University of California, Davis

Bone hemorrhage, infection, and large bone defects following surgical treatment of traumatic bone injury have raised potential concerns, underscoring the urgent need to develop multifunctional therapeutic platforms that can effectively address traumatic bone regeneration. Advancements in three-dimensional (3D) printing technology have propelled the development of several engineering disciplines, such as tissue engineering. Nevertheless, 3D-printed frameworks with conventional materials often lack multifunctional capabilities to promote specific activities for diverse regeneration purposes. In this study, we developed a highly oxidized two-dimensional (2D) graphitic carbon nitride (Ox-gCN) as a nano-photocatalyst to reinforce alginate/gelatin (ALG)-based hydrogel scaffolds (ALG/CN) to achieve an anti-inflammatory and osteo-immunomodulatory niche with superior hemostatic ability for traumatic bone injury repair. Sulfuric acid oxidation enhances the oxygen-containing functional groups of the g-CN surface and promotes cell adhesion and differentiation of human bone marrow-derived mesenchymal stem cells (hBMSCs) in vitro. Moreover, the excellent visible light-activated photocatalytic characteristics of the ALG/CN scaffold were used in antibacterial studies. In addition, the ALG/CN bio/nanocomposite scaffold facilitates M2 polarization of macrophages than did pristine ALG scaffolds. Furthermore, ALG/CN scaffold induced hBMSCs differentiation by upregulating ERK and MAPKs phosphorylation during osteo-immunomodulation. In a rat calvaria defect model, the fabricated ALG/CN scaffolds induced new bone formation through collagen deposition and activation of osteocalcin proteins without inflammation in vivo. These results highlight the potential of 3D-printed functionalized 2D carbon nitrides in regulating the bone immune microenvironment, which may be beneficial for developing advanced tissue constructs, especially for traumatic bone regeneration in clinical settings.

The flow and exchange of information, financial strategic or otherwise, constitute one of the most hotly contested and controversial arenas of global politics right now. The ‘Asian response’, for lack of a better term, to this phenomenon of invisible yet crucial dominance, has largely been through attempts at breaking convention, initiating ‘South-South’ cooperative schemes within or beyond the region, creating institutional alternatives and at worst, making disruptive statements like hacking western financial and tech nerve centers. This chapter attempts to theorize the ‘Asian claim’ to global highways of information, the so-called plumbing of the world’s economy and political exchange. The theoretical offering to the question of ‘how to imagine an Asian claim?’ is made in close reference to the question of ‘who/which groups make this claim in the Asian context?’; the necessity of this implied association is underlined by close observation of the Western case, where industry handed over keys to the doors of the Panopticon. Related to this is our discussion of the possible avenues to risk proof the ‘Asian claim’ from following the same path, and therefore create more resilient, democratic, decentralized and transparent mechanisms that can neither be monopolized by emerging Asian major powers, or by MNC/TNCs that could benefit from such chokeholds.

Introduction Acne pathogenesis is multifactorial, involving systemic factors including gut dysbiosis, hormones, and chronic inflammation. Probiotics, myoinositol, and plant-derived molecules may modulate acne by targeting these factors. The objective is to compare a synbiotic containing herbs against a myoinositol-based herbal supplement on how they influence acne, the gut microbiome, short chain fatty acids (SCFAs), and hormonal profiles. Methods This was an 8-week, randomized study involving 36 male and female patients aged 12 to 45 years with non-cystic acne. Subjects received either a synbiotic or a myoinositol-based herbal supplement (MBHS). Acne lesions were counted, stool samples were collected for gut microbiome and SCFA analyses, and hormone collections were performed at baseline, 4, and 8 weeks. Results Several gut bacteria increased by at least threefold at both week 4 and 8 in the synbiotic (Erysipelatoclostridium merdavium, Blautia argi, Faecalibacterium prausnitzii, Prevotella copri, Streptococcus sp001556435, Blautia sp900541955) and MBHS group (Megamonas funiformis, Ligilactobacillus ruminis, Prevotella ssp015074785, Prevotella copri, Gca-900199835 sp900176495). Acne lesion counts decreased significantly in both groups at week 4 (p < 0.0001) and week 8 (synbiotic, p < 0.0001; MBHS, p < 0.0001). There were significant and trending increases in stool and plasma SCFAs in both cohorts at week 4 and 8. After 8 weeks of MBHS, 17-OHP and androstenedione significantly decreased from 27.3 to 11.3 pg/ml (p = 0.001) and 94.9 to 68.0 pg/ml (p = 0.04), respectively. Conclusion Both the synbiotic and MBHS improved gut health, augmented SCFAs, and reduced lesion counts in those with non-cystic acne. The MBHS may act by reducing hormone levels of 17-OHP and androstenedione.

