How many voices did you hear? Natural variability disrupts identity perception from unfamiliar voices

Our voices sound different depending on the context (laughing vs. talking to a child vs. giving a speech), making within‐person variability an inherent feature of human voices. When perceiving speaker identities, listeners therefore need to not only ‘tell people apart’ (perceiving exemplars from two different speakers as separate identities) but also ‘tell people together’ (perceiving different exemplars from the same speaker as a single identity). In the current study, we investigated how such natural within‐person variability affects voice identity perception. Using voices from a popular TV show, listeners, who were either familiar or unfamiliar with this show, sorted naturally varying voice clips from two speakers into clusters to represent perceived identities. Across three independent participant samples, unfamiliar listeners perceived more identities than familiar listeners and frequently mistook exemplars from the same speaker to be different identities. These findings point towards a selective failure in ‘telling people together’. Our study highlights within‐person variability as a key feature of voices that has striking effects on (unfamiliar) voice identity perception. Our findings not only open up a new line of enquiry in the field of voice perception but also call for a re‐evaluation of theoretical models to account for natural variability during identity perception

Similar representations of emotions across faces and voices

Emotions are a vital component of social communication, carried across a range of modalities and via different perceptual signals such as specific muscle contractions in the face and in the upper respiratory system. Previous studies have found that emotion recognition impairments after brain damage depend on the modality of presentation: recognition from faces may be impaired whilst recognition from voices remains preserved, and vice versa. On the other hand, there is also evidence for shared neural activation during emotion processing in both modalities. In a behavioural study, we investigated whether there are shared representations in the recognition of emotions from faces and voices. We used a within-subjects design in which participants rated the intensity of facial expressions and non-verbal vocalisations for each of the six basic emotion labels. For each participant and each modality, we then computed a representation matrix with the intensity ratings of each emotion. These matrices allowed us to examine the patterns of confusions between emotions and to characterise the representations of emotions within each modality. We then compared the representations across modalities by computing the correlations of the representation matrices across faces and voices. We found highly correlated matrices across modalities, which suggest similar representations of emotions across faces and voices. We also showed that these results could not be explained by commonalities between low-level visual and acoustic properties of the stimuli. We thus propose that there are similar or shared coding mechanisms for emotions which may act independently of modality, despite their distinct perceptual inputs.This research was supported by an ESRC 1+3 PhD studentship to Lisa Kuhn (ES/I90042X/1)

Explaining face-voice matching decisions: the contribution of mouth movements, stimulus effects and response biases

Previous studies have shown that face-voice matching is more consistently above chance for dynamic (i.e. speaking) faces than for static faces. This suggests that dynamic information can play an important role in informing matching decisions. We initially asked whether this advantage for dynamic stimuli is due to shared information across modalities that is encoded in articulatory mouth movements. Participants completed a sequential face-voice matching task with (1) static images of faces, (2) dynamic videos of faces, (3) dynamic videos where only the mouth was visible, and (4) dynamic videos where the mouth was occluded, in a well-controlled stimulus set. Surprisingly, after accounting for random variation in the data due to design choices, accuracy for all 4 conditions was at chance. Crucially, however, exploratory analyses revealed that participants were not responding randomly, with different patterns of response biases being apparent for different conditions. Our findings suggest that face-voice identity matching may not be possible with above-chance accuracy but that analyses of response biases can shed light upon how people attempt face-voice matching. We discuss these findings with reference to the differential functional roles for faces and voices recently proposed for multimodal person perception

MBX-102/JNJ39659100, a Novel Non-TZD Selective Partial PPAR-γ Agonist Lowers Triglyceride Independently of PPAR-α Activation

