Miracle-dz/UniFAR-FTUs huggingface dataset:
Testing for similarity in area‐based spatial patterns: Alternative methods to Andresen's spatial point pattern test

Andresen's spatial point pattern test (SPPT) compares two spatial point patterns on defined areal units; it identifies areas where the spatial point patterns diverge and aggregates these local (dis)similarities to one global measure. We discuss the limitations of the SPPT and provide two alternative methods to calculate differences in the point patterns. In the first approach we use differences in proportions tests corrected for multiple comparisons. We show how the size of differences matters, as with large point patterns many areas will be identified by SPPT as statistically different, even if those differences are substantively trivial. The second approach uses multinomial logistic regression, which can be extended to identify differences in proportions over continuous time. We demonstrate these methods by identifying areas where pedestrian stops by the New York City Police Department are different from violent crimes for 2006–2016.
Inhibition of RON kinase potentiates anti-CTLA-4 immunotherapy to shrink breast tumors and prevent metastatic outgrowth

ABSTRACT The advent of immune checkpoint blockade as a new strategy for immunotherapy has changed the outlook for many aggressive cancers. Although complete tumor eradication is attainable in some cases, durable clinical responses are observed only in a small fraction of patients, underlining urgent need for improvement. We previously showed that RON, a receptor tyrosine kinase expressed in macrophages, suppresses antitumor immune responses, and facilitates progression and metastasis of breast cancer. Here, we investigated the molecular changes that occur downstream of RON activation in macrophages, and whether inhibition of RON can cooperate with checkpoint immunotherapy to eradicate tumors. Activation of RON by its ligand, MSP, altered the gene expression profile of macrophages drastically and upregulated surface levels of CD80 and PD-L1, ligands for T-cell checkpoint receptors CTLA-4 and PD-1. Genetic deletion or pharmacological inhibition of RON in combination with anti-CTLA-4, but not with anti-PD-1, resulted in improved clinical responses against orthotopically transplanted tumors compared to single-agent treatment groups, resulting in complete tumor eradication in 46% of the animals. Positive responses to therapy were associated with higher levels of T-cell activation markers and tumor-infiltrating lymphocytes. Importantly, co-inhibition of RON and anti-CTLA-4 was also effective in clearing metastatic breast cancer cells in lungs, resulting in clinical responses in nearly 60% of the mice. These findings suggest that RON inhibition can be a novel approach to potentiate responses to checkpoint immunotherapy in breast cancer.
HashTran-DNN: A Framework for Enhancing Robustness of Deep Neural Networks against Adversarial Malware Samples

Adversarial machine learning in the context of image processing and related applications has received a large amount of attention. However, adversarial machine learning, especially adversarial deep learning, in the context of malware detection has received much less attention despite its apparent importance. In this paper, we present a framework for enhancing the robustness of Deep Neural Networks (DNNs) against adversarial malware samples, dubbed Hashing Transformation Deep Neural Networks} (HashTran-DNN). The core idea is to use hash functions with a certain locality-preserving property to transform samples to enhance the robustness of DNNs in malware classification. The framework further uses a Denoising Auto-Encoder (DAE) regularizer to reconstruct the hash representations of samples, making the resulting DNN classifiers capable of attaining the locality information in the latent space. We experiment with two concrete instantiations of the HashTran-DNN framework to classify Android malware. Experimental results show that four known attacks can render standard DNNs useless in classifying Android malware, that known defenses can at most defend three of the four attacks, and that HashTran-DNN can effectively defend against all of the four attacks.
Using wearable sensors to classify subject-specific running biomechanical gait patterns based on changes in environmental weather conditions

