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The increased durability and performance features of polymer concrete panels have led to their widespread application in construction. The manufacturing of precise and effective techniques for identifying forms and flaws is vital to guarantee the high quality of these panels. To increase the accuracy of structure and defect-recognition in polymer concrete panels, this study presents a new Stochastic raven roosting optimization enhanced artificial neural network (SRRO-EANN) forecasting technique. The data sample used to assess the completed model fits the training dataset is referred to the test dataset. The Gaussian filter (GF) is a tool used in the pre-processing and Principal Component Analysis (PCA) feature extraction, leading to more effective utilization and understanding the defect capturing. The findings of the research indicate the effectiveness for the future development of forecasting technologies in the realm of quality control and building material inspection.

Direct dynamics simulations on the ozonolysis reaction of catechol is performed in a vacuum and in an N2 bath to study the various aspects of the exit channel dynamics of the reaction. The simulations were conducted at 20 and 324 kg/m3 densities, with 200 and 293 N2 molecules in the bath, respectively. It is a post-transition state (TS) direct dynamics simulation, where the TS of the catechol+O3 system is taken as the reactant. High combustion temperatures of 800, 1000, and 1500 K are considered for the reactant excitation. In contrast, the bath is taken at 300 K. CO, H2O, CO2, O2, and small carboxylic acid (SCA) are the major channels observed for this reaction. Explicit study of the product channels along with rate constants of all the product channels are obtained. The rate constants are in the order of ns−1. The partitioning of the product's internal energy into its various modes and how the energy transfer occurs in the formation of the products is an exciting aspect of the study. Also reported in this work is the branching ratio of the products and comparison with the experimentally reported data. The energy partitioning study reveals that the dissociation dynamics of the catechol-O3 TS is not statistical. In contrast, the bath dynamics, which correspond to the energy transfer from the TS to the bath, is statistical in nature.

Dengue is one of the most critical diseases, caused by Dengue virus (DENV) serotypes (DENV-1 to DENV-4). Study aims to detect DENV in natural A. aegypti from endemic regions of Uttar Pradesh. From 2010- 2013, mosquitoes collected from Uttar Pradesh tested for Dengue virus serotypes by capsid-premembrane gene-based PCR analyzed for genotypes sequencing of the C-prM junction of DENV genome. A total of 4731, 53.54% (n=2671) A. aegypti and 46 % (n=2060) A. albopictus mosquitoes were collected. Of 226 mosquito pools, 10 pools of A. aegypti and 14 pools of A. albopictus were positive for DENV by PCR. All 24 isolates identified as DENV-I; Genotype (G)-III (n=8), G-V (n=1); DENV-2; G-IV (n=4); DENV-3; G-III (n=11). The overall minimum infection rate was much higher in A. albopictus mosquitoes and presence of MIR in male mosquitoes is an indicating natural vertical transmission and important observation in geographical area indicating natural vertical transmission.

IntroductionDengue fever is hyperendemic in several Southeast and South Asian countries, including India, with all four serotypes (DENV 1–4) circulating at different periods and in different locations. Sustainable and improved virological and entomological surveillance is the only tool to prevent dengue and other vector-borne diseases.ObjectivesThe present study has been carried out to detect and characterize the circulating dengue virus (DENV) in field-collected Aedes mosquitoes in Bhopal, Central India.MethodsAedes mosquitoes were collected from 29 localities within Bhopal city during October 2020 to September 2022. DENV infection was assessed in the individual head and thorax regions of Aedes mosquitoes using reverse transcriptase PCR. Positive samples were sequenced, and the circulating serotypes and genotypes were determined using phylogenetic analysis.ResultsDENV RNA was detected in 7 Aedes aegypti and 1 Aedes albopictus, with infection rates of 0.59 and 0.14%, respectively. Phylogenetic analysis revealed all the isolates belonged to DENV serotype 2 and distinctly clustered with the non-Indian lineage (cosmopolitan genotype 4a), which was not recorded from the study area earlier. The time to most common recent ancestor (TMRCA) of these sequences was 7.4 years old, with the highest posterior density (HPD) of 3.5–12.2 years, indicating that this new lineage emerged during the year 2014. This is the first report on the DENV incrimination in both Ae. aegypti and Ae. albopictus mosquitoes collected from Bhopal, Central India.ConclusionThe observed emergence of the non-Indian lineage of DENV-2 in Bhopal, which again is a first report from the area, coincides with the gradual increase in DENV cases in Bhopal since 2014. This study emphasizes the importance of DENV surveillance and risk assessment in this strategically important part of the country to decipher its outbreak and severe disease-causing potential.

