Miracle-dz/UniFAR-FTUs huggingface dataset:
Rivastigmine treatment as an add‑on to antipsychotics in patients with schizophrenia and cognitive deficits

ABSTRACT Objective: Although new atypical antipsychotic agents have been found to improve overall cognitive function in patients with schizophrenia (SZ), some aspects of memory, attention and executive functions still remain impaired. Acetylcholinesterase (AChE) inhibitors, such as rivastigmine, have been shown to improve cognition in other disorders, particularly Alzheimer's disease. Dysfunctions in cholinergic systems, especially in the prefrontal cortex, have been identified in SZ, suggesting that cholinesterase inhibitors may be effective in treating cognitive deficits in this disease. Research design and methods: Using a randomized crossover design, we assessed SZ patients with stable symptoms and poor cognitive functioning. Fifty-eight patients with memory deficits, according to subjective complaints or based on clinicians’ observations, were assessed with the Repeatable Battery for the Assessment of Neuropsychological Status (RBANS) and Subjective Scale to Investigate Cognition in Schizophrenia (SSTICS). Only 24 of these subjects met the inclusion criteria. Twenty patients took part in the study (four dropped out). All subjects meeting the Diagnostic and Statistical Manual of Mental Disorders, fourth edition (DSM‑IV) criteria for SZ were maintained on their current antipsychotic medication (18 atypicals and two typicals) and were randomly assigned to treatment with rivastigmine. Dosage was a function of tolerability, beginning at 3 mg/day and progressively increasing to 9 mg/day. Subjects were given the Cambridge Neuropsychological Test Automated Battery (CANTAB) at baseline and 3 and 6 months. Results: The results revealed no significant improvement in any of the cognitive variables investigated following rivastigmine treatment and symptom severity scores remained unchanged over all recorded time periods. Conclusion: Rivastigmine treatment did not appear to enhance cognition in SZ patients with important cognitive impairments. This finding needs to be interpreted with care and requires substantiation with larger sample size studies with patients treated with cognitive enhancer for longer periods.
Media Rich, Media Poor: Two Studies of Diversity in Agenda-Holding

,Studies of the effects of mass communication normally work from a model of content-specific directional attitudinal influence on individuals. That is, the typical study examines changes in the opinions of a person that correspond to the arguments made in a message t o which he has been exposed. Innovative formulations in media effects analysis consist of deviations from this model. For instance, agenda-setting research involves the traditional content-specific influence of community news sources on individuals, but it does not inquire into the attitudinal direction of that influence.1 Recent conflict and consensus studies are novel in their concern with effects on the local community as a system rather than on specific individuals, although they necessarily build upon a content-specific directional attitudinal influence model.2 Research on the "information gap" is non-attitudinal and concerns an effect of mass communica-
Loss of Heterozygosity at 12q14-15 Often Occurs in Stage I Soft Tissue Sarcomas and Is Associated with MDM2 Amplification in Tumors at Various Stages

Few studies have investigated the loss of heterozygosity and microsatellite instability in soft tissue sarcomas. Therefore, we analyzed samples of human soft tissue sarcomas to determine the status of the chromosomal region 12q14–15, which contains the MDM2 gene encoding the well-known counterpart of the tumor suppressor p53. In addition, we determined whether an amplified MDM2 gene was present in the samples. Of the 88 soft tissue sarcoma samples, 24 (27%) showed evidence of loss of heterozygosity of markers representing 12q14–15, and 12 (14%) showed evidence of microsatellite instability. Of the 72 samples analyzed by semiquantitative polymerase chain reaction, 15 (21%) possessed an amplified MDM2 gene. Loss of heterozygosity (P = .008) and microsatellite instability (P = .035) were significantly more common in Stage I tumors than in higher stage tumors. This result indicated that these alterations occur early in soft tissue sarcoma progression and possibly define a subgroup of soft tissue sarcoma. Surprisingly, MDM2 amplification in soft tissue sarcoma patients was associated with a prognosis better than that of patients without the amplification; however, this difference was not statistically significant (P = .6). Furthermore, of the tumors with an MDM2 amplification, 40% (6/15) also experienced loss of heterozygosity at 12q14–15; in contrast, only 16% of tumors without an MDM2 amplification (9/57) underwent a loss of heterozygosity. A concomitant occurrence of deletions and amplifications resulting from deficiencies in the nonhomologous end-joining pathway could in part explain this finding.
Advances and Applications of Multiplexed Diagnostic Technologies in Autoimmune Diseases

