Whole-room indirect calorimeters (WRICs) have traditionally been used for real-time resting metabolic rate (RMR) measurements, while metabolic rate (MR) during short-interval exercises has commonly been measured by metabolic carts (MCs). This study aims to investigate the feasibility of incorporating short-interval exercises into WRIC study protocols by comparing the performance of WRICs and an MC. We assessed the 40-min RMR of 15 subjects with 2-day repeats and the 10–15 min activity MR (AMR) of 14 subjects at three intensities, using a large WRIC, a small WRIC, and an MC. We evaluated the biases between the instruments and quantified sources of variation using variance component analysis. All three instruments showed good agreement for both RMR  and AMR assessment . Moreover, the majority of the variability was between-subject and between-intensity variation, whereas the types of instrument contributed only a small amount to total variation in RMR (2%) and AMR (0.2%) data. In Conclusion, the good reproducibility among the instruments indicates that they may be used interchangeably in well-designed studies. Overall, WRICs can serve as an accurate and versatile means of assessing MR, capable of integrating RMR and short-interval AMR assessments into a single protocol. A WRIC, an isolated room with a known volume and a controlled air inflow rate, provides minute-by-minute measurements of the subject’s O2 consumption and CO2 production from breaths, via the continuous measurement of O2 and CO2 concentrations in inflow air and outflow air. These data enable precise calculations of metabolic rate  for prolonged periods allowing for activities of daily living3, and energy expenditure , which is the cumulative MR over time. Hence, MR and EE in the general sense are used interchangeably in the medical literature and in this paper.Whole-room indirect calorimeters (WRICs) have been used to assess various metabolic states in both healthy subjects and subjects affected by pathologies4. RMR is influenced by various physiological characteristics, including gender5, ethnicity6, age8, body composition10, various metabolic syndromes16, and gene variations17. Additionally, resting MR (RMR) responds to environmental stimuli, such as cold temperatures19, food intake and dietary composition24. Moreover, RMR drops significantly during sleep25 and varies by circadian phase26. A smaller, yet important component of human MR is activity-induced MR (AMR)27. Activities can be subdivided into two categories: non-exercise activity thermogenesis (NEAT) and volitional exercise29. NEAT includes occupational and leisure activities and any spontaneous activities, such as fidgeting and maintenance of posture. Because of these factors, RMR needs to be assessed under controlled experimental conditions. By providing environmental control and real-time measurements over extended periods, WRICs are the perfect tools to isolate the various components of MR.Human EE is comprised of various components and is influenced by many factors. The largest component of human EE is resting EE, which is the energy required to carry out fundamental physiological functions, contributing 60–80% of the total daily EE32. This may cause claustrophobic sensations in some subjects, potentially impacting RMR measurements30 and limiting the recording duration. For researchers who are interested in assessing MR over a range of activities, MCs can be difficult to maneuver because of the tethering and may alter normal breathing patterns32. Lastly, to assess both RMR and AMR during a single study visit, MCs would require additional time for equipment changes and system re-calibration.Due to the cost of building, maintaining, and operating WRICs, most research aimed at assessing MR has used less costly metabolic carts (MCs). Instead of placing the subjects in a room, MCs require the volunteer to lie under a ventilated hood for RMR measurements, or to wear a mouthpiece and nose clip, or a face mask while tethered to the system33 and various activity types and intensities on total MR38, the “dilution effect” caused by the room size of the WRICs3 limits their temporal resolution, which has traditionally hindered their use in short-interval exercise studies. To remedy the long delay imposed by the room size of WRICs, we have recently devised a method to improve the temporal resolution of WRICs and validated our system against 22 sessions of 24-h gas infusion studies of dynamic metabolic profiles 3. Here, we further evaluate our system using data collected from 29 human subjects under both resting and exercising conditions, investigate the biases between instruments, and quantify the sources of variation in the MR mesurements.Although WRICs provide researchers with more flexibility to study the effects of sedentary behavior3), a small WRIC (5.5 m3), and an MC. The two WRICs had been previously validated using gas-infusion methods3. The MC used in this study (Parvo Medics TrueOne 2400) was in an adjacent room approximately 20 m away from the WRICs. All instruments were calibrated by following the procedures below. Written informed consent was obtained prior to the first study visit and all study procedures related to human subjects were approved by the Institutional Review Board at Virginia Commonwealth University. All research was performed in accordance with the relevant guidelines and regulations.This study was conducted on 29 healthy, non-smoking subjects. Exclusion criteria included age < 18 years, medications affecting metabolism, pregnancy or lactation, reported claustrophobia, and resting blood pressure > 140/90 mmHg. RMR and AMR were measured using three instruments: a large WRIC  onsite to 10 known concentration levels, with O2 ranging from 20.0–21.0% and CO2 ranging from 0.0–1.0%. Gas mixing was automatically performed by a gas blender comprised of mass flow controllers (MFCs). Each MFC was pre-validated against a primary flow standard . During the calibration stage, three MFCs were used to regulate the flow rates of N2, O2, and CO2. These three gases subsequently flowed into a manifold that mixed them at a known combination of flow rates, from which reference values for the O2 and CO2 concentrations were calculated. For the second calibration procedure, N2 and CO2 were infused into an empty chamber to simulate a human subject’s effect on the system and obtain reference values and the in silico performance of the WRICs. Flow rates of N2 and CO2 were pre-determined to generate reference values for volume of O2 (VO2) and volume of CO2 (VCO2), from which the reference values of MR were calculated via the Weir Equation39. The critical parameters for calculating VO2 and VCO2  were estimated given the measurements and the reference MR. These critical parameters were then used in processing the raw data collected in the human studies. All gas samples during the studies were dried below 1,000 ppm using a gas sample dryer . Other operational details of the WRICs have previously been published3.To ensure accurate measurements, the indirect calorimeters were calibrated prior to each testing session by following two procedures: (1) gas analyzer  calibration using mixed gases, and (2) WRIC system calibration using a gas infusion method. For the first calibration procedure, reference points for calibration were obtained by mixing gases , and 4.000% CO2, 16.00% O2, balance N2 (AMR).Prior to each RMR visit and AMR visit, the Parvo Medics TrueOne 2400 was allowed to warm up for a minimum of 30 min. Temperature, barometric pressure, and relative humidity were recorded to ensure accurate calibration of the instrument. Flow calibration was accomplished with a 3-L syringe with an error of ± 1%. Prior to each trial, OTo minimize the confounding effects of natural variations in MR, we randomized the sequence of measurement instruments  to be used for each subject. For RMR measurements, we repeated the experiment on separate days for each subject to allow the assessment of test–retest reliability. For AMR measurements, we devised individualized exercise intensities to ensure that subjects reached a steady-state MR.The RMR study consisted of two visits. To minimize biological variations in RMR, the two visits were scheduled for 2 days within 1 week, and female subjects were scheduled in the early follicular phase of their menstrual cycle (days 2–10). During each visit, the subject underwent RMR tests using all three instruments. The subjects were asked to arrive early in the morning (7:30 am—9:00 am) after an overnight fast (i.e. no food or caffeine intake) and no strenuous exercise for at least 24 h. A brief physical examination was also performed by a study physician to ensure that the inclusion and exclusion criteria were met. Following the physical examination, the subjects were asked to lie in a supine position for approximately 40 min and refrain from sleeping or moving. Subjects were also observed and kept awake during the trials. The room temperature was controlled at 24 ˚C.The AMR study consisted of three visits. The three visits were scheduled for three days within one week, with at least 24 h between each visit. The precautionary steps taken to eliminate biological variation in AMR were similar to the RMR tests, except that subjects could choose to arrive either early in the morning after overnight fasting (7:30 am–9:00 am) or late afternoon (3:00 pm–4:00 pm) after at least a 4-h fast, and this was held constant for each subsequent visit. After a routine examination, the subjects completed a 60-min exercise session on a cycle ergometer (VIAsprint 150P or Monark 928E) using one of the three randomly-assigned instruments. The same ergometer was used for all three visits for each participant. The three exercise intensities for each visit were determined based on the subject’s weight and gender. The workloads of the three intensities were 0.75, 1.5, and 2.25 W/kg for males, and 0.5, 1.0, and 1.5 W/kg for females. Each subject exercised for 15 min at each of the first two intensity levels, and 10 min at the highest intensity level, with a 10-min