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Kleptoparasitic behavior was observed by the authors in P. tiliguerta and P. filfolensis. That this behavior has been observed in two mediterranean species of Podarcis, independently parasitizing on species of Camponotus ant, suggests there is benefit to kleptoparasitism on Camponotus spp. in Mediterranean ecosystems.
Modern biology frequently relies on machine learning to provide predictions and improve decision processes. There have been recent calls for more scrutiny on machine learning performance and possible limitations. Here we present a set of community-wide recommendations aiming to help establish standards of supervised machine learning validation in biology. Adopting a structured methods description for machine learning based on data, optimization, model, evaluation (DOME) will aim to help both reviewers and readers to better understand and assess the performance and limitations of a method or outcome. The recommendations are formulated as questions to anyone wishing to pursue implementation of a machine learning algorithm. Answers to these questions can be easily included in the supplementary material of published papers.
Crosslinking mass spectrometry (Crosslinking MS) has substantially matured as a method over the last two decades through parallel development in multiple labs, demonstrating its applicability for protein structure determination, conformation analysis and mapping protein interactions in complex mixtures. Crosslinking MS has become a much-appreciated and routinely applied tool especially in structural biology. Therefore, it is timely that the community commits to the development of methodological and reporting standards. This white paper builds on an open process comprising a number of events at community conferences since 2015 and identifies aspects of Crosslinking MS for which guidelines should be developed as part of a Crosslinking MS standards initiative.
Seasonality of acute viral respiratory diseases is a well-known and yet not fully understood phenomenon. Several models have been proposed to explain the regularity of yearly recurring outbreaks and the phase-differences observed at different latitudes on Earth. Such models take into account known internal causes, primarily the periodic emergence of new virus variants that evade the host immune response. Yet, this alone, is generally unable to explain the regularity of recurrences and the observed phase-differences. Here we show that seasonality of viral respiratory diseases, as well as its distribution with latitude on Earth, can be fully explained by the virucidal properties of UV-B and A Solar photons through a daily, minute-scale, resonant forcing mechanism. Such an induced periodicity can last, virtually unperturbed, from tens to hundreds of cycles, and even in presence of internal dynamics (host's loss of immunity) much slower than seasonal will, on a long period, generate seasonal oscillations.
Modeling the spread of COVID-19 is crucial for informing public health policy. All models for COVID-19 epidemiology rely on parameters describing the dynamics of the infection process. The meanings of epidemiological parameters like R_0, R_t, the "serial interval" and "generation interval" can be challenging to understand, especially as these and other parameters are conceptually overlapping and sometimes confusingly named. Moreover, the procedures used to estimate these parameters make various assumptions and use different mathematical approaches that should be understood and accounted for when relying on parameter values and reporting them to the public. Here, we offer several insights regarding the derivation of commonly-reported epidemiological parameters, and describe how mitigation measures like lockdown are expected to affect their values. We aim to present these quantitative relationships in a manner that is accessible to the widest audience possible. We hope that better communicating the intricacies of epidemiological models will improve our collective understanding of their strengths and weaknesses, and will help avoid possible pitfalls when using them.
The COVID-19 pandemic has led to significant changes in how people are currently living their lives. To determine how to best reduce the effects of the pandemic and start reopening societies, governments have drawn insights from mathematical models of the spread of infectious diseases. In this article, we give an introduction to a family of mathematical models (called "compartmental models") and discuss how the results of analyzing these models influence government policies and human behavior, such as encouraging mask wearing and physical distancing to help slow the spread of the disease.
Soil organic carbon (SOC) plays an important role on improving soil conditions and soil functions. Increasing land use changes have induced an important decline of SOC content at global scale. Increasing SOC in agricultural soils has been proposed as a strategy to mitigate climate change. Animal manure has the characteristic of enriching SOC, when applied to crop fields, while, in parallel, it could constitute a natural fertilizer for the crops. In this paper, a simulation is performed using the area of Catalonia, Spain as a case study for the characteristic low SOC in the Mediterranean, to examine whether animal manure can improve substantially the SOC of agricultural fields, when applied as organic fertilizers. Our results show that the policy goals of the 4x1000 strategy can be achieved only partially by using manure transported to the fields. This implies that the proposed approach needs to be combined with other strategies.
