Furthermore, we reveal that the molecular fragment has a great influence on the motions of molecules. Combining scanning tunneling microscopy and DFT calculations, two braking mechanisms are addressed for molecular rotors. One is the transformation of adsorption configurations under the external electric field stimulus that enables the molecular rotor to stop/restart its rotation. The other is the introduction of embedded molecular fragments that act as a brake pad and can stop the molecular rotation. We find that the rotation can be recovered by separating the molecule from the fragments. Our study suggests a good system for manipulating molecular rotors' properties in nanophysics and has important value for the design of controllable molecular machines.Manipulation of artificial molecular rotors/motors is a key issue in the field of molecular nanomachines. Here we assemble non-planar SnPc molecules on an FeO film to form two kinds of rotors with different apparent morphologies, rotational speeds and stabilities. Both kinds of rotors can switch to each other  Two “braking mechanisms” for molecular rotors: one is the stimuli-induced conformational change; the other is the embedded molecular fragment acting as a brake pad. In vivo, the molecular machines commonly operate at the interface,2 such as membrane proteins. From the perspective of bionics, surface-mounted molecular rotors are thought as one of the most promising classes of artificial devices for application in nanoscience.Fabrication and manipulation of molecular rotors and motors are crucial for artificial molecular machines,14–18 Gao's group constructed an array of anchored single-molecule rotors on gold surfaces.19,20 Zhong et al. exhibited surface-mounted molecular rotors with variable functional groups and rotation radii.21 Furthermore, Sykes' group explored the rotational mechanism of a single thioether molecule and developed a single-molecule electric motor.22–24 To the best of our knowledge, a small body of work explored nanoscaled molecular machines with controlled properties.25–27 Achieving control of molecular machines is difficult, but quite nontrivial. To realize the practical application, many problems still remain open for controlling molecular machines, such as how to adjust the rotation speed of a nanoscale molecular rotor, how to directly stop/restart the motion of a molecular motor and how to realize the propagation of rotational excitation between adjacent molecules such as a macroscopic scale gear set.28–31Up to now, versatile molecular rotors have been successfully fabricated on solid surfaces. For example, Michl's group synthesized dipolar and nonpolar molecular rotors with an altitudinal axle.d) to the surface32,33 (U(d) being a double-well potential with degenerate minima at ddw and dup. Local external stimulations are available with the tips of scanning probe microscopy. The most direct initiation of atomic motion in this switch is to exert an external stimulation on the central ion, triggering the system switch in |Sn-dw〉 and |Sn-up〉.33 The vertical translation of the central ion goes along with the molecular planar framework affected, i.e. the switching between |Sn-dw〉 and |Sn-up〉, which subtly affects the interaction between the molecular rotor and the substrate, and even influences the probability of the molecule switching between its stable states. These features inspire one to explore controlling the configuration switch and rotation speeds with local electric field applied by the metallic tip of a scanning tunneling microscope (STM). This would have great application prospects in the field of nanomechanical devices.In the current stage, we naturally associate the role of switches and clutches with machinery, and try to install similar functions on nanomachines. A cap-shaped tin phthalocyanine (SnPc) molecule adsorbed on the surface constitutes a single-molecule switch, with the central metal ion pointed either down to the surface or up to the vacuum with different distances  is used as the nanotemplated substrate for molecular adsorption. Two types of SnPc configurations are observed and behave as molecular rotors with different rotational rates and thermal stabilities. The rotation of SnPc molecules can be stopped and restarted, and both configurations can transform into each other under the external electric-field stimulus induced by the STM tip, but with different interconversion barriers. Combining STM and density functional theory (DFT) calculations, we studied the configuration transformation in detail and suggested that the unequal interconversion barriers resulted from the total energy difference between the two configurations when adsorbing on the FeO film. Furthermore, Pt(111) supported FeO films are used as nanotemplates to immobilize individual SnPc molecules. The well-known lattice mismatch between Pt(111) and FeO(111) films gives rise to the Moiré patterns, as shown in 35 The two adsorption configurations of SnPc exhibit distinct STM topographic features under the same tunneling conditions. As shown in a typical STM image in 36 The rotational motion of the molecular configuration is faster than the scanning speed during STM imaging and thereby corresponds to the quick molecular rotation. The other molecules observed in 37 Through detailed examination of the STM frames successively recorded, it is apparent that the orientations of four-lobe shaped molecules changed, as indicated by the colored arrows in the upper parts of I–V) spectral measurements. For HR rotors, the excitation energy threshold required for configuration switching was determined by positioning the STM tip above the centers of blurry circular shaped molecules and gradually increasing the bias voltage. As shown by the red curve in I–V spectrum  shows an obvious tunneling current fluctuation at about −2.30 V, which is associated with the switching process. In subsequently-recorded STM images, via the STM tip.32,33 The rotation speed of SnPc rotors could be similar to those of TiOPc rotors35 due to the nearly same moment of inertia of the two kinds of molecules which determine the pre-exponential frequency factor for rotation speed.22,38–40To explore configuration switch of the molecular rotors, we carried out STM manipulation and current–voltage  bias region the conductance of the HR (LR) rotors is higher (lower) than that of the LR (HR) rotors. In the negative bias region for HR rotors and the positive bias region for LR rotors, there is a significant fluctuation of the tunneling currents as highlighted by ellipses in i.e., it points to the vacuum and the substrate for the |Sn-dw〉 and |Sn-up〉 molecules, respectively. As a result, the responses of the molecules to external fields depend on the polarity of the bias voltage. This should be the reason why LR to HR (HR to LR) conversion commonly occurs in the positive (negative) bias region. The two ground states of this switch labeled as “0” and “1” can be clearly distinguished in STM images due to their high on/off conductance ratio, and thus can be a single-molecule conductance switching as key elements of the molecular devices. On the FeO surface, furthermore, these single-molecule rotors can self-assemble into large-scale ordered arrays in one-monolayer coverage, which can be addressed as “read and overwritten” of high-density storage devices.vs. 4.02 Å) goes along with the molecular planar framework affected as lobe-surface distances changing (3.63 Å vs. 2.54 Å). As a consequence, the interactions between the |Sn-dw〉 molecules and the substrate would be significantly different from those for the |Sn-up〉 molecules. DFT calculations further indicate that the total energy of the |Sn-up〉 molecules is lower than that of the |Sn-dw〉 molecules by about 1.20 eV per cell. Such an energy difference determines the adsorption energy U(d) to be an asymmetric double-well potential, which results in different conversion behaviors of the two kinds of rotors. This point that the vertical translation of the central ion goes along with the lobe-surface distances changing is different from the situation of molecules adsorbed on the metal surface where the changes of the lobe-surface distance can be negligible.33DFT calculations were carried out to determine the structures and energetics for both |Sn-dw〉 and |Sn-up〉 configurations. Two structures resembling the experimental conformations were found, as displayed in θ corresponds to the angles between one of the in-plane symmetry axes of the SnPc molecules and the 〈001〉 direction of the Pt(111) surface. The calculated rotational barriers for |Sn-up〉 and |Sn-dw〉 configurations are around 187 meV and 151 meV, respectively. Once adsorption occurs, the interaction (friction) between the molecules and substrate plays a critical role in determining the molecular rotation characteristics. At a closer distance, the lobes of |Sn-up〉 molecules may simultaneously bond with the FeO surface by formation of carbon–oxygen (C–H⋯O) interaction, which leads to a high rotational energy barrier. In contrast, the lobes of |Sn-dw〉 molecules have a weaker interaction with the FeO surface due to a longer lobe-surface distance and thus show the lower rotational barrier. Accordingly, we can unequivocally assert that |Sn-up〉 is non-degenerate with |Sn-dw〉 as the obvious energy difference and Sn-up (Sn-down) molecules correspond to LR (HR) rotors. As STM does not image the atomic structure, but can resolve molecular orbitals,44 the closer distance between the substrate and the Sn ion in |Sn-dw〉 molecules means stronger electron hybridization . This fact further clarifies that the bright-center molecules correspond to the |Sn-dw〉 configuration and do not undergo configuration transformation when the rotation stops via a fragment embedding. The close-to-perfect molecular manipulation straightforwardly reveals that the rotation of the HR rotor can stop by trapping a small fragment and can be recovered by separating the molecules from the fragment.When manipulating molecular configuration conversion, we find that the molecular fragment has a great influence on the molecular rotations. −8 Pa. Single-crystal Pt(111)  was cleaned by repeated cycles of Ar+ sputtering and annealing until a clean surface was obtained as checked by STM. Iron atoms were evaporated from an electron beam evaporator  equipped with an iron rod . FeO ultrathin films were fabricated by oxidizing iron thin films at an oxygen pressure of 1.0 × 10−4 Pa and the substrate temperature of about 900 K. The temperature was monitored by using an infrared pyrometer and calibrated by using a thermal couple. Thoroughly degassed SnPcs were evaporated from a silica crucible heated at about 450 K with a deposition rate of 0.05 ML min−1. All the STM images were acquired at liquid nitrogen temperature (∼77 K) with electrochemically etched tungsten tips and purchased Pt–Ir tips in the constant-current mode. Differential conductance (dI/dV) measurements were obtained with the help of a lock-in amplifier  with an internal modulation source. The modulation of the lock-in amplifier for dI/dV was 10 mV at 777 Hz. Since in our case bias voltages were applied to samples, modulation probes the unoccupied/occupied states of the sample in positive/negative biases. Each dI/dV curve was obtained by averaging the spectra recorded on the centers of the molecules.Experiments were carried out in a low temperature ultrahigh vacuum STM system  with a base pressure better than 1.0 × 10Ab initio Simulation Package (VASP).45 The exchange and correlation functional with a Perdew–Burke–Ernzerhof generalized gradient corrected approximation (GGA) and ultrasoft pseudopotentials describing the electron–ion interactions was used in the calculations. The wave function was described with a plane wave basis set and an energy cutoff of 400 eV was used. The van der Waals interactions have been calculated using the DFT-D2 method of Grimme.46 Based on the experimental observations,34 the (√91 × √91)R ± 5.2° Moiré pattern for the FeO/Pt(111) substrate was modeled at full scale by a supercell slab consisting of two Pt atomic layers (182 Pt atoms) and one FeO(111) bilayer (73 Fe atoms and 73 O atoms); a vacuum thickness of 12.0 Å was used to separate the slabs. The gamma point was considered in the k-point mesh for such a big supercell. The bottom layer of Pt atoms was fixed and the upper layers of the supercell (containing 1 Pt layer and FeO bilayer) were fully relaxed. All the calculations are spin-polarized. A SnPc molecule was put on the FeO/Pt(111) substrate at different adsorption sites  with the Sn atom pointing to the surface (|Sn-dw〉) and pointing to the vacuum (|Sn-up〉) to search for the most stable adsorption configuration.DFT calculations were performed by using the Vienna Via molecular manipulation, we addressed two “braking mechanisms”: one is the transformation of adsorption configurations under the external electric field stimulus that enables the molecular rotor to stop or restart its rotation; the other is the embedded molecular fragments that can stop the molecular rotation. Our approach develops a good system for manipulating molecular rotors' properties in nanophysics and has significant value for the design of controllable molecular machines.We assembled non-planar SnPc molecules onto a nanotemplated FeO(111) film to form two kinds of rotors with different STM morphologies, rotational speeds and stabilities. The detailed structures of molecular rotors are studied by STM and DFT calculations. It's revealed that the rotors with high and low rotation speeds correspond to the SnPc molecules with the Sn ion pointing to FeO(111) and to the vacuum, respectively. Both kinds of rotors can be transformed into each other by applying external field stimulation. DFT calculations confirm that the total energy of the |Sn-up〉 molecules is lower than that of the |Sn-dw〉 molecules. Such an energy difference determines that the adsorption energy of SnPc is an asymmetric double-well potential, and results in different conversion behaviors of the two kinds of rotors. Furthermore, we find that the molecular fragment has a great influence on the motions of the molecules. Shuangzan Lu: data curation, writing – original draft, writing – review & editing. Min Huang: software, writing – original draft, writing – review & editing. Guodong Huang: data curation, writing – original draft. Qinmin Guo: data curation, writing – original draft. Hongxing Li: data curation, software. Jinghao Deng: data curation. Chendong Zhang: resources, supervision, writing – original draft. Yinghui Yu: conceptualization, methodology, data curtion, writing – original draft, writing – review & editing.There are no conflicts to declare.


