Fundamental behaviors emerge from simulations of a living minimal cell.

We present a whole-cell fully dynamical kinetic model (WCM) of JCVI-syn3A, a minimal cell with a reduced genome of 493 genes that has retained few regulatory proteins or small RNAs. Cryo-electron tomograms provide the cell geometry and ribosome distributions. Time-dependent behaviors of concentrations and reaction fluxes from stochastic-deterministic simulations over a cell cycle reveal how the cell balances demands of its metabolism, genetic information processes, and growth, and offer insight into the principles of life for this minimal cell. The energy economy of each process including active transport of amino acids, nucleosides, and ions is analyzed. WCM reveals how emergent imbalances lead to slowdowns in the rates of transcription and translation. Integration of experimental data is critical in building a kinetic model from which emerges a genome-wide distribution of mRNA half-lives, multiple DNA replication events that can be compared to qPCR results, and the experimentally observed doubling behavior.

Deterministic Evolutionary Trajectories Influence Primary Tumor Growth: TRACERx Renal.

The evolutionary features of clear-cell renal cell carcinoma (ccRCC) have not been systematically studied to date. We analyzed 1,206 primary tumor regions from 101 patients recruited into the multi-center prospective study, TRACERx Renal. We observe up to 30 driver events per tumor and show that subclonal diversification is associated with known prognostic parameters. By resolving the patterns of driver event ordering, co-occurrence, and mutual exclusivity at clone level, we show the deterministic nature of clonal evolution. ccRCC can be grouped into seven evolutionary subtypes, ranging from tumors characterized by early fixation of multiple mutational and copy number drivers and rapid metastases to highly branched tumors with >10 subclonal drivers and extensive parallel evolution associated with attenuated progression. We identify genetic diversity and chromosomal complexity as determinants of patient outcome. Our insights reconcile the variable clinical behavior of ccRCC and suggest evolutionary potential as a biomarker for both intervention and surveillance.

Integration of Multiple Metabolic Signals Determines Cell Fate Prior to Commitment.

Cell-fate decisions are central to the survival and development of both uni- and multicellular organisms. It remains unclear when and to what degree cells can decide on future fates prior to commitment. This uncertainty stems from experimental and theoretical limitations in measuring and integrating multiple signals at the single-cell level during a decision process. Here, we combine six-color live-cell imaging with the Bayesian method of statistical evidence to study the meiosis/quiescence decision in budding yeast. Integration of multiple upstream metabolic signals predicts individual cell fates with high probability well before commitment. Cells "decide" their fates before birth, well before the activation of pathways characteristic of downstream cell fates. This decision, which remains stable through several cell cycles, occurs when multiple metabolic parameters simultaneously cross cell-fate-specific thresholds. Taken together, our results show that cells can decide their future fates long before commitment mechanisms are activated.

Quantum advantage in variational Bayes inference.

Variational Bayes (VB) inference algorithm is used widely to estimate both the parameters and the unobserved hidden variables in generative statistical models. The algorithm-inspired by variational methods used in computational physics-is iterative and can get easily stuck in local minima, even when classical techniques, such as deterministic annealing (DA), are used. We study a VB inference algorithm based on a nontraditional quantum annealing approach-referred to as quantum annealing variational Bayes (QAVB) inference-and show that there is indeed a quantum advantage to QAVB over its classical counterparts. In particular, we show that such better performance is rooted in key quantum mechanics concepts: i) The ground state of the Hamiltonian of a quantum system-defined from the given data-corresponds to an optimal solution for the minimization problem of the variational free energy at very low temperatures; ii) such a ground state can be achieved by a technique paralleling the quantum annealing process; and iii) starting from this ground state, the optimal solution to the VB problem can be achieved by increasing the heat bath temperature to unity, and thereby avoiding local minima introduced by spontaneous symmetry breaking observed in classical physics based VB algorithms. We also show that the update equations of QAVB can be potentially implemented using ⌈logK⌉ qubits and 𝒪(K) operations per step, where K is the number of values hidden categorical variables can take. Thus, QAVB can match the time complexity of existing VB algorithms, while delivering higher performance.

Dissociable Contributions of Basolateral Amygdala and Ventrolateral Orbitofrontal Cortex to Flexible Learning Under Uncertainty.

