Argument mining: A machine learning perspective

Argument mining has recently become a hot topic, attracting the interests of several and diverse research communities, ranging from artificial intelligence, to computational linguistics, natural language processing, social and philosophical sciences. In this paper, we attempt to describe the problems and challenges of argument mining from a machine learning angle. In particular, we advocate that machine learning techniques so far have been under-exploited, and that a more proper standardization of the problem, also with regards to the underlying argument model, could provide a crucial element to develop better systems

The logic-bias effect: The role of effortful processing in the resolution of belief-logic conflict.

According to the default interventionist dual-process account of reasoning, belief-based responses to reasoning tasks are based on Type 1 processes generated by default, which must be inhibited in order to produce an effortful, Type 2 output based on the validity of an argument. However, recent research has indicated that reasoning on the basis of beliefs may not be as fast and automatic as this account claims. In three experiments, we presented participants with a reasoning task that was to be completed while they were generating random numbers (RNG). We used the novel methodology introduced by Handley, Newstead & Trippas (Journal of Experimental Psychology: Learning, Memory, and Cognition, 37, 28-43, 2011), which required participants to make judgments based upon either the validity of a conditional argument or the believability of its conclusion. The results showed that belief-based judgments produced lower rates of accuracy overall and were influenced to a greater extent than validity judgments by the presence of a conflict between belief and logic for both simple and complex arguments. These findings were replicated in Experiment 3, in which we controlled for switching demands in a blocked design. Across all three experiments, we found a main effect of RNG, implying that both instructional sets require some effortful processing. However, in the blocked design RNG had its greatest impact on logic judgments, suggesting that distinct executive resources may be required for each type of judgment. We discuss the implications of our findings for the default interventionist account and offer a parallel competitive model as an alternative interpretation for our findings

Fine Mapping of the Psoriasis Susceptibility Locus PSORS1 Supports HLA-C as the Susceptibility Gene in the Han Chinese Population

PSORS1 (psoriasis susceptibility gene 1) is a major susceptibility locus for psoriasis. Several fine-mapping studies have highlighted a 300-kb candidate region of PSORS1 where multiple biologically plausible candidate genes were suggested. The most recent study has indicated HLA-Cw6 as the primary PSORS1 risk allele within the candidate region in a Caucasian population. In this study, a family-based association analysis of the PSORS1 locus was performed by analyzing 10 polymorphic microsatellite markers from the PSORS1 region as well as HLA-B, HLA-C and CDSN loci in 163 Chinese families of psoriasis. Five marker loci show strong evidence (P<10−3), and one marker locus shows weak evidence (P = 0.04) for association. The haplotype cluster analysis showed that all the risk haplotypes are Cw6 positive and share a 369-kb region of homologous marker alleles which carries all the risk alleles, including HLA-Cw6 and CDSN*TTC, identified in this study. The recombinant haplotype analysis of the HLA-Cw6 and CDSN*TTC alleles in 228 Chinese families showed that the HLA-Cw6−/CDSN*TTC+ recombinant haplotype is clearly not associated with risk for psoriasis (T∶NT = 29:57, p = 0.0025) in a Chinese population, suggesting that the CDSN*TTC allele itself does not confer risk without the presence of the HLA-Cw6 allele. The further exclusion analysis of the non-risk HLA-Cw6−/CDSN*TTC+ recombinant haplotypes with common recombination breakpoints has allowed us to refine the location of PSORS1 to a small candidate region. Finally, we performed a conditional linkage analysis and showed that the HLA-Cw6 is a major risk allele but does not explain the full linkage evidence of the PSORS1 locus in a Chinese population. By performing a series of family-based association analyses of haplotypes as well as an exclusion analysis of recombinant haplotypes, we were able to refine the PSORS1 gene to a small critical region where HLA-C is a strong candidate to be the PSORS1 susceptibility gene

Autoimmune Disease Classification by Inverse Association with SNP Alleles

With multiple genome-wide association studies (GWAS) performed across autoimmune diseases, there is a great opportunity to study the homogeneity of genetic architectures across autoimmune disease. Previous approaches have been limited in the scope of their analysis and have failed to properly incorporate the direction of allele-specific disease associations for SNPs. In this work, we refine the notion of a genetic variation profile for a given disease to capture strength of association with multiple SNPs in an allele-specific fashion. We apply this method to compare genetic variation profiles of six autoimmune diseases: multiple sclerosis (MS), ankylosing spondylitis (AS), autoimmune thyroid disease (ATD), rheumatoid arthritis (RA), Crohn's disease (CD), and type 1 diabetes (T1D), as well as five non-autoimmune diseases. We quantify pair-wise relationships between these diseases and find two broad clusters of autoimmune disease where SNPs that make an individual susceptible to one class of autoimmune disease also protect from diseases in the other autoimmune class. We find that RA and AS form one such class, and MS and ATD another. We identify specific SNPs and genes with opposite risk profiles for these two classes. We furthermore explore individual SNPs that play an important role in defining similarities and differences between disease pairs. We present a novel, systematic, cross-platform approach to identify allele-specific relationships between disease pairs based on genetic variation as well as the individual SNPs which drive the relationships. While recognizing similarities between diseases might lead to identifying novel treatment options, detecting differences between diseases previously thought to be similar may point to key novel disease-specific genes and pathways

Decay in survival motor neuron and plastin 3 levels during differentiation of iPSC-derived human motor neurons

