scTOP: physics-inspired order parameters for cellular identification and visualization.

Advances in single-cell RNA sequencing provide an unprecedented window into cellular identity. The abundance of data requires new theoretical and computational frameworks to analyze the dynamics of differentiation and integrate knowledge from cell atlases. We present 'single-cell Type Order Parameters' (scTOP): a statistical, physics-inspired approach for quantifying cell identity given a reference basis of cell types. scTOP can accurately classify cells, visualize developmental trajectories and assess the fidelity of engineered cells. Importantly, scTOP does this without feature selection, statistical fitting or dimensional reduction (e.g. uniform manifold approximation and projection, principle components analysis, etc.). We illustrate the power of scTOP using human and mouse datasets. By reanalyzing mouse lung data, we characterize a transient hybrid alveolar type 1/alveolar type 2 cell population. Visualizations of lineage tracing hematopoiesis data using scTOP confirm that a single clone can give rise to multiple mature cell types. We assess the transcriptional similarity between endogenous and donor-derived cells in the context of murine pulmonary cell transplantation. Our results suggest that physics-inspired order parameters can be an important tool for understanding differentiation and characterizing engineered cells. scTOP is available as an easy-to-use Python package.

Emergent competition shapes top-down versus bottom-up control in multi-trophic ecosystems.

Ecosystems are commonly organized into trophic levels-organisms that occupy the same level in a food chain (e.g., plants, herbivores, carnivores). A fundamental question in theoretical ecology is how the interplay between trophic structure, diversity, and competition shapes the properties of ecosystems. To address this problem, we analyze a generalized Consumer Resource Model with three trophic levels using the zero-temperature cavity method and numerical simulations. We derive the corresponding mean-field cavity equations and show that intra-trophic diversity gives rise to an effective "emergent competition" term between species within a trophic level due to feedbacks mediated by other trophic levels. This emergent competition gives rise to a crossover from a regime of top-down control (populations are limited by predators) to a regime of bottom-up control (populations are limited by primary producers) and is captured by a simple order parameter related to the ratio of surviving species in different trophic levels. We show that our theoretical results agree with empirical observations, suggesting that the theoretical approach outlined here can be used to understand complex ecosystems with multiple trophic levels.

Phase Transition to Chaos in Complex Ecosystems with Nonreciprocal Species-Resource Interactions.

Nonreciprocal interactions between microscopic constituents can profoundly shape the large-scale properties of complex systems. Here, we investigate the effects of nonreciprocity in the context of theoretical ecology by analyzing a generalization of MacArthur's consumer-resource model with asymmetric interactions between species and resources. Using a mixture of analytic cavity calculations and numerical simulations, we show that such ecosystems generically undergo a phase transition to chaotic dynamics as the amount of nonreciprocity is increased. We analytically construct the phase diagram for this model and show that the emergence of chaos is controlled by a single quantity: the ratio of surviving species to surviving resources. We also numerically calculate the Lyapunov exponents in the chaotic phase and carefully analyze finite-size effects. Our findings show how nonreciprocal interactions can give rise to complex and unpredictable dynamical behaviors even in the simplest ecological consumer-resource models.

Tregs self-organize into a computing ecosystem and implement a sophisticated optimization algorithm for mediating immune response.

Regulatory T cells (Tregs) play a crucial role in mediating immune response. Yet an algorithmic understanding of the role of Tregs in adaptive immunity remains lacking. Here, we present a biophysically realistic model of Treg-mediated self-tolerance in which Tregs bind to self-antigens and locally inhibit the proliferation of nearby activated T cells. By exploiting a duality between ecological dynamics and constrained optimization, we show that Tregs tile the potential antigen space while simultaneously minimizing the overlap between Treg activation profiles. We find that for sufficiently high Treg diversity, Treg-mediated self-tolerance is robust to fluctuations in self-antigen concentrations but lowering the Treg diversity results in a sharp transition-related to the Gardner transition in perceptrons-to a regime where changes in self-antigen concentrations can result in an autoimmune response. We propose an experimental test of this transition in immune-deficient mice and discuss potential implications for autoimmune diseases.

HAL-X: Scalable hierarchical clustering for rapid and tunable single-cell analysis.

