Miracle-dz/UniFAR-FTUs huggingface dataset:
Detection of gene promoter hypermethylation in fine needle washings from breast lesions.

PURPOSE Fine needle aspiration (FNA) is used widely in diagnostic assessment of breast lesions. However, cytomorphological evaluation depends heavily on the proficiency of cytopathologists. Because epigenetic alterations are frequent and specific enough to potentially augment the accuracy of malignant disease detection, we tested whether hypermethylation analysis of a panel of genes would distinguish benign from malignant breast FNA washings. EXPERIMENTAL DESIGN FNA washings were collected from 123 female patients harboring suspicious mammary lesions. Sodium bisulfite-modified DNA was amplified by methyl-specific PCR (MSP) for CDH1, GSTP1, BRCA1, and RARbeta to detect gene promoter CpG island methylation. Paired samples of 27 breast cancer tissue and 7 fibroadenomas and 12 samples of normal breast tissue, collected postoperatively, were also analyzed. MSP results were compared with conventional cytomorphological diagnosis. RESULTS FNAs were cytomorphologically diagnosed as benign (25 cases), malignant (76 cases), suspicious for malignancy (6 cases), and unsatisfactory (16 cases). Percentages of methylated CDH1, GSTP1, BRCA1, and RARbeta in FNA washings were 60, 52, 32, and 16%, and 65.8, 57.9, 39.5, and 34.2% for benign and malignant lesions, respectively. These differences did not reach statistical significance. In all of the paired benign lesions tested, there was absolute concordance. Sixty-seven percent (18 of 27) of FNA washings displayed hypermethylation patterns identical to malignant paired tissue. No methylation was found in the normal breast samples for any of the genes. CONCLUSIONS Detection of gene hypermethylation in FNA washings by MSP analysis is feasible, but the selected gene panel does not discriminate between benign and malignant breast lesions.
Exploring the mechanisms of protein folding

Neither of the two prevalent theories, namely thermodynamic stability and kinetic stability, provides a comprehensive understanding of protein folding. The thermodynamic theory is misleading because it assumes that free energy is the exclusive dominant mechanism of protein folding, and attributes the structural transition from one characteristic state to another to energy barriers. Conversely, the concept of kinetic stability overemphasizes dominant mechanisms that are related to kinetic factors. This article explores the stability condition of protein structures from the viewpoint of meso-science, paying attention to the compromise in the competition between minimum free energy and other dominant mechanisms. Based on our study of complex systems, we propose that protein folding is a meso-scale, dissipative, nonlinear and non-equilibrium process that is dominated by the compromise between free energy and other dominant mechanisms such as environmental factors. Consequently, a protein shows dynamic structures, featuring characteristic states that appear alternately and dynamically, only one of which is the state with minimum free energy. To provide evidence for this concept, we analyzed the time series of energetic and structural changes of three simulations of protein folding/unfolding. Our results indicate that thorough consideration of the multiple dynamic characteristic structures generated by multiple mechanisms may be the key to understanding protein folding.
M3R: Increased performance for in-memory Hadoop jobs

Main Memory Map Reduce (M3R) is a new implementation of the Hadoop Map Reduce (HMR) API targeted at online analytics on high mean-time-to-failure clusters. It does not support resilience, and supports only those workloads which can fit into cluster memory. In return, it can run HMR jobs unchanged -- including jobs produced by compilers for higher-level languages such as Pig, Jaql, and SystemML and interactive front-ends like IBM BigSheets -- while providing significantly better performance than the Hadoop engine on several workloads (e.g. 45x on some input sizes for sparse matrix vector multiply). M3R also supports extensions to the HMR API which can enable Map Reduce jobs to run faster on the M3R engine, while not affecting their performance under the Hadoop engine.
Controlling Influenza by Cytotoxic T-Cells: Calling for Help from Destroyers

