Heliomicrobium modesticaldum has been used as a model organism for the Heliobacteria, the only phototrophic family in the Firmicutes. It is a moderately thermophilic anoxygenic phototrophic bacterium that is capable of fermentative growth in the dark. The genetic manipulation of H. modesticaldum is still in its infancy. Methods to introduce genes through the use of exogenous plasmids and to delete genes from the chromosome through the use of the native CRISPR/Cas system have been developed in the last several years. To expand our genetic toolkit, it was necessary to control gene expression. In this study, we analyzed constitutive and inducible promoters developed for clostridia for their use in H. modesticaldum and further tested two reporters, adhB and lacZ, as indicators of promoter strength. Alcohol dehydrogenase (AdhB) was unsuitable as a reporter in this species due to high endogenous activity and/or low activity of the reporter, but a thermostable LacZ worked well as a reporter. A set of constitutive promoters previously reported to work in Clostridium thermocellum was found to be reliable for controlling the expression of the lacZ reporter gene in H. modesticaldum at a range of activities spanning an order of magnitude. An anhydrotetracycline-inducible promoter was created by inserting tetO operators into a strong constitutive promoter, but it was not fully repressible. The implementation of a xylose-inducible promoter resulted in complete repression of β-gal in the absence of xylose, and reliable expression tunable through the concentration of xylose added to the culture. Heliomicrobium (previously Heliobacterium) modesticaldum, this is primarily pyruvate or lactate, but other species can use a greater range of organic molecules [The Heliobacteria are the only phototrophic members of the phylum Firmicutes. Although unable to fix inorganic carbon, they are capable of growing phototrophically (using light as an energy source) via electron transport driven by their photochemical reaction center (RC) and chemiosmotic ATP production, or chemotrophically through fermentation of a suitable carbon source. In the case of olecules ,2,3. Thiolecules ,5,6.H. modesticaldum is a moderate thermophile of the family Heliobacteriaceae and has been used as a model system for this group of organisms. Since its discovery and isolation from Icelandic volcanic soil in 1995 [Escherichia coli [ in 1995 , its gen in 1995 , transcr in 1995 , and methia coli  now allohia coli ,12,13,14H. modesticaldum has displayed stringency when it comes to genetic elements and manipulation. To introduce non-native plasmids, the plasmids must first be pre-methylated at presumed restriction endonuclease sites and then conjugated using an engineered E. coli host; without the methylation, the DNA will be cleaved and destroyed upon insertion into the cells [Streptococcus pyogenes could be adapted for use in other organisms, H. modesticaldum could not tolerate it. Similar to other clostridia [H. modesticaldum.he cells . In addiostridia , the orgostridia . Both ofH. modesticaldum. A set of constitutive C. thermocellum promoters previously analyzed by Olson et al., 2015 [C. thermocellum is a member of the Clostridiales family much like H. modesticaldum, C. thermocellum grows at a similar temperature to H. modesticaldum, the promoters are orthogonal and therefore should not affect the activities of native genes, and these promoters were already previously characterized in C. thermocellum and E. coli. We then utilized several of the constitutive promoters to attempt to construct an anhydrotetracycline inducible promoter. We have also tested a xylose-inducible promoter. In addition, as a result of this study, two reporters, alcohol dehydrogenase and β-galactosidase, were tested in H. modesticaldum for the first time.In this study, we analyzed a series of promoters used in Clostridia for their activity in l., 2015  was chosE. coli strains  were grown in Luria-Bertani (LB) broth . When needed, the antibiotics erythromycin  and chloramphenicol  were added to a final concentration of 100 µg mL−1 and 15 µg mL−1, respectively. Cultures were grown overnight at 37 °C while shaking at 250 rpm.H. modesticaldum was performed in an anaerobic chamber , with an atmosphere of 20% CO2 and 2–4% H2, and balance N2. H. modesticaldum strains were grown in pyruvate yeast extract (PYE) medium following the recipe and procedure outlined previously [−1. Cultures were grown at 50 °C under 790-nm light-emitting diodes (LEDs)  at a flux of 30 µmol photons m−2 s−1.All work utilizing eviously . When neC. thermocellum and thermostable lacZ and adhB reporter genes were gifts from Daniel Olson (Dartmouth College) [All constructs were made using the vector pMTL86251 . PlasmidCollege) . PromoteadhB, lacZ) were PCR amplified individually from the plasmids provided by Daniel Olson  was synthesized by GenScript  with a NotI site downstream of the xylR and a BamHI site downstream of Pxyl and cloned into pMTL86251 at those sites. Once received from Genscript, the lacZ reporter  was cloned into this plasmid at the BamHI and XhoI sites, resulting in pALX3. Constructs were confirmed by Sanger sequencing.The seven promoters  and two reporters  using Genscript’s GenBuilder Cloning Kit. After confirming the tetO sequences through the use of Sanger sequencing, the PtetOgapDH/—lacZ module was amplified from the pUC19 using primers to add BglII (upstream of PtetOgapDH/) and SalI (downstream of lacZ) sites. The tetR gene was amplified from genomic preps of XL1-Blue E. coli cells using primers to add PspOMI (downstream) and EcoRI (upstream) sites, and the eno promoter was amplified using primers to add EcoRI (downstream) and BglII (upstream) sites. All three products  were digested with their respective enzymes and inserted into pMTL86251 at the NotI and XhoI sites using the modified Golden Gate assembly process described earlier.To add the E. coli TOP10 and s17-1 using standard techniques and were further conjugated into H. modesticaldum using the procedure described previously [Plasmids were transformed into eviously .−1) and added to the appropriate final concentration (ranging from 0 to 200 µg L−1) when the cells entered the log phase (OD735 ≈ 0.5). Cells were assayed after induction for 3 h. For some growth tests, aTc was added at the time of inoculation. For all xylose induction tests, cells were grown in PYE media, with xylose (Sigma-Aldrich) added at the time of inoculation.Anhydrotetracycline (aTc) (Sigma-Aldrich) was prepared in ethanol  containing 100 µL of 3 mg mL−1 lysozyme , then incubated at 37 °C for 20 min. Phenyl-methanesulfonyl fluoride (PMSF) (Sigma-Aldrich) was added to 1 mM, and the suspension was sonicated in an Aquasonic sonication bath  for 20 min with minute-long pauses every 5 min. The lysate was centrifuged at 20,000× g for 30 min, and the pellet was discarded. Protein concentration in the extracts was determined using the Pierce BCA protein assay kit . Cell free extracts were obtained following the protocol outlined previously , with a Activity assays were modeled after those described previously . All assIn this assay, 4 mM acetaldehyde and 0.4 mM NADPH were added to extract (50 µg of protein) in 100 mM Tris-HCl buffer (pH 7.6), and the absorbance at 340 nm was read every 7 s over a period of 10 min to detect the rapid loss of NADPH. 2, and 0.66 mg mL−1 ONPG were added to 50 µg of protein in 100 mM sodium phosphate buffer (pH 7.5). The absorbance at 420 nm was immediately read, and then the plate was incubated at 37 °C. Absorbance was read after 30, 60, and 90 min of incubation to calculate a linear rate of activity corresponding to the cleavage of ONPG. The increase in A420 was converted into units (U) by using the extinction coefficient of ONP (ε = 4500 M−1 s−1) and further to calculate the nmols of ONP produced per second of the reaction. To measure β-galactosidase  activity, 50 mM β-mercaptoethanol, 1 mM MgClhttp://www.quansoft.com/, accessed on 20 March 2022) was used to produce all fittings. Growth data were fit to the logistic equation as described . β-gal aH. modesticaldum. Alcohol dehydrogenase and β-galactosidase have been widely used as reporters and have been tested in many bacterial species, including thermophilic Clostridia [H. modesticaldum have been tested in other clostridia, including C. thermocellum [Clostridium perfringens [Clostridium acetobutilicum [To develop a gene expression system, two enzymes were tested for use as reporters in ostridia . The conmocellum , Clostrifringens , and Cloutilicum .adhB  was cloned behind several clostridial promoters that had been previously tested in C. thermocellum [H. modesticaldum and E. coli by measuring alcohol dehydrogenase activity in cellular extracts after the addition of acetaldehyde and NADPH. The reduction of acetaldehyde to ethanol was monitored by the decrease of absorption of NADPH at 340 nm . E. coli transformants harboring the plasmid containing the adhB reporter gene under the control of promoter 0815 had significantly more alcohol dehydrogenase activity (−0.85 mAU340 s−1), an almost two-fold increase from WT expression levels. In contrast, cellular extracts of H. modesticaldum transformants containing plasmids using several different promoters to drive adhB expression exhibited activities that were barely distinguishable from WT . lacZ gene from Geobacillus stearothermophilus [H. modesticaldum, and cell-free extracts were prepared from them. β-gal activity was assayed by the addition of extract to an assay mixture containing o-nitrophenylgalactoside (ONPG); cleavage of ONPG into o-nitrophenol (ONP) and galactose was monitored by an increase in the absorbance of ONP at 420 nm. Cellular extracts from WT H. modesticaldum cells displayed no detectable β-gal activity . In contrast, extracts from two H. modesticaldum transconjugants with promoters driving lacZ expression displayed much higher levels of β-gal activity: 0.6 mAU420 s−1 (gapDH promoter) and 0.3 mAU420 s−1 (2926 promoter)  per mg of protein.The mophilus , which eromoter) B. In allC. thermocellum [lacZ in H. modesticaldum. Measured activities ranged from 0.067 to 0.79 U/mg protein, with the tested promoters forming two distinct groups . Seven promoters from mocellum  were asst groups . Use of H. modesticaldum requires a conjugation step from E. coli, it is helpful to know the activities of the promoters in both bacterial species. Therefore, activities of E. coli Top10 transformants harboring the same plasmids were also measured. Overall, activities resulting from the promoters in the two species displayed a positive correlation (R2 = 0.486), indicating that if expression of lacZ driven by a given promoter is low or high in one species, it will tend to be similarly low or high in the other  [H. modesticaldum is sensitive to Tc [At the start of these studies, there was no prior knowledge of which inducible systems would work in utylicum , involviistance) ,22. The ve to Tc , we expeve to Tc .H. modesticaldum cells in liquid cultures and found that H. modesticaldum can tolerate up to 100 µg L−1 aTc without noticeable effects on growth. In addition, H. modesticaldum cells can tolerate higher levels of aTc (up to 150 ng mL−1) if the aTc is added