Although important progress has been achieved in understanding the catalytic mechanism of Carbonic Anhydrases, a detailed picture of all factors influencing the catalytic efficiency of the various human isoforms is still missing. In this paper we report a detailed structural study and theoretical pKa calculations on a hCA VII variant. The obtained data were compared with those already known for another thoroughly investigated cytosolic isoform, hCA II. Our structural studies show that in hCA VII the network of ordered water molecules, which connects the zinc bound solvent molecule to the proton shuttle His64, is altered compared to hCA II, causing a reduction of the catalytic efficiency. Theoretical calculations suggest that changes in solvent network are related to the difference in pKa of the proton shuttle in the two enzymes. The residue that plays a major role in determining the diverse pKa values of the proton shuttle is the one in position four, namely His for hCA II and Gly for hCA VII. This residue is located on the protein surface, outside of the active site cavity. These findings are in agreement with our previous studies that highlighted the importance of histidines on the protein surface of hCA II (among which His4) as crucial residues for the high catalytic efficiency of this isoform. Carbonic Anhydrases CAs, EC 4.2.1.1) are ubiquitous metallo-enzymes that are present in most living organisms .2.1.1 ar,6,7,8. H2 to HCO3− and proton, following a two-step mechanism described by Equations (1) and (2) [2+-bound hydroxide on CO2 with consequent formation of HCO3−. The binding of HCO3− to the zinc is rather labile, thus it is replaced by a water molecule generating the catalytically inactive form of the enzyme EZn2+–H2O (Equation (1)). In the second step of the reaction, which is the rate-limiting one, the zinc-bound hydroxide is regenerated through a proton transfer reaction from the zinc-coordinated water molecule to the bulk solvent (B) [hCAs catalyze a simple but fundamental physiological reaction, the reversible hydration of COquations  and (2) vent (B) ,12,13,14In the majority of the isoforms, a histidine residue placed in the middle of the active site cavity, namely His64, assists this step acting as a proton shuttle ,16. Accoin and out. In the in conformation, the imidazole ring points toward the zinc ion, whereas in the out conformation it points out of the active site cavity [2+-bound solvent molecule to His64  [a calculations of the proton shuttle His64 were also carried out.Catalytic assays demonstrated that this enzyme is able to catalyze the COM−1 s−1) . This ficat = 7.0 × 105 s−1 and kcat/KM = 6.5 × 107 M−1 s−1) [In a previous study, we reported the bacterial expression, using pGex-6P-1 vector, of a mutated form of hCA VII, named dmCA VII, which contained two amino acid substitutions. In particular, the cysteine residues in position 183 and 217 were mutated to serines. It was demonstrated that the two mutations did not influence the catalytic activity of the enzyme; indeed, the kinetic constants for native hCA VII and dmCA VII were absolutely comparable (dmCA VII: kM−1 s−1) . dmCA VIM−1 s−1) .E. coli. The purification steps led to obtain the protein with high purity level (>98%) and a final yield of 7 mg/L. The enzyme was concentrated at 5 mg/mL and crystallized, using polyethylene glycol as precipitant, in the space group P21212 with one molecule per asymmetric unit. Using the tagged protein for crystallization experiments, more reproducible results were obtained in comparison with the same trials performed on the protein expressed in the pGex-6P-1 vector.In this paper, dmCA VII was produced using a different vector, pETM13, which allowed us to successfully express the protein with a C-terminal histidine tag in the soluble fraction of The structure was determined at 1.91 Å resolution and atomic coordinates were deposited in the Protein Data Bank (PDB) as entry 6G4T. The crystal parameters, data collection, and refinement statistics are listed in out conformation; however, in the structure under investigation no evidence of the inward conformation was observed  and from the water molecule W1, which in turn interacts with W2  [In the structure of the unbound dmCA VII, the metal ion maintains the tetrahedral coordination already reported for the inhibited enzyme; in this case the fourth coordination position is occupied by a solvent molecule (ZS), which is connected to His64 through a network of well-ordered water molecules A,B. In din and the out conformations  [a value in dmCA VII as well as in hCA II using the PROPKA method [in and out conformations of His64 present in the native hCA II crystal structure (PDB code 1TE3) [out conformation, a model of the in conformation was built using the hCA II structure as template [Several papers highlighted a correlation between the pKcussion) ,29. ThusA method ,44. Bothde 1TE3)  were taktemplate .a values are shown in a lowering. This is the case of His64 in CA structures, which is 63% buried in the out conformation and 100% buried in the in conformation. However, since this effect is present in a comparable manner in all structures under investigation, it is possible to make a relative comparison and to understand the effect of the chemical environment in modulating His64 pKa in hCA VII and hCA II.Predicted pKa value of His64 is higher in dmCA VII, for both in and out conformations  and feels a slight destabilizing charge-charge interaction with Zn2+ ion that further lowers its pKa value. On the contrary, the pKa of the out conformation in both enzymes is affected by the presence of Lys170, a residue located on the protein surface at the top of the active site cavity , being completely solvent exposed. The electrostatic interaction between His4 and the nearby His64 residue contributes to hamper the titration of His64 and thus to lower its pKa in hCA II. This effect is, however, strongly dependent on the distance between His4 and His64. Since from the analysis of the hCA II structures crystallized in different conditions [a calculations on different hCA II structures (see Method paragraph)  than in hCA II (4.3).In the case of hCA II, there is an additional residue, i.e., His4, which further affects the pKormation . Interesnditions ,40,42, iragraph) . The finormation  was obtaa of this residue. Indeed, in previous studies these two features were reported to be important in determining the rate of the proton transfer [The aim of this work was to provide insights into the molecular features responsible for the different catalytic activity of hCA II and hCA VII. Two features of these enzymes were thoroughly analyzed and compared: (i) the network of ordered water molecules, connecting the zinc bound solvent molecule and the proton shuttle His64, and (ii) the pKtransfer .in and the out conformations, whereas only the out conformation is observed for dmCA VII.hCA II and hCA VII present a high degree of sequence identity (55.7%), which is mainly localized in the active site region. Indeed, only seven substitutions  are observed among the 23 residues that delimit the active site cavity . Accordiout conformation is present is quite surprising. Indeed, as mentioned in the Introduction, in many crystallographic structures of hCA II [in and out conformation [in conformation is observed at more alkaline pH [in conformation would have been expected, differently from what experimentally observed. A predominant out conformation of His64 was observed also in the hCA II variants N67L [a of this residue and the out conformation was observed [out conformation [out conformation of His64 and its higher pKa values, our theoretical calculations demonstrated that the pKa of His64 is higher in dmCA VII than in hCA II.The observation that in dmCA VII only the f hCA II ,40,41,42ormation ,20. The aline pH ,20. Sincnts N67L , T200S [nts N67L , W5H [46nts N67L  and N62Dnts N67L . For somobserved ,29. Whenin conformation of His64. Previous studies reported by Fisher and coworkers [2+-bound solvent molecule and the proton shuttle: the smaller the number of connecting water molecules is, the higher the catalytic efficiency will be. These observations were in contrast with the work of Riccardi et al. [The different conformational preferences of His64 in hCA II and dmCA VII have important consequences in the solvent structure within the active site cavity. Indeed, the structural comparison between the two enzymes reveals that in the case of dmCA VII an additional water molecule connecting the zinc-bound solvent molecule and His64 is present in the position that in hCA II is occupied by the oworkers  proposedi et al. , which sout conformation of its side chain and consequently an altered solvent structure that makes less efficient the catalytic mechanism. Interestingly, a key residue in determining the different pKa values of the proton shuttle in the two enzymes, is the one in position four, namely His4 for hCA II and Gly4 for hCA VII. Previously reported studies from our group highlighted the crucial role of histidines on the protein surface of hCA II (among which His4) as important in determining the high catalytic efficiency of this isoform [In conclusion, our studies provide a further piece in the complicated puzzle of the hCA catalytic mechanism. In particular, we found that in hCA VII pKa of His64 is higher than in hCA II. This determines a predominant  isoform ,46. The F: 5′-CGCGCGCCATGGGCATGACCGGCCACCACG-3′R: 5′-CGCGCGCTCGAGGGCCCGGAAGGAGGC-3′The cDNA encoding the C183S/C217S mutant form of hCA VII, called dmCA VII , was PCRE. coli BL21 (DE3) cells were transformed with the recombinant construct. The growth was performed at 37 °C. At OD600 = 0.6, cells were induced by adding IPTG  at a final concentration of 0.1 mM. The expression of the recombinant protein was carried out at 22 °C for 16 h. The culture was centrifuged  and the pellet stored at −80 °C.The resulting fragment was ligated into the expression vector pETM13 . The generated plasmid was checked by sequencing and appropriate digestion with restriction enzymes. g), the protein was purified by FPLC, using an AKTA system on a 1 mL His Trap FF column , by stepwise elution, according to the manufacturer’s instruction . After elution, dmCA VII enzyme was dialyzed in 20 mM Tris, 100 mM NaCl, pH 8.0 buffer. The protein was further purified by affinity chromatography with p-(aminomethyl) benzene sulfonamide (pAMBS) agarose beads . Protein purity was assessed on 15% SDS-PAGE gels, using Biorad Precision Plus Protein All Blue Standards (10–250 kDa) as molecular mass marker.The lysis of the obtained pellet was performed in PBS 1X , in presence of 1 mM phenylmethanesulfonyl fluoride, 5 mg/mL DNaseI, 0.1 mg/mL lysozyme and 1.0 µg/mL Aprotinin, Pepstatin and Leupeptin used as protease inhibitors . Cells were disrupted by sonication. After centrifugation  polyethylene glycol 3350 in 0.1 M Tris-HCl, pH 8.5 as precipitant buffer. Crystals appeared in the drops within 48 h and grew in about 1 week to maximum dimensions of 0.2 × 0.2 × 0.2 mm3. A complete dataset was collected at 1.91 Å resolution from a single crystal at the temperature of 100 K, using a copper rotating-anode generator developed by Rigaku equipped with a Rigaku Saturn CCD detector. Prior to cryogenic freezing, crystals were transferred to the precipitant solution with the addition of 25% (w/v) glycerol. Diffraction data were indexed, integrated and scaled using the HKL2000 software package [1212 with unit cell dimensions of a = 66.3 Å, b = 89.4 Å, c = 44.4 Å and one molecule for asymmetric unit. Data-collection statistics are reported in hCA VII crystallization conditions were identified using Crystal Screen, Crystal Screen 2 and Index kits from Hampton Research ,50. The  package . CrystalThe structure of dmCA VII was solved by the molecular replacement technique using the program AMoRe  and the a values of His64 residue were predicted using PROPKA 3.0 (freely available at http://nbcr-222.ucsd.edu/pdb2pqr_2.0.0/) [a predicting method, which estimates the shift in the pKa arising from hydrogen bonds, relative burial and coulombic interactions [a. Calculations were performed using the dmCA VII crystal structure herein reported, and all the available hCA II crystal structures collected in a pH range 7–9  [pK_2.0.0/) . PROPKA ractions ,44. Thesnd 1TEQ) ,40,42.


