Miracle-dz/UniFAR-FTUs huggingface dataset:
Intranasal bovine β-defensin-5 enhances antituberculosis immunity in a mouse model by a novel protein-based respiratory mucosal vaccine

ABSTRACT Respiratory mucosal immunization is an effective immunization strategy against tuberculosis (TB), and effective mucosal vaccines require adjuvants that can promote protective immunity without deleterious inflammation. Mucosal BCG (Bacille Calmette-Guerin) is effective, but it causes a severe inflammatory response in the lung. A novel less cytotoxic mucosal vaccine AH-PB containing Mycobacterium tuberculosis (Mtb) cell surface antigens Ag85A and HspX (AH), as well as polyinosinic-polycytidylic acid (Poly IC) and bovine neutrophil β-defensin-5 (B5) adjuvants were prepared, with the overarching goal of protecting against TB. Then, the immunogenicity and protective efficacy of these vaccines via the intranasal route were evaluated in a mouse model. Results showed that intranasal AH-PB promoted tissue-resident memory T cells (TRMs) development in the lung, induced antigen-specific antibody response in airway, provided protection against Mycobacterium bovis (M. bovis), conferred better protection than parenteral BCG in the later stage of infection, and boosted the protective immunity generated by BCG in mice. Moreover, both B5 and Poly IC were indispensable for the protection generated by AH-PB. Furthermore, intranasal immunization with AH-B5 fusion vaccines also provided similar protection against M. bovis compared to AH-PB. Collectively, B5-based TB vaccine via the intranasal route is a promising immunization strategy against bovine TB, and this kind of immunization strategy may be applied to human TB vaccine development. These findings highlight the potential importance of B5 as a mucosal adjuvant used in TB vaccines or other respiratory disease vaccines.
Reflections about the construction of a platform LCMS in open code

The management of e-learning environments requires the existence of resources and software tools that make it possible. In the immediate past, there have been two large axis of development: the construction of LMS (Learning Management System)platforms that permit an adequate users management but lack of content management, and the CMS (Content Management System) platforms that permit an efficient management of the contents but not thus of the users. Nevertheless, neither one nor another way has a true LCMS (Learning Content Management System), mainly because they have not used the RLO (Reusable Learning Object) as the basic unit in order to build educational and formative resources suitable to the needs of the users. Anyway, the obtained products should adopt the standards (IMS, AICC, SCORM) in order to be exportable to other contents management environments. In this document, we review the main antecedents of the problem, the initially adopted solutions, some strategic decisions and the reflections that necessarily accompany the development of dynamic experiences, such in this case.
Thor: Wielding Hammers to Integrate Language Models and Automated Theorem Provers

In theorem proving, the task of selecting useful premises from a large library to unlock the proof of a given conjecture is crucially important. This presents a challenge for all theorem provers, especially the ones based on language models, due to their relative inability to reason over huge volumes of premises in text form. This paper introduces Thor, a framework integrating language models and automated theorem provers to overcome this difficulty. In Thor, a class of methods called hammers that leverage the power of automated theorem provers are used for premise selection, while all other tasks are designated to language models. Thor increases a language model's success rate on the PISA dataset from $39\%$ to $57\%$, while solving $8.2\%$ of problems neither language models nor automated theorem provers are able to solve on their own. Furthermore, with a significantly smaller computational budget, Thor can achieve a success rate on the MiniF2F dataset that is on par with the best existing methods. Thor can be instantiated for the majority of popular interactive theorem provers via a straightforward protocol we provide.
HessianFR: An Efficient Hessian-based Follow-the-Ridge Algorithm for Minimax Optimization

