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VEGF inhibitor drugs are part of standard care in oncology and ophthalmology, but not all patients respond to them. Combinations of drugs are likely to be needed for more effective therapies of angiogenesis-related diseases. In this paper we describe naturally occurring combinations of receptors in endothelial cells that might help to understand how cells communicate and to identify targets for drug combinations. We also develop and share a new software tool called DECNEO to identify them. Single-cell gene expression data are used to identify a set of co-expressed endothelial cell receptors, conserved among species (mice and humans) and enriched, within a network, of connections to up-regulated genes. This set includes several receptors previously shown to play a role in angiogenesis. Multiple statistical tests from large datasets, including an independent validation set, support the reproducibility, evolutionary conservation and role in angiogenesis of these naturally occurring combinations of receptors. We also show tissue-specific combinations and, in the case of choroid endothelial cells, consistency with both well-established and recent experimental findings, presented in a separate paper. The results and methods presented here advance the understanding of signaling to endothelial cells. The methods are generally applicable to the decoding of intercellular combinations of signals.

Do psychedelics affect sexual functioning postacutely? Anecdotal and qualitative evidence suggests they do, but this has never been formally tested. While sexual functioning and satisfaction are generally regarded as an important aspect of human wellbeing, sexual dysfunction is a common symptom of mental health disorders. It is also a common side effect of selective serotonin reuptake inhibitors (SSRIs), a first line treatment for depression. The aim of the present paper was to investigate the post-acute effects of psychedelics on self-reported sexual functioning, combining data from two independent studies, one large and naturalistic and the other a smaller but controlled clinical trial. Naturalistic use of psychedelics was associated with improvements in several facets of sexual functioning and satisfaction, including improved pleasure and communication during sex, satisfaction with ones partner and physical appearance. Convergent results were found in a controlled trial of psilocybin therapy versus an SSRI, escitalopram, for depression. In this trial, patients treated with psilocybin reported positive changes in sexual functioning after treatment, while patients treated with escitalopram did not. Despite focusing on different populations and settings, this is the first research study to quantitively investigate the effects of psychedelics on sexual functioning. Results imply a potential positive effect on post-acute sexual functioning and highlight the need for more research on this.

The emergence of long COVID during the ongoing COVID-19 pandemic has presented considerable challenges for healthcare professionals and researchers. The task of identifying relevant literature is particularly daunting due to the rapidly evolving scientific landscape, inconsistent definitions, and a lack of standardized nomenclature. This paper proposes a novel solution to this challenge by employing machine learning techniques to classify long COVID literature. However, the scarcity of annotated data for machine learning poses a significant obstacle. To overcome this, we introduce a strategy called medical paraphrasing, which diversifies the training data while maintaining the original content. Additionally, we propose a Data-Reweighting-Based Multi-Level Optimization Framework for Domain Adaptive Paraphrasing, supported by a Meta-Weight-Network (MWN). This innovative approach incorporates feedback from the downstream text classification model to influence the training of the paraphrasing model. During the training process, the framework assigns higher weights to the training examples that contribute more effectively to the downstream task of long COVID text classification. Our findings demonstrate that this method substantially improves the accuracy and efficiency of long COVID literature classification, offering a valuable tool for physicians and researchers navigating this complex and ever-evolving field.

Pupillometry is a measurement of pupil dilation commonly performed as part of neurological assessments. Prior work have demonstrated the potential for pupillometry in screening or diagnosing a number of neurological disorders including Alzheimers Disease, Schizophrenia, and Traumatic Brain Injury. Unfortunately, the expense and inaccessibility of specialized pupilometers that image in the near infrared spectrum limit the measurement to high resource clinics or institutions. Ideally, this measurement could be available via ubiquitous devices like smartphones or tablets with integrated visible spectrum imaging systems. In the visible spectrum of RGB cameras, the melanin in the iris absorbs light such that it is difficult to distinguish the pupil aperature that appears black. In this paper, we propose a novel pupillometry technique to enable smartphone RGB cameras to effectively differentiate the pupil from the iris. The proposed system utilizes a 630 nm long-pass filter to image in the far red (630-700 nm) spectrum, where the melanin in the iris reflects light to appear brighter in constrast to the dark pupil. Using a convolutional neural network, the proposed system measures pupil diameter as it dynamically changes in a frame by frame video. Comparing across 4 different smartphone models, the pupil-iris contrast of N = 12 participants increases by an average of 451% with the proposed system. In a validation study of N = 11 participants comparing the relative pupil change in the proposed system to a Neuroptics PLR-3000 Pupillometer during a pupillary light response test, the prototype system acheived a mean absolute error of 2.4%.

There have been significant advances in biosignal extraction techniques to drive external biomechatronic devices or to use as inputs to sophisticated human machine interfaces. The control signals are typically derived from biological signals such as myoelectric measurements made either from the surface of the skin or subcutaneously. Other biosignal sensing modalities are emerging. With improvements in sensing modalities and control algorithms, it is becoming possible to robustly control the target position of an end-effector. It remains largely unknown to what extent these improvements can lead to naturalistic human-like movement. In this paper, we sought to answer this question. We utilized a sensing paradigm called sonomyography based on continuous ultrasound imaging of forearm muscles. Unlike myoelectric control strategies which measure electrical activation and use the extracted signals to determine the velocity of an end-effector; sonomyography measures muscle deformation directly with ultrasound and uses the extracted signals to proportionally control the position of an end-effector. Previously, we showed that users were able to accurately and precisely perform a virtual target acquisition task using sonomyography. In this work, we investigate the time course of the control trajectories derived from sonomyography. We show that the time course of the sonomyography-derived trajectories that users take to reach virtual targets reflect the trajectories shown to be typical for kinematic characteristics observed in biological limbs. Specifically, during a target acquisition task, the velocity profiles followed a minimum jerk trajectory shown for point-to-point arm reaching movements, with similar time to target. In addition, the trajectories based on ultrasound imaging result in a systematic delay and scaling of peak movement velocity as the movement distance increased. We believe this is the first evaluation of similarities in control policies in coordinated movements in jointed limbs, and those based on position control signals extracted at the individual muscle level. These results have strong implications for the future development of control paradigms for assistive technologies.

