Anomalous Ferromagnetism of quasiparticle doped holes in cuprate heterostructures revealed using resonant soft X-ray magnetic scattering.

We report strong ferromagnetism of quasiparticle doped holes both within the ab-plane and along the c-axis of Cu-O planes in low-dimensional Au/d-La1.8Ba0.2CuO4/LaAlO3(001) heterostructures (d = 4, 8 and 12 unit-cells) using resonant soft X-ray and magnetic scattering together with X-ray magnetic circular dichroism. Interestingly, ferromagnetism is stronger at a hole doped peak and at an upper Hubbard band of O with spin-polarization degree as high as 40%, revealing strong ferromagnetism of Mottness. For in-ab-plane spin-polarizations, the spin of doped holes in O2p-Cu3d-O2p is a triplet state yielding strong ferromagnetism. For out-of-ab-plane spin-polarization, while the spins of doped holes in both O2p-O2p and Cu3d-Cu3d are triplet states, the spin of doped holes in Cu3d-O2p is a singlet state yielding ferrimagnetism. A ferromagnetic-(002) Bragg-peak of the doped holes is observed and enhanced as a function of d revealing strong ferromagnetism coupling between Cu-O layers along the c-axis.

Quantitative Structure Activity Relationship study of the Anti-Hepatitis Peptides employing Random Forests and Extra-trees regressors.

Antimicrobial peptides are host defense peptides being viewed as replacement to broad-spectrum antibiotics due to varied advantages. Hepatitis is the commonest infectious disease of liver, affecting 500 million globally with reported adverse side effects in treatment therapy. Antimicrobial peptides active against hepatitis are called as anti-hepatitis peptides (AHP). In current work, we present Extratrees and Random Forests based Quantitative Structure Activity Relationship (QSAR) regression modeling using extracted sequence based descriptors for prediction of the anti-hepatitis activity. The Extra-trees regression model yielded a very high performance in terms coefficient of determination (R2) as 0.95 for test set and 0.7 for the independent dataset. We hypothesize that the developed model can further be used to identify potentially active anti-hepatitis peptides with a high level of reliability.

Recent trends in antimicrobial peptide prediction using machine learning techniques.

The importance to develop effective alternatives to known antibiotics due to increased microbial resistance is gaining momentum in recent years. Therefore, it is of interest to predict, design and computationally model Antimicrobial Peptides (AMPs). AMPs are oligopeptides with varying size (from 5 to over100 residues) having key role in innate immunity. Thus, the potential exploitation of AMPs as novel therapeutic agents is evident. They act by causing cell death either by disrupting the microbial membrane by inhibiting extracellular polymer synthesis or by altering intra cellular polymer functions. AMPs have broad spectrum activity and act as first line of defense against all types of microorganisms including viruses, bacteria, parasites, fungi and as well as cancer (uncontrolled celldivision) progression. Large-scale identification and extraction of AMPs is often non-trivial, expensive and time consuming. Hence, there is a need to develop models to predict AMPs as therapeutics. We document recent trends and advancement in the prediction of AMP.

Barriers affecting adherence to radiation treatment and strategies to overcome those barriers.

BACKGROUND: The WHO defines adherence as the extent to which a patient's behavior coincides with recommendations from a health-care provider. Nonadherence to cancer treatment has a major impact on the therapeutic outcome. AIM OF THE STUDY: To assess the prevalence of nonadherence to radiation regimen and to analyze the factors that affect adherence to cancer treatment. MATERIALS AND METHODS: Patients receiving radiation treatment in our hospital were screened for adherence to appointment keeping and to the prescribed radiation regimen and patients who had unplanned treatment breaks during treatment were interviewed. Between January and July 2013, we identified 61 patients who had unplanned breaks during treatment. We analyzed the social, emotional, educational, economic, and therapeutic barriers that led to nonadherence. RESULTS: Of the 61 patients who had unplanned breaks during treatment, 54% were males and 46% were females. Fifty-seven percent of patients had head and neck cancers and 25% had gynecological cancers. Seventy-one percent of patients were planned for concurrent chemoradiation. The number of days of unplanned treatment breaks ranged from 3 to 27 days. Social and therapeutic barriers were found to be the most common factor that led to nonadherence in these patients. CONCLUSION: Identification of barriers that lead to nonadherence, designing strategies to overcome such barriers and effective communication becomes imperative to ensure uninterrupted treatment. Based on the above analysis, we have designed several strategies to improve adherence to treatment among our patients.