Semantic cognition includes taxonomic and thematic relationships, as well as control systems to retrieve and manipulate semantic knowledge to suit specific tasks or contexts. A recent report  suggeste Semantic knowledge is thought to be organized taxonomically, based on feature similarity or categorical membership  framework  offers aanimal) or to a more specific term . This type of task does not require participants to retrieve information about the context. Thus, the identity matching task does not require the same control demands that judging weak and strong relationships does.A recent study by Thompson et al.  dissociadog-wolf are also based on shared features and do not depend on context, so they may similarly not require substantial semantic control. On the other hand, since the feature match is only partial, control may be required in order to focus on the relevant shared features rather than the non-shared features, especially when the feature overlap is low .The Thompson et al.  study leHere we investigate whether retrieving taxonomic and thematic relations differentially relies on control processes. We used a set of materials and methods that allowed a direct test of the taxonomic-thematic distinction, relationship strength, and cognitive control. Specifically, we used a semantic relatedness judgement task with a 2 × 2 factorial manipulation of semantic relation type (thematic vs. taxonomic) and relatedness strength (high vs. low) to examine whether weak thematic relationships (compared to strong thematic relationships) required more or less cognitive control than weak taxonomic relationships (compared to strong taxonomic relationships).In order to make a relatedness judgment for a pair of words, the participant must retrieve the semantic relationship between two words, or fail to retrieve one and decide that no such relationship exists. When the relationship is weak, this retrieval will be particularly difficult and require more cognitive effort. The exertion of cognitive effort is closely linked with cognitive control mechanisms . Two standard behavioral measures – reaction time and accuracy – are sensitive to cognitive effort, though they are also influenced by other factors see, . The tasWhile our behavioral and physiological measures were predicted to converge, it is not uncommon for these measures to vary independently because pupil size is both more sensitive and more specific to cognitive effort  judging thematic relationships appears to be easier than judging taxonomic relationships . Using aThis study was designed to test whether semantic control is differentially recruited for retrieving feature-based (taxonomic) relations vs. event-based (thematic) relations. Both the dual-hub and CSC frameworks predict a main effect of relatedness strength: weak relations should elicit increased pupil dilation, take longer to process, and produce more errors, when compared to strong relations, across both relation types. Event-based thematic relations require focusing on event-relevant features whereas taxonomic relations are based on context-independent shared features, so taxonomic relations may require less control than thematic relations do. Within the CSC framework, the flexibility of thematic relations in comparison to object identity relations has been used to explain why individuals with semantic control deficits appear to be particularly impaired on thematic relations e.g., .In contrast, the dual-hub framework makes no claim about taxonomic or thematic relations requiring more semantic control. Therefore, a main effect of semantic type  would be inconsistent with the dual-hub framework, but could be accommodated by the CSC based on the idea that thematic relations are inherently more flexible. Similarly, the dual-hub framework makes no claim about an interaction between semantic type and relatedness strength, but the CSC could explain such an interaction if the inherent flexibility of thematic relations is particularly control-demanding when the relations are weak.http://osf.io/grwtq). The preregistration contains a description of the research question, hypotheses, the sample size determination, and an analysis plan. We have performed data collection and analyses as described in the preregistration, unless otherwise stated. All data, materials, and scripts  used can be found at http://osf.io/f6axd.We preregistered the experiment before the data collection began on Open Science Framework  in conjunction with a sequential analysis . In short, each pair was rated on a 7-point scale for its taxonomic similarity (sharing similar features and/or belonging to the same category) and its thematic relatedness (co-occurrence in common events). To be included in the current study, the pairs had to show a clear dichotomy in terms of the taxonomic and thematic relationships: taxonomic pairs had to have higher taxonomic similarity than thematic relatedness, and thematic pairs had to have higher thematic relatedness than taxonomic similarity. High strength pairs were selected to ensure relatively distinctive relations (taxonomic-thematic rating difference > 1.75) and weak non-dominant relation strength (mean rating ≤ 3.5). Low strength pairs were selected to exhibit a weaker dominant relationship . These criteria ensured that weak pairs still exhibited a distinction in terms of their relationships but one that was weaker than the strong pairs both in terms of the distinction between taxonomic and thematic relatedness and weaker in terms of the dominant relationship rating. Because the non-dominant relationships were very weak for all word pairs, the relatedness judgment task was expected to be performed on the basis of the dominant relationship, which was substantially stronger for the high-strength pairs than for the low-strength pairs.Filler trials were constructed using 64 unrelated word pairs. These pairs did not share similar features, did not belong to the same category, did not co-occur in common events, and were not associated according to the South Florida free association norms . All conditions were matched on word length in letters and phonemes, word frequency, orthographic neighborhood sizes, and imageability; see Table In sum, each participant completed 128 trials in a 2 (semantic type: Taxonomic vs. Thematic) × 2 (semantic strength: High vs. Low) factorial design: 16 Low-Taxonomic pairs, 16 High-Taxonomic pairs, 16 Low-Thematic pairs, 16 High-Thematic pairs, and 64 filler pairs.Participants were tested individually in a quiet room. The luminance level in the windowless laboratory was held constant at 230 lux. A chin and forehead rest maintained head position and viewing distance at 90 cm. The stimuli were presented in uppercase, 46-pt Courier New font on a constant gray background at 1920 × 1080 resolution on a 24-inch ASUS LCD monitor. Pupil data were continuously acquired monocularly (left eye) at 250 Hz, using a remote EyeLink 1000 plus eye tracker (SR Research). Response latencies were collected using a gamepad controller. The experiment was programmed using the Experiment Builder software provided by SR Research.Following informed consent, a 9-point eye-tracker calibration and validation procedure was used and repeated until average deviations during validation were <0.50 degrees from target fixation. Participants were instructed to judge pairs of words as related or unrelated in meaning. The sequence of stimuli in each trial was the following: 1) a fixation cross (“+”) in the middle of the screen for 1,000 ms; (2) the fixation cross was replaced by a word above another word in the middle of the screen, which remained on the screen until the participant responded by pressing either the left button (“related”) or the right button (“unrelated”) on the gamepad; (3) a blank screen for 1,000 ms served as the inter-trail-interval (ITI) before the start of the next trial. A break was provided after each block of 16 trials (8 blocks). Each block contained filler pairs and either taxonomically or thematically related experimental pairs. Block order was counterbalanced. Before the experiment proper, participants completed 8 practice trials. The trial sequence is illustrated in Figure  a fixatiThe pupil dilates reflexively to changes in luminance and eye position , which must be considered during stimulus generation and development of the procedural details . To minimize these factors, all events in our experimental paradigm were presented in black font on a grey background in the center of the screen. Because pupil size is compared between conditions that are matched on luminance  and eye position, minimal confound is present.https://github.com/dmirman/gazer). The pupil data were first converted from arbitrary units to mm. Trials and participants with greater than 30% missing data were excluded . Unrealistic pupil samples  and pupil samples below and above 3 standard deviations of the mean diameter of each trial were removed (0.01%). Blinks, along with samples that occurred 100 ms before and 100 ms after the blink, were also coded as missing and linearly interpolated. After interpolation, artifacts that arose from quick changes in pupil size based on the median absolute deviation were removed . For interactions, we used an alpha level of 0.01 (two-tailed). Linear mixed-effects models and GCA were implemented in R version 3.4.4  using the lme4 package , and word pairs with accuracy less than 60% were discarded . For our main effect predictions, we used an alpha level of 0.01  and trials with very short correct RTs  were excluded from the latency analysis. Model-estimated RTs for correct responses and overall accuracy are presented in Figure For our reaction time analysis, inverse RT –1000/rt)000/rt1 wEstimate = –0.040, SE = 0.018, p = 0.015. There was also a significant effect of relatedness strength: high strength pairs were judged faster than low strength pairs, Estimate = –0.048, SE = 0.017, p = 0.004. Critically, semantic type did not interact with relatedness strength, Estimate = 0.014, SE = 0.017, p = 0.413. Although not specified in the preregistration, a Bayes factor (BF) derived from the Bayesian Information Criterion .Outliers with a standardized residual greater than 3 standard deviations were removed . This pattern of reaction time and accuracy data shows a clear effect of relatedness strength and mixed evidence suggesting possibly greater cognitive control recruitment for taxonomic relations, particularly low-strength taxonomic pairs. As described in the Introduction, pupil diameter offers a more sensitive and specific measure of cognitive effort, which is described in the next section.The analysis of the accuracy data did not show an effect of semantic type, Estimate = –0.01, SE = 0.007, p = 0.023; all other time terms  did not significantly differ between taxonomic and thematic relations, all ts < 1.01, ps > 0.5. That is, from the beginning of the trial to the end of the trial, the pupillary response increased more for taxonomic pairs than for thematic pairs. The effect of relatedness strength was significant on the linear, Estimate = –0.019, SE = 0.007, p < 0.001 and quadratic, Estimate = –0.014, SE = 0.007, p = .015, terms, but not on the cubic term, Estimate = 0.004, SE = 0.006, p = .500. This suggests that judging low strength pairs resulted in a steeper slope and shallower curvature . The interaction between semantic type and relatedness strength differed on the linear, Estimate = 0.018, SE = 0.006, p = 0.006, and quadratic, Estimate = 0.018, SE = 0.007, p = 0.006, terms. For low relatedness taxonomic pairs, the pupil size continued to increase substantially, even in the late portion of the time window . In contrast, for the low relatedness thematic pairs, the pupil size was not substantially increasing by this point in the time course.The full results along with the model specification are presented in Table The present experiment used a physiological (TEPR) measure of cognitive effort, in addition to RTs and accuracy, to test cognitive control recruitment during retrieval of high and low strength taxonomic and thematic semantic relations. Detecting taxonomic relationships resulted in longer reaction times and a steeper pupil dilation slope than detecting thematic relationships. For relatedness strength, behavioral and pupillometric analyses revealed (not surprisingly) that weak relations were more difficult than strong relations: they took longer to process, were more error-prone, and had steeper pupillary slopes. Our behavioral data did not show an interaction between semantic type and relatedness strength . However, in our pupillometric analysis, we discovered an interaction between relation type and relatedness strength on linear and quadratic time terms. Pupil size increased more throughout the trial for low-relatedness taxonomic compared to low-relatedness thematic pairs. Also, in the late portion of the analysis time window, the pupil size continued to increase for low-relatedness taxonomic pairs more than for low-relatedness thematic pairs, suggesting ongoing cognitive effort.dog – animal). Thompson et al. showed that such relations can vary in difficulty without necessarily differing in semantic control demands. They argued that this was because the relation is determined by feature overlap, making control demands minimal. The present study examined non-identity taxonomic relations , which are also determined by shared features, but found that retrieving such relation does require semantic control. Indeed, retrieving weak taxonomic relations required more cognitive effort than retrieving weak thematic relations.Using a similarity relatedness judgment task, we found that judging taxonomic relations was more difficult than judging thematic relations, and weak relations required greater cognitive control than strong relations did, with greater cognitive control needed to judge low-strength taxonomic pairs. This pattern is opposite to the one observed by Thompson et al. , in whicThe reversal observed in our study compared to Thompson et al.  suggestsThis observation challenges claims that thematic relations are inherently more flexible than taxonomic relations and therefore require more control, and accounts of thematic semantic deficits as semantic control deficits. While we did not directly test the single- vs. dual-hub accounts, the present results are consistent with a dual-hub framework in which taxonomic and thematic semantic relations are represented by complementary semantic systems that integrate sensory-motor features in different ways and both of which engage cognitive control systems for retrieval of weak relations.Although the data support an account that includes two independent semantic hubs, it does not necessarily exclude an account based on a single hub. A potential bridge between the CSC and the dual-hub frameworks is offered by a recent model  that bui


