Idiopathic pulmonary fibrosis (IPF) is a chronic interstitial lung disease characterized by fibrosis and progressive loss of lung function. The pathophysiological pathways involved in IPF are not well understood. Abnormal lipid metabolism has been described in various other chronic lung diseases including asthma and chronic obstructive pulmonary disease (COPD). However, its potential role in IPF pathogenesis remains unclear.In this study, we used ultra-performance liquid chromatography-quadrupole time-of-flight mass spectrometry (UPLC-QTOF-MS) to characterize lipid changes in plasma derived from IPF patients with stable and progressive disease. We further applied a data-independent acquisition (DIA) technique called SONAR, to improve the specificity of lipid identification.Statistical modelling showed variable discrimination between the stable and progressive subjects, revealing differences in the detection of triglycerides (TG) and phosphatidylcholines (PC) between progressors and stable IPF groups, which was further confirmed by mass spectrometry imaging (MSI) in IPF tissue.This is the first study to characterise lipid metabolism between stable and progressive IPF, with results suggesting disparities in the circulating lipidome with disease progression. Idiopathic pulmonary fibrosis (IPF) is a chronic, progressive lung disease characterized by alveolar epithelial cell activation and damage, resulting in proliferation of activated fibroblasts and extracellular matrix deposition leading to irreversible destruction of gas exchange units and lung remodelling . PreviouUltra-performance liquid chromatography-quadrupole time-of-flight mass spectrometry (UPLC-QTOF-MS) is a powerful tool for qualitative characterization of chemical components, providing acquisition of MS spectra with relatively high resolution, sensitivity and mass accuracy, which has become an advanced tool for the qualitative and quantitative analysis of multiple components. Lipidomic studies of complex biological matrices (plasma and tissue extracts) are routinely performed using UPLC-QTOF-MS, resulting in excellent metabolite detection performance at high speeds and sensitivities . A key cThere have been several studies examining circulatory molecules that characterise progressive IPF, focusing mostly on proteomics and genomics –12. Two The study cohort consisted of a total of 58 plasma samples (30 stable and 28 progressors) from the Australian IPF Registry with a clinical diagnosis of IPF Table . BaselinPlasma lipid extraction was carried out as previously described , and eac2O , and was delivered at 10 µL/min to the reference sprayer source of the mass spectrometer.Ultra-performance liquid chromatography (UPLC) is an analytical technique used for the separation, identification and further quantitation of compounds in a mixture using pressurized organic and/or polar solvent systems. This was used for the separation, identification and further quantitation of compounds using an ACQUITY I-class system  equipped with a Waters CSH C18 column . Chromatographic separation was achieved with a gradient of 40 to 99% mobile phase over 18 min. Solvent flow rate and column temperature was maintained at 0.4 mL/min and 55 °C, respectively. The lock-mass compound, leucine enkaphlin (200 pg/μL) was prepared in acetonitrile/HMS-based lipid analysis was performed using a Xevo G2-XS QTOF mass spectrometer  in positive electrospray ionisation (ESI) mode. Note, positive ionization investigates positive ions in low pH, and negative ionization investigates negative ions at high pH. The source temperature and capillary voltage was set to 120 °C and 2.0 kV, respectively. The time-of-flight (TOF) mass analyser of the mass spectrometer was calibrated using a mass to size ratio (m/z) 50 to 1200.. + and [M+NH4]+ showed poor ion spectra -MS methods are successful at resolving the lipid classes chromatographically, however, compound identification is still challenging due to the inaccurate assignment of lipid precursors to their corresponding product ions. In contrast, the precursor and product ions generated by SONAR contributed to the specificity of the method, increasing the probability of successful lipid library matching , 25. FurWith SONAR, a number of TG and PC were identified and found to be expressed at higher levels in the IPF progressor group. Specifically, the levels of these TGs  and PCs (36:3 and 40:6) appeared to be elevated in samples of progressors compared to stable patients. This trend is consistent with the findings of Yan et al.  and KulkTGs along with diglycerides (DGs), are the most abundant lipids found in circulating plasma. Serum total TG has been reported as a biomarker of fatty acid metabolic disturbance . TGs areUnlike TGs, the functional role of PCs has been described in lung surfactant phospholipid metabolism. Pulmonary surfactant forms the lining of the epithelial air-contact surface of the lungs and are essential for the prevention of alveolar collapse during expiration. PC is the major phospholipid comprising around 80% of surfactant lipids and alterations in their composition can cause reduced elasticity, leading to an overall decrease in lung compliance . ChangesTo understand the global dynamics of metabolite differences in IPF, we performed a metabolic network analysis. We found four networks enriched in the progressive IPF samples , which have been previously described in fibrotic lung , 33. BasAlthough a limitation in this study is its small cohort size and the lack of a healthy cohort, abnormalities in plasma lipids in IPF versus healthy controls have been previously described , 14] forIn conclusion, this project successfully profiled plasma samples obtained from two groups of IPF patients using the SONAR acquisition approach enhancing the specificity of unbiased lipid profiling derived from UPLC-QTOF-MS. In particular, we have identified changes related to disease progression in lipid signatures such as TGs and PCs in IPF plasma samples as whole, which have previously been associated with abnormalities in lipid metabolism through mitochondrial-beta oxidation pathways.


The present paper discusses the climatic effects of humidity and temperature on cochlear implant functioning and the quality of the electrical sound signal. MATLAB Simulink simulations were prepared, offering insights into signal behavior under such climatic parameter changes. A simulation setup of the cochlear implant was developed, where a source type selection was used to change between a voice recording and a “chirp” sound. In addition, a DC blocking filter was applied to the input signal. A simulation code, with the application of the climatic influence via the air attenuation function, was developed. Thereby, the attenuation of temperature and humidity in the sound atmospheric circulation of the input signal, at T = 0 °C and RH = 0% and at T = 36 °C and RH = 40% was graphically represented. The results of the electrical pulse generator for each of the eight channels, with the IIR filter, Gaussian noise, temperature variation, humidity influence, and control of denoise block activity, were thus obtained.  Hearing is an important component of human beings’ quality of life, at any age. It is well known that hearing capacity decreases with age or due to some professions. Hearing loss may ultimately lead to deafness. The decrease of hearing capability towards deafness, and its inherent social complications, can be a major source of discomfort. However, many babies are born or develop during early childhood (ages 1–3 years) with profound hearing impairments. As babies learn through speech recognition, the fact that they cannot hear may lead to the mispronunciation of words and difficulties in learning language. These challenges may result in major setbacks in their life and in human interaction, with respect to other children their age. Without proper treatment, they may require special assistance within society. Numerous solutions for resolving this issue, some of which are more or less efficient, have been proposed. Among the best solutions so far, with excellent hopes for positive development in terms of technical performances, is the cochlear implant ,6,7,8,9.The hearing aid, an external device which amplifies the signal, has a retail price between USD 1000 and USD 6000 each, and is available via companies such as Bernafon, Oticon, Phonak, Resound, Signia (formerly Siemens), Starkey, Unitron, and Widex . On the In 2019, the global market value for hearing aids was USD 8.99 billion (with a CAGR of 8.2%) and USD 1.67 billion for cochlear implants (with a CAGR of 10.6%) . The majThe objective of this paper was to study humidity and temperature effects on the quality of sound within cochlear implants via a MATLAB Simulink complete simulation approach. Both humidity and temperature sensors work together to provide a correct and complete response to the sound signal influence of climatic changes. The aim of this work is a theoretical one, of pure precise modeling, as a first step in the validation of our novelty approach. The complex validation process will be made available in a subsequent paper when we will take measurements and the experimental data will be properly analyzed and compared with the simulation data.It is very important to investigate the quality of the environment from the point of view of the sensor response individualization and correctness of the information. In this project, we aimed, on one hand, to study and monitor the environment in the vicinity of the implant. Among the parameters of interest were temperature and humidity. On the other hand, our objective was to individualize the behavior of the cochlear implant for each patient. The latter is an important task in order to properly identify the individual treatment required by each patient. It is said that we have patients, not diseases. Every patient with this implanted device displays a different comportment with respect to individual living and working conditions. As the patient with the cochlear implant will have a dynamic life, including a changing environment, humidity and temperature information from their surroundings will be of crucial importance to the proper functioning of their cochlear implant. This information will be fed into the system. More specifically, the humidity sensor will help in calibrating the loudspeaker of the implant, which receives the information from the exterior and then sends it to the internal unit.Cochlear implants (CIs) are implantable auditory prostheses designed to restore access to sound in deaf individuals via direct electrical stimulation of the auditory nerve. Each CI consists of a speech processor with a microphone worn outside the ear, which processes sounds and then transmits the processed signal via a transmitting coil to a receiver under the skin. This receiver converts the signal into electric current, which is then sent to the cochlear implant electrode array implanted in the cochlea. The microphone transmits processed information via the transmitter coil through the skin, to an internal receiver in the cranial bone that converts this signal to electrical pulse trains to be delivered to the electrodes implanted in the cochlea.Cochlear implants have their own dynamics towards performance, being subject to changes month by month and always offering better, more efficient variants .Used as the main component in implantable stimulators, the second-generation integrated circuit for the Active Books stimulation microsystem in neuro-prostheses, has already been realized. The microprocessor used is composed of two sensors, one temperature and one humidity .The humidity sensor output varies linearly with relative humidity (RH) with a normalized sensitivity