Rate-induced tipping in complex high-dimensional ecological networks.

In an ecosystem, environmental changes as a result of natural and human processes can cause some key parameters of the system to change with time. Depending on how fast such a parameter changes, a tipping point can occur. Existing works on rate-induced tipping, or R-tipping, offered a theoretical way to study this phenomenon but from a local dynamical point of view, revealing, e.g., the existence of a critical rate for some specific initial condition above which a tipping point will occur. As ecosystems are subject to constant disturbances and can drift away from their equilibrium point, it is necessary to study R-tipping from a global perspective in terms of the initial conditions in the entire relevant phase space region. In particular, we introduce the notion of the probability of R-tipping defined for initial conditions taken from the whole relevant phase space. Using a number of real-world, complex mutualistic networks as a paradigm, we find a scaling law between this probability and the rate of parameter change and provide a geometric theory to explain the law. The real-world implication is that even a slow parameter change can lead to a system collapse with catastrophic consequences. In fact, to mitigate the environmental changes by merely slowing down the parameter drift may not always be effective: Only when the rate of parameter change is reduced to practically zero would the tipping be avoided. Our global dynamics approach offers a more complete and physically meaningful way to understand the important phenomenon of R-tipping.

Folding State within a Hysteresis Loop: Hidden Multistability in Nonlinear Physical Systems.

Identifying hidden states in nonlinear physical systems that evade direct experimental detection is important as disturbances and noises can place the system in a hidden state with detrimental consequences. We study a cavity magnonic system whose main physics is photon and magnon Kerr effects. Sweeping a bifurcation parameter in numerical experiments (as would be done in actual experiments) leads to a hysteresis loop with two distinct stable steady states, but analytic calculation gives a third folded steady state "hidden" in the loop, which gives rise to the phenomenon of hidden multistability. We propose an experimentally feasible control method to drive the system into the folded hidden state. We demonstrate, through a ternary cavity magnonic system and a gene regulatory network, that such hidden multistability is in fact quite common. Our findings shed light on hidden dynamical states in nonlinear physical systems which are not directly observable but can present challenges and opportunities in applications.

Association between HNF4A rs1800961 polymorphisms and gallstones in a Taiwanese population.

BACKGROUND AND AIM: A large genetic effect of a novel gallstone-associated genetic variant, the hepatocyte nuclear factor 4α (HNF4A) rs1800961 polymorphism, has been identified through recent genome-wide association studies. However, this effect has not been validated in Asian populations. We investigated the association between the rs1800961 variant and gallstones among a Taiwanese population. METHODS: A total of 20 405 participants aged between 30 and 70 years voluntarily enrolled in the Taiwan Biobank. Self-report questionnaires, physical examinations, biochemical tests, and genotyping were used for analysis. The association of the HNF4A rs1800961 variant and other metabolic risks with gallstone disease was analyzed using multiple logistic regression models. RESULTS: The minor T allele of HNF4A rs1800961 was associated with an increased risk of gallstone, and the association remained significant even after adjustment for other risk factors including age, body mass index (BMI), diabetes, hyperlipidemia, hypertension, and cigarette smoking (adjusted odds ratio [OR] = 1.90, 95% confidence interval [CI] = 1.31 to 2.75) in male participants. When further stratified by BMI and age, the lithogenic effect was the most significant in male participants with obesity (adjusted OR = 3.55, 95% CI = 1.92 to 6.56) and who were younger (adjusted OR = 2.45, 95% CI = 1.49 to 4.04). CONCLUSION: The novel gallstone-associated HNF4A rs1800961 variant was associated with the risk of gallstone in the Taiwanese men. Screening for the rs1800961 polymorphism may be particularly useful in assessing the risk of gallstone formation in younger or obese men.

Adaptable reservoir computing: A paradigm for model-free data-driven prediction of critical transitions in nonlinear dynamical systems.

