Selection Mechanism Research Articles

The effect of rapamycin treatment on mouse ovarian follicle development in dehydroepiandrosterone-induced polycystic ovary syndrome mouse model.

Polycystic ovary syndrome (PCOS) is a complex reproductive and endocrine disorder affecting 5-10% of women of reproductive age, but the pathophysiology of PCOS still remains unknown. Here, the aim of our study was to analyze the effects of rapamycin treatment that may regulate impaired hormonal levels and folliculogenesis in dehydroepiandrosterone (DHEA)-treated PCOS mouse. We hypothesized that rapamycin may ameliorate the negative effects of PCOS in DHEA-induced PCOS mouse model. The target of rapamycin (TOR) gene product is a serine/threonine kinase that has been implicated in the control of cell growth, proliferation and autophagy, and rapamycin is a potent inhibitor of mTORC1 pathway. In this study, for the first time, mTORC1 and activation products are presented at protein and mRNA levels after rapamycin treatment in DHEA-induced PCOS mouse ovary. We showed that rapamycin treatment may regulate follicular development, hormonal levels and provide ovulation in DHEA-induced PCOS mouse. Additionally, we assessed decreased primordial follicle reserve, increased number of primary and secondary follicles, corpus luteum structure forms again after 10 days of rapamycin treatment. This study presented here suggests rapamycin treatment regulates hormonal phenotype and folliculogenesis in the ovary and also mTOR signalling pathway in granulosa cells of DHEA-induced PCOS mouse ovary which may have potential to attenuate understanding the mechanism of dominant follicle selection and anovulatory infertility.

Application of Hydroxyaromatic Aldehydes in Ultra-Efficient and Metal-Free Photocatalytic E→Z Isomerization of Olefin.

The strategy of photocatalyzed E→Z isomerization of olefins to access thermodynamically less stable Z-alkenes has recently received considerable attention. Here, we have discovered a sensitizer of hydroxyaromatic aldehyde that can rapidly achieve olefin E→Z isomerization under blue light irradiation. Notably, 2-hydroxybenzene-1,3,5-tricarbaldehyde, when assisted by blue light, can achieve efficient and selective conversion within just 5 minutes (Z/E=92 : 8). The reaction can be successfully scaled up to gram scale, and exhibits remarkable reactivity toward various derivatives of ethyl cinnamate (27 examples) and other olefins. Furthermore, the former can be directly cyclized by a hydroxyl derivative to produce 4-substituted coumarin. The prominent preponderance of this method includes being metal-free, efficient, convenient, no by-products and achieving high selectivity. Correlation of sensitizer triplet energy (ET) and preliminary mechanistic experiments indicate that the accomplishment of this reaction is based on the selective excitation mechanism.

Modulation of the Chirality and Dynamics of Self-Assembled Nanocellulose-Chiral C-Dot Film for Chiral Sensing Applications.

The detection and sensing of chirality using chiral biomaterials are growing areas of research in advanced bioelectronics. As a result, chiral-controlled biomaterials are crucial for advancing current technologies in chiral sensing applications within biosystems. A chiral carbon dot (C-dot) modulated self-assembled emissive cellulose nanocrystal (CNC) film is developed where the chirality of the CNC film can be tempered between left-handed and right-handed chirality after being doped with chiral L/D-C-dots in CNCs (C-dot-CNC film), transferring the chirality from C-dots to CNCs. The interaction between C-dots, CNCs, and carrier dynamics is investigated using a variety of steady-state and time-resolved PL spectroscopy techniques. The chiral C-dot enhanced the protonic conductivity across the CNC via the formation of hydrogen bonds with its surface functional groups and water molecules. Further, the chiral CNC-C-dots photoelectrodes demonstrate an excellent ability to distinguish between left-handed and right-handed small molecules. These findings on the underlying mechanism of spin selectivity between chiral CNC-C-dot and chiral ligand hold promise for the development of efficient chiral-sensing electronic devices.

