Initial Frequency Research Articles

A new fault diagnosis method for wheelset-bearing system based on VME convergence tendency diagram

Aiming at the difficulty of accurate diagnosis of wheelset-bearing system composite faults, a multi-fault feature extraction method based on self-adaptive variational mode extraction (SAVME) was proposed. Variational mode extraction (VME) can extract a specific sub-signal from a multi-component signal. The key to the success of this algorithm is to determine appropriate initial parameters in advance, including initial center frequency (ICF) and penalty factor. To determine the key parameters of VME adaptively, the convergence characteristics of VME are analyzed deeply, and the VME convergence tendency diagram is proposed creatively according to the trend of the iterative curve of the center frequency of the desired mode. By analyzing the test signal with the VME convergence tendency diagram, the number of main latent sub-signals in the test signal and the ICF of each sub-signal corresponding to the VME can be determined efficiently. Then, according to the position of the ICF of each sub-signal in the frequency domain, the empirical formula of the penalty factor is used to quickly obtain the appropriate penalty factor. The proposed SAVME method not only improves the parameter selection adaptability of the traditional VME algorithm but also extends the VME algorithm to the field of multi-fault diagnosis. By analyzing the simulated signal and two experimental signals, the effectiveness of the SAVME algorithm is verified. Compared with the fast kurtogram method and the adaptive variational mode decomposition method, the proposed method is more accurate and superior in the multi-fault feature extraction of the wheelset-bearing system.

A method for LFM signal parameter estimation based on MWC system and STFT-DPT

A method of estimating linear frequency modulation (LFM) signal parameters using compressed sampling (CS) data of a Modulated Wideband Converter (MWC) system is proposed. The short-time Fourier transform algorithm is used to analyze the CS signal of the MWC system, and the obtained CS signal frequency will present a form of multi-segment distribution. By intercepting the CS signal corresponding to the first segment frequency and analyzing the intercepted segment signal with discrete polynomial transform and Rife algorithm, the chirp rate and initial frequency estimation of the segment CS signal can be preliminarily estimated. According to the random sequence with cyclic displacement, the spectrum shift relationship between the input signal and the output signal can be calculated. Therefore, the parameter of the original signal can be estimated by using single channel CS data. Then, all channels are analyzed with the same method, and a more stable parameter estimation result can be obtained than the single channel output. Finally, combined with the simulation experiment, the proposed method can be used to estimate the initial frequency and chirp rate of the LFM signal using CS data effectively.

Geostrophic Eddies Spread Near-Inertial Wave Energy to High Frequencies

Abstract The generation of broadband wave energy frequency spectra from narrowband wave forcing in geophysical flows remains a conundrum. In contrast to the long-standing view that nonlinear wave–wave interactions drive the spreading of wave energy in frequency space, recent work suggests that Doppler-shifting by geostrophic flows may be the primary agent. We investigate this possibility by ray tracing a large number of inertia–gravity wave packets through three-dimensional, geostrophically turbulent flows generated either by a quasigeostrophic (QG) simulation or by synthetic random processes. We find that, in all cases investigated, a broadband quasi-stationary inertia–gravity wave frequency spectrum forms, irrespective of the initial frequencies and wave vectors of the packets. The frequency spectrum is well represented by a power law. A possible theoretical explanation relies on the analogy between the kinematic stretching of passive tracer gradients and the refraction of wave vectors. Consistent with this hypothesis, the spectrum of eigenvalues of the background flow velocity gradients predicts a frequency spectrum that is nearly identical to that found by integration of the ray tracing equations.

