Miracle-dz/UniFAR-FTUs huggingface dataset:
A novel bispecific antibody for EGFR-directed blockade of the PD-1/PD-L1 immune checkpoint

ABSTRACT PD-L1-blocking antibodies produce significant clinical benefit in selected cancer patients by reactivating functionally-impaired antigen-experienced anticancer T cells. However, the efficacy of current PD-L1-blocking antibodies is potentially reduced by ‘on-target/off-tumor’ binding to PD-L1 widely expressed on normal cells. This lack of tumor selectivity may induce a generalized activation of all antigen-experienced T cells which may explain the frequent occurrence of autoimmune-related adverse events during and after treatment. To address these issues, we constructed a bispecific antibody (bsAb), designated PD-L1xEGFR, to direct PD-L1-blockade to EGFR-expressing cancer cells and to more selectively reactivate anticancer T cells. Indeed, the IC50 of PD-L1xEGFR for blocking PD-L1 on EGFR+ cancer cells was ∼140 fold lower compared to that of the analogous PD-L1-blocking bsAb PD-L1xMock with irrelevant target antigen specificity. Importantly, activation status, IFN-γ production, and oncolytic activity of anti-CD3xanti-EpCAM-redirected T cells was enhanced when cocultured with EGFR-expressing carcinoma cells. Similarly, the capacity of PD-L1xEGFR to promote proliferation and IFN-γ production by CMVpp65-directed CD8+ effector T cells was enhanced when cocultured with EGFR-expressing CMVpp65-transfected cancer cells. In contrast, the clinically-used PD-L1-blocking antibody MEDI4736 (durvalumab) promoted T cell activation indiscriminate of EGFR expression on cancer cells. Additionally, in mice xenografted with EGFR-expressing cancer cells 111In-PD-L1xEGFR showed a significantly higher tumor uptake compared to 111In-PD-L1xMock. In conclusion, PD-L1xEGFR blocks the PD-1/PD-L1 immune checkpoint in an EGFR-directed manner, thereby promoting the selective reactivation of anticancer T cells. This novel targeted approach may be useful to enhance efficacy and safety of PD-1/PD-L1 checkpoint blockade in EGFR-overexpressing malignancies.
Laplace Inference for Multi-fidelity Gaussian Process Classification

In this paper we address a classification problem where two sources of labels with different levels of fidelity are available. Our approach is to combine data from both sources by applying a co-kriging schema on latent functions, which allows the model to account item-dependent labeling discrepancy. We provide an extension for Laplace inference for Gaussian process classification, that takes into account multi-fidelity data. We evaluate the proposed method on real and synthetic datasets and show that it is more resistant to different levels of discrepancy between sources, than other approaches for data fusion. Our method can provide accuracy/cost trade-off for a number of practical tasks such as crowd-sourced data annotation and feasibility regions construction in engineering design.
A novel real‐time computational framework for detecting catheters and rigid guidewires in cardiac catheterization procedures

Purpose Catheters and guidewires are used extensively in cardiac catheterization procedures such as heart arrhythmia treatment (ablation), angioplasty, and congenital heart disease treatment. Detecting their positions in fluoroscopic X‐ray images is important for several clinical applications, for example, motion compensation, coregistration between 2D and 3D imaging modalities, and 3D object reconstruction. Methods For the generalized framework, a multiscale vessel enhancement filter is first used to enhance the visibility of wire‐like structures in the X‐ray images. After applying adaptive binarization method, the centerlines of wire‐like objects were extracted. Finally, the catheters and guidewires were detected as a smooth path which is reconstructed from centerlines of target wire‐like objects. In order to classify electrode catheters which are mainly used in electrophysiology procedures, additional steps were proposed. First, a blob detection method, which is embedded in vessel enhancement filter with no additional computational cost, localizes electrode positions on catheters. Then the type of electrode catheters can be recognized by detecting the number of electrodes and also the shape created by a series of electrodes. Furthermore, for detecting guiding catheters or guidewires, a localized machine learning algorithm is added into the framework to distinguish between target wire objects and other wire‐like artifacts. The proposed framework were tested on total 10,624 images which are from 102 image sequences acquired from 63 clinical cases. Results Detection errors for the coronary sinus (CS) catheter, lasso catheter ring and lasso catheter body are 0.56 ± 0.28 mm, 0.64 ± 0.36 mm, and 0.66 ± 0.32 mm, respectively, as well as success rates of 91.4%, 86.3%, and 84.8% were achieved. Detection errors for guidewires and guiding catheters are 0.62 ± 0.48 mm and success rates are 83.5%. Conclusion The proposed computational framework do not require any user interaction or prior models and it can detect multiple catheters or guidewires simultaneously and in real‐time. The accuracy of the proposed framework is sub‐mm and the methods are robust toward low‐dose X‐ray fluoroscopic images, which are mainly used during procedures to maintain low radiation dose.
Chronic Exposure to Sodium Fluoride Triggers Oxidative Biochemistry Misbalance in Mice: Effects on Peripheral Blood Circulation

