To determine hearing preservation and subjective benefit after cochlear implant (CI) surgery in patients with low frequency hearing in the ear to be implanted  and close to normal hearing in the other.There were two study groups. The test group was made up of 12 adult patients  with normal hearing or mild hearing loss in one ear, and with PD in the ear to be implanted. The reference group consisted of 12 adult patients  who had PD in both ears and who underwent unilateral implantation in their worse ear. Hearing preservation was assessed 1 and 14 months after CI surgery using the Skarzynski Hearing Preservation Classification System. The APHAB questionnaire was used to evaluate the benefit from the CI.The differences in HP% between the groups were not significant: mean hearing preservation (HP%) in the test group was 82% one month after CI surgery and 75% some 14 months after implantation; corresponding results in the reference group were 71% and 69%. However, on the APHAB background noise subscale, the benefit in the test group was significantly larger than in the reference group.To a large extent it was possible to preserve low-frequency hearing in the implanted ear. This means that individuals with low frequency hearing in the implanted ear  and with normal hearing in the other generally received more benefits from cochlear implantation than did patients with partial deafness in both ears. We conclude that residual low frequency hearing in the ear to be implanted should not be considered a contraindication for a CI in a patient with single-sided deafness. With advances in surgical technique and electrode design, preserving residual hearing has now become possible , and thiUnfortunately, direct comparison of many HP case series is difficult in that for many years there was no standardized protocol for inclusion criteria for those with residual hearing and no standard method for reporting HP outcomes. In the literature, different definitions have been used for defining HP , 17, 18.To overcome the lack of an accepted HP standard, Skarzynski et al.  proposedThe Skarzynski HP classification system makes it possible to more easily compare study outcomes and perform meta-analysis of the data. In most HP studies, at least the low frequencies  are reported, so that Snels et al.  was ableThe current study describes our experience of CI with HP in a unique group of patients: they had low-frequency (LF) hearing in the ear to be implanted (they had PD) and normal or close to normal hearing (NH) in the other. Despite having LF hearing in the ear to be implanted, these patients still met the generally acceptable audiometric criteria for single sided deafness (SSD) cochlear implantation, as their pure tone average (PTA) threshold in the ear to be implanted was 90 dB or greater while the PTA in the NH ear was no worse than 30 dB. We therefore called them the SSD-PD group.The study aimed to determine whether HP would be successful in this unique population (the test group), and, if so, what the potential benefit of the CI might be. HP and CI benefit were also evaluated in a reference group: patients with PD in both ears who also underwent unilateral cochlear implantation at about the same time as the study group. The motivation of the study was to gauge what improvement a CI gave to patients with low frequency hearing in the candidate ear when they had normal hearing in the other ear.A group of 12 adult patients with normal hearing or mild hearing loss in one ear, and with partial deafness (hearing thresholds at 0.5 kHz better than 80 dB) in the contralateral ear, were included in the study (SSD-PD or test group). All patients fulfilled the criteria for single-sided deafness (SSD) . Median Patients were implanted at the Institute of Physiology and Pathology of Hearing, Poland, in their poorer-hearing ear  using the six-step round-window approach surgery developed by Skarzynski et al.. The sixThe age at CI surgery ranged from 18 to 61 years . All patients fulfilled 14 months of CI follow-up. Following surgery, the speech processor was fitted for each subject according to the CI manufacturer’s guidelines. Demographic data of the SSD-PD group is presented in Table The second (reference) group consisted of 12 adult patients with partial deafness in both ears (PD group), who underwent unilateral implantation at the Institute of Physiology and Pathology of Hearing using the same surgical procedure (the six-step round-window approach). The patients were matched according to age at implantation , hearing loss in the ear to be implanted (which had largely retained low frequency hearing: hearing thresholds for 0.5 kHz of up to 80 dB), gender, and side of implantation. In addition, their dates of implantation were in the same range as the SSD-PD group. Demographic data of the PD group is also presented in Table Hearing preservation (HP) was assessed using the Skarzynski hearing preservation formula and classification . The degThe APHAB (Abbreviated Profile of Hearing Aid Benefit) questionnaire was used to evaluate the benefit from cochlear implantation . All fouThe hypothesis of a normal distribution of the data was evaluated using a Shapiro–Wilks test. Two-way repeated measures ANOVA was used to determine (1) the effect of time on hearing preservation, HP%, and (2) whether HP% differed significantly between the SSD-PD and PD groups. Two-way repeated measures ANOVA was also used to test (1) the effect of intervention (cochlear implantation) on APHAB results, and (2) whether the APHAB results differed significantly between study groups. The level of significance was set at α = 0.05.The study was designed and conducted according to the Declaration of Helsinki and the study protocol was reviewed and approved by the Institutional Review Board.Figure F = 1.46; p = 0.24); no group effect  = 0.82; p = 0.38); and no interaction between those variables  = 0.4; p = 0.53). This indicates that hearing preservation in the SSD-PD and PD groups did not differ, and that hearing thresholds were stable over 14 months of observation in both groups.Mean hearing preservation HP% for the implanted ear in the SSD-PD group was 82% one month after CI and 75% some 14 months after implantation; corresponding results in the PD group were 71% and 69%. Two-way repeated measures ANOVA showed no significant effect of time  = 1.46; p = 0.24); no group effect  = 0.82; p = 0.38); and no interaction between variables  = 0.4; p = 0.53). The results are plotted in Fig. We also analysed the stability of hearing thresholds in the non-implanted ear using the same formula and classification system. Analysis showed no significant effect of time , for BN = 39.1 p.p. (SD = 18.6), for RV = 33.4 p.p. (SD = 24.9), for AV =  − 14.9 p.p. (SD = 22.9), and for GS = 33.1 p.p. (SD = 20.1). The mean benefit in the PD group was: EC = 28.7 p.p. (SD = 20.4), BN = 23.3 p.p. (SD = 17.2), RV = 25.9 p.p. (SD = 21.9), AV =  − 10.5 p.p. (SD = 11.1), and GW = 25.9 p.p. (SD = 18.3). Detailed results for both groups are presented in Fig. F = 17.4; p < 0.001) and a significant main effect of CI use  = 48.2; p < 0.001). There was no statistically significant interaction between the effects of patient group and CI use  = 0.065; p = 0.8). Similar results were obtained for the RV subscale and for GS: a significant main effect of patient group  = 15.2; p < 0.001; for GS: F = 16.8; p < 0.001) and a significant main effect of CI use  = 38.5; p < 0.001; for GS: F = 56.6; p < 0.001). There was no statistically significant interaction between the effects of patient group and CI use  = 0.61; p = 0.44; for GS: F = 0.84; p = 0.37). This indicates that for the EC and RV sub-scales, as well as for GS, a significant benefit from CI use was observed regardless of the patient group; however, the reported frequency of problems was higher in the PD group compared to the SSD-PD group in both conditions: with CI and without CI  = 10.6; p = 0.003), a significant main effect of CI use  = 72.9; p < 0.001), and a significant interaction between the effects of patient group and CI use  = 4.69; p = 0.04). For the BN sub-scale, the frequency of problems without a CI was similar for both the PD and SSD-PD groups. At the same time, with a CI, the frequency of problems significantly decreased in both groups, and was significantly lower in SSD-PD patients compared to PD patients  = 11.9; p = 0.002). There was no effect of patient group  = 1.5; p = 0.23) or interaction between patient group and CI use  = 0.35; p = 0.56). The frequency of problems reported by patients was significantly lower without a CI compared to the condition with a CI  hearing (NH) in the other ear. Despite low frequency residual hearing in their implanted ear, they met the audiological criteria for SSD. In general, patients with SSD can differ significantly in terms of etiology from the usual group of patients with partial deafness (high frequency profound hearing loss in both ears) when considered as candidates for a CI and electric-acoustic stimulation (EAS). For post-lingual SSD, the results of a study done by Usami et al.  indicateThe outcome measure was hearing preservation as determined by the Skarzynski Classification System. The results showed excellent hearing preservation in the SSD-PD group. In this group, mean hearing preservation (HP%) for the implanted ear was 82% one month after CI surgery and 75% 14 months after implantation; in comparison, results in the PD group were 71% and 69% respectively. However, the differences between the groups were not statistically significant. This tends to indicate that any differences in etiology between the SSD-PD group and the PD group were not relevant for hearing preservation after a CI. At the same time, the figures above for hearing preservation in our SSD-PD group were very similar to the results of Snel’s meta-analysis of hearing preservation in a PD group  . Any sliWhen assessing the benefit from a CI in cases of SSD, SRT in noise is usually measured for three spatially different locations of speech and noise. This will give one of three possible results: (1) the same SNR in the implanted ear (IE) and the non-implanted ear (NE); (2) more favorable SNR at the IE (IE > NE); or (3) less favorable SNR at the IE (IE < NE) . HoweverThe results of the current study indicate that, in terms of APHAB global score, there is no difference in benefit from CI between the SSD-PD and PD groups, a result that also held for the ease of communication and reverberation subscales. However, for the background noise subscale, the benefit for the SSD-PD group was larger than for the PD group. This is understandable by recognising that the ability to discriminate speech in noise depends largely on binaural cues, and in the SSD-PD group a CI restores binaural hearing to a larger extent than in the PD group. In particular, in the SSD-PD group there is a better access to the ILD cue, which is maintained by high frequency hearing through the CI in the implanted ear and natural hearing in the contralateral ear. In the PD group, after unilateral cochlear implantation, access to ILD is limited because of profound high frequency hearing loss in the ear contralateral to the CI.So far as we know, the benefit of a CI in a SSD-PD group has not been tested, and so our results can only be compared with published benefits of a CI in separate SSD and PD groups. The mean CI benefits in our SSD-PD group—revealed by APHAB global score as well as by ease of communication, background noise, and reverberation subscales—are in general larger than those reported in SSD groups –31 or inResults of the current study have demonstrated hearing preservation in a unique population of patients who were qualified as having SSD but still had residual hearing in the implanted ear. Hearing benefit was measured to be larger than that in patients with partial deafness in both ears who also underwent unilateral cochlear implantation. We conclude that for a patient with single-sided deafness, residual LF hearing in the ear to be implanted should not be considered a contraindication for receiving a CI.


