Miracle-dz/UniFAR-FTUs huggingface dataset:
Robust In-Field Testing of Digital Microfluidic Biochips

Microfluidic technology offers vast promise for implementing biochemistry-on-chip with diverse applications to clinical diagnosis, genome analysis, drug design, and point-of-care testing. Among various types of fluid-chips, droplet-based digital microfluidic biochips (DMFBs), which consist of a patterned array of controllable electrodes, provide the advantage of programmability, ease of fluidic operations, and versatile droplet mobility. However, because of manufacturing or field defects, electrode degradation, or dielectric breakdown, these chips may suffer from incorrect fluidic behavior. Reliability of fluidic operations is of utmost concern in DMFBs that are used to perform safety-critical bio-protocols. Various methods are deployed to test these devices, either offline or being overlapped with bioassay operations (termed as concurrent or in-field testing). The main challenge of in-field testing lies in the fact that the test must run concurrently with the execution of the normal assay without hampering the correctness of the latter. In prior work, optimal testing for droplet mobility over all electrodes was formulated in terms of finding either a Hamiltonian path or a Eulerian path in an undirected graph that represents the electrode-adjacency structure. Although these models have been studied for offline testing, no such effort was made in the area of concurrent testing. In this work, we propose, for in-field application, an SAT-based modeling and solution approach to find an optimal test plan that can be used to check droplet movement across the boundary between every pair of adjacent electrodes, which is visited by the droplets of the ongoing assay. The proposed method is robust and determines a test solution successfully regardless of the cover assay that is being executed concurrently. Experiments on several real-life assays and other test cases demonstrate the effectiveness of the method with respect to test completion time.
Optical TDM sorting networks for high-speed switching

The general time-space-time switching problem in telecommunications requires the use of multichannel time slot interchangers. We propose two multichannel time slot sorters which sort N/sup 2/ time-division multiplexed (TDM) optical inputs, arranged as N frames with N time slots per frame using O(Nlog/sup 2/N) optical switch elements. The TDM optical inputs are sorted in place without expanding the space-time fabric into a space-division switch. The hardware components used are 2/spl times/2 optical switches (LiNbO/sub 3/ directional couplers) and optical delay lines connected in a feedforward fashion. Two space-time variants of the spatial odd-even merge algorithm are used to design the sorters. By maintaining the number of shift-exchange operations invariant at each stage, the proposed sorters use fewer switches than previously proposed sorters using switches with feedback line delays. The use of local control at each 2/spl times/2 switch makes the proposed sorters more practical for high-speed optical inputs than Benes-based time slot permuters with global control and high latency, which affects interframe distance. Both time slot sorters support pipelining of input frames and sorted outputs are available at each time slot after an initial frame delay. The proposed sorters find practical application in the time-domain equivalents of space-division, nonblocking, self-routing packet switches using the sort-banyan architecture, such as the Starlite switch, Sunshine switch, etc.
Hispidulin potently inhibits human glioblastoma multiforme cells through activation of AMP-activated protein kinase (AMPK).

Glioblastoma multiforme (GBM) is the most common and lethal type of primary brain tumor. Despite recent therapeutic advances in other cancers, the treatment of GBM remains ineffective and essentially palliative. The current focus lies in the finding of components that activate the AMP-activated protein kinase (AMPK), one key enzyme thought to be activated during the caloric restriction (CR). In the present study, we found that treatment of hispidulin, a flavone isolated from Saussurea involucrate Kar. et Kir. , resulted in dose-dependent inhibition of GBM cellular proliferation. Interestingly, we show that hispidulin activated AMPK in GBM cells. The activation of AMPK suppressed downstream substrates, such as the mammalian target of rapamycin (mTOR) and eukaryotic initiation factor 4E-binding protein-1 (4E-BP1), and resulted in a general decrease in mRNA translation. Moreover, hispidulin-activated AMPK decreases the activity and/or expression of lipogenic enzymes, such as fatty acid synthase (FASN) and acetyl-CoA carboxylase (ACC). Furthermore, hispidulin blocked the progression of the cell cycle at the G1 phase and induced apoptosis by inducing p53 expression and further upregulating p21 expression in GBM cells. On the basis of these results, we demonstrated that hispidulin has the potential to be a chemopreventive and therapeutic agent against human GBM.
A survey on evolutionary machine learning

