Miracle-dz/UniFAR-FTUs huggingface dataset:
Dethroning the Fano Factor: a flexible, model-based approach to partitioning neural variability

Neurons in many brain areas exhibit high trial-to-trial variability, with spike counts that are over-dispersed relative to a Poisson distribution. Recent work (Goris et al., 2014) has proposed to explain this variability in terms of a multiplicative interaction between a stochastic gain variable and a stimulus-dependent Poisson firing rate, which produces quadratic relationships between spike count mean and variance. Here we examine this quadratic assumption and propose a more flexible family of models that can account for a more diverse set of mean-variance relationships. Our model contains additive Gaussian noise that is transformed nonlinearly to produce a Poisson spike rate. Different choices of the nonlinear function can give rise to qualitatively different mean-variance relationships, ranging from sub-linear to linear to multiplicative. Intriguingly, a rectified squaring nonlinearity produces a linear mean-variance function, corresponding to responses with constant Fano factor. We describe a computationally efficient method for fitting this model to data, and demonstrate that a majority of neurons in a V1 population are better described by a model with non-quadratic relationship between mean and variance. Lastly, we develop an application to Bayesian adaptive stimulus selection in closed-loop neurophysiology experiments, which shows that accounting for overdispersion can lead to dramatic improvements in adaptive tuning curve estimation.
GC-MS Metabolomics to Evaluate the Composition of Plant Cuticular Waxes for Four Triticum aestivum Cultivars

Wheat (Triticum aestivum L.) is an important food crop, and biotic and abiotic stresses significantly impact grain yield. Wheat leaf and stem surface waxes are associated with traits of biological importance, including stress resistance. Past studies have characterized the composition of wheat cuticular waxes, however protocols can be relatively low-throughput and narrow in the range of metabolites detected. Here, gas chromatography-mass spectrometry (GC-MS) metabolomics methods were utilized to provide a comprehensive characterization of the chemical composition of cuticular waxes in wheat leaves and stems. Further, waxes from four wheat cultivars were assayed to evaluate the potential for GC-MS metabolomics to describe wax composition attributed to differences in wheat genotype. A total of 263 putative compounds were detected and included 58 wax compounds that can be classified (e.g., alkanes and fatty acids). Many of the detected wax metabolites have known associations to important biological functions. Principal component analysis and ANOVA were used to evaluate metabolite distribution, which was attributed to both tissue type (leaf, stem) and cultivar differences. Leaves contained more primary alcohols than stems such as 6-methylheptacosan-1-ol and octacosan-1-ol. The metabolite data were validated using scanning electron microscopy of epicuticular wax crystals which detected wax tubules and platelets. Conan was the only cultivar to display alcohol-associated platelet-shaped crystals on its abaxial leaf surface. Taken together, application of GC-MS metabolomics enabled the characterization of cuticular wax content in wheat tissues and provided relative quantitative comparisons among sample types, thus contributing to the understanding of wax composition associated with important phenotypic traits in a major crop.
Viral gene therapy for breast cancer: progress and challenges

ABSTRACT Introduction: Breast cancer is the most common cancer in women all over the world. Furthermore, up to one third of breast tumors develop metastases that are resistant to standard therapies. Gene therapeutic strategies have been developed in order to specifically target cancer cells either directly or through the stimulation of antitumor immunity. Areas covered: This review describes the therapeutic strategies that are currently under development to treat this disease using engineered viral vectors including: adenovirus, adeno-associated virus, lentivirus, poxvirus, reovirus, baculovirus, herpesvirus and oncolytic viruses. Advantages and disadvantages of these multiple gene therapy platforms are discussed in detail. Expert opinion: Metastatic breast cancer is a perfect candidate for gene therapy approaches due to the presence of several tumor antigens and the aberrant expression of many molecular pathways. Oncolytic vectors are able to attack tumor cells while sparing normal cells and their activity is often enhanced by the administration of chemotherapy. However, more efforts are needed in order to reduce toxicity and to achieve better transduction efficiency. Improved preclinical models and a more critical patient selection for clinical trials, along with advances in gene therapy regulations, will surely facilitate the evolution of gene therapy for the treatment of metastatic breast cancer.
Exploring high-performance speech recognition in noisy environments using high-order taylor series expansion