Constraining the ellipticity and frequency of binary neutron star remnant via its gravitational-wave and electromagnetic radiations

ABSTRACT The nature of the merger remnant of binary neutron star remains an open question. From the theoretical point of view, one possible outcome is a supra-massive neutron star (SMNS), which is supported by rigid rotation and through its survival of hundreds of seconds before collapsing into a black hole. If this is the case, the SMNS can emit continuous gravitational waves (GW) and electromagnetic radiation, particularly in the X-ray band. In this work, the ellipticity and initial frequency of SMNS are constrained with a Bayesian framework using simulated X-ray and GW signals, which could be detected by The Transient High Energy Sky and Early Universe Surveyor and Einstein Telescope, respectively. We found that only considering the X-ray emission cannot completely constrain the initial frequency and ellipticity of the SMNS, but it can reduce the ranges of the parameters. Afterwards, we can use the posterior distribution of the X-ray parameter estimates as a prior for the GW parameter estimates. It was found that the 95 per cent credible region of the joint X-ray–GW analysis was about 105 times smaller than that of the X-ray analysis alone.

Quasi-Zero Stiffness Isolator Suitable for Low-Frequency Vibration

This paper proposes a quasi-zero stiffness (QZS) isolator based on an inclined trapezoidal beam to explore its advantages in low-frequency passive vibration isolation. The nonlinear stiffness of the inclined trapezoidal beam due to the buckling effect is investigated through finite element simulation, and a linear positive stiffness spring is connected in parallel to form a QZS isolator with high-static and low-dynamic stiffness performance. The natural frequency of the isolator in the QZS region is simulated and analyzed, and the dynamic response of the QZS isolator under different damping ratios, excitation and load conditions is explored. The prototype of the QZS isolator was manufactured, and a static compression experiment was conducted to obtain its nonlinear stiffness. The dynamic experiment results verify the correctness of the simulation conclusions. The simulation and experimental data demonstrate that the QZS isolator has the characteristics of lower initial isolation frequency compared with the equivalent linear isolator. The proposed QZS isolator has an initial isolation frequency of 2.91 Hz and achieves a 90% isolation efficiency at 7.02 Hz. The proposed QZS isolator has great application prospects and can provide a reference for optimizing low-frequency or ultra-low-frequency isolators.

Wireless and Flexible Tactile Sensing Array Based on an Adjustable Resonator with Machine‐Learning Perception

AbstractIntelligent soft robotics and wearable electronics require flexible, wireless radio frequency (RF) pressure sensors for human‐like tactile perception of their moving parts. Existing devices face two challenges for array extension: the construction of sensitive units over a limited area and the handling of resonant peaks overlapping within the channel width. Herein, a simply adjustable RF‐resonator‐based tactile array (RFTA) is reported, in which the initial frequency of each resonator unit is regulated by doping polydimethylsiloxane (PDMS) dielectric layers with various concentrations of multiwalled carbon nanotubes (MWCNTs). An array is constructed using four sensor units with a frequency interval of 15 MHz and a multi‐layer micropyramid structure is employed to obtain a low detection limit (<1 Pa) and high sensitivity (17.49 MHz kPa‐1 in the low‐pressure range). A machine‐learning‐based strategy identifies tactile positions precisely via a one‐time S11 reading, achieving 98.5% accuracy with six stimulation modes. Furthermore, the RFTA distinguishes six objects during the grasping process when installed on a soft manipulator. The device shows considerable potential to be extended for flexible moving scenarios and high‐integrated tactile sensing systems for soft robotics.

Abstract LB170: Artificial intelligence-assisted morphology-based detection and enrichment of malignant effusion tumor cells as a method for molecular profiling

