Miracle-dz/UniFAR-FTUs huggingface dataset:
Recognition of Human Tumor Necrosis Factor α (TNF-α) by Therapeutic Antibody Fragment

Background: Human TNF-α is a cytokine involved in many disease-related cellular processes. Results: High affinity binding of therapeutic antibody (inhibitor) to native and molten globule-like TNF-α conformation is driven by specific noncovalent interactions. Conclusion: Binding-coupled conformational changes are crucial for antibody-TNF-α recognition. Significance: This work helps learn which forces drive unfolding of TNF-α and its recognition by monoclonal antibodies and how they affect TNF-α activity regulation. Human tumor necrosis factor α (TNF-α) exists in its functional state as a homotrimeric protein and is involved in inflammation processes and immune response of a human organism. Overproduction of TNF-α results in the development of chronic autoimmune diseases that can be successfully treated by inhibitors such as monoclonal antibodies. However, the nature of antibody-TNF-α recognition remains elusive due to insufficient understanding of its molecular driving forces. Therefore, we studied the energetics of binding of a therapeutic antibody fragment (Fab) to the native and non-native forms of TNF-α by employing calorimetric and spectroscopic methods. Global thermodynamic analysis of data obtained from the corresponding binding and urea-induced denaturation experiments has been supported by structural modeling. We demonstrate that the observed high affinity binding of Fab to TNF-α is an enthalpy-driven process due mainly to specific noncovalent interactions taking place at the TNF-α-Fab binding interface. It is coupled to entropically unfavorable conformational changes and accompanied by entropically favorable solvation contributions. Moreover, the three-state model analysis of TNF-α unfolding shows that at physiological concentrations, TNF-α may exist not only as a biologically active trimer but also as an inactive monomer. It further suggests that even small changes of TNF-α concentration could have a considerable effect on the TNF-α activity. We believe that this study sets the energetic basis for understanding of TNF-α inhibition by antibodies and its unfolding linked with the concentration-dependent activity regulation.
Hydroxytyrosol (HT) Analogs Act as Potent Antifungals by Direct Disruption of the Fungal Cell Membrane

Fungal infections constitute an emerging threat and a prevalent health problem due to increasing number of immunocompromised people and pharmacological or other treatments aiming at viral infections, cancer or allergies. Currently used antifungals suffer from inefficiency, toxic side effects and developing drug-resistance. Additionally, over the last two decades no new classes of antifungals have been approved, emphasizing the urgent need for developing a novel generation of antifungals. Here, we investigate the antifungal activity of a series of chemically synthesized Hydroxytyrosol (HT) analogs. HT is one of the major phenolic compounds in olive oil, shown to possess radical-scavenging antioxidant, antiproliferative, proapoptotic and anti-inflammatory activities. No previous report has studied HT analogs as antifungals. We show that specific analogs have broad and strong antifungal activity, significantly stronger than the parent compound HT. Using Aspergillus nidulans as an in vivo cellular model system, we show that antifungal HT analogs have an unprecedented efficiency in fungal plasma membrane destruction. Importantly, antifungal HT analogs did not show toxicity in a mammalian cell line, whereas no resistance to HT analogs was obtained by standard mutagenesis. Our results open the way for the development of a novel, efficient and safer class of antifungals.
PGA: Physics Guided and Adaptive Approach for Mobile Fine-Grained Air Pollution Estimation

Large-scale fine-grained air pollution information has both financial for city managers and health benefits for all city residents. Sensors installed on fleet of vehicles (like taxis) to collect air pollution data provides a low cost, low-maintenance and a potentially high coverage approach. The challenges here are: 1) Sensing coverage is sparse in some areas, and 2) it changes over time. This paper presents PGA, a physics guided and adaptive approach to estimate fine-grained air pollution with vehicle fleets. We combine the advantages of a physics guided model and a data-driven model to achieve high accuracy with high spatial-temporal resolution. To evaluate the system, we deploy our air pollution sensing hardware on 29 taxis in the city of Shenzhen, and collected around 26.3 million data samples within 14 days. The results show that our system achieves up to 4.0× reduction on average error compared to state-of-the-art approaches.
Vγ9Vδ2 T Cells as Strategic Weapons to Improve the Potency of Immune Checkpoint Blockade and Immune Interventions in Human Myeloma

