An evaluation of emotion recognition, emotion reactivity, and emotion dysregulation as prospective predictors of 12-month trajectories of non-suicidal self-injury in an adolescent psychiatric inpatient sample.

BACKGROUND: Little is known about trajectories of NSSI. We aimed to identify NSSI trajectories in adolescent psychiatric inpatients and emotional processes that differentiate between trajectories. METHODS: Participants were 180 adolescents (71.7 % female; mean age of 14.89 years, SD = 1.35) from a psychiatric inpatient facility. NSSI was assessed at their index hospitalization, as well as 6, and 12 months after discharge. Emotion recognition, emotion reactivity, and emotion dysregulation were assessed at baseline. Latent class mixture modeling was used to identify different NSSI trajectories and ANOVAs were used to evaluate predictors of the trajectories. RESULTS: Analyses yielded three NSSI trajectories. These included a stable low-frequency class (90.53 % of sample), a stable moderate-frequency class, and a class characterized by high-frequency NSSI at baseline but that largely resolves by 6-month follow-up. After adjustments for multiple comparisons were made, only emotion regulation at baseline differentiated between the trajectories, with greater overall emotion dysregulation and greater emotional non-acceptance (a facet of emotion dysregulation) characterizing the initially high-frequency class and the stable moderate-frequency class more than the stable low-frequency class (ps < .05). Difficulties engaging in goal-directed behavior when distressed characterized the stable moderate-frequency NSSI class more than the stable low-frequency class (p < .05). Limitations The study sample consists predominantly of female and White adolescents and thus may not generalize to other demographic groups. CONCLUSIONS: The current findings suggest that interventions involving emotion regulation with adolescents who engage in NSSI would particularly benefit from a focus on increasing acceptance of emotional experiences.

Observation of a Higher-Order End Topological Insulator in a Real Projective Lattice.

The modern theory of quantized polarization has recently extended from 1D dipole moment to multipole moment, leading to the development from conventional topological insulators (TIs) to higher-order TIs, i.e., from the bulk polarization as primary topological index, to the fractional corner charge as secondary topological index. The authors here extend this development by theoretically discovering a higher-order end TI (HOETI) in a real projective lattice and experimentally verifying the prediction using topolectric circuits. A HOETI realizes a dipole-symmetry-protected phase in a higher-dimensional space (conventionally in one dimension), which manifests as 0D topologically protected end states and a fractional end charge. The discovered bulk-end correspondence reveals that the fractional end charge, which is proportional to the bulk topological invariant, can serve as a generic bulk probe of higher-order topology. The authors identify the HOETI experimentally by the presence of localized end states and a fractional end charge. The results demonstrate the existence of fractional charges in non-Euclidean manifolds and open new avenues for understanding the interplay between topological obstructions in real and momentum space.

Differentiation of Alkyl- and Plasmenyl-phosphatidylcholine by Endogenous Sphingomyelin RT-XLOGP3 Regression for Coronary Artery Disease Plasma Lipidomics Analysis.

Accurate identification between alkyl- and plasmenyl-phosphatidylcholine (PC(O-) and PC(P-)) isomers is a major analytical challenge in lipidomics studies due to a lack of structure-specific ions in conventional tandem mass spectrometry (MS/MS) methods and the absence of universal retention time (RT) references. Given the importance of PC(O-) and PC(P-), an easy-to-apply method for current research is urgently needed. In this study, we present a quadratic RT-XLOGP3SM regression model that uses endogenous sphingomyelin (SM) species in blood samples as retention time (RT) indicators to predict the RTs of PC(O-) and PC(P-) species by coupling their calculated partition coefficients based on XLOGP3. The prediction results were obtained with a root-mean-square error (RMSE) of 0.12 min (1.3%) for the RRHD (rapid resolution high definition) nonlinear LC condition. A lipidomic analysis with RT-XLOGP3SM regression was used to study lipid regulation in coronary artery disease (CAD) outpatient plasma samples, and we found that the types of exhibited regulation were highly dependent on the lipid subclasses in comparison to the healthy control group. In conclusion, given that the quadratic RT-XLOGP3SM regression model predicts the RTs of PC species based on the relative value of XLOGP3 and the RTs of endogenous SM species, it can be expected that most of the C18-based lipidomics analyses could apply this method to increase the identification ability of the PC(O-) and PC(P-) subclasses and to improve the understanding of their physiological functions.