Beauveria bassiana (B. bassiana) is a broad-spectrum entomopathogenic fungus that can control pests in agriculture and forestry. In this study, encoding ecdysteroid uridine diphosphate glucosyltransferase gene (egt) was successfully screened in B. bassiana on the medium containing 500μg/mL G418 sulfate solution through the protoplast transformation method. This enzyme has the function of 20E (20-hydroxyecdysone) inactivation, thus increasing the mortality of the early instar larvae infected with B. bassiana. In this study, we transformed B. bassiana with the egt gene, which deactivates 20-hydroxyecdysone, a key hormone in insect development. The results showed that transgenic B. bassiana killed more silkworms of the 2nd instar larvae than the wild-type with a shorter LT50 time, which was reduced by approximately 20% (day 1 of the 2nd instar silkworm infection of B. bassiana) and 26.4% (day 2 of the 2nd instar silkworm infection of B. bassiana) compared to the wild-type, and also showed a higher mortality number before molting. The transgenic B. bassiana had a higher coverage of the body surface of silkworms compared to the wild type on the 3rd instar. In summary, improving entomopathogenic fungi using biological methods such as genetic engineering is feasible. B. bassiana is the most common and widely used environmentally-friendly fungus in various insects in nature . pMD19-T [sp-6×His-EGT-linker-GFP] and the fungal expression vector pG418, containing the G418 resistance gene, were digested with Xho I and Kpn I, respectively. The recovered sp-6×His-EGT-linker-GFP was ligated into a transgenic vector. The Xho I-Kpn I fragment from pMD19-T [sp-6×His-EGT-linker-GFP] was excised and inserted into the Xho I-Kpn Ⅰ site of pG418 to generate pG418 [sp-6×His-EGT-linker-GFP]. The pG418 fungal expression vector was kindly provided by Professor Yi Zou of the College of Pharmaceutical Sciences at Southwest University .After optimizing the target gene containing EGT by using the preference codon of B. bassiana were generated as previously described [B. bassiana colonies grown on PDA medium for 14 days were collected using a sterile 20% glycerol solution and the mycelia were filtered to obtain a spore suspension. The spore suspension was cultured at 25 °C, 220 rpm shake culture for 12 h, and obtained 70~80% of the spore germinated buds (the length of the buds was approximately 3~4 times the diameter of the conidia). The germinated spore buds in a 50 mL culture medium were collected and washed twice using 15 mL OM buffer  via centrifugation at 4 °C and 4000× g for 5 min. The 10 mM Na–Phosphate buffer was prepared from a 2 M NaPB stock solution containing 90.9 g of Na2HPO4 and 163.4 g of NaH2PO4 per liter, with a pH of 6.5. Next, 10 mL mixed enzyme solution of 0.03 g Lysing Enzymes from Trichoderma harzianum  and 0.02 g Yatalase  dissolved in 10 mL OM buffer were added to digest the new buds. Protoplasts of B. bassiana were obtained via culturing at 28 °C and 80 rpm shake culture for 13 h. Then, 10 mL of trapping buffer  were added and protoplast layers were collected by centrifugation at 4 °C and 4000× g for 20 min. Then, after being added to 10 mL STC buffer , the precipitate was obtained by centrifugation at 4 °C and 4000× g for 8 min and resuspended in 300 μL STC buffer. The 10 μL transgenic fungal expression vector pG418-sp-His-EGT-GFP ultrapure plasmid was mixed with 100 μL protoplasts and allowed to stand on ice for 40 min. Then, 600 μL PEG solution  was added and mixed evenly, standing at 25 °C for 30 min. The mixture was added to an SD-PDA (PDA with 21.86% sorbitol) plate containing 500 μg/mL G418 sulfate solution prepared in advance, and the mixture was manually rolled and spread. Under 26 °C, RH ≥ 90%, the mixture was cultured for 1 day and then inverted for 4 days. A white colony was selected and continuously cultured on PDA plate containing 500 μg/mL G418 sulfate solution, purer transgenic strains were obtained after 20 generations of single-spore cultured on PDA plates containing 500 μg/mL G418 sulfate solution.The protoplast transformation method has been widely applied in the genetic transformation of fungi. Transgenic escribed , the detrpm shake culture for 3 days, the precipitate was collected via centrifugation at 4 °C and 12,000× g for 3 min. After adding 700 μL CTAB buffer  and transferring to 1.5 mL sterile centrifuge tube, the mycelium fluid was quickly frozen with liquid nitrogen for 2 min, followed by a 65 °C water bath for 2 min, and repeatedly freeze-thawed five times. Then, 600 μL of the liquid was piped into a new 1.5 mL sterile centrifuge tube and added to 600 μL phenol: chloroform: isoamyl alcohol . The 500-μL supernate was obtained via centrifugation at 13,000× g for 10 min and then 500 μL phenol: chloroform: isoamyl alcohol  was added. After, 400 μL of supernate was obtained via centrifugation at 13,000× g for 10 min and added to 800 μL precooled isopropanol, then allowed to stand at −20 °C for 40 min. After centrifugation at 13,000× g for 5 min, the precipitate was washed using 600 μL 70% ethanol, dried at room temperature, and dissolved in sterile water sequentially. The total RNA was extracted using the RNA extraction kit SteadyPure Universal RNA Extraction Kit , then first-strand cDNA was generated using reverse transcription kit NovoScript® Plus All-in-one 1st Strand cDNA Synthesis SuperMix (gDNA Purge) , according to the manufacturer’s protocol.The target strain was scraped with a sterile cotton swab and inoculated into a sterile test tube containing 8 mL of PDB medium. After shaking the culture at 26 °C and 200 egt fragment from B. bassiana genomic DNA. The PCR profile was used to amplify target sequences: 94 °C for 5 min; 30 cycles of 94 °C for 30 s, 60 °C for 30 s and 72 °C for 1 min; 72 °C for 10 min. PCR for the full-length sp-His-EGT-GFP from B. bassiana cDNA was used to amplify target sequences: 94 °C for 5 min; 30 cycles of 94 °C for 30 s, 58 °C for 30 s and 72 °C for 2 min 40 s; 72 °C for 10 min. 18S rRNA was used as the control. The PCR profile was 94 °C for 5 min; 30 cycles of 94 °C for 30 s, 58 °C for 30 s and 72 °C for 20 s; 72 °C for 10 min. Electrophoresis confirmed products on a 1.2% (w/v) agarose gel. The primers involved in PCR are shown in Polymerase chain reaction (PCR) for B. bassiana were inoculated in a PDA medium containing effective antibiotics, 26 °C, RH ≥ 90%, and cultured for 14 days. Conidia were scraped and dispersed in 0.05% Tween-80 solution to obtain a spore suspension with 1 × 107 conidia/mL concentration. Silkworm larvae were selected as the test insects on the first day of the second instar, the second day of the second instar, and the first day of the third instar. Each group comprised 300 silkworms of similar size. The spore suspension was evenly sprayed on small silkworms to observe the moisture on the body surface. A 0.05% Tween-80 solution without spore was used as a negative control. After 30 min of inoculation, mulberry leaves were fed to the silkworms, the temperature was raised to 26 °C, and the RH ≥ 90%. Silkworm excrement was cleaned, and fresh mulberry leaves were replaced daily. The death of each treatment was observed and recorded at 12 h intervals, and the median time to death of the test insects was counted prior to the death of wild-type and transgenic B. bassiana-infected silkworm. The transgenic B. bassiana treatment group was the experimental group, and the wild-type B. bassiana treatment group was the control group. Fresh B. bassiana with white villous hyphae and powdery spore on silkworm carcasses were selected, and the hyphae and conidia were collected. The genome and RNA were extracted and detected by PCR, as described above.The wild-type and transgenic Microsoft Excel was used to organize the raw data and graph the survival curves and mortality rates.B. bassiana, sensitive antibiotics and the optimal concentration of B. bassiana were screened using the PDA medium. First, the activated B. bassiana conidia were inoculated on PDA solid medium with 50, 100, and 200 μg/mL of Hygromycin B, Zeocin, and G418 sulfate solution, respectively. After two days of culture, B. bassiana was grown on a medium containing 200 μg/mL Hygromycin B and Zeocin. The growth of B. bassiana was inhibited in the medium containing the G418 sulfate solution, and the higher the concentration, the more pronounced the inhibition  died before molting was still greater than those inoculated with wild-type B. bassiana (88%)  of transgenic ild-type A,B, whilr larvae C. These B. bassiana died (108 h after inoculation), the phenotypes of 30 small silkworms were observed by photography. The proportion of small silkworms with mycelia on their body surface was approximately three times that of the control group. Some dead silkworms  [B. thuringiensis, which can dissolve insect midgut epithelial cells, leading to cell lysis [B. bassiana-infected larvae after molting. Shedding of epidermis significantly affects the success of B. bassiana infection. Shedding of epidermis significantly affects the success of B. bassiana infection. Therefore, we utilized the characteristic of EGT to deactivate 20E [B. bassiana to inhibit the molting hormone in insects, thus extending the interval between molting of early instar larvae to improve the success rate of B. bassiana infection. Due to cost and limitations in experimental facilities, the conclusions of this study have only been validated in the laboratory. To confirm the effectiveness of larger-scale applications or the establishment of a relevant technological systems, further verification is needed by expanding the sample population in different or the same varieties of silkworms.Many genes have been introduced into e (Pr1A)  and Pr1Ae (Pr1A) , and chie (Pr1A) , are syne (Pr1A) ,12. Therll lysis . Scorpioll lysis . The maivate 20E ,25,27 anB. bassiana in this study theoretically has broad-spectrum biological control against Lepidoptera pests, its true effect still needs to be verified in other insects. In addition, it is noted that although the molting of larvae from almost all insects requires 20E to participate [B. bassiana strain compared to the wild-type strain may weaken or even disappear. Therefore, regardless of whether it is the endogenous protein of the fungus or the exogenous protein, the extent of improvement is limited by the expression level of the target protein and the duration of application. In the future, genes that can enhance fungal infection efficiency (such as CDEP1 and CHIT1), genes toxic to insects (such as Vip3A and Cry1Ac proteins), and genes affecting insect molting (EGT) can be simultaneously expressed in B. bassiana or mixed in different strains of B. bassiana containing these proteins to enhance the insecticidal activity of B. bassiana against harmful insects and ultimately expand the application of B. bassiana in biological control.Lepidoptera is the second largest order in the class Insecta, with the most agricultural and forestry pests. Silkworms are commonly used as model insects for research on lepidopteran ,36 and fticipate , the molB. bassiana a new function—deactivating 20E. This successfully caused the death of second instar silkworm larvae, which are usually difficult to infect with B. bassiana because of their short molting period. This approach partially compensates for the limitations of the long pathogenic cycle of B. bassiana in the application of pest biological control and provides insights into the improvement of biocontrol fungi and their efficacy against other insects.Based on the characteristic of insect molting, we focused on “giving”