A problem in nonlinear and complex dynamical systems with broad applications is forecasting the occurrence of a critical transition based solely on data without knowledge about the system equations. When such a transition leads to system collapse, as often is the case, all the available data are from the pre-critical regime where the system still functions normally, making the prediction problem challenging. In recent years, a machine-learning based approach tailored to solving this difficult prediction problem, adaptable reservoir computing, has been articulated. This Perspective introduces the basics of this machine-learning scheme and describes representative results. The general setting is that the system dynamics live on a normal attractor with oscillatory dynamics at the present time and, as a bifurcation parameter changes into the future, a critical transition can occur after which the system switches to a completely different attractor, signifying system collapse. To predict a critical transition, it is essential that the reservoir computer not only learns the dynamical "climate" of the system of interest at some specific parameter value but, more importantly, discovers how the system dynamics changes with the bifurcation parameter. It is demonstrated that this capability can be endowed into the machine through a training process with time series from a small number of distinct, pre-critical parameter values, thereby enabling accurate and reliable prediction of the catastrophic critical transition. Three applications are presented: predicting crisis, forecasting amplitude death, and creating digital twins of nonlinear dynamical systems. Limitations and future perspectives are discussed.

Deep-learning reconstruction of complex dynamical networks from incomplete data.

Reconstructing complex networks and predicting the dynamics are particularly challenging in real-world applications because the available information and data are incomplete. We develop a unified collaborative deep-learning framework consisting of three modules: network inference, state estimation, and dynamical learning. The complete network structure is first inferred and the states of the unobserved nodes are estimated, based on which the dynamical learning module is activated to determine the dynamical evolution rules. An alternating parameter updating strategy is deployed to improve the inference and prediction accuracy. Our framework outperforms baseline methods for synthetic and empirical networks hosting a variety of dynamical processes. A reciprocity emerges between network inference and dynamical prediction: better inference of network structure improves the accuracy of dynamical prediction, and vice versa. We demonstrate the superior performance of our framework on an influenza dataset consisting of 37 US States and a PM2.5 dataset covering 184 cities in China.

Multi-ethnic Imputation System (MI-System): A genotype imputation server for high-dimensional data.

OBJECTIVE: Genotype imputation is a commonly used technique that infers un-typed variants into a study's genotype data, allowing better identification of causal variants in disease studies. However, due to overrepresentation of Caucasian studies, there's a lack of understanding of genetic basis of health-outcomes in other ethnic populations. Therefore, facilitating imputation of missing key-predictor-variants that can potentially improve a risk health-outcome prediction model, specifically for Asian ancestry, is of utmost relevance. METHODS: We aimed to construct an imputation and analysis web-platform, that primarily facilitates, but is not limited to genotype imputation on East-Asians. The goal is to provide a collaborative imputation platform for researchers in the public domain towards rapidly and efficiently conducting accurate genotype imputation. RESULTS: We present an online genotype imputation platform, Multi-ethnic Imputation System (MI-System) (https://misystem.cgm.ntu.edu.tw/), that offers users 3 established pipelines, SHAPEIT2-IMPUTE2, SHAPEIT4-IMPUTE5, and Beagle5.1 for conducting imputation analyses. In addition to 1000 Genomes and Hapmap3, a new customized Taiwan Biobank (TWB) reference panel, specifically created for Taiwanese-Chinese ancestry is provided. MI-System further offers functions to create customized reference panels to be used for imputation, conduct quality control, split whole genome data into chromosomes, and convert genome builds. CONCLUSION: Users can upload their genotype data and perform imputation with minimum effort and resources. The utility functions further can be utilized to preprocess user uploaded data with easy clicks. MI-System potentially contributes to Asian-population genetics research, while eliminating the requirement for high performing computational resources and bioinformatics expertise. It will enable an increased pace of research and provide a knowledge-base for genetic carriers of complex diseases, therefore greatly enhancing patient-driven research. STATEMENT OF SIGNIFICANCE: Multi-ethnic Imputation System (MI-System), primarily facilitates, but is not limited to, imputation on East-Asians, through 3 established prephasing-imputation pipelines, SHAPEIT2-IMPUTE2, SHAPEIT4-IMPUTE5, and Beagle5.1, where users can upload their genotype data and perform imputation and other utility functions with minimum effort and resources. A new customized Taiwan Biobank (TWB) reference panel, specifically created for Taiwanese-Chinese ancestry is provided. Utility functions include (a) create customized reference panels, (b) conduct quality control, (c) split whole genome data into chromosomes, and (d) convert genome builds. Users can also combine 2 reference panels using the system and use combined panels as reference to conduct imputation using MI-System.

