Miracle-dz/UniFAR-FTUs huggingface dataset:
When Learning Disturbs Memory – Temporal Profile of Retroactive Interference of Learning on Memory Formation

Introduction: Consolidation is defined as the time necessary for memory stabilization after learning. In the present study we focused on effects of interference during the first 12 consolidation minutes after learning. Participants had to learn a set of German – Japanese word pairs in an initial learning task and a different set of German – Japanese word pairs in a subsequent interference task. The interference task started in different experimental conditions at different time points (0, 3, 6, and 9 min) after the learning task and was followed by subsequent cued recall tests. In a control experiment the interference periods were replaced by rest periods without any interference. Results: The interference task decreased memory performance by up to 20%, with negative effects at all interference time points and large variability between participants concerning both the time point and the size of maximal interference. Further, fast learners seem to be more affected by interference than slow learners. Discussion: Our results indicate that the first 12 min after learning are highly important for memory consolidation, without a general pattern concerning the precise time point of maximal interference across individuals. This finding raises doubts about the generalized learning recipes and calls for individuality of learning schedules.
Adjuvant chemotherapy versus chemoradiotherapy in the management of patients with surgically resected duodenal adenocarcinoma: A propensity score‐matched analysis of a nationwide clinical oncology database

To the authors' knowledge, optimal adjuvant approaches for resected duodenal adenocarcinoma are not well established. Given the significant risk of locoregional disease recurrence, there may be a subset of patients who demonstrate an improvement in overall survival (OS) from the addition of radiotherapy (chemoradiotherapy [CRT]) to an adjuvant chemotherapy regimen.
Spatial Modulation Assisted Multi-Antenna Non-Orthogonal Multiple Access

Multi-antenna non-orthogonal multiple access (NOMA) is a promising technique to significantly improve spectral efficiency and support massive access, which has received considerable interest from academia and industry. This article first briefly introduces the basic idea of conventional multi-antenna NOMA technique, and then discusses the key limitations, namely, the high complexity of successive interference cancellation and the lack of fairness between a user with a strong channel gain and a user with a weak channel gain. To address these problems, this article proposes a novel spatial modulation assisted multi-antenna NOMA technique, which avoids the use of successive interference cancellation and is able to completely cancel intra-cluster interference. Furthermore, simulation results are provided to validate the effectiveness of the proposed novel technique compared to the conventional multi-antenna NOMA. Finally, this article points out the key challenges and sheds light on the future research directions of the spatial modulation assisted multi-antenna NOMA technique.
Surface plasmon resonance for characterization of large-area atomic-layer graphene film

Characterization of large-area thin films with atomic-scale resolution is challenging but in great demand for diverse applications (e.g., nanotechnology and sensing). Here, we use the Surface Plasmon Resonance (SPR) method to characterize both the thickness and refractive index of chemical-vapor-deposition (CVD)-grown graphene films. The measured refractive index and extinction coefficient values of the CVD-grown graphene monolayer at 670 nm wavelength are 3.135 and 0.897, respectively. Our results demonstrate that SPR shifts generated by graphene films are large (i.e., ∼1°/nm), almost tenfold larger than that observed in SPR measurements of organic monolayers. We find that this significantly large SPR shift easily enables the thickness of a large area sample (i.e., ∼mm2) to be determined with subnanometer-scale resolution. We show that the SPR method can identify thickness of different graphene layers and give an estimate of ∼0.37 nm for the thickness of the CVD-grown graphene layer, which agrees extremely well with the 0.335 nm reported for layer-to-layer carbon atom distance of graphite crystals. Our results open the avenue to fast and cost-effective simultaneous characterization of various parameters (including thickness and optical constants) of thin films at the atomic-scale resolution. The presented characterization method can be applied both to physical characterizations of various two-dimensional layered materials as well as to the use of these layered materials for biosensing applications as shown earlier, due to the favorable properties of graphene plasmons.
To Phrase or Not to Phrase – Impact of User versus System Term Dependence upon Retrieval

