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.

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.

Clinical observation of Porcine Collagen Membrane + artificial Bovine Bone Granules Guided tissue regeneration combined with Autologous CGF in the treatment of severe periodontitis bone defect.

Objective: To investigate and analyze the clinical observation of porcine collagen membrane + artificial bovine bone granules guided tissue regeneration (GTR) combined with autologous concentration of growth factors (CGF) in the treatment of severe periodontitis bone defect. Methods: A total of 94 patients with severe periodontitis bone defects admitted to Shanxi Bethune Hospital from January 2019 to January 2022 were included. They were divided into two groups by simple randomization method. Patients in the control group were treated with porcine collagen membrane + artificial bovine bone granules GTR, while those in the observation group were treated with autologous CGF on the basis of the control group. Before and after treatment, periodontal clinical indicators [sulcus bleeding index (SBI), gingival retreat index (GR), probing depth (PD), clinical attachment loss (CAL), alveolar bone height (AH)] and bone resorption markers [Osteoprotegerin (OPG), bone gla protein (BGP), Type-1 collagen N-terminal peptide (NTX)] were compared between the two groups, and the incidence of postoperative complications in the two groups was recorded. Results: The total efficacy of observation group was significantly higher than that of control group (p<0.05). Three months after surgery, the observation group had lower levels of SBI, PD, CAL and NTX while higher levels of GR, AH, OPG and BGP than the control group (p<0.05). There was no significant difference in complication rate between the two groups (p>0.05). Conclusion: Porcine collagen membrane + artificial bovine bone granules GTR combined with autologous CGF boasts various benefits in the treatment of severe periodontitis bone defects, such as improvement of clinical outcomes, amelioration of periodontal tissue and inhibition of bone resorption.

Reservoir-computing based associative memory and itinerancy for complex dynamical attractors.

Traditional neural network models of associative memories were used to store and retrieve static patterns. We develop reservoir-computing based memories for complex dynamical attractors, under two common recalling scenarios in neuropsychology: location-addressable with an index channel and content-addressable without such a channel. We demonstrate that, for location-addressable retrieval, a single reservoir computing machine can memorize a large number of periodic and chaotic attractors, each retrievable with a specific index value. We articulate control strategies to achieve successful switching among the attractors, unveil the mechanism behind failed switching, and uncover various scaling behaviors between the number of stored attractors and the reservoir network size. For content-addressable retrieval, we exploit multistability with cue signals, where the stored attractors coexist in the high-dimensional phase space of the reservoir network. As the length of the cue signal increases through a critical value, a high success rate can be achieved. The work provides foundational insights into developing long-term memories and itinerancy for complex dynamical patterns.

Effects of growth feedback on gene circuits: A dynamical understanding.

The successful integration of engineered gene circuits into host cells remains a significant challenge in synthetic biology due to circuit-host interactions, such as growth feedback, where the circuit influences cell growth and vice versa. Understanding the dynamics of circuit failures and identifying topologies resilient to growth feedback are crucial for both fundamental and applied research. Utilizing transcriptional regulation circuits with adaptation as a paradigm, we systematically study 435 distinct topological structures and uncover six categories of failures. Three dynamical mechanisms of circuit failures are identified: continuous deformation of the response curve, strengthened or induced oscillations, and sudden switching to coexisting attractors. Our extensive computations also uncover a scaling law between a circuit robustness measure and the strength of growth feedback. Despite the negative effects of growth feedback on the majority of circuit topologies, we identify a few circuits that maintain optimal performance as designed, a feature important for applications.

Total removal of a large esophageal schwannoma by submucosal tunneling endoscopic resection: A case report and review of literature.