Abstract Patients with metastatic breast cancer (MBC) can develop malignant effusions (MEs) when excess fluid and tumor cells accumulate in the pleural or peritoneal space. MEs are associated with significant morbidity and mortality. Tropism of tumor cells in these spaces is poorly understood and tumor cells residing in these fluids are difficult to study with traditional methods due to low abundance. To overcome this limitation and understand the pathobiology of MEs in MBCs, we applied a label-free method to isolate tumor cells based on morphology using an artificial intelligence (AI) model This method enabled isolation of live cells for downstream molecular analysis and characterization of morphological heterogeneity. We collected ME samples from 9 MBC patients with cytology-confirmed MEs, including 3 triple negative and 6 hormone receptor-positive/HER2-negative breast cancers. ME cells were imaged and isolated using a Deepcell AI model trained on a separate cohort of MEs to isolate and enrich tumor cells based on deep learning representations of morphological features. Quantitative morphological features were then extracted from cell images to perform cluster analysis and visualized by dimensionality reduction projections. In parallel, flow cytometry (FC) was used to quantify % EpCAM+/Claudin4+ tumor cells in ME samples. Copy number (CN) analysis was performed before and after enrichment with as few as 200 cells from each sample. Tumor cell fraction (TCF) estimates were calculated from the AI classifier, FC, and CN analyses. CN profiling of malignant cells sorted by the AI classifier revealed genomic alterations common in breast cancer in 7 of 9 patients. We observed increased amplitude of genomic alterations in tumor-enriched vs. unenriched samples, indicating successful tumor cell enrichment in 14 out of 16 samples. We observed a significant correlation in TCF between the AI classifier and FC with a Spearman test (rho=0.568, p0.008). In some cases, the AI classifier reported higher TCF than FC. This could indicate that the AI model detected tumor cells missed by antibody labeling or false positives from morphologically similar cell types, such as macrophages and mesothelial cells. Remarkably, analysis of serial ME samples from the same patient revealed morphological shifts in cell populations which were consistent with changes in TCF by CN analysis. We demonstrate the feasibility of label-free identification and isolation of tumor cells in MEs using an AI classifier. CN profiling of isolated tumor cells verified enrichment from low initial frequencies. Future work will include expression analysis of ME tumor and immune cells, and analysis of organoids derived from MEs. Combining molecular and morphological profiling of MEs will enable new insights into their pathobiology and inform targeted treatment strategies for patients with this condition. Citation Format: Mark J. Magbanua, Laura Huppert, Janifer Cruz, Jackson Goudreau, Shaopu Zhou, Keyi Yin, Pavel Stejskal, Ryan Carelli, Julie Kim, Linda Hsie, Maggie Wang, Zhouyang Lian, Michael Phelan, Manisha Ray, Chassidy Johnson, Amy L. Delson, Ron Balassanian, Jennifer Rosenbluth, Laura van't Veer, Michelle Melisko, Hope S. Rugo, Mahyar Salek, Maddison Masaeli, Hani Goodarzi. Artificial intelligence-assisted morphology-based detection and enrichment of malignant effusion tumor cells as a method for molecular profiling [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 2 (Clinical Trials and Late-Breaking Research); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(8_Suppl):Abstract nr LB170.

Initial phase and frequency modulations of pumping a playground swing.

The playground swing is a dynamic, coupled oscillator system consisting of the swing as an object and a human as the swinger. Here, we propose a model for capturing the effect of the initial phase of natural upper body motion on the continuous pumping of a swing and validate this model from the motion data of ten participants pumping swings of three different swing chain lengths. Our model predicts that the swing pumps the most if the phase of maximum lean back, which we call the initial phase, occurs when the swing is at a vertical (midpoint) position and moving forward when the amplitude is small. As the amplitude grows, the optimal initial phase gradually shifts towards an earlier phase of the cycle, the back extreme of the swing's trajectory. As predicted by our model, all participants shifted the initial phase of their upper body movements earlier as swing amplitude increased. This indicated that swingers adjust both the frequency and initial phase of their upper body movements to successfully pump a playground swing.

Comparative Effects of Domain-Specific Human Monoclonal Antibodies Against LGI1 on Neuronal Excitability.