Short-lived chimera states.

In the classic Kuramoto system of coupled two-dimensional rotators, chimera states characterized by the coexistence of synchronous and asynchronous groups of oscillators are long-lived because the average lifetime of these states increases exponentially with the system size. Recently, it was discovered that, when the rotators in the Kuramoto model are three-dimensional, the chimera states become short-lived in the sense that their lifetime scales with only the logarithm of the dimension-augmenting perturbation. We introduce transverse-stability analysis to understand the short-lived chimera states. In particular, on the unit sphere representing three-dimensional (3D) rotations, the long-lived chimera states in the classic Kuramoto system occur on the equator, to which latitudinal perturbations that make the rotations 3D are transverse. We demonstrate that the largest transverse Lyapunov exponent calculated with respect to these long-lived chimera states is typically positive, making them short-lived. The transverse-stability analysis turns the previous numerical scaling law of the transient lifetime into an exact formula: the "free" proportional constant in the original scaling law can now be precisely determined in terms of the largest transverse Lyapunov exponent. Our analysis reinforces the speculation that in physical systems, chimera states can be short-lived as they are vulnerable to any perturbations that have a component transverse to the invariant subspace in which they live.

Reservoir computing as digital twins for nonlinear dynamical systems.

We articulate the design imperatives for machine learning based digital twins for nonlinear dynamical systems, which can be used to monitor the "health" of the system and anticipate future collapse. The fundamental requirement for digital twins of nonlinear dynamical systems is dynamical evolution: the digital twin must be able to evolve its dynamical state at the present time to the next time step without further state input-a requirement that reservoir computing naturally meets. We conduct extensive tests using prototypical systems from optics, ecology, and climate, where the respective specific examples are a chaotic CO2 laser system, a model of phytoplankton subject to seasonality, and the Lorenz-96 climate network. We demonstrate that, with a single or parallel reservoir computer, the digital twins are capable of a variety of challenging forecasting and monitoring tasks. Our digital twin has the following capabilities: (1) extrapolating the dynamics of the target system to predict how it may respond to a changing dynamical environment, e.g., a driving signal that it has never experienced before, (2) making continual forecasting and monitoring with sparse real-time updates under non-stationary external driving, (3) inferring hidden variables in the target system and accurately reproducing/predicting their dynamical evolution, (4) adapting to external driving of different waveform, and (5) extrapolating the global bifurcation behaviors to network systems of different sizes. These features make our digital twins appealing in applications, such as monitoring the health of critical systems and forecasting their potential collapse induced by environmental changes or perturbations. Such systems can be an infrastructure, an ecosystem, or a regional climate system.

Prognostic nutritional index and neutrophil-lymphocyte ratio predict toxicities and prognosis in patients with cervical cancer treated with curative radiochemotherapy.

BACKGROUND: This study aimed to investigate the influence of immunonutritional factors on treatment-related toxicities and survival outcomes in patients with cervical cancer undergoing definitive radiochemotherapy. METHODS: Patients with cervical cancer who received curative radiochemotherapy between 2016 and 2021 were retrospectively investigated. Pretreatment prognostic nutritional index (PNI), neutrophil-lymphocyte ratio (NLR), monocyte-lymphocyte ratio (MLR), and platelet-lymphocyte ratio (PLR) were measured. Survival outcomes, acute and late toxicities were evaluated. RESULTS: Among the 138 patients, those with larger tumor diameters had significantly lower pre-treatment PNI (p = 0.005). Pre-treatment immunonutritional factors were predictive of clinical survival, whereas post-treatment factors did not correlate with prognosis. Patients with low pre-treatment PNI (<49.5) or high NLR (>2.4) had shorter progression-free survival (PFS, HR: 1.86, p = 0.045 for PNI; HR: 3.15, p = 0.002 for NLR) and overall survival (OS, HR: 1.80, p = 0.048 for PNI; HR: 3.83, p = 0.015 for NLR). High pre-treatment NLR was associated with an increased risk of acute diarrhea (p = 0.049) and late severe toxicities (p = 0.046). Combined analysis revealed that pre-treatment good nutritional status and low systemic inflammation were linked to longer PFS (p = 0.007) and OS (p = 0.002), and poor nutritional status and substantial systemic inflammation were associated with higher rates of late severe toxicities (p = 0.036), with higher prognostic value in advanced stage patients. CONCLUSIONS: Pretreatment immunonutritional measures serve as quantitative biomarkers for predicting survivals and treatment toxicities in patients with cervical cancer treated with definitive radiochemotherapy.

