julilili42/papers huggingface dataset:
Dissociable Contribution of Extrastriate Responses to Representational Enhancement of Gaze Targets
2021-06-30

Abstract Before saccadic eye movements, our perception of the saccade targets is enhanced. Changes in the visual representation of saccade targets, which presumably underlie this perceptual benefit, emerge even before the eye begins to move. This perisaccadic enhancement has been shown to involve changes in the response magnitude, selectivity, and reliability of visual neurons. In this study, we quantified multiple aspects of perisaccadic changes in the neural response, including gain, feature tuning, contrast response function, reliability, and correlated activity between neurons. We then assessed the contributions of these various perisaccadic modulations to the population's enhanced perisaccadic representation of saccade targets. We found a partial dissociation between the motor information, carried entirely by gain changes, and visual information, which depended on all three types of modulation. These findings expand our understanding of the perisaccadic enhancement of visual representations and further support the existence of multiple sources of motor modulation and visual enhancement within extrastriate visual cortex.
A physics perspective on lidar data assimilation for mobile robots
2021-06-30

Abstract This paper presents a new algorithm for lidar data assimilation relying on a new forward model. Current mapping algorithms suffer from multiple shortcomings, which can be related to the lack of clear forward model. In order to address these issues, we provide a mathematical framework where we show how the use of coarse model parameters results in a new data assimilation problem. Understanding this new problem proves essential to derive sound inference algorithms. We introduce a model parameter specifically tailored for lidar data assimilation, which closely relates to the local mean free path. Using this new model parameter, we derive its associated forward model and we provide the resulting mapping algorithm. We further discuss how our proposed algorithm relates to usual occupancy grid mapping. Finally, we present an example with real lidar measurements.
Fifth millennium BC miniature ceramic bottles from the south-eastern Prealps and Central Balkans: A multi-disciplinary approach to study their content and function
2021-06-30

Miniature ceramic bottles with perforated handles entered the pottery repertoire of different Late Neolithic and Chalcolithic communities across the south-eastern Prealps, south-western Transdanubia and the Balkans in the 5th millennium BC. It is hypothesised that these small bottles were personal items that could be hung around the neck or waist, possibly to contain cosmetics or for cultic purposes. The aim of this study was to understand the function of 14 of these miniature bottles recovered from sites attributed to the Lasinja Culture in the south-eastern Prealps and the Vinča Culture in the Central Balkans, by analysing the remains of their contents. A multi-method approach was applied using local high-resolution X-ray micro-diffraction (μ-XRD2) and micro-X-ray fluorescence (μ-XRF) to analyse visible residues in eight bottles, and Gas Chromatography-Mass Spectrometry (GC–MS) to test the absorbed lipid content in nine of them. The analysis showed that cerussite (lead carbonate) was the main component of the white material found in the bottle from Zgornje Radvanje, Slovenia. In the visible residues found in the bottles from Turnišče and Popava 1, the lead minerals plumbogummite and pyromorphite were identified as crystalline components. The identification of lead-containing minerals in this study coincides with the earliest use of lead in south-eastern Europe (ca. 4400–4300 BCE), as described in Hansen et al. (2019). Lipid analysis identified beeswax as the content of three of the vessels, which, together with the detection of lead minerals found in the same vessels, suggests its use as an organic binder, perhaps to form pigments as previously hypothesised, for cosmetic and/or medicinal purposes. This study represents the first application of multidisciplinary scientific methods on miniature bottles from the Lasinja and Vinča cultures in the south-eastern Prealps and Central Balkans. Significantly, this study pushes back the date for the use of lead-based cosmetic/medicinal products in North Africa and the Near East by more than a millennium, and in Europe by more than two millennia.
Systemic bacterial infections affect dendritic cell development and function
2021-06-30

