Automatic design of gene regulatory mechanisms for spatial pattern formation.

Gene regulatory mechanisms (GRMs) control the formation of spatial and temporal expression patterns that can serve as regulatory signals for the development of complex shapes. Synthetic developmental biology aims to engineer such genetic circuits for understanding and producing desired multicellular spatial patterns. However, designing synthetic GRMs for complex, multi-dimensional spatial patterns is a current challenge due to the nonlinear interactions and feedback loops in genetic circuits. Here we present a methodology to automatically design GRMs that can produce any given two-dimensional spatial pattern. The proposed approach uses two orthogonal morphogen gradients acting as positional information signals in a multicellular tissue area or culture, which constitutes a continuous field of engineered cells implementing the same designed GRM. To efficiently design both the circuit network and the interaction mechanisms-including the number of genes necessary for the formation of the target spatial pattern-we developed an automated algorithm based on high-performance evolutionary computation. The tolerance of the algorithm can be configured to design GRMs that are either simple to produce approximate patterns or complex to produce precise patterns. We demonstrate the approach by automatically designing GRMs that can produce a diverse set of synthetic spatial expression patterns by interpreting just two orthogonal morphogen gradients. The proposed framework offers a versatile approach to systematically design and discover complex genetic circuits producing spatial patterns.

When local governments' stay-at-home orders meet the white house's "opening up america again".

In the current polarized political climate, citizens frequently face conflicting directives from their local and federal government officials. For instance, on April 16th, 2020, The White House launched the "Opening up America Again" (OuAA) campaign while many U.S. counties had stay-at-home orders. We created a panel data set of U.S. counties to study the impact of U.S. counties' stay-at-home orders on community mobility before and after The White House's campaign to reopen the country. Our results suggest that before the OuAA campaign, stay-at-home orders substantially decreased the time spent in retail and recreation businesses. However, after the launch of the OuAA campaign, the time spent at retail and recreational businesses in a typical conservative county increased significantly more than in liberal counties (23% increase in a typical conservative county vs. 9% increase in a typical liberal county). We also found that in conservative counties with stay-at-home orders, time spent at retail and recreational businesses increased less than in those without stay-at-home orders. These findings illuminate that when federal and local government policies are at odds, residents decide which policies to adhere to based on the alignment between their political ideology and the government body. Our findings highlight the substantial importance of each government body in forming citizens' behaviors, offering practical implications for policy makers during natural disasters.

Efficacy of a mobile phone application for the improvement of oral hygiene of patients undergoing fixed orthodontic treatment : A randomized controlled clinical trial.

OBJECTIVES: This study aimed to assess the efficacy of a mobile phone application (app) to improve oral hygiene of patients undergoing fixed orthodontic treatment. MATERIALS AND METHODS: This randomized controlled clinical trial was conducted with a total of 60 patients undergoing fixed orthodontic treatment in two groups: intervention and control (n = 30 each). A previously designed mobile app (Labkhand) was used by patients in the intervention group. Orthodontic plaque index (OPI) and modified gingival index (MGI) were recorded in the two groups at baseline (first session or T0), and after 1 (T1) and 3 (T2) months. The number of debonded/broken brackets was also recorded, and pain score of the patients was assessed at nine time points. Data were analyzed using the χ2 test, paired t­test, and repeated measures analysis of variance (ANOVA; α = 0.05). RESULTS: The two groups demonstrated no significant difference in OPI and MGI at T0 (P > 0.05). OPI and MGI at T1 and T2 were significantly lower in the intervention group than in the control group (P < 0.05). The number of patients with broken brackets in the intervention group was significantly lower than that in the control group (P = 0.017). The two groups reported no significant difference in pain score (P > 0.05). CONCLUSION: The Labkhand mobile app successfully improved oral hygiene indices of patients undergoing fixed orthodontic treatment, and decreased the frequency of broken brackets after 1 and 3 months of use.

Automatic design of gene regulatory mechanisms for spatial pattern formation.

