Miracle-dz/UniFAR-FTUs huggingface dataset:
Typical and atypical neuroleptics: differential effects of chronic administration on the activity of A9 and A10 midbrain dopaminergic neurons

Extracellular single unit recording techniques were used to study the effects of both acute and repeated oral neuroleptic administration on the in vivo activity of rat A9 and A10 dopaminergic (DA) neurons. All antipsychotic drugs examined acutely (haloperidol, l-sulpiride, chlorpromazine, and clozapine) increased the number of spontaneously firing DA neurons in both A9 and A10 compared to controls. Repeated (21 day) treatment with haloperidol, l-sulpiride, and chlorpromazine (antipsychotic drugs which can cause extrapyramidal side effects) markedly reduced the number of active DA cells below control levels in both regions. The “silent” DA neurons were in an apparent state of tonic depolarization inactivation since they could be induced to discharge by the microiontophoretic application of the inhibitory neurotransmitter gamma-aminobutyric acid, but not the excitatory amino acid glutamate. The depolarization inactivation observed may be specific for antipsychotic drugs since a non-neuroleptic phenothiazine (promethazine), the inactive isomer of sulpiride (d-sulpiride), and a tricyclic antidepressant (desmethylimipramine) neither increased DA activity when given acutely nor induced depolarization inactivation when administered repeatedly. In contrast to the other drugs tested, repeated treatment with clozapine (an effective antipsychotic drug which does not produce extrapyramidal side effects) resulted in the depolarization inactivation of A10 neurons but not A9 cells. These data suggest that neuroleptics which can induce extrapyramidal side effects produce depolarization inactivation of both A9 and A10 neurons whereas antipsychotic drugs which lack this property inactivate only A10 neurons. It is suggested that the time-dependent development of A9 DA neuron inactivation induced by repeated neuroleptic treatment may provide a mechanism for understanding the delayed onset of extrapyramidal side effects often observed with these drugs.
A simple and improved eye movement features detection process from saccadic samples of electrooculographic data

In this manuscript a simple and improved eye movement features detection process is described. As most of human activities involve eyes, eye movement features detection is very important, particularly in ubiquitous computing and smart human computer interfaces. We all are familiar with Eye movement features detection techniques that involves image or video processing. But in this work we concentrated on a relatively new eye movement features detection technique that uses electrostatic potential from eyes, also known as Electrooculography signal. The work includes the detailed signal processing steps necessary to detect the various eye movement features from the raw Electrooculography signal. The detection process is simple and improved compared to previous counterparts. The detection process is user independent and thus free of any calibration problems.
The Effects of Rest Period Lengths on Muscle Hypertrophy and Intra-Exercise Performance

by Clifton J. Holmes The purpose of this study was to compare the effects of 1-minute versus 3-minute, between-set rest intervals on muscular hypertrophy adaptations in 12 recreationally, resistance-trained males, 18-25 years of age over a 6-week period. Week 1 consisted of dual-energy X-ray absorptiometry scans, and 1-repetition maximum (1RM) testing in the back squat and bench press. Weeks 2 through 5 consisted of four resistance training sessions per week, split between two lower-body and two upper-body workouts. The resistance training protocol started at 3 sets of 10 repetitions at 67% of 1RM and progressed from there. Week 6 consisted of follow-up body composition assessment, using DEXA scan, and 1RM testing of back squat and bench press. The findings of the study revealed no significant differences between the effects of 1-minute and 3-minute rest periods on muscle hypertrophy in recreationally strength-trained males during a 4-week training
Proving and Improving Authorship Attribution Technologies

Who wrote Primary Colors? Can a computer help us make that call? Despite a century of research, statistical and computational methods for authorship attribution are neither reliable, well-regarded, widely-used, or wellunderstood. This paper presents a survey of the current state-of-the-art as well as a framework for uniform and unified development of a tool to apply the state-of-the-art, despite the wide variety of methods and techniques used.
Online critic-identifier-actor algorithm for optimal control of nonlinear systems