The advent of immune checkpoint (ICP) blockade has introduced an unprecedented paradigm shift in the treatment of cancer. Though very promising, there is still a substantial proportion of patients who do not respond or develop resistance to ICP blockade. In vitro and in vivo models are eagerly needed to identify mechanisms to maximize the immune potency of ICP blockade and overcome primary and acquired resistance to ICP blockade. Vγ9Vδ2 T cells isolated from the bone marrow (BM) from multiple myeloma (MM) are excellent tools to investigate the mechanisms of resistance to PD-1 blockade and to decipher the network of mutual interactions between PD-1 and the immune suppressive tumor microenvironment (TME). Vγ9Vδ2 T cells can easily be interrogated to dissect the progressive immune competence impairment generated in the TME by the long-lasting exposure to myeloma cellss. BM MM Vγ9Vδ2 T cells are PD-1+ and anergic to phosphoantigen (pAg) stimulation; notably, single agent PD-1 blockade is insufficient to fully recover their anti-tumor activity in vitro indicating that additional players are involved in the anergy of Vγ9Vδ2 T cells. In this mini-review we will discuss the value of Vγ9Vδ2 T cells as investigational tools to improve the potency of ICP blockade and immune interventions in MM.
The development of a Core Outcome Set for medicines management interventions for people with dementia in primary care

BACKGROUND people with dementia (PWD), and their carers, face challenges with medicines management activities. As interventions to support medicines management for PWD are developed, consideration must be given to the outcomes chosen to measure their effectiveness. A Core Outcome Set (COS) is a minimum set of outcomes to be measured in all trials in a particular clinical area, which seeks to reduce heterogeneity of outcome reporting across trials. OBJECTIVE to develop a COS for trials assessing the effectiveness of medicines management interventions for PWD in primary care. METHODS a comprehensive list of outcomes was compiled through a systematic review and semi-structured interviews with PWD (n = 18), their carers (n = 15), community pharmacists (n = 15) and general practitioners (n = 15). These outcomes were rated by a Delphi panel (n = 52) on a nine-point Likert scale from 1 (limited importance) to 9 (critical) during three sequential rounds of questionnaire distribution. The Delphi panel comprised participants with expertise in dementia and medicines management, including academics and healthcare professionals. An outcome was eligible for inclusion in the COS if ≥70% of participants rated it critical and <15% of participants rated it of limited importance. RESULTS twenty-nine outcomes identified from the systematic review and stakeholder interviews were presented to the Delphi panel. Consensus was reached on 21 outcomes, of which the 7 most highly rated were recommended for inclusion in the COS. CONCLUSION this study used robust methodology to develop a COS for medicines management interventions for PWD. Future work should identify the most appropriate tools to measure these outcomes.
Variation in restricted and repetitive behaviors and interests relates to inhibitory control and shifting in children with autism spectrum disorder

Symptoms of restricted and repetitive behaviors and interests in autism are theoretically linked to executive functioning, which includes problem-solving abilities such as inhibition and cognitive flexibility. This study examined whether inhibition and flexibility are related to higher order restricted and repetitive behaviors and interests (e.g. circumscribed interests and ritualistic behavior) and sensorimotor behaviors (e.g. stereotyped and repetitive movements and sensory preoccupations) among 102 school-aged children with autism spectrum disorder who had cognitive abilities in the average or above average range. The ability to inhibit interfering information and shifting ability were related to higher order restricted and repetitive behaviors and interests, and each uniquely accounted for variance. This suggests that the ability to suppress interfering information as well as the ability to flexibly shift between patterns of responding is protective against higher order restricted and repetitive behaviors and interest symptoms in autism. In addition, the ability to proactively slow one’s reaction time in order to respond more carefully was related to sensorimotor restricted and repetitive behaviors. These results support the importance of distinguishing between higher order and sensorimotor symptoms due to their distinct relationships to executive functioning abilities.
Towards Sparse Hierarchical Graph Classifiers