The chicken D blood system is one of 13 alloantigen systems found on chicken red blood cells. Classical recombinant studies located the D blood system on chicken chromosome 1, but the candidate gene was unknown. Multiple resources were utilized to identify the chicken D system candidate gene, including genome sequence information from both research and elite egg production lines for which D system alloantigen alleles were reported, and DNA from both pedigree and non-pedigree samples with known D alleles. Genome-wide association analyses using a 600 K or a 54 K SNP chip plus DNA from independent samples identified a strong peak on chicken chromosome 1 at 125–131 Mb (GRCg6a). Cell surface expression and the presence of exonic non-synonymous SNP were used to identify the candidate gene. The chicken CD99 gene showed the co-segregation of SNP-defined haplotypes and serologically defined D blood system alleles. The CD99 protein mediates multiple cellular processes including leukocyte migration, T-cell adhesion, and transmembrane protein transport, affecting peripheral immune responses. The corresponding human gene is found syntenic to the pseudoautosomal region 1 of human X and Y chromosomes. Phylogenetic analyses show that CD99 has a paralog, XG, that arose by duplication in the last common ancestor of the amniotes. In humans, CD99 is a cell surface protein, involved with multiple biological processes including cell adhesion, migration, and cell death. It is essential for the transendothelial migration of immune cells. It plays an important role in lymphocyte development, is involved with the upregulation of MHC class I and II and T cell receptor expression on thymocytes, and has important roles in both T-cell activation and early B-cell lymphopoiesis . The CD9a and CD99 glycoproteins, respectively, which are separately expressed on the red blood cell (RBC) membrane. They are located adjacent to each other in the genome in the same orientation. In humans, the CD99 gene lies within pseudoautosomal region 1, while part of the Xg gene is located within the pseudoautosomal region and a portion is X-specific. Thus, in humans, an incomplete Xg is present on the Y chromosome. The expression of Xg and CD99 is coregulated and these two molecules together define the human Xga/CD99 blood group [Human Xg and CD99 genes, each consisting of 10 or 11 exons , encode the Xgod group .An increased understanding of how variability within genes affecting immune responses can impact survival is relevant to improved animal health. The chicken is an excellent non-mammalian model organism for comparative immunology as well as an important food source. Early immunology studies revealed the partitioning of B- and T-cell development ,6. Rous There are 13 blood systems known in the chicken which were named alphabetically in their order of discovery . The B bThe availability of DNA banks comprising multiple DNA samples from individuals serologically typed for various blood systems, plus the availability of genome sequence information for several experimental lines with known blood system alleles, provided the resources for the identification of candidate genes of chicken blood systems. The chicken D blood system was first identified by Briles as the fourth red blood cell antigen . The D s1D2 chickens compared with D1D3, D1D4, D2D3, and D3D3 genotypes within specific MHC-B genotypes [Information on the biological role of the D system in the chicken is scarce; however, the few available studies point to a role in immune response. Divergent selection for bursa of Fabricius size or antibody response against sheep red blood cells in chicken lines resulted in differences in the frequency of D alleles indicating possible relationships with the D system ,15,16. Lenotypes . HoweverLittle research has been carried out with the D blood system since these early studies, most likely due to lack of alloantisera for the detection of the antigens. The primary purpose of the research presented herein was to determine the gene responsible for the D blood system antigen in the chicken. Identification of the causative gene, and DNA-based detection of the variants defining the D alleles would provide the tools needed to expand our information on the D system’s relevance to the chicken immune response. The observation that the candidate gene has an immunological impact on the mammalian immune system adds considerable value to the identification of the chicken D blood system antigen.1D2) each mated to a single dam (D1D2), with serological information for the segregating progeny. In addition, there were NIU non-pedigree samples having known D1 and/or D2 alleles. The HAS/LAS lines are a pair of lines divergently selected for high (HAS) or low (LAS) antibody levels after immunization with sheep red blood cells. Generations 11 and 13 had previously been typed for the D system and were reported as containing D1, D2, and D4 [DNA was available from birds from multiple sources with serologically defined D system alleles and is summarized in , and D4 . DNA was, and D4  as this 1/D2. Three DNA pools were prepared for each family, containing DNA of either D1D1, D1D2, or D2D2 alleles as determined by serology . An additional set of DNA pools was generated from NIU non-pedigree birds, and was comprised of either D1D1, D1D2, or D2D2 individuals. Genome sequences were available for the five experimental lines listed , four of which also had known serologically defined D system alleles. Genome sequences were also available for elite WL, RIR, and WPR layer lines [DNA pools were produced using the two NIU pedigree families for which both sire and dam were heterozygous Der lines .SNP genotypes for the three sets of DNA pools were obtained using the Affymetrix Axiom 600 K chicken SNP array. Genotyping was performed by GeneSeek . Genotype calling was performed with Affymetrix Analysis Power Tools. A quality control filter was applied with minor allele frequency of 0.1 and maximum missing genotypes of 0.1 resulting in genotypes from 580,961 SNP available for the analysis.2r) calculated from regression analysis was used to select SNPs that segregated in accordance with expected pool genotypes. All genes on chromosome 1 were examined, with subsequent focus on region with highest 2r value.Intensity files from the 600 K SNP Affymetrix chip were exported and processed using a custom R script. Calls were recoded as 0 for AA, 1 for AB, and 2 for BB to represent the number of copies of B allele in the individual. A regression equation was developed for each SNP. Expected allele count (based on serology information) was used as response variable and intensity was used as explanatory variable. Coefficient of determination  chemistry  which employs one common primer with two allele-specific primers and fluorescence detection with end-point reads [SNP within the candidate gene were selected based on whether they were predictive for non-synonymous changes, as protein differences are expected to be one cause of serological differences among blood system alleles. Additional synonymous SNP variants were chosen to provide better coverage of the gene, plus one indel within a potential splice region was also included. SNP allele and indel genotypes were identified through PACE nt reads . All funSamples from individuals with serologically defined D alleles, or from lines known to contain specific D system alleles were genotyped with the SNP set identified within the candidate gene. Specific combinations of SNP alleles were identified and where possible, SNP haplotypes were compared with D serological alleles, to associate specific haplotypes with specific D alleles. These haplotypes were then assigned an identifier matched with the serological moniker. DNA from RIR and WPR lines were included to determine if additional novel haplotypes were present in these two different breeds even though no serological information was available.Ortholog and paralog predictions were performed using BLAST ,28,29. S1, D2, or heterozygous pedigree individuals from the NIU DNA bank are shown in n = 50) for which the D allele was known  and 54 K genotypes were available.Results from a GWAS analysis using 600 K chicken SNP array genotypes from the pooled samples containing DNA from either D1 allele (line IAH-72) or the D3   see . A compaA close examination of the sequences of HYL lines for which D allele segregation had been previously identified showed that lines WL1, WL3, and WL4 had SNP segregation within CD99, while lines WL2 and WL5 did not. This specific SNP segregation pattern was not seen within any of the other genes within the candidate region identified in the GWAS analyses, including ARSD and ARSE. With the SNP patterns fitting to the expectation based on serological information, these additional sequences confirmed CD99 as the most likely D blood system candidate gene.3 allele were all homozygous for the same CD99 haplotype, which was subsequently labelled CD99-H03. The inbred line with the D1 allele contained a different haplotype, labelled as CD99-H01.The SNP panel developed for the CD99 gene detected 7 SNP and one indel, encompassing exons 2 through 11. Genotypes for each assay were obtained from individuals from the HYL lines for which D system allele information was known. A total of 11 unique CD99 haplotypes were found see . The thr3 homozygous, contained only CD99-H03, thus confirming that D3 = CD99-H03. For line WL1, CD99 haplotypes were defined for 58 individuals previously typed as either D2 and/or D3. Two haplotypes were found, CD99-H03 and another haplotype subsequently named CD99-H02; thus, D2 = CD99-H02. There was 100% agreement with the serological D alleles previously assigned and the CD99 haplotypes.Lines WL2 and WL5, both of which are D1 and D3, though no individual DNA with known D alleles was available. Within the 120 individuals tested, there were three CD99 haplotypes found: CD99-H01, CD99-H03, and a rare haplotype  which was assigned the label CD99-H06.Line WL3 had previously been reported as segregating for D1 and D3 and contained two CD99 haplotypes, CD99-H01 and CD99-H03, as found in the inbred lines and other Hy-Line lines containing these two serological types. Of the 119 samples for which both serological D alleles and CD99 haplotypes were identified, there was agreement for 106/119 (89%). None of these discrepancies involved the misidentification of homozygotes and are likely due to reagents failing to distinguish heterozygotes accurately.Line WL4 segregated for alleles D1 and D3, which is consistent with the presence of CD99-H01 and CD99-H03 that we found in the generation 41 samples. Haplotype CD99-H06 is identical to CD99-H03 at all non-synonymous SNP and thus is expected to produce a protein identical to D3. The D4 allele previously identified within the LAS line has likely been lost during the intervening 28 generations.The HAS and LAS lines both contained CD99-H01 and CD99-H03 and HAS also contained the CD99-H06 haplotype. The D blood system typing of generations 10 and 13 identified both lines as containing D1, D2, and D3, with the three corresponding CD99 haplotypes also being found. There was agreement between the D allele and CD99 haplotype for 96/104 of the samples (92%). Samples with disparate results always had one allele correct, and the second allele was either not identified, or was mis-identified.There were 104 samples from the NIU DNA bank for which both serological D allele and CD99 haplotype information was