Synthesis and Properties of Two Dimensional Doped Transition Metal Dichalcogenides

Since graphene was discovered in 2004, two-dimensional (2D) materials have been actively studied. Especially, 2D transition metal dichalcogenides (TMDs), such as MoS2 and WS2, have been the subject of significant research because of their exceptional optical, electrical, magnetic, catalytic, and morphological properties. Therefore, these materials are expected to be used in a variety of applications. Furthermore, tuning the properties of TMDs is essential to improve their performance and expand their applications. This review classifies the various doping methods of 2D TMDs, and it summarizes how the dopants interact with the materials and how the performance of the materials improves depending on the synthesis methods and the species of the dopants

Genomic Screening Identifies Novel Human Kinases As Suppressors Of HCV Infection

The Human kinome includes between 500-600 known kinases and open reading frames (ORFs) that play key roles in regulating many cellular processes. Some of these cellular processes include, signal transduction, metabolism, transcription, cell cycle progression, cell movement, apoptosis, differentiation and protein phosphorylation. Various protein and lipid kinases have previously been shown to control viral life cycles. The lipid kinase PI4KIIIA was found to be required for hepatitis C virus (HCV) replication in several genomic screens using small interference RNAs that target human kinases. Although such a loss-of-function approach has led to the identification of host cell factors that are required for HCV replication, no kinases have been found with respect to antiviral activity. Here we adopt a genomic approach to identify human kinases that negatively regulate HCV infection. To this end, a library of activated kinases, which consists of 192 human kinases and kinase-related ORFs, was cloned into a retroviral vector, which adds a myristoylation sequence and flag-epitope tag to each ORF. Using an overexpression screening approach, we identified that several kinases, including Cyclin-dependent kinases regulatory subunit 1 (CKS1B), Mitogen-activated protein kinase kinase 5 (MAP2K5) and Protein kinase C and casein kinase substrate in neurons 1 (PACSIN1), potently suppressed HCV infection. The expression of these active kinases did not activate the nuclear factor-kappaB (NF-κB) pathway or the production of type I interferon and interferon-stimulated genes (ISGs), instead they acted at different post-entry stages. Specifically, CKS1B significantly inhibited viral RNA replication; MAP2K5 also suppressed virus replication and appeared to exert additional inhibition after replication. PACSIN1, by contrast, inhibited HCV infection via a yet-to-be-defined mechanism. Public Health significance: Hepatitis C occurs worldwide and is the major cause of liver diseases. Approximately three quarters of people infected with HCV become chronic carriers of the virus and of these chronically infected, majority of the patients develop liver cirrhosis and hepatocellular carcinoma. Current treatments available to patients are costly and cause severe side-effects that result in poor patient compliance. Thus, the inhibitory effects as shown here by targeting kinases, shows the potential of combating HCV infection by activating specific kinase-mediated pathways, which would be an alternative method of treatment of these patients

Assessing Dengue Risk Globally Using Non-Markovian Models

Dengue is a vector-borne disease transmitted by Aedes mosquitoes. The worldwide spread of these mosquitoes and the increasing disease burden have emphasized the need for a spatio-temporal risk map capable of assessing dengue outbreak conditions and quantifying the outbreak risk. Given that the life cycle of Aedes mosquitoes is strongly influenced by habitat temperature, numerous studies have utilized temperature-dependent development rates of these mosquitoes to construct virus transmission and outbreak risk models. In this study, we advance existing research by developing a mechanistic model for the mosquito life cycle that accurately accounts for the non-Markovian nature of the process. By fitting the model to data on human dengue cases, we estimate several model parameters, allowing the development of a global spatiotemporal dengue risk map. This risk model employs temperature and precipitation data to assess the environmental suitability for dengue outbreaks in a given area. Furthermore, we demonstrate how to reduce the model to the corresponding differential equations, enabling us to utilize existing methods for analyzing the system and fitting the model to observations. This approach can be further applied to similar non-Markovian processes that are currently described with less accurate Markovian models

The Effect of Herbal Diet on Skeletal Muscle Mass After Resistance Training in Rats

Resistance training has been well established as an effective strategy for muscle hypertrophy, increase in skeletal muscle mass and strength. Herbal diet has been introduced as an alternative treatment to alleviate muscle atrophy and therapeutic intervention. However, there is little evidence on the effect of herbal diet on skeletal muscle mass. To investigate whether herbal diet affects skeletal muscle mass after resistance training in rats. Twenty-four rats were randomly divided into 3 groups: 1) Control (CON, N=8), 2) Resistance training (RT, N=8), 3) RT+Herb (RTH, N=8). Resistance training was performed every other day for 8 weeks using ladder climbing. The ladder climbing exercise consisted of 3 sets of 5 repetitions with a 1 min rest interval between the repetitions and a 2 min rest between the sets. Huang Qi (Radix Astragali Membranceus) was given via oral gavage once a day for 8 weeks (1 ml mixed with water based on concentration of 368 mg/kg). All rats received sham treatment, same as treatment groups. All data were analyzed using One-way ANOVA. After 8 weeks of interventions, muscle mass of Gastrocnemius, Plantaris, and Flexor hallucis longus showed significant increases in RT and RTH groups compared to CON (

