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Schottky barrier height modulation in W-based contacts on AlGaN/GaN heterostructures
In this paper, tungsten-based Schottky diodes on AlGaN/GaN heterostructures were fabricated employing reactive sputtering, enabling a controlled modulation of the Schottky barrier height (ΦB). By varying the flux ratios between N2 and Ar during deposition, the electrical properties of both Schottky diodes and high electron mobility transistor (HEMT) devices were tuned. Forward current–voltage (I–V) measurements revealed an increase in the homogeneous Schottky barrier height from 1.26 to 1.39 eV with increasing N2:Ar gas-flow ratio, with an ideality factor of about 2.6. Capacitance–voltage (C–V) measurements, performed to monitor the two-dimensional electron gas concentration (ns) and the threshold voltage (VTH), show a reduction in the carrier concentration (from 4.7 × 1012 to 4.0 × 1012 cm−2) and an increase in the threshold voltage (from −1.36 to −1.17 V) with an increase in the barrier height. The same W-based contacts were then implemented as HEMT gate electrodes. The higher barrier height reduced the gate leakage current by 1 order of magnitude at the cost of 8% reduction in the drain saturation current. These results demonstrate that reactive sputtering is an effective approach to engineer gate contacts in AlGaN/GaN devices.
Neural circuits for stress-induced water drinking in relief of anxiety
Drinking water is a rapid, instinctive behavior of mammals following stress. The role of such stress-induced water drinking behavior and its neural mechanisms remain poorly known. Here, we identify two sequentially activated neural circuits that respectively mediate stress-induced drinking behavior and reward encoding, which collectively promote the extinction of anxiety-like behaviors in mice. Specifically, mild short-term stress induces anxiety-like behaviors and activates glutamatergic neurons in the medial prefrontal cortex (mPFC Glu ) projecting to glutamatergic neurons in the median preoptic nucleus (MnPO Glu ), consequently driving drinking behavior in mice. This drinking behavior subsequently activates GABAergic neurons in the MnPO (MnPO GABA ), which then inhibit GABAergic neurons in the ventral tegmental area (VTA GABA ), leading to disinhibition of reward-encoding dopaminergic VTA neurons (VTA DA ), ultimately mediating the extinction of stress-induced anxiety-like behaviors. The present study defines cortical–hypothalamic–midbrain connections underlying the anxiety-relieving effects of drinking behavior, providing a circuit basis for innate stress coping strategies.
Editorial Note: Optimization of cold chain logistics distribution path considering traffic condition and replenishment along the way
Semantic edge detection of fractures in geological outcrops using edge aware dilated convolutional networks
Abstract Natural fracture networks govern subsurface fluid flow, rock-mass stability, and strain accommodation in the brittle crust, yet their automated delineation from outcrop imagery remains challenging due to multi-scale size variability, low contrast between fracture boundaries and host-rock texture, and scene clutter from vegetation, shadows, and blast artifacts. Standard encoder-decoder networks apply fixed-size receptive fields that inadequately span the fractal-like scale range of natural discontinuity networks, while gradient-based classical detectors lack the semantic context required to suppress non-structural scene edges. We introduce GeoFractNet : a dilated U-Net coupling ConvNeXt encoder blocks with multi-scale dilated convolutions ( $$d \in \{1,\!2,\!4,\!8\}$$ ), gated edge-aware skip connections fusing Scharr and Gabor filter responses, and a Sobel Edge-Aware (SEA) Loss that directly penalizes gradient misalignment at fracture boundaries. The model output is a binary semantic edge map (per-pixel classification of fracture-boundary pixels versus background) rather than a vectorized fracture trace network; conversion to discrete trace objects requires subsequent post-processing. Trained on GeoCrack , an open-access fracture edge dataset of 12,158 annotated patches from 49 structurally and lithologically diverse outcrops across five countries, GeoFractNet achieves mIoU $$= 0.91$$ , Dice $$= 0.92$$ , and Boundary F $$_1 = 0.90$$ , outperforming all evaluated classical and deep-learning baselines. Compared to the best performing network in literature, YOLACT++, GeoFractNet reduces false positives by 38% and recovers 94% of long-range fracture edges missed by classical (Canny) edge detection on a curated hard-negative evaluation subset (420 patches containing vegetation, blast holes, and strong illumination gradients). Ablation studies confirm that the gated edge-aware skip connections and SEA Loss provide the largest incremental gains in boundary-sensitive metrics, with no architectural component degrading performance at any ablation step. Code, model weights, and the GeoCrack dataset are openly released, providing a reproducible benchmark for automated fracture characterization in structural geology, geomechanics, and reservoir analogue studies.
