A document by Dr. Shantia Yarahmadian. Click on the document to view its contents.

Nowadays, one of the appealing choice of transistors for the next generation of high power devices is, AlGaN/GaN high-electron-mobility transistor (AlGaN/GaN-HEMT). Prior to now, numerous experiments were conducted utilizing various gate engineering techniques to propose high threshold voltage enhancement-mode HEMT architectures. In this paper, two new e-mode HEMT structures are proposed by taking motivation from our previously reported structure of Recessed p-GaN gate HEMT. By incorporating p-doped buried layer with recessed p-GaN gate in one HEMT structure, the threshold voltage is significantly increased. In first experiment, a p-doped GaN region is buried in to the Gallium Nitride (GaN) substrate of conventional p-GaN gate HEMT. In second experiment, a p-GaN buried layer is inserted in to GaN substrate of recessed p-GaN gate HEMT. V th of above 3V is obtained from the proposed structures, which is suitable to be implemented for high-frequency power switching applications. The proposed structures provides flexibility to tune V th and I d. The digital circuit compatibility is also tested by using both the proposed structures in a resistive-load inverter circuit. The transient analysis confirms that the HEMTs work properly in inverter circuit and the VTC analysis shows 98.5% of output voltage swing at input voltage of 5V. Highest NM H and NM L is measured as 2.85V and 2.65V respectively. The device simulation, calibration and circuit simulation is done in Silvaco TCAD software.

Online News media which is more accessible, cheaper, and faster to consume, is also of questionable quality as there is less moderation. Anybody with a computing device and internet connection can take part in creating, contributing, and spreading news in online portals. Social media has intensified the problem further. Due to the high volume, velocity, and veracity, online news content is beyond traditional moderation, also known as moderation through human experts. So different machine learning method is being tested and used to spot fake news. One of the main challenges for fake-news classification is getting labeled instances for this high volume of real-time data. In this study, we examined how semi-supervised machine learning can help to decrease the need for labeled instances with an acceptable drop of accuracy. The accuracy difference between the supervised classifier and the semi-supervised classifier is around 0.05 while using only five percent of label instances of the supervised classifier. We tested with logistic regression, SVM, and random forest classifier to prove our hypothesis.

High myopia stands as the primary cause of blindness globally, with cataract emerging as one of the most prevalent complications. However, the underlying mechanism of high myopic cataract remains unknown. The lens capsule is the basement membrane enclosing the lens. In this study, we hypothesized lens capsule tissue-derived EVs (Ti-EVs) play a vital role in the formation of cataract. Ti-EVs were collected from the lens capsule of high myopic and age-related cataract patients during cataract surgery, and isolated by ExoDisc. Then we performed proximity barcoding assay (PBA) for single EV analysis, which enabled us to identify the alteration of Ti-EV subpopulations associated with high myopic cataract. Our findings revealed a predominant immunity cluster within cataracts, characterized by a significantly higher abundance of macrophage-derived EVs in high myopic cataracts, which strongly correlated with the AQP1 cluster, suggesting a potential interaction between these two components in the progression of high myopic cataract. It was also observed that the eye morphogenesis cluster may also work in concert with AQP1, potentially driving the progression of high myopic cataracts through this pathway. These findings not only shed new light on the underlying mechanisms of high myopic cataract, but also pave the way for the development of novel therapeutic strategies to prevent or treat this devastating condition.

The primary method for assessing the depth of anesthesia during clinical surgeries currently relies on physiological and behavioral cues, such as heart rate, blood pressure, and reaction to external stimuli. These measures might not be dependable because their relationship to patients’ levels of awareness is not firmly established. Here, we present a neuromorphic framework comprising a Spiking Neural Network (SNN) with simulated neurons to access conscious states during general anesthesia, such as wakefulness and anesthesia, from intracranial electroencephalography (iEEG) and electroencephalogram (EEG) signals. This framework adjusts synapse weight of each neuron utilizing Spiking-Timing Dependent Plasticity (STDP) rules. Our analysis revealed that the proposed neuromorphic approach can access states of consciousness during anesthesia and characterize the transition from wakefulness to anesthetic-induced unconsciousness. We further implemented this framework on the Field Programmable Gate Array platform to enhance execution efficiency and address the clinical requirements for real-time monitoring of anesthesia states. Importantly, this study signifies an initial viable investigation using neuromorphic computing techniques for evaluating patients’ levels of anesthesia based on iEEG signals.