In this paper, a novel critic-identifier-actor optimal control scheme is designed for discrete-time affine nonlinear systems with uncertainties. A neural identifier is established to learn the unknown control coefficient matrix for affine nonlinear system working together with an actor-critic-based scheme to solve the optimal control in online and forward-in-time manner without value or policy iterations. A critic network learns approximate value function at each step. Another actor network attempts to improve the current policy based on the approximate value function. The weights of all neural networks (NNs) are updated at each sampling instant. Lyapunov theory is utilized to prove the stability of the closed-loop system. A simulation example is provided to illustrate the effectiveness of the developed method.
A survey of social software engineering

Software engineering is a complex socio-technical activity, due to the need for discussing and sharing knowledge among team members. This has raised the need for effective ways of sharing ideas, knowledge, and artifacts among groups and their members. The social aspect of software engineering process also demands computer support to facilitate the development by means of collaborative tools, applications and environments. In this paper, we present a survey of relevant works from psychology, mathematics and computer science studies. The combination of these fields provides the required infrastructure for engineering social and collaborative applications as well as the software engineering process. We also discuss possible solutions for the encountered shortcomings, and how they can improve software development.
Circular backpropagation networks for classification

The class of mapping networks is a general family of tools to perform a wide variety of tasks. This paper presents a standardized, uniform representation for this class of networks, and introduces a simple modification of the multilayer perceptron with interesting practical properties, especially well suited to cope with pattern classification tasks. The proposed model unifies the two main representation paradigms found in the class of mapping networks for classification, namely, the surface-based and the prototype-based schemes, while retaining the advantage of being trainable by backpropagation. The enhancement in the representation properties and the generalization performance are assessed through results about the worst-case requirement in terms of hidden units and about the Vapnik-Chervonenkis dimension and cover capacity. The theoretical properties of the network also suggest that the proposed modification to the multilayer perceptron is in many senses optimal. A number of experimental verifications also confirm theoretical results about the model's increased performances, as compared with the multilayer perceptron and the Gaussian radial basis functions network.
How women's sexual orientation guides accuracy of interpersonal judgements of other women

This research examines how women's sexual orientation guides the accuracy of judgements of other women. One hundred ten judges (67 straight and 43 lesbian women) watched videotapes of 9 targets (4 straight and 5 lesbian) and made judgements about the targets' thoughts, emotions, personality, and sexual orientation. Accuracy scores were created for each judge by comparing judgements to criterion data gathered about targets. Straight judges were significantly more accurate at judging thoughts and marginally more accurate at judging emotions compared to lesbian judges. There were no significant differences in judging personality. Straight targets' thoughts and personality were more easily assessed than lesbian targets' while lesbians' emotions were more easily judged than straight targets'. Lesbian judges were more accurate at judging sexual orientation regardless of their tendency to categorize women as lesbian compared to straight judges. Findings support past research on the accurate perception of sexual orientation and contribute to understanding how sexual orientation guides person perception.
Opportunistic Scheduling in Heterogeneous Networks: Distributed Algorithms and System Capacity

In this work, we design and analyze novel distributed scheduling algorithms for multi-user MIMO systems. In particular, we consider algorithms which do not require sending channel state information to a central processing unit, nor do they require communication between the users themselves, yet, we prove their performance closely approximates that of a centrally-controlled system, which is able to schedule the strongest user in each time-slot. Possible application include, but are not limited to, modern 4G networks such as 3GPP LTE, or random access protocols. The analysis is based on a novel application of the Point-Process approximation, enabling the examination of non-homogeneous cases, such as non-identically distributed users, or handling various QoS considerations, which to date had been open.
DAPID: a 3D-domain annotated protein-protein interaction database.

