But models are subject to contamination; once an unrelated sequence has been added to the model, homologs of the unrelated sequence will also produce high scores, and the model can diverge from the original protein family. Examination of alignment errors during psiblast PSSM contamination suggested a simple strategy for dramatically reducing PSSM contamination. psiblast PSSMs are built from the query-based multiple sequence alignment (MSA) implied by the pairwise alignments between the query model  and the subject sequences in the library. When the original query sequence residues are inserted into gapped positions in the aligned subject sequence, the resulting PSSM rarely produces alignment over-extensions or alignments to unrelated sequences. This simple step, which tends to anchor the PSSM to the original query sequence and slightly increase target percent identity, can reduce the frequency of false-positive alignments more than 20-fold compared with psiblast and jackhmmer, with little loss in search sensitivity.Iterative similarity search programs, like  Protein similarity searching is central to interpreting genome sequence data. The widely used BLAST program  can routpsiblast  or Hidden Markov Model (HMM). In the worst cases, contaminating non-homologous sequences can shift the PSSM away from the original homolog family causing it to detect more non-homologs  than homologs (true positives). In previous work, Gonzalez and Pearson  showed ti et al. .psisearch2, an improved version of psisearch, we found that the strategy used to construct the PSSM in psisearch occasionally produced PSSMs that aligned incorrectly to the homologous domain. To correct this problem, we explored methods to correctly ‘anchor’ the PSSM by replacing gapped positions in the subject sequence alignment with the residues from the query sequence that aligned to the gap in the subject sequence. We were surprised to find that PSSMs constructed using query-seeded subject sequences not only reduced PSSM misalignment, they also produced dramatically fewer false-positives.During the development of psiblast  domain size: Pfam domain model lengths >200 match states; (ii) domain number: >200 members; (iii) diversity: domains were present in at least two of the three kingdoms of life  with at least 20% of the sequences from the second most abundant kingdom (100 domains if the most abundant kingdom had >500 domains); (iv) clan length consistency: domain families from clans were included only if the maximum model length of the domains in the clan was <1.5-fold the minimum model length. In Pfam28, there are 1743 domain families and 155 clans that met the domain length, domain abundance, and clan length consistency criteria. Including the diversity requirement reduced the number of domain families that did not belong to a clan to 299, and the number of clans to 40, for a total of 339 non-homologous queries from 428 Pfam28 domain families.With the dramatic increase in bacterial sequencing over the past 5 years, some of the 339 domain and clan RPD3 families contained many tens of thousands of sequences containing an RPD3 domain. To reduce the differences in abundance between the largest and smallest domain families, large domain families were randomly down-sampled to a maximum of 5000 entries using a strategy that sought to preserve or enhance phylogenetic diversity. Thus, if there were 2000 or fewer sequences in archaea, bacteria, and eukaroyta, all 2000 were included, and if the two kingdoms with fewer domains had <25% of the domains in the largest kingdom, 2000 were taken from the most abundant, and at least 500 taken from the less abundant kingdoms. If the less abundant kingdoms contained >25% of the sequences in the most abundant kingdom, all three kingdoms were sampled randomly. The same strategy was used for domains from the 40 clans, but for domains in clans, all the domain families from the clan were combined, and then the phylogenetic diversity rules were used for sampling.in_full=1) among the RPD3 sequence set.The resulting RPD3 protein set contains 597 753 proteins that contain at least one domain from the 299 Pfam28 families and 40 Pfam28 clans in the RPD3 domain set. The largest clan/domain family is found in 4719 sequences, the smallest domain family in 207 . Thus, the middle 50% of clan/domain families differed ∼2.5-fold in abundance. The full length RPD3 sequences contain many domains in addition to the 339 domains/clans in the RPD3 set. Pfam28 reports 2904 domains in the full alignments , we randomly selected domains from the bottom tenth-percentile of sequence_bits_scores from the pfamA_reg_full_significant table in the Pfam28 MySQL distribution that covered at least 50% of the domain model length, and did not overlap other domains. For the second set of 100 queries (far66), we randomly selected domains that at least 66% of the model length from the bottom tenth-percentile. For domain families that belonged to clans, we selected a domain family near the median in sequence abundance in RPD3. Domains were only selected from sequences that were not marked as is_fragment in Pfam28.To provide a challenging set of domains for evaluating ssearch) of the RPD3 library, and the 100 sequences between the 10th and 40th percentiles by family coverage were selected (far50). For the far66 set of queries, the 100 embedded domain queries were selected randomly from the bottom half of queries ranked by family coverage after an ssearch search. The two query sets sample 150 of the domain/clan families. Forty-nine families are shared (with different embedded domain queries) between the far50 and far66 query sets. We also evaluated the performance of query-seeded iterative searching using full-length protein sequence queries by retrieving the full-length sequences from Pfam28 that contained the 100 far50 embedded domains (far50-full) and the 100 full-length sequences with far66 domains (far66-full).The domain region sequences were then embedded into random sequence, i.e. a 200 residue domain produced a 400 residue query sequence with 100 residues of random sequence on either side of the genuine domain sequence, and used to search the RPD3 sequence set. The embedded domain queries were then ranked by their ability to produce statistically significant alignments in a Smith–Waterman search , FDR), we identified 40 queries with the highest FDR for psisearch2, a new version of the psisearch program  and the sequence library used to construct the PSSM with psiblast.We evaluated the performance of  program  that comt Figure  separatepsiblast (ssearch (psiblast-outfmt 7 or ssearch -m 8CB) format. The alignment results are passed to the m89_btop_msa2.pl script to produce a MSA and subject sequence library. The MSA and subject library files are then passed to psiblast to produce the PSSM.A block diagram of the search/alignment/PSSM construction process is shown in Figure psiblast  or ssear by controlling the boundaries of the sequences specified in the MSA used to make the PSSM ; and (ii) by modifying the subject sequences in the sequence database used to calculate the PSSM .We control the PSSM construction process by using m89_btop_msa2.pl program takes the alignments produced by the psiblast or ssearch similarity search and produces an MSA and a custom subject sequence database that psiblast can convert into a PSSM. m89_btop_msa2.pl options control the MSA boundaries and the sequences in the custom subject sequence database.The psiblast and jackhmmer (unmodified), and psisearch2 modified using the query-seeding and boundary control strategies available with m89_btop_msa2.pl ) or FDR (FP/(TP + FP)) for receiver operator characteristic (ROC) curves and Table Our evaluation of search effectiveness and search selectivity depends on accurate Pfam28 annotations. We used Pfam28 coordinate annotations on the proteins in RPD3 without modification. But when we saw significant false-positive alignments, sometimes with unembedded sequences, and sometimes in the first iteration, we investigated further. In four cases, we concluded that Pfam had missed a homology relationship. We added PF09511 to clan CL0078, PF16332 to CL0579, PF01010 to CL0425, and we formed a new clan (CL9001) from PF01156 and PF07362. Each of these relationships was confirmed by finding alignments where annotated members of the clan aligned with the candidate homologous domain, or where the relationship was supported by SCOOP , or from GitHub (github.com/wrpearson/fasta36). The RPD3 database is available from GitHub (github.com/wrpearson/RPD3).The PSSM corruption often occurs when an aligned homologous region produces a strong similarity score that allows the alignment to be continued into an adjacent non-homologous region, a process we have termed homologous over-extension . HomologWhen we first identified homologous over-extension as a major cause of PSSM contamination, we found that we could reduce contamination using a simple strategy to prevent alignments between the query/PSSM and the subject sequences from extending with successive alignments ,6. This fasta programs have been extended to allow sub-alignment scoring , and with psisearch2 with query seeding to a more limited extent. Many proteins that contain a PF00346 domain also contain a PF00374 domain  for those iterative methods 5–20-fold  to 79-fold (psiblast) at iteration 10 . Thus, we focus on the 20 families from the far50 and far66 query sets that produced the largest FDR  produces six false-positives. jackhmmer reports its first 217 false-positives with PF00346 at iteration 3  at iteration 3. psisearch2 (seeded) does not produce any false-positives for half the queries in the far50 query set after 10 iterations. In the far66 query set, more than half the queries are producing false-positives with psisearch2 (seeded) at iteration 4, but the FDR is an order of magnitude lower than psisearch2 without query-seeding . With the far66 dataset, the numbers are 55 more and 4 fewer false-positive queries without query-seeding (P < 10−12). For psisearch2, 38 families have more false-positives and nine fewer on the far50 dataset (P < 10−5), while 36 have more and five fewer on far66 (P < 10−6). When the same test is done on the number of true positives, query-seeding reduces sensitivity. For psisearch2 and the far50 set, unseeded PSSMs perform better with 63 queries, while query-seeded PSSMs perform better with 32 (P < 0.001). But we believe that false-positives pose much more of a threat to iterative searches than false-negatives (see