Objective: Falun Gong (FLG) is a practice of mind and body focusing on moral character improvement along with meditative exercises. This 2002 pilot study explored perceived health status, medical resource utilization and related factors among Taiwanese FLG practitioners, compared to the general Taiwanese norm estimated by the 2001 National Health Interview Survey (NHIS). Methods: This cross-sectional, observational study was based on a voluntary, paper-based survey conducted from October 2002 to February 2003 using the same Taiwanese SF-36 instrument employed by the NHIS. Primary outcomes included eight SF-36 domain scores and the number of medical visits. One-sample t-tests, one-way ANOVA and multivariate linear regression analyses were performed. Results: The response rate was 75.6% (1,210/1,600). Compared to the norm, the study cohort had significantly higher scores in six of eight SF-36 domains across gender and age (p<0.05). Among those with chronic diseases, 70% to 89% reported their conditions either improved or cured. 74.2%, 79.2%, 83.3%, and 85.6% quitted alcohol drinking, smoking, chewing betel nuts, and gambling. 62.7% reported a reduced number of medical visits (mean=13.53 before; mean=5.87 after). Conclusions: In this subject cohort, practicing FLG led to higher perceived health scores and reduced health resource utilization compared to the norm.
In this paper, we identify the factors associated with cancer screening participation in Korea. We expand upon previous studies through a multilevel mediation model and a composite regional socioeconomic status index which combines education level and income level. Results of the model indicate that education level, nutritional education status and income level are significantly associated with cancer screening participation. With our findings in mind, we recommend health authorities to increase promotional health campaigns toward certain at-risk groups and expand the availability of nutrition education programs.
The epidemic of COVID-19 has caused an unpredictable and devastated disaster to the public health in different territories around the world. Common phenotypes include fever, cough, shortness of breath, and chills. With more cases investigated, other clinical phenotypes are gradually recognized, for example, loss of smell, and loss of tastes. Compared with discharged or cured patients, severe or died patients often have one or more comorbidities, such as hypertension, diabetes, and cardiovascular disease. In this study, we systematically collected and analyzed COVID-19-related clinical phenotypes from 70 articles. The commonly occurring 17 phenotypes were classified into different groups based on the Human Phenotype Ontology (HPO). Based on the HP classification, we systematically analyze three nervous phenotypes (loss of smell, loss of taste, and headache) and four abdominal phenotypes (nausea, vomiting, abdominal pain, and diarrhea) identified in patients, and found that patients from Europe and USA turned to have higher nervous phenotypes and abdominal phenotypes than patients from Asia. A total of 23 comorbidities were found to commonly exist among COVID-19 patients. Patients with these comorbidities such as diabetes and kidney failure had worse outcomes compared with those without these comorbidities.
We compare the quantitative risk of infection from short range airborne transmission of SARS-CoV-2 to the long-range risk from aerosol transmission in an enclosed space.
Cytoskeletons are self-organized networks based on polymerized proteins: actin, tubulin, and driven by motor proteins, such as myosin, kinesin and dynein. Their positive Darwinian evolution enables them to approach optimized functionality (self-organized criticality). Our theoretical analysis uses hydropathic waves to identify and contrast the functional differences between the polymerizing $α$ and $β$ tubulin monomers, which are similar in length and secondary structures, as well as having indistinguishable phylogenetic trees. We show how evolution has improved water-driven flexibility especially for $α$ tubulin, and thus facilitated heterodimer microtubule assembly, in agreement with recent atomistic simulations and topological models. We conclude that the failure of phylogenetic analysis to identify functionally specific positive Darwinian evolution has been caused by 20th century technical limitations. These are overcome using 21st century quantitative mathematical methods based on thermodynamic scaling and hydropathic modular averaging. Our most surprising result is the identification of large level sets, especially in hydrophobic extrema, with both thermodynamically first- and second-order scaled water waves. Our calculations include explicitly long-range water-protein interactions described by fractals. We also suggest a much-needed corrective for large protein drug development costs.
Living systems are subject to the arrow of time; from birth, they undergo complex transformations (self-organization) in a constant battle for survival, but inevitably ageing and disease trap them to death. Can ageing be understood and eventually reversed? What tools can be employed to further our understanding of ageing? The present article is an invitation for biologists and clinicians to consider key conceptual ideas and computational tools (known to mathematicians and physicists), which potentially may help dissect some of the underlying processes of ageing and disease. Specifically, we first discuss how to classify and analyze complex systems, as well as highlight critical theoretical difficulties that make complex systems hard to study. Subsequently, we introduce Topological Data Analysis - a novel Big Data tool - which may help in the study of complex systems since it extracts knowledge from data in a holistic approach via topological considerations. These conceptual ideas and tools are discussed in a rather informal way to pave future discussions and collaborations between mathematicians and biologists studying ageing.