BACKGROUND: Primary schwannoma is a rare submucosal tumor of the esophagus, which is most often benign, and surgery is the only effective treatment. So far, only a few cases have been reported. Herein, we reported a single case diagnosed with primary esophageal schwannoma that was totally removed by submucosal tunneling endoscopic resection (STER). CASE SUMMARY: A 62-year-old man presented to the hospital with a history of resection of a malignant gastric tumor and mild dysphagia. Endoscopic examination revealed a large submucosal elevated lesion in the esophagus 25-30 cm from the incisors. Endoscopic ultrasonography detected a 45 mm × 35 mm × 31 mm hypoechoic lesion; chest computed tomography showed a mass of approximately 55 mm × 35 mm × 29 mm. A preliminary examination showed features suggestive of a stromal tumor. Pathological findings indicated esophageal schwannoma. Next, STER alone was performed to completely resect the mass, and the patient recovered well post-surgery. Afterward, the patient was discharged and showed no tumor recurrence at 33 mo of follow-up. CONCLUSION: Endoscopic resection is still an effective treatment for large esophageal schwannomas (> 30 mm) under meticulous morphological evaluation.

Model-free tracking control of complex dynamical trajectories with machine learning.

Nonlinear tracking control enabling a dynamical system to track a desired trajectory is fundamental to robotics, serving a wide range of civil and defense applications. In control engineering, designing tracking control requires complete knowledge of the system model and equations. We develop a model-free, machine-learning framework to control a two-arm robotic manipulator using only partially observed states, where the controller is realized by reservoir computing. Stochastic input is exploited for training, which consists of the observed partial state vector as the first and its immediate future as the second component so that the neural machine regards the latter as the future state of the former. In the testing (deployment) phase, the immediate-future component is replaced by the desired observational vector from the reference trajectory. We demonstrate the effectiveness of the control framework using a variety of periodic and chaotic signals, and establish its robustness against measurement noise, disturbances, and uncertainties.

[Spectral analysis of organic/microcavity organic light-emitting devices with the change in thickness of organic layer].

Organic light-emitting devices (OLEDs) with emission peak at 520 nm were designed. The electroluminescence (EL) spectra including the integrated intensity, the peak width at half height, and the intensity and the position of the peak of the EL spectra of the OLEDs and microcavity OLEDs (MOLEDs), the total thickness of organic layers which is changeable, were calculated and theoretically analyzed with the thickness of the layer of NPB and light-emitting layer of Alq3 ranging from 10 to 100 nm, respectively. According to these studies, it was found that the optimized OLEDs should be constructed with 70 nm NPB and 62 nm Alq3, and this structure should be more suitable to configurate the MOLEDs. These results suggest that the suitable structure of OLEDs/MOLEDs could be designed with help of theoretical calculation, which is also helpful to the light-emitting properties of OLEDs and MOLEDs.

Predicting amplitude death with machine learning.

In nonlinear dynamics, a parameter drift can lead to a sudden and complete cessation of the oscillations of the state variables-the phenomenon of amplitude death. The underlying bifurcation is one at which the system settles into a steady state from chaotic or regular oscillations. As the normal functioning of many physical, biological, and physiological systems hinges on oscillations, amplitude death is undesired. To predict amplitude death in advance of its occurrence based solely on oscillatory time series collected while the system still functions normally is a challenge problem. We exploit machine learning to meet this challenge. In particular, we develop the scheme of "parameter-aware" reservoir computing, where training is conducted for a small number of bifurcation parameter values in the oscillatory regime to enable prediction upon a parameter drift into the regime of amplitude death. We demonstrate successful prediction of amplitude death for three prototypical dynamical systems in which the transition to death is preceded by either chaotic or regular oscillations. Because of the completely data-driven nature of the prediction framework, potential applications to real-world systems can be anticipated.

Application of modified closed biopsy in rabbit model of VX2-transplanted bone tumor.