MBX-102/JNJ-39659100 (MBX-102) is a selective, partial PPAR-γ agonist that lowers glucose in the absence of some of the side effects, such as weight gain and edema, that are observed with the TZDs. Interestingly MBX-102 also displays pronounced triglyceride lowering in preclinical rodent models and in humans. Although in vitro reporter gene studies indicated that MBX-102 acid is a highly selective PPAR-γ agonist that lacks PPAR-α activity, we sought to determine if PPAR-α activation in vivo could possibly contribute to the triglyceride lowering abilities of MBX-102. In vivo studies using ZDF and ZF rats demonstrated that MBX-102 lowered plasma triglycerides. However in ZF rats, MBX-102 had no effect on liver weight or on hepatic expression levels of PPAR-α target genes. Further in vitro studies in primary human hepatocytes supported these findings. Finally, the ability of MBX-102 to lower triglycerides was maintained in PPAR-α knockout mice, unambiguously establishing that the triglyceride lowering effect of MBX-102 is PPAR-α independent. The in vivo lipid lowering abilities of MBX-102 are therefore mediated by an alternate mechanism which is yet to be determined

Synthetic Protocells Interact with Viral Nanomachinery and Inactivate Pathogenic Human Virus

We present a new antiviral strategy and research tool that could be applied to a wide range of enveloped viruses that infect human beings via membrane fusion. We test this strategy on two emerging zoonotic henipaviruses that cause fatal encephalitis in humans, Nipah (NiV) and Hendra (HeV) viruses. In the new approach, artificial cell-like particles (protocells) presenting membrane receptors in a biomimetic manner were developed and found to attract and inactivate henipavirus envelope glycoprotein pseudovirus particles, preventing infection. The protocells do not accumulate virus during the inactivation process. The use of protocells that interact with, but do not accumulate, viruses may provide significant advantages over current antiviral drugs, and this general approach may have wide potential for antiviral development

Learning Two-input Linear and Nonlinear Analog Functions with a Simple Chemical System

The current biochemical information processing systems behave in a predetermined manner because all features are defined during the design phase. To make such unconventional computing systems reusable and programmable for biomedical applications, adaptation, learning, and self-modification baaed on external stimuli would be highly desirable. However, so far, it haa been too challenging to implement these in real or simulated chemistries. In this paper we extend the chemical perceptron, a model previously proposed by the authors, to function as an analog instead of a binary system. The new analog asymmetric signal perceptron learns through feedback and supports MichaelisMenten kinetics. The results show that our perceptron is able to learn linear and nonlinear (quadratic) functions of two inputs. To the best of our knowledge, it is the first simulated chemical system capable of doing so. The small number of species and reactions allows for a mapping to an actual wet implementation using DNA-strand displacement or deoxyribozymes. Our results are an important step toward actual biochemical systems that can learn and adapt

The effect of SENATOR (Software ENgine for the Assessment and optimisation of drug and non-drug Therapy in Older peRsons) on incident adverse drug reactions (ADRs) in an older hospital cohort - Trial Protocol

Background: The aim of this trial is to evaluate the effect of SENATOR software on incident, adverse drug reactions (ADRs) in older, multimorbid, hospitalized patients. The SENATOR software produces a report designed to optimize older patients' current prescriptions by applying the published STOPP and START criteria, highlighting drug-drug and drug-disease interactions and providing non-pharmacological recommendations aimed at reducing the risk of incident delirium. Methods: We will conduct a multinational, pragmatic, parallel arm Prospective Randomized Open-label, Blinded Endpoint (PROBE) controlled trial. Patients with acute illnesses are screened for recruitment within 48 h of arrival to hospital and enrolled if they meet the relevant entry criteria. Participants' medical history, current prescriptions, select laboratory tests, electrocardiogram, cognitive status and functional status are collected and entered into a dedicated trial database. Patients are individually randomized with equal allocation ratio. Randomization is stratified by site and medical versus surgical admission, and uses random block sizes. Patients randomized to either arm receive standard routine pharmaceutical clinical care as it exists in each site. Additionally, in the intervention arm an individualized SENATOR-generated medication advice report based on the participant's clinical and medication data is placed in their medical record and a senior medical staff member is requested to review it and adopt any of its recommendations that they judge appropriate. The trial's primary outcome is the proportion of patients experiencing at least one adjudicated probable or certain, non-trivial ADR, during the index hospitalization, assessed at 14 days post-randomization or at index hospital discharge if it occurs earlier. Potential ADRs are identified retrospectively by the site researchers who complete a Potential Endpoint Form (one per type of event) that is adjudicated by a blinded, expert committee. All occurrences of 12 pre-specified events, which represent the majority of ADRs, are reported to the committee along with other suspected ADRs. Participants are followed up 12 (+/- 4) weeks post-index hospital discharge to assess medication quality and healthcare utilization. This is the first clinical trial to examine the effectiveness of a software intervention on incident ADRs and associated healthcare costs during hospitalization in older people with multi-morbidity and polypharmacy

