Miracle-dz/UniFAR-FTUs huggingface dataset:
Interaction of Inferior Temporal Cortex with Frontal Cortex and Basal Forebrain: Double Dissociation in Strategy Implementation and Associative Learning

Macaque monkeys learned a strategy task in which two groups of visual objects needed to be treated differently, one with persistent and one with sporadic object choices, to obtain food rewards. After preoperative training, they were divided into two surgical groups of three monkeys each. One group received crossed unilateral removals of frontal cortex and inferior temporal cortex (IT × FC) and were severely impaired in performing the strategy task. The other group received bilateral transection of anterior temporal stem, amygdala, and fornix (TS+AM+FX) and were unimpaired in performing the strategy task. Subsequently the same animals were tested in visual object–reward association learning. Here, confirming previous results, group IT × FC was unimpaired, whereas group TS+AM+FX was severely impaired. The results show that the amnesic effects of TS+AM+FX cannot be generally attributed to the partial temporal–frontal disconnection that this lesion creates, and therefore support the hypothesis that the amnesic effects of this lesion are caused primarily by the disconnection of temporal cortex from ascending inputs from the basal forebrain. The results also show that temporal–frontal interaction in strategy implementation does not require those routes of temporal–frontal interaction that are interrupted in TS+AM+FX, and therefore support the hypothesis that projections to other posterior cortical areas allow temporal and frontal cortex to interact with each other by multisynaptic corticocortical routes in strategy implementation.
Antifungal Activity of Phyllospheric Bacteria Isolated from Coffea arabica against Hemileia vastatrix

Peru is one of the leading countries that produce and export specialty coffees, favorably positioned in the international markets for its physical and organoleptic cup qualities. In recent years, yellow coffee rust caused by the phytopathogenic fungus Hemileia vastatrix stands out as one of the main phytosanitary diseases that affect coffee culture yields. Many studies have demonstrated bacteria antagonistic activity against a number of phytopathogen fungi. In this context, the aim of this work was to select and characterize phyllospheric bacteria isolated from Coffea arabica with antagonistic features against coffee rust to obtain biocontrollers. For that purpose, a total of 82 phyllospheric bacteria were isolated from two coffee leaf rust-susceptible varieties, typica and caturra roja, and one tolerant variety, catimor. Of all the isolates, 15% were endophytic and 85% were epiphytes. Among all the isolates, 14 were capable of inhibiting the mycelial radial growth of Mycena citricolor, and Colletotrichum sp. 16S rRNA gene sequence-based analysis showed that 9 isolates were related to Achromobacter insuavis, 2 were related to Luteibacter anthropi and 1 was related to Rodococcus ceridiohylli, Achromobacter marplatensis and Pseudomonas parafulva. A total of 7 representative bacteria of each group were selected based on their antagonistic activity and tested in germination inhibition assays of coffee rust uredinospores. The CRRFLT7 and TRFLT8 isolates showed a high inhibition percentage of urediniospores germination (81% and 82%, respectively), similar to that obtained with the chemical control (91%). An experimental field assay showed a good performance of both strains against rust damage too, making them a promising alternative for coffee leaf rust biocontrol.
On the Fractional Linear Scale Invariant Systems

The linear scale invariant systems are introduced for both integer and fractional orders. They are defined by the generalized Euler-Cauchy differential equation. The quantum fractional derivatives are suitable for dealing with this kind of systems, allowing us to define impulse response and transfer function with the help of the Mellin transform. It is shown how to compute the impulse responses corresponding to the two half plane regions of convergence of the transfer function.
Data-Flow Testing in the Large

