How could primordial life, before the evolution of genetic systems, adapt to fluctuating environments and achieve homeostasis? This study proposes a minimal, chemically plausible model where homeostasis emerges from a simple chemical reaction network. It utilizes an internal Lotka-Volterra chemical oscillator as a "search engine" to periodically vary a protocell's pigmentation. The system then optimizes its internal state by evaluating the temporal correlation between these internal fluctuations and a single global metric,the cell's self-replication rate, through a mechanism termed "antagonistic memory molecules." Numerical simulations demonstrate that the model can autonomously converge to and maintain the optimal temperature for its self-replication, even amidst significant environmental fluctuations. These findings provide a constructive proof-of-concept for how a core process of active inference can emerge from a simple physicochemical system, offering a concrete scenario for the acquisition of adaptive capabilities at the origin of life.
The genus of Rumex from the Polygonaceae family is widespread in the world, particularly in the northern hemisphere, and includes about 250 species. The species of this genus are used for medicinal purposes and their allelopathic impacts. Regarding allelopathy, many allelochemicals have been detected in different Rumex species. Therefore, plant extracts, leachates, and plant residues of different species of Rumex have been studied with seed germination and plant growth in the recipient plants. Also, various species of Rumex were tested for their allelopathic capacities to control weeds, insects, and plant pathogens. Besides, it was revealed that the allelopathic impact of Rumex spp. was variable depending on extract concentration, the plant part of the Rumex spp., and the species of the recipient plant. In the present review, the results of the studies are exhibited that aimed at the allelopathic effect on different aspects of the plant crops, weeds, insects, and plant pathogens.
The ac4C modification on mRNA has been demonstrated to be associated with various diseases; however, its molecular mechanism remains unclear. The wet lab experiments produced relatively rough data, which lack precise ac4C modification sites, and extracting valuable information from such data remains a challenge. In this study, we integrate linguistics, traditional machine learning, and deep learning, establishing a link between the understanding of mRNA data and natural language processing (NLP). Through our analysis, we successfully revealed key information about ac4C in mRNA and uncovered the information storage mechanism of ac4C redundancy on a single sequence. This redundant information storage method in mRNA facilitates the transmission of ac4C information and promotes the enrichment of ac4C.
As microscopy diversifies and becomes ever-more complex, the problem of quantification of microscopy images has emerged as a major roadblock for many researchers. All researchers must face certain challenges in turning microscopy images into answers, independent of their scientific question and the images they've generated. Challenges may arise at many stages throughout the analysis process, including handling of the image files, image pre-processing, object finding, or measurement, and statistical analysis. While the exact solution required for each obstacle will be problem-specific, by understanding tools and tradeoffs, optimizing data quality, breaking workflows and data sets into chunks, talking to experts, and thoroughly documenting what has been done, analysts at any experience level can learn to overcome these challenges and create better and easier image analyses.
Non-coding RNA are functional molecules that are not translated into proteins. Their function comes as important regulators of biological function. Because they are not translated, they need not be as stable as other types of RNA. The TKF91 Structure Tree from Holmes 2004 is a probability model that effectively describes correlated substitution, insertion, and deletion of base pairs, and found to have some worth in understanding dynamic folding patterns. In this paper, we provide a new probabilistic analysis of the TKF91 Structure Tree. Large deviation principles on stem lengths, helix lengths, and tree size are proved. Additionally, we give a new alignment procedure that constructs accurate sequence and structural alignments for sequences with low identity for a dense enough phylogeny.
A fundamental mistake in receptor theory has led to an enduring misunderstanding of how to estimate the affinity and efficacy of an agonist. These properties are inextricably linked and cannot be easily separated in any case where the binding of a ligand induces a conformation change in its receptor. Consequently, binding curves and concentration-response relationships for receptor agonists have no straightforward interpretation. This problem, the affinity-efficacy problem, remains overlooked and misunderstood despite it being recognised in 1987. To avoid the further propagation of this misunderstanding, we propose that the affinity-efficacy problem should be included in the core curricula for pharmacology undergraduates proposed by the British Pharmacological Society and IUPHAR.
We present q2-fmt, a QIIME 2 plugin that provides diverse methods for assessing the extent of microbiome engraftment following fecal microbiota transplant. The methods implemented here were informed by a recent literature review on approaches for assessing FMT engraftment, and cover aspects of engraftment including Chimeric Asymmetric Community Coalescence, Donated Microbiome Indicator Features, and Temporal Stability. q2-fmt is free for all use, and detailed documentation illustrating worked examples on a real-world data set are provided in the project's documentation.
Real-time forecasting of disease outbreaks requires standardized outputs generated in a timely manner. Development of pipelines to automate infectious disease forecasts can ensure that parameterization and software dependencies are common to any execution of the forecasting code. Here we present our implementation of an automated cloud computing pipeline to forecast infectious disease outcomes, with examples of usage to forecast COVID-19 and influenza targets. We also offer our perspective on the limits of automation and importance of human-in-the-loop automated infectious disease forecasting.
Signal-based early detection of illnesses has been a key topic in research and hospital settings; it reduces technological costs and paves the way for quick and effective patient-care operations. Elementary machine learning and signal processing algorithms have proven to be sufficient in classifying the onset of viral and bacterial conditions before clinical symptoms are shown. Inspired by these recent developments, this project employs signal dynamics analysis to infer changes in vital signs (temperature, respiration, and heart rate). The results demonstrate that the trends of one vital function can be predicted from that of another. In particular, it is shown that heart rate and respiration typically change shortly after body temperature, and aortic blood pressure follows. This is not an etiologically specific approach, but if advanced further, it can enable patients and wearable system users to tame these changes and prevent immediate symptoms.