Discussion). Query-seeding significantly reduces the number of false-positives during iterative searches with only a small decrease in sensitivity.We also compared the performance of PSSMs produced with and without query-seeding by simply tabulating the number of query families where the seeding produced more false-positives, or fewer false positives using the ‘R’ psisearch (psiblast/MSA and psisearch2 (both with seeding). Domain based extension limits only tested with psisearch2, since the approach uses sub-alignment scores to focus on domains with significant similarity.The improvement we see by seeding query residues into gaps in subject sequences is larger than the improvement we found by explicitly limiting alignment extension in sisearch . To see sisearch ,6; and . For the far66 set, 17 families have fewer false-positives and six have more (P < 0.017).Limiting alignment extension using alignment history has a small effect on . Remarkably, when query-seeding is used, the median level of over-extension without alignment boundary modification is <10 residues; query-seeding reduces median over-extension more than 50-fold after five and ten iterations. With so little over-extension without alignment boundary modification, it is difficult to do much better, and domain-based boundary modification looks very similar to query-seeding alone. However, boundary modification using alignment history reduces over-extension even more, particularly after iteration three.Because query-seeding is very effective, we only see a modest improvement in search selectivity (FDR) with our two over-extension control strategies. Figure Seeding query residues into the sequences used to construct PSSMs reduces false-positives by increasing the information content at positions with gaps in many homologs, effectively making the resulting position specific scoring matrix slightly ‘less deep’. But query-seeding also reduces failures of the PSSM construction process to re-align sequences properly. To see whether query-seeding improves searches where over-extension is less likely, we also compared the unseeded domains from the far50 query set to the RPD3 database. When the far50 domains are not embedded in random sequence, there were no significant differences between un-seeded and seeded PSSM searches , and, asHomologous over-extension, the major cause of false-positives in iterative searching, can be reduced by decreasing the evolutionary distance  of the scoring matrix used to produce alignments ,13. QuerE-value, 0.0001 rather than 0.002 (the default), for inclusion in the MSA and PSSM . The more stringent inclusion threshold is less sensitive than query-seeding with E < 0.002, but it does not substantially affect either the amount of over-extension or the target percent identity  to 86.9% , while reducing the FDR from 13.9% to 0.2%. On the far66 dataset, sensitivity drops from 93.0% to 88.3% while the FDR drops from 25.9% to 3.5%. Thus, 5–10% drops in sensitivity yield 8–20-fold, or more, reductions in FDR.Modifying the PSSM by seeding query residues dramatically reduces false-positives, but it also slightly reduces true positives Table . On the We believe that the modest decrease in search sensitivity is more than balanced by 10-fold reductions in FDR. As Pfam clans illustrate, for many large and diverse protein families it is not possible to build a single model, either PSSM or HMM, that can reliably detect all the members of the family. In Pfam30, about one-third of Pfam domain families belong to clans, but even with multiple models in clans representing a single domain family, many Pfam domain homologs remain unannotated. Complete identification of homologous domains requires a mixture of PSSM or HMM models and a strategy for re-starting the iterative search to build a new homologous model. Such transitive strategies are far less likely to become contaminated if false-positives are avoided.Most of our false-positives with query-seeded PSSMs align to the query domain, not to the random sequence surrounding the embedded domain. We cannot be certain that the false-positives that we find with our most selective methods are genuine non-homologs; some are likely to be cryptic homologs. If homologous over-extension can be eliminated with some combination of PSSM adjustment and alignment boundary modification, then the problem of false-positive detection becomes statistical, and it should be possible to develop better methods with even fewer errors.psisearch2 query-seeding and either alignment or domain boundary limits, more than half of our most challenging queries do not produce any false-positives, even after 10 iterations. The higher selectivity of query comes at a cost of slightly lower sensitivity, which can be partially offset by increasing the number of iterations, but alternative strategies, such as re-initiating the search with a distant homolog, may be required to identify the most distant homologs.Improvements in sequence similarity searching require more sensitive scoring matrices—evolutionarily ‘deep’ matrices that can detect homologs with low sequence identity by giving low-identity alignments positive alignment scores. But matrices that give positive scores to low identity homologs are also much more likely to allow alignments from homologous domains to extend into non-homologous regions. Iterative searching is effective because the discovery of one or two distant homologs can reveal hundreds of new homologs in the next iteration. But this same amplification process makes it critical to avoid false-positives; one or two false-positive relationships can quickly lead to hundreds or thousands of misleading results. Query sequence seeding dramatically reduces the incidence of false-positives. With Supplementary DataClick here for additional data file.


In this research, a pruning mechanism for aerial pruning tasks is tested in a real environment. Since the final goal of the aerial pruning robot will be to prune tree branches close to power lines, some experiments related to wireless communication and pruning performance were conducted. The experiments consisted of testing the communication between two XBee RF modules for monitoring purposes as well as testing the speed control of the circular saw used for pruning tree branches. Results show that both the monitoring and the pruning tasks were successfully done in a real environment. In recent years, the popularity of multirotor helicopters has drastically increased to the point that almost everyone has heard the word drone. Low prices and a very competitive market have made possible the access to this technology either for fun or for research activities. Nowadays, the most popular application of multirotor helicopters is aerial video and photography; however, this activity is only for the purpose of capturing video and pictures from the environment without contact.TransportationContact tasksOn the other hand, aerial manipulation focuses on the environment; basically, we can catalog this application in two main activities:Load transportation deals with moving payload from one place to another using an aerial vehicle. It is commonly used a gripper to pick up the payload and move it to the target place; this task requires to control the stability of the aircraft which is affected by the payload –5.Contact tasks using multirotor helicopters are used to interact with the physical environment to perform a specific task. In this case, the multirotor system is endowed either with a tool or with a manipulator. which allows it to execute the task. Examples of this research are: an Asctec Pelican quadrotor endowed with a custom-made manipulator for contact inspection , an aeriThe examples mentioned above are clearly new applications related to multirotor helicopters. Considering that the stability of multirotor helicopters is affected by the payload during the process of grasping and moving and, the flying time is in most of the cases, a crucial factor to perform activities such as inspection by contact operations, we propose aerial manipulation only for the initial operation task. In other words, we propose to carry a tool to the point of interest, fix the multirotor using a gripper, perform the task and finally, return to the ground station. This allows the helicopter to reduce the energy consumption since it is only necessary to fly to the desired position and the rest of the operation will be performed by the tool without flying.Particularly, we are interested in pruning tree branches using an aerial pruning robot as it is shown in Fig. Pruning tree branches close to power lines represent a risk; this means that there is always the possibility of an accident caused by a high-voltage cable. Usually, the minimum required working distance for pruning trees close to a primary distribution line  and a transmission line must be 3 and 6 m, respectively. For a human worker, pruning these branches may become a difficult and hazardous task, that is, it is necessary to find a solution to perform such activity safety without direct human intervention in the task.In this paper, we discuss two important tasks the aerial pruning robot should perform, communication with the user’s interface and pruning a real tree branch. First of all, a description of the aerial pruning robot is explained; next, we discuss the electronic interface and the PI control for the pruning process. We also give a brief introduction to the XBee modules and the interface with the microcontroller, and finally, we give some conclusions and future work.Generally speaking, there are three different ways to trim trees close to electric power lines . These tThe idea we propose to prune tree branches close to power lines is to use a multirotor helicopter, a gripper and a circular saw to perform such activity. This new concept not only reduces the costs of using a truck with a crane but also reduces the human risks of a potential accident because of high voltage around the working area. Figure First of all, the aerial pruning robot should fly to the target branch.When it is reasonable close to the target, a couple of claws-like grippers should close to grab the tree branch.Once the tree branch was grabbed by the gripper and the complete body of the aerial pruning robot is hanging from the tree branch, the pruning mechanism, which is placed on the top of the multirotor, should start pruning the tree branch.Finally, when the pruning task is done, the aerial pruning robot should come back to the home position.With this new