The Optimal Speech-to-Background Ratio for Balancing Speech Recognition With Environmental Sound Recognition.

OBJECTIVES: This study aimed to determine the speech-to-background ratios (SBRs) at which normal-hearing (NH) and hearing-impaired (HI) listeners can recognize both speech and environmental sounds when the two types of signals are mixed. Also examined were the effect of individual sounds on speech recognition and environmental sound recognition (ESR), and the impact of divided versus selective attention on these tasks. DESIGN: In Experiment 1 (divided attention), 11 NH and 10 HI listeners heard sentences mixed with environmental sounds at various SBRs and performed speech recognition and ESR tasks concurrently in each trial. In Experiment 2 (selective attention), 20 NH listeners performed these tasks in separate trials. Psychometric functions were generated for each task, listener group, and environmental sound. The range over which speech recognition and ESR were both high was determined, as was the optimal SBR for balancing recognition with ESR, defined as the point of intersection between each pair of normalized psychometric functions. RESULTS: The NH listeners achieved greater than 95% accuracy on concurrent speech recognition and ESR over an SBR range of approximately 20 dB or greater. The optimal SBR for maximizing both speech recognition and ESR for NH listeners was approximately +12 dB. For the HI listeners, the range over which 95% performance was observed on both tasks was far smaller (span of 1 dB), with an optimal value of +5 dB. Acoustic analyses indicated that the speech and environmental sound stimuli were similarly audible, regardless of the hearing status of the listener, but that the speech fluctuated more than the environmental sounds. Divided versus selective attention conditions produced differences in performance that were statistically significant yet only modest in magnitude. In all conditions and for both listener groups, recognition was higher for environmental sounds than for speech when presented at equal intensities (i.e., 0 dB SBR), indicating that the environmental sounds were more effective maskers of speech than the converse. Each of the 25 environmental sounds used in this study (with one exception) had a span of SBRs over which speech recognition and ESR were both higher than 95%. These ranges tended to overlap substantially. CONCLUSIONS: A range of SBRs exists over which speech and environmental sounds can be simultaneously recognized with high accuracy by NH and HI listeners, but this range is larger for NH listeners. The single optimal SBR for jointly maximizing speech recognition and ESR also differs between NH and HI listeners. The greater masking effectiveness of the environmental sounds relative to the speech may be related to the lower degree of fluctuation present in the environmental sounds as well as possibly task differences between speech recognition and ESR (open versus closed set). The observed differences between the NH and HI results may possibly be related to the HI listeners' smaller fluctuating masker benefit. As noise-reduction systems become increasingly effective, the current results could potentially guide the design of future systems that provide listeners with highly intelligible speech without depriving them of access to important environmental sounds.

Redox Hopping in Metal-Organic Frameworks through the Lens of the Scholz Model.

Initially proposed by Lovric and Scholz to explain redox reactions in solid-phase voltammetry, the Scholz model's applications have expanded to redox reactions in various materials. As an extension of the Cottrell equation, the Scholz model enabled the quantification of electron hopping and ion diffusion with coefficients, De and Di, respectively. Research utilizing the Scholz model indicated that, in most cases, a huge bottleneck results from the ion diffusion which is slower than electron hopping by orders of magnitude. Therefore, electron and ion motion can be tuned and optimized to increase the charge transport and conductivity through systematic investigations guided by the Scholz model. The strategy may be extended to other solid-state materials in the future, e.g., battery anodes/cathodes. In this Perspective, the applications of the Scholz model in different materials will be discussed. Moreover, the limitations of the Scholz model will also be introduced, and viable solutions to those limitations discussed.

Progress made in the efficacy and viability of deep-learning-based noise reduction.