Text：Large language models (LLMs) excel at extracting information from literatures. However, deploying LLMs necessitates substantial computational resources, and security concerns with online LLMs pose a challenge to their wider applications. Herein, we introduce a method for extracting scientific data from unstructured texts using a local large language model (Local LLM), exemplifying its applications to scientific literatures on the topic of on-surface reactions. By combining prompt engineering and multi-step text preprocessing, we show that the local LLM can effectively extract scientific information, achieving a recall rate of 91% and a precision rate of 70%. Moreover, despite significant differences in model parameter size, the performance of the local LLM is comparable to that of GPT-3.5 turbo (97% recall, 79% precision) and GPT-4o (94% recall, 82% precision). The simplicity, versatility, reduced computational requirements, and enhanced privacy of the local LLM make it highly promising for data mining, with the potential to accelerate the ap-plication and development of LLMs across various fields.

The significance of residents’ perception of happiness in fostering individual well-being, social harmony, and the enduring stability of the nation cannot be overstated. Drawing upon data from the ”China Comprehensive Social Survey of 2021” (CGSS2021), this inquiry adopts a stepwise regression analysis, utilizing ordered logistic regression, to elucidate the myriad factors that shape the happiness indices among Chinese citizens. The results reveal that variables such as gender, age, political orientation, educational attainment, health condition, household income, marital status, social class, social trust, and social equity are pivotal determinants of happiness among the Chinese populace. It is imperative to prioritize the enhancement of residents’ sense of accomplishment, well-being, and security through targeted investments in health, education, economic stability, and the cultivation of a positive societal outlook. These endeavors are aimed at providing robust theoretical foundations and practical insights for improving residents’ happiness indices and advancing societal harmony and progress.

Introduction: Cancer and atrial fibrillation (AF) are major public health challenges in the United States, with significant mortality risks and a bidirectional relationship that compounds their burden. Despite substantial documentation of their independent mortality trends, data on concomitant cancer- and AF-related mortality trends remain limited. Methods and Results: We analyzed cancer- and AF-related mortality in the U.S. from 1999 to 2019 using CDC WONDER data, focusing on adults aged ≥25 years. Crude and age-adjusted mortality rates (AAMRs) were calculated, and trends were evaluated using Joinpoint regression. A total of 319,480 deaths were identified, with the AAMR increasing from 4.95 in 1999 to 10.01 in 2019 (AAPC: 3.49%). Males exhibited higher AAMRs than females, while Hispanics demonstrated the greatest increase. Geographic disparities were evident, with the highest AAMRs in Vermont, Minnesota, and Rhode Island and the lowest in Arizona, Georgia, and New Mexico. Conclusion: Cancer- and AF-related mortality has risen significantly over two decades, with notable racial, sex-based, and geographic disparities. These findings underscore the need for targeted interventions to address these inequities and mitigate the growing burden of coexisting cancer and AF.

The AVL and red-black trees are binary search trees that guarantee O ( log n ) insertion, deletion, and search. This guarantee requires rebalancing the trees, potentially for each insertion or deletion. Benchmarks demonstrate that the AVL tree is often faster than three red-black tree variants for insertion and deletion, and as fast or faster for search. An improved AVL tree deletion algorithm reduces the rebalancing operations associated with deletion by 20 percent.

Explicit formulas to calculate MV functions in a basis-free representation are presented for an arbitrary Clifford geometric algebra Cl p , q . The formulas are based on analysis of the roots of minimal MV polynomial and covers defective MVs, i.e. the MVs that have non-diagonalizable matrix representations. The method may be generalized straightforwardly to matrix functions and to finite dimensional linear operators. The results can find wide application in Clifford algebra analysis.

In the process of solving radiation hydrodynamics, the non-physics results often leads to the failure of the solution. In this paper, for radiation hydrodynamics based on maximum entropy approximation under the condition of one-dimensional spherical symmetry, the positive schemes of the operator splitting method are given; the calculation method of spherical symmetric source term is improved, and the time step limitation is reduced; the reconstructing Eddington factor is used instead of the direct reconstructing radiation flux to avoid the non-physics results brought by the reconstructing process. Finally, some examples are given to verify the correctness of the method.

