Miracle-dz/UniFAR-FTUs huggingface dataset:
ProPOSEL: a human-oriented prosody and PoS English lexicon for machine-learning and NLP

ProPOSEL is a prosody and PoS English lexicon, purpose-built to integrate and leverage domain knowledge from several well-established lexical resources for machine learning and NLP applications. The lexicon of 104049 separate entries is in accessible text file format, is human and machine-readable, and is intended for open source distribution with the Natural Language ToolKit. It is therefore supported by Python software tools which transform ProPOSEL into a Python dictionary or associative array of linguistic concepts mapped to compound lookup keys. Users can also conduct searches on a subset of the lexicon and access entries by word class, phonetic transcription, syllable count and lexical stress pattern. ProPOSEL caters for a range of different cognitive aspects of the lexicon©.
INK-SVD: Learning incoherent dictionaries for sparse representations

This work considers the problem of learning an incoherent dictionary that is both adapted to a set of training data and incoherent so that existing sparse approximation algorithms can recover the sparsest representation. A new decorrelation method is presented that computes a fixed coherence dictionary close to a given dictionary. That step iterates pairwise decorrelations of atoms in the dictionary. Dictionary learning is then performed by adding this decorrelation method as an intermediate step in the K-SVD learning algorithm. The proposed algorithm INK-SVD is tested on musical data and compared to another existing decorrelation method. INK-SVD can compute a dictionary that approximates the training data as well as K-SVD while decreasing the coherence from 0.6 to 0.2.
Improved survival after variceal bleeding in patients with cirrhosis over the past two decades

Over the past two decades, new treatment modalities have been introduced for the management of variceal bleeding. The aim of this retrospective study in a single center was to assess whether these treatments have improved the prognosis for cirrhotic patients with variceal bleeding. We reviewed the clinical records of all patients with cirrhosis admitted to our Liver Intensive Care Unit due to variceal bleeding during the years 1980, 1985, 1990, 1995, and 2000. Whereas balloon tamponade was still the first‐line treatment in 1980, patients treated in 2000 received a vasoactive agent, an endoscopic treatment, and an antibiotic prophylaxis in, respectively, 90%, 100%, and 94% of cases. The in‐hospital mortality rate steadily decreased over the study period: 42.6%, 29.9%, 25%, 16.2%, and 14.5% in 1980, 1985, 1990, 1995, and 2000, respectively (P < .05). Mortality decreased from 9% in 1980 to 0% in 2000 in Child‐Turcotte‐Pugh class A patients, from 46% to 0% in class B patients, and from 70% to 32% in class C patients. This improved survival was associated with a decrease of rebleeding (from 47% in 1980 to 13% in 2000) and bacterial infection rates (from 38% to 14%). On multivariable analysis, endoscopic therapy and antibiotic prophylaxis were independent predictors of survival. In conclusion, in‐hospital mortality of patients with cirrhosis and variceal bleeding decreased threefold over the past two decades, in concurrence with an early and combined use of pharmacological and endoscopic therapies and short‐term antibiotic prophylaxis. (HEPATOLOGY 2004;40:652–659.)
PML clastosomes prevent nuclear accumulation of mutant ataxin-7 and other polyglutamine proteins

The pathogenesis of spinocerebellar ataxia type 7 and other neurodegenerative polyglutamine (polyQ) disorders correlates with the aberrant accumulation of toxic polyQ-expanded proteins in the nucleus. Promyelocytic leukemia protein (PML) nuclear bodies are often present in polyQ aggregates, but their relation to pathogenesis is unclear. We show that expression of PML isoform IV leads to the formation of distinct nuclear bodies enriched in components of the ubiquitin-proteasome system. These bodies recruit soluble mutant ataxin-7 and promote its degradation by proteasome-dependent proteolysis, thus preventing the aggregate formation. Inversely, disruption of the endogenous nuclear bodies with cadmium increases the nuclear accumulation and aggregation of mutant ataxin-7, demonstrating their role in ataxin-7 turnover. Interestingly, β-interferon treatment, which induces the expression of endogenous PML IV, prevents the accumulation of transiently expressed mutant ataxin-7 without affecting the level of the endogenous wild-type protein. Therefore, clastosomes represent a potential therapeutic target for preventing polyQ disorders.
Improving Statistical Natural Language Translation with Categories and Rules