period of rest between each level. This exercise protocol was designed to account for the gender difference in power output and provide sufficient recovery time between each short-interval exercise in order to minimize fatigue.We performed data analysis in Matlab 2019b  and R Studio . The resting MR (RMR) was the average of a 40-min MR recording during supine rest, with the initial 10 min before the WRICs reached equilibrium discarded, resulting in 30 min of steady-state RMR. For the AMR measurements, we manually selected the steady-state MR, defined as the flat region between transition edges after plotting the entire session in Matlab. This resulted in approximately 12 min of data for the first two intensity levels and approximately 7 min of data for the highest intensity level, and we averaged the steady-state MR at each intensity.40. Since the true AMR is unknown, we assessed the linear response of the three instruments to various workloads as a proxy for instrument accuracy. Lastly, to assess the variability in MR between instruments over successive visits or at each exercise intensity, we conducted variance component analyses41 to decompose the total variance in the data into the percentage contributions of various factors .In our analysis, we first assessed biases between instruments at each visit or at each exercise intensity level, and tested the group differences using two-sided, paired t-tests. To visualize these biases, we also generated Bland–Altman plots for all comparisons. Test–retest reliability was assessed using the Pearson correlation coefficient between the RMRs measured on two visits. To check the validity of our exercise protocol, we assessed the intensity range using metabolic equivalent of tasks  for each subjectOf the 29 subjects, 15 participated in the RMR trial and 14 participated in the AMR trial  also measured 10% lower RMR than a WRIC . The authors posited that the finding was due to the lack of adjustment for moisture in the gas samples in the MC system, which resulted in underestimation of the VO2 and VCO2. Unlike the WRIC, the ventilated hood method assumes constant environmental factors  using a “reference air” canister as the ground truth which, if not tightly controlled, can critically affect the accuracy of the instrument31. In contrast, our WRIC systems actively dry the gas samples, isolate the influence of incoming air and take the gas concentration of the incoming air into account, leading to more accurate and precise measurements.The RMR readings from both WRICs in the current study were higher than the values obtained via the MC. Similar findings were observed in a study by Rising et al.43, where they found that the energy expenditure assessed with their WRIC system was 30% greater than with their MC system . As we conducted our exercise test at much shorter intervals (10–15 min at each intensity level as opposed to 30 min at one intensity level), we suspect the response time of the WRICs might be responsible for the slight underestimation of AMR. This underestimation is reduced in the small WRIC due to its smaller volume and shorter response time.On average, our large WRIC systems measured about 5% lower than the MC across all exercise intensities, in contrast to the study by Rising et al.Our variance component analyses show that reproducibility across the three instruments was excellent, contributing only 2% of the total variation in the RMR data and 0.2% in the AMR data. This suggests that when assessing cross-sectional RMR, the variation introduced by interchanging the three instruments could be negligible in studies investigating factors  that play a larger role. Moreover, in studies where AMR across activity intensities from low to vigorous activities is of interest, the three instruments can be used interchangeably if necessary.3, our WRICs can capture short-interval exercises (10–15 min), which greatly complement their traditional role as a real-time RMR measurement tool. WRICs could facilitate the recording of AMR, since there is no need to fit mouthpieces and noseclips, substantially reducing the discomfort experienced by study participants. As assessing and modeling MR in free-living situations becomes increasingly important for clinical nutritional research and obesity research, the flexibility provided by accurate WRICs will allow for more complex study designs to better characterize near “free-living” conditions. Collectively, our study demonstrates that modern WRICs can be used as excellent research tools for studying the dynamics of human energy expenditure during both rest and exercise.Overall, our study demonstrates that modern WRICs can be used for studies involving both RMR and exercise measurement, by adequately capturing both in a single setting. Using our previously-validated methods for recovering dynamic WRIC signalsSupplementary Information 1.Supplementary Information 2.Supplementary Information 3.


Sudden sensorineural hearing loss (SSNHL) is an otologic emergency that warrants urgent management. Pure-tone audiometry remains the gold standard for definitively diagnosing SSNHL. However, in clinical settings such as primary care practices and urgent care facilities, conventional pure-tone audiometry is often unavailable.This study aimed to determine the correlation between hearing outcomes measured by conventional pure-tone audiometry and those measured by the proposed smartphone-based Ear Scale app and determine the diagnostic validity of the hearing scale differences between the two ears as obtained by the Ear Scale app for SSNHL.This cross-sectional study included a cohort of 88 participants with possible SSNHL who were referred to an otolaryngology clinic or emergency department at a tertiary medical center in Taipei, Taiwan, between January 2018 and June 2019. All participants underwent hearing assessments with conventional pure-tone audiometry and the proposed smartphone-based Ear Scale app consecutively. The gold standard for diagnosing SSNHL was defined as the pure-tone average (PTA) difference between the two ears being ≥30 dB HL. The hearing results measured by the Ear Scale app were presented as 20 stratified hearing scales. The hearing scale difference between the two ears was estimated to detect SSNHL.The study sample comprised 88 adults with a mean age of 46 years, and 50% (44/88) were females. PTA measured by conventional pure-tone audiometry was strongly correlated with the hearing scale assessed by the Ear Scale app, with a Pearson correlation coefficient of .88 (95% CI .82-.92). The sensitivity of the 5–hearing scale difference (25 dB HL difference) between the impaired ear and the contralateral ear in diagnosing SSNHL was 95.5% (95% CI 87.5%-99.1%), with a specificity of 66.7% (95% CI 43.0%-85.4%).Our findings suggest that the proposed smartphone-based Ear Scale app can be useful in the evaluation of SSNHL in clinical settings where conventional pure-tone audiometry is not available. Sudden sensorineural hearing loss (SSNHL) is an otologic emergency that warrants urgent clinical visits and timely management. SSNHL is commonly defined as a sensorineural hearing loss of 30 or more decibels (dB) over at least 3 consecutive audiometric frequencies occurring within a 72-hour period ; it affeCurrently, pure-tone audiometry remains the gold standard for evaluations of SSNHL since it not only reflects the severity of hearing loss but also provides a baseline hearing status for the assessment of recovery ,8. ConveThe Hearing Scale Test (HST) is a novel hearing screening tool derived from consecutive hearing screening procedures and used to estimate the current hearing status of each ear; it is based on the concepts of the Landolt C vision test chart ,25. WithThis cross-sectional study was conducted at a tertiary medical center in Taipei, Taiwan, from January 2018 to June 2019. The sample size needed to reach a power of 0.80 was 82. We recruited 88 adults with possible SSNHL who visited either an otolaryngology outpatient clinic or emergency department. The study was approved by the institutional review board of the Taipei Veterans General Hospital (2016-12-004BC). Investigators explained the research objectives and process, and written informed consent was obtained from all patients enrolled. Instructions regarding the screening procedures and operations were provided by the trained examiners prior to each hearing screening test.Pure-tone audiometry was administered by certified audiologists in the outpatient department. Otoscopy was performed to examine the clearness of the ear canal. Audiometric examinations were performed with a GSI 61 2-channel audiometer (Grason-Stadler Inc) in a soundproof booth. Standard clinical methods (modified Hughson-Westlake methods) were used to obtain pure-tone air conduction thresholds. To assess the reliability of the threshold measures, 1000 Hz was tested twice in each ear; participants with a >10 dB (dB) change between measures were considered unreliable. The pure-tone average (PTA) was calculated using air conduction thresholds at 0.5 kHz, 1 kHz, 2 kHz, and 4 kHz in each ear. Each individual’s pretreatment hearing status measured by conventional pure-tone audiometry was categorized into the following 5 grades according to the modified Siegel criteria for SSNHL : grade 1The mobile devices used in this study were the iPhone 7 or iPhone 7 Plus (Apple Inc), with iOS software version 13.3.2. The iOS-based automated Ear Scale app (version 2.0) was integrated with the HST and used to measure the hearing statuses of both ears in the enrolled participants a. The itTo calibrate the sound output of the iOS mobile devices to a hearing threshold level of zero at various frequencies, we applied the reference equivalent threshold sound pressure levels for Apple EarPods, which were reported in a prior study with consistent output across different EarPod pairs and between the right and left earphones and therefore could be applied to various Apple mobile devices with EarPods . The KnoPearson correlation coefficients were estimated to investigate the correlation between PTA