Proposal for Observing Yang-Lee Criticality in Rydberg Atomic Arrays.

Yang-Lee edge singularities (YLES) are the edges of the partition function zeros of an interacting spin model in the space of complex control parameters. They play an important role in understanding non-Hermitian phase transitions in many-body physics, as well as characterizing the corresponding nonunitary criticality. Even though such partition function zeroes have been measured in dynamical experiments where time acts as the imaginary control field, experimentally demonstrating such YLES criticality with a physical imaginary field has remained elusive due to the difficulty of physically realizing non-Hermitian many-body models. We provide a protocol for observing the YLES by detecting kinked dynamical magnetization responses due to broken PT symmetry, thus enabling the physical probing of nonunitary phase transitions in nonequilibrium settings. In particular, scaling analyses based on our nonunitary time evolution circuit with matrix product states accurately recover the exponents uniquely associated with the corresponding nonunitary CFT. We provide an explicit proposal for observing YLES criticality in Floquet quenched Rydberg atomic arrays with laser-induced loss, which paves the way towards a universal platform for simulating non-Hermitian many-body dynamical phenomena.

Dimensional Transmutation from Non-Hermiticity.

Dimensionality plays a fundamental role in the classification of novel phases and their responses. In generic lattices of 2D and beyond, however, we found that non-Hermitian couplings do not merely distort the Brillouin zone (BZ), but can in fact alter its effective dimensionality. This is due to the fundamental noncommutativity of multidimensional non-Hermitian pumping, which obstructs the usual formation of a generalized complex BZ. As such, basis states are forced to assume "entangled" profiles that are orthogonal in a lower dimensional effective BZ, completely divorced from any vestige of lattice Bloch states unlike conventional skin states. Characterizing this reduced dimensionality is an emergent winding number intimately related to the homotopy of noncontractible spectral paths. We illustrate this dimensional transmutation through a 2D model whose topological zero modes are protected by a 1D, not 2D, topological invariant. Our findings can be readily demonstrated via the bulk properties of nonreciprocally coupled platforms such as circuit arrays, and provokes us to rethink the fundamental role of geometric obstruction in the dimensional classification of topological states.

Hyperbolic Fringe Signal for Twin Impurity Quasiparticle Interference.

We study the quasiparticle interference (QPI) pattern emanating from a pair of adjacent impurities on the surface of a gapped superconductor (SC). We find that hyperbolic fringes (HFs) in the QPI signal can appear due to the loop contribution of the two-impurity scattering, where the locations of the two impurities are the hyperbolic focus points. For a single pocket Fermiology, a HF pattern signals chiral SC order for nonmagnetic impurities and requires magnetic impurities for a nonchiral SC. For a multipocket scenario, a sign-changing order parameter such as an s_{±} wave likewise yields a HF signature. We discuss twin impurity QPI as a new tool to complement the analysis of superconducting order from local spectroscopy.

Non-Hermitian pseudo-gaps.

The notion of a band gap is ubiquitous in the characterization of matter. Particularly interesting are pseudo-gaps, which are enigmatic regions of very low density of states that have been linked to novel phenomena like high temperature superconductivity. In this work, we discover a novel origin for pseudo-gaps when boundaries are introduced in a non-Hermitian lattice. It generically occurs due to the interference between two or more asymmetric pumping channels, and possess no analog in Hermitian systems. Mathematically, it can be visualized as being created by divergences of spectral flow in the complex energy plane, analogous to how sharp edges creates divergent electric fields near an electrical conductor. A non-Hermitian pseudo-gap can host symmetry-protected mid-gap modes like ordinary topological gaps, but the mid-gap modes are extended instead of edge-localized, and exhibit extreme sensitivity to symmetry-breaking perturbations. Surprisingly, pseudo-gaps can also host an integer number of edge modes even though the pseudo-bands possess fractional topological windings, or even no well-defined Chern number at all, in the marginal case of a phase transition point. Challenging conventional notions of topological bulk-boundary correspondences and even the very concept of a band, pseudo-gaps post profound implications that extend to many-body settings, such as fractional Chern insulators.