Abstract When submitting queries to information retrieval (IR) systems, users often have the option of specifying which, if any, of the query terms are heavily dependent on each other and should be treated as a fixed phrase, for instance by placing them between quotes.In addition to such cases where users specify term dependence, automatic ways also exist for IR systems to detect dependent terms in queries. Most IR systems use both user and algorithmic approaches. It is not however clear whether and to what extent user-defined term dependence agrees with algorithmic estimates of term dependence, nor which of the two may fetch higher performance gains. Simply put, is it better to trust users or the system to detect term dependence in queries? To answer this question, we experiment with 101 crowdsourced search engine users and 334 queries (52 train and 282 test TREC queries) and we record 10 assessments per query. We find that (i) user assessments of term dependence differ significantly from algorithmic assessments of term dependence (their overlap is approximately 30%); (ii) there is little agreement among users about term dependence in queries, and this disagreement increases as queries become longer; (iii) the potential retrieval gain that can be fetched by treating term dependence (both user- and system-defined) over a bag of words baseline is reserved to a small subset (approximately 8%) of the queries, and is much higher for low-depth than deep precision measures. Points (ii) and (iii) constitute novel insights into term dependence.
Equity and Geography: The Case of Child Mortality in Papua New Guinea

Background Recent assessments show continued decline in child mortality in Papua New Guinea (PNG), yet complete subnational analyses remain rare. This study aims to estimate under-five mortality in PNG at national and subnational levels to examine the importance of geographical inequities in health outcomes and track progress towards Millennium Development Goal (MDG) 4. Methodology We performed retrospective data validation of the Demographic and Health Survey (DHS) 2006 using 2000 Census data, then applied advanced indirect methods to estimate under-five mortality rates between 1976 and 2000. Findings The DHS 2006 was found to be unreliable. Hence we used the 2000 Census to estimate under-five mortality rates at national and subnational levels. During the period under study, PNG experienced a slow reduction in national under-five mortality from approximately 103 to 78 deaths per 1,000 live births. Subnational analyses revealed significant disparities between rural and urban populations as well as inter- and intra-regional variations. Some of the provinces that performed the best (worst) in terms of under-five mortality included the districts that performed worst (best), with district-level under-five mortality rates correlating strongly with poverty levels and access to services. Conclusions The evidence from PNG demonstrates substantial within-province heterogeneity, suggesting that under-five mortality needs to be addressed at subnational levels. This is especially relevant in countries, like PNG, where responsibility for health services is devolved to provinces and districts. This study presents the first comprehensive estimates of under-five mortality at the district level for PNG. The results demonstrate that for countries that rely on few data sources even greater importance must be given to the quality of future population surveys and to the exploration of alternative options of birth and death surveillance.
Emerging Technologies for Molecular Diagnosis of Sepsis

SUMMARY Rapid and accurate profiling of infection-causing pathogens remains a significant challenge in modern health care. Despite advances in molecular diagnostic techniques, blood culture analysis remains the gold standard for diagnosing sepsis. However, this method is too slow and cumbersome to significantly influence the initial management of patients. The swift initiation of precise and targeted antibiotic therapies depends on the ability of a sepsis diagnostic test to capture clinically relevant organisms along with antimicrobial resistance within 1 to 3 h. The administration of appropriate, narrow-spectrum antibiotics demands that such a test be extremely sensitive with a high negative predictive value. In addition, it should utilize small sample volumes and detect polymicrobial infections and contaminants. All of this must be accomplished with a platform that is easily integrated into the clinical workflow. In this review, we outline the limitations of routine blood culture testing and discuss how emerging sepsis technologies are converging on the characteristics of the ideal sepsis diagnostic test. We include seven molecular technologies that have been validated on clinical blood specimens or mock samples using human blood. In addition, we discuss advances in machine learning technologies that use electronic medical record data to provide contextual evaluation support for clinical decision-making.
Double Modular Redundancy (DMR) Based Fault Tolerance Technique for Combinational Circuits

