Cobalt-Catalyzed Cascade C-H Activation/Annulation Polymerizations toward Diversified and Multifunctional Sulfur-Containing Fused Heterocyclic Polymers.

Transition-metal-catalyzed C-H activation has greatly benefited the synthesis and development of functional polymer materials, and the construction of multifunctional fused (hetero)cyclic polymers via novel C-H activation-based polyannulations has emerged as a charming but challenging area in recent years. Herein, we report the first cobalt(III)-catalyzed cascade C-H activation/annulation polymerization (CAAP) approach that can efficiently transform readily available aryl thioamides and internal diynes into multifunctional sulfur-containing fused heterocyclic (SFH) polymers. Within merely 3 h, a series of SFH polymers bearing complex and multisubstituted S,N-doped polycyclic units are facilely and efficiently produced with high molecular weights (absolute Mn up to 220400) in excellent yields (up to 99%), which are hard to achieve by traditional methods. The intermediate-terminated SFH polymer can be used as a reactive macromonomer to controllably extend or modify polymer main chains. The structural diversity can be further enriched through facile S-oxidation and N-methylation reactions of the SFH polymers. Benefiting from the unique structures, the obtained polymers exhibit excellent solution processability, high thermal and morphological stability, efficient and readily tunable aggregate-state fluorescence, stimuli-responsive properties, and high and UV-modulatable refractive indices of up to 1.8464 at 632.8 nm. These properties allow the SFH polymers to be potentially applied in diverse fields, including metal ion detection, photodynamic killing of cancer cells, fluorescent photopatterning, and gradient-index optical materials.

Tuneable stimuli-responsive behaviour, spectroscopic signatures and redox properties of indolo[3,2-b]carbazole-based diradicals.

During the last decade, there has been an increasing interest in the rationalisation of how structural changes stabilise (or destabilise) diradical systems. Demonstrated herein is that indolocarbazole (ICz) diradicals, substituted with dicyanomethylene (DCM) groups, are useful motifs for dynamic covalent chemistry by self-assembling from isolated monomers to cyclophane structures. The comparison of ICz-based systems substituted with DCM groups in para- or meta-positions (p-ICz-CN and m-ICz-CN) and their short-chain carbazole analogues (p-Cz-CN and m-Cz-CN) may identify new potential design strategies for stimuli-responsive materials. The principal objectives of this investigation are the elucidation of (i) the connection between diradical character and the cyclophane stability, (ii) the spatial disposition of the cyclophane structures, (iii) the monomer/cyclophane interconversion both in solution and solid state in response to external stimuli and (iv) the impact that the different π-conjugation and electronic communication between the DCM terminals exerts on the electronic adsorption of the diradicals and their redox behavior. The spontaneous nature of the cyclophane structure is supported by the negative relative Gibbs free energies calculated at 298 K and experimentally by UV-Vis and Raman spectroscopy of the initial yellow solid powder. The conversion to monomeric species having diradical character was demonstrated by variable-temperature (VT) EPR, UV-Vis, Raman and IR measurements, resulting in appreciable chromic changes. In addition, electrochemical oxidation and reduction convert the cyclophane dimer (m-ICz-CN)2 to the monomer monocations and dianions, respectively. This research demonstrates how the chemical reactivity and physical properties of π-conjugated diradicals can be effectively tuned by subtle changes in their chemical structures.

Thiophene π-Bridge Manipulation of NIR-II AIEgens for Multimodal Tumor Phototheranostics.

The fabrication of a multimodal phototheranostic platform on the basis of single-component theranostic agent to afford both imaging and therapy simultaneously, is attractive yet full of challenges. The emergence of aggregation-induced emission luminogens (AIEgens), particularly those emit fluorescence in the second near-infrared window (NIR-II), provides a powerful tool for cancer treatment by virtue of adjustable pathway for radiative/non-radiative energy consumption, deeper penetration depth and aggregation-enhanced theranostic performance. Although bulky thiophene π-bridges such as ortho-alkylated thiophene, 3,4-ethoxylene dioxythiophene and benzo[c]thiophene are commonly adopted to construct NIR-II AIEgens, the subtle differentiation on their theranostic behaviours has yet to be comprehensively investigated. In this work, systematical investigations discovered that AIEgen BT-NS bearing benzo[c]thiophene possesses acceptable NIR-II fluorescence emission intensity, efficient reactive oxygen species generation, and high photothermal conversion efficiency. Eventually, by using of BT-NS nanoparticles, unprecedented performance on NIR-II fluorescence/photoacoustic/photothermal imaging-guided synergistic photodynamic/photothermal elimination of tumors was demonstrated. This study thus offers useful insights into developing versatile phototheranostic systems for clinical trials.