after the cells have grown to an O.D. of about 0.5  of 200 µg L−1 on agar plates [We characterized the effects of aTc on the growth of untransformed bout 0.5 . These lutylicum ; howeverH. modesticaldum—gapDH—as the starting point for an aTc-inducible system. The gapDH promoter has two annotated −35/−10 regions [tetO sequence tetO1 [tet operator resulted in only a ~7% drop in β-gal expression  directly upstream and in the reverse orientation of the PtetOgapDH/—lacZ module. The expression of TetR driven by the eno promoter resulted in a ~6-fold reduction of β-gal activity, indicating that it was functioning as a repressor  within the promoter region, which will be bound by the repressor XylR in the absence of xylose. Once xylose is introduced, it will bind to the XylR, which will dissociate from the operator, allowing for the transcription of the downstream gene of interest [H. modesticaldum. At xylose concentrations below 0.35%, there was no discernible inhibition of growth, but at concentrations of 0.5% and higher, growth was noticeably inhibited  was cloned in front of lacZ to make plasmid pALX3. H. modesticaldum transconjugants harboring pALX3 were grown with a range of xylose concentrations, and cellular extracts were prepared from log-phase cells. We found a strikingly linear correlation (R2 = 0.944) between xylose concentration and β-gal activity between 0.03 and 0.3% xylose . Therefore, the lack of detectable inducible alcohol dehydrogenase activity in H. modesticaldum suggests that the exogenous AdhB is inactive or very weakly active in this context. For either of these reasons—the presence of endogenous activity or the lack of detectable exogenous activity—adhB is useless as a reporter gene for H. modesticaldum.From the alcohol dehydrogenase activities assays, we see that H. modesticaldum. There is negligible endogenous β-gal activity displayed by WT or empty-vector-transformed H. modesticaldum samples, indicating that all measured β-gal activity is a result of the promoter driving gene expression. However, we did find evidence that β-galactosidase expression had a negative effect on growth when expressed at high levels . While β-gal is not a perfect reporter, since it does trigger these negative growth effects, it is the one reporter we were able to find during the duration of this study that provided clear, measurable differences in H. modesticaldum conjugants. Unfortunately, we are unable to use other more common fluorescent reporters, such as GFP , due to quenching by the endogenous pigments. We will continue to analyze other reporters for their use in H. modesticaldum, focusing on those that could be used for real-time in vivo monitoring.In contrast, the thermostable β-galactosidase reporter was easily measured and could be used to indicate differences in promoter strength in H. modesticaldum. The promoters covered a range of strengths, making up two distinct groups—high and low strength—while also displaying many differences amongst key sequence elements. If one focuses on the ribosome binding sites, several strong promoters  share a similar sequence (AGGAGG). However, a weak promoter (2638) shares the same sequence, while a strong promoter (eno) has a truncated sequence (GGAG). Looking at the promoter sequences themselves does not provide insight into predictable markers of strength. The predicted ribosome binding sites, −35 regions, and −10 boxes vary amongst all of the promoters and do not exhibit discernible patterns amongst the weak or the strong promoters.Using β-galactosidase as a reporter, we were able to determine the relative activity of a set of clostridial promoters in E. coli, the promoters resulted in higher β-gal specific activity in H. modesticaldum. While H. modesticaldum and E. coli both have very similar ribosome binding sites, sharing AGGAGG, there must be more elements in the actual sequence of the promoter that dictate strength. Studies have shown that some clostridia are sensitive to the full sequence of the promoter, not just the −35 and −10 boxes, displaying drastic decreases in promoter strength when these sequences are altered [H. modesticaldum is more closely related to C. thermocellum, the source of these promoters, than E. coli is, this may explain the higher activities in H. modesticaldum compared to E. coli. In addition, this sensitivity to the full promoter sequence may also explain why the activities of the promoters in H. modesticaldum are still different from those reported in C. thermocellum [In comparison to  altered . Becausemocellum . H. modesticaldum based on the anhydrotetracycline-inducible system developed and used for C. acetobutylicum [gapDH) was used to construct the inducible promoter, assuming that insertion of tetO1 and any additional alterations would not strongly affect the promoter’s strength. The insertion of the tetO sequences into both -35/-10 boxes of gapDH only resulted in a 7% decrease in the correlated β-gal activity. We attempted to construct an inducible promoter for utylicum . To achitetR gene needed to be to guarantee repression. Our initial study using a truncated version of the native Clostridium perfringens thiolase promoter (Pminipthl) [tetR suggested that it was too strong for the system, as the PtetOgapDH/ promoter was strongly repressed but repression could not be relieved by aTc concentrations under which cells could grow (eno). However, even when driving TetR expression from Peno, there was still residual PtetOgapDH/ promoter activity in the absence of aTc . While alleviation of the repression was strong, resulting in almost complete restoration of the activity of the promoter to that of the unmodified PgapDH, the breakthrough activity of this promoter renders it suboptimal for applications in which repression must be very tight . This system would be good, however, for high-level expression of proteins whose constitutive presence can be tolerated by H. modesticaldum.It was unknown how active the promoter in front of the inipthl)  to contruld grow . TherefoC. perfringens is simple and was easily introduced into H. modesticaldum. The xylP promoter was inducible under increasing xylose concentrations from 0.03% to about 0.3%, and H. modesticaldum was shown to tolerate these concentrations of xylose without affecting their growth. The fact that this species does not metabolize xylose should ensure a constant inducer concentration. Thus, the xylP promoter was inducible and highly tunable in the heliobacterial system. While the activity of the fully induced xylP promoter is not the strongest, it should be useful for precise control of long-term expression of a gene of interest. The lack of detectable background activity in the absence of xylose makes this a promising system for the introduction of potentially toxic proteins, or for the complementation of a null mutation in an essential gene.Compared to the aTc system, the xylose-inducible promoter adapted from


ObjectiveTo compare the role of paper-based versus digital record keeping in the orthopaedic ward in terms of staff satisfaction, education of staff, and adherence to British Orthopaedic Association (BOA) guidelines.Materials and methodsForty-four participants including nurses, senior house officers, foundation year trainees, and consultants completed a questionnaire. The first survey was done to introduce electronic records keeping to the participants and the second survey was conducted to review the collected record. Three parameters were assessed, which were adherence to BOA guidelines, staff satisfaction, and effect of education for both paper-based and electronic records. Comparison between two methods of record keeping was done by independent t-test for continuous data and chi-square test for categorical. ResultsFor all four questions about staff satisfaction, the score of the electronic method was higher than paperwork statistically. The score for ‘opportunity to learn images in ward round’ was higher in electronic (3.9±0.8) than paperwork (2.6±1.3) statistically (p<0.001). Comparable results were found for ‘educational usefulness of ward round’ and ‘typing time affecting learning time’. For adherence to guidelines, the electronic record keeping was more effective in storing the patient’s ID and name (p=0.05), details of documenting clinician (p<0.001), time of ward round ((p=0.005), whom to contact in case of concern (p=0.050), and grade of ward round clinician (<0.001).ConclusionElectronic records in the orthopaedic ward were deemed better than paperwork in terms of staff satisfaction, positive effect on the education of doctors, and adherence to BOA guidelines. Proper documentation in patient records is essential for the patient and clinician . These rTraditionally, health records were collected on paper, stored in folders categorized into sections based on the type of note, and a single set was available . Due to Electronic records are associated with job satisfaction among health workers and physicians due to reproducibility, easy handling, ability to share with others, no space issues, and easy manipulation ,7. In moThe BOA guidelines were introduced by The Royal College of Physicians (RCP), London, United Kingdom, for better patient care based on evidence-based research and updated with time. The clinical audit usually involves how much the existing practice in an institute or private practice follows the BOA guidelines ,10.The record-keeping aims are to provide better patient care, offer data to audit, educate young doctors, handle medico-legal issues, perform research, and follow the standard guidelines of care. This study was conducted to compare the role of paper-based versus digital record keeping in the orthopaedic ward in terms of adherence to BOA guidelines, staff satisfaction, and education. To our knowledge, there is no previous study on this perspective.This comparative study was conducted at the Department of Trauma & Orthopaedics at Russells Hall Hospital, Dudley, United Kingdom, on 44 participants including nurses, senior house officers, foundation year trainees, and consultants from February 2021 to August 2021 in the form of two surveys. The first survey was done in February 2021 to introduce electronic records keeping to the staff and the second survey was conducted in August 2021 to review the collected records.Three parameters were assessed, which were adherence to BOA guidelines, staff satisfaction, and the effect of education for both paper-based and electronic records. Adherence to BOA guidelines was assessed in the first loop (survey) review of all inpatient's paper notes, and ward round entries and comparing them against BOA guidelines while the same was done in the second survey for electronic records. The parameters assessed in 'adherence to BOA guidelines' were the efficacy of paper-based or electronic method to record the patient's ID, patient's name, name of the clinician in ward round, details of clinicians, grade of ward round clinician, date and time of ward round, and contact details of the clinician in case of any concern.Staff satisfaction was assessed on a 5-point Likert scale from 1 (extremely dissatisfied) to 5 (extremely satisfied) as a snapshot survey at any one-time point. For recording the effect of education, four