Sensitivity and Specificity of Pulmonary Artery Pressure Measurement in Echocardiography and Correlation with Right Heart Catheterization

ObjectiveEchocardiography is helpful in assessment of pulmonary hemodynamic, however its correlation with Right heart catheterization (RHC) is conflicting. We conducted a study to evaluate sensitivity and specificity of pulmonary hemodynamic parameters measured in echocardiography. Furthermore its correlation with the values measured in RHC was assessed.MethodRetrospective, cross-sectional study conducted at King Fahad medical City, Riyadh, Saudi Arabia. 95 adult patients referred for right heart catheterization were enrolled in the study. All the patients had echocardiography and RHC within one week of each other.ResultDiabetes mellitus, hypertension and dyslipidemia were present among 55%, 66% and 41% of patients respectively. 85% of the study participants were diagnosed to have pulmonary hypertension and 79% of the study participants had postcapillary pulmonary hypertension. Sensitivity of pulmonary artery systolic pressure (PAPs), mean pulmonary artery pressure (PAPm) using PAPs and pulmonary artery acceleration velocity (PAcT) were 86%, 93% and 89% respectively. Correlation of PAPs, PAPm using PAPs and PAcT on echo with invasive hemodynamic in RHC were 0.56, 0.43 and 0.24 respectively. Among patients with moderate to severe Tricuspid Regurgitation (TR) and tricuspid annular plane systolic excursion (TAPSE) <1.5cm correlation of PAPs, PAPm using PAPs and PAcT on echocardiography with right heart catheterization were 0.31, 0.24 and 0.42 respectively.ConclusionEchocardiographic assessment of PAPs and PAPm has high sensitivity and weak to moderate correlation with hemodynamic data in RHC. PAPs measurement on echocardiogram has best correlation with invasive measurement followed by PAPm measurement using PAPs. Among patients with moderate to severe TR and TAPSE <1.5cm PAPm measurement using PAcT has better correlation than using PAPs.     

Laser-Assisted High Speed Machining of 316 Stainless Steel: The Effect of Water-Soluble Sago Starch Based Cutting Fluid on Surface Roughness and Tool Wear

Laser-assisted high speed milling is a subtractive machining method that employs a laser to thermally soften a difficult-to-cut material’s surface in order to enhance machinability at a high material removal rate with improved surface finish and tool life. However, this machining with high speed leads to high friction between workpiece and tool, and can result in high temperatures, impairing the surface quality. Use of conventional cutting fluid may not effectively control the heat generation. Besides, vegetable-based cutting fluids are invariably a major source of food insecurity of edible oils which is traditionally used as a staple food in many countries. Thus, the primary objective of this study is to experimentally investigate the effects of water-soluble sago starch-based cutting fluid on surface roughness and tool’s flank wear using response surface methodology (RSM) while machining of 316 stainless steel. In order to observe the comparison, the experiments with same machining parameters are conducted with conventional cutting fluid. The prepared water-soluble sago starch based cutting fluid showed excellent cooling and lubricating performance. Therefore, in comparison to the machining using conventional cutting fluid, a decrease of 48.23% in surface roughness and 38.41% in flank wear were noted using presented approach. Furthermore, using the extreme learning machine (ELM), the obtained data is modeled to predict surface roughness and flank wear and showed good agreement between observations and predictions.     

