Miracle-dz/UniFAR-FTUs huggingface dataset:
Let the Database Talk Back: Natural Language Explanations for SQL

Database interaction is often characterized as a non-trivial and time-consuming process due to user’s inexperience with the data or the query language. Therefore, there is a need for the databases to be able to "talk back" in order to assist the users during data exploration and eventually lead them to the desired results. In this paper, we tackle the problem of SQL-to-NL by extending the graph-based model of Logos [3]. Our novel extensions include improvements in terms of the system’s translation capabilities and the fluency of the generated explanations. Finally, we report several challenges, highlighted by experiments on different user cases, i.e, astronomy and policy making.
Generate More Imperceptible Adversarial Examples for Object Detection

Object detection methods based on deep neural networks are vulnerable to adversarial examples. The existing attack methods have the following problems: 1) the training generator takes a long time and is difﬁcult to extend to a large dataset; 2) the excessive destruction of the image features does not improve black-box attack effect(the generated adversarial examples have poor transfer-ability) and brings about visible perturbations. In response to these problems, we proposed a more imperceptible attack(MI attack) with a stopping condition of feature destruction and a noise cancellation mechanism. Finally, the generator generates subtle adversarial perturbations, which can not only attack the object detection models that are based on proposal and regression but also boost the training speed by 4-6 times. Experiments show that the MI method has achieved the state-of-the-art attack performance in the large datasets PASCAL VOC.
Proteasome inhibitors decrease paclitaxel-induced cell death in nasopharyngeal carcinoma with the accumulation of CDK1/cyclin B1

Southeast Asia is a region with high incidence of nasopharyngeal carcinoma (NPC). Paclitaxel is the mainstay for the treatment of advanced nasopharyngeal cancer. The present study investigated the effect of proteasome inhibitors on the therapeutic effect of paclitaxel and its related mechanism. The present data from Cell Counting Kit-8 and flow cytometry assays demonstrated that appropriate concentrations of proteasome inhibitors (30 nM PS341 or 700 nM MG132) reduced the lethal effect of paclitaxel on the nasopharyngeal cancer cells. While 400 nM paclitaxel effectively inhibited cell division and induced cell death, proteasome inhibitors (PS341 30 nM or MG132 700 nM) could reverse these effects. Additionally, the western blotting results demonstrated accumulation of cell cycle regulation protein CDK1 and cyclin B1 in proteasome inhibitor-treated cells. In addition, proteasome inhibitors combined with paclitaxel led to decreased MCL1 apoptosis regulator, BCL2 family member/Caspase-9/poly (ADP-ribose) polymerase apoptosis signaling triggered by CDK1/cyclin B1. Therefore, dysfunction of CDK1/cyclin B1 could be defining the loss of paclitaxel lethality against cancer cells, a phenomenon affirmed by the CDK1 inhibitor Ro3306. Overall, the present results demonstrated that a combination of paclitaxel with proteasome inhibitors or CDK1 inhibitors is antagonistic to effective clinical management of NPC.
An Enzyme-Linked Immunosorbent Spot Assay Measuring <named-content content-type='genus-species'>Borrelia burgdorferi</named-content> B31-Specific Interferon Gamma-Secreting T Cells Cannot Discriminate Active Lyme Neuroborreliosis from Past Lyme Borreliosis: a Prospective Study in the Netherlands