idea, new challenges arise as they have to be solved in order to perform the complete task. In order to successfully complete the pruning task, the aerial pruning robot should accomplish four steps:Figure The robotic gripper that we are using in this research is composed by a couple of claws with teeth for grasping the tree branch firmly; since each claw is placed in different planes, we call this configuration “skew-gripper.” The pruning system is composed by a DC motor, a gear box, and a circular saw; Fig. By choosing a multirotor helicopter as a carrier of the pruning mechanism, specifically an hexarotor configuration, the complete mechanism is mounted on the top of it using a 3-mm aluminum base-plate. Figure Figure a and b are the smallest and the largest diameters of the branch, respectively; this is due to the diameter of the circular saw introduce a restriction in the pruning task. In other words, the effectiveness diameter b, of the branch to be pruned is determined by two factors, the circular saw and the outer washer. Figure The skew-gripper can grasp a tree branch with a maximum diameter of 40 mm without any problem; however, the final goal is pruning; therefore, the diameter range, mentclass2pt{minima, was determined experimentally in several branches resulting in On the other hand, the total weight of the aerial pruning robot is another factor which restricts the diameter In order to test the pruning system in a real environment, a hardware for controlling the swinging motion for pruning purposes was designed. A couple of high-torque servo motors from HITEC  and a FuThe hardware designed for controlling the speed of the circular saw is composed by a power electronic module, which is used for commuting the circular saw’s DC motor, a back-electromotive force (back-EMF) module for sensing the motor’s speed, a PI controller programmed in an Arduino Uno board for regulating the speed of the circular saw and a RF XBee wireless module for transmitting the information of the pruning process to the operator. Figure Figure The wireless communication module, which is used to communicate the aerial pruning robot with the ground station, is composed by two XBee series 1 (S1) from DIGI International . Some ofOne of the two XBee modules is connected to an XBee shield from Sparkfun , and thiIn order to validate the performance of the pruning mechanism in a real environment, several experiments were performed. The aim of these experiments was to prove the effectiveness of the wireless communication between the aerial pruning robot and the ground station for monitoring the speed of the circular saw; in addition, the swinging motion for the pruning process produced by the couple of servomotors was also tested. For this experiment, a professional tipped-saw was selected as it will be described later.Outer diameter: 100 mmBlade thickness: 1.3 mmNumber of blades: 36Inside diameter (for attaching to the gear box): 20 mmIn order to prune a tree branch ranging from 12mm to 40 mm, the pruning mechanism should start swinging to go through the tree branch progressively. In these initial tests, the operator decides when the pruning mechanism should turn back and goes again through the tree branch based on a visual inspection of the graphic of the circular saw’s speed provided by the computer placed in the ground station. For the sake of clarity, Fig. Figure The tree branch used in this experiments as well as the performance of the pruning mechanism is mentioned in Table Regarding the energy consumption of the whole system, the main source of energy consumption is the multirotor helicopter, and it consumes around 25 A during flying. On the other hand, the circular saw consumes only 4 A during the pruning process which takes around 8 min or less, depending of the diameter of the tree branch. In these experiments, a 5100-mAh 4S LiPo battery was used for powering the multirotor and the circular saw as well. This battery at full charge gives 16.8 V and should not go down less than 12.8 V at full discharge. For practical applications, we establish a boundary in 14.5 V to have enough time for landing in case the battery has achieved the minimal boundary and thus prevent a permanent damage. This range allows the operator to fly the multirotor around 7 min which is enough time for grasping and pruning at least, one tree branch.In this paper, the hardware description and some experimental results regarding pruning tree branches in a real environment using an aerial pruning robot were shown. Results obtained show that the PI control implemented to control speed of the circular saw was helpful for pruning a real tree branch. In addition, the wireless communication between the aerial pruning robot and a ground station has shown that it is possible to follow the pruning task monitoring the performance of the speed of the circular saw to avoid possible accidents. In the future, there are some necessary improvements to increase the performance of the pruning task such as monitoring the process using either a smart phone or a tablet instead of a PC along with a more powerful wireless communication to cover a large working area. Moreover, a quadrotor helicopter in a coaxial configuration is also being considered to increase the payload capacity and thus allowing the operator to place an extra battery to increase the flying time, which is crucial to accomplish the pruning task.


Herpetosiphon aurantiacus. It is composed of three unusual building blocks, including the non-proteinogenic amino acid 3-chloro-L-tyrosine, the α-hydroxy acid L-isoleucic acid, and a methylmalonyl-CoA-derived ethane unit. A candidate genetic locus for auriculamide biosynthesis was identified and encodes four enzymes. Among them, the non-canonical 199 kDa four-domain nonribosomal peptide synthetase, AulA, is extraordinary in that it features two consecutive adenylation domains. Here, we describe the functional characterization of the recombinantly produced AulA. The observed activation of 3-methyl-2-oxovaleric acid by the enzyme supports the hypothesis that it participates in the biosynthesis of auriculamide. An artificially truncated version of AulA that lacks the first adenylation domain activated this substrate like the full-length enzyme which shows that the first adenylation domain is dispensable. Additionally, we provide evidence that the enzyme tolerates structural variation of the substrate. α-Carbon substituents significantly affected the substrate turnover. While all tested aliphatic α-keto acids were accepted by the enzyme and minor differences in chain size and branches did not interfere with the enzymatic activity, molecules with methylene α-carbons led to low turnover. Such enzymatic plasticity is an important attribute to help in the perpetual search for novel molecules and to access a greater structural diversity by mutasynthesis.Auriculamide is the first natural product known from the predatory bacterium  Herpetosiphon aurantiacus is a filamentous, Gram-negative bacterium with a facultative saprophytic predatory behaviour . Of. Of1-A2-yceticus .H. aurantiacus makes the use of reverse genetics prohibitive, we sought to provide biochemical evidence for the participation of this unusual NRPS in the biosynthesis of 1. AulA is suggested to incorporate L-isoleucic acid . The domain architecture indicates the substrate undergoes no other chemical modification besides a reductive step after being tethered to the PCP domain by the PKS-type ketoreductase domain (KR), as reported for other natural products, such as pyridomycin [3).Since the lack of a genetic system for idomycin , cereuliidomycin , and bacidomycin . Hence, Bacillus brevis led to the identification of ten positions within an A domain (PheA), collectively referred to as nonribosomal code [1. A subsequent manual inspection further revealed that the acyl-activating consensus motif is hardly conserved in AulA-A1. Moreover, the strictly invariant residue Asp413, which is essential for adenylate binding [1. We hence concluded that this domain cannot function as an adenylating enzyme and is likely skipped during the biosynthetic assembly. Inspection of AulA-A2 with PKS/NRPS Analysis [2 posed itself as a conundrum. The first position (D235 in PheA) is normally indicative of the substrate class to be used by the NRPS. Curiously, in AulA-A2 this corresponds to a glycine residue, associated with the activation of anthranilic acid [1 would contribute to the biosynthesis of auriculamide, e.g., through structural support for the catalytic role of AulA-A2, as noticed with a fungal A domain [aulA both as full-length gene and as an artificial open reading frame solely encoding the AulA-A2 domain, the α-keto reductase domain, and the terminal carrier protein. Independently, E. coli KRX was transformed with both constructs for heterologous production of the respective N-terminally hexahistidine fusion proteins, which were purified by metal affinity chromatography . As anticipated, the presence of an α-carbonyl notably influenced a successful adenylation. In the case of the tested α-keto acids, the differences in the chain size or position of the methyl group did not seem to play a role, as demonstrated by the equal enzymatic preference for 3 , 4  and 5 . Conversely, the activation of α-hydroxy acids was not uniform. 2-Hydroxy-4-methylvaleric acid (6) could also be recognized by the NRPS, albeit a slightly lower radiolabel exchange  followed its incubation with the enzyme. Interestingly, the assay of 2-hydroxy-3-methylbutyric acid (7) resulted in a major decrease in the radiolabel exchange  when compared to its α-keto acid analogue. Molecules possessing a methylene α-carbon were not suitable substrates for AulA. 4-Methylpent-2-enoic acid (8) was only modestly activated , while reactions with 9  and 10  resulted in negligible substrate turnover.To probe their enzymatic activity, the two purified AulA fusion proteins were subjected to the ATP-[charcoal . Both returnover  which deOur biochemical in vitro results highlight AulA as apt to take part on a NRPS/PKS complex for the biosynthesis of auriculamide, given the activation of 3-methyl-2-oxovaleric acid by its second adenylation domain. Our results also contribute to hone algorithms used to predict substrates from nonribosomal codes. Moreover, we describe how this enzyme is pliant to minor structural variations of that molecule, enabling future attempts to generate auriculamide analogues as potential new drug candidates.File 1Complete experimental details.


