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Magnetospectroscopic Studies of a Series of Fe(II) Scorpionate Complexes: Assessing the Relationship between Halide Identity and Zero-Field Splitting
No full textFerrous ions in four-coordinate environments are common in protein structures, synthetic catalysts, and molecular magnets. The 3d6 configuration of high-spin Fe(II) imparts an S = 2 ground state, whose analysis using conventional spectroscopic methods is often hindered by substantial zero-field splitting (ZFS). Herein, we provide detailed electronic-structure descriptions for [FeIIX(TptBu,Me)] (1-X; X = F, Cl, Br, I), where (TptBu,Me)− is hydrotris(3-tert-butyl-5-methyl-pyrazol-1-yl)borate. The three pyrazolyl N-donors of the “scorpionate” ligand facially coordinate to Fe(II), giving idealized C3v symmetry with the halide occupying the axial position. Although originally reported by Theopold and co-workers, this series is revisited herein using advanced experimental and theoretical tools. Ground-state transitions were probed by high-frequency and -field electron paramagnetic resonance (HFEPR) and far-infrared magnetic spectroscopy (FIRMS). Variable-temperature/-field (VTVH) 57Fe Mössbauer spectroscopy, paramagnetic susceptibility, and VTVH reduced magnetization were also utilized. This combined approach provided complete sets of spin-Hamiltonian parameters. Interpretation using ab initio multiconfigurational calculations enabled quantification of halide-dependent magnetoelectronic effects. Jahn–Teller distortions induce a descent in symmetry from C3v to Cs in both solution and solid state. Finally, we demonstrate that the 1-X series is ionic, with the ZFS arising from combined Jahn–Teller and ligand field effects, rather than intrinsic spin–orbit coupling from the halides
Sex Differences in Motor Unit Behavior in Patients With Parkinson\u27s Disease
No full textThe aim of this study was to determine whether there are sex differences in motor unit firing behavior in patients with Parkinson\u27s disease. Twenty-seven patients with Parkinson\u27s disease (females = 14 [age = 71.1 ± 6.8], males = 13 [age = 69.2 ± 10.3], Unified Parkinson\u27s Disease Rating Scale Part III score; females = 10.8 ± 4.8, males = 11.4 ± 1.4) performed a contraction at 30% of the maximal voluntary contraction. For each participant, motor unit spike trains were decomposed from high-density surface electromyography data recorded from bilateral vastus lateralis muscles via blind source separation algorithms. In addition to the mean discharge rates, persistent inward currents were estimated via a paired motor unit analysis. Females presented significantly greater laterality of discharge rate (p = 0.001) and persistent inward currents (p = 0.0121) than males. A significant correlation was observed between the discharge rate and the recruitment threshold on the bilateral side of males and the less-affected side of females but not on the more-affected side of females. These findings indicate that sex differences in motor unit behavior exist in Parkinson\u27s disease patients. Motor unit behavior may be a sensitive and quantitative evaluation tool to highlight differences in disease presentation between males and females
PTSD Symptoms Moderate Predictors of Psychophysiological Arousal During Fear Inhibition: Evidence From a Fear, Reward, and Neutral Discrimination Task
No full textThe ability to distinguish between threatening, rewarding, and neutral cues is adaptative and crucial for survival. However, individuals with posttraumatic stress disorder (PTSD) often show poor knowledge of cue contingencies and heightened fear responses even in the presence of cues that signify safety, potentially due to atypical perceptions of neutral cues. We investigated whether perceiving neutral cues as more rewarding or threatening influences conditioned inhibition of fear and whether PTSD symptoms moderate this relationship. Trauma-exposed adults (N = 84; 64 % female; 76 % non-Hispanic white) completed a Fear, Reward, and Neutral Discrimination (FRND) Task involving geometric shapes paired with outcomes (Fear: white noise; Reward: monetary gain; Neutral: no outcome) and conditioned inhibition trials (Fear+Neutral and Reward+Neutral: no outcome). Skin conductance responses (SCR) quantified psychophysiological arousal, and participants rated the valence of each cue. PTSD symptoms were evaluated with the PTSD Checklist for DSM-5. Linear regressions examined PTSD severity as a moderator of the relationship between Reward vs. Neutral or Fear vs. Neutral valence difference and SCR during inhibition. Among individuals with less severe PTSD symptoms, stronger fear inhibition effects were observed when neutral cues were rated more similarly to reward cues (β = 0.12, p = .022); however, this relationship was not significant at average or higher PTSD severity. Our results emphasize that perceptions of neutral cues contribute to fear inhibition and may underlie PTSD-related deficits in safety learning. Future investigations on PTSD and fear inhibition should consider incorporating measures of reward-related processing to examine the overlap between rewarding and inhibitory qualities of safety signals