Thirdhand smoke (THS) is a new term for the toxins in cigarette smoke that linger in the environment long after the cigarettes are extinguished. The effects of THS exposure on male reproduction have not yet been studied. In this study, metabolic changes in male germ cell lines (GC-2 and TM-4) were analyzed after THS treatment for 24 h. THS-loaded chromatography paper samples were generated in a laboratory chamber system and extracted in DMEM. At a paper: DMEM ratio of 50 μg/ml, cell viability in both cell lines was normal, as measured by the MTT assay and markers of cytotoxicity, cell cycle, apoptosis and ROS production were normal as measured by quantitative immunofluorescence. Metabolomic analysis was performed on methanol extracts of GC-2 and TM-4 cells. Glutathione metabolism in GC-2 cells, and nucleic acid and ammonia metabolism in TM-4 cells, was changed significantly by THS treatment. RT-PCR analyses of mRNA for enzyme genes Gss and Ggt in GC-2 cells, and TK, SMS and Glna in TM-4 cells reinforced these findings, showing changes in the levels of enzymes involved in the relevant pathways. In conclusion, exposure to THS at very low concentrations caused distinct metabolic changes in two different types of male reproductive cell lines.

In this paper we have used second harmonic generation (SHG) and phasor approach to auto fluorescence lifetime imaging (FLIM) to obtain fingerprints of different collagens and then used these fingerprints to observe bone marrow fibrosis in the mouse femur. This is a label free approach towards fast automatable detection of fibrosis in tissue samples. FLIM has previously been used as a method of contrast in different tissues and in this paper phasor approach to FLIM is used to separate collagen I from collagen III, the markers of fibrosis, the largest groups of disorders that are often without any effective therapy. Often characterized by an increase in collagen content of the corresponding tissue, the samples are usually visualized by histochemical staining, which is pathologist dependent and cannot be automated.

A rising number of authors are drawing evidence on the diagnostic capacity of specific volatile organic compounds (VOCs) resulting from some body fluids. While cancer incidence in society is on the rise, it becomes clear that the analysis of these VOCs can yield new strategies to mitigate advanced cancer incidence rates. This paper presents the methodology implemented to test whether a device consisting of an electronic nose inspired by a dogs olfactory system and olfactory neurons is significantly informative to detect breast cancer (BC). To test this device, 90 human urine samples were collected from control subjects and BC patients at a hospital. To test this system, an artificial intelligence-based classification algorithm was developed. The algorithm was firstly trained and tested with data resulting from gas chromatography-mass spectrometry (GC-MS) urine readings, leading to a classification rate of 92.31%, sensitivity of 100.00%, and specificity of 85.71% (N = 90). Secondly, the same algorithm was trained and tested with data obtained with our eNose prototype hardware, and class prediction was achieved with a classification rate of 75%, sensitivity of 100%, and specificity of 50%.

In this paper we have used second harmonic generation (SHG) and phasor approach to auto fluorescence lifetime imaging (FLIM) to obtain fingerprints of different collagens and then used these fingerprints to observe bone marrow fibrosis in the mouse femur. This is a label free approach towards fast automatable detection of fibrosis in tissue samples. FLIM has previously been used as a method of contrast in different tissues and in this paper phasor approach to FLIM is used to separate collagen I from collagen III, the markers of fibrosis, the largest groups of disorders that are often without any effective therapy. Often characterized by an increase in collagen content of the corresponding tissue, the samples are usually visualized by histochemical staining, which is pathologist dependent and cannot be automated.

Thirdhand smoke (THS) is a new term for the toxins in cigarette smoke that linger in the environment long after the cigarettes are extinguished. The effects of THS exposure on male reproduction have not yet been studied. In this study, metabolic changes in male germ cell lines (GC-2 and TM-4) were analyzed after THS treatment for 24 h. THS-loaded chromatography paper samples were generated in a laboratory chamber system and extracted in DMEM. At a paper: DMEM ratio of 50 μg/ml, cell viability in both cell lines was normal, as measured by the MTT assay and markers of cytotoxicity, cell cycle, apoptosis and ROS production were normal as measured by quantitative immunofluorescence. Metabolomic analysis was performed on methanol extracts of GC-2 and TM-4 cells. Glutathione metabolism in GC-2 cells, and nucleic acid and ammonia metabolism in TM-4 cells, was changed significantly by THS treatment. RT-PCR analyses of mRNA for enzyme genes Gss and Ggt in GC-2 cells, and TK, SMS and Glna in TM-4 cells reinforced these findings, showing changes in the levels of enzymes involved in the relevant pathways. In conclusion, exposure to THS at very low concentrations caused distinct metabolic changes in two different types of male reproductive cell lines.

After significant earthquakes, we can see images posted on social media platforms by individuals and media agencies owing to the mass usage of smartphones these days. These images can be utilized to provide information about the shaking damage in the earthquake region both to the public and research community, and potentially to guide rescue work. This paper presents an automated way to extract the damaged buildings images after earthquakes from social media platforms such as Twitter and thus identify the particular user posts containing such images. Using transfer learning and ~ 6500 manually labelled images, we trained a deep learning model to recognize images with damaged buildings in the scene. The trained model achieved good performance when tested on newly acquired images of earthquakes at different locations and when ran in near real-time on Twitter feed after the 2020 M7.0 earthquake in Turkey. Furthermore, to better understand how the model makes decisions, we also implemented the Grad-CAM method to visualize the important regions on the images that facilitate the decision.

Human-Building Interaction (HBI) is a convergent field that represents the growing complexities of the dynamic interplay between human experience and intelligence within built environments. This paper provides core definitions, research dimensions, and an overall vision for the future of HBI as developed through consensus among 25 interdisciplinary experts in a series of facilitated workshops. Three primary areas contribute to and require attention in HBI research: humans (human experiences, performance, and well-being), buildings (building design and operations), and technologies (sensing, inference, and awareness). Three critical interdisciplinary research domains intersect these areas: control systems and decision making, trust and collaboration, and modeling and simulation. Finally, at the core, it is vital for HBI research to center on and support equity, privacy, and sustainability. Compelling research questions are posed for each primary area, research domain, and core principle. State-of-the-art methods used in HBI studies are discussed, and examples of original research are offered to illustrate opportunities for the advancement of HBI research.