Designing non-Hermitian real spectra through electrostatics.

Non-hermiticity presents a vast newly opened territory that harbors new physics and applications such as lasing and sensing. However, only non-Hermitian systems with real eigenenergies are stable, and great efforts have been devoted in designing them through enforcing parity-time (PT) symmetry. In this work, we exploit a lesser-known dynamical mechanism for enforcing real-spectra, and develop a comprehensive and versatile approach for designing new classes of parent Hamiltonians with real spectra. Our design approach is based on a new electrostatics analogy for modified non-Hermitian bulk-boundary correspondence, where electrostatic charge corresponds to density of states and electric fields correspond to complex spectral flow. As such, Hamiltonians of any desired spectra and state localization profile can be reverse-engineered, particularly those without any guiding symmetry principles. By recasting the diagonalization of non-Hermitian Hamiltonians as a Poisson boundary value problem, our electrostatics analogy also transcends the gain/loss-induced compounding of floating-point errors in traditional numerical methods, thereby allowing access to far larger system sizes.

Experimental Identification of the Second-Order Non-Hermitian Skin Effect with Physics-Graph-Informed Machine Learning.

Topological phases of matter are conventionally characterized by the bulk-boundary correspondence in Hermitian systems. The topological invariant of the bulk in d dimensions corresponds to the number of (d - 1)-dimensional boundary states. By extension, higher-order topological insulators reveal a bulk-edge-corner correspondence, such that nth order topological phases feature (d - n)-dimensional boundary states. The advent of non-Hermitian topological systems sheds new light on the emergence of the non-Hermitian skin effect (NHSE) with an extensive number of boundary modes under open boundary conditions. Still, the higher-order NHSE remains largely unexplored, particularly in the experiment. An unsupervised approach-physics-graph-informed machine learning (PGIML)-to enhance the data mining ability of machine learning with limited domain knowledge is introduced. Through PGIML, the second-order NHSE in a 2D non-Hermitian topoelectrical circuit is experimentally demonstrated. The admittance spectra of the circuit exhibit an extensive number of corner skin modes and extreme sensitivity of the spectral flow to the boundary conditions. The violation of the conventional bulk-boundary correspondence in the second-order NHSE implies that modification of the topological band theory is inevitable in higher dimensional non-Hermitian systems.

Simulation of Interaction-Induced Chiral Topological Dynamics on a Digital Quantum Computer.

Chiral edge states are highly sought after as paradigmatic topological states relevant to both quantum information processing and dissipationless electron transport. Using superconducting transmon-based quantum computers, we demonstrate chiral topological propagation that is induced by suitably designed interactions, instead of flux or spin-orbit coupling. Also different from conventional 2D realizations, our effective Chern lattice is implemented on a much smaller equivalent 1D spin chain, with sequences of entangling gates encapsulating the required time-reversal breaking. By taking advantage of the quantum nature of the platform, we circumvented difficulties from the limited qubit number and gate fidelity in present-day noisy intermediate-scale quantum era quantum computers, paving the way for the quantum simulation of more sophisticated topological states on very rapidly developing quantum hardware.

Simulating hyperbolic space on a circuit board.

The Laplace operator encodes the behavior of physical systems at vastly different scales, describing heat flow, fluids, as well as electric, gravitational, and quantum fields. A key input for the Laplace equation is the curvature of space. Here we discuss and experimentally demonstrate that the spectral ordering of Laplacian eigenstates for hyperbolic (negatively curved) and flat two-dimensional spaces has a universally different structure. We use a lattice regularization of hyperbolic space in an electric-circuit network to measure the eigenstates of a 'hyperbolic drum', and in a time-resolved experiment we verify signal propagation along the curved geodesics. Our experiments showcase both a versatile platform to emulate hyperbolic lattices in tabletop experiments, and a set of methods to verify the effective hyperbolic metric in this and other platforms. The presented techniques can be utilized to explore novel aspects of both classical and quantum dynamics in negatively curved spaces, and to realise the emerging models of topological hyperbolic matter.