Diffusion MRI Biomarkers for Predicting Treatment Outcomes in Infantile Epileptic Spasms Syndrome with Non-Lesional MRI
No full textBackground
Infantile epileptic spasms syndrome (IESS) is a devastating developmental epileptic encephalopathy (DEE) and patients exhibit diffuse white matter alterations and structural remodeling. However, the correlation between these structural changes and brain network properties, or their effect on the efficacy of treatment outcomes in MRI non-lesional IESS patients is not clear. Method
This retrospective study was conducted on IESS patients using fixel-based analysis (FBA) of diffusion MRI and graph theory analysis of structural connectivity, involving 26 non-lesional IESS patients aged 2 to 12 months and 120 age-matched controls. We further examined the differences between antiseizure medication (ASM) responders and non-responders within the IESS cohort. FBA was performed across three age groups (2–5, 6–7, and 8–12 months) to evaluate white matter integrity at the micro- and macroscale using fiber density (FD), fiber cross-section (FC), and combined fiber density and cross-section (FDC). Graph theory analysis was used to assess global and local network properties. Results
When compared to the control group, IESS patients exhibited significantly lower FD, FC, and FDC across major white matter tracts, including the corticospinal tract, corpus callosum, superior longitudinal fasciculus, optic radiations, and thalamic radiations (family-wise error-corrected, p \u3c 0.05). Graph theory analysis revealed significant alterations in brain network properties, particularly in the age group of 2–5 months, where IESS patients exhibited a significantly lower mean clustering coefficient (p \u3c 0.001, d = -0.74) and global efficiency (p = 0.001, d = -0.69. Small-world network analysis demonstrated a shift toward a more randomized network structure in IESS patients, particularly in the age group of 6–7 months (p = 0.001, d= -0.6182). In the secondary analysis, ASM treatment responders showed higher FD values in regions critical for seizure control, such as the hippocampus. Meanwhile, the ASM treatment non-responders exhibited increased FC in areas such as the pons and brainstem. Although subgroup differences did not achieve statistical significance, trends suggest that white matter integrity and network organization may influence treatment outcomes. Conclusion
The results highlight widespread changes in white matter integrity and network connectivity in non-lesional IESS patients, with preliminary evidence suggesting a relationship between structural brain differences and treatment responsiveness. These findings underscore the potential of advanced neuroimaging analyses to guide personalized interventions in IESS
Towards the Advancement of Violence Recognition in Security Footage with Explainable Neural Networks
No full textThis dissertation investigates the problem of violence recognition in surveillance footage using computer vision and machine learning techniques. More specifically, our goal is to achieve interpretable and explainable deep learning models because violence recognition is a sensitive task. We first propose to perform violence recognition using a 3D convolutional neural network through intuitive hyperparameter tuning and transfer learning. We utilize a state-of-the-art 3D model used for general activity recognition that is lightweight and adjustable. Along with that, we introduce a data augmentation technique called resize-within which uses interpolation, rather than cropping, to resize the original input video to a new width, height, during model training. Using this as the base model, we continue to provide a means for model explainability using class activation maps. That is, during model training, the proposed approach compares the areas of the salient regions that the model uses to make its prediction with that of the active regions pertaining to involved individuals in the violent act. This forces the model to look / focus on the regions related to violence, reducing the ambiguity of where in the frame the model is using to make its