For the sake of achieving high-coercivity HD magnets, two major routes have been developed, namely the grain boundary diffusion process (GBDP) and the dual alloy diffusion process (DADP). In this review, the fundamentals and development of the HD Nd-Fe-B magnets are comprehensively summarized and discussed based on worldwide scientific research. The advances in the GBDP and DADP are investigated and summarized based on the latest progress and results. Additionally, the mechanisms of coercivity enhancement are discussed based on the numerous results of micromagnetic simulations to understand the structure–property relationships of the HD Nd-Fe-B magnets. Lastly, the magnetization reversal behaviors, based on the observation of magneto-optic Kerr effect microscopy, are analyzed to pinpoint the weak regions in the microstructure of the HD Nd-Fe-B magnets.High-performance Nd-Fe-B-based rare-earth permanent magnets play a crucial role in the application of traction motors equipped in new energy automobiles. In particular, the anisotropic hot-deformed (HD) Nd-Fe-B magnets prepared by the hot-press and hot-deformation process show great potential in achieving high coercivity due to their fine grain sizes of 200–400 nm, which are smaller by more than an order of magnitude compared to the traditional sintered Nd-Fe-B magnets. However, the current available coercivity of HD magnets is not as high as expected according to an empirical correlation between coercivity and grain size, only occupying about 25% of its full potential of the anisotropy field of the Nd BH)max from rare-earth-free permanent magnetic materials ((BH)max < 15 MGOe) to rare-earth permanent magnetic materials ((BH)max ≈ 15–55 MGOe) (BH)max and has been widely applied in numerous industrial fields, such as new energy vehicles and wind power generation [AH) of the main phase because the AH values of Dy2Fe14B (μ0AH = 15.0 T) and Tb2Fe14B (μ0AH = 22.0 T) are much higher than those of Nd2Fe14B (μ0AH = 7.6 T). The scarcity of HRE resources has posed a challenge to their sustainability since the “Rare-earth crisis” occurred around 2011. Therefore, numerous researchers are making efforts to develop low-HRE or even HRE-free Nd-Fe-B magnets accompanied with high coercivity [Over the past century, there has been significant development in permanent magnetic materials, resulting in a steady increase in their magnetic energy product 55 MGOe)  [1,2,3,45 MGOe  [neration ,7. Amongneration . Howeverneration ,10,11,12ercivity ,14,15. Tercivity ,17,18. Aercivity ,20,21,22cH = a − b ln D, where D is the grain size and a and b are the logarithmic fitting coefficients. However, when the grain size decreases below 3 μm, the available coercivity drops significantly due to the oxidation of pulverized fine powders during the conventional powder metallurgy route [2Fe14B phase (~250 nm) or even finer, and the oxidation of fine powders can be prevented at the same time. Among various magnet manufacturing processes, the hot-press and hot-deformation process is a promising next-generation industrial process for the production of high-coercivity permanent magnets comprising nanoscale grains. The hot-deformed (HD) Nd-Fe-B magnets, which are produced from melt-spun powders with ultrafine grains (~50 nm), are distinct from the traditional sintered Nd-Fe-B magnets with micron-sized grains. Since the sizes of flake melt-spun powders are much larger than those of grains, the oxidation of nanoscale grains within the flakes are preventable during the process. The HD magnets have grain sizes ranging between 200 and 400 nm and have garnered interest as a promising choice for achieving high coercivity without resorting to HREs.gy route . Hence, β), which is defined by the following equation:T is the working temperature of magnets. The relationship between the temperature coefficient of coercivity (25–180 °C) and grain size for various types of Nd-Fe-B magnets (HRE-free) is illustrated in 2Fe14B grains [For the permanent magnets, the thermal stability generally refers to the ability to resist thermal demagnetization at working temperatures (120–200 °C) in various applications. The thermal stability of coercivity is evaluated by the temperature coefficient of coercivity β=HcT−Hc °CHc25 °B grains ,29.A lot of research has been carried out to enhance the coercivity of HD magnets with low Dy/Tb or even free Dy/Tb. Thus far, two main methods have been proven effective and greatly developed, namely the grain boundary diffusion process (GBDP) and the dual alloy diffusion process (DADP). The crystal alignment mechanism employed in the production of HD magnets completely differs from the traditional sintered magnets. In sintered magnets, the crystallographic alignment is achieved via magnetic alignment, while in HD magnets, such alignment is achieved through plastic deformation at high temperatures . Figure 2Fe14B nanocrystalline grains with a random orientation [2Fe14B nanocrystallites along the c-plane and the rotation of Nd2Fe14B nanocrystallites towards the direction perpendicular to the deformation pressure [BH)max progressively increase from MQ1 to MQ2 and ultimately to MQ3.The schematic diagrams of producing HD magnets are depicted in entation ,34. In tentation ,36,37. Tpressure ,39,40,41pressure . Their rc-axis alignment of grains, while the coercivity is heavily influenced by the intergranular RE-rich phase. For the HRE-free HD magnets, the acquired remanence (1.35–1.50 T) has occupied over 80% of its full potential of the saturation magnetization (μ0sM = 1.61 T) of the Nd2Fe14B phase, while the acquired coercivity (1.5–2.0 T) is only about 25% of its full potential of the anisotropy field (μ0AH = 7.6 T) of the Nd2Fe14B phase, indicating that there is still much room for the improvement of coercivity. Therefore, the current research mainly focuses on the development of high-coercivity HD Nd-Fe-B magnets.The presence of intergranular RE-rich phases is crucial for achieving high coercivity in the HD magnets ,44. Acco70Cu30 infiltrated HD Nd-Fe-B magnet  were also attempted to enhance the coercivity using the DADP. However, the doping of Nd-M alloy powders, such as Nd-Cu, Nd-Cu-Al and Nd-Fe-Ga-Cu, showed unremarkable results in the increase in coercivity (<2.1 T) ,65,69,70ve 2.3 T ,72. It i35Dy35Cu30 alloy with a low melting point of 610 °C achieved an almost identical coercivity compared to the doping with a Dy70Cu30 alloy with high melting point of 795 °C, almost doubling the utilization efficiency of the HREs. Recently, Xia et al. [35Dy35Cu15Ga15 and Nd35Tb35Cu15Ga15 alloys with low melting points of 616 °C and 605 °C, respectively, consuming only 2.2 wt.% HREs  compared to the GBDP, but it can easily realize decent UHRE values in the thick magnets (>3 mm). For the bulk HD magnets with a thickness of 6 mm, the doping of Nd-Dy-Cu-Ga and Nd-Tb-Cu-Ga alloys surpassed that of previous works with regard to achieving a high UHRE.According to the above review, it can be concluded that the diffusion efficiency of HREs is affected not only by the chemical constituents of HRE-containing alloys, but also by the various diffusion processes (GBDP and DADP). The utilization of HREs is roughly evaluated by the following formula:2Fe14B grains are perfectly aligned and magnetically isolated from each other, there is potential to achieve a high coercivity of μ0AH = 6.7 T. However, only about 20–40% of such coercivity is achieved among all the Nd-Fe-B-based permanent magnet materials. The following phenomenological equation first proposed by Kronmüller describes the temperature dependence of coercivity (cH) [AH and sM refer to the anisotropy field and saturation magnetization of the 2:14:1 phase, respectively, and their values can be obtained from ref. [α is a parameter that relates to the reduction in the AH caused by the microstructural defects at grain boundaries, grain surfaces and external surfaces of the sample [α value of 1 is for the magnetically isolated grains without microstructural defects. Conversely, if the microstructural defects completely cover the hard magnetic main phase, the α can reach a minimum value of 0. The second term corresponds to the influence of the demagnetization field to the magnetization reversal process, in which effN is the effective demagnetization factor that is mainly dependent on the grain size and grain shape [effN value decreases with the decreased grain size and aspect ratio. Larger α and AH values and smaller effN and sM values imply higher coercivity. The α and effN values for different types of Nd-Fe-B magnets are listed in α and lower effN, indicating their greater