Structure-Activity Relationship (SAR) Study of Rigid Guanidine and Guanidino-mimetic Analogues of the Neuropeptide FF Receptor Antagonist MES304 (preprint)

The Neuropeptide FF (NPFF) receptor system has been implicated in mediating opioid-induced hyperalgesia and tolerance. Understanding the structural features that contribute to high binding affinity of ligands to the NPFF receptors could help in identifying potent and selective NPFF-R probes. We conducted a structure-activity relationship (SAR) study targeting the guanidine functionality of our previously identified lead molecule MES304 in order to probe the guanidine binding site of the NPFF receptors. By applying several rigidifying modifications and substitutions on the guanidine moiety, we concluded that while the guanidine group is optimal for strong binding affinity at both receptor subtypes (NPFF1-R/NPFF2-R), other groups may be tolerated. Compound 17b, in which the guanidine was replaced with a piperidine ring, displayed robust binding affinity at both receptor subtypes (Ki < 300 nM) despite lacking a guanidine group. This indicated that strong binding to the NPFF receptors can be achieved with analogues devoid of guanidine.

Classification of FLT3 Inhibitors and SAR Analysis by Machine Learning Methods (preprint)

FMS-like tyrosine kinase 3 (FLT3) is a type III receptor tyrosine kinase, which is an important target for anti-cancer therapy. In this work, we conducted a structure-activity relationship (SAR) study on 3867 FLT3 inhibitors we collected. MACCS fingerprints, ECFP4 fingerprints, and TT fingerprints were used to represent the inhibitors in the dataset. A total of 36 classification models were built based on support vector machine (SVM), random forest (RF), eXtreme Gradient Boosting (XGBoost), and deep neural networks (DNN) algorithms. Model 3D_3 built by deep neural networks (DNN) and TT fingerprints performed best on the test set with the highest prediction accuracy of 85.83% and Matthews correlation coefficient (MCC) of 0.72 and also performed well on the external test set. In addition, we clustered 3867 inhibitors into 11 subsets by K-Means algorithm to figure out the structural characteristics of the reported FLT3 inhibitors. Finally, we analyzed the SAR of FLT3 inhibitors by RF algorithm based on ECFP4 fingerprints. The results showed that 2-aminopyrimidine, 1-ethylpiperidine, 2,4-bis(methylamino)pyrimidine, amino-aromatic heterocycle, [(2E)-but-2-enyl]dimethylamine, but-2-enyl, and alkynyl were typical fragments among highly active inhibitors. Besides, three scaffolds in Subset_A (Subset 4), Subset_B, and Subset_C showed a significant relationship to inhibition activity targeting FLT3.

Exploring International Faculty’s Perspectives on Their Campus Life by PLS-SEM (preprint)

Building sustainable campuses has become a priority for various higher education institutes (HEIs). Internalization is one of the important tasks in the progress. Therefore, attracting and maintaining international faculty has converted to action for various internationalized campuses. During the COVID-19 pandemic, it has impacted most of the citizens’ life, while the international faculty members’ life might be tight during this period. The purpose of this study is to explore the perspectives of international faculty members on their life in higher education institutes by using partial least square structural equation modeling (PLS-SEM). Using a self-compiled online survey, we invited 80 international faculty members of HEIs in Taiwan to investigate this issue. The survey covers career and professional status, teaching and research status, and demographics of the faculty. This study proposes a novel conceptual framework for addressing international faculty’s campus life, the design will examine the relationships among working conditions, views of institutions, views of government’s measures, and levels of satisfaction through PLS-SEM. The demographic profile of the participants revealed (a) most international faculty are employed full-time as lecturers or assistant professors in most public universities; (b) most international faculty members earned their first degree outside of Taiwan; however, 66% of them earned their post-doctoral degrees in Taiwan. The results of PLS-SEM suggest that the international faculty perspective of government is more directly related to their current work satisfaction and overall satisfaction. The designed model has shown work well to assess this issue. This can be extended to other higher education settings to tackle similar issues.

STIM1 and ORAI1 mutations leading to tubular aggregate myopathies are sensitive to the Store-operated Ca2+-entry modulators CIC-37 and CIC-39.