Experimental Investigation of the Performance of a Hybrid Self-Healing System in Porous Asphalt under Fatigue Loadings

Self-healing asphalt, which is designed to achieve autonomic damage repair in asphalt pavement, offers a great life-extension prospect and therefore not only reduces pavement maintenance costs but also saves energy and reduces CO₂ emissions. The combined asphalt self-healing system, incorporating both encapsulated rejuvenator and induction heating, can heal cracks with melted binder and aged binder rejuvenation, and the synergistic effect of the two technologies shows significant advantages in healing efficiency over the single self-healing method. This study explores the fatigue life extension prospect of the combined healing system in porous asphalt. To this aim, porous asphalt (PA) test specimens with various healing systems were prepared, including: (i) the capsule healing system, (ii) the induction healing system, (iii) the combined healing system and (iv) a reference system (without extrinsic healing). The fatigue properties of the PA samples were characterized by an indirect tensile fatigue test and a four-point bending fatigue test. Additionally, a 24-h rest period was designed to activate the built-in self-healing system(s) in the PA. Finally, a damaging and healing programme was employed to evaluate the fatigue damage healing efficiency of these systems. The results indicate that all these self-healing systems can extend the fatigue life of porous asphalt, while in the combined healing system, the gradual healing effect of the released rejuvenator from the capsules may contribute to a better induction healing effect in the damaging and healing cycles.     

Bis(2,2′-bipyridil)Copper(II) Chloride Complex: Tyrosinase Biomimetic Catalyst or Redox Mediator?

In this article, construction of amperometric sensor(s) based on screen-printed carbon electrodes covered by thin layers of two types of carbon nanomaterials serving as amplifiers, and containing [Cu(bipy)₂Cl]Cl∙5H₂O complex is reported. Their performance and biomimetic activity towards two selected neurotransmitters (dopamine and serotonin) was studied mainly using flow injection analysis (FIA). The important parameters of FIA such as working potential, flow rate, and pH were optimized. The mechanism of the catalytic activity is explained and experimentally confirmed. It reveals that presence of hydrogen peroxide plays a crucial role which leads to answer the title question: can presented complex really be considered as a tyrosinase biomimetic catalyst or only as a redox mediator?     

Impact of Race and Socioeconomic Status on Outcomes in Patients Hospitalized with COVID-19

BackgroundThe impact of race and socioeconomic status on clinical outcomes has not been quantified in patients hospitalized with coronavirus disease 2019 (COVID-19).ObjectiveTo evaluate the association between patient sociodemographics and neighborhood disadvantage with frequencies of death, invasive mechanical ventilation (IMV), and intensive care unit (ICU) admission in patients hospitalized with COVID-19.DesignRetrospective cohort study.SettingFour hospitals in an integrated health system serving southeast Michigan.ParticipantsAdult patients admitted to the hospital with a COVID-19 diagnosis confirmed by polymerase chain reaction.Main MeasuresPatient sociodemographics, comorbidities, and clinical outcomes were collected. Neighborhood socioeconomic variables were obtained at the census tract level from the 2018 American Community Survey. Relationships between neighborhood median income and clinical outcomes were evaluated using multivariate logistic regression models, controlling for patient age, sex, race, Charlson Comorbidity Index, obesity, smoking status, and living environment.Key ResultsBlack patients lived in significantly poorer neighborhoods than White patients (median income: $34,758 (24,531–56,095) vs. $63,317 (49,850–85,776), p < 0.001) and were more likely to have Medicaid insurance (19.4% vs. 11.2%, p < 0.001). Patients from neighborhoods with lower median income were significantly more likely to require IMV (lowest quartile: 25.4%, highest quartile: 16.0%, p < 0.001) and ICU admission (35.2%, 19.9%, p < 0.001). After adjusting for age, sex, race, and comorbidities, higher neighborhood income ($10,000 increase) remained a significant negative predictor for IMV (OR: 0.95 (95% CI 0.91, 0.99), p = 0.02) and ICU admission (OR: 0.92 (95% CI 0.89, 0.96), p < 0.001).ConclusionsNeighborhood disadvantage, which is closely associated with race, is a predictor of poor clinical outcomes in COVID-19. Measures of neighborhood disadvantage should be used to inform policies that aim to reduce COVID-19 disparities in the Black community.Supplementary InformationThe online version contains supplementary material available at 10.1007/s11606-020-06527-1.KEY WORDS: COVID-19, disparities, disadvantage, socioeconomic status, race     