potential for achieving high coercivities. α through modifying the microstructural defects of grains and the GB. For the HRE-containing alloys, the diffusion of them increases AH and decreases sM because the HREs diffuse into a 2:14:1 phase. Notably, grain growth is almost inevitable during the GBDP, while it is inhibited during the DADP [effN exhibits the opposite trend between the GBDP and DADP.Theoretically, if the Ndity (Hc) :Hc(T) = rom ref. . The fire sample ,81,82. Tin shape . The Nefthe DADP ,72. Ther2Fe14B grains separated by a 4 nm thick grain boundary phase. By varying the μ0sM and exchange stiffness (A) of the grain boundary phase decreased from 1.2 T to 0.0 T and from 8 pJ/m to 0 pJ/m, respectively, the coercivity and nucleation field were enhanced. The decreased ferromagnetism of the grain boundary phase played a critical role in this enhancement. A finite element micromagnetic simulation is a highly practical tool for studying the impact of microstructure, such as grain boundary phase composition, grain size, grain shape, etc., on the magnetization reversals and coercivity mechanisms of Nd-Fe-B permanent magnets . The inf2Fe14B shell. A micromagnetic simulation was utilized to investigate how the presence of the 2Fe14B shell affected the temperature dependence of coercivity, as shown in 0.5Pr0.5)2Fe14B shell remained higher than that without until the temperature reached 107 °C. The modeled sample containing the (Nd0.5Pr0.5)2Fe14B phase exhibited a sharper decrease in coercivity with rising temperature, suggesting that partial Pr substitution for Nd on the surface of platelet-shaped grains slightly worsened the thermal stability of coercivity.It was described above that the Pr-Cu infiltration resulted in a greater increase in coercivity at room temperature, while it showed an inferior thermal stability of coercivity in comparison to the Nd-Cu infiltration . This wa2Fe14B grains were incomplete after the GBDP of HRE-containing alloys in previous works [2Fe14B grains to magnetization reversals and coercivity, micromagnetic simulations were implemented. As shown in 2Fe14B grains with no shell, (ii) Nd2Fe14B cores encompassed by entirely Tb-rich shells, (iii) solely the side of Nd2Fe14B grains covered by a Tb-rich shell and (iv) the c-plane surface of Nd2Fe14B grains covered by a Tb-rich shell [2Fe14B grains resulted in a coercivity increase from 1.8 T to 2.2 T 2Fe14B and (Nd0.9Tb0.1)2Fe14B), two grain shapes (80 × 16 nm2 with an aspect ratio of 5.0 and 57 × 13 nm2 with an aspect ratio of 4.4), and two grain boundary phases (a 3 nm thick ferromagnetic grain boundary phase and a 6 nm thick nonferromagnetic grain boundary phase). As seen in AH of the 2:14:1 phases for models with different grain sizes and grain boundary phases. The models with nonferromagnetic grain boundary phases exhibit higher coercivity values in comparison to those with ferromagnetic grain boundary phases. Additionally, the coercivity increment caused by the nonferromagnetic grain boundary phases demonstrates a positive relationship with the AH of the 2:14:1 phase, while exhibiting negative relations with the grain size and grain aspect ratio. These results indicate that a higher AH of the 2:14:1 phase, coupled with smaller grain sizes and grain aspect ratios, increases the positive effect of the nonferromagnetic grain boundary phase in enhancing coercivity.The results of micromagnetic simulations in a previous work  revealedc-axis in-plane, observed by magneto-optic Kerr effect (MOKE) microscopy [c-axis when the applied magnetic field reached −1.0 T. Conversely, for the diffusion-processed magnet, the propagation of reversed domains occurs along the lateral direction without the cascade propagation, and a higher external field is needed to drive the reversal of domains. The change processes of the magnetization reversal of the as-deformed and diffusion-processed magnets with the c-axis out-of-plane were also observed, as shown in Understanding the evolution of magnetization reversal in HD Nd-Fe-B magnets before and after the GBDP is crucial for the strategy of coercivity enhancement. croscopy . For theIt is worth noting that the reversed magnetic domains originated near the interfaces of the melt-spun ribbons c, which 2Fe14B grains at the ribbon interfaces is effectively inhibited, reducing the width of the CG region  electric vehicles at working temperature ranges of 20–180 °C, the magnetic properties of permanent magnets including μachieved . Figure Anisotropic HD Nd-Fe-B magnets offer a promising option for developing high-coercivity magnets due to their nanoscale grains. To further enhance the coercivity and its thermal stability of HD magnets, two effective approaches were developed: the grain boundary diffusion process and the dual alloy diffusion process. These processes modify the microstructure of the magnets, including the composition of the 2:14:1 phase, the nature of intergranular RE-rich phases, grain sizes, etc., without requiring or with low levels of heavy rare-earth elements. In this context, the mechanisms of coercivity enhancement of the HD magnets processed by the grain boundary diffusion process or the dual alloy diffusion process are summarized and discussed based on the numerous results of microstructure observations and micromagnetic simulations, revealing the respective role of the main phase, intergranular phases and grain size on the increased coercivity. Furthermore, the magnetization reversal behaviors of the as-deformed magnets and the diffusion-processed magnets are observed and discussed by directly observing the migration of magnetic domains. The exchange-coupled as-deformed magnets tend to exhibit cascade-like magnetization reversal, and the reversal process often starts in the regions of misaligned CGs. In contrast, magnetization reversal in the exchange-decoupled diffusion-processed magnets also starts at misaligned grains, but it is hindered by nonferromagnetic grain boundary phases, significantly reducing the cascaded propagation of domain walls. Thus, removing weak regions in the microstructure, such as misaligned CGs, is a practical solution to optimize the microstructure and magnetic properties of HD magnets.


Hearing loss is a sensory deficiency with enormous repercussions on language development, school learning, interpersonal communication, and the psychosocial well-being of individuals. It also has significant economic reflexes on the whole society. A recent publication has brought even more spotlights to the hearing problem: the World Health Organization (WHO) estimates that by 2050, a quarter of the world's population, or 2.5 billion people, will have some degree of hearing loss. In addition, the same report points out that 1.1 billion young people would be at risk of permanent hearing loss due to musical exposure at high and prolonged sound levelsOccupational rules used in the past in several countries, including Brazil, brought results based on a table score developed in the 1940s in the United States, the Fowler table. This table correlated hearing loss, measured in decibels, with a percentage of difficulty in recognizing speech, indicating a rate of hearing lossAnother problem related to the percentage determination of hearing loss concerns mathematics. The unit of measurement of the sound pressure level is called Bel, better represented by its tenth part, the decibel (dB), and is defined on a logarithmic scale. Being a logarithmic scale, it has no extremes that can be delimited from the mathematical point of view, presenting virtually infinite valuesThere are methods of establishing a maximum and minimum hearing value based on hearing averages of groups of individuals, known as the minimum hearing threshold and the maximum hearing threshold or pain threshold. With this, it would be possible to establish the maximum or 100% and the minimum or 0%. These standardizations are performed with groups of healthy individuals and supposedly with normal hearing but do not consider anatomical and physiological variability among individuals, and not even equity of conditions, such as atmospheric pressure, temperature, standardization of acoustic isolation, besides the evident subjectivity about which would be a maximum pain threshold. In the normal audiogram, we know that even what is usually defined as a 0 dB threshold, does not always limit the minimum value of auditory perception. Individuals with Superior Semicircular Canal Dehiscence (SSCD) may sometimes present auditory perception at lower thresholds than the 0 dB. Thus, the minimum threshold would not be the minimum for all patients, and the maximum threshold or pain threshold would not be the maximum for all, given the known variability of the pain threshold among individualsFrom the audiological standards, we know that individuals with conductive hearing loss present communication difficulties differently from those with sensorineural losses. Even among those last ones, we know that there are differences between those with cochlear losses from those with retrocochlear diseases. Similarly, individuals with central auditory processing disorders (CAPD) may present various manifestations of hearing disabilities. It may involve problems of recognition and interpretation of sounds and speech and could happen even in situations where the tonal auditory thresholds are normal.It would still be possible to list here other conditions and factors that make this attempt of quantifying via percentages a failure and susceptible to criticism: the various audible sound frequencies which are not all included in the audiometric thresholds used in pure-tone average; the existence of binaural hearing and age differences.Thus, we believe that the classification of hearing loss in degrees: mild, moderate, severe, profound, or similar, is the most appropriate classification from the medical and scientific perspective, although not fully able to frame any technical, legal, or even medical situation. Moreover, the attempt to quantify, in percentage, a human sensation, is just an empirical simplification of a much more complex event.