available. The D alleles identified included DCD99 haplotypes were also obtained from samples for which no D blood system was available. These were the different breeds Rhode Island Red (RIR) and White Plymouth Rock (WPR)) and were examined to determine if additional haplotypes were present in different breeds. Within the RIR1 line, there were three haplotypes found , and the WPR lines contained CD99-H01, CD99-H04, and novel haplotypes (CD99-H10 and CD00-H11). None of these have any serological information and thus cannot be assigned to specific D alleles; however, these two novel types differ from another haplotype by only one synonymous SNP and thus are likely not another D serological allele. All haplotypes found are reported in Blast , multiple sequence alignments, and synteny analysis were used to further explore the relationships between CD99 and XG. Blast was used to identify CD99 and XG orthologs and a multiple sequence alignment indicates that XG is a paralog of CD99 . CD99 orSynteny analysis shows conservation of the gene order between the CD99 chromosomal regions of zebrafish, chicken, and humans . The regResearch with human CD99 shows that this molecule is involved with multiple critical biological processes, though the mechanism of action is still not clear. It mediates diverse cellular processes which impact immune function, inflammation, and cancer metastasis . The impp = 2.08 × 10−11) is located 325 Kbp away from the start of CD99. The top SNP within the 54 K SNP data set (p = 8.24 × 10−11) was within the ARSD gene, located 104 Kbp away from CD99. Thus, while the GWAS was able to greatly narrow down the genomic region, it could not identify the actual gene. This is due to insufficient SNP in the region of interest, lack of segregation of those SNP in lines of interest, or lack of association of the SNP  with the phenotype. The information from the sequence samples, and use of the candidate gene SNP panel were necessary to unequivocally identify the candidate gene. This shows the SNP chip strength in identifying specific regions, but also shows its limitation in the identification of the correct candidate gene without having additional SNP information from other sources.It is worth noting that neither of the GWAS analyses identified the correct candidate gene. For the 600 K SNP data set, the top SNP (In the human, the CD99 protein is known to be critical for the leukocyte transendothelial migration signaling pathway , thus pla blood system [The thirteen known chicken blood systems were all initially identified with alloantisera, which was often very specific to the lines within which it was produced. The chicken B blood system identifies variations within the chicken MHC, which contains genes with major impacts on the immune system and subsequent response to multiple diseases . It was d system . Thus, t


Obtaining patient feedback is an essential mechanism for health care service providers to assess their quality and effectiveness. Unlike assessments of clinical outcomes, feedback from patients offers insights into their lived experiences. The Department of Health and Social Care in England via National Health Service Digital operates a patient feedback web service through which patients can leave feedback of their experiences in structured and free-text report forms. Free-text feedback, compared with structured questionnaires, may be less biased by the feedback collector and, thus, more representative; however, it is harder to analyze in large quantities and challenging to derive meaningful, quantitative outcomes.The aim of this study is to build a novel data analysis and interactive visualization pipeline accessible through an interactive web application to facilitate the interrogation of and provide unique insights into National Health Service patient feedback.This study details the development of a text analysis tool that uses contemporary natural language processing and machine learning models to analyze free-text clinical service reviews to develop a robust classification model and interactive visualization web application. The methodology is based on the design science research paradigm and was conducted in three iterations: a sentiment analysis of the patient feedback corpus in the first iteration, topic modeling (unigram and bigram)–based analysis for topic identification in the second iteration, and nested topic modeling in the third iteration that combines sentiment analysis and topic modeling methods. An interactive data visualization web application for use by the general public was then created, presenting the data on a geographic representation of the country, making it easily accessible.Of the 11,103 possible clinical services that could be reviewed across England, 2030 (18.28%) different services received a combined total of 51,845 reviews between October 1, 2017, and September 30, 2019. Dominant topics were identified for the entire corpus followed by negative- and positive-sentiment topics in turn. Reviews containing high- and low-sentiment topics occurred more frequently than reviews containing less polarized topics. Time-series analysis identified trends in topic and sentiment occurrence frequency across the study period.Using contemporary natural language processing techniques, unstructured text data were effectively characterized for further analysis and visualization. An efficient pipeline was successfully combined with a web application, making automated analysis and dissemination of large volumes of information accessible. This study represents a significant step in efforts to generate and visualize useful, actionable, and unique information from free-text patient reviews. Patient experience improvement framework of the National Health Service (NHS) defines several quality indicators, among which patient feedback is a priority [Patient experience is described by the Beryl Institute as “the sum of all interactions, shaped by an organisation’s culture, that influence patient perceptions across the continuum of care” . It is apriority . Thus, tpriority , and heaOver the past 2 decades, there has been a greater emphasis on using patient feedback to inform and improve service delivery, largely in the United Kingdom, Europe, and the United States ,10. The star rating out of 6. These publicly available reviews are invaluable insights into the work of the NHS for the service providers themselves, national bodies such as NHS England, and the Care Quality Commission as well as patients deliberating on which services to use. This is a source of vast amounts of rich data, which has the potential to significantly influence the quality of services nationwide as well as policy regarding the NHS. Patient feedback in free-text form is typically hard to analyze on a large scale, which is why standardized scales are more frequently used to generate numerical measures for comparisons [The NHS website allows users to anonymously rate and share their experience in a public forum . All NHSparisons . The difThis type of data lends itself well to analysis using computed natural language processing (NLP) techniques, enabling high-volume automated analysis of text information. In their seminal work, Greaves et al  reportedThe advent of machine learning and the development of sophisticated NLP algorithms have significantly advanced the analysis of text corpora. A significant amount of research has focused on applying advanced NLP methods to web-based reviews. Web-based reviews provide an opportunity to explore free-text corpora that do not usually adhere to a structure or format. The free text in reviews, such as the patient experience, makes the process of automated analysis of the review challenging when compared with closed questions with an expected text input. As web-based patient feedback is extensive, the traditional text analysis methods may provide limited capabilities for analysis. The latest machine learning– and artificial intelligence–based NLP methods have been well explored for analyzing large review data sets, especially for analyzing the user experience. The latest NLP methods provide capabilities to classify the reviews as positive or negative with high accuracy. Identifying the underlying themes and topics in the user experience allows us to understand the frequently reported service areas in user feedback.The aim of the study presented in this paper is to provide an automated solution for the large-scale analysis of patient feedback on health care service providers. This study achieves this by exploring NLP techniques, including sentiment analysis and topic modeling. The objective of this study is also to present an interactive interface that provides stakeholders with a portal to analyze and identify outcomes of the patient feedback analysis. This work builds on previous work in the field ,17 and partifacts that are aimed at addressing a problem. The artifacts address the problems or enhance existing solutions and are important tools for arriving at research outcomes and reviewing them to decide how the adopted artifact can be further used [For this study, the design science research (DSR) methodology was used. The DSR paradigm is a widely popular research approach in information systems research. It is referred to as a problem-solving paradigm as it aims to build her used . The DSRher used . Furtherher used .good corpus and bad corpus based on the associated positive or negative sentiment. The good corpus and bad corpus were analyzed with topic modeling to identify topics within the reviews.cleanliness and dealt with dignity, among others.The NHS website includes a platform where patients provide both ratings and reviews for a particular NHS service. The NHS website rating system provides an outline of patient experience; the rating is an optional feature that is collected for a specific set of parameters such as how likely they are to recommend the particular hospital to family and friends? This is the central question on the NHS website in that the ratings provided are used to calculate the overall rating for a given hospital. The rating for this question provides a quick and easy indicator of a hospital’s performance in providing patient care. However, the rating is single and straightforward, and it is insufficient to obtain a detailed understanding of hospital performance. For a more detailed understanding of patient feedback, the NHS website includes questions where the patients are asked to provide ratings on five parameters: cleanliness, staff co-operation, dignity and respect, involvement in decisions, and same-sex accommodation (out of 6 stars). The website-allocated ratings for these 5 parameters are optional to the users. Finally, there is an optional free-text review of a maximum of 3000 characters. These reviews follow the NHS comment policy and are moderated. The moderators remove any personal information and ensure that the reviews do not cause any legal issues such as defamation [Patients can provide feedback about NHS hospitals in 3 main sections. First, they are asked to rate, on a scale of 1 to 6, famation . ResponsAll available reviews between October 1, 2017, and September 30, 2019, were collected from the NHS website.The NHS platform provides an application programming interface (API) that allows access to the patient ratings and reviews . A custoDate, Comment, and Label columns in the database. The Date and Comment columns referred to the posted date and the content of the participant’s comment, and the Label column was used to hold the sentiment of the comment, classified as either positive or negative.The data set underwent a few processing steps where only columns from the data set that were relevant for this study were selected. Specifically, for parsimony purposes, only relevant data fields were extracted and relabeled into Label column were not defined.Data were organized by posting date ,23 and pHow likely are you to recommend this hospital to friends and family if they needed similar care or treatment?