Critical exponents at the ferromagnetic transition in tetrakis(diethylamino)ethylene-C60_{60} (TDAE-C60_{60})

Critical exponents at the ferromagnetic transition were measured for the first time in an organic ferromagnetic material tetrakis(dimethylamino)ethylene fullerene[60] (TDAE-C$_{60}$). From a complete magnetization-temperature-field data set near $T_{c}=16.1\pm 0.05,$ we determine the susceptibility and magnetization critical exponents $\gamma =1.22\pm 0.02$ and $\beta =0.75 \pm 0.03$ respectively, and the field vs. magnetization exponent at $T_{c}$ of $\delta =2.28\pm 0.14$. Hyperscaling is found to be violated by $\Omega \equiv d^{\prime}-d \approx -1/4$, suggesting that the onset of ferromagnetism can be related to percolation of a particular contact configuration of C$_{60}$ molecular orientations.Comment: 5 pages, including 3 figures; to appear in Phys. Rev. Let

The polymer phase of the TDAE-C60_{60} organic ferromagnet

The high-pressure Electron Spin Resonance (ESR) measurements were preformed on TDAE-C$_{60}$ single crystals and stability of the polymeric phase was established in the $P - T$ parameter space. At 7 kbar the system undergoes a ferromagnetic to paramagnetic phase transition due to the pressure-induced polymerization. The polymeric phase remains stable after the pressure release. The depolymerization of the pressure-induced phase was observed at the temperature of 520 K. Below room temperature, the polymeric phase behaves as a simple Curie-type insulator with one unpaired electron spin per chemical formula. The TDAE$^+$ donor-related unpaired electron spins, formerly ESR-silent, become active above the temperature of 320 K and the Curie-Weiss behavior is re-established.Comment: Submitted to Phys. Rev.

Cardiovascular/Stroke Risk Assessment in Patients with Erectile Dysfunction—A Role of Carotid Wall Arterial Imaging and Plaque Tissue Characterization Using Artificial Intelligence Paradigm: A Narrative Review

Purpose: The role of erectile dysfunction (ED) has recently shown an association with the risk of stroke and coronary heart disease (CHD) via the atherosclerotic pathway. Cardiovascular disease (CVD)/stroke risk has been widely understood with the help of carotid artery disease (CTAD), a surrogate biomarker for CHD. The proposed study emphasizes artificial intelligence-based frameworks such as machine learning (ML) and deep learning (DL) that can accurately predict the severity of CVD/stroke risk using carotid wall arterial imaging in ED patients. Methods: Using the PRISMA model, 231 of the best studies were selected. The proposed study mainly consists of two components: (i) the pathophysiology of ED and its link with coronary artery disease (COAD) and CHD in the ED framework and (ii) the ultrasonic-image morphological changes in the carotid arterial walls by quantifying the wall parameters and the characterization of the wall tissue by adapting the ML/DL-based methods, both for the prediction of the severity of CVD risk. The proposed study analyzes the hypothesis that ML/DL can lead to an accurate and early diagnosis of the CVD/stroke risk in ED patients. Our finding suggests that the routine ED patient practice can be amended for ML/DL-based CVD/stroke risk assessment using carotid wall arterial imaging leading to fast, reliable, and accurate CVD/stroke risk stratification. Summary: We conclude that ML and DL methods are very powerful tools for the characterization of CVD/stroke in patients with varying ED conditions. We anticipate a rapid growth of these tools for early and better CVD/stroke risk management in ED patients

Deep Learning Paradigm for Cardiovascular Disease/Stroke Risk Stratification in Parkinson’s Disease Affected by COVID‐19: A Narrative Review

Background and Motivation: Parkinson’s disease (PD) is one of the most serious, non-curable, and expensive to treat. Recently, machine learning (ML) has shown to be able to predict cardiovascular/stroke risk in PD patients. The presence of COVID‐19 causes the ML systems to be-come severely non‐linear and poses challenges in cardiovascular/stroke risk stratification. Further, due to comorbidity, sample size constraints, and poor scientific and clinical validation techniques, there have been no well‐explained ML paradigms. Deep neural networks are powerful learning machines that generalize non‐linear conditions. This study presents a novel investigation of deep learning (DL) solutions for CVD/stroke risk prediction in PD patients affected by the COVID‐19 framework. Method: The PRISMA search strategy was used for the selection of 292 studies closely associated with the effect of PD on CVD risk in the COVID‐19 framework. We study the hypothesis that PD in the presence of COVID‐19 can cause more harm to the heart and brain than in non‐ COVID‐19 conditions. COVID‐19 lung damage severity can be used as a covariate during DL training model designs. We, therefore, propose a DL model for the estimation of, (i) COVID‐19 lesions in computed tomography (CT) scans and (ii) combining the covariates of PD, COVID‐19 lesions, office and laboratory arterial atherosclerotic image‐based biomarkers, and medicine usage for the PD patients for the design of DL point‐based models for CVD/stroke risk stratification. Results: We validated the feasibility of CVD/stroke risk stratification in PD patients in the presence of a COVID‐ 19 environment and this was also verified. DL architectures like long short‐term memory (LSTM), and recurrent neural network (RNN) were studied for CVD/stroke risk stratification showing powerful designs. Lastly, we examined the artificial intelligence bias and provided recommendations for early detection of CVD/stroke in PD patients in the presence of COVID‐19. Conclusion: The DL is a very powerful tool for predicting CVD/stroke risk in PD patients affected by COVID‐19