DAPID is a database of domain-annotated protein interactions inferred from three-dimensional (3D) interacting domains of protein complexes in the Protein Data Bank (PDB). The DAPID data model allows users to visualize 3D interacting domains, contact residues, and molecular details of any predicted protein-protein interactions. Our model derives these interactions by utilizing a new concept, called the ''3D-domain interologs'' which is similar to ''interologs''. In S. cerevisiae, there is 18.6% overlap between our predicted protein-protein interactions and ones in the DIP database. The mean correlation coefficient of the gene expression profiles of our predicted interactions is significantly higher than that for random pairs in S. cerevisiae. In addition, we find several novel interactions which are consistent with the functions of the proteins. The DAPID currently holds 1008 3D-interacting domain pairs and 101511 predicted 3D-domain annotated protein-protein interactions. It is available online at http://gemdock.life. nctu.edu. tw/dapid.
Algorithms for Parallel Computations on Graphs

The purpose of the consensus algorithms on graphs we have discussed is to find control protocols that result in all nodes reaching the same state values while communication is restricted to local neighbor protocols based on the graph structure. In this section we show that the consensus algorithms are closely related to parallel processing algorithms. Parallel algorithms are important in solving high-dimensional problems with a large number of unknowns, and have been developed in numerical analysis and in computer science for applications to parallel processing computer architectures. They have objectives other than producing consensus. We discuss in this section parallel algorithms for solution of linear equations, for finding the first left eigenvector of a stochastic matrix, and for solving for voltages in large-scale electrical networks. Equation Section 5
Microglial Activation in Perinatal Rabbit Brain Induced by Intrauterine Inflammation: Detection with 11C-(R)-PK11195 and Small-Animal PET

Intrauterine infection can lead to a fetal inflammatory response syndrome that has been implicated as one of the causes of perinatal brain injury leading to periventricular leukomalacia (PVL) and cerebral palsy. The presence of activated microglial cells has been noted in autopsy specimens of patients with PVL and in models of neonatal hypoxia and ischemia. Activated microglial cells can cause oligodendrocyte damage and white matter injury by release of inflammatory cytokines and production of excitotoxic metabolites. We hypothesized that exposure to endotoxin in utero leads to microglial activation in the fetal brain that can be monitored in vivo by 11C-(R)-PK11195 (1-[2-chlorophenyl]-N-methyl-N-[1-methylpropyl]-3-isoquinoline carboxamide)—a positron-emitting ligand that binds peripheral benzodiazepine receptor sites in activated microglia—using small-animal PET. Methods: Pregnant New Zealand White rabbits underwent laparotomy and were injected with 20 and 30 μg/kg of Escherichia coli lipopolysaccharide along the length of the uterus on day 28 of gestation. The pups were born spontaneously at term (31 d) and were scanned using small-animal PET after intravenous administration of 11C-(R)-PK11195 and by MRI on postnatal day 1. The standard uptake values (SUVs) of the tracer were calculated for the whole brain at 10-min intervals for 60 min after tracer injection. The pups were euthanized after the scan, and brains were fixed, sectioned, and stained for microglial cells using biotinylated tomato lectin. Results: There was increased brain retention of 11C-(R)-PK11195—as determined by a significant difference in the slope of the SUV over time—in the endotoxin-treated pups when compared with that of age-matched controls. Immunohistochemical staining showed dose-dependent changes in activated microglia (increased number and morphologic changes) in the periventricular region and hippocampus of the brain of newborn rabbit pups exposed to endotoxin in utero. Conclusion: Intrauterine inflammation leads to activation of microglial cells that may be responsible for the development of brain injury and white matter damage in the perinatal period. PET with the tracer 11C-(R)-PK11195 can be used as a noninvasive, sensitive tool for determining the presence and progress of neuroinflammation due to perinatal insults in newborns.
A fuzzy system for modeling the structure-activity relationships in presence of uncertainties