Non-invasive metabolic biomarkers for early diagnosis of diabetic nephropathy: Meta-analysis of profiling metabolomics studies

AimDiabetic nephropathy (DN) is one of the worst complications of diabetes. Despite a growing number of DN metabolite profiling studies, most studies are suffering from inconsistency in their findings. The main goal of this meta-analysis was to reach to a consensus panel of significantly dysregulated metabolites as potential biomarkers in DN.Data synthesisTo identify the significant dysregulated metabolites, meta-analysis was performed by “vote-counting rank” and “robust rank aggregation” strategies. Bioinformatics analyses were performed to identify the most affected genes and pathways. Among 44 selected studies consisting of 98 metabolite profiles, 17 metabolites (9 up-regulated and 8 down-regulated metabolites), were identified as significant ones by both the meta-analysis strategies (p-value<0.05 and OR>2 or <0.5) and selected as DN metabolite meta-signature. Furthermore, enrichment analyses confirmed the involvement of various effective biological pathways in DN pathogenesis, such as urea cycle, TCA cycle, glycolysis, and amino acid metabolisms. Finally, by performing a meta-analysis over existing time-course studies in DN, the results indicated that lactic acid, hippuric acid, allantoin (in urine), and glutamine (in blood), are the topmost non-invasive early diagnostic biomarkers.ConclusionThe identified metabolites are potentially involved in diabetic nephropathy pathogenesis and could be considered as biomarkers or drug targets in the disease.Prospero registration numberCRD42020197697.     

Spontaneous Coronary Artery Dissection: Does Being Unemployed Matter? Insights from the GSCAD Registry

BackgroundSpontaneous coronary artery dissection (SCAD) has emerged as an important cause of acute coronary syndrome (ACS) and sudden cardiac death. Physical or emotional stressors are the most commonly reported triggers for SCAD. Unemployment has been identified as a source of emotional stress and is linked to poor mental and physical health.ObjectiveTo examine the association between employment status and in-hospital and follow-up adverse cardiovascular events in patients with SCAD.MethodsWe conducted a retrospective, multi-center, observational study of patients undergoing coronary angiography for ACS between January 2011 and December 2017. The total number of patients enrolled was 198,000. Patients were diagnosed with SCAD based on angiographic and intravascular imaging modalities whenever available. There were 83 patients identified with SCAD from 30 medical centers in 4 Arab gulf countries. In-hospital (myocardial infarction, percutaneous intervention, ventricular tachycardia/ventricular fibrillation, cardiogenic shock, death, internal cardioverter/defibrillator placement, dissection extension) and follow-up (myocardial infarction, de novo SCAD, death, spontaneous superior mesenteric artery dissection) cardiac events were compared among those who were employed and those who were not.ResultsThe median age of patients in the study was 44 (37- 55) years. There were 42 (50.6%) female patients, and 41 (49.4) male patients. Of the cohort, 50 (60%) of the patients were employed and the remaining 33 (40%) were unemployed. 66% of all men were employed and 76% of all women were unemployed. After adjusting for gender unemployment was associated with worse in-hospital and follow-up cardiac events (adjusted OR 7.1, [1.3, 37.9]), p = 0.021.ConclusionAdverse cardiovascular events were significantly worse for patients with SCAD who were unemployed.     

