Synthesis of Novel ReIII and ReV Metallointercalator Complexes for Use as DNA Damage Probes

Cancer is a disease that affects millions around the globe. Treatments such as radiation and chemotherapy are often used to treat cancerous tumors, but are not 100% effective in curing the patient. Alternatively, some research groups have proposed the use of metallointercalators, metal complexes consisting of a rhodium, ruthenium, or iridium metal center with bidentate supporting ligands such as bipyridine and phenanthroline, or tridentate ligands such as terpyridine. The complexes also contain a bidentate intercalating group such as dipyridophenazine. These complexes possess the ability to directly bind to DNA through intercalation between the stacks of DNA base pairs, giving them the potential to be used as probes to analyze and identify chemically damaged DNA. The ultimate goal would be to design molecules that could detect damaged DNA at the molecular level and allow treatment before tumors could develop. Our objective is to design and synthesize novel metallointercalator complexes of rhenium, and to examine their physical and spectroscopic properties. Specifically, we are attempting to prepare [Re(Py2NO)(dppz)L]3+, where L = H2O or NH3. (Py2NO = 4,4-dimethyl-2,2-di(2-pyridyl)oxazolidine; dpk-OEt = (py)2C(O)OCH2CH3; dppz = dipyridophenazine). Reaction of TBA2[Re2Cl8] with the nitroxide ligand Py2NO in acetonitrile produces a red/brown compound proposed to be Re(Py­2NO)Cl3. Isolation and purification of this compound has proven to be a challenge. NMR studies indicate the compound is present as an intractable mixture. When the same reaction is performed in ethanol, the blue complex ReO(dpk-OEt)Cl2 is obtained. The same compound can be isolated in good yield by direct reaction of two equivalents of di-2-pyridyl ketone with TBA2[Re2Cl8]. The blue complex has been reacted with two equivalents of Ag+ followed by the addition of dppz to produce a brown solid believed to be the metallointercalator [ReO(dpk-OEt)dppz]2+. Details of the synthesis along with IR, NMR, and UV/Vis spectral characterization of these compounds are described

The Effect of Uncertainty on Explanatory Preference

Previous research on political extremism has led to two competing perspectives. One views extremists as being more knowledgeable and informed about politics than moderates, while the other claims it is moderates who know more. These two views appear to have arisen from studies that examined different types of political knowledge. This phenomenon could be explained by extremists and moderates having different preferences when it comes to their consumption of political information. We hypothesized that participants indirectly manipulated to feel more extreme conviction in their political views by manipulating them to feel uncertain would prefer more simple explanations of political issues compared to a control group. To test this, participants completed a task designed to manipulate their feelings of personal uncertainty, followed by measures designed to gauge their degree of conviction in their political views and their preference for simple vs complex explanations. No significant results were found, but correlational analyses did begin to show a link between conviction and explanatory preference, such that more extreme conviction was associated with preference for more simple explanations. Limitations and implications for future research are discussed

The Effect of Uncertainty on Explanatory Preference

Previous research on political extremism has led to two competing perspectives. One views extremists as being more knowledgeable and informed about politics than moderates, while the other claims it is moderates who know more. These two views appear to have arisen from studies that examined different types of political knowledge. This phenomenon could be explained by extremists and moderates having different preferences when it comes to their consumption of political information. We hypothesized that participants indirectly manipulated to feel more extreme conviction in their political views by manipulating them to feel uncertain would prefer more simple explanations of political issues compared to a control group. To test this, participants completed a task designed to manipulate their feelings of personal uncertainty, followed by measures designed to gauge their degree of conviction in their political views and their preference for simple vs complex explanations. No significant results were found, but correlational analyses did begin to show a link between conviction and explanatory preference, such that more extreme conviction was associated with preference for more simple explanations. Limitations and implications for future research are discussed

PIP: Positional-encoding Image Prior

In Deep Image Prior (DIP), a Convolutional Neural Network (CNN) is fitted to map a latent space to a degraded (e.g. noisy) image but in the process learns to reconstruct the clean image. This phenomenon is attributed to CNN's internal image-prior. We revisit the DIP framework, examining it from the perspective of a neural implicit representation. Motivated by this perspective, we replace the random or learned latent with Fourier-Features (Positional Encoding). We show that thanks to the Fourier features properties, we can replace the convolution layers with simple pixel-level MLPs. We name this scheme ``Positional Encoding Image Prior" (PIP) and exhibit that it performs very similarly to DIP on various image-reconstruction tasks with much less parameters required. Additionally, we demonstrate that PIP can be easily extended to videos, where 3D-DIP struggles and suffers from instability. Code and additional examples for all tasks, including videos, are available on the project page https://nimrodshabtay.github.io/PIP

Deep Phase Coded Image Prior

Phase-coded imaging is a computational imaging method designed to tackle tasks such as passive depth estimation and extended depth of field (EDOF) using depth cues inserted during image capture. Most of the current deep learning-based methods for depth estimation or all-in-focus imaging require a training dataset with high-quality depth maps and an optimal focus point at infinity for all-in-focus images. Such datasets are difficult to create, usually synthetic, and require external graphic programs. We propose a new method named "Deep Phase Coded Image Prior" (DPCIP) for jointly recovering the depth map and all-in-focus image from a coded-phase image using solely the captured image and the optical information of the imaging system. Our approach does not depend on any specific dataset and surpasses prior supervised techniques utilizing the same imaging system. This improvement is achieved through the utilization of a problem formulation based on implicit neural representation (INR) and deep image prior (DIP). Due to our zero-shot method, we overcome the barrier of acquiring accurate ground-truth data of depth maps and all-in-focus images for each new phase-coded system introduced. This allows focusing mainly on developing the imaging system, and not on ground-truth data collection