decision. To the best of our knowledge, this is the first work to provide bounding box labels for involved individuals and saliency evaluation in a violence recognition dataset. Finally, we introduce a deep learning model with built-in model interpretability through case-based reasoning through prototypical examples. This approach utilizes the input latent space, i.e. the input feature maps, and compares it with some learned prototypical feature maps. The nearest prototype feature maps are then concatenated with the input latent space and are used together to make the model prediction. As the model makes a prediction based on multiple sources of information, it increases model performance, as well as adding model prediction interpretability through examples. Since the prototypes are feature maps, we can also show direct active regions the model is associating with the input and its nearest prototype. This work demonstrates that deep learning models can simultaneously improve performance and have greater interpretability. The described proposed methods are evaluated on publicly available benchmark violence recognition datasets (RWF-2000, SCFD, and ViolentFlows)
Inferring Daily Gas Consumption from Multiple Nonuniformly Sampled Billing Cycles with Hierarchical Constraints
No full textLocal natural gas demand forecasting is essential for local distribution companies (LDCs) to optimize resources, maintain efficiency, and meet regulatory requirements. However, inconsistencies arising from nonuniform billing cycles, hierarchical data structures, and aggregated forecasts often degrade accuracy. This dissertation introduces three methodological advancements to address these issues: the Multi-Source Iterative Shifting Disaggregation (ISD) algorithm, Single-Dimension Hierarchical Reconciliation, and Cross-Temporal Hierarchical Forecast Reconciliation. The ISD algorithm disaggregates multiple, overlapping, nonuniformly sampled time series into a coherent high-frequency signal. Unlike existing methods, ISD iteratively refines estimates through constrained load shifting, improving forecast accuracy. Applied to billing cycle data, ISD reduces the Weighted Mean Absolute Percentage Error (WMAPE) by 1.4–4.3%, while residential consumption disaggregation sees a 4.6–10.4% improvement. To enforce coherence in hierarchical forecasting, Single-Dimension Hierarchical Reconciliation corrects inconsistencies within either spatial or temporal hierarchies, enhancing forecast reliability and reducing bias. Cross-Temporal Hierarchical Forecast Reconciliation extends this approach by integrating both spatial and temporal constraints, ensuring consistency across all levels. This method improves hourly forecast accuracy by 10% and daily accuracy by 3%, outperforming Variance Scaling by 7% and 9%, respectively. This dissertation demonstrates that maintaining hierarchical coherence enhances gas demand forecasting. The proposed methods improve accuracy, reduce error, and provide a scalable framework applicable to broader energy forecasting problems. These advancements offer LDCs practical tools to optimize operations and inform decision-making
Interactions Between Explicit and Implicit Memories During Sensorimotor Adaptation and After Concussion
No full textSensorimotor adaptation is a form of motor learning whereby a motor skill is adjusted in the face of a disturbance to return performance to desired performance. This process uses memories of previous experiences to guide future movements. While the temporal characteristics of adaptation have been well studied, the extent to which these memories are explicit, implicit, or a mixture of both is unclear. In this Dissertation, I conduct three studies to investigate the relative contributions of sensorimotor memories when adapting to stochastic spring-like loads applied to the hand. By implementing computational modeling and system identification techniques, I fit memory models to movement errors and external disturbances to yield the relative contributions of sensorimotor memories. In Aim 1, I determined if the memories used in sensorimotor adaptation to stochastic loads are predominantly explicit or implicit. Subjects grasped a handle of the robotic device and performed out-and-back reaches while the robot imposed spring-like forces to oppose movement. Immediately after each reach, subjects reported where they thought they moved – an assay of explicit memory of performance. Model fits revealed that explicit memories of performance did not outperform models that only contained actual reach error – a proxy of implicit memory. Thus, sensorimotor adaptation predominantly used implicit memories. In Aim 