measured by conventional pure-tone audiometry and the hearing scale derived from the Ear Scale app using the HST, as well as the differences in the hearing results between the impaired ear and the contralateral ear of each individual. The corresponding PTA of each hearing scale group was demonstrated using a box plot. Analysis of variance was used to determine the difference in the mean PTA between each scale. Indicators of validity and the predictive value were estimated to determine the diagnostic accuracy of the HST for SSNHL compared with that of the gold standard pure-tone audiometric evaluation. Patients with a PTA difference between the two ears of at least 30 dB within a 72-hour period , as assessed by conventional pure-tone audiometry, were considered positive for SSNHL. We then estimated the sensitivity, specificity, positive predictive value, and negative predictive value for diagnosing SSNHL on the basis of 3 hearing scale differences  between the two ears as measured by the Ear Scale app. Sensitivity was defined as the percentage of individuals with true SSNHL  who werThis study included 88 adults with possible SSNHL who visited the emergency department or an otolaryngology clinic from January 2018 to June 2019; patients with bilateral or conductive hearing loss were excluded. The mean age of the study cohort was 46 years, and 50% (44/88) were females . The aveThe Pearson correlation analyses revealed strong positive correlations between the PTA assessed by pure-tone audiometry and the hearing scale measured by the Ear Scale app as well as between the PTA differences and hearing scale differences between the two ears, with correlation coefficients of .88 (95% CI .82-.92) and .84 (95% CI .77-.90), respectively .P<.05).The association of the PTA and hearing scale differences between the two ears is presented in The diagnostic gold standard for SSNHL used in our study was a PTA difference between the impaired ear and the contralateral ear of ≥30 dB. The indicators of validity of the Ear Scale app and the cutoff values for the hearing scale differences are displayed in This study is the first to investigate the validity of a smartphone-based hearing screening app integrated with the novel HST for the assessment of SSNHL. This study confirmed that there is a strong correlation in hearing results between conventional pure-tone audiometry and the proposed smartphone-based Ear Scale app in a cohort of patients with possible SSNHL. The sensitivity of the hearing scale difference between the two ears measured by the Ear Scale app  in diagnosing SSNHL was high, suggesting that the smartphone-based approach can assist in the evaluation of SSNHL, particularly in clinical settings where conventional pure-tone audiometry is not available.A previous study implemented the proposed Ear Scale app for hearing screening among a pediatric population and reported a strong correlation in the PTA between the app and conventional pure-tone audiometry in a soundproof booth as well as high accuracy in identifying school-aged children with hearing impairment . NotablySeveral practice guidelines and reviews have suggested that patients with possible SSNHL undergo a comprehensive clinical workup upon arrival in the clinic, including thorough history taking, relevant physical examinations, and tuning fork tests to differentiate other types of hearing loss from SSNHL, identify nonidiopathic etiologies, and generate differential diagnoses ,5,33,34.Since the premorbid hearing status is generally unknown among people with possible SSNHL, hearing loss is usually defined on the basis of the difference between the two ears in the thresholds . Based oOur results are limited by a high prevalence of SSNHL because the study cohort consisted of patients referred to tertiary academic medical centers. This factor could potentially bias our estimations of the positive and negative predictive values. Additionally, our screening approach may not be applicable for individuals with bilateral hearing loss. Individuals with conductive hearing loss were excluded from the study cohort, and testing such individuals may pose concerns of safety and feasibility. Despite these limitations, our study confirmed that hearing results can be compared between conventional pure-tone audiometry and the proposed Ear Scale app and that the app has sufficient diagnostic validity for SSNHL. To increase the generalizability and ensure the feasibility and safety of this smartphone-based hearing screening approach in PCP or urgent care settings, future studies with prospective designs and larger sample sizes, acceptability surveys among patients and clinicians, and health-economic analyses are needed. Furthermore, the proposed smartphone-based Ear Scale app creates new possibilities in the management of SSNHL at the PCP level since it can serve as a patient surveillance tool, enabling frequent monitoring and treatment adjustments ,25. GiveThis study demonstrated that the hearing results measured by conventional pure-tone audiometry and the proposed smartphone-based Ear Scale app are strongly correlated among patients with possible SSNHL. Our results showed that the hearing scale difference between the two ears, as measured by the Ear Scale app, has a satisfactory level of validity in detecting SSNHL. The results also suggested that this smartphone-based approach may effectively assist the evaluation of SSNHL in clinical settings where conventional pure-tone audiometry is not available.


To analyze the clinical application of SoundBite bone conduction hearing aids by assessing the improvement of speech recognition and the scores of the benefit scale questionnaire for patients with single-sided deafness (SSD).  Nine patients aged 24 to 61 years with SSD for more than 3 months were enrolled in this study. The patients could understand and repeat Mandarin and have good compliance with the study. The measurements were evaluated before and after one month of wearing hearing aids using the pure tone audiometry threshold, speech recognition in quiet and in noise, and the Glasgow Benefit Inventory (GBI) benefit scale score. SSDNAH), the speech recognition score (SRS) significantly increased by 17 ± 6 and 9 ± 4 at a signal-to-noise ratio (SNR) of -2 dB and -5 dB, respectively, after wearing the hearing aids. When the signal sound came from the front of the patient and the noise from the bad ear side (S0NSSD), the SRS scores were reduced by 5 ± 5 and 7 ± 5 percentage points at SNR equal to -2 dB and -5 dB, which was significantly different from that before wearing the hearing aids. When the signal and noise both came from the front of the patients (S0N0), the SRS was not significantly increased by 5 ± 4 percentage points at SNR equal to -2 dB compared to before wearing hearing aids. However, the SRS was significantly increased by 5 ± 2 percentage points at SNR equal to -5 dB compared to before wearing hearing aids. The average total GBI score was 31 ± 12 for the nine patients, with an average score of 32 ± 10, 31 ± 8, and 30 ± 7 for general conditions, social support, and physical health, respectively. The results of the questionnaires showed that patients' quality of life was improved after wearing SoundBite bone conduction hearing aids.  Pure tone audiometry results showed that the average hearing threshold of good ears and bad ears was 11.4 ± 2.6 dB HL and 89.9 ± 6.4 dB HL, respectively. The average hearing threshold of bad ears after wearing hearing aids was 23.5 ± 9.0 dB HL. Statistical analysis showed that the hearing improvement for the bad ears after wearing hearing aids was significant. The speech audiometry results showed that the disyllable word recognition score of the bad ears in quiet increased significantly at 50 dB SPL by 40 ± 12 percentage points and at 65 dB SPL by 71 ± 15 percentage points. As for the speech recognition in noise, when the signal sound came from the bad ear side and the noise from the good ear side (S SoundBite bone conduction hearing aids are a good choice for patients with SSD, as it could improve the speech recognition ability of patients both in a quiet and noisy environment and improves the quality of life after wearing hearing aids. Single-sided deafness (SSD) refers to severe to profound sensorineural hearing loss on one side (>70 dB HL) and an average hearing threshold of 0.5 to 4 kHz ≤25 dB HL on the good ear . People Current intervention options for SSD include cochlear implants and hearing aids. Previous studies have shown that cochlear implants in SSD patients can improve speech recognition and sound source localization , 6. HoweThe advantage of nonimplantable bone conduction hearing aids is that they do not require complicated surgical procedures. A nonimplantable bone conduction device on the skull  requiresA previous study compared eight bone conduction hearing aids' maximum output and gain; the researchers found that within the frequency range of 4 to 8 kHz, the maximum output and gain for each bone conduction device were different, with SoundBite demonstrating better performance . In anotTo date, however, there is no research investigating SoundBite bone conduction hearing aids in Chinese SSD patients. In this study, Chinese speech recognition in quiet and in noisy environments was evaluated before and after wearing the hearing aids. The GBI scale is designed for use only once postintervention, as a measure of change related to a specific surgical or medical intervention. The questionnaire consists of 18 questions answered using a five-point Likert scale, addressing change in health status post any intervention. The responses are then scaled and averaged to give a score with a range -100 (poorest outcome) through 0 (no change) to +100 (best outcome). A positive value indicates that the patient has benefited to a certain degree in quality of life after medical intervention, a zero score indicates no change, and a negative value indicates that the health level has deteriorated after the intervention. It is widely used in otolaryngology to report change in the quality of life postintervention . The GBIThe study and the informed consent procedures were approved by the local ethics committee , and written informed consent was obtained before participation.SSD patients who were 18 years or older were enrolled in this study. All the enrolled patients should have SSD history longer than 3 months. Before fitting a SoundBite appliance , the teeth of the patients were examined completely by a dentist to make sure that the teeth are healthy; there can be no active caries or periodontal or endodontic conditions affecting the abutment teeth .Subjects were excluded if they met any of the following criteria: (1) currently using other hearing aids, such as BAHA, contralateral routing of signals, and TransEar; (2) speech recognition score (SRS) is disproportionately lower than what would be predicted with the pure tone audiometry (PTA); and (3) patients have psychological or mental conditions that may interfere with understanding, informed consent, compliance, and cooperation.Altogether, 9 patients with SSD for more than 3 months were enrolled in this study. The patients aged between 24 and 61 years with an average age of 39.3 ± 10.8 years, including 4 males and 5 females see . The patDuring the whole study, the devices were fitted using the open SoundBite fitting software. The gain was adjusted to the most comfortable level for the patient. PTA was tested after adjustment. The fitted PTA must meet the criteria that the difference between the average air conduction hearing threshold  after fitting and that in the healthy ear is within 15 dB. Feedback noise cancellation was turned on if there was howling.The patients completed the PTA and tympanogram test before wearing a hearing aid. The Middle Ear Analyzer  was used for the tympanogram. PTA tests were performed in a sound-proofed room with noise less than 30 dB (A) and a calibrated GSI-61™ audiometer (The United States) coupled with TDH 39 headphones.The speech audiometry test was performed in a standard sound-proofed room with noise less than 30 dB (A) calibrated sound field. Before the test, the subjects were familiarized with the test process. The subjects were required to repeat what they heard; then, the audiologist judged whether the restatement was correct. After the test, the system will automatically calculate the SRS and display the results.The materials named XinAiFeiYang issued by the People's Liberation Army General Hospital of Chinese were used for disyllable word recognition in quiet. The material includes 5 test lists, each list contains 40 words, which are enough to make the consonants and tones present in each list representative of those in the language used in daily life . It has SSDNAH), (2) the signal sound came from the front of the patient and the noise from the bad ear side (S0NSSD), and (3) the signal and noise both came from the front of the patients (S0N0). The noise for SRS is steady-state noise, which is spectrally matched to the average spectrum of the sentences. The sentence recognition score was measured at a noise intensity of 65 dB SPL and SNR of -2 and -5 dB.The Mandarin HINT materials were used for SRS under noise. The Mandarin HINT test materials were donated by the House Ear Research Institute. It includes an exercise list, 12 test lists, and 20 sentences each list. Two calibrated loudspeakers  were used to present the sound. Both loudspeakers were placed at 1 m distance from the subject's head. The SRS under noise was evaluated under the following sound field conditions: (1) the signal sound came from the bad ear side and the noise from the good ear side  version 19.0 . The hearing thresholds, SRS under quiet and noisy environment between before and after wearing hearing aids, were compared with paired-sample p < 0.001).The average hearing threshold of good and bad ears before wearing the hearing aids was 11.4 ± 2.6 dB HL and 89.9 ± 6.4 dB HL, respectively. The average hearing threshold of bad ears after fitting the hearing aid was 23.5 ± 9.0 dB HL see , represep < 0.001).The disyllable word SRS under quiet condition at 50 and 65 dB SPL for the good ears was 70 ± 20% and 89 ± 16%. The SRS in quiet at the two intensities was both 0% for the bad ears before wearing the hearing aids. After wearing the hearing aid, the SRS for the bad ears, obtained at 50 and 65 dB SPL, was increased by 40 ± 12 and 71 ± 15 percentage points see , respectSSDNAH), the SRS scores were 28 ± 17% and 9 ± 10% without a hearing aid with the SNR at -2 dB and -5 dB, respectively. After wearing the hearing aid, the SRS scores were 45 ± 16% and 18 ± 9%, which significantly increased by 17 ± 6 and 9 ± 4 percentage points compared to that before wearing the hearing aid (p < 0.001).The speech recognition in noise was evaluated with sentence materials. When the signal came from the bad ear side and the noise came from the good ear side (S0NSSD), the SRS scores were 95 ± 3% and 84 ± 7% without a hearing aid with the SNR at -2 dB and -5 dB, respectively. After wearing the hearing aid, the SRS scores were 90 ± 5% and 77 ± 8%. The SRS scores were reduced by 5 ± 5 and 7 ± 5 percentage points with the two SNRs, respectively. The differences between the two SRSs before and after fitting the hearing aid were statistically significant (p < 0.05).When the signal came from the front of the patient and the noise came from the bad ear side (S0N0), the SRS scores were 75 ± 9% and 43 ± 11% without a hearing aid with the SNR at -2 and -5 dB, respectively. After wearing the hearing aid, the SRS scores were 80 ± 10% and 48 ± 11% with the two SNRs, respectively. The SRS was not significantly increased by5 ± 4 percentage points with the SNR at -2 dB compared with that before wearing hearing aids (p > 0.05); however, the SRS was significantly increased by 5 ± 2 percentage points with the SNR at -5 dB compared with that before wearing hearing aids (p < 0.05)  see .The average GBI total score from the nine patients was 31 ± 12, with the average scores for the three subscales of general, social support, and physical health of 32 ± 10, 31 ± 8, and 30 ± 7, respectively. The results of the questionnaires showed that patients' quality of life improved significantly after wearing SoundBite bone conduction hearing aids see .SSDNAH at SNR -2 and -5 dB, respectively. The SRS scores were reduced by 5 ± 5 and 7 ± 5 percentage points with S0NSSD at SNR -2 and -5 dB, respectively. The SRS scores were improved by 5 ± 4 and 5 ± 2 percentage points with S0N0 at SNR -2 and -5 dB, respectively. The GBI benefit scale showed that the general conditions, social support, and physical health were improved after wearing the hearing aid.In this study, 9 patients with SSD wore SoundBite bone conduction hearing aids for one month. Their hearing, speech recognition, and life benefits were evaluated before and after one month of wearing. This study was first performed in Mandarin Chinese speakers with speech tests using Chinese materials. The results showed that after wearing the hearing aid, the air conduction hearing threshold (across 0.5-4 kHz) decreased by 66.4 ± 14.9 dB for the bad ear. Under a quiet environment, the disyllable word SRS in the bad ear was improved by 40 ± 12 and 71 ± 15 percentage points after wearing the hearing aid with the speech signal at 50 and 65 dB SPL, respectively. Under a noisy environment, the SRS was increased by 17 ± 6 and 9 ± 4 percentage points with SOur study showed that the average aided hearing threshold for frequencies across 0.5, 1, 2, and 4 kHz was 23.5 dB HL. These results are consistent with previous studies, which showed the aided threshold of 21.3 dB for frequencies across 1, 2, 3, 4, and 6 kHz after wearing SoundBite for one month . A previp < 0.001). After wearing SoundBite, the improvement at moderate sound intensity was better than that at soft sound intensity, suggesting that the speech recognition of patients in a quiet environment was greatly improved at the everyday life sound level.Disyllable word has an important value in auditory speech evaluation . The resSpatial hearing and binaural hearing play an important role in the localization of sound sources, especially in a noisy environment. The main reason for speech recognition disturbance in a noisy environment for patients with SSD is the head shadow effect . This is0N0), both ears receive the signal and noise, which is different from listening with one ear, the subject hears louder. The subjects rely on the redundant information provided at the two ears, enhanced detection of smaller differences in signals, and improved speech recognition [0N0 and found the SRS increased by 10.6 percentage points [0N0 showed that the SRS under noise environment increased significantly after wearing the hearing aid, which is consistent with the results obtained from bilateral cochlear implants.When the signal and noise both came from the front of the patients (Sognition . The audognition . Gantz se points . Our res0NSSD, the signal came from the front of the patient and the noise from the bad ear side, the noise and the signals are spatially separated. Our results show that the SRS deteriorates after wearing aid. The main reason is that when the noise is located at the bad ear side, the noise is amplified by the SoundBite and also be transmitted to the good ear side. As a result, the SNR of the good ear decreases and the noise interference results in a decrease in speech recognition.While for the condition SSSD patients cannot accurately determine the sound source and need to turn their heads to find the sound. Moreover, speech recognition is not ideal in a noisy environment, which greatly affects the quality of life of patients , 32. TheSoundBite bone conduction hearing aids are beneficial for SSD patients. It could improve the speech recognition ability of patients in a quiet and noisy environment and quality of life after wearing it for one month. However, the sample size in this experiment is small, and the long-term effects of this device on the speech recognition and quality of life under various listening environment should be explored in further clinical research.