There is a rapid proliferation of new technologies to identify an ever increasing spectrum of autoantibodies in diverse medical conditions that range from organ-specific autoimmune diseases to systemic rheumatic diseases. Although many laboratories have adopted diagnostic platforms, such as enzyme linked immunoassays (ELISAs), to improve turn around times and meet budget constraints, the prevailing evidence is that the rapid adoption of new technologies is not attended by an appropriate balance of assay sensitivity and specificity. Emerging diagnostic technologies include addressable laser bead immunoassays, microarrays in microfluidics platforms and nanobarcode particles. Although these technologies provide advantages of high-throughput, multiplexed autoantibody assays that can be coupled to other disease specific biomarkers (ie, cytokines, single nucleotide polymorphisms) there is a clear need for standardization and internal validation before they are adopted into the clinical diagnostic laboratory.
Home Is Where the Up-Votes Are: Behavior Changes in Response to Feedback in Social Media

Recent research shows that humans are heavily influenced by online social interactions: We are more likely to perform actions which, in the past, have led to positive social feedback. We introduce a quantitative model of behavior changes in response to such feedback, drawing on inverse reinforcement learning and studies of human game playing. The model allows us to make predictions, particularly in the context of social media, about which community a user will select, and to quantify how future selections change based on the feedback a user receives. We show that our model predicts real-world changes in behavior on a dataset gathered from reddit. We also explore how this relatively simple model of individual behavior can lead to complex collective dynamics when there is a population of users, each individual learning in response to feedback and in turn providing feedback to others.
Primary Cilia and Signaling Pathways in Mammalian Development, Health and Disease

Although first described as early as 1898 and long considered a vestigial organelle of little functional importance, the primary cilium has become one of the hottest research topics in modern cell biology and physiology. Primary cilia are nonmotile sensory organelles present in a single copy on the surface of most growth-arrested or differentiated mammalian cells, and defects in their assembly or function are tightly coupled to many developmental defects, diseases and disorders. In normal tissues, the primary cilium coordinates a series of signal transduction pathways, including Hedgehog, Wnt, PDGFRα and integrin signaling. In the kidney, the primary cilium may function as a mechano-, chemo- and osmosensing unit that probes the extracellular environment and transmits signals to the cell via, e.g., polycystins, which depend on ciliary localization for appropriate function. Indeed, hypomorphic mutations in the mouse ift88 (previously called Tg737) gene, which encodes a ciliogenic intraflagellar transport protein, result in malformation of primary cilia, and in the collecting ducts of kidney tubules this is accompanied by development of autosomal recessive polycystic kidney disease (PKD). While PKD was one of the first diseases to be linked to dysfunctional primary cilia, defects in this organelle have subsequently been associated with many other phenotypes, including cancer, obesity, diabetes as well as a number of developmental defects. Collectively, these disorders of the cilium are now referred to as the ciliopathies. In this review, we provide a brief overview of the structure and function of primary cilia and some of their roles in coordinating signal transduction pathways in mammalian development, health and disease.
Toward fluidic microrobots using electrowetting

This paper describes the performance of a fluidic microrobot using Electrowetting on Dielectric (EWOD). A system to control the fluidic microrobot was designed, constructed and deployed in the NIST Mobile Microrobotics Challenge at ICRA 2011. The microrobots (0.1 M KCl and 550 μm diameter) demonstrated the ability to perform controlled maneuvers in 2-D while transporting hydrophilic objects. The EWOD system is composed of a DIP-mounted die produced via standard microfabrication techniques and containing the control electrodes / competition arena, and a transparent ITO cover slip for grounding. Key advantages of this platform include a scalable design for batch EWOD system fabrication, potential simultaneous control of multiple microrobots, and an easily portable, compact system design.
Local tail bounds for functions of independent random variables

It is shown that functions defined on (0, 1, . . r - 1} n satisfying certain conditions of bounded differences that guarantee sub-Gaussian tail behavior also satisfy a much stronger "local" sub-Gaussian property. For self-bounding and configuration functions we derive analogous locally subexponential behavior. The key tool is Talagrand's [Ann. Probab. 22 (1994) 1576-1587] variance inequality for functions defined on the binary hypercube which we extend to functions of uniformly distributed random variables defined on (0, 1, ., r - 1) n for r ≥ 2.
Modeling and control of a transfemoral prosthesis embedding two infinitely variable transmissions

In this paper, we report the model of an original actuation concept for a transfemoral prosthesis, relying on the combination of a single power motor, a compliant element (a spring), a mechanical differential, and two infinitely variable transmissions. It allows to manage the mechanical power flows through the device in both directions (i.e. when energy should be produced or dissipated by the knee and ankle), so that the power motor does not face the sharp load power fluctuations. The paper further reports a preliminary approach to synthesize a closed-loop controller for this device, and simulation results of this closed-loop behavior for three locomotion tasks: level-ground walking and stair ascent/descent. These results illustrate the capacity of this actuation principle to filter the load power profile, and further highlight the necessity to maximize the mechanical efficiency of each part of this actuation scheme.
Biologically Inspired Modular Neural Networks

