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Synthesis of spirooxindole derivatives was achieved by combining Knoevenagel condensation and Michael addition between isatin, malononitrile, and dimedone or cyclohexan-1,3-dione in presence of ammonium acetate by simple stirring in ethanol at room temperature. Spirooxindole derivatives possess promising biological activities like antitumor, antifungal, antimalarial, and anti-HIV. The analytically pure spirooxindoles formed in a concise reaction time without recrystallization or column chromatography. This method is safe and avoids the use of toxic chemicals and excess consumption of energy.

Traumatic dental injuries result in damage to many dental and periradicular structures. They can be conservatively managed depending on the extent of the injury. Maxillary central incisors are most commonly involved in traumatic dental injuries mainly because of their anterior and protrusive positioning. The treatment of immature permanent teeth with severe internal resorption poses a lot of challenges to the clinician. The objective of the present article is to report successful management of traumatised maxillary central incisor with incomplete root formation and severe internal resorption in a 10-year-old boy using triple antibiotic paste, mineral trioxide aggregate and fibre-reinforced composite.

Nowadays, Tumour is one of the important reasons of human death worldwide, producing about 9.6 million people in 2018. BC (breast cancer) is the common reason for cancer deaths in females. BC is a type of cancer that can be treated when detected early. The main motive of this analysis is to detect cancer early in life using ML (machine learning) techniques. The features of the people included in the WDBC (Wisconsin diagnostic breast cancer) and Coimbra BC datasets were classified by SVOF-KNN, KNN, and Naïve Bayes techniques. The pre-processing data phase was applied to the datasets before classification. After the data pre-processing steps, three classification methods were applied to the data. Specificity and Sensitivity rates were used to calculate the success of the techniques. As an outcome of the BC diagnosis classification, the SVOF-KNN technique was found with a 91 percent specificity rate and 90 percent sensitivity rate. When the outcomes attained from feature extraction and selection are calculated. It is seen that feature extraction, selection, and data pre-processing techniques improve the specificity and sensitivity rate of the detection system.

BACKGROUND: Tubal ectopic pregnancies can cause substantial morbidity or even death. Current treatment is with methotrexate or surgery. Methotrexate treatment fails in approximately 30% of women who subsequently require rescue surgery. Gefitinib, an epidermal growth factor receptor inhibitor, might improve the effects of methotrexate. We assessed the efficacy of oral gefitinib with methotrexate, versus methotrexate alone, to treat tubal ectopic pregnancy. METHODS: We performed a multicentre, randomised, double-blind, placebo-controlled trial across 50 UK hospitals. Participants diagnosed with tubal ectopic pregnancy were administered a single dose of intramuscular methotrexate (50 mg/m2) and randomised (1:1 ratio) to 7 days of additional oral gefitinib (250 mg daily) or placebo. The primary outcome, analysed by intention to treat, was surgical intervention to resolve the ectopic pregnancy. Secondary outcomes included time to resolution of ectopic pregnancy and serious adverse events. This trial is registered at the ISRCTN registry, ISCRTN 67795930. FINDINGS: Between Nov 2, 2016, and Oct 6, 2021, 328 participants were allocated to methotrexate and gefitinib (n=165) or methotrexate and placebo (n=163). Three participants in the placebo group withdrew. Surgical intervention occurred in 50 (30%) of 165 participants in the gefitinib group and in 47 (29%) of 160 participants in the placebo group (adjusted risk ratio 1·15, 95% CI 0·85 to 1·58; adjusted risk difference -0·01, 95% CI -0·10 to 0·09; p=0·37). Without surgical intervention, median time to resolution was 28·0 days in the gefitinib group and 28·0 days in the placebo group (subdistribution hazard ratio 1·03, 95% CI 0·75 to 1·40). Serious adverse events occurred in five (3%) of 165 participants in the gefitinib group and in six (4%) of 162 participants in the placebo group. Diarrhoea and rash were more common in the gefitinib group. INTERPRETATION: In women with a tubal ectopic pregnancy, adding oral gefitinib to parenteral methotrexate does not offer clinical benefit over methotrexate and increases minor adverse reactions. FUNDING: National Institute of Health Research.