Autoantibodies to leucine-rich glioma inactivated protein 1 (LGI1) cause an autoimmune limbic encephalitis with frequent focal seizures and anterograde memory dysfunction. LGI1 is a neuronal secreted linker protein with 2 functional domains: the leucine-rich repeat (LRR) and epitempin (EPTP) regions. LGI1 autoantibodies are known to interfere with presynaptic function and neuronal excitability; however, their epitope-specific mechanisms are incompletely understood. We used patient-derived monoclonal autoantibodies (mAbs), which target either LRR or EPTP domains of LGI1 to investigate long-term antibody-induced alteration of neuronal function. LRR- and EPTP-specific effects were evaluated by patch-clamp recordings in cultured hippocampal neurons and compared with biophysical neuron modeling. Kv1.1 channel clustering at the axon initial segment (AIS) was quantified by immunocytochemistry and structured illumination microscopy techniques. Both EPTP and LRR domain-specific mAbs decreased the latency of first somatic action potential firing. However, only the LRR-specific mAbs increased the number of action potential firing together with enhanced initial instantaneous frequency and promoted spike-frequency adaptation, which were less pronounced after the EPTP mAb. This also led to an effective reduction in the slope of ramp-like depolarization in the subthreshold response, suggesting Kv1 channel dysfunction. A biophysical model of a hippocampal neuron corroborated experimental results and suggests that an isolated reduction of the conductance of Kv1-mediated K+ currents largely accounts for the antibody-induced alterations in the initial firing phase and spike-frequency adaptation. Furthermore, Kv1.1 channel density was spatially redistributed from the distal toward the proximal site of AIS under LRR mAb treatment and, to a lesser extant, under EPTP mAb. These findings indicate an epitope-specific pathophysiology of LGI1 autoantibodies. The pronounced neuronal hyperexcitability and SFA together with dropped slope of ramp-like depolarization after LRR-targeted interference suggest disruption of LGI1-dependent clustering of K+ channel complexes. Moreover, considering the effective triggering of action potentials at the distal AIS, the altered spatial distribution of Kv1.1 channel density may contribute to these effects through impairing neuronal control of action potential initiation and synaptic integration.

Coriolis mass flowmeter signal processing system based on time-varying signal model

In order to improve the signal processing effect and efficiency of Coriolis mass flowmeter, a signal processing system of Coriolis mass flowmeter based on time-varying signal model is designed. Based on the analysis of the structure and working principle of Coriolis mass flowmeter, the environmental factors affecting the performance of Coriolis mass flowmeter are discussed. Based on the above analysis, the signal processing environment is designed, including serial signal processing environment and dual core parallel signal processing environment. Then the effective signal processing of Coriolis mass flowmeter is completed from three aspects: initial processing, phase calculation and frequency tracking. The experimental results show that after the time-varying signal model is applied, a complete signal frame can be collected in less than 50 ms in the pre-processing process, and the signal processing frame can be completed in less than 80 ms in the post-processing process. This shows that the signal processing time of the system is short, and the phase difference detection accuracy of the signal processing system based on the time-varying signal model is high, which is of great value to further improve the application effect of Coriolis mass flowmeter.

Abstract 2858: Radiotherapy and NK cell-based immunotherapy: An evolutionary double-bind