In this paper, high-order Taylor Series expansion is proposed to explore the most effective formulas of log-spectral compensation. The power feature, which is crucial to speech recognition in noisy environments and can’t be compensated in usual feature compensation, is processed similarly to spectral subtraction. The modeling accuracy of speech log-spectral Gaussian Mixture Model (GMM) is also discussed and carefully treated. Experimental results show that the log-spectral compensation can greatly improve recognition performance in noisy environments and with the acoustic model trained using multi-condition data, the recognition performance is superior to that in matched conditions
Considerations for the combination of anticancer vaccines and immune checkpoint inhibitors

ABSTRACT Introduction: Over the past few years, trials evaluating immunotherapies, particularly immune checkpoint inhibitors, have revolutionized the standard model of cancer treatment, demonstrating significant antitumor responses and improved clinical outcomes across a wide array of tumors types. Yet, despite these compelling data, a major limitation has been that only a fraction of patients mount a response to single-agent immune checkpoint inhibition. However, a growing amount of preclinical and clinical data suggests that combining immune checkpoint inhibition, either with other immune checkpoint inhibitors or with therapeutic cancer vaccines, has the potential to improve the proportion of patients seeing long-term durable responses with these therapies. Areas Covered: We have reviewed the reported data on immune checkpoint inhibition as monotherapy and as combination therapy with other immune checkpoint inhibitors or therapeutic cancer vaccines. Data is reviewed on agents with FDA approval or breakthrough designation as of the writing of this manuscript. Expert Opinion: Particular focus is given to the combination of immune checkpoint inhibitors and therapeutic cancer vaccines which has the potential to increase efficacy compared to single agent immune checkpoint inhibition with minimal added toxicity.
Perceptual and Category Processing of the Uncanny Valley Hypothesis' Dimension of Human Likeness: Some Methodological Issues

Mori's Uncanny Valley Hypothesis1,2 proposes that the perception of humanlike characters such as robots and, by extension, avatars (computer-generated characters) can evoke negative or positive affect (valence) depending on the object's degree of visual and behavioral realism along a dimension of human likeness (DHL) (Figure 1). But studies of affective valence of subjective responses to variously realistic non-human characters have produced inconsistent findings 3, 4, 5, 6. One of a number of reasons for this is that human likeness is not perceived as the hypothesis assumes. While the DHL can be defined following Mori's description as a smooth linear change in the degree of physical humanlike similarity, subjective perception of objects along the DHL can be understood in terms of the psychological effects of categorical perception (CP) 7. Further behavioral and neuroimaging investigations of category processing and CP along the DHL and of the potential influence of the dimension's underlying category structure on affective experience are needed. This protocol therefore focuses on the DHL and allows examination of CP. Based on the protocol presented in the video as an example, issues surrounding the methodology in the protocol and the use in "uncanny" research of stimuli drawn from morph continua to represent the DHL are discussed in the article that accompanies the video. The use of neuroimaging and morph stimuli to represent the DHL in order to disentangle brain regions neurally responsive to physical human-like similarity from those responsive to category change and category processing is briefly illustrated.
Revisionsplikten för små aktiebolag

“How does accounting firms handle the abolition of the audit requirement? ” Date: June 8 th 2012 Level: Bachelor thesis in Business, 15 ECTS Institution: School of Sustainable development of society and technology, Mälardalen University Authors: Rimon Khames Nazife Sejdija 18 th september 1989 20 th mars 1988 Title: How does accounting firms handle the abolition of the audit requirement? Tutor: Kent Trosander
Consumption of ultra-processed foods and associated sociodemographic factors in the USA between 2007 and 2012: evidence from a nationally representative cross-sectional study