Dedicated preparation for in situ transmission electron microscope tensile testing of exfoliated graphene

Graphene, which is one of the most promising materials for its state-of-the-art applications, has received extensive attention because of its superior mechanical properties. However, there is little experimental evidence related to the mechanical properties of graphene at the atomic level because of the challenges associated with transferring atomically-thin two-dimensional (2D) materials onto microelectromechanical systems (MEMS) devices. In this study, we show successful dry transfer with a gel material of a stable, clean, and free-standing exfoliated graphene film onto a push-to-pull (PTP) device, which is a MEMS device used for uniaxial tensile testing in in situ transmission electron microscopy (TEM). Through the results of optical microscopy, Raman spectroscopy, and TEM, we demonstrate high quality exfoliated graphene on the PTP device. Finally, the stress???strain results corresponding to propagating cracks in folded graphene were simultaneously obtained during the tensile tests in TEM. The zigzag and armchair edges of graphene confirmed that the fracture occurred in association with the hexagonal lattice structure of graphene while the tensile testing. In the wake of the results, we envision the dedicated preparation and in situ TEM tensile experiments advance the understanding of the relationship between the mechanical properties and structural characteristics of 2D materials

Adversarial hypothesis testing and a quantum stein's lemma for restricted measurements

Recall the classical hypothesis testing setting with two convex sets of probability distributions P and Q. One receives either n i.i.d. samples from a distribution p ∈ P or from a distribution q ∈ Q and wants to decide from which set the points were sampled. It is known that the optimal exponential rate at which errors decrease can be achieved by a simple maximum-likelihood ratio test which does not depend on p or q, but only on the sets P and Q. We consider an adaptive generalization of this model where the choice of p ∈ P and q ∈ Q can change in each sample in some way that depends arbitrarily on the previous samples. In other words, in the kth round, an adversary, having observed all the previous samples in rounds 1, ..., κ-1, chooses p_κ ∈ P and q_κ ∈ Q, with the goal of confusing the hypothesis test. We prove that even in this case, the optimal exponential error rate can be achieved by a simple maximum-likelihood test that depends only on P and Q. We then show that the adversarial model has applications in hypothesis testing for quantum states using restricted measurements. For example, it can be used to study the problem of distinguishing entangled states from the set of all separable states using only measurements that can be implemented with local operations and classical communication (LOCC). The basic idea is that in our setup, the deleterious effects of entanglement can be simulated by an adaptive classical adversary. We prove a quantum Stein's Lemma in this setting: In many circumstances, the optimal hypothesis testing rate is equal to an appropriate notion of quantum relative entropy between two states. In particular, our arguments yield an alternate proof of Li and Winter's recent strengthening of strong subadditivity for quantum relative entropy

2010-2011 Master Class - Hyun-Soo Lee (Piano)

https://spiral.lynn.edu/conservatory_masterclasses/1082/thumbnail.jp

In situ electrochemical surface modification for high-voltage LiCoO2 in lithium ion batteries

High-voltage LiCoO2 has been revisited to improve the energy density of lithium ion batteries. LiCoO2 can deliver the reversible capacity of about 200 mA h g(-1) when the upper cut-off voltage increases to 4.55 V (vs. Li/Li+). However, the high upper cut-off voltage causes the severe failures of LiCoO2 such as structural degradation, electrolyte decomposition, and Co dissolution. Various surface-modified LiCoO2 materials have been introduced to suppress electrolyte decomposition and Co dissolution, thereby leading to the improved electrochemical performance. Most of the coated LiCoO2 materials are obtained through a conventional coating process such as sol-gel synthesis, which is complex and high-cost. In this paper, the in situ electrochemical coating method is introduced as a simple and low-cost coating process, where the electrolyte additive of Mg salts is electrochemically decomposed to form a MgF2-based coating layer on the LiCoO2 surface. LiCoO2 electrochemically coated with MgF2 suppresses Co dissolution in electrolytes, resulting in excellent electrochemical performance such as high reversible capacity of 198 mA h g(-1) and stable cycle performance over 100 cycles in the voltage range between 3 and 4.55 V (vs. Li/Li+) at 45 degrees C. The formation mechanism of MgF2 is also demonstrated through ex situ XPS and XANES analyses.

Depression and suicide risk prediction models using blood-derived multi-omics data

More than 300 million people worldwide experience depression; annually, ~800,000 people die by suicide. Unfortunately, conventional interview-based diagnosis is insufficient to accurately predict a psychiatric status. We developed machine learning models to predict depression and suicide risk using blood methylome and transcriptome data from 56 suicide attempters (SAs), 39 patients with major depressive disorder (MDD), and 87 healthy controls. Our random forest classifiers showed accuracies of 92.6% in distinguishing SAs from MDD patients, 87.3% in distinguishing MDD patients from controls, and 86.7% in distinguishing SAs from controls. We also developed regression models for predicting psychiatric scales with R2 values of 0.961 and 0.943 for Hamilton Rating Scale for Depression???17 and Scale for Suicide Ideation, respectively. Multi-omics data were used to construct psychiatric status prediction models for improved mental health treatment