ABSTRACT Artificial intelligence (AI) emphasises the creation of intelligent machines/systems that function like humans. AI has been applied to many real-world applications. Machine learning is a branch of AI based on the idea that systems can learn from data, identify hidden patterns, and make decisions with little/minimal human intervention. Evolutionary computation is an umbrella of population-based intelligent/learning algorithms inspired by nature, where New Zealand has a good international reputation. This paper provides a review on evolutionary machine learning, i.e. evolutionary computation techniques for major machine learning tasks such as classification, regression and clustering, and emerging topics including combinatorial optimisation, computer vision, deep learning, transfer learning, and ensemble learning. The paper also provides a brief review of evolutionary learning applications, such as supply chain and manufacturing for milk/dairy, wine and seafood industries, which are important to New Zealand. Finally, the paper presents current issues with future perspectives in evolutionary machine learning.
Identification of Biomarkers for Predicting Lymph Node Metastasis of Stomach Cancer Using Clinical DNA Methylation Data

Background Lymph node (LN) metastasis was an independent risk factor for stomach cancer recurrence, and the presence of LN metastasis has great influence on the overall survival of stomach cancer patients. Thus, accurate prediction of the presence of lymph node metastasis can provide guarantee of credible prognosis evaluation of stomach cancer patients. Recently, increasing evidence demonstrated that the aberrant DNA methylation first appears before symptoms of the disease become clinically apparent. Objective Selecting key biomarkers for LN metastasis presence prediction for stomach cancer using clinical DNA methylation based on a machine learning method. Methods To reduce the overfitting risk of prediction task, we applied a three-step feature selection method according to the property of DNA methylation data. Results The feature selection procedure extracted several cancer-related and lymph node metastasis-related genes, such as TP73, PDX1, FUT8, HOXD1, NMT1, and SEMA3E. The prediction performance was evaluated on the public DNA methylation dataset. The results showed that the three-step feature procedure can largely improve the prediction performance and implied the reliability of the biomarkers selected. Conclusions With the selected biomarkers, the prediction method can achieve higher accuracy in detecting LN metastasis and the results also proved the reliability of the selected biomarkers indirectly.
Characterization of a novel oligodendrocyte cell surface molecule

Cell‐type‐specific reagents have proven useful in the analysis of central nervous system (CNS) development and function. Most markers of oligodendrocytes are components of myelin sheath, which in the CNS is the specific product of oligodendrocytes. We have isolated a novel monoclonal antibody termed 2B10 which was raised against embryonic rat spinal cord tissue. In adult rat cerebrum, cerebellum, and spinal cord, 2B10 immunoreactivity is predominantly localized in white matter. 2B10 immunoreactivity is absent from peripheral nerve, suggesting that in the nervous system the 2B10 antigen is restricted to the CNS. Dissociated cell culture studies indicate that 2B10 labels a cell surface molecule, and its cellular distribution is coincident with O1 and myelin basic protein‐positive oligodendrocytes. By contrast, 2B10 does not label GFAP‐positive astrocytes. These data suggest that in the CNS the 2B10 antigen is expressed specifically on oligodendrocytes. Biochemical analysis indicates that 2B10 recognizes a protein with an apparent molecular weight of approximately 79,000 in reducing sodium dodecyl sulfate‐polyacrylamide gel electrophoresis. Developmentally, 2B10 immunoreactivity is detectable in rat spinal cord at as early as embryonic day 14. The relative abundance of this molecule decreases during myelination, but is maintained at a sustained level throughout adulthood. The 2B10 antigen appears not to be a myelin‐associated protein since it is not detected in purified myelin fractions. 2B10 immunoreactivity is not detectable in extracts of rat sciatic nerve, heart, kidney, muscle, and skin, but is detectable in extracts of spleen and thymus. These data suggest that the 2B10 antigen is a novel cell surface molecule that is expressed on oligodendrocyte lineage cells throughout development. © 1995 Wiley‐Liss, Inc.
Dynamique et contrôle d'un marché financier avec une approche système multi-agents. (Dynamics and control of financial market with a multi-agent system approach)

