Miracle-dz/UniFAR-FTUs huggingface dataset:
Using Functional Domain Composition and Support Vector Machines for Prediction of Protein Subcellular Location*

Proteins are generally classified into the following 12 subcellular locations: 1) chloroplast, 2) cytoplasm, 3) cytoskeleton, 4) endoplasmic reticulum, 5) extracellular, 6) Golgi apparatus, 7) lysosome, 8) mitochondria, 9) nucleus, 10) peroxisome, 11) plasma membrane, and 12) vacuole. Because the function of a protein is closely correlated with its subcellular location, with the rapid increase in new protein sequences entering into databanks, it is vitally important for both basic research and pharmaceutical industry to establish a high throughput tool for predicting protein subcellular location. In this paper, a new concept, the so-called “functional domain composition” is introduced. Based on the novel concept, the representation for a protein can be defined as a vector in a high-dimensional space, where each of the clustered functional domains derived from the protein universe serves as a vector base. With such a novel representation for a protein, the support vector machine (SVM) algorithm is introduced for predicting protein subcellular location. High success rates are obtained by the self-consistency test, jackknife test, and independent dataset test, respectively. The current approach not only can play an important complementary role to the powerful covariant discriminant algorithm based on the pseudo amino acid composition representation (Chou, K. C. (2001)Proteins Struct. Funct. Genet. 43, 246–255; Correction (2001) Proteins Struct. Funct. Genet. 44, 60), but also may greatly stimulate the development of this area.
Detection of protein three-dimensional side-chain patterns: new examples of convergent evolution.

Detection of recurring three-dimensional side-chain patterns is a potential means of inferring protein function. This paper presents a new method for detecting such patterns and discusses various implications. The method allows detection of side-chain patterns without any prior knowledge of function, requiring only protein structure data and associated multiple sequence alignments. A recursive, depth-first search algorithm finds all possible groups of identical amino acids common to two protein structures independent of sequence order. The search is highly constrained by distance constraints, and by ignoring amino acids unlikely to be involved in protein function. A weighted root-mean-square deviation (RMSD) between equivalenced groups of amino acids is used as a measure of similarity. The statistical significance of any RMSD is assigned by reference to a distribution fitted to simulated data. Searches with the Ser/His/Asp catalytic triad, a His/His porphyrin binding pattern, and the zinc-finger Cys/Cys/His/His pattern are performed to test the method on known examples. An all-against-all comparison of representatives from the structural classification of proteins (SCOP) is performed, revealing several new examples of evolutionary convergence to common patterns of side-chains within different tertiary folds and in different orders along the sequence. These include a di-zinc binding Asp/Asp/His/His/Ser pattern common to alkaline phosphatase/bacterial aminopeptidase, and an Asp/Glu/His/His/Asn/Asn pattern common to the active sites of DNase I and endocellulase E1. Implications for protein evolution, function prediction and the rational design of functional regulators are discussed.
Quantum transport on two-dimensional regular graphs

We study the quantum-mechanical transport on two-dimensional graphs by means of continuous-time quantum walks and analyse the effect of different boundary conditions (BCs). For periodic BCs in both directions, i.e., for tori, the problem can be treated in a large measure analytically. Some of these results carry over to graphs which obey open boundary conditions (OBCs), such as cylinders or rectangles. Under OBCs the long time transition probabilities (LPs) also display asymmetries for certain graphs, as a function of their particular sizes. Interestingly, these effects do not show up in the marginal distributions, obtained by summing the LPs along one direction.
Cloning and functional analysis of an arsB gene responsible for arsenic sequestration in Lysinibacillus sphaericus MB701A VT15: Master Degree Project in Molecular Biotechnology (30 credits) Spring term 2016