Running-related overuse injuries can result from a combination of various intrinsic (e.g., gait biomechanics) and extrinsic (e.g., running surface) risk factors. However, it is unknown how changes in environmental weather conditions affect running gait biomechanical patterns since these data cannot be collected in a laboratory setting. Therefore, the purpose of this study was to develop a classification model based on subject-specific changes in biomechanical running patterns across two different environmental weather conditions using data obtained from wearable sensors in real-world environments. Running gait data were recorded during winter and spring sessions, with recorded average air temperatures of -10° C and +6° C, respectively. Classification was performed based on measurements of pelvic drop, ground contact time, braking, vertical oscillation of pelvis, pelvic rotation, and cadence obtained from 66,370 strides (~11,000/runner) from a group of recreational runners. A non-linear and ensemble machine learning algorithm, random forest (RF), was used to classify and compute a heuristic for determining the importance of each variable in the prediction model. To validate the developed subject-specific model, two cross-validation methods (one-against-another and partitioning datasets) were used to obtain experimental mean classification accuracies of 87.18% and 95.42%, respectively, indicating an excellent discriminatory ability of the RF-based model. Additionally, the ranked order of variable importance differed across the individual runners. The results from the RF-based machine-learning algorithm demonstrates that processing gait biomechanical signals from a single wearable sensor can successfully detect changes to an individual’s running patterns based on data obtained in real-world environments.
MBS: Macroblock Scaling for CNN Model Reduction

In this paper we propose the macroblock scaling (MBS) algorithm, which can be applied to various CNN architectures to reduce their model size. MBS adaptively reduces each CNN macroblock depending on its information redundancy measured by our proposed effective flops. Empirical studies conducted with ImageNet and CIFAR-$10$ attest that MBS can reduce the model size of some already compact CNN models, e.g., MobileNetV$2$ ($25.03\%$ further reduction) and ShuffleNet ($20.74\%$), and even ultra-deep ones such as ResNet-$101$ ($51.67\%$) and ResNet-$1202$ ($72.71\%$) with negligible accuracy degradation. MBS also performs better reduction at a much lower cost than the state-of-the-art optimization-based methods do. MBS's simplicity and efficiency, its flexibility to work with any CNN model, and its scalability to work with models of any depth make it an attractive choice for CNN model size reduction.
Neurobiology of placebo effect in Parkinson's disease: What we have learned and where we are going

The placebo effect is a phenomenon produced when an inert substance administered like a regular treatment improves the clinical outcome. Parkinson's disease (PD) is one of the main clinical disorders for which the placebo response rates are high. The first evidence of the neurobiological mechanisms underlying the placebo effect in PD stems from 2001, when de la Fuente‐Fernandez and colleagues demonstrated that a placebo injection led to the release of dopamine in the striatal nuclei of PD measured with positron emission tomography technology. Since then, several studies have been conducted to investigate the neurobiological underpinnings of placebo responses. This article presents a systematic review following the Preferred Reporting Items for Systematic Reviews and Meta‐Analysis guidelines. Of an initial yield of 143 papers, 19 were included. The lessons learned from these studies are threefold: (i) motor improvement is dependent on the activation of the entire nigrostriatal pathway induced by dopamine release in the dorsal striatum; (ii) the magnitude of placebo‐induced effects is modulated by an expectancy of improvement, which is in turn related to the release of dopamine within the ventral striatum; (iii) the functioning of the neural pathways underlying the placebo response can be tuned by prior exposure and learning strategies. In conclusion, although the neural network underlying the placebo effect in PD has been largely confirmed and accepted, what remains to be established is how, when, and where the expectation of reward (mediated by the ventral striatum) interacts with the primary motor system (mediated by the dorsal striatum) to induce clinical improvement in motor symptoms. © 2018 International Parkinson and Movement Disorder Society
Theoretical Explorations Generate New Hypotheses About the Role of the Cartilage Endplate in Early Intervertebral Disk Degeneration