Speaker diarization systems aim to find ‘who spoke when?’ in multi-speaker recordings. The dataset usually consists of meetings, TV/talk shows, telephone and multi-party interaction recordings. In this paper, we propose a novel multimodal speaker diarization technique, which finds the active speaker through audio-visual synchronization model for diarization. A pre-trained audio-visual synchronization model is used to find the synchronization between a visible person and the respective audio. For that purpose, short video segments comprised of face-only regions are acquired using a face detection technique and are then fed to the pre-trained model. This model is a two streamed network which matches audio frames with their respective visual input segments. On the basis of high confidence video segments inferred by the model, the respective audio frames are used to train Gaussian mixture model (GMM)-based clusters. This method helps in generating speaker specific clusters with high probability. We tested our approach on a popular subset of AMI meeting corpus consisting of 5.4 h of recordings for audio and 5.8 h of different set of multimodal recordings. A significant improvement is noticed with the proposed method in term of DER when compared to conventional and fully supervised audio based speaker diarization. The results of the proposed technique are very close to the complex state-of-the art multimodal diarization which shows significance of such simple yet effective technique. Speaker diarization systems assign speech segments to the people appearing in a dialogue. It is usually an unsupervised technique where the number of speakers is unknown and limited information is available about them. Its applications lay in broadcast news, TV/talk shows, meeting recordings, telephone recordings, etc. Diarization tasks are very challenging when unimodal data is available. Audio-based diarization has lot of complexities due to overlapping speech utterances from various speakers, environmental noise, short utterances and reverberations. Similarly, in video data, speakers may not face the camera, move in a multi-party interaction way or they can be occluded by other speakers. The use of the video modality limits one to using lip and face movement detection for diarization. For each available dataset, the configuration of the recording equipment also varies a lot. For example, audio data may be acquired from a far-field microphone array, individual lapel microphones/headsets or single omnidirectional microphones. Similarly, video recordings comprise of individual speaker closeup cameras, cameras covering some group of speakers or a wide camera covering all the available speakers in an event.Conventional audio-based diarization ,7,8,9,10To solve the shortcomings and limitations in audio-based diarization approaches, multimodal approaches comprised of audio and visual modalities were proposed. Multimodal approaches either use active speaker detection using lip and face movements ,25,26,27In recently published multimodal diarization approaches, e.g., ,33, the The rest of the paper is divided as follows: Speaker diarization requires grouping homogeneous speaker regions when multiple speakers are present in any recording. This task is usually performed with no prior knowledge about speaker voices or their number. The speaker diarization pipeline consists of audio feature extraction where MFCC is usually a choice for representation. Speech activity detection is then applied to separate speech and non-speech MFCC frames followed by segmentation and clustering techniques which results in final diarization. Use of the video modality is motivated by fact that audio and video have correlated factors. For example, the lip, face and head movement of an active speaker are highly correlated with his speech. Hence, features extracted from frontal views of speaker faces can be used to discriminate the active speaker. Such visual features are used in speech recognition ,36, speeFriedland et al.  proposedIn most of the techniques discussed above, either a source localization technique is applied in audio pipeline to locate the active speaker or audio clustering/diarization is applied separately. Similarly, in the video domain face tracking, mouth/lip movement, motion measuring techniques are applied to get diarization results. Finally, audiovisual fusion is applied on the feature level or output level. Both audio and video pipelines require excessive processing to acquire the individual and fusion results. Comparatively, our technique is simple and relies more on a pre-trained SyncNet model to find active speakers. A simple preprocessing pipelines in the audio and video domain is required, which finally ends up in audio-based clustering to acquire diarization. This technique is well suited for formal meeting scenarios where people are static/seated and frontal faces are captured most of the time.In this section we describe each part of our proposed method in detail. First of all, from For audio part we use a mix-headset audio recording that consists of voices from all the speakers. We extract the Mel-Frequency Cepstral Coefficients (MFCC)  featuresFrom the Augmented Multi-party Interaction (AMI)  corpus, The SyncNet architecture was proposed in , where tIn our proposed work, we used a pretrained SyncNet model to determine the the active speaker in each closeup video. As discussed in video preprocessing section, we split the video shots into 2 s segments and then apply SyncNet inference on each video segment. The SyncNet model outputs two metric values: offset and confidence, which are computed to determine the audio-visual relationship. Segments which have lowest offset and high confidence values determine that the visible speaker is the one who is speaking. In our approach, we define two threshold values based on the analysis of those short segments in which complete audio segment belongs to the visible speaker. The first one is the offset threshold, which is defined as Audio preprocessing pipeline provides speech-only MFCC frames after applying speech activity detector and video preprocessing pipeline-provided 2 s video segments. After acquiring the inference results from the SyncNet architecture we used high confidence segments only and take MFCC frames of those corresponding segments. We train a GMM model using Expectation Maximization (EM) with MFCC frames, so for each closeup video a GMM model with K mixtures is trained on the corresponding high confidence MFCC frames only. We represent a mixture of Gaussians as follows: While calculating Now, such a cluster is assumed to be pure and it is most likely to be trained on one speaker. After all the clusters have been trained, which are now speaker-dependent clusters, the likelihood of the rest of the MFCC frames from each cluster is computed. The most likely frames are assigned to that specific cluster. Finally, all the frames are assigned to one of the clusters and the diarization error rate is computed.In speaker diarization problems the number of speakers is usually unknown, so we assume the number of speakers equals to the number of available closeup videos. In such scenarios where only one recording is available, one can assume the number of speakers equals the number of distinct faces in all the video frames. Our diarization technique is designed for such scenarios where all the speaker faces are visible in the video. Additionally, we provided the code of proposed multimodal diarization at .We tested our proposed method on a popular subset of the AMI ,47 meetiIn the audio pipeline we extract 19-dimensional MFCC features with window length of 30 ms and hop length of 10 ms. MFCC frames thus have sample rate of 100 Hz. In the SAD block, MFCC features along with energy features are used with the same window and hop length. An SVM-based classifier is used to train speech and non-speech frames. Further, when the audio pipeline gets high confidence video segment information, a GMM with 20 components and diagonal covariance matrix is trained using an Expectation Maximization algorithm. For each closeup video, the number of high confidence video segments varies, which leads to different numbers of MFCC frames for each speaker. The specification of the GMM model for each speaker is kept same, i.e., 20 components, diagonal covariance.Given each closeup video with a frame rate of 25 fps, we apply face detection with subsequent SAD and shot detection. All the available shots are then further segmented into 2 s chunks to apply SyncNet. This segment length is selected based on the assumption that each speaker may speak for a minimum of 2 s. Moreover, shots and segments smaller than seven frames are also discarded because they are too small to provide reliable audio-visual matching. As two metrics are computed on the output of SyncNet’s inference that is offset and confidence, two threshold values were applied to weed out unsynchronized audio-visual segments. To select high confidence video segments, we choose an offset threshold range between The evaluation metric for speaker diarization is the Diarization Error Rate (DER). It is a combination of four errors: False alarm  consisting of agglomerative hierarchical clustering  based onThirdly, for the completeness of this research a comparison to the state-of-the art multimodal technique is carried out. This multimodal technique is described in  where thComparison to other multimodal techniques are difficult since the scenarios of the recordings and proposed techniques in each of them varies significantly. Specifically, important factors that motivate one to develop any diarization technique vary depending on overlapping speech intervals, the recording equipment used in term of cameras and mic arrays, available speaker information and available training data. Some of the recent multimodal diarization techniques ,33,52 emhttps://github.com/ageitgey/face_recognition, which is based on dlib [We developed our audio-visual speaker diarization system in Python using Anaconda Python distribution on the Windows 10 platform on a computer equipped with a 2.6 GHz CPU, 16 GB RAM and a 3 GB Nvidia GTX 1060 GPU. Librosa  is used  on dlib . Pyannot on dlib  is used In the propose system depicted in Next, the computational complex block is SyncNet which is a two streamed  Convolutional neural network. The input to this block is five frames of size The second comparison to our method is with a fully supervised speaker diarization technique  which isFinally, In this paper we have presented multimodal diarization technique using a pre-trained SyncNet model. In the audio pipeline we apply feature extraction, speech activity detection and finally a clustering technique for speaker diarization. Only high confidence MFCC frames acquired through SyncNet inference are used to train GMM models and then the rest of the audio frames are clustered on the basis of maximum likelihood. In the video pipeline, we apply face detection to crop face-only regions, remove silence frames, shot detection and finally split the video into 2 s short segments. These short video segments are then provided to pre-trained SyncNet model which runs the inference on them. On the output results of this inference we apply two thresholds to select those video segments which are confident enough on the active speaker in the video and its respective audio domain.When compared with audio-based diarization techniques we noticed the effectiveness of such a novel multimodal diarization. The main advantage of our proposed method is in providing pure clusters which are trained on frames belonging to a single speaker, while in the conventional diarization approach which uses an agglomerative hierarchical clustering technique, there is greater chance of impurity in term of merging clusters that have voices of multiple speakers. Our technique, that is based on the threshold selections for offset and confidence metrics in the video domain, robustly prevents any such contamination. Beside this, our proposed technique is fully unsupervised and doesn’t even require any out of domain data for training purposes. Furthermore, the proposed technique is applicable to meeting recordings, TV/talk shows and movies where speakers face the camera most of the time. Finally, it is also concluded that such technique is similar in accuracy to one of the compared multimodal approaches.In the future, we plan to incorporate overlapping speech detection in the video pipeline, where multiple speakers speak simultaneously. For that purpose, a model similar to SyncNet would be explored to label overlapping speech segments. Secondly, sound source localization techniques will also be explored to detect active speakers in scenarios where users move or do not face the camera.