In the face of crises-wars, pandemics, and natural disasters-both increased selfishness and increased generosity may emerge. In this paper, we study the relationship between the presence of COVID-19 threat and generosity using a four-year longitudinal dataset (N = 696,942) capturing real donations made before and during the pandemic, as well as allocations from a 6-month dictator game study (N = 1003 participants) during the early months of the pandemic. Consistent with the notion of "catastrophe compassion" and contrary to some prior research showing a tendency toward self-interested behavior under threat, individuals across both datasets exhibited greater financial generosity when their county experienced COVID-19 threat. While we find that the presence of threat impacted individual giving, behavior was not sensitive to threat level. Our findings have significant societal implications and advance our understanding of economic and psychological theories of social preferences under threat.

Hereditary hemorrhagic telangiectasia (HHT) is an autosomal dominant disorder that causes vascular malformations throughout the body. The most prevalent and accessible of these lesions are found throughout the skin and mucosa, and often rupture causing bleeding and anemia. A recent increase in potential HHT treatments have created a demand for quantitative metrics that can objectively measure the efficacy of new and developing treatments. We employ optical coherence tomography (OCT)-a high resolution, non-invasive imaging modality in a novel pipeline to image and quantitatively characterize dermal HHT lesion behavior over time or throughout the course of treatment. This study is aimed at detecting detailed morphological changes of dermal HHT lesions to understand the underlying dynamic processes of the disease. We present refined metrics tailored for HHT, developed from a pilot study using 3 HHT patients and 6 lesions over the course of multiple imaging dates, totalling to 26 lesion images. Preliminary results from these lesions are presented in this paper alongside representative OCT images. This study provides a new objective method to analyse and understand HHT lesions using a minimally invasive, accessible, cost-effective, and efficient imaging modality with quantitative metrics describing morphology and blood flow.

Alzheimer's disease (AD) is a progressive disorder associated with cognitive dysfunction that alters the brain's functional connectivity. Assessing these alterations has become a topic of increasing interest. However, a few studies have examined different stages of AD from a complex network perspective that cover different topological scales. This study used resting state fMRI data to analyze the trend of functional connectivity alterations from a cognitively normal (CN) state through early and late mild cognitive impairment (EMCI and LMCI) and to Alzheimer's disease. The analyses had been done at the local (hubs and activated links and areas), meso (clustering, assortativity, and rich-club), and global (small-world, small-worldness, and efficiency) topological scales. The results showed that the trends of changes in the topological architecture of the functional brain network were not entirely proportional to the AD progression. There were network characteristics that have changed non-linearly regarding the disease progression, especially at the earliest stage of the disease, i.e., EMCI. Further, it has been indicated that the diseased groups engaged somatomotor, frontoparietal, and default mode modules compared to the CN group. The diseased groups also shifted the functional network towards more random architecture. In the end, the methods introduced in this paper enable us to gain an extensive understanding of the pathological changes of the AD process.

The overarching goal of this paper is to shed light on the human influence on the changing patterns of heat waves in India using the Heat Wave Magnitude Index daily (HWMId). The HWMId obtained from the observational data sets shows a large increase in the heat waves during the past decades. Investigating the effects of natural (e.g., solar variations and volcanic forcings) and anthropogenic (e.g., greenhouse gas emissions, anthropogenic, land use, and land cover) forcings revealed that the anthropogenic factors have cause a two-fold increase in the occurrence probability of severe heat waves in central and mid-southern India during twentieth century. The spatial distribution of maximum HWMId values under natural and all forcings (including anthropogenic) indicates that in most places human activities have increases the frequency, duration and intensity of extreme heat waves. Under the Representative Concentration Pathway (RCP) 4.5, the risk of heat waves is projected to increase tenfold during the twenty-first century. More than ~ 70% of the land areas in India is projected to be influenced by heat waves with magnitudes greater than 9. Furthermore, we find a significant relationship between heat waves and deficits in precipitation. Results show that concurrent heat waves and droughts are projected to increase in most places in India during the twenty-first century.

Over the past two decades, fibrillar collagen reorganization parameters such as the amount of collagen deposition, fiber angle and alignment have been widely explored in numerous studies. These parameters are now widely accepted as stromal biomarkers and linked to disease progression and survival time in several cancer types. Despite all these advances, there has not been a significant effort to make it possible for clinicians to explore these biomarkers without adding steps to the clinical workflow or by requiring high-cost imaging systems. In this paper, we evaluate previously described polychromatic polarization microscope (PPM) to visualize collagen fibers with an optically generated color representation of fiber orientation and alignment when inspecting the sample by a regular microscope with minor modifications. This system does not require stained slides, but is compatible with histological stains such as H&E. Consequently, it can be easily accommodated as part of regular pathology review of tissue slides, while providing clinically useful insight into stromal composition.

The new coronavirus unleashed a worldwide pandemic in early 2020, and a fatality rate several times that of the flu. As the number of infections soared, and capabilities for testing lagged behind, chest X-ray (CXR) imaging became more relevant in the early diagnosis and treatment planning for patients with suspected or confirmed COVID-19 infection. In a few weeks, proposed new methods for lung screening using deep learning rapidly appeared, while quality assurance discussions lagged behind. This paper proposes a set of protocols to validate deep learning algorithms, including our ROI Hide-and-Seek protocol, which emphasizes or hides key regions of interest from CXR data. Our protocol allows assessing the classification performance for anomaly detection and its correlation to radiological signatures, an important issue overlooked in several deep learning approaches proposed so far. By running a set of systematic tests over CXR representations using public image datasets, we demonstrate the weaknesses of current techniques and offer perspectives on the advantages and limitations of automated radiography analysis when using heterogeneous data sources.

We have developed a process for fabricating patient specific Magnetic Resonance Imaging (MRI) Radio-frequency (RF) receive coil arrays using additive manufacturing. Our process involves spray deposition of silver nanoparticle inks and dielectric materials onto 3D printed substrates to form high-quality resonant circuits. In this paper, we describe the material selection and characterization, process optimization, and design and testing of a prototype 4-channel neck array for carotid imaging. We show that sprayed polystyrene can form a low loss dielectric layer in a parallel plate capacitor. We also demonstrate that by using sprayed silver nanoparticle ink as conductive traces, our devices are still dominated by sample noise, rather than material losses. These results are critical for maintaining high Signal-to-Noise-Ratio (SNR) in clinical settings. Finally, our prototype patient specific coil array exhibits higher SNR (5 × in the periphery, 1.4 × in the center) than a commercially available array designed to fit the majority of subjects when tested on our custom neck phantom. 3D printed substrates ensure an optimum fit to complex body parts, improve diagnostic image quality, and enable reproducible placement on subjects.