This study deals with the development of a computer model to estimate the biological activity of a chemical compound by evaluating its molecular structure. There exist uncertainties in a structure-activity modeling problem regarding the numerical representation of the structure via selected molecular descriptors, optimal structure of the model, robustness of the model identification criterion, and presence of noise in experimental activity data used to identify the model. Our approach to deal with the uncertainties in modeling problem consists of following steps: a) The uncertainties in the modeling problem are filtered out using a fuzzy filter. b) Stochastic modeling of the uncertainties using finite mixture models and utilizing this information regarding uncertainties for adapting the structure-activity models to approximate the uncertain relationships. The method is illustrated via considering a case study of modeling the activity of 3-phosphoinositide-dependent protein kinase-1 (PDK1) inhibitors.
Automated Inference of Dependencies of Network Services and Applications via Transfer Entropy

As the scale and complexity of modern computer networks increases, administrators and operators of such networks need tools to accurately infer dependencies between different network services and applications. Such tools can aid in (1) detecting misconfigurations, (2) effectively scheduling major software and hardware maintenance operations with minimal disruptions, and (3) exposing potential anomalies in a timely manner. Existing tools either only consider temporal correlations which require installing additional software to monitor interfaces, ignore network service profiles of more than two services, or do not necessarily capture actual causations. Such shortcomings result in high false detection rates of inferred dependencies. This paper presents the design and evaluation of an algorithm that utilizes the notion of Transfer Entropy (TE) to passively analyze and identify dependencies between various network services and applications. With TE, our algorithm formalizes and measures the amount of information exchanged between two entities (services or applications) in a computer network. By constructing time series of the interactions of such services and applications and computing the pairwise TE from such time series, our algorithm accurately infers dependencies based on causation with low false (positive and negative) alarms. Using collected network traffic from a test and production network, we demonstrate that the algorithm provides lower false alarms with efficient run time and computational requirements.
Effects of normobaric versus hyperbaric oxygen on cell injury induced by oxygen and glucose deprivation in acute brain slices

Normobaric oxygen (NBO) and hyperbaric oxygen (HBO) are emerging as a possible co-treatment of acute ischemic stroke. Both have been shown to reduce infarct volume, to improve neurologic outcome, to promote endogenous tissue plasminogen activator-induced thrombolysis and cerebral blood flow, and to improve tissue oxygenation through oxygen diffusion in the ischemic areas, thereby questioning the interest of HBO compared to NBO. In the present study, in order to investigate and compare the oxygen diffusion effects of NBO and HBO on acute ischemic stroke independently of their effects at the vascular level, we used acute brain slices exposed to oxygen and glucose deprivation, an ex vivo model of brain ischemia that allows investigating the acute effects of NBO (partial pressure of oxygen (pO 2 ) = 1 atmospheres absolute (ATA) = 0.1 MPa) and HBO (pO 2 = 2.5 ATA = 0.25 MPa) through tissue oxygenation on ischemia-induced cell injury as measured by the release of lactate dehydrogenase. We found that HBO, but not NBO, reduced oxygen and glucose deprivation-induced cell injury, indicating that passive tissue oxygenation (i.e. without vascular support) of the brain parenchyma requires oxygen partial pressure higher than 1 ATA.
Effects of Companion Animal Ownership among Canadian Street-involved Youth: A Qualitative Analysis

In Canada, approximately 150,000 youth are homeless on any given night, and many have companion animals. Through a series of semi-structured interviews, this qualitative study explored the issues and effects of companion animal ownership among street-involved youth from the perspective of the youth themselves. “Pet before self” was the substantive theme, with first level sub-themes of “physical” and “emotional” effects. Previously unidentified findings include benefits of having a companion animal, such as creating structure and routine and decreasing use of drugs. Loss of the companion animal was a negative effect. Youth consistently reported making choices to stay with their animal regardless of liabilities for their own health or success. Service providers should understand and support the significant human–animal bond that can exist for these homeless youth.
The Optimal Design of a Market

