Critical effect of dependency groups on the function of networks

Current network models assume one type of links to define the relations between the network entities. However, many real networks can only be correctly described using two different types of relations. Connectivity links that enable the nodes to function cooperatively as a network and dependency links that bind the failure of one network element to the failure of other network elements. Here we present an analytical framework for studying the robustness of networks that include both connectivity and dependency links. We show that a synergy exists between the failure of connectivity and dependency links that leads to an iterative process of cascading failures that has a devastating effect on the network stability. We present exact analytical results for the dramatic change in the network behavior when introducing dependency links. For a high density of dependency links, the network disintegrates in a form of a first-order phase transition, whereas for a low density of dependency links, the network disintegrates in a second-order transition. Moreover, opposed to networks containing only connectivity links where a broader degree distribution results in a more robust network, when both types of links are present a broad degree distribution leads to higher vulnerability.     

Compartmentalized expression of light-induced clock genes in the suprachiasmatic nucleus of the diurnal grass rat (Arvicanthis niloticus)

Photic responses of the circadian system are mediated through light-induced clock gene expression in the suprachiasmatic nucleus (SCN). In nocturnal rodents, depending on the timing of light exposure, Per1 and Per2 gene expression shows distinct compartmentalized patterns that correspond to the behavioral responses. Whether the gene- and region-specific induction patterns are unique to nocturnal animals, or are also present in diurnal species is unknown. We explored this question by examining the light-induced Per1 and Per2 gene expression in functionally distinct SCN subregions, using diurnal grass rats Arvicanthis niloticus. Light exposure during nighttime induced Per1 and Per2 expression in the SCN, showing unique spatiotemporal profiles depending on the phase of the light exposure. After a phase delaying light pulse (LP) in the early night, strong Per1 induction was observed in the retinorecipient core region of the SCN, while strong Per2 induction was observed throughout the entire SCN. After a phase advancing LP in the late night, Per1 was first induced in the core and then extended into the whole SCN, accompanied by a weak Per2 induction. This compartmentalized expression pattern is very similar to that observed in nocturnal rodents, suggesting that the same molecular and intercellular pathways underlying acute photic responses are present in both diurnal and nocturnal species. However, after an LP in early subjective day, which induces phase advances in diurnal grass rats, but not in nocturnal rodents, we did not observe any Per1 or Per2 induction in the SCN. This result suggests that in spite of remarkable similarities in the SCN of diurnal and nocturnal rodents, unique mechanisms are involved in mediating the phase shifts of diurnal animals during the subjective day.     

Low-temperature emergent neuromorphic networks with correlated oxide devices

Neuromorphic computing—which aims to mimic the collective and emergent behavior of the brain’s neurons, synapses, axons, and dendrites—offers an intriguing, potentially disruptive solution to society’s ever-growing computational needs. Although much progress has been made in designing circuit elements that mimic the behavior of neurons and synapses, challenges remain in designing networks of elements that feature a collective response behavior. We present simulations of networks of circuits and devices based on superconducting and Mott-insulating oxides that display a multiplicity of emergent states that depend on the spatial configuration of the network. Our proposed network designs are based on experimentally known ways of tuning the properties of these oxides using light ions. We show how neuronal and synaptic behavior can be achieved with arrays of superconducting Josephson junction loops, all within the same device. We also show how a multiplicity of synaptic states could be achieved by designing arrays of devices based on hydrogenated rare earth nickelates. Together, our results demonstrate a research platform that utilizes the collective macroscopic properties of quantum materials to mimic the emergent behavior found in biological systems.