Exceptional Bound States and Negative Entanglement Entropy.

This Letter introduces a new class of robust states known as exceptional bound (EB) states, which are distinct from the well-known topological and non-Hermitian skin boundary states. EB states occur in the presence of exceptional points, which are non-Hermitian critical points where eigenstates coalesce and fail to span the Hilbert space. This eigenspace defectiveness not only limits the accessibility of state information but also interplays with long-range order to give rise to singular propagators only possible in non-Hermitian settings. Their resultant EB eigenstates are characterized by robust anomalously large or negative occupation probabilities, unlike ordinary Fermi sea states whose probabilities lie between 0 and 1. EB states remain robust after a variety of quantum quenches and give rise to enigmatic negative entanglement entropy contributions. Through suitable perturbations, the coefficient of the logarithmic entanglement entropy scaling can be continuously tuned. EB states represent a new avenue for robustness arising from geometric defectiveness, independent of topological protection or nonreciprocal pumping.

Plasma ceramides are associated with outcomes in acute ischemic stroke patients.

BACKGROUND/PURPOSE: Sphingolipids are major constituents of eukaryotic cell membranes and play key roles in cellular regulatory processes. Our recent results in an experimental stroke animal model demonstrated changes in sphingolipids in response to acute ischemic brain injury. This study aimed to investigate the plasma levels of sphingosine-1-phosphate (S1P) and ceramides in acute ischemic stroke (AIS) patients and their associations with functional outcomes. METHODS: Plasma samples were collected from patients with AIS at <48 and 48-72 h post stroke and from nonstroke controls. The levels of S1P and ceramides with different fatty acyl chain lengths were measured by the ultra-high-pressure liquid chromatography-electrospray ionization tandem mass spectrometry (UHPLC-ESI-MS/MS). A poor functional outcome was defined as a modified Rankin Scale (mRS) score ≥2 at 3 months after AIS. RESULTS: The results showed that S1P and very-long-chain ceramides were significantly decreased in AIS patients (n = 87; poor outcome, 56.3%) compared to nonstroke controls (n = 30). In contrast, long-chain ceramides were significantly increased in AIS patients. More importantly, higher levels of Cer(d18:1/18:0), Cer(d18:1/20:0), and Cer(d18:1/22:0) at 48-72 h were significantly associated with poor functional outcomes after adjusting for potential clinical confounders, including age, sex, hypertension, and National Institutes of Health Stroke Scale score at admission. CONCLUSION: Our study supported the dynamic metabolism of sphingolipids after the occurrence of AIS. Ceramides could be potential prognostic markers for patients with AIS.

Observation of hybrid higher-order skin-topological effect in non-Hermitian topolectrical circuits.

Robust boundary states epitomize how deep physics can give rise to concrete experimental signatures with technological promise. Of late, much attention has focused on two distinct mechanisms for boundary robustness-topological protection, as well as the non-Hermitian skin effect. In this work, we report the experimental realizations of hybrid higher-order skin-topological effect, in which the skin effect selectively acts only on the topological boundary modes, not the bulk modes. Our experiments, which are performed on specially designed non-reciprocal 2D and 3D topolectrical circuit lattices, showcases how non-reciprocal pumping and topological localization dynamically interplays to form various states like 2D skin-topological, 3D skin-topological-topological hybrid states, as well as 2D and 3D higher-order non-Hermitian skin states. Realized through our highly versatile and scalable circuit platform, theses states have no Hermitian nor lower-dimensional analog, and pave the way for applications in topological switching and sensing through the simultaneous non-trivial interplay of skin and topological boundary localizations.

Differentiating ether phosphatidylcholines with a collision energy-optimized MRM method by RPLC-MS/MS and its application to studying ischemia-neuronal injury.