The first cases of coronavirus disease 2019 (COVID-19) were reported in December 2019 and the outbreak of SARS-CoV-2 was declared a pandemic in March 2020 by the World Health Organization. This sparked a plethora of investigations into diagnostics and vaccination for SARS-CoV-2, as well as treatments for COVID-19. Since COVID-19 is a severe disease associated with a high mortality, clinical trials in this disease should be monitored by a data monitoring committee (DMC), also known as data safety monitoring board (DSMB). DMCs in this indication face a number of challenges including fast recruitment requiring an unusually high frequency of safety reviews, more frequent use of complex designs and virtually no prior experience with the disease. In this paper, we provide a perspective on the work of DMCs for clinical trials of treatments for COVID-19. More specifically, we discuss organizational aspects of setting up and running DMCs for COVID-19 trials, in particular for trials with more complex designs such as platform trials or adaptive designs. Furthermore, statistical aspects of monitoring clinical trials of treatments for COVID-19 are considered. Some recommendations are made regarding the presentation of the data, stopping rules for safety monitoring and the use of external data. The proposed stopping boundaries are assessed in a simulation study motivated by clinical trials in COVID-19.
Recent guidelines by WHO recommend delaying non-essential oral health care amid COVID-19 pandemic and call for research on aerosol generated during dental procedures. Thus, this study aims to assess the mechanisms of dental aerosol dispersion in dental offices and to provide recommendations based on a quantitative study to minimize infection transmission in dental offices. The spread and removal of aerosol particles generated from dental procedures in a dental office are measured near the source and at the corner of the office. We studied the effects of air purification (on/off), door condition (open/close), and particle sizes on the temporal concentration distribution of particles. The results show that in the worst-scenario scenario it takes 95 min for 0.5 um particles to settle, and that it takes a shorter time for the larger particles. The indoor air purifier tested expedited the removal time at least 6.3 times faster than the scenario air purifier off. Airborne particles may be transported from the source to the rest of the room, even when the particle concentrations in the generation zone return to the background level. These results are expected to be valuable to related policy making and technology development for infection disease control in dental offices and similar built environments.
Diabetes is considered as an critical comorbidity linked with the latest coronavirus disease 2019 (COVID-19) which spreads through Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-Cov-2). The diabetic patients have higher threat of infection from novel corona virus. Depending on the region in the globe, 20% to 50% of patients infected with COVID-19 pandemic had diabetes. The current article discussed the risk associated with diabetic patients and also recommendation for controlling diabetes during this pandemic situation. The article also discusses the case study of COVID-19 at various regions around the globe and the preventive actions taken by various countries to control the effect from the virus. The article presents several smart healthcare solutions for the diabetes patients to have glucose insulin control for the protection against COVID-19.
What is life. Schrodingers question is discussed here for a specific protein, villin, which builds cells in tissues that detect taste and sound. Villin is represented by a sequence of 827 amino acids bound to a peptide backbone chain. We focus attention on a limited problem, the Darwinian evolution of villin sequences from chickens to humans. This biophysical problem is analyzed using a new physicical method based on thermodynamic domain scaling, a technique that bridges the gap between physical concepts, self-organized criticality, and conventional biostructural practice. It turns out that the evolutionary changes can be explained by Darwinian selection, which is not generally accepted by biologists at the protein level. The presentation is self-contained, and requires no prior experience with proteins at the molecular level.
Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) virus caused the novel coronavirus disease-2019 (COVID-19) affecting the whole world. Like SARS-CoV and MERS-CoV, SARS-CoV-2 are thought to originate in bats and then spread to humans through intermediate hosts. Identifying intermediate host species is critical to understanding the evolution and transmission mechanisms of COVID-19. However, determining which animals are intermediate hosts remains a key challenge. Virus host-genome similarity (HGS) is an important factor that reflects the adaptability of virus to host. SARS-CoV-2 may retain beneficial mutations to increase HGS and evade the host immune system. This study investigated the HGSs between 399 SARS-CoV-2 strains and 10 hosts of different species, including bat, mouse, cat, swine, snake, dog, pangolin, chicken, human and monkey. The results showed that the HGS between SARS-CoV-2 and bat was the highest, followed by mouse and cat. Human and monkey had the lowest HGS values. In terms of genetic similarity, mouse and monkey are halfway between bat and human. Moreover, given that COVID-19 outbreaks tend to be associated with live poultry and seafood markets, mouse and cat are more likely sources of infection in these places. However, more experimental data are needed to confirm whether mouse and cat are true intermediate hosts. These findings suggest that animals closely related to human life, especially those with high HGS, need to be closely monitored.