The excessive fluoride (F) exposure is associated with damage to cellular processes of different tissue types, due to changes in enzymatic metabolism and breakdown of redox balance. However, few studies evaluate doses of F compatible with human consumption. Thus, this study evaluated the effects of chronic exposure to sodium fluoride (NaF) on peripheral blood of mice from the evaluation of biochemical parameters. The animals were divided into three groups (n = 10) and received three concentrations of NaF in the drinking water for 60 days: 0 mg/L F, 10 mg/L F, and 50 mg/L F. The blood was then collected for trolox equivalent antioxidant capacity (TEAC), thiobarbituric acid reactive substances (TBARS), concentrations of nitric oxide (NO), superoxide dismutase (SOD), catalase (CAT), and reduced glutathione (GSH). The results showed that doses of 10 mg/L F and 50 mg/L F were able to increase TBARS concentration and decrease NO levels and CAT activity in the blood, but there was no statistical difference for SOD levels. The 50 mg/L F group showed an increase in TEAC levels and a decrease in the GSH content when compared to the control group. In this way, oxidative changes in blood from chronic exposure to F, especially at the highest dose, indicate that F may be a toxic agent and, therefore, the long-term exposure to excessive doses should be avoided.
Heterogeneous predisposing factors and etiology in uvula edema patients Barbarroja-Escudero

Barbarroja-Escudero J, Asúnsolo-Del-Barco A, Sánchez-González MJ, RodríguezRodríguez M, Alvarez-Mon M Servicio de Enfermedades del Sistema Inmune−Alergia, Hospital Universitario Príncipe de Asturias, Departamento de Medicina y Especialidades Médicas, Facultad de Medicina y Ciencias de la Salud, Universidad de Alcalá, Alcalá de Henares (Madrid), Spain. Department of Surgery, Medical and Social Sciences, University of Alcala, Alcalá de Henares (Madrid), Spain.
Effects of magnetostimulation on muscle activity and pain in edentulous adults with temporomandibular disorders.

INTRODUCTION Multifactorial aetiologies of painful temporomandibular disorders (TMD) have an impact on correct diagnosis and consequently prevent proper treatment. AIM OF THE STUDY The aim of the study was to evaluate the effect of magnetic stimulation on electromyographic activity in temporal muscles and masseters in patients using occlusal splints. MATERIALS AND METHODS The examined group consisted of 40 edentulous patients with TMD. The patients were examined based on Helkimo Index. Next, electromyographic activity of the temporal muscle and masseter were investigated using 8-channel surface electromyography. All patients received acrylic occlusal splints for 12 weeks. The group qualified for the study included 20 randomized patients, whose therapy was additionally carried out by extremely low-frequency magnetic fields for a period of 21 days. Following examinations were conducted after 3, 6 and 12 weeks with surface electromyography recording of the examined muscles. Patients received occlusal splint corrections using the T-Scan III system. The clinical evaluation of TMD was analysed using Helkimo index and VAS scale before and after the treatment. All the data were analysed using Statistica 12.5 PL. RESULTS Patients with combination therapy had lower asymmetry of temporal muscle activity. CONCLUSIONS Combination therapy using magnetic stimulation reduced intensity of pain in patients with TMD and decreased values of the Helkimo indices.
Wolbachia both aids and hampers the performance of spider mites on different host plants

In the last decades, many studies had revealed the potential role of arthropod bacterial endosymbionts in shaping the host range of generalist herbivores and their performance on different host plants, which, in turn, might affect endosymbiont distribution in herbivores populations. We tested this by measuring the prevalence of endosymbionts in natural populations of the generalist spider mite Tetranychus urticae on different host plants. Focusing on Wolbachia, we then analysed how symbionts affected mite life-history traits on the same host-plants in the laboratory. Overall, the prevalences of Cardinium and Rickettsia were low, whereas that of Wolbachia was high, with the highest values on bean and eggplant and the lowest on purple, tomato and zuchini. Although most mite life-history traits were affected by the plant species only, Wolbachia infection was detrimental for egg hatching rate on purple and zucchini, and led to a more female-biased sex ratio on purple and eggplant. These results suggest that endosymbionts may affect the host range of polyphagous herbivores, both by aiding and hampering their performance, depending on the host plant and on the life-history trait that affects performance the most. Conversely, endosymbiont spread may be facilitated or hindered by the plants on which infected herbivores occur.
Balance in signed networks

