Miracle-dz/UniFAR-FTUs huggingface dataset:
Potential contribution of peri-urban wetlands to livelihood of local communities in Shinyanga municipality

The study was conducted in Shinyanga Municipality, Tanzania to assess the potential contribution of peri-urban wetlands to livelihood of adjacent communities. The specific objectives were to examine the contribution of the peri-urban wetland ecosystems to natural capital– (food production/food security), evaluate the contribution of peri-urban wetlands ecosystem to financial capital –(household income) and assess the benefits received by farmer groups/associations formed around the utilization of wetlands (social capital). Data were collected through households and farmer’s group/associations questionnaires, focus group discussion and field observations. Data were analyzed using descriptive and inferential statistics where correlation and t-test were applied. It was observed that 79.2% of the wetland adjacent communities depend on the wetlands for food. The major food crops were paddy, vegetables, fruits and sweet potatoes. The mean production levels of cereals, vegetables and fruits were significantly higher in wetlands compared to uplands (P≤0.001). In the case of household income wetland cultivation (paddy, maize, vegetables and fruits) generated statistically higher household annual mean income of Tshs 2,335,852/year (US$ 1,168) compared to Tshs 197,475/year (US$ 99) generated by upland cultivation (P≤0.001). The findings further indicated that 70% of the farmer groups/associations formed around wetland utilization accessed loans/credit to support agricultural production as benefit from being members. Sustainable utilization of peri-urban wetlands in Shinyanga Municipality generates substantial livelihoods to adjacent communities. Conservation of such wetland ecosystems is imperative for continued contribution to livelihoods while ensuring environmental conservation. Further studies are necessary to unveil the nature of social capital associated with utilization of wetland ecosystems.
Automatic Detection of Gadolinium-Enhancing Multiple Sclerosis Lesions in Brain MRI Using Conditional Random Fields

Gadolinium-enhancing lesions in brain magnetic resonance imaging of multiple sclerosis (MS) patients are of great interest since they are markers of disease activity. Identification of gadolinium-enhancing lesions is particularly challenging because the vast majority of enhancing voxels are associated with normal structures, particularly blood vessels. Furthermore, these lesions are typically small and in close proximity to vessels. In this paper, we present an automatic, probabilistic framework for segmentation of gadolinium-enhancing lesions in MS using conditional random fields. Our approach, through the integration of different components, encodes different information such as correspondence between the intensities and tissue labels, patterns in the labels, or patterns in the intensities. The proposed algorithm is evaluated on 80 multimodal clinical datasets acquired from relapsing-remitting MS patients in the context of multicenter clinical trials. The experimental results exhibit a sensitivity of 98% with a low false positive lesion count. The performance of the proposed algorithm is also compared to a logistic regression classifier, a support vector machine and a Markov random field approach. The results demonstrate superior performance of the proposed algorithm at successfully detecting all of the gadolinium-enhancing lesions while maintaining a low false positive lesion count.
A user's guide to channelrhodopsin variants: features, limitations and future developments

Channelrhodopsins (ChRs) are light‐activated channels from algae that provide these organisms with fast sensors to visible light for phototaxis. Since its discovery, channelrhodopsin‐2 (ChR2) has been used as a research tool to depolarize membranes of excitable cells with light. Subsequent chimeragenesis, mutagenesis and bioinformatic approaches have introduced additional ChR variants, such as channelrhodopsin‐2 with H134R mutation (ChR2/H134R), channelrhodopsin‐2 with E123T mutation (ChETA), Volvox carteri channelrhodopsin‐1 (VChR1), Volvox carteri channelrhodopsin‐2 (VChR2), channelrhodopsin‐2 with C128 or D156A mutations (ChR2/C128X/D156A), chimera D (ChD), chimera EF (ChEF) and chimera EF with I170V mutation (I170V). Each of these ChR variuants has unique features and limitations, but there are few resources summarizing and comparing these ChRs in a systematic manner. In this review, the seven following key properties of ChRs that have significant influences on their effectiveness as research tools are examined: conductance, selectivity, kinetics, desensitization, light sensitivity, spectral response and membrane trafficking. Using this information, valuable qualities and deficits of each ChR variant are summarized. Optimal uses and potential future improvements of ChRs as optogenetic tools are also discussed.
DEPARTMENT OF ECONOMICS

