RegulonDB v12.0: a comprehensive resource of transcriptional regulation in E. coli K-12.

RegulonDB is a database that contains the most comprehensive corpus of knowledge of the regulation of transcription initiation of Escherichia coli K-12, including data from both classical molecular biology and high-throughput methodologies. Here, we describe biological advances since our last NAR paper of 2019. We explain the changes to satisfy FAIR requirements. We also present a full reconstruction of the RegulonDB computational infrastructure, which has significantly improved data storage, retrieval and accessibility and thus supports a more intuitive and user-friendly experience. The integration of graphical tools provides clear visual representations of genetic regulation data, facilitating data interpretation and knowledge integration. RegulonDB version 12.0 can be accessed at https://regulondb.ccg.unam.mx.

Integrating Fossil Flowers into the Angiosperm Phylogeny Using Molecular and Morphological Evidence.

Fossils are essential to infer past evolutionary processes. The assignment of fossils to extant clades has traditionally relied on morphological similarity and on apomorphies shared with extant taxa. The use of explicit phylogenetic analyses to establish fossil affinities has so far remained limited. In this study, we built a comprehensive framework to investigate the phylogenetic placement of 24 exceptionally preserved fossil flowers. For this, we assembled a new species-level data set of 30 floral traits for 1201 extant species that were sampled to capture the stem and crown nodes of all angiosperm families. We explored multiple analytical approaches to integrate the fossils into the phylogeny, including different phylogenetic estimation methods, topological-constrained analyses, and combining molecular and morphological data of extant and fossil species. Our results were widely consistent across approaches and showed minor differences in the support of fossils at different phylogenetic positions. The placement of some fossils agrees with previously suggested relationships, but for others, a new placement is inferred. We also identified fossils that are well supported within particular extant families, whereas others showed high phylogenetic uncertainty. Finally, we present recommendations for future analyses combining molecular and morphological evidence, regarding the selection of fossils and appropriate methodologies, and provide some perspectives on how to integrate fossils into the investigation of divergence times and the temporal evolution of morphological traits. [Angiosperms; fossil flowers; phylogenetic uncertainty; RoguePlots.].

Deep Ordinal Classification in Forest Areas Using Light Detection and Ranging Point Clouds.

Recent advances in Deep Learning and aerial Light Detection And Ranging (LiDAR) have offered the possibility of refining the classification and segmentation of 3D point clouds to contribute to the monitoring of complex environments. In this context, the present study focuses on developing an ordinal classification model in forest areas where LiDAR point clouds can be classified into four distinct ordinal classes: ground, low vegetation, medium vegetation, and high vegetation. To do so, an effective soft labeling technique based on a novel proposed generalized exponential function (CE-GE) is applied to the PointNet network architecture. Statistical analyses based on Kolmogorov-Smirnov and Student's t-test reveal that the CE-GE method achieves the best results for all the evaluation metrics compared to other methodologies. Regarding the confusion matrices of the best alternative conceived and the standard categorical cross-entropy method, the smoothed ordinal classification obtains a more consistent classification compared to the nominal approach. Thus, the proposed methodology significantly improves the point-by-point classification of PointNet, reducing the errors in distinguishing between the middle classes (low vegetation and medium vegetation).

Substitution Effects in Aryl Halides and Amides into the Reaction Mechanism of Ullmann-Type Coupling Reactions.

In this article, we present a comprehensive computational investigation into the reaction mechanism of N-arylation of substituted aryl halides through Ullmann-type coupling reactions. Our computational findings, obtained through DFT ωB97X-D/6-311G(d,p) and ωB97X-D/LanL2DZ calculations, reveal a direct relation between the previously reported experimental reaction yields and the activation energy of haloarene activation, which constitutes the rate-limiting step in the overall coupling process. A detailed analysis of the reaction mechanism employing the Activation Strain Model indicates that the strain in the substituted iodoanilines is the primary contributor to the energy barrier, representing an average of 80% of the total strain energy. Additional analysis based on conceptual Density Functional Theory (DFT) suggests that the nucleophilicity of the nitrogen in the lactam is directly linked to the activation energies. These results provide valuable insights into the factors influencing energetic barriers and, consequently, reaction yields. These insights enable the rational modification of reactants to optimize the N-arylation process.