Listeners with hearing loss often struggle to understand speech in noise, even with a hearing aid. To better understand the auditory processing deficits that underlie this problem, we made large-scale brain recordings from gerbils, a common animal model for human hearing, while presenting a large database of speech and noise sounds. We first used manifold learning to identify the neural subspace in which speech is encoded and found that it is low-dimensional and that the dynamics within it are profoundly distorted by hearing loss. We then trained a deep neural network (DNN) to replicate the neural coding of speech with and without hearing loss and analyzed the underlying network dynamics. We found that hearing loss primarily impacts spectral processing, creating nonlinear distortions in cross-frequency interactions that result in a hypersensitivity to background noise that persists even after amplification with a hearing aid. Our results identify a new focus for efforts to design improved hearing aids and demonstrate the power of DNNs as a tool for the study of central brain structures. Hearing loss is a widespread problem with far-reaching consequences ranging from lost productivity and social isolation to decreases in quality of life and mental health . It alsoThe term ‘hearing loss’ does not capture the full spectrum of the effects of cochlear damage on auditory processing. One common consequence of cochlear damage is a loss of sensitivity that renders low-intensity sounds inaudible. This is the ‘hearing loss’ that is assessed in standard clinical tests and is addressed through amplification with hearing aids. But cochlear damage has other consequences beyond lost sensitivity that cause many people to struggle with high-intensity sounds that are well above their audibility threshold . For exaThe consequences of cochlear damage for the processing of both low- and high-intensity sounds have been well described at the level of the auditory nerve AN; . In addiOne recent study of individual neurons in the inferior colliculus (IC) with and without hearing loss and hearing aids found that some properties, such as phoneme selectivity, were impacted while others, such as frequency selectivity and trial-to-trial variability, were not . But, whOne of the challenges in characterizing neural coding at the network level is the high dimensionality of the activity patterns. If the goal is to gain insight into the underlying computations that the activity reflects, it can be useful to find more compact representations of the full activity that retain its important features. This process, often termed ‘manifold learning’ , typicalWe begin with the traditional approach to manifold learning using PCA to identify and analyze the low-dimensional subspace in which most of the variance in the full network activity patterns resides. We focus on the signal manifold, which captures the features of neural activity that are sound-evoked, first establishing that our recordings are sufficient to identify the signal manifold in individual animals and then that the changes in signal dynamics with hearing loss are fundamental, that is, that the dynamics within the signal manifold are not simply attenuated by hearing loss, but instead are truly distorted. We then continue our analysis using deep neural networks (DNNs) to perform manifold learning within the framework of a stimulus encoding model, which allows us to investigate the impact of hearing loss on the coding of novel sounds.We demonstrate that training DNNs on our recordings allows for accurate prediction of neural activity in conjunction with identification of the signal manifold. We use the trained DNNs to probe the processing of basic acoustic features and show that hearing loss predominantly affects spectral, rather than temporal, processing. We then probe the processing of speech and find that this impaired spectral processing creates a hypersensitivity to background noise that persists even with a hearing aid and appears to arise from aberrant cross-frequency interactions. Our results demonstrate the power of DNNs to provide new insights into neural coding at the network level and suggest that new approaches to hearing aid design are required to address the highly nonlinear nature of the effects of hearing loss on spectral processing.We recorded neural activity from the IC of anesthetized gerbils using electrode arrays with a total of 512 channels , allowinWe began by analyzing activity from animals with normal hearing. As a first step toward characterizing the neural code for speech at the network level, we determined whether there was shared variance across units that would allow us to reduce the dimensionality of the activity patterns. Previous work has shown that the correlations in IC activity are dominated by ‘signal’  rather than ‘noise’  .Signal correlations were also dominant in our recordings: although signal variance  accounted for only 40% of the overall variance in activity, signal correlations accounted for 95% of the total correlation between units . For a nWe partitioned the recordings from each animal into two sets: a training set that was used to identify the principal components (PCs) of the activity , step 1 To measure the signal variance explained , step 3,signal manifold, and that PCA is able to identify the dimensions that define this manifold. To confirm that PCA preferentially identified the signal manifold, we computed the fraction of the variance explained by each PC that was signal rather than noise  and verified that it decreased with each successive PC  linear readout to transform the activations in the bottleneck into the neural activity for each recorded unit. During training, the DNN learns to use the bottleneck to identify the low-dimensional feature space that captures as much of the explainable variance in the recorded neural activity as possible, that is, the signal manifold. Once trained, the DNN can be used to simulate neural activity for any sound, whether it was presented during neural recordings or not, with the activations in the bottleneck layer reflecting the underlying signal dynamics. This supervised approach to identifying the signal manifold also has the added advantage that it eliminates the residual noise that is inevitable with unsupervised methods such as PCA . See de..The utility of the DNN rests, of course, on its ability to faithfully reproduce the recorded neural activity. We trained and tested a separate DNN for each animal (after partitioning the recordings into training and test sets as described above) and found that they performed with remarkable accuracy. The explainable variance explained for activity in the test set approached 100% for the units with highest explainable variance and was far beyond that achieved by a standard single-layer linear–nonlinear model . We variWe also assessed the similarity of the DNN-derived signal dynamics across animals by measuring how much of the variance in the bottleneck activations from one animal could be explained by the bottleneck activations from another  and projected the full dynamics onto the top two PCs, which explained more than 90% of the variance for these simple sounds . For norensities . At low For animals with hearing loss, the signal dynamics were dramatically different. At moderate intensity, only the lowest frequencies elicited any activation  . RSA useThe structure of the RDMs was consistent with the observations about the dynamics made above. For normal hearing animals, the RDMs had a diagonal symmetry with a smooth gradient, indicating that the similarity of the trajectories for two frequencies decreased gradually as the difference between the frequencies increased. For animals with hearing loss, the RDMs had a block structure, indicating that the trajectories formed two clusters . Amplification to the best intensity softened the block structure and shifted the transition between clusters to a lower frequency, but did not fully restore the diagonal structure present in the normal hearing RDMs . To allow for direct comparisons of overall structure, we used canonical correlation analysis (CCA) . CCA ideThe average dynamics after alignment via CCA exhibited phenomena that were similar to those that were evident for individual animals . For norTo measure the similarity between the dynamics for different animals after alignment via CCA, we used a weighted sum of the point-by-point correlations between the two sets of dynamics after projection onto each pair of CCs, with the weight for the correlation associated with each pair of CCs given by the average variance in the original dynamics that those CCs explained (see ‘Methods’ for equation). Overall, the similarity between the dynamics for different normal hearing animals at moderate intensity after alignment via CCA was extremely high  when the consonants were presented at conversational intensity . For aniGiven that the perceptual difficulties experienced by listeners with hearing loss are most pronounced in noisy environments, we expected that the addition of background noise to the speech would create larger distortions in the neural code. We presented the same consonant stream with added speech babble . Comparing the similarity of the dynamics after alignment via CCA yielded similar results. The similarity between the dynamics for different normal hearing animals after alignment via CCA was high . The similarity between normal hearing and hearing loss animals when compared at the same intensity was much lower  and increased when the comparison was made at the best intensity  or after processing with the hearing aid , but remained well below normal levels. Again, for both forms of amplification, the similarity of the signal dynamics for speech in noise to those with normal hearing was much lower than for speech in quiet .When compared at the same high intensity (70 dB SPL), typical of a social setting, both RSA and CCA indicated strong effects of hearing loss on the signal dynamics for speech in noise . The simDecoding the signal dynamics for each animal suggested that the distortions in the signal dynamics for speech in noise had functional consequences . For norTo gain a better understanding of the differential impact of the background noise with and without hearing loss, we used MCCA to jointly align the signal dynamics for all animals with normal hearing and hearing loss so that we could make direct comparisons. We first analyzed the results for speech in quiet. When compared at best intensity , there wWith the hearing aid , the aliFor speech in noise, the alignment was generally worse overall. When compared at best intensity , the corGiven the nature of the hearing loss  becomes less sensitive and may shift toward lower frequencies while the ‘tail’ (corresponding to frequencies below CF) may become more sensitive . It is dOne recent study has provided insight into how the complex spectral distortions in the cochlea impact the neural coding of speech in noise in the AN . In animDistorted spectral processing has also recently been observed in the auditory cortex after mild-to-moderate sloping sensorineural hearing loss . AnimalsThe complex suprathreshold effects of hearing loss that are evident in the distorted neural signal dynamics observed in this study present a difficult challenge for hearing aid designers. Current hearing aids compensate for changes in the threshold and dynamic range of auditory processing using a framework built around a bank of bandpass filters with automatic gain control. The signal processing that can be performed by such a framework is highly constrained, and it is difficult to imagine how it could be used to compensate for problems such as hypersensitivity to background noise that involve highly nonlinear interactions across frequency bands. It is possible that with a better understanding of exactly how different frequencies are interacting, new signal processing frameworks can be designed to offset the more complex effects