Miracle-dz/UniFAR-FTUs huggingface dataset:
Alteration in membrane fluidity and lipid composition, and modulation of H(+)-ATPase activity in Saccharomyces cerevisiae caused by decanoic acid.

Decanoic acid, a lipophilic agent, inhibited in vitro the plasma membrane H(+)-ATPase of Saccharomyces cerevisiae grown in YPD medium. Conversely, when decanoic acid (35 microM) was present in the growth medium, the measured H(+)-ATPase activity was four times higher than that of control cells. Km, and pH and orthovanadate sensitivity were the same for the two growth conditions, which indicated that H(+)-ATPase activation was not due to conformational changes in the enzyme. The activation process was not entirely reversible which showed that plasma membrane H(+)-ATPase activation is due to several mechanisms. 1,6-diphenyl-1,3,5-hexatriene anisotropy performed on protoplasts from cells grown in YPD revealed that as decanoic acid concentration was increased, anisotropy significantly decreased, i.e. membrance fluidity increased. Cells grown in media containing decanoic acid exhibited greater membrane fluidity compared with control cells. Furthermore, these cells did not show any fluidifying effect when increased concentrations of decanoic acid were added. Chemical analysis of cell membrane lipid composition revealed a modification in the distribution of the phospholipid fatty acids and sterols in cells grown in the presence of 35 microM decanoic acid compared with control cells. Our results support the view that the plasma membrane H(+)-ATPase activation induced by decanoic acid is correlated with an alteration in membrane lipid constituents.
Advances in Targeting Src in the Treatment of Breast Cancer and Other Solid Malignancies

Src, a membrane-associated nonreceptor tyrosine kinase, plays a crucial role in the coordination and facilitation of cell-signaling pathways controlling a wide range of cellular functions, including growth, survival, invasion, adhesion, and migration. Deregulation and increased activity of Src has been observed in multiple human malignancies, prompting the development of specific inhibitors of Src. In preclinical studies, Src inhibitors show antitumor effects in multiple solid tumor types. Recently completed early-phase trials using the inhibitors dasatinib and bosutinib have suggested modest activity as monotherapy in breast and prostate cancer, with potentially greater activity in combination regimens. Given the interaction between Src and the estrogen receptor, ongoing trials are exploring combinations with endocrine therapy. The relationship between Src and the vascular endothelial growth factor receptor also justifies investigation of combinations with angiogenesis inhibitors. Future trials will continue to explore the contribution of Src inhibition with both chemotherapy and targeted agents. Clin Cancer Res; 16(14); 3526–32. ©2010 AACR.
Toward a theory for scheduling dags in Internet-based computing

Conceptual and algorithmic tools are developed as a foundation for a theory of scheduling complex computation-dags for Internet-based computing. The goal of the schedules produced is to render tasks eligible for allocation to remote clients (hence, for execution) at the maximum possible rate. This allows one to utilize remote clients well, as well as to lessen the likelihood of the "gridlock" that ensues when a computation stalls for lack of eligible tasks. Earlier work has introduced a formalism for studying this optimization problem and has identified optimal schedules for several significant families of structurally uniform dags. The current paper extends this work via a methodology for devising optimal schedules for a much broader class of complex dags, which are obtained via composition from a prespecified collection of simple building-block dags. The paper provides a suite of algorithms that decompose a given dag Gscr to expose its building blocks and an execution-priority relation xutri on building blocks. When the building blocks are appropriately interrelated under xutri the algorithms specify an optimal schedule for Gscr
Development of new optical fiber toluene sensor

This paper deals with experimental results on detection of toluene dispersed in water by means of a new and simple plastic optical fiber (POF) sensor. Its sensing part consists of swelling polymer cladding layer such as high density polyethylene (HDPE) with slightly larger refractive index n2 than that of poly(methyl methacrylate) (PMMA) core with n1. When is exposed to toluene dispersed in water, HDPE cladding layer causes swelling and the value of n2 decreases quickly. Then the POF structure in the sensing part changes from leaky-type to guided-one and the output light intensity increases dramatically. By measuring this light intensity change, it becomes possible to detect toluene concentration. In addition, as compared with the popular evanescent-wave detection principle type, this sensor operation does not depend on the wavelength of light source. Therefore, we can use an inexpensive LED or LD in visible region. In experiment, detection of less than 1 wt% toluene in water was easily obtained with fast response about only 1 s. Furthermore, this sensor can detect pure toluene even for a small amount below 1 ml.
Being diagnosed with cancer: The experiences of patients with non‐Hodgkin's lymphoma