Hidden intermediate activation: a concept to elucidate the reaction mechanism of the Schmittel cyclization of enyne-allenes.

The mechanistic paradigm in which the Schmittel cyclization transitions from one-step to stepwise has been investigated through the stabilization of a full hidden intermediate in the framework of the Diabatic Model of Intermediate Stabilization. Hidden intermediate activation was studied in silico employing quasi-classical trajectories and the Electron Localization Function. The stabilization of hidden intermediates achieved by substituting enyne-allenes with cyano and nitro groups generates the appearance of a partially hidden and an explicit intermediate, leading to one-step asynchronous biradical and stepwise biradical/zwitterionic mechanisms, respectively. The mechanistic feature associated with the activation level of the hidden intermediate arises from the Thornton effect and non-RRKM dynamics, where in the case of the CN-substituted system, despite having a single transition state, 54% of the effective trajectories remain in the intermediate zone after 540 fs, indicating that a mixture of mechanisms is observed.

Dynamics of the Microbiota and Its Relationship with Post-COVID-19 Syndrome.

Coronavirus disease (COVID-19) is an infection caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), which can be asymptomatic or present with multiple organ dysfunction. Many infected individuals have chronic alterations associated with neuropsychiatric, endocrine, gastrointestinal, and musculoskeletal symptoms, even several months after disease onset, developing long-COVID or post-acute COVID-19 syndrome (PACS). Microbiota dysbiosis contributes to the onset and progression of many viral diseases, including COVID-19 and post-COVID-19 manifestations, which could serve as potential diagnostic and prognostic biomarkers. This review aimed to discuss the most recent findings on gut microbiota dysbiosis and its relationship with the sequelae of PACS. Elucidating these mechanisms could help develop personalized and non-invasive clinical strategies to identify individuals at a higher risk of experiencing severe disease progression or complications associated with PACS. Moreover, the review highlights the importance of targeting the gut microbiota composition to avoid dysbiosis and to develop possible prophylactic and therapeutic measures against COVID-19 and PACS in future studies.

Cluster analysis and forecasting of viruses incidence growth curves: Application to SARS-CoV-2.

The sanitary emergency caused by COVID-19 has compromised countries and generated a worldwide health and economic crisis. To provide support to the countries' responses, numerous lines of research have been developed. The spotlight was put on effectively and rapidly diagnosing and predicting the evolution of the pandemic, one of the most challenging problems of the past months. This work contributes to the existing literature by developing a two-step methodology to analyze the transmission rate, designing models applied to territories with similar pandemic behavior characteristics. Virus transmission is considered as bacterial growth curves to understand the spread of the virus and to make predictions about its future evolution. Hence, an analytical clustering procedure is first applied to create groups of locations where the virus transmission rate behaved similarly in the different outbreaks. A curve decomposition process based on an iterative polynomial process is then applied, obtaining meaningful forecasting features. Information of the territories belonging to the same cluster is merged to build models capable of simultaneously predicting the 14-day incidence in several locations using Evolutionary Artificial Neural Networks. The methodology is applied to Andalusia (Spain), although it is applicable to any region across the world. Individual models trained for a specific territory are carried out for comparison purposes. The results demonstrate that this methodology achieves statistically similar, or even better, performance for most of the locations. In addition to being extremely competitive, the main advantage of the proposal lies in its complexity cost reduction. The total number of parameters to be estimated is reduced up to 93.51% for the short term and 93.31% for the mid-term forecasting, respectively. Moreover, the number of required models is reduced by 73.53% and 58.82% for the short- and mid-term forecasting horizons.

A deeper analysis of the role of synchronicity on the Bell-Evans-Polanyi plot in multibond chemical reactions: a path-dependent reaction force constant.

The role of the degree of synchronicity in the formation of the new single-bonds in a large set of 1,3-dipolar cycloadditions and its relation in the fulfilment of the classical Bell-Evans-Polanyi principle and Hammond-Leffler postulate are deeply investigated. Our results confirm that asynchronicity is an important path-dependent factor to be taken into account: (i) the Bell-Evans-Polanyi is fulfilled as the degree of (a)synchronicity is quite the same, and a linear relationship between reorganisation energy and asynchronicity is found; (ii) the asynchronicity is the origin of deviations of this classical principle of chemical reactivity since any decrease of the energy barrier is due to an increase of asynchronicity at the same exothermicity; and (iii) the less exothermic the reaction is, the more asynchronous the mechanism is, at the same energy barrier. Thus, this implies that TS imbalance decreases the reorganisation energy, consequently affecting the reaction exothermicity as well.

