Search for: ccm

Chlorophyll fluorescence is a well-established method to estimate chlorophyll content in leaves. A popular fluorescence-based meter, the Opti-Sciences CCM-300 Chlorophyll Content Meter (CCM-300), utilizes the fluorescence ratio F735/F700 and equations derived from experiments using broadleaf species to provide a direct, rapid estimate of chlorophyll content used for many applications. We sought to quantify the performance of the CCM-300 relative to more intensive methods, both across plant functional types and years of use. We linked CCM-300 measurements of broadleaf, conifer, and graminoid samples in 2018 and 2019 to high-performance liquid chromatography (HPLC) and/or spectrophotometric (Spec) analysis of the same leaves. We observed a significant difference between the CCM-300 and HPLC/Spec, but not between HPLC and Spec. In comparison to HPLC, the CCM-300 performed better for broadleaves (r = 0.55, RMSE = 154.76) than conifers (r = 0.52, RMSE = 171.16) and graminoids (r = 0.32, RMSE = 127.12). We observed a slight deterioration in meter performance between years, potentially due to meter calibration. Our results show that the CCM-300 is reliable to demonstrate coarse variations in chlorophyll but may be limited for cross-plant functional type studies and comparisons across years. 

Abstract Mitochondrial cytochromecmaturation (CCM) requires heme attachment via distinct pathways termed systems I and III. The mosaic distribution of these systems in Archaeplastida raises questions about the genetic mechanisms and evolutionary forces promoting repeated evolution. Here, we show a recurrent shift from ancestral system I to the eukaryotic-specific holocytochromecsynthase (HCCS) of system III in 11 archaeplastid lineages. Archaeplastid HCCS is sufficient to rescue mutants of yeast system III and Arabidopsis system I. Algal HCCS mutants exhibit impaired growth and respiration, and altered biochemical and metabolic profiles, likely resulting from deficient CCM and reduced cytochromec-dependent respiratory activity. Our findings demonstrate that archaeplastid HCCS homologs function as system III components in the absence of system I. These results elucidate the evolutionary trajectory and functional divergence of CCM pathways in Archaeplastida, providing insight into the causes, mechanisms, and consequences of repeated cooption of an entire biological pathway. 

Abstract Objective. To demonstrate the capability of utilizing graph feature-based supervised machine learning (ML) algorithm on intracranial electroencephalogram recordings for the identification of seizure onset zones (SOZs) in individuals with drug-resistant epilepsy.Approach. Utilizing three model-free measures of effective connectivity (EC)-directed information, mutual information-guided Granger causality index (MI-GCI), and frequency-domain convergent cross-mapping (FD-CCM) - directed graphs are generated. Graph centrality measures at different sparsity are used as the classifier’s features.Main results. The centrality features achieve high accuracies exceeding 90% in distinguishing SOZ electrodes from non-SOZ electrodes. Notably, a sparse graph representation with just ten features and simple ML models effectively achieves such performance. The study identifies FD-CCM centrality measures as particularly significant, with a mean AUC of 0.93, outperforming prior literature. The FD-CCM-based graph modeling also highlights elevated centrality measures among SOZ electrodes, emphasizing heightened activity relative to non-SOZ electrodes during ictogenesis.Significance. This research not only underscores the efficacy of automated SOZ identification but also illuminates the potential of specific EC measures in enhancing discriminative power within the context of epilepsy research. 

ABSTRACT The upside-down jellyfish Cassiopea spp. host their algal symbionts inside a subset of amoebocytes, phagocytic cells that also play innate immune functions akin to macrophages from vertebrate animals. Amoebocyte precursors phagocytose algae from the jellyfish gut and store them inside intracellular compartments called symbiosomes. Subsequently, the precursors migrate to the mesoglea, differentiate into symbiotic amoebocytes, and roam throughout the jellyfish body, where the algae remain photosynthetically active and supply the jellyfish host with a significant portion of their organic carbon needs. Here, we show that the amoebocyte symbiosome membrane contains V-H+-ATPase (VHA), the proton pump that acidifies phagosomes and lysosomes in all eukaryotes. Many symbiotic amoebocytes also abundantly express a carbonic anhydrase (CA), an enzyme that reversibly hydrates CO2 into H+ and HCO3−. Moreover, we found that the symbiosome lumen is pronouncedly acidic and that pharmacological inhibition of VHA or CA activities significantly decreases photosynthetic oxygen production in live jellyfish. These results point to a carbon concentrating mechanism (CCM) that co-opts VHA and CA from the phago-lysosomal machinery that ubiquitously mediates food digestion and innate immune responses. Analogous VHA-dependent CCMs have been previously described in reef-building corals, anemones and giant clams; however, these other two cnidarians host their dinoflagellate algae inside gastrodermal cells – not in amoebocytes – and the clam hosts theirs within the gut lumen. Thus, our study identifies an example of convergent evolution at the cellular level that might broadly apply to invertebrate–microbe photosymbioses while also providing evolutionary links with intracellular and extracellular food digestion and the immune system. 

