Miracle-dz/UniFAR-FTUs huggingface dataset:
In vitro antiproliferative activity of combinations of ether lipid analogues and DNA-interactive agents against human tumor cells.

Ether lipid analogues of platelet-activating factor (1-octadecyl-2-acetyl-sn-glycero-3-phosphocholine) possess a wide range of biological activities, including inhibition of neoplastic cell growth in vitro and in vivo. This activity is believed to be membrane mediated. Three different ether lipid analogues, 1-octadecyl-2-methyl-rac-glycero-3-phosphocholine, 1-thiohexadecyl-2-ethyl-rac-glycero-3-phosphocholine, and 4-amino-methyl-1-[2,3-(di-n-decyloxy)-n-propyl]-4-phenylpiperidine , were combined with three DNA-interactive drugs, Adriamycin, 4-hydroperoxycyclophosphamide, and cisplatin, in the expectation that combinations of drugs with different mechanisms of action might show enhanced antitumor activity. The in vitro antiproliferative activity of the combinations was measured with a semisoft agarose clonogenic assay of an ovarian adenocarcinoma cell line. Various permutations of drug combinations were studied. Isobologram analyses and different treatment schedules were performed. Enhanced antiproliferative activity was found with combinations of ether lipids with DNA-interactive drugs in comparison with single agents. Statistical evaluation of the data indicated that the increase in activity was due to an additivity phenomenon. Neither synergism nor antagonism was found.
Multiband Spectrum Access: Great Promises for Future Cognitive Radio Networks

Cognitive radio has been widely considered as one of the prominent solutions to tackle the spectrum scarcity. While the majority of existing research has focused on single-band cognitive radio, multiband cognitive radio represents great promises toward implementing efficient cognitive networks compared to single-based networks. Multiband cognitive radio networks (MB-CRNs) are expected to significantly enhance the network's throughput and provide better channel maintenance by reducing handoff frequency. Nevertheless, the wideband front-end and the multiband spectrum access impose a number of challenges yet to overcome. This paper provides an in-depth analysis on the recent advancements in multiband spectrum sensing techniques, their limitations, and possible future directions to improve them. We study cooperative communications for MB-CRNs to tackle a fundamental limit on diversity and sampling. We also investigate several limits and tradeoffs of various design parameters for MB-CRNs. In addition, we explore the key MB-CRNs performance metrics that differ from the conventional metrics used for single-band-based networks.
Online Learning with Preference Feedback

We propose a new online learning model for learning with preference feedback. The model is especially suited for applications like web search and recommender systems, where preference data is readily available from implicit user feedback (e.g. clicks). In particular, at each time step a potentially structured object (e.g. a ranking) is presented to the user in response to a context (e.g. query), providing him or her with some unobserved amount of utility. As feedback the algorithm receives an improved object that would have provided higher utility. We propose a learning algorithm with provable regret bounds for this online learning setting and demonstrate its effectiveness on a web-search application. The new learning model also applies to many other interactive learning problems and admits several interesting extensions.
Parallel spring simplifies actuator output torque and improves feed-forward learning

In robotics, particularly legged locomotion, there are situations where high gain feedback is not applicable. It is dangerous and may conduce to instability of the robot. In such situations, presence of a feed-forward controller helps the tracking problem while maintaining stability. However, these controllers usually require the system dynamics which may not be available. Furthermore, There are also situations where the frequency response of the actuator output torque is limited and may not be able to produce torques with high variations. In cases of unknown system dynamics, learning feed-forward scheme has been proposed which requires high number of basis functions according to system and trajectory. In this paper we propose a method that employs parallel spring in order to reduce higher frequency components of the actuator output torque. Moreover, the added spring will simplify the process of learning by reducing number of basis functions. Adaptive parallel spring is proposed for the case where different periodic motions are given to the system. Our simulations results on a two-link manipulator show that the adaptive spring will gradually simplify actuator output torque and improve feed-forward learning.
A Matching-based Placement and Routing System for Analog Design

Matching placement and routing is very important in layout design of high performance analog circuits. This paper presents a matching-based placement and routing system for custom layout design automation especially for analog or mixed-signal designs. The system explores various device-level matching-placement and matching-routing patterns to generate the most compact and high-quality layouts. Inputting a circuit netlist, the system automatically analyzes the circuit and extracts matching devices to form several matching device groups. Then, it selects the best matching placement and routing pattern for each device or device group to optimize and to meet the overall placement objectives and constraints. All patterns are user-configurable, stored in the pattern database, and portable from design to design. After the layout of each device and device group is generated and placed, the constraint-driven shape-based router is invoked to complete the layout. The overall system can easily generate high-quality layouts and greatly reduce the layout design time.
Cognitive neuropsychological functioning in New Zealand Māori diagnosed with schizophrenia

