Miracle-dz/UniFAR-FTUs huggingface dataset:
Emotional range in value-sensitive deliberation

This paper presents a model of agent's behavior that takes into account emotions and moral values. In our proposal, when the description of the current situation reveals that a moral value is 'at stake', the moral goal of re-establishing the threatened value is included among the active goals. The compliance with values generates positive emotions like pride and admiration, while the opposite brings to shame and self-reproach. During the deliberation phase, the agent appraises her plans in terms of the emotional reward they are expected to yield, given the trade off between moral and individual goals. In this phase, the emotional reward affects the agent's choices about her behavior. After the execution phase, one's and others' actions are appraised again in terms of the agent's values, giving rise to moral emotions. The paper shows how emotional appraisal can be coupled with the choice among possible lines of action, presenting a mapping between plans and emotions that integrates and extends preceding proposals.
Learning Sparse, Distributed Representations using the Hebbian Principle

The "fire together, wire together" Hebbian model is a central principle for learning in neuroscience, but surprisingly, it has found limited applicability in modern machine learning. In this paper, we take a first step towards bridging this gap, by developing flavors of competitive Hebbian learning which produce sparse, distributed neural codes using online adaptation with minimal tuning. We propose an unsupervised algorithm, termed Adaptive Hebbian Learning (AHL). We illustrate the distributed nature of the learned representations via output entropy computations for synthetic data, and demonstrate superior performance, compared to standard alternatives such as autoencoders, in training a deep convolutional net on standard image datasets.
Plasma Fatty Acid–Binding Protein 4, Nonesterified Fatty Acids, and Incident Diabetes in Older Adults

OBJECTIVE To examine the relation of fatty acid–binding protein (FABP)4 and nonesterified fatty acids (NEFAs) to diabetes in older adults. RESEARCH DESIGN AND METHODS We ascertained incident diabetes among 3,740 Cardiovascular Health Study participants (1992–2007) based on the use of hypoglycemic medications, fasting glucose ≥126 mg/dL, or nonfasting glucose ≥200 mg/dL. FABP4 and NEFA were measured on specimens collected between 1992 and 1993. RESULTS Mean age of the 3,740 subjects studied was 74.8 years. For each SD increase in log FABP4, hazard ratios (HRs) for diabetes were 1.35 (95% CI 1.10–1.65) for women and 1.45 (1.13–1.85) for men controlling for age, race, education, physical activity, cystatin C, alcohol intake, smoking, self-reported health status, and estrogen use for women (P for sex-FABP4 interaction 0.10). BMI modified the FABP4-diabetes relation (P = 0.009 overall; 0.02 for women and 0.135 for men), in that statistically significant higher risk of diabetes was mainly seen in men with BMI <25 kg/m2 (HR per SD: 1.78 [95% CI 1.13–2.81]). There was a modest and nonsignificant association of NEFA with diabetes (Ptrend = 0.21). However, when restricted to the first 5 years of follow-up, multivariable-adjusted HRs for diabetes were 1.0 (ref.), 1.68 (95% CI 1.12–2.53), and 1.63 (1.07–2.50) across consecutive tertiles of NEFA (Ptrend = 0.03). CONCLUSIONS Plasma FABP4 was positively associated with incident diabetes in older adults, and such association was statistically significant in lean men only. A significant positive association between plasma NEFA and incident diabetes was observed during the first 5 years of follow-up.
Trajectory-based data Forwarding with future Neighbor Prediction in autonomous driving vehicular environments

In VANETs, vehicle-to-vehicle communication is a challenge due to the fast topology changing, frequent disconnections, and highly variable traffic densities. This paper describes a Trajectory-based data Forwarding scheme with future Neighbor Prediction (TFNP) in autonomous driving vehicular environments. The most important characteristic of the environments is that the prediction for future position of vehicles could be sufficiently accurate. With the trajectory information of all autonomous vehicles, the future neighbors of each vehicle can be properly identified. The packet forwarding ability of the future neighbors is evaluated and forwarding sequences are thus constructed. When an autonomous vehicle needs to send data, it will transmit packets based on the constructed forwarding path. The simulation results demonstrate the effectiveness and efficiency of the developed scheme in the autonomous driving vehicular environments.
IQGAP3, a novel effector of Rac1 and Cdc42, regulates neurite outgrowth