Efficient quantum memory for photonic polarization qubits generated by cavity-enhanced spontaneous parametric downconversion.

Quantum memories, for storing then retrieving photonic quantum states on demand, are crucial components for scalable quantum technologies. Spontaneous parametric downconversion (SPDC) with a nonlinear crystal is the most widely used process for generating entangled photon pairs or heralded single photons. Despite the desirability of efficient quantum memories for SPDC-generated single photons, the storage and retrieval efficiencies achieved with this approach still fall below 50%, a threshold value for practical applications. Here, we report an efficiency of > 70% for the storage of heralded single photons generated by cavity-enhanced SPDC using atomic quantum memories based on electromagnetically induced transparency (EIT). In addition, we demonstrate the quantum memory for single-photon polarization qubits with a fidelity of ∼96%. This result paves the way towards the development of large-scale quantum networks.

Endoscopic features and clinical course of patients with asymptomatic cecal ulcers.

BACKGROUND: Cecal ulcers are sometimes encountered in asymptomatic individuals. Their clinical outcomes and management recommendations remain uncertain. METHODS: Asymptomatic patients who underwent a colonoscopic exam for colon cancer screening were retrospectively reviewed from July 2009 to November 2016. Patients with cecal ulcers were included. Patients who had colorectal symptoms, such as abdominal pain, had nonsteroidal anti-inflammatory drugs or were lost to follow-up were excluded. RESULTS: A total of 34,036 patients underwent colon cancer screening. Cecal ulcers were found in 35 patients. After exclusion, 24 patients (mean duration, 52 months) received follow-up colonoscopy. In 20 patients, (83.3%), cecal ulcer resolved without intervention, but 4 patients (16.7%) developed clinical significant diseases, including intestinal tuberculosis (n = 2), Crohn's disease (n = 1), and ulcerative colitis (n = 1). Patients who developed clinically significant diseases had a higher percentage of ulcers larger than 1 cm (75% vs. 15%, p = 0.035), terminal ileum involvement (100% vs. 15.4%, p = 0.006) and ulcers with irregular fold (75% vs. 5%, p = 0.008). CONCLUSIONS: In patients with asymptomatic cecal ulcers, the endoscopic features included larger ulcer size, terminal ileum involvement and ulcers with irregular fold may predict development of clinically significant diseases. If the above-mentioned features are present, even asymptomatic patients should be closely monitored.

Research progress in targeted therapies for gastric cancer.

Patients with advanced gastric cancer experience rapid disease progression with limited survival, high mortality, and a lack of surgical options. Thus, radiochemotherapy or a combination of chemotherapeutics with targeted therapy is the mainstay of treatment. In comparison to the treatment of other malignant tumors, in gastric cancer, the development of molecularly targeted drugs has been relatively slow. Currently, there are two major classes of molecularly targeted drug regimens that have achieved a certain efficacy in clinical practice: anti-vascular endothelial growth factor (anti-VEGF) therapy and anti-epidermal growth factor receptor (anti-EGFR) therapy. Trastuzumab has been approved as the standard of care for first-line treatment in advanced human epidermal growth factor receptor 2 (HER2)-positive gastric cancer. Ramucirumab in combination with paclitaxel is the recommended regimen for second-line treatment, and apatinib is recommended as third-line treatment. This review summarizes the current status of targeted therapies in the treatment of gastric cancer and gives a perspective on the future.

Virtual Reality in Treatment for Psychological Problems in First-Line Health Care Professionals Fighting COVID-19 Pandemic: A Case Series.