Transboundary river basins give rise to complex water-sharing decision making that can be analyzed as a game in the sense of dynamic game theory, as done in this work. The sharing of transboundary water resources depends on the long-term shifting interactions between upstream and downstream countries, which has received limited research attention in the past. The water-sharing strategy of a riparian country depends on the strategies of other countries over time. This paper presents an evolutionary game method to analyze the long-term water-sharing strategies of countries encompassing transboundary river basins over time. The method analyzes the evolutionary strategies of riparian countries and investigates evolutionary stable strategies (ESSs) considering the payoff matrix. The evolutionary game method is applied to a river basin shared by three countries assuming two types of benefits and one type of cost to countries as decision variables of a game that reflects water use, economic and political gains, and socio-economic losses of countries. Numerical examples illustrate the strategies resulting from the evolutionary game processes and the role of several parameters on the interaction between riparian countries. The countries' strategies are analyzed for several levels of benefits and costs, and the convergence of the strategies to a stable point is assessed. Results demonstrate the role that the upstream country's potential benefits and the cost of conflict (i.e., non-cooperation) to other countries has on reaching a stable point in the game. This work's results show the potential benefit to the upstream country under cooperative strategy must exceed its benefits from water use under non-cooperative strategy to gain the full stable cooperation of downstream countries. This work provides a method to resolve water-sharing strategies by countries sharing transboundary river basins and to evaluate the implications of cooperation or non-cooperation.

Emerging and promising therapeutic interventions for Duchenne muscular dystrophy (DMD) are confounded by the challenges of quantifying dystrophin. Current approaches have poor precision, require large amounts of tissue, and are difficult to standardize. This paper presents an immuno-mass spectrometry imaging method using gadolinium (Gd)-labeled anti-dystrophin antibodies and laser ablation-inductively coupled plasma-mass spectrometry to simultaneously quantify and localize dystrophin in muscle sections. Gd is quantified as a proxy for the relative expression of dystrophin and was validated in murine and human skeletal muscle sections following k-means clustering segmentation, before application to DMD patients with different gene mutations where dystrophin expression was measured up to 100 µg kg-1 Gd. These results demonstrate that immuno-mass spectrometry imaging is a viable approach for pre-clinical to clinical research in DMD. It rapidly quantified relative dystrophin in single tissue sections, efficiently used valuable patient resources, and may provide information on drug efficacy for clinical translation.

The new coronavirus unleashed a worldwide pandemic in early 2020, and a fatality rate several times that of the flu. As the number of infections soared, and capabilities for testing lagged behind, chest X-ray (CXR) imaging became more relevant in the early diagnosis and treatment planning for patients with suspected or confirmed COVID-19 infection. In a few weeks, proposed new methods for lung screening using deep learning rapidly appeared, while quality assurance discussions lagged behind. This paper proposes a set of protocols to validate deep learning algorithms, including our ROI Hide-and-Seek protocol, which emphasizes or hides key regions of interest from CXR data. Our protocol allows assessing the classification performance for anomaly detection and its correlation to radiological signatures, an important issue overlooked in several deep learning approaches proposed so far. By running a set of systematic tests over CXR representations using public image datasets, we demonstrate the weaknesses of current techniques and offer perspectives on the advantages and limitations of automated radiography analysis when using heterogeneous data sources.

Better characterization of acute concussion symptomatology is needed in order to advance clinical and scientific understanding of persistent concussion symptoms. This paper aims to illustrate a novel framework for conceptualizing, collecting, and analyzing concussion symptom data. To that end, we describe the temporal and structural dynamics of acute concussion symptoms at the individual-patient level. Ten recently concussion adolescents and young adults completed 20 days of ecological momentary assessment (EMA) of post-concussion symptoms. Follow-up assessments were completed at 3 months post-injury. Network modeling revealed marked heterogeneity across participants. In the overall sample, temporal patterns explained the most variance in light sensitivity (48%) and the least variance in vomiting (5%). About half of the participants had symptom networks that were sparse after controlling for temporal variation. The other individualized symptom networks were densely interconnected clusters of symptoms. Networks were highly idiosyncratic in nature, yet emotional symptoms (nervousness, emotional, sadness), cognitive symptoms (mental fogginess, slowness), and symptoms of hyperacusis (sensitivity to light, sensitivity to noise) tended to cluster together across participants. Person-specific analytic techniques revealed a number of idiosyncratic features of post-concussion symptomatology. We propose applying this framework to future research to better understand individual differences in concussion recovery.

Membrane nanotubes are dynamic structures that may connect cells over long distances. Nanotubes are typically thin cylindrical tubes, but they may occasionally have a beaded architecture along the tube. In this paper, we study the role of membrane mechanics in governing the architecture of these tubes and show that the formation of bead-like structures along the nanotubes can result from local heterogeneities in the membrane either due to protein aggregation or due to membrane composition. We present numerical results that predict how membrane properties, protein density, and local tension compete to create a phase space that governs the morphology of a nanotube. We also find that there exists a discontinuity in the energy that impedes two beads from fusing. These results suggest that the membrane-protein interaction, membrane composition, and membrane tension closely govern the tube radius, number of beads, and the bead morphology.

Better characterization of acute concussion symptomatology is needed in order to advance clinical and scientific understanding of persistent concussion symptoms. This paper aims to illustrate a novel framework for conceptualizing, collecting, and analyzing concussion symptom data. To that end, we describe the temporal and structural dynamics of acute concussion symptoms at the individual-patient level. Ten recently concussion adolescents and young adults completed 20 days of ecological momentary assessment (EMA) of post-concussion symptoms. Follow-up assessments were completed at 3 months post-injury. Network modeling revealed marked heterogeneity across participants. In the overall sample, temporal patterns explained the most variance in light sensitivity (48%) and the least variance in vomiting (5%). About half of the participants had symptom networks that were sparse after controlling for temporal variation. The other individualized symptom networks were densely interconnected clusters of symptoms. Networks were highly idiosyncratic in nature, yet emotional symptoms (nervousness, emotional, sadness), cognitive symptoms (mental fogginess, slowness), and symptoms of hyperacusis (sensitivity to light, sensitivity to noise) tended to cluster together across participants. Person-specific analytic techniques revealed a number of idiosyncratic features of post-concussion symptomatology. We propose applying this framework to future research to better understand individual differences in concussion recovery.