Recent years have brought considerable advances to our ability to increase intelligibility through deep-learning-based noise reduction, especially for hearing-impaired (HI) listeners. In this study, intelligibility improvements resulting from a current algorithm are assessed. These benefits are compared to those resulting from the initial demonstration of deep-learning-based noise reduction for HI listeners ten years ago in Healy, Yoho, Wang, and Wang [(2013). J. Acoust. Soc. Am. 134, 3029-3038]. The stimuli and procedures were broadly similar across studies. However, whereas the initial study involved highly matched training and test conditions, as well as non-causal operation, preventing its ability to operate in the real world, the current attentive recurrent network employed different noise types, talkers, and speech corpora for training versus test, as required for generalization, and it was fully causal, as required for real-time operation. Significant intelligibility benefit was observed in every condition, which averaged 51% points across conditions for HI listeners. Further, benefit was comparable to that obtained in the initial demonstration, despite the considerable additional demands placed on the current algorithm. The retention of large benefit despite the systematic removal of various constraints as required for real-world operation reflects the substantial advances made to deep-learning-based noise reduction.

Aqueous-Phase Destruction of Nerve-Agent Simulants at Copper Single Atoms in UiO-66.

Metal-organic frameworks (MOFs) have shown great success in aqueous-phase hydrolysis of nerve agents, with some even showing promise in the gas phase. However, both aqueous-phase reactivity and gas-phase reactivity are hindered because of the binding of the hydrolyzed products to the MOF nodes in a stable, bridging configuration, which limits turnover. Single transition-metal atoms in MOFs have been a growing field of interest for catalytic applications, and single atoms have been proposed to prevent the unwanted bridged conformation and increase catalytic turnover. To date, there has been little experimental evidence to support the hypothesis. Herein, we report two copper single atom-modified UiO-66 MOFs for nerve-agent simulant degradation. Despite the capping of highly active Zr4+ nodes with fewer Lewis acidic Cun+ atoms, the reactivity of both CuMOFs approaches that of native UiO-66 under aqueous conditions. Computational studies reveal that the Cu coordination environment impairs product inhibition with respect to the native MOF.

EMBEDR: Distinguishing signal from noise in single-cell omics data.

Single-cell "omics"-based measurements are often high dimensional so that dimensionality reduction (DR) algorithms are necessary for data visualization and analysis. The lack of methods for separating signal from noise in DR outputs has limited their utility in generating data-driven discoveries in single-cell data. In this work we present EMBEDR, which assesses the output of any DR algorithm to distinguish evidence of structure from algorithm-induced noise in DR outputs. We apply EMBEDR to DR-generated representations of single-cell omics data of several modalities to show where they visually show real-not spurious-structure. EMBEDR generates a "p" value for each sample, allowing for direct comparisons of DR algorithms and facilitating optimization of algorithm hyperparameters. We show that the scale of a sample's neighborhood can thus be determined and used to generate a novel "cell-wise optimal" embedding. EMBEDR is available as a Python package for immediate use.

Switching Catalyst Selectivity via the Introduction of a Pendant Nitrophenyl Group.

The conversion of abundant small molecules to value-added products serves as an attractive method to store renewable energy in chemical bonds. A family of macrocyclic cobalt aminopyridine complexes was previously reported to reduce CO2 to CO with 98% faradaic efficiency through the formation of hydrogen-bonding networks and with the number of secondary amines affecting catalyst performance. One of these aminopyridine macrocycles, (NH)1(NMe)3-bridged calix[4]pyridine (L5), was modified with a nitrophenyl group to form LNO2 and metalated with a cobalt(II) precursor to generate CoLNO2, which would allow for probing the positioning and steric effects on catalysis. The addition of a nitrophenyl moiety to the ligand backbone results in a drastic shift in selectivity. Large current increases in the presence of added protons and CoLNO2 are observed under both N2 and CO2. The current increases under N2 are ∼30 times larger than the ones under CO2, suggesting a change in the selectivity of CoLNO2 to favor H2 production versus CO2 reduction. H2 is determined to be the dominant reduction product by gas chromatography, reaching faradaic efficiencies up to 76% under N2 with TFE and 71% under CO2 with H2O, in addition to small amounts of formate. X-ray photoelectron spectroscopy (XPS) reveals the presence of a cobalt-containing heterogeneous deposit on the working electrode surface, indicating the addition of the nitrophenyl group reduces the electrochemical stability of the catalyst. These observed catalytic behaviors are demonstrably different relative to the tetra-NH bridged macrocycle, which shows 98% faradaic efficiency for CO2-to-CO conversion with TFE, highlighting the importance of pendant hydrogen bond donors and electrochemically robust functional groups for selective CO2 conversion.