ABSTRACT: Virtual reality therapy (VRT) is a new psychotherapeutic approach integrating virtual reality technology and psychotherapy. This case series aimed to study effectiveness of VRT in treating psychological problems. We described four cases of first-line health care professionals with emerging clinically significant early psychological problems during the COVID-19 outbreak, and specifically received the VRT treatment. We compared the Patient Health Questionnaire 9 items (PHQ-9), Generalized Anxiety Disorder-7 (GAD-7), PHQ-15, and Athens Insomnia Scale to evaluate psychological symptoms and sleep quality before and after sessions. All four cases showed a reduction in scale comparison. General scores of the PHQ-9 reduced 65%, GAD-7 reduced 52.17%, PHQ-15 decreased 38.17%, and scores of the Athens Insomnia Scale reduced 67.44%. Meanwhile, a reduction in depression, anxiety, psychosomatic, and sleeping symptoms was also found, which decreased 76.92% in general. These results are highly significant statistically. This case series demonstrated the effectiveness of VRT on psychological problems as a promising approach to apply on various psychological distress and disorders.

Structural position vectors and symmetries in complex networks.

Symmetries, due to their fundamental importance to dynamical processes on networks, have attracted a great deal of current research. Finding all symmetric nodes in large complex networks typically relies on automorphism groups from algebraic-group theory, which are solvable in quasipolynomial time. We articulate a conceptually appealing and computationally extremely efficient approach to finding and characterizing all symmetric nodes by introducing a structural position vector (SPV) for each node in networks. We establish the mathematical result that symmetric nodes must have the same SPV value and demonstrate, using six representative complex networks from the real world, that all symmetric nodes in these networks can be found in linear time. Furthermore, the SPVs not only characterize the similarity of nodes but also quantify the nodal influences in propagation dynamics. A caveat is that the proved mathematical result relating the SPV values to nodal symmetries is not sufficient; i.e., nodes having the same SPV values may not be symmetric, which arises in regular networks or networks with a dominant regular component. We point out with an analysis that this caveat is, in fact, shared by the known existing approaches to finding symmetric nodes in the literature. We further argue, with the aid of a mathematical analysis, that our SPV method is generally effective for finding the symmetric nodes in real-world networks that typically do not have a dominant regular component. Our SPV-based framework, therefore, provides a physically intuitive and computationally efficient way to uncover, understand, and exploit symmetric structures in complex networks arising from real-world applications.

Metamorphoses and explosively remote synchronization in dynamical networks.

We uncover a phenomenon in coupled nonlinear networks with a symmetry: as a bifurcation parameter changes through a critical value, synchronization among a subset of nodes can deteriorate abruptly, and, simultaneously, perfect synchronization emerges suddenly among a different subset of nodes that are not directly connected. This is a synchronization metamorphosis leading to an explosive transition to remote synchronization. The finding demonstrates that an explosive onset of synchrony and remote synchronization, two phenomena that have been studied separately, can arise in the same system due to symmetry, providing another proof that the interplay between nonlinear dynamics and symmetry can lead to a surprising phenomenon in physical systems.

Beyond Boolean: Ternary networks and dynamics.

Boolean networks introduced by Kauffman, originally intended as a prototypical model for gaining insights into gene regulatory dynamics, have become a paradigm for understanding a variety of complex systems described by binary state variables. However, there are situations, e.g., in biology, where a binary state description of the underlying dynamical system is inadequate. We propose random ternary networks and investigate the general dynamical properties associated with the ternary discretization of the variables. We find that the ternary dynamics can be either ordered or disordered with a positive Lyapunov exponent, and the boundary between them in the parameter space can be determined analytically. A dynamical event that is key to determining the boundary is the emergence of an additional fixed point for which we provide numerical verification. We also find that the nodes playing a pivotal role in shaping the system dynamics have characteristically distinct behaviors in different regions of the parameter space, and, remarkably, the boundary between these regions coincides with that separating the ordered and disordered dynamics. Overall, our framework of ternary networks significantly broadens the classical Boolean paradigm by enabling a quantitative description of richer and more complex dynamical behaviors.

Spin Fano Resonances in Chiral Molecules: An Alternative Mechanism for the CISS Effect and Experimental Implications.

Experiments on spin transport through a chiral molecule demonstrated the attainment of significant spin polarization, demanding a theoretical explanation. We report the emergence of spin Fano resonances as a mechanism in the chiral-induced spin-selectivity (CISS) effect associated with transport through a chiral polyacetylene molecule. Initializing electrons through optical excitation, we derive the Fano resonance formula for the spin polarization. Computations reveal that quasidegeneracy is common in this complex molecular system. A remarkable phenomenon is the generation of pronounced spin Fano resonances due to the contributions of two near-degeneracy states. We also find that the Fano resonance width increases linearly with the coupling strength between the molecule and the lead. Our findings provide another mechanism to explain the experimental observations and lead to new insights into the role of the CISS effect in complex molecules from the perspective of transport and spin polarization resonance, paving the way for chiral molecule-based spintronics applications.