Cette these propose une reflexion autour de l'etude des marches financiers sous le prisme des systemes complexes.Tout d'abord une description mathematique est proposee pour representer le processus de prises de decision des agents des lors ou celui-ci bien que representant les interets individuels d'un agent, est egalement influence par l'emergence d'un comportement collectif. La methode est particulierement applicable lorsque le systeme etudie est caracterise par une dynamique non-lineaire. Une application du modele est proposee au travers de l'implementation d'un marche artificiel boursier avec une approche systeme multi-agents. Dans cette application la dynamique du marche est decrite a la fois aux niveaux microscopiques (comportement des agents) et macroscopique (formation du prix). Le processus de decision des agents est defini a partir d'un ensemble de regles comportementales reposant sur des principes de logique floue. La dynamique de la formation du prix repose sur une description deterministe a partir des regles d'appariement d'un carnet d'ordres central tel que sur NYSE-Euronext-Paris. Il est montre que le marche artificiel boursier tel qu'implemente est capable de repliquer plusieurs faits stylises des marches financiers : queue de distribution des rendements plus epaisse que celle d'une loi normale et existence de grappes de volatilite (ou volatility clustering). Par la suite, a partir de simulations numeriques il est propose d'etudier trois grandes proprietes du systeme : sa capacite d'auto-organisation, de resilience et sa robustesse. Dans un premier temps une methode est introduite pour qualifier le niveau d'auto-organisation du marche. Nous verrons que la capacite d'auto-organisation du systeme est maximisee quand les comportements des agents sont diversifies. Ensuite, il est propose d'etudier la reponse du systeme quand celui-ci est stresse via la simulation de chocs de marche. Dans les deux analyses, afin de mettre en evidence comment la dynamique globale du systeme emerge a partir des interactions et des comportements des agents des resultats numeriques sont systematiquement apportes puis discutes.Nos resultats montrent notamment qu'un comportement collectif gregaire apparait a la suite d'un choc, et, entraine une incapacite temporaire du systeme a s'auto-organiser. Finalement, au travers des simulations numeriques il peut etre egalement remarque que le marche artificiel boursier implemente est plus sensible a de faibles repetitions repetees qu'a un choc plus important mais unique.
User Violence and Nursing Staff Burnout

Exposure to patient violence in health staff can lead to the onset of burnout in these workers. The main goal of this investigation is to study how exposure to this kind of violence affects onset of burnout and to appraise the role of job satisfaction as a modulating variable. A descriptive, cross-sectional study was carried out using a self-administered anonymous questionnaire with the nursing staff of all the public hospitals of the Region of Murcia (Spain), obtaining a sample of 1,489 health professionals. From the results obtained, we underline the modulating role of extrinsic job satisfaction in the relationship between nonphysical violence and emotional exhaustion, and the protective effect of job satisfaction on the impact of nonphysical violence and the level of cynicism. No effects of job satisfaction in the relationship between physical violence and burnout were observed. We therefore conclude that experiencing nonphysical aggression has a lower impact on the psychological health of workers who are satisfied with their job, and interventions aimed at increasing these workers’ extrinsic job satisfaction are highly recommended.
New Low-Complexity Bit-Parallel Finite Field Multipliers Using Weakly Dual Bases

New structures of bit-parallel weakly dual basis (WDB) multipliers over the binary ground field are proposed. An upper bound on the size complexity of bit-parallel multiplier using an arbitrary generating polynomial is given. When the generating polynomial is an irreducible trinomial x/sup m/+x/sup k/+1, 1/spl les/k/spl les/[m/2], the structure of the proposed bit-parallel multiplier requires only m/sup 2/ two-input AND gates and at most m/sup 2/-1 XOR gates. The time delay is no greater than T/sub A/+([log/sub 2/ m]+2)T/sub x/, where T/sub A/ and T/sub X/ are the time delays of an AND gate and an XOR gate, respectively.
Shadow filters based on DDCC