Synthesis, Optical Characterization in Solution and Solid-State, and DFT Calculations of 3-Acetyl and 3-(1'-(2'-Phenylhydrazono)ethyl)-coumarin-(7)-substituted Derivatives.

Intramolecular charge transfer (ICT) effects are responsible for the photoluminescent properties of coumarins. Hence, optical properties with different applications can be obtained by ICT modulation. Herein, four 3-acetyl-2H-chromen-2-ones (1a-d) and their corresponding fluorescent hybrids 3- (phenylhydrazone)-chromen-2-ones (2a-d) were synthesized in 74-65% yields. The UV-Vis data were in the 295-428 nm range. The emission depends on the substituent in position C-7 bearing electron-donating groups. Compounds 1b-d showed good optical properties due to the D-π-A structural arrangement. In compounds 2a-d, there is a quenching effect of fluorescence in solution. However, in the solid, an increase is shown due to an aggregation-induced emission (AIE) effect given by the rotational restraints and stacking in the crystal. Computational calculations of the HOMO-LUMO orbitals indicate high absorbance and emission values of the molecules, and gap values represent the bathochromic effect and the electronic efficiency of the compounds. Compounds 1a-d and 2a-d are good candidates for optical applications, such as OLEDs, organic solar cells, or fluorescence markers.

COVID-19 contagion forecasting framework based on curve decomposition and evolutionary artificial neural networks: A case study in Andalusia, Spain.

Many types of research have been carried out with the aim of combating the COVID-19 pandemic since the first outbreak was detected in Wuhan, China. Anticipating the evolution of an outbreak helps to devise suitable economic, social and health care strategies to mitigate the effects of the virus. For this reason, predicting the SARS-CoV-2 transmission rate has become one of the most important and challenging problems of the past months. In this paper, we apply a two-stage mid and long-term forecasting framework to the epidemic situation in eight districts of Andalusia, Spain. First, an analytical procedure is performed iteratively to fit polynomial curves to the cumulative curve of contagions. Then, the extracted information is used for estimating the parameters and structure of an evolutionary artificial neural network with hybrid architectures (i.e., with different basis functions for the hidden nodes) while considering single and simultaneous time horizon estimations. The results obtained demonstrate that including polynomial information extracted during the training stage significantly improves the mid- and long-term estimations in seven of the eight considered districts. The increase in average accuracy (for the joint mid- and long-term horizon forecasts) is 37.61% and 35.53% when considering the single and simultaneous forecast approaches, respectively.

Report of RILEM TC 281-CCC: outcomes of a round robin on the resistance to accelerated carbonation of Portland, Portland-fly ash and blast-furnace blended cements.

Many (inter)national standards exist to evaluate the resistance of mortar and concrete to carbonation. When a carbonation coefficient is used for performance comparison of mixtures or service life prediction, the applied boundary conditions during curing, preconditioning and carbonation play a crucial role, specifically when using latent hydraulic or pozzolanic supplementary cementitious materials (SCMs). An extensive interlaboratory test (ILT) with twenty two participating laboratories was set up in the framework of RILEM TC 281-CCC 'Carbonation of Concrete with SCMs'. The carbonation depths and coefficients determined by following several (inter)national standards for three cement types (CEM I, CEM II/B-V, CEM III/B) both on mortar and concrete scale were statistically compared. The outcomes of this study showed that the carbonation rate based on the carbonation depths after 91 days exposure, compared to 56 days or less exposure duration, best approximates the slope of the linear regression and those 91 days carbonation depths can therefore be considered as a good estimate of the potential resistance to carbonation. All standards evaluated in this study ranked the three cement types in the same order of carbonation resistance. Unfortunately, large variations within and between laboratories complicate to draw clear conclusions regarding the effect of sample pre-conditioning and carbonation exposure conditions on the carbonation performance of the specimens tested. Nevertheless, it was identified that fresh and hardened state properties alone cannot be used to infer carbonation resistance of the mortars or concretes tested. It was also found that sealed curing results in larger carbonation depths compared to water curing. However, when water curing was reduced from 28 to 3 or 7 days, higher carbonation depths compared to sealed curing were observed. This increase is more pronounced for CEM I compared to CEM III mixes. The variation between laboratories is larger than the potential effect of raising the CO2 concentration from 1 to 4%. Finally, concrete, for which the aggregate-to-cement factor was increased by 1.79 in comparison with mortar, had a carbonation coefficient 1.18 times the one of mortar. Supplementary Information: The online version contains supplementary material available at 10.1617/s11527-022-01927-7.