Emergent behavior, defined as when a system’s collective behavior is different from that of its individual constituents, is widespread and consequential in nature. The brain, for example, is made up of proteins, tissue, and flowing chemicals and charges but functions collectively over a long spatial range as a uniquely powerful and energy-efficient machine. Remarkably, its constituent elements and global architecture feature staggering amounts of seeming randomness and disorder (Fig. 1), yet it has evolved to be capable of astounding computational functionalities: in many ways, still more impressive and energy-efficient than semiconductor-based computers. Analogously, quantum materials, such as strongly correlated systems (1), display collective macroscopic behavior such as superconductivity (2) and metal−insulator transitions (3). These macroscopic collective responses emerge from microscopic quantum mechanical interactions. As a result, brain-inspired computing paradigms—known broadly as neuromorphic (4, 5)—based on these quantum materials are prominent in the goals of various research efforts to explore and hopefully spawn the next technological revolution (6–10).Open in a separate windowFig. 1.A comparison of the emergent behavior that arises in biological systems, including simple living organisms (Left) and artificial (Right) devices. Disorder and randomness play roles at all length scales. In the case of correlated oxides (Right), disorder in the lattice can yield different macroscopic properties that can be used to make devices by controlled light-ion modifications. Moreover, a randomly designed network of said devices can yield exponentially more complex, emergent responses.Natural neural networks in animal brains comprise neurons that are interconnected by synapses. Neurons are capable of integrating charges and releasing them at critical thresholds referred to as action potentials. Synapses can amplify or decrease the signal strength by chemical or electrical pathways (11). Information is encoded temporally in such networks and represents a paradigm distinct from traditional digital electronics. Synapses store memory and can dynamically adjust their weight in response to the time intervals between neuronal stimuli (known as time-dependent plasticity). Neuromorphic hardware networks therefore aspire to capture the key features found in the nervous system such as periodic trains of spiking signals and multistate memory that can be programmed incrementally as well as in a time-dependent manner. Materials that can host diverse electronic structures and/or present nonlinear electrical characteristics often are promising candidate systems to explore as building blocks for neuromorphic networks. Further, to emulate the complexity of natural networks, having multiple control knobs via ionic or electronic inputs to tune the order parameter in neuromorphic devices is desirable.Neuromorphic computing architectures based on correlated transition metal oxides could offer a flexible, low-consumption alternative to von Neumann architectures. The key challenge is to generate flexible material response properties that can harbor multiple states to allow flexibility and an architecture for computation and memory to operate in parallel. Correlated oxides offer a platform to explore high-density and multistate memory because of the opportunity to apply their multiple phases including superconductivity, magnetism, and metal−insulator transitions. While low-temperature complementary metal-oxide-semiconductor (CMOS) and cryocooling have long been an active area of research (12–14), cryoelectronic neuromorphic device technologies based on superconducting materials are gaining interest rapidly due to their unique advantages in power efficiency and in flexibility to generate spiking neuron-like behavior (15–26).In this paper, we report results of simulations that demonstrate classes of low-temperature artificial neural networks that arise from designing controlled disorder in devices based on correlated oxides. We utilize two archetypal properties for this purpose, namely, superconductivity and metal–insulator transitions, with the common theme of creating controlled disorder with light ion incorporation.First, we will discuss how lattice disorder in superconducting YBa2Cu3O7 (YBCO) induced by helium ion implantation can be used to fabricate arrays of superconducting Josephson junction loops that yield an exponential multiplicity of coherent states. This emergent multiplicity can further evolve by randomizing the array’s spatial geometry. The disordered superconducting loops are fast and allow multiple states that have a transient nature with low energy consumption. Second, we discuss how arrays of hydrogen ion incorporated rare earth nickelate devices, each of which are known to individually render synaptic behavior, can also yield a multiplicity of states that rely on the spatial configuration and design of the array. These responses are slower than the superconducting loops but more stable in the various states, and are more memory centric. While the different oxide material systems possess different microscopic behaviors, we illustrate that the flexibility of these two case study systems allows high-density architectures with configurational randomness that yield many possible states that could mimic the animal brain’s notable random-to-collective behavior.Moreover, our simulations outline a broad research effort to harness the properties of strongly correlated electron systems to produce arrays of disordered individual brain-inspired elements that collectively evolve to render emergent functionalities. These quantum materials feature a large range of systems with “binary” states that can be employed, and we show how these can be configured to construct neurons and synapses on the same device. While the two examples we present are near the ends of the spectra of speed, energy, and stability, the range of properties in oxides allows a wide range of speed, power consumption, and volatility, that is, long-term stability or transient volatility. With the wealth of oxides currently being studied throughout a large community, we expect many more systems following the architectural framework that this paper discusses.This paper is organized as follows. First, we summarize the experimentally known properties that enable our neuromorphic simulations—superconductivity and metal−insulator transitions—particularly focusing on the effect that light ions have on them. Then, we discuss how superconducting Josephson junction loops could render neuronal behavior in copper oxide devices. Next, we discuss how synaptic behavior can be achieved in two platforms. Finally, we look outward by presenting examples of connectivity between the devices we propose and other material platforms.     