—were used as the actual data against which the performance of the text-mining model  was tested. Owing to the skewed distribution of the numerical responses and limitations of the machine learning methods, to reduce complexity in the modeling procedure, the continuous-scale patient feedback ratings were discretized. Following discretization, rating scores of 1 and 2 were categorized as negative, and those of 5 and 6 were categorized as positive. Rating scores of 3 and 4 were discarded as they were deemed neutral ratings that did not portray polarization. This categorization served as a binary sentiment label for which each text-mining model was trained and assessed.Within the data set, the ratings given by the participants for the following question—Sentiment analysis, also referred to as opinion mining, refers to the computational study of people’s opinions, sentiments, attitudes, and emotions toward an entity. The entity can be another individual or a public figure, a product such as an electronic device, or service providers such as restaurants and hospitals. The identification of sentiment is performed based on the presence of words or phrases that are likely to refer to an opinion, sentiment, or emotion. If the sentence is identified as having a sentiment or opinion, it is then subjected to the feature selection process. During this process, the identified sentiment is associated with the feature that is being discussed. Finally, the sentiment is categorized into a chosen classification type. For instance, the sentiment can be classified into a binary, such as positive or negative. The sentiment analysis is associated with a score.The fine-grained sentiment score detects polarity within a text; in this case, whether the review is a positive or negative opinion. There are several approaches to sentiment analysis, including the strength of association, naïve Bayes (NB), and the support vector model. NB-based sentiment analysis models are popular and widely used. The NB classifier is a probabilistic classifier, which uses a mixture of models for classification and is widely popular for sentiment classification. Given a document, and based on the distribution of words in the document, the NB approach computes the probability of a document belonging to a class. This model calculates boundaries according to the distribution of the words across the labels while at the same time considering the joint probability of the words occurring independently together. Specifically, NB considers each word independently of one another and then tries to estimate the posterior distribution of a review being positive or negative according to the joint distribution of the words in the review. The probability is computed using the Bayes theorem to predict that a given word belongs to a specific sentiment.One of the popular implementations of sentiment analysis based on NB is the TextBlob rule-based sentiment analysis , which wgood or bad more quickly and consistently than a human-based process.The TextBlob implementation was used to analyze each of the 51,845 reviews to determine their sentiment value. This produced a score for each review between −1 and 1, where 1 represents a wholly positive sentiment, −1 represents an entirely negative sentiment, and 0 represents a neutral sentiment. This provided a method for evaluating whether reviews were Topic modeling is an unsupervised NLP approach where unlabeled documents are used to create a set of topics represented by a list of words that frequently occur in each topic. There are several topic modeling approaches, and most use dimensionality-reducing techniques with the goal of representing a document using fewer words. Some of the most popular topic modeling approaches are probabilistic latent semantic indexing (LSI), latent Dirichlet allocation (LDA), and correlated topic modeling (CTM).bags of words to represent documents. It aims to extract words that carry similar meanings . The LSA disadvantage of the LSI approach is that the number of parameters in the model increases as the volume of data increases, and this could lead to overfitting problems. Furthermore, when the LSI model is used on documents that were not part of the training data set, the topic probabilities have to be assigned again .The CTM approach helps in identifying the correlation between a specific topic and others. The correlation information might help the users in identifying links or associations between a specific topic from a database and other similar topics. The CTM approach uses a logistic normal distribution to identify topics from documents. A covariance matrix used for parameterizing the distribution is then used to identify the correlation between the topics. A topic graph is subsequently drawn, in which the topics are represented by nodes and their correlations with other topics are depicted . The corThe LDA approach also works under the assumption of LSI . However, this assumption is extended by using a hidden variable model of documents that consists of hidden random variables with which the observed data interact . In LDA,bag of words assumption used in the LDA approach makes it invariant to the order of words in the document. Furthermore, the order of documents in the corpus is also not a criterion for the LDA approach to extract topics from the document. This might not be suitable if the patient’s experience needs to be analyzed longitudinally . However, in this study, as the patient experience analysis did not consider the time factor, the LDA method suited its aims.An advantage of the LDA approach is that the statistical assumptions it makes for topic modeling enable it to uncover sophisticated structures in the texts. For instance, the We used the LDA topic modeling approach to categorize each review into computer-generated topics. LDA initially assumes the number of topics and attempts to calculate topics that best represent the documents. It does this by calculating the probability estimate of a word for a given topic as well as the probability of a topic for a given document .We used an LDA implementation called LDA Mallet owing to its use of a more precise sampling method called Gibbs sampling . Data weReviews were then labeled according to the dominant topic. The dominant topic was defined by the LDA model, predicting the percentage contribution of that topic to be ≥50%. The reviews that did not have any topic that contributed ≥50% were not included in the following analysis.The corpus was divided into two subcorpora, the first one being negative-sentiment–scoring reviews and the second one being any positive-sentiment–scoring reviews. Similar to the topic model for the entire corpus, we then performed experiments to determine the optimal parameters for both corpora. Using these parameters, we produced two models, one showing the topics generated from negative-sentiment reviews and the other showing topics generated from positive-sentiment reviews. Applying these new LDA models to their respective corpora produced a topic mixture for each entry of their respective corpora.The nested topic modeling approach enables the identification of the rationale behind a particular sentiment of the patient in each comment or review. The intent is to find out why the patient was happy or unhappy about a particular topic in each comment. The problem being addressed in this iteration of the study was to find the possible reason behind a patient’s sentiment for a particular topic in each comment.Visualizing the results from the NLP methods relied on a Microsoft Azure Cloud Service  to host NHS England is segmented into seven geographical regions with teams supporting the delivery of care locally: London, Midlands, North East and Yorkshire, North West, East of England, South East, and South West. Of the 11,103 possible services across these regions, 2030 (18.28%) services received a combined total of 51,845 reviews between October 1, 2017, and October 31, 2019. Among the reviewed services, the mean number of reviews per service was 26 (SD 60.3), and the highest number of reviews for a single service was 550 for Lincoln County Hospital. During the study period, the mean number of reviews per month across England was 2028 (SD 449.1). The number of reviews per month declined from 2625 in October 2017 to 1611 in September 2019.The number of services per 10,000 population was similar around England, with the lowest being in London, which also has the highest-density population . Across P=.02).To explore the opinions held within the reviews, the sentiment was analyzed for the entire corpus and for individual reviews. The analysis generated a score for how positive  or negative  the sentiment was between −1 (most negative) and 1 (most positive). Examples of reviews and their corresponding score can be found in A total of 14 clusters were identified from the entire corpus, from which themes were derived manually . Reviewsparamedic and ambulance , booking appointment , good experience , operations and surgery , and A&E . These topics comprised 46.49%  of the labeled reviews. Most topics occurred at a steady rate across the study period. No patterns in that variation in topic frequency were identified; however, procedure and dealing with anxieties increased, whereas obstetrics decreased in frequency across the study period  and negative-scoring reviews (≤0.2) were separated into 2 smaller corpora to undergo topic modeling to obtain new sets of positive and negative topics. The optimal number of topics was decided by creating a model for each number of topics in the range of 5 to 20 and using the coherence score as well as human intuition to determine the best topic model. Positive and negative clusters were identified, and the topics were labeled manually  and 5. TThe NLP analytics were then incorporated into a user-friendly web-based interface that enables the exploration of the data through the graphical user interface . Users cThe outcomes of the study presented in this paper demonstrate that NLP-based analysis using sentiment analysis and topic modeling can be used to develop an automated solution for analyzing patient feedback and reviewing the performance of a health care center. The results of the sentiment analysis showed that patient feedback can be accurately classified into positive or negative sentiments and the topic modeling approach can be used to identify topics in a patient review. Furthermore, the identified topics were associated with a sentiment based on the results of the sentiment analysis. This study also presented an interface for stakeholders to view and interact with the outcomes of the patient feedback analysis.In this study, close to 52,000 reviews from 2030 services were considered for analysis over a period of 2 years. The average number of reviews per service was 26 (SD 60.3). This is indicative of a generally low level of patient engagement with providing service feedback. Furthermore, the number of reviews over time shows