Ternary BiSeBr for polarization-sensitive infrared photodetector
Infrared optoelectronic detectors are essential in a variety of applications. This research focuses on investigating the phonon vibrations and optoelectronic properties of BiSeBr, as well as the development of a photodetector based on BiSeBr. The phonon vibration anisotropy was confirmed using angle-resolved polarized Raman spectroscopy. Remarkably, the BiSeBr-based photodetector demonstrates high responsivities of 3.70 and 0.445 A/W when exposed to 638 and 980 nm laser light, respectively. Additionally, the photodetector shows notable polarization sensitivity, with anisotropy ratios of 1.79 and 1.56 under the 980 and 638 nm laser illumination, respectively. These excellent optoelectronic properties make BiSeBr a highly competitive candidate for future polarization-sensitive infrared photodetector applications.
Expression of Concern for Raghuvanshi et al., <i>Mycobacterium tuberculosis</i> evades host immunity by recruiting mesenchymal stem cells
Do China’s low-carbon technology products and environmental goods trade mitigate energy-related carbon emissions in Africa?
Motivated by the rise in green trade between China and Africa and the growing environmental pressures associated with energy-related emissions in Africa, this study examines the impacts of low-carbon technology (LT) products and environmental goods (EGs) imported from China on the energy-related carbon emissions intensity (CEI) of 42 African countries for the period 2003–2023. To account for endogeneity issues and distributional differences, the study employs instrumental variables and panel quantile techniques. The findings reveal several important points. First, LT products and EGs imported from China significantly reduce the energy-related CEI of African countries. Second, the influence of imports of LT products and EGs on energy-related CEI varies by income level: they increase energy-related CEI in low-income African economies but decrease it in middle-income African nations. Third, the import of LT products and EGs has a stronger energy-related CEI-reducing effect in African countries with better institutional frameworks. These findings contribute to the current literature by offering a detailed and nuanced understanding of how trade in LT products and EGs can help reduce energy-related carbon emissions. Policy recommendations are presented based on these findings.
Integrating multi-proxy geochemistry and machine learning to decode cretaceous palaeoenvironments and tectonic evolution in the Nigerian Middle Benue Trough, West Africa
Mathematical expressions of germanium conduction band: Solved using anti-bond orbital model
The formation of α( = s, x, y, z)-like anti-bond orbitals (ABOs) is explained in detail: from atomic orbitals, sp3 tetragonal hybrid orbitals, sp3 tetragonal antibonding orbitals, and finally α-like ABOs. The mathematical formulas for ABO Hamiltonian matrix are derived in detail and illustrated with diagrams. The ABO model is used to explore the conduction band of germanium (Ge). Therefore, the mathematical expressions for its s-, x-, y-, and z-like bands and the ABO composition ratios of each band are obtained. Furthermore, the mathematical expressions for each α-like band derived in this paper are well-suited for computer simulations of advanced Ge-based nanoelectronic devices under conditions of higher electron energies.
Environmental phylogenetics supports a steady diversification of crown eukaryotes starting from the mid-Proterozoic
Molecular clock and preservation of early microfossil assemblages suggest that eukaryotes were present and already diverse more than ~1600 million years ago (Ma). Yet, the earliest identifiable eukaryotic crown group fossil only appeared around 1050 Ma, leaving a ~600 My gap in the Mesoproterozoic during which the evolution and diversification of early eukaryotes remain poorly understood. Here, we infer a timeline of eukaryote evolution using molecular clock and birth–death diversification models, including the large diversity of environmental sequences to take into account the uncultured majority of microbial life. Our analyses, based on a unique dataset of 75,975 nonredundant Operational Taxonomic Units and 77 well-supported fossil calibrations, indicate a steady diversification of crown group eukaryotes during the Proterozoic after the Last Eukaryotic Common Ancestor. We show that Archaeplastida was one of the earliest diversifying supergroups and the most diverse throughout the Proterozoic, suggesting that the first successful plastid endosymbiosis gave Archaeplastida a measurable evolutionary advantage. Discoba, Amoebozoa, and Rhizaria followed Archaeplastida in phylogenetic diversity throughout the Proterozoic, indicating that crown eukaryotes were already thriving in this Era. These results contrast with the common view that the geologically stable Proterozoic Era experienced little eukaryotic evolutionary innovation and indicate that all current supergroups were already established in the Mesoproterozoic.
