Search Results (656)

Hemibagrus guttatus is a large omnivorous fish of significant economic value, listed as a Class II protected species in the National Key Protected Wildlife List in 2021 in China. To provide a theoretical foundation for the artificial breeding of H. guttatus, this study employs high-throughput transcriptome sequencing of testes and ovaries to elucidate the molecular regulatory pathways involved in sex differentiation. Because H. guttatus exhibits no obvious sexual dimorphism even during the breeding season, the distinctive contribution of this study compared with previous gonadal-transcriptomic investigations in other Siluriformes lies not only in documenting sex-biased genes but also in providing a molecular foundation for developing non-lethal sex-identification methods for this morphologically indistinguishable species. A total of 303,192,896 raw reads were obtained, with an effective data rate of 98.4%, indicating high sequencing quality. Differential expression analysis identified 8694 genes, including 6369 upregulated in testes and 2325 upregulated in ovaries. Among these, 88 genes were functionally annotated as sex-related, with 62 testis-biased genes such as spata17, sox9, and dmrt1, and 26 ovary-biased genes including cyp19a, wnt8, and sox12. KEGG pathway enrichment analysis revealed that the TGF-β signaling pathway, insulin secretion, and steroid hormone biosynthesis may play crucial roles in gonadal development and differentiation in H. guttatus. The expression patterns of key genes such as hsd11b1, amh, and insl3 were validated by quantitative real-time PCR, showing consistency with the transcriptome results. These findings lay a molecular foundation for understanding the regulatory mechanisms of sex differentiation in H. guttatus, and provide candidate genes for further investigation into the genetic basis of gonadal development, which is essential for improving artificial reproduction and selective breeding practices. Full article

As corporate sustainability reporting evolves into a pivotal resource for investors, regulators, and stakeholders, the imperative to evaluate and elevate ESG disclosure quality intensifies amid persistent challenges like opacity, inconsistency, and greenwashing. This review synthesizes interdisciplinary insights from accounting, finance, and computational linguistics on artificial intelligence (AI), particularly natural language processing (NLP) and machine learning (ML), as a transformative force in this domain. We delineate ESG disclosure quality across four operational dimensions: readability, comparability, informativeness, and credibility. By integrating cutting-edge methodological innovations (e.g., transformer-based models for semantic analysis), empirical linkages between AI-extracted signals and market/governance outcomes, and normative discussions on AI’s auditing potential, we demonstrate AI’s efficacy in scaling measurement, harmonizing heterogeneous narratives, and prototyping greenwashing detection. Nonetheless, causal evidence linking managerial AI adoption to stakeholder-perceived enhancements remains limited, compounded by biases in multilingual applications and interpretability deficits. We propose a forward-looking agenda, prioritizing cross-lingual benchmarking, curated greenwashing datasets, AI-assurance pilots, and interpretability standards, to harness AI for substantive, equitable improvements in ESG reporting and accountability. Full article

Phase consistency is a critical prerequisite for achieving high-precision time-series InSAR deformation retrieval. However, conventional SBAS-InSAR methods provide only limited consideration of phase consistency during the inversion process. Within the SBAS-InSAR workflow, two principal categories of error sources are primarily responsible for phase inconsistency, manifested as non-zero closure phase (NCP): (1) fading biases introduced during multilooking and filtering prior to phase unwrapping; and (2) unwrapping errors caused by large deformation gradients, low coherence, or inappropriate selection of unwrapping algorithms. To address these issues, this study introduces an improved SBAS-InSAR processing workflow, termed NCP-SBAS, designed to improve the accuracy of deformation field estimation and thereby enhance its applicability to geological hazard monitoring. The key idea of the method is to enforce phase consistency as a constraint, jointly accounting for the spatiotemporal characteristics of fading biases and the valid deformation signals, thereby enabling effective correction of NCP. To evaluate the effectiveness of NCP-SBAS, this study conducted a detailed analysis of deformation differences in Hualong County, Qinghai Province, before and after NCP correction, highlighting the significant advantages of the proposed approach. The results indicate that the influence of fading biases on deformation estimates depends on both the magnitude and direction of deformation, while unwrapping errors primarily lead to an underestimation of deformation. In addition, the study provides an in-depth discussion of how fading biases and unwrapping errors affect landslide monitoring and identification. Full article

