Speech Analysis by Natural Language Processing Techniques: A Possible Tool for Very Early Detection of Cognitive Decline?

Background: The discovery of early, non-invasive biomarkers for the identification of “preclinical” or “pre-symptomatic” Alzheimer's disease and other dementias is a key issue in the field, especially for research purposes, the design of preventive clinical trials, and drafting population-based health care policies. Complex behaviors are natural candidates for this. In particular, recent studies have suggested that speech alterations might be one of the earliest signs of cognitive decline, frequently noticeable years before other cognitive deficits become apparent. Traditional neuropsychological language tests provide ambiguous results in this context. In contrast, the analysis of spoken language productions by Natural Language Processing (NLP) techniques can pinpoint language modifications in potential patients. This interdisciplinary study aimed at using NLP to identify early linguistic signs of cognitive decline in a population of elderly individuals.Methods: We enrolled 96 participants (age range 50–75): 48 healthy controls (CG) and 48 cognitively impaired participants: 16 participants with single domain amnestic Mild Cognitive Impairment (aMCI), 16 with multiple domain MCI (mdMCI) and 16 with early Dementia (eD). Each subject underwent a brief neuropsychological screening composed by MMSE, MoCA, GPCog, CDT, and verbal fluency (phonemic and semantic). The spontaneous speech during three tasks (describing a complex picture, a typical working day and recalling a last remembered dream) was then recorded, transcribed and annotated at various linguistic levels. A multidimensional parameter computation was performed by a quantitative analysis of spoken texts, computing rhythmic, acoustic, lexical, morpho-syntactic, and syntactic features.Results: Neuropsychological tests showed significant differences between controls and mdMCI, and between controls and eD participants; GPCog, MoCA, PF, and SF also discriminated between controls and aMCI. In the linguistic experiments, a number of features regarding lexical, acoustic and syntactic aspects were significant in differentiating between mdMCI, eD, and CG (non-parametric statistical analysis). Some features, mainly in the acoustic domain also discriminated between CG and aMCI.Conclusions: Linguistic features of spontaneous speech transcribed and analyzed by NLP techniques show significant differences between controls and pathological states (not only eD but also MCI) and seems to be a promising approach for the identification of preclinical stages of dementia. Long duration follow-up studies are needed to confirm this assumption

Elotuzumab plus pomalidomide and dexamethasone in relapsed/refractory multiple myeloma: a multicenter, retrospective real-world experience with 200 cases outside of controlled clinical trials

In the ELOQUENT-3 trial, the combination of elotuzumab, pomalidomide and dexamethasone (EloPd) proved a superior clinical benefit over Pd with a manageable toxicity profile, leading to its approval in relapsed/refractory multiple myeloma (RRMM), who had received at least two prior therapies, including lenalidomide and a proteasome inhibitor (PI). We report here a real-world experience of 200 RRMMs treated with EloPd in 35 Italian centers outside of clinical trials. In our dataset, the median number of prior lines of therapy was 2, with 51% of cases undergoing autologous stem cell transplant (ASCT) and 73% exposed to daratumumab. After a median follow-up of 9 months, 126 patients stopped EloPd, most of them (88.9%) because of disease progression. The overall response rate (ORR) was 55.4%, in line with the pivotal trial results. Regarding adverse events, our cohort experienced a toxicity profile similar to the ELOQUENT-3 trial, with no significant differences between younger (<70 years) and older patients. The median progression-free survival (PFS) was 7 months, shorter than that observed in the ELOQUENT-3, probably due to the different clinical characteristics of the two cohorts. Interestingly, the ISS stage III (HR:2.55) was associated with worse PFS. Finally, our series's median overall survival (OS) was shorter than that observed in the ELOQUENT-3 trial (17.5 versus 29.8 months). In conclusion, our real-world study confirms EloPd as a safe and possible therapeutic choice for RRMM who received at least two prior therapies, including lenalidomide and a PI