questions were asked in a staff survey enquiring juniors (house officers and registrars) if they found ward round an educationally enriching experience, comparing paper versus electronic methods.Data analysis was done in IBM SPSS Statistics for Windows, Version 22.0 . Mean with SD was calculated for continuous data like Likert score and frequencies along with percentages for categorical data. Comparison between two methods of record keeping was done by independent t-test for continuous data and chi-square test for categorical. In case of violation of chi-square assumption, Fisher's exact was run. P≤0.05 was kept as the level of significance.The highest number of participants were nurses n=14, 31.82%) and senior house officers/foundation years/physician assistants  followed by registrars  and consultants   higher in electronic 4.2± 0.9) than paperwork (3.5±1.32). Similarly, the Likert score for 'name of ward round clinician', 'documenting clinician', 'date of ward round', 'time of ward round', and 'whom to contact' was very higher statistically (p<0.001) in the electronic method than paperwork (Table ± 0.9 thaFour questions were asked about the effect on staff education. The score for 'opportunity to learn images in ward round' was higher in electronic (3.9±0.8) than paperwork 2.6±1.3) statistically (p<0.001). Comparable results were found for 'educational usefulness of ward round' and 'typing time affecting learning time'. However, though the score for 'confidence to review images with seniors' was a little higher in the electronic method (3.7±1.2) than paperwork (3.2±1.3), it was not statistically significant (p=0.11) , details of documenting clinician (p<0.001), time of ward round (p=0.005), whom to contact in case of concern (p=0.05), and grade of ward round clinician (<0.001). However, the difference was not statistically significant for ward round clinicians (p=0.41) and the date of ward round (p=.21) Table .This study was conducted to know whether electronic record-keeping is better than paperwork in terms of staff satisfaction, the effect on the education of junior doctors, and adherence to BOA guidelines. This study suggests that electronic health records are better in terms of the above-mentioned parameters.We found that staff satisfaction was higher for 'confidence on clarity of treatment plan' in electronic than in paperwork. The treatment plan is the road map by which care is provided to the patient in a customised and orderly way . Proper The results of this study showed that staff satisfaction was higher from electronic record-keeping than paperwork. In North Carolina, United States, a study was conducted on the association between job satisfaction and electronic record-keeping among primary care physicians. They reported a positive association between job satisfaction and electronic health records .BOA established the guidelines for the management of patients in orthopaedics under the standard protocol. These guidelines have been introduced on evidence-based research. In the orthopaedics ward, trauma is the most frequent type of presenting illness, which requires prompt care to prevent complications. BOA guidelines include patients’ ID and name, ward round clinician, documenting clinician, date of ward round, time of ward round, whom to contact in case of concern, and grade of ward round clinician. These guidelines can be documented by both electronic and paper methods. Hence, by following these guidelines, many surgical and medical complications, as well as medico-legal issues, can be overcome. To adhere to the BOA guidelines, proper documentation of a patient's condition and personal data is of paramount importance .Patient safety is key to good medical practice. In centres with busy trauma and workload, following certain pathways and guidelines are of paramount importance in order to improve the documentation quality, especially in handover and continuity of patient care and hence patients safety . Green eThe major limitation of this research was the relatively small sample size, which can reduce the power of the study and, hence, the results of statistical inference. This is a single-centre study; multi-centre studies can better explore this area. Moreover, the participants of this study were having different qualification and experience and were not equally distributed. Also, data were collected separately in each cycle and reflected the presence of documentation of individual criteria and were not cross-checked with paper documentation to make sure whether all documented information was correct or not. Within the limitations of this study, it can be concluded that electronic record-keeping in orthopaedic ward rounds is better than paperwork in terms of staff satisfaction, positive effect on the education of junior doctors, and adherence to BOA guidelines. The most common presentation among orthopaedic emergencies is trauma, which requires prompt treatment to avoid long-term complications. By strictly adhering to BOA guidelines for documenting the ward round, including the patient's condition, and treatment plan, many complications, including medico-legal issues, can be greatly avoided.


It has been shown that there is a non-random association between shape and color. However, the results of previous studies on the shape-color correspondence did not converge. To address the issue, we focused on shape complexity among a number of shape properties, particularly in terms of 3D shape, and parametrically manipulated the shape complexity and all three components of color. With two experiments, the current study aimed to closely examine the correspondence between shape complexity of 3D shape and color in terms of hue (Experiment 1), luminance, and saturation (Experiment 2). Participants were presented with the 3D shapes in either visual or visuo-haptic modes of exploration. Subsequently, they had to pick from a color palette the color best matching each shape of the object. In Experiment 1, we found that as shapes became more complex, the best associated hue changed from those with long wavelengths to ones with short wavelengths. Results of Experiment 2 demonstrated that as the shapes grew more complex, the associated luminance decreased, and saturation increased. Additionally, adding haptic exploration to visual exploration strengthened the association – for saturation in particular – with the pattern of shape-color correspondence maintained. Taken together, we demonstrated that complex shapes are associated with bluish, darker and more saturated colors, suggesting that shape complexity has a systematic relationship with color including hue, luminance, and saturation. Our brain tends to associate a certain feature in one sensory modality with a feature in another modality in a non-random manner, which is known as “cross-modal correspondence” . For exaA classic shape-color correspondence was first suggested by Kandinsky in the early 20th century. He proposed a fundamental association between primary shapes and colors  based on the survey he conducted . HoweverPrevious studies have mainly utilized discrete primary geometric shapes  and colors  in a one-to-one matching task ,2016. HoNext, color is composed of three elements: hue, luminance, and saturation. In contrast to most previous studies which only included hue in the discourse of shape-color correspondence, recent research began considering all three elements when investigating whether a specific shape is associated with a specific color. For example, in the study of A notable addition of the current study is the realistic 3D objects used as stimuli. Previous imaging studies have suggested overlapping activations in the brain during visual and haptic processing of 3D objects . That thWith two experiments, the current study aimed to closely examine the patterns of correspondence between shape complexity and color in terms of hue (Experiment 1), luminance, and saturation (Experiment 2). In Experiments 1 and 2, participants were presented with the 3D shapes – in either visual (video clips of 3D objects) or visuo-haptic (viewing 3D-printed objects while exploring it with their hands) modes of exploration. Subsequently, they had to pick from a color palette the color best matching each shape of the object. To examine the systematic patterns of correspondence between shape complexity and color, we parametrically manipulated the levels of both shape and color stimuli. The shapes of 3D objects were manipulated into three levels of complexity. For the color stimuli, hue was presented at eight levels (Experiment 1) and both luminance and saturation were divided into fifteen levels (Experiment 2). In addition, utilizing 3D shapes allowed us to test the modulatory effect of exploration modality. Reflecting the way our stimuli were manipulated, we hypothesized that shape complexity is systematically related to color including hue, luminance, and saturation. We also hypothesized that the non-arbitrary mapping between shape complexity and color is modulated by the modality of exploration .One hundred ninety participants  participated in Experiment 1, which was an online experiment. Twenty-seven participants  took part in Experiment 2. All participants had normal or corrected-to-normal visual acuity and normal color vision. They gave informed consent approved by the Korea University Institutional Review Board (KUIRB-2020-0174-01).Apparatus differed for participants in Experiment 1 because it was an online experiment and individuals had freedom to choose their own device. For Experiment 2, visual stimuli were presented on a 19-inch CRT monitor . Haptic stimuli were created by printing out the 3D shape models using Stratasys’ J750. The experiment was conducted in a quiet, dark room using MATLAB version 9.5  and Psychophysics Toolbox version 3 .1 Luminance and saturation values were identical across all shapes. To manipulate the complexity of the shape, we first generated a sphere shape–starting from this sphere, we created shapes of which complexity was parametrically modulated by increasing longitudinal frequency in both positive and negative values of a parameter. The increased frequency resulted in an increased number of sides either inflated  or deflated  on the 3D shapes, which was the basic characteristic defining complexity in our stimuli. Thus, an increased number of sides indicated more complex shapes. Out of the generated shapes, we selected the two three-edged shapes and created four more shapes by increasing latitudinal frequency from either shape. Thus, a total of nine shape models were created (The 3D shape stimuli consisted of nine achromatic shapes created with a variant of Superformula . Superfo created .N = 25), we attempted to confirm whether each set’s complexity levels manipulated in latitude or longitude were in line with perceived shape complexity. Within each of the complexity sets, participants rated perceived complexity by listing the shapes from simplest to most complex. We found that the perceived complexity of participants mirrored the manipulated complexity across all shape sets, confirming that shape complexity was successfully manipulated in the shape models .resented . The vidThe experiment was composed of two blocks