Crossroads in Liver Transplantation: Is Artificial Intelligence the Key to Donor-Recipient Matching?

Liver transplantation outcomes have improved in recent years. However, with the emergence of expanded donor criteria, tools to better assist donor-recipient matching have become necessary. Most of the currently proposed scores based on conventional biostatistics are not good classifiers of a problem that is considered "unbalanced." In recent years, the implementation of artificial intelligence in medicine has experienced exponential growth. Deep learning, a branch of artificial intelligence, may be the answer to this classification problem. The ability to handle a large number of variables with speed, objectivity, and multi-objective analysis is one of its advantages. Artificial neural networks and random forests have been the most widely used deep classifiers in this field. This review aims to give a brief overview of D-R matching and its evolution in recent years and how artificial intelligence may be able to provide a solution.

Bioinformatics in Latin America and SoIBio impact, a tale of spin-off and expansion around genomes and protein structures.

Owing to the emerging impact of bioinformatics and computational biology, in this article, we present an overview of the history and current state of the research on this field in Latin America (LA). It will be difficult to cover without inequality all the efforts, initiatives and works that have happened for the past two decades in this vast region (that includes >19 million km2 and >600 million people). Despite the difficulty, we have done an analytical search looking for publications in the field made by researchers from 19 LA countries in the past 25 years. In this way, we find that research in bioinformatics in this region should develop twice to approach the average world scientific production in the field. We also found some of the pioneering scientists who initiated and led bioinformatics in the region and were promoters of this new scientific field. Our analysis also reveals that spin-off began around some specific areas within the biomolecular sciences: studies on genomes (anchored in the new generation of deep sequencing technologies, followed by developments in proteomics) and studies on protein structures (supported by three-dimensional structural determination technologies and their computational advancement). Finally, we show that the contribution to this endeavour of the Iberoamerican Society for Bioinformatics, founded in Mexico in 2009, has been significant, as it is a leading forum to join efforts of many scientists from LA interested in promoting research, training and education in bioinformatics.

Interacting Quantum Atoms Analysis of the Reaction Force: A Tool to Analyze Driving and Retarding Forces in Chemical Reactions.

The analysis of the reaction force and its topology has provided a wide range of fruitful concepts in the theory of chemical reactivity over the years, allowing to identify chemically relevant regions along a reaction profile. The reaction force (RF), a projection of the Hellmann-Feynman forces acting on the nuclei of a molecular system onto a suitable reaction coordinate, is partitioned using the interacting quantum atoms approach (IQA). The exact IQA molecular energy decomposition is now shown to open a unique window to identify and quantify the chemical entities that drive or retard a chemical reaction. The RF/IQA coupling offers an extraordinarily detailed view of the type and number of elementary processes that take reactants into products, as tested on two sets of simple reactions.

1,3-Dipolar Cycloadditions by a Unified Perspective Based on Conceptual and Thermodynamics Models of Chemical Reactivity.