Thirst has evolved for vertebrate terrestrial adaptation. We previously showed that buccal drying induced a series of drinking behaviours  in the amphibious mudskipper goby, thereby discovering thirst in ray-finned fish. However, roles of dipsogenic/antidipsogenic hormones, which act on the thirst center in terrestrial tetrapods, have remained unclear in the mudskipper thirst. Here we examined the hormonal effects on the mudskipper drinking behaviours, particularly the antagonistic interaction between angiotensin II (AngII) and atrial natriuretic peptide (ANP) which is important for thirst regulation in mammalian ‘forebrain’. Expectedly, intracerebroventricular injection of ANP in mudskippers reduced AngII-increased drinking rate. ANP also suppressed the neural activity at the ‘hindbrain’ region for the swallowing reflex, and the maintenance of buccopharyngeal water due to the swallowing inhibition may attenuate the motivation to move to water. Thus, the hormonal molecules involved in drinking regulation, as well as the influence of buccopharyngeal water, appear to be conserved in distantly related species to solve osmoregulatory problems, whereas hormonal control of thirst at the forebrain might have been acquired only in tetrapod lineage during evolution. Intracerebroventricular (ICV) injection of ANP attenuated the AngII-induced thirst by acting on the SFO5. The neuro/endocrine regulatory mechanism of drinking behaviour is poorly understood in non-mammalian vertebrates. Thirst as a regulator of drinking in amphibians has been suggested6. AngII induces thirst and ‘cutaneous drinking’  in toads7. In ray-finned fishes, water is always available so only imbibition is needed, and no searching for water is required8. Indeed, the above systemic hormones may regulate swallowing through the area postrema (AP) in the hindbrain11, and no thirst center in the forebrain has yet been found in ray-finned fishes.Drinking behaviour is important for body fluid regulation throughout the vertebrates. In mammals, the sensory circumventricular organs (sCVOs) in the forebrain, which lack the blood-brain barrier, are critical for motivation  for a series of drinking behaviours. The subfornical organ (SFO), one of the sCVOs, has recently been found in mice to receive information about the local sensation of buccopharyngeal water13. For instance, mudskipper fishes originated from the marine environment15 but spend a significant portion of their time out of water, as a means of predator avoidance and also to forage thus they have evolved numerous physiological and behavioural traits associated with an amphibious lifestyle16. Unlike amphibians and fresh-water fishes, the mudskipper skin secretes Cl− as with the surface area of the gills which is most of the body-surface area of general seawater fish and the transcutaneous water uptake has not been described20. Our very recent study of the mudskipper, Periophthalmus modestus21, has shown that AngII acts centrally at the area postrema (AP) to induce swallowing of buccal water as in other fishes. The loss of buccal water thereby induced the migration to water for refilling, which was the first evidence of thirst in ray-finned fish. This thirst perceived through local sensation  appears to be primarily important for a series of drinking behaviours in the mudskipper, suggesting a common requirement of thirst for terrestrialization throughout the vertebrates21. Local sensation of buccal drying may relay to a possible thirst center, but the neural bases in the forebrain remain unclear even in mammals22. The mudskipper might be a unique experimental model to investigate evolutionary process of thirst and hormonal influences. In particular, antidipsogenic factor(s) can allow mudskippers to retain buccal water which is important for their physiological and behavioural traits in terrestrial lifestyles 23. Analyses of possible antidipsogenic functions of ANP in mudskipper thirst regulation are highly intriguing in order to know how hormonal control contributed to vertebrate terrestrialization, compared with the known mechanisms in tetrapods.The land invasion by vertebrates in the evolutionary process occurred not only in lobe-finned fishes that led to tetrapods but also in ray-finned fishes, independently of the tetrapod lineage26, we also examined the role of BNP. Finally, we discuss the hormonal basis of the requirements for the transition of vertebrates from aquatic to terrestrial habitats during evolution.To clarify the hormonal regulation of the mudskipper thirst, we here analysed whether and how ICV injections of AngII and ANP affect drinking behaviour. We found that ANP attenuated an AngII-increased drinking rate and the neural activity at the AP. We revealed the importance of the AP as the site of action of these hormones, unlike in mammals. Despite such antagonistic effects of ANP on AngII, ICV injection of ANP alone prolonged the period of time in water. Since this might be an intrinsic action of another natriuretic peptide secreted from the heart, B-type natriuretic peptide (BNP) which shares a receptor with ANPanp and bnp were cloned from the mudskipper heart . Molecular phylogenetic analyses of the cDNA sequences are shown in Supplementary Fig. anp and bnp are typical of ray-finned fish anps and bnps, respectively. Mature sequences of the amino acids deduced from the cDNA sequences . These results indicate antagonistic interaction between AngII and ANP at the AP neurons.Direct antagonistic interaction between AngII and ANP was well described in the mammalian SFOurs Fig. . Therefo 21 Fig. , but not31. For two hours after the ICV injection of ANP, no significant change in the migration into water was shown 21. Our comprehensive analyses using the mudskipper, which is independent of the tetrapod lineage13, suggest the importance of the evolutionarily-conserved thirst regulation by local sensation  rather than by direct actions of hormones at the forebrains. Although the hormonal molecules important for drinking appear to be conserved through vertebrates, the neural bases of their actions may differ between the ray-finned fish and tetrapod lineages  in ray-finned fishes has been considered to be the target site of ANP as well as that of AngII11. Further work on identification and localization of ANP receptor in the mudskipper brain is required. Our amphibious mudskipper  as well as aquatic fishes can swallow water without movement to water and the primary site of hormonal action can be limited to the hindbrain, because water is present in the mouth of these fish; some mudskippers store water in the buccal cavity even when they are on land36. In contrast, tetrapods and some other mudskipper species including Periophthalmus barbarus and Periophthalmodon schlosseri38 do not store water in the buccal cavity, and thus must move to water for drinking . In tetrapods, complex neural-circuitry convergence of the direct hormonal actions and buccal-water local sensation in the SFO39 appears to be required for thirst regulation  of both sexes weighing 3 to 5 g were collected from the estuary of the Fujii River, which flows into the Inland Sea of Seto (34°N: 134°E). As previously described50, plasma ions, differentiation of osmoregulatory organs, hormonal status, and amphibious behaviours in mudskippers under varying conditions have been examined. Since we previously showed that no sex differences were found in their amphibious behaviour, both sexes of fish were used21. Fish were acclimated for 2–5 weeks in laboratory tanks (3 L). These fish–collected from brackish water–were held in the tanks that contain diluted seawater , which is almost isotonic to mudskipper plasma. Small plates were placed in each tank to allow mudskippers an opportunity to climb onto them. All specimens were maintained at room temperature of 22–25 °C under a daily photoperiod cycle of 12-h light/12-h dark (lights on at 7:00 a.m.) and were fed daily with Tetrafin flakes . Fish were anesthetized in 0.01% tricaine methanesulfonate  neutralized with sodium bicarbonate before handling. All experiments were approved by the Animal Experiment Committee of the University of Tokyo and Okayama University, and performed in accordance with the manuals prepared by the committees.One year-old mudskippers . Single-stranded cDNA was prepared from 1 μg of heart RNA using High Capacity cDNA Reverse Transcription kit . Partial sequences of anp and bnp were amplified by PCR with degenerate primers: forward primer 5′-AGYYGBHTVCWGGAYCTSCT-3′, reverse primer 5′-CTSGMRKTYCCDATBCGRTCCA-3′ for anp and forward primer 5′-TCMGRGAGYTYCTBTCAKC-3′, reverse primer 5′-CCVAYDGTGTTRCAVCCMAGAGA-3′ for bnp, respectively. cDNAs were amplified with high-fidelity Ex Taq DNA polymerase , ligated into pGEM T-easy plasmid . Amplified products were sequenced by an ABI3130xl DNA sequencer  and BigDye Terminator v3.1 Cycle Sequencing Kit . Based on the determined partial sequences, gene-specific primers: forward primer 5′-CTGCTCAGTGCGCGCCGCTCCTCCTC-3′, reverse primer 5′-CCTCTGCCACAGCCCAGGCCACTAGCG-3′ for anp and forward primer 5′-CCCAGAACCTGCGGACTGTCCGGAA-3′, reverse primer 5′-GAGCTCATGGAGCCGATGCGATCCA-3′ for bnp were designed and used for 3′ and 5′ RACE. Finally, cDNAs encompassing the whole coding regions were amplified using gene-specific primers designed based on the sequences obtained by the RACE method: 5′-ATGAGGGCCGCATTCGTGTGGG-3′, 5′-AGACCAACCTGCAAAATGCGC-3′ for anp and, 5′-ATGGGGAGAGTAGGATCAGAATTGG-3′, 5′-CAAATGTAAACCTTTAATGAACAAAAC-3′ for bnp. Deduced amino acid sequences of ANP and BNP were compared with those of other vertebrates and a phylogenetic tree was generated by maximum likelihood method using the MEGA6 program at http://www.megasoftware.net/. GenBank accession numbers of genes and mRNAs used in this analysis are NM_006172.3 (human ANP), NM_002521.2 (human BNP), NM_012612.2 (rat ANP), NM_031545.1 (rat BNP), AF429999.3 (cane toad ANP), DQ113654.1 (toad BNP), LC348996 (mudskipper ANP), LC348997 (mudskipper BNP), AB087283.1 (Mozambique tilapia ANP), AB087284.1 (tilapia BNP), AB019372.1 (Japanese eel ANP), and AB179820.1 (eel BNP).The procedures for anp and bnp mRNAs was examined by RT-PCR. The brain, pituitary, spinal cord, gill, atrium, ventricle, liver, kidney, spleen, intestine, muscle, and skin were sampled from mudskippers and quickly frozen in liquid nitrogen. Total RNA was extracted and cDNAs of tissues were prepared from 1 μg of total RNA as mentioned above. Elongation Factor 1 α (ef1α) cDNA was used for an internal standard. Gene-specific primers for anp and bnp, which amplify cDNA of the whole coding regions, were designed as described above. Gene-specific primers for ef1α were 5′-GAGCGTGAGCGTGGTATCACCAT-3′ and 5′-GTCTGCCTCATGTCACGCAC-3′. The PCR conditions were: 94 °C, 2 min followed by 30 cycles (ef1α) or 35 cycles (anp and bnp) of denaturation , annealing , and extension . The amplified DNA fragments were electrophoresed on 1% agarose gels and detected by ethidium bromide staining.Tissue distribution of 2), and BNP (NNSPRSSSGCFGRRMDRIGSMSSLGCNTVGNNSPKSA) were synthesized by Peptide Institute  and used in the present study. ICV injection in the mudskipper brain has been established50. Anesthetized fish were injected post-orbitally along the midline into the third ventricle with 0.1 μl/g volume. The concentration of peptide solution was adjusted to 3 × 10−8 to 3 × 