a ABSTRACT Arsenic contamination in environment is serious threat to all living beings. Lysinibacillus sphaericus (B1-CDA) is a Gram positive bacterium that can grow in arsenic contaminated environment. The genes responsible for arsenic resistance of this bacterium have been identified as arsB, arsC and arsR by previous researchers. In the present study, arsB was cloned from genomic DNA of B1-CDA by PCR. The gene was characterized by in silico and in vivo experiments. In silico analysis by Iterative Threading ASSEmbly Refinement (I-TASSER) resulted that ArsB is an integral membrane protein and their putative function included cellular metal homeostasis. Results obtained in silico were in coherence with growth studies. The arsB cDNA was transferred into Escherichia coli JW3469-1, in which arsB gene was knocked out. Statistical analysis of growth indicated that there was significant difference in growth after complementation of arsB in Escherichia coli JW3469-1 when grown with 50 mM arsenic (p=0.04). Further analysis by Inductive Coupled Plasma-Mass Spectroscopy (ICP-MS) confirmed sequestration of arsenic inside the cells and thus its removal from growth media. Preliminary data obtained from ICP-MS indicated that arsenic concentration in cell free broth was decreased by 80.78% (from 50 mM to 9.61 mM) when treated for 24 hrs with transgenic cells (after complementation) and by 78.22% (from 50 mM to 10.89 mM) when treated with mutant cells (before complementation) but the difference was not significant. The results are inconclusive as there was a significant difference in growth between the transgenic and mutant cells but not significant in ICP-MS studies. Heavy metal poisoning is a serious threat to human; one such naturally occurring and highly toxic chemical is arsenic. Arsenic is widely distributed in water and soil, in some part of the few regions of United states) exceeding the maximum allowed limit which is 50 micrograms per cubic meter. As a result there are various diseases caused by arsenic exposure in human from skin rashes to cancer and multiple organ failure. Therefore, there is an alarming need to find ways to remove arsenic from the contaminated environment. There are many methods used for the arsenic removal from environment including oxidation, coagulation, precipitation, adsorption and membrane filtration and recently using microorganisms. Microbial or bio-remediation might be a successful method for avoiding arsenic contamination as they are cost effective and eco-friendly. This method uses microorganism that has resistance against heavy metals which can grow and uptake arsenics from the contaminated …
IMPLEMENTATION OF ANT COLONY OPTIMIZATION ALGORITHM ON THE PROJECT RESOURCE SCHEDULING PROBLEM

Project management in the business field is defined as managing and directing time, material, personnel, and costs to complete a particular project. One important phase in the project planning is Project Resource Scheduling. This process identifies resources amount and type according to the activity that scheduled. Planning the efficient use of resources is a complex task. The purpose is to create a smoother distribution of resource usage i.e. to minimize the fluctuation of the resource usage. The resource leveling is used to produce a solution to the problem. So far, researchers test several heuristic in order to produce the optimal solutions of the problem. In this paper, we discuss an implementation of the Ant Colony Optimization (ACO) to the Project Resource Scheduling Problem (PRSP). To implement the ACO algorithm with a problem, requires defining; an appropriate graph, the autocatalytic feedback process, the heuristic and the constraint satisfaction method. Keyword : Project Resource Scheduling Problem, fluctuation, resource leveling, ACO
Parameters analysis of FitzHugh-Nagumo model for a reliable simulation

Derived from the pioneer ionic Hodgkin-Huxley model and due to its simplicity and richness from a point view of nonlinear dynamics, the FitzHugh-Nagumo model (FHN) is one of the most successful simplified neuron / cardiac cell model. There exist many variations of the original FHN model. Though these FHN type models help to enrich the dynamics of the FHN model, the parameters used in these models are often in biased conditions. The related results would be questionable. So, in this study, the aim is to find the parameter thresholds for one of the commonly used FHN model in order to provide a better simulation environment. The results showed at first that inappropriate time step and integration tolerance in numerical solution of FHN model can give some biased results which would make some publications questionable. Then the thresholds of parameters α, γ and ε are presented. α controls the global dynamics of FHN. α > 0, the cell is in refractory mode; α <; 0, the cell is excitable. ε controls the main morphology of the action potential generated and has a relation with the period (P = 3.065 × αα,γ-0.8275+ 4.397). To show oscillations of relaxation with FHN, ε should be smaller than 0.0085. 7 influences barely action potential, it showed linear relationship with the period and duration of action potential. Even though α <; 0.1, ε <; 0.0085, there is no definite threshold for γ, smaller values are recommended.
Biomechanical and Anatomic Factors Associated with a History of Plantar Fasciitis in Female Runners

