BACKGROUND Miltefosine (MIL), the only oral drug for visceral leishmaniasis (VL), is currently the first-line therapy in the VL elimination program of the Indian subcontinent. Given the paucity of anti-VL drugs and the looming threat of resistance, there is an obvious need for close monitoring of clinical efficacy of MIL. METHODS In a cohort study of 120 VL patients treated with MIL in Nepal,...

A hydrophilic, hydrostable porous metal organic framework (MOF) material-MIL-101 (Cr) was successfully doped into the dense selective polyamide (PA) layer on the polysulfone (PS) ultrafiltration (UF) support to prepare a new thin film nanocomposite (TFN) membrane for water desalination. The TFN-MIL-101 (Cr) membranes were characterized by SEM, AFM, XPS, wettability measurement and reverse osmos...

A cationic porous framework with mobile anions (MIL-101(Cr)-Cl) was easily and successfully synthesized by utilizing the stronger affinity of F- to Al3+ than Cr3+ in the charge-balanced framework of MIL-101(Cr). The structure, morphology and porosity of MIL-101(Cr)-Cl were characterized. The obtained new materials retain the high surface area, good thermostability, and structure topology of MIL...

Multiple Instance Learning (MIL) has been widely used in various applications including image classification. However, existing MIL methods do not explicitly address the multi-target problem where the distributions of positive instances are likely to be multi-modal. This strongly limits the performance of multiple instance learning in many real world applications. To address this problem, this ...

Splenocytes from nonobese diabetic mice overexpressing murine IL (mIL)-4 upon recombinant retrovirus infection lose their capacity to transfer diabetes to nonobese diabetic-scid recipients. Diabetes appeared in 0-20% of mice injected with mIL-4-transduced cells vs 80-100% of controls injected with beta-galactosidase-transduced cells. Protected mice showed a majority of islets (60%) presenting w...

In multiple-Instance Learning (MIL), training class labels are attached to sets of bags composed of unlabeled instances, and the goal is to deal with classification of bags. Most previous MIL algorithms, which tackle classification problems, consider each instance as a represented feature. Although the algorithms work well in some prediction problems, considering diverse features to represent a...

The aim of this study was to evaluate the levels of meaning in life (MIL), psychological well-being (PWB) and quality of life (QOL) in a large sample of school teachers, and to observe to what extent the MIL could act as a moderator variable in the relation between PWB and general QOL. The study included 517 teachers from 57 public and private schools in the city of Campina Grande, Paraíba, Bra...

Since real-world data sets usually contain large instances, it is meaningful to develop efficient and effective multiple instance learning (MIL) algorithm. As a learning paradigm, MIL is different from traditional supervised learning that handles the classification of bags comprising unlabeled instances. In this paper, a novel efficient method based on extreme learning machine (ELM) is proposed...

MIL-101Cr fully or partially (p) postsynthetically modified with nitro (-NO2) or amino (-NH2) groups was shown to be a robust, water stable, selective and enhanced carbon dioxide (CO2) adsorption material with the amine-functionality. The highly microporous amine-modified frameworks (up to 1.6 cm(3) g(-1) total pore volume) exhibit excellent thermal stability (>300 °C) with BET surface areas up...

Multiple instance learning (MIL) is a variation of traditional supervised learning problems where data (referred to as bags) are composed of sub-elements (referred to as instances) and only bag labels are available. MIL has a variety of applications such as content-based image retrieval, text categorization, and medical diagnosis. Most of the previous work for MIL assume that training bags are ...