Rac1 and Cdc42, members of the Rho family GTPases, control diverse cellular processes such as cell migration and morphogenesis through their effectors. Among the effectors, IQGAP1 plays pivotal roles in the establishment of cytoskeletal architecture and intercellular adhesions in various cells. However, its roles remain to be clarified, especially in neuronal cells. We have identified IQGAP3 as a novel member of the IQGAP family, which is highly expressed in brain. We found that IQGAP3, an effector of Rac1 and Cdc42, associates directly with actin filaments and accumulates asymmetrically at the distal region of axons in hippocampal neurons. The depletion of IQGAP3 impairs neurite or axon outgrowth in neuronal cells with the disorganized cytoskeleton, but depletion of IQGAP1 does not. Furthermore, IQGAP3 is indispensable for Rac1/Cdc42-promoted neurite outgrowth in PC12 cells. Taken together, these results indicate that IQGAP3 can link the activation of Rac1 and Cdc42 with the cytoskeletal architectures during neuronal morphogenesis.
Genetic programming with data migration for symbolic regression

In this publication genetic programming (GP) with data migration for symbolic regression is presented. The motivation for the development of the algorithm is to evolve models which generalize well on previously unseen data. GP with data migration uses multiple subpopulations to maintain the genetic diversity during the algorithm run and a sophisticated training subset selection strategy. Each subpopulation is evaluated on a different fixed training subset (FTS) and additionally a variable training subset (VTS) is exchanged between the subpopulations at specific data migration intervals. Thus, the individuals are evaluated on the unification of FTS and VTS and should have better generalization properties due to the regular changes of the VTS. The implemented algorithm is compared to several GP variants on a number of symbolic regression benchmark problems to test the effectiveness of the multiple populations and data migration strategy. Additionally, different algorithm configurations and migration strategies are evaluated to show their impact with respect to the achieved quality.
Verifying, validating and monitoring the open Ravenscar real time kernel

Business and mission critical real-time systems need to be fully predictable, in order that their behavior is known before deployment, even in the presence of faults. The Open Ravenscar Real Time Kernel (ORK) is a small size with reduced complexity kernel designed to be used in this type of applications. This kernel was implemented to be fully compliant with the Ravenscar profile, which defines a subset of the tasking features of Ada which can be used to implement a small and reliable kernel. Critical Software has recently conducted an evaluation on this kernel as well as started a new project to provide a monitoring tool for ORK, thus further enhancing the already existing range of hard real-time verification and validation tools. This paper summarizes the objectives and results of the ORK evaluation and presents the main goals and functionalities of the monitoring tool.
Dynamic gene regulatory network-based inference framework for identifying cancer biomarkers

Identification of reliable cancer biomarkers is a challenging task and great efforts have been devoted to utilize network-based methods to alleviate uncertainty of detected oncogenes. In the paper, an innovative network-based inference method is presented to recognize meaningful cancer-related genes. Firstly, a penalized kernel weighting algorithm is proposed, called ℓ2-norm regularized dynamic Smooth Network Reconstruction (lrSNR), which builds on radial basis function (RBF) kernel smoothing strategy and can be solved efficiently. Secondly, with the lrSNR, the gene regulatory network can be constructed from synthetic and real gene data. Thirdly, by ranking hub genes of the network and performing functional enrichment analysis, cancer biomarkers are obtained. Experimental results on man-made and real-world time series expression data reveal our proposed method significantly improves the computational accuracy and reliability in contrast with three competing methods (GlobalMIT, TSNI and NIR). Furthermore, the dynamic gene regulatory network-based inference schema can be extended to other complex diseases for discovery of disease associated-genes.
Exploiting dynamic timing margins in microprocessors for frequency-over-scaling with instruction-based clock adjustment