BACKGROUND: Razumab is a biosimilar of ranibizumab and its intravitreal injection in various retinal diseases has been reported with good results. However, the incidence of inflammation in the anterior chamber and vitreous after the intravitreal injection of Razumab has not been studied so far. AIMS OF THE STUDY: The aim of this study was to study the incidence of inflammatory reaction in the anterior chamber and vitreous after intravitreal Razumab. MATERIALS AND METHODS: It was a single-center, nonrandomized observational study of eyes that received intravitreal Razumab injection for various retinal indications. The eyes were examined for anterior chamber and vitreous reaction at regular intervals. Preinjection and postinjection visual acuity and intraocular pressure (IOP) were noted. STATISTICAL ANALYSIS: The proportion of eyes developing anterior chamber and vitreous reaction were calculated with confidence intervals. The pre- and postinjection visual acuity and IOP were compared by Student's t-test for significant changes. RESULTS: Eighty-two injections were performed in 40 eyes of 33 patients. Four eyes (4.8%) showed anterior chamber reaction among which one eye had vitreous reaction also. All four eyes had no adverse effect on visual acuity and recovered completely. Mean preinjection logMAR visual acuity of 0.69 ± 0.41 improved to 0.58 ± 0.38 postinjection (P < 0.0001). The mean preinjection IOP changed from 16.2 ± 3.9 mmHg to 14.7 ± 3.4 mmHg postinjection (P = 0.009). CONCLUSIONS: The incidence of inflammatory reaction was found to be higher with Razumab compared to the incidence reported in the literature for ranibizumab. However, visual acuity was not affected by this inflammatory reaction.

Musculoskeletal pain is a common and debilitating symptom in older adults. However, its importance is often underestimated. In this review article, we discuss its proper evaluation and management. Pain evaluation includes detailed history taking, physical examination, imaging, and laboratory investigations. Management of musculoskeletal pain requires a multidomain approach including nonpharmacological, pharmacological, and surgical modalities. A step-wise approach recommended by the World Health Organization can be used for pain management. Common musculoskeletal conditions causing pain are osteoarthritis, low-back pain, gout, pseudogout, rheumatoid arthritis, polymyalgia rheumatica, and fibromyalgia.

RNA-protein interactions play vital roles in driving the cellular machineries. Despite significant involvement in several biological processes, the underlying molecular mechanism of RNA-protein interactions is still elusive. This may be due to the experimental difficulties in solving co-crystallized RNA-protein complexes. Inherent flexibility of RNA molecules to adopt different conformations makes them functionally diverse. Their interactions with protein have implications in RNA disease biology. Thus, study of binding interfaces can provide a mechanistic insight of the molecular functioning and aberrations caused due to altered interactions. Moreover, high-throughput sequencing technologies have generated huge sequence data compared to available structural data of RNA-protein complexes. In such a scenario, efficient computational algorithms are required for identification of protein-binding interfaces of RNA in the absence of known structures. We have investigated several machine learning classifiers and various features derived from nucleotide sequences to identify protein-binding nucleotides in RNA. We achieve best performance with nucleotide-triplet and nucleotide-quartet feature-based random forest models. An overall accuracy of 84.8%, sensitivity of 83.2%, specificity of 86.1%, MCC of 0.70 and AUC of 0.93 is achieved. We have further implemented the developed models in a user-friendly webserver “Nucpred”, which is freely accessible at “http://www.csb.iitkgp.ac.in/applications/Nucpred/index”.

Trackers for activity and physical fitness have become ubiquitous. Although recent work has demonstrated significant relationships between mental effort and physiological data such as skin temperature, heart rate, and electrodermal activity, we have yet to demonstrate their efficacy for the forecasting of mental effort such that a useful mental effort tracker can be developed. Given prior difficulty in extracting relationships between mental effort and physiological responses that are repeatable across individuals, we make the case that fusing self-report measures with physiological data within an internet or smartphone application may provide an effective method for training a useful mental effort tracking system. In this case study, we utilized over 90 h of data from a single participant over the course of a college semester. By fusing the participant’s self-reported mental effort in different activities over the course of the semester with concurrent physiological data collected with the Empatica E4 wearable sensor, we explored questions around how much data were needed to train such a device, and which types of machine-learning algorithms worked best. We concluded that although baseline models such as logistic regression and Markov models provided useful explanatory information on how the student’s physiology changed with mental effort, deep-learning algorithms were able to generate accurate predictions using the first 28 h of data for training. A system that combines long short-term memory and convolutional neural networks is recommended in order to generate smooth predictions while also being able to capture transitions in mental effort when they occur in the individual using the device.