Data-flow (DF) testing was introduced more than thirty years ago aiming at extensively evaluating a program structure. It requires tests that traverse a path in which the definition of a variable and its subsequent use, i.e., a definition-use association (dua), is exercised. While control-flow testing tools have being able to tackle big systems-large and long running programs, DF testing tools have failed to do so. This situation is in part due to the costs associated with tracking duas at run-time. Recently, an algorithm, called Bitwise Algorithm (BA), which uses bit vectors and bitwise operations for tracking intra-procedural duas at run-time, was proposed. This paper presents the implementation of BA for programs compiled into bytecodes. Previous approaches were able to deal with small to medium size programs with high penalties in terms of execution and memory. Our experimental results show that by using BA we are able to tackle large systems with more than 200 KLOCs and 300K required duas. Furthermore, for several programs the execution penalty was comparable with that imposed by a popular control-flow testing tool.
Modeling Shape, Appearance and Motion for Human Movement Analysis

Shape, Appearance and Motion are the most important cues for analyzing human movements in visual surveillance. Representation of these visual cues should be rich, invariant and discriminative. We present several approaches to model and integrate them for human detection and segmentation, person identification, and action recognition. First, we describe a hierarchical part-template matching approach to simultaneous human detection and segmentation combining local part-based and global shape-based schemes. For learning generic human detectors, a pose-adaptive representation is developed based on a hierarchical tree matching scheme and combined with an support vector machine classifier to perform human/non-human classification. We also formulate multiple occluded human detection using a Bayesian framework and optimize it through an iterative process. We evaluated the approach on several public pedestrian datasets. Second, given regions of interest provided by human detectors, we introduce an approach to iteratively estimates segmentation via a generalized Expectation-Maximization algorithm. The approach incorporates local Markov random field constraints and global pose inferences to propagate beliefs over image space iteratively to determine a coherent segmentation. Additionally, a layered occlusion model and a probabilistic occlusion reasoning scheme are introduced to handle inter-occlusion. The approach is tested on a wide variety of real-life images. Third, we describe an approach to appearance-based person recognition. In learning, we perform discriminative analysis through pairwise coupling of training samples, and estimate a set of normalized invariant profiles by marginalizing likelihood ratio functions which reflect local appearance differences. In recognition, we calculate discriminative information-based distances by a soft voting approach, and combine them with appearance-based distances for nearest neighbor classification. We evaluated the approach on videos of 61 individuals under significant illumination and viewpoint changes. Fourth, we describe a prototype-based approach to action recognition. During training, a set of action prototypes are learned in a joint shape and motion space via k-means clustering; During testing, humans are tracked while a frame-to-prototype correspondence is established by nearest neighbor search, and then actions are recognized using dynamic prototype sequence matching. Similarity matrices used for sequence matching are efficiently obtained by look-up table indexing. We experimented the approach on several action datasets.
Relations between fuzzy time intervals

Time intervals like "tonight", which are usually not very precise, can be modeled as fuzzy sets. But this causes the problem that the relations between points and intervals and between two intervals, which are usually very trivial, become very complex when the intervals are fuzzy sets. Moreover, there are many different possibilities to define such relations. In this paper a very flexible operator-based approach to point-interval and interval-interval relations is proposed, where the intervals are fuzzy time intervals over the real numbers. The relations yield non-trivial fuzzy values even if the intervals are crisp. As an example for an application, consider a database with, say, a cinema timetable, and you query the timetable give me all performances ending before midnight. The usual "before" relation will exclude the performances ending a second after midnight. With the fuzzy before relation you can get instead of a sharp drop to 0 at midnight decreasing fuzzy values after midnight, and these can be used to order the results of the query. The intervals and relations are implemented in the Fu-TIRe library (Fuzzy Time intervals and Relations). FuTIRe is an open source C++ library.
Randomized Greedy Algorithms for Independent Sets and Matchings in Regular Graphs: Exact Results and Finite Girth Corrections

