High-speed low-light in vivo two-photon voltage imaging of large neuronal populations.

Monitoring spiking activity across large neuronal populations at behaviorally relevant timescales is critical for understanding neural circuit function. Unlike calcium imaging, voltage imaging requires kilohertz sampling rates that reduce fluorescence detection to near shot-noise levels. High-photon flux excitation can overcome photon-limited shot noise, but photobleaching and photodamage restrict the number and duration of simultaneously imaged neurons. We investigated an alternative approach aimed at low two-photon flux, which is voltage imaging below the shot-noise limit. This framework involved developing positive-going voltage indicators with improved spike detection (SpikeyGi and SpikeyGi2); a two-photon microscope ('SMURF') for kilohertz frame rate imaging across a 0.4 mm × 0.4 mm field of view; and a self-supervised denoising algorithm (DeepVID) for inferring fluorescence from shot-noise-limited signals. Through these combined advances, we achieved simultaneous high-speed deep-tissue imaging of more than 100 densely labeled neurons over 1 hour in awake behaving mice. This demonstrates a scalable approach for voltage imaging across increasing neuronal populations.

Syntaxin-4 defines a domain for activity-dependent exocytosis in dendritic spines.

Changes in postsynaptic membrane composition underlie many forms of learning-related synaptic plasticity in the brain. At excitatory glutamatergic synapses, fusion of intracellular vesicles at or near the postsynaptic plasma membrane is critical for dendritic spine morphology, retrograde synaptic signaling, and long-term synaptic plasticity. Whereas the molecular machinery for exocytosis in presynaptic terminals has been defined in detail, little is known about the location, kinetics, regulation, or molecules involved in postsynaptic exocytosis. Here, we show that an exocytic domain adjacent to the postsynaptic density (PSD) enables fusion of large, AMPA receptor-containing recycling compartments during elevated synaptic activity. Exocytosis occurs at microdomains enriched in the plasma membrane t-SNARE syntaxin 4 (Stx4), and disruption of Stx4 impairs both spine exocytosis and long-term potentiation (LTP) at hippocampal synapses. Thus, Stx4 defines an exocytic zone that directs membrane fusion for postsynaptic plasticity, revealing a novel specialization for local membrane traffic in dendritic spines.

Robust single-shot 3D fluorescence imaging in scattering media with a simulator-trained neural network.

Imaging through scattering is a pervasive and difficult problem in many biological applications. The high background and the exponentially attenuated target signals due to scattering fundamentally limits the imaging depth of fluorescence microscopy. Light-field systems are favorable for high-speed volumetric imaging, but the 2D-to-3D reconstruction is fundamentally ill-posed, and scattering exacerbates the condition of the inverse problem. Here, we develop a scattering simulator that models low-contrast target signals buried in heterogeneous strong background. We then train a deep neural network solely on synthetic data to descatter and reconstruct a 3D volume from a single-shot light-field measurement with low signal-to-background ratio (SBR). We apply this network to our previously developed computational miniature mesoscope and demonstrate the robustness of our deep learning algorithm on scattering phantoms with different scattering conditions. The network can robustly reconstruct emitters in 3D with a 2D measurement of SBR as low as 1.05 and as deep as a scattering length. We analyze fundamental tradeoffs based on network design factors and out-of-distribution data that affect the deep learning model's generalizability to real experimental data. Broadly, we believe that our simulator-based deep learning approach can be applied to a wide range of imaging through scattering techniques where experimental paired training data is lacking.

Simultaneous multiplane imaging with reverberation two-photon microscopy.

Multiphoton microscopy has gained enormous popularity because of its unique capacity to provide high-resolution images from deep within scattering tissue. Here, we demonstrate video-rate multiplane imaging with two-photon microscopy by performing near-instantaneous axial scanning while maintaining three-dimensional micrometer-scale resolution. Our technique, termed reverberation microscopy, enables the monitoring of neuronal populations over large depth ranges and can be implemented as a simple add-on to a conventional design.

Myosin Vb mobilizes recycling endosomes and AMPA receptors for postsynaptic plasticity.