Rational design of multifunctional nanoplatforms capable of combining therapeutic effects with real-time monitoring of drug distribution and tumor status is emerging as a promising approach in cancer nanomedicine. Here, we introduce pyropheophorbide a–bisaminoquinoline conjugate lipid nanoparticles (PPBC LNPs) as a bimodal system for image-guided phototherapy in bladder cancer treatment. PPBC LNPs not only demonstrate both powerful photodynamic and photothermal effects upon light activation, but also exhibit potent autophagy blockage, effectively inducing bladder cancer cell death. Furthermore, PPBC LNPs possess remarkable photoacoustic (PA) and fluorescence (FL) imaging capabilities, enabling imaging with high-resolution, deep tissue penetration and high sensitivity for tracking drug biodistribution and phototherapy efficacy. Specifically, PA imaging confirms the efficient accumulation of PPBC LNPs within tumor and predicts therapeutic outcomes of photodynamic therapy, while FL imaging confirms their prolonged retention at the tumor site for up to 6 days. PPBC LNPs significantly suppress bladder tumor growth, with several tumors completely ablated following just two doses of the nanoparticles and laser treatment. Additionally, PPBC LNPs were formulated with lipid-based excipients and assembled using microfluidic technology to enhance biocompatibility, stability, and scalability, showing potential for clinical translation. This versatile nanoparticle represents a promising candidate for further development in bladder cancer therapy.

Photovoltaic energy development has effectively mitigated energy crises and accelerated global carbon neutrality efforts. However, the increasing photovoltaic installed capacity poses significant challenges to grid scheduling systems. Photovoltaic power forecasting techniques provide crucial basis to formulate scheduling plans, thereby alleviating scheduling pressures. Yet, existing photovoltaic power prediction algorithms have shown unstable performance in complex weather conditions. Therefore, this paper proposes a short-term photovoltaic power prediction method based on the nearest clear sky day decomposition and temporal convolutional network (TCN). The method identifies the photovoltaic output on the nearest clear sky day to the target day and decomposes the photovoltaic power waveform based on the clear sky component removal. TCN combines the feature extraction capabilities of convolutional neural networks (CNNs) with the temporal information mining abilities of sequence-based neural networks like recurrent neural networks (RNNs) and long short-term memory networks (LSTMs), making it suitable for capturing the relationships between various meteorological features and photovoltaic output. Using the Alice Springs dataset in Australia as a case study, the algorithm conducts experiments under different seasons and weather conditions, comparing its performance against other models using metrics such as mean absolute error (MAE), root mean square error (RMSE), and coefficient of determination (R2).

Autonomous driving represents a significant advancement in the transportation industry, enhancing vehicle intelligence, optimizing traffic management, and improving user experiences. Central to these innovations is deep learning, which enables systems to handle complex data and make informed decisions. Our survey explores critical applications of deep learning in autonomous driving, such as perception and detection, localization and mapping, and decision-making and control. We investigate specialized deep learning techniques, including convolutional neural networks, recurrent neural networks, self-attention transformers, and their variants, among others. These methods are applied within various learning paradigms—supervised, unsupervised and reinforcement learning—to suit the specific needs of autonomous driving. Our analysis evaluates the effectiveness, benefits, and limitations of these technologies, focusing on their integration with other intelligent algorithms to enhance system performance. Furthermore, we examine the architectures of autonomous systems, analyzing how knowledge and information are organized from modular, pipeline-based frameworks to comprehensive end-to-end models. By presenting an exhaustive overview of the progressing domain of autonomous driving and bridging various research areas, our survey aims to synthesize diverse research threads into a unified narrative. This effort not only aims to enhance our understanding but also pushes the boundaries of what is achievable in this interdisciplinary field.