Multiwavelength behaviour of the blazar OJ 248 from radio to γ-rays

We present an analysis of the multiwavelength behaviour of the blazar OJ 248 at z = 0.939 in the period 2006-2013. We use low-energy data (optical, near-infrared, and radio) obtained by 21 observatories participating in the Gamma-Ray Large Area Space Telescope (GLAST)-AGILE Support Program of the Whole Earth Blazar Telescope, as well as data from the Swift (optical-UV and X-rays) and Fermi (γ-rays) satellites, to study flux and spectral variability and correlations among emissions in different bands. We take into account the effect of absorption by the Damped Lyman α intervening system at z = 0.525. Two major outbursts were observed in 2006-2007 and in 2012-2013 at optical and near-IR wavelengths, while in the high-frequency radio light curves prominent radio outbursts are visible peaking at the end of 2010 and beginning of 2013, revealing a complex radio-optical correlation. Cross-correlation analysis suggests a delay of the optical variations after the γ-ray ones of about a month, which is a peculiar behaviour in blazars. We also analyse optical polarimetric and spectroscopic data. The average polarization percentage P is less than 3 per cent, but it reaches ∼19 per cent during the early stage of the 2012-2013 outburst. A vague correlation of P with brightness is observed. There is no preferred electric vector polarization angle and during the outburst the linear polarization vector shows wide rotations in both directions, suggesting a complex behaviour/structure of the jet and possible turbulence. The analysis of 140 optical spectra acquired at the Steward Observatory reveals a strong Mg II broad emission line with an essentially stable flux of 6.2 × 10- 15 erg cm- 2 s- 1 and a full width at half-maximum of 2053 km s- 1

Proceedings of the Fifth Italian Conference on Computational Linguistics CLiC-it 2018

On behalf of the Program Committee, a very warm welcome to the Fifth Italian Conference on Computational Linguistics (CLiC-­‐it 2018). This edition of the conference is held in Torino. The conference is locally organised by the University of Torino and hosted into its prestigious main lecture hall “Cavallerizza Reale”. The CLiC-­‐it conference series is an initiative of the Italian Association for Computational Linguistics (AILC) which, after five years of activity, has clearly established itself as the premier national forum for research and development in the fields of Computational Linguistics and Natural Language Processing, where leading researchers and practitioners from academia and industry meet to share their research results, experiences, and challenges

Linguistic features and automatic classifiers for identifying mild cognitive impairment and dementia

Almost 50 million people are living with dementia in 2018 worldwide, and the number will double every 20 years. The effectiveness of existing pharmacologic treatments for the disease is limited to symptoms control, and none of them are able to prevent, reverse or turn off the neurodegenerative process that leads to dementia; therefore, a prompt detection of the “disease signature” is a key problem, in order to develop and test new drugs and to support the management of clinical and domestic context. Recent studies showed that linguistic alterations may be one of the earliest signs of the pathology, years before other neurocognitive deficits become evident. Traditional tests fail to identify these slight but noticeable changes; whereas, the analysis of spoken language productions by Natural Language Processing (NLP) techniques can ecologically and inexpensively identify minor language modifications in potential patients. This interdisciplinary study aims at quantifying and describing alterations of linguistic features due to cognitive decline and build an automatic system for early diagnosis and screening purpose. To this aim, we enrolled 96 participants: 48 healthy controls and 48 impaired subjects. Of the latter, 32 was diagnosed with Mild Cognitive Impairment and 16 with early Dementia (eD). Each subject underwent a brief neuropsychological screening, and samples of semi-spontaneous speech productions was collected by means of three elicitation tasks. Recorded sessions were orthographically transcribed, PoS tagged and parsed building two different corpora: in the first we kept the automatic annotations, while in the second the transcripts were manually corrected in order to remove all mistakes. A multidimensional parameter computation was performed on the data, taking into consideration a set of 87 acoustical, rhythmical, morpho-syntactic and lexical feature as well as some readability indexes and demographic information. After these preparatory steps, some automatic classifiers were trained to distinguish healthy controls from MCI subjects employing two different algorithms, Support Vector (SVC) and Random Forest Classifiers (RFC). Our system was able to distinguish between controls and MCI subjects exhibiting high F1 scores, around 75%, thus it seems to be a promising approach for the identification of preclinical stages of dementia

