Signals of selection and ancestry in independently feral Gallus gallus populations.

Recent work indicates that feralisation is not a simple reversal of domestication, and therefore raises questions about the predictability of evolution across replicated feral populations. In the present study we compare genes and traits of two independently established feral populations of chickens (Gallus gallus) that inhabit archipelagos within the Pacific and Atlantic regions to test for evolutionary parallelism and/or divergence. We find that feral populations from each region are genetically closer to one another than other domestic breeds, despite their geographical isolation and divergent colonisation histories. Next, we used genome scans to identify genomic regions selected during feralisation (selective sweeps) in two independently feral populations from Bermuda and Hawaii. Three selective sweep regions (each identified by multiple detection methods) were shared between feral populations, and this overlap is inconsistent with a null model in which selection targets are randomly distributed throughout the genome. In the case of the Bermudian population, many of the genes present within the selective sweeps were either not annotated or of unknown function. Of the nine genes that were identifiable, five were related to behaviour, with the remaining genes involved in bone metabolism, eye development and the immune system. Our findings suggest that a subset of feralisation loci (i.e. genomic targets of recent selection in feral populations) are shared across independently established populations, raising the possibility that feralisation involves some degree of parallelism or convergence and the potential for a shared feralisation 'syndrome'.

Characterization of the Protein Corona of Three Chairside Hemoderivatives on Melt Electrowritten Polycaprolactone Scaffolds.

In tissue engineering, the relationship between a biomaterial surface and the host's immune response during wound healing is crucial for tissue regeneration. Despite hemoderivative functionalization of biomaterials becoming a common tissue-engineering strategy for enhanced regeneration, the characteristics of the protein-biomaterial interface have not been fully elucidated. This study characterized the interface formed by the adsorbed proteins from various hemoderivatives with pristine and calcium phosphate (CaP)-coated polycaprolactone (PCL) melt electrowritten scaffolds. PCL scaffolds were fabricated by using melt electrospinning writing (MEW). Three hemoderivatives (pure platelet-rich plasma (P-PRP), leucocyte platelet-rich plasma (L-PRP) and injectable platelet-rich fibrin (i-PRF)) and total blood PLASMA (control) were prepared from ovine blood. Hemoderivatives were characterized via SEM/EDX, cross-linking assay, weight loss, pH and protein quantification. The interface between PCL/CaP and hemoderivative was examined via FTIR, XPS and electrophoresis. i-PRF/PCL-CaP (1653 cm-1), PLASMA/PCL-CaP (1652 cm-1) and i-PRF/PCL (1651 cm-1) demonstrated a strong signal at the Amide I region. PLASMA and i-PRF presented similar N1s spectra, with most of the nitrogen involved in N-C=O bonds (≈400 eV). i-PRF resulted in higher adsorption of low molecular weight (LMW) proteins at 60 min, while PLASMA exhibited the lowest adsorption. L-PRP and P-PRP had a similar pattern of protein adsorption. The characteristics of biomaterial interfaces can be customized, thus creating a specific hemoderivative-defined layer on the PCL surface. i-PRF demonstrated a predominant adsorption of LMW proteins. Further investigation of hemoderivative functionalized biomaterials is required to identify the differential protein corona composition, and the resultant immune response and regenerative capacity.

Trainability of Dissipative Perceptron-Based Quantum Neural Networks.

Several architectures have been proposed for quantum neural networks (QNNs), with the goal of efficiently performing machine learning tasks on quantum data. Rigorous scaling results are urgently needed for specific QNN constructions to understand which, if any, will be trainable at a large scale. Here, we analyze the gradient scaling (and hence the trainability) for a recently proposed architecture that we call dissipative QNNs (DQNNs), where the input qubits of each layer are discarded at the layer's output. We find that DQNNs can exhibit barren plateaus, i.e., gradients that vanish exponentially in the number of qubits. Moreover, we provide quantitative bounds on the scaling of the gradient for DQNNs under different conditions, such as different cost functions and circuit depths, and show that trainability is not always guaranteed. Our work represents the first rigorous analysis of the scalability of a perceptron-based QNN.