Influenza is a vaccine preventable disease that causes severe illness and excess mortality in humans. Licensed influenza vaccines induce humoral immunity and protect against strains that antigenically match the major antigenic components of the vaccine, but much less against antigenically diverse influenza strains. A vaccine that protects against different influenza viruses belonging to the same subtype or even against viruses belonging to more than one subtype would be a major advance in our battle against influenza. Heterosubtypic immunity could be obtained by cytotoxic T-cell (CTL) responses against conserved influenza virus epitopes. The molecular mechanisms involved in inducing protective CTL responses are discussed here. We also focus on CTL vaccine design and point to the importance of immune-related databases and immunoinformatics tools in the quest for new vaccine candidates. Some techniques for analysis of T-cell responses are also highlighted, as they allow estimation of cellular immune responses induced by vaccine preparations and can provide correlates of protection.
γδ T Cell-Mediated Antibody-Dependent Cellular Cytotoxicity with CD19 Antibodies Assessed by an Impedance-Based Label-Free Real-Time Cytotoxicity Assay

γδ T cells are not MHC restricted, elicit cytotoxicity against various malignancies, are present in early post-transplant phases in novel stem cell transplantation strategies and have been shown to mediate antibody-dependent cellular cytotoxicity (ADCC) with monoclonal antibodies (mAbs). These features make γδ T cells promising effector cells for antibody-based immunotherapy in pediatric patients with B-lineage acute lymphoblastic leukemia (ALL). To evaluate combination of human γδ T cells with CD19 antibodies for immunotherapy of B-lineage ALL, γδ T cells were expanded after a GMP-compliant protocol and ADCC of both primary and expanded γδ T cells with an Fc-optimized CD19 antibody (4G7SDIE) and a bi-specific antibody with the specificities CD19 and CD16 (N19-C16) was evaluated in CD107a-degranulation assays and intracellular cytokine staining. CD107a, TNFα, and IFNγ expression of primary γδ T cells were significantly increased and correlated with CD16-expression of γδ T cells. γδ T cells highly expressed CD107a after expansion and no further increased expression by 4G7SDIE and N19-C16 was measured. Cytotoxicity of purified expanded γδ T cells targeting CD19-expressing cells was assessed in both europium-TDA release and in an impedance-based label-free method (using the xCELLigence system) measuring γδ T cell lysis in real-time. Albeit in the 2 h end-point europium-TDA release assay no increased lysis was observed, in real-time xCELLigence assays both significant antibody-independent cytotoxicity and ADCC of γδ T cells were observed. The xCELLigence system outperformed the end-point europium-TDA release assay in sensitivity and allows drawing of conclusions to lysis kinetics of γδ T cells over prolonged periods of time periods. Combination of CD19 antibodies with primary as well as expanded γδ T cells exhibits a promising approach, which may enhance clinical outcome of patients with pediatric B-lineage ALL and requires clinical evaluation.
A dynamic algorithm for local community detection in graphs

A variety of massive datasets, such as social networks and biological data, are represented as graphs that reveal underlying connections, trends, and anomalies. Community detection is the task of discovering dense groups of vertices in a graph. Its one specific form is seed set expansion, which finds the best local community for a given set of seed vertices. Greedy, agglomerative algorithms, which are commonly used in seed set expansion, have been previously designed only for a static, unchanging graph. However, in many applications, new data is constantly produced, and vertices and edges are inserted and removed from a graph. We present an algorithm for dynamic seed set expansion, which incrementally updates the community as the underlying graph changes. We show that our dynamic algorithm outputs high quality communities that are similar to those found when using a standard static algorithm. The dynamic approach also improves performance compared to re-computation, achieving speedups of up to 600x.
Organization and VLSI implementation of MIPS

MIPS is an 32-bit, high performance processor architecture implemented as an nMOS VLSI chip. The processor uses a low level, streamlined instruction set coupled with a fast pipeline to achieve an instruction rate of two million instructions per second. Close interaction between the processor design and compilers for the machine yields efficient execution of programs on the chip. Simplifying the instruction set and the requirements placed on the hardware by the architecture, facilitates both processor control and interrupt handling in the pipeline. High speed MOS circuit design techniques and a sophisticated timing methodology enable the processor to achieve a 250nS clock cycle.
An effective S-transform feature extraction method for classification of power quality disturbance Signals