We derive new results for the performance of a simple greedy algorithm for finding large independent sets and matchings in constant-degree regular graphs. We show that for r-regular graphs with n nodes and girth at least g, the algorithm finds an independent set of expected cardinality \[ f(r)n - O\biggl(\frac{(r-1)^{\frac{g}{2}}}{ \frac{g}{2}! } n\biggr), \] where f(r) is a function which we explicitly compute. A similar result is established for matchings. Our results imply improved bounds for the size of the largest independent set in these graphs, and provide the first results of this type for matchings. As an implication we show that the greedy algorithm returns a nearly perfect matching when both the degree r and girth g are large. Furthermore, we show that the cardinality of independent sets and matchings produced by the greedy algorithm in arbitrary bounded-degree graphs is concentrated around the mean. Finally, we analyse the performance of the greedy algorithm for the case of random i.i. d. weighted independent sets and matchings, and obtain a remarkably simple expression for the limiting expected values produced by the algorithm. In fact, all the other results are obtained as straightforward corollaries from the results for the weighted case.
Distinct Temporal and Anatomical Distributions of Amyloid-β and Tau Abnormalities following Controlled Cortical Impact in Transgenic Mice

Traumatic brain injury (TBI) is a major environmental risk factor for Alzheimer's disease. Intracellular accumulations of amyloid-β and tau proteins have been observed within hours following severe TBI in humans. Similar abnormalities have been recapitulated in young 3xTg-AD mice subjected to the controlled cortical impact model (CCI) of TBI and sacrificed at 24 h and 7 days post injury. This study investigated the temporal and anatomical distributions of amyloid-β and tau abnormalities from 1 h to 24 h post injury in the same model. Intra-axonal amyloid-β accumulation in the fimbria was detected as early as 1 hour and increased monotonically over 24 hours following injury. Tau immunoreactivity in the fimbria and amygdala had a biphasic time course with peaks at 1 hour and 24 hours, while tau immunoreactivity in the contralateral CA1 rose in a delayed fashion starting at 12 hours after injury. Furthermore, rapid intra-axonal amyloid-β accumulation was similarly observed post controlled cortical injury in APP/PS1 mice, another transgenic Alzheimer's disease mouse model. Acute increases in total and phospho-tau immunoreactivity were also evident in single transgenic TauP301L mice subjected to controlled cortical injury. These data provide further evidence for the causal effects of moderately severe contusional TBI on acceleration of acute Alzheimer-related abnormalities and the independent relationship between amyloid-β and tau in this setting.
Day 5 morning serum cortisol predicts hypothalamic-pituitary-adrenal function after transsphenoidal surgery for pituitary tumors.

BACKGROUND Adrenal insufficiency is a complication of transsphenoidal surgery (TSS) for pituitary adenoma, and correct identification of patients requiring glucocorticoid replacement is important. Controversy exists over which early postoperative 9 AM cortisol concentration reliably predicts hypothalamic-pituitary-adrenal (HPA) axis reserve, as defined by the insulin tolerance test (ITT). METHODS Data were reviewed for 36 patients undergoing TSS followed by day 5 postoperative 9 AM cortisol measurement and ITT 6 weeks postsurgery. All patients received postoperative glucocorticoid replacement, which was discontinued if the 9 AM serum cortisol was >300 nmol/L. RESULTS Of 23 patients who failed the ITT (peak cortisol <500 nmol/L), 20 also had a day 5, 9 AM serum cortisol <300 nmol/L. Nine of 13 patients who passed the ITT had a day 5, 9 AM cortisol >300 nmol/L. The cutoff cortisol concentration of 300 nmol/L had 86.9% (66.4%-97.2%) diagnostic sensitivity, 69.2% (38.6%-90.9%) diagnostic specificity, and 83.3% (61.8%-94.5%) positive predictive value (PPV) for detecting secondary adrenal insufficiency. Increasing the cutoff to 392 nmol/L resulted in 100% (85.2%-100%) sensitivity, 46.1% (19.2%-74.9%) specificity, and 76.6% (57.3%-89.4%) PPV. Decreasing the cutoff to 111 nmol/L resulted in 100% (75.3%-100%) specificity and 100% (67.9%-100%) PPV, although sensitivity was 47.8% (26.8%-69.4%). CONCLUSIONS A day 5 post-TSS 9 AM serum cortisol <111 nmol/L reliably detects secondary adrenal insufficiency, and concentrations >392 nmol/L support intact HPA function. Because concentrations of 111-392 nmol/L are poorly predictive of HPA function, glucocorticoid replacement should continue in such cases until definitive testing is performed using an ITT.
From regularization to radial, tensor and additive splines