Landslides pose a significant threat to infrastructure, ecosystems, and human safety, necessitating accurate and efficient susceptibility assessment methods. Traditional models often struggle to capture the complex spatial dependencies and interactions between geological and environmental factors. To address this gap, this study employs a deep learning approach, utilizing a convolutional neural network (CNN) for high-precision landslide susceptibility mapping in the Bakhtegan watershed, southwestern Iran. A comprehensive landslide inventory was compiled using 235 documented landslide locations, validated through remote sensing and field surveys. An equal number of non-landslide locations were systematically selected to ensure balanced model training. Fifteen key conditioning factors—including topographical, geological, hydrological, and climatological variables—were incorporated into the model. While traditional statistical methods often fail to extract spatial hierarchies, the CNN model effectively processes multi-dimensional geospatial data, learning intricate patterns influencing slope instability. The CNN model outperformed other classification approaches, achieving an accuracy of 95.76% and a precision of 95.11%. Additionally, error metrics confirmed its reliability, with a mean absolute error (MAE) of 0.11864, mean squared error (MSE) of 0.18796, and root mean squared error (RMSE) of 0.18632. The results indicate that the northern and northeastern regions of the Bakhtegan watershed are highly susceptible to landslides, highlighting areas where proactive mitigation strategies are crucial. This study demonstrates that deep learning, particularly CNNs, offers a powerful and scalable solution for landslide susceptibility assessment. The findings provide valuable insights for urban planners, engineers, and policymakers to implement effective risk reduction strategies and enhance resilience in landslide-prone regions.

We aimed to understand the relation between child maltreatment and SPED service evaluation referrals in childcare settings. Extant data (n = 1,354) from the Longitudinal Studies of Child Abuse and Neglect (LONGSCAN) was used to address the study aims. Results indicated that the odds of teacher-initiated service evaluation referrals at age 6 were 2.5 times higher for boys than girls. Results for model 2 indicated that parental perception of the need for services was 2.6 times higher for children who were screened for developmental delays and 0.08 times lower for children who attended center-based childcare or preschool. Early identification and intervention are critical to improving the long-term outcomes for young children with disabilities. More research is needed to understand how to accurately make referrals for evaluation of SPED service needs and how to best support early childhood educators in accurately and effectively referring students for evaluation of SPED service needs.

Merriam’s kangaroo rat (Dipodomys merriami) is a member of a unique family of primarily desert-adapted North American rodents (Heteromyidae). Of the 20 species in the genus, D. merriami is one of the most wide-ranging and ecologically flexible, inhabiting desert scrub, grassland, sagebrush steppe, and juniper-piñon woodland in the southwestern deserts of the United States and Mexico. We present a de novo reference genome for D. merriami generated from PacBio HiFi long-read and Omni-C chromatin proximity sequencing as a part of the California Conservation Genomics Project. The primary pseudo-haplotype assembly comprises 3,110 scaffolds, with a contig N50 of 8.6 Mb, scaffold N50 of 49.1 Mb, and a total length of 3.57 Gb. Further, a BUSCO completeness score of 97.8% suggests that the assembly is highly complete. This reference genome will serve as a resource for future studies of Dipodomys conservation genomics, desert adaptation, and phylogeography.

Microbially induced calcite precipitation (MICP) is an emerging ground improvement technique that uses microbes to induce cementation between soil particles. To date, the majority of research has focused on exploring MICP with silica-rich sands; however, the present study investigates the process and efficacy of MICP in a carbonate-rich natural soil, and a comparison is made with benchmark silica-rich sands. MICP column experiments were performed with a range of treatment formulations to optimize and understand the MICP process in carbonate-rich soil. Performance was quantified using chemical (pH, urea, and ammonium concentrations) and physical measurements (TGA and LOI tests). Micro-scale characterization of the cemented soils was performed with XRD, SEM, and EDS, while shear-wave velocity (Vs) and unconfined compressive strength tests were performed to evaluate the effect of precipitated calcite on macroscopic engineering properties. Natural carbonates were found to have a significant impact on the MICP process, resulting in an increase in MICP efficiency of 23% and increases in precipitated calcite contents by as much as 82% when compared to benchmark silica-rich soils receiving similar treatments. These results suggest that the presence of natural carbonate minerals within soils may lower the energy barrier and act as preferential sites for calcite precipitation during the MICP process. Furthermore, SEM images highlighted the association of bacterial cells with precipitated calcite crystals, differences in calcite morphologies and more widespread cementation bonds in carbonate-rich soil when compared to silica sand. Generated cementation also resulted in a linear increase in Vs with increases in precipitated calcite contents for MICP treated carbonate-rich soil, consistent with past results for silica sands. Lastly, differences in yeast extract concentrations applied in treatment solutions were also found to significantly impact the development of ureolytic microbial capacity and the efficiency of the MICP process in the considered soils.

