What does a wounded body testify to, i.e., what is nonsuicidal self-injury and how to recognize it?

This article is a review, its aim is to present the phenomenon of nonsuicidal self-injury (NSSI) in the context of current research. The article discusses the terminological, definitional and epidemiological issues, as well as the forms, functions and determinants of NSSI, according to the analysis of Polish and foreign literature. Particularly important are the functions of NSSI, which allow you to understand why an individual hurts his own body and thus better understand such people. The article also includes an application aspect by presenting warning signals of NSSI and the methods of reacting to information about nonsuicidal self-injury committed by a person in the immediate vicinity.

Rising early warning signals in affect associated with future changes in depression: a dynamical systems approach

Background Predicting future states of psychopathology such as depressive episodes has been a hallmark initiative in mental health research. Dynamical systems theory has proposed that rises in certain ‘early warning signals’ (EWSs) in time-series data (e.g. auto-correlation, temporal variance, network connectivity) may precede impending changes in disorder severity. The current study investigates whether rises in these EWSs over time are associated with future changes in disorder severity among a group of patients with major depressive disorder (MDD). Methods Thirty-one patients with MDD completed the study, which consisted of daily smartphone-delivered surveys over 8 weeks. Daily positive and negative affect were collected for the time-series analyses. A rolling window approach was used to determine whether rises in auto-correlation of total affect, temporal standard deviation of total affect, and overall network connectivity in individual affect items were predictive of increases in depression symptoms. Results Results suggested that rises in auto-correlation were significantly associated with worsening in depression symptoms (r = 0.41, p = 0.02). Results indicated that neither rises in temporal standard deviation (r = −0.23, p = 0.23) nor in network connectivity (r = −0.12, p = 0.59) were associated with changes in depression symptoms. Conclusions This study more rigorously examines whether rises in EWSs were associated with future depression symptoms in a larger group of patients with MDD. Results indicated that rises in auto-correlation were the only EWS that was associated with worsening future changes in depression.

Structure-based identification of sensor species for anticipating critical transitions

Ecological systems can undergo sudden, catastrophic changes known as critical transitions. Anticipating these critical transitions remains challenging in systems with many species because the associated early warning signals can be weakly present or even absent in some species, depending on the system dynamics. Therefore, our limited knowledge of ecological dynamics may suggest that it is hard to identify those species in the system that display early warning signals. Here, we show that, in mutualistic ecological systems, it is possible to identify species that early anticipate critical transitions by knowing only the system structure—that is, the network topology of plant–animal interactions. Specifically, we leverage the mathematical theory of structural observability of dynamical systems to identify a minimum set of “sensor species,” whose measurement guarantees that we can infer changes in the abundance of all other species. Importantly, such a minimum set of sensor species can be identified by using the system structure only. We analyzed the performance of such minimum sets of sensor species for detecting early warnings using a large dataset of empirical plant–pollinator and seed-dispersal networks. We found that species that are more likely to be sensors tend to anticipate earlier critical transitions than other species. Our results underscore how knowing the structure of multispecies systems can improve our ability to anticipate critical transitions.

The price of defence: toxins, visual signals and oxidative state in an aposematic butterfly

In a variety of aposematic species, the conspicuousness of an individual's warning signal and the quantity of its chemical defence are positively correlated. This apparent honest signalling in aposematism is predicted by resource competition models which assume that the production and maintenance of aposematic defences compete for access to antioxidant molecules that have dual functions as pigments directly responsible for colouration and in protecting against oxidative lipid damage. Here we study a model aposematic system - the monarch butterfly (Danaus plexippus) and make use of the variable phytochemistry of its larval host-plant, milkweeds (Asclepiadaceae), to manipulate the concentration of sequestered cardenolides. We test two fundamental assumptions of resource competition models: that (1) the possession of secondary defences is associated with costs in the form of oxidative lipid damage and reduced antioxidant defences; and (2) that oxidative damage or decreases in antioxidant defences can reduce the capacity of individuals to produce aposematic displays. Monarch caterpillars that sequestered the highest concentrations of cardenolides exhibited higher levels of oxidative lipid damage as adults. The relationship between warning signals, cardenolide concentrations and oxidative damage differed between the sexes. In male monarchs conspicuousness was explained by an interaction between oxidative damage and sequestration: as males sequester more cardenolides, those with high levels of oxidative damage become less conspicuous, while those that sequester lower levels of cardenolides equally invest in conspicuous with increasing oxidative damage. There was no significant effect of oxidative damage or concentration of sequestered cardenolides on female conspicuousness. Our results demonstrate physiological linkage between the production of coloration and protection from autotoxicity, that warning signals can be honest indicators of defensive capability, and that the relationships are different between the sexes.