Environmental conditions such as temperature and resource availability can shape disease transmission by altering contact rates and/or the probability of infection given contact. However, interactive effects of these factors on transmission processes remain poorly understood. Here, we develop mechanistic models and fit them to experimental data to uncover how temperature and resources jointly affect transmission of fungal parasites (Metschnikowia bicuspidata) in zooplankton hosts (Daphnia dentifera). Model competition revealed interactive effects of temperature and resources on both contact rates (host foraging) and the probability of infection given contact (per-parasite susceptibility). Foraging rates increased with temperature and decreased with resources (via type-II foraging), but this resource effect weakened at warmer temperature due to shorter handling times. Per-parasite susceptibility increased with resources at cooler temperatures but remained consistently high when warmer. Our analysis demonstrates that temperature and resources interact to shape transmission processes and provides a general theoretical framework for other host-parasite systems.

Over 40% of amphibian species are threatened, urbanization being a major threat to their survival. Conservation efforts to sustain viable amphibian populations within urban spaces may play a meaningful part in protecting amphibian species. To explore the factors that influence the viability of urban amphibian population in Mediterranean environments, we used a capture-recapture analysis applied to a large dataset collected in a multi-year citizen-science program, focused on two urban Salamandra infraimmaculata populations, and to a second dataset of two Bufotes variabilis populations in an urban park and in a nearby nature reserve. Individuals of both species have individually-unique patterns of dorsal spots, allowing for noninvasive recapture identification. Using the salamander dataset, we developed a length-based age-estimation method and discovered a prolonged period of increased vulnerability throughout their first years of life, even after reaching sexual maturity, a finding with important implications for management. Additionally, the two case studies suggest that the creation of fish-containing artificial water bodies in Mediterranean habitats can have highly detrimental impacts on amphibian populations. The study uncovered population-specific information, such as unknown breeding sites and population size estimates, of conservation importance, and demonstrates the utility of citizen science in study and conservation of urban ecology.

Background Fever is a common biological phenomenon during the occurrence of infections and tumors in the body. However, it is unclear whether the increase in body temperature of tumor patients has an impact on immune cells and patient prognosis, as well as the underlying molecular regulatory mechanisms. Its effects on the patient’s disease outcome and immunotherapy efficacy are also uncertain. Clarifying the impact mechanism of fever on immune cells will provide new ideas for predicting tumor prognosis and formulating immunotherapy strategies. Methods T cells were cultured at 37.0°C and 39.5°C for 3, 24, and 72 h. Cell samples were collected for transcriptome RNA sequencing. Gene set enrichment analysis (GSEA) and TCellSI analysis were used to explore the effects of high-temperature culture on T cell function. Thermosensitive genes related to high-temperature induction were identified via differential gene analysis. A risk scoring model was constructed based on these thermosensitive genes using the least absolute shrinkage and selection operator (LASSO) algorithm and subsequently validated utilizing independent queue data (GSE68645). Independent prognostic factors were determined through univariate and multivariate Cox regression analyses. The impact of each factor on patient prognosis was evaluated using Kaplan-Meier survival curves. In addition, the impact of lactate dehydrogenase A (LDHA) expression on patient immune characteristics was evaluated using algorithms such as CIBERSORT and ESTIMATE. Results The study results indicated that T cell immune activation pathways were significantly enriched under fever stimulation, especially the Toll-like receptor signaling, macrophage activation, and interleukin-6 production. Differential gene analysis identified 840 thermosensitive genes, of which seven highly correlated genes were used to construct a risk scoring model based on the LASSO algorithm. This model successfully distinguished between high- and low-risk groups of lung adenocarcinoma (LUAD) patients in The Cancer Genome Atlas cohort. Patient prognosis in the high-risk group was significantly worse. The prognostic ability of the model was validated in the GSE68645 cohort. In addition, three independent prognostic factors (LDHA, IRX5, and CIITA) were identified and utilized to construct a nomogram. IRX5 and CIITA were associated with better prognosis, while LDHA was associated with poor prognosis (P < 0.05). Further analysis showed that LDHA was significantly correlated (P < 0.05) with the APC_combination and parainflammation pathway enrichment, PDCD1 inhibitory immune checkpoint upregulation, and tumor mutation burden. Conclusion The present study identified thermosensitive genes and their molecular characteristics. The discovery of thermosensitive genes is closely related to the prognosis of non-small cell lung cancer patients. The research results also suggested that T cell fever-related genes are significantly correlated with the upregulation of PDCD1 inhibitory immune checkpoint and tumor mutation burden, providing new ideas for lung cancer prognosis and immunotherapy treatment response.