Reformulation of the No-Free-Lunch Theorem for Entangled Datasets.

The no-free-lunch (NFL) theorem is a celebrated result in learning theory that limits one's ability to learn a function with a training dataset. With the recent rise of quantum machine learning, it is natural to ask whether there is a quantum analog of the NFL theorem, which would restrict a quantum computer's ability to learn a unitary process with quantum training data. However, in the quantum setting, the training data can possess entanglement, a strong correlation with no classical analog. In this Letter, we show that entangled datasets lead to an apparent violation of the (classical) NFL theorem. This motivates a reformulation that accounts for the degree of entanglement in the training set. As our main result, we prove a quantum NFL theorem whereby the fundamental limit on the learnability of a unitary is reduced by entanglement. We employ Rigetti's quantum computer to test both the classical and quantum NFL theorems. Our Letter establishes that entanglement is a commodity in quantum machine learning.

Inference-Based Quantum Sensing.

In a standard quantum sensing (QS) task one aims at estimating an unknown parameter θ, encoded into an n-qubit probe state, via measurements of the system. The success of this task hinges on the ability to correlate changes in the parameter to changes in the system response R(θ) (i.e., changes in the measurement outcomes). For simple cases the form of R(θ) is known, but the same cannot be said for realistic scenarios, as no general closed-form expression exists. In this Letter, we present an inference-based scheme for QS. We show that, for a general class of unitary families of encoding, R(θ) can be fully characterized by only measuring the system response at 2n+1 parameters. This allows us to infer the value of an unknown parameter given the measured response, as well as to determine the sensitivity of the scheme, which characterizes its overall performance. We show that inference error is, with high probability, smaller than δ, if one measures the system response with a number of shots that scales only as Ω(log^{3}(n)/δ^{2}). Furthermore, the framework presented can be broadly applied as it remains valid for arbitrary probe states and measurement schemes, and, even holds in the presence of quantum noise. We also discuss how to extend our results beyond unitary families. Finally, to showcase our method we implement it for a QS task on real quantum hardware, and in numerical simulations.

Computable and Operationally Meaningful Multipartite Entanglement Measures.

Multipartite entanglement is an essential resource for quantum communication, quantum computing, quantum sensing, and quantum networks. The utility of a quantum state |ψ⟩ for these applications is often directly related to the degree or type of entanglement present in |ψ⟩. Therefore, efficiently quantifying and characterizing multipartite entanglement is of paramount importance. In this work, we introduce a family of multipartite entanglement measures, called concentratable entanglements. Several well-known entanglement measures are recovered as special cases of our family of measures, and hence we provide a general framework for quantifying multipartite entanglement. We prove that the entire family does not increase, on average, under local operations and classical communications. We also provide an operational meaning for these measures in terms of probabilistic concentration of entanglement into Bell pairs. Finally, we show that these quantities can be efficiently estimated on a quantum computer by implementing a parallelized SWAP test, opening up a research direction for measuring multipartite entanglement on quantum devices.

The Temporal Dimension in Maternal Sensitivity Predicting Organized Attachment in Children.

This study analyzed Maternal Sensitivity in the interaction of mother-infant dyads to distinguish different dynamics associated with their attachment group membership - Insecure-Avoidant (A), Secure (B), or Insecure-Resistant (C), with no Disorganized traits - that they developed at 15 months. Participants were 26 eight-month old infants interacting with their mothers in a free-play setting. The analyses used sequential streams of infant actions and maternal responses, and state-space grids to study the temporal organization of the sequences. We examined appropriateness and promptness of the maternal response to infant behavior and a dynamic analysis of the interactive process. In Group B, when the antecedent behavior was infant social approach, the appropriateness of the maternal profile (sensitive vs. intrusive responses) was related to their children's subsequent security. However, how promptly mothers responded was not predictive. Conversely, when the antecedent behavior was infant play, how promptly the mothers responded was associated with securely attached children while mothers' profile of appropriate responses did not distinguish the subsequent quality of attachment. The indices derived from state-space grids were associated with attachment type and distinguished Group C from Groups B and A. However, results from analysis of a specific region of states, 'maternal interfering response to infant social approach,' showed significant discriminant function that correctly classified 78% of Group B, 73% of Group A, and 83% of Group C. Group B infants received less intrusive behavior than their counterparts, and when they did, it involved positive affection or playfulness. Finally, regarding attractors, although more Group B dyads showed multi-stability of coexisting attractors, than Groups A or C, the differences were not statistically significant.

