Developmental dyscalculia: compensatory mechanisms in left intraparietal regions in response to nonsymbolic magnitudes

BACKGROUND: Functional magnetic resonance imaging (fMRI) studies investigating the neural mechanisms underlying developmental dyscalculia are scarce and results are thus far inconclusive. Main aim of the present study is to investigate the neural correlates of nonsymbolic number magnitude processing in children with and without dyscalculia. METHODS: 18 children (9 with dyscalculia) were asked to solve a non-symbolic number magnitude comparison task (finger patterns) during brain scanning. For the spatial control task identical stimuli were employed, instructions varying only (judgment of palm rotation). This design enabled us to present identical stimuli with identical visual processing requirements in the experimental and the control task. Moreover, because numerical and spatial processing relies on parietal brain regions, task-specific contrasts are expected to reveal true number-specific activations. RESULTS: Behavioral results during scanning reveal that despite comparable (almost at ceiling) performance levels, task-specific activations were stronger in dyscalculic children in inferior parietal cortices bilaterally (intraparietal sulcus, supramarginal gyrus, extending to left angular gyrus). Interestingly, fMRI signal strengths reflected a group x task interaction: relative to baseline, controls produced significant deactivations in (intra)parietal regions bilaterally in response to number but not spatial processing, while the opposite pattern emerged in dyscalculics. Moreover, beta weights in response to number processing differed significantly between groups in left - but not right - (intra)parietal regions (becoming even positive in dyscalculic children). CONCLUSION: Overall, findings are suggestive of (a) less consistent neural activity in right (intra)parietal regions upon processing nonsymbolic number magnitudes; and (b) compensatory neural activity in left (intra)parietal regions in developmental dyscalculia.(VLID)218888

Polyoxazoline-Based Nanovaccine Synergizes with Tumor-Associated Macrophage Targeting and Anti-PD-1 Immunotherapy against Solid Tumors

Nanovaccines; Tumor immune microenvironment; Tumor-associated macrophagesNanovacunes; Microambient immune tumoral; Macròfags associats al tumorNanovacunas; Microambiente inmune tumoral; Macrófagos asociados al tumorImmune checkpoint blockade reaches remarkable clinical responses. However, even in the most favorable cases, half of these patients do not benefit from these therapies in the long term. It is hypothesized that the activation of host immunity by co-delivering peptide antigens, adjuvants, and regulators of the transforming growth factor (TGF)-β expression using a polyoxazoline (POx)-poly(lactic-co-glycolic) acid (PLGA) nanovaccine, while modulating the tumor-associated macrophages (TAM) function within the tumor microenvironment (TME) and blocking the anti-programmed cell death protein 1 (PD-1) can constitute an alternative approach for cancer immunotherapy. POx-Mannose (Man) nanovaccines generate antigen-specific T-cell responses that control tumor growth to a higher extent than poly(ethylene glycol) (PEG)-Man nanovaccines. This anti-tumor effect induced by the POx-Man nanovaccines is mediated by a CD8+-T cell-dependent mechanism, in contrast to the PEG-Man nanovaccines. POx-Man nanovaccine combines with pexidartinib, a modulator of the TAM function, restricts the MC38 tumor growth, and synergizes with PD-1 blockade, controlling MC38 and CT26 tumor growth and survival. This data is further validated in the highly aggressive and poorly immunogenic B16F10 melanoma mouse model. Therefore, the synergistic anti-tumor effect induced by the combination of nanovaccines with the inhibition of both TAM- and PD-1-inducing immunosuppression, holds great potential for improving immunotherapy outcomes in solid cancer patients.Funding: R.S.-F. and H.F.F. thank the following funding agencies for their generous support: The project that gave rise to these results has received funding from the “la Caixa” Foundation under the grant agreements LCF/PR/HR22/52420016, LCF/PR/HR19/52160021, and LCF/TR/CD20/52700005 (R.S.-F. and H.F.F). H.F.F thanks the generous financial support from The Fundação para a Ciência e Tecnologia-Ministério da Ciência, Tecnologia e Ensino Superior (FCT-MCTES) (EXPL/MED-QUI/1316/2021, PTDC/BTM-SAL/4350/2021, UTAP-EXPL/NPN/0041/2021, UIDB/04138/2020, UIDP/04138/2020). R.S.-F. thanks to the European Research Council (ERC) PoC Grant Agreement no. 101113390 and ERC Advanced Grant Agreement no. 835227, the Israel Science Foundation (1969/18), the Melanoma Research Alliance (Established Investigator Award no. 615808 to R.S.-F.), the Israel Cancer Research Fund (ICRF) Professorship award (no. PROF-18-682), the Morris Kahn Foundation. B.C. is supported by the FCT-MCTES (Ph.D. Fellowship SFRH/BD/131969/2017). The authors also acknowledge the NIH Tetramer Core Facility for the provision of Adpgk tetramers, in addition to the Comparative Pathology Unit of IMM and the Histopathology Facility of IGC for supporting the histopathological study