of hearing loss. But engineering such a framework that is flexible enough to provide benefit in a wide range of real-world settings will require conceptual advances that may not be forthcoming in the near term.One alternative approach to improving the perception of speech in noise that is already showing promise is speech enhancement, that is, the suppression of background noise. Hearing aids have offered noise suppression for many years, but the simple filters based on low-order statistics that are used by current devices often provide little real-world benefit . Recent A more flexible alternative is to identify optimal processing algorithms for hearing aids empirically by providing DNNs with the data they need to learn how best to transform sounds in order to elicit normal neural activity from an impaired auditory system . By takiBuilding computational models of sensory processing has been a long-standing goal in systems neuroscience. Current models of the sensory periphery can be highly accurate. For example, there are numerous models of the cochlea that faithfully capture the transformation of incoming sound into basilar membrane motion and AN activity . Models Until recently, efforts to model central sensory processing were constrained by the difficulties of fitting high-capacity models with limited experimental data. But deep learning has provided a new approach to fitting models that are well matched to sensory processing, and new methods for large-scale electrophysiology can provide the required big data. Initial efforts to use DNNs to model the sensory periphery have shown that they can be as accurate as hand-designed biophysical models. In one recent study, a DNN trained to replicate an established model of the cochlea provided accurate and generalizable predictions of the model’s response to speech and tones . In anotDNN models of sensory processing in the brain have also been shown to outperform traditional models. DNN models of V1 responses to natural images explained 50 and 80% of the explainable variance in single-unit spike counts and calcium activity, respectively , while DOur results improve on these initial successes in several important ways. Firstly, our models simulate neural activity with full temporal resolution, that is, spike times with millisecond precision. While lower temporal resolution may be sufficient to describe sensory processing in some contexts, precise spike timing carries critical information about speech . SecondlWith these advances, it should now be possible to use computational models of the brain for exploratory basic research, with benefits that are both scientific (studies are no longer data limited) and ethical , as well as for technology development . With further effort, it may be possible to build models that are not only black-box simulators but also mirror the underlying structure of biological systems . Such moExperiments were performed on 12 young-adult gerbils of both sexes that were born and raised in standard laboratory conditions. Six of the animals were exposed to noise when they were 16–18 weeks old. (These six were chosen from among many that were noise exposed based on the pattern of hearing loss that they exhibited: sloping mild-to-moderate in both ears.) The number of animals used was not predetermined. Because of the investigative nature of the study, the key outcome measures were not known in advance and, thus, a pre-study power analysis based on anticipated effect sizes was not possible. The duration of the data collection from each animal was predetermined based on the results of preliminary experiments in which the amount of neural activity required for manifold analysis and deep learning to yield stable results was assessed. Assignment to the control and hearing loss groups was random on a per-animal basis . Investigators were not blinded during data collection or analysis , but all analyses were fully automated and objective. ABR recordings and large-scale IC recordings were made from all animals when they were 20–24 weeks old. All experimental protocols were approved by the UK Home Office (PPL P56840C21).Mild-to-moderate sensorineural hearing loss was induced by exposing anesthetized gerbils to high-pass filtered noise with a 3 dB/octave roll-off below 2 kHz at 118 dB SPL for 3 hr . For aneAnimals were placed in a sound-attenuated chamber and anesthetized for surgery with an initial injection of 1 ml per 100 g body weight of ketamine (100 mg per ml), xylazine (20 mg per ml), and saline in a ratio of 5:1:19. The same solution was infused continuously during recording at a rate of approximately 2.2 μl per min. Internal temperature was monitored and maintained at 38.7°C. A small metal rod was mounted on the skull and used to secure the head of the animal in a stereotaxic device. The pinnae were removed and speakers (Etymotic ER-2) coupled to tubes were inserted into both ear canals along with microphones (Etymotic ER-10B+) for calibration. The frequency response of these speakers measured at the entrance of the ear canal was flat (±5 dB) between 0.2 and 8 kHz. Two craniotomies were made along with incisions in the dura mater, and a 256-channel multi-electrode array was inserted into the central nucleus of the IC in each hemisphere . The arrBefore beginning the IC recordings, ABRs were measured. Subdermal needles were used as electrodes with the active electrodes placed behind the ear over the bulla (one on each side), the reference placed over the nose, and the ground placed in a rear leg. Recordings were bandpass-filtered between 300 and 3000 Hz. The parallel ABR method  was usedSentences were taken from the TIMIT corpus  that conBackground noise sounds were taken from the Microsoft Scalable Noisy Speech Dataset , which iMUA was measured from recordings on each channel of the electrode array as follows: (1) a bandpass filter was applied with cutoff frequencies of 700 and 5000 Hz; (2) the standard deviation of the background noise in the bandpass-filtered signal was estimated as the median absolute deviation/0.6745 ; (3) times at which the bandpass-filtered signal made a positive crossing of a threshold of 3.5 standard deviations were identified and grouped into bins with a width of 1.3 ms. Only units with a signal correlation  of 0.2 or higher were used for manifold learning and DNN training .’ above).For each animal, the MUA was represented as an We applied PCA to We measured the similarity between the signal dynamics for different animals as the variance explained after linear regression of one set of dynamics DNNs were used to transform sound input into neural activity across four stages: (1) a SincNet layer  with 48 , where Models were trained to transform 24,414.0625 kHz sound input frames of length 8192 samples into 762.9395 Hz neural activity frames of length 192 samples . Sound inputs were scaled such that an RMS of 1 corresponded to a level of 94 dB SPL. Training was performed in MATLAB on a local PC with GPUs (2x NVIDIA RTX 3080) with a batch size of 64 for 10 epochs and took about 8 hr for a typical model. The Adam optimizer was used with a learning rate of 0.0001. The optimization was framed as Poisson regression with loss function For each animal, data were split into training and test sets (see ‘Speech’ above). The training set was used to learn the optimal values of the DNN parameters. The final performance of the optimized network was measured on the test set by calculating the percent of the explainable variance in the recorded responses that was explained by the network outputs based on the ratio of the covariance between For each animal, the activations in the bottleneck layer for all sounds from a given class  were extracted to form the 100 ms tones with frequencies ranging from 500 Hz to 8000 Hz in 0.2 octave steps; intensities ranging from 25 dB SPL to 100 dB SPL in 5 dB steps; 10 ms cosine on and off ramps; and a 100 ms pause between tones.100 ms bursts of bandpass noise with cutoff frequencies of 500 and 8000 Hz; a sinusoidal envelope with frequencies ranging from 10 Hz to 240 Hz in 10 Hz steps and a modulation depth of 1; intensities ranging from 25 dB SPL to 100 dB SPL in 5 dB steps; 10 ms cosine on and off ramps; and a 100 ms pause between tones.100 ms bursts of bandpass noise with cutoff frequencies of 500 and 8000 Hz; a sinusoidal envelope with a modulation depth ranging from 0.1 to 1 in 20 logarithmic steps and a frequency of 30 Hz; intensities ranging from 25 dB SPL to 100 dB SPL in 5 dB steps; 10 ms cosine on and off ramps; and a 100 ms pause between tones.linkage] was applied to the spectrogram time bins to identify two clusters based on a correlation metric and the border between them was inspected and corrected if needed), values after the end of the consonant were set to zero (with a 2 ms linear ramp), and the utterance was truncated to 200 ms. Utterances were presented in random order with a 175 ms pause between sounds at intensities ranging from 25 dB SPL to 100 dB SPL in 5 dB steps.Speech utterances were taken from the Articulation Index LSCP (LDC Cat# LDC2015S12). Utterances were from 10 American English speakers . Each speaker pronounced consonant-vowel syllables made from all possible combinations of 22 consonants and 13 vowels. For each utterance, the border between the consonant and vowel was identified in a semi-automated manner  was summed to create speech babble. The intensity of the babble was set based on the intensity of the isolated consonants to achieve a speech-to-noise ratio of 3 dB.Continuous speech from 16 different British English speakers from the UCL Scribe database  with a modulation depth ((peak – trough)/peak) of 1; an intensity matched to that of the narrowband target; 10 ms cosine on and off ramps; and a 100 ms pause between tones.A 10-channel wide-dynamic range compression hearing aid was simulated using a program provided by Prof. Johsua Alexander (Purdue University) . The croFor each animal, the signal dynamics matrix cannoncorr), where nt_mcca).To align two sets of signal dynamics, was used  (MATLAB fitcecoc) to identify consonants from signal dynamics with a max-wins voting strategy based on all possible combinations of binary classifiers and tenfold cross-validation.For each animal, the signal dynamics matrix  This fundamental work uses deep neural networks to simulate activity evoked by a wide range of stimuli and demonstrates systematic differences in latent population representations between hearing-impaired and normal-hearing animals that are consistent with impaired representations of speech in noise. The evidence supporting the conclusions is compelling, and the neural-network approach is novel with potential future applications. The research will be of interest to auditory neuroscientists and computational scientists. public reviews designed to be posted alongside the preprint for the benefit of readers; (ii) feedback on the manuscript for the authors, including requests for revisions, shown below. We also include an acceptance summary that explains what the editors found interesting or important about the work.Our editorial process produces two outputs: (i) Decision letter after peer review:eLife. Your article has been reviewed by 2 peer reviewers, and the evaluation has been overseen by a Reviewing Editor and Barbara Shinn-Cunningham as the Senior Editor. The following individual involved in review of your submission has agreed to reveal their identity: Stephen V David (Reviewer #2).Thank you for submitting your article "Distorted neural signal dynamics create hypersensitivity to background noise after hearing loss" for consideration by The reviewers have discussed their reviews with one another, and the Reviewing Editor has drafted this to help you prepare a revised submission.Essential revisions:1) The reviewers raised concerns about the generalizability of the study's approach. The approach hinges on the deep neural network getting things right, such that it generalizes across sounds. The study's claims would