Local number fluctuations in ordered and disordered phases of water across temperatures: Higher-order moments and degrees of tetrahedrality.

The isothermal compressibility (i.e., related to the asymptotic number variance) of equilibrium liquid water as a function of temperature is minimal under near-ambient conditions. This anomalous non-monotonic temperature dependence is due to a balance between thermal fluctuations and the formation of tetrahedral hydrogen-bond networks. Since tetrahedrality is a many-body property, it will also influence the higher-order moments of density fluctuations, including the skewness and kurtosis. To gain a more complete picture, we examine these higher-order moments that encapsulate many-body correlations using a recently developed, advanced platform for local density fluctuations. We study an extensive set of simulated phases of water across a range of temperatures (80-1600 K) with various degrees of tetrahedrality, including ice phases, equilibrium liquid water, supercritical water, and disordered nonequilibrium quenches. We find clear signatures of tetrahedrality in the higher-order moments, including the skewness and excess kurtosis, which scale for all cases with the degree of tetrahedrality. More importantly, this scaling behavior leads to non-monotonic temperature dependencies in the higher-order moments for both equilibrium and non-equilibrium phases. Specifically, under near-ambient conditions, the higher-order moments vanish most rapidly for large length scales, and the distribution quickly converges to a Gaussian in our metric. However, under non-ambient conditions, higher-order moments vanish more slowly and hence become more relevant, especially for improving information-theoretic approximations of hydrophobic solubility. The temperature non-monotonicity that we observe in the full distribution across length scales could shed light on water's nested anomalies, i.e., reveal new links between structural, dynamic, and thermodynamic anomalies.

Free-energy landscape and spinodals for the liquid-liquid transition of the TIP4P/2005 and TIP4P/Ice models of water.

Continued increases in computational power now make it possible to evaluate the free-energy landscape associated with the first-order liquid-liquid transition in realistic models of water for which an accurate estimate of the liquid-liquid critical point exists, and to explore its change with pressure near the coexistence line. We report the results of 50 µs-long NPT umbrella sampling simulations for two realistic models for water, TIP4P/2005 and TIP4P/ice, 3-9 K below their critical temperatures. The free energy profile at different pressures clearly shows the presence of two well-defined free energy basins and makes it possible to identify the liquid-liquid spinodal points, the limits of stability that define the (temperature dependent) pressure range within which two distinct free energy basins exist. The results show that for temperatures less than 10 K below the critical temperature, metastable states are possible across a very limited pressure interval, information that is relevant to the interpretation of experiments probing the metastable phase behavior of deeply supercooled water in the so-called no-man's land.

Comparison of Bayesian Networks, G-estimation and linear models to estimate causal treatment effects in aggregated N-of-1 trials with carry-over effects.

BACKGROUND: The aggregation of a series of N-of-1 trials presents an innovative and efficient study design, as an alternative to traditional randomized clinical trials. Challenges for the statistical analysis arise when there is carry-over or complex dependencies of the treatment effect of interest. METHODS: In this study, we evaluate and compare methods for the analysis of aggregated N-of-1 trials in different scenarios with carry-over and complex dependencies of treatment effects on covariates. For this, we simulate data of a series of N-of-1 trials for Chronic Nonspecific Low Back Pain based on assumed causal relationships parameterized by directed acyclic graphs. In addition to existing statistical methods such as regression models, Bayesian Networks, and G-estimation, we introduce a carry-over adjusted parametric model (COAPM). RESULTS: The results show that all evaluated existing models have a good performance when there is no carry-over and no treatment dependence. When there is carry-over, COAPM yields unbiased and more efficient estimates while all other methods show some bias in the estimation. When there is known treatment dependence, all approaches that are capable to model it yield unbiased estimates. Finally, the efficiency of all methods decreases slightly when there are missing values, and the bias in the estimates can also increase. CONCLUSIONS: This study presents a systematic evaluation of existing and novel approaches for the statistical analysis of a series of N-of-1 trials. We derive practical recommendations which methods may be best in which scenarios.