This study presents a new realisation of voltage-mode shadow filters based on low-voltage low-power differential difference current conveyor (DDCC). Thanks to the attractive features of the DDCC, including its capability of performing arithmetic operations, the proposed filters offer the advantage of circuit simplicity, minimum number of active and passive elements, and no need for additional summing circuit, compared to the previous available shadow filter designs. The DDCC was designed and fabricated in Cadence platform using 0.35 μm CMOS AMIS process with supply voltage and power consumption of 1 V and 37 μW, respectively. The presented simulation and experimental results using a real chip validate the functionality of the proposed filters.
Speech recognition using hidden Markov models based on segmental statistics

It is well-known that standard hidden Markov models (HMMs) cannot adequately represent time-variant features while staying in a single state. In order to capture the dynamic characteristics of speech, various methods have been proposed and studied widely; use of linear regression coefficients of time, introduction of conditional density HMMs, and so forth. In this paper, segmental unit input HMMs are described. In this modeling, several successive frames are combined and treated as an input vector. When using segmental statistics, the increased dimension of the parameters degrades the precision in estimating covariance matrices. There, Karhunen-Loeve expansion was used to reduce the dimension. In this paper, the segmental unit input HMMs were compared with the following other methods for modeling the dynamics of speech: conditional density HMMs and use of regression coefficients. These comparisons were done through evaluation experiments in different tasks, namely, continuous syllable recognition, sentence recognition, and isolated word recognition tasks. The results show that utilization of segmental features as input vectors to basic HMMs gave us better recognition rates than those by the traditional methods. Furthermore, integration of regression coefficients into the segmental unit HMMs yielded the best results. © 1997 Scripta Technica, Inc. Syst Comp Jpn, 28(7): 31–38, 1997
A 20-kilodalton protein is required for efficient production of the Bacillus thuringiensis subsp. israelensis 27-kilodalton crystal protein in Escherichia coli

The 27-kilodalton (kDa) mosquitocidal protein gene from Bacillus thuringiensis subsp. israelensis has been cloned as a 10-kilobase (kb) HindIII fragment from plasmid DNA; efficient expression in Escherichia coli KM1 depends on a region of DNA located approximately 4 kb upstream (K. McLean and H. R. Whiteley, J. Bacteriol. 169:1017-1023, 1987). We have cloned the upstream DNA region and show that it contains a complete open reading frame (ORF) encoding a protein with a molecular mass of 19,584 Da. Sequencing of adjacent stretches of DNA revealed two partial ORFs: one has 55.2% identity in an overlap of 319 amino acids to the putative transposase of IS231 of B. thuringiensis subsp. thuringiensis, and the other, a 78-codon partial ORF, may be the carboxyl terminus of the 67-kDa protein previously observed in maxicells of strain KM1. A 0.8-kb fragment containing only the 20-kDa protein gene greatly enhanced the expression of the 27-kDa protein in E. coli. The introduction of nonsense codons into the 20-kDa protein gene ORF abolished this effect, indicating that the gene product, not the mRNA or DNA, is required for the enhancement. The effect of the 20-kDa protein gene on various fusions of lacZ to the 27-kDa protein gene suggests that the 20-kDa protein acts after the initiation of translation of the 27-kDa protein gene.
Early identification of autism by the CHecklist for Autism in Toddlers (CHAT)

Acknowledgements 2 We are grateful to the MRC for support for this work through 3 successive project grants to SBC, AC, and GB. Carol Brayne gave us valuable feedback on an earlier draft of this paper. 3 The CHecklist for Autism in Toddlers (CHAT) is a screening instrument which identifies children aged 18 months who are at risk for autism. This article explains how the CHAT was developed, how the CHAT should be used, and provides a brief introduction to autism.
Generation and Characterization of a Human Bradykinin Receptor B1 Transgenic Rat as a Pharmacodynamic Model