Wide applications of differentiable two-player sequential games (e.g., image generation by GANs) have raised much interest and attention of researchers to study efficient and fast algorithms. Most of the existing algorithms are developed based on nice properties of simultaneous games, i.e., convex-concave payoff functions, but are not applicable in solving sequential games with different settings. Some conventional gradient descent ascent algorithms theoretically and numerically fail to find the local Nash equilibrium of the simultaneous game or the local minimax (i.e., local Stackelberg equilibrium) of the sequential game. In this paper, we propose the HessianFR, an efficient Hessian-based Follow-the-Ridge algorithm with theoretical guarantees. Furthermore, the convergence of the stochastic algorithm and the approximation of Hessian inverse are exploited to improve algorithm efficiency. A series of experiments of training generative adversarial networks (GANs) have been conducted on both synthetic and real-world large-scale image datasets (e.g. MNIST, CIFAR-10 and CelebA). The experimental results demonstrate that the proposed HessianFR outperforms baselines in terms of convergence and image generation quality.
Visualizing CoAtNet Predictions for Aiding Melanoma Detection

Melanoma is considered to be the most aggressive form of skin cancer. At present, the evaluation of malignancy is performed primarily by invasive histological examination of the suspicious lesion. Developing an accurate classifier for early and efficient detection can minimize and monitor the harmful effects of skin cancer and increase patient survival rates. Due to the similar shape of malignant and benign cancerous lesions, doctors spend considerably more time when diagnosing these findings. However, using a deep learning approach as a computer vision tool can overcome some of the challenges. This paper proposes a multi-class classification task using the CoAtNet architecture, a hybrid model that combines the deep depthwise convolution matrix operation of traditional convolutional neural networks with the strengths of Transformer models and self-attention mechanics to achieve better generalization and capacity. The model was evaluated based on precision, recall, and AP. The proposed multi-class classifier achieves an overall precision of 0.901, recall 0.895, and AP 0.923, indicating high performance compared to other state-of-the-art networks. The proposed approach should provide a less complex framework to automate the melanoma diagnostic process and speed up the life-saving process.
Bacterial Adaptation to Venom in Snakes and Arachnida

Notwithstanding their 3 to 5% mortality, the 2.7 million envenomation-related injuries occurring annually—predominantly across Africa, Asia, and Latin America—are also major causes of morbidity. Venom toxin-damaged tissue will develop infections in some 75% of envenomation victims, with E. faecalis being a common culprit of disease; however, such infections are generally considered to be independent of envenomation. ABSTRACT Animal venoms are considered sterile sources of antimicrobial compounds with strong membrane-disrupting activity against multidrug-resistant bacteria. However, venomous bite wound infections are common in developing nations. Investigating the envenomation organ and venom microbiota of five snake and two spider species, we observed venom community structures that depend on the host venomous animal species and evidenced recovery of viable microorganisms from black-necked spitting cobra (Naja nigricollis) and Indian ornamental tarantula (Poecilotheria regalis) venoms. Among the bacterial isolates recovered from N. nigricollis, we identified two venom-resistant, novel sequence types of Enterococcus faecalis whose genomes feature 16 virulence genes, indicating infectious potential, and 45 additional genes, nearly half of which improve bacterial membrane integrity. Our findings challenge the dogma of venom sterility and indicate an increased primary infection risk in the clinical management of venomous animal bite wounds. IMPORTANCE Notwithstanding their 3 to 5% mortality, the 2.7 million envenomation-related injuries occurring annually—predominantly across Africa, Asia, and Latin America—are also major causes of morbidity. Venom toxin-damaged tissue will develop infections in some 75% of envenomation victims, with E. faecalis being a common culprit of disease; however, such infections are generally considered to be independent of envenomation. Here, we provide evidence on venom microbiota across snakes and arachnida and report on the convergent evolution mechanisms that can facilitate adaptation to black-necked cobra venom in two independent E. faecalis strains, easily misidentified by biochemical diagnostics. Therefore, since inoculation with viable and virulence gene-harboring bacteria can occur during envenomation, acute infection risk management following envenomation is warranted, particularly for immunocompromised and malnourished victims in resource-limited settings. These results shed light on how bacteria evolve for survival in one of the most extreme environments on Earth and how venomous bites must be also treated for infections.
Named Entity Linking on Namesakes

