Miracle-dz/UniFAR-FTUs huggingface dataset:
THE ISOLATION OF SYNAPTOSOMES FROM THE BRAIN OF A TELEOST FISH, CENTRIOPRISTES STRIATUS

Abstract— The brain of a typical teleost fish, sea bass (Centrioprisles striatus), yielded subcellular fractions similar to those previously isolated from mammalian brain upon homogenization and differential and density gradient centrifuging. In particular, a well‐defined synaptosomal fraction accounting for most of the bound acetylcholine of the tissue was obtained, although possibly because of the large amount of myelin in fish brain this and other fractions were not as homogeneous as the corresponding ones from guinea pig cerebral cortex. No difficulty was experienced in adapting the methods to the relatively small amounts of tissue (about 200 mg) obtainable from each fish. The isolation of synaptosomes in good yield indicates that teleost central presynaptic terminals have essentially the same physical properties as those of higher forms, permitting detachment and sealing under conditions of moderate liquid shear.
Amygdala mu‐opioid receptors mediate the motivating influence of cue‐triggered reward expectations

Environmental reward‐predictive stimuli can retrieve from memory a specific reward expectation that allows them to motivate action and guide choice. This process requires the basolateral amygdala (BLA), but little is known about the signaling systems necessary within this structure. Here we examined the role of the neuromodulatory opioid receptor system in the BLA in such cue‐directed action using the outcome‐specific Pavlovian‐to‐instrumental transfer (PIT) test in rats. Inactivation of BLA mu‐, but not delta‐opioid receptors was found to dose‐dependently attenuate the ability of a reward‐predictive cue to selectively invigorate the performance of actions directed at the same unique predicted reward (i.e. to express outcome‐specific PIT). BLA mu‐opioid receptor inactivation did not affect the ability of a reward itself to similarly motivate action (outcome‐specific reinstatement), suggesting a more selective role for the BLA mu‐opioid receptor in the motivating influence of currently unobservable rewarding events. These data reveal a new role for BLA mu‐opioid receptor activation in the cued recall of precise reward memories and the use of this information to motivate specific action plans.
Torsional vibration damper design using Augmented Lagrangian Particle Swarm Optimization

Torsional vibration dampers are comfort relevant components of automotive powertrains. Transferring the requirements imposed on their design into mathematical models leads to a nonlinear constrained optimization task. Given this problem’s structure, stochastic methods like the Augmented Lagrangian Particle Swarm Optimization (ALPSO) algorithm [1] offer suitable tools to find solutions. This procedure showed desirable convergence properties on various test functions and was used successfully to solve real-world problems in engineering [1, 2]. To handle the comparably high problem dimension and the large amount of constraints, a two-stage optimization process has been implemented. The first pre-conditioning step improves the initial population’s quality by applying gradientbased search methods to a reduced problem. ALPSO is then used in the subsequent main optimization of the full problem.
Blur Map Generation Based on Local Natural Image Statistics for Partial Blur Segmentation

Partial blur segmentation is one of the most interesting topics in computer vision, and it has practical value. The generation of blur maps is a crucial part of partial blur segmentation because partial blur segmentation involves producing a blur map and applying a segmentation algorithm to the blur map. In this study, we address two important issues in order to improve the discrimination of blur maps: (1) estimating a robust local blur feature to consider variations in the intensity amplitude and (2) a scheme for generating blur maps. We propose the ANGHS (AmplitudeNormalized Gradient Histogram Span) as a local blur feature. ANGHS represents the heavy-tailedness of a gradient distribution, where it is calculated from an image gradient normalized using the intensity amplitude. ANGHS is robust to variations in the intensity amplitude, and it can handle local regions in a more appropriate manner than previously proposed local blur features. Blur maps are affected by local blur features but also by the contents and sizes of local regions, and the assignment of blur feature values to pixels. Thus, multiple-sized grids and the EAI (Edge-Aware Interpolation) are employed in each task to improve the discrimination of blur maps. The discrimination of the generated blur maps is evaluated visually and statistically using numerous partial blur images. Comparisons with the results obtained by state-of-the-art methods demonstrate the high discrimination of the blur maps generated using the proposed method. key words: ANGHS, blur map generation, natural image statistics, partial blur segmentation
Nanozyme applications in biology and medicine: an overview

Abstract Nanozymes, in nature, are artificial enzymes. Innovated by Ronald Breslow to mimic enzymes. Nanozymes have widespread applications including targeted cancer therapy, diagnostic medicine and bio-sensing even environmental toxicology. However, these applications are a novel research field in biomedicine, but are growing fast. Enzyme-based applications such as immune-absorbent assay (ELIZA) are expensive because of the complexity of producing enzymes and antibodies. Not only, some nanoparticles can mimic these enzymes such as superoxides, but also they can manipulate biological pathways directly like autophagy. These abilities make them a suitable alternative for both therapy and diagnosis. In this review, we opted on metal nanoparticles and application of this cutting edged technology into modern medicine.
Stereotactic ventral pallidotomy for Parkinson's disease