Unilateral euroization is underexplored even in comparison to unilateral dollarization (taken to mean the adoption of the US dollar as legal tender). This paper attempts to partly fill this gap in the literature by investigating the case of Montenegro, which is one of the two countries that have unilaterally adopted the euro as the legal tender. Montenegro’s limited monetary policy options make the nature of business cycles important. The evidence presented here suggests that Montenegro has a low degree of synchronization, limited structural similarity, and weak trade integration with the Eurozone. Moreover, there is limited evidence for endogenous structural assimilation following euroization. The case for currency union is weak for Montenegro and appears to be defensible only on grounds of policy credibility. JEL classifications: F15, F45, E32, E52
Recursive dynamic node creation in multilayer neural networks

The derivations of a novel approach for simultaneous recursive weight adaptation and node creation in multilayer backpropagation neural networks are presented. The method uses time and order update formulations in the orthogonal projection method to derive a recursive weight updating procedure for the training process of the neural network and a recursive node creation algorithm for weight adjustment of a layer with added nodes during the training process. The proposed approach allows optimal dynamic node creation in the sense that the mean-squared error is minimized for each new topology. The effectiveness of the algorithm is demonstrated on several benchmark problems (the multiplexer and the decoder problems) as well as a real world application for detection and classification of buried dielectric anomalies using a microwave sensor.
Service Clouds: Distributed Infrastructure for Adaptive Communication Services

This paper describes service clouds, a distributed infrastructure designed to facilitate rapid prototyping and deployment of adaptive communication services. The infrastructure combines adaptive middleware functionality with an overlay network substrate in order to support dynamic instantiation and reconfiguration of services. The service clouds architecture includes a collection of low-level facilities that can be invoked directly by applications or used to compose more complex services. After describing the service clouds architecture, we present results of experimental case studies conducted on the PlanetLab Internet testbed alone and a mobile computing testbed.
Mechanism of Pyrite Dissolution in the Presence ofThiobacillus ferrooxidans

ABSTRACT In spite of the environmental and commercial interests in the bacterial leaching of pyrite, two central questions have not been answered after more than 35 years of research: does Thiobacillus ferrooxidans enhance the rate of leaching above that achieved by ferric sulfate solutions under the same conditions, and if so, how do the bacteria affect such an enhancement? Experimental conditions of previous studies were such that the concentrations of ferric and ferrous ions changed substantially throughout the course of the experiments. This has made it difficult to interpret the data obtained from these previous works. The aim of this work was to answer these two questions by employing an experimental apparatus designed to maintain the concentrations in solution at a constant value. This was achieved by using the constant redox potential apparatus described previously (P. I. Harvey, and F. K. Crundwell, Appl. Environ. Microbiol. 63:2586–2592, 1997; T. A. Fowler, and F. K. Crundwell, Appl. Environ. Microbiol. 64:3570–3575, 1998). Experiments were conducted in both the presence and absence of T. ferrooxidans, maintaining the same conditions in solution. The rate of dissolution of pyrite with bacteria was higher than that without bacteria at the same concentrations of ferrous and ferric ions in solution. Analysis of the dependence of the rate of leaching on the concentration of ferric ions and on the pH, together with results obtained from electrochemical measurements, provided clear evidence that the higher rate of leaching with bacteria is due to the bacteria increasing the pH at the surface of the pyrite.
Block-based compressed sensing of images via deep learning

