Prior Studies Research Articles

A Multi-Level Interpretable Sleep Stage Scoring System by Infusing Experts' Knowledge Into a Deep Network Architecture.

In recent years, deep learning has shown potential and efficiency in a wide area including computer vision, image and signal processing. Yet, translational challenges remain for user applications due to a lack of interpretability of algorithmic decisions and results. This black box problem is particularly problematic for high-risk applications such as medical-related decision-making. The current study goal was to design an interpretable deep learning system for time series classification of electroencephalogram (EEG) for sleep stage scoring as a step toward designing a transparent system. We have developed an interpretable deep neural network that includes a kernel-based layer guided by a set of principles used for sleep scoring by human experts in the visual analysis of polysomnographic records. A kernel-based convolutional layer was defined and used as the first layer of the system and made available for user interpretation. The trained system and its results were interpreted in four levels from microstructure of EEG signals, such as trained kernels and effect of each kernel on the detected stages, to macrostructures, such as transitions between stages. The proposed system demonstrated greater performance than prior studies and the system learned information consistent with expert knowledge.

Data Complexity: A New Perspective for Analyzing the Difficulty of Defect Prediction Tasks

Defect prediction is crucial for software quality assurance and has been extensively researched over recent decades. However, prior studies rarely focus on data complexity in defect prediction tasks, and even less on understanding the difficulties of these tasks from the perspective of data complexity. In this article, we conduct an empirical study to estimate the hardness of over 33,000 instances, employing a set of measures to characterize the inherent difficulty of instances and the characteristics of defect datasets. Our findings indicate that: (1) instance hardness in both classes displays a right-skewed distribution, with the defective class exhibiting a more scattered distribution; (2) class overlap is the primary factor influencing instance hardness and can be characterized through feature, structural, and instance-level overlap; (3) no universal preprocessing technique is applicable to all datasets, and it may not consistently reduce data complexity, fortunately, dataset complexity measures can help identify suitable techniques for specific datasets; (4) integrating data complexity information into the learning process can enhance an algorithm’s learning capacity. In summary, this empirical study highlights the crucial role of data complexity in defect prediction tasks, and provides a novel perspective for advancing research in defect prediction techniques.

Association of cord plasma metabolites with birth weight: results from metabolomic and lipidomic studies of discovery and validation cohorts.

Birth weight is a good predictor of fetal intrauterine growth and long-term health. Although several studies have evaluated the relationship between metabolites and birth weight, no prior study has comprehensively investigated the metabolomic and lipidomic and further validated and quantified meaningful metabolites. Firstly, a pseudotargeted metabolomics approach was applied to detect 2418 metabolites in 504 cord blood samples in the discovery set enrolled from the Wuhan Healthy Baby Cohort (HBC), China. Metabolome-wide association scan (MWAS) analysis and pathway enrichment were applied to discover metabolites and metabolic pathways that were significantly associated with birth weight for gestational age (BWGA) z-score. Logistic regression models were used to analyze the association of metabolites in the most significantly associated pathways with small for gestational age (SGA) and low birth weight (LBW). Subsequently, 350 cord blood samples in a validation cohort were subjected to targeted analysis to validate the metabolites screened from the discovery cohort. In the discovery set, 513 metabolites were significantly associated with BWGA z-score (PFDR <0.05), of which 298 KEGG-annotated metabolites were included in the pathway analysis. The primary bile acid biosynthesis pathway was the most relevant metabolic pathway associated with BWGA z-score in our study. Elevated cord plasma primary bile acids were associated with lower BWGA z-score and higher odds of SGA or LBW in the discovery and validation cohorts. In the validation set, a 2-fold increase in taurochenodeoxycholic acid (TCDCA) and taurocholic acid (TCA) was associated with 0.10 (95% CI: 0.00, 0.20) and 0.18 (95 %CI: 0.04, 0.31) decrease in BWGA z-score, respectively, after adjusting for covariates. In addition, a 2-fold increase in cord plasma TCDCA and TCA was associated with an adjusted odds ratio of 1.52 (1.00, 2.30) and 1.77 (1.05, 2.98) for SGA, respectively. The adjusted ORs for a 2-fold increase in TCDCA and TCA concentrations were 2.39 (95% CI 1.00, 5.71) and 3.21 (0.96, 10.74) for LBW, respectively. The results indicate a significant association between primary bile acids and lower BWGA z-score, as well as higher risk of SGA and LBW. Abnormalities of primary bile acid metabolism may play an important role in restricted fetal development. This article is protected by copyright. All rights reserved.