This paper describes an all level approach on statistical natural language translation (SNLT). Without any predefined knowledge the system learns a statistical translation lexicon (STL), word classes (WCs) and translation rules (TRs) from a parallel corpus thereby producing a generalized form of a word alignment (WA). The translation process itself is realized as a beam search. In our method example-based techniques enter an overall statistical approach leading to about 50 percent correctly translated sentences applied to the very difficult English-German VERBMOBIL spontaneous speech corpus.
Bayesian pattern ranking for move prediction in the game of Go

We investigate the problem of learning to predict moves in the board game of Go from game records of expert players. In particular, we obtain a probability distribution over legal moves for professional play in a given position. This distribution has numerous applications in computer Go, including serving as an efficient stand-alone Go player. It would also be effective as a move selector and move sorter for game tree search and as a training tool for Go players. Our method has two major components: a) a pattern extraction scheme for efficiently harvesting patterns of given size and shape from expert game records and b) a Bayesian learning algorithm (in two variants) that learns a distribution over the values of a move given a board position based on the local pattern context. The system is trained on 181,000 expert games and shows excellent prediction performance as indicated by its ability to perfectly predict the moves made by professional Go players in 34% of test positions.
Solving Difficult HTM Problems Without Difficult Hardware

There are several classes of operations, including I/O and memory allocation, that are considered difficult to perform as part of a transaction. To allow such operations inside of transactions, previous hardware transactional memory systems have proposed additional mechanisms such as opennested transactions that use hardware management of software handlers. Open-nested transactions are not necessary, and add significant complexity to both HTM systems and the software written to take advantage of them. MetaTM is an HTM system designed to run TxLinux, an operating system that uses transactions for some synchronization. Inside the operating system, it is necessary to efficiently handle I/O and memory allocation. MetaTM and TxLinux handle both of these without requiring the significant extra hardware or overhead associated with open-nested transactions. The TxLinux kernel uses cooperative transactional spinlocks which provide the concurrency of transactions with the mutual exclusion needed to perform I/O. Through explicit management of transactional system calls, TxLinux ensures strong atomicity for system calls within a transaction, providing user-level transactions with a powerful and simple transactional programming model.
The Role of p53 in Marijuana Smoke Condensates-induced Genotoxicity and Apoptosis

Objectives Marijuana is one of the most frequently abused drug in Korea and its adverse health effects are controversial. p53 is known to be crucial in regulating the DNA damage responses, and adverse effects can occur when it is regulated by marijuana smoke. We evaluated a role of p53 on genotoxic effect and apoptosis in lung cancer cells exposed to marijuana smoke condensates (MSCs). Methods The p53-related genotoxicity and apoptosis of MSCs were evaluated using in vitro bioassay, viz., comet assay, cytokinesis-block micronucleus assay and apoptosis assay. We used two cell lines with differential p53 expression (p53-wildtype (WT) H460 and p53-null H1299). Results MSCs significantly increased DNA breakages and chromosomal changes in p53-WT H460 and p53-null H1299 cells. The genotoxicity induced by MSCs in p53-null H1299 cells showed greater sensitivity than p53-WT H460 cells. Moreover, MSCs showed a significant increase in reactive oxygen species production and apoptosis. The apoptotic responses induced by MSCs were higher in p53-WT H460 cells than in p53-null H1299 cells. Significantly increased mRNA expression or apoptosis related genes, including p53, caspase-3, and Bax/Bcl-2 ratio were observed in the p53-WT H460 cells exposed to MSCs. Conclusions These results suggest that MSCs induce DNA/chromosomal damages and apoptosis in human lung cancer cells and p53 plays an important role in the cellular response to MSCs. The present study may have border implications for our understanding of pulmonary diseases.
ChainReaction: a causal+ consistent datastore based on chain replication