Objective: Previous research suggests that New Zealand Māori may have an elevated rate of schizophrenia. However, there is limited evidence on important clinical features of the illness in this population. This study examined cognitive neuropsychological functioning in 54 adult Māori diagnosed with schizophrenia and 56 Māori controls. This study also examined associations between cognition, medication and symptoms of psychosis in the schizophrenia group. Method: The groups were matched on socio-demographic variables, handedness and premorbid cognitive ability. Participants were assessed on neuropsychological tests of attention, executive ability, motor, premorbid ability, verbal/non-verbal memory and verbal fluency (English/Māori versions). The Positive and Negative Syndrome Scale was used to assess psychotic symptoms. Information on cultural identity, duration of illness, duration of untreated psychosis, medication and substance abuse was collected. Results: The performance of the schizophrenia group was significantly lower than the control group on all the neuropsychological tests, except the test of attention. The effect sizes were moderate to large: 0.78 for motor function; 1.3 for executive ability, verbal fluency and visual memory; 1.6 for verbal learning and 1.8 for verbal memory. These differences remained after adjustment for multiple comparisons and covariates. A higher dose of antipsychotic medication and a higher anticholinergic load were associated with greater verbal memory impairment (r = −0.38 and r = −0.38, respectively). A longer duration of illness was associated with greater impairment of verbal memory (rho = −0.48), verbal learning (rho = −0.41) and visual memory (rho = −0.44). Conclusion: The findings for the schizophrenia group show a profile of generalised cognitive impairment with greater impairment of verbal memory. The cognitive impairment in this group was independent of psychotic symptoms, but was associated with a higher antipsychotic dose, higher anticholinergic load and longer duration of illness. These findings have implications for clinical prescribing practices and rehabilitation for New Zealand Māori diagnosed with schizophrenia.
An averaged switch model including conduction losses for boundary conduction mode dc-to-dc converters

In this paper, following the averaged switch approach, an averaged model, including conduction losses for the switch network of the pulse width modulated (PWM) dc-to-dc converters operating at the boundary between continuous conduction mode (CCM) and discontinuous inductor current mode (DICM) is developed. The model is verified through PSpice and CASPOC simulation for the boost converter. The averaged model is general, and can be applied to different dc-to-dc converters, without deriving a different averaged switch network model for each converter configuration.
Motion pattern analysis enabling accurate travel mode detection from GPS data only

Travel modes are one of the crucial pieces of information to characterize one's travel behavior. In recent years several approaches of mode detection from GPS data have been proposed. The approach presented in this paper uses machine learning to evaluate a set of GPS-based features for their ability to recognize the common modes walk, bicycle, car, bus, and train. The proposed features describe motion characteristics from GPS-trajectories by relative frequencies. Compared to previous work the proposed feature set leads to higher average recognition rates around 92% without relying on additional GIS or real-time information. The evaluation compares detection rates from multilayer perceptrons, logistic model trees, and C4.5 decision trees and is complemented by an evolutionary feature selection for selecting the most beneficial feature subsets leading to the best quality gain. In contrast to other research, this study uses a comparatively large set of 400 GPS trajectories which have been recorded in rural and urban European areas. Results contribute to a higher reliability as well as a broader applicability of GPS-only travel mode detection.
Estrogen Replacement Regulates Vaginal Innervations in Ovariectomized Adult Virgin Rats: A Histological Study

Background. Our previous Gräfenberg spot findings confirmed that the distal-third areas of the anterior vaginal wall bore a significantly greater number of nerves and sexual hormone may have certain degree of influence on these significant differences. However, the role of estrogen in vaginal innervations remains controversial. Methods. To investigate whether hormonal-neural interactions occur in the vagina, sixty rats were randomly divided into six groups: Sham-operated, ovariectomy, and 4 treatment groups. After 2 weeks of treatment, vaginal biopsies were prepared with hematoxylin and eosin and PGP9.5 using immunohistochemistry. Results. The density of small nerve fibers was significantly higher in the distal-half areas of intact vaginal walls than the proximal-half areas (P = 0.001). In contrast, the overall PGP 9.5-ir fiber innervation density was significantly decreased in the OVX rats subjected to surgical menopause. Sustained estrogen administration for 2 weeks resulted in nerve fiber proliferation, with values reaching normal levels in the low-dose estradiol valerate group. Conclusion. Our findings indicate that systemic hormonal therapy with low-dose estradiol valerate is effective and safe for treating deficient vaginal innervation caused by low level of estrogen activity in menopausal women and may aid studies to identify an optimal estradiol dose to provide relief from vaginal discomfort.
Tetrameric Subunit Structure of the Native Brain Inwardly Rectifying Potassium Channel Kir 2.2*

