Surprising Effect Research Articles

Management of Tanning Waste from Leather Processing by Anaerobic Digestion Using a Dynamic Method on a Semi-Technical Scale

In the context of climate policy, measures are being taken around the world to reduce pollution. These have been intensified in the areas of transport, industry, and energy, with the goal of zero emissions. The role of the biogas plant in energy transition and as a waste treatment plant for disposal is very important. This article describes research on a dynamic anaerobic digestion (AD) process plant. The subject of this study was leather shavings, which is a problematic waste. The research presented here is intended to demonstrate the decomposition of the flesh in the process, to confirm its biogas yield, and to evaluate the biological and technical parameters of the process. High biochemical stability was achieved for each of the tests evaluated, and no specific technical requirements were demonstrated. The only technical aspect to be addressed during operation was sedimentation, which can be solved by preparing the mixture earlier or by changing the mixing method. This made it reasonable to investigate the material further in the context of an industrial project. The characteristics of protein degradation in the AD process resulted in a high methane content in the biogas, above 65%. It was also observed that the long conditioning time of biogas in the gas cushion favourably affected the proportion of methane in biogas. Analytical results confirmed 77.5% methane content, which was a very good result. This paper presents the results of a surprising effect of chromium, primarily Cr (III), on the performance of anaerobic digestion.

Epi-Allele: CRISPR-mediated allele-specific epigenetic editing alleviates hypertrophic cardiomyopathy

Abstract Background Hypertrophic cardiomyopathy (HCM) is a prevalent genetic heart disease caused by variants in sarcomere genes like MYH7, which accounts for 40% of cases with 75% being heterozygous. HCM shows incomplete penetrance, where mutant allele expression correlates with severity. Thus, allele-specific suppression is a potential therapy. While allele-specific RNAi or CRISPR/Cas9 showed moderate efficacy, low expression level of MYH7 is associated with the risk of heart failure and dilated cardiomyopathy. We hypothesized CRISPR-based epigenetic editing（dCas9 fused with DNA methylation and histone regulation elements） may potently achieve phenotypic modification without impacting overall gene expression. Objective We investigated allele-specific CRISPR epigenetic editing (Epi-Allele) to selectively suppress mutant Myh6 allele expression without affecting total expression, as a novel HCM therapy in a mouse model. Results We first generated C57R403Q/DBA mice to simulate heterozygous HCM patients. Allele-specific Myh6 gRNAs reduced C57 allele expression and increased DBA expression without impacting total Myh6 expression in primary cardiomyocytes, effects sustained for one year in report cells. HCM mice with Epigenetic editor and allele-specific gRNAs (Epi-Allele) showed reduced left ventricular wall thickness, normalized ECG, and less cardiac fibrosis. Similar therapeutic effects were observed in HCM mice treated by a dual AAV delivery system of Epi-Allele. We further found that Epi-Allele could reverse established disease in the inducible transgene mice. We’ve also seen similar surprising therapeutic effects from Epi-Alleles in patient-derived pluripotent stem cell-induced cardiomyocytes (iPSC-CMs). Conclusion We suggest that Epi-Allele may be a novel therapy for HCM and other dominant diseases where the mutant gene plays a vital role, overcoming the limitations of previous allele-specific suppression strategy.Figure abstract of Epi-AlleleThe universality of Epi-Alleles

An attractor state zone precedes neural crest fate in melanoma initiation.