Coevolution and dynamic processes: an introduction to this issue and avenues for future research

AbstractIn this paper, drawing upon previous contributions to modern Schumpeterian economics, we argue that coevolution in economic systems operates when considering several evolving populations within a socio-economic system, these populations mutually shape their respective selection, learning, and/or novelty generation mechanisms. The properties that arise from coevolution should be analyzed as emerging from multiple populations in co-determination. The notion of coevolution appears not only in Schumpeterian economics but, in general, in many branches of heterodox thought. Likewise, it can also be found in Biology, Sociology, Political Science, History, Philosophy, Law, and Computational studies. In this introduction, after providing a neat definition of coevolution, we illustrate with formal examples how coevolution can be represented and, potentially, empirically tested. Finally, we present the contributions to the SI and suggest avenues for future research.

Three-Level Chirality Transfer and Amplification in Liquid Crystal Supramolecular Assembly for Achieving Full-Color and White Circularly Polarized Luminescence.

Chiral liquid crystal supramolecular assembly provides an ideal strategy for constructing excellent circularly polarized luminescence (CPL) materials. However, the chirality transfer in chiral liquid crystals normally occurs at two levels from the configurational chirality to the supramolecular phase chirality. The more precise and more levels of chirality transmission are fascinating but remain challenging. The present work reports the first success of three-level chirality transfer and amplification from configurationally point chirality of small molecules to conformationally helical chirality of helical polymers and finally to supramolecular phase chirality of cholesteric liquid crystals composed of chiral nonfluorescent polymers (P46) and nematic liquid crystals. Noticeably, the helical twisting power of P46 is five-fold larger than its monomer. Full-color and white CPL with maximum luminescence dissymmetry factor up to 1.54 and photoluminescence quantum yield up to 63.8% are realized utilizing helical supramolecular assembly combined with selective reflection mechanism. Also significantly, the electrically stimuli-responsive CPL switching device as well as anti-counterfeiting security, information encryption, and chiral logic gate applications are developed. This study deepens the understanding of chirality transfer and amplification across different hierarchical levels.

Guidance for establishing and applying tolerable upper intake levels for vitamins and essential minerals.

Vitamins and essential minerals are micronutrients that are required for the normal functioning of the human body. However, they may lead to adverse health effects if consumed in excess. A tolerable upper intake level (UL) is a science-based reference value that supports policy-makers and other relevant actors in managing the risks of excess nutrient intake. EFSA's principles for establishing ULs for vitamins and minerals were originally developed by the Scientific Committee on Food in 2000. This guidance from the EFSA Panel on Nutrition, Novel Foods and Food Allergens provides an updated framework for UL assessments. A draft was published in 2022 and underwent a 2-year piloting period. The present document incorporates revisions based on the experience gained through its practical implementation. It covers aspects related to the planning of the risk assessment (problem formulation and definition of methods) and its implementation (evidence retrieval, appraisal, synthesis, integration, uncertainty analysis). As in the previous framework, the general principles developed for the risk assessment of chemicals in food are applied, i.e. hazard identification, hazard characterisation, intake assessment, risk characterisation. Specific to nutrients are their biochemical and physiological roles and the specific and selective mechanisms that maintain the systemic homeostasis and accumulation of the nutrient in the body. Such considerations must also be taken into account when conducting risk assessments of nutrients.

Wood meet MOFs: Facile, green, and highly selective adsorbent for textile wastewater