10−5 M. The doses were chosen based on the results of preliminary studies and published reports on effective physiological doses21. Fish treated with artificial cerebrospinal fluid served as handling controls. Evans blue (0.1%) was used to confirm the success of ICV injection. To minimize the leakage from the injection site, 30 sec was allowed to elapse after each injection. Fish were fully recovered from anesthetization in 1–2 min.Mudskipper AngII (NRVYVHPF), ANP (SSSGCFGARMDRIGNASGLGCGR-NHn = 7), ANP or BNP alone (n = 4–6), ANP in the presence of AngII (n = 4–7), or vehicle (n = 7), each fish was placed in the water area of experimental tank  were recorded for 8 h.Immediately after ICV injection with AngII alone  after ICV injection with ANP (n = 5), BNP (n = 5), AngII (n = 6), ANP in the presence of AngII (n = 5–6), or vehicle (n = 6–8). The tank was filled with 0.004% phenol red in 10-ppt seawater so as not to expose fish to air. The amount of water in the whole gastrointestinal tract was measured according to the colorimetric method of previous studies51. Briefly, the whole tracts were dissected out and cut open into a petri dish. Phenol red was thoroughly washed from the tract by 1 ml saline. The 0.5-ml aliquots were mixed with 0.5 ml 5% trichloroacetic acid (Sigma-Aldrich), and centrifuged at 10,000 rpm for 5 min. The supernatant was mixed with 0.5 ml of 1 M NaOH, and absorbance was determined at 550 nm wave length by a spectrophotometer .As previously described−6 M AngII alone (n = 5), or 3 × 10−5 M ANP in the presence of AngII (n = 5). The procedure for the immunohistochemistry was previously described52. The brains were fixed in 4% paraformaldehyde in phosphate buffer (PB) and embedded in Paraplast . Serial sections were prepared at 7 μm and mounted onto MAS-coated slides . The sections were deparaffinized, rehydrated and then rinsed in phosphate buffered saline (PBS). The sections were immersed in methanol containing 0.3% H2O2 for 30 min at room temperature. The sections were rinsed thoroughly in PBS after the antigen activation and then pretreated with a blocking solution  for 1 h at room temperature and incubated with a polyclonal antibody raised against human c-Fos  for 12 h at 4 °C. Specificity of the antibody against c-Fos protein of the mudskipper was previously confirmed by Western blotting analysis52. After rinsing in PBS, the sections were treated with ABC Elite kit  according to the manufacturer’s instruction. The sections were rinsed in 0.1 M PB and immersed in 0.01% 3,3′-diaminobenzidine tetrahydrochloride (DAB) in PB for 3 min to intensify colorization and then incubated in 0.01% DAB solution containing 0.01% H2O2 for 3 min in dark. Finally, the sections were rinsed in PB and distilled water, and immunoreactive c-Fos signals were examined and photographed using a digital camera . The photomicrographs were binarized by graphic software Image J (https://imagej.nih.gov/ij/) and the number of c-Fos positive neurons was counted manually and compared between the cohorts. Every third section was analysed in order not to count positive cells repeatedly.The brains were dissected out 1 h after ICV injection with 3 × 10t-test was used for analyses of the number of c-Fos immunoreactive cells after the assumptions was checked.The data for amphibious behaviour, drinking rate, and the number of c-Fos immunoreactive cells are expressed as means ± standard error of the mean (s.e.m.). Kyplot 5.0  was used for statistics analysis. The data for amphibious behaviour were analyzed with two-way or three-way repeated measures analysis of variance (ANOVA) followed by Tukey’s post-hoc test. The drinking rate was analysed with one-way factorial ANOVA followed by Dunnett’s post-hoc test or with Kruskal-Wallis followed by Steel-Dwass post-hoc test. Unpaired Supplementary information


Natural independent walking mostly occurs during infant´s everyday explorations of their home environment. Gait characteristics of infant walkers at different developmental stages exist in literature, however, data has been only collected in laboratory environments, which may reduce gait variability, therefore mask differences between developmental stages of natural gait. The aim of the study was to provide the first data set of temporal and functional gait characteristics of novice and improver infant walkers in familiar environment conditions in their home. We hypothesised that familiar environment conditions may effectively demonstrate natural gait characteristics and real differences in gait variables differing between 2 groups of developing infant walkers.p ≤ 0.05) with determination of effect size of Pearson r correlation.In a cross-sectional design; we used open-source videos of infants in their home environments: twenty videos of 10 novice  and 10 improver  walkers were chosen from an open-source website. 2-D video gait analysis was undertaken for these parameters: falls frequency, frequency of stops, gait cadence, and time of stance phase, swing phase, and double support. Between groups comparison for novice versus improver was investigated by Mann-Whitney U tests ; cadence ; stance phase duration of right leg ; stance phase duration of left leg ; and double support phase duration . Novices scored higher in comparison with improver walkers in all the parameters except cadence.Statistically significant differences between groups with large effect sizes were found for these parameters: falls frequency (This study presents the first data set of functional and temporal gait parameters of novice and improver infant walkers in their home environments. As an addition to recent research, novice infants walk with lower cadence and higher falls frequency, stance phase time and double support in their familiar environments. With increasing experiences, infant´s cadence increases while the other parameters decrease. Ontogenesis of walking starts very early in human development, even intra-uterine at the nervous system level . Just afCharacteristics of the early stage of walking show a large inter- and intra-individual variability and variable walking maturation strategies: for example twister, faller, stepper, pendulum . DespiteNatural walking of infants mostly occurs during their everyday explorations of environment and interactions with their parents and caregivers . Whilst The aim of this study was to provide the first data set of temporal and functional characteristics of novice and improved infants walking in familiar environment conditions in their homes. Inspired by the study of Gardiner et al.  who usedwww.youtube.com) was searched for videos of walking infants in their home environments. The single researcher conducted searches of YouTube’s database between the 25th of January and the 13th March 2018 (www.clipconverter.cc). Only good resolution videos that contained information about the age and gender specification of the infant, where both feet were visible to observe clear footage of independent walking on level ground and recorded a walking bout consisting of a minimum of five complete gait cycles were selected for further analysis, regardless of language or ethnicity. Videos that contained walking outside were rejected due to the potential confounding factors of wide varieties of surfaces and environments. This resulted in 20 videos being selected. All data from the YouTube´s videos were made anonymous and collected under the ‘Fair Use’ (https://www.youtube.com/yt/copyright/en-GB/fair-use.html) and ‘Exceptions to Copyright’ (https://www.gov.uk/guidance/exceptions-to-copyright) rules for non-commercial research.The open-source video sharing website YouTube  functional characteristics—frequency of falls  and frequency of stops (stops per minute); and (ii) temporal characteristics—gait cadence (steps per minute), stance phase time (seconds) for right leg and left leg, swing phase time (seconds) for right leg and left leg, and double support phase time (seconds). Foot contact and foot off times were recorded for the both legs during whole walking sequence of each video. Foot contact was defined as a moment when the foot first visibly touched the ground. Foot off described the occasion when the foot first visibly left the ground. Stance phase time was defined as time elapsed between foot contact and the consecutive foot off the same leg. Thus, it is a summation of double support time and single leg stance time within one stride. Swing phase time was between foot off and the consecutive foot contact of the same foot and is equal to single leg stance of opposite leg. Double support time was defined as the duration elapsed during which both feet contacted the ground. A fall was defined as a sudden unintended loss of balance and collapse of the participant, down to the ground, but with evident willingness to stand up and continue to walk. A stop was defined as an intended break in walking mainly for recovery of balance or short change of the focus of infant . Based on our observations, the time of stop lasting less than 4 s was counted as double support phase time.www.opensourcephysics.org), which allows processing of the video footage frame by frame and show the sampling rate of the original video recording. From recorded times of foot contact and foot off, the average of stance phase and swing phase time for both legs and the double support time was calculated. The researcher recorded the age, gender, ethnicity assumption, clothing conditions, and counted number of steps, falls, and stops to calculate gait cadence and frequency of falls and stops. The original video sampling rate was recorded also, as it may influence time dependent results.The videos were systematically analysed by one single evaluator and 2D video analysis was performed using free accessible tool Tracker 4.11.0  were used. For inductive statistic, Mann-Whitney U test for two independent groups and comparison of mean ranks was performed due to difference in distribution of scores for both groups. Effect size of Pearson relation  was calcrelation .p ≤ 0.05 was set as a level of significance.All statistical tests were performed using the statistical software SPSS  and p < 0.05) in functional and temporal gait characteristics. Novices scored higher for five parameters with large effect sizes: frequency of falls , stance phase time for right leg , left leg  and double support phase time , but lower for gait cadence   Tables  and 3. T) Tables .This first non-laboratory investigation of infant´s gait in their home environments has described how infants move in familiar environments and identified significant differences in functional and temporal gait parameters between novice and improver walkers. Consistent with the hypothesis, specific differences included a reduced frequency of falls in more experienced walkers , and in terms of temporal aspects an increase in gait cadence, and a decrease in stance phase time and double support time, all with large effect sizes.t tests comparing home (n = 15) and laboratory (n = 70) observations of infants with equivalent walking age) [Analysis of the video data in the current study showed that functional characteristics of infant gait in real-world environments differ dependent on their walking experience, specifically there was a decreasing frequency of falls with increasing gait experience during infant walking. Alongside this change in falls we