The field cancerization theory suggests that a group of cells containing oncogenic mutations are predisposed to transformation 1, 2 . We previously identified single cells in BRAF V600E ;p53 -/- zebrafish that reactivate an embryonic neural crest state before initiating melanoma 3-5 . Here we show that single cells reactivate the neural crest fate from within large fields of adjacent abnormal melanocytes, which we term the "cancer precursor zone." These cancer precursor zone melanocytes have an aberrant morphology, dysplastic nuclei, and altered gene expression. Using single cell RNA-seq and ATAC-seq, we defined a distinct transcriptional cell attractor state for cancer precursor zones and validated the stage-specific gene expression initiation signatures in human melanoma. We identify the cancer precursor zone driver, ID1, which binds to TCF12 and inhibits downstream targets important for the maintenance of melanocyte morphology and cell cycle control. Examination of patient samples revealed precursor melanocytes expressing ID1, often surrounding invasive melanoma, indicating a role for ID1 in early melanomagenesis. This work reveals a surprising field effect of melanoma initiation in vivo in which tumors arise from within a zone of morphologically distinct, but clinically covert, precursors with altered transcriptional fate. Our studies identify novel targets that could improve early diagnosis and prevention of melanoma.

The unique contribution of uncertainty reduction during naturalistic language comprehension

Language comprehension is an incremental process with prediction. Delineating various mental states during such a process is critical to understanding the relationship between human cognition and the properties of language. Entropy reduction, which indicates the dynamic decrease of uncertainty as language input unfolds, has been recognized as effective in predicting neural responses during comprehension. According to the entropy reduction hypothesis (Hale, 2006), entropy reduction is related to the processing difficulty of a word, the effect of which may overlap with other well-documented information-theoretical metrics such as surprisal or next-word entropy. However, the processing difficulty was often confused with the information conveyed by a word, especially lacking neural differentiation. We propose that entropy reduction represents the cognitive neural process of information gain that can be dissociated from processing difficulty. This study characterized various information-theoretical metrics using GPT-2 and identified the unique effects of entropy reduction in predicting fMRI time series acquired during language comprehension. In addition to the effects of surprisal and entropy, entropy reduction was associated with activations in the left inferior frontal gyrus, bilateral ventromedial prefrontal cortex, insula, thalamus, basal ganglia, and middle cingulate cortex. The reduction of uncertainty, rather than its fluctuation, proved to be an effective factor in modeling neural responses. The neural substrates underlying the reduction in uncertainty might imply the brain’s desire for information regardless of processing difficulty.

New advances in the role of JAK2 V617F mutation in myeloproliferative neoplasms.

The JAK2 V617F mutation is the most common driver gene in myeloproliferative neoplasm (MPN), which means that the JAK/STAT signaling pathway is persistently activated independent of cytokines, and plays an important part in the onset and development of MPN. The JAK inhibitors, although widely used in the clinical practice, are unable to eradicate MPN. Therefore, the unavoidable long-term treatment poses a serious burden for patients with MPN. It is established that the JAK2 V617F mutation, in addition its role in the JAK/STAT pathway, can promote cell proliferation, differentiation, anti-apoptosis, DNA damage accumulation, and other key biologic processes through multiple pathways. Other than that, the JAK2 V617F mutation affects the cardiovascular system through multiple mechanisms. Although JAK inhibitors cannot eradicate MPN cells, the combined use of JAK inhibitors and other drugs may have surprising effects. This requires an in-depth understanding of the mechanism of action of the JAK2 V617F mutation. In this review, the authors explored the role of the JAK2 V617F mutation in MPN from multiple aspects, including the mechanisms of non-JAK/STAT pathways, the regulation of cellular methylation, the induction of cellular DNA damage accumulation, and effects on the cardiovascular system, with the objective of providing valuable insights into multidrug combination therapy for MPN.

Surprising sounds influence risky decision making

Adaptive behavior depends on appropriate responses to environmental uncertainty. Incidental sensory events might simply be distracting and increase errors, but alternatively can lead to stereotyped responses despite their irrelevance. To evaluate these possibilities, we test whether task-irrelevant sensory prediction errors influence risky decision making in humans across seven experiments (total n = 1600). Rare auditory sequences preceding option presentation systematically increase risk taking and decrease choice perseveration (i.e., increased tendency to switch away from previously chosen options). The risk-taking and perseveration effects are dissociable by manipulating auditory statistics: when rare sequences end on standard tones, including when rare sequences consist only of standard tones, participants are less likely to perseverate after rare sequences but not more likely to take risks. Computational modeling reveals that these effects cannot be explained by increased decision noise but can be explained by value-independent risky bias and perseveration parameters, decision biases previously linked to dopamine. Control experiments demonstrate that both surprise effects can be eliminated when tone sequences are presented in a balanced or fully predictable manner, and that surprise effects cannot be explained by erroneous beliefs. These findings suggest that incidental sounds may influence many of the decisions we make in daily life.