AIMS AND OBJECTIVES To explore the lived experiences of patients on receiving a diagnosis of non-Hodgkin's lymphoma. BACKGROUND Persons with non-Hodgkin's lymphoma are often diagnosed with cancer when at an advanced stage. This may arise due to a lack of screening programmes and delays in help seeking as the symptoms experienced are fairly nonspecific in nature. Consequently, receiving an unexpected diagnosis of non-Hodgkin's lymphoma may pose a psychosocial challenge to the individual. DESIGN An exploratory qualitative phenomenological design was used. METHODS Six adult patients diagnosed with non-Hodgkin's lymphoma were recruited and participated in two semi-structured interviews at different timepoints. Transcribed texts were analysed using interpretative phenomenological analysis. RESULTS Three main themes emerged: "Emotional reaction to the diagnosis," "Struggling with a lack of understanding" and "Searching through past experiences. " Most of the participants expressed shock, disbelief and fear at the news of being diagnosed with non-Hodgkin's lymphoma. A sense of relief was expressed by one participant, who could now provide a label to the pain experienced. CONCLUSIONS This study highlights the responses of patients with non-Hodgkin's lymphoma on receiving a diagnosis. These responses are influenced by their knowledge about the illness, type of symptoms experienced, their own personal circumstances and the manner in which the diagnosis was given. RELEVANCE TO CLINICAL PRACTICE Nursing care relates to an understanding of the patients' experiences. By reflecting on these findings, nurses are empowered to communicate with persons who have received a diagnosis of non-Hodgkin's lymphoma in a patient-sensitive manner, which establishes a relationship of trust and respect.
A Grassmann-Rayleigh quotient iteration for computing invariant subspaces

The classical Rayleigh quotient iteration (RQI) computes a 1-dimensional invariant subspace of a symmetric matrix A with cubic convergence. We propose a generalization of the RQI which computes a p-dimensional invariant subspace of A. The geometry of the algorithm on the Grassmann manifold Gr(p,n) is developed to show cubic convergence and to draw connections with Newton algorithms on Riemannian manifolds.
Future image frame generation using Artificial Neural Network with selected features

This paper presents a novel approach for the generation of Future image frames using Artificial Neural Network (ANN) on spatiotemporal framework. The input to this network are hyper-dimensional color and spatiotemporal features of every pixel of an image in an image sequence. Principal Component Analysis, Mutual Information, Interaction Information and Bhattacharyya Distance measure based feature selection techniques have been used to reduce the dimensionality of the feature set. The pixel values of an image frame are predicted using a simple ANN back propagation algorithm. The ANN network is trained for R, G and B values for each and every pixel in an image frame. The resulting model is successfully applied on an image sequence of a landing fighter plane. As Mentioned above four feature selection techniques are used to compare the performance of the proposed ANN model. The quality of the generated future image frames is assessed using, Canny edge detection based Image Comparison Metric(CIM) and Mean Structural Similarity Index Measure(MSSIM) image quality measures. The proposed approach is found to have generated six future image frames successfully with acceptable quality of images.
Learning a synaptic learning rule

Summary form only given, as follows. The authors discuss an original approach to neural modeling based on the idea of searching, with learning methods, for a synaptic learning rule which is biologically plausible and yields networks that are able to learn to perform difficult tasks. The proposed method of automatically finding the learning rule relies on the idea of considering the synaptic modification rule as a parametric function. This function has local inputs and is the same in many neurons. The parameters that define this function can be estimated with known learning methods. For this optimization, particular attention is given to gradient descent and genetic algorithms. In both cases, estimation of this function consists of a joint global optimization of the synaptic modification function and the networks that are learning to perform some tasks. Both network architecture and the learning function can be designed within constraints derived from biological knowledge. <<ETX>>
A Guaranteed Cost Approach to Robust Model Predictive Control of Uncertain Linear Systems