This paper presents a S-transform effective feature extraction method for power quality (PQ) disturbance classification problem. Firstly, S-transform technique is used to extract the significant features of distorted signal. Then, an optimum combination of the most useful features is identified for increasing the accuracy of classification. Features extracted by using the S-transform are applied as input to support vector machines (SVM) for automatic classification of the power quality (PQ) disturbances that solves a relatively complex problem. Seven single disturbances and six complex disturbances selected as reference are considered for the classification. Sensitivity of proposed classifier under different noise conditions is investigated. The analysis and results show that the classifier can effectively classify different PQ disturbances.
Death Education and the Management of Fear of Death Via Photo-Voice: An Experience Among Undergraduate Students

Abstract The article presents the results of a research intervention aimed at exploring the differences among some factors (death anxiety, ontological representation of death as annihilation, alexithymia, spirituality, resilience, happiness) in two different groups of university students: one that attended a course of Death Education (DeEd; 83 students: DeEd-gr) and another one that did not participate in it (149: NoDeEd-gr). The DeEd course utilized the photo-voice as an elective strategy aimed at facilitating the performative experience. It was hypothesized that this method could help DeEd-gr in sharing emotions and spiritual experiences while managing the increase of death anxiety, inevitably caused by the death-related issues introduced during the lessons. The results confirmed that, despite the higher death anxiety, the DeEd-gr had a greater level of happiness and lower alexithymia compared with NoDeEd-gr, suggesting that the use of photo-voice can be useful in the course because it facilitates the management of communication and relationships. A further analysis was realized to explain the relationships between the different dimensions considered. Results evidenced that spirituality was inversely correlated with the representation of death as annihilation and with alexithymia, which was positively related to personal happiness. However, in DeEd-gr, spirituality impacted less on happiness, while the representation of death as annihilation impacted significantly more on resilience among participants of NoDeEd-gr. The discussion analyzes some characteristics that differentiated women and men, compared with men, and considers how DeEd courses should recognize the role of the ontological representations of death with their relationships with spirituality.
Conceptual Spaces—The Geometry of Thought

nightmare—small changes in the intended semantics of predicates or procedures cause large and usually catastrophic changes in performance. There have been, I think, two responses to this. One is owed to my colleague Paul Compton and his coworkers, using ripple-down rules (RDRs), which at the risk of oversimplification is the online adaptation of an expert sys-tem—users provide feedback as they apply the system in real life. The beau-Conceptual Spaces—The Geometry of Thought is a book by Peter Gärdenfors, professor of cognitive science at Lund University, Sweden. Gärdenfors has authored another book in this series (based on work with Carlos Alchour-ron and David Makinson), Knowledge in Flux, a definitive account of the widely examined AGM (after Alchour-ron, Gärdenfors, and Makinson) theory of belief revision. The AGM theory is firmly based on classical logic and its model theory, and by his founding participation in developing it, Gärden-fors has earned the right to critique knowledge representation. His new book is not primarily about logic, but it is certainly not an apostasy either. If I may be permitted a minor irrever-ence, I would say that this book came not to destroy logic but to fulfill. Knowledge representation as we know it has reached an impasse. There seems to be two problems, or perhaps, they are merely different perspectives of an underlying problem. The first is knowledge acquisition, and the second is machine learning. Conceptual Spaces has important messages for both. For machines to use knowledge it has to be somehow acquired. In principle , this knowledge can be hand coded into machines by experts or even elicited from them by an automated process. The practice is disappointing. The systems so constructed suffer from the aptly named brittle-ness problem, which is AI's version of the control engineer's sensitivity to be organized as collections of classes that reside in trees with their implicit hierarchy. Much AI a decade or more ago was devoted to the algebras and logics of property and method inheritance among classes so ordered. Today, a lot of effort is going into the automation, or at least semi-automation, of the construction of such ontologies. There is as yet no convincing evidence that these two responses will succeed in large-scale practice. Machine learning is commercially hot. There is no enterprise with access to large customer databases (social class, behavior patterns, credit history, and so on) that has not attempted to data mine them. My …
Heat shock decreases the embryonic quality of frozen-thawed bovine blastocysts produced in vitro