Two variants differing in domain wall alignment and vertical lattice parameters have been identified and independently analysed.Scanning X-ray nanodiffraction with 100 nm spatial resolution has been applied to investigate the ferroelectric domain structure of K 0.75Na0.25NbO3 epitaxial layer has been performed by using a focused X-ray beam of about 100 nm probe size. A 90°-rotated domain variant which is aligned along [1TSO has been found in addition to the predominant domain variant where the domains are aligned along the [TSO direction of the underlying (110) TbScO3 (TSO) orthorhombic substrate. Owing to the larger elastic strain energy density, the 90°-rotated domains appear with significantly reduced probability. Furthermore, the 90°-rotated variant shows a larger vertical lattice spacing than the 0°-rotated domain variant. Calculations based on linear elasticity theory substantiate that this difference is caused by the elastic anisotropy of the K0.75Na0.25NbO3 epitaxial layer.Scanning X-ray nanodiffraction on a highly periodic ferroelectric domain pattern of a strained K Here, the unit cells of the epitaxial layer are rotated in-plane by 90°, leading to a corresponding rotation of the one-dimensional domain pattern, which is now aligned along the lateral Anisotropic strain can be induced in KTC) are strongly correlated with the incorporated lattice strain, the emergence of either one or two phases may drastically change the film’s ferroelectric properties on the nanoscale (e.g. the temperature or broadness of a phase transition). In this regard, it is highly desirable to verify experimentally the different strain states of the two domain variants for a fundamental understanding of their utility for ferroic applications.But, simultaneously, calculations based on linear elasticity also predict a larger vertical strain in the 90° domain variant compared with the 0° domain variant. Since ferroelectric properties like the Curie temperature  and high-resolution X-ray diffraction have frequently been employed  TbScO3, both domain variants can be independently identified and analysed. Thereby, the theoretical prediction of a different vertical lattice spacing between the two could be verified and evaluated. The observations are discussed within the framework of calculations based on linear elastic theory.In this paper, we report an advanced X-ray diffraction experiment using a nanofocused monochromatic X-ray beam where high strain sensitivity is combined with spatial resolution in the 100 nm regime. We will show that, for K2.0.75Na0.25NbO3 thin films were grown on (110) TbScO3 substrates using liquid-delivery spin metal–organic chemical vapour deposition (MOCVD). Since it is performed close to thermodynamic equilibrium and at high oxygen partial pressures, MOCVD potentially provides highly perfect films with nearly stoichiometric composition and smooth surfaces/interfaces. A detailed description of the growth parameters can be found in the work by Schwarzkopf et al. . The ferroelectric domain state and local piezoelectric responses of the K0.75Na0.25NbO3 films were studied by the PFM setup of the atomic force microscope, utilizing the dual alternating-current resonance tracking (DART) mode . The principal experimental setup has already been reported by Chahine  al. 2014, 2015 ▸.et al., 2011A fast-readout two-dimensional detector  and pseudocubic  latttice parameters are interconnected via apc = co, bpc = cpc = pc = 2tan−1(bo/co), where αpc is the angle between the bpc and cpc axes. Throughout the following we will use the pseudocubic notation for the KxNax1−NbO3 epitaxial layers, whereas the orthorhombic notation will be used for the TbScO3 substrate   notation, which can be easily obtained from the orthorhombic notation by a simple coordinate transformation. Similar to the notation of Vailionis  al. 2011, the orta) the elastic strain energy density F(∊) of a KxNax1−NbO3 epitaxial layer is displayed as a function of potassium content (x > 0.5) for different surface orientations of the pseudocubic unit cell. These calculations are based on the elastic coefficients of KNbO3  is significantly higher for the (100)pc surface orientation than for the (001)pc surface orientation. This energy difference forces the KxNax1−NbO3 epitaxial layers to grow on the TbScO3 substrate with the (001)pc surface orientation. In this case a strongly anisotropic epitaxial strain emerges, illustrated in Fig. 1b). For a potassium content of x = 0.75, which is applied in this study, the epitaxial strain is highly compressive in one in-plane direction (∊yy) and very weakly tensile in the corresponding orthogonal direction (∊xx).In Fig. 1pc surface orientation depends on the azimuthal epitaxial relationship of the pseudocubic unit cell on the TbScO3 substrate. For the 0° domain variant this is given by [100]pc || [001]TSO and [010]pc || pc || [001]TSO and [100]pc || a), the elastic strain energy density F(∊) is slightly smaller for the 0° variant. The observed energy difference can be attributed to the elastic anisotropy of the epitaxial layer, which is also reflected in a different vertical lattice parameter for each domain variant. The calculations predict a slightly larger vertical lattice parameter for the 90° variant by about (Δd/d)z = 2.4 × 10−4.The elastic strain energy density of the (001)4.0.75Na0.25NbO3 epitaxial layer shows atomically smooth terraces about 400 nm broad, which correspond to the 0.05° off-orientation of the TbScO3 substrates . It contains both the 0°  and 90°  variants, which differ in their azimuthal alignment . In both cases the domain periodicity can be determined to be about 46 nm. X-ray rocking curves and reciprocal-space maps obtained on a conventional X-ray diffractometer prove that the K0.75Na0.25NbO3 epitaxial layer is coherently grown onto the TbScO3 substrate with (001)pc orientation.The surface morphology of the 23 nm Kes Fig. 2. The rooes Fig. 2a. It co0° Fig. 3b and 900° Fig. 3c varianpc reciprocal-lattice point  of the strained K0.75Na0.25NbO3 layer [which appears in the vicinity of the (422)TSO reflection peak of the substrate], a detector frame has been accumulated for a selected position of the X-ray beam on the sample . Near to the strong central scattering feature P0, which we assign to the crystal truncation rod (CTR), two weak side maxima P1 and P2 can be observed, which are caused by the domain periodicity. In the chosen scattering geometry the Close to the asymmetric coplanar (113)le Fig. 4a. Near L by applying the expression L = λ/Δ. From Fig. 4a) a value of Δ = 0.19 ± 0.01° is derived, from which a domain periodicity of L ≃ 47 ± 2 nm can be evaluated, which is in very good agreement with the PFM data shown in Fig. 3a). However, owing to the large convergence of the primary beam all scattering features are remarkably broadened. Nevertheless, they do not overlap and can be analysed independently. The integrated intensity of the CTR [accumulated within the central dashed area of Fig. 4a)] is displayed in Fig. 4b) as a function of the vertical scattering vector component zQ. The intensity distribution shows a broad maximum at zQ = 4.65 Å−1 accompanied by periodic thickness fringes, which prove the coherent growth of the epitaxial layer and smooth interfaces. From the distance between adjacent fringes, a film thickness of t = 23 ± 1 nm can be derived, which is in agreement with conventional high-resolution X-ray diffraction curves measured using a large X-ray beam (not shown here).The angular distance, Δ, of P1 and P2 from the CTR (P0) on the detector can be used to determine the domain period zQ = 4.65 Å−1, the integrated intensities of the features P1 and P2 [accumulated within the outer two dashed areas of Fig. 4a)] were analysed as a function of the position of the X-ray spot on the sample within a 5 × 5 µm area . Two different regions, denoted A and B, can be distinguished. Inside the needle-shaped region A, the intensities of P1 and P2 are strong. By contrast, in the surrounding region B the integrated intensities of P1 and P2 vanish. A complementary behaviour is observed in the vicinity of the asymmetric pc Bragg reflection, we observe the scattering features P0, P1 and P2, but the intensities of P1 and P2 are now strong in the surrounding matrix B , while the intensities are strongly suppressed within the needle-shaped area A .For d)]. On the other hand, if the domain walls are perpendicular to the plane of incidence no satellite peaks can be observed . From our experimental findings we can therefore conclude that the needle-shaped region A can be assigned to the 90° domain variant which appears with reduced probability, while the surrounding matrix can be assigned to the 0° domain variant. However, it must be noted that we know the centre of sample rotation with respect to the X-ray spot with an accuracy of 10 µm for all rotation circles. Without a clear two-dimensional feature in the sample observable in both reflections, we are unlikely to be able to overlap the images perfectly  for the (113)pc Bragg reflection, where again areas A and B can be resolved. While the contrast in P1 and P2 maps  is produced by the different domain alignment of the 0 and 90° variants, the contrast mechanism of the central CTR is not completely understood yet. Presumably, it arises from the different vertical lattice parameters in the 0 and 90° variants. The maximum of the CTR of the 90° domain variant appears at a slightly smaller zQ value than that of the 0° variant. This effect is observed for both the (113)pc and b) and 6c). The apparent peak shift proves a slightly enlarged vertical lattice parameter by about (Δd/d)z = (6 ± 1) × 10−4 for the 90° domain variant compared with the 0° domain variant.Surprisingly, the central CTR also shows local differences in the contrast, although the observed effect is much weaker than for the satellite peaks P1 and P2. This is demonstrated in Fig. 6xx = 0.05% and ∊yy = −1.47% [see also Fig. 1b)], whereas for the 90° variant we obtain ∊yy = 0.07% and ∊xx = −1.49%. However, although the net in-plane strains 〈∊〉 = ∊xx + ∊yy are identical for both domain variants, the elastic strain energy density F(∊) slightly favours the 0° variant . As already stated, this energy difference can be attributed to the