ATP-binding cassette transporters play an important role in drug resistance and nutrient transport. In the human malaria parasite Plasmodium falciparum, a homolog of the human p-glycoprotein (PfPgh-1) was shown to be involved in resistance to several drugs. More recently, many transporters were associated with higher IC 50 levels in responses to chloroquine (CQ) and quinine (QN) in field isolates. Subsequent studies, however, could not confirm the associations, although inaccuracy in drug tests in the later studies could contribute to the lack of associations. Here we disrupted a gene encoding a putative multidrug resistance-associated protein (PfMRP) that was previously shown to be associated with P. falciparum responses to CQ and QN. Parasites with disrupted PfMRP (W2/MRP⌬) could not grow to a parasitemia higher than 5% under normal culture conditions, possibly because of lower efficiency in removing toxic metabolites. The W2/MRP⌬ parasite also accumulated more radioactive glutathione, CQ, and QN and became more sensitive to multiple antimalarial drugs, including CQ, QN, artemisinin, piperaquine, and primaquine. PfMRP was localized on the parasite surface membrane, within membrane-bound vesicles, and along the straight side of the D-shaped stage II gametocytes. The results suggest that PfMRP plays a role in the efflux of glutathione, CQ, and QN and contributes to parasite responses to multiple antimalarial drugs, possibly by pumping drugs outside the parasite.

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Post translational modifications (PTMs) are involved in variety of cellular activities and phosphorylation is one of the most extensively studied PTM, which regulates a number of cellular functions like cell growth, differentiation, apoptosis and cell signaling in healthy condition. However, alterations in phosphorylation pathways result in serious outcomes in the form of diseases, especially cancer. Many signalling pathways including Tyrosine kinase, MAP kinase, Cadherin-catenin complex, Cyclin-dependent kinase etc. are major players of the cell cycle and deregulation in their phosphorylation-dephosphorylation cascade has been shown to be manifested in the form of various types of cancers. Tyrosine kinase family encompasses the greatest number of oncoproteins. MAPK cascade has an importance role in cancer growth and progression. Bcl-2 family proteins serve either proapoptotic or antiapoptotic function. Cadherin-catenin complex regulates cell adhesion properties and cyclins are the key regulators of cell cycle. Altered phosphorylations in any of the above pathways are strongly associated with cancer, at the same time they serve as the potential tergets for drug development against cancer. Drugs targeting tyrosine kinase are potent anticancer drugs. Inhibitors of MEK, PI3K and ERK signalling pathways are undergoing clinical trials. Thus, drugs targeting phosphorylation pathways represent a promising area for cancer therapy.

Choroidal melanoma is the most common primary intra-ocular malignant tumor and second most common site of ten malignant melanoma sites in the body. Current diagnosis of choroidal melanoma is based on both the clinical experience of the specialist and modern diagnostic techniques such as indirect ophthalmoscopy, A- and B-ultrasonography scans, fundus fluorescein angiography, and transillumination. Invasive studies such as fine needle aspiration cytology can have significant morbidity and should only be considered if therapeutic intervention is indicated and diagnosis cannot be established by any other means. Several modes of treatment are available for choroidal melanoma. Multiple factors are taken into account when deciding one approach over other approaches, such as visual acuity of the affected eye, visual acuity of the contralateral eye, tumor size, location, ocular structures involved and presence of metastases. A comprehensive review of literature available in books and indexed journals was done. This article discusses in detail epidemiology, diagnosis, current available treatment options, and prognosis and survival of choroidal melanoma.

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The delayed rectifier potassium current plays a critical role in cellular physiology. This current (I(K)) in Drosophila larvae is believed to be a single current. However, a likely null mutation in the Shab K(+) channel gene (Shab(3)) reduces I(K) but does not eliminate it. This raises a question as to whether or not the entire I(K) passes through channels encoded by one gene. Similarly, an incomplete blockade of I(K) by high concentrations of quinidine, a selective I(K) blocker, raises a question as to whether I(K) consists of two components that are differentially sensitive to quinidine. We have addressed these questions by a combined use of genetics, pharmacology, and physiology. The current component removed by the Shab(3) mutation differed from the remaining component in activation kinetics, inactivation kinetics, threshold of activation, and voltage dependence. The two components showed strong differences in sensitivity to quinidine. Physiological properties of the current component removed by the Shab(3) mutation were similar to those of the quinidine-sensitive fraction of I(K). Complementary to this, properties of the current component remaining in the Shab(3) mutant muscles were similar to those of the quinidine-resistant fraction of I(K). These observations strongly suggest that, in contrast to the current belief, I(K) consists of two components in Drosophila, which are genetically, pharmacologically, and physiologically distinct. These components are being called I(KS) and I(KF). I(KS) is carried via Shab-encoded channels. I(KF) defines a new voltage-activated K(+) current in Drosophila.

We compare communication efficiencies of two compelling distributed machine learning approaches of split learning and federated learning. We show useful settings under which each method outperforms the other in terms of communication efficiency. We consider various practical scenarios of distributed learning setup and juxtapose the two methods under various real-life scenarios. We consider settings of small and large number of clients as well as small models (1M -6M parameters), large models (10M -200M parameters) and very large models (1 Billion-100 Billion parameters). We show that increasing number of clients or increasing model size favors split learning setup over the federated while increasing the number of data samples while keeping the number of clients or model size low makes federated learning more communication efficient.Preprint. Under review.