—This paper proposes a constrained guaranteed cost robust model predictive controller (GCMPC) for uncertain discrete time systems. Such controller enables the design of Robust Model Predictive Controllers with quadratic cost functional and quadratically bound uncertainties, which is currently intractable on Min-Max Model Predictive Control techniques. The developed technique requires a Quadratically Constraint Quadratic Problem (QCQP) optimization while LMI-based GCMPC approaches require a Semi-Deﬁnite Programming (SDP) optimization. A conservative approximation which sim-pliﬁes the optimization problem to a Quadratic Programming (QP) is also proposed.
DAWN: Energy efficient data aggregation in WSN with mobile sinks

The benefits of using mobile sink to prolong sensor network lifetime have been well recognized. However, few provably theoretical results remain are developed due to the complexity caused by time-dependent network topology. In this work, we investigate the optimum routing strategy for the static sensor network. We further propose a number of motion stratifies for the mobile sink(s) to gather real time data from static sensor network, with the objective to maximize the network lifetime. Specially, we consider a more realistic model where the moving speed and path for mobile sinks are constrained. Our extensive experiments show that our scheme can significantly prolong entire network lifetime and reduce delivery delay.
A Real-Time Algorithm for Accurate Collision Detection for Deformable Polyhedral Objects

We propose an accurate collision detection algorithm for use in virtual reality applications. The algorithm works for three-dimensional graphical environments where multiple objects, represented as polyhedra (boundary representation), are undergoing arbitrary motion (translation and rotation). The algorithm can be used directly for both convex and concave objects and objects can be deformed (nonrigid) during motion. The algorithm works efficiently by first reducing the number of face pairs that need to be checked accurately for interference, by first localizing possible collision regions using bounding box and spatial subdivision techniques. Face pairs that remain after this pruning stage are then accurately checked for interference. The algorithm is efficient, simple to implement, and does not require any memory-intensive auxiliary data structures to be precomputed and updated. The performance of the proposed algorithm is compared directly against other existing algorithms, e.g., the separating plane algorithm, octree update method, and distance-based method. Results are given to show the efficiency of the proposed method in a general environment.
Changing the picture of Earth's earliest fossils (3.5–1.9 Ga) with new approaches and new discoveries

Significance Precambrian fossils are essential for understanding the emergence of complex life. New analytical tools and new fossil discoveries are now changing the picture, allowing us to refine and extend our knowledge about the early fossil record. High-resolution data from 3.46-Ga Apex chert microbiota help us to test rigorous criteria for studying the early fossil record. Preservational windows in the 1.88-Ga Gunflint chert allow us to posit novel cellular forms, and emphasize the critical role played by the fossil record in understanding early biodiversity. Micromapping of 3.43-Ga Strelley Pool sandstone reveals microfossils preserved between sand grains from the earliest known shoreline, reminding us that many kinds of ancient habitat have yet to be explored in this way. New analytical approaches and discoveries are demanding fresh thinking about the early fossil record. The 1.88-Ga Gunflint chert provides an important benchmark for the analysis of early fossil preservation. High-resolution analysis of Gunflintia shows that microtaphonomy can help to resolve long-standing paleobiological questions. Novel 3D nanoscale reconstructions of the most ancient complex fossil Eosphaera reveal features hitherto unmatched in any crown-group microbe. While Eosphaera may preserve a symbiotic consortium, a stronger conclusion is that multicellular morphospace was differently occupied in the Paleoproterozoic. The 3.46-Ga Apex chert provides a test bed for claims of biogenicity of cell-like structures. Mapping plus focused ion beam milling combined with transmission electron microscopy data demonstrate that microfossil-like taxa, including species of Archaeoscillatoriopsis and Primaevifilum, are pseudofossils formed from vermiform phyllosilicate grains during hydrothermal alteration events. The 3.43-Ga Strelley Pool Formation shows that plausible early fossil candidates are turning up in unexpected environmental settings. Our data reveal how cellular clusters of unexpectedly large coccoids and tubular sheath-like envelopes were trapped between sand grains and entombed within coatings of dripstone beach-rock silica cement. These fossils come from Earth’s earliest known intertidal to supratidal shoreline deposit, accumulated under aerated but oxygen poor conditions.
Protein transport and trafficking in Plasmodium falciparum-infected erythrocytes

