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2022 ◽  
Vol 15 ◽  
Author(s):  
Kyra T. Newmaster ◽  
Fae A. Kronman ◽  
Yuan-ting Wu ◽  
Yongsoo Kim

The brain is composed of diverse neuronal and non-neuronal cell types with complex regional connectivity patterns that create the anatomical infrastructure underlying cognition. Remarkable advances in neuroscience techniques enable labeling and imaging of these individual cell types and their interactions throughout intact mammalian brains at a cellular resolution allowing neuroscientists to examine microscopic details in macroscopic brain circuits. Nevertheless, implementing these tools is fraught with many technical and analytical challenges with a need for high-level data analysis. Here we review key technical considerations for implementing a brain mapping pipeline using the mouse brain as a primary model system. Specifically, we provide practical details for choosing methods including cell type specific labeling, sample preparation (e.g., tissue clearing), microscopy modalities, image processing, and data analysis (e.g., image registration to standard atlases). We also highlight the need to develop better 3D atlases with standardized anatomical labels and nomenclature across species and developmental time points to extend the mapping to other species including humans and to facilitate data sharing, confederation, and integrative analysis. In summary, this review provides key elements and currently available resources to consider while developing and implementing high-resolution mapping methods.


Symmetry ◽  
2021 ◽  
Vol 14 (1) ◽  
pp. 19
Author(s):  
Blanca E. Garcia ◽  
Emmanuel Rodriguez ◽  
Yolocuauhtli Salazar ◽  
Paul A. Valle ◽  
Adriana C. Flores-Gallegos ◽  
...  

The authors wish to make the following corrections to this paper [...]


Webology ◽  
2021 ◽  
Vol 18 (2) ◽  
pp. 135-153
Author(s):  
Khadijeh Moulaei ◽  
Shirin Ayani ◽  
Kambiz Bahaadinbeigy ◽  
Rafat Bayat ◽  
Farhad Fatehi ◽  
...  

The Internet of Things (IOT) has led to ground-breaking changes in the healthcare industry as a promising technological solution. Yet, despite its many benefits, its application has always proven to be challenging. Hence, the purpose of this study is to identify the challenges of using the IOT with the intention of proposing a model for implementing Internet of Things in Iranian hospitals. This study was performed in three phases. In the first phase, the challenges of using the IoT were outlined and introduced. In the second phase the challenges of using the Internet of Things in Iranian hospitals (according to experts) was established during the completion of a two-round Delphi. In the last phase, a novel model for implementing the IoT in Iranian hospitals was proposed. The identified primary model consisted of six groups, namely privacy and security, Big data, hardware, network, software, and organizational-cultural and environmental issues with 78 challenges. Out of the 78 identified challenges, 46 were approved by experts as essential elements for providing IoT patterns in Iranian hospitals. The highest and lowest averages were related to the subgroups "Failure to provide regular IoT rules and programs by governments" and "Absence of single, integrated and efficient platforms with high data transfer capacity and fast processing", respectively. The final model for implementing IoT in Iranian hospitals was designed and presented using Edraw Max10.0.4+Portable software. Providing this model can provide a sufficient basis, information and knowledge for policymakers, government authorities and managers of organizations to use the IoT in hospitals of Iran and other countries. Also, the application of the proposed model can result in the improved special capabilities in the realm of system design, and offer grounds for saving time and money as well as reducing failures in the initial design of IoT projects.


2021 ◽  
Vol 4 (1) ◽  
Author(s):  
Naari Jeong ◽  
Dennis Savaiano

Background and Objective: Depression is the leading cause of disability worldwide, yet approximately one-third of adults diagnosed with major depressive disorder do not receive treatment. Health coalitions are one strategy towards addressing this issue through collaborative, multi-sector interventions implemented within community organizations. In this narrative review, we examine what evidence exists that coalitions may improve outcomes of depression.     Methods: A search for peer-reviewed literature was conducted using PubMed, CINHAL, MEDLINE, and PsychINFO databases. The search was limited to studies published in the English language. No limitations were placed on location or date of publication. Initial search produced a total of 236 articles. Of these, 34 met inclusion criteria. Papers that did not address depression interventions in the context of community health coalitions were excluded.     Results: Community Partners in Care, a double-blinded randomized control trial, is the primary model in literature that describes outcomes of coalitions in addressing depression. 6-month outcomes revealed that participants treated through a coalition model (n=504) as opposed to those in a non-coalition approach (n=512) had significantly improved self-reported mental health related quality of life and a reduced number of behavioral health hospitalizations. 3-year and 4-year outcomes revealed participants in the coalition model had increased odds of clinical depression remission. While many other studies documented coalition-based interventions, there was little to no report of treatment effectiveness.      Project Impact: Current literature suggests that coalitions may be an effective method of addressing depression, particularly in under-resourced areas where community members are more likely to access care through social services. In addition, churches and schools were identified as key partners for coalitions, as trusted informal networks for mental health support. While results support coalition utility for mental health intervention, more research is needed to determine what, if any, unique attributes of coalitions are necessary to insure effective mental health interventions.   