Effects of Cognitive Diversity Amid Earnings Surprises on Market Reaction and Financial Performance

Effects of Cognitive Diversity Amid Earnings Surprises on Market Reaction and Financial Performance

Persistence of large-scale coherent structures in a turbulent pipe flow through an improved lattice Boltzmann approach

We simulated a turbulent pipe flow within the lattice Boltzmann method using a multiple-relaxation-time collision operator with Maxwell–Boltzmann equilibrium distribution expanded, for the sake of a more accurate description, up to the sixth order in Hermite polynomials. The moderately turbulent flow (Reτ≈181.3) is able to reproduce up to the fourth statistical moment with great accuracy compared with other numerical schemes and with experimental data. A coherent structure identification was performed based on the most energetic streamwise turbulent mode, which revealed a surprising memory effect related to the large-scale forcing scheme that triggered the pipe's turbulent state. We observe that the existence of large-scale motions that are out of the pipe's stationary regime does not affect the flow's detailed single-point statistical features. Furthermore, the transitions between the coherent structures of different topological modes were analyzed as a stochastic process. We find that for finely resolved data, the transitions are effectively Markovian, but for larger decimation time lags, due to topological mode degeneracy, non-Markovian behavior emerges, in agreement with previous experimental studies.

Syntactic Adaptation and Word Learning in 3‐ to 4‐Year‐Olds

AbstractIn a recent study, preschoolers adapted their syntactic expectations about a familiar phrase in French; this adaptation affected later word learning. In two experiments, we probed the generality of this finding by replicating the experiment and extending it to a different expression in English. We examined the ambiguous phrase the baby, which can be followed by nouns (the baby monkeys) or verbs (the baby sleeps). In induction trials, the baby consistently preceded either familiar nouns (noun condition) or verbs (verb condition). In later novel‐word trials, children in the verb condition were more likely to interpret novel words following the baby (The baby gorps!) as verbs than were children in the noun condition. In Experiment 2, a modified design isolated the effect of experience with the critical phrase from possible effects of task structure, and an added baseline condition showed the adaptation effect to be asymmetrical, suggesting frequency or surprisal effects on adaptation.

Mass transfer at vapor-liquid interfaces of H2O + CO2 mixtures studied by molecular dynamics simulation

Abstract In many industrial applications as well as in nature, the mass transfer of CO2 at vapor-liquid interfaces in aqueous systems plays an important role. In this work, this process was studied on the atomistic level using non-equilibrium molecular dynamics simulations. In a first step, a molecular model of the system water + CO2 was developed that represents both bulk and interfacial equilibrium properties well. This system is characterized by a very large adsorption and enrichment of CO2 at the vapor-liquid interface. Then, non-equilibrium mass transfer simulations were carried out using a method that was developed recently: CO2 is inserted into the vapor phase of a simulation box which contains a liquid slab. Surprising effects are observed at the interface such as a net repulsion of CO2 particles from the interface and a complex time dependence of the amount of CO2 adsorbed at the interface.