be substantially more convincing if the authors included data that validates their model's predictions about coding of speech in noise, tones in noise, and SAM noise in NH vs. HL animals. It appears that they have data in hand for speech and speech-in-noise from the same animals that they could analyze using methods already in the manuscript. If they are unable to validate any of these predictions, the authors should revise the manuscript to emphasize that they remain predictions until they can be validated with additional data in a different study.eLife 2018). The authors would want to place their work in the context of these and other works more clearly.2) Several labs have studied changes in inferior colliculus and cortex, but their work is not acknowledged in this manuscript. For example, the work by the Sanes lab at NYU and Polley lab at Harvard have advanced theories around decreased inhibition to accommodate reduced peripheral input. This work has also implicated deficits in temporal processing that do not at the surface appear consistent with the current study  Details about some statistical tests were hard to find , but it also appears that the authors still make important statements without statistical justification, for example, related to NH/HL+noise vs. NH/HL+quiet . There are several other cases where conclusions, e.g., about dimensionality, are not supported by a statistical test. The authors would want to make sure that all their conclusions are supported quantitatively.4) The authors would also want to flesh out the argument for why the same effects would not be present in the nerve.Reviewer #1 (Recommendations for the authors):Suggestions for authors:-at the end of intro, I think you could make it a little more explicit that the DNN is being trained to predict PC responses from sound.For people who will not be familiar with the practical constraints that necessitate a design with separate groups of normal-hearing and hearing-impaired gerbils, you might state explicitly early on that you are comparing separate groups of NH and HI gerbils.Figure 1 caption – why are there two sample sizes given in the last sentence?Line 83 – you might give some flavor as to the noise types that were usedFigure 2A is really well done -- you made a pretty complicated list of comparisons quite straightforward to followLine 111 and Figure 2 – I must be missing something, but I don't see how you can approach 100% explained signal variance given the way that I think it is calculated. Doesn't the noise variance show up in the denominator?Line 130 – I suggest motivating/justifying the additional linear transformation for the readerFigure 3 – can anything be said about how the left panel of Figure 3d looks pretty different from Figure 2i?I don't understand what constitutes a "recorded unit". The methods refer to multi-unit activity. Is a unit just an electrode from the array of 512? Or is there spike sorting being performed? How is any non-stationarity in the recordings dealt with ?Line 177 – I found myself wondering how the results would compare to a more conventional model with a STRF for each neuron. I suspect lots of people will wonder how the DNN compares to something like that.Lines 183-185 – give numbers for the similarity, to be parallel to earlier sectionsLines 219-220 – the "clustering of dynamics" referred to here was not all that evident to this reviewer from eyeballing the figure – please make what you mean more explicit, and clarify how this is different from refs 18 and 19Lines 232-233 – I recommend making the RDMs more conventional and just having more of them in the figure – I think people will find the asymmetry confusing when they page through the paperLine 241 – are the numbers mean and SD? Please specify.Line 246 – I didn't completely understand what would constitute a distortion to the "overall structure of the dynamics" – could you give an example?Multiple figures – I don't think the asterisks are defined clearly, and I believe the mean different things in different figures. Please label more explicitly, and/or mention in each caption.Line 287 – I found myself wondering about the possible effect of phase shifts or increases in response latency, which one might imagine could occur with hearing loss. I think the analysis would be highly vulnerable to this, especially given that the encoding of modulation is partly synchrony-based. The fact that the modulation analysis shows pretty similar results for NH and HI suggests there is not much of this, but some readers may wonder about this.At several points throughout the paper, I found myself wondering about the effects of compression. I would have been greatly interested to see an analysis that separately manipulated compression , to see how much benefit it produces on restoring normal-like responses. I also would have liked to see some discussion of its effects.Line 344 – for this analysis, I was hoping to have chance levels explicitly stated, and ideally labeled on the graph.Figure 7d – this panel is pretty confusing, partly because the SPL numbers are inside particular plots, so it is not completely clear what they apply to, and partly because the little numbers in the plots are not labeled or defined anywhere.Line 404-406 – how does this jive with the findings of distorted tonotopy from the Heinz lab?Line 408 and onwards – the text refers to CCs but the figure is labeled as PCsLine 438 – why is this coherence rather than correlation?Line 457-459 – these lines state the conclusions of the paper, but I think they could be more explicitly linked to the results that come earlier. Explain why the distortions are nonlinear, and explain why the effects involve cross-frequency interactions.Lines 472-473 – the statement here (and the earlier one on line 33) seems a little too strong given the widespread prevalence of noise reduction, and the widespread use of speech in noise diagnostics in audiometric evaluationsLine 484 and earlier – can the clustering be explained merely by audibility ?Line 496 – the claim here needs a referenceLine 511 – I wanted to know more about the absence of evidence of clustering in nerve responses. This seems critical.Line 586 and onwards – I think the conclusions/suggestions here should be tempered given that there are almost surely going to be limits to how well DNN models trained in this way will generalize to arbitrary stimuli. And you might acknowledge some of these limitations.Line 606 – I think it might be helpful to specify what a material transfer agreement would involve – does this just mean someone agrees not to share it with anyone else?Line 691 – why is "/ 0.6745" here? Is this a typo?Line 697 – what is a "unit"?Line 768 – I wondered whether setting values to 0 without any windowing might induce artifacts…Line 784 – it seems plausible that the hearing aid settings are suboptimal. In particular, the extent of compression is based on some informal optimization in humans. Could this partly explain the less than complete restoration of normal responses?Line 810 – it would help to link this to the weights that are described in the Results section. It took me a couple reads to make the connection.Overall, the statistical tests and quantitative comparisons are somewhat buried. There are a lot of statistical comparisons via color map  where a scatter or bar plot with error bars might be more helpful.Reviewer #2 (Recommendations for the authors):eLife 2018). Hearing loss and the neural coding of sound are complex, so the concern is not about the validity of the current results as much as how they fit in with the existing literature. Currently, the manuscript reads as if this previous work was never completed, and that issue should be addressed.1. While there are many open questions around central deficits following hearing loss, several labs have studied changes in IC and cortex, but their work is not acknowledged in this manuscript. In particular the Sanes lab at NYU and Polley lab at Harvard have advanced theories around decreased inhibitory tone to accommodate diminished bottom-up drive. Relevant to the current study, this work has implicated deficits in temporal processing that do not at the surface appear consistent with the current study  needs to be demonstrated by a quantitative comparison between them. Table S1 doesn't appear to contain anything about comparisons between data in the different figures. Please provide quantitative support for the statement that "… neither was sufficient to bring the similarity close to normal levels" (Line 379). There are several other cases where conclusions, eg, about dimensionality, are not supported by a statistical test. The authors should make sure that all their conclusions are supported quantitatively. It would also3. The performance of the DNN is impressive, providing a reasonable motivation for the subsequent analysis of "bottleneck PCs" for activity simulated by the model. However, one worries that since the models were not fit to stimuli tested in the simulation, that the results may not actually be reciprocated in actual neural activity. One contrast, in particular (speech in quiet vs. speech in noise), was actually collected experimentally, and it seems like the authors could validate their decoding analysis with the actual neural data. Can't the neural responses be projected back into the bottleneck space and be used to decode the same way as the DNN simulations? Such an analysis would substantially strengthen the study. Alternatively, the authors should include a caveat in the Discussion that the DNN simulations may not actually generalize to actual neural activity. The authors may wish to argue that this is a small concern, but the finding of such low-dimensional PC bottleneck is quite surprising, and it's not clear if dimensionality would be as small if the actual stimuli  were included in the fit set. Essential revisions:1) The reviewers raised concerns about the generalizability of the study's approach. The approach hinges on the deep neural network getting things right, such that it generalizes across sounds. The study's claims would be substantially more convincing if the authors included data that validates their model's predictions about coding of speech in noise, tones in noise, and SAM noise in NH vs. HL animals. It appears that they have data in hand for speech and speech-in-noise from the same animals that they could analyze using methods already in the manuscript. If they are unable to validate any of these predictions, the authors should revise the manuscript to emphasize that they remain predictions until they can be validated with additional data in a different study.We understand the concern about generalization to out-of-sample inputs. A model trained on one set of inputs may not necessarily produce accurate simulations of responses to a different set of inputs. Thus, in order for our analysis of model latent representations of non-speech sounds to be compelling, we must demonstrate that the model responses to such sounds are an accurate simulation of the true responses.The non-speech sounds that we used in the study were pure tones and SAM noise. For pure tones, we were able to verify that the models generalized well by using recordings from the same animals on which the models were originally trained (without including the tone responses in training). For SAM noise, we did not have recordings from the original animals. However, this provided an opportunity to further test the generality of the model by using transfer learning to predict responses from new animals for which we had responses to SAM noise as well as a small sample of speech. We froze the DNN encoder after training on animals from the original dataset and retrained only the linear readout using the speech responses for the new animals. We then tested the ability of the updated models to predict SAM noise responses for the new animals, and they performed well. This new work is described in the revised Results .The one class of sounds for which we were not explicitly able to validate the models is hearing aid-amplified speech. While these sounds are not qualitatively different from standard speech and we have no particular reason to believe the model predictions for these sounds would be inaccurate, we have added a note to the text to indicate the lack of validation.2) Several labs have studied changes in inferior colliculus and cortex, but their work is not acknowledged in this manuscript. For example, the work by the Sanes lab at NYU and Polley lab at Harvard have advanced theories around decreased inhibition to accommodate reduced peripheral input. This work has also implicated deficits in temporal processing that do not at the surface appear consistent with the current study . The authors would want to place their work in the context of these and other works more clearly.We have tried to do more to place our results within the context other related studies. The study from the Polley lab that is most closely related to ours is McGill et al. (2022) in which they study the downstream effects of mild-to-moderate sensorineural hearing loss.  