In this study, the effect of heat shock on frozen-thawed blastocysts was evaluated using in vitro-produced (IVP) bovine embryos. In experiment 1, the effects of 6 h of heat shock at 41.0 C on fresh blastocysts were evaluated. HSPA1A expression as a reflection of stress was increased by heat shock (P < 0.05), but the expressions of the quality markers IFNT and POU5F1 were not affected. In experiment 2, frozen-thawed blastocysts were incubated at 38.5 C for 6 h (cryo-con) or exposed to heat shock at 41.0 C for 6 h (cryo-HS). Then, blastocysts were cultured at 38.5 C until 48 h after thawing (both conditions). Cryo-HS blastocysts exhibited a decreased recovery rate: HSPA1A expression was dramatically increased compared with that in fresh or cryo-con blastocysts at 6 h, and IFNT expression was decreased compared with that in cryo-con blastocysts at 6 h (both P < 0.05). Cryo-con blastocysts at 6 h also exhibited higher HSPA1A expression than fresh blastocysts (P < 0.05). At 48 h after thawing, the number of hatched blastocysts and blastocyst diameter were lower in cryo-HS blastocysts (P < 0.05). Cryo-con blastocysts showed lower POU5F1 levels at 48 h than fresh, cryo-con or cryo-HS blastocysts at 6 h (P < 0.05), but their POU5F1 levels were not different from those of cryo-HS blastocysts at 48 h. These results indicated that application of heat shock to frozen-thawed blastocysts was highly damaging. The increase in damage by the interaction of freezing-thawing and heat shock might be one reason for the low conception rate in frozen-thawed embryo transfer in summer.
Sequential Variety in Work Processes

This paper introduces the concept of sequential variety as a way to measure variability in the sequence of steps used to perform a work process. There is reason to expect that such variability may influence a range of outcomes, such as flexibility and quality. Two alternative ways to operationalize sequential variety are demonstrated and compared to traditional measures of task variety in four task units. The findings provide empirical support for treating sequential variety as a distinct construct. In particular, sequential variety appears to provide an index of tacit, procedural knowledge, while task variety may relate more closely to explicit, declarative knowledge.
Ecohydrological advances and applications in plant-water relations research: a review

Aims The field of ecohydrology is providing new theoretical frameworks and methodological approaches for understanding the complex interactions and feedbacks between vegetation and hydrologic flows at multiple scales. Here we review some of the major scientific and technological advances in ecohydrology as related to understanding the mechanisms by which plant–water relations influence water fluxes at ecosystem, watershed and landscape scales. Important Findings We identify several cross-cutting themes related to the role of plant– water relations in the ecohydrological literature, including the contrasting dynamics of water-limited and water-abundant ecosystems, transferring information about water fluxes across scales, understanding spatiotemporal heterogeneity and complexity, ecohydrological triggers associated with threshold behavior and shifts between alternative stable states and the need for long-term data sets at multiple scales. We then show how these themes are embedded within three key research areas where improved understanding of the linkages between plant–water relations and the hydrologic cycle have led to important advances in the field of ecohydrology: upscaling water fluxes from the leaf to the watershed and landscape, effects of plant–soil interactions on soil moisture dynamics and controls exerted by plant water use patterns and mechanisms on streamflow regime. In particular, we highlight several pressing environmental challenges facing society today where ecohydrology can contribute to the scientific knowledge for developing sound management and policy solutions. We conclude by identifying key challenges and opportunities for advancing contributions of plant–water relations research to ecohydrology in the future.
One-way and two-way coupled PDF-PBE/LES simulations of turbulent particle-laden flows