We propose a simple and practical method of named entity linking (NEL), and explore its features and performance on a dataset of ambiguous named entities - Namesakes. We represent knowledge base (KB) entity by a set of embeddings. Our observations suggest that it is reasonable to keep a limited number of such embeddings, and that the number of mentions required to create a KB entity is important. We show that representations of entities in the knowledge base (KB) can be adjusted us-ing only KB data, and the adjustment improves NEL performance.
Multi-task Learning for Gaussian Graphical Regressions with High Dimensional Covariates

Gaussian graphical regression is a powerful means that regresses the precision matrix of a Gaussian graphical model on covariates, permitting the numbers of the response variables and covariates to far exceed the sample size. Model ﬁtting is typically carried out via separate node-wise lasso regressions, ignoring the network-induced structure among these regressions. Consequently, the error rate is high, especially when the number of nodes is large. We propose a multi-task learning estimator for ﬁtting Gaussian graphical regression models; we design a cross-task group sparsity penalty and a within task element-wise sparsity penalty, which govern the sparsity of active covariates and their eﬀects on the graph, respectively. For computation, we consider an eﬃcient augmented Lagrangian algorithm, which solves subproblems with a semi-smooth Newton method. For theory, we show that the error rate of the multi-task learning based estimates has much improvement over that of the separate node-wise lasso estimates, because the cross-task penalty borrows information across tasks. To address the main challenge that the tasks are entangled in a complicated correlation structure, we establish a new tail probability bound for correlated heavy-tailed (sub-exponential) variables with an arbitrary correlation structure, a useful theoretical result in its own right. Finally, the utility of our method is demonstrated through simulations as well as an application to a gene co-expression network study with brain cancer patients.
Least-to-Most Prompting Enables Complex Reasoning in Large Language Models

Chain-of-thought prompting has demonstrated remarkable performance on various natural language reasoning tasks. However, it tends to perform poorly on tasks which requires solving problems harder than the exemplars shown in the prompts. To overcome this challenge of easy-to-hard generalization, we propose a novel prompting strategy, least-to-most prompting. The key idea in this strategy is to break down a complex problem into a series of simpler subproblems and then solve them in sequence. Solving each subproblem is facilitated by the answers to previously solved subproblems. Our experimental results on tasks related to symbolic manipulation, compositional generalization, and math reasoning reveal that least-to-most prompting is capable of generalizing to more difficult problems than those seen in the prompts. A notable finding is that when the GPT-3 code-davinci-002 model is used with least-to-most prompting, it can solve the compositional generalization benchmark SCAN in any split (including length split) with an accuracy of at least 99% using just 14 exemplars, compared to only 16% accuracy with chain-of-thought prompting. This is particularly noteworthy because neural-symbolic models in the literature that specialize in solving SCAN are trained on the entire training set containing over 15,000 examples. We have included prompts for all the tasks in the Appendix.
Modeling Neutron Star Matter in the Age of Multimessenger Astrophysics

The interpretation of available and forthcoming data obtained from multimessenger astrophysical observations—potentially providing unprecedented access to neutron star properties—will require the development of novel, accurate theoretical models of dense matter. Of great importance, in this context, will be the capability for devising a description of thermal effects applicable to the study of quantities other than the equation of state, such as the transport coefficients and the neutrino mean free path in the nuclear medium. A formalism based on correlated basis states and the cluster expansion technique has been previously employed to derive a well-behaved effective interaction—suitable for use in standard perturbation theory—from a state-of-the-art nuclear Hamiltonian, including phenomenological two- and three-nucleon potentials. Here, we provide a comprehensive and self-contained account of the extension of this approach to the treatment of finite-temperature effects, and report the results of numerical calculations of a number of properties of nuclear matter with arbitrary neutron excess and temperature up to 50 MeV.
Towards Automated Document Revision: Grammatical Error Correction, Fluency Edits, and Beyond