that patient review activities have seen a consistent downward trend.A larger corpus of patient reviews can significantly contribute to building analytical models with capabilities to perform more granular analyses such as individual service performance, areas of concern for a selected service, and similar fine-grained analyses. They improve the communication channel between patients and services, and incentives for patient engagement are necessary for increasing the average number of patient reviews and contributing to the development of improved assessment tools.procedure and dealing with anxieties , implying that undertaking a procedure might be an anxious process for patients, and the average sentiment score of 0.33  indicates that the patients are generally happy with the service and care provided by the NHS services.The identification of a topic based on topic modeling is a useful tool as it helps in understanding the areas of performance and areas of concern for the NHS services. In this study, the most frequent topic was booking appointments  implies that the patients might be unhappy with the current appointment booking service. Therefore, topic identification, along with the associated sentiment score, allows policy makers to use the learnings from areas of success and potentially apply them to areas of concern to improve patient satisfaction with the NHS services.On the contrary, another frequently reviewed topic was The study presented demonstrates a multifactorial analysis that can be performed using topic modeling and sentiment analysis approaches. A large corpus helps in achieving temporal analysis of patient feedback, as demonstrated in A&E was most frequently reviewed across the regions. Procedure and dealing with anxieties was another frequently reviewed topic across regions. Region-based, frequently reviewed topics are a piece of beneficial information for policy makers to gain insights into the weak areas of NHS services for each region and, in general, across all regions.The combined approach of sentiment analysis of topics identified from topic modeling methods helps collect insightful information about health care services. In our study, the sentiment classification of each topic helped in assessing the public’s perception of the NHS services for a given topic, thus reflecting the service quality. With a large corpus of reviews, the sentiment analysis of identified topics over time can potentially explore the links between temporal factors, such as seasons, and patient experience for each topic. The results of this study demonstrate a decrease in sentiment during the winter. A more extensive and diverse data set of patient reviews has the potential to extract links between seasons and specific topics for further causal inferences to be made. For instance, a variation in sentiment scores for a topic across seasons can be analyzed for external causal factors such as influenza outbreaks, pandemics, staff shortages, technological hindrances, political changes, and similar outside factors.good staff, rather than just the topic itself. Although this did not affect the accuracy of the results, it could cause the topics to be less useful.We note that there are limitations to this study; the first one was that we discounted reviews that did not include a dominant topic. This reduced the population size and ignored reviews that might provide valuable insight. Second, both sentiment analysis and topic modeling inevitably have misclassification errors, especially when the user reviews can be misspelled or have a double meaning (sarcasm). The third limitation is that the topics from the topic modeling are influenced by words that carry sentiment; this causes some topics to carry sentimental meaning, such as Providing patient reviews in an easily inferable format through visualization tools to the public can foster competitiveness to improve among the NHS services. It is essential to provide analytical outcomes in an accessible format to general users to support patient autonomy and decision-making. The visualization tool developed in this study has the potential to rapidly and easily disseminate the findings of this study using maps and graph-based visual analytics. The visualization further enables users to view the feedback of an NHS service based on the key areas identified from the topics.This study presents an automated system for the analysis of patient feedback based on NLP techniques and topic modeling. The DSR methodology was adopted for this study to conduct the patient feedback analysis in 3 iterations. In the first iteration, sentiment analysis of the patient feedback corpus was performed followed by a topic modeling–based analysis of the corpus. In the third iteration, the sentiment scores and topics identified were further analyzed to associate the sentiment scores with the topics identified and categorize the corpus into good and bad corpora. Furthermore, we provided a data visualization interface with potential use for policy makers and associated stakeholders to review the performance of health care centers individually as well as by regions and identify the possible causes behind the performance of a health care center. The future work of our study aims to collect and analyze larger patient feedback databases to build more accurate analytical models. We are exploring artificial intelligence–based NLP models to include them in our analysis.


We present here anovel integrated approach employing machine learningalgorithms for predicting thermophysical properties of fluids. Theapproach allows obtaining molecular parameters to be used in the polarsoft-statistical associating fluid theory (SAFT) equation of stateusing molecular descriptors obtained from the conductor-like screeningmodel for real solvents (COSMO-RS). The procedure is used for modeling18 refrigerants including hydrofluorocarbons, hydrofluoroolefins,and hydrochlorofluoroolefins. The training dataset included six inputsobtained from COSMO-RS and five outputs from polar soft-SAFT parameters,with the accurate algorithm training ensured by its high statisticalaccuracy. The predicted molecular parameters were used in polar soft-SAFTfor evaluating the thermophysical properties of the refrigerants suchas density, vapor pressure, heat capacity, enthalpy of vaporization,and speed of sound. Predictions provided a good level of accuracy(AADs = 1.3–10.5%) compared to experimental data, and withina similar level of accuracy using parameters obtained from standardfitting procedures. Moreover, the predicted parameters provided acomparable level of predictive accuracy to parameters obtained fromstandard procedure when extended to modeling selected binary mixtures.The proposed approach enables bridging the gap in the data of thermodynamicproperties of low global warming potential refrigerants, which hinderstheir technical evaluation and hence their final application. However, HFCs are known as potent GHGs,with high global warming potential (GWP), and are to be phased outdue to the Kigali Amendment to Montreal’s Protocol.6 The gradual phase-out of HFCs under current environmentallegislations led to the search for alternative sustainable refrigerantsanew, focusing on desirable properties including the benign environmentaleffect , an acceptable level of safety , and excellent technical performance.7 For a while, the search for 4th generation refrigerants remainedelusive,8 until the principles of greenchemistry and engineering came to the rescue,9 identifying hydrofluoroolefins (HFOs), hydrochlorofluoroolefins(HCFOs), hydrofluoroethers (HFEs), and blends of natural or syntheticrefrigerants as potential alternatives.12Our planet is witnessing one of its most notorious problems todate with the emergence of the global warming phenomenon and climatechange, liable for rising atmospheric and marine temperatures, increasedsea levels, extreme weather conditions, and many other environmentalissues.13Hence, refrigerantsto replace the high-GWP ones currently usedmust meet three criteria: (1) environmentally friendly, (2) safetyrequirements, and (3) excellent technical performance. The first twocriteria are inherent to the pure refrigerant depending on their atomicconstituents and structure, acting as the first layer of screeningby discarding those unable to meet environmental and safety standards.In contrast, the third criterion relies on detailed knowledge of arefrigerant’s thermodynamic properties required for accurateprocess design and evaluation. This is the main hurdle in the commercializationof newly developed refrigerants achieving the environmental and safetyrequirements, given the large number of thermodynamic properties requiredfor accurate technical evaluation.11 exemplifies the difficulties associated with the search for greeneralternative refrigerants. Their systematic screening of a large arrayof possible substances from available databases and experimental datasetsresulted in a pool of 138 pure candidate refrigerants meeting therequired environmental and safety limits. The additional technicalperformance assisted in the identification of 27 possible low-GWPsingle-component refrigerants finely balancing the trade-offs betweenthe aforementioned two selection criteria. Such an effort was facilitatedby the availability of extensive databases on the relevant propertiesof known substances studied over the past 20 years. However, thisbecomes increasingly difficult in the case of newly developed greenrefrigerants, as the standard experimental route to obtaining propertymeasurements has long ceased to be capable of meeting the exponentiallygrowing number of newly developed refrigerants and blends. This hasaccelerated the increasing need for predictive computational modelingtools for evaluating the thermodynamic behavior of these refrigerantsand obtaining their relevant properties required for technical evaluation.16 Of particular prominence are molecular modeling techniques, bridgingmicroscopic characteristics of a fluid with its observable macroscopicproperties, becoming indispensable for accurately predicting thermodynamicproperties and enhancing the fundamental understanding of complexsystems.20 Outstanding contributions havebeen made in this field. For instance, Raabe and co-workers26 employed classical molecular simulations to predict the thermophysicalproperties of pure HFOs and HCFOs, for which few experimental datawere available. Recently, García et al.27 performed a computational screening of 40 experimentallyavailable metal–organic frameworks (MOFs) exploring their applicationin adsorption air-conditioning with selected low-GWP refrigerants,searching for the optimal MOF–refrigerant pair. The computationalstudy provides useful guidance on the MOF topology, composition, andpore sizes needed to achieve the best performance with the selectedlow-GWP refrigerants for this application. Mambo–Lomba andParicaud28 developed a thermodynamic modelfor different pure refrigerants and their binary mixtures, based ondifferent versions of the conductor-like screening model for realsolvents (COSMO-RS).30 Vega and co-workers38 employed different versions of the statistical associating fluidtheory (SAFT)39 equation of state (EoS),namely, the Lennard–Jones version (soft-SAFT) and polar perturbedchain version (PC-SAFT), to study a wide range of thermodynamic, interfacial,and transport properties of pure HFCs, HFOs, and HCFOs demonstratingthe efficacy of molecular modeling in the search for alternative