Dendritic cells (DCs) are critical in host defense against infection. DC depletion is an early event in the course of sepsis that may impair the host defense mechanisms. Here, we addressed whether DC depletion and dysfunction are pathogen-independent, mediated via pattern recognition receptors, and are due to impaired DC development upon systemic infection with the Gram-negative bacterium Escherichia coli and the Gram-positive pathogen Staphylococcus aureus. Infection with E. coli and S. aureus led to reduced numbers of splenic DC subsets and of DC progenitors in the bone marrow (BM) with this effect persisting significantly longer in mice infected with S. aureus than with E. coli. The reduction of DC subsets and their progenitors was mainly TLR-independent as was the infection-induced monopoiesis. Moreover, de novo DC development was impaired in mice infected with S. aureus, and BM cells from E. coli or S. aureus infected mice favored macrophage differentiation in vitro. As a consequence of reduced DC numbers and their reduced expression of MHC II less CD4<sup>+</sup> and CD8<sup>+</sup> T cells, especially Th1 and IFN-γ producing CD8<sup>+</sup> T cells, could be detected in S. aureus compared to E. coli infected mice. These differences are reflected in the rapid killing of E. coli as opposed to an increase in bacterial load in S. aureus. In summary, our study supports the idea that systemic bacterial infections generally affect the number and development of DCs and thereby the T cell responses, but the magnitude is pathogen-dependent.
The paracaspase MALT1 in psoriasis
2021-06-30

Abstract Psoriasis is a frequent autoimmune-related skin disease, which involves various cell types such as T cells, keratinocytes and dendritic cells. Genetic variations, such as mutations of CARD14 , can promote the development of the disease. CARD14 mutations as well as the stimulation of immune and cytokine receptors activate the paracaspase MALT1, a potent activator of the transcription factors NF-κB and AP-1. The disease-promoting role of MALT1 for psoriasis is mediated by both its protease activity as well as its molecular scaffold function. Here, we review the importance of MALT1-mediated signaling and its therapeutic implications in psoriasis.
Mixture-of-Experts Variational Autoencoder for clustering and generating from similarity-based representations on single cell data
2021-06-30

Clustering high-dimensional data, such as images or biological measurements, is a long-standing problem and has been studied extensively. Recently, Deep Clustering has gained popularity due to its flexibility in fitting the specific peculiarities of complex data. Here we introduce the Mixture-of-Experts Similarity Variational Autoencoder (MoE-Sim-VAE), a novel generative clustering model. The model can learn multi-modal distributions of high-dimensional data and use these to generate realistic data with high efficacy and efficiency. MoE-Sim-VAE is based on a Variational Autoencoder (VAE), where the decoder consists of a Mixture-of-Experts (MoE) architecture. This specific architecture allows for various modes of the data to be automatically learned by means of the experts. Additionally, we encourage the lower dimensional latent representation of our model to follow a Gaussian mixture distribution and to accurately represent the similarities between the data points. We assess the performance of our model on the MNIST benchmark data set and challenging real-world tasks of clustering mouse organs from single-cell RNA-sequencing measurements and defining cell subpopulations from mass cytometry (CyTOF) measurements on hundreds of different datasets. MoE-Sim-VAE exhibits superior clustering performance on all these tasks in comparison to the baselines as well as competitor methods.
Brain signaling dynamics after vagus nerve stimulation
2021-06-30