Data Mining and Pattern Recognition Models for Identifying Inherited Diseases: Challenges and Implications.

Data mining and pattern recognition methods reveal interesting findings in genetic studies, especially on how the genetic makeup is associated with inherited diseases. Although researchers have proposed various data mining models for biomedical approaches, there remains a challenge in accurately prioritizing the single nucleotide polymorphisms (SNP) associated with the disease. In this commentary, we review the state-of-art data mining and pattern recognition models for identifying inherited diseases and deliberate the need of binary classification- and scoring-based prioritization methods in determining causal variants. While we discuss the pros and cons associated with these methods known, we argue that the gene prioritization methods and the protein interaction (PPI) methods in conjunction with the K nearest neighbors' could be used in accurately categorizing the genetic factors in disease causation.

A stepwise strategy employing automated screening and DryLab modeling for the development of robust methods for challenging high performance liquid chromatography separations: a case study.

A stepwise method development strategy has been employed to develop a robust HPLC method to resolve several closely eluting structurally related impurities in an active pharmaceutical ingredient (API). This strategy consisted of automated column screening, optimization of the most critical chromatographic parameters, DryLab(®) modeling, and experimental verification of optimized separation conditions. DryLab(®) was used to predict an optimized gradient profile and separation temperature and these predictions were verified experimentally. A discussion of the accuracy of these predictions is presented. The robustness of the method was verified and the ability of DryLab(®) to predict, with reasonable accuracy, the outcome of such robustness studies was also examined. Once the robustness was established by the DryLab(®) predictions the remainder of the subsequent verification by experiment becomes a simple reiterative exercise. This study also demonstrates that factors such as column chemistry and critical chromatographic parameters can have a profound and oftentimes interrelated effect on the chromatographic separation of isomers, bromo analogs and other structurally very similar impurities. Therefore, it is critical to adopt a rational strategy, as demonstrated here, to evaluate the interplay of these factors, thereby greatly enhancing method development efficiency.

Ammonia volatilization loss from surface applied livestock manure.

Ammonia (NH(3)) emission from livestock manures used in agriculture reduces N uptake by crops and negatively impacts air quality. This laboratory study was conducted to evaluate NH(3)emission from different livestock manures applied to two soils: Candler fins sand (CFS; light-textured soil, pH 6.8 and field capacity soil water content of 70 g kg(-1)) from Lake Alfred, Florida and Ogeechee loamy sand (OLS; medium-textured soil, pH 5.2 and field capacity soil water content of 140 g kg(-1)) from Savannah, Georgia. Poultry litter (PL) collected from a poultry farm near Douglas, Georgia, and fresh solid separate of swine manure (SM) collected from a farm near Clinton, North Carolina were used. Each of the soil was weighed in 100 g sub samples and amended with either PL or SM at rates equivalent to either 0, 2.24, 5.60, 11.20, or 22.40 Mg ha(-1) in 1L Mason jars and incubated in the laboratory at field capacity soil water content for 19 days to monitor NH(3) volatilization. Results indicated a greater NH(3) loss from soils amended with SM compared to that with PL. The cumulative NH(3)volatilization loss over 19 days ranged from 4 to 27% and 14 to 32% of total N applied as PL and SM, respectively. Volatilization of NH(3) was greater from light-textured CFS than that from medium-textured OLS. Volatilization loss increased with increasing rates of manure application. Ammonia volatilization was lower at night time than that during the day time. Differences in major factors such as soil water content, temperature, soil type and live stock manure type influenced the diurnal variation in volatilization loss of NH(3) from soils. A significant portion (> 50%) of cumulative NH(3) emission over 19 d occurred during the first 5-7 d following the application of livestock manures. Results of this study demonstrate that application of low rates of livestock manure (< or = 5.60 Mg ha(-1)) is recommended to minimize NH(3) emissions.