Poggio and Girosi showed that regularization principles lead to approximation schemes which are equivalent to networks with one layer of hidden units, called regularization networks. They summarize their results (1993) that show that regularization networks encompass a much broader range of approximation schemes, including many of the general additive models and some of the neural networks. In particular, additive splines as well as some tensor product splines can be obtained from appropriate classes of smoothness functionals. The same extension that extends radial basis functions to hyper basis functions leads from additive models to ridge approximation models, containing as special cases Breiman's hinge functions and some forms of projection pursuit regression. The authors propose to use the term generalized regularization networks for this broad class of approximation schemes that follow from an extension of regularization. <<ETX>>
Why are median phosphorus concentrations improving in New Zealand streams and rivers?

ABSTRACT The enrichment of phosphorus (P) in streams and rivers can impair water quality, but concentrations have decreased. We found little evidence that this improvement was caused by a decrease in soil Olsen P concentrations or imported P (e.g. fertiliser), a change to low water-soluble P fertilisers, or that greater nitrate loads were assimilating P from groundwater or sediments. Possible causes of improvement were that land use change had decreased erosion, more nitrogen fertiliser use was assimilating soil P, and a greater awareness of P as an environmental issue. However, the most probable causes were that strategies were mitigating P loss from land, guidelines were directing where to best use strategies, and policy instruments were including P management. These findings support the development and implementation of mitigations, supported by voluntary guidelines and regulation. However, our findings can be strengthened if data are referenced to equivalent, and finer spatial and temporal scales.
Polyunsaturated fatty acid-rich diets: effect on adipose tissue metabolism in rats

The aim of the present study was to evaluate the effect of diets rich in n-6 and n-3 fatty acids on adipose tissue metabolism. Starting at weaning, male Wistar rats were fed ad libitum, for 8 weeks with one of the following diets: C, rat chow; S, rat chow containing 15 % (w/w) soyabean oil; F, rat chow containing 15 % (w/w) fish oil; SF, rat chow containing 15 % (w/w) soyabean and fish oil (5:1, w/w). Casein was added to the fat diets to achieve the same 20 % (w/w) protein content as in the control chow. Food intake and body weight were measured weekly. The rats were killed by decapitation and the retroperitoneal (RET) and epididymal (EPI) white adipose tissues were removed and weighed. Tissue lipid and protein content, in vivo lipogenesis rate, uptake of diet-derived lipids, in vitro lipolytic rate, adipocyte area, lipoprotein lipase, ATP citrate lyase, and malic enzyme activities were evaluated. Carcass lipid and protein contents were also measured. Energy intake was reduced while carcass lipid content was increased in the three fat-fed groups. However, carcass protein and body weight gains were elevated only with diets F and SF. Lipolysis rate was diminished by diets F and SF, while the uptake of diet-derived lipids was elevated by the diet S in both RET and EPI tissues. These metabolic alterations may have contributed to the increase in in vivo lipogenesis rate in the presence of decreased ATP citrate lyase and malic enzyme activities induced by the three lipid diets. These results indicate that enrichment of the diet with polyunsaturated fatty acids causes changes in adipose tissue metabolism that favour fat deposition. Different metabolic pathways were preferentially affected by each type of fatty acid used.
Mutation of a single residue in the ba3 oxidase specifically impairs protonation of the pump site