Owing to their unique physicochemical features, ionic liquids (ILs) are extensively utilized to dissolve cellulose for the preparation of regenerated cellulose fibers (RCFs) in the textile industry. However, Cu2+ is introduced during the cellulose dissolution process and gradually accumulates, seriously affecting the RCF performance and IL recovery. Herein, to achieve an efficient and selective adsorption of Cu2+ from ILs aqueous solutions, a facile and green method based on the in situ growth of zeolitic imidazolate framework-67 in wood hydrogels (ZIF-67@WH) is presented. The morphology, functional groups, crystallinity, thermal stability, and pore structure of the resulting ZIF-67@WH were investigated using various characterization techniques, and the adsorption selectivity, adsorption thermodynamics, adsorption kinetics, and recoverability were examined in depth by performing batch experiments. The ZIF-67@WH exhibits high adsorption selectivity for Cu2+ and ILs with adsorption ratios of 92.8 % and 2.2 %, respectively. The selective adsorption mechanism was elucidated by means of FT-IR, XPS, DFT, and MD. As the key for the selective adsorption, the N site in ZIF-67 dramatically promotes the coordination with Cu2+, while the adsorption for ILs mainly occurs via hydrogen bonding and van der Waals interactions with adsorption energies of –80.61 and –35.33 kcal/mol, respectively. The results demonstrate that this approach constitutes a feasible technique for the efficient separation of Cu2+ and ILs in the textile industry.

Digital models for the selection of party organs and election candidates in Spanish political parties: the impact of intraparty democracy

Processes for selecting party officials and election candidates are key aspects of the internal democracy of political parties. This is not only because they reveal who is appointed, but also because they provide insight into intra-party dynamics and the levels of internal democracy within the parties. The aim of this article is to offer a general overview of the internal selection models used by Spanish political parties with representation in the Congress of Deputies, in order to determine whether online procedures are as widespread as is often believed, and, whether they have any real impact on intra-party democracy. We look into whether political parties are making widespread use of digital tools to promote new selection processes, whether the establishment of online selection mechanisms impacts the internal democracy of political parties and what types of parties are primarily using these tools. For this purpose we have conducted a taxonomy of the selection processes in Spanish political parties represented in the Congress of Deputies, based on the development of a model of indicators drawing on some previous studies.

Selective Clonal Regression After Interferon Therapy in Metastatic Melanoma.

Regression (total or partial) is a common phenomenon in melanoma. From a pathogenic perspective, it is highly complex and only partially understood, involving aspects of both the tumor and the individual. One of the determining factors is the clonal selection of the tumor, wherein some clones within the tumor survive while others perish. This clonal selection can sometimes occur as a selective mechanism after the initiation of a therapeutic intervention. In many of these cases, the effect is detrimental, because the surviving clone is resistant to the applied therapy. However, occasionally, the therapy can successfully select the less harmful clone. We present an example of the latter, where therapy with interferon induced regression of the metastatic-capable melanocytic population, with only the primary tumor melanocytic population persisting. To confirm this, we demonstrated BRAF mutational similarity between the 2 populations, and an additional NRAS mutation in the metastatic population, which was absent in the primary tumor.

Medical image segmentation network based on feature filtering with low number of parameters

In recent years, the medical image segmentation method based on hybrid convolutional neural network (CNN) and Vision Transformer (ViT) has made great progress, but it still faces the challenge of unbalanced global and local modeling, and excessive parameters. In addition, ViT repeatedly uses the whole feature map to model the global information, thus generating irrelevant and weakly related information, which will weaken the performance of the model when facing small datasets and segmentation targets. Therefore, this paper proposes a feature screening network based on similarity, named Screening Feature (SF)-MixedNet. Specifically, this paper first proposes a new feature extractor, namely Correlation based Similarity Transformer (CSimFormer). On the basis of parameter pruning, it uses the Screening Feature Multi-head Self Attention (SF-MSA) to establish the remote dependency, and calculates the similarity between local elements through the Location-Sensitive Mechanism (LsM) to obtain the weight matrix. Then, the correlation between regional elements is mined by Region Matching and Selection (RMS) mechanism, and the obtained information is filtered according to the corresponding rules to reduce the side effects of redundant information. Extensive experiments on Synapse dataset, ACDC dataset and SegPC-2021 dataset show that the segmentation accuracy reaches 83.51%, 92.20% and 81.27% respectively. Especially in the Synapse dataset, our method is 6.31% higher than the baseline. The method proposed in this paper effectively improves the segmentation accuracy, provides more detailed information for medical diagnosis and promotes the development of medical artificial intelligence technology.

