Browse Articles

Utilizing hybridization effects to tune morphology and electron mobility of Y6 through asymmetric small- and large-scale modifications of terminal groups

While exploring molecular modifications of the high-performance acceptor Y6 with an A-DA′D-A framework, researchers have discovered that asymmetric modification of terminal groups (TGs) appears to be a promising approach as it frequently enhances the photovoltaic performance of organic solar cells (OSCs) effectively. However, the underlying mechanism about how asymmetric TG modifications influence morphology and charge carrier mobility remains unclear. We have conducted a systematic study in this work to investigate the morphology and electron mobility of two asymmetric Y6 derivatives with the A1-DA′D-A2 framework: Y6-asym-IM2O (A1 = IM-2F and A2 = IM2O, representing small-scale TG modification) and Y6-asym-BR (A1 = IM-2F and A2 = BR, representing large-scale TG modification), along with their symmetric counterparts (A1 = A2 = IM-2F/BR/IM2O). The results demonstrate that small-scale asymmetric TG modifications such as Y6-asym-IM2O fine-tune molecular packing, while large-scale modifications such as Y6-asym-BR drastically alter stacking patterns. In addition, hybridization effects are found in the frontier molecular orbital energy, electrostatic potential, and electron mobility of the asymmetric molecules, which fall between the values of their symmetric counterparts. In particular, the results of small-scale asymmetric modification of Y6 reveal that the introduction of promising TGs in an asymmetric manner can further improve electron mobility by tuning reorganization energy and morphology, and vice versa. While previous studies focused on symmetric modifications, this work systematically investigates asymmetric substitution patterns and further elucidates the impact of these methods on charge transfer for the first time. These discoveries underscore the potential of utilizing asymmetric modification of TGs as a quantitative means to regulate electron mobility in Y6-based OSCs.

Memory Monitoring Recognition Test (MMRT), a new measurement of stimular source monitoring: Software and comprehension

Background Reality monitoring allows the evaluation and monitoring of reality through the assignment of information to internal or external sources, which is crucial to differentiate real events from imaginary ones. In schizophrenics, monitoring seems to be related to an error in the allocation processes, giving rise to false perceptions such as visual hallucinations, which are associated with a poor prognosis. This error can appear almost imperceptibly at an early age in life, making carrying out predictive or evaluation tests with paper and pencil unattractive. The computerization of technical resources that allow the monitoring of reality offers a new tool to evaluate the attribution process, in an effective and agile way and with easy understanding of cognitive deficits in a friendly environment. Objective Computerize the Memory Monitoring and Recognition Test (MMRT) evaluate reality monitoring through verbal memory tasks, improving its implementation, optimizing interaction with the user and perfecting the recording of memory errors that could indicate psychotic symptoms. Method The MMRT was developed using Python and Kivy, facilitating the creation of cross-platform user interfaces. The test is structured in stages, allows voice accessibility for people with visual disabilities and provides comprehensive user management. The test data is stored in the cloud using MongoDB as the database system. Additionally, the software incorporates speech recognition using the gTTS library and generates a performance report in PDF format, documenting external, internal and global attribution errors. Result The computerized version of the MMRT allowed the detection of specific errors in memory monitoring, as well as the performance of repeated measurements to evaluate long-term memory and working memory. Conclusion Preliminary applications suggest its usefulness in identifying early cognitive markers of schizophrenia, facilitating the measurement of reality monitoring through attribution errors. Developed with open-source technology and an interface adaptable to various platforms, the MMRT represents an accessible and efficient tool for psychological evaluation, with innovative potential in the study of reality monitoring.