Contemporary medical imaging is becoming increasingly more quantitative. The emerging field of radiomics is a leading example. By translating unstructured data (i.e., images) into structured data (i.e., imaging features), radiomics can potentially characterize clinically useful imaging phenotypes. In this paper, an exploratory radiomics approach is used to investigate the potential association between quantitative imaging features and pulmonary function in CT images. Thirty-nine radiomic features were extracted from the lungs of 64 patients as potential imaging biomarkers for pulmonary function. Collectively, these features capture the morphology of the lungs, as well as intensity variations, fine-texture, and coarse-texture of the pulmonary tissue. The extracted lung radiomics data was compared to conventional pulmonary function tests. In general, patients with larger lungs of homogeneous, low attenuating pulmonary tissue (as measured via radiomics) were found to be associated with poor spirometry performance and a lower diffusing capacity for carbon monoxide. Unsupervised dynamic data clustering revealed subsets of patients with similar lung radiomic patterns that were found to be associated with similar forced expiratory volume in one second (FEV1) measurements. This implies that patients with similar radiomic feature vectors also presented with comparable spirometry performance, and were separable by varying degrees of pulmonary function as measured by imaging.

Contemporary medical imaging is becoming increasingly more quantitative. The emerging field of radiomics is a leading example. By translating unstructured data (i.e., images) into structured data (i.e., imaging features), radiomics can potentially characterize clinically useful imaging phenotypes. In this paper, an exploratory radiomics approach is used to investigate the potential association between quantitative imaging features and pulmonary function in CT images. Thirty-nine radiomic features were extracted from the lungs of 64 patients as potential imaging biomarkers for pulmonary function. Collectively, these features capture the morphology of the lungs, as well as intensity variations, fine-texture, and coarse-texture of the pulmonary tissue. The extracted lung radiomics data was compared to conventional pulmonary function tests. In general, patients with larger lungs of homogeneous, low attenuating pulmonary tissue (as measured via radiomics) were found to be associated with poor spirometry performance and a lower diffusing capacity for carbon monoxide. Unsupervised dynamic data clustering revealed subsets of patients with similar lung radiomic patterns that were found to be associated with similar forced expiratory volume in one second (FEV1) measurements. This implies that patients with similar radiomic feature vectors also presented with comparable spirometry performance, and were separable by varying degrees of pulmonary function as measured by imaging.

Contemporary medical imaging is becoming increasingly more quantitative. The emerging field of radiomics is a leading example. By translating unstructured data (i.e., images) into structured data (i.e., imaging features), radiomics can potentially characterize clinically useful imaging phenotypes. In this paper, an exploratory radiomics approach is used to investigate the potential association between quantitative imaging features and pulmonary function in CT images. Thirty-nine radiomic features were extracted from the lungs of 64 patients as potential imaging biomarkers for pulmonary function. Collectively, these features capture the morphology of the lungs, as well as intensity variations, fine-texture, and coarse-texture of the pulmonary tissue. The extracted lung radiomics data was compared to conventional pulmonary function tests. In general, patients with larger lungs of homogeneous, low attenuating pulmonary tissue (as measured via radiomics) were found to be associated with poor spirometry performance and a lower diffusing capacity for carbon monoxide. Unsupervised dynamic data clustering revealed subsets of patients with similar lung radiomic patterns that were found to be associated with similar forced expiratory volume in one second (FEV1) measurements. This implies that patients with similar radiomic feature vectors also presented with comparable spirometry performance, and were separable by varying degrees of pulmonary function as measured by imaging.

Deletion of phenylalanine 508 (F508del) in the cystic fibrosis transmembrane conductance regulator (CFTR) anion channel is the most frequent mutation causing cystic fibrosis (CF). F508del-CFTR is misfolded and prematurely degraded. Recently thymosin a-1 (Tα-1) was proposed as a single molecule-based therapy for CF, improving both F508del-CFTR maturation and function by restoring defective autophagy. However, three independent laboratories failed to reproduce these results. Lack of reproducibility has been ascribed by the authors of the original paper to the use of DMSO and to improper handling. Here, we address these potential issues by demonstrating that Tα-1 changes induced by DMSO are fully reversible and that Tα-1 peptides prepared from different stock solutions have equivalent biological activity. Considering the negative results here reported, six independent laboratories failed to demonstrate F508del-CFTR correction by Tα-1. This study also calls into question the autophagy modulator cysteamine, since no rescue of mutant CFTR function was detected following treatment with cysteamine, while deleterious effects were observed when bronchial epithelia were exposed to cysteamine plus the antioxidant food supplement EGCG. Although these studies do not exclude the possibility of beneficial immunomodulatory effects of thymosin α-1, they do not support its utility as a corrector of F508del-CFTR.

The potential for behaviours to spread via cultural transmission has profound implications for our understanding of social dynamics and evolution. Several studies have provided empirical evidence that local traditions can be maintained in animal populations via conformist learning (i.e. copying the majority). A conformist bias can be characterized by a sigmoidal relationship between a behavior's prevalence in the population and an individual's propensity to adopt that behavior. For this reason, the presence of conformist learning in a population is often inferred from a sigmoidal acquisition curve in which the overall rate of adoption for the behavior is taken as the dependent variable. However, the validity of sigmoidal acquisition curves as evidence for conformist learning has recently been challenged by models suggesting that such curves can arise via alternative learning rules that do not involve conformity. We review these models, and find that the proposed alternative learning mechanisms either rely on faulty or unrealistic assumptions, or apply only in very specific cases. We therefore recommend that sigmoidal acquisition curves continue to be taken as evidence for conformist learning. Our paper also highlights the importance of understanding the generative processes of a model, rather than only focusing solely on the patterns produced. By studying these processes, our analysis suggests that current practices by empiricists have provided robust evidence for conformist transmission in both humans and non-human animals.Arising from: Acerbi, A. et al. Sci. Rep. 6, 36068 (2016); https://doi.org/10.1038/srep36068 .