eco-friendlymaterials.40 Polishuk et al.41 implemented PC-SAFT and the variable attractiverange SAFT version (SAFT-VR) to study the thermodynamic propertiesof 3rd generation HFCs and their mixtures. Vinš et al.42 developed a property model using PC-SAFT forthe density and surface tension of 4th generation HFEs. Mickoleitet al.43 examined the effect of the thermodynamicmodel on the accuracy of screening and designing refrigeration systems,examining models including classical cubic equations of state, COSMO-basedmodels, and PCP-SAFT. Yang et al.44 appliedentropy scaling with the REFPROP45 referenceEoS to model the thermal conductivity of several pure and mixed refrigerants.Wang et al.46 integrated a polar versionof PC-SAFT with process modeling and optimization to determine optimalrefrigerants based on technical evaluation.The monumental work of McLinden et al.28 Finally, SAFT-basedEoSs require the availability of experimental data,47 even if somewhat limited in terms of type and range, toregress the parameters descriptive of the pure refrigerant. Grantedthese parameters can be transferable across molecular families withspecific analoguous molecular characteristics,48 yet this approach is not fully foolproof when applied tonew and emerging refrigerants with unavailable data.Despite this, itremains difficult to assert the adequacy of onethermodynamic model over another for property predictions for emerginggreen refrigerants, as all of these models suffer from some limitations,with no singular universal model. For example, molecular simulationsare computationally expensive for force-field parametrization anddata generation over a wide range of operating conditions of interest,as required for the detailed characterization of these compounds.Conversely, the accuracy of purely predictive COSMO-based models isqualitative rather than quantitative, which can be enhanced by adjustingsome of the model’s parameters at the expense of losing itsfully predictive nature.49 coupled molecularsimulations and PC-SAFT to study the phase equilibria of some 4thgeneration refrigerants with 3rd generation refrigerants and CO2. Similarly, Fouad and Alasiri50 employed molecular dynamics simulation to generate data for theviscosity and self-diffusion coefficient required for regressing parametersof polar PC-SAFT of selected low-GWP refrigerants. Moreover, Li etal.51 employed molecular dynamics simulationand polar PC-SAFT to examine the interfacial anomaly for selectedrefrigerant blends of HFOs and n-alkanes. These contributionsrelied on molecular simulations for data generation in the absenceof experimental data. The simulation data was then used to parametrizethe more rapid molecular-based EoSs, enabling extrapolation of propertymeasurements at industrially relevant conditions without incurringhigh computational costs.Conversely,the combination of different modeling approaches exploitingthe synergetic benefits of several modeling tools has proved to bea beneficial endeavor for accelerating the development of molecularmodeling tools for assessing emerging low-GWP refrigerants, even inthe absence of data. Raabe52 The integrationof machine learning with other molecular modeling approaches suchas COSMO-RS has found success in predicting the thermodynamic behaviorof several classes of green materials as shown in refs  and machinelearning with molecular modeling approaches for a number of applicationsincluding the rational design of new green materials, predicting thermodynamicbehavior of complex systems, and accelerating the development of force-fieldsfor molecular simulations. in refs −62. Ther63 applied toeighteen 3rd and 4th generation refrigerants including HFCs, HFOs,and HCFOs, provided in In this contribution,we present for the first time a novel paradigmfor predicting molecular parameters of pure refrigerants, modeledusing polar soft-SAFT EoS,2The general paradigm of the methodologyadopted in this work, highlightedin 2.130 the surface charge density of a molecule,obtained from density functional theory (DFT), is transformed intodiscrete surface segments with a specific screening charge density(σ), incorporating contributions from different intermolecularinteractions. This generates the so-called σ-profile and σ-potential,representing the probability of specific charge density on a surfacesegment, and the affinity to a specific polarity surface, respectively.30 To do so, the three-dimensional (3D) molecular structures for theeighteen refrigerants were built using Turbomole software (TmoleXversion 4.5.1)64 and then geometricallyoptimized at the DFT level using BP86 functional with the def-TZVPbasis set, as provided in 65 The generated σ-profileswere then discretized into eight regions, each with a screening chargedensity of 0.00625 e/Å2, used to compute the moleculardescriptors namely, Sσ-profile, as integrals of the area under the σ-profile curves in thoseeight regions.66 Though the σ-profilescan be divided into 51 regions for more accurate models,30 it was opted to maintain the relative simplicityof the model by minimizing the number of descriptors. The advantageof using these molecular descriptors as ANN inputs is that aside frombeing obtained a priori without fitting, they alsocontain sufficient information indicative of the structural and energeticnature of the molecule, needed to predict its governing intermolecularinteractions. Additional details on the implementation of COSMO-RSto obtain σ-profiles and their discretization can be found elsewhere.66Inthis work, COSMO-RS is used to obtain molecular descriptors representativeof the studied refrigerants, used as inputs into the ANN model. WithCOSMO-RS,2.263 the residual Helmholtz energy density (ares) of a pure fluid is computed as the sumof various microscopic contributionsaref) represents the repulsiveand dispersive interactions between the individual Lennard–Jones(LJ) group integrating the molecule . The chain term (achain)represents the chain formation from connected LJ segments. The associationterm (aassoc) denotes highly directionaland short-range intermolecular interactions such as hydrogen bonding.Finally, the polar term (apolar) standsfor the explicit contribution from multipolar intermolecular interactionssuch as a permanent dipole or quadrupole. Additional details on thetheory and mathematical expressions for each term can be found inthe original publications.67Polar soft-SAFT is used to obtain molecular parametersdescriptiveof the studied refrigerants for their holistic thermodynamic evaluation,used as outputs into the ANN model. With polar soft-SAFT,i.e., structure, energy, interactions, etc.) capturedthrough a set of molecular parameters. The eighteen refrigerants includedin this work were modeled as non-associating LJ chainlike fluids withexplicit consideration of their permanent dipole moments, arisingfrom the presence of halogen atoms in their structure, leading toasymmetrical charge distribution. As such, five molecular parameterswere required to characterize each refrigerant, including LJ segmentdiameter (σ), chain length (m), LJ segment’sdispersive energy (ε), dipole moment (μ), and the fractionof dipolar segments (xp), as shown in a priori based on a physical argumenthighlighted in previous contributions.70 With that, only the three remaining parameters  were simultaneously fittedto limited data sets including each refrigerant’s liquid densityand vapor pressure.Applying thetheory to pure fluids requires proposing a simplified coarse-grainmodel representative of the key molecular features  to hidden neurons (Hj), while the output neurons(Ok) are linearly connectedto the hidden neurons, as72wij and wjk are theweights for links of type [input neuron i—hiddenneuron j] and [hidden neuron j—outputneuron k], respectively, while bj and bk are the biases of hidden neuron j and output neuron k, respectively.To predict polar soft-SAFTmolecular parameters descriptive of the thermodynamic behavior ofpure refrigerants, a feed-forward ANN is used to map the relationbetween COSMO-RS molecular descriptors and polar soft-SAFT molecularparameters, in the absence of experimental data for parametrization.The network is composed of three layers: (1) the input layer withthe refrigerant molecular weight, and eight COSMO-RS molecular descriptors,(2) hidden layer, with several processing neurons, and (3) outputlayer with the five polar soft-SAFT molecular parameters. The neuronsin each layer are interconnected 73 with the supervised training task done using the training datasetpairs of COSMO-RS descriptors and polar soft-SAFT parameters for fifteenrefrigerants while the three remaining refrigerants, namely, R134a,R32, and R1243zf, were used for internal validation. It is grantedthat the size of the dataset is relatively small compared to otherapplications employing machine learning, however, this is sufficientfor a proof of concept, as will be shown later.The ANNmodel was developed using the neural networks toolbox of the John’sMacintosh Project (JMP) statistical software,R2), absolute average relative deviation (AARD), root meansquare error (RMSE), and average standard deviation (SDavg), for each output. Nevertheless, the most crucial evaluation ofthe developed ANN model is by utilizing its predicted polar soft-SAFTmolecular parameters in characterizing the thermodynamic behaviorof the three refrigerants not included in the training set and checkingtheir accuracy compared to experimental data.Several statisticalindicators were used to evaluate the developedANN model including coefficient of determination (33.12) denoting positive polarity surfaces with a hydrogen donatingcharacter, while positive charge densities (σ > +0.01 e/Å2) denoting negative polarity surfaces with a hydrogen acceptingcharacter. Meanwhile, charge densities within the range of −0.01≤ σ ≤ +0.01 e/Å2 represent neutralnonpolar surfaces within the molecule. The discretization of the σ-profilesinto the eight molecular descriptors highlighted earlier with a σstep size of 0.00625 e/Å2, provides insight into theatomistic nature of each molecule and their contribution to its governingintermolecular interactions based on the location, height, and widthof the peaks. For example, for HFCs with one carbon atom, such asR41, R32, and R23, seen in S4 to S2 with decreasing peaks),while their fluorine-less counterparts negatively polarized (transitionfrom S6 to S5 with increasing peaks). The same is observed for HFCs with 2 or3 carbon backbones, as shown in S4) can be attributed to the double-bonded carbons in their structure.For all examined refrigerants, no distinct peaks were observed instrongly positive polarity (S1), and stronglynegative polarity (S7 and S8), as these regions are attributed to ions , as such, these descriptors were removed from the training dataset.In some cases, such as those for R23, R125, and R227ea, the smallprobability of strongly positive polarizable atoms shown from thesmall peaks in (S2) was attributed tothe effect of the halogens on polarizing the single hydrogen in theirmolecular structure. The ANN inputs dataset including molecular weightand the five COSMO-RS descriptors  are provided in Table S1 in the Supporting Information (SI).Based on the aforementionedapproach in 3.238 with the parametersincluded in Table S2 in the SI. The robustnessof these molecular parameters was rigorously tested by predictingother thermodynamic properties not included in the fitting process,comprising single-phase density, isobaric heat capacity, enthalpyof vaporization, and speed