Article abstract-Eighteen patients with medically intractable Parkinson's disease that was characterized by bradykinesia, rigidity, and marked "on-off" fluctuations underwent stereotactic ventral pallidotomy under local anesthesia. Targeting was aided by anatomic coordinates derived from the MRI, intraoperative cell recordings, and electrical stimulation prior to lesioning. A nonsurgically treated group of seven similarly affected individuals was also followed. Assessment of motor function was made at baseline and at 3-month intervals for 1 year. Following the lesioning, patients improved in bradykinesia, rigidity, resting tremor, and balance with resolution of medication-induced contralateral dyskinesia. When compared with preoperative baseline, all quantifiable test scores after surgery improved significantly with the patients off medications for 12 hours: UPDRS by 65%, and CAPIT subtest scores on the contralateral limb by 38.2% and the ipsilateral limb by 24.2%. Walk scores improved by 45%. Medication requirements were unchanged, but the patients who had had surgery were able to tolerate larger doses because of reduced dyskinesia. Ventral pallidotomy produces statistically significant reduction in parkinsonism and contralateral "on" dyskinesia without morbidity or mortality and with a short hospitalization in Parkinson's disease patients for whom medical therapy has failed. NEUROLOGY 1995;45: 753-761
It takes two (or more) to tango: Partnerships within the education sector in Timor-Leste

Recent international declarations stress the importance of partnerships between and amongst donors, the state and civil society, in order to improve service delivery and promote qualities of good governance, particularly in key sectors such as education. However, in conditions of state fragility—where high levels of distrust between and amongst these ‘partners’ may exist; large and often ungovernable flows of assistance are common; and the capacity of state institutions is weak or non-existent—the feasibility of such partnerships must be critically examined. This paper examines these dilemmas within the context of Timor-Leste’s basic education sector, with specific attention paid to how external actors, internal service providers and the Ministry of Education have ‘partnered’ to qualify more than 8,000 teachers through in-service training. The author suggests that donors are increasingly compelled to work within a framework, which presents both practical challenges and deeper ideological conundrums. Ultimately, the interests of teachers and children are being left behind in the drive for alignment, coordination and harmonisation.
Effects of backward difference on DFA of RR interval data of CHF subjects

In this study, the detrended fluctuation analysis (DFA) is applied to examine characteristics of RR interval time series of subjects with congestive heart failure (CHF) and those of subjects with normal sinus rhythm (NSR). In addition to original RR interval time series, the differenced RR interval time series of both NSR and CHF subjects are obtained using the backward difference. From the computational results, it is shown that there is a significant difference between the scaling exponents of differenced RR interval time series of CHF subjects and those of NSR subjects. Such significant difference is however not obtained for the original RR interval time series. The computational results therefore suggest that the backward difference may be applied to enhance the classification of CHF using the DFA.
Learning Feature-based Semantics with Autoencoder

It is essential to reduce the dimensionality of features, not only for computational efficiency, but also for extracting the most meaningful pattern of features. This is particularly important in certain area such as computer vision or natural language processing, in which researchers usually have difficulty to manually specify feature. In this project, we applied the autoencoder model to represent the semantics of phrases and evaluated it by computing the correlation between the similarity of autoencoded feature and the rating manually tagged by human.
A multi-surface sliding-mode extremum seeking controller for alternator maximum power point tracking

Power maximization is of great interest in energy conversion systems including renewable sources and alternators used in automotive systems. This paper presents a multi-surface sliding-mode extremum seeking controller for achieving Maximum Power Point Tracking (MPPT) for energy conversion involving a Lundell alternator. The proposed multi-surface sliding scheme results in smooth and fast convergence to the extremum point while achieving robust performance in face of parametric uncertainties in the alternator model. In particular, it is shown that using multiple sliding surfaces would help to reduce chattering and increase the performance speed and precision of the system. The behavior of the system in terms of convergence characteristics is analyzed using a Lyapunov-like stability analysis. Simulation results demonstrate the efficiency of the proposed controller in terms of power maximization, robustness, and convergence speed.
Model-based synthesis of sensor location

A system that automatically generates the possible camera locations for observing an object is described. The approach, which uses models of the object and the camera, is based on meeting the requirements that the spatial resolution be above a minimum value, all surface points be in focus, all surfaces lie within the sensor field-of-view, and no surface points be occluded. Each sensing requirement is converted into a geometric constraint on the sensor location, from which the three-dimensional region of viewpoints that satisfies that constraint is computed. The intersection of these regions is the space where a sensor may be located. <<ETX>>
Politics and the German language: Testing Orwell's hypothesis using the Google N-Gram corpus