Learning-related plasticity at excitatory synapses in the mammalian brain requires the trafficking of AMPA receptors and the growth of dendritic spines. However, the mechanisms that couple plasticity stimuli to the trafficking of postsynaptic cargo are poorly understood. Here we demonstrate that myosin Vb (MyoVb), a Ca2+-sensitive motor, conducts spine trafficking during long-term potentiation (LTP) of synaptic strength. Upon activation of NMDA receptors and corresponding Ca2+ influx, MyoVb associates with recycling endosomes (REs), triggering rapid spine recruitment of endosomes and local exocytosis in spines. Disruption of MyoVb or its interaction with the RE adaptor Rab11-FIP2 abolishes LTP-induced exocytosis from REs and prevents both AMPA receptor insertion and spine growth. Furthermore, induction of tight binding of MyoVb to actin using an acute chemical genetic strategy eradicates LTP in hippocampal slices. Thus, Ca2+-activated MyoVb captures and mobilizes REs for AMPA receptor insertion and spine growth, providing a mechanistic link between the induction and expression of postsynaptic plasticity.

Institutional influences on the supervision of GP trainees: a documentary analysis.

INTRODUCTION: The supervisory relationship is a key source of support for postgraduate GP trainees in the United Kingdom. This article focuses on the institutional influences on GP supervision through an analysis of training documentation. METHODS: Training documents were identified through a search of key sources of institutional influence: General Medical Council, Royal College of General Practitioners, Health Education West Midlands and a local university's supervisor-training material. Searches were run from September 2016 until February 2019, and 60 documents identified. Content analysis was undertaken, and documents were considered based on audience, context, language and purpose. RESULTS: Institutional expectations regarding the functions of trainees and supervisors were identified, and supervisory relationships appeared entangled within the broader contexts of the training practice, wider profession and political events. Collation of evidence, quality assurance and patient safety were prominent messages within the documents. The institutional hierarchy was accentuated through these messages, and through processes for trainees to raise concerns. Moving down this hierarchy, messages from within the profession changed in emphasis and content. CONCLUSION: With patient safety paramount, and high-quality training and supervision expected, the hierarchical system outlined by the documents is perhaps unsurprising. However, unintended messages may result: collation of evidence may be prized above quality and trainees may feel unable to raise legitimate concerns. Furthermore, conflicting messages from different institutions illustrate the tensions and complexities of GP supervision. For trainees and supervisors, these inconsistencies could lead to different perspectives and expectations as they interact within the supervisory relationship.

Synchronous Infra-Slow Oscillations Organize Ensembles of Accessory Olfactory Bulb Projection Neurons into Distinct Microcircuits.

The accessory olfactory system controls social and sexual behavior. In the mouse accessory olfactory bulb, the first central stage of information processing along the accessory olfactory pathway, projection neurons (mitral cells) display infra-slow oscillatory discharge with remarkable periodicity. The physiological mechanisms that underlie this default output state, however, remain controversial. Moreover, whether such rhythmic infra-slow activity patterns exist in awake behaving mice and whether such activity reflects the functional organization of the accessory olfactory bulb circuitry remain unclear. Here, we hypothesize that mitral cell ensembles form synchronized microcircuits that subdivide the accessory olfactory bulb into segregated functional clusters. We use a miniature microscope to image the Ca2+ dynamics within the apical dendritic compartments of large mitral cell ensembles in vivo We show that infra-slow periodic patterns of concerted neural activity, indeed, reflect the idle state of accessory olfactory bulb output in awake male and female mice. Ca2+ activity profiles are distinct and glomerulus-specific. Confocal time-lapse imaging in acute slices reveals that groups of mitral cells assemble into microcircuits that exhibit correlated Ca2+ signals. Moreover, electrophysiological profiling of synaptic connectivity indicates functional coupling between mitral cells. Our results suggest that both intrinsically rhythmogenic neurons and neurons entrained by fast synaptic drive are key elements in organizing the accessory olfactory bulb into functional microcircuits, each characterized by a distinct default pattern of infra-slow rhythmicity.SIGNIFICANCE STATEMENT Information processing in the accessory olfactory bulb (AOB) plays a central role in conspecific chemosensory communication. Surprisingly, many basic physiological principles that underlie neuronal signaling in the AOB remain elusive. Here, we show that AOB projection neurons (mitral cells) form parallel synchronized ensembles both in vitro and in vivo Infra-slow synchronous oscillatory activity within AOB microcircuits thus adds a new dimension to chemosensory coding along the accessory olfactory pathway.