Reversal learning measures the ability to form flexible associations between choice outcomes with stimuli and actions that precede them. This type of learning is thought to rely on several cortical and subcortical areas, including the highly interconnected orbitofrontal cortex (OFC) and basolateral amygdala (BLA), and is often impaired in various neuropsychiatric and substance use disorders. However, the unique contributions of these regions to stimulus- and action-based reversal learning have not been systematically compared using a chemogenetic approach particularly before and after the first reversal that introduces new uncertainty. Here, we examined the roles of ventrolateral OFC (vlOFC) and BLA during reversal learning. Male and female rats were prepared with inhibitory designer receptors exclusively activated by designer drugs targeting projection neurons in these regions and tested on a series of deterministic and probabilistic reversals during which they learned about stimulus identity or side (left or right) associated with different reward probabilities. Using a counterbalanced within-subject design, we inhibited these regions prior to reversal sessions. We assessed initial and pre-/post-reversal changes in performance to measure learning and adjustments to reversals, respectively. We found that inhibition of the ventrolateral orbitofrontal cortex (vlOFC), but not BLA, eliminated adjustments to stimulus-based reversals. Inhibition of BLA, but not vlOFC, selectively impaired action-based probabilistic reversal learning, leaving deterministic reversal learning intact. vlOFC exhibited a sex-dependent role in early adjustment to action-based reversals, but not in overall learning. These results reveal dissociable roles for BLA and vlOFC in flexible learning and highlight a more crucial role for BLA in learning meaningful changes in the reward environment.

Murburn posttranslational modifications of proteins: Cellular redox processes and murzyme-mediated metabolo-proteomics.

Murburn concept constitutes the thesis that diffusible reactive species or DRS are obligatorily involved in routine metabolic and physiological activities. Murzymes are defined as biomolecules/proteins that generate/modulate/sustain/utilize DRS. Murburn posttranslational modifications (PTMs) result because murburn/murzyme functionalism is integral to cellular existence. Cells must incorporate the inherently stochastic nature of operations mediated by DRS. Due to the earlier/inertial stigmatic perception that DRS are mere agents of chaos, several such outcomes were either understood as deterministic modulations sponsored by house-keeping enzymes or deemed as unregulated nonenzymatic events resulting out of "oxidative stress". In the current review, I dispel the myths around DRS-functions, and undertake systematic parsing and analyses of murburn modifications of proteins. Although it is impossible to demarcate all PTMs into the classical or murburn modalities, telltale signs of the latter are evident from the relative inaccessibility of the locus, non-specificities and mechanistic details. It is pointed out that while many murburn PTMs may be harmless, some others could have deleterious or beneficial physiological implications. Some details of reversible/irreversible modifications of amino acid residues and cofactors that may be subjected to phosphorylation, halogenation, glycosylation, alkylation/acetylation, hydroxylation/oxidation, etc. are listed, along with citations of select proteins where such modifications have been reported. The contexts of these modifications and their significance in (patho)physiology/aging and therapy are also presented. With more balanced explorations and statistically verified data, a definitive understanding of normal versus pathological contexts of murburn modifications would be obtainable in the future.

Continuous and deterministic all-photonic cluster state of indistinguishable photons.

Cluster states are key resources for measurement-based quantum information processing. Photonic cluster and graph states, in particular, play indispensable roles in quantum network and quantum metrology. We demonstrate a semiconductor quantum dot based device in which the confined hole spin acts as a needle in a quantum knitting machine producing continuously and deterministically at sub-Gigahertz repetition rate single indistinguishable photons which are all polarization entangled to each other and to the spin in a one dimensional cluster state. By projecting two nonadjacent photons onto circular polarization bases we disentangle the spin from the photons emitted in between. This way we demonstrate a novel way for producing deterministic and continuous all-photonic cluster states. We use polarization tomography on four sequentially detected photons to demonstrate and to directly quantify the robustness of the cluster's entanglement and the determinism in its photon generation.

An Automated Single-Cell Droplet-Digital Microfluidic Platform for Monoclonal Antibody Discovery.