Mycorrhizal tomato plants fine tunes the growth-defence balance upon N depleted root environments.

In low nutritive environments, the uptake of N by arbuscular mycorrhizal (AM) fungi may confer competitive advantages for the host. The present study aims to understand how mycorrhizal tomato plants perceive and then prepare for an N depletion in the root environment. Plants colonized by Rhizophagus irregularis displayed improved responses to a lack of N than nonmycorrhizal (NM) plants. These responses were accomplished by a complex metabolic and transcriptional rearrangement that mostly affected the gibberellic acid and jasmonic acid pathways involving DELLA and JAZ1 genes, which were responsive to changes in the C/N imbalance of the plant. N starved mycorrhizal plants showed lower C/N equilibrium in the shoots than starved NM plants and concomitantly a downregulation of the JAZ1 repressor and the increased expression of the DELLA gene, which translated into a more active oxylipin pathway in mycorrhizal plants. In addition, the results support a priorization in AM plants of stress responses over growth. Therefore, these plants were better prepared for an expected stress. Furthermore, most metabolites that were severely reduced in NM plants following the N depletion remained unaltered in starved AM plants compared with those normally fertilized, suggesting that the symbiosis buffered the stress, improving plant development in a stressed environment.

Factorization and Criticality in Finite XXZ Systems of Arbitrary Spin.

We analyze ground state (GS) factorization in general arrays of spins s_{i} with XXZ couplings immersed in nonuniform fields. It is shown that an exceptionally degenerate set of completely separable symmetry-breaking GSs can arise for a wide range of field configurations, at a quantum critical point where all GS magnetization plateaus merge. Such configurations include alternating fields as well as zero-bulk field solutions with edge fields only and intermediate solutions with zero field at specific sites, valid for d-dimensional arrays. The definite magnetization-projected GSs at factorization can be analytically determined and depend only on the exchange anisotropies, exhibiting critical entanglement properties. We also show that some factorization-compatible field configurations may result in field-induced frustration and nontrivial behavior at strong fields.

Mother-Infant Verbal and Nonverbal Interaction as Predictor of Attachment: Nonlinear Dynamic Analyses.

This longitudinal study examined flexibility in early mother-infant interaction at the age of approximately 6 months (N=30) and whether flexibility indices predicted (in) secure child attachment at 15 months. Dyadic flexibility was measured using dynamic systems-based modelling of patterns during mother-child free play in terms of NDS variables derived from SSG: the propensity to change states (dynamic flexibility), number of states visited (diversity) and predictability (dispersion). Results showed significant discriminant functions on the attachment type groups, A, B & C, for the total grid, which included verbal and non-verbal, and for the reciprocal verbal region. Specifically, the prediction outcomes seem to work better in total grid for A-dyads and in the reciprocal verbal region for B and C-dyads. Diversity emerged as the most relevant index in dyadic flexibility: A-dyads showed the least diversity, distinguished them from B-dyads in the verbal regions, (both the reciprocal and non-reciprocal, 'child verbal-mother non-verbal' one), and, from C-dyads in the reciprocal non-verbal region. A-dyads showed remarkably low activity in the regions involving child verbal behavior, showing that children who became avoidant attached at 15 months of age, were mostly silent at approximately 6 months, when they interacted with their mothers. Findings in this study contribute to advancing conceptually informed measurement of dyadic interaction to provide a new perspective on maternal sensitivity and early markers of child insecure/secure attachment.