The main aim in the present report is to gain a deeper understanding of typical 1,3-dipolar cycloadditions by means of three chemical reactivity models in a unified perspective: conceptual density functional theory, distortion/interaction, and reaction force analysis. The focus is to explore the information provided by each reactivity model and how they complement or reinforce each other. Our results showed that the Bell-Evans-Polanyi (BEP) relationship is fulfilled, which is consistent with the Hammond-Leffler postulate. The electronic chemical potential based analysis classifies the reactions as HOMO-, HOMO/LUMO-, and LUMO-controlled reactions as the activation energy increases. It seems likely that HOMO-controlled reaction shifts into LUMO-controlled one as the transition state (TS) position does from early into late. Therefore, the transition from HOMO- (and early TS) into LUMO-controlled (and late TS) is paid by shifting the overall energy change into an endothermic direction, thus supporting the fulfillment of the BEP principle. While thermodynamic models unveil that the distortion or structural rearrangements mainly drive the activation barriers rather than interaction or electronic rearrangements in accord with the distortion/interaction and reaction force analysis, respectively. It is also found that both models are consistent when energy associated with structural and electronic reordering from reaction force analysis is respectively confronted with destabilizing (distortion and Pauli repulsion) and stabilizing (electrostatic and orbital interactions) contributions from the distortion/interaction model, which, on the other hand, increases as low activation barrier and high exothermicity are converted into the high barrier and low exothermicity along with the BEP relation. Finally, the reaction force constant reveals that all 1,3-dipolar cycloaddition reactions proceed by a synchronous single-step mechanism, unveiling that the degree of synchronicity is quite the same in all reactions, confirming the statement that BEP is fulfilled for similar reactions proceeding by a quite alike degree of synchronicity.

RegulonDB v 10.5: tackling challenges to unify classic and high throughput knowledge of gene regulation in E. coli K-12.

RegulonDB, first published 20 years ago, is a comprehensive electronic resource about regulation of transcription initiation of Escherichia coli K-12 with decades of knowledge from classic molecular biology experiments, and recently also from high-throughput genomic methodologies. We curated the literature to keep RegulonDB up to date, and initiated curation of ChIP and gSELEX experiments. We estimate that current knowledge describes between 10% and 30% of the expected total number of transcription factor- gene regulatory interactions in E. coli. RegulonDB provides datasets for interactions for which there is no evidence that they affect expression, as well as expression datasets. We developed a proof of concept pipeline to merge binding and expression evidence to identify regulatory interactions. These datasets can be visualized in the RegulonDB JBrowse. We developed the Microbial Conditions Ontology with a controlled vocabulary for the minimal properties to reproduce an experiment, which contributes to integrate data from high throughput and classic literature. At a higher level of integration, we report Genetic Sensory-Response Units for 200 transcription factors, including their regulation at the metabolic level, and include summaries for 70 of them. Finally, we summarize our research with Natural language processing strategies to enhance our biocuration work.

Evolutionary dynamics of natural product biosynthesis in bacteria.

Covering: 2008 up to 2019The forces of biochemical adaptive evolution operate at the level of genes, manifesting in complex phenotypes and the global biodiversity of proteins and metabolites. While evolutionary histories have been deciphered for some other complex traits, the origins of natural product biosynthesis largely remain a mystery. This fundamental knowledge gap is surprising given the many decades of research probing the genetic, chemical, and biophysical mechanisms of bacterial natural product biosynthesis. Recently, evolutionary thinking has begun to permeate this otherwise mechanistically dominated field. Natural products are now sometimes referred to as 'specialized' rather than 'secondary' metabolites, reinforcing the importance of their biological and ecological functions. Here, we review known evolutionary mechanisms underlying the overwhelming chemical diversity of bacterial secondary metabolism, focusing on enzyme promiscuity and the evolution of enzymatic domains that enable metabolic traits. We discuss the mechanisms that drive the assembly of natural product biosynthetic gene clusters and propose formal definitions for 'specialized' and 'secondary' metabolism. We further explore how biosynthetic gene clusters evolve to synthesize related molecular species, and in turn how the biological and ecological roles that emerge from metabolic diversity are acted on by selection. Finally, we reconcile chemical, functional, and genetic data into an evolutionary model, the dynamic chemical matrix evolutionary hypothesis, in which the relationships between chemical distance, biomolecular activity, and relative fitness shape adaptive landscapes.

Genotoxicity assessment of four novel quinazoline-derived trypanocidal agents in the Drosophila wing somatic mutation and recombination test.