Melting curves of ice polymorphs in the vicinity of the liquid-liquid critical point.

The possible existence of a liquid-liquid critical point in deeply supercooled water has been a subject of debate due to the challenges associated with providing definitive experimental evidence. The pioneering work by Mishima and Stanley [Nature 392, 164-168 (1998)] sought to shed light on this problem by studying the melting curves of different ice polymorphs and their metastable continuation in the vicinity of the expected liquid-liquid transition and its associated critical point. Based on the continuous or discontinuous changes in the slope of the melting curves, Mishima [Phys. Rev. Lett. 85, 334 (2000)] suggested that the liquid-liquid critical point lies between the melting curves of ice III and ice V. We explore this conjecture using molecular dynamics simulations with a machine learning model based on ab initio quantum-mechanical calculations. We study the melting curves of ices III, IV, V, VI, and XIII and find that all of them are supercritical and do not intersect the liquid-liquid transition locus. We also find a pronounced, yet continuous, change in the slope of the melting lines upon crossing of the liquid locus of maximum compressibility. Finally, we analyze the literature in light of our findings and conclude that the scenario in which the melting curves are supercritical is favored by the most recent computational and experimental evidence. Although the preponderance of evidence is consistent with the existence of a second critical point in water, the behavior of ice polymorph melting lines does not provide strong evidence in support of this viewpoint, according to our calculations.

Depressive symptoms and weight change in inpatients with anorexia nervosa: A cross-lagged panel model.

OBJECTIVE: Anorexia nervosa (AN) is marked by a high rate of comorbid depression, which raises the question whether depressive symptoms may adversely affect treatment outcome. Thus, we examined whether depressive symptoms at admission would predict weight change from admission to discharge in a large sample of inpatients with AN. In addition, we also explored the reverse direction, that is, whether body mass index (BMI) at admission would predict changes in depressive symptoms. METHODS: A sample of 3011 adolescents and adults with AN (4% male) who received inpatient treatment at four Schoen Clinics was analyzed. Depressive symptoms were measured with the Patient Health Questionnaire-9. RESULTS: BMI significantly increased and depressive symptoms significantly decreased from admission to discharge. BMI and depressive symptoms were unrelated at admission and discharge. Higher BMI at admission predicted smaller decreases in depressive symptoms and higher depressive symptoms at admission predicted larger weight gain. The latter effect, however, was mediated by longer length of stay. CONCLUSION: Results indicate that depressive symptoms do not adversely affect weight gain during inpatient treatment in persons with AN. Instead, higher BMI at admission is predictive of smaller improvements in depressive symptoms but this effect seems to be negligible in terms of clinical relevance.

Krein support vector machine classification of antimicrobial peptides.

Antimicrobial peptides (AMPs) represent a potential solution to the growing problem of antimicrobial resistance, yet their identification through wet-lab experiments is a costly and time-consuming process. Accurate computational predictions would allow rapid in silico screening of candidate AMPs, thereby accelerating the discovery process. Kernel methods are a class of machine learning algorithms that utilise a kernel function to transform input data into a new representation. When appropriately normalised, the kernel function can be regarded as a notion of similarity between instances. However, many expressive notions of similarity are not valid kernel functions, meaning they cannot be used with standard kernel methods such as the support-vector machine (SVM). The Krein-SVM represents generalisation of the standard SVM that admits a much larger class of similarity functions. In this study, we propose and develop Krein-SVM models for AMP classification and prediction by employing the Levenshtein distance and local alignment score as sequence similarity functions. Utilising two datasets from the literature, each containing more than 3000 peptides, we train models to predict general antimicrobial activity. Our best models achieve an AUC of 0.967 and 0.863 on the test sets of each respective dataset, outperforming the in-house and literature baselines in both cases. We also curate a dataset of experimentally validated peptides, measured against Staphylococcus aureus and Pseudomonas aeruginosa, in order to evaluate the applicability of our methodology in predicting microbe-specific activity. In this case, our best models achieve an AUC of 0.982 and 0.891, respectively. Models to predict both general and microbe-specific activities are made available as web applications.