Pregaming potentiates risk between UPPS-P impulsivity and day-level drinking behavior: A test of person-environment transactions theory.

Pregaming represents a uniquely high-risk drinking event for young adults, and subfacets of impulsivity are robust predictors of alcohol use and related negative outcomes. Further, it is likely that pregame events contain social and physical stimuli that are particularly appealing for impulsive individuals, thus exacerbating risk for negative outcomes. However, no prior studies have investigated the extent to which impulsive personality traits interact with pregame events to confer alcohol-related risk. Thus, the present study examined the extent to which UPPS-P (urgency-perseverance-premeditation-sensation seeking-positive urgency) subfacets of impulsivity interact with the occurrence of pregaming to predict relations between pregaming, drinking quantity, and negative alcohol-related outcomes. College students (N = 737) completed a modified, online version of the 30-day Timeline Followback in which they reported drinking quantity, negative consequences, and whether they engaged in pregaming on a given day. Results indicated that sensation seeking and a lack of premeditation moderated relations between pregaming and drinking quantity such that those who are higher in sensation seeking and lower in their ability to plan ahead drank more on pregaming days. Sensation seeking and positive urgency moderated relations between pregaming and negative consequences such that those who are higher in positive urgency experience more consequences on pregaming days whereas those who are higher in sensation seeking experience less consequences. Future studies may benefit from more granular assessments of pregame-related risk as subfacets of impulsivity may confer momentary risk. Prevention efforts targeting the reduction of pregaming frequency, particularly among impulsive individuals, may lower overall risk for heavy drinking. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Strategic information planning and performance of SMEs: A structural equation modelling approach

BACKGROUND: The business environment is getting unstable due to which execution of decision support systems has become significant to maintain competitive advantage. Small and medium enterprises in particular confront issues, such as insufficiency of devising strategic planning, strategic decision-making, and information exchange, as well as difficulty increasing performance. Because functional strategic management and decision-making are required, strategic information systems planning is employed to accumulate information and assist decision-makers in developing and implementing the best strategy for gaining superior performance. Executives in information systems focus on technical difficulties while ignoring decisions to support strategic plans. OBJECTIVE: The purpose of this article is to investigate how strategic information systems planning contributes to increased performance in SMEs. METHODS: Data was gathered through questionnaires distributed to information system executives in SMEs, primarily in Jordan. The data was collected with the help of questionnaires adopted from prior studies. For Analysis after applying the diagnostic tests, structural equation modelling has been applied to test the framework developed based on the literature. RESULTS: A structural equation modeling (SEM) was conducted, the individual loadings of the SEM items are investigated, reliability and discriminant validity is tested, and path coefficients of hypothesized relationships of the developed model are tested. CONCLUSIONS: The findings suggested that managers should prioritize implementing strategic information systems planning so that they can gain superior performance with better agility in the future. This research not only enhances the current understanding of the significance of strategic information systems planning but also assists supervisors to improve the procedure.

Contraction Decomposition in Unit Disk Graphs and Algorithmic Applications in Parameterized Complexity

We give a new decomposition theorem in unit disk graphs (UDGs) and demonstrate its applicability in the fields of Structural Graph Theory and Parameterized Complexity. First, our new decomposition theorem shows that the class of UDGs admits an “almost” Contraction Decomposition Theorem. Prior studies on this topic exhibited that the classes of planar graphs [Klein, SICOMP, 2008], graphs of bounded genus [Demaine, Hajiaghayi and Mohar, Combinatorica 2010], and H -minor free graphs [Demaine, Hajiaghayi and Kawarabayashi, STOC 2011] admit a Contraction Decomposition Theorem. Even bounded-degree UDGs can contain arbitrarily large cliques as minors, and therefore our result is a significant advance in the study of contraction decompositions. Additionally, this result answers an open question posed by Hajiaghayi ( www.youtube.com/watch?v=2Bq2gy1N01w ) regarding the existence of contraction decompositions for classes of graphs beyond H -minor free graphs though under a relaxation of the original formulation. Second, we present a “parameteric version” of our new decomposition theorem. We prove that there is an algorithm that, given a UDG G and a positive integer k , runs in polynomial time and outputs a collection of \(\mathcal {O}(k)\) tree decompositions of G with the following properties. Each bag in any of these tree decompositions can be partitioned into \(\mathcal {O}(k)\) connected pieces (we call this measure the chunkiness of the tree decomposition). Moreover, for any subset S of at most k edges in G , there is a tree decomposition in the collection such that S is well preserved in the decomposition in the following sense. For any bag in the tree decomposition and any edge in S with both endpoints in the bag, either its endpoints lie in different pieces or they lie in a piece that is a clique. Having this decomposition at hand, we show that the design of parameterized algorithms for some cut problems becomes elementary. In particular, our algorithmic applications include single-exponential (or slightly super-exponential) algorithms for well-studied problems such as Min Bisection , Steiner Cut , s -Way Cut , and Edge Multiway Cut-Uncut on UDGs; these algorithms are substantially faster than the best-known algorithms for these problems on general graphs.