Due to the continuous scaling of digital systems and the increased demand on low power devices, design of effective soft error tolerance techniques is of high importance to cope with the increased susceptibility of systems to soft errors and to enhance system reliability. In this work, we propose a double modular redundancy (DMR) technique that aims to achieve high reliability with reduced area overhead. Furthermore, we propose an improved application of DMR based on the use of C-element (DMR-CEL). The proposed technique is compared with Triple Modular Redundancy (TMR) technique and DMR-CEL. Simulations performed for LGSynth’91 benchmark circuits demonstrate that applying the proposed DMR technique achieves improved reliability with significantly lower area overhead than TMR without voter protection. Furthermore, improved reliability with lower area overhead is achieved by the proposed DMR technique in comparison to DMR-CEL without C-element protection. In addition, applying a recently proposed transistor.
Understanding the Consequences of Newcomer Integration Processes: The Sport Team Socialization Tactics Questionnaire.

The ways in which newcomers are integrated into sport teams may have broad consequences for the athletes entering the group, as well as for the existing team members. Drawing from organizational socialization theory, the current research developed a questionnaire to assess athletes' perceptions of how newcomers are socialized into their group. Across four studies, think-aloud interviews (N = 8), an expert panel review (N = 6), cross-sectional tests of the factor structure (NStudy 2 = 197; NStudy 3 = 460), and a two-wave correlational design (NStudy 4 = 194) were used to evaluate the construct validity and the internal consistency of the Sport Team Socialization Tactics Questionnaire (STSTQ). Collectively, these efforts identified a three-factor structure underlying the STSTQ and provided preliminary evidence for its validity. The STSTQ enables researchers to systematically examine the individual- and group-level consequences associated with the socialization tactics implemented in sport teams.
Compiling Process Networks to Interaction Nets

Kahn process networks are a model of computation based on a collection of sequential, deterministic processes that communicate by sending messages through unbounded channels. They are well suited for modelling stream-based computations, but are in no way restricted to this application. Interaction nets are graph rewriting systems that have many interesting properties for implementation. In this paper we show how to encode process networks using interaction nets, where we model both networks and messages in the same framework.
ADAMTS13 autoantibodies cloned from patients with acquired thrombotic thrombocytopenic purpura: 1. Structural and functional characterization in vitro

Acquired thrombotic thrombocytopenia purpura (TTP) is a life‐threatening illness caused by autoantibodies that decrease the activity of ADAMTS13, the von Willebrand factor–cleaving protease. Despite efficacy of plasma exchange, mortality remains high and relapse is common. Improved therapies may come from understanding the diversity of pathogenic autoantibodies on a molecular or genetic level. Cloning comprehensive repertoires of patient autoantibodies can provide the necessary tools for studying immunobiology of disease and developing animal models.
NeMoFinder: dissecting genome-wide protein-protein interactions with meso-scale network motifs

Recent works in network analysis have revealed the existence of network motifs in biological networks such as the protein-protein interaction (PPI) networks. However, existing motif mining algorithms are not sufficiently scalable to find meso-scale network motifs. Also, there has been little or no work to systematically exploit the extracted network motifs for dissecting the vast interactomes.We describe an efficient network motif discovery algorithm, NeMoFinder, that can mine meso-scale network motifs that are repeated and unique in large PPI networks. Using NeMoFinder, we successfully discovered, for the first time, up to size-12 network motifs in a large whole-genome S. cerevisiae (Yeast) PPI network. We also show that such network motifs can be systematically exploited for indexing the reliability of PPI data that were generated via highly erroneous high-throughput experimental methods.
A simple yet efficient algorithm to turn one oriented triangular mesh connectivity into another

This paper addresses the problem of efficiently turning the connectivity of an initial triangular mesh into a new one, using combinatorial edge flip operations only, excluding vertex insertion and deletion. More precisely, we provide a practical but provably good solution for determining a sequence of edge flips between two surface triangulations sharing the same topological genus and the same number of vertices, with a one-to-one correspondence between the vertices of the two meshes. This is original in that the sequence of edge flips can be determined without using any canonical geometric or topological configuration, classically acting as a pivot between the two triangulations. The paper identifies the main difficulties that must be overcome by this direct algorithm, putting in place the foundation for a generic strategy. An efficient instantiation of this strategy is then presented, with an algorithm that proceeds by successive conformation of the facets, through a region-growing mechanism. The effectiveness of this approach is illustrated by practical benchmarks.
Optimal Boundary Surface for Irreversible Investment with Stochastic Costs