2, I determined if instructing subjects to explicitly attempt to underperform during the reaching test would influence how these memories were used in adaptation. Subjects were instructed to simulate having a concussion (moving slower, less accurate, and having memory deficits). The relative contributions of sensorimotor memories did not significantly change when subjects attempted to sabotage the test, further supporting the notion of inaccessible implicit mechanisms at play. In Aim 3, recently concussed individuals participated in a longitudinal study wherein I observed changes in the relative contributions of motor memories as their injury recovered. Concussion did impact the process of sensorimotor adaptation, but practice effects obscured initial effects of the injury on sensorimotor memories. This work provides novel insights into how explicit and implicit processes interact during motor learning and adaptation, offering potential applications for assessing motor control after brain injury
Synthetic and Mechanistic Investigations of Ruthenium Catalyzed Coupling Reactions via C−C and C−N Bond Activation
No full textTransition-metal catalyzed C-N and C-C bond activation reactions are synthetically valuable reactions for selective and atom-economical synthesis of complex organic molecules. Catalytic reactions that involve activation of unreactive bonds are important for the stereoselective synthesis of molecular scaffolds from readily available substrates derived from bio-mass feedstock. Despite these advances, designing a broadly applicable catalytic method faces challenges of selectivity and harsh reaction conditions. A five coordinate Ru-H complex was found to be an effective catalyst for promoting the multicomponent deaminative coupling reaction of anilines, aldehydes and tertiary amines to selectively afford 2,3-disubstituted quinoline products. The scope of the reaction was expanded to enamines to generate 2,3,4-trisubstituted quinolines. We devised a stereoselective synthesis of (Z)-acrylic nitriles from the Ru-catalyzed coupling reaction of nitriles with unsaturated carbonyl compounds via C–C bond cleavage. Mechanistic studies revealed that the C-C bond cleavage step is the rate-determining step of the reaction mechanism. A tetranuclear Ru-H complex was found to be an effective catalyst for the hydrodeaminative coupling reaction of nitriles and amides to afford secondary amides. The inverted V-shaped Hammett plot revealed a change in reaction mechanism depending on the electronic environment of the nitrile substrate. We detected a catalytically relevant intermediate by generating a Ru-H species in situ. A tentative mechanism for the coupling reaction was proposed based on the experimental data which involves nucleophilic attack of amide on the nitrile substrate followed by amine hydrogenolysis
Preparedness of Speech Language Pathologists and Occupational Therapists to Treat Pediatric Feeding Disorder: A Cross-Sectional Survey
No full textBackground
Pediatric feeding disorder (PFD) is increasingly common and is often treated by speech language pathologists (SLPs) and occupational therapists (OTs) in the community setting. However, the preparedness of these disciplines to effectively address PFD is relatively unknown. Methods
A national (US), online survey was disseminated to providers who assess and treat PFD. For the present analysis, the responses of SLPs (N = 418) and OTs (N = 195) related to their clinical background, educational background, post-graduate training, and self-rated clinical effectiveness were statistically analyzed and compared across the two disciplines. Results
Both SLPs and OTs report feeling underprepared to work with PFD clients immediately following their academic training, but time spent in post-graduate training and years of clinical practice both significantly (p \u3c 0.0001) increased feelings of effectiveness in assessing and treating PFD. Most SLPs and OTs pursued self-directed learning activities to increase competence, with the most common activities being article review, podcasts, and peer case review, although SLPs were significantly more likely to use podcasts (p \u3c 0.0001) and peer review (p = 0.0004) than OTs. The most common barriers for providers were financial, time, travel, and institutional support barriers. Conclusions
While PFD is a key practice area of both SLPs and OTs, both provider groups feel unprepared and under-supported in providing competent care to these patients upon graduation. Future research and policy should support advancements in training for current SLPs and OTs related to PFD and address current barriers to a specialized educational pathway