SUN: Exploring Intrinsic Uncertainties in Text-to-SQL Parsers

This paper aims to improve the performance of text-to-SQL parsing by exploring the intrinsic uncertainties in the neural network based approaches (called SUN). From the data uncertainty perspective, it is indisputable that a single SQL can be learned from multiple semantically-equivalent questions.Different from previous methods that are limited to one-to-one mapping, we propose a data uncertainty constraint to explore the underlying complementary semantic information among multiple semantically-equivalent questions (many-to-one) and learn the robust feature representations with reduced spurious associations. In this way, we can reduce the sensitivity of the learned representations and improve the robustness of the parser. From the model uncertainty perspective, there is often structural information (dependence) among the weights of neural networks. To improve the generalizability and stability of neural text-to-SQL parsers, we propose a model uncertainty constraint to refine the query representations by enforcing the output representations of different perturbed encoding networks to be consistent with each other. Extensive experiments on five benchmark datasets demonstrate that our method significantly outperforms strong competitors and achieves new state-of-the-art results. For reproducibility, we release our code and data at https://github.com/AlibabaResearch/DAMO-ConvAI/tree/main/sunsql.Comment: Accepted at COLING 202

Robust estimation of bacterial cell count from optical density

Optical density (OD) is widely used to estimate the density of cells in liquid culture, but cannot be compared between instruments without a standardized calibration protocol and is challenging to relate to actual cell count. We address this with an interlaboratory study comparing three simple, low-cost, and highly accessible OD calibration protocols across 244 laboratories, applied to eight strains of constitutive GFP-expressing E. coli. Based on our results, we recommend calibrating OD to estimated cell count using serial dilution of silica microspheres, which produces highly precise calibration (95.5% of residuals <1.2-fold), is easily assessed for quality control, also assesses instrument effective linear range, and can be combined with fluorescence calibration to obtain units of Molecules of Equivalent Fluorescein (MEFL) per cell, allowing direct comparison and data fusion with flow cytometry measurements: in our study, fluorescence per cell measurements showed only a 1.07-fold mean difference between plate reader and flow cytometry data

Rhizopus microsporus and Mucor racemosus coinfection following COVID-19 detected by metagenomics next-generation sequencing: A case of disseminated mucormycosis

Mucormycosis is an invasive opportunistic fungal infection, which may be lethal and mostly affects patients with immunodeficiency or diabetes mellitus. Among Mucorales fungi, Rhizopus spp. is the most common cause of mucormycosis, followed by genera such as Mucor and Lichtheimia. Here we report a patient with severe COVID-19 infection who developed nasal pain, facial swelling, prominent black eschar on the nasal root. CT scan revealed pansinusitis along the maxillary, ethmoidal, and sphenoid sinuses. Mixed mold infection with Rhizopus microsporus and Mucor racemosus was detected by blood metagenomics next-generation sequencing (mNGS) and later nasal mucosa histological investigation confirmed mucormycosis. Severe COVID-19 infection led to the patient's thrombocytopenia and leukopenia. Later disseminated mucormycosis aggravated the infection and sepsis eventually resulted in death.It is the first case report of mucormycosis in which R. microsporus and M. racemosus as the etiologic agents were found simultaneously in one patient. COVID-19 infection combined with disseminated mucormycosisis can be fatal and mNGS is a fast, sensitive and accurate diagnostic method for fungi detection

Natural mycotoxin contamination in dog food: A review on toxicity and detoxification methods

Nowadays, the companion animals (dogs or other pets) are considered as members of the family and have established strong emotional relationships with their owners. Dogs are long lived compared to food animals, so safety, adequacy, and efficacy of dog food is of great importance for their health. Cereals, cereal by-products as well as feedstuffs of plant origin are commonly employed food resources in dry food, yet are potential ingredients for mycotoxins contamination, so dogs are theoretically more vulnerable to exposure when consumed daily. Aflatoxins (AF), deoxynivalenol (DON), fumonisins (FUM), ochratoxin A (OTA), and zearalenone (ZEA) are the most frequent mycotoxins that might present in dog food and cause toxicity on the growth and metabolism of dogs. An understanding of toxicological effects and detoxification methods (physical, chemical, or biological approaches) of mycotoxins will help to improve commercial ped food quality, reduce harm and minimize exposure to dogs. Herein, we outline a description of mycotoxins detected in dog food, toxicity and clinical findings in dogs, as well as methods applied in mycotoxins detoxification. This review aims to provide a reference for future studies involved in the evaluation of the risk, preventative strategies, and clear criteria of mycotoxins for minimizing exposure, reducing harm, and preventing mycotoxicosis in dog

Co-infecting pathogens can contribute to inflammatory responses and severe symptoms in COVID-19

Background: The current COVID-19 pandemic is posing a major challenge to public health on a global scale. While it is generally believed that severe COVID-19 results from over-expression of inflammatory mediators (i.e., a "cytokine storm"), it is still unclear whether and how co-infecting pathogens contribute to disease pathogenesis. To address this, we followed the entire course of the disease in cases with severe or critical COVID-19 to determine the presence and abundance of all potential pathogens present-the total "infectome"-and how they interact with the host immune system in the context of severe COVID-19. Methods: We examined one severe and three critical cases of COVID-19, as well as a set of healthy controls, with longitudinal samples (throat swab, whole blood, and serum) collected from each case. Total RNA sequencing (meta-transcriptomics) was performed to simultaneously investigate pathogen diversity and abundance, as well as host immune responses, in each sample. A Bio-Plex method was used to measure serum cytokine and chemokine levels. Results: Eight pathogens, SARS-CoV-2, Aspergillus fumigatus (A. fumigatus), Mycoplasma orale (M. orale), Myroides odoratus (M. odoratus), Acinetobacter baumannii (A. baumannii), Candida tropicalis, herpes simplex virus (HSV) and human cytomegalovirus (CMV), identified in patients with COVID-19 appeared at different stages of the disease. The dynamics of inflammatory mediators in serum and the respiratory tract were more strongly associated with the dynamics of the infectome compared with SARS-CoV-2 alone. Correlation analysis revealed that pulmonary injury was directly associated with cytokine levels, which in turn were associated with the proliferation of SARS-CoV-2 and co-infecting pathogens. Conclusions: For each patient, the cytokine storm that resulted in acute lung injury and death involved a dynamic and highly complex infectome, of which SARS-CoV-2 was a component. These results indicate the need for a precision medicine approach to investigate both the infection and host response as a standard means of infectious disease characterization