Deep multiscale feature fusion network with dual attention for rolling bearing remaining useful life prediction

Abstract Aiming at the existing life prediction methods for rolling bearing degradation information mining is not sufficient, the critical time step information degree is insufficient, resulting in the loss of key degradation information, model prediction accuracy and model generalization ability is insufficient, this paper proposes a novel deep multiscale feature fusion network with dual attention for rolling bearing remaining useful life (RUL) prediction. First, multi-domain feature sets of rolling bearing vibration signals are acquired. Subsequently, it is proposed to use Squeeze-and-Excitation (SE) attention mechanism to calibrate and weigh temporal significance of feature sequences, thereby capturing critical temporal information. Then, a multi-scale feature extraction and fusion module with deep network is constructed, consisting of multiple identical multi-scale residual pyramid layers connected in series to further delve into the state information of rolling bearings. Additionally, a relative position encoding method suitable for time series prediction is introduced within the multi-head attention mechanism, a network architecture based on a dual attention-enhanced Transformer encoding layer is established. This enhancement significantly improves model prediction accuracy, generalization capability, and sequence data comprehension ability. Finally, the high-level features output from the feed-forward layer are mapped through a regression layer to obtain the final prediction results. Experimental results demonstrate the superior performance of the proposed method in terms of both prediction accuracy and generalization capability for rolling bearing life prediction. A more robust and accurate framework for RUL prediction in rolling bearings is provided.

Data efficient learning of molecular slow modes from nonequilibrium metadynamics

Enhanced sampling simulations help overcome free energy barriers and explore molecular conformational space by applying external bias potential along suitable collective variables (CVs). However, identifying optimal CVs that align with the slow modes of complex molecular systems with many coupled degrees of freedom can be a significant challenge. Deep time-lagged independent component analysis (Deep-TICA) addresses this issue by employing an artificial neural network that generates non-linear combinations of molecular descriptors to learn the slowest degrees of freedom. Training Deep-TICA CVs, however, typically requires long equilibrium simulations that can sample multiple recrossing events across various metastable conformations of the molecule. This requirement can often be prohibitively expensive, thereby limiting its widespread application. In this study, we present an algorithm that enables the training of Deep-TICA CVs using a limited amount of trajectory data obtained from short, non-equilibrium metadynamics simulations that only sample one forward transition from the initial to the final state. We achieve this by utilizing the variational Koopman algorithm, which reweights short off-equilibrium trajectories to reflect the equilibrium probability densities. We demonstrate that enhanced sampling simulations conducted along the Koopman reweighted Deep-TICA CV can accurately and efficiently converge the free energy surface for systems such as the Müller–Brown potential, alanine dipeptide, and the chignolin mini-protein. Our approach, therefore, addresses the key challenge of inferring slow modes from limited trajectory data, making it more feasible to use deep learning CVs to study molecular processes of practical relevance.

Platelet-rich plasma: A promising therapy for mitigating sperm oxidative stress and mitochondrial dysfunction in subfertile men

Platelet-rich plasma (PRP) is a pioneering therapy widely used in various medical fields, showing promising outcomes. However, its impact on human sperm quality remains poorly explored among emerging therapies. This study aims to investigate the effect of autologous PRP supplementation on oxidative stress levels and mitochondrial activity in human sperm. PRP was freshly prepared from venous blood and added to each ejaculated semen sample at different concentrations of 2%, 5%, and 10%. Reactive oxygen species (ROS) in spermatozoa were measured after 24 hours of incubation at 37° (5% CO2), using nitro-blue tetrazolium (NBT) test. The MTT test was used to measure the mitochondrial succinate deshydrogenase activity. A total of 180 semen samples were obtained from 15 patients. The supplementation with PRP significantly reduced the reactive oxidative species levels and improved mitochondrial activity in spermatozoa. The level of oxidative stress in sperm was significantly decreased after 24h of incubation with PRP at 2% (p = 0.001), 5% (p = 0.001) and 10% (p = 0.001) when compared to the control group. The succinate dehydrogenase activity was enhanced in the three groups when compared to the control group. It increased from 0.667 ± 0.313 to 0.952 ± 0.499 (p = 0.018), 1.201 ± 0.657 (p = 0.002) and 1.159 ± 0.607 (p = 0.001) after incubation with 2%, 5% and 10% of PRP, respectively. This study has shown that PRP supplementation could be a promising tool to enhance sperm quality against oxidative stress and mitochondrial dysfunction. These findings could be a starting point to investigate the usefulness of PRP in ART procedures.