This paper studies the influence of the fluid-particle interaction for the prediction of turbulent particle-laden flows in a bluff body configuration. The simulations are performed using a probability density function form of the population balance equation (PDF-PBE) in the large eddy simulation (LES) context. The inertia of particles of different sizes is effectively modelled using a newly developed Stokes binning method which divides the PDF-PBE into classes based on the particle Stokes numbers. A two-way coupling is applied considering the interaction between the continuous and particulate phase only by momentum exchange terms. The studied case is an experiment of glass beads injected into a recirculating flow. Good agreement is observed between the simulations and the measurements. The results also confirm that the flow field of the continuous phase is influenced by the particulate phase and this is consistent with the experimental observations.
Measuring Social Aspects of Distributed Learning Groups

Computer-supported group-based learning requires that interaction has to be structured within the group; otherwise it will not happen. Also, it is important to pay attention to social aspects of a distributed learning group (i.e. group dynamics); otherwise a good working team will not emerge. This article proposes a process-oriented framework that considers five critical elements for enhancing interaction in the cognitive dimension. Enhancing social interaction for achieving a sound social space requires a sociable computer-supported collaborative learning environment (CSCL) in which social presence of the group members exists. Such sound social space is considered by many, if not most, educational researchers to be a necessary prerequisite for epistemic interaction. In order to allow designers and teachers to develop sociable CSCL environments, this article proposes three scales to measure social aspects. Although the validation of these scales is somewhat limited, the scales are a first step for making certain characteristics of distributed interaction visible. To provide a more firm base for the scales, this article also calls for more research on this issue.
Becoming Acrobat, Becoming Academic: An Affective, Autoethnographic Inquiry Into Collective Practices of Knowing and Becoming

This article mobilizes a Spinozo–Deleuzian understanding of affect to articulate connections between embodied sensation and academic thinking, connections which surfaced during my ethnographic and autoethnographic research as a circus performer. I argue against reifying differences between the production of knowledge and of movement, suggesting we explore similarities in the conditions of their emergence including reflection, multiplicity, and responsiveness to repetition. In so doing, I challenge hegemonic ideas about who belongs in the body of an academic; inviting us to better understand our “less-rational” and more collective selves in becoming purveyors of academic knowledge.
Descriptive Geometric Kinematic Analysis of Clavel's "Delta" Robot

Certain high speed industrial assembly robots share a peculiar three legged parallel architecture wherein three “hips”, attached to a fixed upper base or “pelvis”, actuate “thighs” connected by “knees” to “shins”. Each shin is a parallelogramic four-bar linkage. “Ankles” are connected to a common end effector “foot” which executes spatial translatory motion. Inverse and direct kinematic analyses of such manipulators have simple geometric solutions reducable to intersection of line and sphere. Computation is carried out efficiently in a common fixed reference frame.
Learning to Draw Dynamic Agent Goals with Generative Adversarial Networks

We address the problem of designing artificial agents capable of reproducing human behavior in a competitive game involving dynamic control. Given data consisting of multiple realizations of inputs generated by pairs of interacting players, we model each agent's actions as governed by a time-varying latent goal state coupled to a control model. These goals, in turn, are described as stochastic processes evolving according to player-specific value functions depending on the current state of the game. We model these value functions using generative adversarial networks (GANs) and show that our GAN-based approach succeeds in producing sample gameplay that captures the rich dynamics of human agents. The latent goal dynamics inferred and generated by our model has applications to fields like neuroscience and animal behavior, where the underlying value functions themselves are of theoretical interest.
Effectively using search-based software engineering techniques within model checking and its applications

In this position paper, we affirm that there are synergies to be gained by using search-based techniques within software model checking. We will show from the literature how meta-heuristic search based techniques can augment both the model checking process and its applications. We will provide evidence to support this assertion in the form of existing research work and open problems that may benefit from combining Search-Based Software Engineering (SBSE) techniques and software model checking.