elastic anisotropy of the epitaxial layer, likewise leading to different vertical lattice parameters for each domain variant. Indeed, the calculations predict a slightly larger vertical lattice parameter for the 90° variant, in agreement with the experimental findings. However, the calculated numerical value of (Δd/d)z = 2.4 × 10−4 is smaller than the experimental value of 6 × 10−4. Nevertheless, our experimental results demonstrate that linear elasticity theory is qualitatively suitable for the correct prediction of the surface unit-cell orientation and consecutive strain field in KxNax1−NbO3 epitaxial layers. In particular, our calculationsWe can interpret the observed vertical lattice parameter difference by a simple model: for the 0° variant the corresponding in-plane lattice strains are given by ∊nt Fig. 1a. As alpc surface orientation of the pseudocubic unit cell;(i) indicate a preferential (001)(ii) predict that the 90° domain variant appears with lower probability than the 0° variant; and(iii) show a larger vertical lattice parameter in the 90° variant than in the 0° variant.TC. This has been theoretically demonstrated, for example, for SrTiO3  strain has a strong impact on the Curie temperature 5.0.75Na0.25NbO3 epitaxial layer using a focused monochromatic X-ray beam. With this technique, we were able to investigate the highly periodic one-dimensional ferroelectric domain pattern with a spatial resolution of about 100 nm. Besides the dominant 0° domain variant, where the domains are aligned along the 3 substrate, we were able to identify the 90° domain variant, which is aligned in the orthogonal lateral 0.75Na0.25NbO3 epitaxial layer and can be qualitatively confirmed by calculations based on linear elasticity theory.In summary, we have presented a scanning X-ray diffraction study of an anisotropically strained K


Music and speech exhibit striking similarities in the communication of emotions in the acoustic domain, in such a way that the communication of specific emotions is achieved, at least to a certain extent, by means of shared acoustic patterns. From an Affective Sciences points of view, determining the degree of overlap between both domains is fundamental to understand the shared mechanisms underlying such phenomenon. From a Machine learning perspective, the overlap between acoustic codes for emotional expression in music and speech opens new possibilities to enlarge the amount of data available to develop music and speech emotion recognition systems. In this article, we investigate time-continuous predictions of emotion  in music and speech, and the Transfer Learning between these domains. We establish a comparative framework including intra-  and cross-domain experiments . In the cross-domain context, we evaluated two strategies—the direct transfer between domains, and the contribution of Transfer Learning techniques (feature-representation-transfer based on Denoising Auto Encoders) for reducing the gap in the feature space distributions. Our results demonstrate an excellent cross-domain generalisation performance with and without feature representation transfer in both directions. In the case of music, cross-domain approaches outperformed intra-domain models for Valence estimation, whereas for Speech intra-domain models achieve the best performance. This is the first demonstration of shared acoustic codes for emotional expression in music and speech in the time-continuous domain. It is common knowledge that music has the remarkable capacity to stir human emotions and affect our moods in everyday life. Although this is an intuitive fact for most people, understanding the process of emotion induction through music proved to be a major challenge to research in various disciplines. It is not until the past few decades that a consistent body of evidence started unveiling key aspects of such a pervasive phenomenon and shedding new light on the underlying mechanisms supporting the link between music perception and emotion production see , 2). It . It 2]).The fact that the most consistent relationships between musical structure and emotional qualities involve basic variables in human audition rather than complex, music-specific cues, indicates that the way emotions are conveyed through acoustic patterns embedded in music is remarkably similar to the expressive patterns characteristic of speech prosody . In a meta-analysis that reviews 104 studies of vocal expression and 41 studies of music performance and compared the acoustic characteristics of speech and music associated with particular emotions , the autIn this article, we propose to investigate the level of overlap between the acoustic cues to emotion in music and speech. From an Affective Sciences perspective, it is important to explore the level of overlap between the acoustic cues to emotion in music and speech, because it can help explain the reasons why listeners perceive music as expressive of emotion  emotions perceived in music and natural speech was conducted by . The autThe first direct comparison between both domains , albeit in the categorical domain, was presented by . The autIn the context of this paper, our aims are two-fold. First, we want to explore the extent to which time-continuous affective-acoustic patterns are generalisable across music and speech, and to predict emotion in music using models trained on emotional speech, and vice-versa. Second, we want to explore the use of knowledge transfer techniques to deal with differences in the feature spaces and distributions of both types of stimuli.When modelling emotion it is fundamental to define the conceptual model used for its numerical representation. Early work on music emotion recognition focused on classifying music pieces into categorical labels describing specific emotions . The typA framework that has proved to be particularly adequate to address these issues are the so-called dimensional models of affects. According to dimensional theorists, the subjective experience of emotion can be depicted by the combination of two or more underlying psychological ‘dimensions’, and therefore that human emotions can be represented as positions in a multidimensional space . A very popular dimensional taxonomy in this domain is the circumplex model of affect proposed by Russell , which cFrom the machine learning point of view, time-continuous predictions of continuous dimensions in a two-dimensional space largely benefit from context-sensitive models. Given their proven ability to model time-continuous emotions in both music  and speW as the weight matrix, xt the input vector, ht the hidden vector, bt the hidden bias vector, the activation vector of the input gate it can be expressed as follows:fg is the logistic sigmoid function.ft is expressed as:Similarly, the activation of the forget gate ct is then the sum of the input vector at time t and its own activation in the previous time step:fi is the tanh activation function.The memory cell value The output of the memory cell is controlled by the output gate activation:fo is the tanh activation function.Finally, the output of the memory block is:Generally, LSTM-RNNs have shown remarkable performance in a variety of Machine Learning tasks, including, handwriting recognition , keywordComParE). This feature set is already a standard in SER , which cover a broad set of descriptors from the fields of speech processing, Music Information Retrieval, and general sound analysis. For the computation of all LLDs, we used overlapping windows with a step size of 10 ms. LLDs related to voice were computed using 60 ms long time frames and Gaussian windows (σ = 0.4), whereas LLDs related to all other features were calculated using 25 ms long time frames and Hamming window functions . In order to adapt the sampling rate of the feature set to that of the annotations, we also computed the mean and standard deviation functionals of each feature over 2 s time windows with 50% overlap (step size of 1.0 s). This resulted in a total of 260 features extracted at a rate of 1 Hz. The computation of these features was done using the open-source feature extractor openSMILE  and andComPaR e.g., , 33). Th. ThComPaenSMILE . For furefer to .The concept of Transfer Learning (TL) is inspired by the fact that humans can use previously acquired knowledge in one domain to solve new problems, faster and efficiently, in new domains. In the realm of Machine Learning, TL has been proposed to deal with the problematic situation of having training and test samples derived from different feature spaces and data distributions. In this scenario, most statistical models need to be retrained with new data that matches the new distributions, which is an expensive process and often unrealistic. As an example, in the SER field, a common limitation of models trained on a specific speech corpus is the tendency to under-perform with recordings from other sources. This can be due to various reasons, but often is related to the characteristics of the speakers in each corpus, the type of emotions being conveyed, the level of portrayal vs spontaneity, the recording conditions, among others (see ). In poitransductive transfer learning (tTL) setting  in order to obtain a more robust performance estimation. Training was stopped once there was no improvement after 20 consecutive learning iterations, or a maximum of 300 iterations was reached. The architecture implemented for each DAE consisted of an input layer composed of 260 units with sigmoidal activation functions, one hidden layer composed of a variable number of LSTM blocks (#HBlayer1), and one output layer composed of 260 units also with sigmoidal activation functions.The first step was to pre-train the first layer of the network using a DAE framework. The DAE takes as input time-continuous feature vectors of acoustic descriptors, and attempt to reproduce these time series at the output units from corrupted version of the inputs. In this paper, we inject Gaussian noise with zero mean and variable standard deviation , we trained different DAEs with a variable number of hidden layer sizes  and different noise parameters. Each combination of #HBlayer1 and σ will later be used tested in the regression experiments , we computed 10 independent trials of the models with randomised