Synchronous caregiving from birth to adulthood tunes humans’ social brain

Mammalian young are born with immature brain and rely on the mother’s body and caregiving behavior for maturation of neurobiological systems that sustain adult sociality. While research in animal models indicated the long-term effects of maternal contact and caregiving on the adult brain, little is known about the effects of maternal–newborn contact and parenting behavior on social brain functioning in human adults. We followed human neonates, including premature infants who initially lacked or received maternal–newborn skin-to-skin contact and full-term controls, from birth to adulthood, repeatedly observing mother–child social synchrony at key developmental nodes. We tested the brain basis of affect-specific empathy in young adulthood and utilized multivariate techniques to distinguish brain regions sensitive to others’ distinct emotions from those globally activated by the empathy task. The amygdala, insula, temporal pole (TP), and ventromedial prefrontal cortex (VMPFC) showed high sensitivity to others’ distinct emotions. Provision of maternal–newborn contact enhanced social synchrony across development from infancy and up until adulthood. The experience of synchrony, in turn, predicted the brain’s sensitivity to emotion-specific empathy in the amygdala and insula, core structures of the social brain. Social synchrony linked with greater empathic understanding in adolescence, which was longitudinally associated with higher neural sensitivity to emotion-specific empathy in TP and VMPFC. Findings demonstrate the centrality of synchronous caregiving, by which infants practice the detection and sharing of others’ affective states, for tuning the human social brain, particularly in regions implicated in salience detection, interoception, and mentalization that underpin affect sharing and human attachment.