Ultrasound images are severely degraded by the presence of obstacles such as bones and air gaps along the beam path. This paper describes a method for imaging structures that are distal to obstacles that are otherwise impenetrable to ultrasound. The method uses an optically-inspired holographic algorithm to beam-shape the emitted ultrasound field in order to bypass the obstacle and place the beam focus beyond the obstruction. The resulting performance depends on the transducer aperture, the size and position of the obstacle, and the position of the target. Improvement compared to standard ultrasound imaging is significant for obstacles for which the width is larger than one fourth of the transducer aperture and the depth is within a few centimeters of the transducer. For such cases, the improvement in focal intensity at the location of the target reaches 30-fold, and the improvement in peak-to-side-lobe ratio reaches 3-fold. The method can be implemented in conventional ultrasound systems, and the entire process can be performed in real time. This method has applications in the fields of cancer detection, abdominal imaging, imaging of vertebral structure and ultrasound tomography. Here, its effectiveness is demonstrated using wire targets, tissue mimicking phantoms and an ex vivo biological sample.

Alzheimer's Disease (AD) is a progressive neurodegenerative disease where biomarkers for disease based on pathophysiology may be able to provide objective measures for disease diagnosis and staging. Neuroimaging scans acquired from MRI and metabolism images obtained by FDG-PET provide in-vivo measurements of structure and function (glucose metabolism) in a living brain. It is hypothesized that combining multiple different image modalities providing complementary information could help improve early diagnosis of AD. In this paper, we propose a novel deep-learning-based framework to discriminate individuals with AD utilizing a multimodal and multiscale deep neural network. Our method delivers 82.4% accuracy in identifying the individuals with mild cognitive impairment (MCI) who will convert to AD at 3 years prior to conversion (86.4% combined accuracy for conversion within 1-3 years), a 94.23% sensitivity in classifying individuals with clinical diagnosis of probable AD, and a 86.3% specificity in classifying non-demented controls improving upon results in published literature.

Ultrasound images are severely degraded by the presence of obstacles such as bones and air gaps along the beam path. This paper describes a method for imaging structures that are distal to obstacles that are otherwise impenetrable to ultrasound. The method uses an optically-inspired holographic algorithm to beam-shape the emitted ultrasound field in order to bypass the obstacle and place the beam focus beyond the obstruction. The resulting performance depends on the transducer aperture, the size and position of the obstacle, and the position of the target. Improvement compared to standard ultrasound imaging is significant for obstacles for which the width is larger than one fourth of the transducer aperture and the depth is within a few centimeters of the transducer. For such cases, the improvement in focal intensity at the location of the target reaches 30-fold, and the improvement in peak-to-side-lobe ratio reaches 3-fold. The method can be implemented in conventional ultrasound systems, and the entire process can be performed in real time. This method has applications in the fields of cancer detection, abdominal imaging, imaging of vertebral structure and ultrasound tomography. Here, its effectiveness is demonstrated using wire targets, tissue mimicking phantoms and an ex vivo biological sample.

The potential for behaviours to spread via cultural transmission has profound implications for our understanding of social dynamics and evolution. Several studies have provided empirical evidence that local traditions can be maintained in animal populations via conformist learning (i.e. copying the majority). A conformist bias can be characterized by a sigmoidal relationship between a behavior's prevalence in the population and an individual's propensity to adopt that behavior. For this reason, the presence of conformist learning in a population is often inferred from a sigmoidal acquisition curve in which the overall rate of adoption for the behavior is taken as the dependent variable. However, the validity of sigmoidal acquisition curves as evidence for conformist learning has recently been challenged by models suggesting that such curves can arise via alternative learning rules that do not involve conformity. We review these models, and find that the proposed alternative learning mechanisms either rely on faulty or unrealistic assumptions, or apply only in very specific cases. We therefore recommend that sigmoidal acquisition curves continue to be taken as evidence for conformist learning. Our paper also highlights the importance of understanding the generative processes of a model, rather than only focusing solely on the patterns produced. By studying these processes, our analysis suggests that current practices by empiricists have provided robust evidence for conformist transmission in both humans and non-human animals.Arising from: Acerbi, A. et al. Sci. Rep. 6, 36068 (2016); https://doi.org/10.1038/srep36068 .

Alzheimer's Disease (AD) is a progressive neurodegenerative disease where biomarkers for disease based on pathophysiology may be able to provide objective measures for disease diagnosis and staging. Neuroimaging scans acquired from MRI and metabolism images obtained by FDG-PET provide in-vivo measurements of structure and function (glucose metabolism) in a living brain. It is hypothesized that combining multiple different image modalities providing complementary information could help improve early diagnosis of AD. In this paper, we propose a novel deep-learning-based framework to discriminate individuals with AD utilizing a multimodal and multiscale deep neural network. Our method delivers 82.4% accuracy in identifying the individuals with mild cognitive impairment (MCI) who will convert to AD at 3 years prior to conversion (86.4% combined accuracy for conversion within 1-3 years), a 94.23% sensitivity in classifying individuals with clinical diagnosis of probable AD, and a 86.3% specificity in classifying non-demented controls improving upon results in published literature.

Single-cell transcriptomic data have rapidly become very popular in genomic science. Genomic science also has a long history of using network models to understand the way in which genes work together to carry out specific biological functions. However, working with single-cell data presents major challenges, such as zero inflation and technical noise. These challenges require methods to be specifically adapted to the context of single-cell data. Recently, much effort has been made to develop the theory behind statistical network models. This has lead to many new models being proposed, and has provided a thorough understanding of the properties of existing models. However, a large amount of this work assumes binary-valued relationships between network nodes, whereas genomic network analysis is traditionally based on continuous-valued correlations between genes. In this paper, we assess several established methods for genomic network analysis, we compare ways that these methods can be adapted to the single-cell context, and we use mixture-models to infer binary-valued relationships based on gene-gene correlations. Based on these binary relationships, we find that excellent results can be achieved by using subnetwork analysis methodology popular amongst network statisticians. This methodology thereby allows detection of functional subnetwork modules within these single-cell genomic networks.