Hierarchical and dynamic graph attention network for drug-disease association prediction.

In the realm of biomedicine, the prediction of associations between drugs and diseases holds significant importance. Yet, conventional wet lab experiments often fall short of meeting the stringent demands for prediction accuracy and efficiency. Many prior studies have predominantly focused on drug and disease similarities to predict drug-disease associations, but overlooking the crucial interactions between drugs and diseases that are essential for enhancing prediction accuracy. Hence, in this paper, a resilient and effective model named Hierarchical and Dynamic Graph Attention Network (HDGAT) has been proposed to predict drug-disease associations. Firstly, it establishes a heterogeneous graph by leveraging the interplay of drug and disease similarities and associations. Subsequently, it harnesses the capabilities of graph convolutional networks and bidirectional long short-term memory networks (Bi-LSTM) to aggregate node-level information within the heterogeneous graph comprehensively. Furthermore, it incorporates a hierarchical attention mechanism between convolutional layers and a dynamic attention mechanism between nodes to learn embeddings for drugs and diseases. The hierarchical attention mechanism assigns varying weights to embeddings learned from different convolutional layers, and the dynamic attention mechanism efficiently prioritizes inter-node information by allocating each node with varying rankings of attention coefficients for neighbour nodes. Moreover, it employs residual connections to alleviate the over-smoothing issue in graph convolution operations. The latent drug-disease associations are quantified through the fusion of these embeddings ultimately. By conducting 5-fold cross-validation, HDGAT's performance surpasses the performance of existing state-of-the-art models across various evaluation metrics, which substantiates the exceptional efficacy of HDGAT in predicting drug-disease associations.

Differences in presurgical MMPI-3 scores across trajectories of recovery from spine surgery.

Approximately 30% of patients who undergo spinal surgery for chronic back pain continue to experience significant pain and disability up to 2 months following surgery. Prior studies have identified mental health variables including depression and anxiety as predictors of poorer postsurgical outcomes using screening instruments, but no studies have examined long-term outcomes using the Minnesota Multiphasic Personality Inventory-3 (MMPI-3), a commonly used tool used in presurgical psychological evaluations (PPE). Using group-based trajectory modeling and a sample of 404 spine surgery evaluees, the present study examined the trajectories of changes in disability scores from presurgery through 3, 12, and 24 months postsurgery. We then compared scores on MMPI-3 scales between trajectory groups. We identified three trajectory groups of change in disability over time: a rapid-remitting group (8%), characterized by moderate presurgical disability that rapidly and substantially remitted by 12 and 24 months; a steady-recovering group (68%), characterized by moderate presurgical disability, slower change over time, and mild levels of disability at the 2-year time point; and a persisting disability group (24%), characterized by severe presurgical disability that continued into long-term follow-ups. Participants in the persisting pain group produced higher presurgical scores on somatic/cognitive and internalizing MMPI-3 scales than participants in the rapid-remitting and steady-recovering groups. Our results support the clinical utility of the MMPI-3 in PPEs and highlight the importance of evaluating somatic/cognitive concerns and internalizing dysfunction to identify patients who are likely to have poorer postsurgical outcomes. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Comparative kinetics of humans and non-human primates during vertical climbing.