Compressed sensing (CS) is a signal processing framework for efficiently reconstructing a signal from a small number of measurements, obtained by linear projections of the signal. Block-based CS is a lightweight CS approach that is mostly suitable for processing very high-dimensional images and videos: it operates on local patches, employs a low-complexity reconstruction operator and requires significantly less memory to store the sensing matrix. In this paper we present a deep learning approach for block-based CS, in which a fully-connected network performs both the block-based linear sensing and non-linear reconstruction stages. During the training phase, the sensing matrix and the non-linear reconstruction operator are jointly optimized, and the proposed approach out-performs state-of-the-art both in terms of reconstruction quality and computation time. For example, at a 25% sensing rate the average PSNR advantage is 0.77dB and computation time is over 200-times faster.
Speech Enhancement in Hearing Aids Using Conjugate Symmetry of DFT and SNR-Perception Models

Most of the speech enhancement algorithms use the magnitude of STFT while phase is kept unchanged [3]. In this paper the magnitude of STFT of noisy speech is kept unchanged while the phase is modified. Modified complex spectrum of speech is obtained by combining unchanged magnitude spectrum and modified phase spectrum [3]. This modification results into cancellation of low energy components (noise) of complex spectrum more than the high-energy (speech) components leading to reduction in background noise which is essential in hearing aids. SNR-Perception models for AWGN, Train noise, and Babble noise are developed using Nonlinear Regression modeling technique. Normal hearing and hearing loss subjective listening tests and spectrogram analysis show that the proposed method gives improved speech quality.
Bilateral Teleoperation of Groups of Mobile Robots With Time-Varying Topology

In this paper, a novel decentralized control strategy for bilaterally teleoperating heterogeneous groups of mobile robots from different domains (aerial, ground, marine, and underwater) is proposed. By using a decentralized control architecture, the group of robots, which is treated as the slave side, is made able to navigate in a cluttered environment while avoiding obstacles, interrobot collisions, and following the human motion commands. Simultaneously, the human operator acting on the master side is provided with a suitable force feedback informative of the group response and of the interaction with the surrounding environment. Using passivity-based techniques, we allow the behavior of the group to be as flexible as possible with arbitrary split and join events (e.g., due to interrobot visibility/packet losses or specific task requirements) while guaranteeing the stability of the system. We provide a rigorous analysis of the system stability and steady-state characteristics and validate performance through human/hardware-in-the-loop simulations by considering a heterogeneous fleet of unmanned aerial vehicles (UAVs) and unmanned ground vehicles as a case study. Finally, we also provide an experimental validation with four quadrotor UAVs.
LAMOST telescope reveals that Neptunian cousins of hot Jupiters are mostly single offspring of stars that are rich in heavy elements

Significance Hot Jupiters are Jupiter-size planets at £1/10 of the Sun–Earth distance, and even though they were the first exoplanet population discovered around sun-like stars, their origins still remain elusive. Using data from NASA’s Kepler satellite and China’s Large Sky Area Multi-Object Fiber Spectroscopic Telescope, we discover a population of close-in Neptune-size planets (called “Hoptunes”) that share key similarities with hot Jupiters. Like hot Jupiters, Hoptunes prefer to reside around stars with higher metal abundance than the Sun. Nearly half of the Kepler planets are discovered in systems with multiple transiting planets, but both hot Jupiters and Hoptunes are preferentially found in single-transiting planet systems. The “kinship” between hot Jupiters and Hoptunes suggests likely common origins and offers fresh clues into the formation of these exotic close-in planets. We discover a population of short-period, Neptune-size planets sharing key similarities with hot Jupiters: both populations are preferentially hosted by metal-rich stars, and both are preferentially found in Kepler systems with single-transiting planets. We use accurate Large Sky Area Multi-Object Fiber Spectroscopic Telescope (LAMOST) Data Release 4 (DR4) stellar parameters for main-sequence stars to study the distributions of short-period (1d<P<10d) Kepler planets as a function of host star metallicity. The radius distribution of planets around metal-rich stars is more “puffed up” compared with that around metal-poor hosts. In two period–radius regimes, planets preferentially reside around metal-rich stars, while there are hardly any planets around metal-poor stars. One is the well-known hot Jupiters, and the other one is a population of Neptune-size planets (2R⊕≲Rp≲6R⊕), dubbed “Hoptunes. ” Also like hot Jupiters, Hoptunes occur more frequently in systems with single-transiting planets although the fraction of Hoptunes occurring in multiples is larger than that of hot Jupiters. About 1% of solar-type stars host Hoptunes, and the frequencies of Hoptunes and hot Jupiters increase with consistent trends as a function of [Fe/H]. In the planet radius distribution, hot Jupiters and Hoptunes are separated by a “valley” at approximately Saturn size (in the range of 6R⊕≲Rp≲10R⊕), and this “hot-Saturn valley” represents approximately an order-of-magnitude decrease in planet frequency compared with hot Jupiters and Hoptunes. The empirical “kinship” between Hoptunes and hot Jupiters suggests likely common processes (migration and/or formation) responsible for their existence.
Pose-Configurable Generic Tracking of Elongated Objects