Two-tier serology testing is most frequently used for the diagnosis of Lyme borreliosis (LB); however, a positive result is no proof of active disease. To establish a diagnosis of active LB, better diagnostics are needed. Tests investigating the cellular immune system are available, but studies evaluating the utility of these tests on well-defined patient populations are lacking. Therefore, we investigated the utility of an enzyme-linked immunosorbent spot (ELISpot) assay to diagnose active Lyme neuroborreliosis. Peripheral blood mononuclear cells (PBMCs) of various study groups were stimulated by using Borrelia burgdorferi strain B31 and various recombinant antigens, and subsequently, the number of Borrelia-specific interferon gamma (IFN)-secreting T cells was measured. We included 33 active and 37 treated Lyme neuroborreliosis patients, 28 healthy individuals treated for an early manifestation of LB in the past, and 145 untreated healthy individuals. The median numbers of B. burgdorferi B31-specific IFN-secreting T cells/2.5 105 PBMCs did not differ between active Lyme neuroborreliosis patients (6.0; interquartile range [IQR], 0.5 to 14.0), treated Lyme neuroborreliosis patients (4.5; IQR, 2.0 to 18.6), and treated healthy individuals (7.4; IQR, 2.3 to 14.9) (P 1.000); however, the median number of B. burgdorferi B31-specific IFN-secreting T cells/2.5 105 PBMCs among untreated healthy individuals was lower (2.0; IQR, 0.5 to 3.9) (P 0.016). We conclude that the Borrelia ELISpot assay, measuring the number of B. burgdorferi B31-specific IFN-secreting T cells/2.5 105 PBMCs, correlates with exposure to the Borrelia bacterium but cannot be used for the diagnosis of active Lyme neuroborreliosis.
Evaluations of the Gap between Supervised and Reinforcement Lifelong Learning on Robotic Manipulation Tasks

: Overcoming catastrophic forgetting is of great importance for deep learning and robotics. Recent lifelong learning research has great advances in supervised learning. However, little work focuses on reinforcement learning(RL). We focus on evaluating the performances of state-of-the-art lifelong learning algorithms on robotic reinforcement learning tasks. We mainly focus on the properties of overcoming catastrophic forgetting for these algorithms. We summarize the pros and cons for each category of lifelong learning algorithms when applied in RL scenarios. We propose a framework to modify supervised lifelong learning algorithms to be compatible with RL. We also develop a manipulation benchmark task set for our evaluations.
SpRY Cas9 Can Utilize a Variety of Protospacer Adjacent Motif Site Sequences To Edit the <named-content content-type='genus-species'>Candida albicans</named-content> Genome

Candida albicans is a human fungal pathogen capable of causing lifethreatening infections. The ability to edit the C. albicans genome using CRISPR/Cas9 is an important tool investigators can leverage in their search for novel therapeutic targets. However, wild-type Cas9 requires an NGG protospacer adjacent motif (PAM), leaving many AT-rich regions of DNA inaccessible. A recently described near-PAMless CRISPR system that utilizes the SpRY Cas9 variant can target non-NGG PAM sequences. Using this system as a model, we developed C. albicans CRISPR/SpRY. We tested our system by mutating C. albicans ADE2 and show that CRISPR/SpRY can utilize non-NGG PAM sequences in C. albicans. Our CRISPR/SpRY system will allow researchers to efficiently modify C. albicans DNA that lacks NGG PAM sequences. IMPORTANCE Genetic modification of the human fungal pathogen Candida albicans allows us to better understand how fungi differ from humans at the molecular level and play essential roles in the development of therapeutics. CRISPR/Cas9-mediated genome editing systems can be used to introduce site-specific mutations to C. albicans. However, wild-type Cas9 is limited by the requirement of an NGG PAM site. CRISPR/SpRY targets a variety of different PAM sequences. We modified the C. albicans CRISPR/Cas9 system using the CRISPR/SpRY as a guide. We tested CRISPR/SpRY on C. albicans ADE2 and show that our SpRY system can facilitate genome editing independent of an NGG PAM sequence, thus allowing the investigator to target AT-rich sequences. Our system will potentially enable mutation of the 125 C. albicans genes which have been previously untargetable with CRISPR/Cas9. Additionally, our system will allow for precise targeting of many genomic locations that lack NGG PAM sites.
Real Robot Challenge Stage 1 Team: Three Wolves

Deep reinforcement learning has shown its advantages in real-time decision-making based on the state of the agent. In this stage, we solved the task of using a real robot to manipulate the cube to a given trajectory. The task is broken down into diﬀerent procedures and we propose a hierarchical structure, the high-level deep reinforcement learning model selects appropriate contact positions and the low-level control module performs the position control under the corresponding trajectory. Our framework reduces the disadvantage of low sample eﬃciency of deep re-inforcement learning and lacking adaptability of traditional robot control methods. Our algorithm is trained in simulation and migrated to reality without ﬁne-tuning. The experimental results show the eﬀectiveness of our method both simulation and reality.
Modifying Simulated Perfect Oracles for Interactive Reinforcement Learning: a Participatory Design Methodology