Being born a mammal implies that the brain is immature at birth and develops in the context of the mother’s body, lactation, and caregiving behavior (1). Infants rely on the provisions embedded in the mother’s body, sensory stimuli (2), and the expression of well-adapted caregiving for maturation of neurobiological systems that sustain participation in the social world. Extant research in animal models has shown that breeches in the mother’s continuous presence and variability in the consistency of caregiving carry long-term effects on brain structure and function, particularly on systems that underpin sociality, and these effects are maintained throughout life, altering the adult animal’s capacity to coordinate social bonds, manage hardships, and parent the next generation (3, 4). However, while the human brain is slowest to mature and requires the most extended period of dependence (5), the long-term consequences of caregiving for the human social brain are largely unknown. The current birth-to-adulthood study examines the effects of maternal–newborn skin-to-skin contact (Kangaroo Care, KC) and parent–child social synchrony experienced across development on the brain’s empathic response to others’ emotional states in young adulthood. Social synchrony describes the coordination between the parent’s and child’s nonverbal behavior and communicative signals during social interactions in ways that enhance positivity, reciprocity, and mutual engagement (6, 7), and we tested its longitudinal impact on the brain basis of empathy, a core feature of the social brain.The human social brain integrates activity of subcortical, paralimbic, and cortical structures to sustain human social life, which requires rapid processing of social inputs, top–down regulation of intention and affect, and coordination of the two into the present moment (8). The social brain has undergone massive expansion across primate evolution to support humans’ exquisite social skills, communicative competencies, and mindreading capacities. It has been suggested that Homo sapiens’ success over other hominin owes to their unique empathic abilities, which allow humans to quickly identify and mentally share others’ affective states (9). Such multifaceted empathy, which integrates automatic identification of others’ distinct emotions with the ability to use interoceptive signals to detect others’ specific affect and the capacity to reflect on the changing emotional states of social partners, marks a fundamental achievement of the human social brain. The empathic social brain, in turn, enabled humans to coordinate actions for survival, fine-tune communicative signal systems, and partake in the joys and sorrows of others (10). Yet, while empathy is a core feature of human sociality that is tuned in mammals by patterns of parental care, the relational precursors of the neural empathic response have not been fully explored in human studies.Social synchrony is first observed in the third month of life when parents begin to coordinate with the infant’s nonverbal signals and interactive rhythms. Synchrony continues to mature across childhood and adolescence with the parent’s and child’s increasing reciprocity and adaptation to each other’s verbal and nonverbal communications, affective state, and pace of dialogue and is considered a prototypical experience that prepares children to life with others (11, 12). Through ongoing adaptation first to the infant’s nonverbal cues and then to the older child’s verbal and affective communications, parents orient children to social moments, practice rapid assessment of distinct emotional states, and, over time, enable children to simulate others’ mental states, fine-tuning the social brain and its capacity for empathy (13). Social synchrony undergoes maturation across development and evolves from nonverbal matching to a verbal dialogue that acknowledges others’ emotions, engages multiple perspectives, and reflects on feelings while retaining the interactive rhythms of the familiar dialogue from infancy to adulthood (1). The early experience of synchrony plays a key role in children’s social–emotional development and has been shown to predict the child’s later ability to engage with peers (14, 15), regulate emotions (4), exhibit cognitive control (16), manage stress (17), and display empathic understanding (18), indicating that improvements in mother–infant synchrony during its early stages may have long-term effects on the capacity for empathy and its neural underpinnings.The development of synchrony is highly sensitive to initial conditions. Conditions that compromise maternal–infant bonding bear long-term negative consequences for the development of social synchrony and, consequently, for maturation of human social abilities (19, 20). When infants are born prematurely and full maternal–infant bodily contact is initially lacking, the development of synchrony is halted and socioemotional competencies compromised. Notably, when we provided structured maternal–infant skin-to-skin contact (KC) to premature neonates during the postpartum period, the intervention improved not only social synchrony but also the functioning of regulatory support systems, such as circadian rhythmicity, autonomic maturity, stress responsivity, and exploratory behavior, the same systems that are shaped in young mammals by contact with the mother’s body (17, 18) and consistent presence (21).What may be the effects of maternal–newborn skin-to-skin contact and synchronous caregiving across development on the social brain in young adulthood? Utilizing our unique cohort, we imaged the neural empathic response in three groups of healthy young adults who were recruited at birth: infants born at full-term (FT), preterm infants receiving kangaroo contact (KC), and demographically and medically matched preterm infants receiving standard incubator care (SC) who were followed in our laboratory for two decades (Fig. 1A). We focused on the neural basis of empathy, particularly on the brain’s capacity to detect, affectively share, reflect, and empathize with the different emotions of others (22). Two key hypotheses were tested. First, we expected that the provision of maternal bodily contact in the neonatal period would enhance the expression of social synchrony in infancy and across development. This hypothesis is based on research in animal models which shows that maternal bodily contact, consistent presence, and sensory stimuli improve maternal caregiving and have long-term effects on brain and behavior (21, 23–25). Second, we hypothesized that the experience of synchrony would augment the brain’s capacity to differentiate among others’ emotional states. Synchrony is a dyadic experience by which infants practice the identification and sharing of others’ emotions and, as they develop, learn to imbue others’ feelings with meaning and representations (26). We expected that such practice would tune the brain of young adults to empathize with others’ distinct emotions, particularly in areas that have been linked with parent–child synchrony in the parental brain, the amygdala (27, 28) and insula (29).Open in a separate windowFig. 1.Birth-to-adulthood longitudinal study design and fMRI paradigm. (A) Three cohorts of infants and parents recruited at birth: full-term (FT) infants and two case-matched neurologically intact premature infants assigned to either Kangaroo Care (KC: infants receiving skin-to-skin contact with mother) or matched controls receiving standard incubator care (SC). Mother–child social synchrony was assessed at 4 mo (SD =1.14), 3 y (SD = 1.38), 12 y (SD = 1.62), and 20 y (SD = 2.01). (B) fMRI empathy paradigm. Example illustrates a pseudorandomized design in which participants were presented with an emotional probe followed by four photos depicting this probe. Participants were asked to empathize with the protagonists, and five blocks per condition were presented.Using a longitudinal sample of n = 96 young adults who were followed from infancy, we first examined the neural basis of affect-specific empathy. We employed a validated functional MRI (fMRI) paradigm that exposed participants to others’ distinct emotions (joy, sadness, and distress) and asked them to mentally empathize with the protagonists (30) (Fig. 1B). Consistent with prior imaging studies on the brain regions activated during empathy tasks (31–33), we focused on a network of regions sustaining empathy. This included limbic regions: the amygdala, a key player in emotion detection (34), and the parahippocampal gyrus. Also included were the anterior insula, superior temporal sulcus (STS), and temporal pole (TP) that have been repeatedly implicated in human empathy research (32, 35). We also examined the ventromedial prefrontal cortex (VMPFC), precuneus, and inferior parietal gyrus, known as hubs of the default mode network, which is related to self-referential processing, perspective-taking, and theory of mind tasks (36, 37) and plays a key role in social understanding (38).We used Representational Similarity Analysis (RSA), a multivariate brain pattern analytic technique, to differentiate brain areas that show a distinct neural pattern while empathizing with specific affective states from those generally activated by the empathy task but without a unique response to each emotion. By using RSA, we aimed to compare the distinct neural patterns activated during empathy to different emotions and characterize the brain basis of affect-specific empathy (39). A recent study employing RSA to pinpoint the neural signature of basic emotions indicated that the amygdala, insula, medial prefrontal cortex, frontal pole, and precuneus showed distinct representations for different emotional states (40). Consequently, and in light of research highlighting the role of the amygdala in fear (41) and empathy for negative affective states (42), we focused on the amygdala as a key area that may present differential response during empathy to positive versus negative emotions. Similarly, the insula exhibits similar activations during empathy for physical pain and emotional distress (43, 44), and we expected the insula to show differential activations during empathy to distressing versus nondistressing affective states. Areas including the dorsomedial prefrontal cortex (DMPFC), VMPFC, insula, TP, and precuneus have been shown in research using multivoxel pattern analysis to display specific activation patterns to emotion-related actions and mentalization (36), and we expected these areas to exhibit specific activation patterns during empathy with others’ distinct emotions.     