Misinformation or hard to tell? An eye-tracking study to investigate the effects of food crisis misinformation on social media engagement

Visual misinformation about ongoing contaminated food crises poses a significant threat to organizational well-being and public health, particularly when people share incorrect images on social media. Corrective responses and highly credible media sources as effective strategies geared toward combating crisis misinformation. Extending Lewandowsky and colleagues’ (2012) corrective strategies for debunking misinformation, the concept of visual misinformation, cognitive process, and the theory of social sharing of emotion, this study aims to advance research on visual misinformation in public relations and crisis communication. A 2 (image veracity: incorrect vs. true) x 2 (corrective strategy: simple rebuttal vs. simple rebuttal + fact elaboration) x 2 (source credibility: high vs. low) between-subjects eye-tracking experiment was conducted to test the effects of these features on visual attention and intention to share. Additionally, we explored the mediation effects of emotional surprise and perceived crisis severity on sharing posts. Results showed visual cues (e.g., images and sources) and textual cues (e.g., corrective strategies) led to different allocations of visual attention. We found visual attention significantly mediated the effects of combined corrective messages on sharing. Additionally, feeling surprised also significantly mediated the effects of messages with low credible sources on sharing. This study provides insights into advancing crisis communication theory and offers evidence-based recommendations for health organizations and practitioners to better fight against food crisis misinformation.

Exploring the influence of pore shape on conductance and permeation

There are increasing numbers of ion channel structures featuring heteromeric subunit assembly, exemplified by synaptic α1βB glycine and α4β2 nicotinic receptors. These structures exhibit inherent pore asymmetry, but the relevance of this to function is unknown. Furthermore, molecular dynamics simulations performed on symmetrical homomeric channels often lead to thermal distortion whereby conformations of the resulting ensemble are also asymmetrical. When functionally annotating ion channels, researchers often rely on minimal constrictions determined via radius-profile calculations performed with computer programs, such as HOLE or CHAP, coupled with an assessment of pore hydrophobicity. However, such tools typically employ spherical probe particles, limiting their ability to accurately capture pore asymmetry. Here, we introduce an algorithm that employs ellipsoidal probe particles, enabling a more comprehensive representation of the pore geometry. Our analysis reveals that the use of nonspherical ellipsoids for pore characterization provides a more accurate and easily interpretable depiction of conductance. To quantify the implications of pore asymmetry on conductance, we systematically investigated carbon nanotubes with varying degrees of pore asymmetry as model systems. The conductance through these channels shows surprising effects that would otherwise not be predicted with spherical probes. The results have broad implications not only for the functional annotation of biological ion channels but also for the design of synthetic channel systems for use in areas such as water filtration. Furthermore, we make use of the more accurate characterization of channel pores to refine a physical conductance model to obtain a heuristic estimate for single-channel conductance. The code is freely available, obtainable as pip-installable python package and provided as a web service.

Financial information and diverging beliefs

Standard Bayesians’ beliefs converge when they receive the same piece of new information. However, when agents initially disagree and have uncertainty about the precision of a signal, their disagreement might instead increase, despite receiving the same information. We demonstrate that this divergence of beliefs leads to a unimodal effect of the absolute surprise in the signal on trading volume. We show that this prediction is consistent with the empirical evidence using trading volume around earnings announcements of U.S. firms. We find evidence of elevated volume following moderate surprises and depressed volume following more extreme surprises, a pattern that is more pronounced when investors hold more distant prior beliefs and are more uncertain about earnings’ precision. The evidence is consistent with the model where investors disagree about stocks’ expected returns and do not know the precision of earnings as a signal about the firm’s value.

PDMS microspheres as rheological additives for PDMS-based DIW inks

Direct Ink Writing holds vast potential for additive manufacturing with broad material compatibility as long as appropriate rheological properties are exhibited by the material of choice. Additives are often included to attain the desired rheological properties for printing, but these same additives can yield products with undesirable mechanical properties. For example, silica fillers are used to create silicone inks appropriate for printing but yield cured structures that are too stiff. In this work, we investigate the applicability of PDMS microspheres as a rheological and thixotropic additive for PDMS based DIW inks. We utilize a facile oil-in-water emulsion method to reproducibly obtain small (∼5 μm) PDMS microspheres, which are then incorporated into PDMS-based inks. More traditional inks with fumed silica and thixotropic additive were compared with inks containing PDMS microspheres at equal volume loadings to determine whether the PDMS microspheres could impart the desired rheological properties for DIW. Inks including PDMS microspheres exhibited surprising thixotropic effects, which enabled prints with fidelity analogous to traditional ink employing silica filler, while producing mechanically softer prints.