Monoclonal antibody (mAb) discovery plays a prominent role in diagnostic and therapeutic applications. Droplet microfluidics has become a standard technology for high-throughput screening of antibody-producing cells due to high droplet single-cell confinement frequency and rapid analysis and sorting of the cells of interest with their secreted mAbs. In this work, a new method is described for on-demand co-encapsulation of cells that eliminates the difficulties associated with washing in between consecutive steps inside the droplets and enables the washing and addition of fresh media. The new platform identifies hybridoma cells that are expressing antibodies of interest using antibody-characterization assays to find the best-performing or rare-cell antibody candidates.

Sub-nanometre quality X-ray mirrors created using ion beam figuring.

Ion beam figuring (IBF) is a powerful technique for figure correction of X-ray mirrors to a high accuracy. Here, recent technical advancements in the IBF instrument developed at Diamond Light Source are presented and experimental results for figuring of X-ray mirrors are given. The IBF system is equipped with a stable DC gridded ion source (120 mm diameter), a four-axis motion stage to manipulate the optic, a Faraday cup to monitor the ion-beam current, and a camera for alignment. A novel laser speckle angular measurement instrument also provides on-board metrology. To demonstrate the IBF system's capabilities, two silicon X-ray mirrors were processed. For 1D correction, a height error of 0.08 nm r.m.s. and a slope error of 44 nrad r.m.s. were achieved. For 2D correction over a 67 mm × 17 mm clear aperture, a height error of 0.8 nm r.m.s. and a slope error of 230 nrad r.m.s. were obtained. For the 1D case, this optical quality is comparable with the highest-grade, commercially available, X-ray optics.

Sensitivity analyses of probabilistic and deterministic DTI tractography methodologies for studying arm muscle architecture.

PURPOSE: To determine the sensitivity profiles of probabilistic and deterministic DTI tractography methods in estimating geometric properties in arm muscle anatomy. METHODS: Spin-echo diffusion-weighted MR images were acquired in the dominant arm of 10 participants. Both deterministic and probabilistic tractography were performed in two different muscle architectures of the parallel-structured biceps brachii (and the pennate-structured flexor carpi ulnaris. Muscle fascicle geometry estimates and number of fascicles were evaluated with respect to tractography turning angle, polynomial fitting order, and SNR. The DTI tractography estimated fascicle lengths were compared with measurements obtained from conventional cadaveric dissection and ultrasound modalities. RESULTS: The probabilistic method generally estimated fascicle lengths closer to ranges reported by conventional methods than the deterministic method, most evident in the biceps brachii (p > 0.05), consisting of longer, arc-like fascicles. For both methods, a wide turning angle (50º-90°) generated fascicle lengths that were in close agreement with conventional methods, most evident in the flexor carpi ulnaris (p > 0.05), consisting of shorter, feather-like fascicles. The probabilistic approach produced at least two times more fascicles than the deterministic approach. For both approaches, second-order fitting yielded about double the complete tracts as third-order fitting. In both muscles, as SNR decreased, deterministic tractography produced less fascicles but consistent geometry (p > 0.05), whereas probabilistic tractography produced a consistent number but altered geometry of fascicles (p < 0.001). CONCLUSION: Findings from this study provide best practice recommendations for implementing DTI tractography in skeletal muscle and will inform future in vivo studies of healthy and pathological muscle structure.

Community assembly of organisms regulates soil microbial functional potential through dual mechanisms.

Unraveling the influence of community assembly processes on soil ecosystem functioning presents a major challenge in the field of theoretical ecology, as it has received limited attention. Here, we used a series of long-term experiments spanning over 25 years to explore the assembly processes of bacterial, fungal, protist, and nematode communities using high-throughput sequencing. We characterized the soil microbial functional potential by the abundance of microbial genes associated with carbon, nitrogen, phosphorus, and sulfur cycling using GeoChip-based functional gene profiling, and determined how the assembly processes of organism groups regulate soil microbial functional potential through community diversity and network stability. Our results indicated that balanced fertilization (NPK) treatment improved the stochastic assembly of bacterial, fungal, and protist communities compared to phosphorus-deficient fertilization (NK) treatment. However, there was a nonsignificant increase in the normalized stochasticity ratio of the nematode community in response to fertilization across sites. Our findings emphasized that soil environmental factors influenced the assembly processes of the biotic community, which regulated soil microbial functional potential through dual mechanisms. One mechanism indicated that the high phosphorus levels and low soil nutrient stoichiometry may increase the stochasticity of bacterial, fungal, and protist communities and the determinism of the nematode community under NPK treatment, ultimately enhancing soil microbial functional potential by reinforcing the network stability of the biotic community. The other mechanism indicated that the low phosphorus levels and high soil nutrient stoichiometry may increase the stochastic process of the bacterial community and the determinism of the fungal, protist, and nematode communities under NK treatment, thereby enhancing soil microbial functional potential by improving the ß-diversity of the biotic community. Taken together, these results provide valuable insights into the mechanisms underlying the assembly processes of the biotic community that regulate ecosystem functioning.

