Rethinking the Role of Recourse in the Sale of Financial Assets

The presence of recourse in the sale of a financial asset is generally thought to jeopardize the true sale treatment of the sale, especially in the event of the seller\u27s bankruptcy. This article examines the existing law and concludes that a transfer that qualifies as a sale under state law should be treated as a sale even if the buyer retains recourse to the seller, so long as recourse is limited to warranting that the asset will perform in accordance with its terms

Sustainable Recycling of Insoluble Rust Waste for the Synthesis of Iron-Containing Perovskite-Type Catalysts

Insoluble rust waste from the scraping of rusted iron-containing materials represents a cheap, eco-friendly, and available source of iron. LaFeO3 perovskite-type powders were successfully prepared by solution combustion synthesis using rust waste from an electricity transmission tower manufacturer. Solution combustion synthesis enabled introduction of this insoluble iron precursor directly into the final product, bypassing complex extraction procedures. Catalytic activity in the propylene oxidation of the waste-derived LaFeO3 with stoichiometric Fe/La ratio was almost identical to the commercial iron nitrate-derived LaFeO3 , thus demonstrating the viability of this recycling solution. The amount of waste iron precursor was varied and its effect on the powder properties was investigated. A lesser stoichiometric amount of precursor produced a LaFeO3 -La2O3 binary system, whereas a higher stoichiometric amount led to a LaFeO3 -Fe2O3 binary system. Catalytic activity of iron-rich compositions in the propylene oxidation was only slightly lower than the stoichiometric one, whereas iron-poor compositions were much less active. This eco-friendly methodology can be easily extended to other iron perovskites with different chemical compositions and to other iron-containing compounds

Magnetic Monopoles in Ferromagnetic Spin-Triplet Superconductors

Using the $\phi$-mapping method, we argue that ferromagnetic spin-triplet superconductors allow formation of unstable magnetic monopoles. In particular, we show that the limit points and the bifurcation points of the $\phi$-mapping will serve as the interaction points of these magnetic monopoles.Comment: 4 pages, no figure

Essential Role of the cVRG in the Generation of Both the Expiratory and Inspiratory Components of the Cough Reflex.

As stated by Korpáš and Tomori (1979), cough is the most important airway protective reflex which provides airway defensive responses to nociceptive stimuli. They recognized that active expiratory efforts, due to the activation of caudal ventral respiratory group (cVRG) expiratory premotoneurons, are the prominent component of coughs. Here, we discuss data suggesting that neurons located in the cVRG have an essential role in the generation of both the inspiratory and expiratory components of the cough reflex. Some lines of evidence indicate that cVRG expiratory neurons, when strongly activated, may subserve the alternation of inspiratory and expiratory cough bursts, possibly owing to the presence of axon collaterals. Of note, experimental findings such as blockade or impairment of glutamatergic transmission to the cVRG neurons lead to the view that neurons located in the cVRG are crucial for the production of the complete cough motor pattern. The involvement of bulbospinal expiratory neurons seems unlikely since their activation affects differentially expiratory and inspiratory muscles, while their blockade does not affect baseline inspiratory activity. Thus, other types of cVRG neurons with their medullary projections should have a role and possibly contribute to the fine tuning of the intensity of inspiratory and expiratory efforts

Antibodies to combat viral infections: development strategies and progress.

Monoclonal antibodies (mAbs) are appealing as potential therapeutics and prophylactics for viral infections owing to characteristics such as their high specificity and their ability to enhance immune responses. Furthermore, antibody engineering can be used to strengthen effector function and prolong mAb half-life, and advances in structural biology have enabled the selection and optimization of potent neutralizing mAbs through identification of vulnerable regions in viral proteins, which can also be relevant for vaccine design. The COVID-19 pandemic has stimulated extensive efforts to develop neutralizing mAbs against severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), with several mAbs now having received authorization for emergency use, providing not just an important component of strategies to combat COVID-19 but also a boost to efforts to harness mAbs in therapeutic and preventive settings for other infectious diseases. Here, we describe advances in antibody discovery and engineering that have led to the development of mAbs for use against infections caused by viruses including SARS-CoV-2, respiratory syncytial virus (RSV), Ebola virus (EBOV), human cytomegalovirus (HCMV) and influenza. We also discuss the rationale for moving from empirical to structure-guided strategies in vaccine development, based on identifying optimal candidate antigens and vulnerable regions within them that can be targeted by antibodies to result in a strong protective immune response

Quadruplewild-type (WT) GIST: defining the subset of GIST that lacks abnormalities of KIT, PDGFRA, SDH, or RAS signaling pathways

A subset of GISTs lack mutations in the KIT/PDGFRA or RAS pathways and yet retain an intact succinate dehydrogensase (SDH) complex. We propose that these KIT/PDGFRA/SDH/RAS-P WT GIST tumors be designated as quadruple wild-type (WT) GIST. Further molecular and clinicophatological characterization of quadruple WT GIST will help to determine their prognosis as well as assist in the optimization of medical management, including clinical test of novel therapies

Country-level factors dynamics and ABO/Rh blood groups contribution to COVID-19 mortality

The identification of factors associated to COVID-19 mortality is important to design effective containment measures and safeguard at-risk categories. In the last year, several investigations have tried to ascertain key features to predict the COVID-19 mortality tolls in relation to country-specific dynamics and population structure. Most studies focused on the first wave of the COVID-19 pandemic observed in the first half of 2020. Numerous studies have reported significant associations between COVID-19 mortality and relevant variables, for instance obesity, healthcare system indicators such as hospital beds density, and bacillus Calmette-Guerin immunization. In this work, we investigated the role of ABO/Rh blood groups at three different stages of the pandemic while accounting for demographic, economic, and health system related confounding factors. Using a machine learning approach, we found that the “B+” blood group frequency is an important factor at all stages of the pandemic, confirming previous findings that blood groups are linked to COVID-19 severity and fatal outcome

Multi-time-scale features for accurate respiratory sound classification

The COVID-19 pandemic has amplified the urgency of the developments in computer-assisted medicine and, in particular, the need for automated tools supporting the clinical diagnosis and assessment of respiratory symptoms. This need was already clear to the scientific community, which launched an international challenge in 2017 at the International Conference on Biomedical Health Informatics (ICBHI) for the implementation of accurate algorithms for the classification of respiratory sound. In this work, we present a framework for respiratory sound classification based on two different kinds of features: (i) short-term features which summarize sound properties on a time scale of tenths of a second and (ii) long-term features which assess sounds properties on a time scale of seconds. Using the publicly available dataset provided by ICBHI, we cross-validated the classification performance of a neural network model over 6895 respiratory cycles and 126 subjects. The proposed model reached an accuracy of 85% ± 3% and an precision of 80% ± 8%, which compare well with the body of literature. The robustness of the predictions was assessed by comparison with state-of-the-art machine learning tools, such as the support vector machine, Random Forest and deep neural networks. The model presented here is therefore suitable for large-scale applications and for adoption in clinical practice. Finally, an interesting observation is that both short-term and long-term features are necessary for accurate classification, which could be the subject of future studies related to its clinical interpretation