initial weights in the range ).In order to understand how the transfer learning process is affected by the size of the hidden layer  and the lrdae). This optimisation was done in two steps. First, for each value of #HBlayer1, we trained the model with different learning rates  (again this process was repeated 10 times for each #HBlayer1/lrdae pair). The optimal lrdae for each #HBlayer1 was determined by selecting the lowest average reconstruction error (computed using the rmse) over the 10 trials. Second, for each #HBlayer1 (using the optimised lrdae for each layer size), we varied the characteristics of the noise injected at the inputs  . As a result, we created 10 different DAEs (each with an optimised learning rate) that correspond to 30 different initialisation of the first layer to be used in the regression tests .The only parameter optimised in the pre-training phase was the learning rate , cross-domain (CD), and cross-domain with TL (CDTL):ID experiments correspond to the traditional approaches—separate models were created for each domain (music and speech) using the extracted acoustic features. During training, for each domain, we used a leave-one-out nested cross-validation schema (Nmusic = 33 and Nspeech = 23). In each fold, N − 2 instances are used for training (training set), one is used for parameter optimisation , and another instance is left out for testing (test set). The best parameter set is determined as the average performance over the left-out instances of each fold. Once the best model was found, its performance to unseen data was determined as the average performance over the test instances of each fold.CD experiments focused on developing the model with data from one domain (music or speech) and directly tested on the other domain . In the model development and optimisation phase, we used a leave-one-out cross-validation strategy. The performance for each condition was estimated as the average performance over the left-out instances of each training fold. The optimised model was then tested on cross-domain data .CDTL experiments were also focused on creating the models with data from the source domain (music or speech) and test them on the target domain . Again, we used a leave-one-out cross-validation strategy in the development phase using the source domain database, and the model performance was estimated on the database of the target domain. The key difference between CDTL and CD is that the first layer was not randomly initialised. Instead, we initialised the first layer with the weights and biases matrix of the DAEs described in the previous sections. Tests were conducted with each  pair.We used a multi-task learning framework for the joint learning of Arousal and Valence time-continuous values. In all tests, the network’s basic architecture consisted of 260 inputs (the number of acoustic features), two hidden layers (with LSTM memory blocks) of variable size, and two output units with linear activation functions . In all cases, mirroring the pre-training phase, we computed ten trials (repetitions) in each test condition, each with randomised initial weights in the range . We conducted three separate blocks of experiments—intra-domain (Each sequence (music or speech instance) was presented randomly to the model during training, and both the input and output data were standardised to the mean zero and unit variance .e−6 . The size of the first hidden layer was varied between 50 and 200 neurons . As noted, in the case of the CDTL experiments, we initialised the weight and biases matrices of the first layer with the parameters of the previously trained DAEs (note that the sizes of the first hidden layer are the same). In practice, this was done by removing the output layer of the previously trained DAEs, and adding an extra hidden layer and a new output layer . These weights were kept fixed while training the models on the target task. Given that we trained 6 DAEs with the same number of hidden units (due to the various σDAE used), unlike the ID and CD experiments, we conducted 6 tests (instead of one) per hidden layer in order to test the impact of the σDAE value used in the pre-training phase.For all models, we optimised the number of hidden LSTM blocks in each layer using a learning rate of 1HBlayer1 size (or #HBlayer1/σDAE pair in the case of the CDTL experiments) yielding the best performance on the target tasks, we optimised the size of the second hidden layer on the target task (#HBlayer2 ∈ , corresponds to a maximum normalised rmse of approximately 11% (0.223/2), and therefore also an excellent precision. The results concerning Valence are clearly less positive, which is not surprising given the fact that Valence is harder to be perceived by people and automatically estimated , but not so accentuated in terms of precision . Generally, these results indicate a good fit the data in both domains, and confirm our expectations in terms of modelling emotional responses to music and speech using acoustic descriptors (in this case the ComPaRe 2013 feature set).Before describing the results pertaining to the focus of this article , it is important to make some considerations about the quality of the predictions. As it can be observed in nstance, , 21). ThM2M, S2M and S2MTL).We focus now on the statistical analysis of the experiments where Music was used as the target domain, i.e., ccc and r . In terms of precision, the performance of the cross-domain models was slightly worse than the model only trained on music  = −3.96, p = 0.004, t = −4.12, p = 0.003).In relation to Arousal, results show that all conditions achieved statistically the same performance in terms of ccc values showed that the performances of S2M and S2MTL were significantly higher than the baseline model  = −3.84, p = 0.005 and t = −2.86, p = 0.021). Furthermore, the S2M model performed better than S2MTL  = 2.36, p = 0.046), and therefore yielded the best performance. There were no differences in terms of precision, but the S2M lead to statistically better results than the M2M model in terms of r  = −3.51, p = 0.008).Regarding Valence, the two cross-modal models performed generally better than the baseline model. Indeed, the statistical analysis of the S2M model delivered the best results for Valence. In Figs M, S2M and S2MTL). We also represent the respective golden standards.In sum, the models trained on speech (with or without the pre-trained layer) delivered statistically the same performance of the baseline model trained and tested on music for Arousal, and the ccc than the cross-domain models  = 31.2, p < 0.001, t = 14.7, p < 0.001). This was also the case both in terms of r  = 7.21, p < 0.001; t = 9.59, p < 0.001), and rmse  = −9.54, p < 0.001; t = −10.7, p < 0.001). There were no statistically significant differences between both cross-domain models (p > 0.05). The results pertaining Valence are identical. The intra-domain model S performed globally better than the cross-domain models  = 24.2, p < 0.001, t = 15.9, p < 0.001), and there were no significant differences between the cross-domain models M2S and M2STL. The differences in the global performance are related to a decreased performance in terms of r for both cross-domain models  = 15.1, p < 0.001, t = 16.3, p < 0.001), and a decrease in terms of precision  = −2.43, p = 0.041; t = −5.28, p = 0.001). In Figs S, M2S and M2STL)) against the respective golden standards for two speech samples (once more the two instances selected do not necessarily represent the best or worst instances—they are used for illustrative purposes only).We turn now to the experiments where Speech was the target domain. Looking first at Arousal, the baseline model yielded higher In this article, we focused on time-continuous predictions of emotion in music and speech, and investigating the overlap between these two tasks. Indeed, as discussed at the outset, given the tight relationships between the acoustic codes for emotional expression in music and speech, it is plausible to assume that, the communication of emotion in both domains may be achieved by means of shared acoustic patterns. In this context, we proposed to transfer knowledge from one domain to the other with the aims of exploring the extent of the overlap between domains, and testing whether it is possible to enlarge the amount of data available to develop MER and SER systems by combining both domains. In our experiments, we established a comparative framework including intra-  and cross-domain experiments . The intra-domain models were used as a baseline. In the cross-domain context, we evaluated two strategies—the direct transfer of knowledge between domains, and the contribution of Transfer Learning techniques, namely feature-representation-transfer based on Denoising Auto Encoders.There are various relevant conclusions and observations deriving from our results and analyses. Starting with overall performances per domain, we found that the speech regression tasks delivered a much better performance compared to the music task for both affective dimensions . Furthermore, as expected from most previous work on Affective Sciences and Affective Computing, Arousal predictions were more accurate than those of Valence for both domains. The fact that we obtained a better performance for the speech compared to music may simply be the consequence of the datasets used, nonetheless there are at least two other possible interpretations. First, predicting emotion in music may be a more challenging task . This is plausible to assume for various reasons, including the fact that emotion recognition in music is more stereotyped and listener dependant , and thM2M, S2M and S2MTL) were similar, although the cross-domain models were slightly worse in terms of precision. The cross-modal models (S2M and S2MTL) performed significantly better than the baseline model (M2M) when predicting Valence, and there were no significant differences between both. In relation to Speech as the target domain, for both Arousal and Valence predictions, the intra-domain models (S2S) performed significantly better than the cross-domain models (M2S and M2STL), which achieved statistically the same performance. These results evidence two important aspects relating to the transfer of knowledge