Functionalization of Polycaprolactone Electrospun Osteoplastic Scaffolds with Fluorapatite and Hydroxyapatite Nanoparticles: Biocompatibility Comparison of Human Versus Mouse Mesenchymal Stem Cells

A capability for effective tissue reparation is a living requirement for all multicellular organisms. Bone exits as a precisely orchestrated balance of bioactivities of bone forming osteoblasts and bone resorbing osteoclasts. The main feature of osteoblasts is their capability to produce massive extracellular matrix enriched with calcium phosphate minerals. Hydroxyapatite and its composites represent the most common form of bone mineral providing mechanical strength and significant osteoinductive properties. Herein, hydroxyapatite and fluorapatite functionalized composite scaffolds based on electrospun polycaprolactone have been successfully fabricated. Physicochemical properties, biocompatibility and osteoinductivity of generated matrices have been validated. Both the hydroxyapatite and fluorapatite containing polycaprolactone composite scaffolds demonstrated good biocompatibility towards mesenchymal stem cells. Moreover, the presence of both hydroxyapatite and fluorapatite nanoparticles increased scaffolds’ wettability. Furthermore, incorporation of fluorapatite nanoparticles enhanced the ability of the composite scaffolds to interact and support the mesenchymal stem cells attachment to their surfaces as compared to hydroxyapatite enriched composite scaffolds. The study of osteoinductive properties showed the capacity of fluorapatite and hydroxyapatite containing composite scaffolds to potentiate the stimulation of early stages of mesenchymal stem cells’ osteoblast differentiation. Therefore, polycaprolactone based composite scaffolds functionalized with fluorapatite nanoparticles generates a promising platform for future bone tissue engineering applications.     

From the Cover: Convolutional networks for fast,energy-efficient neuromorphic computing