Objectives To compare ultra-processed food consumption across sociodemographic groups and over time (2007–2008, 2009–2010, 2011–2012) in the USA. Design Cross-sectional study. Setting National Health and Nutrition Examination Survey (NHANES) 2007–2012. Participants All individuals aged ≥2 years with at least one 24-hour dietary recall were included (n=23 847). Main outcome measures Average dietary contribution of ultra-processed foods (expressed as a percentage of the total caloric value of the diet), obtained after classifying all food items according to extent and purpose of industrial food processing using NOVA classification. Data analysis Linear regression was used to evaluate the association between sociodemographic characteristics or NHANES cycles and dietary contribution of ultra-processed foods. Results Almost 60% of calories consumed in the period 2007–2012 came from ultra-processed foods. Consumption of ultra-processed foods decreased with age and income level, was higher for non-Hispanic whites or non-Hispanic blacks than for other race/ethnicity groups and lower for people with college than for lower levels of education, all differences being statistically significant. Overall contribution of ultra-processed foods increased significantly between NHANES cycles (nearly 1% point per cycle), the same being observed among males, adolescents and high school education-level individuals. Conclusions Ultra-processed food consumption in the USA in the period 2007–2012 was overall high, greater among non-Hispanic whites or non-Hispanic blacks, less educated, younger, lower-income strata and increased across time.
Skeletal Muscle Dysfunction in Chronic Obstructive Pulmonary Disease. What We Know and Can Do for Our Patients

&NA; Skeletal muscle dysfunction occurs in patients with chronic obstructive pulmonary disease (COPD) and affects both ventilatory and nonventilatory muscle groups. It represents a very important comorbidity that is associated with poor quality of life and reduced survival. It results from a complex combination of functional, metabolic, and anatomical alterations leading to suboptimal muscle work. Muscle atrophy, altered fiber type and metabolism, and chest wall remodeling, in the case of the respiratory muscles, are relevant etiological contributors to this process. Muscle dysfunction worsens during COPD exacerbations, rendering patients progressively less able to perform activities of daily living, and it is also associated with poor outcomes. Muscle recovery measures consisting of a combination of pulmonary rehabilitation, optimized nutrition, and other strategies are associated with better prognosis when administered in stable patients as well as after exacerbations. A deeper understanding of this process’ pathophysiology and clinical relevance will facilitate the use of measures to alleviate its effects and potentially improve patients’ outcomes. In this review, a general overview of skeletal muscle dysfunction in COPD is offered to highlight its relevance and magnitude to expert practitioners and scientists as well as to the average clinician dealing with patients with chronic respiratory diseases.
Automatic fault localization for client‐side JavaScript

JAVASCRIPT is a scripting language that plays a prominent role in web applications today. It is dynamic, loosely typed and asynchronous and is extensively used to interact with the Document Object Model (DOM) at runtime. All these characteristics make JAVASCRIPT code error‐prone; unfortunately, JAVASCRIPT fault localization remains a tedious and mainly manual task. Despite these challenges, the problem has received very limited research attention. This paper proposes an automated technique to localize JAVASCRIPT faults based on dynamic analysis, tracing and backward slicing of JAVASCRIPT code. This technique is capable of handling features of JAVASCRIPT code that have traditionally been difficult to analyse, including eval, anonymous functions and minified code. The approach is implemented in an open source tool called AUTOFLOX, and evaluation results indicate that it is capable of (1) automatically localizing DOM‐related JAVASCRIPT faults with high accuracy (over 96%) and no false‐positives and (2) isolating JAVASCRIPT faults in production websites and actual bugs from real‐world web applications. Copyright © 2015 John Wiley & Sons, Ltd.
Correction for Saintyves et al., Self-sustained lift and low friction via soft lubrication

