Permeability Prediction of Sandstones Based on Mercury Intrusion Method

The use of the mercury intrusion method has been one of the most relevant trends in determining the permeability of porous media in the past decades. In this paper, general knowledge of sandstone reservoir evaluation is delineated including the pore distribution of sandstones and air permeability measurement. Based upon the paradigmatic study conducted by Purcell, a schematic diagram illustrating apparatus used in mercury intrusion is shown and introduced, and the relevant procedure is also outlined. Four significant permeability prediction models are described respectively and compared based on researches focusing on tight rocks. By doing so, this article reveals that the performance of the models is different despite the painstaking analysis and the significance of these studies. The contribution of this present study is providing a general reference of permeability prediction by mercury intrusion method as well as its previous momentous studies, giving a comparison among the given models.

Commercial SARS-CoV-2 Targeted, Protease Inhibitor Focused and Protein–Protein Interaction Inhibitor Focused Molecular Libraries for Virtual Screening and Drug Design

Since December 2019, the new SARS-CoV-2-related COVID-19 disease has caused a global pandemic and shut down the public life worldwide. Several proteins have emerged as potential therapeutic targets for drug development, and we sought out to review the commercially available and marketed SARS-CoV-2-targeted libraries ready for high-throughput virtual screening (HTVS). We evaluated the SARS-CoV-2-targeted, protease-inhibitor-focused and protein–protein-interaction-inhibitor-focused libraries to gain a better understanding of how these libraries were designed. The most common were ligand- and structure-based approaches, along with various filtering steps, using molecular descriptors. Often, these methods were combined to obtain the final library. We recognized the abundance of targeted libraries offered and complimented by the inclusion of analytical data; however, serious concerns had to be raised. Namely, vendors lack the information on the library design and the references to the primary literature. Few references to active compounds were also provided when using the ligand-based design and usually only protein classes or a general panel of targets were listed, along with a general reference to the methods, such as molecular docking for the structure-based design. No receptor data, docking protocols or even references to the applied molecular docking software (or other HTVS software), and no pharmacophore or filter design details were given. No detailed functional group or chemical space analyses were reported, and no specific orientation of the libraries toward the design of covalent or noncovalent inhibitors could be observed. All libraries contained pan-assay interference compounds (PAINS), rapid elimination of swill compounds (REOS) and aggregators, as well as focused on the drug-like model, with the majority of compounds possessing their molecular mass around 500 g/mol. These facts do not bode well for the use of the reviewed libraries in drug design and lend themselves to commercial drug companies to focus on and improve.

Rheumatoid Arthritis in Silica-Exposed Workers

Few studies have examined rheumatoid arthritis (RA) risk and severity in Korean workers exposed to silica. We compared the hospitalization risk of RA between silica-exposed workers and the general Korean population. The study cohort consisted of male workers exposed to silica who had undergone at least one silica-associated special medical examination between 1 January 2000 and 31 December 2004 (N = 149,948). The data were from the Korea Occupation Safety and Health Agency. RA morbidity based on hospital admission records was estimated from 2000 to 2005 using the Korea National Health Insurance Service claims data. The standardized admission ratio (SAR) was calculated by dividing the observed number of admissions in silica-exposed workers by the expected number of admissions in the general reference population. For the sum of “Seropositive rheumatoid arthritis” (M05) and “Other rheumatoid arthritis” (M06), the SAR was higher in the silica-exposed group (1.34, 95% CI 1.08–1.64). For M05, workers with <10 years of silica exposure had a significantly higher SAR (2.54, 95% CI 1.10–5.01) than the general population. More silica-exposed workers without a diagnosis of pneumoconiosis were hospitalized for RA than the general population. Our analysis reaffirms the link between silica exposure and RA and suggests that the severity of RA is increased by silica. Further studies of silica-exposed workers with longer follow-up are needed.

One of Everything: The Breakthrough Listen Exotica Catalog

We present Breakthrough Listen’s Exotica Catalog as the centerpiece of our efforts to expand the diversity of targets surveyed in the Search for Extraterrestrial Intelligence (SETI). As motivation, we introduce the concept of survey breadth, the diversity of objects observed during a program. Several reasons for pursuing a broad program are given, including increasing the chance of a positive result in SETI, commensal astrophysics, and characterizing systematics. The Exotica Catalog is a 963 entry collection of 816 distinct targets intended to include “one of everything” in astronomy. It contains four samples: the Prototype sample, with an archetype of every known major type of nontransient celestial object; the Superlative sample of objects, with the most extreme properties; the Anomaly sample of enigmatic targets that are in some way unexplained; and the Control sample, with sources not expected to produce positive results. As far as we are aware, this is the first object list in recent times with the purpose of spanning the breadth of astrophysics. We share it with the community in hopes that it can guide treasury surveys and as a general reference work. Accompanying the catalog is an extensive discussion of the classification of objects and a new classification system for anomalies. Extensive notes on the objects in the catalog are available online. We discuss how we intend to proceed with observations in the catalog, contrast it with our extant Exotica efforts, and suggest how similar tactics may be applied to other programs.