(ABSTRACT) This dissertation explores the modular learning in artificial neural networks that mainly driven by the inspiration from the neurobiological basis of the human learning. The presented modu-larization approaches to the neural network design and learning are inspired by the engineering, complexity, psychological and neurobiological aspects. The main theme of this dissertation is to explore the organization and functioning of the brain to discover new structural and learning inspirations that can be subsequently utilized to design artificial neural network. The artificial neural networks are touted to be a neurobiologicaly inspired paradigm that emulate the functioning of the vertebrate brain. The brain is a highly structured entity with localized regions of neurons specialized in performing specific tasks. On the other hand, the mainstream monolithic feed-forward neural networks are generally unstructured black boxes which is their major performance limiting characteristic. The non explicit structure and monolithic nature of the current mainstream artificial neural networks results in lack of the capability of systematic incorporation of functional or task-specific a priori knowledge in the artificial neural network design process. The problem caused by these limitations are discussed in detail in this disserta-tion and remedial solutions are presented that are driven by the functioning of the brain and its structural organization. Also, this dissertation presents an in depth study of the currently available modular neural network architectures along with highlighting their shortcomings and investigates new modular artificial neural network models in order to overcome pointed out shortcomings. The resulting proposed modular neural network models have greater accuracy, generalization, comprehensi-ble simplified neural structure, ease of training and more user confidence. These benefits are readily obvious for certain problems, depending upon availability and usage of available a priori knowledge about the problems. The modular neural network models presented in this dissertation exploit the capabilities of the principle of divide and conquer in the design and learning of the modular artificial neural networks. The strategy of divide and conquer solves a complex computational problem by dividing it into simpler sub-problems and then combining the individual solutions to the sub-problems into a solution to the original problem. The divisions of a task considered in this dissertation are the automatic decomposition of the mappings to be learned, decompositions of the artificial neural networks to minimize harmful interaction during the learning process, and explicit decomposition of the application task into sub-tasks that are learned separately. The versatility and capabilities of the …
Electromagnetic surface integral equation representations in terms of scalar functions

In this paper surface integral equations are developed for electromagnetics and acoustics. It is demonstrated that in acoustics conventional discretization strategies lead to much better conditioned matrix equations than in electromagnetics. This analysis suggests that in order to obtain better conditioned systems in electromagnetics the surface integral equation representations should written in terms of similar scalar unknowns as in acoustics.
Red Galaxy Clustering in the NOAO Deep Wide-Field Survey

We have measured the clustering of 0.30 < z < 0.90 red galaxies and constrained models of the evolution of large-scale structure using the initial 1.2 deg2 data release of the NOAO Deep Wide-Field Survey (NDWFS). The area and BWRI passbands of the NDWFS allow samples of ≳103 galaxies to be selected as a function of spectral type, absolute magnitude, and photometric redshift. Spectral synthesis models can be used to predict the colors and luminosities of a galaxy population as a function of redshift. We have used PEGASE2 models, with exponentially declining star formation rates, to estimate the observed colors and luminosity evolution of galaxies and to connect, as an evolutionary sequence, related populations of galaxies at different redshifts. A red galaxy sample, with present-day rest-frame Vega colors of BW-R > 1.44, was chosen to allow comparisons with the 2dF Galaxy Redshift Survey and Sloan Digital Sky Survey. We find the spatial clustering of red galaxies to be a strong function of luminosity, with r0 increasing from 4.4 ± 0.4 h-1 Mpc at MR - 5 log h ≈ -20.0 to 11.2 ± 1.0 h-1 Mpc at MR - 5 log h ≈ -22.0. Clustering evolution measurements using samples where the rest-frame selection criteria vary with redshift, including all deep single-band magnitude limited samples, are biased because of the correlation of clustering with rest-frame color and luminosity. The clustering of -21.5 < MR - 5 log h < -20.5, BW-R > 1.44 galaxies exhibits no significant evolution over the redshift range observed with r0 = 6.3 ± 0.5 h-1 Mpc in comoving coordinates. This is consistent with recent ΛCDM models in which the bias of L* galaxies undergoes rapid evolution and r0 evolves very slowly at z < 2.
Current Concepts in Stem Cell Therapy for Cardiovascular Diseases: What We Know and Don't Know