Brain imaging genetics intends to uncover associations between genetic markers and neuroimaging quantitative traits. Sparse canonical correlation analysis (SCCA) can discover bi-multivariate associations and select relevant features, and is becoming popular in imaging genetic studies. The L1-norm function is not only convex, but also singular at the origin, which is a necessary condition for sparsity. Thus most SCCA methods impose [Formula: see text]-norm onto the individual feature or the structure level of features to pursuit corresponding sparsity. However, the [Formula: see text]-norm penalty over-penalizes large coefficients and may incurs estimation bias. A number of non-convex penalties are proposed to reduce the estimation bias in regression tasks. But using them in SCCA remains largely unexplored. In this paper, we design a unified non-convex SCCA model, based on seven non-convex functions, for unbiased estimation and stable feature selection simultaneously. We also propose an efficient optimization algorithm. The proposed method obtains both higher correlation coefficients and better canonical loading patterns. Specifically, these SCCA methods with non-convex penalties discover a strong association between the APOE e4 rs429358 SNP and the hippocampus region of the brain. They both are Alzheimer's disease related biomarkers, indicating the potential and power of the non-convex methods in brain imaging genetics.

The human chaperonin TRiC consists of eight non-identical subunits, and its protein-folding activity is critical for cellular health. Misfolded proteins are associated with many human diseases, such as amyloid diseases, cancer, and neuropathies, making TRiC a potential therapeutic target. A detailed structural understanding of its ATP-dependent folding mechanism and substrate recognition is therefore of great importance. Of particular health-related interest is the mutation Histidine 147 to Arginine (H147R) in human TRiC subunit 5 (CCT5), which has been associated with hereditary sensory neuropathy. In this paper, we describe the crystal structures of CCT5 and the CCT5-H147R mutant, which provide important structural information for this vital protein-folding machine in humans. This first X-ray crystallographic study of a single human CCT subunit in the context of a hexadecameric complex can be expanded in the future to the other 7 subunits that form the TRiC complex.

The human chaperonin TRiC consists of eight non-identical subunits, and its protein-folding activity is critical for cellular health. Misfolded proteins are associated with many human diseases, such as amyloid diseases, cancer, and neuropathies, making TRiC a potential therapeutic target. A detailed structural understanding of its ATP-dependent folding mechanism and substrate recognition is therefore of great importance. Of particular health-related interest is the mutation Histidine 147 to Arginine (H147R) in human TRiC subunit 5 (CCT5), which has been associated with hereditary sensory neuropathy. In this paper, we describe the crystal structures of CCT5 and the CCT5-H147R mutant, which provide important structural information for this vital protein-folding machine in humans. This first X-ray crystallographic study of a single human CCT subunit in the context of a hexadecameric complex can be expanded in the future to the other 7 subunits that form the TRiC complex.

Single-cell transcriptomic data have rapidly become very popular in genomic science. Genomic science also has a long history of using network models to understand the way in which genes work together to carry out specific biological functions. However, working with single-cell data presents major challenges, such as zero inflation and technical noise. These challenges require methods to be specifically adapted to the context of single-cell data. Recently, much effort has been made to develop the theory behind statistical network models. This has lead to many new models being proposed, and has provided a thorough understanding of the properties of existing models. However, a large amount of this work assumes binary-valued relationships between network nodes, whereas genomic network analysis is traditionally based on continuous-valued correlations between genes. In this paper, we assess several established methods for genomic network analysis, we compare ways that these methods can be adapted to the single-cell context, and we use mixture-models to infer binary-valued relationships based on gene-gene correlations. Based on these binary relationships, we find that excellent results can be achieved by using subnetwork analysis methodology popular amongst network statisticians. This methodology thereby allows detection of functional subnetwork modules within these single-cell genomic networks.

Brain imaging genetics intends to uncover associations between genetic markers and neuroimaging quantitative traits. Sparse canonical correlation analysis (SCCA) can discover bi-multivariate associations and select relevant features, and is becoming popular in imaging genetic studies. The L1-norm function is not only convex, but also singular at the origin, which is a necessary condition for sparsity. Thus most SCCA methods impose [Formula: see text]-norm onto the individual feature or the structure level of features to pursuit corresponding sparsity. However, the [Formula: see text]-norm penalty over-penalizes large coefficients and may incurs estimation bias. A number of non-convex penalties are proposed to reduce the estimation bias in regression tasks. But using them in SCCA remains largely unexplored. In this paper, we design a unified non-convex SCCA model, based on seven non-convex functions, for unbiased estimation and stable feature selection simultaneously. We also propose an efficient optimization algorithm. The proposed method obtains both higher correlation coefficients and better canonical loading patterns. Specifically, these SCCA methods with non-convex penalties discover a strong association between the APOE e4 rs429358 SNP and the hippocampus region of the brain. They both are Alzheimer's disease related biomarkers, indicating the potential and power of the non-convex methods in brain imaging genetics.

Biomacromolecular pKa shifting is considered as one of the most ubiquitous processes in biochemical events, e.g., the enzyme-catalyzed reaction and protein conformational stabilization. In this paper, we report on the construction of biocompatible polysaccharide nanoparticle with targeting ability and lower toxicity by supramolecular pKa shift strategy. This was realized through a ternary assembly constructed by the dual host‒guest interactions of an adamantane-bis(diamine) conjugate (ADA) with cucurbit[6]uril (CB[6]) and a polysaccharide. The potential application of such biocompatible nanostructure was further implemented by the selective transportation of small interfering RNA (siRNA) in a controlled manner. It is demonstrated that the strong encapsulation of the ADA's diammonium tail by CB[6] not only reduced the cytotoxicity of the nano-scaled vehicle but also dramatically enhanced cation density through an obvious positive macrocycle-induced pKa shift, which eventually facilitated the subsequent siRNA binding. With a targeted polysaccharide shell containing a cyclodextrin‒hyaluronic acid conjugate, macrocycle-incorporated siRNA polyplexes were specifically delivered into malignant human prostate PC-3 cells. The supramolecular polysaccharide nanoparticles, the formation of which was enabled and promoted by the complexation-assisted pKa shift, may be used as a versatile tool for controlled capture and release of biofunctional substrates.