Spinal muscular atrophy (SMA) is a neuromuscular disease caused by mutations in Survival Motor Neuron 1 (SMN1), leading to degeneration of alpha motor neurons (MNs) but also affecting other cell types. Induced pluripotent stem cell (iPSC)-derived human MN models from severe SMA patients have shown relevant phenotypes. We have produced and fully characterized iPSCs from members of a discordant consanguineous family with chronic SMA. We differentiated the iPSC clones into ISL-1+/ChAT+ MNs and performed a comparative study during the differentiation process, observing significant differences in neurite length and number between family members. Analyses of samples from wild-type, severe SMA type I and the type IIIa/IV family showed a progressive decay in SMN protein levels during iPSC-MN differentiation, recapitulating previous observations in developmental studies. PLS3 underwent parallel reductions at both the transcriptional and translational levels. The underlying, progressive developmental decay in SMN and PLS3 levels may lead to the increased vulnerability of MNs in SMA disease. Measurements of SMN and PLS3 transcript and protein levels in iPSC-derived MNs show limited value as SMA biomarkers

Multiple Loci within the Major Histocompatibility Complex Confer Risk of Psoriasis

Psoriasis is a common inflammatory skin disease characterized by thickened scaly red plaques. Previously we have performed a genome-wide association study (GWAS) on psoriasis with 1,359 cases and 1,400 controls, which were genotyped for 447,249 SNPs. The most significant finding was for SNP rs12191877, which is in tight linkage disequilibrium with HLA-Cw*0602, the consensus risk allele for psoriasis. However, it is not known whether there are other psoriasis loci within the MHC in addition to HLA-C. In the present study, we searched for additional susceptibility loci within the human leukocyte antigen (HLA) region through in-depth analyses of the GWAS data; then, we followed up our findings in an independent Han Chinese 1,139 psoriasis cases and 1,132 controls. Using the phased CEPH dataset as a reference, we imputed the HLA-Cw*0602 in all samples with high accuracy. The association of the imputed HLA-Cw*0602 dosage with disease was much stronger than that of the most significantly associated SNP, rs12191877. Adjusting for HLA-Cw*0602, there were two remaining association signals: one demonstrated by rs2073048 (p = 2×10−6, OR = 0.66), located within c6orf10, a potential downstream effecter of TNF-alpha, and one indicated by rs13437088 (p = 9×10−6, OR = 1.3), located 30 kb centromeric of HLA-B and 16 kb telomeric of MICA. When HLA-Cw*0602, rs2073048, and rs13437088 were all included in a logistic regression model, each of them was significantly associated with disease (p = 3×10−47, 6×10−8, and 3×10−7, respectively). Both putative loci were also significantly associated in the Han Chinese samples after controlling for the imputed HLA-Cw*0602. A detailed analysis of HLA-B in both populations demonstrated that HLA-B*57 was associated with an increased risk of psoriasis and HLA-B*40 a decreased risk, independently of HLA-Cw*0602 and the C6orf10 locus, suggesting the potential pathogenic involvement of HLA-B. These results demonstrate that there are at least two additional loci within the MHC conferring risk of psoriasis

Determination of sin2 θeff w using jet charge measurements in hadronic Z decays

The electroweak mixing angle is determined with high precision from measurements of the mean difference between forward and backward hemisphere charges in hadronic decays of the Z. A data sample of 2.5 million hadronic Z decays recorded over the period 1990 to 1994 in the ALEPH detector at LEP is used. The mean charge separation between event hemispheres containing the original quark and antiquark is measured for bb̄ and cc̄ events in subsamples selected by their long lifetimes or using fast D*'s. The corresponding average charge separation for light quarks is measured in an inclusive sample from the anticorrelation between charges of opposite hemispheres and agrees with predictions of hadronisation models with a precision of 2%. It is shown that differences between light quark charge separations and the measured average can be determined using hadronisation models, with systematic uncertainties constrained by measurements of inclusive production of kaons, protons and A's. The separations are used to measure the electroweak mixing angle precisely as sin2 θeff w = 0.2322 ± 0.0008(exp. stat.) ±0.0007(exp. syst.) ± 0.0008(sep.). The first two errors are due to purely experimental sources whereas the third stems from uncertainties in the quark charge separations

Measurement of the W mass by direct reconstruction in e+e−e^+ e^- collisions at 172 GeV

The mass of the W boson is obtained from reconstructed invariant mass distributions in W-pair events. The sample of W pairs is selected from 10.65~pb$^{-1}$ collected with the ALEPH detector at a mean centre-of-mass energy of 172.09 \GEV. The invariant mass distribution of simulated events are fitted to the experimental distributions and the following W masses are obtained: $WW \to q\overline{q}q\overline{q } m_W = 81.30 +- 0.47(stat.) +- 0.11(syst.) GeV/c^2$, $WW \to l\nu q\overline{q}(l=e,\mu) m_W = 80.54 +- 0.47(stat.) +- 0.11(syst.) GeV/c^2$, $WW \to \tau\nu q\overline{q} m_W = 79.56 +- 1.08(stat.) +- 0.23(syst.) GeV/C62$. The statistical errors are the expected errors for Monte Carlo samples of the same integrated luminosity as the data. The combination of these measurements gives: $m_W = 80.80 +- 0.11(syst.) +- 0.03(LEP energy) GeV/^2$