Many organisms respond to alarm calls of neighbors to change their behavior and morphology and thereby reduce their risk of consumption. The effectiveness of responses to alarm calls in animals can be enhanced by learning, and animals increase their responses to individuals known to provide more reliable or pertinent information. Sagebrush becomes more resistant to chewing herbivores after exposure to the volatiles of experimentally clipped conspecifics. This induced resistance has been shown to be more effective if the volatile cues come from individuals of the same chemotype as the receiver. It is not known if long term exposure to the cues of other chemotypes enhances the effectiveness of eavesdropping on different volatile cues. We conducted an experiment testing the effectiveness of damage-induced volatiles in a meadow of sagebrush dominated by individuals of two chemotypes -- thujone and camphor. As in previous experiments, branches exposed to damage-induced volatiles received less chewing damage over the growing season compared to control branches which did not receive volatiles. Plants of the camphor chemotype received less damage than those of the thujone chemotype. However, regardless of one's chemotype, when the damage-induced volatile cues and the long-term cues from neighbors matched, the induced defense became significantly stronger. These results raise the possibility that plants may learn to respond to different cues.

The presence of anti-polyethylene glycol (PEG) antibodies significantly impacts the clinical translation and therapeutic efficacy of PEGylated liposomes. Current research on anti-PEG antibodies primarily focuses on strategies to reduce their production. However, the growing prevalence of anti-PEG antibody-positive individuals has become an undeniable challenge. To address this issue, we have designed an asymmetrical branched PEG lipid derivative (PEG2,5K-DSPE) aimed at minimizing the binding of anti-PEG antibodies. We have further prepared 1,1’-dioctadecyl-3,3,3,3’-tetramethylindodicarbocyanine iodide (DiR) liposomes (P2,5K-DiR) and mitomycin C lipid prodrug (MSC) liposomes (P2,5K-MSC) modified with this derivative. Classic PEG2000 lipid derivatives, after binding to anti-PEG IgG, are not only cleared by the liver but can also be taken up by tumor-associated macrophages in the tumor stroma, leading to reduced anti-tumor efficacy. Notably, the PEG2,5K-DSPE can form a bilayer structure on the surface of liposomes, which reduces the recognition and binding of anti-PEG IgG antibody. This modification enhances DiR liposomes and MSC liposomes accumulation in tumor tissues and promotes cancer-immune circulation. As a result, these liposomes significantly improve the effectiveness of photothermal therapy and chemotherapy in cancer treatment. Overall, our study provides a new strategy for the clinical application of PEGylated liposomes in anti-PEG antibody-positive populations.

Background and purpose The 68Ga-labeled nanobody SNA006 marks a significant advancement in noninvasive ImmunoPET imaging of CD8+ T cells, facilitating real-time tracking of cellular immune responses in cancer, yet its pharmacokinetic properties remain suboptimal. This study aimed to develop a next-generation CD8-targeting immunoPET nanobody probe by incorporating a PEGylated brush border membrane enzyme-cleavable linker to improve pharmacokinetics and to evaluate its characterization in CD8-positive intrapulmonary tumors. Experimental approach A precursor based on SNA006, containing a PEGylated brush border membrane enzyme-cleavable linker, was designed, synthesized, and radiolabeled with gallium-68 to yield 68Ga-NOTA-m-SNA006. The probe was subsequently assessed both in vitro and in vivo. Key results The probe exhibited high radiochemical yield, purity, and favorable stability, and demonstrated binding to the CD8 protein with high affinity. PET/CT imaging and biodistribution studies revealed that 68Ga-NOTA-m-SNA006 exhibited favorable pharmacokinetic properties, including rapid clearance from the kidneys, reduced liver uptake, and sustained retention in the tumor, compared with 68Ga-NODAGA-SNA006. 68Ga-NOTA-m-SNA006 exhibited high uptake in lung lesions during in vivo PET imaging, reflecting CD8 expression in an intrapulmonary tumor model. Conclusion and implication In summary, we present a novel 68Ga-labeled SNA006 radiotracer with an optimized linker moiety, 68Ga-NOTA-m-SNA006, which effectively decreases renal and hepatic uptake while maintaining tumor uptake, thereby enhancing the tumor-to-background ratio. This approach represents a critical advancement in addressing the persistent challenge of radioactivity in the kidneys and liver associated with nanobody-based radiopharmaceuticals.