Local Community Assembly Mechanisms and the Size of Species Pool Jointly Explain the Beta Diversity of Soil Fungi.

Fungi play vital regulatory roles in terrestrial ecosystems. Local community assembly mechanisms, including deterministic and stochastic processes, as well as the size of regional species pools (gamma diversity), typically influence overall soil microbial community beta diversity patterns. However, there is limited evidence supporting their direct and indirect effects on beta diversity of different soil fungal functional groups in forest ecosystems. To address this gap, we collected 1606 soil samples from a 25-ha subtropical forest plot in southern China. Our goal was to determine the direct effects and indirect effects of regional species pools on the beta diversity of soil fungi, specifically arbuscular mycorrhizal (AM), ectomycorrhizal (EcM), plant-pathogenic, and saprotrophic fungi. We quantified the effects of soil properties, mycorrhizal tree abundances, and topographical factors on soil fungal diversity. The beta diversity of plant-pathogenic fungi was predominantly influenced by the size of the species pool. In contrast, the beta diversity of EcM fungi was primarily driven indirectly through community assembly processes. Neither of them had significant effects on the beta diversity of AM and saprotrophic fungi. Our results highlight that the direct and indirect effects of species pools on the beta diversity of soil functional groups of fungi can significantly differ even within a relatively small area. They also demonstrate the independent and combined effects of various factors in regulating the diversities of soil functional groups of fungi. Consequently, it is crucial to study the fungal community not only as a whole but also by considering different functional groups within the community.

Biogeographic patterns of meio- and micro-eukaryotic communities in dam-induced river-reservoir systems.

Although the Three Gorges Dam (TGD) is the world's largest hydroelectric dam, little is known about the spatial-temporal patterns and community assembly mechanisms of meio- and micro-eukaryotes and its two subtaxa (zooplankton and zoobenthos). This knowledge gap is particularly evident across various habitats and during different water-level periods, primarily arising from the annual regular dam regulation. To address this inquiry, we employed mitochondrial cytochrome c oxidase I (COI) gene-based environmental DNA (eDNA) metabarcoding technology to systematically analyze the biogeographic pattern of the three communities within the Three Gorges Reservoir (TGR). Our findings reveal distinct spatiotemporal characteristics and complementary patterns in the distribution of meio- and micro-eukaryotes. The three communities showed similar biogeographic patterns and assembly processes. Notably, the diversity of these three taxa gradually decreased along the river. Their communities were less shaped by stochastic processes, which gradually decreased along the longitudinal riverine-transition-lacustrine gradient. Hence, deterministic factors, such as seasonality, environmental, and spatial variables, along with species interactions, likely play a pivotal role in shaping these communities. Environmental factors primarily drive seasonal variations in these communities, while hydrological conditions, represented as spatial distance, predominantly influence spatial variations. These three communities followed the distance-decay pattern. In winter, compared to summer, both the decay and species interrelationships are more pronounced. Taken together, this study offers fresh insights into the composition and diversity patterns of meio- and micro-eukaryotes at the spatial-temporal level. It also uncovers the mechanisms behind community assembly in various environmental niches within the dam-induced river-reservoir systems. KEY POINTS: • Distribution and diversity of meio- and micro-eukaryotes exhibit distinct spatiotemporal patterns in the TGR. • Contribution of stochastic processes in community assembly gradually decreases along the river. • Deterministic factors and species interactions shape meio- and micro-eukaryotic community.

Effects of anthropometrics, thrust, and drag on stroke kinematics and 100 m performance of young swimmers using path-analysis modeling.