of sound, along with predictive extensionof the model to binary mixtures with n-alkanes.38The second part ofthe dataset development for ANN training is obtaining the polar soft-SAFTparameters for the eighteen selected refrigerants. The molecular parametersdescriptive of the modeled refrigerants using polar soft-SAFT weretransferred from earlier contributions based on the aforementionedparametrization approach,i.e., m, σ, ε) can be correlated to the molecular size, volume,and energy of each refrigerant, with mσ3 representing the volume of the molecule, while m(ε/kB) stands for its dispersiveenergy. As such it was opted to construct the polar soft-SAFT outputdataset with five parameters  to ensure meaningfulphysically oriented trends, included in Table S3 in the SI.Here, we will focus on evaluating theirphysical meaning as it can help in assessing their validity and possibletransferability for similar compounds, and hence, their role in thedataset. The nonpolar parameters (mσ3) as shown in i.e., R1233zd(E) vs R1234ze(E), and R1224yd(Z) vs R1225ye(Z)) has a larger impact on increasing the molecularvolume of the refrigerant associated with chlorine’s higheratomic weight. These trends are also supported by the increased contributionof the chain term (achain) to the residualHelmholtz energy of the molecules (ares), obtained from T = 250 K, shown in For the sake of highlighting the meaningof these parameters, wepresent the analysis of selected HFCs , HFOs (three-carbon chain: R1234ze(E), R1225ye(Z)), and HCFOs(three-carbon chain: R1233zd(E), R1224yd(Z)) to isolate the effectof carbon number and degree of halogenation on the structure and energyof the refrigerants, as provided in i.e., m(ε/kB), μ, xp), two opposingtrends are observed, depending on the degree of saturation of thecarbon backbone, as highlighted in mxp). This is responsible for R125 lower polarcontribution compared to R134a, as seen in S4 in their σ-profiles in vs HCFOs), reducing the polarity of the HCFOs and their polar contributions,due to the lower electronegativity of chlorine atoms.Regarding the molecular parameters representing energetic contributions(3.3xp), and dipole moment (μ) of the molecule, with3 and 9 neurons for each, respectively. The second hidden layer receivesinputs from the neurons in the first hidden layer, into 4 neuronsfor computing the remaining polar soft-SAFT parameters such as chainlength, volume, and LJ energy of the molecule. The network was trainedwith data for 15 refrigerants, while the remaining three  were used for testing thetrained ANN model. Overall, the developed network required 120 weights,and 16 biases, to correlate COSMO-RS molecular descriptors with polarsoft-SAFT molecular parameters of the studied refrigerants. The associatedweights and biases for all links between neurons in each layer areprovided in Tables S4–S6 in theSI for completeness.With the datasetsfor the eighteen refrigerants obtained using theaforementioned molecular modeling approaches (COSMO-RS and polar soft-SAFT),the ANN model for predicting polar soft-SAFT molecular parameterswas developed, with the network topography shown in i.e., dispersive, associating, polar, etc.) from those specific for polar fluids , within the same integrated network. Thisframework has the possibility of flexible future application and extensionto other molecular families with different chemical nature than thedipolar refrigerants included in this work. Additionally, during theANN model development, it was established that the number of hiddenneurons in each processing layer was decisive in controlling the accuracyand complexity of the model. The number of neurons in each layer wereincrementally changed up to the number of critical neurons in eachlayer without substantial improvement in modeling accuracy while maintaininga relatively simple ANN architect.The architecture of the developednetwork was chosen to segregatethe molecular parameters applicable for all molecular families , especially for μ, and xp parameters due to the high number of processing neurons,used in their correlation. Conversely, for the remaining parameters,the goodness of fit is with a good level of accuracy (R2 > 0.97) and an acceptable level of dispersion forsomedata points. This can also be visually supported from the parity plotsshown in y-axis)and those obtained using the standard parametrization approach, fittingto experimental data (x-axis). It should be notedthat the higher values of the statistical indicators for trainingand testing ANN for m(ε/kB) output parameter are associated with its larger magnitude.However, these results indicate the proper training of the developedmodel and its ability to map the relation between the chosen inputsand outputs. The subsequent testing of the model’s externalpredictive power using refrigerants R32, R134a, and R1243zf, alsoshown in Provided in 82 quantified in terms of absolute average deviation (AAD). As highlightedin mε/kB), in To better elucidate the impact of ANN accuracy onpredicting molecularparameters for molecular theory, the ANN-predicted parameters wereutilized to predict the vapor pressure and saturated liquid densityof the pure refrigerants using polar soft-SAFT compared to experimentalproperty dataTable S1 in the SI. It can be seen thatthe increase in the degree of fluorination was accompanied by an increasein the chain length and molecular volume of fluoroethanes  and the molecular weight. The connection between the molecularweight and the chain length and molecular volume has been highlightedpreviously in S5 input feature, as it has the largest magnitudeamong all COSMO-RS inputs, related to the degree of fluorination,being the most influential factor on the intermolecular interactionsof the refrigerants. In the case of the fraction of polar segments,the importance of S2 feature is the largest.Overall, both S5 and molecular weighthave the largest impact on correlating the polar soft-SAFT parameters,while the remaining input features have a similar level of importance≈15%.For the developed ANN, the relative importance of each input parameterand usefulness in predicting the polar soft-SAFT output parametersare provided in 3.4An ultimate test forassessing the adequacy of the polar soft-SAFT molecular parameterspredicted by the ANN was performed using these parameters for theneeded thermodynamic characterization of selected refrigerants, includingcoexisting densities, vapor pressure, single-phase density, heat capacity,enthalpy of vaporization, and speed of sound. This is a rigorous testingframework applicable for any thermodynamic model, and at the sametime, showcases the versatility of polar soft-SAFT as a holistic thermodynamicmodel.i.e., fine-tune fitting to experimental data), and the availableexperimental data and results are presented in The thermodynamic behavior using ANN-predicted parameterswas compared to those obtained using standard parametrization , and vapor pressure (AAD = 6.587%). These deviations are satisfactoryconsidering that these parameters were predicted from the ANN withoutusing any experimental data. The most consistent trends between fittedand predicted parameters were obtained for the R236fa refrigerant,as the training set was more biased to refrigerants with similar molecularstructures.Shown in 85 for properties such as single-phase density (AADANN-MPs = 3.488%) and isobaric heat capacity (AADANN-MPs = 1.311%).The second stage of evaluating the adequacy of ANN-predicted polarsoft-SAFT parameters is by examining other thermodynamic propertiesnot included in the standard parametrization approach. Obtaining theseproperties is considered a stringent criterion in evaluating any thermodynamicmodel, as first and second-order derivative properties are very sensitiveto small errors in modeling phase equilibria of pure fluids. As demonstratedin 85 was obtained using fitted parameters rather thanANN-predicted parameters in the cases of enthalpy of vaporization(AADANN-MPs = 5.981%); owing to deviations in thepredicted vapor pressures and speed of sound (AADANN-MPs = 10.525%) as this property encompasses other derivative properties, increasing its sensitivity to errorsarising from those properties.86A better agreement with experimental datan-alkane + HFC,HFC + HFC, and HFO + HFO for selected mixtures to ensure the accuratequantification of dipolar interactions in refrigerants.70 The examined mixtures include R23 + ethane ,87 R23 + R134a, and R1123+ R1234ze(E), with polar soft-SAFT employed in a predictive manner(binary interaction parameters kept at unity). Using the fitted parametersas opposed to the ANN-predicted parameters proved to be more accuratein capturing the reported experimental/molecular simulation behaviorof these binary mixtures90 as shown in The last examination of the adequacy of ANN-predicted parametersis through their extension to computing vapor–liquid equilibria(VLE) of binary mixtures such as The propagation of error in estimating additional thermodynamicproperties and VLE of binary mixtures with ANN-predicted parametersstems from the errors seen earlier for coexisting densities and vaporpressure. This limitation can be attributed to the narrow trainingset used in ANN development, with expected enhanced accuracy witha larger training dataset. However, these errors are within an acceptablelevel of accuracy needed for the rapid screening of alternative eco-friendlyrefrigerants based on technical efficacy, guiding where the experimentalefforts should be put for a detailed characterization of the mostpromising refrigerants.4In thiswork, a novel integrated approach was developed using amachine learning algorithm to map the relationships between moleculardescriptors obtained from COSMO-RS with the molecular parameters requiredfor obtaining the needed thermodynamic properties of refrigerantsusing the polar soft-SAFT equation of state.With this approach,a direct link was established between specificmolecular features of emerging sustainable refrigerants and theircorresponding polar soft-SAFT molecular parameters. This enabled theacquisition of parameters for the equation of state for emerging systemseven in the absence of experimental data, which effectively filledthe gap in thermodynamic data required for evaluating their technicalperformance for different cooling applications.The accuracyof the trained machine learning algorithm was verifiedwith various statistical approaches. Additional tests were conductedon the adequacy of the ANN-predicted parameters in modeling thermodynamicproperties from polar soft-SAFT with acceptable levels of errors versusthe available experimental data in the range of 1.3–10.5% forproperties such as coexisting densities, vapor pressure, enthalpyof vaporization, isobaric heat capacity, and speed of sound. Moreover,the ANN-predicted molecular parameters proved to be capable of characterizingthe VLE behavior of selected binary mixtures with acceptable deviations.These deviations were attributable to the limited dataset used inthe development of the machine learning model, which can be enhancedusing large datasets, requiring a library of polar soft-SAFT molecularmodels for a larger variety of systems.Despite the alreadymentioned limitations, the results in thiswork showcase the potentiality of this novel integrated approach forthe first technical evaluation of newly developed refrigerants, evenin the absence of sufficient experimental data, facilitating the searchfor green alternatives meeting technical and environmental requirements.