How negative mood hinders belief updating in depression: results from two experimental studies.

BACKGROUND: In two experimental studies, we tested the hypothesis that negative mood would hinder the revision of negative beliefs in response to unexpectedly positive information in depression, whereas positive mood was expected to enhance belief updating. METHODS: In study 1 (N = 101), we used a subclinical sample to compare the film-based induction of sad v. happy mood with a distraction control group. Subsequently, participants underwent a well-established paradigm to examine intra-individual changes in performance-related expectations after unexpectedly positive performance feedback. In study 2, we applied the belief-updating task from study 1 to an inpatient sample (N = 81) and induced sad v. happy mood via film-clips v. recall of autobiographic events. RESULTS: The results of study 1 showed no significant group differences in belief updating; the severity of depressive symptoms was a negative predictor of belief revision, though, and there was a non-significant trend suggesting that the presence of sad mood hindered belief updating in the subgroup of participants with a diagnosed depressive episode. Study 2 revealed that participants updated their expectations significantly less in line with positive feedback when they underwent the induction of negative mood prior to feedback, relative to positive mood. CONCLUSIONS: By indicating that the presence of negative mood can hinder the revision of negative beliefs in clinically depressed people, our findings suggest that learning from new experiences can be hampered if state negative mood is activated. Thus, interventions relying on learning from novel positive experiences should aim at reducing state negative mood in depression.

Pirfenidone Has Anti-fibrotic Effects in a Tissue-Engineered Model of Human Cardiac Fibrosis.

A fundamental process in the development and progression of heart failure is fibrotic remodeling, characterized by excessive deposition of extracellular matrix proteins in response to injury. Currently, therapies that effectively target and reverse cardiac fibrosis are lacking, warranting novel therapeutic strategies and reliable methods to study their effect. Using a gelatin methacryloyl hydrogel, human induced pluripotent stem cell-derived cardiomyocytes (hiPSC-CM) and human fetal cardiac fibroblasts (hfCF), we developed a multi-cellular mechanically tunable 3D in vitro model of human cardiac fibrosis. This model was used to evaluate the effects of a promising anti-fibrotic drug-pirfenidone-and yields proof-of-concept of the drug testing potential of this platform. Our study demonstrates that pirfenidone has anti-fibrotic effects but does not reverse all TGF-ß1 induced pro-fibrotic changes, which provides new insights into its mechanism of action.

Anomalies and Local Structure of Liquid Water from Boiling to the Supercooled Regime as Predicted by the Many-Body MB-pol Model.

For the past 50 years, researchers have sought molecular models that can accurately reproduce water's microscopic structure and thermophysical properties across broad ranges of its complex phase diagram. Herein, molecular dynamics simulations with the many-body MB-pol model are performed to monitor the thermodynamic response functions and local structure of liquid water from the boiling point down to deeply supercooled temperatures at ambient pressure. The isothermal compressibility and isobaric heat capacity show maxima near 223 K, in excellent agreement with recent experiments, and the liquid density exhibits a minimum at ∼208 K. A local tetrahedral arrangement, where each water molecule accepts and donates two hydrogen bonds, is found to be the most probable hydrogen-bonding topology at all temperatures. This work suggests that MB-pol may provide predictive capability for studies of liquid water's physical properties across broad ranges of thermodynamic states, including the so-called water's "no man's land" which is difficult to probe experimentally.

Inpatient psychotherapy for depression in a large routine clinical care sample: A Bayesian approach to examining clinical outcomes and predictors of change.