BACKGROUND: This study was aimed to explore the application value of modified closed biopsy technique in puncture biopsy of rabbit VX2 transplanted bone tumor model. METHODS: VX2 tumor was transplanted into the bilateral tibia of 30 rabbits through the tibial plateau to make the model of VX2 transplanted bone tumor. Seven days after modeling, the proximal tibia biopsy was performed under the guidance of X-ray, and the biopsy specimen was examined pathologically. The left leg was biopsied with modified closed biopsy technique (experimental group), and the right leg was biopsied with hollow needle (control group). After 14 days of modeling, all rabbits were killed after X-ray examination around the puncture hole, and the soft tissue around the puncture hole was taken for pathological examination, and the expression levels of PCNA and CD34 in the tissue extract were detected by enzyme-linked immunosorbent assay (ELISA). RESULTS: By the end of the experiment, a total of 3 rabbits died, and finally, 27 rabbits were included in the study. Tumor cells were detected in all the intramedullary specimens obtained by puncture biopsy. On the 14th day after modeling, X-ray showed that the occurrence rate of periosteal reaction and extraosseous high-density shadow around the puncture hole was 14.81% (4/27) in the experimental group and 40.74% (11/27) in the control group. The difference was statistically significant (P<0.05). The pathological results of soft tissue around the puncture hole showed that the tumor cell metastasis rate was 29.63% (8/27) in the experimental group and 100% (27/27) in the control group, and the difference was statistically significant (P<0.05). The expression levels of PCNA and CD34 in the experimental group were lower than those in the control group (P < 0.05). CONCLUSION: Both the modified closed biopsy technique and needle aspiration biopsy can provide sufficient biopsy tissue for the diagnosis of VX2-transplanted bone tumor in rabbits. At the same time, the improved closed biopsy technique has a certain application value in preventing local metastasis of tumor cells along the puncture channel.

Synchronization within synchronization: transients and intermittency in ecological networks.

Transients are fundamental to ecological systems with significant implications to management, conservation and biological control. We uncover a type of transient synchronization behavior in spatial ecological networks whose local dynamics are of the chaotic, predator-prey type. In the parameter regime where there is phase synchronization among all the patches, complete synchronization (i.e. synchronization in both phase and amplitude) can arise in certain pairs of patches as determined by the network symmetry-henceforth the phenomenon of 'synchronization within synchronization.' Distinct patterns of complete synchronization coexist but, due to intrinsic instability or noise, each pattern is a transient and there is random, intermittent switching among the patterns in the course of time evolution. The probability distribution of the transient time is found to follow an algebraic scaling law with a divergent average transient lifetime. Based on symmetry considerations, we develop a stability analysis to understand these phenomena. The general principle of symmetry can also be exploited to explain previously discovered, counterintuitive synchronization behaviors in ecological networks.

[Studies on the characters and microstructure of enzyme and octenyl succinic anhydride modified starch].

Fourier transform infrared spectroscopy (FTIR), X-ray diffraction (XRD), UV-Vis spectra, laser nano size detector (LNSD) and scanning electron microscope (SEM) were employed to analyze the characters and structure of enzyme and octenyl succinic anhydride modified starch. The results indicated that the enzymatic starch reacted with octenyl succinic anhydride, bringing only octenyl succinic anhydride groups but not any other groups. The esterification of enzymatic starch only took place in amorphous region, but had no effect on the crystal form of starch granule. The clarity of EOSS increased with the increase in substitution degree. The particle size of oil emulsion made by EOSS was fine and well-distributed, meaning that the emulsion has excellent emulsibility and emulsifying stability. The embedding of oil encapsulated with EOSS is fine. It can be concluded that the properties of EOSS is excellent, and can be used as emulsifier and wall material of microcapsule.

Intratumoral Susceptibility Signals Reflect Biomarker Status in Gliomas.