Two-component systems (TCS) are signaling machinery that consist of a histidine kinases (HK) and response regulator (RR). When an environmental change is detected, the HK phosphorylates its cognate response regulator (RR). While cognate interactions were considered orthogonal, experimental evidence shows the prevalence of crosstalk interactions between non-cognate HK–RR pairs. Currently, crosstalk interactions have been demonstrated for TCS proteins in a limited number of organisms. By providing specificity predictions across entire TCS networks for a large variety of organisms, the ELIHKSIR web server assists users in identifying interactions for TCS proteins and their mutants. To generate specificity scores, a global probabilistic model was used to identify interfacial couplings and local fields from sequence information. These couplings and local fields were then used to construct Hamiltonian scores for positions with encoded specificity, resulting in the specificity score. These methods were applied to 6676 organisms available on the ELIHKSIR web server. Due to the ability to mutate proteins and display the resulting network changes, there are nearly endless combinations of TCS networks to analyze using ELIHKSIR. The functionality of ELIHKSIR allows users to perform a variety of TCS network analyses and visualizations to support TCS research efforts. Two-component systems (TCSs) are ubiquitous in bacteria and archaea and are the key signaling transduction machineries for sensing and responding to the environment. TCSs consist of sets of interaction signaling partners, histidine kinases (HKs) that phosphorylate their cognate response regulators (RRs). Interactions, however, are often not one-to-one. Multiple HKs can interact with multiple RRs. Identifying relevant interactions among TCS is an important task that has been addressed experimentally only for a limited number of organisms.We advanced the study of interaction specificity in TCS by creating a model based on amino acid coevolution at the interface of HKs and RRs. Our Direct Coupling Analysis (DCA)  based inEvolutionary Links Inferred for Histidine Kinase Sensors Interacting with Response regulators (ELIHKSIR) and it can be accessed at https://elihksir.org.We decided to make this model and tools available to the scientific community in an interactive web server that facilitates the analysis and prediction of TCS networks as well as the exploration of the effects of mutation in these proteins prior to experimental work. We named the service In recent years, online repositories of sequence data have seen a large influx of sequences and are painting a more refined picture of protein families. Using these data, one can construct global probabilistic models that verify the observed statistics and relate them to inter-residual couplings. Cheng et al.  have useZ to verify normalization for the probabilities. A sufficient form for i and j; and the local field i. The sites a can have i and j. The inference of the coupling matrix Using the set of sequences or H(σ→)  is givenor H(σ→) :(2)H(σ→)ollowing:P(σ→)=1Z·to do so ,13. We iFrom these coupling parameters, we can introduce and create objective functions to measure varying effects. In the Material and Methods, we introduce an objective function ELIHKSIR is a user-friendly and accessible tool that displays TCS signaling networks. The breadth of the web server allows for analysis of TCS networks in both common and uncommon species and strains. E. coli, there were fourteen true positives and three false negatives for seventeen cognate pairs, shown in S. elongatus, there were five true positives and one false negative for the six cognate pairs, shown in E. faecalis, there were seven true positives and one false negative for the eight cognate pairs, shown in Validation of the ELIHKSIR web server was performed through detailed investigation using three model organisms: Escherichia coli, Synechococcus elongatus, and Enterococcus faecalis. True positive specificity predictions were defined by either positive selection and/or negative selection for a cognate pair. Positive selection is defined as an HK having its highest specificity with a single RR. Negative selection is defined as an RR having poor specificity across all HKs but having its relative highest specificity with an HK. False negatives were defined as selection towards a noncognate partner that is greater than that of the cognate partner, in which both positive and negative selection fail to identify the cognate pair. Only cognate pairs in which the HK contains a HisKA domain were evaluated. For DCA identifies coevolving residues at the HK–RR interface for HisKA and REC domains that have been used to accurately predict the structure of the HK–RR complex . Out of The interface is aligned for each TCS pair during the construction of the MSA, which was performed using a hidden Markov model. The sequences displayed in ELIHKSIR are the aligned residues and gaps. Predictions made based on HK and RR sequences only consider residues which align with their respective protein family. Insertions and deletions are not considered in the alignment of the interface and may result in deviations in the three dimensional structure of the resulting signaling complex. The model assumes no changes in the three dimensional structure of the HK–RR interface during evaluation of different TCS pairs.A key functionality of the ELIHKSIR server is the ability to interactively perform in silico mutations on a HK–RR pair. In the mutation screen, as shown in Only a subset of the positions in the genetic sequence correspond to an actual interfacial residue of the protein interface between Thermotoga maritima class I HK853 and the response regulator RR468, (PDB ID: 3DGE). Because of this, not every change in the sequence performed by a user will translate into a change in the specificity score. Furthermore, some types of amino acids can play similar roles in a specific residue position. In this case, the model accounts for this and only reflects minor or no changes in the total score.An interesting application of the mutation user interface is shown in Export to PNG button that is located inside the opened histogram. The histogram export will also include the names of each response regulator. Finally, the user may export a CSV representation of the user’s arbitrary selections of the cells of the heatmap. After the user makes selections of the cells on the heatmap, the Export to CSV button on the right panel can be clicked to download a file that contains a comma delimited list of the user’s selections. All these methods of exporting will take into consideration the mutated Hamiltonian values, if any, of the response regulator and histidine kinase pairs.The user has three options to export data from ELIHKSIR. First, the user may export a PNG image, as shown in E. coli (ECOLI). Besides the heatmap, a good indicator for the interactions is a look at the histograms  change interaction specificity with all possible TCS partners. A feature that becomes important when scientists would like to assess the network effect of mutations as opposed to single pairwise interactions. The ELIHKSIR web server also separates organisms by strain, allowing interaction specificities to be compared between different strains of the same organism. Accessibility of specificity predictions for different mutants and strains may reveal differences in TCS signaling of clinical and environmental variants and may assist in the engineering of sensory kinases and response regulators as it has been shown in previous studies .Raw HMM profiles for HisKA and REC were obtained through Pfam’s hidden Markov models (HMM) ,28. Theni and j; and the local field i. m HKs in the data set with any of n RRs in the dataset:Mean field DCA (mfDCA)  was usedTo find The web server has a custom-built front end running React  for enhaThe ELIHKSIR web server is a valuable tool for analyzing TCS specificity landscapes in a growing list of 6412 species and strains of bacteria, 65 species and strains of archaea, and 188 species and strains of eukaryotes. This allows users to find potential cross-talk interactions and characterize existing orthogonality for many organisms across different kingdoms. For each organism, heatmaps and histograms of TCS networks are easily accessed, displayed, and exported. Furthermore, the ability to compute, display, and export changes in specificity for mutated HK or RR proteins allows users to explore potential interactions and visualize changes in specificity over an entire signaling network. This ability can assist in the analysis of engineered mutants, clinical and environmental variants, and cross-talk behavior. While ELIHKSIR is useful for interactions between HisKA family HKs and the REC domain of RRs, there exist other HK families in which the ELIHKSIR model does not evaluate. Building and validating models to predict specificity for other families of HK would further assist TCS research. Even though ELIHKSIR only displays specificity scores for HisKA and REC domains, these domains are critical in determining specificity for many TCS interactions, as demonstrated by the 6,272,607 HK-RR pairs evaluated. Due to the ability to mutate each protein and recalculate network-wide specificity scores, there are nearly endless possibilities of HK–RR pairs to evaluate using ELIHKSIR. The accessibility, breadth, and functionality of ELIHKSIR allows a variety of researchers  to harness TCS specificity predictions, supporting research efforts through a tool that did not previously exist.


Several studies have demonstrated that age-related hearing loss (defined as >25 dB pure tone average [PTA]) is longitudinally associated with worse cognition. We aimed to investigate whether subclinical hearing loss (SCHL), or imperfect hearing traditionally categorized as normal (PTA ≤25 dB), may be similarly linked to cognitive decline. Subjects included cognitively normal adults ≥50 years old in the Baltimore Longitudinal Study of Aging with PTA ≤25 dB measured between January 1991 - September 1994 who had repeated cognitive assessments from January 1991 - November 2019 (n=263). The exposure was hearing based on the better ear PTA. The outcomes were standardized test scores in the following domains: learning/memory, mental status, executive function, visuospatial ability, and language. Multivariable linear-mixed effects models with random intercepts and slopes and unstructured variance-covariance structure were used to model the association between hearing and change in cognition over time, adjusting for baseline age, sex, years of education, and race. Mean age was 68.3 years (standard deviation [SD]=8.9) and follow-up ranged from 0-27.7 years . A 10-dB worsening in hearing was longitudinally associated with an annual decline of 0.016 SDs  in California Verbal Learning Test (CVLT) short-delayed recall, 0.019 SDs  in CVLT long-delayed recall, and 0.017 SDs  in letter fluency after covariate adjustment. Poorer hearing among those with SCHL was associated with steeper declines in memory and verbal fluency scores. This relationship may begin at earlier levels of hearing loss than previously recognized.


Treponema pallidum. It spreads usually via sexual contact. Syphilis generally presents as a multisystem disease, with symptoms resembling and often confused with those of other diseases, thus often called “the great mimicker”. Neurosyphilis is a rare but late course of the disease process when the meninges and central nervous system (CNS) are involved. Otosyphilis is an even rarer, yet important complication of neurosyphilis and a rare cause of sensorineural deafness often misdiagnosed. We present the case of a 46-year-old Caucasian male admitted for acute onset bilateral hearing loss caused by otosyphilis. We include a discussion about cerebrospinal fluid (CSF) protein analysis in individuals diagnosed with neurosyphilis.Syphilis is a bacterial infection caused by  Hearing loss or loss of any sense is a very concerning event for any individual, more so especially when this is sudden. The differential diagnosis to consider for sudden hearing loss is very extensive, including but not limited to acoustic neuroma, immune-mediated disorders, Meniere's disease, vascular alterations and vasculitis, membrane rupture, and infectious agents which can be viral or bacterial in origin . With teTreponema pallidium fluorescent treponemal antibody absorption-Ab was also positive which confirmed the diagnosis of syphilis. A lumbar puncture was then performed prior to the beginning of treatment and cerebrospinal fluid (CSF) Venereal Disease Research Laboratory (VDRL) was positive with a 1:4 VDRL titer establishing the diagnosis of neurosyphilis. Considering the initial MRI scan in Figure A 46-year-old male with medical history significant for uncontrolled hypertension admitted to the hospital for acute onset bilateral hearing loss. Symptoms had started four hours prior to presentation while he was watching television. Initial admission computed tomography (CT) scan was negative for any acute infarcts or other intracranial processes. His initial physical exam was significant for mild gait impairment, with Weber and Rinne test consistent with bilateral neurosensory hearing loss. Patient was initially admitted as a possible stroke rule out. Work up for stroke including carotid ultrasound, Cardiac echo were all negative except for Magnetic Resonance Imaging (MRI) which showed some periventricular and deep white matter lesions demonstrating T2 hyper-intensity without enhancement consistent with questionable demyelinating disease as seen in Figure The definite diagnosis of isolated otosyphilis is challenging and it should be made on the basis of a typical clinical presentation i.e. hearing impairment in the setting of positive serological test results for syphilis. Also, this is a potentially reversible cause of hearing loss if treatment is initiated early. For this reason, overdiagnosis is justifiable. Based on a retrospective review of 85 cases by Kwanchanok et al., comprising 56 males and 29 females, the most common presenting symptoms included hearing loss (90.6%), tinnitus 72.9%), and vertigo (52.9%). The cerebrospinal fluid analysis was positive for VRDL in 5.4% [2.9%, andOtosyphilis remains to be a rare cause of hearing loss with varying response to treatment if diagnosed early. It should always be part of the differential diagnosis in patients suffering from sudden sensorineural hearing loss , fluctuating hearing loss, with tinnitus and vertigo, in which acute cerebrovascular events have been ruled out. In our patient, it is uncertain whether this patient will regain hearing later down the road. Unfortunately tone and speech audiogram was not done for this patient during his hospital stay. It is however recommended to do audiogram during the management of otosyphilis as it helps establishes a baseline and also helps in evaluating the response to the treatment. Moreover, it is important to understand early diagnosis and proper management of this disease prevent progression of the disease and subsequent cardiovascular or ophthalmologic complications which can be really devastating.