We study the optimal design of the rules of trade in a two-period market given that agents arrive at different times and may only trade with agents present contemporaneously. First period agents face a fixed cot of trading across periods, and their decisions of whether or not to trade in the second period result in externalities relative to the agents arriving in the second period. Given the non-convexities associated with the fixed cost, competitve trading rules can result in inefficienceis in such a market and, in fact, anonymity must be sacrificed to achieve efficiency. Efficient trading rules have a market maker (i.e., an agent who is given some market power and the right to trade across periods) who faces some competition within period trading, but not across periods. The efficient choice of who should be market maker can be made by auctionaing rights to this position. If there is uncertainty across periods, then efficient mechanisms may involve multiple market makers, and the optimal number of market makers depends on the cost of trading, level of risk aversion, and presence of scymmetric information.
Distributed Smart Cameras for Aging in Place

Abstract : This paper describes the design and preliminary implementation of two distributed smart camera applications: a fall detector and an object finder. These functions are part of a novel suite of applications being developed to address aging in place health care technologies. Our approach to these applications is unique in that they are based heavily on video data, whereas other solutions may require devices that must be worn or attached to objects. The fall detector relies on features extracted from video by the camera nodes, which are sent to a central processing node where one of several machine learning techniques are applied to detect a fall. If a fall is detected, alerts are triggered both in the home and to a third party. The object finder similarly uses a boosted cascade of classifiers to visually recognize objects either by request of the user or automatically when an object is moved.
Hydrophobic and Ionic Factors in the Binding of Local Anesthetics to the Major Variant of Human α1-Acid Glycoprotein

Understanding the interaction of local anesthetics (LAs) with plasma proteins is essential to understanding their systemic pharmacology and toxicology. The molecular determinants of LA binding to the major variant (F1*S) of human α1-acid glycoprotein (AGP) were therefore investigated spectrofluorometrically using whole AGP and a novel, F1*S variant-selective probe previously developed in our laboratory. Equilibrium- competitive displacement of this probe by LAs, observed by the recovery of AGP's fluorescence as the quenching probe was displaced from its high-affinity site, was characterized by inhibitory dissociation constants for the various LAs. The importance of electrostatic factors was assessed by examining the pH dependent binding of an ionizable LA, lidocaine, using the quaternary lidocaine derivative QX-314 [N-(2,6-dimethylphenylcarbamoylmethyl) triethylammonium chloride] to control for pH dependent ionization of AGP. Uncharged lidocaine bound with at least 8 times the affinity of protonated lidocaine (KD = 4.0 ± 0.6 μM and >32 μM, respectively). This result is inconsistent with the current model of the AGP-binding site, which depicts a buried pocket having a negatively charged region that interacts with the amino termini of basic drugs. Consistent with the model, however, two sets of structurally homologous LAs (mepivacaine, ropivacaine, bupivacaine, and lidocaine, RAD-240, RAD-241, RAD-242, L-30, W-6603) demonstrated a strong positive correlation between hydrophobicity (measured as the octanol:buffer partition coefficient of the neutral species) and their free energies of dissociation. Given that the tertiary structure of AGP has proven refractory to resolution, these structure-activity studies should contribute to understanding the nature of the binding site on this important protein.
What drives 'Unconscious' Multi-Attribute Decision-Making?

This study aims to further investigate the Unconscious Thought Theory (UTT, Dijksterhuis & Nordgren, 2006), namely whether individual differences account for differences in choice made after either deliberation (conscious thought, CT) or distraction (unconscious thought, UT). Also, subjective weighting was considered and choice options were constructed following individual preferences, hence avoiding choices biased by differences in preferences. The main effect was replicated with a big sample (N=120, CT: 50.8%, UT: 70.5% chose the best alternative), using four different dependent measures. The results show further that the main effect is driven by underperformance of women in the CT condition. Stereotype threat is discussed as a possible explanation.