Significance Cytochrome c oxidase is the terminal electron acceptor in mitochondria and aerobic bacteria, where O2 reduction is linked to proton pumping across the membrane. Understanding the mechanism by which the enzyme pumps protons requires identification of the so-called proton-loading site, which is the controlling element that assures unidirectionality in the proton flux. The position of this site has been predicted on the basis of theoretical calculations but has not been identified in experimental studies. Here we have used sophisticated biophysical techniques to investigate intraprotein electron and proton transfer in the thermophilic bacterium Thermus thermophilus. The data made it possible to identify the destination of the pumped proton and to unravel the sequence of reactions that leads to proton translocation. The ba3-type cytochrome c oxidase from Thermus thermophilus is a membrane-bound protein complex that couples electron transfer to O2 to proton translocation across the membrane. To elucidate the mechanism of the redox-driven proton pumping, we investigated the kinetics of electron and proton transfer in a structural variant of the ba3 oxidase where a putative “pump site” was modified by replacement of Asp372 by Ile. In this structural variant, proton pumping was uncoupled from internal electron transfer and O2 reduction. The results from our studies show that proton uptake to the pump site (time constant ∼65 μs in the wild-type cytochrome c oxidase) was impaired in the Asp372Ile variant. Furthermore, a reaction step that in the wild-type cytochrome c oxidase is linked to simultaneous proton uptake and release with a time constant of ∼1.2 ms was slowed to ∼8.4 ms, and in Asp372Ile was only associated with proton uptake to the catalytic site. These data identify reaction steps that are associated with protonation and deprotonation of the pump site, and point to the area around Asp372 as the location of this site in the ba3 cytochrome c oxidase.
Design of Experiments for the Tuning of Optimisation Algorithms

This thesis presents a set of rigorous methodologies for tuning the performance of algorithms that solve optimisation problems. Many optimisation problems are difficult and time-consuming to solve exactly. An alternative is to use an approximate algorithm that solves the problem to an acceptable level of quality and provides such a solution in a reasonable time. Using optimisation algorithms typically requires choosing the settings of tuning parameters that adjust algorithm performance subject to this compromise between solution quality and running time. This is the parameter tuning problem. This thesis demonstrates that the Design Of Experiments (DOE) approach can be adapted to successfully address the parameter tuning problem for algorithms that find approximate solutions to optimisation problems. The thesis introduces experiment designs and analyses for (1) determining the problem characteristics affecting algorithm performance (2) screening and ranking the most important tuning parameters and problem characteristics and (3) tuning algorithm parameters to maximise algorithm performance for a given problem instance. Desirability functions are introduced for tackling the compromise of achieving satisfactory solution quality in reasonable running time. Five case studies apply the thesis methodologies to the Ant Colony System and the Max-Min Ant System algorithms for the Travelling Salesperson Problem. New results are reported and open questions are answered regarding the importance of both existing tuning parameters and proposed new tuning parameters. A new problem characteristic is identified and shown to have a very strong effect on the quality of the algorithms’ solutions. The tuning methodologies presented here yield solution quality that is as good as or better than than the general parameter settings from the literature. Furthermore, the associated running times are orders of magnitude faster than the results obtained with the general parameter settings. All experiments are performed with publicly available algorithm code, publicly available problem generators and benchmarked experimental machines.
Allocating Classes for Soft-Then-Hard Subpixel Mapping Algorithms in Units of Class

There is a type of algorithm for subpixel mapping (SPM), namely, the soft-then-hard SPM (STHSPM) algorithm that first estimates soft attribute values for land cover classes at the subpixel scale level and then allocates classes (i.e., hard attribute values) for subpixels according to the soft attribute values. This paper presents a novel class allocation approach for STHSPM algorithms, which allocates classes in units of class (UOC). First, a visiting order for all classes is predetermined, and the number of subpixels belonging to each class is calculated using coarse fraction data. Then, according to the visiting order, the subpixels belonging to the being visited class are determined by comparing the soft attribute values of this class, and the remaining subpixels are used for the allocation of the next class. The process is terminated when each subpixel is allocated to a class. UOC was tested on three remote sensing images with five STHSPM algorithms: back-propagation neural network, Hopfield neural network, subpixel/pixel spatial attraction model, kriging, and indicator cokriging. UOC was also compared with three existing allocation methods, i.e., linear optimization technique (LOT), sequential assignment in units of subpixel (UOS), and a method that assigns subpixels with highest soft attribute values first (HAVF). Results show that for all STHSPM algorithms, UOC is able to produce higher SPM accuracy than UOS and HAVF; compared with LOT, UOC is able to achieve at least comparable accuracy but needs much less computing time. Hence, UOC provides an effective and real-time class allocation method for STHSPM algorithms.