Gain-of-function mutations on STIM1 and ORAI1 genes are responsible for an increased store-operated calcium entry, and underlie the characteristic symptoms of three overlapping ultra-rare genetic disorders (i.e tubular aggregate myopathy, Stormorken syndrome, York platelet syndrome) that can be grouped as tubular aggregate myopathies. These mutations lead to a wide spectrum of defects, which usually include muscle weakness and cramps. Negative modulators of store-operated Ca2+-entry targeting wild-type STIM1 and ORAI1 have entered clinical trials for a different array of disorders, including pancreatitis, COVID-19, cancer, and autoimmune disorders and, while efficacy data is awaited, safety data indicates tolerability of this STIM1/ORAI1 mutations are amenable to pharmacological intervention. If this were so, given that there are no approved treatments or clinical trials ongoing for these rare disorders, it could be envisaged that these agents could also rehabilitate tubular aggregate myopathy patients. In the present contribution we characterized the Ca2+-entry patterns induced by eleven STIM1 and three ORAI1 mutations in heterologous systems or in patient-derived cells, i.e. fibroblasts and myotubes, and evaluated the effect of CIC-37 and CIC-39, two novel store-operated calcium entry modulators. Our data show that all STIM1 and ORAI1 gain-of-function mutations tested, with the possible exception of the R304Q STIM1 mutation, are amenable to inhibition, albeit with slightly different sensitivities, paving the way to the development of SOCE modulators in tubular aggregate myopathies.

Impact of Salmonella genome rearrangement on gene expression (preprint)

In addition to nucleotide variation, many bacteria also undergo changes at a much larger scale via rearrangement of their genome structure around long repeat sequences. These rearrangements result in genome fragments shifting position and/or orientation in the genome without necessarily affecting the underlying nucleotide sequence. To date, scalable techniques have not been applied to genome structure (GS) identification, so it remains unclear how extensive this variation is and the extent of its impact upon gene expression. However, the emergence of multiplexed, long-read sequencing overcomes the scale problem, as reads of several thousand bases are routinely produced that can span long repeat sequences to identify the flanking chromosomal DNA, allowing GS identification. Genome rearrangements were generated in Salmonella enterica serovar Typhi through long-term culture at ambient temperature. Colonies with rearrangements were identified via long-range PCR and subjected to long-read nanopore sequencing to confirm genome variation. Four rearrangements were investigated for differential gene expression using transcriptomics. All isolates with changes in genome arrangement relative to the parent strain were accompanied by changes in gene expression. Rearrangements with similar fragment movements demonstrated similar changes in gene expression. The most extreme rearrangement caused a large imbalance between the origin and terminus of replication and was associated with differential gene expression as a factor of distance moved towards or away from the origin of replication. Genome structure variation may provide a mechanism through which bacteria can quickly adapt to new environments and warrants routine assessment alongside traditional nucleotide level measures of variation.

Lossless Distributed Linear Mixed Model with Application to Integration of Heterogeneous Healthcare Data (preprint)

Linear mixed models (LMMs) are commonly used in many areas including epidemiology for analyzing multi-site data with heterogeneous site-specific random effects. However, due to the regulation of protecting patients’ privacy, sensitive individual patient data (IPD) are usually not allowed to be shared across sites. In this paper we propose a novel algorithm for distributed linear mixed models (DLMMs). Our proposed DLMM algorithm can achieve exactly the same results as if we had pooled IPD from all sites, hence the lossless property. The DLMM algorithm requires each site to contribute some aggregated data (AD) in only one iteration. We apply the proposed DLMM algorithm to analyze the association of length of stay of COVID-19 hospitalization with demographic and clinical characteristics using the administrative claims database from the UnitedHealth Group Clinical Research Database.

The Italian Response to Exploitation of Migrant Workers in the Agricultural Sector: Between Criminalization and Prevention (preprint)

The exploitation of migrant workers in the agricultural sector in Italy remains common. This is so despite the fact that, first, Italy has ratified the vast majority of instruments dealing with serious forms of labour exploitation at the international and regional level and has adopted legislation implementing its international obligations, by creating offences which criminalize practices amounting to slavery, servitude, forced labour and exploitation of labour, and second, that the interpretation adopted by the Italian courts of the relevant concepts appears to be in line with that of international bodies. Clearly, an approach which goes beyond the criminal law and includes measures of prevention is therefore needed in order to address the phenomenon of exploitation of migrant workers at its roots. In addition to structural deficiencies in the system of labour inspections, a major factor contributing to the vulnerability of migrant workers results from Italy’s immigration law and policy, which has as a result that the immigration status of many migrant workers is irregular, or at least precarious. Notwithstanding the existence of (narrow) paths for legal migration and special provisions for the regularization of victims of severe labour exploitation, the current legislation remains insufficient and ineffective in combating exploitation. Whilst recent legislation adopted to deal with the Covid-19 emergency specifically provides for the regularization of irregular agricultural workers already present in Italy, it remains an ad hoc measure which does not address the underlying systemic problems and is not a substitute for an approach to immigration control which puts the right of migrant workers not to be exploited at the core of the debate.