depending on the modality of exploration condition , with the visual-only block preceding the visuo-haptic block. In the visual condition, participants viewed each of the video clips for 3 s, then were presented with a palette manipulated in luminance and saturation dimensions for a hue –3. UsingIn the subsequent visuo-haptic condition, participants were handed a white 3D shape object. They viewed the object while exploring it with both hands for 3 s. After the 3 s of exploration, the object was taken from them. Then the participants were presented with a palette and responded in the same manner as the visual condition . Again, We investigated the hue that is best associated with the shape according to the shape complexity level. We calculated the percentage of each hue selected for each shape for each complexity set. We then organized the shapes based on their complexity levels  and averaged the calculated percentage for hue.2 = 45.291, p < 0.001). Post-hoc paired comparisons showed that hue distribution of simple condition was statistically significantly different from those of intermediate and complex conditions . Similarly, when distributions of the percentage for hues were fitted depending on the complexity level using spline interpolation  on the associations. Based on the results from Experiment 1, we took five hues that had the best matched, highest percentage of responses for each shape from the eight hues. Participants chose a color chip that they thought would best match the shape in a palette with modulated luminance and saturation. We collected the luminance and saturation values of the chosen color. The two values were averaged, respectively, across all the color palettes within each shape.F = 1.834, p = 0.17, F = 8.892, p < 0.001, F = 0.669, p = 0.421]. Post-hoc Bonferroni comparisons of shape complexity showed that luminance value was lower in the complex level than in the simple or intermediate level . These findings suggest that as the shape grew more complex, it was associated with a color with lower luminance. The pattern of association did not differ depending on what the exploration modality was.We conducted two-way repeated-measures ANOVAs with the factors “shape complexity”  and “modality of exploration”  for luminance and saturation. Results are shown in F = 6.432, p = 0.003, F = 14.739, p = 0.002, F = 0.018, p = 0.894]. Post-hoc Bonferroni comparisons of shape complexity showed that saturation value was higher in the complex level than in the simple or intermediate level . These results suggest that higher saturation values are associated with more complex shapes. In addition, the pattern of association was modulated according to the modality of exploration, meaning that participants showed a more pronounced modulation of responses according to shape complexity in the visuo-haptic condition when compared to the visual condition for saturation while only showing a tendency to do so for luminance.As for saturation, the interaction effect was statistically significant [The present study demonstrates a non-arbitrary association between shape and color. Importantly, using parametrically manipulated 3D shape and color stimuli enabled us to more closely examine the shape-color correspondence. The associated colors showed systematic modulations according to the complexity of 3D shapes. Specifically, results of Experiment 1 showed that as shapes became more complex, the best associated hue changed from those with long wavelengths to ones with short wavelengths. In Experiment 2, we found that with increasing shape complexity, the associated luminance decreased and the associated saturation increased. Moreover, when haptic exploration was combined with visual exploration, there was an added effect to the association between shape complexity and elements of color–for saturation in particular. The results imply a non-random mapping between shape complexity and color including hue, luminance, and saturation. It also suggests the importance of considering haptic characteristics of shape when examining shape-color correspondence.Our results of Experiment 1 are in line with those from previous studies to show associations between 2D shape complexity and hues. Although results from three experiments of Our study is distinguished from previous studies by investigating luminance and saturation on a continuum. For instance, We used 3D modeling to create objects that enable dynamic haptic exploration, which proved to have several advantages. In our everyday lives, we encounter and interact with 3D objects. Among these interactive experiences, shape perception is a multisensory experience not only of vision but also including haptics. This has been evidenced by both behavioral and neuroscientific studies . It is pUsing the 3D stimuli, our results displayed tighter patterns of associations in the multimodal exploration condition. Specifically, the association observed in the visual-only condition was significantly strengthened in the visuo-haptic condition for saturation while showing a tendency to do so for luminance. The results of visuo-haptic exploration are displaying a similar pattern of results with visual exploration can be attributed to the fact that vision and haptics share common brain regions such as the middle occipital (MO) cortex , the latThe current study revealed that perceptual inputs from a single modality or different sensory modalities are closely associated with each other in a systematic and non-random manner. Yielding results that serve as significant empirical evidence for intra-modal/cross-modal correspondence between shape and color, the present study entailed several methodological advantages. First, parametrically manipulating the level of shape complexity and color and presenting the wide spectrum of both luminance and saturation allowed us to systematically examine the shape-color correspondence. Second, utilizing 3D shapes enabled us to consider the multimodal nature of shapes when examining shape-color correspondence. Our results have an important implication in the sense that it has potential for application in the industrial field. There have been attempts to apply the knowledge of cross-modal correspondence to branding and marketing . In a siThe raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.The studies involving human participants were reviewed and approved by the Korea University Institutional Review Board. The patients/participants provided their written informed consent to participate in this study.JS, HS, MP, SN, and C-YK designed the experiment, analyzed the results, and revised the manuscript. JS, HS, MP, and SN conducted the experiment. JS, HS, MP, and C-YK drafted the manuscript. All authors contributed to the article and approved the submitted version.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


When the left versus right foot movements were directly contrasted, the third representation could be clearly distinguished from the second representation in multiple individuals. Functional connectivity from seed regions in the third somatomotor representation confirmed anatomically specific connectivity with the cerebral cortex, paralleling the patterns observed for the two well-established maps. All results were prospectively replicated in an independent dataset with new individuals (n = 4). These collective findings provide direct support for a third somatomotor representation in the vermis of the cerebellum that may be part of a third map. We discuss the relations of this candidate third map to the broader topography of the cerebellum as well as its implications for understanding the specific organization of the human cerebellar vermis where distinct zones appear functionally specialized for somatomotor and visual domains.Seminal neurophysiological studies in the 1940s discovered two somatomotor maps in the cerebellum—an inverted anterior lobe map and an upright posterior lobe map. Both maps have been confirmed in the human using noninvasive neuroimaging with additional hints of a third map within and near to the cerebellar vermis. Here, we sought direct evidence for the third somatomotor map by using intensive, repeated functional MRI (fMRI) scanning of individuals performing movements across multiple body parts . An initial discovery sample (NEW & NOTEWORTHY A third somatomotor representation exists in the vermis of the human cerebellum. Evidence for this elusive representation arises specifically from mapping the foot. Separate foot representations distinguish the third from the nearby second somatomotor representation. A third somatomotor representation in the posterior vermis supports a large-scale organization hypothesis in which the cerebellum possesses three sets of roughly homotopic representations of the full cerebrum. A somatomotor topography was first described in the anterior lobe of the cerebellum by Adrian . In catsA half century after their discovery, the anatomical basis of the two separate somatomotor maps was revealed using transneuronal viral tracing techniques. A barrier to measuring anatomical connectivity between the cerebral cortex and the cerebellum was that the pathways are polysynaptic and did not yield to traditional monosynaptic anatomical tracing techniques –6. UsingThe two distinct somatomotor maps have been repeatedly observed in humans using noninvasive neuroimaging techniques evoked by movements –15 and tBeyond these two established somatomotor body maps, we have also postulated the existence of a third, smaller body map in the posterior extent of the cerebellum within and near to the vermis . There aSpecifically, cerebral networks associated with higher-order functions display representations in the posterior lobe of the cerebellum near lobule IX, which would be expected if there was a third gradient, including somatomotor representation. In particular, the network referred to in the human literature as the default network possesses a robust representation in lobule IX , 22, 23.Hints of a third somatomotor representation exist in the literature. In an early electrophysiological study, Dow and Anderson  documentZ = −38 and the coronal section at Y = −68 in Fig. 8 of Ref. In humans, one of the first studies to use fMRI to map the cerebellum noted that there might be a third somatomotor representation in the vermis . HoweverA definitive demonstration of a third somatomotor map, if one exists, faces multiple challenges. First, the hypothesized map is expected to be found in a small region within the already small cerebellum. Second, the inferior portions of the cerebellum near the vermis have low signal-to-noise ratio (SNR) in fMRI compared with the cerebral cortex.In the present work, we took a number of steps to overcome these challenges. First, we acquired a substantial amount of data within individual participants during task-based motor movements to boost SNR. Each participant was scanned on four days with six motor runs per day, for a total of 576 separate blocks of motor movements (96 blocks for each of six separate motor conditions). Second, to avoid between-individual spatial blurring, each participant was initially analyzed separately , 28. ThiEight healthy adults, aged 19–25 , were recruited from