Strongly inwardly rectifying potassium channels of the Kir 2 subfamily (IRK1, IRK2, and IRK3) are involved in maintenance and modulation of cell excitability in brain and heart. Electrophysiological studies of channels expressed in heterologous systems have suggested that the pore-conducting pathway contains four subunits. However, inferences from electrophysiological studies have not been tested on native channels and do not address the possibility of nonconducting auxiliary subunits. Here, we investigate the subunit stoichiometry of endogenous inwardly rectifying potassium channel Kir 2.2 (IRK2) from rat brain. Using chemical cross-linking, immunoprecipitiation, and velocity sedimentation, we report physical evidence demonstrating the tetrameric organization of the native channel. Kir 2.2 was sequentially cross-linked to produce bands on SDS-polyacrylamide gel electrophoresis corresponding in size to monomer, dimer, trimer, and three forms of tetramer. Fully cross-linked channel was present as a single band of tetrameric size. Immunoprecipitation of biotinylated membranes revealed a single band corresponding to Kir 2.2, suggesting that the channel is composed of a single type of subunit. Hydrodynamic properties of 3-[(3-cholamidopropyl)dimethylammonio]-1-propane sulfonic acid-solubilized channel were used to calculate the molecular mass of the channel. Velocity sedimentation in H2O or D2O gave a sharp peak with a sedimentation coefficient of 17.3 S. Gel filtration yielded a Stokes radius of 5.92 nm. These data indicate a multisubunit protein with a molecular mass of 193 kDa, calculated to contain 3.98 subunits. Together, these results demonstrate that Kir 2.2 channels are formed by the homotetrameric association of Kir 2.2 subunits and do not contain tightly associated auxiliary subunits. These studies suggest that Kir 2.2 channels differ in structure from related heterooctomeric ATP-sensitive K channels and heterotetrameric G-protein-regulated inward rectifier K channels.
Chapter 11. Nonfactual meanings in early use of evidentials in Turkish child-caregiver interactions

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Expanding EOS Developing and Understanding a Stable Baseline for the Economics by Object-oriented Simulation Framework

EOS is a framework and computational laboratory for agent-based computational economics developed in spring 2009 by Chris Rucinski ’10, Ye “Cody” Wang ’10, and myself under the guidance of Professor Steiglitz. Here, we build upon this earlier EOS work by developing a stable baseline model and accompanying agent implementations as well as an I/O package to support both development and experimentation. The steps leading up to, and the analysis motivating the final “baseline 3” model are described, and the model is tested with a set of simple experiments. Finally, the baseline 3 model is compared to an agent-based economic model created and presented by Oeffner [4]. Differences in design and approach are discussed, as are implications for future work.
Separating the EPS in a biofilm: Models and simulations of movement of the EPS within

In this dissertation two models are investigated for describing movement of different components within a biofilm. The first model uses a single fluid three component formulation of the biofilm to model the movement within the biofilm in 1D. This is done using a system partial differential equations to model the expansion or contraction of the biofilm in order for the three components to reach an ideal concentration. The model is further refined to include separate velocities for each component as well as using zeroth order kinetics for the growth. In order to solve this system of partial differential equations a finite difference method with an upwind scheme was used to solve the system numerically. The second model is an energy based approach done in both 1D and 2D. An energy, in the case the Flory-Huggins free energy density, is used to describe the interactions of different components within the biofilm. The Cahn-Hilliard equation with the Flory-Huggins free energy density is used to model the separation of the biofilm into two phases. A brief derivation of both the Flory-Huggins equation and the Cahn-Hilliard equation is given using a lattice model and thermodynamic properties. The Flory-Huggins equation is modified slightly for simplicity. A movement energy is also added to the Flory-Huggins equation in order to allow the polymers within the biofilm to move around the domain. In the 1D case the numerical solution was found using finite differences with an upwind scheme similar to the first model. The 2D case is more difficult to solve due to the extra dimension. Due to this the projection method was used to solve part of the system of equations and finite difference using central difference instead of upwind is used to solve the rest.
rePLay: A Hardware Framework for Dynamic Optimization