BACKGROUND: A routinely collected dataset was analyzed (1) to determine the naturalistic effectiveness of inpatient psychotherapy for depression in routine psychotherapeutic care, and (2) to identify potential predictors of change. METHODS: In a sample of 22,681 inpatients with depression, pre-post and pre-follow-up effect sizes were computed for various outcome variables. To build a probabilistic model of predictors of change, an independent component analysis generated components from demographic and clinical data, and Bayesian EFA extracted factors from the available pre-test, post-test and follow-up questionnaires in a subsample (N = 6377). To select the best-fitted model, the BIC of different path models were compared. A Bayesian path analysis was performed to identify the most important factors to predict changes. RESULTS: Effect sizes were large for the primary outcome and moderate for various secondary outcomes. Almost all pretreatment factors exerted significant influences on different baseline factors. Several factors were found to be resistant to change during treatment: suicidality, agoraphobia, life dissatisfaction, physical disability and pain. The strongest cross-loadings were observed from suicidality on negative cognitions, from agoraphobia on anxiety, and from physical disability on perceived disability. LIMITATIONS: No causal conclusions can be drawn directly from our results as we only used cross-lagged panel data without control group. CONCLUSIONS: The results indicate large effects of inpatient psychotherapy for depression in routine clinical care. The direct influence of pretreatment factors decreased over the course of treatment. However, some factors appeared stable and difficult to treat, which might hinder treatment outcome. Findings of different predictors of change are discussed.

Liquid-Liquid Transition in Water from First Principles.

A long-standing question in water research is the possibility that supercooled liquid water can undergo a liquid-liquid phase transition (LLT) into high- and low-density liquids. We used several complementary molecular simulation techniques to evaluate the possibility of an LLT in an ab initio neural network model of water trained on density functional theory calculations with the SCAN exchange correlation functional. We conclusively show the existence of a first-order LLT and an associated critical point in the SCAN description of water, representing the first definitive computational evidence for an LLT in water from first principles.

Kernel Methods for Predicting Yields of Chemical Reactions.

The use of machine learning methods for the prediction of reaction yield is an emerging area. We demonstrate the applicability of support vector regression (SVR) for predicting reaction yields, using combinatorial data. Molecular descriptors used in regression tasks related to chemical reactivity have often been based on time-consuming, computationally demanding quantum chemical calculations, usually density functional theory. Structure-based descriptors (molecular fingerprints and molecular graphs) are quicker and easier to calculate and are applicable to any molecule. In this study, SVR models built on structure-based descriptors were compared to models built on quantum chemical descriptors. The models were evaluated along the dimension of each reaction component in a set of Buchwald-Hartwig amination reactions. The structure-based SVR models outperformed the quantum chemical SVR models, along the dimension of each reaction component. The applicability of the models was assessed with respect to similarity to training. Prospective predictions of unseen Buchwald-Hartwig reactions are presented for synthetic assessment, to validate the generalizability of the models, with particular interest along the aryl halide dimension.

Vapor-liquid equilibrium of water with the MB-pol many-body potential.

Among the many existing molecular models of water, the MB-pol many-body potential has emerged as a remarkably accurate model, capable of reproducing thermodynamic, structural, and dynamic properties across water's solid, liquid, and vapor phases. In this work, we assessed the performance of MB-pol with respect to an important set of properties related to vapor-liquid coexistence and interfacial behavior. Through direct coexistence classical molecular dynamics simulations at temperatures of 400 K < T < 600 K, we calculated properties such as equilibrium coexistence densities, vapor-liquid interfacial tension, vapor pressure, and enthalpy of vaporization and compared the MB-pol results to experimental data. We also compared rigid vs fully flexible variants of the MB-pol model and evaluated system size effects for the properties studied. We found that the MB-pol model predictions are in good agreement with experimental data, even for temperatures approaching the vapor-liquid critical point; this agreement was largely insensitive to system sizes or the rigid vs flexible treatment of the intramolecular degrees of freedom. These results attest to the chemical accuracy of MB-pol and its high degree of transferability, thus enabling MB-pol's application across a large swath of water's phase diagram.