This paper proposes a Geo-distributed key-value datastore, named ChainReaction, that offers causal+ consistency, with high performance, fault-tolerance, and scalability. ChainReaction enforces causal+ consistency which is stronger than eventual consistency by leveraging on a new variant of chain replication. We have experimentally evaluated the benefits of our approach by running the Yahoo! Cloud Serving Benchmark. Experimental results show that ChainReaction has better performance in read intensive workloads while offering competitive performance for other workloads. Also we show that our solution requires less metadata when compared with previous work.
Improving the Performance of Neural Machine Translation Involving Morphologically Rich Languages

The advent of the attention mechanism in neural machine translation models has improved the performance of machine translation systems by enabling selective lookup into the source sentence. In this paper, the efficiencies of translation using bidirectional encoder attention decoder models were studied with respect to translation involving morphologically rich languages. The English - Tamil language pair was selected for this analysis. First, the use of Word2Vec embedding for both the English and Tamil words improved the translation results by 0.73 BLEU points over the baseline RNNSearch model with 4.84 BLEU score. The use of morphological segmentation before word vectorization to split the morphologically rich Tamil words into their respective morphemes before the translation, caused a reduction in the target vocabulary size by a factor of 8. Also, this model (RNNMorph) improved the performance of neural machine translation by 7.05 BLEU points over the RNNSearch model used over the same corpus. Since the BLEU evaluation of the RNNMorph model might be unreliable due to an increase in the number of matching tokens per sentence, the performances of the translations were also compared by means of human evaluation metrics of adequacy, fluency and relative ranking. Further, the use of morphological segmentation also improved the efficacy of the attention mechanism.
Hand-Held texting is less distracting than texting with the phone in a holder: anyway, don't do it

We studied the effects of texting while driving and the effects of mobile phone position (hand-held, holder) on drivers' lane-keeping performance, experienced workload, and in-car glance durations in a motion-platform driving simulator with 24 participants. Overall, we found the known negative effects of texting on lane-keeping performance, workload, and visual attention on road, suggesting that texting on the road in any manner is not risk-free. As a novel finding, we found that hand-held texting led to fewer lane-keeping errors and shorter total glance times off road compared to texting with the phone in a holder. We suggest the explanation is that the drivers had considerably more experience on texting hand-held than texting with the phone in a holder. In addition, the instability caused by the movements of the simulator was presumably easier to control while the phone was in hand compared to the holder. Surprisingly, there was a significant inverse correlation between the distance of the phone from the driving scene and total in-car glance duration as well as the number of in-car glances. The finding suggests that the participants made more inefficient but brief in-car glances towards the phone when the phone was closer to the driving scene than farther apart. The findings should be considered when planning legislation and designing novel in-car touch screen based interaction methods to replace interactions with mobile phones while driving.
Discrete Nondeterministic Modeling of the Fas Pathway

Computer modeling of molecular signaling cascades can provide useful insight into the underlying complexities of biological systems. We present a refined approach for the discrete modeling of protein interactions within the environment of a single cell. The technique we offer utilizes the Membrane Systems paradigm which, due to its hierarchical structure, lends itself readily to mimic the behavior of cells. Since our approach is nondeterministic and discrete, it provides an interesting contrast to the standard deterministic ordinary differential equations techniques. We argue that our approach may outperform ordinary differential equations when modeling systems with relatively low numbers of molecules – a frequent occurrence in cellular signaling cascades. Refinements over our previous modeling efforts include the addition of nondeterminism for handling reaction competition over limited reactants, increased efficiency in the storing and sorting of reaction waiting times, and modifications of the model reactions. Results of our discrete simulation of the type I and type II Fas-mediated apoptotic signaling cascade are illustrated and compared with two approaches: one based on ordinary differential equations and another based on the well-known Gillespie algorithm.
Targeted delivery of NRASQ61R and Cre‐recombinase to post‐natal melanocytes induces melanoma in Ink4a/Arflox/lox mice