Background: Melanin protects skin and hair from the effects of ultraviolet (UV) radiation damage, which contributes to all forms of skin cancer, including melanoma. Human melanocytes produce two main types of melanin: eumelanin provides effective photoprotection, and pheomelanin offers less protection against UV-induced skin damage. The agouti signaling protein (ASIP) antagonizes the melanocortin-1 receptor (MC1R), hinders melanocyte signaling, and shifts pigmentation toward pheomelanin, promoting UV vulnerability. In this study, we aim to discover compounds that inhibit ASIP–MC1R interaction and effectively preserve eumelanogenic signaling. Methods: The ASIP–MC1R interface-based pharmacophore model from ASIP is implicated in MC1R receptor protein engagement. We performed virtual screening with a validated pharmacophore model for ~4000 compounds curated from ZINCPharmer and applied drug-likeness filters, viz. ADMET and toxicity profiling tests. Further, the screened candidates were targeted for docking to the ASIP C-terminal domain corresponding to the MC1R-binding moiety. Top compounds underwent a 100-nanosecond (ns) run of molecular dynamics (MD) simulations to assess complex stability and persistence of key contacted residues. Results: Sequential triage, including pharmacophore, ADME–toxicity (ADMET), and docking/ΔG, yielded a focused group of candidates against ASIP antagonists with a favorable fit value. The MD run for 100 ns supported pose stability at the targeted pocket. Based on these predictions and analyses, compound ZINC14539068 was screened as a new potent inhibitor of ASIP to preserve α-MSH-mediated signaling of MC1R. Conclusions: Our in silico pipeline identifies ZINC14539068 as a potent inhibitor of ASIP at its C-terminal interface. This compound is predicted to disrupt ASIP–MC1R binding, thereby maintaining eumelanin-biased signaling. These findings motivate experimental validation in melanocytic models and in vivo studies to confirm pathway modulation and anti-melanoma potential. Full article

Background: The anterior cruciate ligament (ACL) and medial collateral ligament (MCL) display strikingly different healing behaviors, despite their similar structural roles within the knee. The epiligament (EL)—a vascular and cellular envelope surrounding each ligament—has emerged as a critical determinant of repair capacity. The aim of this study was to perform a region-specific, comparative analysis of EL molecular profiles in the ACL and MCL to elucidate the mechanisms underlying their contrasting reparative outcomes. Methods: Human ACL and MCL specimens were obtained from 12 fresh knee joints. Immunohistochemical labeling for CD34, α-smooth muscle actin (α-SMA), and vascular endothelial growth factor (VEGF) was performed across proximal, mid-substance, and distal EL regions. Quantitative image analysis using IHC Profiler for ImageJ generated semiquantitative (negative, low-positive, positive) distributions, and inter-ligament comparisons were quantified using t-tests (p  <  0.05). Results: Distinct, region-specific EL signatures were identified. The ACL EL exhibited strong proximal α-SMA expression (0% neg/66.8% low+/33.2%+) and notable distal CD34 positivity (0% neg/83.3% low+/16.7%+), while VEGF expression was confined to the mid-substance (≈55% low+/26%+). In contrast, the MCL EL was largely negative for CD34 and VEGF across all regions, showing a homogeneous but functionally oriented α-SMA profile: proximally negative, sparse mid positivity, and high distal low-positive staining (93.4% low+). Differences in proximal and distal CD34 and α-SMA expression between the ACL and MCL were highly significant (p  <  0.0001–0.001), confirming a mechanistic divergence in EL organization. Conclusions: The ACL EL is regionally heterogeneous, vascularly biased, and enriched in contractile α-SMA+ cells, suggesting localized but poorly coordinated reparative potential. In contrast, the MCL EL is structurally uniform, with distributed α-SMA activity supporting stable wound contraction and tissue continuity, despite limited angiogenic signaling. These findings indicate that the ACL’s failure to heal is not attributable to the absence of progenitor or angiogenic factors, but rather to its fragmented spatial organization and dominant contractile phenotype. Therapeutically, preserving and modulating the EL, particularly its CD34+ and α-SMA+ compartments, could be key to enhancing intrinsic ACL repair and improving outcomes in ligament reconstruction and regeneration. Full article

