set of image characteristics (mathematic descriptors) were automatically extracted from image data of the lesions and surrounding tissues; and 3) selected features were merged into an estimate of the probability of malignancy using a Bayesian artificial neural network classifier. Performance of the analyses was evaluated at various stages of the conversion using receiver-operating characteristic analysis. RESULTS: An area under the curve value of 0.81 was obtained in the task of distinguishing between malignant and benign mass lesions in a round-robin by case evaluation on the entire FFDM dataset. We failed to show a statistically significant difference (P = .83) compared to results from our previous study in which the computerized classification was performed on digitized screen-film mammograms. CONCLUSIONS: Our computerized analysis methods developed on digitized screen-film mammography can be converted for use with FFDM. Results show that the computerized analysis methods for the diagnosis of breast mass lesions on FFDM are promising, and can potentially be used to aid clinicians in the diagnostic interpretation of FFDM.