Cardiovascular Risk Stratification in Diabetic Retinopathy via Atherosclerotic Pathway in COVID-19/non-COVID-19 Frameworks using Artificial Intelligence Paradigm: A Narrative Review

Diabetes is one of the main causes of the rising cases of blindness in adults. This microvascular complication of diabetes is termed diabetic retinopathy (DR) and is associated with an expanding risk of cardiovascular events in diabetes patients. DR, in its various forms, is seen to be a powerful indicator of atherosclerosis. Further, the macrovascular complication of diabetes leads to coronary artery disease (CAD). Thus, the timely identification of cardiovascular disease (CVD) complications in DR patients is of utmost importance. Since CAD risk assessment is expensive for lowincome countries, it is important to look for surrogate biomarkers for risk stratification of CVD in DR patients. Due to the common genetic makeup between the coronary and carotid arteries, lowcost, high-resolution imaging such as carotid B-mode ultrasound (US) can be used for arterial tissue characterization and risk stratification in DR patients. The advent of artificial intelligence (AI) techniques has facilitated the handling of large cohorts in a big data framework to identify atherosclerotic plaque features in arterial ultrasound. This enables timely CVD risk assessment and risk stratification of patients with DR. Thus, this review focuses on understanding the pathophysiology of DR, retinal and CAD imaging, the role of surrogate markers for CVD, and finally, the CVD risk stratification of DR patients. The review shows a step-by-step cyclic activity of how diabetes and atherosclerotic disease cause DR, leading to the worsening of CVD. We propose a solution to how AI can help in the identification of CVD risk. Lastly, we analyze the role of DR/CVD in the COVID-19 framework

Conditional targeting of MAD1 to kinetochores is sufficient to reactivate the spindle assembly checkpoint in metaphase

Fidelity of chromosome segregation is monitored by the spindle assembly checkpoint (SAC). Key components of the SAC include MAD1, MAD2, BUB1, BUB3, BUBR1, and MPS1. These proteins accumulate on kinetochores in early prometaphase but are displaced when chromosomes attach to microtubules and/or biorient on the mitotic spindle. As a result, stable attachment of the final chromosome satisfies the SAC, permitting activation of the anaphase promoting complex/cyclosome (APC/C) and subsequent anaphase onset. SAC satisfaction is reversible, however, as addition of taxol during metaphase stops cyclin B1 degradation by the APC/C. We now show that targeting MAD1 to kinetochores during metaphase is sufficient to reestablish SAC activity after initial silencing. Using rapamycin-induced heterodimerization of FKBP-MAD1 to FRB-MIS12 and live monitoring of cyclin B1 degradation, we show that timed relocalization of MAD1 during metaphase can stop cyclin B1 degradation without affecting chromosome-spindle attachments. APC/C inhibition represented true SAC reactivation, as FKBP-MAD1 required an intact MAD2-interaction motif and MPS1 activity to accomplish this. Our data show that MAD1 kinetochore localization dictates SAC activity and imply that SAC regulatory mechanisms downstream of MAD1 remain functional in metaphase. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1007/s00412-014-0458-9) contains supplementary material, which is available to authorized users

Singlet-triplet energy gap in the non-ferromagnetic

The ESR intensity of the $\alpha'$ modification of TDAE-$\rm C_{60}$ vanishes below 20 K in contrast to the α modification where the ESR intensity tremendously increases due to the ferromagnetic transition. The proton NMR spectra of the $\alpha'$ modification show a single temperature independent NMR line close to the Larmor frequency. Both the ESR and the NMR results can be understood by the existence of S=0 spin pairing in the ground state of the non-ferromagnetic $\alpha'$ modification of TDEA-$\rm C_{60}$ at low temperatures

Single-particle and spin-density wave charge dynamics in (TMTSF)2PF6 and (TMTSF)2AsF6 : A comparative overview

We present the results of DC and AC (100 mHz - 1MHz) electrical transport measurements in low and high electric fields performed in the spin-density wave (SDW) state of the Bechagaards salts (TMTSF)2PF6 and (TMTSF)2AsF6. We argue that a degree of complex structure of the SDW ground state which is unfolded in a particular experiment depends strongly on the chosen experimental probe and the crystal measured