Abstract The vagus nerve projects to a well-defined neural circuit via the nucleus tractus solitarii (NTS) and its stimulation elicits a wide range of metabolic, neuromodulatory, and behavioral effects. Transcutaneous vagus nerve stimulation (tVNS) has been established as a promising technique to non-invasively alter brain function. However, the precise dynamics elicited by tVNS in humans are still largely unknown. Here, we performed fMRI with concurrent right-sided tVNS (vs. sham) following a randomized cross-over design ( N =40). First, to unravel the temporal profile of tVNS-induced changes in the NTS, we compared fMRI time series to canonical profiles for stimulation ON and OFF cycles. Model comparisons indicated that NTS time series were best fit by block-wise shifts in signal amplitude with stimulation ON and OFF estimates being highly correlated. Therefore, we compared stimulation (ON + OFF) versus baseline phases and found that tVNS increased fMRI BOLD activation in the NTS, but this effect was dependent on sufficient temporal signal-to-noise ratio (tSNR) in the mask. Second, to identify the spatiotemporal evolution of tVNS-induced changes in the brain, we examined lagged co-activation patterns and phase coherence. In contrast to our hypothesis, tVNS did not alter dynamic functional connectivity after correction for multiple comparisons. Third, to establish a positive control for future research, we measured changes in gastric myoelectrical frequency via an electrogastrogram. Again, in contrast to our hypothesis, tVNS induced no changes in gastric frequency. Collectively, our study provides evidence that tVNS can perturb brain signaling in the NTS, but these effects are dependent on tSNR and require precise localization. In light of an absence of acute tVNS-induced effects on dynamic functional connectivity and gastric motility, we discuss which steps are necessary to advance future research on afferent and efferent effects of tVNS.
Comprehension and production of Kinyarwanda verbs in the Discriminative Lexicon
2021-06-30

How do speakers comprehend and produce complex words? In the theory ofthe Discriminative Lexicon this is hypothesized to be the results of mapping the phonology of whole word forms onto their semantics and vice versa, without recourse to morphemes. This raises the question whether this hypothesis also holds true in highly agglutinative languages, which are oǒten seen to exemplify the compositional nature of morphology. On the one hand, one could expect that the hypothesis for agglutinative languages is correct, since it remains unclear whether speakers are able to isolate the morphemes they need to achieve this. On the other hand, agglutinative languages have so many different words that it is not obvious how speakers can use their knowledge of words to comprehend and produce them.In this paper, we investigate comprehension and production of verbs in Kinyarwanda,an agglutinative Bantu language, by means of computational modeling within the theDiscriminative Lexicon, a theory of the mental lexicon, which is grounded in word andparadigm morphology, distributional semantics, error-driven learning, and uses insightsof psycholinguistic theories, and is implemented mathematically and computationallyas a shallow, two-layered network.In order to do this, we compiled a data set of 11528 verb forms and annotated for eachverb form its meaning and grammatical functions, and, additionally, we used our dataset to extract 573 verbs that are present in our full data set and for which meanings ofverbs are based on word embeddings. In order to assess comprehension and production of Kinyarwanda verbs, we fed both data sets into the Linear Discriminative Learningalgorithm, a two-layered, fully connected network. One layer represent the phonological form and the layer represents meaning. Comprehension is modeled as a mapping from phonology to meaning and production is modeled as a mapping from meaning to phonology. Both comprehension and production is learned with high accuracy in all data and in held-out data, both for the full data set, with manually annotated semantic features, and for the data set with meanings derived from word embeddings.Our findings provide support for the various hypotheses of the Discriminative Lexicon:Words are stored as wholes, meanings are a result of the distribution of words in utterances, comprehension and production can be successfully modeled from mappings from form to meaning and vice versa, which can be modeled in a shallow two-layered network, and these mappings are learned in by minimizing errors.
Analysis of Factors that Influenced the Participation Rates in Upper-secondary Vocational Schools in China
2021-06-30

Vocational education and training has been treated as a good pathway to smoothly promote youth from school to the workplace. However, for many reasons, the vocational education in Asian countries is not as popular as general education, and the participation rate in vocational schools is normally lower than it is in general schools. This paper builds a theoretical model about the factors that influence the participation rate in Chinese upper-secondary vocational schools and explains the possible related indicators through a regression analysis method. The results show that the most positive factor is related to the number of male students in vocational schools, followed by the number of female vocational teachers. Other staff in vocational schools also have a positive effect, while the youth unemployment rate and the number of men vocational teachers both have negative relationships with the participation rate. In China, it is possible to improve the rates of new entrants into vocational schools from the aspects of the gender issues, the training methods for vocational teachers, and the social recognition of vocational education.ract is compulsory. First sentence describes the nature or the background information on the field of study. Subsequent sentences provide the problem statement or objectives and scope of the research. Next sentences explain the methods and materials used in the work. Main results and important findings are then highlighted. Finally, a summary of conclusions is put forth. Length of abstract can be proportional to the length of the article.
Regulation of Platelet-Derived ADAM17: A Biomarker Approach for Breast Cancer?
2021-06-30