The effect of COVID-19 and socioeconomic inequalities on emergency department accesses for psychiatric conditions
Background COVID-19 produced a detrimental effect on mental health that could not be appropriately addressed due to a reduced accessibility of healthcare services, as patients with socioeconomic vulnerabilities faced the largest barriers in accessing healthcare during the pandemic. This study aimed to investigate emergency department (ED) accesses with a psychiatric diagnosis before and after COVID-19, with a specific focus on the role of socioeconomic factors. Methods This is a multicentre longitudinal retrospective study that involves a population-based open cohort of residents and National Health Service (NHS)beneficiaries aged ≥ 10, enrolled in three large Italian areas from 1 January 2018 to 31 December 2021 (n = 5,159,363). The primary outcome of interest was the First Mental Health Emergency Access (FMHEA); sex, age, deprivation level, citizenship and groups of diagnosis were considered as covariates. Crude incidence rates were calculated comparing the two time periods (pre- and post-COVID-19) by each covariate. In the context of interrupted time series (ITS), incidence rate ratios (IRR) of FMHEAs with 95%CI were estimated via an ITS analysis using a step-change negative binomial model. Temporal trend of FMHEAs was investigated through an ITS analysis on their monthly numbers. Results During follow-up, 56,762 FMHEAs were observed. The multivariate analysis showed that before COVID-19, the rate of FMHEA was higher among the highly deprived (IRR: 1.28; 95%CI:1.22.-1.35 for highest vs lowest deprivation level), females, people aged ≤24 and ≥85 and immigrants. A significant interaction between the COVID-19 period and deprivation level was found: the probability of reduced FMHEA increased with the deprivation level. The greater impact of COVID-19 was found in the central age classes resulting in a reduction in the differences in FMHEA rates by age in the post-Covid-19 period. Conclusion COVID-19 reduced incidence rates of ED accesses, but increased socioeconomic inequalities in the use of ED for mental health disorders.
Vitamin D3 reverses intestinal epithelial cell injury in septic rats by alleviating toll-like receptor 4 mediated endoplasmic reticulum stress
Impact of Zn penetration on the tensile deformation behavior of Fe symmetric tilt grain boundaries
Zn-induced liquid metal embrittlement (LME) hinders the widespread application of galvanized advanced high-strength steels (AHSS) in the automotive industry. This phenomenon originates from the penetration of molten Zn along Fe grain boundaries (GBs) during thermomechanical processing, where Zn segregation weakens the interatomic bonding and degrades the mechanical integrity of the GBs. This work employed molecular-dynamics simulations to investigate the tensile deformation behavior of three representative Fe symmetric tilt grain boundaries (STGBs), namely, Σ5(31¯0)[001], Σ9(11¯4)[110], and Σ21(3¯12)[111], both with and without Zn penetration. The simulations reveal that Zn alloying intrinsically weakens Fe by reducing the stacking fault energies for both {112}⟨111⟩ twinning and 〈111〉 dislocation slip. Governed by segregation energy, Zn atoms preferentially occupy Fe GB sites with large local free space, and tensile loading further enhances this segregation as STGBs expand. In contrast, {112}⟨111⟩ twin boundaries mitigate Zn segregation at STGBs by serving as alternative segregation sites. While Zn segregation and twin nucleation degrade STGB structural order, dislocations enhance it, thereby modulating dislocation nucleation through GB structural unit transitions. Due to loading orientation effects, the {110}⟨111⟩ and {123}⟨111⟩ slip systems, characterized by low Peierls stresses, exhibit the highest Schmid factors in Σ5(31¯0)[001] while the lowest in Σ9(11¯4)[110], resulting in pronounced dislocation activity in Σ5(31¯0)[001] and hardly any dislocation nucleation in Σ9(11¯4)[110]. These findings underscore the potential of GB engineering guided by elemental segregation energetics, combined with texture design to accommodate multi-mode deformation, as an effective strategy to mitigate LME in AHSS.
Whistleblowers can contain the unethical externalities of human–AI delegation
Prior work using controlled principal-agent experiments suggests two risks from delegating tasks to AI systems: Human principals are more likely to request profit-maximizing misconduct from AI agents than from human agents, and AI agents are more likely to comply. Here we test whether third-party observers can contain the resulting harm. In an incentivized die-reporting paradigm, principals instructed either a human or an AI agent how strongly to prioritize profit over accuracy, creating potential financial harm to a charity. We first confirm, with human principals ( N = 600) and three large language models as AI agents, that delegation to AI produces larger negative externalities than delegation to humans. We then study observers who could pay a personal cost to flag a principal’s instruction, canceling the principal’s gain in favor of the charity, as a laboratory analogue of whistleblowing. In this observer study ( N = 300), the probability of flagging increased with how unethical the principal’s request was, but did not depend on whether the request was directed to a human or an AI agent. Because principals made more unethical requests under AI delegation, flagging was more frequent under AI delegation. When combined with agent behavior, this increase in flagging fully neutralized the negative externalities of AI delegation in our experimental setting. These findings support institutional protections for whistleblowers as one potential organizational safeguard against the harms of human–AI delegation.