The human malarial parasite Plasmodium falciparum extensively modifies its host erythrocyte, and to this end, is faced with an interesting challenge. It must not only sort proteins to common organelles such as endoplasmic reticulum, Golgi and mitochondria, but also target proteins across the ‘extracellular’ cytosol of its host cell. Furthermore, as a member of the phylum Apicomplexa, the parasite has to sort proteins to novel organelles such as the apicoplast, micronemes and rhoptries. In order to overcome these difficulties, the parasite has created a novel secretory system, which has been characterized in ever-increasing detail in the past decade. Along with the ‘hardware’ for a secretory system, the parasite also needs to ‘program’ proteins to enable high fidelity sorting to their correct subcellular location. The nature of these sorting signals has remained until relatively recently, enigmatic. Experimental work has now begun to dissect the sorting signals responsible for correct subcellular targeting of parasite-encoded proteins. In this review we summarize the current understanding of such signals, and comment on their role in protein sorting in this organism, which may become a model for the study of novel protein trafficking mechanisms.
Map Building from Laser Range Sensor Information in Noisy Environment using Mixed Clustering , Singular Value Decomposition and Hough Transform

The goal of an autonomous mobile robot is to navigate without human intervention in a dynamic and real environment by sensing the world around using sensors mounted on it. A robot in an unknown environment, which has no prior information of the world around with respect to position and map, is rather difficult to imagine. Simultaneous Localization and Mapping, also known as SLAM addresses this problem of a mobile robot moving through an environment of which no map is available priori. The goal of the SLAM problem is to reconstruct the map of the environment and path taken by the robot simultaneously. The SLAM problem focuses two major problems-
Denotational validation of higher-order Bayesian inference

We present a modular semantic account of Bayesian inference algorithms for probabilistic programming languages, as used in data science and machine learning. Sophisticated inference algorithms are often explained in terms of composition of smaller parts. However, neither their theoretical justification nor their implementation reflects this modularity. We show how to conceptualise and analyse such inference algorithms as manipulating intermediate representations of probabilistic programs using higher-order functions and inductive types, and their denotational semantics. Semantic accounts of continuous distributions use measurable spaces. However, our use of higher-order functions presents a substantial technical difficulty: it is impossible to define a measurable space structure over the collection of measurable functions between arbitrary measurable spaces that is compatible with standard operations on those functions, such as function application. We overcome this difficulty using quasi-Borel spaces, a recently proposed mathematical structure that supports both function spaces and continuous distributions. We define a class of semantic structures for representing probabilistic programs, and semantic validity criteria for transformations of these representations in terms of distribution preservation. We develop a collection of building blocks for composing representations. We use these building blocks to validate common inference algorithms such as Sequential Monte Carlo and Markov Chain Monte Carlo. To emphasize the connection between the semantic manipulation and its traditional measure theoretic origins, we use Kock's synthetic measure theory. We demonstrate its usefulness by proving a quasi-Borel counterpart to the Metropolis-Hastings-Green theorem.
Transmogrification: causal manipulation of visualizations

A transmogrifier is a novel interface that enables quick, on-the-fly graphic transformations. A region of a graphic can be specified by a shape and transformed into a destination shape with real-time, visual feedback. Both origin and destination shapes can be circles, quadrilaterals or arbitrary shapes defined through touch. Transmogrifiers are flexible, fast and simple to create and invite use in casual InfoVis scenarios, opening the door to alternative ways of exploring and displaying existing visualizations (e.g., rectifying routes or rivers in maps), and enabling free-form prototyping of new visualizations (e.g., lenses).
Frequent Expression of Glypican-3 in Merkel Cell Carcinoma: An Immunohistochemical Study of 55 Cases