The Novel Phosphate and Bile Acid Sequestrant Polymer SAR442357 Delays Disease Progression in a Rat Model of Diabetic Nephropathy.

As a gut-restricted, nonabsorbed therapy, polymeric bile acid sequestrants (BAS) play an important role in managing hyperlipidemia and hyperglycemia. Similarly, nonabsorbable sequestrants of dietary phosphate have been used for the management of hyperphosphatemia in end-stage renal disease. To evaluate the potential utility of such polymer sequestrants to treat type 2 diabetes (T2D) and its associated renal and cardiovascular complications, we synthesized a novel polymeric sequestrant, SAR442357, possessing optimized bile acid (BA) and phosphate sequestration characteristics. Long-term treatment of T2D obese cZucker fatty/Spontaneously hypertensive heart failure F1 hybrid (ZSF1) with SAR442357 resulted in enhanced sequestration of BAs and phosphate in the gut, improved glycemic control, lowering of serum cholesterol, and attenuation of diabetic kidney disease (DKD) progression. In comparison, colesevelam, a BAS with poor phosphate binding properties, did not prevent DKD progression, whereas losartan, an angiotensin II receptor blocker that is widely used to treat DKD, showed no effect on hyperglycemia. Analysis of hepatic gene expression levels of the animals treated with SAR442357 revealed upregulation of genes responsible for the biosynthesis of cholesterol and BAs, providing clear evidence of target engagement and mode of action of the new sequestrant. Additional hepatic gene expression pathway changes were indicative of an interruption of the enterohepatic BA cycle. Histopathological analysis of ZSF1 rat kidneys treated with SAR442357 further supported its nephroprotective properties. Collectively, these findings reveal the pharmacological benefit of simultaneous sequestration of BAs and phosphate in treating T2D and its associated comorbidities and cardiovascular complications. SIGNIFICANCE STATEMENT: A new nonabsorbed polymeric sequestrant with optimum phosphate and bile salt sequestration properties was developed as a treatment option for DKD. The new polymeric sequestrant offered combined pharmacological benefits including glucose regulation, lipid lowering, and attenuation of DKD progression in a single therapeutic agent.

Clinical scientists' early career choices and progression: an exploratory mixed methods study.

BACKGROUND: Understanding the influences on healthcare professionals' career choices and progression can inform interventions to improve workforce retention. Retention of health professionals is a high priority worldwide, in order to maintain expertise and meet the needs of national populations. In the UK, investment in clinical scientists' pre-registration education is high and the need to retain motivated scientists recognised. METHODS: We conducted a mixed methods study to investigate the career choices and progression of early career clinical scientists. First job sector and salary of trainees who completed the UK pre-registration Scientist Training Programme (STP) between 2014 and 2019 were analysed using descriptive statistics and Chi-Squared tests. Semi-structured interviews conducted with volunteer practising clinical scientists who completed the programme in 2015 or 2016 were analysed thematically and reviewed for alignment with theories for understanding career choice and workforce retention. RESULTS: Most scientists who completed the STP between 2014 and 2019 obtained a post in the UK National Health Service (NHS) and achieved the expected starting salary. Life scientists were more likely to work in non-NHS healthcare settings than other scientific divisions; and physiological scientists less likely to achieve the expected starting salary. Experiences during training influenced career choice and progression 0-3 years post qualification, as did level of integration of training places with workforce planning. Specialty norms, staff turnover, organisational uncertainty and geographical preferences influenced choices in both the short (0-3 years) and longer term (5 + years). Interviewees reported a strong commitment to public service; and some could foresee that these priorities would influence future decisions about applying for management positions. These factors aligned with the components of job embeddedness theory, particularly that of 'fit'. CONCLUSIONS: Training experiences, personal values, specialty norms and organisational factors all influence UK clinical scientists' early career choices and progression. Job embeddedness theory provides a useful lens through which to explore career choice and progression; and suggests types of intervention that can enhance the careers of this essential group. Interventions need to take account of variations between different scientific specialties.