Elongated objects have various shapes and can shift, rotate, change scale, and be rigid or deform by flexing, articulating, and vibrating, with examples as varied as a glass bottle, a robotic arm, a surgical suture, a finger pair, a tram, and a guitar string. This generally makes tracking of poses of elongated objects very challenging. We describe a unified, configurable framework for tracking the pose of elongated objects, which move in the image plane and extend over the image region. Our method strives for simplicity, versatility, and efficiency. The object is decomposed into a chained assembly of segments of multiple parts that are arranged under a hierarchy of tailored spatio-temporal constraints. In this hierarchy, segments can rescale independently while their elasticity is controlled with global orientations and local distances. While the trend in tracking is to design complex, structure-free algorithms that update object appearance on-line, we show that our tracker, with the novel but remarkably simple, structured organization of parts with constant appearance, reaches or improves state-of-the-art performance. Most importantly, our model can be easily configured to track exact pose of arbitrary, elongated objects in the image plane. The tracker can run up to 100 fps on a desktop PC, yet the computation time scales linearly with the number of object parts. To our knowledge, this is the first approach to generic tracking of elongated objects.
Identification of androgen‐regulated genes in the prostate cancer cell line LNCaP by serial analysis of gene expression and proteomic analysis

A common therapy for nonorgan‐confined prostate cancer involves androgen deprivation. To develop a better understanding of the effect of androgen on prostatic cells, we have analyzed gene expression changes induced by dihydrotestosterone (DHT) in the androgen responsive prostate cancer line LNCaP, at both RNA and protein levels. Changes at the RNA level induced by DHT were determined by means of serial analysis of gene expression (SAGE), and protein profiling was done by means of quantitative two‐dimensional polyacrylamide gel electrophoresis. Among 123 371 transcripts analyzed, a total of 28844 distinct SAGE tags were identified representing 16 570 genes. Some 351 genes were significantly affected by DHT treatment at the RNA level (p<0.05), of which 147 were induced and 204 repressed by androgen. In two independent experiments, the integrated intensity of 32 protein spots increased and 12 decreased at least two‐fold in response to androgen, out of a total of 1031 protein spots analyzed. The change in intensity for most of the affected proteins identified could not be predicted based on the level of their corresponding RNA. Our study provides a global assessment of genes regulated by DHT and suggests a need for profiling at both RNA and protein levels for a comprehensive evaluation of patterns of gene expression.
The variation of productivity and its allocation along a tropical elevation gradient: a whole carbon budget perspective.

Why do forest productivity and biomass decline with elevation? To address this question, research to date generally has focused on correlative approaches describing changes in woody growth and biomass with elevation. We present a novel, mechanistic approach to this question by quantifying the autotrophic carbon budget in 16 forest plots along a 3300 m elevation transect in Peru. Low growth rates at high elevations appear primarily driven by low gross primary productivity (GPP), with little shift in either carbon use efficiency (CUE) or allocation of net primary productivity (NPP) between wood, fine roots and canopy. The lack of trend in CUE implies that the proportion of photosynthate allocated to autotrophic respiration is not sensitive to temperature. Rather than a gradual linear decline in productivity, there is some limited but nonconclusive evidence of a sharp transition in NPP between submontane and montane forests, which may be caused by cloud immersion effects within the cloud forest zone. Leaf-level photosynthetic parameters do not decline with elevation, implying that nutrient limitation does not restrict photosynthesis at high elevations. Our data demonstrate the potential of whole carbon budget perspectives to provide a deeper understanding of controls on ecosystem functioning and carbon cycling.
Increasing the resilience of atomic commit, at no additional cost

