Intergroup Variability in Personality Recognition

Automatic Identification of personality in conversational speech has many applications in natural language processing such as leader identification in a meeting, adaptive dialogue systems, and dating websites. However, the widespread acceptance of automatic personality recognition through lexical and vocal characteristics is limited by the variability of error rate in a general purpose model among speakers from different demographic groups. While other work reports accuracy, we explored error rates of automatic personality recognition task using classification models for different genders and native language groups (L1). We also present a statistical experiment showing the influence of gender and L1 on the relation between acoustic-prosodic features and NEO- FFI self-reported personality traits. Our results show the impact of demographic differences on error rate varies considerably while predicting “Big Five” personality traits from speaker’s utterances. This impact can also be observed through differences in the statistical relationship of voice characteristics with each personality inventory. These findings can be used to calibrate existing personality recognition models or to develop new models that are robust to intergroup variability

Results of the Second SIGMORPHON Shared Task on Multilingual Grapheme-to-Phoneme Conversion

Grapheme-to-phoneme conversion is an important component in many speech technologies, but until recently there were no multilingual benchmarks for this task. The second iteration of the SIGMORPHON shared task on multilingual grapheme-to-phoneme conversion features many improvements from the previous year's task (Gorman et al. 2020), including additional languages, a stronger baseline, three subtasks varying the amount of available resources, extensive quality assurance procedures, and automated error analyses. Four teams submitted a total of thirteen systems, at best achieving relative reductions of word error rate of 11% in the high-resource subtask and 4% in the low-resource subtask

DLK induces developmental neuronal degeneration via selective regulation of proapoptotic JNK activity

DLK is part of a specialized JNK signaling complex in axons that promotes apoptosis via c-Jun but axon degeneration via distinct JNK substrates

Zn²⁺–Silica Modified Cobalt Ferrite Magnetic Nanostructured Composite for Efficient Adsorption of Cationic Pollutants from Water

A Zn²⁺–silica modified CoFe₂O₄ (CZFS) nanostructured composite, useful for adsorbing cationic pollutants from water, was prepared by a wet-chemical method. The composite comprises cubic spinel crystallites (average 18 nm size) with amorphous silica clusters decorated on the crystallites-surface. Improved surface area (59.8 m²/g) of CZFS over those of Zn²⁺ modified CoFe₂O₄, CZF (32.6 m²/g), and CoFe₂O₄, CF (42.8 m²/g), together with its high negative ζ-potential of −35.4 mV (from surface SiO^–) provides CZFS with improved adsorption capacity for Methylene blue (MB) over that of CZF and CF. MB adsorption (initial adsorbate concentration <i>C</i>₀ = 5–25 mg/L) conforms to the Langmuir isotherm model, with maximum monolayer adsorption capacity <i>Q</i>_m = 25.6 mg/g. CZFS exhibits adsorption efficiency <i>A</i>_e ≥ 98% for removal of heavy metal ions Cr³⁺, Cu²⁺ and Pb²⁺ (<i>C</i>₀ = 5 mg/L). High <i>A</i>_e = 99.9% for Pb²⁺ dropped only to <i>A</i>_e = 98.8% for higher <i>C</i>₀ = 20 mg/L (Q_m = 19.8 mg/g). Saturation magnetization of 39 emu/g enables easy magnetic separation of CZFS from water. Good reusability of CZFS adsorbent was observed for up to three cycles. In summary, CZFS efficiently removes MB as well as heavy metal ions (especially Pb²⁺) from contaminated water

Activation of Muscle-Specific Kinase (MuSK) Reduces Neuromuscular Defects in the Delta7 Mouse Model of Spinal Muscular Atrophy (SMA)

Spinal muscular atrophy (SMA) is a motor neuron disease caused by insufficient levels of the survival motor neuron (SMN) protein. One of the most prominent pathological characteristics of SMA involves defects of the neuromuscular junction (NMJ), such as denervation and reduced clustering of acetylcholine receptors (AChRs). Recent studies suggest that upregulation of agrin, a crucial NMJ organizer promoting AChR clustering, can improve NMJ innervation and reduce muscle atrophy in the delta7 mouse model of SMA. To test whether the muscle-specific kinase (MuSK), part of the agrin receptor complex, also plays a beneficial role in SMA, we treated the delta7 SMA mice with an agonist antibody to MuSK. MuSK agonist antibody #13, which binds to the NMJ, significantly improved innervation and synaptic efficacy in denervation-vulnerable muscles. MuSK agonist antibody #13 also significantly increased the muscle cross-sectional area and myofiber numbers in these denervation-vulnerable muscles but not in denervation-resistant muscles. Although MuSK agonist antibody #13 did not affect the body weight, our study suggests that preservation of NMJ innervation by the activation of MuSK may serve as a complementary therapy to SMN-enhancing drugs to maximize the therapeutic effectiveness for all types of SMA patients