Synthetic developmental biology aims to engineer gene regulatory mechanisms (GRMs) for understanding and producing desired multicellular patterns and shapes. However, designing GRMs for spatial patterns is a current challenge due to the nonlinear interactions and feedback loops in genetic circuits. Here we present a methodology to automatically design GRMs that can produce any given spatial pattern. The proposed approach uses two orthogonal morphogen gradients acting as positional information signals in a multicellular tissue area or culture, which constitutes a continuous field of engineered cells implementing the same designed GRM. To efficiently design both the circuit network and the interaction mechanisms-including the number of genes necessary for the formation of the target pattern-we developed an automated algorithm based on high-performance evolutionary computation. The tolerance of the algorithm can be configured to design GRMs that are either simple to produce approximate patterns or complex to produce precise patterns. We demonstrate the approach by automatically designing GRMs that can produce a diverse set of synthetic spatial expression patterns by interpreting just two orthogonal morphogen gradients. The proposed framework offers a versatile approach to systematically design and discover pattern-producing genetic circuits.

Mobile app for comprehensive management of orthodontic patients with fixed appliances : Design and use.

OBJECTIVES: This study aimed to design and implement a mobile phone application (app) aiming to enhance the cooperation and oral hygiene of orthodontic patients. METHODS: An orthodontic app named "Labkhand" was designed by a team of orthodontists and programmers according to the needs and scheduled interventions for patients. The aim of this app was to obviate the orthodontic needs of patients by providing educational content in the form of texts and videos, oral hygiene reminders, appointment reminders, and nutrition reminders. It also enabled instant messaging and chatting between patients and dental personnel. In this study, 61 patients were monitored during their orthodontic treatment procedure. All patients were evaluated using a questionnaire with 7 questions before and after a 6-month treatment period. A total of 31 patients were instructed to use the app, while the other 30 patients received treatment without the app. The collected data were analyzed using paired t­test and analysis of covariance (ANCOVA) test. RESULTS: While the control group's questionnaire score remained almost the same (P > 0.05), the mean questionnaire score of patients in the intervention group significantly increased after using the app compared to the baseline scores acquired before using the app (P < 0.001). The greatest improvement was noted in topics related to "reminding of appointments", "general information about orthodontic treatment", and "oral hygiene maintenance and instructions". "Quality of services" and "patient-orthodontist communication" showed the least improvement in patient satisfaction. CONCLUSIONS: The Labkhand orthodontic app has multiple functions and can obviate the needs of orthodontic patients easily and at a low cost.

Reconstruction of C1 lateral mass with an expandable cage in addition to vertebral artery preservation: presenting two cases.

PURPOSE: C1 lateral mass reconstruction is recommended, in cases of instability caused by tumor involvement or extensive C1 lateral mass resection. However, because of the anatomical complexity of the area and, most importantly, the proximity to vertebral arteries, few cases of reconstruction have been reported to date. The purpose of this report is to present technical details of C1 lateral mass reconstruction in conjunction with vertebral artery preservation from a posterior approach. METHODS: Two cases of one stage craniovertebral junction instrumentation and C1 lateral mass reconstruction in conjunction with vertebral artery preservation from a posterior approach are presented. RESULTS: In both cases of extensive resection of lateral mass due to tumor involvement, an expandable cage was used for C1 lateral mass reconstruction, which has been used only in one patient in literature. Complementary pathological examinations of the two cases indicated two rare tumors that had been reported in the upper cervical region so far. The first case became an unknown origin metastatic cancer and the second was reported to be a primary non- Hodgkin lymphoma. CONCLUSIONS: C1 lateral mass reconstruction with an expandable cage together with VA preservation is recommended in cases of extensive C1 lateral mass resection to increase the total strength and to shorten the length of the posterior device and probably better fusion. The expandable cage is preferred because of safer placement under compression instead of the lateral mass.

Computational Systems Biology of Morphogenesis.

Extracting mechanistic knowledge from the spatial and temporal phenotypes of morphogenesis is a current challenge due to the complexity of biological regulation and their feedback loops. Furthermore, these regulatory interactions are also linked to the biophysical forces that shape a developing tissue, creating complex interactions responsible for emergent patterns and forms. Here we show how a computational systems biology approach can aid in the understanding of morphogenesis from a mechanistic perspective. This methodology integrates the modeling of tissues and whole-embryos with dynamical systems, the reverse engineering of parameters or even whole equations with machine learning, and the generation of precise computational predictions that can be tested at the bench. To implement and perform the computational steps in the methodology, we present user-friendly tools, computer code, and guidelines. The principles of this methodology are general and can be adapted to other model organisms to extract mechanistic knowledge of their morphogenesis.

Damage-tolerant 3D-printed ceramics via conformal coating.