The key findings of McGill et al. that relate to our study are (1) that hearing loss induces behavioral hypersensitivity for detection of tones at the frequencies around which the hearing loss increases from mild to moderate; (2) that these frequencies are overrepresented after rearrangement of the cortical tonotopic map; and (3) that (some) cortical neurons located in this region of the tonotopic map exhibit increased gain and synchrony in their responses.Our work does not investigate the circuit-level mechanisms that underlie the observed effects of hearing loss . The work from the Sanes lab is focused on these mechanisms and it is difficult for us to see how further consideration of our results in conjunction with theirs can lead to additional insights. The specific study suggested by Reviewer 2, Yao and Sanes (2018) is focused on developmental hearing loss, which makes it even more difficult to compare with our work. Also, the reviewer suggests that their results are somehow inconsistent with ours, but they are not. Their abstract states “We found that developmental HL … did not alter brainstem temporal processing.” Our results also suggest that HL does not alter brainstem temporal processing, and this is consistent with many other studies that have found that HL does not impact temporal processing in the early auditory pathway (see Parida and Heinz (2022) for another recent example). Understanding how temporal processing deficits arise at the level of the cortex after hearing loss is not something that our work can help with; for that we must continue to look to the Sanes lab and others who are focused on such questions.What we can do is try to synthesize our results with others related to mild-to-moderate sensorineural hearing loss from the auditory nerve and cortex in order to better understand the transformation that takes place along the way. We have added a new section to the Discussion “Distorted spectral processing from cochlea to cortex” along these lines. Perhaps the most salient point we can take from this exercise is the recognition that coordinated studies are needed to develop a coherent picture.3) Details about some statistical tests were hard to find , but it also appears that the authors still make important statements without statistical justification, for example, related to NH/HL+noise vs. NH/HL+quiet . There are several other cases where conclusions, e.g., about dimensionality, are not supported by a statistical test. The authors would want to make sure that all their conclusions are supported quantitatively.We have added statistical tests to support our assertion that the distortions in signal dynamics caused by hearing loss are more pronounced for speech in noise than for speech in quiet . Whether compared at best intensity or after amplification with a hearing aid, and whether measured via RSA or CCA, the distortions were much smaller for speech in quiet than for speech in noise, with all differences highly significant . We have included this information in the revised Results.We have also added distribution plots and statistical tests to support our assertion that the signal dynamics differ between pairs of animals with normal hearing and hearing loss more than between pairs of animals with the same hearing status . Whether based on the signal manifold as identified via PCA or via DNN, signal dynamics were much more similar for pairs of animals with the same hearing status than for pairs of animals with different hearing status.Reviewer 2 also suggested statistical tests in two instances where we did not make explicit comparisons between groups because we did not feel that these comparisons would be informative. But we include the statistical tests here for completeness:Figure 2c,g. The dimensionality of the signal manifoldWe assert only that the signal manifold is low dimensional with both normal hearing and hearing loss. We have added the range of values for the dimensionality of the signal manifold for each group of animals to the revised text. A t-test indicates that the average dimensionality with hearing loss is significantly lower than with normal hearing in the statistical sense . But since we cannot say whether or not this difference is significant in the functional sense in and of itself , we did not include it in the Results.Figure 3c. The predictive power of the DNN modelWe assert only that the model performs well for both normal hearing and hearing loss. In fact, the predictive power was generally higher for hearing loss than for normal hearing: separate t-tests for each bottleneck dimensionality  yielded p-vales of 0.08, 4e-4, 1e-3, 3e-3, 1e-3, and 1e-3; all but the first of these indicates significantly better performance for hearing loss even after (Bonferroni correction). But, inasmuch as we do not follow up on this difference to understand how it arises, we don’t think it is appropriate to include it in the Results.4) The authors would also want to flesh out the argument for why the same effects would not be present in the nerve.Broadly speaking, many of the effects that we see  are present in the AN. What does not appear to be present in the AN is the specific form of distorted spectral processing that we observe in the IC as a clustering of signal dynamical trajectories within the latent representation of the DNN model.Providing a definitive answer to the question of why these effects are present in the IC and not the AN is beyond us (though we added some speculative ideas to the new section in the revised Discussion). We can, however, provide more explicit evidence that the distortions in spectral processing in the AN and the IC are, in fact, different. To do this, we simulated AN responses to pure tone sounds with and without hearing loss, as suggested by Reviewer 1, and performed the same analyses of the signal dynamics as we did for the IC.We found no evidence in the AN of the clustering of dynamical trajectories that is present in the IC. . We also demonstrated that the clustering of dynamical trajectories that we observed in the latent representation of the IC DNN model was also evident in experimental IC responses. These new analyses are described in Figure 4 —figure supplement 1 of the revised manuscript.Reviewer #1 (Recommendations for the authors):Suggestions for authors:Figure 1 caption – why are there two sample sizes given in the last sentence?It is just one (large) sample size: n = 544,362.Line 83 – you might give some flavor as to the noise types that were usedThis information is in the Methods.Line 111 and Figure 2 – I must be missing something, but I don't see how you can approach 100% explained signal variance given the way that I think it is calculated. Doesn't the noise variance show up in the denominator?With the full complement of PCs, all of the variance in a dataset can be fully explained. So, in general, it should be no surprise that a subset of that variance can also be fully explained; in fact, it would be impossible for this not to be the case. It is, however, potentially surprising that so much of the signal variance can be explained with so few PCs.I don't understand what constitutes a "recorded unit". The methods refer to multi-unit activity. Is a unit just an electrode from the array of 512? Or is there spike sorting being performed? How is any non-stationarity in the recordings dealt with ?Spike sorting was not performed, but the extraction of multi-unit activity involved some processing. This is described in the Methods. Non-stationarity was ignored.Line 287 – I found myself wondering about the possible effect of phase shifts or increases in response latency, which one might imagine could occur with hearing loss. I think the analysis would be highly vulnerable to this, especially given that the encoding of modulation is partly synchrony-based. The fact that the modulation analysis shows pretty similar results for NH and HI suggests there is not much of this, but some readers may wonder about this.RSA is insensitive to phase shifts; CCA is not. This difference is part of the motivation for using both methods rather than just one or the other.At several points throughout the paper, I found myself wondering about the effects of compression. I would have been greatly interested to see an analysis that separately manipulated compression , to see how much benefit it produces on restoring normal-like responses. I also would have liked to see some discussion of its effects.We did an extensive analysis of the effects of compression on the neural coding of speech in a previous paper .Figure 7d – this panel is pretty confusing, partly because the SPL numbers are inside particular plots, so it is not completely clear what they apply to, and partly because the little numbers in the plots are not labeled or defined anywhere.We understand that this figure can be confusing, but the formatting and labelling are exactly the same as in all of the previous figures, e.g., 4d and 5d. We tried several other designs for this figure, but none were judged to be better.Line 404-406 – how does this jive with the findings of distorted tonotopy from the Heinz lab?The relationship between our findings and the distorted tonotopy that has been observed in the auditory nerve is considered in detail in the Discussion. To our knowledge, the potential for (frequency-weighted) amplification to mitigate the effects of distorted tonotopy on speech coding at the level of the auditory nerve has not been tested.Line 438 – why is this coherence rather than correlation?As used here, they are equal. We have relabeled as correlation since that is likely to be more familiar to readers.Line 484 and earlier – can the clustering be explained merely by audibility ?That would be possible, but (1) there is also clustering of responses to low-frequency tones that evoke strong responses and (2) the high-frequency tones do, in fact, elicit a response .Line 496 – the claim here needs a referenceThe relevant papers are cited throughout the paragraph.Line 691 – why is "/ 0.6745" here? Is this a typo?No. That is the scaling factor required to transform an estimate of mean absolute deviation into an estimate of standard deviation.(See https://en.wikipedia.org/wiki/Median_absolute_deviation)Line 697 – what is a "unit"?The MUA-processed signal from one recording channel.Line 784 – it seems plausible that the hearing aid settings are suboptimal. In particular, the extent of compression is based on some informal optimization in humans. Could this partly explain the less than complete restoration of normal responses?We agree that it is plausible that the hearing aid settings are suboptimal. But we think it is unlikely that this suboptimal fitting is the main reason why hearing aids are unable to restore neural responses to normal. We did an extensive analysis of the effects of hearing aids on the neural coding of speech in a previous paper .