Small “Nested” Introgressions from Wild Thinopyrum Species, Conferring Effective Resistance to Fusarium Diseases, Positively Impact Durum Wheat Yield Potential

Today wheat cultivation is facing rapidly changing climate scenarios and yield instability, aggravated by the spreading of severe diseases such as Fusarium head blight (FHB) and Fusarium crown rot (FCR). To obtain productive genotypes resilient to stress pressure, smart breeding approaches must be envisaged, including the exploitation of wild relatives. Here we report on the assessment of the breeding potential of six durum wheat-Thinopyrum spp. recombinant lines (RLs) obtained through chromosome engineering. They are characterized by having 23% or 28% of their 7AL chromosome arm replaced by a “nested” alien segment, composed of homoeologous group 7 chromosome fractions from Th. ponticum and Th. elongatum (=7el1L + 7EL) or from different Th. ponticum accessions (=7el1L + 7el2L). In addition to the 7el1L genes Lr19 + Yp (leaf rust resistance, and yellow pigment content, respectively), these recombinant lines (RLs) possess a highly effective QTL for resistance to FHB and FCR within their 7el2L or 7EL portion. The RLs, their null segregants and well-adapted and productive durum wheat cultivars were evaluated for 16 yield-related traits over two seasons under rainfed and irrigated conditions. The absence of yield penalties and excellent genetic stability of RLs was revealed in the presence of all the alien segment combinations. Both 7el2L and 7EL stacked introgressions had positive impacts on source and sink yield traits, as well as on the overall performance of RLs in conditions of reduced water availability. The four “nested” RLs tested in 2020 were among the top five yielders, overall representing good candidates to be employed in breeding programs to enhance crop security and safety

Linguistic features and automatic classifiers for identifying mild cognitive impairment and dementia

Almost 50 million people are living with dementia in 2018 worldwide, and the number will double every 20 years. The effectiveness of existing pharmacologic treatments for the disease is limited to symptoms control, and none of them are able to prevent, reverse or turn off the neurodegenerative process that leads to dementia; therefore, a prompt detection of the \u201cdisease signature\u201d is a key problem, in order to develop and test new drugs and to support the management of clinical and domestic context. Recent studies showed that linguistic alterations may be one of the earliest signs of the pathology, years before other neurocognitive deficits become evident. Traditional tests fail to identify these slight but noticeable changes; whereas, the analysis of spoken language productions by Natural Language Processing (NLP) techniques can ecologically and inexpensively identify minor language modifications in potential patients. This interdisciplinary study aims at quantifying and describing alterations of linguistic features due to cognitive decline and build an automatic system for early diagnosis and screening purpose. To this aim, we enrolled 96 participants: 48 healthy controls and 48 impaired subjects. Of the latter, 32 was diagnosed with Mild Cognitive Impairment and 16 with early Dementia (eD). Each subject underwent a brief neuropsychological screening, and samples of semi-spontaneous speech productions was collected by means of three elicitation tasks. Recorded sessions were orthographically transcribed, PoS tagged and parsed building two different corpora: in the first we kept the automatic annotations, while in the second the transcripts were manually corrected in order to remove all mistakes. A multidimensional parameter computation was performed on the data, taking into consideration a set of 87 acoustical, rhythmical, morpho-syntactic and lexical feature as well as some readability indexes and demographic information. After these preparatory steps, some automatic classifiers were trained to distinguish healthy controls from MCI subjects employing two different algorithms, Support Vector (SVC) and Random Forest Classifiers (RFC). Our system was able to distinguish between controls and MCI subjects exhibiting high F1 scores, around 75%, thus it seems to be a promising approach for the identification of preclinical stages of dementia