A causal and talker-independent speaker separation/dereverberation deep learning algorithm: Cost associated with conversion to real-time capable operation.

The fundamental requirement for real-time operation of a speech-processing algorithm is causality-that it operate without utilizing future time frames. In the present study, the performance of a fully causal deep computational auditory scene analysis algorithm was assessed. Target sentences were isolated from complex interference consisting of an interfering talker and concurrent room reverberation. The talker- and corpus/channel-independent model used Dense-UNet and temporal convolutional networks and estimated both magnitude and phase of the target speech. It was found that mean algorithm benefit was significant in every condition. Mean benefit for hearing-impaired (HI) listeners across all conditions was 46.4 percentage points. The cost of converting the algorithm to causal processing was also assessed by comparing to a prior non-causal version. Intelligibility decrements for HI and normal-hearing listeners from non-causal to causal processing were present in most but not all conditions, and these decrements were statistically significant in half of the conditions tested-those representing the greater levels of complex interference. Although a cost associated with causal processing was present in most conditions, it may be considered modest relative to the overall level of benefit.

Deep learning based speaker separation and dereverberation can generalize across different languages to improve intelligibility.

The practical efficacy of deep learning based speaker separation and/or dereverberation hinges on its ability to generalize to conditions not employed during neural network training. The current study was designed to assess the ability to generalize across extremely different training versus test environments. Training and testing were performed using different languages having no known common ancestry and correspondingly large linguistic differences-English for training and Mandarin for testing. Additional generalizations included untrained speech corpus/recording channel, target-to-interferer energy ratios, reverberation room impulse responses, and test talkers. A deep computational auditory scene analysis algorithm, employing complex time-frequency masking to estimate both magnitude and phase, was used to segregate two concurrent talkers and simultaneously remove large amounts of room reverberation to increase the intelligibility of a target talker. Significant intelligibility improvements were observed for the normal-hearing listeners in every condition. Benefit averaged 43.5% points across conditions and was comparable to that obtained when training and testing were performed both in English. Benefit is projected to be considerably larger for individuals with hearing impairment. It is concluded that a properly designed and trained deep speaker separation/dereverberation network can be capable of generalization across vastly different acoustic environments that include different languages.

Nanoconfinement and mass transport in metal-organic frameworks.

The ubiquity of metal-organic frameworks in recent scientific literature underscores their highly versatile nature. MOFs have been developed for use in a wide array of applications, including: sensors, catalysis, separations, drug delivery, and electrochemical processes. Often overlooked in the discussion of MOF-based materials is the mass transport of guest molecules within the pores and channels. Given the wide distribution of pore sizes, linker functionalization, and crystal sizes, molecular diffusion within MOFs can be highly dependent on the MOF-guest system. In this review, we discuss the major factors that govern the mass transport of molecules through MOFs at both the intracrystalline and intercrystalline scale; provide an overview of the experimental and computational methods used to measure guest diffusivity within MOFs; and highlight the relevance of mass transfer in the applications of MOFs in electrochemical systems, separations, and heterogeneous catalysis.

Defect Level and Particle Size Effects on the Hydrolysis of a Chemical Warfare Agent Simulant by UiO-66.

Defect engineering in metal-organic frameworks (MOFs) has recently become an area of significant research due to the possibility of enhancing material properties such as internal surface area and catalytic activity while maintaining stable 3D structures. Through a modulator screening study, the model Zr4+ MOF, UiO-66, has been synthesized with control of particle sizes (100-1900 nm) and defect levels (2-24%). By relating these properties, two series were identified where one property remained constant, allowing for independent analysis of the defect level or particle size, which frequently change coincident with the modulator choice. The series were used to compare UiO-66 reactivity for the hydrolysis of a chemical warfare agent simulant, dimethyl 4-nitrophenylphosphate (DMNP). The rate of DMNP hydrolysis displayed high dependence on the external surface area, supporting a reaction dominated by surface interactions. Moderate to high concentrations of defects (14-24%) allow for the accessibility of some interior MOF nodes but do not substantially promote diffusion into the framework. Individual control of defect levels and particle sizes through modulator selection may provide useful materials for small molecular catalysis and provide a roadmap for similar engineering of other zirconium frameworks.