Abstract Convergent cross-mapping (CCM) has attracted increased attention recently due to its capability to detect causality in nonseparable systems under deterministic settings, which may not be covered by the traditional Granger causality. From an information-theoretic perspective, causality is often characterized as the directed information (DI) flowing from one side to the other. As information is essentially nondeterministic, a natural question is: does CCM measure DI flow? Here, we first causalize CCM so that it aligns with the presumption in causality analysis—the future values of one process cannot influence the past of the other, and then establish and validate the approximate equivalence of causalized CCM (cCCM) and DI under Gaussian variables through both theoretical derivations and fMRI-based brain network causality analysis. Our simulation result indicates that, in general, cCCM tends to be more robust than DI in causality detection. The underlying argument is that DI relies heavily on probability estimation, which is sensitive to data size as well as digitization procedures; cCCM, on the other hand, gets around this problem through geometric cross-mapping between the manifolds involved. Overall, our analysis demonstrates that cross-mapping provides an alternative way to evaluate DI and is potentially an effective technique for identifying both linear and nonlinear causal coupling in brain neural networks and other settings, either random or deterministic, or both. 

Constant coefficient multipliers are widely used in digital signal processing and machine learning architectures. Researchers have proposed HBU-CCM (hybrid binary-unary constant coefficient multiplier), which is an approximate method that outperforms conventional binary and FloPoCo-KCM (table-based real multiplier) methods in terms of hardware cost at the expense of accuracy due to aliasing issues. SimBU (self-similarity-based hybrid binary-unary) is another method that was recently proposed to implement general nonlinear functions using self-similarities leading to few hardware resources. In this work, we use a simplified version of the SimBU algorithm to address the aliasing issues of HBU-CCM and improve accuracy. We also implement a convolution kernel for a Gaussian blurring filter to evaluate our method and compare it to previous works. Our method outperforms conventional binary and FloPoCo-KCM methods in terms of hardware cost with desired accuracy and with no aliasing error as opposed to HBU-CCM. 

In this article, a high-efficiency and high-density 2.5 kW four-level interleaved flying capacitor multilevel (FCML) totem-pole bridgeless power-factor-correction (PFC) rectifier with 200 V GaN devices is analyzed, designed, and tested. This 2.5 kW four-level continuous conduction mode (CCM) GaN totem pole PFC operates with three times inductor current ripple frequency than that of the switching frequency which significantly reduces the size of the inductors while also supporting switching loss reduction. This article compares the loss of the two-level CCM GaN totem-pole PFC, four-level non-interleaved FCML PFC and interleaved four-level FCML PFC with the same ripple frequency (300 kHz) and shows that the interleaved four-level CCM GaN PFC has much less device loss. In addition, this article discusses the detailed EMI spectrum analysis and derivation of the mathematical model for determining the attenuation requirement of the four-level interleaved FCML PFC converter followed by volumetric co-optimization of AC-side passives i.e., the boost inductor and differential mode (DM) EMI filter. A 2.5 kW four-level interleaved FCML GaN totem-pole PFC prototype with an optimized 94 kHz switching frequency is developed and tested in this article. The converter exhibits a peak efficiency of 99.14% with system power density reaching 89.47 W/inch3. 

Hornworts are a deeply diverged lineage of bryophytes and a sister lineage to mosses and liverworts. Hornworts have an array of unique features that can be leveraged to illuminate not only the early evolution of land plants, but also alternative paths for nitrogen and carbon assimilation via cyanobacterial symbiosis and a pyrenoid-based CO2-concentrating mechanism (CCM), respectively. Despite this, hornworts are one of the few plant lineages with limited available genetic tools. Here we report an efficient biolistics method for generating transient expression and stable transgenic lines in the model hornwort, Anthoceros agrestis. An average of 569 (±268) cells showed transient expression per bombardment, with green fluorescent protein expression observed within 48–72 h. A total of 81 stably transformed lines were recovered across three separate experiments, averaging six lines per bombardment. We followed the same method to transiently transform nine additional hornwort species, and obtained stable transformants from one. This method was further used to verify the localization of Rubisco and Rubisco activase in pyrenoids, which are central proteins for CCM function. Together, our biolistics approach offers key advantages over existing methods as it enables rapid transient expression and can be applied to widely diverse hornwort species. 

Rooted in dynamic systems theory, convergent cross mapping (CCM) has attracted increased attention recently due to its capability in detecting linear and nonlinear causal coupling in both random and deterministic settings. One limitation with CCM is that it uses both past and future values to predict the current value, which is inconsistent with the widely accepted definition of causality, where it is assumed that the future values of one process cannot influence the past of another. To overcome this obstacle, in our previous research, we introduced the concept of causalized convergent cross mapping (cCCM), where future values are no longer used to predict the current value. In this paper, we focus on the implementation of cCCM in causality analysis. More specifically, we demonstrate the effectiveness of cCCM in identifying both linear and nonlinear causal coupling in various settings through a large number of examples, including Gaussian random variables with additive noise, sinusoidal waveforms, autoregressive models, stochastic processes with a dominant spectral component embedded in noise, deterministic chaotic maps, and systems with memory, as well as experimental fMRI data. In particular, we analyze the impact of shadow manifold construction on the performance of cCCM and provide detailed guidelines on how to configure the key parameters of cCCM in different applications. Overall, our analysis indicates that cCCM is a promising and easy-to-implement tool for causality analysis in a wide spectrum of applications.