Positive psychology group intervention for breast cancer patients: a randomised trial.

This study assessed the effects of a psychological group intervention based on positive psychology in women with breast cancer. 175 women were randomly assigned either to an experimental group, receiving the 14-session intervention (n = 87), or to a wait list group (n = 88) that did not receive any type of intervention. For treatment, a group intervention was applied, based on improving psychological strengths and enhancing positive psychology-based styles of coping. Strength-related outcomes, self-esteem, well-being, and happiness were assessed before and after the intervention. The experimental group showed higher scores on all of the study variables after the intervention. Participants reported improved self-esteem, emotional intelligence-related abilities, resilience, and optimism, as well as positive affectivity, well-being, and happiness. The results show a beneficial effect of this psychological intervention based on positive psychology on female breast cancer patients' psychological health.

Exponential concentration in quantum kernel methods.

Kernel methods in Quantum Machine Learning (QML) have recently gained significant attention as a potential candidate for achieving a quantum advantage in data analysis. Among other attractive properties, when training a kernel-based model one is guaranteed to find the optimal model's parameters due to the convexity of the training landscape. However, this is based on the assumption that the quantum kernel can be efficiently obtained from quantum hardware. In this work we study the performance of quantum kernel models from the perspective of the resources needed to accurately estimate kernel values. We show that, under certain conditions, values of quantum kernels over different input data can be exponentially concentrated (in the number of qubits) towards some fixed value. Thus on training with a polynomial number of measurements, one ends up with a trivial model where the predictions on unseen inputs are independent of the input data. We identify four sources that can lead to concentration including expressivity of data embedding, global measurements, entanglement and noise. For each source, an associated concentration bound of quantum kernels is analytically derived. Lastly, we show that when dealing with classical data, training a parametrized data embedding with a kernel alignment method is also susceptible to exponential concentration. Our results are verified through numerical simulations for several QML tasks. Altogether, we provide guidelines indicating that certain features should be avoided to ensure the efficient evaluation of quantum kernels and so the performance of quantum kernel methods.

The Flourishing Scale: Psychometric Properties in Breast Cancer Patients.

BACKGROUND/OBJECTIVE: Promoting well-being is a key goal of cancer care, and it needs to be assessed using appropriate instruments. Flourishing is considered part of psychological well-being and it is commonly assessed with the Flourishing Scale (FS). To our knowledge, no studies have analyzed the psychometric properties of the FS in breast cancer patients. Our aim here was to provide validity evidence for use of the FS in this context. METHOD: Participants were 217 Spanish women with breast cancer who completed the FS and other scales assessing positive psychology constructs (life satisfaction, positive affect, resilience, self-esteem, optimism) and indicators of psychological maladjustment (negative affect, depression, anxiety, and stress). The internal structure of the FS was analyzed using confirmatory factor analysis (CFA). We calculated the average variance extracted (AVE) to evaluate convergent validity, and both McDonald's omega and Cronbach's alpha coefficients to estimate reliability. Item analysis was performed by computing corrected item-total correlations. Validity evidence based on relationships with other variables was obtained through Pearson correlation analysis, controlling for age and cancer stage. RESULTS: The CFA supported a single-factor structure, with adequate goodness-of-fit indices (CFI = 0.997, NNFI = 0.996, RMSEA = 0.069, and SRMR = 0.047) and standardized factor loadings ranging from 0.70 to 0.87. The value of the AVE was 0.63, and the reliability coefficient obtained with both procedures was 0.91. Corrected item-total correlations ranged from .62 to .78. Correlation analysis showed direct and strong associations between the FS score and scores on positive psychology constructs (range from 0.43 to 0.74), the strongest correlations being with positive affect and life satisfaction. The FS score was inversely correlated with scores on depression, anxiety, stress, negative affect, and pessimism (range from -0.14 to -0.52), the strongest association being with stress. DISCUSSION: The FS is a useful tool for exploring well-being in the breast cancer context, providing useful information for psychological assessment.

A Lie algebraic theory of barren plateaus for deep parameterized quantum circuits.