of 0.04% RH over the range of 20–90% RH. The temperature sensor has a nonlinearity of 0.4% over the range of 20–90 °C and a resolution of 0.12 °C. The stimulator is the first of its kind to include two integrated temperature and humidity sensors.The idea, presented in this paper, of using a single temperature and humidity sensor in the IC was developed during an activity carried out at the University of Cambridge, which was published in specialized journals . This avThe relationship between different weather variables, such as temperature and humidity, can be quite confusing. Temperature and humidity are connected either directly or indirectly. For the purpose of analyzing the results, it is of the essence to understand their mutual dependence.3.Humidity is the amount of water vapor, the gaseous state of water, in the air, and it is usually invisible. The maximum amount of water vapor in the air depends on air temperature. Absolute humidity (AH) is the water content in the air, i.e., the mass of water vapor included in a particular volume of air, expressed in g/mTemporal variation of signal strength, when exploring the results from our experiments in both summer and winter, made it evident that signal strength had both short-term  and long-term  variation. Interestingly, the variation was notably different in each period. Diurnal variation (day/night) was clearly apparent in summer, whereas seasonal variation was easier to detect in winter and between different seasons (summer/winter). These variations were not random, but were mainly cyclic, following a certain distinct pattern.This paper is organized into five sections. After the introduction, the paper presents the materials and methods. In the third section, the paper presents the results, while in fourth section the obtained results are discussed. Finally, the work is concluded in the fifth section. This section is intended to present the materials used in setting up our analysis and problem solving. We start by giving the main scientific arguments for our work, then we present the cochlear implant and make an important analysis of the electrical signal in the cochlear implant, and its behavior based on temperature and humidity, via MATLAB Simulink simulations. Previous works analyzed temperature effects on CMOS  Hall sensors  and prop® HFSS software, which is used for electro-magnetic simulations. Results were presented in papers [A precise three-dimensional model of a cochlear implant, together with extensive simulations, were previously performed in Ansoft Ansysn papers ,30,31, wHowever, the purpose of this paper was to model the electrical signal in the cochlear implant in a simulation based on discrete components (functioning blocks) in a Simulink environment, in remote powering mode, subject to various climatic changes. At this time, we also have the pleasure to introduce the unique idea of adding, for the very first time, temperature and humidity sensors in the circuit in order to compensate for these variations that greatly affect the quality of the sound signal, with arguments being given later on in this section. These temperature and humidity sensors  will give an idea of the electrical signal behavior with these climatic changes. This project aimed to improve the wellbeing of patients with cochlear implants who, for example, work outside, participate in outdoors activities, have a fever, etc. In these situations, the external medium changes in temperature and humidity. This influences the quality of the sound signal. The quality of the external sound is affected by temperature and humidity in the following ways. Hot air is less dense than the cold air and therefore the sound travels faster through the external medium. The attenuation of sound in air is also affected by humidity. The moist air (higher relative humidity) is less dense than dry air and, by consequence, like with the higher temperature, the sound travels more easily through that medium. Dry air absorbs more acoustical energy than the moist air.In essence, we have two media: the air from the exterior, where the sound propagates, and the internal medium, which should receive the undistorted information. Additionally, there is the synchronization of the sensors, one on the outside and the other on the inside, whose conditions do not change much. We are greatly interested in the effect of temperature and humidity on the correct functioning of the sensors, as well as their reliability. For example, there is an important difference between the level of humidity indoors and outdoors, as well as in different cities. The humidity and temperature also change with respect to the seasons, as well as from daytime to nighttime. There is likewise an increase in temperature in cities which have lakes . The quality of sound in air does not change with altitude directly, but does so through the agency of temperature, which, once again, tends to decrease if we go higher in altitude. The schematic diagram for a cochlear implant is illustrated in Some of the design goals of intracochlear electrodes are the insertion depth, coupling efficiency, and insertion trauma . It is kHerein, we will address issues such as the effect of temperature and humidity on the speed of sound associated to the quality of the signal sound, therefore understanding the impact on the reception of sound in the microphone of the cochlear implant, attenuation of the signal, and the decrease of sound quality.As we know, the speed of sound is affected by temperature and humidity; because it is less dense, sound passes through hot air faster than it passes through cold air. For this reason, temperature gradients cause refraction effects. Moreover, as sound propagates through the air, the air absorbs energy from the sound wave, attenuating (weakening) it. The effect is significant only at frequencies greater than 2 kHz, and increases with frequency. The attenuation of the sound in the air is affected by the relative humidity. Dry air absorbs far more acoustical energy than moist air. This is because moist air is less dense than dry air (water vapor weighs less than air) . The speed of sound in air varies depending on several factors, including humidity , and is Temperature will have an effect on microphone performance. Sensitivity levels can be directly affected by extreme environmental conditions. As the temperature approaches the maximum specifications of the microphone, its sensitivity specification will decrease. The owner will need to be aware of not only the operating temperature, but also the storage temperature of the microphones. If operated or stored in extreme conditions, the microphone can be adversely affected and will also require calibration more often .It is worth noting that the speed of sound changes with temperature, slightly with humidity, but not with air pressure (atmospheric pressure). Thus, it is only because of the decreasing air temperature, which decreases with altitude, that the speed of sound decreases.Changes in weather conditions affect radio signal strength. In terms of signal strength variation, temperature as a variable has a negative, linear effect on signal strength in general, while high relative humidity may have some effect, particularly when temperature is below 0 °C. The effect of weather conditions on response quality  were explored in several studies. The related variables used to measure radio signal strength, temperature, and humidity, were defined in . Some stAH (g/m3) can be defined, e.g., as a function of temperature and relative humidity as [t (°C) is the actual temperature, RH the actual relative humidity (%), m = 17.62, Tn = 243.12 °C, and A = 6.112 h Pa.The relationship between different weather variables, such as temperature and humidity, can be quite confusing. Temperature and humidity are connected to each other either directly or indirectly. For the purpose of analyzing the study results, it is of the essence to understand their mutual dependence. Absolute humidity idity as : (1)AHt,AH at various temperatures to be used as the input data in the module for adapting the frequency and the sound power, in order to obtain the necessary corrections for a normal standard sound.It is the basic formula used in the module for correcting atmospheric attenuation of sound, when traversing space through the air, from the emitting source to the patient’s ear. Equation (1) has the role of calculating AH, were calculated according to the above formula. The AH values which were obtained are AH  = 0.02 g/m3, AH  = 0.97 g/m3, AH  = 10.37 g/m3, and AH  = 14.7 g/m3.Formula validity. The temperature must be greater or equal to −40 °C, and less or equal to 100 °C. Several values for absolute humidity, In this section, the simulation of the cochlear implant using MATLAB Simulink is detailed, including system blocks, block parameters, some associated code, and the model hierarchy used to evaluate the electrical behavior of the cochlear implant. This tool allows us to rapidly and correctly investigate the device under discussion, as well as to assess the quality of the electrical radio signals.This paper discusses, for the first time in literature, the effect of humidity and temperature on the quality of processed sound. The calibration of sound was carried out on the real temperature and on the existing humidity in the sound emission space. Prior to this, these considerations were not taken into account when designing cochlear implants.The responses of the sensors to the change of temperature (temperature effect) and to the change of the environmental conditions regarding the humidity (effect of humidity) were highlighted by numerical simulation. These evidently marked the alteration of sound quality depending on the change of environmental conditions, namely the change of temperature and ambient humidity.The designed and developed conceptual blocks, such as the cochlear implant signal source block and cochlear implant signal from Workspace block, were both represented in MATLAB Simulink. The first concept, made to work like source type selection, was used to change between a voice recording and a “chirp” sound, and a DC blocking filter was applied at the input signal.In addition, the cochlear implant filter bank block was represented in the MATLAB Simulink so that signal processing of the input source would use two filter types: FIR (finite impulse response) and IIR (infinite impulse response). To increase the degree of confidentiality, the FIR filter was applied in a cascade to the eight frequency bands.Finally, the denoise stage block was schematically represented, together with stimulation pulse generator block.In the standard cochlear implant, an adequate frequency range can be set from 70–350 Hz up to 3500–8500 Hz, but as far as limits are considered 75 Hz is the lower limit and 8500 Hz is the upper limit. However, it is necessary to find the best frequency range for CI speech perception, thus, we found the best frequencies in the range considered optimal for very clear language transmission. Making an analysis almost exhaustive based on several frequencies in the simulations demonstrates that comb filtered speech  can be used in the modeling of CI speech perception. Thus, we carried out this study of comb filtered speech recognition in eight frequency ranges, chosen as reference intervals for the whole process. ® to perform multidomain modeling and simulation, because it can reuse models across environments to simulate how all parts of the system work together. With Simulink, we can do the following:Model our system across domains using specific tools and prebuilt blocks;Develop large-scale models through componentization with reusable system components and libraries;Combine our models into one