Natural language processing technology has rapidly improved automated grammatical error correction tasks, and the community begins to explore document-level revision as one of the next challenges. To go beyond sentence-level automated grammatical error correction to NLP-based document-level revision assistant, there are two major obstacles: (1) there are few public corpora with document-level revisions being annotated by professional editors, and (2) it is not feasible to elicit all possible references and evaluate the quality of revision with such references because there are infinite possibilities of revision. This paper tackles these challenges. First, we introduce a new document-revision corpus, TETRA, where professional editors revised academic papers sampled from the ACL anthology which contain few trivial grammatical errors that enable us to focus more on document- and paragraph-level edits such as coherence and consistency. Second, we explore reference-less and interpretable methods for meta-evaluation that can detect quality improvements by document revision. We show the uniqueness of TETRA compared with existing document revision corpora and demonstrate that a fine-tuned pre-trained language model can discriminate the quality of documents after revision even when the difference is subtle. This promising result will encourage the community to further explore automated document revision models and metrics in future.
Effect of external load on muscle activation during the barbell back squat

ABSTRACT The aim of this study was to compare gluteus maximus-to-hamstring (GM:H) co-activation, hamstrings-to-quadriceps (H:Q) co-activation, and mean muscle activity in the vastus lateralis (VL), vastus medialis (VM), rectus femoris (RM), gluteus maximus (GM), semitendinosus (ST), and bicep femoris (BF) muscles across a range of training loads (40%, 50%, 50%, 70%, 80% 90% 1RM) of the barbell back squat in resistance trained females. Surface electromyography (EMG) was observed in 18 healthy resistance trained females for the VL, VM, RM, GM, ST, and BF during the ascending and descending phase of the back squat. During the ascending phase, the VL, VM, RM, GM, ST, and BF displayed significantly more EMG activation at 90% 1RM compared to 40%–70% (p < .03). During the descending phase, there were no significant differences displayed in mean muscle activity at 40%–80% of 1RM compared to 90% of 1RM. There were no significant differences in H-Q co-activation and GM-H co-activation during the ascending or descending phase across external loads. These findings are similar to the results found in similar studies using male participants and support that higher training intensities (80%–90% 1RM) of the barbell back squat significantly activate muscles surrounding the hip compared to lower loads (40%–70%). The results of this study also suggest that training intensities of 80% 1RM and 90% 1RM elicits the similar musculature activation in the muscle surrounding the hip in resistance trained females. Highlights During the back squat, mean muscle activity of the hip and leg muscles increased linearly with exercise intensity (40%–90% of one-repetition max) in resistance trained females. No significant difference in mean activity were noted between 80% and 90% of one-repetition max during the ascending and descending phases. During the ascending phase, 80% of one-repetition max generated the highest gluteus maximus to hamstring activity co-activation ratio. During the descending phase, 40% of one-repetition max produced a gluteus maximus to hamstring activity co-activation ratio closest to 1.0.
Reconstitution and mechanistic dissection of the human microtubule branching machinery

Zhang et al. reconstitute branching microtubule nucleation using native and recombinant human augmin–γ-TuRC complex and highlight the importance of the adaptor protein NEDD1 and CDK1/PLK1-mediated phosphorylation in this process. This study also unravels a novel augmin’s oligomerization-dependent mechanism for γ-TuRC activation.
Modelling the secular evolution of proto-planetary disc dust sizes – A comparison between the viscous and magnetic wind case