Ceramic materials, despite their high strength and modulus, are limited in many structural applications due to inherent brittleness and low toughness. Nevertheless, ceramic-based structures, in nature, overcome this limitation using bottom-up complex hierarchical assembly of hard ceramic and soft polymer, where ceramics are packaged with tiny fraction of polymers in an internalized fashion. Here, we propose a far simpler approach of entirely externalizing the soft phase via conformal polymer coating over architected ceramic structures, leading to damage tolerance. Architected structures are printed using silica-filled preceramic polymer, pyrolyzed to stabilize the ceramic scaffolds, and then dip-coated conformally with a thin, flexible epoxy polymer. The polymer-coated architected structures show multifold improvement in compressive strength and toughness while resisting catastrophic failure through a considerable delay of the damage propagation. This surface modification approach allows a simple strategy to build complex ceramic parts that are far more damage-tolerant than their traditional counterparts.

Inference of dynamic spatial GRN models with multi-GPU evolutionary computation.

Reverse engineering mechanistic gene regulatory network (GRN) models with a specific dynamic spatial behavior is an inverse problem without analytical solutions in general. Instead, heuristic machine learning algorithms have been proposed to infer the structure and parameters of a system of equations able to recapitulate a given gene expression pattern. However, these algorithms are computationally intensive as they need to simulate millions of candidate models, which limits their applicability and requires high computational resources. Graphics processing unit (GPU) computing is an affordable alternative for accelerating large-scale scientific computation, yet no method is currently available to exploit GPU technology for the reverse engineering of mechanistic GRNs from spatial phenotypes. Here we present an efficient methodology to parallelize evolutionary algorithms using GPU computing for the inference of mechanistic GRNs that can develop a given gene expression pattern in a multicellular tissue area or cell culture. The proposed approach is based on multi-CPU threads running the lightweight crossover, mutation and selection operators and launching GPU kernels asynchronously. Kernels can run in parallel in a single or multiple GPUs and each kernel simulates and scores the error of a model using the thread parallelism of the GPU. We tested this methodology for the inference of spatiotemporal mechanistic gene regulatory networks (GRNs)-including topology and parameters-that can develop a given 2D gene expression pattern. The results show a 700-fold speedup with respect to a single CPU implementation. This approach can streamline the extraction of knowledge from biological and medical datasets and accelerate the automatic design of GRNs for synthetic biology applications.

GlyGen data model and processing workflow.

SUMMARY: Glycoinformatics plays a major role in glycobiology research, and the development of a comprehensive glycoinformatics knowledgebase is critical. This application note describes the GlyGen data model, processing workflow and the data access interfaces featuring programmatic use case example queries based on specific biological questions. The GlyGen project is a data integration, harmonization and dissemination project for carbohydrate and glycoconjugate-related data retrieved from multiple international data sources including UniProtKB, GlyTouCan, UniCarbKB and other key resources. AVAILABILITY AND IMPLEMENTATION: GlyGen web portal is freely available to access at https://glygen.org. The data portal, web services, SPARQL endpoint and GitHub repository are also freely available at https://data.glygen.org, https://api.glygen.org, https://sparql.glygen.org and https://github.com/glygener, respectively. All code is released under license GNU General Public License version 3 (GNU GPLv3) and is available on GitHub https://github.com/glygener. The datasets are made available under Creative Commons Attribution 4.0 International (CC BY 4.0) license. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Clinical, Functional, and Radiologic Outcome of Single- and Double-Level Transforaminal Lumbar Interbody Fusion in Patients with Low-Grade Spondylolisthesis.