system-level simulation even if they were not built in Simulink;Run massive simulations in parallel on a multicore desktop, computer cluster, or the cloud, without writing large amounts of code;Share simulations as standalone executables, web apps, and Functional Mockup Units (FMUs).Here, we briefly present Simulink software, including what it is in principle and what it can be used for. In general, engineers and scientists use SimulinkIndustry-specific applications can also be designed in Simulink using prebuilt blocks, so we do not have to create them ourselves. For example, aircraft propulsion systems can be designed with Aerospace Blockset™, or audio and video systems with Signal Processing tools, as in our case. As for the math behind the application Simulink, we will refer to the simulation applications for making graphics. Standalone executables can be complete simulation apps that use MATLAB graphics and UIs designed with the MATLAB App Designer. To provide browser-based access to a deployed simulation, it will create a web app and host it with the MATLAB Web App Server™. Standalone Functional Mockup Units (FMU) are binaries that adhere to the Functional Mockup Interface (FMI) standard and can be used in an external simulation environment.A standalone executable is a program that does not depend an Objective Caml installation to run. This facilitates the distribution of binary applications and robustness against runtime library changes across Objective Caml versions. ocamlrun. For example, imagine the file example.ml is as follows: The Objective Caml native compiler produces standalone executables by default. Without the custom option, however, the byte code compiler produces an executable which requires the byte code interpreter Let f (x) = x + 1;Print_int ;Print_newline (…);Then the following command produces the (approximately 8 k) file example.exe: Ocamlc -o example.exe example.mlThis file can be executed by the Objective Caml bytecode interpreter: $ ocamlrun example.exeThese are some of the subroutines defined as mathematical applications in the software used to run programs in Simulink.The purpose of the filter bank signal processing block was to decompose the input speech signal into eight overlapping subbands. There is more information contained in the lower frequencies of speech signals than in the higher frequencies. To get as much resolution as possible where the most information is contained, the subbands were spaced in such a way that the lower-frequency bands were narrower than the higher-frequency bands. The advanced CI system is capable of detecting a change in the background noise with no user intervention and changes the noise suppression parameters to previously determined (during training) optimal parameters for that particular background noise. A block diagram with a first-order low-pass filter, which is used at the end to smooth out any fluctuations, was proposed.mx(n), md(n), and my(n), respectively, where m denotes the window number. A priori and a posteriori SNRs  for the speech spectral estimation are given as follows: mξ(k) denotes the a priori SNR, mγ(k) denotes a posteriori SNR at the frequency bin k, dλ denotes the noise variance, and xλ denotes the clean speech variance. Frequency bin is a discretized frequency, which transforms continuous frequency into a discrete one. A priori SNR and a posteriori SNRs are obtained by using the “decision-directed” approach as: ddα is a smoothing parameter [minξ is a small number greater than 0.Let us consider an additive noise scenario, with clean, noise, and noisy received signals represented by arameter ,43, and SNR is defined as the ratio of signal power to the power of background noise. According to ,43, the In the present study, we used eight bands for the eight frequency ranges, from the lower limit of 70 Hz to the upper limit of 8500 Hz. The eight frequency ranges chosen were the following: 70–250 Hz, 70–350 Hz, 350–6500 Hz, 250–6500 Hz, 70–6500 Hz, 350–8500 Hz, 250–8500 Hz, and 70–8500 Hz. We used the model of 8-channel implants. The best results of comb filtered words frequency were recognized in the frequency range of 250–6500 Hz, with minimal recognition in the broadest frequency range.Since the discovery of multi-channel devices, research has demonstrated that hearing performance improves with increasing numbers of spectral channels ,45,46. HAt equidistance on the basilar membrane, distribution spectral bands, in accordance with normal tonotopical organization of the cochlea , was choThe cochlear implant block representation was effectuated in MATLAB Simulink, and contained an input source, cochlear speech processor, filter bank signal processing with designer filter bank, FIR and IIR filter bank response, an optional denoise stage, and electrical pulses with a pulse generation by 8 channels.Thereby, the cochlear implant signal source block, (represented in MATLAB Simulink) was introduced, where source type selection was used to change between a voice recording and a “chirp” sound, and again on the DC blocking filter, which was applied to the input signal. Evidently, a major focus on detailing software was associated with the block structure of the input signal source, when the FIR filter was applied in cascade to the 8 frequency bands .In the following, the most important functional blocks of the electrical model of the cochlear implant will be described. Among the most relevant was the cochlear implant signal from the Workspace block representation in MATLAB Simulink. In this description we will discuss the sampling of the input signal, which was done at a frequency of sampling set at the fixed value of fs = 22,050 Hz.Two more of the modules used included the cascade FIR bank block representation, with three successive cascaded high band/low band blocks, and the IIR filter bank block representation, with the IIR filter bank (with 8 outputs), the rectifier (with 8 inputs), and the supplementary block of 8 biquads applications.This section presents the simulation results and interprets them.The filter bank frequency specifications, with normalized band edges in the  interval, were as follows: channel 0: ; channel 1: ; channel 2: ; and channel 3: . In this example, the four low-frequency bands were equally spaced, while each of the four remaining high-frequency bands was twice the bandwidth of its lower-frequency neighbor. The filter bank frequency specifications, with normalized band edges in the  interval with twice the bandwidth, were as follows: channel 4: ; channel 5: ; channel 6: ; and channel 7: . In order to examine the frequency contents of the eight filter banks, we ran the model using the chirp source type in the input source block, as shown in Further on, we applied the FIR filter. In the following, we selected the speech input source for the simulation and ran the MATLAB simulation with various parameters, selecting from a range of filters, noise sources, and denoise functions. This section highlights the novelty of the investigation of climatic parameters (humidity and temperature) through the addition of such sensors in the discrete components circuit simulation. The sensors were simulated using constants integrated into the simulation environment, as can be seen in T = 36 °C) and relative humidity (RH = 40%). Further on, in The atmospheric attenuation of sound was computed for the input source and applied for different temperature and humidity values. For example, Details of the modified input source diagram, showing the temperature and humidity effects blocks, which were added in the multiplication stage as noise, are included in T = 0 °C and RH = 0%. Further, T = 36 °C and RH = 40%. In order to simulate the influence of humidity and temperature, an atmospheric attenuation of sound model was used. T = 36 °C and RH = 40% to T = 0 °C and RH = 0%. A more detailed analysis of these results will be carried out in the dedicated discussion section.T = 0 °C and RH = 0%, compared to three different temperatures and humidities, namely T = 20 °C and RH = 20% , PRR (packet reception ratio), SNR , and LQI .In An additional correction step can be applied using a denoise function that corrects the temperature and humidity influence on the system, as in At the beginning of the processing chain, where there is an external microphone, the sound waves arrive already affected by their way through the air and the distance from the source to the ear, which will then have to be processed electronically to reach the optimal standard values of the cochlear implant type used.A Simulink diagram of the complete simulation featuring the microphone input, the temperature and humidity sensor, as well as the additional filters and denoise blocks, is displayed in The simulation of a cochlear implant using MATLAB Simulink started by organizing a simulation setup of the cochlear implant, where a source type selection was used to change between a voice recording and a “chirp” sound. In addition, a DC blocking filter to the input signal was applied.In a natural sequence, simulation stages were presented in detail, among which the most important ones included the cochlear implant signal source block representation in MATLAB Simulink, the denoise stage block representation, and, finally, the stimulation pulse generator block. Thus, source type selection was used to change between a voice recording and a “chirp” sound, while a DC blocking filter was applied to the input signal. Special attention was paid to simulation code with the application of the temperature and humidity influence via the air attenuation function. T = 0 °C and RH = 0% and at T = 36 °C and RH = 40% was graphically represented. The atmospheric attenuation of sound was computed for the input source and applied for different temperature and humidity values. Moreover, the attenuation of temperature and humidity of the input signal at Propagation of sound close to the ground outdoors (at levels of 1–2 m) involves geometric spreading, air absorption, and interaction with the ground. The principle effect is owed to air absorption, which is manifested by proportion of sound energy that is converted to heat as a sound wave travels through the air.p at distance x from a position where the pressure is 0p is given by 0expp = p(−αx/2). In previous formulas, p was the pressure at temperature T (absolute temperature of the atmosphere in degrees Kelvin), and 0p was the reference atmospheric pressure (1 atm = 1.01325 × 105 Pa). The attenuation coefficient α, for air absorption, is dependent on frequency, humidity, temperature, and pressure.The resulting air absorption becomes significant at high frequencies and at long range, so air acts as a low-pass filter at long range. For a plane wave, the pressure Absolute humidity (AH) was an important factor in the diurnal variation and usually peaked in the afternoon. The diurnal variations were greatest in the summer (from 30% to 70%). It should be noted, that the use of (arithmetic) mean values of atmospheric absorption may lead to overestimates of attenuation when attempting to establish worst-case exposures for the purposes of environmental noise impact assessment.T = 0 °C and RH = 0% and at T = 36 °C and RH = 40% was graphically represented. The atmospheric attenuation of sound was computed for the input source and applied for different temperature and humidity values. Moreover, the attenuation of temperature and humidity of the input signal at T = 36 °C is the body temperature and RH = 40% is the average humidity of concert halls and of air-conditioned rooms. At the same time, they are considered as the average for reference values.These values of temperature and humidity were used because As an important observation, in the power graphs, as a function