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Lewis acid catalyzed condensation of pyrrole and 4-fluoro-2,6-dimethylbenzaldehyde followed by chemical oxidation afforded the corresponding chlorin along with the parent porphyrin. The subsequent metalation of the porphyrin-chlorin mixture in the presence of Zn(OAc)₂•2H₂O afforded Zn monoand di-hydroxychlorins in addition to the Zn porphyrin in a one-flask synthesis. This new direct hydroxylation reaction eliminates the need for highly toxic OsO₄and H₂S that are traditionally used for the generation of hydroxy chlorins. In addition to the full characterization of the zinc chlorins, we present cyclic voltammograms, steady-state absorption, and emission profiles of this rarely available class of compounds. Our findings show that Zn mono- and di-hydroxychlorins are stable compounds that possess exceptionally long triplet excited states in solution, making them promising candidates for photodynamic therapy. 

Pediatric epilepsy due to drug-resistant Focal Cortical Dysplasia (FCD) presents significant healthcare challenges. Precise preoperative identification of FCD lesions is imperative for surgical planning and patient outcomes. This paper presents a proof-of-concept for an integrated methodology that combines Electroencephalogram (EEG)-based functional connectivity analysis with Magnetic Resonance Imaging (MRI)-derived cortical thickness measurements to identify FCD lesions in pediatric epileptic patients. We examined a single-case clinical scenario from Oregon Health Science and University, consistently identifying the Caudal Middle Frontal (cMFG) region across both EEG and MRI modalities, a finding that was confirmed in the postoperative MRI scan. This cross-validation underscores the potential of the precision of our approach in pinpointing the surgical target region. Despite being constrained by its preliminary nature, our research offers a valuable foundation for a personalized, rigorous method of detecting the location of the FCD lesions. It holds significant clinical implications for managing FCD-related epilepsy. It also portends broader applications in neurology and precision medicine. Nonetheless, further large-scale studies are needed to validate and fine-tune our methodology. Clinical Relevance - This study offers clinicians an advanced, integrated approach to preoperative assessment of FCD lesions, potentially improving the precision of surgical planning in pediatric epilepsy. 

Deep Neural Networks (DNNs) trained for classification tasks are vulnerable to adversarial attacks. But not all the classes are equally vulnerable. Adversarial training does not make all classes or groups equally robust as well. For example, in classification tasks with long-tailed distributions, classes are asymmetrically affected during adversarial training, with lower robust accuracy for less frequent classes. In this regard, we propose a provable robustness method by leveraging the continuous piecewise-affine (CPA) nature of DNNs. Our method can impose linearity constraints on the decision boundary, as well as the DNN CPA partition, without requiring any adversarial training. Using such constraints, we show that the margin between the decision boundary and minority classes can be increased in a provable manner. We also present qualitative and quantitative validation of our method for class-specific robustness. Our code is available at https: //github.com/Josuelmet/CROP