Altered cell nutrition in the intervertebral disk (IVD) is considered a main cause for disk degeneration (DD). The cartilage endplate (CEP) provides a major path for the diffusion of nutrients from the peripheral vasculature to the IVD nucleus pulposus (NP). In DD, sclerosis of the adjacent bony endplate is suggested to be responsible for decreased diffusion and disk cell nutrition. Yet, experimental evidence does not support this hypothesis. Hence, we evaluated how moderate CEP composition changes related to tissue degeneration can affect disk nutrition and cell viability. A novel composition-based permeability formulation was developed for the CEP, calibrated, validated, and used in a mechano-transport finite element IVD model. Fixed solute concentrations were applied at the outer surface of the annulus and the CEP, and three cycles of daily mechanical load were simulated. The CEP model indicated that CEP permeability increases with the degeneration/aging of the tissue, in accordance with recent measurements reported in the literature. Additionally, our results showed that CEP degeneration might be responsible for mechanical load-induced NP dehydration, which locally affects oxygen and lactate levels, and reduced glucose concentration by 16% in the NP-annulus transition zone. Remarkably, CEP degeneration was a condition sine-qua-non to provoke cell starvation and death, while simulating the effect of extracellular matrix depletion in DD. This theoretical study cast doubts about the paradigm that CEP calcification is needed to provoke cell starvation, and suggests an alternative path for DD whereby the early degradation of the CEP plays a key role.
In Search of Biomarkers for Pathogenesis and Control of Leishmaniasis by Global Analyses of Leishmania-Infected Macrophages

Leishmaniasis is a vector-borne, neglected tropical disease with a worldwide distribution that can present in a variety of clinical forms, depending on the parasite species and host genetic background. The pathogenesis of this disease remains far from being elucidated because the involvement of a complex immune response orchestrated by host cells significantly affects the clinical outcome. Among these cells, macrophages are the main host cells, produce cytokines and chemokines, thereby triggering events that contribute to the mediation of the host immune response and, subsequently, to the establishment of infection or, alternatively, disease control. There has been relatively limited commercial interest in developing new pharmaceutical compounds to treat leishmaniasis. Moreover, advances in the understanding of the underlying biology of Leishmania spp. have not translated into the development of effective new chemotherapeutic compounds. As a result, biomarkers as surrogate disease endpoints present several potential advantages to be used in the identification of targets capable of facilitating therapeutic interventions considered to ameliorate disease outcome. More recently, large-scale genomic and proteomic analyses have allowed the identification and characterization of the pathways involved in the infection process in both parasites and the host, and these analyses have been shown to be more effective than studying individual molecules to elucidate disease pathogenesis. RNA-seq and proteomics are large-scale approaches that characterize genes or proteins in a given cell line, tissue, or organism to provide a global and more integrated view of the myriad biological processes that occur within a cell than focusing on an individual gene or protein. Bioinformatics provides us with the means to computationally analyze and integrate the large volumes of data generated by high-throughput sequencing approaches. The integration of genomic expression and proteomic data offers a rich multi-dimensional analysis, despite the inherent technical and statistical challenges. We propose that these types of global analyses facilitate the identification, among a large number of genes and proteins, those that hold potential as biomarkers. The present review focuses on large-scale studies that have identified and evaluated relevant biomarkers in macrophages in response to Leishmania infection.
Insulin regulates POMC neuronal plasticity to control glucose metabolism

Hypothalamic neurons respond to nutritional cues by altering gene expression and neuronal excitability. The mechanisms that control such adaptive processes remain unclear. Here we define populations of POMC neurons in mice that are activated or inhibited by insulin and thereby repress or inhibit hepatic glucose production (HGP). The proportion of POMC neurons activated by insulin was dependent on the regulation of insulin receptor signaling by the phosphatase TCPTP, which is increased by fasting, degraded after feeding and elevated in diet-induced obesity. TCPTP-deficiency enhanced insulin signaling and the proportion of POMC neurons activated by insulin to repress HGP. Elevated TCPTP in POMC neurons in obesity and/or after fasting repressed insulin signaling, the activation of POMC neurons by insulin and the insulin-induced and POMC-mediated repression of HGP. Our findings define a molecular mechanism for integrating POMC neural responses with feeding to control glucose metabolism.
Mental Health in Anesthesiology and ICU Staff: Sense of Coherence Matters