of normalized frequency (Power (dB) versus Normalized Frequency), power decreased rapidly until to a frequency of 0.6 (×π rad/sample), followed by a somewhat significant increase around a frequency value of 0.8 (×π rad/sample).Worth mentioning are also the results of the electrical pulse generator for each of the 8 channels with the IIR filter, Gaussian noise, temperature and humidity influence, and the denoise block active.Following the comparison, we can consider that for the first 4 channels (channels 0–3) we had a maximum fluctuation around the 1 s value (marked on the abscissa), and for the last 4 channels (channels 4–7) we had massive fluctuations for at least three values of time measured on the abscissa , as happened in channel 6 for the values 1.7 s, 2.9 s, and 4 s.To obtain the results in this project, a thorough understanding of MATLAB Simulink software was necessary. MATLAB Simulink was used to characterize the electrical behavior of the cochlear implant and to assess the audio signal and quantify the atmospheric perturbation impact on the signal quality. A simulation setup of the cochlear implant, where a source type selection was used to change between a voice recording and a “chirp” sound, was used. In addition, a DC blocking filter was applied to the input signal. Taking the initial intake data into account, the cochlear implant signal from the Workspace block representation in MATLAB Simulink, when the input sound was sampled, was included.T = 0 °C and RH = 0% and at T = 36 °C and RH = 40% was graphically represented. It can be easily seen that in the power graphs as a function of normalized frequency (Power (dB) versus Normalized Frequency), the power decreased rapidly until to frequency 0.6 (×π rad/sample), followed by a somewhat significant increase around the frequency value of 0.8 (×π rad/sample). Filter bank signal processing block was introduced, the role of which was to decompose the input speech signal into eight overlapping subbands.A simulation code with the application of the climatic influence, via the air attenuation function, was developed. Thereby, the attenuation of temperature and humidity in the sound atmospheric circulation of the input signal at The results of the electrical pulse generator for each of the 8 channels with an IIR filter, Gaussian noise, temperature variation, humidity influence, and by control of denoise block activity were thus obtained accurately. For the first 4 channels, we had a maximum fluctuation around 1 s and for the last 4 channels we had massive fluctuations for at least three values of time. For the sake of succinct presentation and article volume limitation, we have selected to present only two channels (channel 0 and channel 7) out of the eight. It can be said now that the novelty consists in the very idea of adding these sensors to a cochlear implant and that we obtained a modified signal, which can then be considered for future compensation. Electronic sound processing to change the parameters affected by air travel, from the sound source to the patient’s ear, is a technical novelty, as shown in this article. In the present, it was accepted and mastered in the serial production of cochlear implants by important players from the hearing aids industry , which now are announcing massive efforts in the future of the development of new cochlear implants.As for future directions, the effective production of the circuit with discrete components and its subsequent testing should be carried out.


Electrically conductive, highly flexible graphene membranes hold great promise for harvesting energy from ambient vibrations. For this study, we built numerous three-dimensional graphene ripples, with each featuring a different amount of compression, and performed molecular dynamics simulations at elevated temperatures. These ripples have a convex cosine shape, then spontaneously invert their curvature to concave. The average time between inversion events increases with compression. We use this to determine how the energy barrier height depends on strain. A typical convex-to-concave curvature inversion process begins when the ripple’s maximum shifts sideways from the normal central position toward the fixed outer edge. The ripple’s maximum does not simply move downward toward its concave position. When the ripple’s maximum moves toward the outer edge, the opposite side of the ripple is pulled inward and downward, and it passes through the fixed outer edge first. The ripple’s maximum then quickly flips to the opposite side via snap-through buckling. This trajectory, along with local bond flexing, significantly lowers the energy barrier for inversion. The large-scale coherent movement of ripple atoms during curvature inversion is unique to two-dimensional materials. We demonstrate how this motion can induce an electrical current in a nearby circuit. Recent developments in low power-consuming circuit designs have reduced input power to nanowatts in active mode and picowatts in standby mode. In addition, some applications operate with an ultra-low duty cycle, which further lowers the overall power requirement . These bMicroelectromechanical systems (MEMS) are an attractive approach to energy harvesting. For example, a 14 nm thick silicon nitride cantilever is extremely flexible and will vibrate under the slightest influence. The kinetic energy of the cantilever can be converted into stored electrical charge using an electrical circuit with a rectifier and a capacitor. Materials thinner than silicon nitride would be better, as the flexural rigidity decreases with thickness to the third power. The ultimate thinness is found with two-dimensional (2D) materials and graphene is the most durable of these. A graphene membrane can be ten times thinner than silicon nitride and, therefore, 1000 times more flexible ,10. In aWhen graphene is suspended, the membrane can be held under tension or compression. When taut, the vibrational modes produced are the well-studied drumhead vibrations. For graphene, these modes have very high frequencies . One potWhen graphene was first suspended in the early 2000s ,26,27,28Some dynamic properties of ripples have been studied using scanning probe microscopy (SPM). However, the shape transformation details of a ripple during curvature inversion cannot be imaged using SPM, as the process takes place over too large an area and too short a time for SPM. Molecular dynamics simulations (MDS), on the other hand, are an ideal method for investigating the evolution of ripple inversion. In this study, we used MDS to quantify the dynamics of graphene ripples as they invert their curvature. To achieve this, we first determined the energy required to compress and stretch graphene. Second, we used the strain–energy relationship to build a double-well potential for a particular ripple, then investigated the average inversion rate as a function of strain. This allowed us to report the energy barrier as a function of compressive strain. Finally, we derived a detailed picture of the shape transformations the ripple undergoes during the curvature inversion process through cross-sectional analysis.We first created a flat circular lattice of about 7000 carbon atoms arranged in a honeycomb structure. The potential energy of the lattice was found for a number of different bond lengths in LAMMPS using the AIREBO C-C potential at zero Kelvin. Atomic pairs were set to interact within three standard deviations of their equilibrium distance ,41. The We constructed three-dimensional (3D) ripples starting from the flat circular lattice and using a bond length smaller than 0.140 nm. Next, inside a radius of 7.5 nm, a third dimension was added to the carbon atoms using the following cosine function:This formula comes from a simple geometric analysis and computes the strain using the diameter and smooth arc length of the cosine ripple. After forming the ripple, we retain a 1.5 nm wide graphene annulus around the ripple at zero height. The annulus is always fixed in place, its position defines the The zero Kelvin potential energy of a −0.7% strained ripple is the double-well potential, as shown in ry (DFT) . Note thurvature .Next, we constructed numerous 15 nm diameter ripples, each featuring a different strain, using the method described above. After ripple construction, the ripple temperature was set to 3000 K and maintained at that temperature using a Nosé–Hoover thermostat. The equilibrium bond length changed from 0.140 nm at zero K to 0.143 nm at 3000 K. The equations of motion were integrated using a time step of 1 fs. The system was equilibrated for 0.5 ns starting from the initial cosine shape. The subsequent trajectory from a production run of 40 ns was used for analysis in this study.The cross-sectional profile of a typical equilibrated cosine-like shaped ripple is shown in We tracked the central height of the ripple in time. One such example is shown in We narrowed the strain values to those that yield bistable ripples that invert their curvature on a time scale accessible for this study. We then created 10 more ripples with similar but varying strains and ran 40 ns MDS for each. For all these simulations, we found the average time between curvature inversions, then plotted this time versus the calculated strain found using Equation (2), as shown in rs’ rate :(4)〈t〉∝eThe best fit to the average time between flips yields Using the energy barrier results shown in To study the shape transformations, we first identified all the inversion events in a particular ripple simulation by plotting the central height over time, as shown in 2) are shown for each function in time, as the ripple inverts. Early on, the goodness-of-fit for cosine is close to one, while sine is close to zero, as shown in Next, we analyzed the shape of the ripple in time. This was carried out by using regression analysis on the cross-sectional datasets taken in both the x- and y-directions. For convenience, we redrew The primary path for the ripple curvature inversion process as determined by MDS is illustrated in For comparison, we calculated the energy barrier from elastic theory ,47,48. TThe timescale for the curvature inversion process is remarkably short, at about 20–30 ps. During this time, the ripple height changes by 1.6 nm, which is significantly larger than the bond length. This yields an average speed of about 700 m/s. It is also noteworthy that the carbon atoms are moving together. This type of collective movement is like snap-through buckling ,49,50,51We can utilize the MDS data to predict the current produced by the thermal movement of the graphene. We imagine forming a parallel plate capacitor with a time-varying capacitance given by We studied the dynamics of graphene ripples using molecular dynamics simulations (MDS). The ripples are made by compressing flat graphene, then allowing the graphene to relax into the third dimension. For a range of strain values, the ripples are bistable and invert their curvature on a timeframe accessible with MDS. The transition rate for barrier crossing was found to be exponential in strain. Using the average transition rate, along with Kramers’ formula, we determined the height of the barrier in the double-well potential as a function of strain. The energy barrier is much lower than what one would expect if the ripples uniformly flattened before crossing. Using cosine and sine regression analysis on cross-sections of the ripple, we showed that the ripple distorts its shape to allow only part of the ripple to pass through the fixed frame at a time. Finally, we used the movement of the graphene to create a variable capacitor, which then induced a current to flow in a nearby circuit, thereby quantifying the energy harvesting promise of this system.


