Confidence voting method ensemble applied to off-line signature verification

In this paper, a new approximation to off-line signature verification is proposed based on two-class classifiers using an expert decisions ensemble. Different methods to extract sets of local and a global features from the target sample are detailed. Also a normalization by confidence voting method is used in order to decrease the final equal error rate (EER). Each set of features is processed by a single expert, and on the other approach proposed, the decisions of the individual classifiers are combined using weighted votes. Experimental results are given using a subcorpus of the large MCYT signature database for random and skilled forgeries. The results show that the weighted combination outperforms the individual classifiers significantly. The best EER obtained were 6.3 % in the case of skilled forgeries and 2.31 % in the case of random forgeries.     

IR and Raman studies and effect of γ radiation on crystallization of some lead borate glasses containing Al2O3

IR and Raman spectra of glass ceramics based on PbO-Cr₂O₃-B₂O₃glass composition system have been studied. The bands characteristic of BO₃. and BO₄ functional groups are present in all the samples. An incorporation of Al₂O₃(up to 5 mol%) in the initial glass composition considerably changes the glass network structure and relative concentrations of BO₃ and BO₄ groups. The composition 50 PbO-20 Cr₂O₃-25 B₂O₃-5 Al₂O₃(in mol%) reveals a maximum fraction of boron in the BO₄ group. A sample of this composition heat treated at 850° C for 25 h shows a maximum crystallization fraction with Pb₂O · CrO₄ as a prominent crystalline phase. The glasses irradiated with rays inhibit the crystallization into the Pb₂O · CrO₄ phase. They also show relatively smaller thermal conductivity.     

Peptides to Tackle Leishmaniasis: Current Status and Future Directions

Peptide-based drugs are an attractive class of therapeutic agents, recently recognized by the pharmaceutical industry. These molecules are currently being used in the development of innovative therapies for diverse health conditions, including tropical diseases such as leishmaniasis. Despite its socioeconomic influence on public health, leishmaniasis remains long-neglected and categorized as a poverty-related disease, with limited treatment options. Peptides with antileishmanial effects encountered to date are a structurally heterogeneous group, which can be found in different natural sources—amphibians, reptiles, insects, bacteria, marine organisms, mammals, plants, and others—or inspired by natural toxins or proteins. This review details the biochemical and structural characteristics of over one hundred peptides and their potential use as molecular frameworks for the design of antileishmanial drug leads. Additionally, we detail the main chemical modifications or substitutions of amino acid residues carried out in the peptide sequence, and their implications in the development of antileishmanial candidates for clinical trials. Our bibliographic research highlights that the action of leishmanicidal peptides has been evaluated mainly using in vitro assays, with a special emphasis on the promastigote stage. In light of these findings, and considering the advances in the successful application of peptides in leishmaniasis chemotherapy, possible approaches and future directions are discussed here.     

Inhibitors of Chemoresistance Pathways in Combination with Ara-C to Overcome Multidrug Resistance in AML. A Mini Review

Acute myeloid leukemia (AML), the most common type of leukemia in older adults, is a heterogeneous disease that originates from the clonal expansion of undifferentiated hematopoietic progenitor cells. These cells present a remarkable variety of genes and proteins with altered expression and function. Despite significant advances in understanding the molecular panorama of AML and the development of therapies that target mutations, survival has not improved significantly, and the therapy standard is still based on highly toxic chemotherapy, which includes cytarabine (Ara-C) and allogeneic hematopoietic cell transplantation. Approximately 60% of AML patients respond favorably to these treatments and go into complete remission; however, most eventually relapse, develop refractory disease or chemoresistance, and do not survive for more than five years. Therefore, drug resistance that initially occurs in leukemic cells (primary resistance) or that develops during or after treatment (acquired resistance) has become the main obstacle to AML treatment. In this work, the main molecules responsible for generating chemoresistance to Ara-C in AML are discussed, as well as some of the newer strategies to overcome it, such as the inclusion of molecules that can induce synergistic cytotoxicity with Ara-C (MNKI-8e, emodin, metformin and niclosamide), subtoxic concentrations of chemotherapy (PD0332991), and potently antineoplastic treatments that do not damage nonmalignant cells (heteronemin or hydroxyurea + azidothymidine).     