Author Correction: Circulating microRNA-155-3p levels predicts response to first line immunotherapy in patients with metastatic renal cell carcinoma

Path-integral Monte Carlo simulations of solid parahydrogen using two-body, three-body, and four-body <i>ab initio</i> interaction potential energy surfaces

We present path integral Monte Carlo simulation results for the equation of state of solid parahydrogen between 0.024 and 0.1Å−3 at T = 4.2 K. The simulations are performed using non-additive isotropic ab initio two-body, three-body, and four-body potential energy surfaces (PESs). We apply corrections to account for both the finite size simulation errors and the Trotter factorization errors. Simulations that use only the two-body PES during sampling yield an equation of state similar to that of simulations that use both the two-body and three-body PESs during sampling. With the four-body interaction energy, we predict an equilibrium density of 0.02608Å−3, very close to the experimental result of 0.0261Å−3. The inclusion of the four-body interaction energy also brings the simulation results in excellent agreement with the experimental pressure–density data until around 0.065Å−3, beyond which the simulation results overestimate the pressure. These PESs overestimate the average kinetic energy per molecule at the equilibrium density by about 7% compared to the experimental result. Our findings suggest that, at higher densities, we require five-body and higher-order many-body interactions to quantitatively improve the agreement between the pressure-density curve produced by simulations and that of the experiment. Using the four-body PES during sampling at excessively high densities, where such higher-order many-body interactions are likely to be significant, causes an artificial symmetry breaking in the hcp lattice structure of the solid.

RWOA: A novel enhanced whale optimization algorithm with multi-strategy for numerical optimization and engineering design problems

Whale Optimization Algorithm (WOA) is a biologically inspired metaheuristic algorithm with a simple structure and ease of implementation. However, WOA suffers from issues such as slow convergence speed, low convergence accuracy, reduced population diversity in the later stages of iteration, and an imbalance between exploration and exploitation. To address these drawbacks, this paper proposed an enhanced Whale Optimization Algorithm (RWOA). RWOA utilized Good Nodes Set method to generate evenly distributed whale individuals and incorporated Hybrid Collaborative Exploration strategy, Spiral Encircling Prey strategy, and an Enhanced Spiral Updating strategy integrated with Levy flight. Additionally, an Enhanced Cauchy Mutation based on Differential Evolution was employed. Furthermore, we redesigned the update method for parameter a to better balance exploration and exploitation. The proposed RWOA was evaluated using 23 classical benchmark functions and the impact of six improvement strategies was analyzed. We also conducted a quantitative analysis of RWOA and compared its performance with other state-of-the-art (SOTA) metaheuristic algorithms. Finally, RWOA was applied to nine engineering design optimization problems to validate its ability to solve real-world optimization challenges. The experimental results demonstrated that RWOA outperformed other algorithms and effectively addressed the shortcomings of the canonical WOA.

Circadian syndrome and mortality risk in adults aged ≥ 40 years: a prospective cohort analysis of CHARLS and NHANES

Coupled-trajectory surface hopping with sign consistency

The framework of exact factorization (XF) has inspired a series of trajectory-based nonadiabatic dynamics methods by introducing different approximations. Recently, the coupled-trajectory surface hopping (CTSH) method has been proposed to combine the key advantages of the coupled-trajectory mixed quantum–classical method based on XF and the fewest switches surface hopping. We here present a novel variant of CTSH, namely, sign-consistent CTSH (SC-CTSH), which considers proper trajectory clustering to reconstruct the nuclear density distribution and the consistency between wave function and active states to introduce decoherence. Using the exact quantum solutions as references, the high performance of SC-CTSH is benchmarked in the widely studied scattering models and compared with other related XF-based methods. Due to the incorporation of new trajectory clustering and sign consistency algorithms, SC-CTSH obtains more accurate quantum momentum and decoherence during the nonadiabatic dynamics, which makes the combination of XF and surface hopping more consistent and reliable. This study further highlights the significance of internal consistency between wave function and active states, which is important in the further development of mixed quantum–classical dynamics methods.