The influence of neighbor selection on self-organized UAV swarm based on finite perception vision.

Recently, vision-based unmanned aerial vehicle (UAV) swarming has emerged as a promising alternative that can overcome the adaptability and scalability limitations of distributed and communication-based UAV swarm systems. While most vision-based control algorithms are predicated on the detection of neighboring objects, they often overlook key perceptual factors such as visual occlusion and the impact of visual sensor limitations on swarm performance. To address the interaction problem of neighbor selection at the core of self-organizing UAV swarm control, a perceptually realistic finite perception visual (FPV) neighbor selection model is proposed, which is based on the lateral visual characteristics of birds, incorporates adjustable lateral visual field widths and orientations, and is able to ignore occluded agents. Based on the FPV model, a neighbor selection method based on the Acute Angle Test (AAT) is proposed,
which overcomes the limitation that the traditional neighbor selection mechanism can only interact with the nearest neighboring agents. A large number of Monte Carlo simulation comparison experiments show that the proposed FPV+AAT neighborselection mechanism can reduce the redundant communication burden between large scale self-organized UAV swarms, and outperforms the traditional neighbor selection method in terms of order, safety, union, connectivity, and noise resistance.

AFA–Mamba: Adaptive Feature Alignment with Global–Local Mamba for Hyperspectral and LiDAR Data Classification

The joint classification of hyperspectral imagery (HSI) and LiDAR data is an important task in the field of remote sensing image interpretation. Traditional classification methods, such as support vector machine (SVM) and random forest (RF), have difficulty capturing the complex spectral–spatial–elevation correlation information. Recently, important progress has been made in HSI-LiDAR classification using Convolutional Neural Networks (CNNs) and Transformers. However, due to the large spatial extent of remote sensing images, the vanilla Transformer and CNNs struggle to effectively capture global context. Moreover, the weak misalignment between multi-source data poses challenges for their effective fusion. In this paper, we introduce AFA–Mamba, an Adaptive Feature Alignment Network with a Global–Local Mamba design that achieves accurate land cover classification. It contains two main core designs: (1) We first propose a Global–Local Mamba encoder, which effectively models context through a 2D selective scanning mechanism while introducing local bias to enhance the spatial features of local objects. (2) We also propose an SSE Adaptive Alignment and Fusion (A2F) module to adaptively adjust the relative positions between multi-source features. This module establishes a guided subspace to accurately estimate feature-level offsets, enabling optimal fusion. As a result, our AFA–Mamba consistently outperforms state-of-the-art multi-source fusion classification approaches across multiple datasets.

Charge Generation and Enhancement of Key Components of Triboelectric Nanogenerators: A Review.

The past decade has witnessed remarkable progress in high-performance Triboelectric nanogenerators (TENG) with the design and synthesis of functional dielectric materials, the exploration of novel dynamic charge transport mechanisms, and the innovative design of architecture, making it one of the most crucial technologies for energy harvesting. High output charge density is fundamental for TENG to expand its application scope and accelerate industrialization; it depends on the dynamic equilibrium of charge generation, trapping, de-trapping, and migration within its core components. Here, this review classifies and summarizes innovative approaches to enhance the charge density of the charge generation, charge trapping, and charge collection layers. The milestone of high charge density TENG is reviewed based on material selection and innovative mechanisms. The state-of-the-art principles and techniques for generating high charge density and suppressing charge decay are discussed and highlighted in detail, and the distinct charge transport mechanisms, the technologies of advanced materials preparation, and the effective charge excitation strategy are emphatically introduced. Lastly, the bottleneck and future research priorities for boosting the output charge density are summarized. A summary of these cutting-edge developments intends to provide readers with a deep understanding of the future design of high-output TENG.

Mechanisms of Copper Selectivity and Release by the Metallochaperone CusF: Insights from CO-Binding, Rapid-Freeze-Quench EXAFS, and Unnatural Amino Acid Substitution.