OBJECTIVES:. The optimal staffing model for physicians in the ICU is unknown. Patient-to-intensivist ratios may offer a simple measure of workload and be associated with patient mortality and physician burnout. To evaluate the association of physician workload, as measured by the patient-to-intensivist ratio, with physician burnout and patient mortality. DESIGN:. Cross-sectional observational study. SETTING:. Fourteen academic centers in the United States from August 2020 to July 2021. SUBJECTS:. We enrolled ICU physicians and collected data on adult ICU patients under the physician’s care on the single physician-selected study day for each physician. MEASUREMENTS and MAIN RESULTS:. The primary exposure was workload (self-reported number of patients’ physician was responsible for) modeled as high (>14 patients) and low (≤14 patients). The primary outcome was burnout, measured by the Well-Being Index. The secondary outcome measure was 28-day patient mortality. We calculated odds ratio for burnout and patient outcomes using a multivariable logistic regression model and a binomial mixed effects model, respectively. We enrolled 122 physicians from 62 ICUs. The median patient-to-intensivist ratio was 12 (interquartile range, 10–14), and the overall prevalence of burnout was 26.4% (n = 32). Intensivist workload was not independently associated with burnout (adjusted odds ratio, 0.74; 95% CI, 0.24–2.23). Of 1,322 patients, 679 (52%) were discharged alive from the hospital, 257 (19%) remained hospitalized, and 347 (26%) were deceased by day 28; 28-day outcomes were unknown for 39 of patients (3%). Intensivist workload was not independently associated with 28-day patient mortality (adjusted odds ratio, 1.33; 95% CI, 0.92–1.91). CONCLUSIONS:. In our cohort, approximately one in four physicians experienced burnout on the study day. There was no relationship be- tween workload as measured by patient-to-intensivist ratio and burnout. Factors other than the number of patients may be important drivers of burnout among ICU physicians.

Heavy metal pollution is a growing environmental and health concern. Heavy metal ions, like Cu(II), undergo the Fenton reaction, producing hydroxide radicals and superoxides that cause DNA mutation (crosslinking and depurination) and carcinogenic damage to the living organisms. This study evaluates the performance of olive stones for Cu(II) removal from effluents in a continuous simulated process under different operating conditions using Aspen Adsorption® V8.0. Flow rate, bed height, and initial metal ion concentration were varied to analyze the effect of these parameters on the bed performance. The fastest breakthrough time was 5 h when the system was run at a flow rate of 2 L/s, 1 m bed height, and 5 ppm initial metal ion concentration. The optimal conditions for the process were found to be 1 m bed height, 2 ppm initial metal ion concentration, and 0.5 L/s flow rate at which 0.543 mg/g metal adsorption capacity of the bed was observed. Under these conditions, bed life was observed to be 163.8 h. The aim of this work is to provide motivation towards usage of low-cost biosorbents for industrial scale adsorption processes.

We assess the underlying topics, sentiment, and types of information regarding COVID-19 vaccines cycling through Twitter during the initiation of the vaccine rollout. Once tweets about COVID-19 vaccine posted between 1 December 2020 and 28 February 2021 were collected and preprocessed, they were categorized as either relevant or irrelevant by a classifier trained by the research team. Latent Dirichlet Allocation was used to discover the topics of discussion in the relevant tweets. The NRC lexicon was used to quantify positive and negative sentiment found in the tweets. The types of information (information, misinformation, opinion, or question) in positive and negative sentiment tweets were assessed and distributions were compared. A total of 1,386,390 tweets were collected, out of which 210,657 relevant tweets were identified by the relevancy classifier. Eight topics provided the best representation of the corpus of relevant tweets. Tweets with a negative sentiment were associated with a higher percentage of misinformation whereas tweets with positive sentiment showed a higher percentage of information, opinions, and questions. The proliferation of information and misinformation on social media platforms is associated with building public trust and mitigating negative sentiment associated with COVID-19 vaccines.