Background: Hospitals, and particularly intensive care units (ICUs), are demanding and stressful workplaces. Physicians and nurse staff are exposed to various stressors: emergency situations, patients' deaths, and team conflicts. Correspondingly, several studies describe increased rates of PTSD symptoms and other mental health problems in hospital staff. Therefore, it is important to identify factors that lower the risk of psychopathological symptoms. High levels of sense of coherence (SOC) and general resilience as well as an internal locus of control (LOC) have already been identified as important health-benefitting factors in medical staff. The current study aimed to evaluate their unique impact in an ICU and an anesthesiology unit. Method: The cross-sectional online survey investigated SOC, LOC, general resilience, general mental health problems as well as PTSD symptoms in nurses and physicians within an ICU and an anesthesiology unit (N = 52, 65.4% female). General mental health problems were assessed using the ICD-10-Symptom-Rating (ISR) and PTSD symptoms were measured using the PTSD Checklist for DSM-5 (PCL-5). The Sense of Coherence Scale (SOC-L9) assessed SOC, the Resilience Scale (RS-11) measured general resilience, and LOC was determined using a 4-item scale for the assessment of control beliefs (IE-4). Results: As expected, SOC, r = −0.72, p < 0.001, general resilience, r = −0.46, p < 0.001, and internal LOC, r = −0.51, p < 0.001, were negatively correlated with general mental health problems while an external LOC showed a positive association, r = 0.35, p = 0.010. However, in a multiple regression model, R2 = 53.9%, F(4, 47) = 13.73, p < 0.001, only SOC significantly predicted general mental health problems by uniquely accounting for 13% of the variance. For PTSD symptoms, which were highly correlated with general mental health problems, a similar pattern of results was found. Conclusion: SOC was found to be the most important correlate of both general mental health problems and PTSD symptoms in an ICU and an anesthesiology unit. Thus, if further evidenced by longitudinal studies, implementing interventions focusing on an enhancement of SOC in training programs for ICU and anesthesiology unit staff might be a promising approach to prevent or reduce psychopathological symptoms.
Active Tuberculosis Is Characterized by Highly Differentiated Effector Memory Th1 Cells

Despite advances in diagnosing latent Mycobacterium tuberculosis infection (LTBI), we still lack a diagnostic test that differentiates LTBI from active tuberculosis (TB) or predicts the risk of progression to active disease. One reason for the absence of such a test may be the failure of current assays to capture the dynamic complexities of the immune responses associated with various stages of TB, since these assays measure only a single parameter (release of IFN-γ) and rely on prolonged (overnight) T cell stimulation. We describe a novel, semi-automated RNA flow cytometry assay to determine whether immunological differences can be identified between LTBI and active TB. We analyzed antigen-induced expression of Th1 cytokine mRNA after short (2- and 6-h) stimulation with antigen, in the context of memory T cell immunophenotyping. IFNG and TNFA mRNA induction was detectable in CD4+ T cells after only 2 h of ex vivo stimulation. Moreover, IFNG- and TNFA-expressing CD4+ T cells (Th1 cells) were more frequent in active TB than in LTBI, a difference that is undetectable with conventional, protein-based cytokine assays. We also found that active TB was associated with higher ratios of effector memory to central memory Th1 cells than LTBI. This effector memory phenotype of active TB was associated with increased T cell differentiation, as defined by loss of the CD27 marker, but not with T cell exhaustion, as determined by PD-1 abundance. These results indicate that single-cell-based, mRNA measurements may help identify time-dependent, quantitative differences in T cell functional status between latent infection and active tuberculosis.
The prevalence of obesity among adult population and its association with food outlet density in a hilly area of Uttarakhand