Static timing analysis provides the basis for setting the clock period of a microprocessor core, based on its worst-case critical path. However, depending on the design, this critical path is not always excited and therefore dynamic timing margins exist that can theoretically be exploited for the benefit of better speed or lower power consumption (through voltage scaling). This paper introduces predictive instruction-based dynamic clock adjustment as a technique to trim dynamic timing margins in pipelined microprocessors. To this end, we exploit the different timing requirements for individual instructions during the dynamically varying program execution flow without the need for complex circuit-level measures to detect and correct timing violations. We provide a design flow to extract the dynamic timing information for the design using post-layout dynamic timing analysis and we integrate the results into a custom cycle-accurate simulator. This simulator allows annotation of individual instructions with their impact on timing (in each pipeline stage) and rapidly derives the overall code execution time for complex benchmarks. The design methodology is illustrated at the microarchitecture level, demonstrating the performance and power gains possible on a 6-stage OpenRISC in-order general purpose processor core in a 28nm CMOS technology. We show that employing instruction-dependent dynamic clock adjustment leads on average to an increase in operating speed by 38% or to a reduction in power consumption by 24%, compared to traditional synchronous clocking, which at all times has to respect the worst-case timing identified through static timing analysis.
Antibodies to interleukin 12 abrogate established experimental colitis in mice

In this study, we describe a novel murine model of chronic intestinal inflammation induced by the hapten reagent 2,4,6-trinitrobenzene sulfonic acid (TNBS). Rectal application of low doses of TNBS in BALB/c and SJL/J mice resulted in a chronic transmural colitis with severe diarrhea, weight loss, and rectal prolapse, an illness that mimics some characteristics of Crohn's disease in humans. The colon of TNBS-treated mice on day 7 was marked by infiltration of CD4+ T cells; furthermore, in situ polymerase chain reaction studies revealed high levels of interferon (IFN)-gamma mRNA in diseased colons. Isolated lamina propria (LP) CD4+ T cells from TNBS-treated mice stimulated with anti-CD3 and anti-CD28 antibodies exhibited a Th1 pattern of cytokine secretion: a 20-50-fold increase in IL-2 and IFN-gamma levels and a 5-fold decrease in IL-4 levels as compared with those of stimulated LP CD4+ T cells from control BALB/c mice. Administration of monoclonal anti-IL-12 antibodies to the TNBS-treated mice both early (at 5 d) and late (at 20 d) after induction of colitis led to a striking improvement in both the clinical and histopathological aspects of the disease and frequently abrogated the established colitis completely. Furthermore, LP CD4+ T cells isolated from anti-IL-12-treated mice failed to secrete IFN-gamma upon in vitro stimulation. In summary, the data demonstrate the pivotal role of IL-12 and IFN-gamma in a TNBS-induced murine model of chronic intestinal inflammation. Furthermore, they suggest the potential utility of anti-IL-12 antibodies in patients with Crohn's disease.
Towards tuberculosis elimination: an action framework for low-incidence countries

This paper describes an action framework for countries with low tuberculosis (TB) incidence (<100 TB cases per million population) that are striving for TB elimination. The framework sets out priority interventions required for these countries to progress first towards “pre-elimination” (<10 cases per million) and eventually the elimination of TB as a public health problem (less than one case per million). TB epidemiology in most low-incidence countries is characterised by a low rate of transmission in the general population, occasional outbreaks, a majority of TB cases generated from progression of latent TB infection (LTBI) rather than local transmission, concentration to certain vulnerable and hard-to-reach risk groups, and challenges posed by cross-border migration. Common health system challenges are that political commitment, funding, clinical expertise and general awareness of TB diminishes as TB incidence falls. The framework presents a tailored response to these challenges, grouped into eight priority action areas: 1) ensure political commitment, funding and stewardship for planning and essential services; 2) address the most vulnerable and hard-to-reach groups; 3) address special needs of migrants and cross-border issues; 4) undertake screening for active TB and LTBI in TB contacts and selected high-risk groups, and provide appropriate treatment; 5) optimise the prevention and care of drug-resistant TB; 6) ensure continued surveillance, programme monitoring and evaluation and case-based data management; 7) invest in research and new tools; and 8) support global TB prevention, care and control. The overall approach needs to be multisectorial, focusing on equitable access to high-quality diagnosis and care, and on addressing the social determinants of TB. Because of increasing globalisation and population mobility, the response needs to have both national and global dimensions. Action framework for countries with low tuberculosis incidence: a coherent approach for eliminating tuberculosis http://ow.ly/H03ZZ
Unlocked capacity of proteins to attack membranes characteristic of aggregation: the evil for diseases and aging from Pandora’s box