We have developed a somatic cell gene delivery mouse model of melanoma that allows for the rapid validation of genetic alterations identified in this disease. A major advantage of this system is the ability to model the multi‐step process of carcinogenesis in immune‐competent mice without the generation and cross breeding of multiple strains. We have used this model to evaluate the role of RAS isoforms in melanoma initiation in the context of conditional Ink4a/Arf loss. Mice expressing the tumor virus A (TVA) receptor specifically in melanocytes under control of the dopachrome tautomerase (DCT) promoter were crossed to Ink4a/Arflox/lox mice and newborn DCT‐TVA/Ink4a/Arflox/lox mice were injected with retroviruses containing activated KRAS, NRAS and/or Cre‐recombinase. No mice injected with viruses containing KRAS and Cre or NRAS alone developed tumors; however, more than one‐third of DCT‐TVA/Ink4a/Arflox/lox mice injected with NRAS and Cre viruses developed melanoma and two‐thirds developed melanoma when NRAS and Cre expression was linked.
Moving to dynamic computational lexicons with LeXFlow

In this paper we present LeXFlow, a web application framework where lexicons already expressed in standardised format semi-automatically interact by reciprocally enriching themselves. LeXFlow is intended for, on the one hand, paving the way to the development of dynamic multi-source lexicons; and on the other, for fostering the adoption of standards. Borrowing from techniques used in the domain of document workflows, we model the activity of lexicon management as a particular case of workflow instance, where lexical entries move across agents and become dynamically updated. To this end, we have designed a lexical flow (LF) corresponding to the scenario where an entry of a lexicon A becomes enriched via basically two steps. First, by virtue of being mapped onto a corresponding entry belonging to a lexicon B, the entry(LA) inherits the semantic relations available in lexicon B. Second, by resorting to an automatic application that acquires information about semantic relations from corpora, the relations acquired are integrated into the entry and proposed to the human encoder. As a result of the lexical flow, in addition, for each starting lexical entry(LA) mapped onto a corresponding entry(LB) the flow produces a new entry representing the merging of the original two.
A Transgenic Marker for Newly Born Granule Cells in Dentate Gyrus

Neurogenesis in the dentate gyrus continues into adulthood, yet little is known about the function of newly born neurons or how they integrate into an existing network of mature neurons. We made transgenic mice that selectively and transiently express enhanced green fluorescent protein (EGFP) in newly born granule cells of the dentate gyrus under the transcriptional control of proopiomelanocortin (POMC) genomic sequences. Analysis of transgenic pedigrees with truncation or deletion mutations indicated that EGFP expression in the dentate gyrus required cryptic POMC promoter regions dispensable for arcuate hypothalamic or pituitary expression. Unlike arcuate neurons, dentate granule cells did not express the endogenous POMC gene. EGFP-positive neurons had immature properties, including short spineless dendrites and small action potentials. Colocalization with bromodeoxyuridine indicated that EGFP-labeled granule cells were ∼2 weeks postmitotic. EGFP-labeled cells expressed markers for immature granule cells but not the glial marker GFAP. The number of EGFP-labeled neurons declined with age and increased with exercise, paralleling neurogenesis. Our results indicate that POMC-EGFP marks immature granule cells and that adult-generated granule cells integrate quite slowly into the hippocampal circuitry.
Cytogenetic effects from exposure to mixed pesticides and the influence from genetic susceptibility.