Does peer tutoring have negative effects? An investigation and intervention on tutors’ implicit theories and beliefs of intelligence

Peer-tutoring (PT) is a widely studied learning strategy. No studies, to our knowledge, have examined the effects of PT on tutors’ metacognitive abilities, particularly in relation to implicit theories of intelligence. Study 1 aimed to determine the effects of PT on tutors’ theory and beliefs of intelligence and self-confidence in their own intelligence. Study 2 aimed to explore the efficacy of a metacognitive training administered to tutors on some metacognitive dimensions for both tutors and tutees. In Study 1, we compared tutors and no-tutors before and after PT; in Study 2 we compared tutors, who completed a brief metacognitive training, with a group of standard tutors, as well as their relative tutees. Participants were assigned to experimental conditions and assessed using various standardised questionnaires. Study 1 revealed a surprising negative effect: after PT, tutors tended to abandon the idea of intelligence as a modifiable construct and adopt a more dysfunctional theory of entity intelligence (Cohen’s d = .66). Study 2 also showed that the PT experience negatively affected tutors’ theory of intelligence (Cohen’s d = .94), but this held only for untrained tutors. We also found a positive indirect effect of the training on tutees’ attitudes towards maths.

Exact Results for a Boundary-Driven Double Spin Chain and Resource-Efficient Remote Entanglement Stabilization

We derive an exact solution for the steady state of a setup where two XX-coupled N-qubit spin chains (with possibly nonuniform couplings) are subject to boundary Rabi drives and common boundary loss generated by a waveguide (either bidirectional or unidirectional). For a wide range of parameters, this system has a pure entangled steady state, providing a means for stabilizing remote multiqubit entanglement without the use of squeezed light. Our solution also provides insights into a single boundary-driven dissipative XX spin chain that maps to an interacting fermionic model. The nonequilibrium steady state exhibits surprising correlation effects, including an emergent pairing of hole excitations that arises from dynamically constrained hopping. Our system could be implemented in a number of experimental platforms, including circuit QED. Published by the American Physical Society 2024

Preparation of hyperbranched hydrophobic nano-silica and its superior needling-effect in PDMS defoam agent

Hydrophobic nano silica powder is a kind of important synergist to silicone defoaming agents. The large pore volume and branched chain conformation of silica nanoparticles present superior effects on defoaming properties. However, silica nanoparticles synthesized by liquid phase easily aggregate and pore collapse because of their high surface activity and polarity, leading to poorer dispersity and limited practicability. In this paper, a novel hydrophobic silica with a hyperbranched structure was designed through in-situ modifying method with hexamethyldisilazane (HMDS) and polydimethylsiloxane (PDMS) in the liquid phase. The trimethylsilanol generated by HMDS hydrolysis reacts quickly with the highly active hydroxyl groups on the silica, causing the surface properties of the nanoparticles to transform from polar to non-polar properties. The steric hindrance of the trimethyl silicon and the reduction of the surface polarity effectively prevent silica pores from collapsing and maintain the macropore structures to realize the hyperbranched silica. At the same time, the −Si (CH3)2− from PDMS endowed the hyperbranched silica with excellent hydrophobicity. When applied in the defoaming agent, the hydrophobicity of silica contributes to dewetting the foams, and the hyperbranched spatial structures play an enhanced needling effect. Therefore, this hydrophobic hyperbranched silica exhibited a surprising defoaming effect, which significantly reduced the defoaming time from 464.4 s to less than 2 s, superior to commercial defoaming silica (155.3 s). The defoaming efficiency reached 100 % within 2 s of the end of the shaking, and the defoamer antifoaming ability was improved to reach 27.5 min, which was 77 % higher than that of commercial defoamer.