Climbing represents a critical behavior in the context of primate evolution. However, anatomically modern human populations are considered ill-suited for climbing. This adaptation can be attributed to the evolution of striding bipedalism, redirecting anatomical traits away from efficient climbing. Although prior studies have speculated on the kinetic consequences of this anatomical reorganization, there is a lack of data on the force profiles of human climbers. This study utilized high-speed videography and force plate analysis to assess single limb forces during climbing from 44 human participants of varying climbing experience and compared these data with climbing data from eight species of non-human primates (anthropoids and strepsirrhines). Contrary to expectations, experience level had no significant effect on the magnitude of single limb forces in humans. Experienced climbers did, however, demonstrate a predictable relationship between center of mass position and peak normal forces, suggesting a better ability to modulate forces during climbing. Humans exhibited significantly higher peak propulsive forces in the hindlimb compared with the forelimb and greater hindlimb dominance overall compared with non-human primates. All species sampled demonstrated exclusively tensile forelimbs and predominantly compressive hindlimbs. Strepsirrhines exhibited a pull-push transition in normal forces, while anthropoid primates, including humans, did not. Climbing force profiles are remarkably stereotyped across humans, reflecting the universal mechanical demands of this form of locomotion. Extreme functional differentiation between forelimbs and hindlimbs in humans may help to explain the evolution of bipedalism in ancestrally climbing hominoids.

Blockchain adoption in supply chains: implications for sustainability

This study aims to systematically review the current academic literature on blockchain and supply chain management (SCM) to explore the antecedents and sustainable outcomes of blockchain adoption and identify factors that influence blockchain adoption at different stages of implementation in supply chains (SC). This review follows the 6 steps and 14 decisions of conducting a systematic literature review (SLR) to comprehensively review 69 papers published in Academic Journal Guide (AJG) 3 and above journals between 2008 and 2023. Based on the content analysis of the selected papers, this study identifies antecedents and outcomes for blockchain adoption. This study further identifies influential factors in each blockchain implementation stage. A conceptual framework is developed to facilitate the conceptual development of blockchain adoption for sustainability in SCs. This study extends the prior studies of blockchain adoption in SCM with the consideration of how blockchain affects the SCM from the aspects of economic, social, and environmental. This review identifies four major themes in the topic of blockchain implementation and SCM, thus assisting researchers in avoiding focusing on the investigation of mature and saturated topics. The research gaps are identified, and future research directions are recommended for scholars, thereby enabling the comprehensive development of blockchain adoption in SCs.

Bovine serum albumin uptake and polypeptide disaggregation studies of hypoglycemic ruthenium(II) uracil Schiff-base complexes

Our prior studies have illustrated that the uracil ruthenium(II) diimino complex, [Ru(H3ucp)Cl(PPh3)] (1) (H4ucp = 2,6-bis-((6-amino-1,3-dimethyluracilimino)methylene)pyridine) displayed high hypoglycemic effects in diet-induced diabetic rats. To rationalize the anti-diabetic effects of 1, three new derivatives have been prepared, cis-[Ru(bpy)2(urdp)]Cl2 (2) (urdp = 2,6-bis-((uracilimino)methylene)pyridine), trans-[RuCl2(PPh3)(urdp)] (3), and cis-[Ru(bpy)2(H4ucp)](PF6)2 (4). Various physicochemical techniques were utilized to characterize the structures of the novel ruthenium compounds. Prior to biomolecular interactions or in vitro studies, the stabilities of 1–4 were monitored in anhydrous DMSO, aqueous phosphate buffer containing 2% DMSO, and dichloromethane (DCM) via UV–Vis spectrophotometry. Time-dependent stability studies showed ligand exchange between DMSO nucleophiles and chloride co-ligands of 1 and 3, which was suppressed in the presence of an excess amount of chloride ions. In addition, the metal complexes 1 and 3 are stable in both DCM and an aqueous phosphate buffer containing 2% DMSO. In the case of compounds 2 and 4 with no chloride co-ligands within their coordination spheres, high stability in aqueous phosphate buffer containing 2% DMSO was observed. Fluorescence emission titrations of the individual ruthenium compounds with bovine serum albumin (BSA) showed that the metal compounds interact non-discriminately within the protein's hydrophobic cavities as moderate to strong binders. The metal complexes were capable of disintegrating mature amylin amyloid fibrils. In vivo glucose metabolism studies in liver (Chang) cell lines confirmed enhanced glucose metabolism as evidenced by the increased glucose utilization and glycogen synthesis in liver cell lines in the presence of complexes 2–4.