Antagonists of the B1 bradykinin receptor (B1R) offer the promise of novel therapeutic agents for the treatment of inflammatory and neuropathic pain. However, the in vivo characterization of the pharmacodynamics of B1R antagonists is hindered by the low level of B1R expression in healthy tissue and the profound species selectivity exhibited by many compounds for the human B1R. To circumvent these issues, we generated a transgenic rat expressing the human B1R under the control of the neuron-specific enolase promoter. Membranes prepared from whole brain homogenates of heterozygous transgenic rats indicate a B1R expression level of 30 to 40 fmol/mg; there is no detectable B1R expression in control nontransgenic rats. The pharmacological profile of the B1R expressed in the transgenic rat matches that expected of the human, but not the rat receptor. The mapping of the transgene insertion site to rat chromosome 1 permitted the development of a reliable assay for the identification of homozygous transgenic rats. Significantly, homozygous transgenic rats express 2-fold more B1R than heterozygous animals. Autoradiographic analyses of tissue sections from transgenic rats reveal that the B1R is broadly expressed in both the brain and spinal cord. The human B1R expressed in the transgenic rat functions in an in vitro contractile assay and thus has the potential to elicit a functional response in vivo. Using the humanized B1R transgenic rat, an assay was developed that is suitable for the routine evaluation of a test compound's ability to occupy the human B1R in the central nervous system.
Complexity-reduced FootSLAM for indoor pedestrian navigation using a geographic tree-based data structure

FootSLAM or simultaneous localisation and mapping (SLAM) for pedestrians is a technique that addresses the indoor positioning and mapping problem based on human odometry (aka pedestrian dead reckoning), for example with a foot-mounted inertial sensor. FootSLAM follows the FastSLAM factorisation, using a Rao-Blackwellised particle filter to simultaneously estimate the building layout and the pedestrian's pose – his position and orientation. To that end, FootSLAM divides the 2D space into a grid of uniform and adjacent hexagons and counts the number of times that each particle crosses the edges of the hexagons it visits. As we shall show, the complexity of FootSLAM grows quadratically with time, preventing the mapping of large areas. In this paper, we present a new geographic tree-based data structure, called H-tree, to reduce the quadratic-in-time computational growth rate of naïve FootSLAM to t times log t. In addition, we introduce a compact representation (alphabet) for the set of six counters that are used to map the transitions of the particles across the edges of each hexagon. This alphabet is particularly effective during the exploration phases of FootSLAM that requires much particle diversity. In this contribution, the computational savings of the H-tree are presented both theoretically and with real-world data. In practice, we believe that FootSLAM can be applied in quasi real-time applications that require rapid mapping of unknown areas. Additionally, the mass market offline mapping process can be undertaken much more efficiently.
SULF2 strongly prediposes to fasting and postprandial triglycerides in patients with obesity and type 2 diabetes mellitus

Hepatic overexpression of sulfatase‐2 (SULF2), a heparan sulfate remodeling enzyme, strongly contributes to high triglyceride (TG) levels in obese, type 2 diabetic (T2DM) db/db mice. Nevertheless, data in humans are lacking. Here, the association of human hepatic SULF2 expression and SULF2 gene variants with TG metabolism in patients with obesity and/or T2DM was investigated.
Efect of frequency of burst pulse high electric field and burst pulse high intensity electromagnetic wave on microorganisms

As applications of pulsed power, there are biological and medical applications, like sterilization, cancer treatment, etc. This work focuses on investigating biological effect of high intensity and high frequency electric field and electromagnetic field on cells. The frequency components applying to those were 25∼70 MHz, and field strength was about 100 kV/cm. The numbers of pulses were 5, 10 and 20. Survival ratios of yeast decreased at all frequency and every number of pulses. At 70 MHz of every number of pulses, survival ratios of yeast were most decreased. The survival ratio of yeast decreased to 30 % at the frequency of 70 MHz on 20 pulses. Meanwhile, a survival ratio of E. coli decreased only 25 % at 70 MHz by applying 20 pulses. In experiment of radiating electromagnetic wave to microorganisms, yeast was exposed to the burst pulse electromagnetic wave with the intensity of 9 kV/cm and the frequency components of 135 and 280 MHz in water. On 100 and 200 pulses, survival ratios of yeast decreased to 70 and 35 %, respectively. For comparison, the pulsed voltage was also applied directly to yeast at same condition of the electric field intensity and frequency components. On 100 pulses, the survival ratio of yeast decreased to 70 % as with the result in electromagnetic wave.