Abstract Evolution-informed therapies exploit the ecological and evolutionary consequences of drug resistance to inhibit the expansion of treatment resistant populations and prolong time to progression. One strategy, termed an evolutionary double-bind, uses an initial therapy to elicit a specific adaptive mechanism that is then selectively targeted by a follow-on therapy. Here we examine combining radiation therapy followed by immunotherapy as a double bind strategy. Radiotherapy (RT) induces lethal double-strand DNA breaks in tumor cells and radiosensitivity is governed by the cells’ DNA repair capabilities. Radioresistant cancer cells upregulate DNA damage response pathways. However, successful adaptations to RT and other DNA damaging agents can increase natural killer (NK) cell ligand expression on tumor cells increasing their vulnerability to the immune system. We conducted a multidisciplinary investigation of this potential evolutionary double bind using an isogenic cell line model of radiation resistance derived from an androgen-independent human prostate carcinoma cell line (22Rv1). We demonstrate that RT-resistant cells maintain higher expression of NK cell ligands and have a 2-fold increase in sensitivity to NK cell mediated killing. Furthermore, we show that preferential targeting of the RT-resistant population by NK cells is preserved in competition assays with varying initial population frequencies. Scope of treatment responses are dependent on population frequencies in our in vitro analysis. Mathematical modeling quantified three key elements of a double-bind: cost of resistance, inter-specific competition between RT-sensitive and RT-resistant lines, and preferential targeting of RT-resistant lines by NK cells. We conclude that RT followed by immunotherapy produces an evolutionary double bind that can be exploited in heterogenous tumors to limit RT resistance. Furthermore, model simulations predict that extinction of both populations is achievable by sequential RT and NK cell therapy. Citation Format: Kimberly Luddy, Jeffery West, Mark Robertson-Tessi, Bina Desai, Taylor M. Bursell, Sarah Barrett, Joel Brown, Jacintha O'Sullivan, Robert Gatenby, Laure Marignol, Alexander R. Anderson, Cliona O'Farrelly. Radiotherapy and NK cell-based immunotherapy: An evolutionary double-bind [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 2858.

Abstract 3164: Disulfiram re-sensitizes ER+ breast cancer cells resistant to chemotherapy through an evolutionary double-bind

Abstract Most ER+ metastatic breast cancer patients develop resistance to endocrine therapy and are also typically cross-resistant to chemotherapy, resulting in disease progression. However, chemotherapy resistant lines have been shown to acquire cross-sensitivity to therapies such as disulfiram, a drug used to treat alcoholism. Because of this cross-sensitivity, we hypothesized the existence of an “evolutionary double-bind”, a situation where treatment with one drug improves the response to a different agent. This can occur when resistance mechanisms rewire key proliferative pathways, creating potentially targetable vulnerabilities to be exploited. While recent studies have applied theoretical ecological and evolutionary principles in understanding cancer progression, it remains a new and exciting endeavor to quantify effective evolutionary games in refractory cancer to design alternative treatment strategies that induce an evolutionary double-bind. Here, we developed ER+ breast cancer cell lines sensitive and resistant to paclitaxel or doxorubicin to test the cross-sensitivity and efficacy of chemotherapy and disulfiram as an alternative treatment strategy. To quantify this cross-sensitivity of chemotherapy and disulfiram and use the double-bind to mitigate resistance, we also developed a game-theoretic mathematical modeling framework with parametrization performed using the “evolutionary game assay” technique, a direct measurement of payoff matrices that specify evolutionary games measured in cancer. Spheroids of varied initial frequencies of sensitive and resistant cells (100% monoculture; 75/25, 50/50, and 25/75 sensitive and resistant coculture) were treated with chemotherapy and disulfiram. The mathematical model was parameterized from the in vitro experimental data and as expected, treating with chemotherapy selects for resistant cells. However, disulfiram alone shifts the selection dynamics to favor sensitive cells. These modeling results predict the existence of a double-bind between chemotherapy and disulfiram where disulfiram selects for the sensitive subpopulation, which then re-sensitizes the spheroids to subsequent administration of chemotherapy. Importantly, the model predicts that the double-bind is non-symmetric and does not exist when treatments are given in combination. These predictions suggest the importance of timing/dosing strategies in which several schemes were further validated in vitro, confirming the superior efficacy of chemotherapy followed by disulfiram in blocking resistance emergence. This project integrates experimental data with mathematical modeling to identify a treatment double-bind in ER+ breast cancer. The integrative experimental-mathematical framework developed here has broad applicability in designing evolution-based or adaptive therapy treatment strategies across a wide range of cancer types. Citation Format: Rena Emond, Jeffrey West, Alexander Anderson, Andrea H. Bild. Disulfiram re-sensitizes ER+ breast cancer cells resistant to chemotherapy through an evolutionary double-bind [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 3164.