Multi-objective optimization of a sequentially inspected system prone to revealing and non-revealing failures via particle swarm optimization and a fuzzy method

ABSTRACT Maintenance models rely heavily on the failure characteristics and inspection or maintenance actions. This paper introduced a sequentially inspected system whose failure alternated between revealing and non-revealing failures. The distinctive characteristic of the proposed model is that inspections are imperfect, inspection and maintenance time are non-negligible, and corrective maintenance is perfect. The proposed study contributed on the multi-objective availability and cost optimization issue of the aforementioned system, which was lacking in the prior studies. This paper focused on evaluating the general expressions for limiting availability and long-run average cost of the sequentially inspected system and addressed the multi-objective optimization issue using particle swarm optimization to generate the Pareto optimal solutions followed by a fuzzy method to select the best compromise solution. Moreover, reasonable results were achieved using a numerical case study and the findings of the existing studies were found to be the special case of this model.

SECap: Speech Emotion Captioning with Large Language Model

Speech emotions are crucial in human communication and are extensively used in fields like speech synthesis and natural language understanding. Most prior studies, such as speech emotion recognition, have categorized speech emotions into a fixed set of classes. Yet, emotions expressed in human speech are often complex, and categorizing them into predefined groups can be insufficient to adequately represent speech emotions. On the contrary, describing speech emotions directly by means of natural language may be a more effective approach. Regrettably, there are not many studies available that have focused on this direction. Therefore, this paper proposes a speech emotion captioning framework named SECap, aiming at effectively describing speech emotions using natural language. Owing to the impressive capabilities of large language models in language comprehension and text generation, SECap employs LLaMA as the text decoder to allow the production of coherent speech emotion captions. In addition, SECap leverages HuBERT as the audio encoder to extract general speech features and Q-Former as the Bridge-Net to provide LLaMA with emotion-related speech features. To accomplish this, Q-Former utilizes mutual information learning to disentangle emotion-related speech features and speech contents, while implementing contrastive learning to extract more emotion-related speech features. The results of objective and subjective evaluations demonstrate that: 1) the SECap framework outperforms the HTSAT-BART baseline in all objective evaluations; 2) SECap can generate high-quality speech emotion captions that attain performance on par with human annotators in subjective mean opinion score tests.

Abstract 6615: Reduced penetrance BRCA1 and BRCA2 pathogenic variants in clinical germline genetic testing

Abstract Despite the current binary clinical classification of variants (pathogenic versus benign), it is widely recognized that risk is a continuum. Prior studies have suggested the existence of reduced penetrance pathogenic variants (RPPVs), which pose challenges for identification, reporting, patient counseling and care. Through the current study, we sought to establish RPPVs as a new category of variants, with efforts focused specifically on BRCA1 and BRCA2 (BRCA). We compiled, compared, and assessed data for BRCA RPPVs from two large clinical diagnostic laboratories to establish an initial set of reduced penetrance pathogenic variants. Specifically, candidate BRCA RPPVs provided by clinical diagnostic laboratories were compiled to identify those with the highest prior likelihood of being a RPPV, based on concordant interpretations. Evidence of reduced penetrance included: 1) the identification of biallelic Fanconi Anemia-affected carriers; 2) splicing, missense, and nonsense-mediated RNA decay (NMD)-escaping variants; 3) laboratory-validated cancer history weighting models; 4) published variant-specific risk estimation data; 5) extrapolation of a reduced-risk interpretation onto close match variants that are expected to have the same effect; and 6) functional data. Sixteen candidate BRCA RPPV present in both lists from both laboratories were systematically assessed. For BRCA1, variants included c.5096G&amp;gt;A p.(Arg1699Gln) and variants impacting the canonical c.671 splice acceptor site. For BRCA2, variants included three frameshift (c.658_658delGT, c.9672dupA, c.9699_9702delTATG), two spliceogenic (c.8488-1G&amp;gt;A and c.8488-1G&amp;gt;T), and three missense (c.7878G&amp;gt;C, T, p.(Trp2626Cys); and c.9302T&amp;gt;G p.(Leu3101Arg)) variants. Our study is the first to clearly evaluate and establish BRCA RPPVs as a new class of variants, based on consistency in results across 16 variants using different interpretation strategies between laboratories. These variants impart a moderately increased risk of breast cancer, often considered to be between 2-4 fold, which should be considered when determining risk-informed cancer prevention strategies. Furthermore, our study establishes a framework to harmonize interpretation and standardize reporting of BRCA RPPV, by providing evidence to support this new classification for 16 variants. Further work to define clinically meaningful risk thresholds and categories for reporting BRCA RPPV are needed to personalize cancer risks in conjunction with other risk factors. Citation Format: Tuya Pal, Erin Mundt, Marcy Richardson, Elizabeth Chao, Tina Pesaran, Thomas P. Slavin, Fergus Couch, Alvaro N. Monteiro. Reduced penetrance BRCA1 and BRCA2 pathogenic variants in clinical germline genetic testing [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 6615.