This paper examines a Markovian model for the optimal irreversible investment problem of a firm aiming at minimizing total expected costs of production. We model market uncertainty and the cost of investment per unit of production capacity, as two independent one-dimensional regular diffusions, and we consider a general convex running cost function. The optimization problem is set as a three-dimensional degenerate singular stochastic control problem. We provide the optimal control as the solution of a reflected diffusion at a suitable boundary surface. Such boundary arises from the analysis of a family of two-dimensional parameter-dependent optimal stopping problems, and it is characterized in terms of the family of unique continuous solutions to parameter-dependent, nonlinear integral equations of Fredholm type.
Connectivity map identifies HDAC inhibition as a treatment option of high-risk hepatoblastoma

ABSTRACT Hepatoblastoma (HB) is the most common liver tumor of childhood, usually occurring in children under the age of 3 y. The prognosis of patients presenting with distant metastasis, vascular invasion and advanced tumor stages remains poor and children that do survive often face severe late effects from the aggressive chemotherapy regimen. To identify potential new therapeutics for high risk HB we used a 1,000-gene expression signature as input for a Connectivity Map (CMap) analysis, which predicted histone deacetylase (HDAC) inhibitors as a promising therapy option. Subsequent expression analysis of primary HB and HB cell lines revealed a general overexpression of HDAC1 and HDAC2, which has been suggested to be predictive for the efficacy of HDAC inhibition. Accordingly, treatment of HB cells with the HDAC inhibitors SAHA and MC1568 resulted in a potent reduction of cell viability, induction of apoptosis, reactivation of epigenetically suppressed tumor suppressor genes, and the reversion of the 16-gene HB classifier toward the more favorable expression signature. Most importantly, the combination of HDAC inhibitors and cisplatin – a major chemotherapeutic agent of HB treatment - revealed a strong synergistic effect, even at significantly reduced doses of cisplatin. Our findings suggest that HDAC inhibitors skew HB cells toward a more favorable prognostic phenotype through changes in gene expression, thus indicating a targeted molecular mechanism that seems to enhance the anti-proliferative effects of conventional chemotherapy. Thus, adding HDAC inhibitors to the treatment regimen of high risk HB could potentially improve outcomes and reduce severe late effects.
Unique and Novel Urinary Metabolomic Features in Malignant versus Benign Adrenal Neoplasms

Purpose: Adrenal incidentalomas must be differentiated from adrenocortical cancer (ACC). Currently, size, growth, and imaging characteristics determine the potential for malignancy but are imperfect. The aim was to evaluate whether urinary small molecules (<800 Da) are associated with ACC. Experimental Design: Preoperative fasting urine specimens from patients with ACC (n = 19) and benign adrenal tumors (n = 46) were analyzed by unbiased ultraperformance liquid chromatography/mass spectrometry. Creatinine-normalized features were analyzed by Progenesis, SIMCA, and unpaired t test adjusted by FDR. Features with an AUC >0.8 were identified through fragmentation patterns and database searches. All lead features were assessed in an independent set from patients with ACC (n = 11) and benign adrenal tumors (n = 46) and in a subset of tissue samples from patients with ACC (n = 15) and benign adrenal tumors (n = 15) in the training set. Results: Sixty-nine features were discovered and four known metabolites identified. Urinary creatine riboside was elevated 2.1-fold (P = 0.0001) in patients with ACC. L-tryptophan, Nϵ,Nϵ,Nϵ-trimethyl-L-lysine, and 3-methylhistidine were lower 0.33-fold (P < 0.0001), 0.56-fold (P < 0.0001), and 0.33-fold (P = 0.0003) in patients with ACC, respectively. Combined multivariate analysis of the four biomarkers showed an AUC of 0.89 [sensitivity 94.7% (confidence interval {CI}, 73.9%–99.1%), specificity 82.6% (CI, 68.6%–92.2%), PPV 69.2% (CI, 48.2%–85.6%), and NPV 97.4% (CI, 86.5%–99.6%)] for distinguishing ACC from benign tumors. Of the four, creatine riboside and four unknown features were validated. Creatine riboside, Nϵ,Nϵ,Nϵ-trimethyl-L-lysine, and two unknown features were elevated in ACC tumors. Conclusions: There are unique urinary metabolic features in patients with ACC with some metabolites present in patient tumor samples. Urinary creatine riboside can differentiate benign adrenal neoplasms from ACC. Clin Cancer Res; 23(17); 5302–10. ©2017 AACR.
Modeling Trajectories of Adolescent‐Perceived Family Conflict: Effects of Marital Dissatisfaction and Parental Alcoholism