the Boston area. Four participants contributed data to the initial Discovery sample, and four participants contributed data to the Replication sample. All participants were screened to exclude a history of neurological and psychiatric illness or ongoing use of psychoactive medications. Paid participants provided written informed consent through a protocol approved by the Institutional Review Board of Harvard University.Scanning was conducted at the Harvard Center for Brain Science using a 3 T Siemens Magnetom Prisma-fit MRI scanner and a 64-channel phased-array head-neck coil . Inflatable padding provided comfort and helped immobilize the head. Participants viewed a rear-projected display through a mirror attached to the head coil. Before each session, the screen presentation was adjusted so that the center point could be comfortably viewed without any eye strain. Participants’ eyes were monitored and video-recorded using the Eyelink 1000 Core Plus with Long-Range Mount , and alertness was scored during each functional run.S5–S8) were scanned in an additional session not used here.All participants were scanned across four MRI sessions on separate nonconsecutive days. Six task-based runs were acquired each day where participants made active movements (motor runs) as well as two runs where participants fixated on a centrally presented black crosshair on a light gray background (fixation runs). In total, each participant had 24 motor and 8 fixation runs. Four participants (S1–S4) were scanned with two different spatial resolutions: 1.8 mm (2 sessions) and 2.4 mm (2 sessions) isotropic voxels . All sessions of the four Replication sample participants (S5–S8) were scanned with 2.4 mm isotropic voxels. Acquisition parameters for 1.8 mm resolution: TR = 2,000 ms, TE = 30 ms, flip-angle = 80°, matrix 122 × 122 × 87 (FOV = 220 × 220), multislice three times acceleration. Two hundred eleven volumes were acquired for each fixation run and 214 volumes for each motor run. Acquisition parameters for 2.4 mm resolution: TR = 1,000 ms, TE = 33 ms, flip-angle = 64°, matrix 92 × 92 × 65 (FOV = 221 × 221), multislice five times acceleration. Four hundred twenty-two volumes were acquired for each fixation run and 428 volumes for each motor run. The two resolutions in the Discovery sample were acquired to explore the possibility of higher (1.8 mm) resolution disambiguating functional details better than lower (2.4 mm) resolution. For the present purposes, the two resolutions were combined and smoothed with a uniform kernel. The first two sessions of S5 and the first session of S6 were acquired in a different FOV (211 × 211); therefore, the matrix for both BOLD runs and field maps was: 88 × 88 × 65 and BOLD TE = 32.6 ms. The change in FOV, which occurred to accommodate larger heads, did not affect the quality of registration or impact the analyses in any way we could detect.Functional data used a multiband gradient-echo echo-planar pulse sequence sensitive to blood oxygenation level-dependent (BOLD) contrast –32, geneA T1-weighted structural image was obtained in each session using a rapid multiecho magnetization-prepared rapid gradient echo (MPRAGE) three-dimensional sequence : TR = 2,S3, two motor runs for S4, one motor and two fixation runs for S6, and one fixation run for S8 due to motion. One border case motor run for S4 (with maximum absolute motion of 1.88 mm) was included as the motion estimate was mostly the result of a linear drift. In three motor runs of S3, an artifact in the form of a peak translation (in the z axis and to a lesser extent in the y-axis) was spotted in approximately the same time point across these three runs. Maximum absolute motion did not exceed the set threshold for these runs , but we conservatively excluded them from analysis. Runs were excluded based on BOLD data quality before examination of task response patterns to avoid bias.Exclusion criteria included a maximum absolute motion of no more than 1.8 mm. One motor run was excluded for 1) tongue: participants moved their tongue from right to left, touching their upper premolar teeth; 2 and 3) right and left hands: participants moved their fingers alternating between tapping the thumb with the index finger and the thumb with the middle finger; 4) glutes: participants contracted and relaxed their gluteal muscles; and 5 and 6) right and left feet: participants alternated dorsiflexion and plantarflexion of their toes.Participants performed a blocked-task paradigm consisting of six types of movements that targeted four separate parts along the anterior-posterior body axis: Each movement was performed repeatedly in 10-s active movement blocks. Prior to a movement block, a visual cue of a drawn body part with a text label was presented for 2 s, informing the participant to initiate one of the six movement types. Then, the fixation crosshair was presented with a black circle surround that repeatedly flashed on and off to pace the movements (1 s on then 1 s off). The onset of the black circle cued movement of the tongue to the right, thumb to index finger, glutes contraction, and toes plantarflexion. The black circle turning off cued movement of the tongue to the left, thumb to middle finger, glutes relaxation, and toes dorsiflexion. The word “End” was presented for 1 s at the end of each movement block to instruct the participant to stop, and there was a 1-s fixation gap before the onset of the next movement cue.Twenty-four movement blocks (4/body part) occurred within each motor run, with 16-s blocks of passive fixation providing a break after each set of six movement blocks. All runs began and ended with fixation yielding five fixation blocks per run. Six separate runs with distinct orders of movement conditions were performed on each day. Counterbalancing of the movement conditions across the six runs on each day ensured that each condition appeared exactly once in each of the run positions.Participants extensively practiced the intended movements before the first scanning session. First, participants practiced remotely during a consent and training session, after watching a video demonstrating how to execute the movements. Emphasis was placed on how to localize each movement in a subtle way to avoid head motion. Next, when participants arrived for their first session, they practiced again while lying on the scanner bed outside of the bore and then a final time inside the bore. To further reduce extraneous motion, participants’ legs were supported in a semiflexed position using an ergonomic knee-to-ankle cushion.1) a motion correction matrix for each volume to the run’s middle volume , 2) a matrix for field-map-unwarping the run’s middle volume , 3) a matrix for registering the field-map-unwarped middle volume to a mean BOLD template , 4) a matrix for registering the mean BOLD template to the participant’s native space T1 image , and 5) a matrix for registering the native space T1 to the MNI152 1 mm atlas . The mean BOLD template was created by taking the mean of all field-map-unwarped middle volumes after registration to an upsampled (1.2 mm), unwarped mid-volume template . The native space template was one of the participant’s T1 structural images, upsampled to 1 mm isotropic resolution. Given that multiple structural images were available for each individual, a single reference image was chosen that possessed a robust estimate of the pial and white-matter boundaries .A custom analysis pipeline for individualized data processing was used, as described in detail in the study by Braga et al. . BrieflyS1–S4) that had two spatial resolutions (two sessions with 1.8 mm isotropic voxels and two sessions with 2.4 mm isotropic voxels), each resolution was processed separately and then combined. All functional data were smoothed in the volume with a 3-mm full width at half maximum (FWHM) kernel.Data were checked for registration errors. In cases of suboptimal registration, a different mid-volume temporary template was chosen, or a different/additional field map was used for unwarping (since two field maps were acquired in each session). Before the calculation of these matrices, for each BOLD run, the first 12 volumes were discarded for T1 equilibration (6 volumes in 1.8 mm resolution). For the participants . The projection method uses an approximate surface of the gray and white matter defined on the SUIT template . The vertices on the two surfaces come in pairs. When projecting, the algorithm samples the data along the line connecting the two vertices and then takes the mean of these numbers to determine the value of the vertex.To visualize the spatial extent of maps in the cerebellum, the data were projected onto flat and inflated representations of the cerebellar surface [similar to our prior work in Xue et al. ]. These The surface representation was used for display purposes only. The surface is not unique to each individual. In addition, the flat surface may not be optimal for the cerebellar vermis. To enable flattening, a cut was made between the posterior vermis and lobules HVII-HIX that can introduce distortions to the vermal region. Nonetheless, the surface representation can display the individual participant’s topographic details in a uniform, comprehensive display framework. To avoid ambiguities in anatomical localization, key findings were also displayed across multiple slices through the cerebellar volume, including the vermis.Motor run data in MNI152 atlas space were initially analyzed within each individual separately. A high-pass filter with a 100-s (0.01 Hz) cut-off was applied to remove low-frequency drifts within each run. Run-specific general linear model  analyses were performed, including the six movement conditions as regressors of interest. The onsets of the six movement blocks were modeled 1 s before the initiation of movement . The duration of each event block was set to 10 s. Events were modeled with a canonical hemodynamic response function (double-γ) along with its temporal derivative. GLM analyses resulted in voxel-level β values for each movement.S1–S4). To visualize somatomotor topography, a winner-take-all map was generated. Each voxel was assigned with the highest β value across all movement contrasts  and was given the color of the corresponding movement: tongue—red, right and left hand—yellow, glutes—green, and right and left foot—blue. In this visualization, right and left representations were not distinguished. Maps were visualized separately for each individual in the volume and also projected onto the SUIT flatmap template of the cerebellum. Maps were thresholded within individuals to best capture somatomotor topography, separately for the cerebrum and for the cerebellum.Initial analyses focused on estimating the somatomotor map across the four body parts in the cerebral cortex and separately in the cerebellum. In total there were six conditions because there were two (right and left) movements for the hands and feet. A contrast map was computed for each of the movements by subtracting all of the other five movements’ β values . This was done in each motor run separately and then averaged across runs (including averaging across resolutions in S1–S4). Contrast maps were projected onto a flatmap template of the