The pathologic diagnosis of Merkel cell carcinoma (MCC) is mainly based on routine morphology and a characteristic cytokeratin 20 (CK20) immunohistochemical staining pattern. However, about 10% to 15% of MCCs are reportedly CK20 negative and about 10% to 20% of MCCs have only focal CK20 positivity. We aimed to study glypican-3 (GPC3) expression in MCC and to compare it with CK20 expression in MCC. Immunohistochemical expression of GPC-3, CK20, and CD23 were studied in 55 cases of MCC and 21 cases of noncutaneous small cell neuroendocrine carcinoma (SCNC), including 10 cases of pulmonary SCNC and 11 cases of nonpulmonary SCNC. The results of GPC3 expression in these tumors were then compared with that of CK20. Of the 55 cases, 39 were positive for GPC3 (70.9%). Seventy-seven percent of the positive MCCs (30/39) showed GPC3 immunoreactivity in ≥30% tumor cells. Of the 55 cases, 53 were positive for CK20 (96%); 49 of them showed CK20 immunoreactivity in ≥30% tumor cells. All MCC cases were either positive for both GPC3 and CK20 (37 cases) or at least 1 of the markers (18 cases, 16 CK20 only and 2 GPC3 only). In addition, we studied GPC3 and CK20 expression in 10 cases of pulmonary SCNC and 11 cases of nonpulmonary SCNC. Three of ten pulmonary SCNC and 5 of 11 nonpulmonary SCNC expressed GPC3. All pulmonary SCNC and 9 of 11 nonpulmonary SCNC were negative for CK20. All cases of MCC and SCNC were negative for CD23. In conclusion, GPC3 is frequently expressed in SCNC of various origins, in particular in MCC, which, in combination with CK20, may represent another useful marker in the diagnosis of MCC.
A No-Reference Quality Metric for Blur Image

In this paper, we address the problem of no-reference quality assessment for blurred picture from digital camera. To evaluate the digital picture corrupted by defocus and motion artifact, the inherent information of blur kernel is estimated from the blurred image. We consider blur priori based on specific gradient domain, which is inferred in Alpha channel image. This metric is validated in LIVE Blur database and CSIQ Blur database, the results shows the high prediction accuracy with human judgment.
Temporal Relation Classification Based on Temporal Reasoning

The area of temporal information extraction has recently focused on temporal relation classification. This task is about classifying the temporal relation (precedence, overlap, etc.) holding between two given entities (events, dates or times) mentioned in a text. This interest has largely been driven by the two recent TempEval competitions. Even though logical constraints on the structure of possible sets of temporal relations are obvious, this sort of information deserves more exploration in the context of temporal relation classification. In this paper, we show that logical inference can be used to improve—sometimes dramatically— existing machine learned classifiers for the problem of temporal relation classification.
Random Dopant Fluctuation-Induced Threshold Voltage Variation-Immune Ge FinFET With Metal–Interlayer–Semiconductor Source/Drain

The impact of process-induced random dopant fluctuation (RDF)-induced threshold voltage (Vth) variation on the performance of 7-nm n-type germanium (Ge) FinFETs with and without a metal-interlayer-semiconductor (MIS) source/drain (S/D) structure is investigated using 3-D TCAD simulations. In order to reduce the RDF-induced Vth variation, an MIS S/D structure with a heavily doped n-type zinc oxide (ZnO) interlayer is used in the S/D region of the Ge FinFET. Thus, without performance degradation, the Ge FinFET with an MIS S/D structure achieves approximately threefold reduction in the RDF-induced Vth variation (versus without an MIS S/D structure). The impact of various fin parameters (i.e., fin height and fin width) on the RDF-induced Vth variation is also investigated. It is noteworthy that variation is suppressed as the fin height (fin width) increases (decreases).
Body sensor network security: an identity-based cryptography approach

A body sensor network (BSN), is a network of sensors deployed on a person's body, usually for health care monitoring. Since the sensors collect personal medical data, security and privacy are important components in a body sensor network. At the same time, the collected data has to readily available in the event of an emergency. In this paper, we present IBE-Lite, a lightweight identity-based encryption suitable for sensors, and developed protocols based on IBE-Lite for a BSN.