TDP-43 is a nuclear protein involved in pivotal processes, extensively studied for its implication in neurodegenerative disorders. TDP-43 cytosolic inclusions are a common neuropathologic hallmark in amyotrophic lateral sclerosis (ALS) and related diseases, and it is now established that TDP-43 misfolding and aggregation play a key role in their etiopathology. TDP-43 neurotoxic mechanisms are not yet clarified, but the identification of proteins able to modulate TDP-43-mediated damage may be promising therapeutic targets for TDP-43 proteinopathies. Here we show by the use of refined yeast models that the nucleolar protein nucleolin (NCL) acts as a potent suppressor of TDP-43 toxicity, restoring cell viability. We provide evidence that NCL co-expression is able to alleviate TDP-43-induced damage also in human cells, further supporting its beneficial effects in a more consistent pathophysiological context. Presented data suggest that NCL could promote TDP-43 nuclear retention, reducing the formation of toxic cytosolic TDP-43 inclusions. Neurodegenerative diseases (NDs) represent a huge health problem worldwide. In most cases, the origin of neurodegeneration remains largely unknown, but it is now commonly accepted that many of these disorders share the accumulation and aggregation of misfolded proteins as a major hallmark of neuropathology (for which they are often referred to as proteinopathies). Despite the role of such a protein misbehavior and the formation of protein assemblies may differ in distinct NDs, possibly also in different subtypes of the same ND, in all cases disease-associated misfolded proteins are supposed to either acquire toxic properties or lose their normal functions, or both, thereby driving neuropathogenic mechanisms .Among NDs, the vast majority of amyotrophic lateral sclerosis (ALS), frontotemporal dementia (FTD), and also a subset of late-onset Alzheimer’s disease (AD) cases are characterized by the presence of neuronal cytoplasmic inclusions mainly composed of an ubiquitinated and hyper-phosphorylated form of the transactive response DNA binding 43 kDa protein (TDP-43) . TDP-43 The above concept is further reinforced by acknowledging that intracellular TDP-43 aggregates are a major histopathological hallmark of ALS, found in post-mortem samples of 97% ALS patients . AlthougSaccharomyces cerevisiae , and Huntington’s disease (HD), have been successfully modeled in the yeast revisiae .In the last decade, different yeast models have been generated ectopically expressing TDP-43 at either high or low levels, mainly by the use of multicopy or centromeric plasmids, respectively . IntegraDrosophila and human cells) supported the linkage between ALS/FTD and the function of intra- and extra-nuclear membrane-less organelles, identifying some nucleolar proteins, cytosolic stress granules components and nucleocytoplasmic trafficking factors as genetic modifiers  of the cytotoxicity triggered by ALS-related proteins, such as C9orf72 DPRs  in light of multiple aspects, including the following structural and functional features of the protein that are reminiscent of TDP-43 attributes: (i), NCL is able to bind DNA/RNA molecules thanks to multiple RNA recognition motifs (RRM) domains; (ii), NCL carries an intrinsically disordered C-terminal region able to interact with different functional partners; (iii) NCL is one of the most abundant non-ribosomal proteins in the nucleolus, where it plays fundamental roles in nucleolar assembly and function, rRNA and ribosome metabolism , which aWe are here providing evidence that NCL is able to act as a potent suppressor of TDP-43 toxicity, relieving the lethal damage induced by WT or ALS-mutant TDP-43 overexpression in yeast cells. Additional experiments in mammalian HEK293T cell models further support that the NCL overexpression can prevent TDP-43-dependent toxicity, reducing the formation of detergent-insoluble assemblies and restoring cell viability, possibly by avoiding the non-physiologic translocation of TDP-43 out of the nuclear compartment.All yeast strains and plasmids used in this study are listed in Procedures for yeast growth and manipulation were performed according to standard protocols .Aldrich. Induction of gene expression under Gal1 promoter control was obtained by growing transformed yeast cells in raffinose containing medium for 16 h, followed by dilution of the cultures to an optical density at 600 nm light adsorption (OD600) of 0.1 and growth in galactose containing medium. Cells were routinely incubated at 30°C.Yeast cells were grown in standard rich media  supplemented with 20 g/L of glucose or galactose as carbon source, or in synthetic minimal media  containing 20 g/L of glucose, raffinose or galactose and lacking the specific nutrients allowing for selection of transformed yeast clones. Media components were from Difco (Thermo Fischer Scientific), and auxotrophic requirements were from Sigma-The PEG/lithium acetate method was used to transform yeast  and then600 culture turbidity measurements). In particular, for spotting assays, yeast cells were grown overnight at 30°C in liquid media containing raffinose. Cultures were then normalized to OD600 = 1, serially diluted and spotted onto synthetic solid media containing glucose or galactose and the auxotrophic requirements as needed. Plates were maintained at 30°C for different days, as indicated. Conversely, OD600 measurements were performed in a Microplate Reader spectrophotometer (TECAN) at a 600 nm wavelength (bandwidth 9 nm) with 15 flashes and 200 μL of culture per well. Briefly, three yeast clones were grown overnight in a raffinose containing medium (non-inducing condition), diluted in triplicate to OD600 = 0.1 in a galactose containing medium (inducing condition), and grown for 24 h. OD600 was then recorded immediately after the transfer (t0) or after 24 h of growth (t24), in the galactose medium, and the OD600 ratio (t24/t0) was calculated. Data were then normalized to the mean ratio of control cultures CEN.PK IMX672 (hereafter abbreviated as CENPK).Yeast cell viability was performed as previously described  by measuex = 488 nm for the excitation of GFP) and a computer-assisted charge-coupled device camera (Hamamatsu Orca flash 4.0), which allowed the acquisition of digital micrographs for either fluorescence or brightfield microscopy.For fluorescence microscopy observations, cells grown for 6 h in a galactose-containing liquid medium were used, while for cell shape analysis by brightfield microscopy cells were cultured on galactose containing solid substrate for 2 days. In both cases, before microscopy analysis, harvested cells were washed and resuspended in PBS, and then mounted on a coverslip with a thin agar slab as described previously . Images For morphometric measurements, the cell length-to-width ratio was calculated on digitalized images using the Fiji ImageJ software.For confocal microscopy observations, 1 mL of yeast cultures grown 24 h in galactose-containing medium were fixed in 4% formaldehyde . To stain nuclei, DAPI  was added during cell fixation. After fixation, cells were washed twice with sterile water and then resuspended in 1 M sorbitol. Cell suspension was dropped onto a slide  and covered with a coverslip . Cells wg, 10 min, 4°C). Cells were then resuspended in 0.1 M NaOH and incubated at room temperature for 20 min. After pelleting, cells were finally lysed by vortexing (2 min) in a buffer containing 62.5 mM Tris-HCl (pH 6.8), 2.3% (w/v) SDS and 10% (w/v) glycerol (buffer O). Lysates were boiled (3 min) and, after centrifugation to remove cell debris, protein quantification was performed in the supernatant using a bicinchoninic acid (BCA) assay kit (Thermo Fisher Scientific), according to the manufacturer’s instructions.The extraction of proteins from yeast cells for Western blot (WB) and MS analyses was performed as described elsewhere  with som–1 in 100 mM TEAB, pH 8.0) to each filter . After digestion, filters were subjected to two subsequent centrifugation step  adding 50 μL of 100 mM TEAB for peptide recovery. Peptide mixtures were then labeled using 6-plex TMT reagents (Thermo Scientific), and subjected to fractionation and purification as previously described (18 BioPure spin columns (The Nest Group). To reduce sample complexity, TMT-labeled peptides were fractionated by strong cation exchange fractionation (SCX) before liquid chromatography (LC) and MS. Briefly, labeled samples were diluted with four volumes of 0.1% formic acid and then applied to the SCX macro spin columns (The Nest Group). After two washings, retained peptides were stepwise eluted in five fractions containing increasing concentrations of ammonium formate . Prior to sample injection into the high-performance liquid chromatography-high resolution tandem mass spectrometry (HPLC-HRMS/MS) workstation, each fraction was desalted by means of the C18 BioPure spin columns. The five labeled fractions of each peptide extract were finally dried using a stream of nitrogen (RT) and suspended in 100 μL of 0.1% formic acid for the subsequent MS analysis.For tandem mass tag (TMT)-based quantitative proteomic analysis, protein extracts (50 μg) from three biological replicates of parental CENPK and TDP 1C yeast strains were processed according to the filter-aided sample preparation method , using fescribed . The thr18, 150 mm × 2.1 mm, 2.6 μm, Phenomenex) kept at 30°C.Relative quantification of yeast proteins was achieved using a hybrid quadrupole-orbitrap Q-Exactive (Thermo Fisher Scientific) mass spectrometer, coupled to a UHPLC system Ultimate 3000 (Thermo Fisher Scientific). Each SCX fraction