Factors affecting recruitment into General Practice: a double binary choice approach.

Recruitment to General Practice (GP) is currently low in many countries. Here we focus on two binary choices for junior doctors: first, whether to apply to GP; second, whether to accept a GP training place if offered. Previous attitudinal studies have indicated factors claimed to affect recruitment. The current study goes further by quantifying the relative impact of different factors on the propensity of candidates to apply to GP and accept a training place. An online questionnaire was sent to candidates applying to United Kingdom (UK) specialty training in 2015. Descriptive statistics and a path analysis evaluated the importance of various factors on GP applications. Our results were synthesised with an analysis of data from the online applications portal. With 3838 candidates responding to the survey, the path analysis showed that personality and previous GP experiences were strongly associated with the decision to apply. There was some evidence that it was easier to enter GP than other specialties; in terms of deciding whether to accept, the evidence suggests GP was a backup plan for around 9% of candidates who accepted a GP post. Our results indicate that recruitment initiatives should focus on candidates who apply to GP but not as first choice or consider GP but do not apply, particularly by providing substantial experience of GP and accentuating the positives of the specialty such as work-life balance and the intellectual challenge of working with patients in primary care. Acceptance of a GP place may also depend on competition for places in other specialties.

Variation in pigmentation gene expression is associated with distinct aposematic color morphs in the poison frog Dendrobates auratus.

BACKGROUND: Color and pattern phenotypes have clear implications for survival and reproduction in many species. However, the mechanisms that produce this coloration are still poorly characterized, especially at the genomic level. Here we have taken a transcriptomics-based approach to elucidate the underlying genetic mechanisms affecting color and pattern in a highly polytypic poison frog. We sequenced RNA from the skin from four different color morphs during the final stage of metamorphosis and assembled a de novo transcriptome. We then investigated differential gene expression, with an emphasis on examining candidate color genes from other taxa. RESULTS: Overall, we found differential expression of a suite of genes that control melanogenesis, melanocyte differentiation, and melanocyte proliferation (e.g., tyrp1, lef1, leo1, and mitf) as well as several differentially expressed genes involved in purine synthesis and iridophore development (e.g., arfgap1, arfgap2, airc, and gart). CONCLUSIONS: Our results provide evidence that several gene networks known to affect color and pattern in vertebrates play a role in color and pattern variation in this species of poison frog.

A systematic review of supervisory relationships in general practitioner training.

OBJECTIVES: The educational alliance is argued to be at the heart of supervision in medical education. This review aims to map the research field and develop a conceptualisation of the nature of such educational alliances within postgraduate supervision for general practitioners. METHODS: An integrative review of the international literature on supervision from 2011 to 2018 was undertaken, and papers assessed for relevance and quality. Data analysis incorporated framework analysis techniques. Bordin's working alliance-based model of supervision was used as a springboard for synthesis, as well as allowing for the emergence of new ideas, theories and concepts from the literature. RESULTS: A total of 49 full texts were included for analysis. There was evidence of the importance of trust, agreement and bond in accordance with Bordin's model. The results also highlighted the importance of greater clarity on supervisory goals, and the tasks to support these goals, to effectively address competing priorities and roles within supervision. Non-hierarchical relationships were advocated, although supervisors must remain impartial in their assessment and monitoring roles. The influence of the wider practice community and situated learning through legitimate peripheral participation are documented. A model of General practice (GP) supervision is proposed that integrates the findings. CONCLUSIONS: GP supervision requires a greater emphasis than is suggested by the working alliance model, both on the clarity of expectations and the appreciation of the multiple roles and competing priorities of both trainee and supervisor. Furthermore, as GP supervision develops within the rising workload of contemporary general practice, the role of the wider community of practice may become more prominent. We have adapted the working alliance model for postgraduate General practice (GP) supervision, emphasising the explicit sharing of expectations relating to goals, tasks and roles to facilitate negotiation and agreement.

Using recruitment and selection to build a primary care workforce for the future.