Susceptibility-weighted imaging (SWI) can be a useful tool to depict vascular structures in brain tumors as well as micro-bleedings, which represent tumor invasion to blood vessels and could also be representative of tumoral angiogenesis. In this study, we investigated the relationship between SWI features and glioma grades, and the expression of key molecular markers isocitrate dehydrogenase 1 (IDH1), O-6-methylguanine-DNA methyltransferase (MGMT), and 1p19q. The gliomas were graded according to the intratumoral susceptibility signals (ITSS). We used the Mann-Whitney test to analyze the relationship between ITSS grades and the pathological level and status of these markers. Additionally, the area under the curve (AUC) was used to determine the predictive value of glioma SWI characteristics for the molecular marker status. In these cases, the ITSS grades of low-grade gliomas (LGG) were significantly lower than those of high-grade gliomas (HGG). Similarly, the ITSS grades of gliomas with IDH1 mutations and MGMT methylation were significantly lower than those of gliomas with Wild-type IDH1 and unmethylated MGMT. However, ITSS grades showed no relationship with 1p19q deletion status, while they did show significant predictive ability for glioma grade, IDH1 mutation, and MGMT methylation. These findings indicate an association between some molecular markers and cerebral microbleeds in gliomas, providing a new avenue for non-invasive prediction of molecular genetics in gliomas and an important basis for preoperative personalized surgical treatment based on molecular pathology.

Percolation on networks with weak and heterogeneous dependency.

In real networks, the dependency between nodes is ubiquitous; however, the dependency is not always complete and homogeneous. In this paper, we propose a percolation model with weak and heterogeneous dependency; i.e., dependency strengths could be different between different nodes. We find that the heterogeneous dependency strength will make the system more robust, and for various distributions of dependency strengths both continuous and discontinuous percolation transitions can be found. For Erdos-Rényi networks, we prove that the crossing point of the continuous and discontinuous percolation transitions is dependent on the first five moments of the dependency strength distribution. This indicates that the discontinuous percolation transition on networks with dependency is determined not only by the dependency strength but also by its distribution. Furthermore, in the area of the continuous percolation transition, we also find that the critical point depends on the first and second moments of the dependency strength distribution. To validate the theoretical analysis, cases with two different dependency strengths and Gaussian distribution of dependency strengths are presented as examples.

Preoperative inflammation markers and IDH mutation status predict glioblastoma patient survival.

Recent studies suggest that inflammation response biomarkers are prognostic indicators of solid tumor outcomes. Here, we quantify the prognostic value of the neutrophil-to-lymphocyte ratio (NLR), platelet-to-lymphocyte ratio (PLR), and lymphocyte-to-monocyte ratio (LMR) in glioblastomas (GBMs), taking into consideration the role of the isocitrate dehydrogenase (IDH) mutation status. We examined 141 primary glioblastomas (pGBMs) and 25 secondary glioblastomas (sGBMs). NLRs, PLRs, and LMRs were calculated before surgery. IDH mutations were detected immunohistochemically after tumor resection, and patients' clinical outcomes were analyzed after classification into GBM, pGBM, and IDH-wild type glioblastoma (IDH-wt GBM) groups. To make comparisons, we set cutoffs for NLR, PLR and LMR of 4.0, 175.0, and 3.7, respectively. In a multivariate analysis, both NLR (HR=1.712, 95% CI 1.026-2.858, p=0.040) and PLR (HR=2.051, 95% CI 1.288-3.267, p=0.002) had independent prognostic value. While a low NLR was associated with a better prognosis only in the IDH-wt GBM group, PLR was predictive of patient survival in the GBM, pGBM, and IDH-wt GBM groups. By contrast, LMR exhibited no prognostic value for any of the 3 types of GBM.

[Beijing common green tree leaves' accumulation capacity for heavy metals].