would have anticipated a reduction in stops as the infants become more experienced walkers, however this was not the case. This may be influenced by the definition of a stop in the study being longer than 4s, which could make it difficult to find statistical differences between the two groups. However, slow transitions to the alternate leg in our sample lasted approximately 2 s, thus, we assumed pauses longer than 4 s were an appropriate definition. Furthermore, the nature of the data sourcing, inclusion criteria and analysis in the current study allowed infants to stop, turn or fall just as a result of natural movement consequences as we analysed open-source video and did not apply a protocol to the data collection. Distractions or other occurrences within the home environment might also be the reason for infants stopping or falling, on the other hand. The increasing confidence of the more experienced walkers in our research is also evidenced by their ability to carry objects and tendency to walk in self-motivated situations without being encouraged . This coing age) . HoweverIn addition to the functional differences identified between novice and improver walkers, this open-source data also enabled the quantification of temporal characteristics. The normal gait cadence data in literature shows comparable results to the results of the current study ,22. HoweFurther temporal characteristics of infant novice and improver gait differed as significant difference in stance phase time between the two have been presented in our results. Novice infant walkers have longer stance phase duration  in comparison with more mature improver walkers (0.59 s for both legs). In comparison to other literature collected in laboratory environments the mature improver walker values are most consistent with those reported as adopting gait 0.52 s SD 0.11  and withThe novice infants in this study had a longer single leg stance time coupled with a longer double support time to maximise stability in this early stage of gait . Double Current results show greater inter-individual variability in novice in comparison with improver walkers in stance phase time and double support time . HoweverThis study has some limitations largely due to the open-source nature of the videos. Firstly, the study has been undertaken with a relatively small sample number due to inclusion criteria which limited number of available videos significantly. This low availability of videos has also led to a lack of standardisation of video; with the environment such as other individuals, flooring and room size varying between videos. Secondly, due to the nature of the data in this research study it can only provide limited functional and temporal kinematic gait data. The size effect i.e. lower limb size in the obtained data was not possible control as limb size could not be effectively computed from the video with any certainty due to different perspectives and zoom/resolution of data participants. The data accuracy may also be influenced by the parent´s self-reporting of age and additional information, the quality of non-professional video recording, video resolutions, and inconsistent range of video sampling rates. These ranged between 0.033 Hz and 0.67 Hz with the following distribution of various frame rates: 0.033 Hz (n = 13); 0.040 Hz (n = 2); 0.042 Hz (n = 2); 0.050 Hz (n = 1); and 0.067 Hz (n = 2) with almost equal distribution in both groups. Additionally, there is a lack of information about the infant participant such as a birth date, health condition details, or basic anthropometry which limits the generalisability of the sample and would be available in a standard gait laboratory study. Similarly, the videos of the infants may have been recorded and published due to them walking early for example or undertaking a specific task which might lead to task specific outcomes, and therefore their relevance to the wider population of walking infants is unclear and undefined.Our study is the first to investigate infant´s functional and temporal gait parameters in their home environments using open-source gait data. Analysed data presented significant differences between novice and improver walkers in falls frequency, gait cadence, stance phase time, and double support phase time. The recent study results can serve as basic, but limited, data set for further research, for example, external validation of some laboratory protocols for infants. There are clear challenges with collection and interpretation of, and long-term access to, open-source gait data. However, it offers a rich availability for gait analysis in familiar environments of participants whose behaviour would otherwise be significantly influenced in a laboratory. Future research should be focused on familiar environment gait data collection in infants to identify their natural movement patterns more precisely consecutively with the psycho-social context.


KAs were in the range of 32.6–131 µM; for hCA II of 16.2–116 µM, and for hCA VII of 17.1–131 µM. The structure-activity relationship was intricate and not easy to rationalize, but the most effective activators were 1-(2-piperidinyl)-piperazine (KA of 16.2 µM for hCA II), 2-benzyl-piperazine (KA of 17.1 µM for hCA VII), and 1-(3-benzylpiperazin-1-yl)propan-1-one (KA of 32.6 µM for hCA I). As CAAs may have interesting pharmacologic applications in cognition and for artificial tissue engineering, investigation of new classes of activators may be crucial for this relatively new research field.Four human (h) carbonic anhydrase isoforms , hCA I, II, IV, and VII, were investigated for their activation profile with piperazines belonging to various classes, such as N-aryl-, N-alkyl-, N-acyl-piperazines as well as 2,4-disubstituted derivatives. As the activation mechanism involves participation of the activator in the proton shuttling between the zinc-coordinated water molecule and the external milieu, these derivatives possessing diverse basicity and different scaffolds were appropriate for being investigated as CA activators (CAAs). Most of these derivatives showed CA activating properties against hCA I, II, and VII (cytosolic isoforms) but were devoid of activity against the membrane-associated hCA IV. For hCA I, the  For each activator, at least six traces of the initial 5–10% of the reaction have been used for determining the initial velocity. The uncatalyzed rates were determined in the same manner and subtracted from the total observed rates. Stock solutions of activator (0.1 mM) were prepared in distilled-deionized water and dilutions up to 0.1 nM were done thereafter with the assay buffer. The activation constant (KA), defined similarly with the inhibition constant KI, was obtained by considering the classical Michaelis–Menten equation , and considering that [A]f can be represented in the form of the total concentration of the enzyme ([E]t) and activator ([A]t), the obtained competitive steady-state equation for determining the activation constant is given by Equation (5)v0 represents the initial velocity of the enzyme-catalyzed reaction in the absence of an activator. All CA isozymes used in the experiments were purified recombinant proteins obtained as reported earlier by our groupWorking at substrate concentrations considerably lower than 3.3.1.1–24  groups from the heterocyclic ring is influenced by the diverse substitution patterns present in them. Indeed, both electron withdrawing as well as electron donating moieties are present in these compounds which may lead to a different basicity of the moieties able to shuttle protons between the enzyme active site and the reaction medium (C) was also tested for comparison.Piperazines 1–24  were chon medium . The uns3.2.,Activation data against four physiologically relevant hCA isoforms, hCA I, II, IV, and VII, are shown in 1–24 and two standard activators (compounds A and B) shown in KAs > 150 µM against all investigated enzymes), the substituted-piperazines 1–24 showed CA activating properties against hCA I  with activation constants ranging between 32.6 and 131 µM, being thus moderate – weak activators. Indeed, the leads A and B were much more potent, low micromolar activators of this isoform, with KAs of 2.1–7.4 µM , and they belong to variously substituted piperazines. Small variations on the core structure of these compounds generally led to a diminution of the activity. For example, 19, the best hCA I activator, carries a propionyl group on the piperazine ring and a benzyl moiety in the 3 position. Its deacylated analog, 18, was almost two times a less effective hCA I activator, with a KA of 73.7 µM, compared to 19.Although unsubstituted piperazine C was inactive as a CAA (1–7.4 µM 18. The b1–24 investigated here compared to hCA I (22 was inactive (KA > 150 µM), and the range of the activation constants for the remaining derivatives was of 16.2–116 µM. A number of compounds showed KAs in the range of 16.2–50.1 µM: for example, 2, 4, 14, 19, 21, 23, and 24. They belong to various chemical classes and incorporated different substituents, which demonstrates that it might be possible to design much more efficient CAAs incorporating this interesting ring. However, the simple lead compound A was a much more potent hCA II activator compared to the other piperazines investigated here, whereas histamine B was a very inefficient hCA II activator with a KA of 125 µM , whereas the remaining derivatives showed a profile of medium – weak activator, with KAs in the range of 17.1–131 µM . For this isoform, the SAR of the couple 18/19 is completely different compared to what is mentioned above for the activation of hCA I. In this case, the deacetylatyed derivative 18 was 4.9 times a better hCA VII activator compared to the acetylated one 19. Thus, small changes in the scaffold lead to a very different activation profile in this series of piperazines and their derivatives.The brain cytosolic isoform hCA VII was not activated by piperazines 1–131 µM . The besThe activation of the four CA isoforms mentioned above with the piperazine derivatives 4.KAs in the range of 17.1–131 µM. The structure-activity relationship was intricate and not easy to rationalize for each isoform, but the most effective activators were 1-(2-piperidinyl)-piperazine 14 (KA of 16.2 µM for hCA II), 2-benzyl-piperazine 18 (KA of 16.2 µM for hCA VII), and 1-(3-benzylpiperazin-1-yl)propan-1-one 19 (KA of 32.6 µM for hCA I). As CAAs may have interesting pharmacologic applications in cognition and for artificial tissue engineering, investigation of new classes of activators as the ones reported here may be crucial for this relatively new research field.We report here an activation study of four physiologically and pharmacologically relevant CA isoforms, hCA I, II, IV, and VII with a rather large series of piperazines and their derivatives. The compounds were included in order to investigate whether the fine tuning of the basicity correlated with the various shapes of these molecules may lead to efficient activators, considering the fact that the only piperazine investigated till now as activator (4-(2-aminoethy)l-piperazine) showed such interesting properties. hCA I was activated by most of the investigated derivatives, with activation constants of 32.6–131 µM; hCA II with activation constants of 16.2–116 µM, whereas the membrane-bound isoform hCA IV was not activated by the investigated piperidines. The brain-associated cytosolic isoform hCA VII was activated with