Abstract Aggregation of specific proteins is characteristic of a large spectrum of human diseases including all neurodegenerative diseases, while aggregation of non-specific proteins has been now identified to be a biomarker for cellular aging down to Escherichia coli. Previously, as facilitated with our discovery in 2005 that “completely insoluble” proteins could be all solubilized in unsalted water [Song (2009) FEBS Lett. 583: 953], we found that the TDP-43 prion-like domain in fact contains an intrinsic membrane-interacting subdomain [Lim et al. [2016] PLoS Biol. 14, e1002338]. We decrypted that ALS-causing mutations/cofactor-depletion act to render the wellstructured folds of cytosolic VAPB-MSP domain and SOD1 into highly disordered states, thus becoming buffer-insoluble. Most surprisingly, this also unlocks the amphiphilic/hydrophobic regions universally exiting in proteins, which thus acquire a novel capacity in abnormally interacting with membranes [Qin et al. (2013) F1000Res 2-221.v2; Lim (2016) BBA-Biomembranes. 1858: 2223]. Here we aimed extend our discovery to address two fundamental questions: 1) why many E. coli proteins become aggregated in aging; and 2) whether aggregation-prone proteins can also acquire a novel capacity in interacting with membranes; by dissecting the 557-residue S1 ribosomal protein into 7 fragments to disrupt its 6 S1 folds, followed by extensive CD and NMR characterizations. The results reveal that we have successfully eliminated all 6 S1 folds and fragment 4 becomes highly disordered and thus buffer-insoluble. Most strikingly, F4 does acquire a capacity in transforming into a helical conformation in membrane environments. Here, for the first time, our study deciphers that like ALScausing mutants, the disruption of a well-folded E. coli cytosolic protein also unlocks its amphiphilic/hydrophobic regions which are capable of abnormally interacting with membranes. Therefore, proteins, the most important functional players for all forms of life, can transform into membrane-toxic forms triggering diseases and aging, if their hydrophobic/amphiphilic regions are unlocked by genetic, pathological or/and environmental factors, which is characteristic of severe aggregation.
The consistency of superior face recognition skills in police officers

In recent years, there has been increasing interest in people with superior face recognition skills. Yet identification of these individuals has mostly relied on criterion performance on a single attempt at a single measure of face memory. The current investigation aimed to examine the consistency of superior face recognition skills in 30 police officers, both across tests that tap into the same process and between tests that tap into different components of face processing. Overall indices of performance across related measures were found to identify different superior performers to isolated test scores. Further, different top performers emerged for target‐present versus target‐absent indices, suggesting that signal detection measures are the most useful indicators of performance. Finally, a dissociation was observed between superior memory and matching performance. Super‐recognizer screening programmes should therefore include overall indices summarizing multiple attempts at related tests, allowing for individuals to rank highly on different (and sometimes very specific) tasks.
Isolation and Characterization of Cellulose-decomposing Bacteria Inhabiting Sawdust and Coffee Residue Composts