: Interactive reinforcement learning (I-RL) is a widely-used technique 1 for agent learning, which allows the agent to receive reward signals not only from 2 the environment but also from a human teacher. In the early stage of I-RL algo-3 rithm implementation, prior work usually tests their I-RL algorithms with simu-4 lated perfect oracles which are assumed to know the optimal policy for any states 5 and can provide accurate feedback to the learning agent without delay. However, 6 a perfect oracle is not an accurate reﬂection of human feedback behaviors. To 7 solve this problem, in this paper, we present a participatory design approach that 8 can modify perfect oracles to human-like oracles for I-RL algorithm testing. We 9 conduct a user study using our proposed methodology. We also run experiments 10 with modiﬁed oracles generated from our participants. Our modiﬁed oracles were 11 found to be more human-like and possibly can provide more realistic testing re-12 sults compared to perfect oracles. 13
Few-shot Learning for Cardiac Segmentation via Self-supervised Multi-task Learning

In this paper, we propose two novel self-supervised tasks, which encode the spatial and modality appearance information of cardiac images, respectively. Furthermore, we propose to ensemble multiple self-supervised tasks in a multi-task learning framework to learn more eﬀective semantic representation for cardiac segmentation. The proposed approach was validated on the late gadolinium enhancement (LGE) cardiac magnetic resonance (CMR) images from the public MICCAI 2019 MSCMRseg Dataset, and was compared with two popular self-supervised tasks, including context inpainting (CI) and context restoration (CR). Results demonstrated that the proposed self-supervised tasks, as well as hybridized multi-task learning strategies, are eﬀective in few-shot cardiac segmentation.
Binary Search in Secure Computation

—Binary search is one of the most popular algo- rithms in computer science. Realizing it in the context of secure multiparty computation which demands data-oblivious execu- tion, however, is extremely non-trivial. It has been previously implemented only using oblivious RAM (ORAM) for secure computation and in this work we initiate the study of this topic using conventional secure computation techniques based on secret sharing. We develop a suite of protocols with different properties and of different structure for searching a private dataset of m elements by a private numeric key. Our protocols result in O ( m ) and O ( √ m ) communication using only standard and readily available operations based on secret sharing. We further extend our protocols to support write operations, namely, binary search that obliviously updates the selected element, and realize two variants: updating non-key ﬁelds and updating the key ﬁeld. Our implementation results indicate that even after applying known and our own optimizations to the fastest ORAM constructions, our solutions are faster than optimized ORAM schemes for datasets of up to 2 30 elements and by up to two orders of magnitude. We hope that this work will prompt further interest in seeking efﬁcient realizations of this important problem.
Species Distribution Modeling and Niche Overlap for the Louisiana Pine Snake (Pituophis ruthveni) and Baird’s Pocket Gopher (Geomys breviceps)

The Louisiana pine snake (Pituophis ruthveni) is one of the least studied snakes in North America and recently was listed as Threatened under the United States Endangered Species Act. They often are associated with longleaf pine (Pinus palustris) savanna and rely on the Baird’s pocket gopher (Geomys breviceps) as a major part of their diet, as well as using their burrows for hibernacula. A comparison of geographic ranges, however, show that Baird’s pocket gopher is more widespread than the Louisiana pine snake. This suggests that factors other than prey availability limit the overall distribution of the Louisiana pine snake. Our objectives were to generate species distribution models, identify environmental variables that best predicted occupancy, and perform tests of niche overlap between species. Maxent was used to build the species distribution models and tests of niche overlap were performed using ENMTools. Measures of precipitation were most important for predicting occupancy for the Baird’s pocket gopher, whereas precipitation and temperature were most important for the Louisiana pine snake. Tests of niche overlap between the Baird’s pocket gopher and the Louisiana pine snake were significantly different than the null distribution, which indicate different niche requirements for each species. The Red River in Louisiana appears to have separated regions of habitat for the Louisiana pine snake, creating potential conservation concerns if populations are isolated. Finally, predictions of distribution for the Louisiana pine snake closely resemble the historical distribution of longleaf pine savanna, which is indicative of their close association.
Lifestyle, WCRF/AICR Recommendations and Esophageal Adenocarcinoma Risk. A Systematic Review of the Literature