A Novel Strategy for the Development of Vaccines for SARS-CoV-2 (COVID-19) and Other Viruses Using AI and Viral Shell Disorder (preprint)

A model that predicts levels of coronavirus (CoV) respiratory/fecal-oral transmission potentials based on the outer shell hardness has been built using neural network (artificial intelligence, AI) analysis of the percentage of disorder (PID) in the nucleocapsid, N, and membrane, M, proteins of the inner and outer viral shells, respectively. Based mainly on the PID of N, SARS-CoV-2 is categorized as having intermediate levels of both respiratory and fecal oral transmission potential. Related to this, other studies have found strong positive correlations between virulence and inner shell disorder among numerous viruses, including Nipah, Ebola, and Dengue viruses. There is some evidence that this is also true for SARS-CoV-2 and SARS-CoV, which have N PIDs of 48% and 50%, and are characterized by case-fatality rates of 7.1% and 10.9%, respectively. The link between levels of respiratory transmission and virulence lies in viral load of body fluids and organ respectively. A virus can be infectious via respiratory modes only if the viral loads in saliva and mucus exceed certain minima. Likewise, a person may die, if the viral load is too high especially in viral organs. Inner shell proteins of viruses play important roles in the replication of viruses, and structural disorder enhances these roles by providing greater efficiency in protein-protein/DNA/RNA/lipid binding. This paper outlines a novel strategy in attenuating viruses involving comparison of disorder patterns of inner shells of related viruses to identify residues and regions that could be ideal for mutation. The M protein of SARS-CoV-2 has one of the lowest M PID values (6%) in its family, and therefore this virus has one of the hardest outer shells, which makes it resistant to antimicrobial enzymes in body fluid. While this is likely responsible for its contagiousness, the risks of creating an attenuated virus with a more disordered M are discussed.

A cascade network for Detecting COVID-19 using chest x-rays (preprint)

The worldwide spread of pneumonia caused by a novel coronavirus poses an unprecedented challenge to the world's medical resources and prevention and control measures. Covid-19 attacks not only the lungs, making it difficult to breathe and life-threatening, but also the heart, kidneys, brain and other vital organs of the body, with possible sequela. At present, the detection of COVID-19 needs to be realized by the reverse transcription-polymerase Chain Reaction (RT-PCR). However, many countries are in the outbreak period of the epidemic, and the medical resources are very limited. They cannot provide sufficient numbers of gene sequence detection, and many patients may not be isolated and treated in time. Given this situation, we researched the analytical and diagnostic capabilities of deep learning on chest radiographs and proposed Cascade-SEMEnet which is cascaded with SEME-ResNet50 and SEME-DenseNet169. The two cascade networks of Cascade - SEMEnet both adopt large input sizes and SE-Structure and use MoEx and histogram equalization to enhance the data. We first used SEME-ResNet50 to screen chest X-ray and diagnosed three classes: normal, bacterial, and viral pneumonia. Then we used SEME-DenseNet169 for fine-grained classification of viral pneumonia and determined if it is caused by COVID-19. To exclude the influence of non-pathological features on the network, we preprocessed the data with U-Net during the training of SEME-DenseNet169. The results showed that our network achieved an accuracy of 85.6\% in determining the type of pneumonia infection and 97.1\% in the fine-grained classification of COVID-19. We used Grad-CAM to visualize the judgment based on the model and help doctors understand the chest radiograph while verifying the effectivene.

Proceedings Eighth Workshop on Mathematically Structured Functional Programming (preprint)

This volume contains the proceedings of the Eighth Workshop on Mathematically Structured Functional Programming (MSFP 2020). The meeting was originally scheduled to take place in Dublin, Ireland on the 25th of April as a satellite event of the European Joint Conferences on Theory & Practice of Software (ETAPS 2020). Due to the COVID-19 pandemic, ETAPS 2020, and consequently MSFP 2020, has been postponed to a date yet to be determined. The MSFP workshop highlights applications of mathematical structures to programming applications. We promote the use of category theory, type theory, and formal language semantics to the development of simple and reasonable programs. This year's papers cover a variety of topics ranging from array programming to dependent types to effects.