Scientific research is often characterized by schools of thought. We investigate whether these divisions are associated with differences in researchers’ cognitive traits such as tolerance for ambiguity. These differences may guide researchers to prefer different problems, tackle identical problems in different ways, and even reach different conclusions when studying the same problems in the same way. We surveyed 7,973 researchers in psychological sciences and investigated links between what they research, their stances on open questions in the field, and their cognitive traits and dispositions. Our results show that researchers’ stances on scientific questions are associated with what they research and with their cognitive traits. Further, these associations are detectable in their publication histories. These findings support the idea that divisions in scientific fields reflect differences in the researchers themselves, hinting that some divisions may be more difficult to bridge than suggested by a traditional view of data-driven scientific consensus.

Root barrier cell types, like the endodermis and exodermis, are crucial for plant acclimation to environmental stresses. Deposition of suberin, a hydrophobic polymer, in these cell layers restricts the movement of molecules and plays a vital role in stress responses. This study investigates the role of SlMYB41, SlMYB92 and SlWRKY71 transcription factors (TFs) in regulating suberin biosynthesis in the tomato (Solanum lycopersicum) root exodermis by genetic perturbation. Genetic perturbation of these TFs altered exodermal suberin deposition patterns, indicating the SlMYBs as positive and SlWRKY71 negative regulators of suberization. RNA sequencing revealed a significant overlap between differentially expressed genes regulated by these TFs, suggesting a shared regulatory network. Gene set enrichment analyses highlighted their role in lipid and suberin biosynthesis as well as overrepresentation of exodermis-enriched transcripts. Furthermore, transactivation assays demonstrated that these two MYBs promote the expression of suberin-related genes, while SlWRKY71 represses them. These results indicate a complex antagonistic relationship, advancing our understanding of the regulatory mechanisms controlling exodermis suberization in tomato roots.

Small-conductance Ca²⁺-activated K⁺ (KCa2.1-KCa2.3) channels modulate neuronal and cardiac excitability. We report cryo-electron microscopy structures of the KCa2.2 channel in complex with calmodulin and Ca²⁺, alone or bound to two small molecule inhibitors, at 3.18, 3.50, 2.99 and 2.97 angstrom resolution, respectively. Extracellular S3-S4 loops in β-hairpin configuration form an outer canopy over the pore with an aromatic box at the canopy’s center. Each S3-S4 β-hairpin is tethered to the selectivity filter in the neighboring subunit by inter-subunit hydrogen bonds. This hydrogen bond network flips the aromatic residue (Tyr362) in the filter’s GYG signature by 180°, causing the outer selectivity filter to widen and water to enter the filter. Disruption of the tether by a mutation narrows the outer selectivity filter, realigns Tyr362 to the position seen in other K⁺ channels, and significantly increases unitary conductance. UCL1684, a mimetic of the bee venom peptide apamin, sits atop the canopy and occludes the opening in the aromatic box. AP14145, an analogue of a therapeutic for atrial fibrillation, binds in the central cavity below the selectivity filter and induces closure of the inner gate. These structures provide a basis for understanding the small unitary conductance and pharmacology of KCa2.x channels.

Background Adolescent high body mass index (BMI) is a growing global health problem. This study analyzes global, regional, and national prevalence and trends of high BMI among adolescents (aged 10–19 years) from 1990 to 2021, investigates disparities by sex, country, and socio-demographic index (SDI), and projects prevalence to 2030. Methods This study analyzed Global Burden of Disease (GBD) study 2021 data. Prevalence and trends of high BMI among adolescents were stratified by sex, SDI, and region. Estimated annual percentage change (EAPC), joinpoint regression, and Bayesian age-period-cohort (BAPC) analysis were used to quantify trends and project prevalence to 2030. Results Global adolescent high BMI prevalence has increased from 8.36% (1990) to 17.64% (2021), with females having a slightly higher prevalence than males. Marked disparities are observed across SDI levels; high SDI countries have the highest prevalence, but middle SDI countries are experiencing the fastest increases. Substantial geographic variations are also evident, with particularly rapid increases in some regions, such as the Pacific Island nations, and slower growth or declines in others, such as parts of East Asia. The BAPC model projects a continued rise in global high BMI prevalence up to 2030, with considerable variation across individual countries. Conclusions The global rise of high BMI among adolescents, coupled with projections of continued increases, presents a pressing public health concern. The observed disparities across SDI levels and geographic regions necessitate tailored interventions to address this growing epidemic effectively.