between domains:Knowledge transfer (with or without domain adaptation) from speech to music was more successful than in the opposite direction. Indeed, emotions in music could be predicted with models trained on speech or music equally, whereas the speech model trained on speech data outperformed those trained on music.Knowledge transfer approaches with and without domain adaptation lead to the statistically same results for both Music and Speech.We turn now to our considerations concerned with the central focus of this paper—the comparison between intra- and cross-domain experiments. In the of case of Music as the target domain, we found that the Arousal predictions in all three conditions (musilanguage model [In relation to (1), our results can simply reflect the nature of the datasets, and particularly the distributions of targets for each database. This is evident in ge model , which pge model . Such brRegarding (2), our findings reveal two important pieces of evidence: a) the standard cross-domain approach led to excellent cross-domain generalisations; b) knowledge transfer by feature-representation-learning did not lead to significant improvements over the standard cross-domain approach. One the one hand these findings indicate that the cross-domain models perform well even without adaptation of the feature space, reinforcing the ideas of a close coupling between both domain in terms of acoustic codes for emotional communication. On the other hand, considering that we have used deep neural networks in both cross-domain experiments (with the same number of hidden layers), it may indicate that, the pre-training process did not extract additional, relevant statistical information from the features spaces of the source and target domains. In this case, larger, more varied unlabelled databases may be needed to sample a sufficient amount of information from the features in both domains. Also, deeper architectures may favour the extraction of relevant representations at different structural levels . This is a clear demonstration that there is a substantial overlap between the acoustic codes for emotional expression in music and speech, which can help explain the power of music to communicate emotions to its listeners by confirming the existence of shared acoustic codes for emotional communication. These findings are also beneficial for computer scientists interested in automatic estimation of emotion due to the fact that data from either domain (music or speech) can be used to develop models emotion recognition models. This is particularly relevant in contexts where the amount of data in either domain is scarce, which is clearly the case of music, but also in contexts where the whole emotional spectrum is not represented by a particular dataset and can be complement by data from another domain (the emotions typically experienced with music are a different subset from those most commonly experiences in everyday life circumstances; see for instance , 29. Fin


Oreochromis niloticus) flour were investigated. Five formulations were prepared with different concentrations of tilapia flour as partial substitute of wheat flour: pasta without tilapia flour (PTF0%), pasta with 6% (PTF6%), 12% (PTF12%), 17% (PTF17%), and 23% (PTF23%) of tilapia flour. The formulations were assessed for proximate composition, fatty acid and amino acid profile on day 1 whereas, instrumental color parameters , pH, water activity (aw), and lipid and protein oxidation were evaluated on days 1, 7, 14, and 21 of storage at 25°C. Fortification with tilapia flour increased (p < 0.05) protein, lipid, ash, total essential amino acids, and total polyunsaturated fatty acids contents. In addition, supplementation of pasta with tilapia flour decreased (p < 0.05) lightness and water activity while redness, yellowness, pH values, and lipid oxidation were increased (p < 0.05) in a level-dependent manner. Nevertheless, all formulations were exhibited storage stability at 25°C. In general, protein oxidation was greater (p < 0.05) in the pasta containing 12%, 17%, and 23% of tilapia flour than their counterparts, and the storage promoted an increase (p < 0.05) on the carbonyl content in all formulations. Thus, pasta with 6% of tilapia flour has the potential to be a technological alternative to food industry for the nutritional enrichment of traditional pasta with negligible negative effects on the chemical stability of the final product during 21 days at 25°C.Physicochemical parameters of pasta enriched with tilapia ( The global pasta consumption is expanding in recent years with reports documenting an increase of 1 million ton in 2013 in relation to 2012 ), it is Oreochromis niloticus) contributes with approximately 9% of the total amount of fish produced [Fish flour is a by-product from fish processing and represent a cheap source of high-quality nutrients for the human diet, mainly due to high levels of essential amino acids and polyunsaturated fatty acids ,9. Furthproduced  however,produced ,13. On tSardinella longiceps [Catla Catla mince [Previous studies observed the nutritional and physicochemical aspects of dried pasta manufactured with the mince and the oil of ongiceps , Catla Cla mince , and frela mince . Howeverla mince ,17,18, tA total of 9.0 ± 0.2 kg of mechanically separated meat (MSM) of tilapia was purchased from a commercial fish farm  located in Rio de Janeiro, Brazil (22°33'58.3"S 42°41'48.2"W). The MSM was packed in low density polyethylene bags, frozen at –80°C, and transported during 3 h in a styrofoam box containing ice to the bakery pilot plant of the Department of Food Engineering, Universidade Federal de Goiás, Goiânia, Brazil. The tilapia flour was obtained by drying the MSM for 12 hours at 65°C in a forced-air convection oven , and tilapia flour was obtained.Triticum aestivum L.) and whole powdered egg were purchased from a local market in Goiânia, Brazil. All ingredients were manually mixed and the dough formed was drawn between rolls of a pasta machine with a gap of 4 mm , and cut into 260 mm length and 5 mm width. Then, fresh pasta was dried in a forced-air convection oven  at 45°C for 2 hours.Five pasta formulations (PF) were prepared using different wheat flour (WF) and tilapia flour (TF) levels (w/w) as following: 58 g of WF, 52 g of WF plus 6 g of TF, 46 g of WF plus 12 g of TF, 41 g of WT plus 17 g of TF, and 35 g of WF plus 23 g of TF, representing 0%, 10.34%, 20.68%, 29.31%, and 39.65% of WF replacement by TF, respectively. All formulations were optimized to 37% of added water and 5% of whole powdered egg before the drying step  and refeL*, a*, and b* values), water activity (aw), pH, and lipid and protein oxidation were analyzed on days 1, 7, 14 and 21 of storage at 25°C. Prior to all analysis, samples were homogenized in a commercial blender  in order to standardization of the samples. The whole experiment, consisting of tilapia MSM and pasta manufacture followed by physicochemical analyses, was repeated three times (n = 3).All formulations were aerobically packaged and transported (< 3 h) to the Department of Food Technology, Universidade Federal Fluminense, Rio de Janeiro, Brazil. Samples were analyzed for proximate composition, fatty acids profile, and amino acid content on day 1 whereas, instrumental color parameters  for 10 min at 25°C. After derivatization, an aliquot of 300 μL of water was added, and then 20 μL was injected into the high performance liquid chromatograph  equipped with a Phenomenex Gemini 5 μm ODS (250 mm × 3.0 mm i.d.) column. Amino acids were separated at 33°C with an A:B gradient wherein A was methanol and B was 50 mM triethylammonium phosphate buffer (pH 2.5) at a flow-rate of 0.32 mL/min. The gradient profile was set as follows: t0min 8% A, t10min 32% A, t25min 50% A, and t30min 8% A. The amino acids were detected utilizing a diode array detector at 320 nm, and quantified through calibration curves of each amino acid analyzed. The amino acids analysis was performed in duplicate.The protein hydrolysis was performed at day 1 according to protocol described by Chlou and Wang  and MarcL* (lightness) a* (redness) b* (yellowness) values were recorded from four distinct sampling areas from the air-exposed surface utilizing illuminant A, 8 mm aperture, and 10° observer at 25°C. Prior to color measurements, the spectrophotometer was calibrated following standard protocol described in American Meat Science Association [L*, a* and b* values were determined in quadruplicate.The instrumental color parameters were determined using a Minolta CM-600D Spectrophotometer . An aliquot of 25 g was placed into a polystyrene petri dishes (60 mm × 15 mm), and subsequently, CIE ociation . L*, a* w value was directly recorded at 25°C in a Pawkit water activity meter  according to manufacturer guidelines. The pH values were determined after homogenizing 10 g of sample in 90 mL of distilled water using a penetration electrode of a digital pH meter  [w and pH were determined in triplicate and quadruplicate, respectively on each storage day analyzed.Five grams of sample were transferred to a disposable sample cup  and the aet, USA) . Prior tLipid oxidation was evaluated by the thiobarbituric acid-reactive substances (TBARS) method proposed by Yin et al. , whereasFor the TBARS measurement, an aliquot of 5 g of sample was homogenized with 22.5 mL of 11% trichloroacetic acid for 1 min in a T18 basic Ultra-Turrax at 11,000 rpm  followed by 1 min in an ice bath, and another 1 min of homogenization at equal conditions. The homogenate was filtered using Whatman paper N°1, and 1 mL of 20 mM TBA was added to 1 mL of the filtrate following incubation in dark conditions for 20 h at 25°C. The absorbance value at 532 nm was read using a UV-1800 spectrophotometer  and the results were expressed as TBARS number.g for 5 min at 4°C. After discarding the supernatant 10 mM 2,4-dinitrophenylhydrazine (DNPH) in 2 N HCl was added to the precipitate and incubated for 1 h at 25°C in the dark with brief vortexing every 15 min. Then the DNPH-reacted substrate was precipitated with 10% TCA following centrifugation at 11,000 × g for 10 min at 4°C. The precipitate was washed three times with 1:1 (v/v) ethanol/ethyl acetate solution and then solubilized with 6M guanidine hydrochloride