The aim of this study was to understand the interactions between anthropometric, kinetic, and kinematic variables and how they determine the 100 m freestyle performance in young swimmers. Twenty-five adolescent swimmers (15 male and 10 female, aged 15.75 ± 1.01 years) who regularly participated in regional and national competitions were recruited. The 100 m freestyle performance was chosen as the variable to be predicted. A series of anthropometric (hand surface area-HSA), kinetic (thrust and active drag coefficient (CDA )), and kinematic (stroke length (SL); stroke frequency (SF), and swimming speed) variables were measured. Structural equation modeling (via path analysis) was used to develop and test the model. The initial model predicted performance with 90.1% accuracy. All paths were significant (p < 0.05) except the thrust-SL. After deleting this non-significant path (thrust-SL) and recalculating, the model goodness-of-fit improved and all paths were significant (p < 0.05). The predicted performance was 90.2%. Anthropometrics had significant effects on kinetics, which had significant effects on kinematics, and consequently on the 100 m freestyle performance. The cascade of interactions based on this path-flow model allowed for a meaningful prediction of the 100 m freestyle performance. Based on these results, coaches and swimmers should be aware that the swimming predictors can first meaningfully interact with each other to ultimately predict the 100 m freestyle performance.

Evaluating Linkage Quality of Population-Based Administrative Data for Health Service Research.

BACKGROUND: To overcome the limitations of relying on data from a single institution, many researchers have studied data linkage methodologies. Data linkage includes errors owing to legal issues surrounding personal information and technical issues related to data processing. Linkage errors affect selection bias, and external and internal validity. Therefore, quality verification for each connection method with adherence to personal information protection is an important issue. This study evaluated the linkage quality of linked data and analyzed the potential bias resulting from linkage errors. METHODS: This study analyzed claims data submitted to the Health Insurance Review and Assessment Service (HIRA DATA). The linkage errors of the two deterministic linkage methods were evaluated based on the use of the match key. The first deterministic linkage uses a unique identification number, and the second deterministic linkage uses the name, gender, and date of birth as a set of partial identifiers. The linkage error included in this deterministic linkage method was compared with the absolute standardized difference (ASD) of Cohen's according to the baseline characteristics, and the linkage quality was evaluated through the following indicators: linked rate, false match rate, missed match rate, positive predictive value, sensitivity, specificity, and F1-score. RESULTS: For the deterministic linkage method that used the name, gender, and date of birth as a set of partial identifiers, the true match rate was 83.5 and the missed match rate was 16.5. Although there was bias in some characteristics of the data, most of the ASD values were less than 0.1, with no case greater than 0.5. Therefore, it is difficult to determine whether linked data constructed with deterministic linkages have substantial differences. CONCLUSION: This study confirms the possibility of building health and medical data at the national level as the first data linkage quality verification study using big data from the HIRA. Analyzing the quality of linkages is crucial for comprehending linkage errors and generating reliable analytical outcomes. Linkers should increase the reliability of linked data by providing linkage error-related information to researchers. The results of this study will serve as reference data to increase the reliability of multicenter data linkage studies.

Inverse Modeling of Heterogeneous Structures in Electron Probe Microanalysis.

Electron probe microanalysis (EPMA) is a powerful tool for chemical characterization of materials on a microscopic scale. However, EPMA has the drawback that its information volume has a spatial extent of some 100 nm to a few µm. With the introduction of new electron sources, i.e., Schottky Thermal Field and Cold Field Emitter, where the electron beam is focused down to a few nm, measurements can be nowadays performed on the sub-micrometer scale. The goal of the work is to reveal the chemical composition of structures smaller than the excitation volume. New strategies are presented where the acquisition is performed at different positions on the sample and as a scan across a fine structure by using one or more single beam energies. Besides the well-known Monte-Carlo simulation, a deterministic model is also used. The deterministic model is based on moment equations of the Boltzmann equation. Inverse modeling is presented for several case studies. Due to the highly complex nonlinearity of the inverse model, an ill-posed and well-posed problem is shown as well. Finally, the method is extended to reconstruct 2D structures, i.e., rectangular shaped particles, with heterogeneous composition on lateral and depth scale.

The predicted benefits of genomic selection on pig breeding objectives.