was separated on a reverse-phase analytical column (Aeris peptide C–1 (35 min) using a gradient elution with the following composition (expressed as A:B ratio): 97.5:2.5 for 1 min; from 97.5:2.5 to 70:30 in 19 min (following a linear gradient), from 70:30 to 50:50 in 4 min; from 50:50 to 5:95 in 2 min and maintained for 4 min to wash the column; then back to 97.5:2.5 (to re-equilibrate the system) in 0.5 min and maintained for 4.5 min. The injection volume was 10 μL.Elution solvents for peptide separation were water (A) and acetonitrile (ACN) (B), both containing 0.1% (v/v) formic acid. The chromatographic separation was carried out at a flow rate of 200 μL × min6 ions, an injection time (IT) of 250 ms, an isolation window of 2.0 Th, and a scan range from 300 to 2000 Th. Higher energy C-trap dissociation (HCD) was performed with a normalized collision energy of 30, AGC target of 2 × 105 ions, an IT of 120 ms, a dynamic exclusion of 30 s and a resolution of 17,500. Fragmentation spectra were used for peptide identification and quantification, setting a fixed starting mass of 100 Th. To increase the number of identified peptides, each SCX fraction was analyzed twice. To this purpose, the m/z values of peptides positively identified in the first analysis (as described in details in the next section) were used to create a static exclusion list that was then applied to a second HPLC-HRMS/MS analysis  for each sample fraction.Ion source capillary temperature was 325°C, the sheath gas flow rate 35 (arbitrary instrument units), auxiliary gas flow rate 10 (arbitrary instrument units), S-lens voltage 55 V, heater temperature 325°C, and the spray was optimized at 3 kV. The instrument operated in data-dependent mode with a top-7 acquisition method . Full scan spectra were acquired using an automatic gain control (AGC) target of 3 × 10S. cerevisiae database (3AUP000002311). Enzyme specificity was set to trypsin, and a maximum of one missed cleavage was allowed. The precursor and product ion mass tolerances were set to 10 ppm and 0.2 Da, respectively. Oxidation of methionine was selected as variable modification, while 6-plex TMT at N-termini and at lysine residues, and carbamidomethylation of cysteines were set as fixed modification. Relative quantification was performed directly by the Proteome Discoverer software and only unique peptides were considered for quantification purposes. Based on the Percolator algorithm, proteins were considered as correctly identified if at least two unique peptides were quantified with an individual q-value < 0.05. Proteins were then grouped according to the principle of maximum parsimony. For quantification, the reporter mass tolerance was set to 20 ppm. The software detected the reporter ions , and performed the quantification of relative peptide abundance normalizing the intensity of each reporter ion to that of CENPK 1 sample (126.1 Th). Normalized intensity values of proteins derived from Proteome Discoverer were exported in an Excel spreadsheet, and the matrix was arranged for further analysis. The final fold-change of a given protein was calculated as the mean value of the normalized ratios (TDP 1C/CENPK) of the three replicates. Finally, a two-tailed t-test was performed, and only proteins with a ratio > 1.33 or <0.77, and a p-value < 0.05, were considered over-expressed or under-expressed, respectively.Raw files derived from HPLC-HRMS/MS runs were analyzed with a MudPIT protocol using the Proteome Discoverer 1.4 software (Thermo Fisher Scientific) interfaced to a SEQUEST HT search engine (Thermo Fisher Scientific). All MS/MS data were searched against the UniProt 18 spin columns (The Nest Group), following manufacturer’s instructions. Briefly, samples were loaded in pre-activated C18 spin columns, that were washed twice with 200 μL of 0.5% formic acid (v/v), and then peptides were eluted using 75% ACN containing 0.1% formic acid (v/v). Peptide extracts were dried under a stream of nitrogen and, immediately before HPLC-HRMS/MS analyses, dissolved in a solution (100 μL) containing 5% (v/v) ACN and 0.1% (v/v) formic acid to obtain a final concentration of 0.5 μg × μL–1 of protein digest. Confirmatory analyses were performed using the same HPLC-HRMS/MS apparatus described above operating in the PRM mode. Peptides were separated using the same chromatographic gradient described above. The scheduled PRM method was developed by recording and selecting the most intense charge state of the considered peptides and four diagnostic precursor-to-product ion transitions in serial injections of a representative yeast digest sample. The same was done for glyceraldehyde 3-phosphate dehydrogenase (GAPDH), which was chosen as housekeeping protein. PRM acquisition parameters were as follows: HCD fragmentation with normalized collision energy (NCE) of 27, AGC target of 5 × 105 ions, an IT of 120 ms, an isolation window of 1.6 Th with an offset of 0.4 Th. Two proteotypic peptides were selected for each protein included in the PRM analysis. The selected precursor-to-product ion transitions, together with the retention time and the instrumental settings used in the acquisition method are reported in The targeted MS-based parallel reaction monitoring (PRM) analysis was carried out in WT CENPK, and TDP-43 1C and 2C strains expressing, or not, NCL. To this end, 50 μg of yeast lysates from nine different cultures for WT CENPK and for each of the four different TDP-43 clones  were prepared and processed according to the filter-aided sample preparation (FASP) method as described above. After digestion, peptide mixtures were acidified (pH < 3) by adding formic acid and desalted using BioPure CTo ensure that no instrumental drift occurred during PRM analysis, a pooled sample prepared by mixing together equal amounts of peptide digests was injected and analyzed at the beginning and at the end of the analytical sequence. The Skyline software (version 3.5.0.9191)  was usedThe MS proteomics data have been deposited to the ProteomeXchange Consortium via the PRIDE partner repository  with theL-glutamine, 100 U/mL penicillin, and 100 μg/mL streptomycin and maintained at 37°C in a humidified incubator with 5% CO2. HEK293T cells were seeded at 25% of confluence in multi-well culture plates 24 h before transfection. Cells were (co-)transfected using the Lipofectamine 3000 transfection kit (Invitrogen) following the manufacturer’s instructions and analyzed 48 h after transfection. Plasmids used to transfect HEK293T cells are reported in Human epithelial kidney HEK293T cells were grown in Dulbecco’s modified Eagle medium (DMEM) supplemented with 10% (v/v) foetal bovine serum (FBS), 2 mM Cell viability was assessed using the MTS  assay , based on the reduction of MTS by viable cells, following the manufacturer’s instructions. Briefly, cells were seeded in 96-wells and co-transfected with the desired plasmids (as indicated in the figure legends), and – 48 h after transfection – the cell culture medium was removed and the MTS reagent [20 μL in 100 μL of phosphate-buffered saline (PBS)] was added to each culture well. Cells were then incubated at 37°C (90 min), after which the absorbance of reduced MTS (λ = 490) was determined using a Microplate Reader (TECAN) spectrophotometer.For confocal microscopy observations, HEK293T cells were seeded onto 13 mm coverslips and co-transfected using plasmids coding for the desired fluorescent constructs. 48 h after transfection, cells were rinsed twice with PBS, fixed  with paraformaldehyde [2% (w/v) in PBS], rinsed again and permeabilized  with Triton-X100 [0.1% (w/v) in PBS]. Cell nuclei were counter-stained with Hoechst 33342 , and coverslips were finally washed in PBS and mounted in Mowiol 40–88 (Sigma-Aldrich) [8% (w/v) in glycerol:PBS ]. Images were collected with a Leica SP5 confocal microscope using 40X or 63X HCX PL APO  oil-immersion objectives. Settings of confocal microscope and image acquisition and analysis were performed as described above. For quantitative analyses reported in g, 10 min, 4°C) to precipitate cell debris, the total protein content in the supernatant was determined by the BCA assay kit (Thermo Fisher Scientific).For protein extraction, HEK293T cells, grown onto 12-wells plates and transfected as described above, were washed twice with ice-cold PBS and lysed with buffer O (60 μL/well). After centrifugation ] or Mini-Protean TGX precast gels ] and electroblotted onto polyvinylidene difluoride (PVDF) membranes . Membranes were stained with Coomassie brilliant blue (Sigma-Aldrich) to check for even protein loading, and digitalized images were collected for subsequent densitometric analysis. After Coomassie destaining with methanol, membranes were incubated  in a blocking solution[5% (w/v) non-fat dry milk (Bio-Rad Laboratories) in TRIS-buffered saline (TBS) added with 0.1% (w/v) Tween-20 (TBS-T)], and then probed  with the desired primary antibody diluted in TBS-T containing 1% (w/v) bovine serum albumin (BSA). After three washings with TBS-T, membranes were incubated  with horseradish peroxidase-conjugated anti-mouse or anti-rabbit IgG secondary antibody  , depending on the primary antibody. Immunoreactive bands were visualized using an enhance chemiluminescence reagent kit (EMD Millipore) and digitalized by means of an UVItec imaging system (Eppendorf). For densitometric analyses, the intensity of each immunoreactive band was normalized to the optical density of the corresponding Coomassie blue-stained lane  bromophenol blue] were subjected to SDS-PAGE . Cells were lysed by vortexing  in a MagnaLyser apparatus (Roche Diagnostics). The protein fraction was obtained after precipitation of cell debris by centrifugation , and proteins were quantified by a Lowry assay kit (Sigma-Aldrich). 