We evaluated the effects of marital dissatisfaction on adolescent-perceived conflict in 435 families with and without a parental history of alcoholism. On average, family conflict decreased linearly as adolescents aged. Families with an alcoholic parent demonstrated higher adolescent-reported family conflict and this effect was partially mediated by higher mother- and father-reported marital dissatisfaction. Families with higher marital dissatisfaction had greater conflict when adolescents were young (based on fathers' marital dissatisfaction) and as they aged (based on mother's marital dissatisfaction). Years in which mothers reported higher marital dissatisfaction than usual coincided with years in which adolescents reported greater family conflict. Results indicate that marital dissatisfaction has both within and between-family effects on adolescent perceptions of conflict.
Active Expert Learning for the Digital Humanities

Current platforms for paper sharing among scholars, such as Research Gate, could support Active Expert Learning, whereby the paper being uploaded is processed using human language technology techniques, and feedback is asked of the scholar doing the upload using active learning techniques to minimize the amount of feedback requested. We show that this approach could outperform traditional active learning as well as randomly asking for feedback.
Q-learning with Nearest Neighbors

We consider model-free reinforcement learning for infinite-horizon discounted Markov Decision Processes (MDPs) with a continuous state space and unknown transition kernel, when only a single sample path under an arbitrary policy of the system is available. We consider the Nearest Neighbor Q-Learning (NNQL) algorithm to learn the optimal Q function using nearest neighbor regression method. As the main contribution, we provide tight finite sample analysis of the convergence rate. In particular, for MDPs with a $d$-dimensional state space and the discounted factor $\gamma \in (0,1)$, given an arbitrary sample path with "covering time" $ L $, we establish that the algorithm is guaranteed to output an $\varepsilon$-accurate estimate of the optimal Q-function using $\tilde{O}\big(L/(\varepsilon^3(1-\gamma)^7)\big)$ samples. For instance, for a well-behaved MDP, the covering time of the sample path under the purely random policy scales as $ \tilde{O}\big(1/\varepsilon^d\big),$ so the sample complexity scales as $\tilde{O}\big(1/\varepsilon^{d+3}\big). $ Indeed, we establish a lower bound that argues that the dependence of $ \tilde{\Omega}\big(1/\varepsilon^{d+2}\big)$ is necessary.
A Role for Human N-alpha Acetyltransferase 30 (Naa30) in Maintaining Mitochondrial Integrity*

N-terminal acetylation (Nt-acetylation) by N-terminal acetyltransferases (NATs) is one of the most common protein modifications in eukaryotes. The NatC complex represents one of three major NATs of which the substrate profile remains largely unexplored. Here, we defined the in vivo human NatC Nt-acetylome on a proteome-wide scale by combining knockdown of its catalytic subunit Naa30 with positional proteomics. We identified 46 human NatC substrates, expanding our current knowledge on the substrate repertoire of NatC which now includes proteins harboring Met-Leu, Met-Ile, Met-Phe, Met-Trp, Met-Val, Met-Met, Met-His and Met-Lys N termini. Upon Naa30 depletion the expression levels of several organellar proteins were found reduced, in particular mitochondrial proteins, some of which were found to be NatC substrates. Interestingly, knockdown of Naa30 induced the loss of mitochondrial membrane potential and fragmentation of mitochondria. In conclusion, NatC Nt-acetylates a large variety of proteins and is essential for mitochondrial integrity and function.