Daily high doses of atorvastatin alter neuronal morphology in a juvenile songbird model

Statins are highly effective and widely prescribed cholesterol lowering drugs. However, statins cross the blood-brain barrier and decrease neural cholesterol in animal models, raising concern that long-term statin use may impact cholesterol-dependent structures and functions in the brain. Cholesterol is a fundamental component of cell membranes and experimentally decreasing membrane cholesterol has been shown to alter cell morphology in vitro. In addition, brain regions that undergo adult neurogenesis rely on local brain cholesterol for the manufacture of new neuronal membranes. Thus neurogenesis may be particularly vulnerable to long-term statin use. Here we asked whether oral statin treatment impacts neurogenesis in juveniles, either by decreasing numbers of new cells formed or altering the structure of new neurons. The use of statins in children and adolescents has received less attention than in older adults, with few studies on potential unintended effects in young brains. We examined neurons in the juvenile zebra finch songbird in telencephalic regions that function in song perception and memory (caudomedial nidopallium, NCM) and song production (HVC). Birds received either 40 mg/kg of atorvastatin in water or water vehicle once daily for 2–3 months until they reached adulthood. We labeled newborn cells using systemic injections of bromodeoxyuridine (BrdU) and quantified cells double-labeled with antibodies for BrdU and the neuron-specific protein Hu 30–32 days post mitosis. We also quantified a younger cohort of new neurons in the same birds using antibody to the neuronal protein doublecortin (DCX). We then compared numbers of new neurons and soma morphology of BrdU + /Hu+ neurons between statin-treated and control birds. We did not find an effect of statins on the density of newly formed neurons in either brain region, suggesting that statin treatment did not impact neurogenesis or young neuron survival in our paradigm. However, we found that neuronal soma morphology differed significantly between statin-treated and control birds. Somata of BrdU + /Hu+ (30–32 day old) neurons were flatter and had more furrowed contours in statin-treated birds relative to controls. In a larger, heterogeneous cohort of non-birthdated BrdU-/Hu+ neurons, largely born prior to statin treatment, somata were smaller in statin-treated birds than in controls. Our findings indicate that atorvastatin may affect neural cytoarchitecture in both newly formed and mature neurons, perhaps as a consequence of decreased cholesterol availability in the brain.

Study on energy retrofits for rural residential envelopes in Northwest China

Structure of liquids from reference hard body fluids: Additive vs non-additive hard body models

Following the early simulation results for simple liquids, various hard body fluids have been used in molecular-based equations of state as a leading/reference term. This has been justified for normal liquids by the similarity of their structure, but for polar and associating ones, a direct application of hard body models has not been considered so far. Viewing the hard body models, fused-hard-sphere bodies, as simple geometrical objects, their mutual interaction is additive. However, when accounting for the mutual effect of the site–site interactions, the individual hard sphere–hard sphere interactions may become non-additive, and consequently, the resulting interaction between the hard bodies becomes non-additive, which may also affect their structure. The effect of the non-additivity on the structural properties, the site–site and dipole–dipole correlation functions are analyzed in detail by considering three polar fluids, quadrupolar carbon dioxide, dipolar acetonitrile, and acetone, as well as two associating fluids, methanol and water. The modification of the mutual geometry in the non-additive models leads to differences both in their structural and orientation correlations. The comparison of the structure of the non-additive purely repulsive hard-body models with those of the empirical models of the chosen real liquids shows surprising similarities, which extends the possibilities of the direct application of the hard-body fluids as reference systems in perturbation theories.