Recruitment and selection are critical components of human resource management. They influence both the quantity and quality of the healthcare workforce. In this article, we use two different examples of primary care workers, General Practitioners in the UK and Community Health Workers in low- and middle- income countries, to illustrate how recruitment and selection are, and could be, used to enhance the primary care workforce in each setting. Both recruitment and selection can be costly, so when funding is limited, decisions on how to spend the human resources budget must be made. It could be argued that human resource management should focus on recruitment in a seller's market (an insufficient supply of applicants) and on selection in a buyer's market (sufficient applicants but concerns about their quality). We use this article to examine recruitment and selection in each type of market and highlight the interactions between these two human resource management decisions. Recruitment and selection, we argue, must be considered in both types of market; particularly in sectors where workers' labour impacts upon population health. We note the paucity of high-quality research in recruitment and selection for primary care and the need for rigorous study designs such as randomised trials.

Would changing the selection process for GP trainees stem the workforce crisis? A cohort study using multiple-imputation and simulation.

BACKGROUND: There is currently a shortage of qualified GPs in the UK and not all of the training posts available each year are filled. Changing the way in which GP trainees are selected could help increase the training post fill rate and the number of new entrants to the GP Register. The aim of this study was to model the impact of changing the selection process for GP training on the number of trainees obtaining GP Registration, either with or without extensions. METHOD: This was a cohort study using UK applications for GP training in 2011-14. Application data were linked using GMC numbers to training outcome data where available, and imputed using multiple imputation where missing. The number of trainees appointed and GP Registrations within three and five years' full-time-equivalent were estimated for four different selection processes. RESULTS: The cut scores used in the actual 2015 selection process makes it impossible to fill all training posts. Random selection is the worst option, but the difference between this and other processes modelled falls as more trainees are selected. There are large marginal effects on outcomes: those with the highest selection scores are more likely to obtain GP Registration than those with the lowest scores. CONCLUSIONS: Changing the selection process alone would have a small impact on the number of GP Registrations; reducing/removing cut scores would have a much larger impact. This would also increase the number of trainees requiring extensions and being released from training which would have adverse consequences for the profession.

The tacit rules of the game in the GP trainee-trainer supervisory relationship: experienced educators describe GP supervision.

A key aspect of support in UK General Practice training is the trainee-trainer supervisory relationship. A small but significant number of trainees struggle in training, and relationship 'breakdown' can result. This study aims to better understand the nature of the supervisory interaction when a trainee faces difficulty. Using Bordin's 'Supervisory Working Alliance' and Egan's 'Skilled Helper Model' as a conceptual framework, four semi-structured interviews were undertaken with GP educators all experienced with trainees in difficulty, purposively sampled based on geography and gender. Interviews were transcribed verbatim, and content and coding analysis were undertaken to identify key themes. Trainee factors (insight, engagement, GP as 'best fit' career and difficulties in training) and trainer factors (failure to fail, tensions in role) were perceived as contributing to relationship breakdown. A lack of agreement in the goals and tasks of supervision was described when relationships broke down. Relating to Bordin's model, the trainee and trainer may hold differing expectations, particularly relating to the goals and tasks of supervision. Making expectations more explicit could be part of the solution to an improved supervisory working alliance. Further study on the influence of structure and agency is required to better understand the relationship in context.

Forest fire surveillance systems: A review of deep learning methods.

This review aims to critically examine the existing state-of-the-art forest fire detection systems that are based on deep learning methods. In general, forest fire incidences bring significant negative impact to the economy, environment, and society. One of the crucial mitigation actions that needs to be readied is an effective forest fire detection system that are able to automatically notify the relevant parties on the incidence of forest fire as early as possible. This review paper has examined in details 37 research articles that have implemented deep learning (DL) model for forest fire detection, which were published between January 2018 and 2023. In this paper, in depth analysis has been performed to identify the quantity and type of data that includes images and video datasets, as well as data augmentation methods and the deep model architecture. This paper is structured into five subsections, each of which focuses on a specific application of deep learning (DL) in the context of forest fire detection. These subsections include 1) classification, 2) detection, 3) detection and classification, 4) segmentation, and 5) segmentation and classification. To compare the model's performance, the methods were evaluated using comprehensive metrics like accuracy, mean average precision (mAP), F1-Score, mean pixel accuracy (MPA), etc. From the findings, of the usage of DL models for forest fire surveillance systems have yielded favourable outcomes, whereby the majority of studies managed to achieve accuracy rates that exceeds 90%. To further enhance the efficacy of these models, future research can explore the optimal fine-tuning of the hyper-parameters, integrate various satellite data, implement generative data augmentation techniques, and refine the DL model architecture. In conclusion, this paper highlights the potential of deep learning methods in enhancing forest fire detection that is crucial for forest fire management and mitigation.