Risk factors for drug-resistant epilepsy (DRE) in children and a model to predict development of DRE

Purpose. This study was conducted to investigate possible risk factors that could increase the occurrence of drug-resistant epilepsy (DRE), in hopes that the results could be used to educate the patient and their caregivers as well as increase early detection efforts. Methods. Case control study was conducted at neurology outpatient pediatric RSDS between May to December 2022. Risk factor of DRE such as sex, age of onset, type of seizure, initial seizure frequencies, history of cranial hemorrhage, cerebral infection, febrile seizure, status epilepticus, neonatal seizure, neonatal asphyxia, family history of epilepsy, present of neurological deficit, electroencephalogram (EE) finding, and result of neuroimaging examination were obtained through anamnesis and clinical examination. Risk factors were analyzed with bivariate analysis and multivariate analysis. A model was generated to predict probabilities of DRE in children with epilepsy. Results. DRE was observed in 84/137 (54%) patients. Bivariate analysis showed age of onset <1 years old (OR 2.31, p = 0.016), initial seizure frequencies >5 times/day (OR 3.0, p = 0.011), neonatal seizure (OR 3, p = 0.034), presence of neurologic deficit (OR 3.1, p=0.002), and abnormality of EEG (OR 2.82, p = 0.013) are significantly associated with DRE. Logistic regression revealed that initial seizure frequencies > 5 times/day (OR=2.5; 95%CI 1.051 to 6.028; P=0.038), present of neurological deficit (OR=2.58; 95%CI 1.205 to 5.531; P=0.031), and EE abnormality (OR=2.84; 95%CI 1.170 6.914; P=0.021) were significantly correlated with DRE. Our model sensitivity was 75.3% and 55.76% to predict DRE (AUC = 0.704, p=0.000). Conclusion. Seizure onsets of >5 times, neurological deficits, and EEG abnormality were found to be associated with drug resistant epilepsy.

Covalent Triazine Framework Encapsulated Ultrafine PdAu Alloy Nanoclusters as Additive-Free Catalysts for Efficient Hydrogen Production from Formic Acid

Dehydrogenation of formic acid (FA) represents a promising route for clean hydrogen production, the economic viability of which, however, is largely hindered by the catalyst inefficiency. Here, ultrafine Au–Pd bimetallic nanoclusters (NCs) confined within the cavities of bipyridyl covalent triazine frameworks (CTFs) are constructed via a metal-nitrogen coordination reduction strategy, which is enabled by the chelation between bidentate nitrogen sites of the bipyridine ligands and metal ion precursors. The resulting CTF-confined PdAu NCs exhibit a high initial turnover frequency up to 12,368 h–1 (333 K) toward the dehydrogenation of FA without any additives. The experimental and theoretical studies disclose that the pyridinic nitrogen sites of CTF not only facilitate the formation of monodisperse small-sized metal NCs via the anchoring and the pore confinement effect but also serve as a proton buffer which can store excess protons and thus suppress the recombination of adsorbed formate and hydrogen on the PdAu alloy surface. These findings would have significant implications for designing high-performance hydrogen production catalysts.

Comparison of Metastable Pitting Behavior of Austenitic and Duplex Stainless Steels under U‐Bending Deformation

The metastable pitting behavior of austenitic stainless steel (304 SS) and duplex stainless steel (2205 DSS) under U‐bending deformation is comparatively investigated. This study highlights the effects of differences in inclusions and matrix between 304 SS and 2205 DSS with regard to the metastable pitting behavior under tensile and compressive deformation. The results show that the pit initiation frequency of 304 SS increases in the order: tensile < as‐received < compressive; in contrast, the initiation frequency of 2205 DSS increases in the order: as‐received < tensile < compressive. In addition, tensile and compressive deformation change the preferential initiation sites of metastable pitting in 2205 DSS. It is proposed that the types of transient signals can be comprehensively and objectively distinguished by the two parameters (t g/t rep) and [(I p/Q g):(I p/Q rep)]. The correlation relationship between transient signals and pitting morphologies is clarified by cluster analysis. Both tensile and compressive deformations promote the growth of a few metastable pits, thus increasing the transition probability from metastability to stability. Overall, both tensile and compressive deformations are detrimental to the pitting corrosion resistance of the experimental steels.