APPLIED PHYSICAL SCIENCES Correction for “Self-sustained lift and low friction via soft lubrication,” by Baudouin Saintyves, Theo Jules, Thomas Salez, and L. Mahadevan, which appeared in issue 21, May 24, 2016, of Proc Natl Acad Sci USA (113:5847–5849; first published May 9, 2016; 10.1073/pnas.1525462113). The authors note that Fig. 2 and its corresponding legend appeared incorrectly. The corrected figure and its corrected legend appear below. This error does not affect the conclusions of the article. The authors thank Martin Essink, Anupam Pandey, and Jacco Snoeijer for a careful reanalysis of the data that led to the authors’ noticing this typographical mistake, and for discussions.
Evaluation of Compound Optical Interference in High-Content Screening

Compound optical interference remains an inherent problem in chemical screening and has been well documented for biochemical assays and less so for automated microscopy-based assays. It has also been the assumption that the latter should not suffer from such interference because of the washing steps involved in the process, thus eliminating the residual nonspecific compound effects. Instead, these compounds may have no relevance to the actual target, and as such, compound optical interference contributes to a number of false-positives, resulting in a high attrition rate during subsequent follow-up studies. In this report, we analyze the outcome of a high-content screen using enhanced green fluorescent protein as a reporter in a gain-of-function cell-based assay in search of modulators of the micro RNA (miRNA) biogenesis pathway. Using a previously validated image-based biosensor, we screened a diverse library collection of ~315,000 compounds covering natural and synthetic derivatives in which 1130 positives were identified to enhance green fluorescence expression. Lateral confirmation and dose-response studies revealed that all of these compounds were the result of optical interference and not specific inhibition of miRNA biogenesis. Here, we highlight the chemical classes that are susceptible to compound optical interference and discuss their implications in automated microscopy-based assays.
Adopting Meta-modelling for ITU-T Languages : Language Tool Prototypes as a by-Product of Language Specifications

Grammars have been used to describe computer languages since the age of the first programming languages. But the kind of languages that we use in modern engineering has changed drastically over the last decade. Modern computer languages come in a variety of forms and their characteristics demand more from a description technique than grammars can offer. In this paper, we examine two widely different approaches to language descriptions: grammars and object-oriented modelling. We argue about language requirements and that grammars struggle where object-oriented modelling promises new efficient ways to not only describe languages, but also prototype language tools. In this paper we briefly show the differences between grammars and meta-modelling and show how meta-modelling can be used to automatically develop language tool prototypes from language specifications.
Gold nanoparticle-based theranostics: disease diagnostics and treatment using a single nanomaterial

Nanotheranostics takes advantage of nanotechnology-based systems in order to diagnose and treat a specific disease. This approach is particularly relevant for personalized medicine, allowing the detection of a disease at an early stage, to direct a suitable therapy toward the target tissue based on the molecular profile of the altered phenotype, subsequently facilitating disease monitoring and following treatment. A tailored strategy also enables to reduce the off-target effects associated with universal treatments and improve the safety profile of a given treatment. The unique optical properties of gold nanoparticles, their ease of surface modification, and high surface-to-volume ratio have made them central players in this area. By combining imaging, targeting, and therapeutic agents in a single vehicle, these nanoconjugates are (ought to be) an important tool in the clinics. In this review, the multifunctionality of gold nanoparticles as theranostics agents will be highlighted, as well as the requirements before the translation of these nanoplatforms into routine clinical practice.
Exploiting Multiple Detections for Person Re-Identification

Re-identification systems aim at recognizing the same individuals in multiple cameras, and one of the most relevant problems is that the appearance of same individual varies across cameras due to illumination and viewpoint changes. This paper proposes the use of cumulative weighted brightness transfer functions (CWBTFs) to model these appearance variations. Different from recently proposed methods which only consider pairs of images to learn a brightness transfer function, we exploit such a multiple-frame-based learning approach that leverages consecutive detections of each individual to transfer the appearance. We first present a CWBTF framework for the task of transforming appearance from one camera to another. We then present a re-identification framework where we segment the pedestrian images into meaningful parts and extract features from such parts, as well as from the whole body. Jointly, both of these frameworks contribute to model the appearance variations more robustly. We tested our approach on standard multi-camera surveillance datasets, showing consistent and significant improvements over existing methods on three different datasets without any other additional cost. Our approach is general and can be applied to any appearance-based method.
Transposon‐driven transcription is a conserved feature of vertebrate spermatogenesis and transcript evolution