Chagas disease, caused by the protozoan Trypanosoma cruzi, has increased in the world due to migration, travelling and climate change; at present, the principal problem is that common trypanocidal agents have resulted in toxic or inconvenient side effects. We tested for genotoxicity in the standard (ST) and high bioactivation (HB) crosses of Drosophila wing somatic mutation and recombination test, four novel trypanocidal agents derived from 2, 4, 6-triaminquinazoline (TAQ): 2,4-diamino-6 nitro-1,3 diazonaftalene (S-1QN2-1), 2,4-diacetamino-6-amino 1,3 diazonaftalene (D-1), N6-(4,methoxybenzyl)quinazoline-2,4,6-triamine (GHPM) and N6-[4-(trifluoromethoxy)benzyl]quinazoline-2,4,6-triamine (GHPMF) at 1.9, 3.9, 7.9 and 15 µM, respectively. Also, high-pressure liquid chromatography (HPLC) analysis was run to determine the remanence of either drug in flare, and Oregon R(R)-flare flies emerged from treated larvae. S-1QN2-1 showed genotoxicity only in the ST cross, increasing the small, large and total spot frequencies at all concentrations and twin spots only at 1.9 µM; D-1 and GHPM showed significant increments of large spots only at 15 µM in the ST cross; GHPMF was not genotoxic at any concentration or either cross. In the mwh clones accumulated distribution frequencies analysis, associated with disrupted cell division, S-1QN2-1 caused alterations in the ST cross at all concentrations but only at 15 µM in the HB cross; D-1 caused alterations at 3.9, 7.9 and 15 µM in the ST cross and at 1.9 and 15 µM in the HB cross; GHPM caused alterations at 7.9 and 15 µM in the ST cross and also at 1.9, 3.9 and 7.9 µM in the HB cross; GHPMF caused those alterations at all concentrations in the ST cross and at 1.9, 3.9 and 7.9 µM in the HB cross. The HPLC results indicated no traces of either agent in the flare and Oregon R(R)-flare flies. We conclude that S-1QN2-1 is clearly genotoxic, D-1 and GHPM have an unclear genotoxicity and GHPMF was not genotoxic; all quinazoline derivatives disrupted cell division. GHPMF is a good candidate to be tested in other genotoxicity and cytotoxic bioassays. The differences in the genotoxic activity of these trypanocidal agents are correlated with differences in their chemical structure.

Machine learning methods in organ transplantation.

PURPOSE OF REVIEW: Machine learning techniques play an important role in organ transplantation. Analysing the main tasks for which they are being applied, together with the advantages and disadvantages of their use, can be of crucial interest for clinical practitioners. RECENT FINDINGS: In the last 10 years, there has been an explosion of interest in the application of machine-learning techniques to organ transplantation. Several approaches have been proposed in the literature aiming to find universal models by considering multicenter cohorts or from different countries. Moreover, recently, deep learning has also been applied demonstrating a notable ability when dealing with a vast amount of information. SUMMARY: Organ transplantation can benefit from machine learning in such a way to improve the current procedures for donor--recipient matching or to improve standard scores. However, a correct preprocessing is needed to provide consistent and high quality databases for machine-learning algorithms, aiming to robust and fair approaches to support expert decision-making systems.

Effect of concomitant antiplatelet therapy in patients with nonvalvular atrial fibrillation initiating non-vitamin K antagonists.

BACKGROUND: Antiplatelet therapy (APT) use in combination with oral anticoagulation is common among patients with atrial fibrillation, but there is scarce information regarding its effect on outcomes in patients on non-vitamin K antagonist oral anticoagulants (NOAC). We aimed to evaluate the safety and efficacy of APT use in a 'real-world' cohort of nonvalvular atrial fibrillation (NVAF) patients initiating NOAC. DESIGN: We conducted a retrospective multicentre study including 2361 consecutive NVAF patients initiating NOAC between January 2013 and December 2016. Patients with an acute ischaemic event within the last 12 months (acute coronary syndrome, stroke or revascularization) were excluded. Patients were followed up, and all clinical events were recorded at 3 months. The primary outcome of the study was major bleeding, and the secondary outcomes were stroke, nonfatal myocardial infarction, intracranial bleeding and death. RESULTS: One hundred forty-five (6.1%) patients received concomitant APT, and aspirin was the more common (79%). At 3 months, 25 (1.1%) patients had major bleeding, 8 (0.3%) had nonfatal myocardial infarction, 7 (0.3%) had ischaemic stroke, and 40 (1.7%) died. After multivariate adjustment, concomitant APT was associated with higher risk for major bleeding (HR = 3.62, 95% CI 1.32-9.89; P = .012), but was not associated with a higher risk of other clinical outcomes. CONCLUSIONS: Concomitant APT use is uncommon among these patients and does not seem to be associated with lower rates of ischaemic events or death. However, there are signals for an increased risk of bleeding, which reinforces current guideline recommendations.