Recent advances in representation learning on graphs, mainly leveraging graph convolutional networks, have brought a substantial improvement on many graph-based benchmark tasks. While novel approaches to learning node embeddings are highly suitable for node classification and link prediction, their application to graph classification (predicting a single label for the entire graph) remains mostly rudimentary, typically using a single global pooling step to aggregate node features or a hand-designed, fixed heuristic for hierarchical coarsening of the graph structure. An important step towards ameliorating this is differentiable graph coarsening---the ability to reduce the size of the graph in an adaptive, data-dependent manner within a graph neural network pipeline, analogous to image downsampling within CNNs. However, the previous prominent approach to pooling has quadratic memory requirements during training and is therefore not scalable to large graphs. Here we combine several recent advances in graph neural network design to demonstrate that competitive hierarchical graph classification results are possible without sacrificing sparsity. Our results are verified on several established graph classification benchmarks, and highlight an important direction for future research in graph-based neural networks.
Discrepancy in random hypergraph models

We study hypergraph discrepancy in two closely related random models of hypergraphs on $n$ vertices and $m$ hyperedges. The first model, $\mathcal{H}_1$, is when every vertex is present in exactly $t$ randomly chosen hyperedges. The premise of this is closely tied to, and motivated by the Beck-Fiala conjecture. The second, perhaps more natural model, $\mathcal{H}_2$, is when the entries of the $m \times n$ incidence matrix is sampled in an i.i. d. fashion, each with probability $p$. We prove the following: 1. In $\mathcal{H}_1$, when $\log^{10}n \ll t \ll \sqrt{n}$, and $m = n$, we show that the discrepancy of the hypergraph is almost surely at most $O(\sqrt{t})$. This improves upon a result of Ezra and Lovett for this range of parameters. 2. In $\mathcal{H}_2$, when $p= \frac{1}{2}$, and $n = \Omega(m \log m)$, we show that the discrepancy is almost surely at most $1$. This answers an open problem of Hoberg and Rothvoss.
How to aggregate Top-lists: Score based approximation schemes

We study the aggregation of partial rankings and give a PTAS for TOP-AGG, the problem of aggregating a collection of top-lists into a single full-ranking. When the input top-lists all have length less than some fixed constant, we provide an EPTAS. In terms of technical contributions, an important notion is the score of a candidate, namely, the number of times it has been selected by voters: low-score candidates can be sorted by score at the end of the output without deteriorating its quality by much. Another important observation is that an optimal output never differs too much from the sorted order, in the sense that in any optimal ranking, a candidate with score smaller than one fourth of the score of another candidate is ranked after that latter candidate.
The Potential of Quantum Computing and Machine Learning to Advance Clinical Research and Change the Practice of Medicine.

dmitry solenov, phd, (above), is in the department of physics, saint louis university, st. louis, Missouri. Jay Brieler, Md, and Jeffrey F. scherrer, phd, are in the department of Family and community Medicine, saint louis university school of Medicine, st. louis, Missouri. Contact: dmitry.solenov@slu.edu Introduction We may be at the advent of a revolution in computer applications in clinical care and medical research. Quantum computing can exponentially advance computational power and promises to usher in a new epoch in computing technology. Quantum computing compared to current computing could be what the first personal computer was to Charles Babbage’s 1833 primitive computer the ‘difference engine”.
Session-based Recommendation with Graph Neural Networks

The problem of session-based recommendation aims to predict user actions based on anonymous sessions. Previous methods model a session as a sequence and estimate user representations besides item representations to make recommendations. Though achieved promising results, they are insufficient to obtain accurate user vectors in sessions and neglect complex transitions of items. To obtain accurate item embedding and take complex transitions of items into account, we propose a novel method, i.e. Session-based Recommendation with Graph Neural Networks, SR-GNN for brevity. In the proposed method, session sequences are modeled as graphstructured data. Based on the session graph, GNN can capture complex transitions of items, which are difficult to be revealed by previous conventional sequential methods. Each session is then represented as the composition of the global preference and the current interest of that session using an attention network. Extensive experiments conducted on two real datasets show that SR-GNN evidently outperforms the state-of-the-art session-based recommendation methods consistently.
Ubiquitous Emotion Recognition with Multimodal Mobile Interfaces