2021 ◽  
Author(s):  
Nana Tang ◽  
Han Chen ◽  
Ruidong Chen ◽  
Wen Tang ◽  
Hongjie Zhang

Abstract Background Mucosal healing (MH) has become the treatment goal of patients with Crohn’s disease (CD). This study aims to develop a noninvasive and reliable clinical tool for individual evaluation of mucosal healing in patients with Crohn’s disease. Results The following variables were independently associated with the MH and were subsequently included into the prediction model: PLR (platelet to lymphocyte ratio), CAR (C-reactive protein to albumin ratio), ESR (erythrocyte sedimentation rate), HBI (Harvey-Bradshaw Index) score and infliximab treatment. A primary model and a simple model were established, respectively. The primary model performed better than the simple one in C-index (87.5% vs 83.0 %, p=0.004). There was no statistical significance between these two models in sensitivity (70.43% vs 62.61%, p=0.467), specificity (87.12% vs 80.69%, p=0.448), PPV (72.97% vs 61.54%, p=0.292), NPV (85.65% vs 81.39%, p=0.614), and accuracy (81.61% vs 74.71%, p=0.303). The primary model had good calibration and high levels of explained variation and discrimination in validation cohort. Conclusions This model can be used to predict MH in post-treatment CD patients. It can also be used as an indication of endoscopic surveillance to evaluate mucosal healing in patients with CD after treatment.


2021 ◽  
pp. 108201322110496
Author(s):  
Fatih Tarlak ◽  
Fernando Pérez-Rodríguez

The main objective of the present study was to investigate the effect of storage temperature on aerobically stored chicken meat spoilage using the two-step and one-step modelling approaches involving different primary models namely the modified Gompertz, logistic, Baranyi and Huang models. For this purpose, growth data points of Pseudomonas spp. were collected from published studies conducted in aerobically stored chicken meat product. Temperature-dependent kinetic parameters (maximum specific growth rate ‘µ max’ and lag phase duration ‘ λ’) were described as a function of storage temperature through the Ratkowsky model based on the different primary models. Then, the fitting capability of both modelling approaches was compared taking into account root mean square error, adjusted coefficient of determination (adjusted-R2) and corrected Akaike information criterion. The one-step modelling approach showed considerably improved fitting capability regardless of the used primary model. Finally, models developed from the one-step modelling approach were validated for the maximum growth rate data extracted from independent published literature using the statistical indexes Bias (Bf) and Accuracy (Af) factors. The best prediction capability was obtained for the Baranyi model with Bf and Af being very close to 1. The shelf-life of chicken meat as a function of storage temperature was predicted using both modelling approaches for the Baranyi model.


2021 ◽  
Vol 1197 (1) ◽  
pp. 012011
Author(s):  
Aniket Patkar ◽  
Santosh Mukkawar

Abstract In this paper analyzed the RC a nd PT Beam against variation in the clear span length of the beam. This work includes the design and estimate of Cost/Beam from 5m span up to 15m span length of the beam. Also, The response of the frame following two variation in its modelling. Initially, The primary model consists of a conventional RCC frame with all beams and columns as RCC. The secondarily model considers peripheral beams as RCC and interior beams with PT. Such as ETABS software used to designed RC beam element and ADAPT-PTRC used to designed PT beam element. However it has been note that variation of cost with respect to the span of beam where the break-even point between RCC and PT technique is approx 7m Span. Also the control on deflection of beam by restrict the depth of beam by using unbonded Post-tensioned prestress concrete beam method. There is very good understand all aspects PT beam better than as compared with to RC beam in deflection against longer span length of beams. This paper gives suggestion about to reach a decidedly conclusion regarding which technique is superior over one another.