Selection Forces Driving Herding of Herbivorous Insect Larvae

Herding behavior is widespread among herbivorous insect larvae across several orders. These larval societies represent one of several different forms of insect sociality that have historically received less attention than the well-known eusocial model but are showing us that social diversity in insects is broader than originally imagined. These alternative forms of sociality often focus attention on the ecology, rather than the genetics, of sociality. Indeed, mutually beneficial cooperation among individuals is increasingly recognized as important relative to relatedness in the evolution of sociality, and I will explore its role in larval insect herds. Larval herds vary in in the complexity of their social behavior but what they have in common includes exhibiting specialized social behaviors that are ineffective in isolated individuals but mutually beneficial in groups. They hence constitute cooperation with direct advantages that doesn’t require kinship between cooperators to be adaptive. Examples include: trail following, head-to-tail processions and other behaviors that keep groups together, huddling tightly to bask, synchronized biting and edge-feeding to overwhelm plant defenses, silk production for shelter building or covering plant trichomes and collective defensive behaviors like head-swaying. Various selective advantages to group living have been suggested and I propose that different benefits are at play in different taxa where herding has evolved independently. Proposed benefits include those relative to selection pressure from abiotic factors (e.g., thermoregulation), to bottom-up pressures from plants or to top-down pressures from natural enemies. The adaptive value of herding cooperation must be understood in the context of the organism’s niche and suite of traits. I propose several such suites in herbivorous larvae that occupy different niches. First, some herds aggregate to thermoregulate collectively, particularly in early spring feeders of the temperate zone. Second, other species aggregate to overwhelm host plant defenses, frequently observed in tropical species. Third, species that feed on toxic plants can aggregate to enhance the warning signal produced by aposematic coloration or stereotyped defensive behaviors. Finally, the combination of traits including gregariousness, conspicuous behavior and warning signals can be favored by a synergy between bottom-up and top-down selective forces. When larvae on toxic plants aggregate to overcome plant defenses, this grouping makes them conspicuous to predators and favors warning signals. I thus conclude that a single explanation is not sufficient for the broad range of herding behaviors that occurs in phylogenetically diverse insect larvae in different environments.

Eigenstate Transition of Multi-Channel Time Series Data around Earthquakes

To decrease human and economic damage owing to earthquakes, it is necessary to discover signals preceding earthquakes. We focus on the concept of “early warning signals” developed in bifurcation analysis, in which an increase in the variances of variables precedes its transition. If we can treat earthquakes as one of the transition phenomena that moves from one state to the other state, this concept is useful for detecting earthquakes before they start. We develop a covariance matrix from multi-channel time series data observed by an observatory on the seafloor and calculate the first eigenvalue and corresponding eigenstate of the matrix. By comparing the time dependence of the eigenstate to some past earthquakes, it is shown that the contribution from specific observational channels to the eigenstate increases before earthquakes, and there is a case in which the eigenvalue increases as predicted in early warning signals. This result suggests the first eigenvalue and eigenstate of multi-channel data are useful to identify signals preceding earthquakes.

Deciphering early-warning signals of SARS-CoV-2 elimination and resurgence from limited data at multiple scales

Inferring the transmission potential of an infectious disease during low-incidence periods following epidemic waves is crucial for preparedness. In such periods, scarce data may hinder existing inference methods, blurring early-warning signals essential for discriminating between the likelihoods of resurgence versus elimination. Advanced insight into whether elevating caseloads (requiring swift community-wide interventions) or local elimination (allowing controls to be relaxed or refocussed on case-importation) might occur can separate decisive from ineffective policy. By generalizing and fusing recent approaches, we propose a novel early-warning framework that maximizes the information extracted from low-incidence data to robustly infer the chances of sustained local transmission or elimination in real time, at any scale of investigation (assuming sufficiently good surveillance). Applying this framework, we decipher hidden disease-transmission signals in prolonged low-incidence COVID-19 data from New Zealand, Hong Kong and Victoria, Australia. We uncover how timely interventions associate with averting resurgent waves, support official elimination declarations and evidence the effectiveness of the rapid, adaptive COVID-19 responses employed in these regions.