Objective: The aim of this study was to investigate the effect of factors such as neutrophils lymphocytes ratio(NLR) on the prognosis of patients with treated metastatic colorectal cancer who undergo immunotherapy, and the associated factors that influence the efficacy of immunotherapy. Methods: Colorectal cancer patients who attended the Department of Oncology of the Second Affiliated Hospital of Air Force Medical University from October 2019 to February 2023 were initially enrolled in this study, and 127 patients were finally included after the inclusion and exclusion criteria. Statistical methods were used to analyze the effects of whether or not to receive immune checkpoint inhibitors, the type of immune checkpoint inhibitors, and NLR on the evaluation of the efficacy of tumor treatment for patients with treated metastatic colorectal cancer. The effects of different factors such as gender, tumor site, KRAS status, mismatch repair protein(MMR), and NLR on the prognosis of treated metastatic colorectal cancer. Results: Of the 127 patients, 65 received immunotherapy and 62 did not. There was no statistically significant difference between the therapeutic efficacy of domestic and imported immune checkpoint inhibitors for metastatic colorectal cancer patients who had previously received more than one first-line treatment . Kaplan-Meier survival curves plotted by SPSS found that there was a statistical difference between NLR ( P=0.007) and MMR (P=0.029) for progression free survival(PFS) in patients with treated metastatic colorectal cancer.Cox univariate survival analysis found that NLR (P=0.012) and MMR (P=0.046) were two factors affecting PFS in colorectal cancer patients. Cox multivariate analysis concluded that NLR<3 (P=0.024) was an independent risk factor affecting survival time of patients with treated metastatic colorectal cancer who received immune checkpoint inhibitors, with a statistically significant difference. Conclusion: For patients with treated metastatic colorectal cancer, NLR and MMR are important indicators of PFS, and NLR<3 is an independent risk factor for survival time of patients with treated metastatic colorectal cancer who receive immune checkpoint inhibitors, which has an important predictive value of prognosis.

The deep-seated and multi-faceted cancer, driven by genetic mutations, has remained a major hurdle for both detection as well as therapeutic management. Single-cell RNA sequencing (scRNA-seq) has emerged as a powerful approach for understanding the heterogeneity found in cancer and identifying potential therapeutic targets. In this study, we elucidate its function in the context of laryngeal squamous cell carcinoma (LSCC). In order to explore novel molecular-targeted biomarkers and establish precise monitoring strategies for the diagnosis, and treatment of LSCC based on acute transcriptome screening results by using scRNA-seq data (GSE252490). After data processing, quality control and standardization took place followed by the detection of highly variable genes optimization using principal component analysis. Further cell clustering revealed 12 discrete clusters with unique molecular features. We finally detected 6434 differentially expressed genes (DEGs) by comparing the marker gene with the human homolog. Gene ontology enrichment analysis indicates the biological processes associated with LSCC progression. Protein-protein interaction (PPI) network construction revealed twenty central genes associated with multiple key pathways involved in building up cancer. Pathway enrichment by the KEGG pathway was used to underline the involvement of these centric genes in various cancer-related pathways. Moreover, the evaluation of mRNA expression and clinical prognosis further confirmed their significance in LSCC. Of these genes, CCL3, EPCAM, and IL8 with increased expression levels are linked to the survival rate in LSCC. The present analysis provides insight into the molecular landscape of LSCC, thereby identifying potential candidates for diagnosis and targeted therapy.

Considering the local magnetic characteristics of surface-mounted permanent magnet (SPM) motors, the paper proposes an adaptive connecting equivalent magnetic network (ACEMN) model to accurately predict SPM motor performance. First, for modeling the magnetic field at the inclined boundary of the stator pole shoe, a diagonal hybrid permeance element covering two materials is developed. And considering the parallel magnetization of PMs, a branching calculation of the magnetomotive force source is performed inside a cross-shaped permeance of a fan-shaped mesh. Then, by analyzing the phenomenon of magnetic field line deflection at the air gap boundary, an air gap node connecting way based on adaptive conversion of connecting permeances is built. Thereby, the rotating magnetic field of the air gap can be accurately described using the different connecting permeances with variable size. To accelerate the nonlinear solution for saturated element permeability, a hybrid iterative method is used. The validity of this modeling method is verified by finite element analysis (FEA) and prototype experiments, which allows a satisfactory compromise between accuracy and calculation speed.