cerebellar surface and thresholded within individuals.A critical aspect of the design is the ability to contrast left and right movements separately for the hands and feet. This enables direct contrast of the left and right movements within each body part, allowing a clean estimate of the representation of the body part because nonspecific noise and other features of the data are largely nulled . CoS3 had 28 runs (193.3 min), S4 had 30 runs (207.2 min), S6 had 29 runs (200.5 min), and S8 had 31 runs (214.2 min). All other participants utilized all 32 runs (221.1 min). Note that unlike typical task-free data (fixation), the present data included substantial modulation due to active task demands.To assess representations’ specificity, we conducted a seed-based functional connectivity analysis. This analysis should not be considered independent as we utilized all available data, including the motor task runs used to make the contrast maps. Specifically, in addition to the 24 motor runs (across 4 sessions) of each individual, we used the eight fixation runs (2 per session), resulting in 32 possible runs. After quality control exclusions, Preprocessing included all steps as used for the prior task-based analyses but also regression of nuisance variables  as is typical in functional connectivity analysis . The residual BOLD data were then band-pass filtered at 0.01–0.1 Hz and smoothed with a 3 mm FWHM kernel paralleling the task-based analysis.S8, we selected right and left foot tertiary coordinates to be similar to the ones of other individuals given the lack of detectable representations.Seed regions were defined in the right and left primary, secondary, and tertiary somatomotor cerebellar representations. For each seed region, an MNI152 coordinate was selected in the volume to best capture the center of the representation in the right versus left hand and foot contrast maps. In one individual, S1–S4) after being Fisher’s r-to-z transformed.Seed regions were defined with a 2-mm radius centered at the selected MNI152 coordinate (33 voxels). For each run, the Pearson’s correlation was computed between the averaged time course of the seed region and that of all other voxels. The functional connectivity values were averaged across all runs (and resolutions for S1–S4) before any analysis was attempted on the second, independent group of participants .A key aspect of our methods was prospective replication. Data from the initial four participants were fully analyzed and graphed . Although not common in the literature, contraction and relaxation of the gluteal muscles was included to evoke a representation of the trunk. As predicted by the body topography, representation of the glutes fell between the hand and the foot. The observed M1 motor topography validates the experimental paradigm and the processing methods applied.The body map in the primary motor cortex  is well established. The anterior (head) to posterior (foot) axis is topographically organized as one moves from the lateral extent of the motor strip to the midline . Data frmiddle and bottom). The ordering of body parts was inverted between the two maps. In the anterior lobe, the foot was anterior to the tongue and in the posterior lobe the tongue was anterior to the foot . In both maps, the glutes representation falls between the hand and foot, filling in a gap often observed in motor body maps  than others (S3 and S4). The current data thus reveal strong and reliable evidence for the anterior lobe and posterior lobe somatomotor maps.The two well-established cerebellar somatomotor maps were apparent in all participants (bottom) is that the tongue movements induced noise centered outside of the cerebellum (but extending into the cerebellum). Given the sensitivity of fMRI to motion artifact, it is not surprising that movements within the field of view produce artifact. Fortunately, the predicted tongue representation is spatially distant from the region of artifact and thus easy to distinguish.One other aspect of the results , bottom To visualize the entirety of the somatomotor cerebellar topography, maps with representations of the four body part movements were projected onto a flatmap view of the cerebellum . In thisS2 and S3. This feature is consistent with the hypothesized third (tertiary) somatomotor map of the cerebellum. However, idiosyncrasies in the data, variability across participants, and the adjacency to the secondary representation make it uncertain. To further explore the possibility of a third map, we focused specifically on hand and foot movements where direct contrasts between the right and left movements were possible.S1 and S2 there were two distinct representations for the second and the candidate third map. In S3 and S4, the two representations appeared as one continuous cluster. Consequently, it is still ambiguous as to whether the medial representation is a distinct third representation or simply a continuation of the second representation. It seems that even if there is a third map, the hand representation is not positioned to provide clear evidence for dissociation. Therefore, we repeated the same analyses focused on the foot representation.Contrasting right and left hand movements yielded robust responses in the cerebellum for all participants . The priContrasting right and left foot movements yielded robust responses that included a representation in the vermis medial to the second representation in multiple participants . Unlike 1) the spatially separate cerebellar foot representations should both couple to the same midline cerebral zone (see 2) the hand representations, which sit between the two foot representations in the cerebellum, should couple to a distinct cerebral zone near the hand knob of the precentral gyrus  yrus see .To explore these hypotheses, we first examined the specificity of the hand representations by applying seed-based functional connectivity analysis to the cerebellum and examining the correlation pattern in the cerebral cortex. Three sets of right and left seed regions were defined in the primary, secondary, and candidate tertiary representations  reveal strong evidence for a third somatomotor map in the cerebellum. Given the importance of this observation and also that we have previously failed to find clear evidence for a third map,1 we sought to replicate all of the observations in an independent set of new participants. Critically, none of the Replication sample data (S5 to S8) were processed until all of the analyses on the initial Discovery sample (S1 to S4) were completed and plotted.The findings from the Discovery sample (top) and in the cerebellar anterior lobe of all four individuals with the expected order of the tongue-hand-glutes-foot , consistent with prior studies of within-individual precision mapping that do not always achieve robust results in every individual  shows the full somatomotor topography across all eight individuals . A winner-take-all map was generated after averaging each movement contrast across individuals and projected onto flat map (left) and inflated cerebellar pial surface (right) visualizations. The primary and secondary somatomotor maps clearly present inverted and upright body topography correspondingly, suggesting that averaging data across participants can be effective. The posterior vermis shows hints of the tertiary map, clearer on the inflated surface, with the foot representation separated from the two others.Although our studies were designed with the goal of preserving idiosyncratic anatomical details within the individual, the results suggested that there may be sufficient positional stability of the three somatomotor maps to examine the data at the group level. bottom) also shows the left versus right contrasts of the hand and foot movements averaged across all individuals. Critically, the small discontinuous responses linked to the right and left foot movements are evident within the vermis as would be expected from the analyses of the individuals. Once the location is known, it is now possible to see evidence in the group data. 3) along with the primary (F1) and secondary (F2) representations. The positioning and small size of the response also suggest why the third map representation is challenging to detect (and notice) as separate. We will return to this point in the discussion where the existing literature was reexamined for prior overlooked evidence for the third map.For another perspective on the relative location of the hand and the foot representations of the candidate tertiary map, Our data provide evidence for a third somatomotor representation within and near to the vermis of the human cerebellum. Unlike the two well-established somatomotor maps that can be distinguished as separate by exploring the hand, foot, or other body parts bilaterally, the linchpin for detecting the separation between the second map and candidate third map was examining the multiple representations of the foot. When left and right foot movements were contrasted, a response was detected in the cerebellar vermis that was spatially discontinuous from the known second map. Moreover, the region of the posterior cerebellum between the two foot representations was associated selectively with the hand, providing evidence that the two foot representations are parts of spatially- and functionally distinct maps. All findings were detected in multiple independent participants and replicated in a second, prospectively analyzed cohort. We discuss these observations in the context of what they reveal about the broad topography of the cerebellum as well as implications for the specific organization of the vermis.1 Guell et al.  can result in a different pattern of cerebellar activity and engage regions that are not activated during simple movements . The preWe provide reliable evidence that the human cerebellum possesses three spatially distinct representations of the foot. The location of the third foot representation is in the vermis, consistent with a third somatomotor map continuous with the tertiary representations of cognitive and affective networks. The third map’s small size and location may have contributed to its evasiveness in past explorations, and suggests examination at high spatial resolution will be necessary to fully understand the third map’s spatial orientation and its relation to functionally distinct zones of the vermis.This study was supported by National Institutes of Health (NIH) Grant MH124004 and NIH Shared Instrumentation Grant S10OD020039. This work was also supported by the National Science Foundation Graduate Research Fellowship Program under Grant No. DGE1745303 (to L.M.D.). Any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the National Science Foundation.No conflicts of interest, financial or otherwise, are declared by the authors.N.S.-G. and R.L.B. conceived and designed research; P.A.A. and L.M.D. performed experiments; N.S.-G., P.A.A., and L.M.D. analyzed data; N.S.-G., P.A.A., L.M.D., and R.L.B. interpreted results of experiments; N.S.-G. and R.L.B. prepared figures; N.S.-G. and R.L.B. drafted manuscript; N.S.-G., P.A.A., L.M.D., and R.L.B. edited and revised manuscript; N.S.-G., P.A.A., L.M.D., and R.L.B. approved final version of manuscript.