We consider signed networks in which connections or edges can be either positive (friendship, trust, alliance) or negative (dislike, distrust, conflict). Early literature in graph theory theorized that such networks should display "structural balance," meaning that certain configurations of positive and negative edges are favored and others are disfavored. Here we propose two measures of balance in signed networks based on the established notions of weak and strong balance, and we compare their performance on a range of tasks with each other and with previously proposed measures. In particular, we ask whether real-world signed networks are significantly balanced by these measures compared to an appropriate null model, finding that indeed they are, by all the measures studied. We also test our ability to predict unknown signs in otherwise known networks by maximizing balance. In a series of cross-validation tests we find that our measures are able to predict signs substantially better than chance.
SCORES: Shape Composition with Recursive Substructure Priors

We introduce SCORES, a recursive neural network for shape composition. Our network takes as input sets of parts from two or more source 3D shapes and a rough initial placement of the parts. It outputs an optimized part structure for the composed shape, leading to high-quality geometry construction. A unique feature of our composition network is that it is not merely learning how to connect parts. Our goal is to produce a coherent and plausible 3D shape, despite large incompatibilities among the input parts. The network may significantly alter the geometry and structure of the input parts and synthesize a novel shape structure based on the inputs, while adding or removing parts to minimize a structure plausibility loss. We design SCORES as a recursive autoencoder network. During encoding, the input parts are recursively grouped to generate a root code. During synthesis, the root code is decoded, recursively, to produce a new, coherent part assembly. Assembled shape structures may be novel, with little global resemblance to training exemplars, yet have plausible substructures. SCORES therefore learns a hierarchical substructure shape prior based on per-node losses. It is trained on structured shapes from ShapeNet, and is applied iteratively to reduce the plausibility loss.We showresults of shape composition from multiple sources over different categories of man-made shapes and compare with state-of-the-art alternatives, demonstrating that our network can significantly expand the range of composable shapes for assembly-based modeling.
Extending Neural Generative Conversational Model using External Knowledge Sources

The use of connectionist approaches in conversational agents has been progressing rapidly due to the availability of large corpora. However current generative dialogue models often lack coherence and are content poor. This work proposes an architecture to incorporate unstructured knowledge sources to enhance the next utterance prediction in chit-chat type of generative dialogue models. We focus on Sequence-to-Sequence (Seq2Seq) conversational agents trained with the Reddit News dataset, and consider incorporating external knowledge from Wikipedia summaries as well as from the NELL knowledge base. Our experiments show faster training time and improved perplexity when leveraging external knowledge.
Icaritin Provokes Serum Thrombopoietin and Downregulates Thrombopoietin/MPL of the Bone Marrow in a Mouse Model of Immune Thrombocytopenia

Immune thrombocytopenia (ITP) is a common acquired autoimmune disease, and thrombopoietin (TPO) is an important cytokine that regulates the production of megakaryocytes and platelets. We have identified a biologically active component, icaritin, from a Chinese herba epimedii extract. Icaritin promotes platelet production and regulates T cell polarization, but its mechanism is not clear. In this study, the BALB/c mouse model of ITP was established by injection of an antiplatelet antibody every other day for seven total times. The antiplatelet sera were derived from guinea pigs immunized with the platelets of BALB/c mice. Mice with ITP were treated with icaritin at low, moderate, or high doses of 4.73, 9.45, and 18.90 mg/kg, respectively, for fourteen consecutive days. The present study shows that icaritin can significantly increase peripheral blood platelet counts and thrombocytocrit, increase the TPO level in serum, attenuate splenomegaly, and reduce the abnormal proliferation of megakaryocytes in the spleen and bone marrow. Icaritin can also downregulate the expression of bone marrow TPO, myeloproliferative leukemia virus oncogene (MPL), and p-Stat3. Our results suggest that icaritin can significantly improve the health of mice with ITP via possible downregulation of p-Stat3 expression in the JAK2/Stat3 phosphorylation signaling pathway and regulation of bone marrow TPO/MPL metabolism.
Who Is This “We” You Speak of? Grounding Activist Identity in Social Psychology

What is an activist identity? Prior answers have focused almost exclusively on collective identity, without (a) considering the possibility of role-based identities or (b) grounding collective identities in broader social-psychological theories. The present study investigates activist identity through the lens of role-based and category-based identities and reports two major findings. First, there is a distinct role-based activist identity, one that involves internalizing role responsibilities and the expectations of friends and family. Second, collective identity represents a relationship between a social identity and an injustice frame; it either involves incorporating an injustice frame into a preexisting social identity or using the injustice frame to create a new in-group. The present findings help to illuminate the processes underlying collective identity, indicate that a great deal of role-based activist identity is mistaken for collective identity, and suggest new directions for the study of micro-mobilization and organizational forms and tactics in social movements.
On Automatic Formation of Effective Therapy Groups in Social Networks