1 mg of total proteins (in IP buffer) was added with either 1 μg of anti-NCL rabbit pAb , or anti-GFP mouse mAb , or anti-RFP mouse mAb  antibody and incubated  under continuous gentle inversion mixing. As negative control, the same amount of lysate was incubated in the absence of antibody.For protein co-immunoprecipitation (co-IP) assays from 2HPO4, and 2 mM KH2PO4, 0.2% NP-40, 10% glycerol (w/v), 5 mM EDTA and Roche complete EDTA-free protease inhibitor cocktail), and maintained on ice for 15 min. Cell lysates were then passed (five times) through 21-gauge needles and centrifuged . The total protein content in the supernatant was determined using the BCA assay kit (ThermoFisher Scientific), and 200 μg of total proteins were incubated  with 0.2 μg of the rabbit polyclonal anti-NCL  in lysis buffer under continuous gentle inversion mixing. When needed, cell lysates were treated with RNAse A  prior to immunoprecipitation. Non-transfected cells were processed as described above as negative control.The co-IP protocol for HEK293T cells was adapted from g, 4°C), washed three times with lysis buffer and once with 50 mM Tris–HCl (pH 7.5), and finally boiled (3 min) in reducing sample buffer. Immunoprecipitated proteins were separated onto 10% SDS-PAGE gel, electroblotted onto PVDF membranes (Millipore) and analyzed by WB with antibodies to target proteins.For both yeast and HEK293T samples, protein-antibody complexes were precipitated by adding protein A-Sepharose . After incubation  under gentle shaking, sepharose bead-bound immunocomplexes were collected by centrifugation  to precipitate cell debris. Fractionation was performed by centrifuging  the cleared lysate, the supernatant (“detergent-soluble” fraction) was saved, and the pellet was resuspended in lysis buffer and centrifuged again as above. The final pellet (“detergent-insoluble” fraction) was resuspended in a urea-based buffer containing 7 M urea, 2 M thiourea, 4% CHAPS and 30 mM Tris (pH 8.5).The protocol for detergent-soluble and -insoluble protein fractionation from yeast cells was adapted from g, 5 min, 4°C). Cells were then resuspended in 300 μL of radio-immunoprecipitation assay (RIPA) buffer  supplemented with 2 mM EDTA, 1 mM EGTA and complete EDTA-free protease inhibitor cocktail (Roche). Following incubation on ice (15 min), cells were sonicated (10 s) and the obtained lysate was cleared by centrifugation  to remove cell debris. A small amount of the lysate  was kept for subsequent analysis, while the remaining lysate was centrifuged . The resulting supernatant (“detergent-soluble” fraction) was saved and the pellet, after washing in RIPA buffer, was centrifuged again as above. The final pellet (“detergent-insoluble” fraction) was resuspended in the same urea-based buffer used for yeast samples.Detergent solubility fractionation of proteins from HEK293T cells was performed according to N,N’-methylenebisacrylamide 37.5:1 (w/w)] and WB for the detection of the target proteins as described previously. To determine the amount of RIPA-soluble (or insoluble) TDP-43, the percentage ratio between the intensity of TDP-43 soluble (or insoluble) immunoreactive bands and the sum of the intensity of TDP-43 bands in the soluble and insoluble fractions were calculated. The reliability of the fractionation protocol was confirmed by evaluating the absence of GAPDH in the insoluble fractions.The quantification of the protein content in all fractions of both yeast and HEK293T samples was carried out by the Lowry assay kit (Sigma-Aldrich). Protein fractions were then analyzed by SDS/PAGE [using 10% (w/v) acrylamide-The protein–protein interaction network and gene ontology enrichment analysis was performed using the publicly available Cytoscape 3.7 software  integratp-value < 0.05 was considered statistically significant .Data were analyzed using Prism 7 (GraphPad Software) and Microsoft Excel (Microsoft Corporation) software. Data are reported as mean ± standard error of the mean (SEM). The number of biological replicates (n) reported in figure legends indicates the number of cultures/transformations, or the number of transfection experiments performed in different days, for yeast and HEK293T cells, respectively. Depending on the experiment, the statistical analysis was carried out as indicated in each figure legend. A S. cerevisiae yeast strains in the CEN.PK IMX672 genetic background , or strongly overexpressing the protein by means of an ectopic galactose-inducible multi-copy (MC) plasmid. The genome editing correctness in the obtained yeast strains was verified by PCR , although the molecular mechanisms underlying such a defect remained unsolved . We thusA strict correlation between the elongated phenotype and cell death, however, is ruled out by the observation that such morphological defect also appears in yeast cells expressing non-toxic levels of TDP-43  human TDP-43 bearing the ALS-related Q331K and M337V missense mutations that are known to exacerbate TDP-43 cytotoxicity . These mThe above data demonstrated that expression of WT or ALS-associated mutant human TDP-43 markedly affects cell viability and growth in yeast strains carrying two or more copies of the transgene, while the expression of TDP-43 (either WT or mutated) from a single genomic copy was not sufficient to cause cell death, pointing to a saturating effect of two transgene copies for TDP-43 cytotoxicity.This experimental evidence validates the use of the here presented yeast strains as valuable cell models for the identification of biological factors and/or chemical compounds that can rescue TDP-43-dependent harm.Once established, we used such yeast models for TDP-43 proteinopathies to test the possible effects of NCL, which – as detailed in the Introduction – could be a promising player in the mechanisms of TDP-43 cytotoxicity. To this purpose, we overexpressed NCL (as a fusion construct with the red fluorescent protein mKate2) in the yeast strains bearing two or three integrated chromosomal copies of the TDP-43-GFP construct (WT or mutated), by means of an ectopic MC plasmid driven by a galactose-inducible promoter.Both cell viability  and growNotably, we also observed that the genetic 1:1 stoichiometry of NCL with respect to TDP-43 was not sufficient to prevent TDP-43 cytotoxicity, as demonstrated by the loss of viability of cells carrying two integrated chromosomal copies of both TDP-43-GFP and NCL-mKate2 constructs, driven by the same galactose-inducible promoter .In the attempt to shed light on the ways by which NCL prevented TDP-43 toxicity, we next investigated the effects of NCL overexpression on TDP-43 behavior and the TDP-43-related morphologic phenotype in the TDP 2C strain. By biochemical fractionation assays, we found that the co-expression of NCL significantly increased the detergent-soluble fraction of WT TDP-43 . Quite sNotably, untagged NCL, expressed by a MC plasmid, still retained the capability to counteract the toxicity of untagged TDP-43, as demonstrated by cell viability assays , ruling Collectively taken, the above data indicated that NCL overexpression was able to potently counteract the toxic effects of TDP-43 in yeast cells, and that such beneficial effect could be related to the aggregation dynamics of TDP-43, i.e., NCL reduced the detergent-insoluble fraction of TDP-43.NSR1 gene. We found that NSR1 deletion did not impinge on the toxic phenotype induced by TDP-43 overexpression and that the ability of NCL to suppress TDP-43 toxicity was unperturbed in the ΔNSR1 genetic background  by multicopy plasmids , or the ckground .We then applied different approaches aimed at exploring the mechanisms by which NCL was able to counteract TDP-43-related toxic phenotypes.p-value < 0.05) up-regulated  or down-regulated  in the TDP 1C strain , both grown to log phase in raffinose medium (non-inducing) and transferred to galactose medium for 24 h to induce transgene expression. We chose to run the preliminary untargeted MS analysis on the TDP 1C strain, in which TDP-43 expression levels were sub-lethal, in order to possibly identify cell pathways induced by TDP-43 (and that NCL is able to counteract) whilst avoiding proteome alterations due to secondary events in downstream cell death pathways. By this assay, a total of 631 proteins were properly identified and quantified in all biological replicates of both strains. Interestingly, only 38 proteins were either significantly  and that the vast majority of them belonged to discrete clusters. Notably, a gene ontology (GO) analysis showed that the most enriched cluster of up-regulated proteins included proteins involved in cellular response to stress, such as heat and oxidative stress, and oxidant detoxification  in both transgenic strains  on the levels of the subset of 19 proteins that were mostly deregulated by TDP-43 expression (in both the 1C and 2C strains) according to the unbiased proteomic analysis, and whose alteration was confirmed by the validation study of  strains  and that strains .In summary, the MS data indicated that the expression of TDP-43 (in the TDP 1C strain) triggered weak changes (∼6%) of the whole yeast proteome, resulting, however, in the significant up-regulation of specific proteins implicated in the response to oxidative and heat stress, and in protein folding and refolding processes that may be related to a proteostatic stress induced by TDP-43 expression. Most importantly, we also found that NCL brought the levels of some of these proteins in TDP-43-expressing strains toward