As Aggregation-Induced Emission Meets with Noncovalent Conformational Locks: Subtly Regulating NIR-II Molecules for Multimodal Imaging-Navigated Synergistic Therapies.

Phototheranostics is growing into a sparking frontier in disease treatment. Developing single molecular species synchronously featured by powerful absorption capacity, superior second near-infrared (NIR-II) fluorescence and prominent photothermal conversion ability is highly desirable for phototheranostics, yet remains formidably challenging. In this work, we propose a molecular design philosophy that the integration of noncovalent conformational locks (NoCLs) with aggregation-induced emission (AIE) in a single formulation is able to boost multiple photophysical properties for efficient phototheranostics. The introduction of NoCLs skeleton with conformation-locking feature in the center of molecular architecture indeed elevates the structural planarity and rigidity, which simultaneously promotes the absorption capacity and bathochromic-shifts the emission wavelength centered in NIR-II region. Meanwhile, the AIE tendency mainly originated from flexibly propeller-like geometry at the ends of molecular architecture eventually endows the molecule with satisfactory emission intensity and photothermal conversion in aggregates. Consequently, by utilizing the optimized molecule, unprecedented performance on NIR-II fluorescence-photoacoustic-photothermal trimodal imaging-guided photothermal-chemo synergistic therapy is demonstrated by the precise tumor diagnosis and complete tumor ablation.

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.

A comparison of bilateral and unilateral cerebral perfusion for total arch replacement surgery for non-marfan, type A aortic dissection.

OBJECTIVES: Acknowledging lacking of consensus exist in total aortic arch (TAA) surgery for acute type A aortic dissection (AAD), this study aimed to investigate the neurologic injury rate between bilateral and unilateral cerebrum perfusion on the specific population. METHODS: A total of 595 AAD patients other than Marfan syndrome receiving TAA surgery since March 2013 to March 2022 were included. Among them, 276 received unilateral cerebral perfusion (via right axillary artery, RCP) and 319 for bilateral cerebral perfusion (BCP). The primary outcome was neurologic injury rate. Secondary outcomes were 30-day mortality, serum inflammation response (high sensitivity C reaction protein, hs-CRP; Interleukin-6, IL-6; cold-inducible RNA binding protein, CIRBP) and neuroprotection (RNA-binding motif 3, RBM3) indexes. RESULTS: The BCP group reported a significantly lower permanent neurologic deficits [odds ratio: 0.481, Confidence interval (CI): 0.296-0.782, p = 0.003] and 30-day mortality (odds ratio: 0.353, CI: 0.194-0.640, p < 0.001) than those received RCP treatment. There were also lower inflammation cytokines (hr-CRP: 114 ± 17 vs. 101 ± 16 mg/L; IL-6: 130 [103,170] vs. 81 [69,99] pg/ml; CIRBP: 1076 [889, 1296] vs. 854 [774, 991] pg/ml, all p < 0.001), but a higher neuroprotective cytokine (RBM3: 4381 ± 1362 vs 2445 ± 1008 pg/mL, p < 0.001) at 24 h after procedure in BCP group. Meanwhile, BCP resulted in a significantly lower Acute Physiology, Age and Chronic Health Evaluation (APACHE) Ⅱscore (18 ± 6 vs 17 ± 6, p < 0.001) and short stay in intensive care unit (4 [3,5] vs. 3 [2,3] days, p < 0.001) and hospital (16 ± 4 vs 14 ± 3 days, p < 0.001). CONCLUSIONS: This present study indicated that BCP compared with RCP was associated with lower permanent neurologic deficits and 30-day mortality in AAD patients other than Marfan syndrome receiving TAA surgery.

Ferulic Acid Induces Autophagy and Apoptosis in Colon Cancer CT26 Cells via the MAPK Pathway.

Ferulic acid (FA) is a bioactive compound found in traditional Chinese herbal medicine; for example, it is present in Xinjiang Ferula, but also in strong-flavor Chinese baijiu. FA has been shown to play a crucial role in treating oxidative stress, skin whitening, and eye diseases. In this study, the potential role of FA as a means of inducing apoptosis and inhibiting colon cancer induced by the transplantation of CT26 cells was investigated. The results show that FA adjuvant treatment caused an upregulation in the expression of genes related to autophagy while simultaneously suppressing the expression of inflammatory response elements and improving the bodyweight, glutamic pyruvic transaminase (ALT), and glutamic oxaloacetic transaminase (AST) in vivo. Furthermore, FA inhibited the proliferation of CT26 cells and induced apoptosis, specifically by activating the phosphorylation of ERK and JNK to enhance the essential proteins BCL-2 and BAX in the apoptosis pathway. These results suggest that FA could be a promising auxiliary therapeutic agent for the treatment of colon cancer. Further research is needed to better understand the mechanisms underlying the beneficial effects of FA and its synergistic effects with other compounds.