Dynamics of photoinduced ferromagnetism in oxides with orbital degeneracy

By using intense coherent electromagnetic radiation, it may be possible to manipulate the properties of quantum materials very quickly, or even induce new and potentially useful phases that are absent in equilibrium. For instance, ultrafast control of magnetic dynamics is crucial for a number of proposed spintronic devices, and it can also shed light on the possible dynamics of correlated phases out of equilibrium. Inspired by recent experiments on spin-orbital ferromagnet ${\mathrm{YTiO}}_{3}$, we consider the nonequilibrium dynamics of a Heisenberg ferromagnetic insulator with low-lying orbital excitations. We model the dynamics of the magnon excitations in this system following an optical pulse that resonantly excites infrared-active phonon modes. As the phonons ring down, they can dynamically couple the orbitals with the low-lying magnons, leading to a dramatically modified effective bath for the magnons. We show that this transient coupling can lead to a dynamical acceleration of the magnetization dynamics, which is otherwise bottlenecked by small anisotropy. Exploring the parameter space more, we find that the magnon dynamics can also even completely reverse, leading to a negative relaxation rate when the pump is blue-detuned with respect to the orbital bath resonance. We therefore show that by using specially targeted optical pulses, one can exert a much greater degree of control over the magnetization dynamics, allowing one to optically steer magnetic order in this system. We conclude by discussing interesting parallels between the magnetization dynamics we find here and recent experiments on photoinduced superconductivity, where it is similarly observed that depending on the initial pump frequency, an apparent metastable superconducting phase emerges.

Maintenance of genetic diversity in subdivided populations using genomic coancestry matrices.

For both undivided and subdivided populations, the consensus method to maintain genetic diversity is the Optimal Contribution (OC) method. For subdivided populations, this method determines the optimal contribution of each candidate to each subpopulation to maximize global genetic diversity (which implicitly optimizes migration between subpopulations) while balancing the relative levels of coancestry between and within subpopulations. Inbreeding can be controlled by increasing the weight given to within-subpopulation coancestry (λ). Here we extend the original OC method for subdivided populations that used pedigree-based coancestry matrices, to the use of more accurate genomic matrices. Global levels of genetic diversity, measured as expected heterozygosity and allelic diversity, their distributions within and between subpopulations, and the migration pattern between subpopulations, were evaluated via stochastic simulations. The temporal trajectory of allele frequencies was also investigated. The genomic matrices investigated were (i) the matrix based on deviations of the observed number of alleles shared by two individuals from the expected number under Hardy-Weinberg equilibrium; and (ii) a matrix based on a genomic relationship matrix. The matrix based on deviations led to higher global and within-subpopulation expected heterozygosities, lower inbreeding and similar allelic diversity than the second genomic and pedigree-based matrices when a relatively high weight was given to the within-subpopulation coancestries (λ ≥ 5). Under this scenario, allele frequencies moved only slightly away from the initial frequencies. Therefore, the recommended strategy is to use the former matrix in the OC methodology giving a high weight to the within-subpopulation coancestry.

Chaotic mode-competition dynamics in a multimode semiconductor laser with optical feedback and injection.