Few-Shot Text Classification (FSTC) aims to classify text accurately into predefined categories using minimal training samples. Recently, prompt-tuning-based methods have achieved promising results by constructing verbalizers that map input data to the label space, thereby maximizing the utilization of pre-trained model features. However, existing verbalizer construction methods often rely on external knowledge bases, which require complex noise filtering and manual refinement, making the process time-consuming and labor-intensive, while approaches based on pre-trained language models (PLMs) frequently overlook inherent prediction biases. Furthermore, conventional data augmentation methods focus on modifying input instances while overlooking the integral role of label semantics in prompt tuning. This disconnection often leads to a trade-off where increased sample diversity comes at the cost of semantic consistency, resulting in marginal improvements. To address these limitations, this paper first proposes a novel Bayesian Mutual Information-based method that optimizes label mapping to retain general PLM features while reducing reliance on irrelevant or unfair attributes to mitigate latent biases. Based on this method, we propose two synergistic generators that synthesize semantically consistent samples by integrating label word information from the verbalizer to effectively enrich data distribution and alleviate sparsity. To guarantee the reliability of the augmented set, we propose a Low-Entropy Selector that serves as a semantic filter, retaining only high-confidence samples to safeguard the model against ambiguous supervision signals. Furthermore, we propose a Difficulty-Aware Adversarial Training framework that fosters generalized feature learning, enabling the model to withstand subtle input perturbations. Extensive experiments demonstrate that our approach outperforms state-of-the-art methods on most few-shot and full-data splits, with F1 score improvements of up to +2.8% on the standard AG’s News benchmark and +1.0% on the challenging DBPedia benchmark. Full article

This paper proposes an optimized digital predistortion (DPD) framework. Firstly, a peak-detection-based loop-delay estimation is developed by leveraging the unique peak distribution of Orthogonal Frequency Division Multiplexing (OFDM) signals. It reduces the required number of samples to as small as two without compromising estimation accuracy. Then, a Biased Memory Polynomial (BMP) model is proposed for power amplifier modeling. It addresses low-power inaccuracies caused by circuit imperfections (e.g., DC offsets) by adding a bias term to conventional memory polynomials, improving linearization accuracy in low-power regime. Last, to improve the accuracy of coefficient derivation, Memory-Clustering Biased Memory Polynomial (MBMP) is proposed by grouping signals into clusters based on memory-attenuated input vectors and processing them with dedicated sub-models. It improves linearization accuracy in high-power regime. Experimental results demonstrate that the MBMP model reduces normalized mean square error (NMSE) by 16.12 dB, and reduces adjacent channel power ratio (ACPR) by about 12 dBm compared to conventional MP. Full article

Pollination syndromes reflect the convergence of floral traits among plants sharing the same pollinator guild. However, bee-pollinated orchids exhibit striking variation in color and size. This diversity reflects the multiple reward strategies that evolved within the family, each interacting differently with bee sensory biases. Here, we tested whether the complex floral visual displays of orchids differ in signal identity and intensity among reward systems. We also considered intrafloral modularity, measured as the color differentiation among flower parts, and color–size integration. For this, we measured and modeled floral morphometric and reflectance data from sepals, petals, lip tips, and lip bases under bee vision from 95 tropical Epidendroid species to compare chromatic and achromatic contrasts, spectral purity, and mean reflectance across wavebands, plus flower and display size, among reward systems. Reward types included 19 food-deceptive, 8 nectar-offering, 10 oil-offering, 11 fragrance-offering, and 47 orchid species of unknown reward strategy. Principal component analyses on 34 color and 9 size variables summarized major gradients of visual trait variation: first component (19.1%) represented overall green-red reflectance and achromatic contrasts, whereas the second (16.5%) captured chromatic contrast–size covariation. Reward systems differed mostly in signal identity rather than signal intensity. Flower chromatic contrasts presented strong integration with flower size, while achromatic contrasts were negatively associated with display size. While deceptive and nectar-offering orchids tend toward larger solitary flowers with bluer and spectrally purer displays, oil- and fragrance-offering orchids tend toward smaller, brownish, or yellow to green flowers, with larger inflorescences. Rewardless orchids presented more achromatically conspicuous signals than rewarding orchids, but smaller displays. Orchid species clustered by reward both in PCA spaces and in bee hexagon color space. Deceptive orchids were typically associated with UV + White colors, oil orchids with UV + Yellow lip tips, and fragrance orchids with UV-Black lip bases and UV-Green lip tips. Together, these results indicate that orchid reward systems promote qualitative rather than quantitative differentiation in visual signals, integrating display color and size. These long-evolved distinct signals potentially enable foraging bees to discriminate among resource types within the community floral market. Our results demonstrate that color and flower display size are important predictors of reward strategy, likely used by foraging bees for phenotype-reward associations, thus mediating the evolution of floral signals. Full article