In 1997 Rosalind Picard introduced fundamental concepts of affect recognition [1]. Since this time, multimodal interfaces such as Brain-computer interfaces (BCIs), RGB and depth cameras, physiological wearables, multimodal facial data and physiological data have been used to study human emotion. Much of the work in this field focuses on a single modality to recognize emotion. However, there is a wealth of information that is available for recognizing emotions when incorporating multimodal data. Considering this, the aim of this workshop is to look at current and future research activities and trends for ubiquitous emotion recognition through the fusion of data from various multimodal, mobile devices.
VIREL: A Variational Inference Framework for Reinforcement Learning

Applying probabilistic models to reinforcement learning (RL) enables the application of powerful optimisation tools such as variational inference to RL. However, existing inference frameworks and their algorithms pose significant challenges for learning optimal policies, e.g., the absence of mode capturing behaviour in pseudo-likelihood methods and difficulties learning deterministic policies in maximum entropy RL based approaches. We propose VIREL, a novel, theoretically grounded probabilistic inference framework for RL that utilises a parametrised action-value function to summarise future dynamics of the underlying MDP. This gives VIREL a mode-seeking form of KL divergence, the ability to learn deterministic optimal polices naturally from inference and the ability to optimise value functions and policies in separate, iterative steps. In applying variational expectation-maximisation to VIREL we thus show that the actor-critic algorithm can be reduced to expectation-maximisation, with policy improvement equivalent to an E-step and policy evaluation to an M-step. We then derive a family of actor-critic methods from VIREL, including a scheme for adaptive exploration. Finally, we demonstrate that actor-critic algorithms from this family outperform state-of-the-art methods based on soft value functions in several domains.
MACRO: A Meta-Algorithm for Conditional Risk Minimization

We study conditional risk minimization (CRM), i.e. the problem of learning a hypothesis of minimal risk for prediction at the next step of sequentially arriving dependent data. Despite it being a fundamental problem, successful learning in the CRM sense has so far only been demonstrated using theoretical algorithms that cannot be used for real problems as they would require storing all incoming data. In this work, we introduce MACRO, a meta-algorithm for CRM that does not suffer from this shortcoming, but nevertheless offers learning guarantees. Instead of storing all data it maintains and iteratively updates a set of learning subroutines. With suitable approximations, MACRO applied to real data, yielding improved prediction performance compared to traditional non-conditional learning.
Lessons from Vienna: stakeholder perceptions of functional electrical stimulation technology and a conceptual model for practice

Abstract Aim: Functional electrical stimulation (FES) is a technology that can be used on paralyzed muscles to allow them to move. It has been used in populations with muscle paralysis or weakness for exercise, such as spinal cord injury (SCI) and multiple sclerosis. In order to improve technology, it is vital to understand from a qualitative perspective, issues surrounding device development and implementation. Materials and Methods: In 2016, a study was conducted at the Medical University of Vienna that sought to unravel perspectives of FES exercise from the perspective of clinicians, engineers and researchers. Semi-structured, qualitative interviews were conducted on a sample of participants from the conference (n = 22). Interviews were transcribed verbatim, and text data were analysed. Results: Following this analysis, a conceptual model of FES application in the home environment was derived. We show that the likelihood of continuing FES over time may be influenced by expectations and initial education, as perceived by stakeholders. Conclusion: This model provides a tool by which researchers or clinicians may implement FES in the home environment and may assist in the increased uptake of FES exercise at home for people who may reap benefits from its use. Implications for Rehabilitation Functional electrical stimulation (FES) is a technology that enables individuals with paralysis, such as Spinal Cord Injury or Multiple Sclerosis, to exercise. Motivation and support networks, along with adequate initial education, are essential should patients be able to successfully use FES for exercise. There are unique issues associated with performing FES in the home, and compliance may be influenced by how patients perceive FES with regard to providing benefits, and what their initial expectations are. Communication and education are essential for all parties involved in the provision of FES treatment, to ensure successful treatment with FES at home.
Learning Shared Dynamics with Meta-World Models