Background: The rising burden of obesity is a threat to both developed and developing countries. Obesity is a killer lifestyle disease. Different aspects of the local environment play a key role in the occurrence of obesity. The present study was conducted to determine the prevalence of obesity among adult, its associated factors, and its relationship with retail food outlet density in Srikot, Uttarakhand. Materials and Methods: This study was a community-based cross-sectional study, carried out in Srikot, Uttarakhand, in 2016 among 155 adults systematically sampled, aged above 20 years. A semi-structured pretested questionnaire was used to interview the participants, and anthropometry measurements were done. The details regarding the geographic location and count of outlets were carried out. Obesity was considered body mass index ≥25 kg/m2. Results: In the present study, 61.3% were females. The mean age of the study participants was 38.3 (±13.6) years. The prevalence of overweight was 14.8% and obesity was 55.5%. A total of 92.9% of the study participants reported consumption of junk food. On mapping the study area, it was found that the total number of food outlets were 116. It was found that density of any food outlet per study participant was 0.7. Conclusion: The Srikot in Uttarakhand is in an early stage of urbanization as evident from the burden of obesity, and this might worsen by prevalent outlet density of food in the environment.
Space-Time Second-Quantization Effects and the Quantum Origin of Cosmological Constant in Covariant Quantum Gravity

Space-time quantum contributions to the classical Einstein equations of General Relativity are determined. The theoretical background is provided by the non-perturbative theory of manifestly-covariant quantum gravity and the trajectory-based representation of the related quantum wave equation in terms of the Generalized Lagrangian path formalism. To reach the target an extended functional setting is introduced, permitting the treatment of a non-stationary background metric tensor allowed to depend on both space-time coordinates and a suitably-defined invariant proper-time parameter. Based on the Hamiltonian representation of the corresponding quantum hydrodynamic equations occurring in such a context, the quantum-modified Einstein field equations are obtained. As an application, the quantum origin of the cosmological constant is investigated. This is shown to be ascribed to the non-linear Bohm quantum interaction of the gravitational field with itself in vacuum and to depend generally also on the realization of the quantum probability density for the quantum gravitational field tensor. The emerging physical picture predicts a generally non-stationary quantum cosmological constant which originates from fluctuations (i.e., gradients) of vacuum quantum gravitational energy density and is consistent with the existence of quantum massive gravitons.
Brain targeting of chitosan-based diazepam mucoadhesive microemulsions via nasal route: formulation optimization, characterization, pharmacokinetic and pharmacodynamic evaluation

Abstract Objective: The aim of present investigation was to develop microemulsions (MEs) and mucoadhesive microemulsions (MME) of diazepam for brain uptake through nasal administration for the treatment of seizure emergency. Significance: Status epilepticus (SE) is a medical emergency, requires intravenous administration of diazepam which requires hospitalization of patient. Initiation of therapy at home via nasal administration of diazepam could prevent the damage of brain due to delay of therapy initiation. Methods: Diazepam MEs were prepared by phase titration method, optimized by using Box–Behnken design. The influence of independent variables oleic acid, surfactant mixture (tween 80:propylene glycol), and water on dependent variables size, flux, and zeta potential was investigated. Optimized MEs, MMEs, and Calmpose (i.v route) were evaluated for pharmacokinetic and pharmacodynamic studies on rats. Results: MME2 composed of oleic acid (5), surfactant mixture (50), water (45), and chitosan (0.5) showed size of 96.45 nm, PDI 0.21 and zeta potential 13.52 mV. MME2 showed significantly high flux of 846.96 ± 34 µg/cm2/h and AUCbrain 1206.49 ± 145.8. The drug targeting efficiency (314%) and direct nose-to-brain transport (68.1%) of MME2 were significantly high compared to Calmpose (i.v) and ME. The latency periods of minimal clonal seizures and generalized tonic–clonic seizures of MME2 was significantly increased (p < 0.0001) compared to drug solution and Calmpose (i.v). Conclusion: The brain uptake of diazepam from chitosan-based MMEs via nasal route is significantly high compared to i.v route.