For many years proto-planetary discs have been thought to evolve viscously: angular momentum redistribution leads to accretion and outward disc spreading. Recently, the hypothesis that accretion is due, instead, to angular momentum removal by magnetic winds gained new popularity: no disc spreading is expected in this case. In this paper, we run several one-dimensional gas and dust simulations to make predictions on the time-evolution of disc sizes in the dust and to assess whether they can be used to understand how discs evolve. We show that viscous and magnetic wind models have very different dust disc radii. In particular, MHD wind models are compact and their sizes either remain constant or decrease with time. On the contrary, discs become larger with time in the viscous case (when α ≳ 10−3). Although current observations lack enough sensitivity to discriminate between these two scenarios, higher-sensitivity surveys could be fruitful to this goal on a 1 to 10 Myr age range. When compared with the available ALMA Band 7 data, both viscous and magnetic wind models are compatible with the observationally-inferred dust radii in Lupus, Chamaeleon I and Upper Sco. Furthermore, in the drift-dominated regime, the size-luminosity correlation is reproduced in Lupus, both in Band 7 and 3, while in Upper Sco a different slope than in the data is predicted. Sub-structures (potentially undetected) can explain several outliers with large observed sizes. Higher-angular-resolution observations will be helpful to test our predictions in the case of more compact discs, expected in both frameworks, particularly at the age of Upper Sco.
Pareto-Improving Data-Sharing✱

We study the effects of data sharing between firms on prices, profits, and consumer welfare. Although indiscriminate sharing of consumer data decreases firm profits due to the subsequent increase in competition, selective sharing can be beneficial. We show that there are data-sharing mechanisms that are strictly Pareto-improving, simultaneously increasing firm profits and consumer welfare. Within the class of Pareto-improving mechanisms, we identify one that maximizes firm profits and one that maximizes consumer welfare.
What Makes Data-to-Text Generation Hard for Pretrained Language Models?

Expressing natural language descriptions of structured facts or relations – data-to-text generation (D2T) – increases the accessibility of structured knowledge repositories. Previous work shows that pre-trained language models (PLMs) perform remarkably well on this task after fine-tuning on a significant amount of task-specific training data. On the other hand, while auto-regressive PLMs can generalize from a few task examples, their efficacy at D2T is largely unexplored. Furthermore, we have an incomplete understanding of the limits of PLMs on D2T. In this work, we conduct an empirical study of both fine-tuned and auto-regressive PLMs on the DART multi-domain D2T dataset. We consider their performance as a function of the amount of task-specific data and how the data is incorporated into the models: zero and few-shot learning, and fine-tuning of model weights. In addition, we probe the limits of PLMs by measuring performance on subsets of the evaluation data: novel predicates and abstractive test examples. To improve the performance on these subsets, we investigate two techniques: providing predicate descriptions in the context and re-ranking generated candidates by information reflected in the source. Finally, we conduct a human evaluation of model errors and show that D2T generation tasks would benefit from datasets with more careful manual curation.
Heterogeneity-aware P2P Wireless Energy Transfer for Balanced Energy Distribution

The recent advances in wireless energy transfer (WET) provide an alternate and reliable option for replenishing the battery of pervasive and portable devices, such as smart-phones. The peer-to-peer (P2P) mode of WET brings improved flexibility to the charging process among the devices as they can maintain their mobility while replenishing their battery. Few existing works in P2P-WET unrealistically assume the nodes to be exchanging energy at every opportunity with any other node. Also, energy exchange between the nodes is not bounded by the energy transfer limit in that inter-node meeting duration. In this regard, the parametric heterogeneity (in terms of device's battery capacity and WET hardware) among the nodes also affects the energy transfer bound in each P2P interaction, and thus, may lead to unbalanced network energy distributions. This inherent heterogeneity aspect has not been adequately covered in the P2P-WET literature so far, especially from the point of view of maintaining a balanced energy distribution in the networked population. In this work, we present a Heterogeneity-aware Wireless Energy Transfer (HetWET) method. In contrast to the existing literature, we devise a fine-grained model of wireless energy transfer while considering the parametric heterogeneity of the participating devices. Thereafter, we enable the nodes to explore and dynamically decide the peers for energy exchange. The performance of HetWET is evaluated using extensive simulations with varying heterogeneity settings. The evaluation results demonstrate that HetWET can maintain lower energy losses and achieve more balanced energy variation distance compared to three different state-of-the-art methods.