Mechanistic Understanding of Efficient Polyethylene Hydrocracking over Two-Dimensional Platinum-Anchored Tungsten Trioxide.

Chemical upcycling of polyethylene (PE) can convert plastic waste into valuable resources. However, engineering a catalyst that allows PE decomposition at low temperatures with high activity remains a significant challenge. Herein, we anchored 0.2 wt.% platinum (Pt) on defective two-dimensional tungsten trioxide (2D WO3 ) nanosheets and achieved hydrocracking of high-density polyethylene (HDPE) waste at 200-250 °C with a liquid fuel (C5-18 ) formation rate up to 1456 gproducts ⋅ gmetal species -1 ⋅ h-1 . The reaction pathway over the bifunctional 2D Pt/WO3 is elucidated by quasi-operando transmission infrared spectroscopy, where (I) well-dispersed Pt immobilized on 2D WO3 nanosheets trigger the dissociation of hydrogen; (II) adsorption of PE and activation of C-C cleavage on WO3 are through the formation of C=O/C=C intermediates; (III) intermediates are converted to alkane products by the dissociated H. Our study directly illustrates the synergistic role of bifunctional Pt/WO3 catalyst in the hydrocracking of HDPE, paving the way for the development of high-performance catalysts with optimized chemical and morphological properties.

Double-pronged Antimicrobial Agents based on a Donor-π-Acceptor Type Aggregation-Induced Emission Luminogen.

Novel antibacterial agents are urgently needed to control the infections induced by multidrug-resistant (MDR) bacteria. Herein, we rationally designed and facilely synthesized a new D-π-A type luminogen with strong red/near-infrared fluorescence emission, great aggregation-induced emission (AIE) features, and excellent reactive oxygen species (ROS) production. The newly developed molecule TTTh killed the methicillin-resistant Staphylococcus aureus (MRSA) by triggering the ROS accumulation in bacteria and interrupting the membrane integrity. Moreover, TTTh specifically targeted the lysosomes and potentiated their maturation to accelerate the clearance of intracellular bacteria. Additionally, reduced bacterial burden and improved healing were observed in TTTh-treated wounds with negligible side effects. Our study expands the biological design and application of AIE luminogens (AIEgens), and provides new insights into discovering novel antibacterial targets and agents.

Dynamic Covalent Properties of a Novel Indolo[3,2-b]carbazole Diradical.

This work describes the synthesis and properties of a dicyanomethylene-substituted indolo[3,2-b]carbazole diradical ICz-CN. This quinoidal system dimerises almost completely to (ICz-CN)2 , which contains two long C(sp3 )-C(sp3 ) σ-bonds between the dicyanomethylene units. The minor open-shell ICz-CN component in the solid-state mixture was identified by EPR spectroscopy. Cyclic voltammetry and UV-visible spectroelectrochemical data, as well as comparison with reference monomer ICz-Br reveal that the nature of the one-electron oxidation of (ICz-CN)2 at ambient temperature and ICz-CN at elevated temperature is very similar in all these compounds due to the prevailing localization of their HOMO on the ICz backbone. The peculiar cathodic behaviour reflects the co-existence of (ICz-CN)2 and ICz-CN. The involvement of the dicyanomethylene groups stabilizes the close-lying LUMO and LUMO+1 of (ICz-CN)2 and especially ICz-CN compared to ICz-Br, resulting in a distinctive cathodic response at low overpotentials. Differently from neutral ICz-CN, its radical anion and dianion are remarkably stable under ambient conditions. The UV/Vis(-NIR) electronic transitions in parent (ICz-CN)2 and ICz-CN and their different redox forms have been assigned convincingly with the aid of TD-DFT calculations. The σ-bond in neutral (ICz-CN)2 is cleaved in solution and in the solid-state upon soft external stimuli (temperature, pressure), showing a strong chromism from light yellow to blue-green. Notably, in the solid state, the monomeric diradical species is predominantly formed under high hydrostatic pressure (>1 GPa).

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.

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.

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.

Polymers and protein-associated vesicles for the microencapsulation of anthocyanins from grape skins used for food applications.