Medical therapies and mechanical interventions for the treatment of myocardial infarction (MI) and ischemic heart failure have seen great progress. However, current therapies only slow the progression to heart failure, but do not stimulate regeneration to recover the loss of functional myocytes. Stem cell-based therapy is a novel modality that can potentially be used for the treatment of ischemic cardiac injury and heart failure wherein cardiac tissue is regenerated thereby improving cardiac function and reducing the morbidity and mortality of patients. Many preclinical and clinical trials have employed various types of stem cells including bone marrow-derived mononuclear cells, skeletal myoblasts, resident cardiac stem cells, mesenchymal stem cells, and endothelial progenitor cells, and these studies have suggested great potential for the clinical application of stem cell therapy for cardiac disease. Here we will review and summarize the current evidence indicating the potential of stem cell therapy, focusing on the clinical trials conducted to date.
Effects of interleukin-37 on cardiac function after myocardial infarction in mice.

BACKGROUND Interleukin-37 (IL-37) is a new discovered member of the interleukin family and plays anti-inflammatory effect in some inflammatory disease. A recent study found that IL-37 elevated significantly in peripheral blood of patients with acute myocardial infarction. We aimed to explore the effect IL-37 on cardiac function after mice myocardial infarction (MI) and its mechanism. METHODS Acute MI mouse model was established and divided into three groups: sham group, MI group and IL-37 treatment group. MPO expression was detected by immunohistochemistry; NF-κB signaling pathway was tested by Western blot; and cardiac function was measured by echocardiography. RESULTS Compared with MI mice, IL-37 treatment showed an obvious decrease of MPO expression, suppression of p-p65 expression, and improved cardiac function by decreasing left ventricular shortening fraction (LVFS). CONCLUSION IL-37 may improve MI mice cardiac function via inhibition of inflammatory NF-κB signaling pathway.
An Assessment Of Integrated Water Resources Management Practices In Gucha River Catchment

Gucha River catchment is one of the main rivers draining into Lake Victoria. The river is targeted for irrigation, power generation, domestic and industrial water use development in addition to serving a population estimated at 1,460,884 people. In this study, the assessment of water resources, water use water balance and the catchment status was done, in order to determine the implications of the IWRM practices on the catchment water resources. This was carried out through analysis of water resources data, stakeholder and community interviews, measurement of water quality parameters and physical observations on the catchment status. From this study it was evident that policy and strategy which does not match the institutional capacities has resulted in deficient IWRM practices. From the study, the water resources in the catchment were estimated at 1,204 MCM/year, which is equivalent to 824 m/cap/year. It was also determined that in the advent of proposed developments, construction of water storage structures is required for sustainability of proposed developments. Study of six rainfall stations showed increase in rainfall trend in the upper catchment and decrease in the lower catchment. The Gucha River at RGS KB01A showed substantial increasing variability in the average and high flows with annual increase of 1.2 m3/sec and 21 m3/sec respectively. These observations allude to possible climate change effects and land use and catchment ecological condition changes. Effects of poor IWRM which include high river sediment load, estimated at 60 mg/ltr, river riparian encroachment, pollution and lack of data were observed and the study recommended development of adequate hydro-meteorological stations and information management system, human resource capacity development and review of the IWRM implementation strategy.
Combinatorial Cascading Bandits

We propose combinatorial cascading bandits, a class of partial monitoring problems where at each step a learning agent chooses a tuple of ground items subject to constraints and receives a reward if and only if the weights of all chosen items are one. The weights of the items are binary, stochastic, and drawn independently of each other. The agent observes the index of the first chosen item whose weight is zero. This observation model arises in network routing, for instance, where the learning agent may only observe the first link in the routing path which is down, and blocks the path. We propose a UCB-like algorithm for solving our problems, CombCascade; and prove gap-dependent and gap-free upper bounds on its $n$-step regret. Our proofs build on recent work in stochastic combinatorial semi-bandits but also address two novel challenges of our setting, a non-linear reward function and partial observability. We evaluate CombCascade on two real-world problems and show that it performs well even when our modeling assumptions are violated. We also demonstrate that our setting requires a new learning algorithm.
Noisy Clockwork: Time Series Analysis of Population Fluctuations in Animals

Both biotic interactions and abiotic random forcing are crucial influences on population dynamics. This frequently leads to roughly equal importance of deterministic and stochastic forces. The resulting tension between noise and determinism makes ecological dynamics unique, with conceptual and methodological challenges distinctive from those in other dynamical systems. The theory for stochastic, nonlinear ecological dynamics has been developed alongside methods to test models. A range of dynamical components has been considered—density dependence, environmental and demographic stochasticity, and climatic forcing—as well as their often complex interactions. We discuss recent advances in understanding ecological dynamics and testing theory using long-term data and review how dynamical forces interact to generate some central field and laboratory time series.