The identification of ether-phosphatidylcholine (ether-PC) isomers, including alkyl-PC (PC(O-)) and plasmalogen-PC (PC(P-)), is technically challenging in MS/MS analysis, which hinders scientists from gaining a deeper understanding of such important lipids. In this study, we developed a sensitive and specific LC-MS/MS-MRM method to accurately identify PC(O-) and PC(P-). We first deciphered the specific fragmentation rules from LPC(O-) and LPC(P-) isomers, in which the product ion of LPC(P-) would be dominated by alkenyl ions (A). In contrast, LPC(O-) only provided a ring-structure fragment (R) without further fragmentation to the alkyl ion, showing completely different characteristics between LPC(O-) and LPC(P-) in negative ion mode. Next, to overcome the sensitivity issue, the MRM approach based on fragmentation rules was used to differentiate PC(O-) and PC(P-). The CE-optimized MRM method increased the alkenyl-to-ring ratio (A/R) between PC(O-) and PC(P-), in which A/R was almost equal to zero for PC(O-) but A/R ≥ 3 for PC(P-). This highly selective property of the CE-optimized MRM method provides accurate identification of PC(O-) and PC(P-) in whole blood samples. The proposed method was applied in primary neuronal cultures with oxygen-glucose deprivation (OGD) treatment to investigate the regulation of PCs under hypoxic stress. The results showed that the regulation of ether-PCs was mainly related to the sn-1 chain length, and the concentration changes of diacyl-PCs were highly dependent on the degree of unsaturation. In summary, the CE-optimized MRM method enables users to distinguish between PC(O-) and PC(P-) in a simple way.

Quantized classical response from spectral winding topology.

Topologically quantized response is one of the focal points of contemporary condensed matter physics. While it directly results in quantized response coefficients in quantum systems, there has been no notion of quantized response in classical systems thus far. This is because quantized response has always been connected to topology via linear response theory that assumes a quantum mechanical ground state. Yet, classical systems can carry arbitrarily amounts of energy in each mode, even while possessing the same number of measurable edge states as their topological winding. In this work, we discover the totally new paradigm of quantized classical response, which is based on the spectral winding number in the complex spectral plane, rather than the winding of eigenstates in momentum space. Such quantized response is classical insofar as it applies to phenomenological non-Hermitian setting, arises from fundamental mathematical properties of the Green's function, and shows up in steady-state response, without invoking a conventional linear response theory. Specifically, the ratio of the change in one quantity depicting signal amplification to the variation in one imaginary flux-like parameter is found to display fascinating plateaus, with their quantized values given by the spectral winding numbers as the topological invariants.

Topological Defect Engineering and PT Symmetry in Non-Hermitian Electrical Circuits.

We employ electric circuit networks to study topological states of matter in non-Hermitian systems enriched by parity-time symmetry PT and chiral symmetry anti-PT (APT). The topological structure manifests itself in the complex admittance bands which yields excellent measurability and signal to noise ratio. We analyze the impact of PT-symmetric gain and loss on localized edge and defect states in a non-Hermitian Su-Schrieffer-Heeger (SSH) circuit. We realize all three symmetry phases of the system, including the APT-symmetric regime that occurs at large gain and loss. We measure the admittance spectrum and eigenstates for arbitrary boundary conditions, which allows us to resolve not only topological edge states, but also a novel PT-symmetric Z_{2} invariant of the bulk. We discover the distinct properties of topological edge states and defect states in the phase diagram. In the regime that is not PT symmetric, the topological defect state disappears and only reemerges when APT symmetry is reached, while the topological edge states always prevail and only experience a shift in eigenvalue. Our findings unveil a future route for topological defect engineering and tuning in non-Hermitian systems of arbitrary dimension.

Development of an Efficient and Sensitive Chemical Derivatization-Based LC-MS/MS Method for Quantifying Gut Microbiota-Derived Metabolites in Human Plasma and Its Application in Studying Cardiovascular Disease.