Exploring vaccination attitudes in African communities in Canada: A mixed-methods study protocol

Introduction Vaccine hesitancy is a complex issue influenced by many interacting factors. While literature on its contributing causes continues to expand, there is limited research on the contextual and cultural dynamics that shape vaccine hesitancy among African-born individuals in Canada. Identifying and understanding these factors is critical in developing targeted health interventions that address specific barriers to vaccination within this community. The study aims to explore the unique socio-cultural and context-specific elements of vaccine hesitancy among African community members living in Canada. Methods and analysis The study will use a mixed-methods approach to investigate vaccine hesitancy among African community members living in Southwestern Ontario. In the qualitative study, we will conduct semi-structured interviews and participatory focus groups within each of the selected study areas: London, Windsor and Chatham-Kent. The qualitative data will be collected, transcribed and then analyzed thematically using NVivo 12. For the quantitative study, we will provide participants with surveys to accurately assess the predictors of vaccine hesitancy. The quantitative data will be analyzed using logistic regression to explore how socio-cultural influences, trust, and accessible information impact vaccine hesitancy. Discussion This study addresses a significant gap in existing literature by providing cultural and contextual insights on the drivers of vaccine hesitancy among African-born individuals. Using a mix-method design, the study offers a rich understanding of the influences shaping vaccine decision-making. The findings will support the development of health policies and interventions aimed at improving overall health outcomes for African communities within Canada.

Alterations in looking at face-pareidolia images in autism

Abstract Face tuning is vital for adaptive and effective social cognition and interaction. This capability is impaired in a wide range of mental conditions including autism spectrum disorder (ASD). Yet the origins of this deficit are largely unknown. Here, an eye-tracking methodology had been implemented in adolescents with high-functioning ASD and in typically developing (TD) matched controls while administering a face-pareidolia task. The spatial distributions of eye fixation in five regions of interest [face, eyes, mouth, CFA (complementary face area, a face area beyond eyes and mouth) and non-face area (a screen area outside a face)] were recorded during spontaneous recognition of a set of Arcimboldo-like Face-n-Food images presented in a predetermined order from the least to most resembling a face. Individuals with ASD gave significantly fewer face responses and looked more often at the mouth, CFA, and non-face areas. By contrast, TD controls mostly fixated the face and eyes areas. The atypical visual scanning strategies could, at least partly, account for the lower face tuning in ASD, supporting the eye avoidance hypothesis, according to which ASD individuals concentrate less on the eyes because the eyes represent a source of emotional information that may make them feel uncomfortable.

The impact of hydration shell inclusion and chain exclusion in the efficacy of reaction coordinates for homogeneous and heterogeneous ice nucleation

Ice nucleation plays a pivotal role in many natural and industrial processes, and molecular simulations have proven vital in uncovering its kinetics and mechanisms. A fundamental component of such simulations is the choice of an order parameter (OP) that quantifies the progress of nucleation, with the efficacy of an OP typically measured by its ability to predict the committor probabilities. Here, we leverage a machine learning framework introduced in our earlier work [Domingues et al., J. Phys. Chem. Lett. 15, 1279, (2024)] to systematically investigate how key implementation details influence the efficacy of standard Steinhardt OPs in capturing the progress of both homogeneous and heterogeneous ice nucleation. Our analysis identifies distance and q6 cutoffs as the primary determinants of OP performance, regardless of the mode of nucleation. We also examine the impact of two popular refinement strategies, namely chain exclusion and hydration shell inclusion, on OP efficacy. We find neither strategy to exhibit a universally consistent impact. Instead, their efficacy depends strongly on the chosen distance and q6 cutoffs. Chain exclusion enhances OP efficacy when the underlying OP lacks sufficient selectivity, whereas hydration shell inclusion is beneficial for overly selective OPs. Consequently, we demonstrate that selecting optimal combinations of such cutoffs can eliminate the need for these refinement strategies altogether. These findings provide a systematic understanding of how to design and optimize OPs for accurately describing complex nucleation phenomena, offering valuable guidance for improving the predictive power of molecular simulations.