Understanding the relationship between political regimes and language, while a popular theme among historians and linguists, is empirically difficult. This study suggests a preliminary empirical framework. A quantitative analysis of the Google Books 1-Gram German corpus from the year 1870 to 1945 provides empirical evidence consistent with George Orwell’s hypothesis that everyday language deteriorates under dictatorships, through the inversion of words’ underlying meanings. More specifically, this article shows that six non-technical non-Nazi words— Demokratie (democracy), Freiheit (freedom), Frieden (peace), Herrlichkeit (glory), Gerechtigkeit (justice), and Heldentumd (heroism)—are (1) highly correlated with explicitly Nazi words; (2) negatively correlated with Germany’s level of democracy; and (3) negatively correlated with the count of riots, anti-government protests, and government crises, implying that these words were not used as a form of protest. The use of these words increased sharply under the Nazi government, which banned all publications that were critical of the government. These correlations cannot tell us whether the relationship is causal, and we cannot be sure whether the corpus under study is truly representative. Replicating this empirical framework on other corpora, pushing the period under study further back in time, or using 2-gram data sets can all help assess this study’s findings.
Treatment of a Compulsive Horse Race Gambler by Aversion Therapy

References to compulsive gambling are found in the psychiatric literature as early as 1914 (von Hattingberg) and 1920 (Simmel). These and subsequent literature on the psychodynamics and treatment by analytical methods were summarized by Harris in 1964. In the past year references have been made to the treatment by aversion therapy of isolated cases of horse race gamblers and of a “one-armed bandit” gambler (Barker and Miller, 1966b, Seager et al., 1966, Barker and Miller, 1966a). A further case of a compulsive horse race gambler treated by aversion therapy seems worth presenting, in view of the sparse literature on treatment of the condition by this method, and also because in this case, the therapy was immediately followed by remission of a longstanding marital disharmony which is believed to have been one major precipitating cause of the gambling.
Anomaly-based Self-Healing Framework in Distributed Systems

One of the important design criteria for distributed systems and their applications is their reliability and robustness to hardware and software failures. The increase in complexity, interconnectedness, dependency and the asynchronous interactions between the components that include hardware resources (computers, servers, network devices), and software (application services, middleware, web services, etc.) makes the fault detection and tolerance a challenging research problem. In this dissertation, we present a self healing methodology based on the principles of autonomic computing, statistical and data mining techniques to detect faults (hardware or software) and also identify the source of the fault. In our approach, we monitor and analyze in real-time all the interactions between all the components of a distributed system using two software modules: Component Fault Manager (CFM) to monitor all set of measurement attributes for applications and nodes and Application Fault Manager (AFM) that is responsible for several activities such as monitoring, anomaly analysis, root cause analysis and recovery. We used three-dimensional array of features to capture spatial and temporal features to be used by an anomaly analysis engine to immediately generate an alert when abnormal behavior pattern is detected due to a software or hardware failure. We use several fault tolerance metrics (false positive, false negative, precision, recall, missed alarm rate, detection accuracy, latency and overhead) to evaluate the effectiveness of our self healing approach when compared to other techniques. We applied our approach to an industry standard web e-commerce application to emulate a complex e-commerce environment. We evaluate the effectiveness of our approach and its performance to detect software faults that we inject asynchronously, and compare the results for different noise levels. Our experimental results showed that by applying our anomaly based approach, false positive, false negative, missed alarm rate and detection accuracy can be improved significantly. For example, evaluating the effectiveness of this approach to detect faults injected asynchronously shows a detection rate of above 99.9% with no false alarms for a wide range of faulty and normal operational scenarios.
Color-avoiding percolation.

Many real world networks have groups of similar nodes which are vulnerable to the same failure or adversary. Nodes can be colored in such a way that colors encode the shared vulnerabilities. Using multiple paths to avoid these vulnerabilities can greatly improve network robustness, if such paths exist. Color-avoiding percolation provides a theoretical framework for analyzing this scenario, focusing on the maximal set of nodes which can be connected via multiple color-avoiding paths. In this paper we extend the basic theory of color-avoiding percolation that was published in S. M. Krause et al. [Phys. Rev. X 6, 041022 (2016)]2160-330810.1103/PhysRevX.6. 041022. We explicitly account for the fact that the same particular link can be part of different paths avoiding different colors. This fact was previously accounted for with a heuristic approximation. Here we propose a better method for solving this problem which is substantially more accurate for many avoided colors. Further, we formulate our method with differentiated node functions, either as senders and receivers, or as transmitters. In both functions, nodes can be explicitly trusted or avoided. With only one avoided color we obtain standard percolation. Avoiding additional colors one by one, we can understand the critical behavior of color-avoiding percolation. For unequal color frequencies, we find that the colors with the largest frequencies control the critical threshold and exponent. Colors of small frequencies have only a minor influence on color-avoiding connectivity, thus allowing for approximations.
Pension Funds’ Asset Allocation and Participant Age: A Test of the Life-Cycle Model