Photonic computing has attracted increasing interest for the acceleration of information processing in machine learning applications. The mode-competition dynamics of multimode semiconductor lasers are useful for solving the multi-armed bandit problem in reinforcement learning for computing applications. In this study, we numerically evaluate the chaotic mode-competition dynamics in a multimode semiconductor laser with optical feedback and injection. We observe the chaotic mode-competition dynamics among the longitudinal modes and control them by injecting an external optical signal into one of the longitudinal modes. We define the dominant mode as the mode with the maximum intensity; the dominant mode ratio for the injected mode increases as the optical injection strength increases. We deduce that the characteristics of the dominant mode ratio in terms of the optical injection strength are different among the modes owing to the different optical feedback phases. We propose a control technique for the characteristics of the dominant mode ratio by precisely tuning the initial optical frequency detuning between the optical injection signal and injected mode. We also evaluate the relationship between the region of the large dominant mode ratios and the injection locking range. The region with the large dominant mode ratios does not correspond to the injection-locking range. The control technique of chaotic mode-competition dynamics in multimode lasers is promising for applications in reinforcement learning and reservoir computing in photonic artificial intelligence.

Rapid adaptation of recombining populations on tunable fitness landscapes.

How does standing genetic variation affect polygenic adaptation in recombining populations? Despite a large body of work in quantitative genetics, epistatic and weak additive fitness effects among simultaneously segregating genetic variants are difficult to capture experimentally or to predict theoretically. In this study, we simulated adaptation on fitness landscapes with tunable ruggedness driven by standing genetic variation in recombining populations. We confirmed that recombination hinders the movement of a population through a rugged fitness landscape. When surveying the effect of epistasis on the fixation of alleles, we found that the combined effects of high ruggedness and high recombination probabilities lead to preferential fixation of alleles that had a high initial frequency. This indicates that positive epistatic alleles escape from being broken down by recombination when they start at high frequency. We further extract direct selection coefficients and pairwise epistasis along the adaptive path. When taking the final fixed genotype as the reference genetic background, we observe that, along the adaptive path, beneficial direct selection appears stronger and pairwise epistasis weaker than in the underlying fitness landscape. Quantitatively, the ratio of epistasis and direct selection is smaller along the adaptive path ( ) than expected. Thus, adaptation on a rugged fitness landscape may lead to spurious signals of direct selection generated through epistasis. Our study highlights how the interplay of epistasis and recombination constrains the adaptation of a diverse population to a new environment.

Evaluating the Catalytic Activities of PNCNP Pincer Group 10 Metal Hydride Complexes: Pd-Catalyzed Reduction of CO2 to the Formic Acid Level with NH3·BH3 and NaBH4 under Ambient Conditions.

In order to develop efficient protocols for CO2 reduction with less expensive and more convenient hydrogen sources, the catalytic reactivities of group 10 metal hydride complexes supported by a PNCNP pincer ligand, [2,6-(tBu2PNH)2C6H3]MH (M = Ni, 1a; Pd, 1b; Pt, 1c), against the hydroboration of CO2 with NH3·BH3 and NaBH4 have been explored. Both 1a and 1b readily react with CO2 at room temperature to form the corresponding formato complexes, [2,6-(tBu2PNH)2C6H3]MOC(O)H (M = Ni, 2a; Pd, 2b), in nearly quantitative yields. Treatment of NH3·BH3 with CO2 (1 atm) in 1,4-dioxane or THF at room temperature in the presence of 0.05-1.0 mol % of 1b followed by hydrolysis of the resulting mixtures produces formic acid in 105-186% yields, and initial turnover frequencies of up to 2000 h-1 are observed. In the presence of 1.0 mol % of 1b, NaBH4 reacts with CO2 (1 atm) in THF at room temperature to form NaB[OC(O)H]4 (3) in 87% isolated yield. In situ NMR spectroscopy indicates that the reactions proceed through the insertion of the C═O bond in CO2 into the Pd-H bond in 1b to form 2b, which sequentially reacts with the hydrides in NH3·BH3 or NaBH4 to produce boron formato species and regenerate 1b. This work represents one of the rare examples of catalytic transfer hydrogenation of CO2 with NH3·BH3 to the formic acid level under very mild conditions without any additives and also the first example of 4 equiv of CO2 uptake by NaBH4 in a reaction.