This paper presents a new atomic commitment protocol that always allows a quorum in the system to make progress. Previously suggested quorum-based protocols (e.g. [12]) allow a quorum to make progress in case of one failure. If failures cascade, however, and the quorum in the system is \lost" (i.e. at a given time no quorum component exists, e.g. because of a total crash), a quorum can later become connected and still remain blocked. The importance of this work is in demonstrating, using a simple algorithm, how protocols that always allow a majority to make progress can be constructed.
Manganese homeostasis in the nervous system

Manganese (Mn) is an essential heavy metal that is naturally found in the environment. Daily intake through dietary sources provides the necessary amount required for several key physiological processes, including antioxidant defense, energy metabolism, immune function and others. However, overexposure from environmental sources can result in a condition known as manganism that features symptomatology similar to Parkinson's disease (PD). This disorder presents with debilitating motor and cognitive deficits that arise from a neurodegenerative process. In order to maintain a balance between its essentiality and neurotoxicity, several mechanisms exist to properly buffer cellular Mn levels. These include transporters involved in Mn uptake, and newly discovered Mn efflux mechanisms. This review will focus on current studies related to mechanisms underlying Mn import and export, primarily the Mn transporters, and their function and roles in Mn‐induced neurotoxicity.
Socially Acceptable Behaviour for Robots Approaching Humans using an Adaptable Personal Space

In this paper a new adaptable model of the personal space is proposed. This model takes into account the position of a persons hands aiming to facilitate interactions with the human while maintaining an appropriate social distance during the approach. This personal space model has been used as a cost function in the path planning algorithm Transition-based Rapidly exploring Random Trees. It allows users to influence the robot’s generated approach at the planning stage by varying their body and hand positions. Results from an online survey, where participants were shown different simulated approach behaviours, indicate that the model performs well when it comes to distance regulation and how close the robot comes during the approach. An interesting discovery from the survey is that the maintenance of eye contact, i.e. the robot keeping oriented towards the person during the approach, was positively associated with both the closeness of the robot during movement and judging the intentions of the robot for unaware users.
Association between Frequency of Breakfast Consumption and Academic Performance in Healthy Korean Adolescents

Background The purpose of this study was to examine whether the frequency of breakfast consumption was related to academic performance in healthy Korean adolescents. Methods: We analyzed data from the seventh Korea Youth Risk Behavior Web-based Survey conducted in 2011, in which 75,643 adolescents from school grades 7–12 participated. We assessed the association between the frequency of breakfast consumption (per week) and academic performance using multivariate logistic regression analysis after adjusting for covariates such as age, body mass index, frequency of smoking, frequency of drinking, parents’ education level, family economic status, frequency of vigorous physical activity (PA), frequency of moderate PA, frequency of muscular strength exercises, and level of mental stress. Results: For male adolescents, the odds ratios (ORs) for achieving average or higher academic performance according to the breakfast frequency per week were once per week, 1.004 (P=0.945); twice per week, 0.915 (P=0.153); 3 days per week, 0.928 (P=0.237); 4 days per week, 1.087 (P=0.176); 5 days per week, 1.258 (P<0.001); 6 days per week, 1.473 (P<0.001); and every day, 1.700 (P<0.001), compared to no breakfast per week. For female adolescents, the ORs for achieving average or higher academic performance according to the breakfast frequency were once per week, 1.068 (P=0.320); twice per week, 1.140 (P=0.031); 3 days per week, 1.179 (P=0.004); 4 days per week, 1.339 (P<0.001); 5 days per week, 1.449 (P<0.001); 6 days per week, 1.768 (P<0.001); and every day, 1.922 (P<0.001), compared to no breakfast per week. Conclusion: The frequency of breakfast consumption is positively correlated with academic performance in both male and female healthy adolescents in Korea.