Objective:To compare selected structural and biomechanical factors between female runners with a history of plantar fasciitis and healthy control subjects. Design:Cross-sectional. Setting:University of Delaware Motion Analysis Laboratory, Newark, Delaware; and University of Massachusetts Biomechanics Laboratory, Amherst, Massachusetts. Participants:Twenty-five female runners with a history of plantar fasciitis were recruited for this study. A group of 25 age- and mileage-matched runners with no history of plantar fasciitis served as control subjects. Interventions:The independent variable was whether or not subjects had a history of plantar fasciitis. Main Outcome Measures:Subjects ran overground while kinematic and kinetic data were recorded using a motion capture system and force plate. Rearfoot kinematic variables of interest included peak dorsiflexion, peak eversion, time to peak eversion along with eversion excursion. Vertical ground reaction force variables included impact peak and the maximum instantaneous load rate. Structural measures were taken for calcaneal valgus and arch index during standing and passive ankle dorsiflexion range of motion. Results:A significantly greater maximum instantaneous load rate was found in the plantar fasciitis group along with an increased ankle dorsiflexion range of motion compared with the control group. The plantar fasciitis group had a lower arch index compared with control subjects, but calcaneal valgus was similar between groups. No differences in rearfoot kinematics were found between groups. Conclusion:These data indicate that a history of plantar fasciitis in runners may be associated with greater vertical ground reaction force load rates and a lower medial longitudinal arch of the foot.
Compressive data gathering for large-scale wireless sensor networks

This paper presents the first complete design to apply compressive sampling theory to sensor data gathering for large-scale wireless sensor networks. The successful scheme developed in this research is expected to offer fresh frame of mind for research in both compressive sampling applications and large-scale wireless sensor networks. We consider the scenario in which a large number of sensor nodes are densely deployed and sensor readings are spatially correlated. The proposed compressive data gathering is able to reduce global scale communication cost without introducing intensive computation or complicated transmission control. The load balancing characteristic is capable of extending the lifetime of the entire sensor network as well as individual sensors. Furthermore, the proposed scheme can cope with abnormal sensor readings gracefully. We also carry out the analysis of the network capacity of the proposed compressive data gathering and validate the analysis through ns-2 simulations. More importantly, this novel compressive data gathering has been tested on real sensor data and the results show the efficiency and robustness of the proposed scheme.
Supramolecular assemblies of histidinylated β-cyclodextrin for enhanced oligopeptide delivery into osteoclast precursors

Abstract Much attention has been given to the problem of drug delivery through the cell membrane in order to treat and manage bone diseases recently. The aim of this study was to develop nanoparticles made of amino- and histidinyl-modified amphiphilic β-cyclodextrins (β-CDs) entrapping osteoclast inhibitor, a hydrophobic oligopeptides drug, across the membrane of bone marrow-derived macrophages (BMMs). Drug-loaded β-CDs nanoparticles (NPs) were prepared by the emulsion solvent evaporation technique and fully characterized for size, zeta potential, and entrapment efficiency. Spherical NPs displaying a hydrodynamic radius of about 295 nm which did not change upon storage as an aqueous dispersion, a positive zeta potential, and entrapment efficiency of drug very close to 98% were produced. Flow cytometry and spectrofluorimetry analysis indicated that the model drug itself was not taken up by the BMMs; however, NP systems underwent significant cellular uptake. In particular, histidinyl group-modified CD (β-CD-H) NPs were taken up more efficiently than amino group-modified (β-CD-A) ones. Cellular uptake mechanism study demonstrated that the permeability of drug-loaded NPs across the membrane of BMMs is probably due to macropinocytosis pathway. Cell viability studies showed that both β-CD-A and β-CD-H exhibited no significant cytotoxicity up to 1.0 mg/ml against the cells. These results highlight the developed β-CD-H NPs have great potential in safely and effectively delivering osteoclast inhibitors and other therapeutic agents toward bone disease.
Auto-associative learning of on-line handwriting using recurrent neural networks