Seasonal variation of heavy metal contents in leaves and their relationships with soil heavy metal pollution levels were studied through measuring and analyzing the leaves of the common tree species in Beijing and soil heavy metal contents, to detect heavy metal accumulation ability of plant leaves. The results showed that: (1) the contents of Cu, Pb, Zn in plant leaves first decreased and then increased, again declined with changing the seasons (from spring to winter). Cr concentration showed the trend of first increase and then decrease from spring to winter, and the highest in the autumn; the accumulation capacities of Cu for Babylonica and Japonica were higher in the spring, summer and autumn, while Tabuliformis was in winter; the higher accumulation capacities for Cr, Pb were Japonica and Platycladus, and in winter were Platycladus and Bungeana; the higher accumulation capacities for Zn were Babylonica and Bungeana, while Platycladus in winter; (2) the pollution degree of four kinds of heavy metals (Cu, Cr, Pb, Zn) from downtown to suburbs showed that: Jingshan (C =2.48, C is contamination factor) > Olympic (C = 1.27) > Songshan (C = 1.20) > Shuiguan (C = 1. 18); (3) the heavy metals concentration of same plant leaves in the water of the Great Wall changed larger, but those in the other three areas showed that: Jingshan > Olympic > Songshan; the ability of same species leaf to absorb different sorts of heavy metals showed that: Zn >Cu >Pb >Cr; the difference between Zn content and Cr content was significant (P <0.01); (4) the relationship between heavy metal content in plant leaves and soil heavy metal pollution levels presented a quadratic polynomial relation; the significant correlation was found between other three heavy metal contents of plant samples and soil samples, but they were not the case for the Cu, and the correlation coefficients were above 0. 9.

Carboxylic acid functionalized ortho-linked oxacalix[2]benzene[2]pyrazine: synthesis, structure, hydrogen bond and metal directed self-assembly.

Cyclooligomerization of 2,6-dichloropyrazine 4 and benzyl 2,3-dihydroxybenzoate 5 under microwave irradiation resulted in a racemic pair of ester functionalized ortho-linked oxacalix[2]benzene[2]pyrazine 6, which was further transformed to the corresponding racemic carboxylic acid functionalized ortho-linked oxacalix[2]benzene[2]pyrazine 3. Both enantiomers of 3 adopt 1,3-alternate conformations with their two carboxylic acid groups pointing to opposite directions in the solid state. Enantiomers of 3 form a step-like one-dimensional supramolecular polymer via intermolecular hydrogen bond interactions between the carboxylic acids for crystals obtained in methanol. No hydrogen bonds were formed between the carboxylic acids for crystals of 3 obtained in pyridine and aqueous guanidine solutions; instead, intermolecular hydrogen bonds between the carboxylic acid groups of 3 and pyridine, as well as guanidinium ions were formed. Under metal-mediated self-assembly conditions, the pyrazinyl nitrogen atoms in 3 interacted with transition metal ions, such as Ag(I), Cu(II) and Zn(II), and resulted in the formation of four new metal-containing supramolecular complexes. Metallomacrocycles 7, 8 and 9 were formed by reactions of 3 with Ag(I) or Cu(II) ions by bridging two ligands 3 in the equatorial region via M-N coordination bonds. A one-dimensional coordination polymer 10 was generated by reaction between ligand 3 and Zn(II) ions, and a cage-based structure is presented in 10 by bridging of the cyclophane units by Zn(2+) ions via Zn-N and Zn-O bonds.

Identification and characterization of a rat novel gene RSEP4 expressed specifically in central nervous system.

The low-abundantly expressed genes composed the majorities of the mRNAs expressed in the central nervous system (CNS), and were thought to be important for the normal brain functions. Through differential screening a low-abundance cDNA sublibrary with mRNA from neuropathic pain of chronic constriction injury (CCI) model, we have identified a novel rat gene, rat spinal-cord expression protein 4 gene (RSEP4). The total length of RSEP4 cDNA is 2006 bp, with a 501 nucleotide open reading frame (ORF) that encodes a 167 amino acid polypeptide. Northern blot revealed that RSEP4 was expressed specifically in the CNS. In situ hybridization showed that the mRNA of RSEP4 was strongly expressed in the CA1, CA2, CA3 and DG regions of hippocampus, the Purkinje cells of cerebellum, and the small sensory neurons of dorsal horn and large motor neurons of ventral horn of spinal cord. Over-expression of RSEP4-EGFP fusion protein in the human embryonic kidney 293T cells showed that RSEP4 protein was mainly localized in the cell cytoplasm. These results suggest that RSEP4 may play some roles in the CNS.