Variational quantum computing schemes train a loss function by sending an initial state through a parametrized quantum circuit, and measuring the expectation value of some operator. Despite their promise, the trainability of these algorithms is hindered by barren plateaus (BPs) induced by the expressiveness of the circuit, the entanglement of the input data, the locality of the observable, or the presence of noise. Up to this point, these sources of BPs have been regarded as independent. In this work, we present a general Lie algebraic theory that provides an exact expression for the variance of the loss function of sufficiently deep parametrized quantum circuits, even in the presence of certain noise models. Our results allow us to understand under one framework all aforementioned sources of BPs. This theoretical leap resolves a standing conjecture about a connection between loss concentration and the dimension of the Lie algebra of the circuit's generators.

[Prevention of thromboembolism in bariatric surgery]. / Prevencíon del tromboembolismo en cirugía bariátrica.

Disease thromboembolic (ETEV) is one of the major complications that can occur after surgery, and is the leading cause of death in the postoperative period of Bariatric Surgery. Among other factors, should be aware that these patients are at additional risk of ETEV due to own obesity (IMC > 30). After Bariatric Surgery the risk of thromboembolic events varies according to the surveyed series. In general, it is estimated that danger EP (pulmonary embolism) is 0.8% and DVT (deep vein thrombosis) of 1.7%. ETEV global mortality estimates of 0.1 to 2%.

Effectiveness of child sexual abuse prevention programs on knowledge acquisition: A meta-analytical study.

BACKGROUND: Child sexual abuse (CSA) is a type of maltreatment considered a global health problem. CSA is a traumatic experience with important consequences for the victim's health. It is essential to report the effectiveness of CSA prevention programs to offer society useful tools to combat this abuse. OBJECTIVE: We aimed to study the effectiveness of CSA prevention programs on the knowledge acquisition based on comparing pre- and post-treatment changes, and also if their effectiveness is related to program-related and methodological variables. PARTICIPANTS AND SETTINGS: Standardised mean change (with studies that report pre-post program measures) of the effectiveness of CSA prevention programs published between 2014 and 2021 was carried out. METHODS: The general effectiveness of these programs and whether the results were influenced by program-related variables (the duration, the target population, participants' age, or the type of intervention) or by methodology-related factors (the agent who taught them, the geographical area where they were carried out or the way the programs were evaluated) were analysed. A total of 43 samples analysing knowledge about CSA as a dependent variable were included. RESULTS: The results reported a combined effect size considered large (dMR = -0.96, 95 % CI [-1.10, -0.82], p < .001). High inter-study heterogeneity was observed in the meta-analysis, although only the geographic area where the studies were conducted appears as a significant moderator. CONCLUSIONS: In conclusion, the prevention programs included in this analysis significantly improved the participants' knowledge acquisition.

Life Satisfaction and Character Strengths in Women With Breast Cancer: Zest and Hope as Predictors.

BACKGROUND: Empirical evidence shows that life satisfaction is positively related to character strengths, and although this association has been observed in different populations, it is scarce in breast cancer patients. This study analyzes the relationship between character strengths and life satisfaction in Spanish women diagnosed with breast cancer. METHODS: A sample of 117 women completed the Satisfaction with Life Scale (SWLS) and the Spanish version of the VIA Inventory of Strengths (VIA-IS). Correlation analysis and regression modeling were performed to determine which strengths predict life satisfaction. RESULTS: The results of the correlation analysis showed that 15 strengths were positively and significantly associated with life satisfaction, with the highest correlations corresponding to zest, hope, curiosity, social intelligence, love, gratitude, and judgment. Regression modeling indicated that of these, zest and hope were key strengths for predicting life satisfaction. CONCLUSIONS: These findings suggest that intervention programs based on the development of zest and hope could help to improve life satisfaction and, therefore, the psychological well-being of women with breast cancer.

Psychometric properties of the perceived stress scale (pss-10) with breast cancer patients.