the values observed in control cells, suggesting that it could decrease in some ways the proteostatic stress associated to TDP-43 intoxication.As mentioned, human NCL prevalently localizes in the nucleolar compartment where it plays multiple roles , but it may also translocate outside the nucleus taking part to nucleocytoplasmic trafficking . We therThe above described genetic/functional interaction between TDP-43 and NCL might also be related to a physical interaction between the two proteins, considering that both proteins, although being mainly nuclear, may also localize in the cytosol. This possibility was suggested by a previous study showing – by immunoprecipitation of overexpressed TDP-43 followed by identification of co-purified proteins by MS – that NCL was a physical interactor of TDP-43 in HEK293T cells . We thusWe next asked if NCL was able to suppress the TDP-43-induced phenotypes observed in the yeast models also in mammalian cells. To answer this question, we overexpressed human TDP-43 and NCL in the immortalized human cell line HEK293T by transient transfection with plasmids encoding fluorescence-tagged TDP-43-mKate2 and NCL-GFP chimeric constructs see .We firstly assessed the viability of transfected cells by the MTS assay. Cells overexpressing the TDP-43 (either WT or bearing the Q331K missense mutation) chimera were significantly less viable (around 50%) compared to control cells  was toxic to, and formed detergent-insoluble aggregates in, HEK293T cells and that NCL was able to attenuate both phenotypes, facilitating the nuclear localization of TDP-43. These data substantiated a functional role of NCL in antagonizing TDP-43 proteinopathies.The finding that extranuclear NCL is prominent in abolishing TDP-43 toxicity in yeast , prompteFinally, following the co-IP observations in yeast cells  and the TDP-43-mediated neurodegeneration is characterized by the depletion of nuclear TDP-43 and the concomitant accumulation of the protein in aberrant cytoplasmic assemblies. These two hallmarks suggested that TDP-43 proteinopathies arise from either a loss of function, by impairing the normal function of nuclear TDP-43 in regulating transcription and mRNA processing, or a gain of toxicity of the cytosolic fraction of the protein, or both . Whatevein vitro cell models.Here we showed that NCL, sharing different structural and functional attributes with TDP-43, is able to revert cell death and other phenotypes related to TDP-43 overexpression in both yeast and human S. cerevisiae yeast model already provided important hints into NDs caused by protein misfolding and aggregation, including TDP-43 proteinopathies , that are known to increase TDP-43 toxicity in yeast cells . HoweverOur models also recapitulated recently described alterations of yeast cell morphology caused by TDP-43 expression , but theTaken together, the above findings demonstrated that the novel models here generated were able to recapitulate in a more stable genetic context the major effects of TDP-43 expression in yeast cells, i.e., cytotoxicity, increased propensity to protein aggregation/condensation, and specific morphological alterations, and were therefore suited for the identification of possible antagonists of TDP-43-induced cellular damage and to investigate the cell pathways thereof.Drosophila model  the most abundant nucleolar protein and is the first identified RNA binding protein able to shuttle from nucleus to cytoplasm, being involved in the transport of RNA molecules and proteins across the nuclear membrane . Importala model , althougla model .Another link between NCL and ALS/FTD was suggested by the co-segregation of NCL and some RNAs  or RNA binding proteins (including TDP-43 and FUS/TLS), in RNA foci accumulated in ALS patient tissues, resulting in the altered expression of specific RNA targets, RNA misprocessing events and disruption of nucleolar functions .Moreover, NCL was also reported to play a role in the long-term maintenance of mature neurons , and hasImportantly, we confirmed in mammalian cells the evidence that NCL can potently counteract TDP-43 (WT or bearing ALS-related missense mutations) cytotoxicity and TDP-43 detergent-insolubility observed in our yeast models. Indeed, data from human HEK293T cells indicated that NCL overexpression markedly decreased the number of cells with cytoplasmic TDP-43 aggregates, while concurrently increasing the number of cells displaying proper TDP-43 nuclear localization. We further observed that the TDP-43-dependent activation of apoptotic cell death in human cells was significantly reduced by NCL co-expression, suggesting that NCL could suppress TDP-43 toxicity also by directly acting as an antiapoptotic factor, in light of its reported ability to stabilize the mRNA of the Bcl-2 anti-apoptotic factor , and to However, in yeast cells, NCL was not able to counteract the cytotoxicity induced by the overexpression of FUS and C9orf72 DPR, both of which were recognized – similarly to TDP-43 – to induce cell death via the mitochondrial apoptotic pathway , suggestFurther studies are needed to establish the exact mechanisms by which NCL specifically target TDP-43 pathology. However, the observation that  the removal of the NLS did not influence NCL ability to rescue TDP-43 toxicity, which was conversely abrogated by introducing an additional NLS signal forcing NCL retention in the nuclear compartment, strongly suggested that the extranuclear localization of NCL, possibly linked to its nucleocytoplasmic shuttling role, is required to suppress TDP-43 proteotoxicity.According to the evidence provided by this work, a possible scenario could be proposed, whereby the extranuclear fraction of NCL interacts with TDP-43 in the cytosol, restoring its correct movement across the nuclear membrane and promoting its nuclear localization and solubility, therefore preventing the formation of cytosolic TDP-43 aggregates, and finally alleviating the proteostatic stress induced by TDP-43 overexpression and/or disease-related mutations.An emerging theme in TDP-43 proteinopathies is the ability of the protein to undergo phase transitions, forming liquid-like condensates that may be more harmful to neurons than insoluble aggregates. The nature  and the cell localization  of TDP-43-containing structures that NCL is able to cure may thus worth further investigation.Concurrently, another sensible hypothesis entails that NCL exerts a chaperone-like activity for TDP-43, as already suggested for histones, whereby NCL helps histone nuclear import and storage when not assembled with DNA . By thisThis hypothesis is supported by the MS analysis performed in yeast cells providing evidence that among numerous altered proteins by TDP-43 expression, the presence of NCL restored the normal levels of some HSPs. Interestingly, NCL restored the levels of HSP70 and of HSP82 (the yeast homolog of HSP90), both previously suggested to functionally interact with TDP-43 . PossiblThe targeted proteomic approach was based on a preliminary untargeted large-scale MS analysis of yeast cells, which identified several proteins dysregulated by TDP-43 expression. Although we may possibly have identified a higher number of dysregulated proteins in the toxic TDP 2C strain, we chose to run such a first large-scale study in the non-toxic TDP 1C strain in order to avoid foreseeable secondary whole proteome alterations related to cell death pathway activation. Such a choice was corroborated by the subsequent PRM targeted analysis, demonstrating that most of proteins involved in cell stress response, protein folding, and proton transport that we found to be modified in TDP 1C cells were equally altered in the (toxic) TDP 2C strain.Specifically, we found in both strains the upregulated expression of proteins involved in the response to oxidative stress or also of some chaperone proteins belonging to the HSP subfamily involved in protein folding and stress granule disassembly, which may speculatively be associated to a compensative response to TDP-43-induced protein misfolding stress. Conversely, among the MS identified proteome no evident alteration was observed for proteins directly involved in nucleocytoplasmic trafficking, while – unfortunately – our analysis failed in detecting any protein possibly implicated in the filamentous yeast growth or autophagy. Importantly, however, we also found that TDP-43 expression in yeast is associated to a down-regulation of several subunits of ATP synthase, supporting the idea that TDP-43-induced damages may also involve mitochondrial disfunctions, as reported previously .in vivo ALS experimental settings, NCL seems a promising means to understand the mechanisms of TDP-43-associated proteinopathies and a possible therapeutic target for ALS and related disorders.Here, reported findings clearly identified NCL as a powerful modifier of TDP-43 proteotoxicity in both yeast and mammalian cells. Although studies are needed to assess if the above proposition still stands in ALS selectively vulnerable MNs and in The mass spectrometry proteomics data have been deposited to the ProteomeXchange Consortium via the PRIDE partner repository with the dataset identifier PXD022432.RL, AB, and CP: conceptualization. RL, RS, and CP: methodology. CP, RS, and RB: formal analysis. CP, RB, RS, MM, JA, and AM: investigation. FT, GS, and MM: resources. RL, AB, and CP: writing – original draft. RL, AB, CP, FT, MM, GS, and RS: writing – review and editing. RL and AB: supervision and funding acquisition. All authors contributed to the article and approved the submitted version.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.