An effectively causal deep learning algorithm to increase intelligibility in untrained noises for hearing-impaired listeners.

Real-time operation is critical for noise reduction in hearing technology. The essential requirement of real-time operation is causality-that an algorithm does not use future time-frame information and, instead, completes its operation by the end of the current time frame. This requirement is extended currently through the concept of "effectively causal," in which future time-frame information within the brief delay tolerance of the human speech-perception mechanism is used. Effectively causal deep learning was used to separate speech from background noise and improve intelligibility for hearing-impaired listeners. A single-microphone, gated convolutional recurrent network was used to perform complex spectral mapping. By estimating both the real and imaginary parts of the noise-free speech, both the magnitude and phase of the estimated noise-free speech were obtained. The deep neural network was trained using a large set of noises and tested using complex noises not employed during training. Significant algorithm benefit was observed in every condition, which was largest for those with the greatest hearing loss. Allowable delays across different communication settings are reviewed and assessed. The current work demonstrates that effectively causal deep learning can significantly improve intelligibility for one of the largest populations of need in challenging conditions involving untrained background noises.

Design Strategies for Enhanced Conductivity in Metal-Organic Frameworks.

Metal-organic frameworks (MOFs) are a class of materials which exhibit permanent porosity, high surface area, and crystallinity. As a highly tunable middle ground between heterogeneous and homogeneous species, MOFs have the potential to suit a wide variety of applications, many of which require conductive materials. The continued development of conductive MOFs has provided an ever-growing library of materials with both intrinsic and guest-promoted conductivity, and factors which limit or enhance conductivity in MOFs have become more apparent. In this Outlook, the factors which are believed to influence the future of MOF conductivity most heavily are highlighted along with proposed methods of further developing these fields. Fundamental studies derived from these methods may provide pathways to raise conductivity across a wide range of MOF structures.

A talker-independent deep learning algorithm to increase intelligibility for hearing-impaired listeners in reverberant competing talker conditions.

Deep learning based speech separation or noise reduction needs to generalize to voices not encountered during training and to operate under multiple corruptions. The current study provides such a demonstration for hearing-impaired (HI) listeners. Sentence intelligibility was assessed under conditions of a single interfering talker and substantial amounts of room reverberation. A talker-independent deep computational auditory scene analysis (CASA) algorithm was employed, in which talkers were separated and dereverberated in each time frame (simultaneous grouping stage), then the separated frames were organized to form two streams (sequential grouping stage). The deep neural networks consisted of specialized convolutional neural networks, one based on U-Net and the other a temporal convolutional network. It was found that every HI (and normal-hearing, NH) listener received algorithm benefit in every condition. Benefit averaged across all conditions ranged from 52 to 76 percentage points for individual HI listeners and averaged 65 points. Further, processed HI intelligibility significantly exceeded unprocessed NH intelligibility. Although the current utterance-based model was not implemented as a real-time system, a perspective on this important issue is provided. It is concluded that deep CASA represents a powerful framework capable of producing large increases in HI intelligibility for potentially any two voices.

Does Time Compression Decrease Intelligibility for Female Talkers More Than for Male Talkers?

Purpose This preliminary investigation compared effects of time compression on intelligibility for male versus female talkers. We hypothesized that time compression would have a greater effect for female talkers. Method Sentence materials from four talkers (two males) were time compressed, and original-speed and time-compressed speech materials were presented in a background of 12-talker babble to young adult listeners with normal hearing. Each talker/processing condition was heard by eight listeners (total N = 64). Generalized linear mixed-effects models were used to determine the effects of and interaction between processing condition and talker sex on keyword intelligibility. Additional post hoc analyses examined whether processing condition effects were related to talker vowel space and word frequency. Results For original-speed sentences, female and male talkers were essentially equally intelligible. Time compression reduced intelligibility for all talkers, but the effect was significantly greater for the female talkers. Supplementary analyses revealed that the effect of time compression depended on both talker vowel space and word frequency: The detrimental effect decreased significantly as word frequency and vowel space increased. Word frequency effects were also greater overall for talkers with larger vowel spaces. Conclusions While the small talker sample limits conclusions about the effects of talker sex, the secondary analyses suggest that intelligibility of talkers with larger vowel spaces is less susceptible to the negative effect of time compression, especially for high-frequency words.