Clarifying the identity and enzymatic activities of microorganisms associated with the decomposition of organic materials is expected to contribute to the evaluation and improvement of composting processes. In this study, we examined the cellulolytic and hemicellulolytic abilities of bacteria isolated from sawdust compost (SDC) and coffee residue compost (CRC). Cellulolytic bacteria were isolated using Dubos mineral salt agar containing azurine cross-linked (AZCL) HE-cellulose. Bacterial identification was performed based on the sequence analysis of 16S rRNA genes, and cellulase, xylanase, β-glucanase, mannanase, and protease activities were characterized using insoluble AZCL-linked substrates. Eleven isolates were obtained from SDC and 10 isolates from CRC. DNA analysis indicated that the isolates from SDC and CRC belonged to the genera Streptomyces, Microbispora, and Paenibacillus, and the genera Streptomyces, Microbispora, and Cohnella, respectively. Microbispora was the most dominant genus in both compost types. All isolates, with the exception of two isolates lacking mannanase activity, showed cellulase, xylanase, β-glucanase, and mannanase activities. Based on enzyme activities expressed as the ratio of hydrolysis zone diameter to colony diameter, it was suggested that the species of Microbispora (SDCB8, SDCB9) and Paenibacillus (SDCB10, SDCB11) in SDC and Microbispora (CRCB2, CRCB6) and Cohnella (CRCB9, CRCB10) in CRC contribute to efficient cellulolytic and hemicellulolytic processes during composting.
Genome Sequencing of the Phytoseiid Predatory Mite Metaseiulus occidentalis Reveals Completely Atomized Hox Genes and Superdynamic Intron Evolution

Metaseiulus occidentalis is an eyeless phytoseiid predatory mite employed for the biological control of agricultural pests including spider mites. Despite appearances, these predator and prey mites are separated by some 400 Myr of evolution and radically different lifestyles. We present a 152-Mb draft assembly of the M. occidentalis genome: Larger than that of its favored prey, Tetranychus urticae, but considerably smaller than those of many other chelicerates, enabling an extremely contiguous and complete assembly to be built—the best arachnid to date. Aided by transcriptome data, genome annotation cataloged 18,338 protein-coding genes and identified large numbers of Helitron transposable elements. Comparisons with other arthropods revealed a particularly dynamic and turbulent genomic evolutionary history. Its genes exhibit elevated molecular evolution, with strikingly high numbers of intron gains and losses, in stark contrast to the deer tick Ixodes scapularis. Uniquely among examined arthropods, this predatory mite’s Hox genes are completely atomized, dispersed across the genome, and it encodes five copies of the normally single-copy RNA processing Dicer-2 gene. Examining gene families linked to characteristic biological traits of this tiny predator provides initial insights into processes of sex determination, development, immune defense, and how it detects, disables, and digests its prey. As the first reference genome for the Phytoseiidae, and for any species with the rare sex determination system of parahaploidy, the genome of the western orchard predatory mite improves genomic sampling of chelicerates and provides invaluable new resources for functional genomic analyses of this family of agriculturally important mites.
On the number of candidates in opportunistic routing for multi-hop wireless networks

Opportunistic Routing (OR) is a new paradigm that has been investigated as a new way to improve the performance of multihop wireless networks by exploiting the broadcast nature of the wireless medium. In contrast to traditional routing, in OR an ordered set of nodes is selected as potential next-hop forwarders (candidates). Using more number of candidates in OR decreases the number of transmissions in the network, but this comes at the cost of increasing the signaling overhead and also the possibility of having duplicated transmissions which in turn reduces the performance of the OR protocol. The number of candidates that each node can select is an issue which is not well investigated in the literature. In this paper, we propose a Distance-based MAximum number of Candidate Estimation (D-MACE) as an approach to find the number of candidates in each node. In contrast to the traditional approaches in OR which consider an identical number of candidates for all nodes, D-MACE reduces the number of candidates in each node according to the distance between the node and the destination. We evaluate the performance of our proposal, using two relevant candidate selection algorithms. Our results show that D-MACE reduces the number of selected candidates effectively in the network, which improves the network performance compared to the case with the same number of candidates in all nodes.