One of the most notable changes in the Esophageal Cancer (EC) epidemiology is the rising incidence and prevalence of esophageal adenocarcinoma (EAC) in developed countries, likely due to lifestyle and/or environmental factors that may play an important role in EAC onset. The aim of this systematic review was to collect and summarize all the available evidence regarding lifestyle, diet and EAC risk. We searched the PubMed and Scopus databases in January 2021 for studies providing information about lifestyle, diet, WCRF/AICR recommendations and EAC risk. A total of 106 publications met the inclusion criteria. Body mass index (BMI) and waist circumference (WC) are associated with increased EAC risk. Physical activity does not appear to have a significant direct role in EAC risk. A diet rich in fruit, vegetables, and whole grains appeared to be more protective than a diet rich in animal fat, red meat, and processed meat. Alcohol does not seem to be related to EAC whereas smokers, particularly heavy smokers, have an increased risk of EAC. Primary prevention remains the best option to avert EAC. BMI and WC, along with low consumption of red and processed meat, high consumption of plant food, and the avoidance of smoking are pivotal for EAC prevention.
Trigeminal neuralgia New classification and diagnostic grading for practice and research

Trigeminal neuralgia (TN) is an exemplary condition of neuropathic facial pain. However, formally classifying TN as neuropathic pain based on the grading system of the International Association for the Study of Pain is complicated by the requirement of objective signs confirming an underlying lesion or disease of the somatosensory system. The latest version of the International Classification of Headache Disorders created similar difficulties by abandoning the term symptomatic TN for manifestations caused by major neurologic disease, such as tumors or multiple sclerosis. These diagnostic challenges hinder the triage of TN patients for therapy and clinical trials, and hamper the design of treatment guidelines. In response to these shortcomings, we have developed a classification of TN that aligns with the nosology of other neurologic disorders and neuropathic pain. We propose 3 diagnostic categories. Classical TN requires demonstration of morphologic changes in the trigeminal nerve root from vascular compression. Secondary TN is due to an identifiable underlying neurologic disease. TN of unknown etiology is labeled idiopathic. Diagnostic certainty is graded possible when pain paroxysms occur in the distribution of the trigeminal nerve branches. Triggered paroxysms permit the designation of clinically established TN and probable neuropathic pain. Imaging and neurophysiologic tests that establish the etiology of classical or secondary TN determine definite neuropathic pain. Neurology
Global existence and stabilization in a forager-exploiter model with general logistic sources1

Abstract. We study a forager-exploiter model with generalized logistic sources in a smooth bounded domain with homogeneous Neumann boundary conditions. A new boundedness criterion is developed to prove the global existence and boundedness of the solution. Under some conditions on the logistic degradation rates, the classical solution exists globally and remain bounded in the high dimensions. Moreover, the large time behavior of the obtained solution is investigated in the case of the nutrient supply is a positive constant or has fast decaying property.
Modeling Dengue Immune Responses Mediated by Antibodies: A Qualitative Study