Direction of arrival (DOA) estimation via sensor array is a crucial component of any passive sonar signal processing technology. In certain practical applications, however, the interested far-field targets are frequently affected by near-field interference, which may result in degradation of DOA estimation. Aiming at the direction estimation problems of far-field targets under strong near-field interference, a unified sparse representation model of far-field and near-field hybrid sources is constructed according to the various correlations in steering vectors between the planar wave and spherical wave in this paper. A high-resolution spatial spectrum reconstruction algorithm based on a sparse Bayesian framework is then exploited to constrain the energy of near-field interference in the preset near-field steering vector over-complete dictionary, thus ensuring the accurate detection and estimation of far-field targets. An expectation-maximization (EM) algorithm approach is introduced to estimate the number of sources and noise power iteratively, which will reduce the dependence of the algorithm on such prior information. Several state-of-art algorithms are mentioned and discussed (Minimum Variance Distortionless Response (MVDR) method, Multiple Signal Classification (MUSIC) algorithm and conventional beamforming (CBF) algorithm) to compare with the one proposed in this manuscript that achieves higher accuracy of estimation and resolution under low SNR level with limited samples, which is verified by simulation and for the results obtained in an experimental case study.  Direction of arrival (DOA) estimation via an acoustic sensor array mounted on underwater vehicles is an important research subject in the field of passive sonar signal processing ,2. It isExtant interference suppression methods usually rely on the adaptive noise canceller (ANC) ,8,9 or sThe matrix filter is a kind of spatial filter that passes sector-of-interest signals and suppresses out-of-sector interference. Researchers have developed numerous matrix filters with nulls in the stopband to suppress interference in the far-field. ,20,21,22Sparse signal recovery (SSR) based methods have been applied to solve the problem of DOA estimation of far-field signals in recent years ,29,30,31The direction estimation problems of far-field targets under near-field strong interference were investigated in this study by analyzing the differences in steering vectors between far-field and near-field signals. The near-field interference is modeled in this paper as a spherical wave rather than a Fresnel Approximation. A unified sparse representation model of far-field and near-field hybrid sources is constructed considering the correlation difference between the planar waveguide vector and the spherical waveguide vector. A sparse Bayesian learning algorithm to separate far-field and near-field signals is then proposed and the expectation-maximization (EM) algorithm is exploited to provide the DOA estimation automatically without preset regularization parameters and the prior knowledge of the number of sources. The proposed method enhances the ability of sparse signal processing for weak far-field target localization in the strong near-field interference environment.The remainder of this paper is organized as follows. K1 far-field targets and K2 near-field interferences) impinging on an array composed of M sensors. The sensor spacing of the uniform linear array (ULA) is d and the aperture of the array is expressed as D = (M − 1) d. For the kth  signal in the far-field, the wavefront of acoustic propagation is assumed to be a plane. In this case, the amplitudes of the signals are all the same on each sensor; the phase differences between the sensors are determined by the angle of targets. For the other kth  interferences in the near-field, the acoustic wavefront is assumed to propagate on the spherical wavelet. The locations of the interferences relative to the reference sensor are determined by two-dimensional (2D) parameters which are described in polar coordinates as Suppose there are K narrowband signals  sensor can be expressed as follows:The receiving interference amplitudes at different sensors are different. The phase difference between sensors is determined by the angle and distance of the impinging signals. Therefore, with the presence of near-field interference, the received signal of the mth  is a so-called Fresnel approximation and its significance lies in pointing out that the wavefront of the spherical wave received by the array is no longer a plane and can be approximated to quadric surface form in the Fresnel region. The phase difference between different sensors can be separated into two parts via Fresnel approximation, the first term of which is independent of the distance of sources. This property provides a great deal of convenience to relative array signal processing. However, model errors are inevitably introduced by omitting the higher-order Taylor expansion terms and neglecting the amplitude difference among sensors. Errors in this case are related to the specific location of near-field interference.As shown in x-axis. The two black solid lines in the figure represent the Fresnel area of the receiving array. The x and y-axis directions are the end-fire and normal direction of the ULA, respectively. Along the x-axis direction, the phase difference among different sensors at any distance only depends on the sensor spacing and there is no approximate error. In other areas of the figure, model error due to Fresnel approximation gradually emerges and decreases as distance increases. In the normal direction of the array, the phase difference error between the symmetrical sensors of the origin is zero; there are still errors between the asymmetrical sensors introduced by the approximation. It is assumed that the reference sensor is at the coordinate origin and the array is placed along the The Fresnel zone determined by the empirical formula does not take the model errors caused by the impinging signals at different locations to the array into account. In other words, the Fresnel regional boundary is not an appropriate basis for distinguishing the near-field from the far-field. To minimize the influence of model errors, the approximate Equation (3) can be used to model the near-field interferences.The similarity between plane and spherical wave is closely related to the specific position of the sound source relative to the receiving array. Liang  analyzedH denotes the conjugate transpose. The spatial power as a function of the steering angle is given by:T denotes the number of snapshots. CBF is robustness against mismatch between the assumed and the actual signal steering vector . As is well known, CBF suffers from poor resolution because of the wide beam. The high sidelobe also makes it difficult to detect a weak signal in the presence of strong interferences.The conventional beamforming (CBF) is that many sensors coherently combine the received signal, thus producing a sufficient array gain for target detection (whether a target is present or not). To achieve spatial filtering, the received signal is multiplied by the steering vector MVDR beamformer minimizes the beam output power in the noise and interference direction while keeping a distortionless response in the observed direction. The beamforming weight of MVDR is optimized by:Using the Lagrangian multiplier, the minimization yields the following MVDR beamformer produces a null response in the direction of interferences and hence can provide a far better resolution and stronger interference suppression capability than CBF. In practice, there is always an error between the estimated covariance matrix in Equation (10) and the true covariance matrix because of the limited snapshots. When the number of snapshots is insufficient, the estimated covariance matrix can often be singular and cause an ill-conditioned problem of a matrix inverse. MVDR is also known to be sensitive to the signal mismatch problem.M eigenvalues and the corresponding eigenvectors can be obtained by:MUSIC is an eigenvector-based algorithm. For a stationary isotropic noise environment, where a sufficient number of snapshots can be used to estimate covariance matrix accurately, the K largest eigenvalues and the corresponding eigenvectors can be associated with uncorrelated sources (including targets and interferences). The MUSIC makes full use of the orthogonal characteristics of the signal subspace and the noise subspace and hence achieves super-resolution and allows for better performance of interference rejection. Similar to the MVDR algorithm, MUSIC also suffers from insufficient snapshots. The source number is also assumed to be prior information to separate the signal subspace from the noise subspace reliably. Previous researches have proven the sparse reconstruction algorithm as applied to far-field DOA estimation to be rational. Numerous high-resolution DOA estimation algorithms based on sparse signal reconstruction have been proposed to date including the L1-singular value decomposition (L1-SVD) and Spare Bayesian Learning (SBL). Sparse signal reconstruction algorithms can manage coherent signals and perform well even when snapshots are scarce and SNR is low.The sparse representation model assumes that the array received signal consists of linear combinations of a few far-field steering vectors in The spatial spectrum estimation result of the sparse Bayesian learning method of a ULA is simulated, which is shown in An over-complete model is established in this study to separate far-field targets from near-field interferences. the model incorporates the entirety of far-field and near-field by discretizing the spatial regions of far-field and near-field sources, respectively, giving the definition:Here, the far-field space is quantized to K, so that the array output can be expressed in an over-complete form:Set Equations (15) and (16) gives the uniform sparse representation of signals where both far-field targets and near-field interferences exist. The matrix form with the multiple measurement vector can be rewritten as:In practical applications, although the location of near-field interference cannot be accurately obtained due to the complex distribution of noise sources, the pre-existing information regarding the region of near-field interferences can still be obtained. The positions of major noise sources such as propellers or motors are always relatively fixed. Therefore, to construct a complete dictionary for near-field interferences, the prior information of the interference locations should be properly accounted for to minimize the dictionary size and computational complexity of the reconstruction algorithm.K1 far-field targets should be all composed of the basis of 2 near-field interferences should be fitted by the basis in the dictionary After correctly reconstructing the signal matrix Lp-norm penalization is a commonly used penalty function for the sparse constraint. Algorithms of this type typically require prior estimation of noise power, and/or the source number as input. In fact, algorithms using Lp-norm as sparse constraint penalty function are equivalent from the perspective of Bayesian estimation, assuming that the signal vectors