Humans have consciousness as the ability to perceive events and objects: a mental model of the world developed from the most impoverished of visual stimuli, enabling humans to make rapid decisions and take actions. Although spatial and temporal aspects of different scenes are generally diverse, the underlying physics among environments still work the same way, thus learning an abstract description of shared physical dynamics helps human to understand the world. In this paper, we explore building this mental world with neural network models through multi-task learning, namely the meta-world model. We show through extensive experiments that our proposed meta-world models successfully capture the common dynamics over the compact representations of visually different environments from Atari Games. We also demonstrate that agents equipped with our meta-world model possess the ability of visual self-recognition, i.e., recognize themselves from the reflected mirrored environment derived from the classic mirror self-recognition test (MSR).
Endometrial mesenchymal stem/stromal cell modulation of T cell proliferation.

Perivascular mesenchymal stem/stromal cells can be isolated from the human endometrium using the surface marker SUSD2, and are being investigated for use in tissue repair. Mesenchymal stem/stromal cells from other tissues modulate T cell responses via mechanisms including interleukin-10, prostaglandin E2, TGF-β1 and regulatory T cells. Animal studies demonstrate that endometrial mesenchymal stem/stromal cells can also modify immune responses to implanted mesh, but the mechanism/s they employ have not been explored. We examined the immunomodulatory properties of human endometrial mesenchymal stem/stromal cells on lymphocyte proliferation using mouse splenocyte cultures. Endometrial mesenchymal stem/stromal cells inhibited mitogen-induced lymphocyte proliferation in vitro in a dose-dependent manner. Inhibition of lymphocyte proliferation was not affected by blocking the mouse interleukin-10 receptor or inhibiting prostaglandin production. Endometrial mesenchymal stem/stromal cells continued to restrain lymphocyte proliferation in the presence of an inhibitor of TGF-β receptors, despite a reduction in regulatory T cells. Thus the in vitro inhibition of mitogen-induced lymphocyte proliferation by endometrial mesenchymal stem/stromal cells occurs by a mechanism distinct from the interleukin-10, prostaglandin E2, TGF-β1, and regulatory T cell-mediated mechanisms employed by MSC from other tissues. eMSC were shown to produce interleukin-17A and Dickkopf-1 which may contribute to their immunomodulatory properties. In contrast to MSC from other sources, systemic administration of endometrial mesenchymal stem/stromal cells did not inhibit swelling in a T cell-mediated model of skin inflammation. We conclude that, while endometrial mesenchymal stem/stromal cells can modify immune responses, their immunomodulatory repertoire may not be sufficient to restrain some T cell-mediated inflammatory events.
Integrated Surface and Subsurface Hydrological Modeling with Snowmelt and Pore Water Freeze–Thaw

For the simulation of winter hydrological processes a gap in the availability of flow models existed: one either had the choice between (1) physically‐based and fully‐integrated, but computationally very intensive, or (2) simplified and compartamentalized, but computationally less expensive, simulators. To bridge this gap, we here present the integration of a computationally efficient representation of winter hydrological processes (snowfall, snow accumulation, snowmelt, pore water freeze–thaw) in a fully‐integrated surface water‐groundwater flow model. This allows the efficient simulation of catchment‐scale hydrological processes in locations significantly influenced by winter processes. Snow accumulation and snowmelt are based on the degree‐day method and pore water freeze–thaw is calculated with a vertical heat conduction approach. This representation of winter hydrological processes is integrated into the fully‐coupled surface water‐groundwater flow model HydroGeoSphere. A benchmark for pore water freeze–thaw as well as two illustrative examples are provided.