For the study, the animals were observed exclusively by video cameras and no manipulations of the animals were necessary. Therefore, ethical approval for animal experimentation by an institutional animal care and use committee was not considered necessary in our jurisdiction.”The animal ethics statement on page 218 should be revised as follows (addition in bold): “This study was conducted on a commercial Austrian dairy farm in May 2019. The farmer gave his informed consent to the use of the accelerometer data and video recordings of his animals.


In summer 2020, 68 clinicians in the New England area caring for Veterans with dementia as part of a specialty team were surveyed regarding caregiver support services, with a 46% response rate (n=31). When faced with the need to abruptly discontinue in-person dementia support services, the majority of respondents offered caregivers support via telephone rather than video telehealth. Only 4 of 31 (13%) mentioned offering video visits for the first time to replace face-to-face visits. Clinician choice of modality largely reflected shifts among preestablished communication modalities/patterns and were influenced by clinician perception that older patients and their caregivers would prefer the telephone. Clinicians without experience using video telehealth with older adults were unlikely to offer video visits despite evidence that many older adults are willing and able to participate. Assessing caregiver wish/ability to participate in video visits may inform and shape clinician choice of telehealth modality.


The whole sentence representation reasoning process simultaneously comprises a sentence representation module and a semantic reasoning module. This paper combines the multi-layer semantic representation network with the deep fusion matching network to solve the limitations of only considering a sentence representation module or a reasoning model. It proposes a joint optimization method based on multi-layer semantics called the Semantic Fusion Deep Matching Network (SCF-DMN) to explore the influence of sentence representation and reasoning models on reasoning performance. Experiments on text entailment recognition tasks show that the joint optimization representation reasoning method performs better than the existing methods. The sentence representation optimization module and the improved optimization reasoning model can promote reasoning performance when used individually. However, the optimization of the reasoning model has a more significant impact on the final reasoning results. Furthermore, after comparing each module’s performance, there is a mutual constraint between the sentence representation module and the reasoning model. This condition restricts overall performance, resulting in no linear superposition of reasoning performance. Overall, by comparing the proposed methods with other existed methods that are tested using the same database, the proposed method solves the lack of in-depth interactive information and interpretability in the model design which would be inspirational for future improving and studying of natural language reasoning. Natural language inference (NLI) has become one of the essential benchmark tasks in natural language understanding because of its complex language understanding and in-depth information involved in reasoning . NaturalWith the development of neural networks and deep learning, the sentence representation reasoning method has gradually developed from the traditional manual feature extraction method combined with the logical relationship reasoning method to the current end-to-end sentence representation reasoning method . The endWith deep learning in natural language processing, semantic reasoning technology gradually changes from logic-based to deep learning-based reasoning technology . The corInfluenced by the seq2seq model in machine translation, The construction and optimization of sentence semantic representation and reasoning models have become the two core problems in sentence representation reasoning . No mattThis paper will further improve and optimize the existing sentence representation reasoning methods. This paper aims to solve the incomplete and inaccurate sentence semantic information expression, the inference model’s lack of in-depth interactive information, and the lack of interpretability during the reasoning process. The whole sentence representation reasoning process simultaneously comprises a sentence representation module and a semantic reasoning module. Therefore, this paper comprehensively considers the two modules’ improvement and optimization, combining the multi-layer semantic representation and deep fusion matching networks. Finally, it proposes a joint optimization sentence representation reasoning method to explore sentence representation and reasoning, learning the prediction results.In order to verify the feasibility of the semantic-based sentence representation reasoning method in the field of text entailment and to explore the influence of sentence representation and reasoning learning on sentence representation reasoning, experiments were carried out on SNLI data sets and Multi-NLI data sets commonly used in the field of text entailment. All the methods and experiment are based on python 2.7 based on pyTorch environment.SNLI data set is a text entailment data set published by Stanford University. SNLI is manually annotated and contains 570k text pairs. There are three kinds of marks: entailment, contradiction, and neutral. In this paper, data are divided into a training set , a verification set , and a test set  according to The Multi-NLI dataset  containsIn this paper, the text entailment task is carried out on the unmatched and matching sets. The data are divided into the training set  and the matching/mismatching verification set .The word vector pre-training is initialized by GloVe-840B-300D , in whicThe gradient update of the model adopts Adam  algorithThe training process adopts the early stop strategy and combines dropout and data batching to avoid model overfitting. Dropout is set to 0.8. For the SNLI dataset, each batch contains 32 sentence pairs, and for the Multi-NLI dataset, each training batch contains 8 sentence pairs.The learning rate is initialized to 0.0002. After each iteration, the model uses the results of the verification set to adjust the learning parameters. For example, when the verification set’s accuracy decreases, the learning rate is reduced by half, and the early stop flag is increased by one.All experiments are based on the Theano deep learning framework. The NVIDIA GeForce GTX 1070 graphics card and 16 g memory train and test the model performance. The experimental parameters are as follows:Preprocessing: preprocessing the premise text P and hypothesis text H by word segmentation and part of speech restoration to generate the premise hypothesis text pair.Text representation: a multi-layer semantic representation network is used to obtain the sentence’s semantic representation of premise text P and hypothesis text H, respectively.Relational reasoning: deep fusion matching network is used to infer the possible score of implication relationship between premise text P and hypothesis text H.Result prediction: the score of implication relation is classified. The result with the highest possibility is output as the result of the relational reasoning system.A. PreprocessingThe existing research only considers the optimization and promotion of sentence representation module or semantic reasoning model. Suppose the two modules are optimized at the same time. In that case, the limitation of single optimization can be solved to a certain extent. However, there is no research content to consider the joint optimization of the two modules simultaneously. Therefore, this paper combines the multi-layer semantic representation network and deep fusion matching model to form the sentence representation optimization methods. The joint optimization method consists of four modules: preprocessing, text representation, relational reasoning, and result predictionB. Sentence representationThe first step is the natural language text preprocessing. The input pairs are all English texts, so the preprocessing includes the English word segmentation, speech restoration, removing stop words, and processing non-standard numbers. The word is the smallest language unit in the natural language reasoning task, so segmenting the input text before sentence representation is necessary. Stop words generally refer to the words which appear frequently but have no practical significance compared with other words in sentences. In this paper, to save storage space and improve efficiency, stop words are removed. In this paper, all the contents related to numbers are unified into Arabic numerals.After the preprocess module, the next module in the process is the sentence representation module. Itis based on the multi-layer semantic representation network. The sentence’s semantic representation of the text vector of premise text P and hypothesis text h is carried out. The specific operation is as follows:First of all, all words in the question Embedding encoding C. Relational reasoningLike the hypothesis text, the premise text P is input into the multi-layer semantic representation network with the same configuration. The sentence representation of the premise text is obtained as follows. The number of layers and word order information weight alpha of the multi-layer semantic representation network is adjusted according to the model’s training results.1. Convolution layerThe third module would be the relational reasoning module. The semantic fusion deep matching network (SCF-DMN) is used in the relational reasoning module to obtain inference between premise text P and hypothesis text h based on the text pair’s embedded representation. The relational reasoning module mainly includes the convolution layer, matching layer, and information aggregation layer.The text representation module obtains the embedded representation of premise hypothesis pairs but does not contain syntactic structure information. Therefore, this module’s convolution layer uses the dependency tree convolution network (d-TBCNN) to obtain the syntactic structure information of premise text P and hypothesis text H . The speFirstly, the natural language parser in NLTK is used to transform the premise text P  into dependency syntax tree form. For example, the visual dependency tree structure is shown in Then, according to the sequence of dependent subtrees obtained, the convolution operation is performed on the dependency results of each subtree in turn. The convolution results of all subtrees are summarized to form the syntactic structure information 2. Matching layerThe subtree contains two directly connected nodes, “I” and “like.” All nodes put forward the syntactic structure information carried by the convolution layer. The convolution result of the subtree went through the pooling layer and the hidden layer and are summarized to generate the structure information corresponding to the sentence.1) Get interactive informationThe matching layer adopts the chain structure, composed of M matching modules with the same configuration to collect local reasoning information based on the sequence. The specific calculation steps are as follows:2) Get self-interested informationFor section i-th matching module, the cross attention between the premise hypothesis pairs is calculated, and then the cross attention is used to weigh the text pair embed representation (3) Information fusionFor the generated premise-hypothesis interaction information 4) Generating local reasoning informationThe interactive information and self-attention information generated in step 1) and step 2) are fused to form the reasoning information 3. Information aggregation layerAlong the chain structure, based on the reasoning information generated by the previous matching module, repeat steps 1–3 m times, and finally take the output of the top-level as the output of the matching layer, the premise text matching semantics D. Result predictionThe information aggregation layer is used to construct the input of inference prediction layer fuse the text syntactic structure features The final module is the result prediction module. The inference and prediction layer comprises three layers of the fully connected neural network. The output of the information aggregation layer passes through a single hidden layer and activation function. Finally, it classifies by the Softmax function