Data clustering plays a significant role in biomedical sciences, particularly in single-cell data analysis. Researchers use clustering algorithms to group individual cells into populations that can be evaluated across different levels of disease progression, drug response, and other clinical statuses. In many cases, multiple sets of clusters must be generated to assess varying levels of cluster specificity. For example, there are many subtypes of leukocytes (e.g. T cells), whose individual preponderance and phenotype must be assessed for statistical/functional significance. In this report, we introduce a novel hierarchical density clustering algorithm (HAL-x) that uses supervised linkage methods to build a cluster hierarchy on raw single-cell data. With this new approach, HAL-x can quickly predict multiple sets of labels for immense datasets, achieving a considerable improvement in computational efficiency on large datasets compared to existing methods. We also show that cell clusters generated by HAL-x yield near-perfect F1-scores when classifying different clinical statuses based on single-cell profiles. Our hierarchical density clustering algorithm achieves high accuracy in single cell classification in a scalable, tunable and rapid manner.

Multipotent Embryonic Lung Progenitors: Foundational Units of In Vitro and In Vivo Lung Organogenesis.

Transient, tissue-specific, embryonic progenitors are important cell populations in vertebrate development. In the course of respiratory system development, multipotent mesenchymal and epithelial progenitors drive the diversification of fates that results to the plethora of cell types that compose the airways and alveolar space of the adult lungs. Use of mouse genetic models, including lineage tracing and loss-of-function studies, has elucidated signaling pathways that guide proliferation and differentiation of embryonic lung progenitors as well as transcription factors that underlie lung progenitor identity. Furthermore, pluripotent stem cell-derived and ex vivo expanded respiratory progenitors offer novel, tractable, high-fidelity systems that allow for mechanistic studies of cell fate decisions and developmental processes. As our understanding of embryonic progenitor biology deepens, we move closer to the goal of in vitro lung organogenesis and resulting applications in developmental biology and medicine.

The Neurostimulation Appropriateness Consensus Committee (NACC): Recommendations for Surgical Technique for Spinal Cord Stimulation.

INTRODUCTION: The field of neurostimulation for the treatment of chronic pain is a rapidly developing area of medicine. Although neurostimulation therapies have advanced significantly as a result of technologic improvements, surgical planning, device placement, and postoperative care are of equal importance to optimize outcomes. This Neurostimulation Appropriateness Consensus Committee (NACC) project intends to provide evidence-based guidance for these often-overlooked areas of neurostimulation practice. MATERIALS AND METHODS: Authors were chosen based on their clinical expertise, familiarity with the peer-reviewed literature, research productivity, and contributions to the neuromodulation literature. Section leaders supervised literature searches of MEDLINE, BioMed Central, Current Contents Connect, Embase, International Pharmaceutical Abstracts, Web of Science, Google Scholar, and PubMed from the last NACC publication in 2017 to the present. Identified studies were graded using the United States Preventive Services Task Force criteria for evidence and certainty of net benefit. Recommendations are based on evidence strength and consensus when evidence was scant. RESULTS: This NACC project provides guidance on preoperative assessment, intraoperative techniques, and postoperative management in the form of consensus points with supportive evidence. These results are based on grade of evidence, strength of consensus, and expert opinion. CONCLUSIONS: The NACC has given guidance for a surgical plan that encompasses the patient journey from the planning stage through the surgical experience and postoperative care. The overall recommendations are designed to improve efficacy and the safety of patients undergoing these neuromodulation procedures and are intended to apply throughout the international community.

The role of third-generation dual-source dual-energy computed tomography in characterizing the composition of renal stones with infrared spectroscopy as the reference standard.