Nonsurgical spinal decompression system traction combined with electroacupuncture in the treatment of multi-segmental cervical disc herniation: A case report

Rationale:With the spread of computers and mobile phones, cervical spondylosis has become a common occupational disease in clinics, which seriously affects the quality of life of patients. We used a nonsurgical spinal decompression system (SDS) combined with physical therapy electroacupuncture (EA) to treat a case of mixed cervical spondylosis caused by multi-level cervical disc herniation, and we achieved satisfactory results.Patient concerns:A 44-year-old Caucasian Asian woman presented with neck pain and numbness on the left side of the limb. MRI showed the patient''s C3–C7 segment cervical disc herniation, and the flexion arch of the cervical spine was reversed.Diagnosis:The patient was diagnosed with a mixed cervical spondylosis.Interventions:The patient received a month of physical therapy (SDS traction combined with EA).Outcomes:Before and after treatment: VAS score of neck pain decreased from 8 to 0; Cervical spine mobility returned to normal; The grip strength of left hand increased from 7.5 kg to 19.2 kg; Cervical curvature index changed from −16.04% to −3.50%; the physiological curvature of the cervical spine was significantly restored. There was no dizziness or neck discomfort at 6 month and 1 year follow-up.Lessons subsetions:SDS traction combined with EA is effective for the treatment of cervical disc herniation and can help restore and rebuild the biomechanical balance of the cervical spine.     

肝硬化门静脉高压症患者心脏泵功能减退

心脏血流动力学参数足反映人体生命信息和心脏泵功能的重要指标.同前,尢创性血流动力学检测已广泛应用于心力衰竭、休克等危重症患者的病情监测、治疗方案的确定等,以便使患者得到科学、有效的治疗[1-2].     

Ultrafast four-dimensional imaging of cardiac mechanical wave propagation with sparse optoacoustic sensing

Propagation of electromechanical waves in excitable heart muscles follows complex spatiotemporal patterns holding the key to understanding life-threatening arrhythmias and other cardiac conditions. Accurate volumetric mapping of cardiac wave propagation is currently hampered by fast heart motion, particularly in small model organisms. Here we demonstrate that ultrafast four-dimensional imaging of cardiac mechanical wave propagation in entire beating murine heart can be accomplished by sparse optoacoustic sensing with high contrast, ∼115-µm spatial and submillisecond temporal resolution. We extract accurate dispersion and phase velocity maps of the cardiac waves and reveal vortex-like patterns associated with mechanical phase singularities that occur during arrhythmic events induced via burst ventricular electric stimulation. The newly introduced cardiac mapping approach is a bold step toward deciphering the complex mechanisms underlying cardiac arrhythmias and enabling precise therapeutic interventions.