Biomacromolecular pKa shifting is considered as one of the most ubiquitous processes in biochemical events, e.g., the enzyme-catalyzed reaction and protein conformational stabilization. In this paper, we report on the construction of biocompatible polysaccharide nanoparticle with targeting ability and lower toxicity by supramolecular pKa shift strategy. This was realized through a ternary assembly constructed by the dual host‒guest interactions of an adamantane-bis(diamine) conjugate (ADA) with cucurbit[6]uril (CB[6]) and a polysaccharide. The potential application of such biocompatible nanostructure was further implemented by the selective transportation of small interfering RNA (siRNA) in a controlled manner. It is demonstrated that the strong encapsulation of the ADA's diammonium tail by CB[6] not only reduced the cytotoxicity of the nano-scaled vehicle but also dramatically enhanced cation density through an obvious positive macrocycle-induced pKa shift, which eventually facilitated the subsequent siRNA binding. With a targeted polysaccharide shell containing a cyclodextrin‒hyaluronic acid conjugate, macrocycle-incorporated siRNA polyplexes were specifically delivered into malignant human prostate PC-3 cells. The supramolecular polysaccharide nanoparticles, the formation of which was enabled and promoted by the complexation-assisted pKa shift, may be used as a versatile tool for controlled capture and release of biofunctional substrates.

There is an increasing interest in developing microfluidic bioreactors and organs-on-a-chip platforms combined with sensing capabilities for continual monitoring of cell-secreted biomarkers. Conventional approaches such as ELISA and mass spectroscopy cannot satisfy the needs of continual monitoring as they are labor-intensive and not easily integrable with low-volume bioreactors. This paper reports on the development of an automated microfluidic bead-based electrochemical immunosensor for in-line measurement of cell-secreted biomarkers. For the operation of the multi-use immunosensor, disposable magnetic microbeads were used to immobilize biomarker-recognition molecules. Microvalves were further integrated in the microfluidic immunosensor chip to achieve programmable operations of the immunoassay including bead loading and unloading, binding, washing, and electrochemical sensing. The platform allowed convenient integration of the immunosensor with liver-on-chips to carry out continual quantification of biomarkers secreted from hepatocytes. Transferrin and albumin productions were monitored during a 5-day hepatotoxicity assessment in which human primary hepatocytes cultured in the bioreactor were treated with acetaminophen. Taken together, our unique microfluidic immunosensor provides a new platform for in-line detection of biomarkers in low volumes and long-term in vitro assessments of cellular functions in microfluidic bioreactors and organs-on-chips.

There is an increasing interest in developing microfluidic bioreactors and organs-on-a-chip platforms combined with sensing capabilities for continual monitoring of cell-secreted biomarkers. Conventional approaches such as ELISA and mass spectroscopy cannot satisfy the needs of continual monitoring as they are labor-intensive and not easily integrable with low-volume bioreactors. This paper reports on the development of an automated microfluidic bead-based electrochemical immunosensor for in-line measurement of cell-secreted biomarkers. For the operation of the multi-use immunosensor, disposable magnetic microbeads were used to immobilize biomarker-recognition molecules. Microvalves were further integrated in the microfluidic immunosensor chip to achieve programmable operations of the immunoassay including bead loading and unloading, binding, washing, and electrochemical sensing. The platform allowed convenient integration of the immunosensor with liver-on-chips to carry out continual quantification of biomarkers secreted from hepatocytes. Transferrin and albumin productions were monitored during a 5-day hepatotoxicity assessment in which human primary hepatocytes cultured in the bioreactor were treated with acetaminophen. Taken together, our unique microfluidic immunosensor provides a new platform for in-line detection of biomarkers in low volumes and long-term in vitro assessments of cellular functions in microfluidic bioreactors and organs-on-chips.

Roll-to-roll (R2R) printing has been pursued as a commercially viable high-throughput technology to manufacture flexible, disposable, and inexpensive printed electronic devices. However, in recent years, pessimism has prevailed because of the barriers faced when attempting to fabricate and integrate thin film transistors (TFTs) using an R2R printing method. In this paper, we report 20 × 20 active matrices (AMs) based on single-walled carbon nanotubes (SWCNTs) with a resolution of 9.3 points per inch (ppi) resolution, obtained using a fully R2R gravure printing process. By using SWCNTs as the semiconducting layer and poly(ethylene terephthalate) (PET) as the substrate, we have obtained a device yield above 98%, and extracted the key scalability factors required for a feasible R2R gravure manufacturing process. Multi-touch sensor arrays were achieved by laminating a pressure sensitive rubber onto the SWCNT-TFT AM. This R2R gravure printing system overcomes the barriers associated with the registration accuracy of printing each layer and the variation of the threshold voltage (Vth). By overcoming these barriers, the R2R gravure printing method can be viable as an advanced manufacturing technology, thus enabling the high-throughput production of flexible, disposable, and human-interactive cutting-edge electronic devices based on SWCNT-TFT AMs.

Roll-to-roll (R2R) printing has been pursued as a commercially viable high-throughput technology to manufacture flexible, disposable, and inexpensive printed electronic devices. However, in recent years, pessimism has prevailed because of the barriers faced when attempting to fabricate and integrate thin film transistors (TFTs) using an R2R printing method. In this paper, we report 20 × 20 active matrices (AMs) based on single-walled carbon nanotubes (SWCNTs) with a resolution of 9.3 points per inch (ppi) resolution, obtained using a fully R2R gravure printing process. By using SWCNTs as the semiconducting layer and poly(ethylene terephthalate) (PET) as the substrate, we have obtained a device yield above 98%, and extracted the key scalability factors required for a feasible R2R gravure manufacturing process. Multi-touch sensor arrays were achieved by laminating a pressure sensitive rubber onto the SWCNT-TFT AM. This R2R gravure printing system overcomes the barriers associated with the registration accuracy of printing each layer and the variation of the threshold voltage (Vth). By overcoming these barriers, the R2R gravure printing method can be viable as an advanced manufacturing technology, thus enabling the high-throughput production of flexible, disposable, and human-interactive cutting-edge electronic devices based on SWCNT-TFT AMs.