Color, an important visual cue for survival, is encoded by comparing signals from photoreceptors with different spectral sensitivities. The mouse retina expresses a short wavelength-sensitive and a middle/long wavelength-sensitive opsin (S- and M-opsin), forming opposing, overlapping gradients along the dorsal-ventral axis. Here, we analyzed the distribution of all cone types across the entire retina for two commonly used mouse strains. We found, unexpectedly, that ‘true S-cones’ (S-opsin only) are highly concentrated (up to 30% of cones) in ventral retina. Moreover, S-cone bipolar cells (SCBCs) are also skewed towards ventral retina, with wiring patterns matching the distribution of true S-cones. In addition, true S-cones in the ventral retina form clusters, which may augment synaptic input to SCBCs. Such a unique true S-cone and SCBC connecting pattern forms a basis for mouse color vision, likely reflecting evolutionary adaptation to enhance color coding for the upper visual field suitable for mice’s habitat and behavior. Many primates, including humans, can see color better than most other mammals. This difference is due to the variety of light-detecting proteins – called opsins – that are produced in the eye by cells known as cones. While humans have three, mice only have two different opsins, known as S and M, which detect blue/UV and green light, respectively. Mouse cones produce either S-opsins, M-opsins or both. Fewer than 10 percent of cone cells in mice produce just the S-opsin, and these cells are essential for color vision.Mice are commonly used in scientific research, and so their vision has been well studied. However, previous research has produced conflicting results. Some studies report that cone cells that contain only S-opsin are evenly spread out across the retina. Other evidence suggests that color vision in mice exists only for the upper field of their vision, in other words, that mice can only distinguish colors that appeared above them.Nadal-Nicolás et al. set out to understand how to reconcile these contrasting findings. Molecular tools were used to detect S- and M-opsin in the retina of mice and revealed large differences between the lower part, known as the ventral retina, and the upper part, known as the dorsal retina. The ventral retina detects light coming from above the animal, and about a third of cone cells in this region produced exclusively S-opsin, compared to only 1 percent of cones in the dorsal retina. These S-opsin cone cells in the ventral retina group into clusters, where they connect with a special type of nerve cells that transmit this signal.To better understand these findings, Nadal-Nicolás et al. also studied albino mice. Although albino mice have a different distribution of S-opsin protein in the retina, the cone cells producing only S-opsin are similarly clustered in the ventral retina. This suggests that the concentration of S-opsin cone cells in the ventral retina is an important feature in mouse sight. This new finding corrects the misconception that S-opsin-only cone cells are evenly spread throughout the retina and supports the previous evidence that mouse color vision is greatest in the upper part of their field of vision. Nadal-Nicolás et al. suggest this arrangement could help the mice to detect predators that may attack them from above during the daytime. Together, these new findings could help to improve the design of future studies involving vision in mice and potentially other similar species. Thus, S+S+) , while c+) (+S+)  broadens+) (+S+) .+M-). These true S-cones are thought to be evenly distributed across the retina (+S-), a pattern akin to mammalian retinas in general  were relatively evenly distributed across three regions, S opsin-expressing cones (S+: M+S+ + S+M-) showed considerable anisotropy, with a high density in the ventral retina and a precipitous drop in the dorsal retina, confirming previous observations .In mouse retina, the gradients of S- and M-opsin expression along the dorsal-ventral axis have been well documented , but thervations . Surprisrvations , we founrvations  than did+S- cones were very sparse  in four retinal quadrants centered upon the optic nerve head reveals different profiles between pigmented and albino strain for mixed and M+S- cones (+S- cones from the center to the periphery (green line) in pigmented mice, compared to a majority of mixed cones (gray line) in albino mice. However, true S-cone profiles (magenta lines) were similar between the two strains in all quadrants, except for a slightly increased density along the edge of the ventronasal (VN) quadrant in pigmented mice. A recent study successfully modeled cone opsin expression and type determination according to graded thyroid hormone signaling in a pigmented mouse strain (C57BL/6)  . It woul+M- cones may instead be mixed cones with low M-opsin expression , we compared the observed true S-cone distributions within 1 mm diameter VN and DT retinal samples to artificially generated alternative populations . To thisTo quantitatively compare the patterning of real true S-cone populations to their artificial counterparts, we first computed two measures of regularity for true S-cones: nearest neighbor and Voronoi diagram regularity indices  Copine9_gRNA_R(62/4.40), 5’GCCTCGGAGCGTAGCGTCC3’. A mixture of the targeting plasmid  with two tested gRNAs (25 ng/µl each) and the SpCas9 protein  were microinjected into mouse fertilized eggs and transferred to pseudopregnant female recipients as described elsewhere , albino  mice were obtained from the National Eye Institute breeding colony. The Venus-Cpne9 mouse line  carrieslsewhere . With a Mice were housed a 12:12 hr light/dark cycle. All experiments and animal care are conducted in accordance with protocols approved by the Animal Care and Use Committee of the National Institutes of Health and following the Association for Research in Vision and Ophthalmology guidelines for the use of animals in research.2 and perfused transcardially with saline followed by 4% paraformaldehyde. To preserve retinal orientation, eight retinas per mouse strain/line were dissected as flat whole-mounts by making four radial cuts . The twImmunodetection of flat-mounted retinas or retinal sections was carried out as previously described . ImportaRetinal whole-mounts were imaged with a 20x objective using a LSM 780 Zeiss confocal microscope equipped with computer-driven motorized stage controlled by Zen Lite software . M- and S-opsins were imaged together to allow the identification and quantification of different cone types. Magnifications from flat mounts and retinal cross-sections  were tak+S-, true S- (S+M-) or mixed (M+S+) cones depending on their opsin expression. Data representation was performed using GraphPad Prism 8.3 software.In four retinas per strain, we acquired images from three 135 × 135 μm samples (63x) per each area of interest . These areas were selected according to the S-opsin gradient in wholemount retinas , true S-cones (both strains) (+ SCBCs (Cpne9-Venus mouse line) were manually marked on the retinal photomontage (Adobe Photoshop CC).To characterize the distribution of the different cone photoreceptor types in the mouse retina, we developed and validated an automatic routine  to identify, quantify the total number of outer segments and finally extract the location of each individual cone . Brieflystrains)  and Venu2 for all cone types except for true S-cones and M+S--cone in the albino strain (5000 cones/mm2), as represented in the last image of each row of 2 (+S-- and true S-cones) dividing the retina in four quadrants: dorsotemporal, dorsonasal, ventrotemporal and ventronasal . From these populations, isodensity maps were created using Sigmaplot 13.0 (Systat Software). These maps are filled contour plots generated by assigning to each area of interest (83.3 × 83.3 μm) a color code according to its cone density, ranging from 0 (purple) to 17,300 cones/mmrow of 2  within a+ S-cone bipolar cells (Venus+ SCBCs) with true S-cone terminals, we acquired images from the same area  at two focal planes: First, we focused upon the INL+OPL, then the corresponding photoreceptor outer segment (OS) layer, respectively, for two areas of interest (DT and VN). To verify connectivity between Venus+ SCBC dendrites and true S-cone pedicles in the OPL, in addition to S-opsin immunodetection, we also labeled retinas using cone arrestin antibodies to discriminate mixed cone pedicles from true S-cone pedicles, because true S-cone pedicles contain either low or no cone arrestin (+M- opsin labeling (+ SCBCs (133 and 53 for DT and VN respectively) and 263 true S-cone pedicles . The number of synaptic contacts was assessed by tracking manually each SCBC-branch from the cell body using the Zen lite black visualization package . Multiple branch contacts in one true S-cone pedicle from a single SCBC were considered a single contact and counted only once  was calculated as r = 3√ (A / (√2 πN)), where A is the circular area of the 1 mm diameter retinal sample and N is the total number of cones in that sample. For a highly regular cell mosaic containing N cells filling an area A, this calculation estimates the location of the first minimum in the density recovery profile (To assess the true S-cones and S-cone bipolar cell (SCBC) clustering, we performed two comparable sets of analyses. First, we extracted two circular areas (1 mm diameter) in the DT-VN axis at 1 mm from the optic disc center (scheme in 4B). A K-nearest neighbor algorithm  was used profile , providi+ SCBC connectivity, we identified numerically, and color coded each true S-outer segment form a cluster. The corresponding true S-pedicles were identified by tracking the cell body from their S+M-OSs. Focusing on the outer plexiform layer (OPL), each individual true S-cone pedicle -that form a cluster- was manually outlined and color coded accordingly. Lastly, the SCBC synaptic terminals that belong to a single SCBC, were identified by their specific contacts to the respective true S-cone pedicle , and eye orientation angles of 22° (elevation) and 64° . For Retinal images were reconstructed and projected into visual space using R software v.3.5.2 for 64-bit Microsoft Windows using Retistruct v.0.6.2 as in +SCBCs ; Raunak Sinha (Reviewer #3).Thank you for submitting your article "True S-cones are concentrated in the ventral mouse retina for color detection in the upper visual field" for consideration by The reviewers have discussed the reviews with one another and the Reviewing Editor has drafted this decision to help you prepare a revised submission.eLife papers without additional data, even if they feel that they would make the manuscript stronger. Thus the revisions requested below only address clarity and presentation. There are  comments about further experiments, which we have retained so that you can see those suggestions but which are not