This paper examines the impact of participants’ age distribution on the asset allocation of Dutch pension funds, using a unique data set of pension fund investment plans for 2007. Theory predicts a negative effect of age on (strategic) equity exposures. We observe that pension funds do indeed take the average age of their participants into account. However, the average age of active participants has been incorporated much more strongly in investment behaviour than the average ages of retired or dormant participants. This suggests that both employers and employees, who dominate pension fund boards, tend to show more interest in active participants. A one-year higher average age in active participants leads to a significant and robust reduction in the strategic equity exposure by around 0.5 percentage point. Larger pension funds show a stronger age-equity exposure effect than smaller pension funds. This age-dependent asset allocation of pension funds aligns with the original life-cycle model by which young workers should invest more in equity than older workers because of their larger human capital. Other factors, viz. fund size, funding ratio, and average pension wealth of participants, influence equity exposure positively and significantly, in line with theory. Pension plan type and pension fund type have no significant impact.
Specificity of antisera to mouse ascites tumor in rabbits immunologically depressed with mouse red cells.

The concept tested is whether antisera showing increased specificity against a mouse ascites tumor can be obtained by immunizing with this tumor rabbits that were immunologically depressed by injection of mouse red cells when newborn. One-half the young in three litters of rabbits (depressed) were injected with washed mouse red cells on the day of birth and several times thereafter until day 38 after birth. The other half (controls) were left uninjected. At 9 ½ to 12 months, one-half the rabbits in both depressed and control groups were immunized with mouse red cells, the other half with L4946 mouse tumor. Antisera from depressed rabbits finally immunized with red cells had hemagglutination, hemolysin and cytolytic titers respectively 17, 6 and 6 times lower than their controls, indicating considerable immunologic depression. Antisera from depressed rabbits finally immunized with tumor cells had titers respectively 6, 2 and 2 times lower than their controls, indicating some cross-depression against tumor cells. The specificity of antisera against L4946 tumor was precisely the same in depressed rabbits and in nondepressed controls; absence of results for the latter control group in recent studies by Levi et al. and by Garb et al. throws serious doubt on a positive interpretation of their data.
Proof-of-relevance: Filtering false data via authentic consensus in Vehicle Ad-hoc Networks

Emerging applications in vehicle ad hoc networks (VANETs) not only open tremendous business opportunities and navigation benefits; they also pose formidable research challenges in security provisioning. A critical security threat to VANETs is false data injection, i.e., an attacker disseminates false information to disrupt the behavior of the other drivers. Information driven operations of vehicular networks make false data injection a very effective attack. As the first line of defense, this paper presents the notion of proof-of-relevance (PoR), which consists in proving that the event reporter is authentically relevant to the event it has reported. The PoR is accomplished by collecting authentic consensus on the event from witness vehicles in a cooperative way. Event reports from attackers who fail to provide this PoR are disregarded, making the network immune to bogus data. Performance evaluation and security analysis demonstrate the efficiency and security of the proposed scheme.
Metastatic Renal Cell Carcinoma: Sunitinib as First-Line Treatment; Results of a Retrospective Study

Abstract Background: Targeted agents were introduced over past years because of better understanding of oncogenetic mechanisms in metastatic renal cell carcinoma (mRCC). These agents include tyrosine kinase inhibitors (TKIs) such as sorafenib, sunitinib, pazopanib and axitinib. Methods: Review of recorded data from patients’ files with mRCC were analysed during the period from August 2008 to December 2014. Those patients were treated by sunitinib as first-line therapy. The data included patients characteristics such as age, sex, ECOG performance status (ECOG PS), number and sites of metastasis and pathological type. Also, we reviewed response to sunitinib therapy, its adverse events and progression-free survival (PFS). Results: This study included 26 patients; median age was 56 years with male predominance (76.9%) and 61.5% of patients were of ECOG PS0. Lymph nodes were the most common site of metastasis (38.5%) and 46.2% presented with ≥3 sites of disease. Clear cell pathology was reported in 96.2%. No grade IV adverse events to sunitinib reactions were observed. Thrombocytopenia was the most predominant haematological reaction (46%) followed by neutropenia (38.6%), whilst fatigue was the most reported non-haematological one (50%) followed by diarrhoea (42.3%). Partial response (PR) was found in 30.8% and stable disease (SD) in 46.2%. One-year PFS was 57.7% with median PFS time of 12 months. Conclusion: This study proved effectiveness and safety of sunitinib as first-line treatment for mRCC. However, this is a retrospective study and relatively small numbers of patients were included, so prospective studies with larger number of patients are needed for further evaluation.