Traditionally the parametric grammar based approach to the modeling and recognition of temporal sequences using hidden Markov models HMMs involves a very crucial step which requires human experts to determine a priori the appropriate model architecture to work on This includes among other things determining the number of states in the proba bilistic grammar and the probabilistic transitions between states As a long term e ort we attempt to develop a more domain independent principled approach to the modeling of grammatical structures in temporal sequences without knowing in advance the topology of the underlying grammars This is achieved through an unsupervised learning process In particular a discrete time recurrent neural network model called ASCOC we proposed before is trained to learn separately the dynamics of each individual embedded subgrammar or subpattern class These subgrammar network models are trained in an auto associative or self supervised manner which is similar in spirit to the principal component analysis PCA learning paradigm for feedforward neural networks except that our focus here is on recurrent neural networks that model dynamical behaviors In the pilot study presented in this paper some issues of this new approach to temporal sequence processing are investi gated in the domain of on line handwriting modeling and recognition Some possible future research directions are also discussed
Ultra-low power Al2O3-based RRAM with 1μA reset current

Transition metal oxide (TMO) resistance random access memory (RRAM) is considered a promising candidate for future non-volatile memory application for fast switching speed, long retention time and superior scaling properties [1]. In order to satisfy product requirements, elimination of the electroforming process of fresh RRAM device, sufficient program/erase voltage margin, and low power consumption are required. Among the various resistive layer materials, Al2O3 prepared by traditional sputtering method is so far not an attractive candidate due to its relatively small active voltage [2] and large programming current [3] compared to other TMO materials. In this paper, RRAM devices with atomic-layer deposited (ALD) Al2O3 as resistive layer material and Al electrodes are fabricated which show stable bipolar resistive switching behavior without the electro-forming process. In addition, the reset current is ultra-low (~1μA) with adequate switching voltage margin. To investigate the influence of Ti-interfacial layer on the Al2O3 switching mechanism, bi-layer top electrode Al/Ti/Al2O3/Pt memory cells were also fabricated for comparison. For these devices, a unipolar switching behavior was observed.
When parasitic wasps hijacked viruses: genomic and functional evolution of polydnaviruses

The Polydnaviridae (PDV), including the Bracovirus (BV) and Ichnovirus genera, originated from the integration of unrelated viruses in the genomes of two parasitoid wasp lineages, in a remarkable example of convergent evolution. Functionally active PDVs represent the most compelling evolutionary success among endogenous viral elements (EVEs). BV evolved from the domestication by braconid wasps of a nudivirus 100 Ma. The nudivirus genome has become an EVE involved in BV particle production but is not encapsidated. Instead, BV genomes have co-opted virulence genes, used by the wasps to control the immunity and development of their hosts. Gene transfers and duplications have shaped BV genomes, now encoding hundreds of genes. Phylogenomic studies suggest that BVs contribute largely to wasp diversification and adaptation to their hosts. A genome evolution model explains how multidirectional wasp adaptation to different host species could have fostered PDV genome extension. Integrative studies linking ecological data on the wasp to genomic analyses should provide new insights into the adaptive role of particular BV genes. Forthcoming genomic advances should also indicate if the associations between endoparasitoid wasps and symbiotic viruses evolved because of their particularly intimate interactions with their hosts, or if similar domesticated EVEs could be uncovered in other parasites.
Differentiation of hES Cells to a Parathyroid-Like Phenotype