A deep learning algorithm to increase intelligibility for hearing-impaired listeners in the presence of a competing talker and reverberation.

For deep learning based speech segregation to have translational significance as a noise-reduction tool, it must perform in a wide variety of acoustic environments. In the current study, performance was examined when target speech was subjected to interference from a single talker and room reverberation. Conditions were compared in which an algorithm was trained to remove both reverberation and interfering speech, or only interfering speech. A recurrent neural network incorporating bidirectional long short-term memory was trained to estimate the ideal ratio mask corresponding to target speech. Substantial intelligibility improvements were found for hearing-impaired (HI) and normal-hearing (NH) listeners across a range of target-to-interferer ratios (TIRs). HI listeners performed better with reverberation removed, whereas NH listeners demonstrated no difference. Algorithm benefit averaged 56 percentage points for the HI listeners at the least-favorable TIR, allowing these listeners to perform numerically better than young NH listeners without processing. The current study highlights the difficulty associated with perceiving speech in reverberant-noisy environments, and it extends the range of environments in which deep learning based speech segregation can be effectively applied. This increasingly wide array of environments includes not only a variety of background noises and interfering speech, but also room reverberation.

A deep learning based segregation algorithm to increase speech intelligibility for hearing-impaired listeners in reverberant-noisy conditions.

Recently, deep learning based speech segregation has been shown to improve human speech intelligibility in noisy environments. However, one important factor not yet considered is room reverberation, which characterizes typical daily environments. The combination of reverberation and background noise can severely degrade speech intelligibility for hearing-impaired (HI) listeners. In the current study, a deep learning based time-frequency masking algorithm was proposed to address both room reverberation and background noise. Specifically, a deep neural network was trained to estimate the ideal ratio mask, where anechoic-clean speech was considered as the desired signal. Intelligibility testing was conducted under reverberant-noisy conditions with reverberation time T 60 = 0.6 s, plus speech-shaped noise or babble noise at various signal-to-noise ratios. The experiments demonstrated that substantial speech intelligibility improvements were obtained for HI listeners. The algorithm was also somewhat beneficial for normal-hearing (NH) listeners. In addition, sentence intelligibility scores for HI listeners with algorithm processing approached or matched those of young-adult NH listeners without processing. The current study represents a step toward deploying deep learning algorithms to help the speech understanding of HI listeners in everyday conditions.

Covalent-Organic Frameworks Composed of Rhenium Bipyridine and Metal Porphyrins: Designing Heterobimetallic Frameworks with Two Distinct Metal Sites.

The incorporation of homogeneous catalysts for CO2 reduction into extended frameworks has been a successful strategy for increasing catalyst lifetime and activity, but the effects of the linkers on catalysis are underexplored. In this work, a novel rhenium bipyridine complex was synthesized for the purpose of designing a covalent-organic framework (COF) with both metalloporphyrin and metal bipyridine moieties. Investigation of the rhenium complex as a homogeneous catalyst shows a faradaic efficiency of 81(8)% for the electrocatalytic conversion of CO2 to CO upon the addition of methanol as the proton source. Treatment of the rhenium complex with tetra(4-aminophenyl)porphyrin under Schiff base conditions produces the desired COF, as indicated by powder X-ray diffraction (PXRD) studies. Metalation of the porphyrins was accomplished through postsynthetic modification with CoCl2 and FeCl3 metal precursors. The retention of the PXRD peaks and appearance of new Co and Fe peaks in the corresponding X-ray photoelectron spectroscopy spectra suggest the successful incorporation of a secondary metal site into the framework. Cyclic voltammetry measurements display increases in current densities when the atmosphere is changed from N2 to CO2. Controlled potential electrolyses show that the cobalt-postmetalated COF has the highest activity toward CO2 reduction, reaching a faradaic efficiency of 18(2)%.

An eight-gene molecular phylogeny of the Kickxellomycotina, including the first phylogenetic placement of Asellariales.