obey the following prior-distribution:The probability density function in Equation (19) reaches its peak at 0. The function value is closer to 0 when the row vector in the signal matrix deviates farther from the zero vector. Assuming that the mean Gaussian noise is 0 and the variance is p = 1. Equation (20) shows that the convex relaxation method essentially adds the same prior distribution assumption to the sources of different directions while the sparseness of the impinging signals requires a larger weight of The convex relaxation algorithm based on the L1-norm is a special case of Equation (20) with The sparse Bayesian learning method  can be uCorresponding to the signal matrix Unlike the assumption in Equation (19), each row of the signal matrix is assumed to conform to the Gaussian distribution of different parameters, thus avoiding the negative impact of L1-norm on all signals with constant weights. The noise power The probability density function of the Gamma distribution can be defined as:To secure reasonable values of When noise power When noise power However, in the actual application of the algorithm, the noise power as obtained by Equation (31) is often far smaller than the real noise power. This prevents the learning process of In the EM algorithm architecture, an iterative approach to noise power estimation and sparse spectrum reconstruction can be deployed under a sparse Bayesian learning framework. The spatial spectrum estimation of each discrete point in space  into Equation (25). If the number of sources is already known, the noise power can be estimated directly from Equation (25).Determine the region of near-field interference according to the real array configuration.According to Equation (15), sample the far-field and near-field spatial regions of interest separately at certain intervals to obtain Initialize Calculate Update parameter If the number of sources is unknown, estimate the number of signals and interferences according to Equations (33) and (34), and update the noise power Repeat Steps 4, 5 and 6 steps until Calculate the mean of the posterior probability Based on the principle of the EM algorithm and the improved iterative process for noise power estimation presented above, we now summarize the proposed DOA estimation and parameter optimization algorithm like the following steps:The over-complete dictionary N × N matrix, which leads to the complexity of For each iteration of the proposed algorithm, the computational complexity of the algorithm is dominated by evaluating the posterior probability density function in the E-step, computing the derivatives and updating the number of sources and noise power in the M-step. To obtain mean and variance of multivariate normal distribution expressed in Equation (26), it is required for computing the inversion of It is worth mentioning that the superiority of the proposed algorithm comes at the cost of the increased computational complexity. It is a compromise between the performance of computational complexity when other state-of-art algorithms fail to provide satisfactory results.Simulations and experiments were conducted to assess the performance of the proposed algorithm. In the simulations, CBF, MVDR, and MUSIC were compared with the proposed algorithm to observe the presence of near-field interference, the spatial spectrum of each algorithm, the probability of successful resolution and the RMS error of far-field direction estimation versus SNR and snapshot numbers.Assume a ULA composed of 11 hydrophones with half-wavelength corresponding to the target signal sensor space. The reference sensor is located at the center of the array. Far-field signals and near-field interference exist simultaneously in the space, and the noise is additive Gaussian noise. The INR is defined as Three unrelated far-field narrowband signals impinge on the array with the same power from 63°, 70°, and 150°, respectively. There are two near-field interferences in the space and the positions relative to the central sensor are The number of near-field interferences plus far-field signals in the MUSIC algorithm is assumed to be prior known so that the signal plus interference subspace and noise subspace can be distinguished accurately. For other algorithms, there is no prior knowledge of noise power or source number. The spatial spectrum estimated results under simulation conditions of different algorithms are shown in As shown in The MVDR algorithm can restrain the interference from unknown directions adaptively, including near-field interference when designing beams, thus yielding better spatial spectrum results than the CBF algorithm. However, under low SNR level (<0 dB), the adaptive beamforming algorithm does not have the ability to distinguish the two targets from 63° and 70°. Further, the position of near-field interference is close to the far-field target from 150°, which weakens the interference suppression ability of beams near 150°. The spectrum under low SNR does not form the sharp peaks and deviates from the real DOA of the signals. However, these problems grow less intense as SNR increases. The MUSIC algorithm utilizes the orthogonality of the signal subspace and noise subspace to enhance the resolution to adjacent targets compared with MVDR. Its spatial spectrum estimation performance is also better than that of MVDR. However, under the low SNR level, the target from 150° also shows peak-broadening characteristics; further, the MUSIC algorithm is very sensitive to the prior-number of interferences and signals.Under the simulated SNR and INR levels, the proposed algorithm gives the most stable far-field spatial spectrum estimation and has the sharpest peaks among the algorithms simulated in this study. In addition, only the proposed algorithm can distinguish the two targets from 63° and 70° and correctly estimate all three target arrivals under the SNR of −10 dB. Unlike the MUSIC method, the proposed algorithm does not need the number of signals and interferences as its prior-input. Instead of suppressing the near-field interferences, the proposed algorithm effectively separates the far-field targets signals from the near-field interferences thanks to the high resolution of the sparse Bayesian learning framework. The estimated spatial near-field spectrum is shown in The proposed spatial spectrum sparse reconstruction algorithm iteratively optimizes the signal power spectrum and noise power under the framework of the EM algorithm. The convergence of the iterative optimization strategy and EM algorithm has been verified in many pieces of literature. In order to verify the convergence of the proposed DOA estimation algorithm, the relative update ratio of the signal power spectrum defined by As shown in The root mean square error (RMSE) of DOA estimation versus SNR and the number of snapshots were next investigated over 500 independent Monte Carlo simulations. To adjust the resolution of various algorithms with adjacent angles, two far-field signals were selected with DOAs of 90° and 130°; this prevented the large RMSE due to the algorithm’s poor resolution. One near-field source interference, in this case, was located at The RMSEs of DOA estimation versus SNR with MVDR, MUSIC and the proposed algorithm are shown in The RMSE of each algorithm versus the number of snapshots (10 to 1260) under SNR of 0 dB is shown in As shown in We also investigated the resolution of each algorithm to adjacent targets. The angles of the two far-field signals, in this case, were 90° and 94°, respectively. The snapshot number was 200 and the SNR varied from −10 dB to 20 dB. We ran 500 Monte Carlo simulations under each SNR level and determined the resolution of dual targets as:We next fixed the location of the targets from 90° and varied the angle of the other source from 91° to 105°. The probability of source resolution for each algorithm versus the angle interval of the two targets with 0 dB SNR and 200 snapshots is shown in The simulations conducted in this study show that properly accounting for the sparsity constraint during spatial spectrum estimation can significantly enhance algorithm performance. The proposed algorithm thus has a lower SNR threshold and angle interval threshold in resolving the adjacent targets in space compared to other state-of-art algorithms. We also conducted an in-situ experiment to verify the proposed algorithm. Experimental data was collected with 10 acoustic hydrophones uniformly spaced at 4 m. The array was placed at 10 m under the sea surface. The observed space  was uniformly discretized at 1° interval where 0° is the end-fire direction. There was interference at the distance of 40 m and the direction of around 60°. A few weak targets moved around 100° to 160° throughout the experiment. The near-field interference region was discretized with As shown in As shown in The algorithm is proposed to solve the problem of high-resolution DOA estimation of far-field targets in presence of near-field interferences. In practical sonar system applications, how to achieve fast implementation of the sparse spectrum reconstruction is a practical problem. Strong demand for wideband signal processing and vector hydrophone array processing under random interferences still exists. How to use prior signal information such as signal structure is an open challenge.Local interferences like towing vessels or propeller vibration noise in the array mounted on an underwater vehicle can be expressed as intensive near-field sources, which poses a severe challenge to the accurate DOA estimation of far-field targets. Far-field target detection and DOA estimation problems in the near-field interference environment were investigated in this study. The far-field target location estimation problem under near-field interference was transformed into a problem of separating far-field and near-field sources; a sparse Bayesian learning algorithm was proposed in this paper based on this idea. The spherical wave propagation model was first applied to simulate the near-field source, which prevents any model error otherwise caused by Fresnel approximation. A unified sparse representation model of far-field and near-field mixed sources was constructed considering the correlation difference between the planar waveguide vector and the spherical waveguide vector. The sparseness of the Bayesian model was then exploited to fit and constrain the array data while the high resolution of the sparse reconstruction algorithm was used to constrain the energy of near-field interference in the preset near-field steering vector over-complete dictionary. This, as demonstrated by simulation and experimental results, ensures the accurate detection of far-field targets. The number of sources, the power of noise, and the sparse reconstruction of signals are solved by joint iteration in the proposed sparse optimization process. The prior information such as the number of sources and power of noise is thus unnecessary and parameter adjustment is not needed.Simulation and experimental results validated the effectiveness of the proposed algorithm in the presence of intensive near-field interference. The performance of the proposed algorithm in an experimental case study that has been carried out is better than similar state-of-the-art algorithms in the perspectives of SNR level, the number of samples and angle resolution.