Metallochaperones are small proteins that shuttle essential metal ions such as Cu selectively to their cellular targets. CusF has unusual Cu(I) coordination, bound by two methionines, one histidine and a capping tryptophan residue, W44. Here we compare the CO binding reactivity of the wild type (WT) protein and its W44A, F, and M variants. Fourier-transform infrared (FTIR) indicates that W44A provides unhindered access for CO, while W44M is unreactive. WT is also largely unreactive to CO suggesting that the tryptophan cap is effective in shielding the Cu(I) center from exogenous adduct formation, while the Phe variant shows partial reactivity suggestive of an equilibrium between cap-on and cap-off conformers. Rates of metal transfer to the partner CusB are consistent with the π-cation cap providing both selectivity and redox protection. Unnatural amino acid substitutions of the W44 ligand with cyano-Phe and Br-Phe underpin the conclusion that the Phe ligand is a less effective capping residue. Finally, density functional theory (DFT) calculations validate the CO-binding strategy. Overall, the study suggests that CusF uses the tryptophan cap to protect against exogenous ligand (O2) attack while the mechanism of protein-protein complex formation allows the cap to swing out of the way, and thus have minimal effect on the rates of metal transfer.

Challenges in using data on fathers/partners to study prenatal exposures and offspring health.

Paternal exposures (and other non-maternal factors) around pregnancy could have important effects on offspring health. One challenge is that data on partners are usually from a subgroup of mothers with data, potentially introducing selection bias, limiting generalisability of findings. We aimed to investigate the potential for selection bias in studies using partner data.We characterise availability of data on father/partner and mother health behaviours (smoking, alcohol, caffeine and physical activity) around pregnancy from three UK cohort studies: the Avon Longitudinal Study of Parents and Children (ALSPAC), Born in Bradford and the Millennium Cohort Study. We assess the extent of sample selection by comparing characteristics of families where fathers/partners do and do not participate. Using the association of parental smoking during pregnancy and child birthweight as an example, we perform simulations to investigate the extent to which missing father/partner data may induce bias in analyses conducted only in families with participating fathers/partners.In all cohorts, father/partner data were less detailed and collected at fewer timepoints than mothers. Partners with a lower socio-economic position were less likely to participate. In simulations based on ALSPAC data, there was little evidence of selection bias in associations of maternal smoking with birthweight, and bias for father/partner smoking was relatively small. Missing partner data can induce selection bias. In our example analyses of the effect of parental smoking on offspring birthweight, the bias had a relatively small impact. In practice, the impact of selection bias will depend on both the analysis model and the selection mechanism.

Adaptive time step selection for spectral deferred correction

AbstractSpectral Deferred Correction (SDC) is an iterative method for the numerical solution of ordinary differential equations. It works by refining the numerical solution for an initial value problem by approximately solving differential equations for the error, and can be interpreted as a preconditioned fixed-point iteration for solving the fully implicit collocation problem. We adopt techniques from embedded Runge-Kutta Methods (RKM) to SDC in order to provide a mechanism for adaptive time step size selection and thus increase computational efficiency of SDC. We propose two SDC-specific estimates of the local error that are generic and do not rely on problem specific quantities. We demonstrate a gain in efficiency over standard SDC with fixed step size and compare efficiency favorably against state-of-the-art adaptive RKM.

Attrition in a large-scale habituation task administered at home.

Infant research often struggles with selective samples, especially when focusing on behavioural measures, such as those drawn from habituation tasks. However, selectivity may threaten the generalizability and interpretation of results, which is why the current study investigates attrition in a habituation task administered in a household setting in 7-month-old infants. We used a large-scale German dataset, focusing on the children's socioeconomic background, and investigated two aspects of attrition, namely, participation and task completion. The findings suggest significant effects of the children's socioeconomic background on attrition: Maternal education, parental occupation, household income and household language (German vs. other) were positively related to participation and task completion. The analyses indicate that multiple barriers may prevent parents from lower socioeconomic backgrounds from letting their children participate. The study concludes with a critical discussion of possible mechanisms of selectivity in behavioural measures as well as the household setting, in which the data were collected.