Abstract 5589: CBFB::MYH11 expression activates Trp53 in myeloid cells, which inhibits cell growth and sensitizes cells to cytarabine

Abstract CBFB::MYH11 acute myeloid leukemia (AML) is a subset of Core Binding Factor AML initiated by an oncofusion in which Core Binding Factor Beta (CBFB) is fused to the C-terminus of smooth muscle myosin heavy chain 11 (MYH11). CBFB::MYH11 AML has a high cure rate with conventional anthracycline/cytarabine induction and high-dose cytarabine consolidation in patients under the age of 60, and remains chemosensitive after multiple relapses; the mechanisms underlying chemosensitivity are unknown. TP53 is a key regulator of DNA damage response and apoptosis, and TP53-mutated AML is often chemoresistant. Large sequencing studies have revealed only rare co-occurrence of TP53 mutations with CBFB::MYH11. We initially used CBFB::MYH11 as a negative control for an unrelated study of TP53 function and unexpectedly found striking synergy. While expression of CBFB::MYH11 in WT murine hematopoietic stem and progenitor cells (HSPCs) induced a low-penetrance (~20%), long latency (~6 months) myelomonocytic AML, expression of CBFB::MYH11 in Trp53-/- HSPCs induced AML with 100% penetrance and a median latency of 8 weeks. CBFB::MYH11-induced AMLs arising in Trp53-/- cells were similar by morphology and flow cytometry phenotype. We validated these findings in a second model, using a conditional Trp53flox/flox x Vav1-Cre model in which Trp53 is inactivated in HSPCs, with nearly identical results. Trp53-/- cells transduced with Empty Vector did not develop AML, but did develop T cell lymphoma with a median latency of 16 weeks, similar to prior studies. Trp53 deficiency also cooperated with RUNX1::RUNX1T1, the other Core Binding Factor oncofusion, to induce a high-penetrance (100%) and short latency AML (median latency 10 weeks); consistent with prior data, RUNX1::RUNX1T1 expression alone in Trp53 WT cells was insufficient to induce AML. To determine whether Trp53 influences the chemosensitivity of CBFB::MYH11 expressing cells, we performed cytarabine dose-response experiments. While CBFB::MYH11-transduced WT HSPCs were very sensitive to cytarabine (relative to EV), sensitivity was reduced in CBFB::MYH11-transduced Trp53-/- cells, suggesting that CBFB::MYH11-induced chemosensitivity is, at least in part, mediated by the TP53 pathway. To determine whether CBFB::MYH11 expression results in upregulation of key TP53 pathway genes, we transduced WT HSPCs with EV or CBFB::MYH11, transplanted cells into WT mice, harvested bone marrow at 3 months, and performed RNA-Seq. Key TP53 pathway genes were upregulated, including Bbc3/Puma, Bax, Cdkn1a/p21, and Trp53 (all 2.6-4.6-fold, p≤0.05). These data suggest that the chemosensitivity and the favorable prognosis of CBFB::MYH11 AML may be due to the direct activation of the TP53 pathway by CBFB::MYH11. Similar studies are underway with RUNX1::RUNX1T1-transduced cells to determine whether TP53 is relevant for the chemosensitivity of that CBF AML as well. Citation Format: Ryan B. Day, Julia A. Hickman, Casey D. Katerndahl, Sai Mukund Ramakrishnan, Christopher A. Miller, Timothy J. Ley. CBFB::MYH11 expression activates Trp53 in myeloid cells, which inhibits cell growth and sensitizes cells to cytarabine [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 5589.