Purpose: The aim of our study was to prospectively evaluate the role of third-generation, dual-source, dual-energy computed tomography (DECT) in the characterization of renal calculi, with ex vivo renal stone evaluation using Fourier transform infrared spectroscopy (IS) as the reference standard. Material and methods: In our study 50 patients with history suggestive of renal calculi were subjected to DECT using 100 kVp and Sn150 kVp. With DECT, renal stone attenuation at low and high kVp was attained, and the attenuation ratios were measured. The result of DECT was compared with IS to identify the chemical composition of the extracted renal stones. IBM SPSS version 22 was used for statistical analysis. Results: In our study, the mean attenuation ratio of the renal stone was 1.57 ± 0.25. Out of 50 patients, the stones of 39 patients were predicted as calcium-containing stones, in 4 patients as cystine stones, and in 7 as uric acid stones on DECT. In IS analysis, 43 patients had calcium-containing stones, and 7 patients had uric acid stones. The accuracy rate of DECT for detecting calcium and uric acid stones in our study were 90% and 100%, respectively. The positive predictive value for the DECT to assess the chemical composition of renal calculi was found to be 92%. Conclusions: Third-generation DECT scan had 100% accuracy in differentiating uric acid stones from non-uric acid stones in our study. Because the treatment is different for different chemical compositions of stones, identification of specific chemical components is very important, and it can be accurately done by DECT.

Multicenter Retrospective Analysis of Dorsal Root Ganglion Stimulator Placement Using Intraoperative Neuromonitoring in Asleep Patients During Early Periods of Adoption.

OBJECTIVES: Intraoperative neuromonitoring (IONM) has been used in the implantation of spinal cord stimulation for both safety and confirmation of lead placement. It is less well defined in its use for dorsal root ganglion (DRG) stimulator placement. MATERIALS AND METHODS: This is a retrospective analysis of 304 leads placed in 93 patients undergoing DRG stimulation therapy with its placement utilizing IONM in asleep patients by four implanting physicians in four separate centers. The first year, or early adoption period, of placements for each site was chosen as the included cases to evaluate. RESULTS: There were a total of 14 IONM alerts across the 304 lead placements. There were two complications, no permanent or severe adverse events, and no revisions. All alerts led to a change in approach as a corrective action. The two complications were a patient requiring a blood patch for an undetected CSF leak, while the other was a generator site seroma that resolved with conservative care. A single patient experienced transient calf paresthesia's in the post-operative period. CONCLUSION: This retrospective series demonstrates the utility and accuracy of IONM in not only confirming proper dorsal placement of a DRG electrode but also in maintaining a low adverse event profile. It further demonstrates that its utility in the real world with new users can be safe and accurate with an ease of integration.

Novel Intermittent Dosing Burst Paradigm in Spinal Cord Stimulation.

INTRODUCTION: Intermittent dosing (ID), in which periods of stimulation-on are alternated with periods of stimulation-off, is generally employed using 30 sec ON and 90 sec OFF intervals with burst spinal cord stimulation (SCS). The goal of this study was to evaluate the feasibility of using extended stimulation-off periods in patients with chronic intractable pain. MATERIALS AND METHODS: This prospective, multicenter, feasibility trial evaluated the clinical efficacy of the following ID stimulation-off times: 90, 120, 150, and 360 sec with burst waveform parameters. After a successful trial (≥50% pain relief) using ID stimulation, subjects were titrated with OFF times beginning with 360 sec. Pain, quality of life, disability, and pain catastrophizing were evaluated at one, three, and six months after permanent implant. RESULTS: Fifty subjects completed an SCS trial using ID stimulation settings of 30 sec ON and 90 sec OFF, with 38 (76%) receiving ≥50% pain relief. Pain scores were significantly reduced from baseline at all time points (p < 0.001). Improvements in quality of life, disability, and pain catastrophizing were aligned with pain relief outcomes; 45.8% of the subjects that completed the six-month follow-up visit used an OFF period of 360 seconds. CONCLUSIONS: ID burst SCS effectively relieved pain for six months. The largest group of subjects used IDB settings of 30 sec ON and 360 sec OFF. These findings present intriguing implications for the optimal "dose" of electricity in SCS and may offer many advantages such as optimizing the therapeutic window, extending battery life, reducing recharge burden and, potentially, mitigating therapy habituation or tolerance.

Elastography: a surrogate marker of renal allograft fibrosis - quantification by shear-wave technique.