Fault diagnosis of driving gear in battery swapping system based on auditory bionics

Rack and pinion drives (RPD) are widely used in battery swapping system (BSS) for electric heavy trucks (EHT), and due to the continuous heavy-load and high-intensity operation, along with the electric erosion, the gears in the RPD are always damaged, which causes unexpected consequences such as downtime or safety incidents. The working conditions of the RPD in BSS include uncertain noises, fluctuant and low speed, which pose steep challenges to accurate fault diagnosis. Considering the auditory resistance of interference, the low-frequency sensitivity of auditory perception, and the auditory saliency mechanism, to leverage the advantages of auditory perceptual mechanism in addressing the above challenges, as the contribution in artificial intelligence, we propose an entire vibration signal processing scheme based on auditory bionics, including some mathematical models for auditory mechanisms. For the application in engineering, the proposed scheme is employed for fault diagnosis of RPD in BSS in unique working conditions. First, adaptive resampling is used to smooth the speed fluctuation, then, Gammatone filters are employed to transform vibration signals to cochleograms, after that, based on auditory stream segregation and selective attention mechanisms, effective frequency channels and salient features are extracted from the cochleograms, besides, to improve the diagnosis accuracy, binaural features are also extracted, finally, based on (sectional) sparse representation and fusion, fault diagnosis is achieved. The effectiveness of the fault diagnosis scheme is demonstrated using a BSS prototype system.

The effect of grandparental involvement on grandchildren's school grades: Heterogeneity by the extended family characteristics

As the early years are crucial for individuals' lifelong socioeconomic success, extensive research has examined the impact of non-maternal childcare on children's development. This study aims to enhance the understanding of the relationship between grandparental involvement (defined as grandparent childcare, frequency of contact, and financial support) and grandchildren's school grades, exploring a mechanism of positive selection: children from extended families with specific socioeconomic characteristics are more likely to spend time with grandparents and benefit the most from this involvement.We utilize data from the German Pairfam survey, which uniquely provides rich information on three family generations. By conducting a heterogeneous treatment effect analysis, we account for confounding factors associated with grandparental involvement and school performance that could bias our findings. For example, children from advantaged families could be more likely to spend time with grandparents and achieve better school grades. Additionally, this approach examines whether the effect of grandparental involvement systematically varies across children based on the extended family's characteristics. For example, children from advantaged families may benefit the most from spending time with grandparents who possess social, cultural, and cognitive resources conducive to their development.After accounting for confounding factors and heterogeneity, our analyses do not reveal a statistically significant effect of grandparental investment on children's school grades. The study concludes by discussing possible reasons for this result and highlighting the implications for the intergenerational transmission of inequality.

Cognitive Radar Waveform Selection for Low-Altitude Maneuvering-Target Tracking: A Robust Information-Aided Fusion Method

In this paper, we introduce an innovative interacting multiple-criterion selection (IMCS) idea to design the optimal radar waveform, aimingto reduce tracking error and enhance tracking performance. This method integrates the multiple-hypothesis tracking (MHT) and Rao–Blackwellized particle filter (RBPF) algorithms to tackle maneuvering First-Person-View (FPV) drones in a three-dimensional low-altitude cluttered environment. A complex hybrid model, combining linear and nonlinear states, is constructed to describe the high maneuverability of the target. Based on the interacting multiple model (IMM) framework, our proposed IMCS method employs several waveform selection criteria as models and determines the optimal criterion with the highest probability to select waveform parameters. The simulation results indicate that the MHT–RBPF algorithm, using the IMCS method for adaptive parameter selection, exhibits high accuracy and robustness in tracking a low-altitude maneuvering target, resulting in lower root mean square error (RMSE) compared with fixed- or single-waveform selection mechanisms.