Incident adverse drug reactions in geriatric inpatients : a multicentred observational study

Background: Adverse drug reactions (ADRs) are common in older adults and frequently have serious clinical and economic consequences. This study was conducted as a feasibility study for a randomized control trial (RCT) that will investigate the efficacy of a software engine to optimize medications and reduce incident (in-hospital) ADRs. This study's objectives were to (i) establish current incident ADR rates across the six sites participating in the forthcoming RCT and (ii) assess whether incident ADRs are predictable. Methods: This was a multicentre, prospective observational study involving six European hospitals. Adults aged 65 years, hospitalized with an acute illness and on pharmacological treatment for three or more conditions were eligible for inclusion. Adverse events (AEs) were captured using a trigger list of 12 common ADRs. An AE was deemed an ADR when its association with an administered drug was adjudicated as being probable/certain, according to the World Health Organization Uppsala Monitoring Centre causality assessment. The proportion of patients experiencing at least one, probable/certain, incident ADR within 14 days of enrolment/discharge was recorded. Results: A total of 644 patients were recruited, evenly split by sex and overwhelmingly of White ethnicity. Over 80% of admissions were medical. The median number of chronic conditions was five (interquartile range 4-6), with eight or more conditions present in approximately 10%. The mean number of prescribed medications was 9.9 (standard deviation 3.8), which correlated strongly with the number of conditions (r = 0.54, p < 0.0001). A total of 732 AEs were recorded in 382 patients, of which 363 were incident. The majority of events were classified as probably or possibly drug related, with heterogeneity across sites (chi(2) = 88.567, df = 20, p value < 0.001). Out of 644 patients, 139 (21.6%; 95% confidence interval 18.5-25.0%) experienced an ADR. Serum electrolyte abnormalities were the most common ADR. The ADRROP (ADR Risk in Older People) and GerontoNet ADR risk scales correctly predicted ADR occurrence in 61% and 60% of patients, respectively. Conclusion: This feasibility study established the rates of incident ADRs across the six study sites. The ADR predictive power of ADRROP and GerontoNet ADR risk scales were limited in this population

Atomic Force Microscopy Images Label-Free, Drug Encapsulated Nanoparticles In Vivo and Detects Difference in Tissue Mechanical Properties of Treated and Untreated: A Tip for Nanotoxicology

Overcoming the intractable challenge of imaging of label-free, drug encapsulated nanoparticles in tissues in vivo would directly address associated regulatory concerns over 'nanotoxicology'. Here we demonstrate the utility of Atomic Force Microscopy (AFM) for visualising label-free, drug encapsulated polyester particles of ~280 nm distributed within tissues following their intravenous or peroral administration to rodents. A surprising phenomenon, in which the tissues' mechanical stiffness was directly measured (also by AFM) and related to the number of embedded nanoparticles, was utilised to generate quantitative data sets for nanoparticles localisation. By coupling the normal determination of a drug's pharmacokinetics/pharmacodynamics with post-sacrifice measurement of nanoparticle localisation and number, we present for the first time an experimental design in which a single in vivo study relates the PK/PD of a nanomedicine to its toxicokinetics