Recently, the gut microbiota has been found to be associated with many diseases, such as inflammatory bowel disease, depression, Parkinson's disease, cancer, metabolic syndrome, and cardiovascular disease (CVD). Among various gut microbiota-derived metabolites (GMs), short-chain fatty acids (SCFAs), bile acids (BAs), and tryptophan (TRP) metabolites are the most frequently discussed metabolites. LC-MS/MS shows advantages in quantifying the levels of metabolites with good sensitivity and selectivity; however, the poor ionization efficiency and polar characteristics of SCFAs make their analysis challenging, especially when analyzing plasma samples with low SCFA concentrations. Moreover, without characteristic fragment ions for unconjugated BAs and different detection ion modes for TRP metabolites and BAs, GM analysis is complex and time-consuming. To overcome these problems, we developed a derivatization method combined with LC-MS/MS to enhance the sensitivity and LC retention of GMs. Through derivatization with 3-nitrophenylhydrazine (3-NPH), 7 SCFAs, 9 bile acids, and 6 tryptophan metabolites can be simultaneously analyzed via separation within 14 min on a reversed-phase C18 column. For accurate quantification, 13C6-3NPH-labeled standards were used as one-to-one internal standards. This derivatization approach was optimized and then validated. We further applied this method to investigate the targeted GM profile in patients with CVD. The results showed a significant reduction in plasma butyrate levels in CVD patients compared with healthy controls, suggesting its potentially protective role in CVD. In summary, this work provides a sensitive and effective LC-MS/MS method for simultaneously quantifying gut microbiota-related metabolites in human plasma, which could benefit various future gut microbiota-related studies.

Proportionate Adaptive Filtering Algorithms Derived Using an Iterative Reweighting Framework.

In this paper, based on sparsity-promoting regularization techniques from the sparse signal recovery (SSR) area, least mean square (LMS)-type sparse adaptive filtering algorithms are derived. The approach mimics the iterative reweighted ℓ 2 and ℓ 1 SSR methods that majorize the regularized objective function during the optimization process. We show that introducing the majorizers leads to the same algorithm as simply using the gradient update of the regularized objective function, as is done in existing approaches. Different from the past works, the reweighting formulation naturally leads to an affine scaling transformation (AST) strategy, which effectively introduces a diagonal weighting on the gradient, giving rise to new algorithms that demonstrate improved convergence properties. Interestingly, setting the regularization coefficient to zero in the proposed AST-based framework leads to the Sparsity-promoting LMS (SLMS) and Sparsity-promoting Normalized LMS (SNLMS) algorithms, which exploit but do not strictly enforce the sparsity of the system response if it already exists. The SLMS and SNLMS realize proportionate adaptation for convergence speedup should sparsity be present in the underlying system response. In this manner, we develop a new way for rigorously deriving a large class of proportionate algorithms, and also explain why they are useful in applications where the underlying systems admit certain sparsity, e.g., in acoustic echo and feedback cancellation.

ResNEsts and DenseNEsts: Block-based DNN Models with Improved Representation Guarantees.

Models recently used in the literature proving residual networks (ResNets) are better than linear predictors are actually different from standard ResNets that have been widely used in computer vision. In addition to the assumptions such as scalar-valued output or single residual block, the models fundamentally considered in the literature have no nonlinearities at the final residual representation that feeds into the final affine layer. To codify such a difference in nonlinearities and reveal a linear estimation property, we define ResNEsts, i.e., Residual Nonlinear Estimators, by simply dropping nonlinearities at the last residual representation from standard ResNets. We show that wide ResNEsts with bottleneck blocks can always guarantee a very desirable training property that standard ResNets aim to achieve, i.e., adding more blocks does not decrease performance given the same set of basis elements. To prove that, we first recognize ResNEsts are basis function models that are limited by a coupling problem in basis learning and linear prediction. Then, to decouple prediction weights from basis learning, we construct a special architecture termed augmented ResNEst (A-ResNEst) that always guarantees no worse performance with the addition of a block. As a result, such an A-ResNEst establishes empirical risk lower bounds for a ResNEst using corresponding bases. Our results demonstrate ResNEsts indeed have a problem of diminishing feature reuse; however, it can be avoided by sufficiently expanding or widening the input space, leading to the above-mentioned desirable property. Inspired by the densely connected networks (DenseNets) that have been shown to outperform ResNets, we also propose a corresponding new model called Densely connected Nonlinear Estimator (DenseNEst). We show that any DenseNEst can be represented as a wide ResNEst with bottleneck blocks. Unlike ResNEsts, DenseNEsts exhibit the desirable property without any special architectural re-design.