Physicochemical control of solvation and molecular assembly of charged amphiphilic oligomers at air-aqueous interfaces

Hypothesis: Understanding the rules that control the assembly of nanostructured soft materials at interfaces is central to many applications. We hypothesize that electrolytes can be used to alter the hydration shell of amphiphilic oligomers at the air-aqueous interface of Langmuir films, thereby providing a means to control the formation of emergent nanostructures.Experiments: Three representative salts – (NaF, NaCl, NaSCN) were studied for mediating the self-assembly of oligodimethylsiloxane methylimidazolium (ODMS-MIM+) amphiphiles in Langmuir films. The effects of the different salts on the nanostructure assembly of these films were probed using vibrational sum frequency generation (SFG) spectroscopy and Langmuir trough techniques. Experimental data were supported by atomistic molecular dynamic simulations.Findings: Langmuir trough surface pressure – area isotherms suggested a surprising effect on oligomer assembly, whereby the presence of anions affects the stability of the interfacial layer irrespective of their surface propensities. In contrast, SFG results implied a strong anion effect that parallels the surface activity of anions. These seemingly contradictory trends are explained by anion driven tail dehydration resulting in increasingly heterogeneous systems with entangled ODMS tails and appreciable anion penetration into the complex interfacial layer comprised of headgroups, tails, and interfacial water molecules. These findings provide physical and chemical insight for tuning a wide range of interfacial assemblies.

Xylazine is an agonist at kappa opioid receptors and exhibits sex-specific responses to opioid antagonism

Xylazine is in the unregulated drug supply at increasing rates, usually combined with fentanyl, necessitating understanding of its pharmacology. Despite commentary from politicians, and public health officials, it is unknown how xylazine impacts naloxone efficacy, and. few studies have examined it alone. Here, we examine the impact of xylazine alone and in combination with fentanyl on several behaviors in mice. Surprisingly, naloxone precipitates withdrawal from xylazine and fentanyl/xylazine coadministration, with enhanced sensitivity in females. Further, xylazine is a full agonist at kappa opioid receptors, a potential mechanism for its naloxone sensitivity. Finally, we demonstrate surprising effects of xylazine to kappa opioid antagonism, which are relevant for public health considerations. These data address an ongoing health crisis and will help inform critical policy and healthcare decisions. One-sentence summaryWe present surprising new insights into xylazine and fentanyl pharmacology with immediate implications for clinical practice and frontline public health.

Machine learning to identify structural motifs in asphaltenes

Asphaltenes are organic compounds that aggregate in crude oil with two dominant molecular architectures: archipelago and continental. Continental architectures possess a single uniform island structure composed of aromatic rings in contrast to archipelago architectures with aromatic cores interconnected through aliphatic chains. The structural composition of asphaltenes varies globally due to geographical differences, posing challenges in their classification due to a lack of uniformity. This study is the first known exploration of using image-based supervised machine learning, particularly the ResNet-50 neural network, for the binary classification of asphaltenes into continental and archipelago motifs. 255 continental and archipelago models underwent structural augmentations to create a sample size of 1,530 asphaltene structures that is robust enough for accurate results in both the training and testing portions of the machine learning. These augmentations included the repeated addition of carbons until a complete pentane chain was added to a specified carbon on each asphaltene structure. Using Mathematica, supervised ResNet-50 image-based classification was used on both original and augmented structure datasets to classify as either archipelago or continental. The classification was also implemented using topological similarity searching for association between atoms and the distance between them for further molecule identification. This study demonstrates the surprising effectiveness of image-based classification compared to traditional topological feature-based methods. Our results reveal that deep learning techniques, especially image-based approaches, provide novel and insightful ways to differentiate complex molecular structures like asphaltenes, challenging the traditional reliance on topological features alone. This research opens new avenues in chemical analysis and molecular characterization, highlighting the potential of machine learning in complex molecular systems.