Tumor progression and metastasis are critically dependent on the tumor microenvironment. A disintegrin and metalloproteinase 17 (ADAM17) is associated with shedding of several substrates involved in tumor progression and known to be expressed by platelets of healthy donors and patients with solid tumors. Here, we report that platelet-derived ADAM17 (pADAM17) is regulated upon platelet activation of breast cancer patients, but not of healthy individuals. The observed downregulation of pADAM17 on platelets of cancer patients correlated with clinical parameters related to tumor progression including tumor stage and the occurrence of metastasis. Our data identify an association between platelet activation, modulation of platelet-derived ADAM17, and metastasis. In conclusion, we demonstrate that further development of pADAM17 as a liquid biomarker is warranted for monitoring disease progression in breast cancer.
When quantum state tomography benefits from willful ignorance
2021-06-30

Abstract We show that quantum state tomography with perfect knowledge of the measurement apparatus proves to be, in some instances, inferior to strategies discarding all information about the measurement at hand, as in the case of data pattern tomography. In those scenarios, the larger uncertainty about the measurement is traded for the smaller uncertainty about the reconstructed signal. This effect is more pronounced for minimal or nearly minimal informationally complete measurement settings, which are of utmost practical importance.
ppAURORA: Privacy Preserving Area Under Receiver Operating Characteristic and Precision-Recall Curves with Secure 3-Party Computation
2021-06-30

Computing an AUC as a performance measure to compare the quality of different machine learning models is one of the final steps of many research projects. Many of these methods are trained on privacy-sensitive data and there are several different approaches like -differential privacy, federated machine learning and methods based on cryptographic approaches if the datasets cannot be shared or evaluated jointly at one place. In this setting, it can also be a problem to compute the global performance measure like an AUC, since the labels might also contain privacy-sensitive information. There have been approaches based on -differential privacy to deal with this problem, but to the best of our knowledge, no exact privacy preserving solution has been introduced. In this paper, we propose an MPC-based framework, called \\fw{}, with private merging of sorted lists and novel methods for comparing two secret-shared values, selecting between two secret-shared values, converting the modulus, and performing division to compute the exact AUC as one could obtain on the pooled original test samples. With \\fw{} computation of the exact area under precision-recall curve and receiver operating characteristic curve is even possible when ties between prediction confidence values exist. To show the applicability of \\fw{}, we use it to evaluate a model trained to predict acute myeloid leukemia therapy response and we also assess its scalability via experiments on synthetic data. The experiments show that we efficiently compute exactly the same AUC with both evaluation metrics in a privacy preserving manner as one can obtain on the pooled test samples in the plaintext domain. Our solution provides security against semi-honest corruption of at most one of the servers performing the secure computation.
SOX11, CD70, and Treg cells configure the tumor immune microenvironment of aggressive mantle cell lymphoma
2021-07-01

Abstract Mantle cell lymphoma (MCL) is a mature B-cell neoplasm with a heterogeneous clinical and biological behavior. SOX11 oncogenic expression contributes to the aggressiveness of these tumors by different mechanisms, including tumor and stromal cell interactions. However, the precise composition of the immune cell microenvironment of MCL, its possible relationship to SOX11 expression, and how it may contribute to tumor behavior is not well known. Here, we performed an integrative transcriptome analysis of 730 immune-related genes combined with the immune cell phenotype analysis by immunohistochemistry in SOX11+ and SOX11− primary nodal MCL cases and non-neoplastic reactive lymph nodes. SOX11+ MCL had a significant lower T-cell intratumoral infiltration compared with negative cases. A reduced expression of MHCI/II-like and T-cell costimulation and signaling activation related transcripts was significantly associated with poor clinical outcome. Moreover, we identified CD70 as a SOX11 direct target gene, whose overexpression was induced in SOX11+, but not SOX11− tumor cells by CD40L in vitro. CD70 was overexpressed in primary SOX11+ MCL and it was associated with an immune unbalance of the tumor microenvironment characterized by increased number of effector regulatory T (Treg) cell infiltration, higher proliferation, and aggressive clinical course. CD27 was expressed with moderate to strong intensity in 76% of cases. Overall, our results suggest that SOX11 expression in MCL is associated with an immunosuppressive microenvironment characterized by CD70 overexpression in tumor cells, increased Treg cell infiltration and downmodulation of antigen processing, and presentation and T-cell activation that could promote MCL progression and represent a potential target for tailored therapies.
Effects of nintedanib by inclusion criteria for progression of interstitial lung disease
2021-07-01