The duration of nutrient limiting conditions can contribute to shaping subsequent diatom community composition: Insights from laboratory experiments
Climate-driven increases in global surface water temperatures are enhancing upper ocean stratification and likely resulting in more prolonged periods of nutrient limitation. Although nutrient limitation in diatoms and their growth responses to increasing temperatures have been studied extensively, much less is known about their growth response to nutrient injection after prolonged durations of nutrient limitation. This study examines the growth response of three bloom-forming diatom species: Pseudo-nitzschia pungens , P. australis , and Skeletonema costatum after short-term (~2 week) and prolonged (~4 week) periods of nutrient limitation at five temperatures (9, 12, 15, 20, and 25°C). Pseudo-nitzschia species showed shorter lag times and higher specific growth rates than S. costatum after prolonged nutrient stress. These findings demonstrate that certain diatom species can exhibit faster growth recovery after prolonged nutrient limitation and in warmer conditions compared to others, providing new insights on drivers that shape phytoplankton communities.
Correction: The intrinsically disordered protein SPE-56 is required for acrosomal-like exocytosis and fertility in Caenorhabditis elegans
Canonical structure of the LLG equation for exponential updates in micromagnetism
In this contribution, we propose an exponential update algorithm for magnetic moments appearing in the framework of micromagnetics and the Landau–Lifshitz–Gilbert (LLG) equation. This algorithm can be interpreted as the geometric integration on spheres that a priori satisfies the unit length constraint of the normalized magnetization vector. Even though the geometric structures for this are obvious and some works already use an exponential algorithm, to the best of the authors’ knowledge, there is no canonical structure of the LLG equation for the exponential update algorithm in micromagnetism. Tensor algebraic reformulations of the LLG equation allow the canonical representation of the evolution equation for magnetization, which serves as the basis for different integrators. Based on the specific structure of the exponential of skew symmetric matrices, an efficient update scheme is derived. The excellent performance of the proposed exponential update algorithm is demonstrated in representative examples.
Productivity gains and work conditions in coercive labor markets: Experimental evidence from the Bangladesh brick sector
Productivity growth in competitive labor markets can improve worker welfare over time through higher wages or better conditions, but it is unclear if productivity gains benefit workers in coercive labor markets (where force or threats shape employment). We examine this issue in the Bangladesh brick sector using a randomized trial that introduced a more efficient production method. Despite large productivity improvements, we find no reduction in (high) rates of labor trafficking or child labor. These findings suggest that in the short-term, productivity growth alone may be insufficient to improve work conditions in coercive settings.
Distinct gut and oral microbiome patterns associated with dyslexia in a family-based cohort: A preliminary exploratory study
Background Many neurodevelopmental disorders, including dyslexia and childhood apraxia of speech (CAS), have genetic predispositions that are understood to varying degrees. However, the microbiome in individuals with dyslexia and CAS remains underexplored. The goal of this exploratory study was to determine whether fecal and saliva microbiome diversity and composition are associated with dyslexia or CAS. Methods To this end, we examined the fecal and saliva microbiota of individuals with dyslexia, CAS, and their neurotypical family members using 16S rRNA gene amplicon sequencing in a family-based cohort composing of 7 individuals with dyslexia, 11 with CAS, and 10 neurotypical family members (n = 28). Participants with dyslexia and CAS were drawn from separate families, with neurotypical relatives serving as within-family controls. A total of 19 fecal and 29 saliva samples were collected, with paired fecal-saliva samples available for 19 individuals. Taxonomic classification was performed using four 16S rRNA reference databases, and microbial diversity, composition, and functional potential were analyzed. Results Individuals with dyslexia consistently showed distinct fecal microbiome alpha and beta diversity patterns at the species level compared to neurotypical family members and participants with CAS histories, irrespective of taxonomic database employed. Both fecal and saliva datasets identified key taxa associated with dyslexia, but not with CAS. Predicted functional profiling further identified dyslexia-associated pathways in the fecal microbiome, whereas no functional differences were detected in saliva. Conclusion Although these results suggest that individuals with dyslexia may harbor distinct fecal and saliva microbiomes, the findings are exploratory and should be considered as hypothesis-generating. Future studies leveraging larger, independent cohorts will be essential to validate these findings and to more rigorously examine the oral-gut-brain axis in language-based syndromes.