Virtual Screening of FDA-Approved Drugs against Triose Phosphate Isomerase from Entamoeba histolytica and Giardia lamblia Identifies Inhibitors of Their Trophozoite Growth Phase

Infectious diseases caused by intestinal protozoan, such as Entamoeba histolytica (E. histolytica) and Giardia lamblia (G. lamblia) are a worldwide public health issue. They affect more than 70 million people every year. They colonize intestines causing primarily diarrhea; nevertheless, these infections can lead to more serious complications. The treatment of choice, metronidazole, is in doubt due to adverse effects and resistance. Therefore, there is a need for new compounds against these parasites. In this work, a structure-based virtual screening of FDA-approved drugs was performed to identify compounds with antiprotozoal activity. The glycolytic enzyme triosephosphate isomerase, present in both E. histolytica and G. lamblia, was used as the drug target. The compounds with the best average docking score on both structures were selected for the in vitro evaluation. Three compounds, chlorhexidine, tolcapone, and imatinib, were capable of inhibit growth on G. lamblia trophozoites (0.05–4.935 μg/mL), while folic acid showed activity against E. histolytica (0.186 μg/mL) and G. lamblia (5.342 μg/mL).     

2-DOF vibratory gyroscope fabricated by SU-8 based UV-LIGA process

This paper reports fabrication of 2-DOF vibratory gyroscope using SU-8 based UV-LIGA process. The device structure is designed to be symmetrical in order to match the resonance frequencies of drive and sense mode oscillators and also to minimize their relative temperature dependent drift. The overall arrangement is such that the two vibration modes do not affect each other and therefore, mechanical decoupling is achieved which helps in minimizing bias drift. The design is optimized to be compatible with the UV-LIGA process having 10 μm thick electroformed nickel as structural layer. Photolithography to create 11 μm thick SU-8 molds for electroforming sacrificial copper and structural nickel layer is optimized using multiple exposure technique that ensures near vertical side walls. Since the highly cross-linked SU-8 remaining after development is difficult to remove reliably from high aspect ratio structures without damage or alteration to the electroformed metals, a 2.45 GHz MW plasma etching process is developed with CF₄/O₂ mixes. The fabricated device is checked for off-plane misalignment between the stationary and movable comb fingers using white light interferometry and it is found to be almost negligible. Also, the prototype device is characterized for amplitude and phase spectral responses using Polytec MSA-500 Micro System Analyzer. The drive and sense mode resonance frequencies are observed at 7.3 and 7.1 kHz respectively against the mode matched designed frequency of 7.5 kHz.     

Epileptic seizure classification in EEG signals using second-order difference plot of intrinsic mode functions

Epilepsy is a neurological disorder which is characterized by transient and unexpected electrical disturbance of the brain. The electroencephalogram (EEG) is a commonly used signal for detection of epileptic seizures. This paper presents a new method for classification of ictal and seizure-free EEG signals. The proposed method is based on the empirical mode decomposition (EMD) and the second-order difference plot (SODP). The EMD method decomposes an EEG signal into a set of symmetric and band-limited signals termed as intrinsic mode functions (IMFs). The SODP of IMFs provides elliptical structure. The 95% confidence ellipse area measured from the SODP of IMFs has been used as a feature in order to discriminate seizure-free EEG signals from the epileptic seizure EEG signals. The feature space obtained from the ellipse area parameters of two IMFs has been used for classification of ictal and seizure-free EEG signals using the artificial neural network (ANN) classifier. It has been shown that the feature space formed using ellipse area parameters of first and second IMFs has given good classification performance. Experimental results on EEG database available by the University of Bonn, Germany, are included to illustrate the effectiveness of the proposed method.     

Hierarchical abnormal event detection by real time and semi-real time multi-tasking video surveillance system

In this paper, we describe how to detect abnormal human activities taking place in an outdoor surveillance environment. Human tracks are provided in real time by the baseline video surveillance system. Given trajectory information, the event analysis module will attempt to determine whether or not a suspicious activity is currently being observed. However, due to real-time processing constrains, there might be false alarms generated by video image noise or non-human objects. It requires further intensive examination to filter out false event detections which can be processed in an off-line fashion. We propose a hierarchical abnormal event detection system that takes care of real time and semi-real time as multi-tasking. In low level task, a trajectory-based method processes trajectory data and detects abnormal events in real time. In high level task, an intensive video analysis algorithm checks whether the detected abnormal event is triggered by actual humans or not.