required.We would like to draw your attention to changes in our revision policy that we have made in response to COVID-19 (https://elifesciences.org/articles/57162). Specifically, we are asking editors to accept without delay manuscripts, like yours, that they judge can stand as The reviewers were all in agreement that the paper provides important new information about cone types in mouse retina, and that the paper is in good shape. Comments in the individual reviews (below) highlight some points that could be strengthened.Reviewer #1:The sampling of visual space by different cone photoreceptor types is an important determinant of how color vision works. Mice have long been known to show a dorsal-ventral gradient in the relative expression of the S and M cone opsin. Many mouse cones express both opsins, with a ratio in expression level of cones in different parts of the retina reflecting the dorsal-ventral gradient. In addition to these "mixed" cones, mice also have true S cones – cones that only express the S cone opsin. Those cones, and their distribution across the retina, are the topic of this paper.The authors find that true S cones are highly concentrated in the ventral retina. This is based on direct labeling of the cone opsin, and also by generation of a mouse with a class of bipolar cells that exclusively contact S cones labeled. This conclusion is well supported by the data shown, and provides interesting and new information about the location of S cones in mouse retina. A final conclusion – that the true S cones form clusters – could be presented more clearly, as detailed below along with several other concerns/suggestions.S cone clusteringOne of the highlighted results is that the true S cones form clusters, and that this may lessen the required length of the dendrites of the S cone bipolar cells. Clustering to me implies a non-random distribution, and indeed the data provide support for this. But much stronger is the evidence that the distribution is not regular. The text on this point is confusing, and I think it would help to set up the issue more clearly from the start by introducing the range of possibilities .Related to this point is the relation between cone locations and bipolar dendritic length. It would be helpful to generate models for the required bipolar dendritic length given the number of cone contacts for the three cone distributions and see what those predict. At present, given the apparent lack of evidence for clustering, it is hard to evaluate the suggestion about wiring length.PresentationThe text in general could be expanded to help guide a reader through the figures. I found myself reading portions of the text and figure legends multiple times before I could understand the content of a given figure, and I am not sure I would have had that patience had I not been reviewing the paper. The results are clear and quite beautiful, so guiding someone through them clearly is important! As a general guide I think the paper could expand in length by a factor of ~2.Here are some examples of places I found myself spending a lot of time to understand. (1) Figure 1C. Visually I expected the outer ring to follow from the inner pie chart, and it took a while to realize what was plotted there. (2) Figure 2C. More description in the text of what is plotted here would help. Maybe pick a quadrant and describe each line carefully. (3) A more complete description of how different cone terminals were identified for Figure 3 would be helpful. This is not a complete list – so I would ask the authors to go through the paper and make sure they lead the reader through each figure carefully and not just give the result.QuantificationIn general, the results are nicely described and then quantified. Figure 3 is an exception, where there are several statements made without any quantitative analysis. It is important, for example, to see some numbers about the types of cone contacts made by the S cone bipolar cells. Similarly, more complete numbers for both convergence and divergence in dorsal and ventral retina are needed .Reviewer #2:Nadal-Nicolas et al., present an elegant and important survey of mouse cone distributions across the retina. They show that many S-opsin expressing cones in the ventral retina are in fact "true" S-cones, unlike previously thought. This has important consequences for retinal wiring, colour vision and our understanding of ecological adaptation in vision.I had commented on an earlier version of this work elsewhere. At the time I thought it was excellent and certainly worth publishing in a broad journal, and I continue to think so also now. From that previous version, the authors suitably addressed most of my points raised at the time, accordingly all my remaining points on this new and improved version are minor.Reviewer #3:+S+, M+S- and S+M-, and surprisingly find that the concentration of true S-cones (S+M-) is remarkably higher in the ventral retina than in the dorsal retina. When comparing this cone distribution with albino mouse, they find that even though there is robust S opsin expression in the dorsal retina (unlike pigmented mouse), the pattern of true S cone distribution remained mostly unchanged. The authors further tested if the bipolar cells that receive synaptic inputs from the S cones follow the uneven distribution of the true S-cones. They convincingly demonstrate using a novel transgenic mouse, which selectively labels the S-cone bipolar cells, that these bipolar cells mimic the dorsal ventral concentration gradient as the true-S cones. These findings are novel and important for a mechanistic understanding of color vision. The data quality is high and the analysis is robust. Overall, it is well-laid out study with beautiful figures and exciting results.In this study, Nadal-Nicolas and colleagues have used immunohistochemistry and careful anatomical analysis to identify an interesting distribution of cone photoreceptors, that only express short wavelength-sensitive opsin pigment (true S-cones), in mouse retina. Most cones in mouse retina express both the medium wavelength (M) and short-wavelength (S) sensitive opsin. It is well established that there is spatial gradient of opsin expression across the mouse retina – with middle wavelength-sensitive and S opsin enriched in the dorsal and ventral retina respectively. However, previous studies have proposed a uniform distribution of true S-cones across the dorso-ventral axis. This issue has been revisited by Nadal-Nicolas and colleagues where they systematically map the distribution of all cone types i.e. MI have a few comments that could potentially add to their findings:+S+ cones compared to S+M- cones. How does this change going between dorsal, medial and ventral retina? This is important for making the argument about sensitivity of color opponent signals being higher in the ventral retina.i) Is there divergent contacts from a single true-S cone to SCBCs in the ventral retina like in the dorsal retina? Also, it would be informative to know if the SCBCs also get input from the M+S+ cones as true S cones since M opsin expression is low?ii) The authors have used a robust automated and a manual analysis method to identify and count each of the cone types. In subsection “10.3.6. Image processing: manual and automated whole quantification”, the authors mention using a threshold to create a binary mask and subsequent filtering. It would be great to mention in the methods where the threshold is placed relative to the background noise in terms of standard deviations above noise. Also, could the placement of the threshold be a reason for mis-classifying a small fraction of M+S-) in the dorsal retina? Moreover, is it possible to compare a M cone contacting bipolar cell (any type) and if it has preference of contacting the true M cones vs the M+S+ cones and if this changes between dorsal and ventral retina. The authors have a great opportunity to also expand on the true M cone distribution and its connectivity across mouse retina.iii) It is indeed interesting to see the clustered arrangement of the true-S cones in the ventral retina. Is this also a feature of the true M cones (Miv) The authors have generated a SCBC specific mouse transgenic line which could be very useful for targeting this bipolar cell type for single cell electrophysiology to substantiate the anatomical findings in this study. In particular, the divergent vs convergent inputs from true S cones to SCBCs in dorsal vs ventral retina should have a direct bearing on the strength of the synaptic inputs on SCBCs and this could be validated by measuring short-wavelength light evoked excitatory currents from SCBCs. Reviewer #1:The sampling of visual space by different cone photoreceptor types is an important determinant of how color vision works. Mice have long been known to show a dorsal-ventral gradient in the relative expression of the S and M cone opsin. Many mouse cones express both opsins, with a ratio in expression level of cones in different parts of the retina reflecting the dorsal-ventral gradient. In addition to these "mixed" cones, mice also have true S cones – cones that only express the S cone opsin. Those cones, and their distribution across the retina, are the topic of this paper.The authors find that true S cones are highly concentrated in the ventral retina. This is based on direct labeling of the cone opsin, and also by generation of a mouse with a class of bipolar cells that exclusively contact S cones labeled. This conclusion is well supported by the data shown, and provides interesting and new information about the location of S cones in mouse retina. A final conclusion – that the true S cones form clusters – could be presented more clearly, as detailed below along with several other concerns/suggestions.S cone clusteringOne of the highlighted results is that the true S cones form clusters, and that this may lessen the required length of the dendrites of the S cone bipolar cells. Clustering to me implies a non-random distribution, and indeed the data provide support for this. But much stronger is the evidence that the distribution is not regular. The text on this point is confusing, and I think it would help to set up the issue more clearly from the start by introducing the range of possibilities .We have carefully edited that section, after a brief review of the current knowledge on true S-cones and S-cone bipolar cells distribution in the retina, we have included a paragraph to concisely explain the purpose of generating different simulated cone populations and to guide the readers through the corresponding results and figures.Related to this point is the relation between cone locations and bipolar dendritic length. It would be helpful to generate models for the required bipolar dendritic length given the number of cone contacts for the three cone distributions and see what those predict. At present, given the