2021 ◽  
Vol 11 (20) ◽  
pp. 9723
Author(s):  
Carlo Galli ◽  
Elena Landi ◽  
Silvana Belletti ◽  
Maria Teresa Colangelo ◽  
Stefano Guizzardi

Strontium (Sr) and Magnesium (Mg) are bioactive ions that have been proven to exert a beneficial effect on bone; therefore, their incorporation into bone substitutes has long been viewed as a possible approach to improve tissue integration. However, the thermal instability of Mg-substituted hydroxyapatites has hitherto limited development. We previously described the creation of thermally consolidated porous constructs of Mg,Sr co-substituted apatites with adequate mechanical properties for their clinical use. The present paper describes the biocompatibility of Mg,Sr co-substituted granules using an alveolar-bone-derived primary model of human osteoblasts. Cells were cultured in the presence of different amounts of hydroxyapatite (HA), Sr-substituted HA, or MgSrHA porous macrogranules (with a size of 400–600 microns, obtained by grinding and sieving the sintered scaffolds) for three and seven days, and their viability was measured by a 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide (MTT) assay. Protein content was measured using the Lowry assay at the same time points. Cell viability was not impaired by any of the tested compounds. Indirect and direct biocompatibility of these macrogranules was assessed by culturing cells in a previously conditioned medium with HA, SrHA, or MgSrHA, or in the presence of material granules. Osteoblasts formed larger and more numerous nodules around SrHA or MgSrHA granules. Furthermore, cell differentiation was evaluated by alkaline phosphatase staining of primary cells cultured in the presence of HA, SrHA, or MgSrHA granules, confirming the increased osteoconductivity of the doped materials.


2021 ◽  
Author(s):  
Jadelyn M Hoerr ◽  
Ahmed E Dhamad ◽  
Thomas M Deere ◽  
Melissa Chanderban ◽  
Daniel J Lessner

Methanosarcina acetivorans is the primary model to understand the physiology of methanogens that do not use hydrogenase to consume or produce hydrogen (H2) during methanogenesis. The genome of M. acetivorans encodes putative methanophenazine-reducing hydrogenases (Vht and Vhx), F420-reducing hydrogenase (Frh), and hydrogenase maturation machinery (Hyp), yet cells lack significant hydrogenase activity under all growth conditions tested to date. Thus, the importance of hydrogenase to the physiology of M. acetivorans has remained a mystery. M. acetivorans can fix dinitrogen (N2) using nitrogenase that is documented in bacteria to produce H2 during the reduction of N2 to ammonia. Therefore, we hypothesized that M. acetivorans uses hydrogenase to recycle H2 produced by nitrogenase during N2 fixation. Results demonstrate that hydrogenase expression and activity is higher in N2-grown cells compared to cells grown with fixed nitrogen (NH4Cl). To test the importance of each hydrogenase and the maturation machinery, the CRISPRi-dCas9 system was used to generate separate M. acetivorans strains where transcription of the vht, frh, vhx, or hyp operons is repressed. Repression of vhx and frh does not alter growth with either NH4Cl or N2 and has no effect on H2 metabolism. However, repression of vht or hyp results in impaired growth with N2 but not NH4Cl. Importantly, H2 produced endogenously by nitrogenase is detected in the headspace of culture tubes containing the vht or hyp repression strains. Overall, the results reveal that Vht hydrogenase recycles H2 produced by nitrogenase that is required for optimal growth of M. acetivorans during N2 fixation.


2021 ◽  
Vol 13 (3) ◽  
pp. 1546-1555
Author(s):  
Masduki Ahmad

This study aims to analyze the impact given by the learning environment and learning motivation on the student learning effectiveness at As-Syafiiyah Islamic University. This research is based on the problem that education in Indonesia is currently paralyzed, including higher education at universities, one of which is the As-Syafiiyah Islamic University. A quantitative methodology is a technique used in this study with the primary model of multiple linear regression. Using the Slovin formula, the research participants were 340 students from various study programs at the As-Syafiiyah Islamic University. Research data was obtained through a questionnaire distributed online. Statistical calculations were carried out using SPSS 25, and the hypothesis test used the t-test and f-test. It was revealed that: 1) the learning environment significantly and positively affects the learning effectiveness at As-Syafiiyah Islamic University; 2) the learning motivation significantly and positively affects the learning effectiveness at As-Syafiiyah Islamic University, and 3)both of learning environment and learning motivation impact student learning effectiveness at As-Syafiiyah Islamic University by 77.5%. This research is expected to have implications for improving the quality of education during the pandemic. In particular, it can illustrate how important the learning environment and learning motivation are to the university's effectiveness.


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