Exposure to pesticides remains a major environmental health problem. Health risk from such exposure needs to be more precisely understood. We conducted three different cytogenetic assays to elucidate the biological effects of exposure to mixed pesticides in 20 Costa Rica farmers (all nonsmokers) compared with 20 matched controls. The farmers were also exposed to dibromochloropropane during the early employment years, and most of them experienced sterility/fertility problems. Our data show that the farmers had consistently higher frequencies of chromosome aberrations, as determined by the standard chromosome aberration assay, and significantly abnormal DNA repair responses (p < 0.05), as determined by the challenge assay, but no statistically significant differences in the tandem-probe fluorescence in situ hybridization (FISH) assay (p > 0.05). Genotype analysis indicates that farmers with certain "unfavorable" versions of polymorphic metabolizing genes (cytochrome P4502E1, the glutathione S-transferases mu and theta, and the paraoxonase genes) had significantly more biological effects, as determined by all three cytogenetic assays, than both the farmers with the "favorable" alleles and the matched controls. A unique observation is that, in individuals who had inherited any of the mentioned "unfavorable" alleles, farmers were consistently underrepresented. In conclusion, the Costa Rican farmers were exposed to genotoxic agents, most likely pesticides, which expressed the induction of biological and adverse health effects. The farmers who had inherited "unfavorable" metabolizing alleles were more susceptible to genotoxic effects than those with "favorable" alleles. Our genotype data suggest that the well-recognized "healthy worker effect" may be influenced by unrecognized occupational selection pressure against genetically susceptible individuals. ImagesFigure 1
Mitigating DDoS attacks using data mining and density-based geographical clustering

DDoS attacks have for the last two decades been among the greatest threats facing the internet infrastructure. Mitigating DDoS attacks is a particularly challenging task. It is known that ordinary signature based detection techniques are inefficient in undermining DDoS attacks as this type of attacks has the ability to mask itself among legitimate traffic. In this thesis, we present an paradigm for countering DDoS attacks at the targeted victim by using elements from data mining and machine learning. Two novel methods that focus on identifying hidden data structures in historical traffic are proposed, to differentiate legitimate traffic from abnormal traffic. In the first method, we resort to data mining techniques to find association rules which are able to describe the part of traffic that has higher likelihood of re-occurring. As a data structure for storing those data driven rules, we employ a binary tree structure. The second method builds on previously uncharted areas within mitigation techniques, where clustering techniques are used to create geographical clusters. In order to summarize the clustering information for real-life traffic filtering scenarios, we use the concept of bloom filters. The results show that these mitigation approaches improve the ability to separate between unknown abnormalities in the dataset and the legitimate traffic structure. Our proposed DDoS filtering schemes are able to mitigate 99% of the botnet traffic and thus countering significantly the magnitude of those attacks.
The transcription factor ST18 regulates proapoptotic and proinflammatory gene expression in fibroblasts

Suppression of tumorigenicity 18 (ST18) and the homologues neural zinc‐finger protein‐3 (NZF3) and myelin transcription factor 3 (Myt3) are transcription factors with unknown function. Previous studies have established that they repress transcription of a synthetic reporter construct consisting of the consensus sequence AAAGTTT linked to the thymidine kinase promoter. In addition, ST18 exhibits significantly reduced expression in breast cancer and breast cancer cell lines. We report here for the first time evidence that ST18 mediates tumor necrosis factor (TNF)‐α induced mRNA levels of proapoptotic and proinflammatory genes in fibroblasts by mRNA profiling and silencing with ST18 small interfering RNA (siRNA). Gene set enrichment analysis and mRNA profiling support this conclusion by identifying several apoptotic and inflammatory pathways that are downregulated by ST18 siRNA. In addition, ST18 siRNA reduces TNF‐induced fibroblast apoptosis and caspase3/7 activity. Fibroblasts that overexpress ST18 by transient transfection exhibit significantly increased apoptosis and increased expression of TNF‐α, interleukin (IL)‐1α, and IL‐6. In addition, cotransfection of ST18 and a TNF‐α or IL‐1α reporter construct demonstrates that ST18 overexpression in fibroblasts significantly enhanced promoter activity of these genes. Taken together, these studies demonstrate that the transcription factor ST18/NZF3 regulates the mRNA levels of proapoptotic and proinflammatory genes in revealing a previously unrecognized function. — Yang, J., Siqueira, M. F. , Behl, Y. , Alikhani, M. , and Graves, D. T. The transcription factor ST18 regulates proapoptotic and proinflammatory gene expression in fibroblasts. FASEB J. 22, 3956–3967 (2008)