apparent lack of evidence for clustering, it is hard to evaluate the suggestion about wiring length.Thank you for the insightful suggestion. We have classified cone populations here using opsin expression, which clearly labels outer segments, but for technical reasons, m-opsin antibodies very poorly penetrate to synaptic terminals, preventing reliable classification of cone pedicles. In addition, we do not yet have true S-cone and SCBC maps from the same retina, which would be the most useful information for making such estimates. Currently, we are in the process of crossing the SCBC line with an S-opsin reporter line and with a trb2-GFP line, which will make it possible to better visualize and quantify cone pedicle distribution of different types as well as SCBC distributions. It will definitely be valuable to revisit this topic once these materials are available. Nonetheless, we did perform some additional analysis in retinal areas that allowed us to trace S-opsin labeling to the pedicles, where we found an interesting pattern of clustering in their pedicles, and importantly, a closely grouped set of contacts to a nearby SCBC. We have added an example of such a scenario to Figure 4  as a proof-of-principle for how such true S-cone clustering may facilitate wiring with SCBCs.PresentationThe text in general could be expanded to help guide a reader through the figures. I found myself reading portions of the text and figure legends multiple times before I could understand the content of a given figure, and I am not sure I would have had that patience had I not been reviewing the paper. The results are clear and quite beautiful, so guiding someone through them clearly is important! As a general guide I think the paper could expand in length by a factor of ~2.Here are some examples of places I found myself spending a lot of time to understand. (1) Figure 1C. Visually I expected the outer ring to follow from the inner pie chart, and it took a while to realize what was plotted there. (2) Figure 2C. More description in the text of what is plotted here would help. Maybe pick a quadrant and describe each line carefully. (3) A more complete description of how different cone terminals were identified for Figure 3 would be helpful. This is not a complete list – so I would ask the authors to go through the paper and make sure they lead the reader through each figure carefully and not just give the result.eLife “Short Report” format.In our original submission, we regrettably omitted detailed explanations in the main text in several locations in the interests of shortening the manuscript to observe the length guidelines for the Following the reviewers’ feedback, we have carefully edited the manuscript adding clarification text and new relevant references to better guide the readers through the manuscript and figures. We have also eliminated the outer ring in Figure 1C which, as pointed out, was confusing and did not convey critical information. We added explanations to the main text describing each graph in Figure 2C, and we have expanded upon the method for identifying true S-cone terminals as suggested.QuantificationIn general, the results are nicely described and then quantified. Figure 3 is an exception, where there are several statements made without any quantitative analysis. It is important, for example, to see some numbers about the types of cone contacts made by the S cone bipolar cells. Similarly, more complete numbers for both convergence and divergence in dorsal and ventral retina are needed .We have now performed quantification for the numbers of SCBCs contacting each true S-cone terminal as well as the numbers of true S-cones contacted by each SCBC. This data has been added Figure 3 and described accordingly in the main text.Reviewer #2:Nadal-Nicolas et al., present an elegant and important survey of mouse cone distributions across the retina. They show that many S-opsin expressing cones in the ventral retina are in fact "true" S-cones, unlike previously thought. This has important consequences for retinal wiring, colour vision and our understanding of ecological adaptation in vision.I had commented on an earlier version of this work elsewhere. At the time I thought it was excellent and certainly worth publishing in a broad journal, and I continue to think so also now. From that previous version, the authors suitably addressed most of my points raised at the time, accordingly all my remaining points on this new and improved version are minor.We thank reviewer 2 for the fair and constructive review. We found much of the feedback from the previous reviews helpful and felt the manuscript would be improved if we followed those suggestions.Reviewer #3:+S+, M+S- and S+M-, and surprisingly find that the concentration of true S-cones (S+M-) is remarkably higher in the ventral retina than in the dorsal retina. When comparing this cone distribution with albino mouse, they find that even though there is robust S opsin expression in the dorsal retina (unlike pigmented mouse), the pattern of true S cone distribution remained mostly unchanged. The authors further tested if the bipolar cells that receive synaptic inputs from the S cones follow the uneven distribution of the true S-cones. They convincingly demonstrate using a novel transgenic mouse, which selectively labels the S-cone bipolar cells, that these bipolar cells mimic the dorsal ventral concentration gradient as the true-S cones. These findings are novel and important for a mechanistic understanding of color vision. The data quality is high and the analysis is robust. Overall, it is well-laid out study with beautiful figures and exciting results.In this study, Nadal-Nicolas and colleagues have used immunohistochemistry and careful anatomical analysis to identify an interesting distribution of cone photoreceptors, that only express short wavelength-sensitive opsin pigment (true S-cones), in mouse retina. Most cones in mouse retina express both the medium wavelength (M) and short-wavelength (S) sensitive opsin. It is well established that there is spatial gradient of opsin expression across the mouse retina – with middle wavelength-sensitive and S opsin enriched in the dorsal and ventral retina respectively. However, previous studies have proposed a uniform distribution of true S-cones across the dorso-ventral axis. This issue has been revisited by Nadal-Nicolas and colleagues where they systematically map the distribution of all cone types i.e. MI have a few comments that could potentially add to their findings:+S+ cones compared to S+M- cones. How does this change going between dorsal, medial and ventral retina? This is important for making the argument about sensitivity of color opponent signals being higher in the ventral retina.i) Is there divergent contacts from a single true-S cone to SCBCs in the ventral retina like in the dorsal retina? Also, it would be informative to know if the SCBCs also get input from the M+S+ cones. However, we have observed some “blind” endings that did not reach any cone pedicle as previously reported .These are interesting questions. Although convergent as well divergent connections were found between true S-cones and SCBCs in both dorsal and ventral retina, we noted different connectivity patterns. We now quantified the connections between true S-cones and SCBCs in both dorsal and ventral retina. These results agree well with the true S-cone to SCBC ratios calculated from cell densities. Accordingly, both data sets support the presence of a prevalent divergence of true S-cone to SCBC connections in the dorsal retina, in comparison to a prominent convergence of contacts from true S-cones to SCBCs in VN retina. From the data we collected so far, we have not seen evidence that SCBCs contact M+S+ cones as true S cones since M opsin expression is low?ii) The authors have used a robust automated and a manual analysis method to identify and count each of the cone types. In subsection “10.3.6. Image processing: manual and automated whole quantification”, the authors mention using a threshold to create a binary mask and subsequent filtering. It would be great to mention in the methods where the threshold is placed relative to the background noise in terms of standard deviations above noise. Also, could the placement of the threshold be a reason for mis-classifying a small fraction of MWe were aware of the possible lower M-opsin expression in some mixed cones especially in the ventral retina. To avoid false classification, in addition to automated analysis, we manually identified the true S-cones in both mouse strains. Of note on this point, the importance of removing thoroughly the RPE to obtain optimal opsin immunodetection was a crucial step to enable high fidelity detection of opsins. During manual classification, we only considered true-S cones as those cells completely lacking any detectable M-opsin signal. The mean values of background noise for our data sets were 9.6±1.2% and 15.2±3.2% for S- and M-opsin respectively, and the threshold was applied at 15.7%. Later, the remaining noise signals were excluded consistently by applying the ‘despeckle’ filter and by fixing shape and size parameters.+S-) in the dorsal retina? Moreover, is it possible to compare a M cone contacting bipolar cell (any type) and if it has preference of contacting the true M cones vs the M+S+ cones and if this changes between dorsal and ventral retina. The authors have a great opportunity to also expand on the true M cone distribution and its connectivity across mouse retina.iii) It is indeed interesting to see the clustered arrangement of the true-S cones in the ventral retina. Is this also a feature of the true M cones . However, the vast difference between two strains would implicate very different connectivity patterns for such bipolar cells. We have not examined the connection of M+S- cones with other bipolar types, which may involve using a different bipolar mouse line, but it would be interesting to probe in the future, perhaps in albino mice.These are interesting notions. It is possible that Miv) The authors have generated a SCBC specific mouse transgenic line which could be very useful for targeting this bipolar cell type for single cell electrophysiology to substantiate the anatomical findings in this study. In particular, the divergent vs convergent inputs from true S cones to SCBCs in dorsal vs ventral retina should have a direct bearing on the strength of the synaptic inputs on SCBCs and this could be validated by measuring short-wavelength light evoked excitatory currents from SCBCs.We thank for the encouraging suggestions. The SCBC reporter mouse provides a very useful tool for future color vision study in mouse. We are definitely committed to further investigations of the physiology of SCBCs. However, such experiments are probably beyond the scope of this study and will certainly evolve into a separate story.