PURPOSE: Renal fibrosis is the most common cause of allograft failure in kidney transplantations. Evaluation of renal abnormalities has progressed considerably over the past years. Currently, the diagnosis of intrarenal fibrosis and quantification of its development with non-invasive assessment tools is possible. This may help in early detection of renal allograft dysfunction. This study sought to assess the efficacy of 2D real-time shear-wave elastography (SWE) in the quantitative measurement of renal allograft dysfunction. METHODS: A total of 172 patients were included in our study. SWE was performed in all these patients just before renal allograft biopsy. The cortical elasticity was assessed and described in terms of Young's modulus (kPa). Banff histopathological grading obtained from transplant kidney tissue biopsy was taken as the reference standard. The potential correlation between SWE scores and Banff classification was performed. RESULTS: There was a significant correlation between the Banff grade and mean SWE score, with a correlation coefficient of 0.665 (p < 0.001). The individual correlation coefficients of interstitial fibrosis and tubular atrophy with mean SWE score stood at 0.667 and 0.649 respectively (p < 0.001). The correlation of resistive indices was insignificant when compared to mean polar SWE score in respective poles and the Banff grading of fibrosis. CONCLUSIONS: Renal stiffness quantified by 2D SWE showed significant correlation with histopathological renal fibrosis. Thus, the study suggests that shear-wave elastography could be used as a surrogate marker for early detection of renal fibrosis.

Comparison of T2 relaxometry and PET CT in the evaluation of patients with mesial temporal lobe epilepsy using video EEG as the reference standard.

PURPOSE: Our study aimed to compare the sensitivity of T2 relaxometry and positron emission tomography - computed tomography (PET/CT) in patients with a history suggestive of mesial temporal lobe epilepsy using video electroencephalography (EEG) as the reference standard. MATERIAL AND METHODS: In our study, 35 patients with a history suggestive of mesial temporal lobe epilepsy were subjected to conventional magnetic resonance imaging (MRI), T2 relaxometry, and PET/CT. The results of each of the studies were compared with video EEG findings. Analyses were performed by using statistical software (SPSS version 20.0 for windows), and the sensitivity of conventional MRI, T2 relaxometry, and PET/CT were calculated. RESULTS: The sensitivity of qualitative MRI (atrophy and T2 hyperintensity), quantitative MRI (T2 relaxometry), and PET/CT in lateralizing the seizure focus were 68.6% (n = 24), 85.7% (n = 30), and 88.6% (n = 31), respectively. CONCLUSIONS: The sensitivity of MRI in lateralization and localization of seizure focus in temporal lobe epilepsy can be increased by adding the quantitative parameter (T2 relaxometry) with the conventional sequences. T2 Relaxometry is comparable to PET/CT for localization and lateralization of seizure focus and is a useful tool in the workup of TLE patients.

The Minimum Environmental Perturbation Principle: A New Perspective on Niche Theory.

AbstractFifty years ago, Robert MacArthur showed that stable equilibria optimize quadratic functions of the population sizes in several important ecological models. Here, we generalize this finding to a broader class of systems within the framework of contemporary niche theory and precisely state the conditions under which an optimization principle (not necessarily quadratic) can be obtained. We show that conducting the optimization in the space of environmental states instead of population sizes leads to a universal and transparent physical interpretation of the objective function. Specifically, the equilibrium state minimizes the perturbation of the environment induced by the presence of the competing species, subject to the constraint that no species has a positive net growth rate. We use this "minimum environmental perturbation principle" to make new predictions for evolution and community assembly, where the minimum perturbation increases monotonically under invasion by new species. We also describe a simple experimental setting where the conditions of validity for this optimization principle have been empirically tested.

Effect of Resource Dynamics on Species Packing in Diverse Ecosystems.

The competitive exclusion principle asserts that coexisting species must occupy distinct ecological niches (i.e., the number of surviving species cannot exceed the number of resources). An open question is to understand if and how different resource dynamics affect this bound. Here, we analyze a generalized consumer resource model with externally supplied resources and show that-in contrast to self-renewing resources-species can occupy only half of all available environmental niches. This motivates us to construct a new schema for classifying ecosystems based on species packing properties.

Available energy fluxes drive a transition in the diversity, stability, and functional structure of microbial communities.