The premise was tested that the additional genetic gain was achieved in the overall breeding objective in a pig breeding program using genomic selection (GS) compared to a conventional breeding program, however, some traits achieved larger gain than other traits. GS scenarios based on different reference population sizes were evaluated. The scenarios were compared using a deterministic simulation model to predict genetic gain in scenarios with and without using genomic information as an additional information source. All scenarios were compared based on selection accuracy and predicted genetic gain per round of selection for objective traits in both sire and dam lines. The results showed that GS scenarios increased overall response in the breeding objectives by 9% to 56% and 3.5% to 27% in the dam and sire lines, respectively. The difference in response resulted from differences in the size of the reference population. Although all traits achieved higher selection accuracy in GS, traits with limited phenotypic information at the time of selection or with low heritability, such as sow longevity, number of piglets born alive, pre- and post-weaning survival, as well as meat and carcass quality traits achieved the largest additional response. This additional response came at the expense of smaller responses for traits that are easy to measure, such as back fat and average daily gain in GS compared to the conventional breeding program. Sow longevity and drip loss percentage did not change in a favourable direction in GS with a reference population of 500 pigs. With a reference population of 1000 pigs or onwards, sow longevity and drip loss percentage began to change in a favourable direction. Despite the smaller responses for average daily gain and back fat thickness in GS, the overall breeding objective achieved additional gain in GS.

Deterministic Localization for Fully Automatic Operation: A Survey and Experiments.

With the rapid development of fully automatic operation (FAO) and location-based services, the evaluation criteria of average localization accuracy can no longer meet our demands, in favor of deterministic localization. However, most localization researches modeled localization performance function and enhanced it by minimizing average localization root mean square error (RMSE). The performance degradation in a small region was not considered. In this paper, we present a survey of deterministic localization and analyze the relationship between accuracy and certainty. In this paper, two common solutions of localization enhancement are presented and their localization certainties are discussed. Furthermore, we carry out related localization enhancement experiments in rail transit line and analyze their improvement on deterministic localization. The experimental results show that the overall localization performance is improved, while the deterministic localization requires the stricter solution to promote.

Acceleration for Efficient Automated Generation of Operational Amplifiers.

Operational amplifiers (Op-Amps) are critical to sensor systems because they enable precise, reliable, and flexible signal processing. Current automated Op-Amp generation methods suffer from extremely low efficiency because the time-consuming SPICE-in-the-loop sizing is normally involved as its inner loop. In this paper, we propose an efficiently automated Op-Amp generation tool using a hybrid sizing method, which combines the merits together from a deterministic optimization algorithm and differential evolution algorithm. Thus, it can not only quickly find a decent local optimum, but also eventually converge to a global optimum. This feature is well fit to be serving as an acute filter in the circuit structure evaluation flow to efficiently eliminate any undesirable circuit structures in advance of detailed sizing. Our experimental results demonstrate its superiority over traditional sizing approaches and show its efficacy in highly boosting the efficiency of automated Op-Amp structure generation.

Community assembly of ectomycorrhizal fungal communities in pure and mixed Pinus massoniana forests.

Ectomycorrhizal (EcM) fungi play an important role in nutrient cycling and community ecological dynamics and are widely acknowledged as important components of forest ecosystems. However, little information is available regarding EcM fungal community structure or the possible relationship between EcM fungi, soil properties, and forestry activities in Pinus massoniana forests. In this study, we evaluated soil properties, extracellular enzyme activities, and fungal diversity and community composition in root and soil samples from pure Pinus massoniana natural forests, pure P. massoniana plantations, and P. massoniana and Liquidambar gracilipes mixed forests. The mixed forest showed the highest EcM fungal diversity in both root and bulk soil samples. Community composition and co-occurrence network structures differed significantly between forest types. Variation in the EcM fungal community was significantly correlated with the activities of ß-glucuronidase and ß-1,4-N-acetylglucosaminidase, whereas non-EcM fungal community characteristics were significantly correlated with ß-1,4-glucosidase and ß-glucuronidase activities. Furthermore, stochastic processes predominantly drove the assembly of both EcM and non-EcM fungal communities, while deterministic processes exerted greater influence on soil fungal communities in mixed forests compared to pure forests. Our findings may inform a deeper understanding of how the assembly processes and environmental roles of subterranean fungal communities differ between mixed and pure plantations and may provide insights for how to promote forest sustainability in subtropical areas.