Abstract 4208: In-vitro modeling of Sonic Hedgehog signaling identifies the extracellular matrix as a regulator of tumor invasion outcomes

Abstract Preclinical models of Sonic Hedgehog (SHH)-driven tumors support the therapeutic benefit of pharmacological inhibitors, yet clinical outcomes are conflicted due to biphasic tumor responses that include complete remission or faster disease progression. In such studies, the activity of the SHH pathway in the tumor-adjacent stroma supported or restrained tumor progression. Our prior studies show that altered SHH ligand levels can lead to faster tumor growth during pharmacological inhibition suggesting a role for the SHH pathway strength in the biphasic tumor responses of a triple-negative breast cancer (TNBC) model. In this study, the role of SHH pathway strength altered by tumor-fibroblast proximity was examined on the tumor growth, invasion, and transcriptional activity using in vitro and in vivo models. Tumor spheroid models composed of TNBC cell lines and fibroblasts were used to monitor SHH signal strength as a function of the culture modality and SHH ligand levels. In vitro, SHH signal strength was altered by tumor-fibroblast proximity and ligand concentration. Mixed cultures exhibited significantly higher SHH activity and reduced invasion than adjacent cultures. This effect was reversed by pharmacological inhibition of SMO, confirming the involvement of an SHH-dependent mechanism. In a tumor xenograft model, tumor metastasis outcomes were suppressed by pharmacological inhibition of the SHH pathway, but opposite results were observed in tumor xenografts composed of co-injected with fibroblasts indicating that the biphasic tumor response can be driven by the fibroblasts. Spatial transcriptional analysis identified extracellular matrix (ECM) activities in fibroblasts as the top altered bioprocesses in both in vitro and in vivo. This finding was confirmed in cultures, where fibroblast-derived decellularized matrix stimulated tumor invasion of single TNBC spheroids at levels observed in co-cultures. Further analysis of differentially expressed genes (DEGs) identified a small leucine-rich repeat proteoglycan (SLRP) as the primary ECM target altered by variations in SHH signal strength in fibroblasts. Furthermore, we confirmed the role of SLRP cues in governing tumor invasion potency within spheroid cultures embedded in a collagen hydrogel matrix. While high levels of SLRP in the matrix inhibited spheroid invasion, low SLRP matrix promoted spheroid invasion and increased secondary invasion sites. These findings agreed with the Vimentin expression levels observed in TNBC cells and confirmed the potency of SLRP in regulating tumor invasion outcomes. In summary, our study underscores the significance of ECM remodeling activities in fibroblasts and the role of SLRPs in governing tumor invasion, with implications for understanding the impact of altering SHH signaling activity in TNBC. Citation Format: Emanuel A. Carrasquillo-Dones, Heizel M. Rosado-Galindo, Ana M. Reyes-Ramos, Wandaliz Torres-Garcia, Said Cifuentes, Jan P. Rios-Grant, Gabriela Ortiz-Soto, Natalia A. Ramos-Acevedo, Miosotis Acevedo-Esquilin, Monica Colon-Vargas, Israel Almodovar-Rivera, Camilo Mora, Michelle M. Martinez-Montemayor, Maribella Domenech. In-vitro modeling of Sonic Hedgehog signaling identifies the extracellular matrix as a regulator of tumor invasion outcomes [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 4208.

Wellness Tourism Attributes and Tourist Outcomes: An Analysis of Configurational Effects

Prior studies have identified the complexity involved in explaining the relationship between wellness tourism attributes and tourist behavior. This study investigated the configurational effects of 10 constructs in predicting the ambiguous area between overall satisfaction and destination behavioral loyalty. To achieve this, the study used a fuzzy set qualitative comparative analysis (fsQCA) and necessary condition analysis (NCA). The results of the fsQCA generated a range of models that effectively predicted tourists’ high or low levels of overall satisfaction and behavioral loyalty. Furthermore, the NCA results identified perceived quality as an essential construct for achieving the combination of overall satisfaction and behavioral loyalty. The study enhances our current knowledge by broadening our comprehension of the intricate aspects of tourists’ decision-making processes and the formation of their behaviors. Additionally, it provides a roadmap for the industry to effectively address challenges in managing wellness tourism resources.