Mannheimia haemolytica, Pasteurella multocida, Histophilus somni, and Mycoplasma bovis. While most bacterial isolates recovered from healthy cattle are susceptible to a repertoire of antimicrobials, multidrug resistance is common in isolates recovered from cattle suffering from BRD. Integrative and conjugative elements (ICE) have gained increasing notoriety in BRD-Pasteurellaceae as they appear to play a key role in the concentration and dissemination of antimicrobial resistant genes. Likewise, low macrolide susceptibility has been described in feedlot isolates of M. bovis. Horizontal gene transfer has also been implicated in the spread of AMR within mycoplasmas, and in-vitro experiments have shown that exposure to antimicrobials can generate high levels of resistance in mycoplasmas via a single conjugative event. Consequently, antimicrobial use (AMU) could be accelerating AMR horizontal transfer within all members of the bacterial BRD complex. While metagenomics has been applied to the study of AMR in the microbiota of the respiratory tract, the potential role of the respiratory tract microbiome as an AMR reservoir remains uncertain. Current and prospective molecular tools to survey and characterize AMR need to be adapted as point-of-care technologies to enhance prudent AMU in the beef industry.Numerous antimicrobial resistance (AMR) surveillance studies have been conducted in North American feedlot cattle to investigate the major bacterial pathogens of the bovine respiratory disease (BRD) complex, specifically:  Mannheimia haemolytica, Pasteurella multocida, Histophilus somni, and Mycoplasma bovis . In . In 1]. ma bovis . With thBRD accounts for 70–80% and 40–50% of total feedlot cattle morbidities and mortalities, respectively . In addiPrevention of bacterial diseases through the administration of antimicrobials via the feed or parenterally to a large cohort of cattle or the entire herd is known as metaphylaxis . MetaphyPasteurellaceae ARGs can be clustered within integrative and conjugative elements (ICE), which are mobile genetic elements (MGE) integrated in the bacterial host chromosome. ICE are considered the most abundant conjugative elements in prokaryotes, outnumbering conjugative plasmids ;  AND “Pasteurella multocida” [MeSH];  AND “Histophilus somni” [MeSH]; (“Mycoplasma bovis” [MeSH]) AND “Drug Resistance, Microbial” [MeSH];  AND “Bovine Respiratory Disease Complex” [MeSH]; and ((“Pasteurellaceae Infections/microbiology” [MeSH]) AND “Cattle” [MeSH]) AND “Microbial Sensitivity Tests/veterinary” [MeSH].During December 2021, a search building strategy was followed through the medical subject headings (MeSH) at the PubMed website  guidelines for antimicrobial susceptibility testing (AST) were included. However, no CLSI guidelines exist for AST in M. bovis. The time frame was from January 2000 to December 2021, with a main focus on cattle raised for meat production. Data on dairy isolates were included when AST results were not differentiated between beef and dairy cattle. Only observational studies were included, and experimental studies were excluded. Among AMR reports, only those that possessed minimum metadata, including if the isolates were collected from clinical BRD or healthy cattle, originated from a diagnostic laboratory, stated country of origin and year of isolation, were considered. Additionally, those authors that appeared in ≥3 publications were manually searched in PubMed to ensure that relevant publications were not missed.The articles included AMR in BRD clinical cases (passive surveillance) and surveillance studies that assessed AMR in BRD bacteria in the general cattle population (active surveillance) . The focA number of culture-dependent methods, such as disk diffusion, broth, and agar dilution, are used for AST of BRD bacteria ,43. UsinM. haemolytica (n = 4548 isolates) was undertaken . Su. SuPaste in 2018 . FurtherA cattle . Later w as 2002 . Clawson as 2002  analyzed classes . Additio the USA  and H. sellaceae . Over haPasteurellaceae  can occur . Mycoplaellaceae . One of ectively  51]..PasteureMycoplasma agalactiae has been used as a model for in silico and in vitro analysis of conjugation with M. bovis [Mycoplasma bovis appears to harbor ICE more frequently than other mycoplasmas within their Hominis group [parE and parC) from M. bovis to M. agalactiae can occur via conjugation [in vitro exposure of M. agalactiae to fluoroquinolones increased resistance as a result of conjugative transfer of several resistant alleles [M. bovis have been isolated from beef cattle at feedlot entry prior to the administration of antimicrobials [M. bovis isolated from feedlot cattle.M. bovis . Mycoplais group . Interesjugation . Further alleles . The inc alleles ,56,90,91crobials ,41. Mycocrobials , and onetet(H) significantly increased between both sampling points in the OXY treated group, whereas the proportion of sul1 decreased. In contrast, the relative abundance of sul2 and tet(W) was similar between the two sampling points. Another study investigated the effects of the same drugs on the nasopharynx and gut microbiota of beef cattle after feedlot placement [erm(X), sul2, and tet(M), whereas OXY increased tet(H) and tet(W) as well. Noteworthy, the effects that OXY had on the relative abundance of antimicrobial determinants was observed after 14 DOF, whereas it was after 36 DOF for cattle treated with TUL. Interestingly, a higher relative abundance of sul2, tet(H) tet(M), and tet(W) was observed at 36 DOF among all treatments including the control group . The detection of comparable antimicrobial determinant levels in the control and treated cattle could have been a consequence of environmental transmission of resistant bacteria/determinants across individuals within different experimental groups.Only a few studies have used metagenomic approaches to investigate AMR in the microbiota of the respiratory tract of feedlot cattle in North America, using either quantitative PCR (qPCR) or metagenomic DNA sequencing  29,57,6,69. The blaROB-1, a β-lactamase-encoding gene, was two to ten times more abundant than any other AMR gene. In Canada, β-lactams are mainly used only for therapeutic purposes in feedlot cattle, thus having a limited usage compared to metaphylactically used TET or macrolides [Salmonella spp. did [M. haemolytica and P. multocida but were also identified in Bacteroides ovatus (human pathogen) and Bacteroides xylanisolvens . Additionally, AMR genes not previously described in Pasteurellaceae were identified among these samples, raising the possibility that non-BRD bacteria may act as a reservoir of AMR in the bovine respiratory tract.Bronchoalveolar lavage samples (BAL) were taken from feedlot cattle mortalities for the study of the lower respiratory tract microbiome and resistome . The metcrolides . In the crolides ,46. Yet,crolides ,76, wherspp. did . This suspp. did , a diffespp. did  also invsul2 and tet(H) was observed after feedlot placement in the herd that had received in-feed CTET, suggesting that metaphylaxis selected for AMR. However, a correlation between AMU and AMR is not always clear among BRD bacteria [per se. This suggests that the feedlot environment could serve as a source of AMR bacteria and/or antimicrobial residues that could exert selective pressures for AMR in environmental bacteria [A cohort of steers was used to evaluate the evolution of AMR from spring processing to ≥40 DOF . Deep nabacteria ,95. Rathbacteria ,97. Recebacteria . The opeThe process from respiratory specimen collection to bacterial antimicrobial susceptibility testing (AST) of individual bacterial isolates can be time consuming. A number of different technologies that have been assessed for their ability or have the potential to detect AMR-BRD bacteria in a timely manner are discussed in this section . As an aM. haemolytica serotypes A1 and A6, P. multocida, H. somni, and M. bovis) and ARGs related to Pasteurellaceae ICE in metagenomic DNA collected from DNPS [Recombinase polymerase amplification (RPA) is a simple, sensitive, and rapid technology based on isothermal DNA-amplification with a multiplexing option . It is lrom DNPS . AlthougP. multocida, M. haemolytica, H. somni, and M. bovis from bovine and swine respiratory samples, as well as for the detection of AMR (ARGs and mutations) in non-BRD bacterial species  [Pasteurellaceae, demonstrating the utility of LAMP for field applications.Another detection technology is loop-mediated isothermal amplification (LAMP), which enables DNA amplification at a constant temperature, resulting in high sensitivity, specificity, and a short turnaround time . LAMP isus spp.) ,110,111.us spp.)  reportedMolecular approaches used in diagnostics present their own advantages and disadvantages . DNA isoin-situ sample preparation, i.e., DNA extraction and library preparation, for MinION metagenomic sequencing under field conditions [M. haemolytica and M. bovis [M. bovis gene mutations that result in AMR because, unlike ARGs, they cannot be detected by a simple PCR without implementing post-amplification sequencing-based procedures.Metagenomic applications have shown promising results in the rapid diagnostics field. MinION (Oxford Nanopore Technologies) is a third generation sequencer that produces long reads that can be analyzed in real-time; is highly portable; has a relatively low cost and can be interfaced with a desktop or laptop computer for data analysis . Nanopornditions . Howevernditions . A combinditions . NanoporM. bovis ,119, butvanA, 96.7% sensitivity and 98.1% specificity), activity quantification of antibiotic-inactivating enzymes , and measurement of bacterial growth exposed to different antimicrobials [P. multocida isolates tested (n = 100) within 3 h after isolation [While third generation sequencing evolves towards becoming a routine procedure for the phenotypic prediction of AMR in metagenomic samples, there are current technologies that are already being used in diagnostic laboratories for the rapid detection of AMR in individual bacterial isolates. An excellent example of this is MALDI-TOF, as it provides AST results faster than conventional methods that require bacteria to be cultured. This technology has been used for the detection of ARGs  when ARG-ICE were first detected in Canadian M. haemolytica isolates (2008) [Pasteurellaceae isolates presenting up to 13 ARGs were isolated from feedlot cattle in western Canada and, although at low prevalence, they were more common in feedlot dairy than beef calves [Pasteurellaceae isolates obtained from BRD clinical cases than healthy cattle [Pasteurellaceae should be evaluated in the general feedlot cattle population as well as their possible emergence and impact on BRD treatment failure and mortality  . Recentlf calves . ARG-ICEy cattle ,30,35,86ortality .Pasteurellaceae can vary substantially in structure, and ARG can also be associated with other MGE or the bacterial chromosome [M. haemolytica isolated from BRD clinical cases than in those obtained from healthy cattle (up to five) [Pasteurellaceae isolated from clinical BRD cases could be a consequence of co-selection for other ARGs. Newer statistical approaches, such as additive Bayesian networks (ABN), could assist in elucidating the systematic associations amongst phenotypic antimicrobial resistance or ARG in Pasteurellaceae from cattle [Pasteurellaceae.The interdisciplinary approach of combining genomics and systematically collected epidemiological data should be the cornerstone of future AMR studies, and for informing the prudent use of antimicrobials. ICE described in romosome . Interesto five) ,86. Geneto five) ,20,82. Tm cattle . Couplinm cattle  could heIn vitro ICE can be transferred via conjugation between Pasteurellaceae bacteria and to other non-Pasteurellaceae species, especially γ-proteobacterial pathogens of clinical importance [Pasteurellaceae and the magnitude of MGEs exchange at the population level within the bovine respiratory tract is unknown  or targeting their ARGs  initiated a national feedlot AMU/AMR surveillance program that targets selected respiratory and enteric bacterial pathogens . The int