Background The INBUILD trial investigated nintedanib versus placebo in patients with progressive fibrosing interstitial lung diseases (ILDs). We investigated the decline in forced vital capacity (FVC) in subgroups based on the inclusion criteria for ILD progression. Methods Subjects had a fibrosing ILD other than idiopathic pulmonary fibrosis and met the following criteria for ILD progression within the 24 months before screening despite management deemed appropriate in clinical practice: Group A, relative decline in FVC ≥10% predicted; Group B, relative decline in FVC ≥5–&lt;10% predicted with worsened respiratory symptoms and/or increased extent of fibrosis on high-resolution computed tomography (HRCT); Group C, worsened respiratory symptoms and increased extent of fibrosis on HRCT only. Results In the placebo group, the rates of FVC decline over 52 weeks in Groups A, B and C, respectively, were −241.9, −133.1 and −115.3 mL per year in the overall population (p=0.0002 for subgroup-by-time interaction) and −288.9, −156.2 and −100.1 mL per year among subjects with a usual interstitial pneumonia (UIP)-like fibrotic pattern on HRCT (p=0.0005 for subgroup-by-time interaction). Nintedanib had a greater absolute effect on reducing the rate of FVC decline in Group A than in Group B or C. However, the relative effect of nintedanib versus placebo was consistent across the subgroups (p&gt;0.05 for heterogeneity). Conclusions The inclusion criteria used in the INBUILD trial, based on FVC decline or worsening of symptoms and extent of fibrosis on HRCT, were effective at identifying patients with progressive fibrosing ILDs. Nintedanib reduced the rate of decline in FVC across the subgroups based on the inclusion criteria related to ILD progression.
Precision medicine and digital phenotyping: Digital medicine's way from more data to better health
2021-07-01

Precision medicine and digital phenotyping are two prominent data-based approaches within digital medicine. While precision medicine historically used primarily genetic data to find targeted treatment options, digital phenotyping relies on the usage of big data deriving from digital devices such as smartphones, wearables and other connected devices. This paper first focusses on the aspect of data type to explore differences and similarities between precision medicine and digital phenotyping. It outlines different ways of data collection and production and the consequences thereof. Second, it shows how these sorts of data influence dominant beliefs in the field: The field of precision medicine relying on the dominant understanding of ‘genetic determinism’ imported from genetics, digital phenotyping building on the logic of ‘data fundamentalism’. In the end, the analysis shows how digital data informs potentials as well as challenges of precision medicine and digital phenotyping: a better health care for (some) individuals connected with individualisation and responsibilisation for all, with a prognosed shift from reactive to preventive medicine. Additionally, data-based approaches might facilitate epistemological and ontological redirections for the whole field of medicine that will also affect knowledge production and a reassessment of the value of different types of knowledge (quantifiable vs. non-quantifiable) with all its consequences. Institutionally, it might lead to shifts in distribution of power to experts in big data related technologies, i.e. private companies.
Optically pumped magnetometers reveal fasciculations non-invasively
2021-07-01

We show that other forms of SA are not detectable with current OPM and propose necessary technical solutions to overcome this circumstance. Our results motivate to pursue OPM-MMG as a new clinical neurophysiological diagnostic.