Targeted micro-fiber arrays for measuring and manipulating localized multi-scale neural dynamics over large, deep brain volumes during behavior.

Neural population dynamics relevant to behavior vary over multiple spatial and temporal scales across three-dimensional volumes. Current optical approaches lack the spatial coverage and resolution necessary to measure and manipulate naturally occurring patterns of large-scale, distributed dynamics within and across deep brain regions such as the striatum. We designed a new micro-fiber array approach capable of chronically measuring and optogenetically manipulating local dynamics across over 100 targeted locations simultaneously in head-fixed and freely moving mice, enabling the investigation of cell-type- and neurotransmitter-specific signals over arbitrary 3D volumes at a spatial resolution and coverage previously inaccessible. We applied this method to resolve rapid dopamine release dynamics across the striatum, revealing distinct, modality-specific spatiotemporal patterns in response to salient sensory stimuli extending over millimeters of tissue. Targeted optogenetics enabled flexible control of neural signaling on multiple spatial scales, better matching endogenous signaling patterns, and the spatial localization of behavioral function across large circuits.

Pupil engineering for extended depth-of-field imaging in a fluorescence miniscope.

Significance: Fluorescence head-mounted microscopes, i.e., miniscopes, have emerged as powerful tools to analyze in-vivo neural populations but exhibit a limited depth-of-field (DoF) due to the use of high numerical aperture (NA) gradient refractive index (GRIN) objective lenses. Aim: We present extended depth-of-field (EDoF) miniscope, which integrates an optimized thin and lightweight binary diffractive optical element (DOE) onto the GRIN lens of a miniscope to extend the DoF by 2.8× between twin foci in fixed scattering samples. Approach: We use a genetic algorithm that considers the GRIN lens' aberration and intensity loss from scattering in a Fourier optics-forward model to optimize a DOE and manufacture the DOE through single-step photolithography. We integrate the DOE into EDoF-Miniscope with a lateral accuracy of 70 µm to produce high-contrast signals without compromising the speed, spatial resolution, size, or weight. Results: We characterize the performance of EDoF-Miniscope across 5- and 10-µm fluorescent beads embedded in scattering phantoms and demonstrate that EDoF-Miniscope facilitates deeper interrogations of neuronal populations in a 100-µm-thick mouse brain sample and vessels in a whole mouse brain sample. Conclusions: Built from off-the-shelf components and augmented by a customizable DOE, we expect that this low-cost EDoF-Miniscope may find utility in a wide range of neural recording applications.

Robust single-shot 3D fluorescence imaging in scattering media with a simulator-trained neural network.

Imaging through scattering is a pervasive and difficult problem in many biological applications. The high background and the exponentially attenuated target signals due to scattering fundamentally limits the imaging depth of fluorescence microscopy. Light-field systems are favorable for high-speed volumetric imaging, but the 2D-to-3D reconstruction is fundamentally ill-posed, and scattering exacerbates the condition of the inverse problem. Here, we develop a scattering simulator that models low-contrast target signals buried in heterogeneous strong background. We then train a deep neural network solely on synthetic data to descatter and reconstruct a 3D volume from a single-shot light-field measurement with low signal-to-background ratio (SBR). We apply this network to our previously developed Computational Miniature Mesoscope and demonstrate the robustness of our deep learning algorithm on scattering phantoms with different scattering conditions. The network can robustly reconstruct emitters in 3D with a 2D measurement of SBR as low as 1.05 and as deep as a scattering length. We analyze fundamental tradeoffs based on network design factors and out-of-distribution data that affect the deep learning model's generalizability to real experimental data. Broadly, we believe that our simulator-based deep learning approach can be applied to a wide range of imaging through scattering techniques where experimental paired training data is lacking.