to obtain the probability of implication relationship between current text pairs. The output result module selects the most probable result according to the probability of implication relation.1) No optimizationIn order to explore the influence of sentence representation module and relational reasoning model on reasoning results, four groups of experimental schemes were designed to compare and analyze the performance of sentence representation module and relational reasoning model without optimization, single optimization, combinatorial optimization, and joint optimization, to understand the interaction between modules.This scheme does not consider the optimization of the sentence representation module or relational reasoning module. Instead, this paper uses the general sentence representation reasoning method proposed by 2) Separate optimization1. Optimizing sentence representation module separatelyThe single optimization experiment only optimizes the sentence representation module or the relational reasoning model. The non-optimized module is consistent with the corresponding module in the non-optimization experimental scheme. The single optimization experiment’s result prediction module uses average pooling and maximum pooling to fix the sentence pair vector’s length. It then obtains the final implication through the full connection layer and Softmax functions. The single optimization experiment includes two parts2. Single optimization of relational reasoning moduleThe network structure diagram of the single optimized relational reasoning module is shown in 3) Combination optimizationFor the SNLI dataset, the number of matching modules in the matching layer is set to 3; for the number of Multi-NLI matching modules, T is set to 2. The parameters of other parts are set with general parameters. The initial numbers for modules are chosen based on the number used by other studies using similar networks on the same datasets.The combination optimization of the sentence representation module and the relational reasoning model is realized by taking the average output probability distribution of the two optimization modules’ implication relation as the final probability distribution.The combination optimization model’s network structure is shown in Firstly, the left SCF-DMN network is trained. Then, the bidirectional long-short memory network learns the sentence semantics and context information. The interactive information is obtained by using the deep fusion matching network designed in this paper. After pooling and classification functions, the left network’s implication relationship’s probability distribution results are obtained.The multi-layer semantic representation network on the right is trained synchronously. The multi-layer semantic representation network designed in this paper is used to capture multiple sentences’ semantic information. The inference information between sentences is obtained by interactive matching. Finally, the probability distribution results of this part’s implication relation are obtained through average pooling, maximum pooling, and full connection layer.Finally, the average output probability distribution of the left and right networks is taken as the final result of the combination optimization scheme.4) Joint optimizationThe parameters in the combination optimization scheme are updated and learned synchronously with the training process. All neural network dimensions are set to 300D. The number of initial matching modules is set to 2. The initial semantic layer level is 5. The initial weight parameter alpha of word order information is set to 1.5. The remaining network parameters are randomly initialized by the Gaussian function .4) JoinThe joint optimization scheme of the sentence representation module and relation module is the joint optimization method based on the multi-layer semantic representation network and deep fusion matching reasoning model proposed in this paper. The joint optimization method is shown in The sentence representation module adopts the multi-layer semantic representation network in this paper in the joint optimization scheme. The relational reasoning model adopts the deep fusion matching network designed in this paper. Firstly, a complete sentence embedding representation is obtained through the multi-layer semantic representation network. On this basis, the local reasoning information and syntactic structure information between sentence pairs are captured through the deep fusion matching network. Finally, the implicative relation between sentence pairs is obtained by pool level and full connection layer reasoning.The first mock exam results on the SNLI dataset are shown in Compared with the combined optimization scheme’s experimental results and the joint optimization scheme, the combination optimization scheme’s accuracy is higher in both sets. The accuracies are 96.4% and 89.4%, respectively. Thus, the results show that the sentence representation module and reasoning module as a joint optimization method of the overall model training optimization can improve sentences’ reasoning performance. However, the improvement effect is not as good as the combination optimization scheme formed by linear superposition.It is speculated that this result is the mutual restriction between sentence representation and reasoning model. When all parameters are trained as a whole, each part cannot achieve the best performance. Thus, although the overall performance is improved, there is no expected substantial improvement.As shown in The Multi-NLI data set’s experimental results show that the sentence representation reasoning method’s performance is affected by both sentence representation and the reasoning model. Therefore, the optimization of both helps to improve reasoning performance. However, combining the two parts is not good when the two modules are optimized jointly due to the interaction between the two modules.1. Comparison between joint optimization and combinatorial optimizationTwo groups of comparative experiments were conducted to explore further the influence of the optimization model of sentence representation module and reasoning learning module on reasoning results. First, the influence of combinatorial and joint optimization on sentence representation reasoning and joint optimization and individual optimization of each module on reasoning performance are analyzed.The experimental results show that the combination optimization of the sentence representation module and inference module to achieve the optimization and then take the average prediction value as the final result can weaken the restriction. However, the time-consuming and the number of parameters in the process increase significantly, improving the reasoning process’s complexity and reducing the interpretability.2. Comparison of joint optimization and individual optimization of each moduleIt can be observed from the figure that the combination optimization method has higher iteration times than the joint optimization method at the same time. The experimental results also show that the sentence representation module and the reasoning module’s optimization will be affected by the interaction between the two modules in the same model. Therefore, the performance cannot be improved linearly as the combination optimization.When the number of iterations is between 1–3, this model’s learning rate is close to the rate of the multi-layer semantic representation network. When the number of iterations is 4–15, this model’s changing trend is consistent with a deep fusion matching network. In the later stage of training, the learning curve matches that based on deep fusion.This model’s learning curve (joint optimization) shows that sentence representation greatly influences the early reasoning ability of the model. On the other hand, the inference model strongly influences the reasoning ability in the middle and later stages of the model. Simultaneously, it is also found that the combination of sentence representation and reasoning methods can achieve better results because of their respective network structures. However, it is limited by the mutual constraints between the various parts, and there is no greater improvement.1. The influence on sentence representation moduleBy comparing different optimization methods, it is found that the sentence representation module and relational reasoning module are optimized simultaneously. Their interaction restricts the model’s performance improvement, but each module’s specific impact is unknown. Therefore, this paper further studies the impact of this interaction on each module. The specific experimental results and analysis are as follows.This paper experimented on the semantic layer level change of sentence representation module in the joint optimization scheme on SNLI dataset to explore the influence of inference module on sentence representation module. The experimental results are shown in 2. Influence on reasoning moduleSimilarly, this paper explores the impact of the interaction between the two on the inference module’s performance. The optimized inference module uses the deep fusion matching network, mainly affected by the number of matching layers and syntactic structure. Since the performance changes in the syntactic structure cannot directly reflect the model’s performance changes, this paper mainly explores the performance changes of the reasoning module by comparing and analyzing the learning curves of the reasoning models with different matching layer levels on the SNLI dataset.As shown in Based on the experimental results of this part, it is found that the interaction between the sentence representation module and the reasoning model can improve the overall performance of the joint model. However, on the other hand, it will restrict the performance of each module.In this paper, considering the joint optimization of the sentence representation module and the relational reasoning module, a sentence representation reasoning method that combines a multi-layer semantic network and a deep fusion matching network is proposed. Specifically, firstly, the complete sentence embedding representation is obtained by fusing a multi-layer semantic network. Next, a deep fusion matching network obtains the local reasoning and syntactic structure information between sentence pairs. Then the reasoning results are obtained through the pooling layer and full connection layer. Finally, the performance of the joint optimization model is explored on several data sets containing recognition tasks. The joint optimization model’s advantages and disadvantages are compared with those without optimization, individual optimization, and combinatorial optimization. Experimental results show that the sentence reasoning method’s joint optimization has better accuracy than other existing characterization reasoning methods. However, comparing separate optimization sentence characterization and optimization effect between reasoning models, the optimization effect is closer. This result suggests that sentence representation and reasoning model optimization function have a larger influence on the final result of reasoning on promoting the reasoning and inference model of optimizing. After comparing the experimental results of joint optimization and combinatorial optimization, there are mutual constraints. These constraints restrict optimal performance, resulting in no linear superposition of reasoning performance after joint optimization.At present, the field of natural language reasoning has become another research hotspot after the field of images. Many scholars have carried out research work in this field. However, sentence representational reasoning is still the focus and difficulty in this field due to cognition and understanding. Although this paper explores sentence representation and reasoning methods that improve reasoning accuracy, it is far from the best effect. Given this, future research can be further studied from the following contents:This article explores the characterization of the sentence module and a reasoning model for sentences expressing the influence of reasoning methods and found mutual promotion and mutual constraints. However, this article does not put forward specific proposals for how to solve the constraints between them. Subsequent can consider solving the constraints between the two to realize partial performance improvement and improve the overall performance.10.7717/peerj-cs.908/supp-1Supplemental Information 1Click here for additional data file.