in 20 mM sodium phosphate buffer (pH 6.5). A final centrifugation at 11,000 × g for 10 min at 25°C was employed to remove insoluble particles. The absorbance value was measured at 370 nm using a UV-1800 spectrophotometer , and carbonyls concentration was calculated using an absorptivity coefficient for the protein hydrazones of 21.0 × mM-1 × cm-1 for the protein hydrazones. The results were expressed as nmol of carbonyl/mg of protein. For protein content measurement, the aforementioned steps were performed with 2N HCl instead of the DNPH solution, and the absorbance value at 280 nm was recorded from the homogenate solubilized in 6M guanidine hydrochloride. The results were expressed as nmol of carbonyl/mg of protein.For determination of carbonyl content, 3 g of sample was homogenized with 0.15 M KCl (pH 7.4) using a T18 basic Ultra-Turrax at 7,100 rpm  for 90 s. The proteins present in the homogenate were precipitated with 10% TCA, and centrifuged  at 5,000 × p < 0.05). Data of proximate composition were also analyzed by linear regression. These analyses were carried out utilizing XLSTAT software, version 2012.6.08 . In addition, the data of instrumental color parameters , pH, aw, lipid and protein oxidations were plotted using SigmaPlot software, version 11 . The whole experiment comprising the manufacture of tilapia flour and pasta formulations, and physicochemical analyses were repeated three times (n = 3).The influence of partial replacement of wheat flour by tilapia flour  and storage period  at 25°C were separately analyzed using one-way ANOVA followed by Tukey test (p < 0.05) on the proximate composition of the pasta formulations (p < 0.05) moisture and carbohydrate contents whereas increased (p < 0.05) the levels of lipid, protein, and ash, potentially due to the tilapia flour compositions. While tilapia flour exhibits low carbohydrate level (< 1.5%) and increased protein (> 45%), lipid (> 25%) and ash (> 3%) contents [The addition of tilapia flour demonstrated a level-dependent effect  energy value. No difference (p > 0.05) was observed between PTF17% and PTF12%, as well as between PTF6% and PTF0%. In addition, PTF17% and PTF12% presented greater (p < 0.05) energy value than PTF6% and PTF0%. Our findings of energy value can be explained by the changes on the lipid, protein, and carbohydrate contents promoted by tilapia flour addition in association to their respective individual weights in the formula proposed by Merrill and Watt [p < 0.05) energy value than control, fish by-products exhibit high quality of nutrients such as polyunsaturated fatty acids and essential amino acids [p < 0.05) the contents of protein, lipid, ash contents and the energy value, whereas it decreased (p < 0.05) moisture and carbohydrate level. Furthermore, carbohydrate and protein were the compounds affected in greatest extent  by the tilapia flour inclusion.Pasta formulation with 23% of tilapia flour (PTF23%) exhibited the greatest  amount of pentadecylic (C15:0), palmitic (C16:0), stearic (C18:0) and heneicosylic acids (C21:0) whereas, lower (p < 0.05) myristic acid (C14:0) level than pasta made without tilapia flour. Nonetheless, no difference (p > 0.05) was observed in total SFA amongst all formulations. With regards to monounsatured fatty acids (MUFA) tilapia flour fortification increased (p < 0.05) the contents of palmitoleic (C16:1) and paullinic acids (C20:1), and decreased (p < 0.05) the amount of the heptadecenoic (C17:1) and vaccenic acids (C18:1) resulting on a decrease (p < 0.05) of total MUFA content in tilapia flour-added pastas when compared to control. Nonetheless, all the identified polyunsaturated fatty acids (PUFA) exhibited an increased (p < 0.05) due to tilapia flour addition leading to the greatest (p < 0.05) total PUFA level in PTF23% and PTF17% followed by PTF12%, PTF6%, and the lowest (p < 0.05) value reported in the PTF0% formulation.A total of fifteen fatty acids were identified  in tilapp > 0.05) in total SFA level, and the decrease (p < 0.05) in MUFA contents followed by tilapia flour addition is potentially due to the binding of amylose to fatty acids [Studies evaluating the fatty acid profile of pasta products fortified with fish by-products are limited and there are no reports regarding fatty acid composition in dried pasta enriched with tilapia meat and/or tilapia flour at the present moment. Our findings can be attributed to the difference between fatty acid composition of wheat flour and tilapia flour ,42. Accoty acids ,44 hindety acids  reportedp < 0.05) the levels of essential amino acids (EAA) such as histidine, lysine, threonine, methionine, valine, and leucine, and some non-essential amino acids (NEAA) including arginine, serine, glutamine, glycine, aspartic acid, glutamic acid, alanine, and tyrosine. Amongst the EAA, histidine and lysine levels were increased (p < 0.05) in a level-dependent manner of tilapia flour inclusion. While, the contents of threonine and valine were higher (p < 0.05) in the formulations containing 12%, 17% and 23% of tilapia flour, methionine and leucine levels were increased (p < 0.05) by tilapia flour inclusions greater or equal to 6% and 17%, respectively. No difference (p > 0.05) was observed in the tryptophan and phenylalanine contents. Amongst the NEAA, arginine, serine, glutamine, glycine, and tyrosine levels were increased (p < 0.05) by tilapia flour fortifications greater than 6%; whereas, glutamic acid content was greater (p < 0.05) in PTF17% than in PTF6% and PTF0%. Similarly, aspartic acid and alanine contents exhibited an increase (p < 0.05) due to the addition of tilapia flour. Pasta manufactured with tilapia flour demonstrated greater (p < 0.05) total EAA and NEAA contents than the control counterpart and the increase exhibited a tilapia flour inclusion level-dependent manner (PTF23% > PTF17% > PTF12% > PTF6% > PTF0%).Sixteen amino acids were identified  in the pTinca tinca mince, respectively. On the other hand, these authors also reported an increase on tryptophan and phenylalanine contents due to soybean and T. tinca fortification, which disagrees with our present findings. According to Gaie-Levrel et al. [The amino acid profile of pasta products enriched with protein sources is scarce in the literature and the amino acid composition of dried pasta fortified with tilapia flour is still unknown. Pasta products manufactured with wheat contain a limited amount of essential amino acids representing a potential target for the incorporation of protein sources such as fish by-products  which arl et al. , tryptopL*, a* and b* values are presented in p < 0.05) the L* values (p < 0.05) both a*  was observed in L*, a*, and b* values during the entire storage in all formulations. Pasta stability is directly affected by formulation and the drying procedure which together dictate the water dynamic in the final product [L* values did not change in storage period potentially due to very low water mobility in this type of product [a* values, by reducing the water activity of foodstuff there is a mitigation of Maillard reaction [w values bellow 0.64 [w values lower than 0.6.No difference  whereas the aw (p < 0.05) as the level of tilapia flour increased in pasta formulations. There are no reports regarding pH and aw in pasta formulations enriched with protein and lipid sources. Wheat flour exhibits pH values around 6.01 [w in fish pasta formulations compared to control counterparts can be attributed to the capacity of protein to bind water molecules [The pH value  increases the aw  decreaseund 6.01 , while tund 6.01  thus, tholecules  and to tolecules ,39. Moreolecules ,35, whicolecules .p < 0.05) from day 7 of storage until the end of the storage period in all formulations. A decrease in the pH values during storage can be attributed to the formation of organic acids [p < 0.05) was found in aw of pasta formulations due to stored during the 21 days at 25°C potentially due to the low water mobility in pasta products [Furthermore, the pH values decreased (ic acids . Similaric acids  observedproducts . In partproducts  observedp < 0.05) on TBARS values in a tilapia flour inclusion level-dependent manner (The replacement of wheat flour by tilapia flour promoted an increase (t manner . Fish cop < 0.05) carbonyl content (p > 0.05) was observed between PTF17% and PTF12%, and between PTF12% and PTF6%. Whereas, PTF17% exhibited greater (p < 0.05) carbonyl content than PTF6%. Although carbonyl content was not affected (p > 0.05) by the inclusion of tilapia flour at 6%, at levels greater or equal to 12% it was observed an increased (p < 0.05) on protein oxidation in comparison to control after the 1st day of storage. Nonetheless, there was a trend of greater carbonyl content following a inclusion level-dependent manner of tilapia flour on each storage point. Fish by-products exhibit a potential alternative to increase the protein levels in wheat-based products which are usually limited in such nutrient [In terms of protein oxidation, while PTF23% demonstrated the greatest  in all formulations  by the storage period (w values with a lower reaction rate in the aw range of 0.3–0.5, which includes most of the formulation investigated in the present study. Another potential explanation for the lipid oxidative stability during storage is related to the unavailability of lipid molecules due to their binding with proteins, carbohydrates or both compounds during extrusion preventing oxidation [Lipid oxidation was not influenced (e period . Lipase xidation . In agrexidation  observedxidation  also repThe replacement of wheat flour by tilapia flour increased the nutritional quality of dried pasta including greater protein and lipid contents containing high quality essential fatty acid and amino acid profiles. Although some formulations of tilapia flour-enriched pasta exhibited an increase in the values of redness, yellowness, lipid oxidation and protein oxidation, the substitution of wheat flour by tilapia flour up to 6% can be considered a suitable strategy to improve the nutritional aspects of dried pasta without negatively affecting the storage quality of this product during at least 21 days at 25°C. Such strategy represents an alternative to the food industry to satisfy marketing trends and consumption patterns.