Deep networks are now able to achieve human-level performance on a broad spectrum of recognition tasks. Independently, neuromorphic computing has now demonstrated unprecedented energy-efficiency through a new chip architecture based on spiking neurons, low precision synapses, and a scalable communication network. Here, we demonstrate that neuromorphic computing, despite its novel architectural primitives, can implement deep convolution networks that (i) approach state-of-the-art classification accuracy across eight standard datasets encompassing vision and speech, (ii) perform inference while preserving the hardware’s underlying energy-efficiency and high throughput, running on the aforementioned datasets at between 1,200 and 2,600 frames/s and using between 25 and 275 mW (effectively >6,000 frames/s per Watt), and (iii) can be specified and trained using backpropagation with the same ease-of-use as contemporary deep learning. This approach allows the algorithmic power of deep learning to be merged with the efficiency of neuromorphic processors, bringing the promise of embedded, intelligent, brain-inspired computing one step closer.The human brain is capable of remarkable acts of perception while consuming very little energy. The dream of brain-inspired computing is to build machines that do the same, requiring high-accuracy algorithms and efficient hardware to run those algorithms. On the algorithm front, building on classic work on backpropagation (1), the neocognitron (2), and convolutional networks (3), deep learning has made great strides in achieving human-level performance on a wide range of recognition tasks (4). On the hardware front, building on foundational work on silicon neural systems (5), neuromorphic computing, using novel architectural primitives, has recently demonstrated hardware capable of running 1 million neurons and 256 million synapses for extremely low power (just 70 mW at real-time operation) (6). Bringing these approaches together holds the promise of a new generation of embedded, real-time systems, but first requires reconciling key differences in the structure and operation between contemporary algorithms and hardware. Here, we introduce and demonstrate an approach we call Eedn, energy-efficient deep neuromorphic networks, which creates convolutional networks whose connections, neurons, and weights have been adapted to run inference tasks on neuromorphic hardware.For structure, typical convolutional networks place no constraints on filter sizes, whereas neuromorphic systems can take advantage of blockwise connectivity that limits filter sizes, thereby saving energy because weights can now be stored in local on-chip memory within dedicated neural cores. Here, we present a convolutional network structure that naturally maps to the efficient connection primitives used in contemporary neuromorphic systems. We enforce this connectivity constraint by partitioning filters into multiple groups and yet maintain network integration by interspersing layers whose filter support region is able to cover incoming features from many groups by using a small topographic size (7).For operation, contemporary convolutional networks typically use high precision ( ≥ 32-bit) neurons and synapses to provide continuous derivatives and support small incremental changes to network state, both formally required for backpropagation-based gradient learning. In comparison, neuromorphic designs can use one-bit spikes to provide event-based computation and communication (consuming energy only when necessary) and can use low-precision synapses to colocate memory with computation (keeping data movement local and avoiding off-chip memory bottlenecks). Here, we demonstrate that by introducing two constraints into the learning rule—binary-valued neurons with approximate derivatives and trinary-valued ({−1,0,1}) synapses—it is possible to adapt backpropagation to create networks directly implementable using energy efficient neuromorphic dynamics. This approach draws inspiration from the spiking neurons and low-precision synapses of the brain (8) and builds on work showing that deep learning can create networks with constrained connectivity (9), low-precision synapses (10, 11), low-precision neurons (12–14), or both low-precision synapses and neurons (15, 16). For input data, we use a first layer to transform multivalued, multichannel input into binary channels using convolution filters that are learned via backpropagation (12, 16) and whose output can be sent on chip in the form of spikes. These binary channels, intuitively akin to independent components (17) learned with supervision, provide a parallel distributed representation to carry out high-fidelity computation without the need for high-precision representation.Critically, we demonstrate that bringing the above innovations together allows us to create networks that approach state-of-the-art accuracy performing inference on eight standard datasets, running on a neuromorphic chip at between 1,200 and 2,600 frames/s (FPS), using between 25 and 275 mW. We further explore how our approach scales by simulating multichip configurations. Ease-of-use is achieved using training tools built from existing, optimized deep learning frameworks (18), with learned parameters mapped to hardware using a high-level deployment language (19). Although we choose the IBM TrueNorth chip (6) for our example deployment platform, the essence of our constructions can apply to other emerging neuromorphic approaches (20–23) and may lead to new architectures that incorporate deep learning and efficient hardware primitives from the ground up.