Group therapy is one of the major clinical interventions to help patients with mental disorders. However, finding an effective therapy group is very challenging, since there are three crucial criteria that should be carefully considered simultaneously: 1) avoidance of isolation and loneliness; 2) unfamiliarity of patients; and 3) size of the therapy group. Manual selection of therapy group members, which is the common approach for most psychiatrists and mental health professionals today, may incur human bias and is very time-consuming. Therefore, we propose a new research problem, namely, Unfamiliarity-Aware Therapy Group Selection with Noah’s Ark Principle (UTNA) for automatic selections of therapy group members from the social network while addressing the three crucial criteria mentioned above. In this paper, we first analyze the NP-hardness and inapproximability of the UTNA problem. Then, we consider a special case of UTNA on trees, namely, Tree-based UTNA, and propose a linear-time dynamic programming algorithm to find the optimal solution. We then design an efficient algorithm, Effective Therapy Group Discovery, to select suitable members efficiently and effectively for the general UTNA problem. We invite 10 psychiatrists and clinical psychologists to conduct an expert study for validating our problem formulation, where all experts agree that our problem formulation is helpful for them, and the groups selected by our proposed approach are better than or similar to their manual configurations. In addition, we also conduct extensive experiments on three real data sets. Experimental results show that our proposed algorithms outperform other baselines in both efficiency and solution quality.
An Incentive Mechanism for Crowd Sensing with Colluding Agents

Vehicular mobile crowd sensing is a fast-emerging paradigm to collect data about the environment by mounting sensors on vehicles such as taxis. An important problem in vehicular crowd sensing is to design payment mechanisms to incentivize drivers (agents) to collect data, with the overall goal of obtaining the maximum amount of data (across multiple vehicles) for a given budget. Past works on this problem consider a setting where each agent operates in isolation---an assumption which is frequently violated in practice. In this paper, we design an incentive mechanism to incentivize agents who can engage in arbitrary collusions. We then show that in a "homogeneous" setting, our mechanism is optimal, and can do as well as any mechanism which knows the agents' preferences a priori. Moreover, if the agents are non-colluding, then our mechanism automatically does as well as any other non-colluding mechanism. We also show that our proposed mechanism has strong (and asymptotically optimal) guarantees for a more general "heterogeneous" setting. Experiments based on synthesized data and real-world data reveal gains of over 30\% attained by our mechanism compared to past literature.
Dynamic clinical prediction models for discrete time‐to‐event data with competing risks—A case study on the OUTCOMEREA database

The development of clinical prediction models requires the selection of suitable predictor variables. Techniques to perform objective Bayesian variable selection in the linear model are well developed and have been extended to the generalized linear model setting as well as to the Cox proportional hazards model. Here, we consider discrete time‐to‐event data with competing risks and propose methodology to develop a clinical prediction model for the daily risk of acquiring a ventilator‐associated pneumonia (VAP) attributed to P. aeruginosa (PA) in intensive care units. The competing events for a PA VAP are extubation, death, and VAP due to other bacteria. Baseline variables are potentially important to predict the outcome at the start of ventilation, but may lose some of their predictive power after a certain time. Therefore, we use a landmark approach for dynamic Bayesian variable selection where the set of relevant predictors depends on the time already spent at risk. We finally determine the direct impact of a variable on each competing event through cause‐specific variable selection.
Current Research Trends of SLA and Challenges and Strategies for Native Chinese English-learners

Current research of second language Acquisition (SLA) has several main trends: cognitive oriented perspective, social oriented perspective, and holistic perspective. Under these perspectives, many theories have been used to explain the process of SLA such as, primary theory, the theory of affordances, and sociocognitive approach. These provided solid foundations towards understanding second language acquisition. Based on the perspectives of SLA, this paper aims to re-explore the challenges and strategies of native Chinese English-learners. This group of people may have more challenges to learn English as a second/foreign language compared with other ethnic groups whose mother tongues are alphabetic based languages. It could be related to thinking differences, language distance, as well as effective teaching methods. As a result, teaching under holistic perspective may be a practical method to enhance Chinese English-learners.
Associations among two vitamin D receptor (VDR) gene polymorphisms (ApaI and TaqI) in acne vulgaris: A pilot susceptibility study

Acne vulgaris (AV) pathogenesis is multifactorial. Vitamin D (VitD) plays an important role in sebocytes' differentiation and function. Most VitD functions are mediated by the nuclear VitD receptor (VDR) following binding of its biologically active form (1,25 dihydroxyvitamin D3). Genetic variations in VDR gene may cause significant receptor dysfunction and have been found to be associated with many inflammatory skin diseases. Two adjacent single nucleotide polymorphisms of VDR, ApaI (rs7975232) and TaqI (rs731236), were commonly studied.