Hasanain N. Ezzat, Ihsan M. Shihab, Samer S. Hameed
This experiment was conducted to study the effect of administration Ξ³-amino butyric acid (GABA) on physiology performance of broiler chicks. The following treatments were used: T1:control treatment, T2: the birds were administration 0.2ml 0.4% GABA solution daily, T3: the birds were administration 0.2ml 0.5% GABA solution daily and T4: the birds were administration 0.2ml 0.6% GABA solution daily. The results of the experiment indicated that there were significant differences for the calculating blood lipids .When calculating blood enzymes, there are significant differences between the treatments
Many people are still facing hunger and the global food shortages is still an urgent problem. Meanwhile, global warming is still severe. Therefore, we propose a simulation-based optimization approach for improving crop yield and reducing the greenhouse gas emissions (GHG) of agriculture system. We simulated and verified the crop yield and carbon/nitrogen cycle with Denitrification-Decomposition (DNDC) model. A set of empirical equations of DNDC model were selected and implemented in gPROMS for obtaining the optimal solution of fertilizer usage. A case study shows that the optimized framework improves crop yield by 18%, when 72.42kg N/ha urea was used. Meanwhile the GHG emission of the system was reduced by 10%. The results show the necessity of optimal planning and usage of fertilizer in agriculture system.
Steven G. Krantz, Peter Polyakov, Arni S. R. Srinivasa Rao
In this paper, we have proposed a two phase procedure (combining discrete graphs and wavelets) for constructing a true epidemic growth. In the first phase graph theory based approach was developed to update partial data available and in the second phase we used this partial data to generate a plausible complete data through wavelets. This procedure although novel and implementable, still leave some questions unanswered.
Michael Sadovsky, Tatiana Guseva, Vladislav Birukov
et al.
We studied the structuredness ensemble of transcriptome of Siberian larch. The clusters in 64-dimensional space were identified with $K$-means technique, where the objects to be clusterized are the different fragments of the genome. A tetrahedron like structure in distribution of these fragments was found. Chargaff's discrepancy measure was determined for each class, as well as that latter between the classes. It reveals a relative similitude of the classes. The results have been compared to those obtained for specific transcriptome of each tissue. Also, a surrogate transcriptome has been developed comprising the contigs assembled for specific tissues; that latter has been compared with the real total transcriptome, and significant difference has been observed.
Degeneracy is a salient feature of genetic codes, because there are more codons than amino acids. The conventional table for genetic codes suffers from an inability of illustrating a symmetrical nature among genetic base codes. In fact, because the conventional wisdom avoids the question, there is little agreement as to whether the symmetrical nature actually even exists. A better understanding of symmetry and an appreciation for its essential role in the genetic code formation can improve our understanding of nature coding processes. Thus, it is worth formulating a new integrated symmetrical table for genetic codes, which is presented in this paper. It could be very useful to understand the Nobel laureate Crick wobble hypothesis: how one transfer ribonucleic acid can recognize two or more synonymous codons, which is an unsolved fundamental question in biological science.
For more than fifty years, taxonomists have proposed numerous alternative definitions of species while they searched for a unique, comprehensive, and persuasive definition. This monograph shows that these efforts have been unnecessary, and indeed have provably been a pursuit of a will o' the wisp because they have failed to recognize the theoretical impossibility of what they seek to accomplish. A clear and rigorous understanding of the logic underlying species definition leads both to a recognition of the inescapable ambiguity that affects the definition of species, and to a framework-relative approach to species definition that is logically compelling, i.e., cannot not be accepted without inconsistency. An appendix reflects upon the conclusions reached, applying them in an intellectually whimsical taxonomic thought experiment that conjectures the possibility of an emerging new human species.
Vahid Salari, Felix Scholkmann, Farhad Shahbazi
et al.
It is known that the Arrhenius equation, based on the Boltzmann distribution, can model only a part (e.g. half of the activation energy) for retinal discrete dark noise observed for vertebrate rod and cone pigments. Luo et al (Science, 332, 1307-312, 2011) presented a new approach to explain this discrepancy by showing that applying the Hinshelwood distribution instead the Boltzmann distribution in the Arrhenius equation solves the problem successfully. However, a careful reanalysis of the methodology and results shows that the approach of Luo et al is questionable and the results found do not solve the problem completely.
Quantum theory of hydrogen atoms distribution between two complementary nucleotide bases in DNA double helix at moment of replication has been proposed in this work. It bases on two mechanisms of proton tunneling: the Andreev-Meyerovich mechanism with spontaneous phonon radiation and the Kagan-Maximov (Flynn-Stoneham) mechanism at phonon scattering. According to the presented model, the probability of proton location in shallow potential well (point mutation form) is directly proportional to temperature. It was shown that the point mutation probability decreases with increasing replication velocity.
The dynamic instability of the living systems and the "superposition" of different forms of randomness are viewed as a component of the contingently increasing organization of life along evolution. We briefly survey how classical and quantum physics define randomness differently. We then discuss why this requires, in our view, an enrichment of the understanding of the effects of their concurrent presence in biological systems' dynamics. Biological randomness is then presented as an essential component of the heterogeneous determination and intrinsic unpredictability proper to life phenomena, due to the nesting and interaction of many levels of organization. Even increasing organization itself induces growing disorder, by energy dispersal effects of course, but also by variability and differentiation. Co-operation between diverse components in networks implies at the same time the presence of constraints due to the peculiar forms of (bio-)resonance and (bio-)entanglement we discuss.