Breast cancer diagnosis is one of the most difficult events that a woman can experience during her life and it usually produces high levels of stress. Global measures of perceived stress are useful for screening and for comparing stress levels between cancer patients and other clinical and nonclinical populations. One such instrument that is widely used is the Perceived Stress Scale (pss-10), but its psychometric properties have scarcely been analysed with breast cancer patients. The aim of this study was to provide validity evidence regarding the use of the 10-item version of the pss-10 as a tool for measuring perceived stress in this context. Participants were 215 Spanish breast cancer patients who completed the PSS-10 and the DASS-21, a measure of affective distress (depression, anxiety, and stress). The internal structure of the PSS-10 was examined through confirmatory factor analysis (CFA), and the reliability of test scores was estimated using McDonald's omega coefficient. Validity evidence based on relationships with other variables was also obtained using correlation analysis. The CFA supported a correlated two-factor structure: perceived helplessness (six negatively worded items) and perceived self-efficacy (four positively worded items). Reliability coefficients for scores on these two factors were 0.87 and 0.73, respectively. Scores on affective distress (DASS-21) were strongly and positively correlated with perceived helplessness and moderately and negatively correlated with perceived self-efficacy. The PSS-10 is an adequate tool for measuring perceived stress in the breast cancer context and it may be useful for identifying women at risk of psychological maladjustment.

Development and psychometric properties of the Stressors in Breast Cancer Scale.

Background: A diagnosis of breast cancer generates psychological stress, due not only to treatment and its side effects but also to the impact on different areas of the patient's daily life. Although there are instruments for measuring psychological stress in the cancer context, there is currently no tool for assessing stressors specific to breast cancer. Aims: The aim of this study was to develop the Stressors in Breast Cancer Scale (SBCS). Method: A panel of experts evaluated the clarity and relevance of scale items, providing validity evidence based on test content. Psychometric properties of the scale were then analyzed. Results: Validity evidence based on the internal structure of the SBCS was obtained through exploratory factor analysis (EFA) and confirmatory factor analysis (CFA), following a cross-validation strategy. The CFA supported a second-order factor model with five dimensions: physical appearance and sex strains, health and daily difficulties, interpersonal relationship strains, healthcare strains, and worries and concerns about the future. This structure was invariant across two groups distinguished by time from cancer diagnosis (less than 3 and 3 years or more from diagnosis). Reliability, based on McDonald's omega and Cronbach's alpha coefficients, ranged from 0.83 to 0.89 for factor scores, and reached 0.95 for total scores. Validity evidence was also provided by correlations with depression, anxiety, perceived stress, and perceived health and quality of life. Discussion: The results support the use of the SBCS for measuring stress as a stimulus in the breast cancer context. Implications for clinical practice and research are discussed.

Instruments to measure interaction of mothers and newborns: A systematic review.

Interactions between newborns and their parents/primary caregivers are characterized by asymmetric and dependent relationships. This systematic review mapped, identified, and described the psychometric parameters, categories, and items of instruments used to assess mother-newborn interaction. Seven electronic databases were accessed in this study. Furthermore, this research included neonatal interaction studies describing instruments' items, domains, and psychometric properties while excluding studies that focused on maternal interactions and lacked items for assessing newborns. Additionally, studies validated with older infants that did not have a newborn in the sample were used for test validation, which is a criterion used to decrease the risk of bias. Fourteen observational instruments from 1047 identified citations were included that addressed interactions using varying techniques, constructs, and settings. Particularly, we focused on observational settings that assessed interactions with communication-based constructs in the context of proximity or distance as influenced by physical, behavioral, or procedural barriers. These tools are also used to predict risk behaviors in a psychological context, mitigate feeding difficulties, and conduct neurobehavioral assessments of mother-newborn interactions. The elicited imitation was also an observational setting. This study found that the most described properties in the included citations were inter-rater reliability followed by criterion validity. However, only two instruments reported content, construct, and criterion validity, as well as a description of an internal consistency assessment and inter-rater reliability. Finally, the synthesis of the instruments reported in this study can guide clinicians and researchers in selecting the most appropriate one for their own application.