Detecting the causal influence of thermal environments among climate regions in the United States.

The quantification of cross-regional interactions for the atmospheric transport processes is of crucial importance to improve the predictive capacity of climatic and environmental system modeling. The dynamic interactions in these complex systems are often nonlinear and non-separable, making conventional approaches of causal inference, such as statistical correlation or Granger causality, infeasible or ineffective. In this study, we applied an advanced approach, based on the convergent cross mapping algorithm, to detect and quantify the causal influence among different climate regions in the contiguous U.S. in response to temperature perturbations using the long-term (1901-2018) climatology of near surface air temperature record. Our results show that the directed causal network constructed by convergent cross mapping algorithm, enables us to distinguish the causal links from spurious ones rendered by statistical correlation. We also find that the Ohio Valley region, as an atmospheric convergent zone, acts as the regional gateway and mediator to the long-term thermal environments in the U.S. In addition, the temporal evolution of dynamic causality of temperature exhibits superposition of periodicities at various time scales, highlighting the impact of prominent low frequency climate variabilities such as El Niño-Southern Oscillation. The proposed method in this work will help to promote novel system-based and data-driven framework in studying the integrated environmental system dynamics.

Vascular Trauma in the Extremities: Factors Associated with the Outcome and Assessment of Amputation Indexes.

Background: Traumatic vascular injury in the extremities may be associated with a low mortality rate but can lead to limb loss that seriously affects patients' functionality. Multiple scoring systems have been designed to evaluate the prognosis, but none are 100% predictive. The management of traumatic vascular injury remains challenging and depends mostly on the surgeon's experience. Objectives: We identified the risks associated with limb loss and further investigated the utility of current amputation indexes. Methods: We retrospectively reviewed 53 cases of traumatic vascular injury in the extremities at a tertiary referral medical center over the past ten years (January 2011-December 2020). The mangled extremity severity score (MESS), limb salvage index (LSI), and predictive salvage index (PSI) were used to assess the traumatized limbs. The injury characteristics and outcomes were evaluated using regression analysis. Results: The incidence of limb loss was 20.8% (n = 11), and open fractures were the most related factor. Extensive involvement of soft tissue, vascular injury combined with tibia or fibula fractures, initial shock status, and the amount of transfusion were associated with limb loss. Conclusions: Our study identified the risk factors and clinical utility of MESS, PSI, and LSI. While both LSI and PSI had acceptable diagnostic accuracy, amputation should be decided based on a variety of criteria and clinical features. Salvaging any limb that has not become apparently futile seems logical, yet the presence of certain factors may suggest a worse outcome.

Generation of sub-MHz and spectrally-bright biphotons from hot atomic vapors with a phase mismatch-free scheme.

We utilized the all-copropagating scheme, which maintains the phase-match condition, in the spontaneous four-wave mixing (SFWM) process to generate biphotons from a hot atomic vapor. The linewidth and spectral brightness of our biphotons surpass those of the biphotons produced with the hot-atom SFWM in the previous works. Moreover, the generation rate of the sub-MHz biphoton source in this work can also compete with those of the sub-MHz biphoton sources of the cold-atom SFWM or cavity-assisted spontaneous parametric down conversion. Here, the biphoton linewidth is tunable for an order of magnitude. As we tuned the linewidth to 610 kHz, the generation rate per linewidth is 1,500 pairs/(s·MHz) and the maximum two-photon correlation function, gs,as(2), of the biphotons is 42. This gs,as(2) violates the Cauchy-Schwarz inequality for classical light by 440 folds, and demonstrates that the biphotons have a high purity. By increasing the pump power by 16 folds, we further enhanced the generation rate per linewidth to 2.3×104 pairs/(s·MHz), while the maximum gs,as(2) became 6.7. In addition, we are able to tune the linewidth down to 290±20 kHz. This is the narrowest linewidth to date among all single-mode biphoton sources of room-temperature and hot media.