In this paper, we propose a new processor framework that supports dynamic optimization. The rePLay Framework embeds an optimization engine atop a high-performance execution engine. The heart of the rePLay Framework is the concept of a frame. Frames are large, single-entry, single-exit optimization regions spanning many basic blocks in the program's dynamic instruction stream, yet containing only a single flow of control. This atomic property of frames increases the flexibility in applying optimizations. To support frames, rePLay includes a hardware-based recovery mechanism that rolls back the architectural state to the beginning of a frame if, for example, an early exit condition is detected. This mechanism permits the optimizer to make speculative, aggressive optimizations upon frames. In this paper, we investigate some of the underlying phenomenon that support rePLay. Primarily, we evaluate rePLay's region formation strategy. A rePLay configuration with a 256-entry frame cache, using 74 KB frame constructor and frame sequencer, achieves an average frame size of 88 Alpha AXP instructions with 68 percent coverage of the dynamic istream, an average frame completion rate of 92.81 percent, and a frame predictor accuracy of 81.26 percent. These results soundly demonstrate that the frames upon which the optimizations are performed are large and stable. Using the most frequently initiated frames from rePLay executions as samples, we also highlight possible strategies for the rePLay optimization engine. Coupled with the high coverage of frames achieved through the dynamic frame construction, the success of these optimizations demonstrates the significance of the rePLay Framework. We believe that the concept of frames, along with the mechanisms and strategies outlined in this paper, will play an important role in future processor architecture.
Channel Capacity of MIMO Multi-Carrier Modulation in Additive Noise

In this paper, we investigate the capacity of multi-carrier modulation (MCM) system based on multiple input multiple output (MIMO) antennae for single user communications over the additive noise channel. We define a two variable energy spectrum function for each antenna which describes energy distribution in time-frequency-space (T-F-S) subchannels. The general wavelet packet basis functions are employed as sub-carrier to give more flexibility to partition of T-F plane compared with Inverse Fast Fourier Transform and Fast Fourier Transform (IFFT/FFT). The total energy and T-F space constraints are given. Under the prior information of additive noise energy distribution, the water-filling resolution is derived to obtain maximum objective capacity function. Furthermore, the optimal resource distribution in three dimensions space is obtained finally.
Comparative study of induction motor efficiency optimization control strategy for electric vehicle

Electric vehicles must extremely improve its electric drive system efficiency and effectively use its limited energy. In this paper, a structure diagram of the induction motor in synchronously rotating frame of reference including iron loss is given at first, the losses of induction motor in operation is discussed and an efficiency optimization control strategy of induction motor driving system based on loss model is proposed. then a comprehensive analysis of the efficiency optimization control strategy based on search, minimum stator current and loss model respectively is presented to discuss the distinction and links among them. The experiment system is built with DSP TMS320LF2407A, the experimental results show that the loss model based efficiency optimization control strategy can reach global optimum without additional hardware. It has a fast optimization speed and a fairly stable torque and speed in the optimization process.
Outcome reporting bias in evaluations of public health interventions: evidence of impact and the potential role of a study register

Background Systematic reviews of the effectiveness of interventions are increasingly used to inform recommendations for public health policy and practice, but outcome reporting bias is rarely assessed. Methods Studies excluded at full-text stage screening for a systematic review of a public health intervention were assessed for evidence of study exclusion resulting from non-reporting of relevant outcomes. Studies included in the review were assessed for evidence of outcome reporting bias and the impact that this had on the evidence synthesised using a formal tool (Outcome Reporting Bias in Trials (ORBIT)). Results None of the reports excluded at full-text stage were excluded because of non-reporting of relevant outcomes. Of the 26 included papers, six were identified as having evidence of missing or incompletely reported outcomes, with 64% of unreported or incompletely reported outcomes identified as to leading to a high risk of bias according to the ORBIT tool. Where there was evidence of the effectiveness of interventions before an assessment of outcome reporting bias was undertaken, identifying possible instances of outcome reporting bias generally led to a reduction in the strength of evidence for the effectiveness of the interventions. Conclusion The findings from this single evaluation provide empirical data to support the call for a prospective public health interventions study registry to aid the identification of unreported or incompletely reported outcomes. Critical appraisal tools can also be used to identify incompletely reported outcomes, but a tool such as ORBIT requires development to be suitable for public health intervention evaluations.
On Combining Backpropagation with Boosting

Boosting is a method for learning combined classifiers. In a boosting ensemble of classifiers trained by the backpropagation algorithm, the learning rate takes much smaller value comparing with the backpropagation applied alone. We propose a method which overcomes the above drawback and test it on neuro-fuzzy systems constituting a classifier ensemble using some well known benchmarks.