Manifestations of metastable criticality in the long-range structure of model water glasses.

Much attention has been devoted to water's metastable phase behavior, including polyamorphism (multiple amorphous solid phases), and the hypothesized liquid-liquid transition and associated critical point. However, the possible relationship between these phenomena remains incompletely understood. Using molecular dynamics simulations of the realistic TIP4P/2005 model, we found a striking signature of the liquid-liquid critical point in the structure of water glasses, manifested as a pronounced increase in long-range density fluctuations at pressures proximate to the critical pressure. By contrast, these signatures were absent in glasses of two model systems that lack a critical point. We also characterized the departure from equilibrium upon vitrification via the non-equilibrium index; water-like systems exhibited a strong pressure dependence in this metric, whereas simple liquids did not. These results reflect a surprising relationship between the metastable equilibrium phenomenon of liquid-liquid criticality and the non-equilibrium structure of glassy water, with implications for our understanding of water phase behavior and glass physics. Our calculations suggest a possible experimental route to probing the existence of the liquid-liquid transition in water and other fluids.

Psychological consequences and differential impact of the COVID-19 pandemic in patients with mental disorders.

OBJECTIVE: People with mental disorders might be differentially affected by the COVID-19 pandemic. The aim of the current study was to evaluate the impact of the pandemic on patients with various psychiatric disorders who were admitted to inpatient treatment. METHODS: Five-hundred thirty-eight inpatients with mental disorders participated in a survey about psychological consequences of the pandemic between March-December 2020. We examined the perceived burden by restrictions and worries, changes in health care utilization, and helpfulness of coping strategies. RESULTS: More than 50% reported any worsening of symptoms, 40% stated increased need of therapeutic support. High rates of symptom deterioration were observed for depressive symptoms (>55%), anxiety (>40%), and sleeping behavior (>40%). Treatment impairment was stated by 27.9%. Patients with anxiety disorders were less affected by contact restrictions compared with eating disorders and depression. Patients with anorexia nervosa and post-traumatic stress disorder experienced higher helpfulness by daily structuring than patients with depression. DISCUSSION: About half of our sample of psychiatric patients experienced symptom deterioration due to the pandemic and about one quarter reported impairment of treatment modalities. Especially patients with eating disorders and depressive disorders were more frequently affected. The results indicate a needed improvement of treatment options during a pandemic.

Advanced In Vitro Modeling to Study the Paradox of Mechanically Induced Cardiac Fibrosis.

In heart failure, cardiac fibrosis is the result of an adverse remodeling process. Collagen is continuously synthesized in the myocardium in an ongoing attempt of the heart to repair itself. The resulting collagen depositions act counterproductively, causing diastolic dysfunction and disturbing electrical conduction. Efforts to treat cardiac fibrosis specifically have not been successful and the molecular etiology is only partially understood. The differentiation of quiescent cardiac fibroblasts to extracellular matrix-depositing myofibroblasts is a hallmark of cardiac fibrosis and a key aspect of the adverse remodeling process. This conversion is induced by a complex interplay of biochemical signals and mechanical stimuli. Tissue-engineered 3D models to study cardiac fibroblast behavior in vitro indicate that cyclic strain can activate a myofibroblast phenotype. This raises the question how fibroblast quiescence is maintained in the healthy myocardium, despite continuous stimulation of ultimately profibrotic mechanotransductive pathways. In this review, we will discuss the convergence of biochemical and mechanical differentiation signals of myofibroblasts, and hypothesize how these affect this paradoxical quiescence. Impact statement Mechanotransduction pathways of cardiac fibroblasts seem to ultimately be profibrotic in nature, but in healthy human myocardium, cardiac fibroblasts remain quiescent, despite continuous mechanical stimulation. We propose three hypotheses that could explain this paradoxical state of affairs. Furthermore, we provide suggestions for future research, which should lead to a better understanding of fibroblast quiescence and activation, and ultimately to new strategies for the prevention and treatment of cardiac fibrosis and heart failure.