The first observation of irregular spikes in electrocardiogram (ECG) signals in the early 1900s (1) has fostered vast research efforts to unravel the fundamental mechanisms underlying cardiac arrhythmias (2–4). The information encoded in ECG signals corresponds to the integrated electrical activity of cardiac cells. Point-by-point ECG mapping combined with modeling of tissue electrical properties has enabled localizing the onset and propagation paths of irregular electric signaling in the heart (5). Electrocardiographic imaging further combined multiple ECG body-surface readings with full-body X-ray computed tomography (CT) or MRI scans to map the electrical activity on the epicardial heart surface (6, 7). While delivering excellent contrast for imaging cardiac anatomy and blood flow (8–10), the temporal resolution of CT and MRI is insufficient for three-dimensional (3D) visualization of transient nonperiodic events in the heart. Models linking the electrophysiology of single cells to the propagation of electric signals in cardiac muscles have subsequently been developed, yet complete depiction of the entire heart motion in real-time and 3D remains challenging without employing cardiac gating (11). Progress in ultrasound (US) imaging recently enabled attaining millisecond-scale temporal resolutions, leading to major advances in characterization of the cardiac motion. Particularly, visualization of electromechanical and shear waves in the heart was possible with ultrafast US (12, 13). In parallel, development of optical probes of transmembrane voltage (14, 15) and intracellular calcium (16, 17) has provided unprecedented insights into cardiovascular physiology and the mechanisms of electrical activity (18, 19). A combination between US and optical mapping has further enabled the multimodal characterization of scroll waves in an isolated Langendorff pig heart model (20), a pivotal step toward better understanding of the heart rhythm disorders.Despite major technical advances, the spatiotemporal resolution and penetration of the currently available cardiac imaging modalities hinders the transmural visualization of fast electromechanical phenomena across the entire beating heart, particularly in small model organisms. The effective spatiotemporal resolution of ultrafast volumetric US is ultimately limited by the need for compounding multiple plane-wave transmissions in order to achieve sufficient image contrast (21, 22). While offering important benefits, such as fast response times (23) and accurate spatial mapping capacity (24), the strong photon scattering in biological tissues fundamentally confines optical mapping to surface-weighted two-dimensional (2D) observations (25, 26), whereas motion artifacts associated with the myocardial contractions further lead to out-of-focus artifacts complicating action potential readings (18, 27–29). Other common impediments of extrinsic cardiac labeling approaches include phototoxicity (30), photobleaching (31), and nonphysiological changes in the heart (32).Recent progress in the development of optoacoustic (OA) tomography methods have attained a unique combination between fast volumetric imaging speed, deep-tissue penetration, and high molecular sensitivity (26, 33–35), making this modality particularly attractive for studying cardiac function. Whole-heart OA imaging at 50-Hz volumetric frame rates was demonstrated for the Langendorff-perfused model (36) as well as in vivo by capturing bulk perfusion of optical contrast agents (37, 38). Healing of infarcted myocardium in models of coronary occlusion and c-kit mutants has further been studied using this approach (39), highlighting its unique capacity for multiparametric characterization of morphological and functional changes in murine models of myocardial infarction.Yet, accurate mapping of microscopic tissue deformations in a rapidly beating murine heart implies 3D imaging at significantly faster frame rates on a submillisecond temporal resolution scale (39, 40). Here, we demonstrate that ultrafast four-dimensional imaging of cardiac mechanical wave propagation in the entire beating murine heart can be accomplished using sparse OA sensing (SOS) of volumetric cardiac motion. Our approach is based on a rapid compressed acquisition of OA responses from randomized subsets of US detection channels followed by iterative reconstruction of the entire image sequence with infimal convolution of the total variation (ICTV) functional. In this way, we were able to efficiently delineate the multiscale spatiotemporal information encoded in the volumetric heart motion with high-contrast, ∼115-µm spatial, and submillisecond temporal resolution.     

安宁缓和医疗中的心理及社会评估

心理社会评估是安宁疗护工作的核心内容之一.本文以北京市海淀医院安宁疗护病房开展心理社会评估的经验体会为基础,阐述了心理社会评估的意义以及有效开展评估工作的相关要点,供开展安宁缓和医疗的医护人员参考.     

Circumstances of Falls During Sit-to-Stand Transfers in Older People: A Cohort Study of Video-Captured Falls in Long-Term Care

ObjectiveTo characterize the circumstances of falls during sit-to-stand transfers in long-term care (LTC), including the frequency, direction, stepping and grasping responses, and injury risk, based on video analysis of real-life falls.DesignCohort study.SettingLTC.ParticipantsWe analyzed video footage of 306 real-life falls by 183 LTC residents that occurred during sit-to-stand transfers, collected from 2007 to 2020. The mean age was 83.7 years (SD=9.0 years), and 93 were female (50.8%).InterventionNot applicable.Main Outcome MeasuresWe used Generalized Estimating Equations to test for differences in the odds that a resident would fall at least once during the rising vs stabilization phases of sit-to-stand and to test the association between the phase of the transfer when the fall occurred (rising vs stabilization) and the following outcomes: (1) the initial fall direction; (2) the occurrence, number, and direction of stepping responses; (3) grasping of environmental supports; and (4) documented injury.ResultsFalls occurred twice as often in the rising phase than in the stabilization phase of the transfer (64.0% and 36.0%, respectively). Falls during rising were more often directed backward, while falls during stabilization were more likely to be sideways (odds ratio [OR]=1.95; 95% confidence interval [CI]=1.07-3.55). Falls during rising were more often accompanied by grasping responses, while falls during stabilization were more likely to elicit stepping responses (grasping: OR=0.30; 95% CI=0.14-0.64; stepping: OR=8.29; 95% CI=4.54-15.11). Injuries were more likely for falls during the stabilization phase than the rising phase of the transfer (OR=1.73; 95% CI=1.04-2.87).ConclusionMost falls during sit-to-stand transfers occurred from imbalance during the rising phase of the transfer. However, falls during the subsequent stabilization phase were more likely to cause injury.