Longitudinal effects of prenatal alcohol exposure on visual neurodevelopment over infancy.

Prenatal alcohol exposure (PAE) affects neurodevelopment in over 59 million individuals globally. Prior studies using dichotomous categorization of alcohol use and comorbid substance exposures provide limited knowledge of how prenatal alcohol specifically impacts early human neurodevelopment. In this longitudinal cohort study from Cape Town, South Africa, PAE is measured continuously-characterizing timing, dose, and drinking patterns (i.e., binge drinking). High-density electroencephalography (EEG) during a visual-evoked potential (VEP) task was collected from infants aged 8 to 52 weeks with prenatal exposure exclusively to alcohol and matched on sociodemographic factors to infants with no substance exposure in utero. First trimester alcohol exposure related to altered timing of the P1 VEP component over the first 6 months postnatally, and first trimester binge drinking exposure altered timing of the P1 VEP components such that increased exposure was associated with longer VEP latencies while increasing age was related to shorter VEP latencies (n = 108). These results suggest alcohol exposure in the first trimester may alter visual neurodevelopmental timing in early infancy. Exploratory individual-difference analysis across infants with and without PAE tested the relation between VEP latencies and myelination for a subsample of infants with usable magnetic resonance imaging (MRI) T1w and T2w scans collected at the same time point as EEG (n = 47). Decreased MRI T1w/T2w ratios (an indicator of myelin) in the primary visual cortex (n = 47) were linked to longer P1 VEP latencies. Results from these two sets of analyses suggest that prenatal alcohol and postnatal myelination may both separately impact VEP latency over infancy. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Extreme Outcomes Accentuate Overweighting of Low Probabilities and Underweighting of High Probabilities in Experience‐Based Choice

ABSTRACTSubjective inferences of probability are critical for decisions involving uncertainty. Recent studies have suggested that extreme outcomes bias beliefs about the value of uncertain options toward the best/worst outcome possible when learning the odds through experience, leading to increased preferences for uncertain options over equivaluable sure bets when there is the prospect of gain and, conversely, aversion to uncertain options when there is the prospect of loss. However, prior studies regarding the influence of extreme outcomes on decisions involving uncertainty have only done so using 50/50 gambles, and it was unclear whether extreme outcomes biased probability perception more broadly. Across three pre‐registered experiments, we found that when people made decisions between equivaluable certain and uncertain options, they particularly preferred uncertain options at low probabilities (20%) when there was the prospect of gain and avoided them when there was the prospect of loss, with these preferences being reduced or even reversed at medium (50%) and high (80%) probabilities. We also found that uncertainty preferences were influenced by outcome extremity and the relative certainty associated with safe reference options. We conclude that extreme outcomes accentuate the overweighting of low probabilities and the underweighting of high probabilities in experience‐based choice.

Detection of hepatocellular carcinoma methylation markers in salivary DNA.

Alterations to DNA methylation have been identified in both hepatocellular carcinoma (HCC) tumor and circulating DNA from affected individuals. These markers have potential utility in HCC screening. Adherence to HCC screening is poor and acceptable HCC screening tests are needed. A feasibility study was performed on a subset of case patients and control subjects from a prior study of risk factors for HCC. Case patients (n=12) included adults aged 47-85 years with a first diagnosis of HCC between 2011 and 2016 and without viral hepatitis. Control subjects (n=12) were matched on age, sex, and state of residence. Participants provided saliva samples for DNA genotyping. Log fold change in salivary DNA methylation at 1359 CpG sites representing 25 candidate genes previously associated with HCC was compared across case patients and control subjects. The quantity of DNA ranged from 9.65 to 257.79 μg. The purity of DNA isolates was good, with mean OD260/280 ratio of 1.78 (SD: 0.14). Of 25 candidate genes, 16 had at ≥1 CpG site with detectable differences in methylation across HCC case patients and control subjects. Sites differentially methylated in HCC case patients included genes encoding tumor suppressors (PRDM2, RUNX3, p15/16, and RASSF1/5), regulators of cell cycle progression (DAPK1 and TP73), and DNA repair (MGMT and GSTP1). No associations met the significance threshold 3.7 × 10-5 required for multiple comparisons. Salivary DNA may be a feasible alternative to blood samples in the era of novel DNA-based screening tests for HCC. The ease of saliva-based testing supports further investigation of its potential.