Authigenic minerals reflect microbial control on pore waters in a ferruginous analogue

Ferruginous conditions prevailed in the oceans through much of Earth’s history. However, minerals recording these conditions remain difficult to interpret in terms of biogeochemical processes prior to lithification. In Lake Towuti, Indonesia, ferruginous sediments are deposited under anoxic sulfate-poor conditions similar to the Proterozoic oceans, allowing the study of mineralogical (trans)formations during microbial diagenesis. Comprehensive pore water geochemistry, high resolution geochemical core profiles, and electron microscopy of authigenic minerals revealed in situ formation of magnetite, millerite, and abundant siderite and vivianite along a 100 m long sequence. Framboidal magnetites represent primary pelagic precipitates, whereas millerite, a sulfide mineral often overlooked under sulfate-poor conditions, shows acicular aggregates entangled with siderite and vivianite resulting from saturated pore waters and continuous growth during burial. These phases act as biosignatures of microbial iron and sulfate reduction, fermentation and methanogenesis, processes clearly traceable in pore water profiles. Variability in metal and organic substrates attests to environment driven processes, differentially sustaining microbial processes along the stratigraphy. Geochemical profiles resulting from microbial activity over 200 kyr after deposition provide constraints on the depth and age of mineral formation within ferruginous records

Investigating Paraphrasing-Based Data Augmentation for Task-Oriented Dialogue Systems

With synthetic data generation, the required amount of human-generated training data can be reduced significantly. In this work, we explore the usage of automatic paraphrasing models such as GPT-2 and CVAE to augment template phrases for task-oriented dialogue systems while preserving the slots. Additionally, we systematically analyze how far manually annotated training data can be reduced. We extrinsically evaluate the performance of a natural language understanding system on augmented data on various levels of data availability, reducing manually written templates by up to 75% while preserving the same level of accuracy. We further point out that the typical NLG quality metrics such as BLEU or utterance similarity are not suitable to assess the intrinsic quality of NLU paraphrases, and that public task-oriented NLU datasets such as ATIS and SNIPS have severe limitations

Towards Foundation Models for Relational Databases Vision Paper

Tabular representation learning has recently gained a lot of attention. However, existing approaches only learn a representation from a single table, and thus ignore the potential to learn from the full structure of relational databases, including neighboring tables that can contain important information for a contextualized representation. Moreover, current models are significantly limited in scale, which prevents that they learn from large databases. In this paper, we thus introduce our vision of relational representation learning, that can not only learn from the full relational structure, but also can scale to larger database sizes that are commonly found in real-world. Moreover, we also discuss opportunities and challenges we see along the way to enable this vision and present initial very promising results. Overall, we argue that this direction can lead to foundation models for relational databases that are today only available for text and images

Investigating User Radicalization: A Novel Dataset for Identifying Fine-Grained Temporal Shifts in Opinion

There is an increasing need for the ability to model fine-grained opinion shifts of social media users, as concerns about the potential polarizing social effects increase. However, the lack of publicly available datasets that are suitable for the task presents a major challenge. In this paper, we introduce an innovative annotated dataset for modeling subtle opinion fluctuations and detecting fine-grained stances. The dataset includes a sufficient amount of stance polarity and intensity labels per user over time and within entire conversational threads, thus making subtle opinion fluctuations detectable both in long term and in short term. All posts are annotated by non-experts and a significant portion of the data is also annotated by experts. We provide a strategy for recruiting suitable non-experts. Our analysis of the inter-annotator agreements shows that the resulting annotations obtained from the majority vote of the non-experts are of comparable quality to the annotations of the experts. We provide analyses of the stance evolution in short term and long term levels, a comparison of language usage between users with vacillating and resolute attitudes, and fine-grained stance detection baselines

WannaDB: Ad-hoc SQL Queries over Text Collections

In this paper, we propose a new system called WannaDB that allows users to interactively perform structured explorations of text collections in an ad-hoc manner. Extracting structured data from text is a classical problem where a plenitude of approaches and even industry-scale systems already exists. However, these approaches lack in the ability to support the ad-hoc exploration of texts using structured queries. The main idea of WannaDB is to include user interaction to support ad-hoc SQL queries over text collections using a new two-phased approach. First, a superset of information nuggets from the texts is extracted using existing extractors such as named entity recognizers. Then, the extractions are interactively matched to a structured table definition as requested by the user based on embeddings. In our evaluation, we show that WannaDB is thus able to extract structured data from a broad range of (real-world) text collections in high quality without the need to design extraction pipelines upfront