Mathematical modelling of reoviruses in cancer cell cultures

Oncolytic virotherapy has emerged as a potential cancer therapy, utilizing viruses to selectively target and replicate within cancer cells while preserving normal cells. In this paper, we investigate the oncolytic potential of unmodified reovirus T3wt relative to a mutated variant SV5. In animal cancer cell monolayer experiments it was found that SV5 was more oncolytic relative to T3wt. SV5 forms larger sized plaques on cancer cell monolayers and spreads to farther distances from the initial site of infection as compared to T3wt. Paradoxically, SV5 attaches to cancer cells less efficiently than T3wt, which lead us to hypothesize that there might be an optimal binding affinity with maximal oncolytic activity. To understand the relationship between the binding process and virus spread for T3wt and SV5, we employ mathematical modelling. A reaction-diffusion model is applied, which is fit to the available data and then validated on data that were not used for the fit. Analysis of our model shows that there is an optimal binding rate that leads to maximum viral infection of the cancer monolayer, and we estimate this value for T3wt and SV5. Moreover, we find that the viral burst size is an important parameter for viral spread, and that a combination of efficient binding and large burst sizes is a promising direction to further develop anti-cancer viruses.

Semantic segmentation model of multi-source remote sensing images was used to extract winter wheat at tillering stage

Electronic structure of graphene films prepared from water dispersions and their energy level alignments with organic semiconductors

The unique physical and chemical properties of graphene offer significant potential in a wide range of applications, particularly as a flexible electrode in electronic devices. Solution-processable graphene, especially graphene dispersion in water (GDW), has emerged as a promising candidate for cost-effective, environmentally friendly, and large-scale production. However, the energy level alignment between GDW and semiconductors, which is critical for designing efficient device architectures, remains insufficiently understood. In this study, we investigated the interfacial electronic structures of GDW electrodes with organic semiconductors (C60 and rubrene) using in situ x-ray/ultraviolet photoelectron spectroscopy. We also explored the effect of ultraviolet–ozone (UV–O3) treatment on the charge injection barriers. After 5 min of UV–O3 treatment, the work function of GDW increased by ∼0.4 eV due to surface oxidation, shifting the electron and hole injection barriers for C60 from 0.59 and 1.66 eV to 0.84 and 1.41 eV, respectively, and shifting those for rubrene from 1.82 and 0.74 eV to 2.14 and 0.42 eV, respectively. Weak interactions of both organic semiconductors with the GDW were observed, in contrast to metal electrodes. These results provide valuable insights into the design of future high-efficiency devices using GDW.

Research on the balanced, coordinated and sustainable development of China manufacturing industry

China’s manufacturing industry faces the multiple goals of balanced, coordinated and sustainable development. This paper clarifies the connotation of balanced, coordinated and sustainable development of the manufacturing industry from regional structure, industrial structure and development structure. The level of balanced, coordinated and sustainable development of the manufacturing industry is measured using various methods such as index construction model, coupled coordination model and objective assignment method. The temporal and spatial evolution characteristics of the balanced, coordinated and sustainable development of the manufacturing industry are analysed. The following conclusions were obtained: the overall level of manufacturing equilibrium in the east is high, and the level of manufacturing equilibrium in the west and northeast is low. Therefore, it is necessary to promote the level of manufacturing development in the central and western regions through industrial transfer and other means. The overall coordination level of manufacturing industry shows a clear upward trend. The coordination level of manufacturing industry in the east ranks first among the four regions, and the coordination level of manufacturing industry in the west has made the most obvious progress. The overall level of sustainable development of the manufacturing industry is on an upward trend, with the highest level of sustainable development of the manufacturing industry in the east and a relatively low level of sustainable development in the west. There is a need to achieve sustainable development of the manufacturing industry by promoting the integration and development of the digital economy and the manufacturing industry.