Modular space systems are an innovative and future-oriented concept, offering the flexibility to adapt to diverse mission requirements. In our previous paper, we introduced the Twins4Space project, a modular and flexible architecture utilizing a SpaceWire network to connect distributed nodes. In this paper, we focus on the empirical performance evaluation of the system, presenting detailed measurements of its communication layer. Key performance indicators such as SpaceWire Time-Code latency and network data rates are assessed, which are essential for maintaining predictable operational conditions within decentralized nodes. Our findings demonstrate that Twins4Space can effectively meet the rigorous demands of future space applications, providing robust communication and swift reconfiguration capabilities. Furthermore, areas with optimization potential are identified, offering opportunities for further performance improvements. This validation underscores the practical potential of the Twins4Space approach in enhancing the efficiency and reliability of space mission infrastructure.

ABSTRACT Diabetes control relies heavily on very pricy medical care, which has emerged as a major global health concern. As a result, it is imperative to look at potential alternatives to treatment, the cost-effective ways and reduce side effects on our health from synthetic drugs. Purpose of this study mainly emphasizes on evaluation of rhizomes of twenty three plants of the family Zingiberaceae for the presence of active phytochemicals that inhibiting anti-diabetic, inflammatory, and oxidant activities. Anti-oxidant effect of these extracts were examined by Reducing Power, DPPH, and ABTS assays. The methanolic extract showed potent anti-oxidant activity which is comparable to ascorbic-acid and trolox used as control standard. While anti-diabetic activity was evaluated based on α-glucosidase and α-amylase enzymes inhibition assays with acarbose drug act as standard. Out of the 23 samples,10 samples were showed significant (IC50 values< 100 μg/mL) by α-glucosidase inhibitory effect inclusive of which rhizomes of Alpinia calcarata had the most optimum activity in-vitro with IC50 values 2.58±0.16 μg/mL for α-glucosidase and IC50=145.8±0.09 μg/mL for α-amylase enzymes assays. Further, anti-inflammatory potential of these extracts were performed using Heat-induced and Hypoxicity-induced hemolysis methods with Indomethacin used as standard. Entire aforementioned activities assay gives significant results for respective methanolic extracts. Using standard deviation, the results were determined to be statistically significant (p<0.005).

Aim This study aims to evaluate the risk factors associated with the development of prediabetes (preDM) in patients following COVID-19 infection. Given the potential metabolic impacts of COVID-19, understanding the relationship between post-COVID-19 health and preDM can provide insights into the long-term consequences of the disease. Method This descriptive case series study included five patients diagnosed with new-onset preDM after COVID-19, who visited the COVID-19 Follow-up Clinic. Demographic data, medical history, and laboratory findings were collected retrospectively from the Health Management System of the Ministry of Health. Risk factors such as physical activity levels, smoking, body mass index, and the use of corticosteroids were assessed concerning preDM development. Results PreDM was diagnosed in 4.2% of patients after Covid-19 disease. The findings revealed that 80% of the patients were male, with an average age over 65 years. Several risk factors including age, gender, low physical activity levels, and corticosteroid use during COVID-19 treatment were associated with an increased likelihood of preDM. One patient was found to have high clinical frailty, which may further predispose to metabolic imbalances. Conclusion COVID-19 may contribute not only to acute health issues but also to long-term metabolic risks such as preDM. Post-COVID-19 patients with preDM require close monitoring and lifestyle interventions to mitigate the risk of progression to diabetes mellitus (DM). This study underscores the importance of assessing metabolic health in COVID-19 survivors and calls for further research with larger sample sizes to deepen our understanding of COVID-19’s long-term effects on metabolic health.

Prior human and animal research suggests prenatal stress alters developmental systems that support stress and emotion regulation in offspring, which may underlie vulnerability to psychopathology risk. We examined prospective associations between prenatal stress from a natural disaster and infant biobehavioral outcomes across toddlerhood. Women pregnant during Hurricane Harvey in 2017 completed assessments of their objective levels of hardship and their subjective distress from the disaster. Offspring (n = 47) completed follow-up assessments at 24 months to collect autonomic nervous system activity (respiratory sinus arrythmia, RSA; heart rate, HR) during a stressor task. Parents assessed their children’s behavioral problems at 24 and 48 months of age. Increased prenatal subjective distress was associated with a blunted RSA response to stress and less RSA recovery, whereas increased objective hardship was associated with greater HR reactivity and recovery. Prenatal subjective distress was associated with increased child behavior problems at 24 months. Higher behavior problems at 24 months of age were also associated with RSA and HR reactivity and recovery. Findings suggest that prenatal stress is an important predictor of offspring biobehavioral risk. Further research is needed to explore longitudinal pathways.