Iatrogenic hypoparathyroidism is the most common complication of cervical endocrine surgery. Current management is limited and palliative. As the molecular steps in parathyroid development have been defi ned, they may be replicable in vitro, with a goal of cellular replacement therapy. Human embryonic stem cell (hESC) lines were investigated as a model for parathyroid regeneration in vitro. BG01 was selected as a model based on expression of genes of interest in embryoid bodies (EBs). Established strategies for mouse embryonic stem cell differentiation into defi nitive endoderm were modifi ed and extended to maximize the expression of defi nitive markers of parathyroid development. The optimal approach included the use of Activin A at 100 ng/mL with BG01 cells grown on murine embryonic fi broblasts for 5 days under conditions of increasing serum concentration. After 5 days, the cells were allowed to mature further in tissue culture without murine fi broblasts but with continuous Activin A. Our strategy produced differentiated cell cultures that expressed intermediate markers of endoderm and parathyroid development (CXCR4, EYA1, Six1, and Pax1), as well as markers of committed parathyroid precursors or developed parathyroid glands (glial cell missing-2 [Gcm2], CCL21, calcium sensing receptor [CaSR], and parathyroid hormone [PTH]). We further characterized the cells by testing conditioned medium from various time points in our differentiation scheme for the presence of PTH. We found that by keeping the cells in culture 2 weeks after the withdrawal of Activin A, the cells were able to produce PTH. Further in vivo work will be needed to demonstrate proper functionality of the cells developed in this way.
Comparison of different secure network coding paradigms concerning transmission efficiency

Preventing the success of active attacks is of essential importance for network coding since even the infiltration of one single corrupted data packet can jam large parts of the network. The existing approaches for network coding schemes preventing such pollution attacks can be divided into two categories: utilize cryptographic approaches or utilize redundancy similar to error correction coding. Within this paper, we compared both paradigms concerning efficiency of data transmission under various circumstances. Particularly, we considered an attacker of a certain strength as well as the influence of the generation size. The results are helpful for selecting a suitable approach for network coding taking into account both security against pollution attacks and efficiency.
A Quantitative Analysis of Reordering Phenomena

Reordering is a serious challenge in statistical machine translation. We propose a method for analysing syntactic reordering in parallel corpora and apply it to understanding the differences in the performance of SMT systems. Results at recent large-scale evaluation campaigns show that synchronous grammar-based statistical machine translation models produce superior results for language pairs such as Chinese to English. However, for language pairs such as Arabic to English, phrase-based approaches continue to be competitive. Until now, our understanding of these results has been limited to differences in Bleu scores. Our analysis shows that current state-of-the-art systems fail to capture the majority of reorderings found in real data.
Broadcasting with side information

We consider the problem of multicasting data from a source to receivers that possess arbitrary subsets of the data apriori as side information. Fountain codes, which are an ideal solution to the standard multicasting problem without any side information, have also been proposed as a potential approach for the side information problem in multiple independent studies recently. Relevant to such a context, we formulate and study an optimization problem over degree distributions to minimize the overhead necessary for complete decoding, and prove that: (i) Degree distributions converging to the standard soliton distribution cannot exploit side information in terms of the overhead necessary for complete decoding. (ii) An asymptotic shifted soliton distribution achieves an overhead which is within a constant factor (<; 2) of the optimal overhead (iii) There exist no degree distributions which achieve asymptotically optimal overhead for any non trivial constant fraction of the data as side information. While (iii) is discouraging, this limitation can be sidestepped by using systematic versions, where intermediate symbols are generated from the source symbols, to which the fountain code is then applied. One important implication of this is that the systematic versions are in a sense indispensable to achieve asymptotic rate optimality for the side information problem.