BACKGROUND: Anthocyanins were extracted from grape skins by a combination of ethanolic-ultrasonic assisted methods and were then encapsulated by freeze-drying in soy phosphatidylcholine vesicles with the addition of different polymers, such as pectin, acacia gum, and whey protein isolate. The goal of this research was to microencapsulate anthocyanin compounds extracted from grape skins, to characterize the stability and behavior of the vesicles and then to use them to obtain a new light formulated mayonnaise. RESULTS: The particle size ranged from 900 nm in the control condition to 250 nm in vesicles loaded with whey proteins. The powders showed higher encapsulation efficiency for all variants, ranging from 81 to 96%. Vibrational spectroscopy revealed better inclusion of anthocyanins in polysaccharide-based coacervates, whereas in protein-based coacervates a possible interaction of anthocyanins with amine groups was observed. The vesicles were tested for in vitro release, and the results confirmed the gradual release of the anthocyanins in both stages of digestion, with a residual content of about 50% in the vesicles. The powders displayed high stability during storage in the dark at 4 °C. The panelists appreciated the new light formulated mayonnaises enriched with 10% dried vesicles compared with the control sample, in particular samples with acacia gum. CONCLUSION: The study revealed that polymer-loaded vesicles presented stability in simulated gastrointestinal fluids and have proved successful in obtaining new light enriched mayonnaises. © 2020 Society of Chemical Industry.

Genetic dynamics of earlier maturity group emergence in south-to-north extension of Northeast China soybeans.

KEY MESSAGE: This population genetic study is characterized with direct comparisons of days to flowering QTL-allele matrices between newly evolved and originally old maturity groups of soybeans to explore its evolutionary dynamics using the RTM-GWAS procedure. The Northeast China (NEC) soybeans are the major germplasm source of modern soybean production in Americas (> 80% of the world total). NEC is a relatively new soybean area in China, expanded after its nomadic status in the seventeenth century. At nine sites of four ecoregions in NEC, 361 varieties were tested for their days to flowering (DTF), a geography-sensitive trait as an indicator for maturity groups (MGs). The DTF reduced obviously along with soybeans extended to higher latitudes, ranging in 41-83 days and MG 000-III. Using the RTM-GWAS (restricted two-stage multi-locus model genome-wide association study) procedure, 81 QTLs with 342 alleles were identified, accounting for 77.85% genetic contribution (R2 = 0.01-7.74%/locus), and other 20.75% (98.60-77.85%, h2 = 98.60%) genetic variation was due to a collective of unmapped QTLs. With soybeans northward, breeding effort made the original MG I-III evolved to MG 0-00-000. In direct comparisons of QTL-allele matrices among MGs, the genetic dynamics are identified with local exotic introduction/migration (58.48%) as the first and selection against/exclusion of positive alleles causing new recombination (40.64%) as the second, while only a few allele emergence/mutation happened (0.88%, limited in MG 0, not in MG 00-000). In new MG emergence, 24 QTLs with 19 candidate genes are the major sources. A genetic potential of further DTF shortening (13-21 days) is predicted for NEC population. The QTL detection in individual ecoregions showed various ecoregion-specific QTLs-alleles/genes after co-localization treatment (removing the random environment shifting ones).

A two-stage deep learning algorithm for talker-independent speaker separation in reverberant conditions.

Speaker separation is a special case of speech separation, in which the mixture signal comprises two or more speakers. Many talker-independent speaker separation methods have been introduced in recent years to address this problem in anechoic conditions. To consider more realistic environments, this paper investigates talker-independent speaker separation in reverberant conditions. To effectively deal with speaker separation and speech dereverberation, extending the deep computational auditory scene analysis (CASA) approach to a two-stage system is proposed. In this method, reverberant utterances are first separated and separated utterances are then dereverberated. The proposed two-stage deep CASA system significantly outperforms a baseline one-stage deep CASA method in real reverberant conditions. The proposed system has superior separation performance at the frame level and higher accuracy in assigning separated frames to individual speakers. The proposed system successfully generalizes to an unseen speech corpus and exhibits similar performance to a talker-dependent system.

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.

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.

The optimal threshold for removing noise from speech is similar across normal and impaired hearing-a time-frequency masking study.

Hearing-impaired listeners' intolerance to background noise during speech perception is well known. The current study employed speech materials free of ceiling effects to reveal the optimal trade-off between rejecting noise and retaining speech during time-frequency masking. This relative criterion value (-7 dB) was found to hold across noise types that differ in acoustic spectro-temporal complexity. It was also found that listeners with hearing impairment and those with normal hearing performed optimally at this same value, suggesting no true noise intolerance once time-frequency units containing speech are extracted.

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.