Predictors of outcome during inpatient psychotherapy for posttraumatic stress disorder: a single-treatment, multi-site, practice-based study.

Objective: The aims of this study were to determine the effectiveness of a routine clinical care treatment and to identify predictors of treatment outcome in PTSD inpatients. Methods: A routinely collected data set of 612 PTSD inpatients (M = 42.3 years [SD = 11.6], 75.7% female) having received trauma-focused psychotherapy was analyzed. Primary outcome was the clinical symptom severity change score, secondary outcomes were assessed using functional, anxiety, and depression change scores. Hedges g-corrected pre-post effect sizes (ES) were computed for all outcomes. Elastic net regulation as a data-driven, stability-based machine-learning approach was used to build stable clinical prediction models. Results: Hedges g ES indicated medium to large effects on all outcomes. The results of the predictor analyses suggested that a combined predictor model with sociodemographic, clinical, and psychometric variables contribute to predicting different treatment outcomes. Across the clinical and functional outcome, psychoticism, total number of diagnoses, and bronchial asthma consistently showed a stable negative predictive relationship to treatment outcome. Conclusion: Trauma-focused psychotherapy could effectively be implemented in a routine inpatient setting. Some important prognostic variables could be identified. If the proposed models of predictors are replicated, they may help personalize treatment for patients receiving routine clinical care.

Signatures of a liquid-liquid transition in an ab initio deep neural network model for water.

The possible existence of a metastable liquid-liquid transition (LLT) and a corresponding liquid-liquid critical point (LLCP) in supercooled liquid water remains a topic of much debate. An LLT has been rigorously proved in three empirically parametrized molecular models of water, and evidence consistent with an LLT has been reported for several other such models. In contrast, experimental proof of this phenomenon has been elusive due to rapid ice nucleation under deeply supercooled conditions. In this work, we combined density functional theory (DFT), machine learning, and molecular simulations to shed additional light on the possible existence of an LLT in water. We trained a deep neural network (DNN) model to represent the ab initio potential energy surface of water from DFT calculations using the Strongly Constrained and Appropriately Normed (SCAN) functional. We then used advanced sampling simulations in the multithermal-multibaric ensemble to efficiently explore the thermophysical properties of the DNN model. The simulation results are consistent with the existence of an LLCP, although they do not constitute a rigorous proof thereof. We fit the simulation data to a two-state equation of state to provide an estimate of the LLCP's location. These combined results-obtained from a purely first-principles approach with no empirical parameters-are strongly suggestive of the existence of an LLT, bolstering the hypothesis that water can separate into two distinct liquid forms.

Drawing the borderline: Predicting treatment outcomes in patients with borderline personality disorder.

BACKGROUND: A routinely collected big data set was analyzed to determine the effectiveness of naturalistic inpatient treatment and to identify predictors of treatment outcome and discontinuation. METHODS: The sample included 878 patients with borderline personality disorder who received non-manualized dialectic behavioral therapy in a psychosomatic clinic. Effect sizes (Hedge's g) were calculated to determine effectiveness. A bootstrap-enhanced regularized regression with 91 potential predictors was used to identify stable predictors of residualized symptom- and functional change and treatment discontinuation. Results were validated in a holdout sample and repeated cross validation. RESULTS: Effect sizes were small to medium (g = 0.28-0.51). Positive symptom-related outcome was predicted by low affect regulation skills and no previous outpatient psychotherapy. Lower age, absence of work disability, high emotional and physical role limitations and low bodily pain were associated with greater improvement in functional outcome. Higher education and comorbid recurrent depressive disorder were the main predictors of treatment completion. The predictive quality of the models varied, with the best being found for symptom-related outcome (R2 = 18%). CONCLUSION: While the exploratory process of variable selection replicates previous findings, the validation results suggest that tailoring treatment to the individual patient might not be based solely on sociodemographic, clinical and psychological baseline data.