Kickxellomycotina is a recently described subphylum encompassing four zygomycete orders (Asellariales, Dimargaritales, Harpellales, Kickxellales). These fungi are united by the formation of disciform septal pores containing lenticular plugs. Morphological diversification and life history evolution has made the relationships within and among the four orders difficult to resolve on those grounds alone. Here we infer the phylogeny of the Kickxellomycotina based on an eight-gene supermatrix including both ribosomal rDNA (18S, 28S, 5.8S) and protein sequences (MCM7, TSR1, RPB1, RPB2, ß-tubulin). The results of this study demonstrate that Kickxellomycotina is monophyletic and related to members of the Zoopagomycotina. Eight unique clades are distinguished in the Kickxellomycotina, including the four defined orders (Asellariales, Dimargaritales, Harpellales, Kickxellales) as well as four genera previously placed within two of these orders (Barbatospora, Orphella, Ramicandelaber, Spiromyces). Dimargaritales and Ramicandelaber are the earliest diverging members of the subphylum, although the relationship between these taxa remains uncertain. The remaining six clades form a monophyletic group, with Barbatospora diverging first. The next split divides the remaining members of the subphylum into two subclades: (i) Asellariales and Harpellales and (ii) Kickxellales, Orphella and Spiromyces. Estimation of ancestral states for four potentially informative morphological and ecological characters reveals that arthropod endosymbiosis might have been an important factor in the early evolution of the Kickxellomycotina.

Using a five-gene phylogeny to test morphology-based hypotheses of Smittium and allies, endosymbiotic gut fungi (Harpellales) associated with arthropods.

Smittium, one of the first described genera of gut fungi, is part of a larger group of endosymbiotic microorganisms (Harpellales) that live predominantly in the digestive tracts of aquatic insects. As a diverse and species-rich taxon, Smittium has helped to advance our understanding of the gut fungi, in part due to the relative success of attempts to culture species of Smittium as compared to other members of Harpellales. Approximately 40% of the 81 known species of Smittium have been cultured. This is the first Smittium multigene dataset and phylogenetic analysis, using the 18S and 28S rRNA genes, as well as RPB1, RPB2, and MCM7 translated protein sequences. Several well-supported clades were recovered within Smittium. One includes the epitype S. mucronatum (the "True Smittium" clade), and another contains many species including S. simulii and S. orthocladii (the "Parasmittium" clade). Ancestral states were reconstructed for holdfast shape, thallus branching type, as well as asexual (trichospore) and sexual (zygospore) spore morphology. Two of these characters, holdfast shape and trichospore morphology, supported the split of the two main clades revealed by the molecular phylogeny, suggesting these are natural clades and these traits may have evolutionary and perhaps ecological significance.

Information technology and hospital patient safety: a cross-sectional study of US acute care hospitals.

OBJECTIVES: To determine whether health information technology (IT) systems are associated with better patient safety in acute care settings. STUDY DESIGN: In a cross-sectional retrospective study, data on hospital patient safety performance for October 2008 to June 2010 were combined with 2007 information technology systems data. The sample included 3002 US non-federal acute care hospitals. Electronic health record (EHR) system was coded as a composite dichotomous variable based on the presence of 10 major clinical and administrative applications that (if in use) could potentially meet stage 1 "meaningful use" objectives. The surgical IT system was measured as a dichotomous variable if a hospital used at least 1 of the perioperative, preoperative, or postoperative information systems. Hospital patient safety performance was measured by risk-standardized estimated rates per 1000 admissions. Statistical analyses were conducted using an estimated dependent variable methodology with gamma-log link-based weighted generalized linear models, adjusting for hospital characteristics, historical composite process quality, and propensity for EHR adoption. RESULTS: We found that the use of surgical IT systems was associated with 7% to 26% lower rates for 7 of 8 patient safety indicators (incidence rate ratio [IRR] range from 0.74 to 0.93; all P values < .01). Further, stage 1 meaningful use-capable EHR systems were associated with 7% to 11% lower rates on 3 of 8 measures (IRR range from 0.89 to 0.93; all P values < .01). CONCLUSIONS: Our results suggest that the use of IT is associated with modestly lower rates of adverse events in hospitals. However, the cross-sectional design limits our ability to make causal conclusions.