In recent years, e-bikes have rapidly gained popularity in China. However, riders frequently engage in aberrant behaviors, posing significant traffic safety concerns. Field observation combined with traffic conflict techniques offer an effective approach for identifying risky riding behaviors that significantly affect traffic safety. This study aims to address two major limitations in existing research that can lead to estimation biases: the unsystematic and incomplete inclusion of potential risky riding behaviors, and the insufficient consideration of unobserved heterogeneity in conflict data. Data on 437 e-bike–motor vehicle conflicts were collected at four signalized intersections in Hefei, covering 21 variables including illegal, negligent, and error-prone riding behaviors, as well as sociodemographic factors. Appropriate conflict risk indicators were selected for straight-line and angle conflicts, respectively. A random parameters binary logit model with heterogeneity in means and variances (RPBL-HMV) was developed and compared against binary logistic and mixed logit models. The results indicate that the RPBL-HMV model provides a significantly better goodness-of-fit than the other two models. Six factors with fixed parameters are positively associated with high-risk conflicts, while two factors exhibit random parameters—one of which decreases in mean when riders fail to slow down before turning. The identified risky behaviors and the corresponding targeted countermeasures offer practical insights for regulating unsafe e-bike riding and improving intersection safety. Full article

This study examined the correlation between the number of delay flip-flops (D-FFs) connected after each ring oscillator (RO) and the bit distribution of random number sequences in an RO-based random number generator (RNG). In our previous research, unstable input signals to the XOR gate contributed to differences in bit distribution. Based on these results, we simulated how combining signals with biased distributions through XOR gates affects the overall bit distribution. Beyond this, we also conducted simulations where the inputs to the XOR gate included not just {0, 1} signals, but also three-state signals incorporating metastable states. We then proposed using multi-D-FFs as synchronization circuits for RO signals and performed analyses on RO-based RNG implementations by estimating metastable output conditions and conducting NIST Special Publication 800-22 tests regarding bit distributions. These results confirm that inserting two or more D-FFs after RO signals improves the bit distribution of RO-based RNG implementations. Full article

Background/Objectives: Early larval development is critical for caste and sex differentiation in honeybees. This study investigates molecular divergence in 4-day-old Apis mellifera larvae and introduces a customized deep learning model for hub-gene discovery. Methods: Genome-guided RNA-seq, DEGs, WGCNA, and splicing analyses were integrated. A hybrid convolution–attention model, ACmix-Swin, combined with WGAN-GP augmentation, was developed to classify larvae and prioritize caste-biased genes. Selected genes were validated by qPCR. Results: Significant caste- and sex-specific divergence was detected in cuticle formation, hormone metabolism, and reproductive signaling. ACmix-Swin achieved the highest accuracy among baseline models and consistently identified key regulators, including Vg, LOC725841, LOC412768, and LOC100576841. qPCR confirmed RNA-seq trends. Conclusions: Caste- and sex-specific transcriptional programs are established early in larval development. The ACmix-Swin framework provides an effective strategy for high-dimensional transcriptome interpretation and robust hub-gene identification. Full article

Visible Light Communication (VLC) systems commonly employ optical orthogonal frequency division multiplexing (O-OFDM) to achieve high data rates, benefiting from its robustness against multipath effects and intersymbol interference (ISI). However, a key limitation of asymmetrically clipped direct current biased optical–OFDM (ACO-OFDM) systems lies in their inherently high peak-to-average power ratio (PAPR), which significantly affects signal quality and system performance. This paper proposes a joint chaotic encryption and modified μ-non-linear logarithmic companding (μ-MLCT) scheme for ACO-OFDM–based VLC systems to simultaneously enhance security and reduce PAPR. First, image data is encrypted at the upper layer using a hybrid chaotic system (HCS) combined with Arnold’s cat map (ACM), mapped to quadrature amplitude modulation (QAM) symbols and further encrypted through chaos-based symbol scrambling to strengthen security. A μ-MLCT transformation is then applied to mitigate PAPR and enhance both peak signal-to-noise ratio (PSNR) and bit-error-ratio (BER) performance. A mathematical model of the proposed secured ACO-OFDM system is developed, and the corresponding BER expression is derived and validated through simulation. Simulation results and security analyses confirm the effectiveness of the proposed solution, showing gains of approximately 13 dB improvement in PSNR, 2 dB in BER performance, and a PAPR reduction of about 9.2 dB. The secured μ-MLCT-ACO-OFDM not only enhances transmission security but also effectively reduces PAPR without degrading PSNR and BER. As a result, it offers a robust and efficient solution for secure image transmission with low PAPR, making it well-suitable for emerging wireless networks such as cognitive and 5G/6G systems. Full article