Spermatogenesis is associated with major and unique changes to chromosomes and chromatin. Here, we sought to understand the impact of these changes on spermatogenic transcriptomes. We show that long terminal repeats (LTRs) of specific mouse endogenous retroviruses (ERVs) drive the expression of many long non‐coding transcripts (lncRNA). This process occurs post‐mitotically predominantly in spermatocytes and round spermatids. We demonstrate that this transposon‐driven lncRNA expression is a conserved feature of vertebrate spermatogenesis. We propose that transposon promoters are a mechanism by which the genome can explore novel transcriptional substrates, increasing evolutionary plasticity and allowing for the genesis of novel coding and non‐coding genes. Accordingly, we show that a small fraction of these novel ERV‐driven transcripts encode short open reading frames that produce detectable peptides. Finally, we find that distinct ERV elements from the same subfamilies act as differentially activated promoters in a tissue‐specific context. In summary, we demonstrate that LTRs can act as tissue‐specific promoters and contribute to post‐mitotic spermatogenic transcriptome diversity.
Predictive power of abnormal electroencephalogram for post-cerebral infarction depression

Electroencephalography is a sensitive indicator for measuring brain condition, and can reflect early changes in brain function and severity of cerebral ischemia. However, it is not yet known whether electroencephalography can predict development of post-cerebral infarction depression. A total of 321 patients with ischemic stroke underwent electroencephalography and Hamilton Depression Rating Scale assessment to analyze the relationship between electroencephalography and post-cerebral infarction depression. Our results show that electroencephalograms of ischemic stroke patients with depression exhibit low-amplitude alpha activity and slow theta activity. In contrast, electroencephalograms of ischemic stroke patients without depression show fast beta activity and slow delta activity. These findings confirm that low-amplitude alpha activity and slow theta activity can be considered as independent predictors for post-cerebral infarction depression.
Excessive Daytime Sleepiness Independently Predicts Increased Cardiovascular Risk After Myocardial Infarction

Background Excessive daytime sleepiness (EDS), a common symptom among patients with sleep‐disordered breathing, is closely associated with the development of cardiovascular diseases, but its long‐term prognostic value is not completely understood. The aim of this study was to investigate whether EDS would be an independent prognostic factor after myocardial infarction. Methods and Results We prospectively recruited 112 post–myocardial infarction patients. The Epworth Sleepiness Scale was completed before polysomnography, and EDS was defined as a score ≥11. After exclusion of 8 patients who accepted treatment with continuous positive airway pressure, 104 patients were followed up for 48 months. The primary composite end point was major adverse cardiac events. Patients with EDS had higher rates of major adverse cardiac events (48.4% versus 27.4%, χ2=5.27, P=0.022) and reinfarction (29.0% versus 5.5%, χ2=13.51, P=0.0002) compared with those without EDS. In the Cox proportional hazards model, patients with EDS had 2.15 times (95% confidence interval, 1.08–4.18; P=0.030) higher crude risk of major adverse cardiac events, with prognostic significance persisting after adjusting for age, diabetes mellitus, depression, left ventricular ejection fraction, apnea–hypopnea index, and nocturnal nadir oxygen saturation (hazard ratio: 2.13, 95% confidence interval, 1.04–4.26, P=0.039). Furthermore, among participants with moderate to severe sleep‐disordered breathing, the presence of EDS was associated with higher risk of major adverse cardiac events than those without EDS, after adjusting for age and nadir oxygen saturation (hazard ratio: 3.17, 95% confidence interval, 1.22–7.76, P=0.019). Conclusions EDS may be an independent prognostic factor of adverse outcome in post–myocardial infarction patients with moderate to severe sleep‐disordered breathing. Evaluation of EDS may shed new light on risk stratification and identify treatment responders for this patient population.