The First Two Complete Mitochondrial Genomes of Neoephemeridae (Ephemeroptera): Comparative Analysis and Phylogenetic Implication for Furcatergalia

Mayflies of the family Neoephemeridae are widespread in the Holarctic and Oriental regions, and its phylogenetic position is still unstable in the group Furcatergalia (mayflies with fringed gills). In the present study, we determined the complete mitogenomes of two species, namely Potamanthellus edmundsi and Pulchephemera projecta, of this family. The lengths of two mitogenomes were 15,274 bp and 16,031 bp with an A + T content of 73.38% and 73.07%, respectively. Two neoephemerid mitogenomes had a similar gene size, base composition, and codon usage of protein-coding genes (PCGs), and the sequenced gene arrangements were consistent with the putative ancestral insect mitogenomes as understood today. The most variable gene of Furcatergalia mitogenomes was ND2, while the most conserved gene was COI. Meanwhile, the analysis of selection pressures showed that ND6 and ATP8 exhibited a relaxed purifying selection, and COI was under the strongest purifying selection. Phylogenetic trees reconstructed based on two concatenated nucleotide datasets using both maximum likelihood (ML) and Bayesian inference (BI) estimations yielded robust identical topologies. These results corroborated the monophyly of seven studied families and supported the family Leptophlebiidae as being of the basal lineage of Furcatergalia. Additionally, the sister-group relationship of Caenidae and Neoephemeridae was well supported. Methodologically, our present study provides a general reference for future phylogenetic studies of Ephemeroptera at the mitogenome level.

Cycling Activity Dataset Creation and Application for Feedback Giving

Our research aims at supporting personal cycling trainer applications in training planning and feedback giving to nonprofessional outdoor cyclists, based on a general reference. In this paper we present the created dataset. According to our present knowledge, this data collection is the first public dataset containing cycling activities recorded outdoor. Its usability for training planning and feedback giving is demonstrated through an example. The dataset is clustered according to age groups, considering distance and average speed as the two most influential features when predicting the time required for training. These clusters are then applied as references in feedback giving and goal setting.

COVID-19 Comorbidity and Non-Communicable Diseases (NCDs) General Reference Hospital (HGR), Niamey, Niger

The COVID-19 infection has highlighted the most vulnerable patients. Indeed, COVID-19 patients suffering from another pathology including NCDs such as Arterial Hypertension (Hypertension), diabetes, cancers and respiratory diseases are paying a heavy price for this pandemic. We undertook a study in Niger to better document this comorbidity in a cross-sectional study that brought together patients hospitalized at the Niamey General Reference Hospital for COVID-19 infection and suffering from one or more NCDs. Among 273 patients hospitalized from March 19 to June 03, 2020, 34.8% had a non-communicable disease associated with COVID-19. The average age of the patients was 55 years (22 years to 94 years) and the sex ratio was 2.64 (72.5 % men and 27.5% women). Hypertension was the most represented NCD with 24.5%, followed by diabetes in 17.9% of cases, respiratory diseases 3.66% and other diseases (Heart disease, Obesity, Dyslipidemia, Gout, Chronic renal failure) with 3%. Health workers were the most affected by the disease with 38.6% of cases. The average consultation time was 3.77 days with extremes ranging from 1 to 8 days. The clinical symptoms characterizing the two main groups of patients (hypertensive and diabetic) were almost identical. It consisted mainly of cough, fever, chills, sore throat and rhinorrhea. According to the WHO clinical criteria for the severity of COVID-19, 34 patients or 16.11% were severe and 177 patients or 83% moderate. The clinical severity of the disease is significantly correlated with the patient's age (over 50 years) and the presence of an NCD associated with COVID-19. A total of 35.8% were hospitalized in intensive care in the NCD and COVID-19 group and 14.6% in the NCD group (p <0.001). The average length of patient hospitalization was 6.87 days overall, it was 7 days in intensive care. In 61.7% of cases the length of hospitalization was greater than 4 days. There was 22.1% in the NCD group and 7.3% in the just COVID-19 group (p <0.001). A total of 90.6% of registered deaths occurred in intensive care.

Deep Composition of Tensor-Trains Using Squared Inverse Rosenblatt Transports

Characterising intractable high-dimensional random variables is one of the fundamental challenges in stochastic computation. The recent surge of transport maps offers a mathematical foundation and new insights for tackling this challenge by coupling intractable random variables with tractable reference random variables. This paper generalises the functional tensor-train approximation of the inverse Rosenblatt transport recently developed by Dolgov et al. (Stat Comput 30:603–625, 2020) to a wide class of high-dimensional non-negative functions, such as unnormalised probability density functions. First, we extend the inverse Rosenblatt transform to enable the transport to general reference measures other than the uniform measure. We develop an efficient procedure to compute this transport from a squared tensor-train decomposition which preserves the monotonicity. More crucially, we integrate the proposed order-preserving functional tensor-train transport into a nested variable transformation framework inspired by the layered structure of deep neural networks. The resulting deep inverse Rosenblatt transport significantly expands the capability of tensor approximations and transport maps to random variables with complicated nonlinear interactions and concentrated density functions. We demonstrate the efficiency of the proposed approach on a range of applications in statistical learning and uncertainty quantification, including parameter estimation for dynamical systems and inverse problems constrained by partial differential equations.