Social isolation and loneliness represent critical psychosocial stressors associated with profound hormonal dysregulation and adverse behavioral outcomes. This review synthesizes current evidence on neuroendocrine mechanisms linking perceived and objective social disconnection to health consequences, emphasizing hypothalamic–pituitary–adrenal axis dysfunction, altered glucocorticoid signaling, and inflammatory pathways. Loneliness activates conserved transcriptional responses with upregulated proinflammatory gene expression and downregulated antiviral responses, mediated through sustained cortisol elevation and glucocorticoid resistance. Neural circuit alterations in reward processing, particularly the ventral tegmental area-nucleus accumbens pathway, contribute to anhedonia, social withdrawal, and cognitive decline. Sex differences in neuroendocrine responses reveal distinct hormonal profiles and circuit-specific adaptations. Emerging interventions targeting oxytocin and arginine vasopressin systems, alongside behavioral approaches addressing loneliness-induced cognitive biases, show promise. Critical research gaps include a mechanistic understanding of epigenetic modifications, sex-specific therapeutic responses, and translational applications across diverse populations. Understanding the endocrine–behavior interface in social disconnection offers opportunities for targeted interventions addressing this growing public health challenge. Full article

A central obstacle to the practical deployment of Reinforcement Learning (RL) is the prevalence of sparse rewards, which often necessitates task-specific dense signals crafted through costly trial-and-error. Automated reward decomposition and return–redistribution methods can reduce this burden, but they are largely semantically agnostic and may fail to capture the multifaceted nature of task performance, leading to reward hacking or stalled exploration. Recent work uses Large Language Models (LLMs) to generate reward functions from high-level task descriptions, but these specifications are typically static and may encode biases or inaccuracies from the pretrained model, resulting in a priori reward misspecification. To address this, we propose the Metacognitive Introspective Reward Architecture (MIRA), a closed-loop architecture that treats LLM-generated reward code as a dynamic object refined through empirical feedback. An LLM first produces a set of computable reward factors. A dual-loop design then decouples policy learning from reward revision: an inner loop jointly trains the agent’s policy and a reward-synthesis network to align with sparse ground-truth outcomes, while an outer loop monitors learning dynamics via diagnostic metrics and, upon detecting pathological signatures, invokes the LLM to perform targeted structural edits. Experiments on MuJoCo benchmarks show that MIRA corrects flawed initial specifications and improves asymptotic performance and sample efficiency over strong reward-design baselines. Full article

In this study, we present the design and analysis of a stacked inverter-based millimeter-wave (mmWave) power amplifier (PA) in 90 nm CMOS-targeting wideband Q-band operation. The PA employs two PMOS and two NMOS devices in a fully stacked inverter topology to distribute device stress, remove the need for an RF choke, and increase effective transconductance while preserving compact layout. A resistor ladder biases the stack near $V_{\mathrm{DD}}/4$ per device, and capacitive division steers intermediate-node swings to enable class-E-like voltage shaping at the output. Closed-form models are developed for gain, output power, drain efficiency/PAE, and linearity, alongside a small-signal stacked-ladder formulation that quantifies stress sharing and the impedance presented to the matching networks; L/T network synthesis relations are provided to co-optimize bandwidth and insertion loss. Post-layout simulation in 90 nm CMOS shows $|S_{21}|$ = 10 dB at 39.84 GHz with 3 dB bandwidth from 36.8 to 42.4 GHz, peak PAE of 18.38% near 41 GHz, and saturated output power $P_{\mathrm{sat}}=8.67$ dBm at $V_{\mathrm{DD}}=4$ V, with $S_{11}<-15$ dB and reverse isolation $\approx -16$ dB. The layout occupies $1.6\times 1.6$ mm² and draws 31.08 mW. Robustness is validated via a 200-run Monte Carlo showing tight clustering of $P_{\mathrm{sat}}$ and PAE, sensitivity sweeps identifying sizing/tolerance trade-offs ($\pm 10\%$ devices/passives), and EM co-simulation of on-chip passives indicating only minor loss/shift relative to schematic while preserving the target bandwidth and efficiency. The results demonstrate a balanced gain–efficiency–power trade-off with layout-aware resilience, positioning stacked-inverter CMOS PAs as a power- and area-efficient solution for mmWave front-ends. Full article