A fundamental goal of microbial ecology is to understand what determines the diversity, stability, and structure of microbial ecosystems. The microbial context poses special conceptual challenges because of the strong mutual influences between the microbes and their chemical environment through the consumption and production of metabolites. By analyzing a generalized consumer resource model that explicitly includes cross-feeding, stochastic colonization, and thermodynamics, we show that complex microbial communities generically exhibit a transition as a function of available energy fluxes from a "resource-limited" regime where community structure and stability is shaped by energetic and metabolic considerations to a diverse regime where the dominant force shaping microbial communities is the overlap between species' consumption preferences. These two regimes have distinct species abundance patterns, different functional profiles, and respond differently to environmental perturbations. Our model reproduces large-scale ecological patterns observed across multiple experimental settings such as nestedness and differential beta diversity patterns along energy gradients. We discuss the experimental implications of our results and possible connections with disorder-induced phase transitions in statistical physics.

A Systematic Literature Review of Dorsal Root Ganglion Neurostimulation for the Treatment of Pain.

OBJECTIVE: To conduct a systematic literature review of dorsal root ganglion (DRG) stimulation for pain. DESIGN: Grade the evidence for DRG stimulation. METHODS: An international, interdisciplinary work group conducted a literature search for DRG stimulation. Abstracts were reviewed to select studies for grading. General inclusion criteria were prospective trials (randomized controlled trials and observational studies) that were not part of a larger or previously reported group. Excluded studies were retrospective, too small, or existed only as abstracts. Studies were graded using the modified Interventional Pain Management Techniques-Quality Appraisal of Reliability and Risk of Bias Assessment, the Cochrane Collaborations Risk of Bias assessment, and the US Preventative Services Task Force level-of-evidence criteria. RESULTS: DRG stimulation has Level II evidence (moderate) based upon one high-quality pivotal randomized controlled trial and two lower-quality studies. CONCLUSIONS: Moderate-level evidence supports DRG stimulation for treating chronic focal neuropathic pain and complex regional pain syndrome.

A high-bias, low-variance introduction to Machine Learning for physicists.

Machine Learning (ML) is one of the most exciting and dynamic areas of modern research and application. The purpose of this review is to provide an introduction to the core concepts and tools of machine learning in a manner easily understood and intuitive to physicists. The review begins by covering fundamental concepts in ML and modern statistics such as the bias-variance tradeoff, overfitting, regularization, generalization, and gradient descent before moving on to more advanced topics in both supervised and unsupervised learning. Topics covered in the review include ensemble models, deep learning and neural networks, clustering and data visualization, energy-based models (including MaxEnt models and Restricted Boltzmann Machines), and variational methods. Throughout, we emphasize the many natural connections between ML and statistical physics. A notable aspect of the review is the use of Python Jupyter notebooks to introduce modern ML/statistical packages to readers using physics-inspired datasets (the Ising Model and Monte-Carlo simulations of supersymmetric decays of proton-proton collisions). We conclude with an extended outlook discussing possible uses of machine learning for furthering our understanding of the physical world as well as open problems in ML where physicists may be able to contribute.

Glassy Phase of Optimal Quantum Control.

We study the problem of preparing a quantum many-body system from an initial to a target state by optimizing the fidelity over the family of bang-bang protocols. We present compelling numerical evidence for a universal spin-glasslike transition controlled by the protocol time duration. The glassy critical point is marked by a proliferation of protocols with close-to-optimal fidelity and with a true optimum that appears exponentially difficult to locate. Using a machine learning (ML) inspired framework based on the manifold learning algorithm t-distributed stochastic neighbor embedding, we are able to visualize the geometry of the high-dimensional control landscape in an effective low-dimensional representation. Across the transition, the control landscape features an exponential number of clusters separated by extensive barriers, which bears a strong resemblance with replica symmetry breaking in spin glasses and random satisfiability problems. We further show that the quantum control landscape maps onto a disorder-free classical Ising model with frustrated nonlocal, multibody interactions. Our work highlights an intricate but unexpected connection between optimal quantum control and spin glass physics, and shows how tools from ML can be used to visualize and understand glassy optimization landscapes.

DRG FOCUS: A Multicenter Study Evaluating Dorsal Root Ganglion Stimulation and Predictors for Trial Success.