Simple Summary With more than one-third of the world population at risk of acquiring the disease, dengue fever is a major public health problem. Caused by four antigenically distinct but related serotypes, disease severity is associated with the immunological status of the individual, seronegative or seropositive, prior to a natural dengue infection. While a primary natural dengue infection is often asymptomatic or mild, individuals experiencing a secondary dengue infection with a heterologous serotype have higher risk of developing the severe form of the disease, linked to the antibody-dependent enhancement (ADE) process. We develop a modeling framework to describe the dengue immune responses mediated by antibodies. Our model framework can describe qualitatively the dynamic of the viral load and antibodies production for scenarios of primary and secondary infections, as found in the empirical immunology literature. Studies such as the one described here serve as a baseline to further model extensions. Future refinements of our framework will be of use to evaluate the impact of imperfect dengue vaccines. Abstract Dengue fever is a viral mosquito-borne infection and a major international public health concern. With 2.5 billion people at risk of acquiring the infection around the world, disease severity is influenced by the immunological status of the individual, seronegative or seropositive, prior to natural infection. Caused by four antigenically related but distinct serotypes, DENV-1 to DENV-4, infection by one serotype confers life-long immunity to that serotype and a period of temporary cross-immunity (TCI) to other serotypes. The clinical response on exposure to a second serotype is complex with the so-called antibody-dependent enhancement (ADE) process, a disease augmentation phenomenon when pre-existing antibodies to previous dengue infection do not neutralize but rather enhance the new infection, used to explain the etiology of severe disease. In this paper, we present a minimalistic mathematical model framework developed to describe qualitatively the dengue immunological response mediated by antibodies. Three models are analyzed and compared: (i) primary dengue infection, (ii) secondary dengue infection with the same (homologous) dengue virus and (iii) secondary dengue infection with a different (heterologous) dengue virus. We explore the features of viral replication, antibody production and infection clearance over time. The model is developed based on body cells and free virus interactions resulting in infected cells activating antibody production. Our mathematical results are qualitatively similar to the ones described in the empiric immunology literature, providing insights into the immunopathogenesis of severe disease. Results presented here are of use for future research directions to evaluate the impact of dengue vaccines.
Fungal Isocyanide Synthases and Xanthocillin Biosynthesis in <named-content content-type='genus-species'>Aspergillus fumigatus</named-content>

Microbial secondary metabolites, including isocyanide moieties, have been extensively mined for their repertoire of bioactive properties. Although the first naturally occurring isocyanide (xanthocillin) was isolated from the fungus Penicillium notatum over half a century ago, the biosynthetic origins of fungal isocyanides remain unknown. Here we report the identification of a family of isocyanide synthases (ICSs) from the opportunistic human pathogen Aspergillus fumigatus. Comparative metabolomics of overexpression or knockout mutants of ICS candidate genes led to the discovery of a fungal biosynthetic gene cluster (BGC) that produces xanthocillin (xan). Detailed analysis of xanthocillin biosynthesis in A. fumigatus revealed several previously undescribed compounds produced by the xan BGC, including two novel members of the melanocin family of compounds. We found both the xan BGC and a second ICS-containing cluster, named the copper-responsive metabolite (crm) BGC, to be transcriptionally responsive to external copper levels and further demonstrated that production of metabolites from the xan BGC is increased during copper starvation. The crm BGC includes a novel type of fungus-specific ICS-nonribosomal peptide synthase (NRPS) hybrid enzyme, CrmA. This family of ICS-NRPS hybrid enzymes is highly enriched in fungal pathogens of humans, insects, and plants. Phylogenetic assessment of all ICSs spanning the tree of life shows not only high prevalence throughout the fungal kingdom but also distribution in species not previously known to harbor BGCs, indicating an untapped resource of fungal secondary metabolism. IMPORTANCE Fungal ICSs are an untapped resource in fungal natural product research. Their isocyanide products have been implicated in plant and insect pathogenesis due to their ability to coordinate transition metals and disable host metalloenzymes. The discovery of a novel isocyanide-producing family of hybrid ICSNRPS enzymes enriched in medically and agriculturally important fungal pathogens may reveal mechanisms underlying pathogenicity and afford opportunities to discover additional families of isocyanides. Furthermore, the identification of noncanonical ICS BGCs will enable refinement of BGC prediction algorithms to expand on the secondary metabolic potential of fungal and bacterial species. The identification of genes related to ICS BGCs in fungal species not previously known for secondary metabolite-producing capabilities (e.g., Saccharomyces spp.) contributes to our understanding of the evolution of BGC in fungi.