OBJECTIVE: The main objective is to determine the functional, clinical, and radiological outcome of patients with low-grade spondylolisthesis undergoing single- or double-level transforaminal lumbar interbody fusion (TLIF). MATERIALS AND METHODS: This quasi-interventional study was conducted during a 2-year period from 2016 to 2018 in Shiraz, Southern Iran. We included all the adult (≥18 years) patients with low-grade spondylolisthesis (Meyerding Grade I and II) who underwent single- or double-level TLIF in our center. The spinopelvic parameters including pelvic incidence (PI), pelvic tilt (PT), sacral slope (SS), lumbar lordosis (LL), and segmental LL (SLL) were measured. The pain intensity and disability were measured utilizing the visual analog scale (VAS) for back and leg pain and Oswestry Disability Index (ODI), respectively, after 1 year. RESULTS: Overall, we included a total number of 50 patients with mean age of 54.1 ± 10.48 years. After the surgery, the PI (P = 0.432), PT (P = 0.782), and SS (P = 0.466) were not found to be statistically changed from the baseline. However, we found that single- or double-level TLIF was associated with increased LL (P < 0.001) and SLL (P < 0.001). Regarding the clinical outcome measures, both back (P = 0.001) and leg (P < 0.001) VAS improved after the surgery significantly. In addition, we found that improved leg VAS was positively correlated with improved ODI (r = 0. 634; P < 0.001). CONCLUSION: Single- or double-level TLIF is associated with increased global and SLL along with improved leg and back pain and disability in patients with low-grade spondylolisthesis. Interestingly, improved leg pain is correlated to improved disability in these patients.

Aloe vera Gel: Effective Therapeutic Agent against Multidrug-Resistant Pseudomonas aeruginosa Isolates Recovered from Burn Wound Infections.

Objective. Aloe vera is an herbal medicinal plant with biological activities, such as antimicrobial, anticancer, anti-inflammatory, and antidiabetic ones, and immunomodulatory properties. The purpose of this study was investigation of in vitro antimicrobial activity of A. vera gel against multidrug-resistant (MDR) Pseudomonas aeruginosa isolated from patients with burn wound infections. Methods. During a 6-month study, 140 clinical isolates of P. aeruginosa were collected from patients admitted to the burn wards of a hospital in Tehran, Iran. Antimicrobial susceptibility test was carried out against the pathogens using the A. vera gel and antibiotics (imipenem, gentamicin, and ciprofloxacin). Results. The antibiogram revealed that 47 (33.6%) of all isolates were MDR P. aeruginosa. The extract isolated from A. vera has antibacterial activity against all of isolates. Also, 42 (89.4%) isolates were inhibited by A. vera gel extract at minimum inhibitory concentration (MIC) ≤ 200 µg/mL. MIC value of A. vera gel for other isolates (10.6%) was 800 µg/mL. All of MDR P. aeruginosa strains were inhibited by A. vera at similar MIC50 and MIC90 200 µg/mL. Conclusion. Based on our results, A. vera gel at various concentrations can be used as an effective antibacterial agent in order to prevent wound infection caused by P. aeruginosa.

A new approach to human microRNA target prediction using ensemble pruning and rotation forest.

MicroRNAs (miRNAs) are small non-coding RNAs that have important functions in gene regulation. Since finding miRNA target experimentally is costly and needs spending much time, the use of machine learning methods is a growing research area for miRNA target prediction. In this paper, a new approach is proposed by using two popular ensemble strategies, i.e. Ensemble Pruning and Rotation Forest (EP-RTF), to predict human miRNA target. For EP, the approach utilizes Genetic Algorithm (GA). In other words, a subset of classifiers from the heterogeneous ensemble is first selected by GA. Next, the selected classifiers are trained based on the RTF method and then are combined using weighted majority voting. In addition to seeking a better subset of classifiers, the parameter of RTF is also optimized by GA. Findings of the present study confirm that the newly developed EP-RTF outperforms (in terms of classification accuracy, sensitivity, and specificity) the previously applied methods over four datasets in the field of human miRNA target. Diversity-error diagrams reveal that the proposed ensemble approach constructs individual classifiers which are more accurate and usually diverse than the other ensemble approaches. Given these experimental results, we highly recommend EP-RTF for improving the performance of miRNA target prediction.

Combining classifiers generated by multi-gene genetic programming for protein fold recognition using genetic algorithm.

In this study the problem of protein fold recognition, that is a classification task, is solved via a hybrid of evolutionary algorithms namely multi-gene Genetic Programming (GP) and Genetic Algorithm (GA). Our proposed method consists of two main stages and is performed on three datasets taken from the literature. Each dataset contains different feature groups and classes. In the first step, multi-gene GP is used for producing binary classifiers based on various feature groups for each class. Then, different classifiers obtained for each class are combined via weighted voting so that the weights are determined through GA. At the end of the first step, there is a separate binary classifier for each class. In the second stage, the obtained binary classifiers are combined via GA weighting in order to generate the overall classifier. The final obtained classifier is superior to the previous works found in the literature in terms of classification accuracy.