INTRODUCTION: Dorsal root ganglion stimulation (DRGS) is a powerful tool in the treatment of chronic, neuropathic pain. The premise of DRGS is similar to that of conventional spinal cord stimulation (cSCS), however, there is more variability in how it can be utilized. While it is this variability that likely gives it its versatility, DRGS is not as straightforward to implement as cSCS. The purpose of this study was to assess the efficacy of DRGS on a broad number of diagnoses, determine which dorsal root ganglia were associated with better outcomes for particular body parts/diagnoses, and evaluate what factors/parameters were associated with higher rates of trial success. METHODS: This is a physician initiated, multicenter retrospective registry of 217 patients trialed with DRGS. Data were collected via an online questionnaire that assessed specifics regarding the patient's pain, distribution, size, and response to treatment. The data were analyzed to see if there were certain diagnoses and/or parameters that were more or less associated with trial success. RESULTS: DRGS was found to be an effective treatment in all diagnoses evaluated within this study, most of which had statistically significant improvements in pain. The most important predictor of trial success was the amount of painful area covered by paresthesias during the programing phase. The number of leads utilized had a direct and indirect impact on trial success. Pain in the distribution of a specific peripheral nerve responded best and there was no statistical difference based on what body part was being treated. CONCLUSION: DRGS can be an effective treatment for a variety of neuropathic pain syndromes, in addition to CRPS. It is recommended that a minimum of 2 leads should be utilized per area being treated. In addition, this therapy was shown to be equally efficacious in any body part/region so long as the area being treated is focal and not widespread.

Innate Immunity Stimulation via Toll-Like Receptor 9 Ameliorates Vascular Amyloid Pathology in Tg-SwDI Mice with Associated Cognitive Benefits.

Alzheimer's disease (AD) is characterized by the presence of parenchymal amyloid-ß (Aß) plaques, cerebral amyloid angiopathy (CAA) and neurofibrillary tangles. Currently there are no effective treatments for AD. Immunotherapeutic approaches under development are hampered by complications related to ineffectual clearance of CAA. Genome-wide association studies have demonstrated the importance of microglia in AD pathogenesis. Microglia are the primary innate immune cells of the brain. Depending on their activation state and environment, microglia can be beneficial or detrimental. In our prior work, we showed that stimulation of innate immunity with Toll-like receptor 9 agonist, class B CpG (cytosine-phosphate-guanine) oligodeoxynucleotides (ODNs), can reduce amyloid and tau pathologies without causing toxicity in Tg2576 and 3xTg-AD mouse models. However, these transgenic mice have relatively little CAA. In the current study, we evaluated the therapeutic profile of CpG ODN in a triple transgenic mouse model, Tg-SwDI, with abundant vascular amyloid, in association with low levels of parenchymal amyloid deposits. Peripheral administration of CpG ODN, both before and after the development of CAA, negated short-term memory deficits, as assessed by object-recognition tests, and was effective at improving spatial and working memory evaluated using a radial arm maze. These findings were associated with significant reductions of CAA pathology lacking adverse effects. Together, our extensive evidence suggests that this innovative immunomodulation may be a safe approach to ameliorate all hallmarks of AD pathology, supporting the potential clinical applicability of CpG ODN. SIGNIFICANCE STATEMENT: Recent genetic studies have underscored the emerging role of microglia in Alzheimer's disease (AD) pathogenesis. Microglia lose their amyloid-ß-clearing capabilities with age and as AD progresses. Therefore, the ability to modulate microglia profiles offers a promising therapeutic avenue for reducing AD pathology. Current immunotherapeutic approaches have been limited by poor clearance of a core AD lesion, cerebral amyloid angiopathy (CAA). The present study used Tg-SwDI mice, which have extensive CAA. We found that stimulation of the innate immune system and microglia/macrophage activation via Toll-like receptor 9 using CpG (cytosine-phosphate-guanine) oligodeoxynucleotides (ODNs) leads to cognitive improvements and CAA reduction, without associated toxicity. Our data indicate that this novel concept of immunomodulation represents a safer method to reduce all aspects of AD pathology and provide essential information for potential clinical use of CpG ODN.