An effective way to identify breast cancer is by creating a prediction algorithm using risk factors. Models for ML have been used to improve the effectiveness of early detection. This article analyses a KNN combined with singular value decomposition and Grey wolf optimization(GWO) method to give a detection of breast cancer(BC) at the early phase depending on risk metrics. The SVD technique was utilized to eliminate the reliable feature vectors, the GW optimizer was used to select the feature vectors, and while KNN model was used to diagnose the BC status. The proposed hybrid recommendation model (SVOF-KNN) for BC prediction's main objective is to give an accurate recommendation for BC prognosis through four different steps such as;BCCD dataset collection, data pre-processing, feature selection, and classification/recommendation. It is implemented to classify the consequence of risk metrics connected withregular blood analysis(BA) in the BCCD database. The aspects of the BC dataset are insulin, glucose, HOMA, Leptin, resistin, etc. The error categories such as RMSE and MAE are used to calculate the exception values for each instance of the BC dataset. It hybrid model has recommended the best score instance having the minimumexception rateas the defined features for BC prediction. It improves significance in automatic BC classification with the optimum solution. The hybrid recommendation model (SVOF-KNN) also recommends the accurateclassification method for BC diagnosis. The results of this work shall enhance the QoS in BC care.

The desire to develop software with more and more functionalities to make human work easier pushes the industry towards developing various programming languages. The existence of the various programming languages in today's scenario raises the need for their evaluation. The motive of this research is the development of a deterministic decision support framework to solve the object-oriented programming (OOP) language's selection problem. In the present study, OOP language's selection problem is modeled as a multi-criteria decision-making, and a novel fuzzy-distance based approach is anticipated to solve the same. To demonstrate the working of developed framework, a case study consisting of the selection of seven programming languages is presented. The results of this study depict that Python is the most preferred language compared to other object-oriented programming languages. Selection of OOP languages helps to select the most appropriate language, which provides better opportunities in the business domain and will result in high success for engineering students.

Recently, the research on sentimental analysis has been growing rapidly. The tweets of social media are extracted to analyze the user sentiments. Many of the studies prefer to apply machine learning algorithms for performing sentiment analysis. In the current pandemic, there is an utmost importance to analyze the sentiments or behavior of a person to make the decisions as the whole world is facing lockdowns in multiple phases. The lockdown is psychologically affecting the human behavior. This study performs a sentimental analysis of Twitter tweets during lockdown using multinomial logistic regression algorithm. The proposed system framework follows the pre-processing, polarity and scoring, and feature extracting before applying the machine learning model. For validating the performance of proposed framework, other three majorly used machine learning based models-- namely decision tree, naïve Bayes, and K-nearest neighbors-- are implemented. Experimental results prove that the proposed framework provides improved accuracy over other models.

OBJECTIVE The middle fossa approach is an effective option for the treatment of small (Koos grade I and II) vestibular schwannomas (VSs) when the goal is hearing preservation. The authors evaluated the rates of hearing preservation and examined the factors associated with improved hearing outcomes after the middle fossa approach for VSs. METHODS In this retrospective, single-center cohort study evaluating the clinical outcomes after resection of small VSs using the middle fossa approach, consecutive adult patients (> 18 years) who underwent surgery between January 2000 and December 2021 were included. Clinical and imaging characteristics were analyzed, including baseline hearing status, duration of surgery, anesthetic parameters, and imaging characteristics of the surgically treated tumors. RESULTS Among the 131 included patients, 102 had valid and discoverable pre- and postoperative audiology assessments. The mean follow-up was 26 months (range 1–180 months). There were 85 patients with serviceable hearing preoperatively, defined as American Academy of Otolaryngology–Head and Neck Surgery (AAO-HNS) class A or B, of whom 78% retained class A or B hearing at the last follow-up. Binary logistic regression demonstrated that preoperative hearing AAO-HNS class (OR 0.19, 95% CI 0.05–0.77; p = 0.02), overlap between fundus and cochlea (OR 0.32, 95% CI 0.11–0.96; p = 0.04), and duration of anesthesia (OR 0.98, 95% CI 0.97–0.99; p = 0.03) were independent predictors of hearing outcomes. Additionally, 75% of patients with high diffusion-weighted imaging signal in the tumor (p = 0.009) and 67% of patients with the tumor originating at the modiolus of the cochlea (p = 0.004) had poor hearing outcomes. CONCLUSIONS The hearing preservation rates after microsurgical resection of small VSs using the middle fossa approach are high, with 78% of patients maintaining AAO-HNS class A or B hearing. Poor hearing status at baseline, longer duration of anesthesia, and large overlap between the fundus of the internal auditory canal and the cochlea were independently associated with unfavorable hearing outcomes. Imaging characteristics can be used to stratify patients’ risk of hearing loss.

This study reported the results of groundwater quality for drinking and irrigation purposes in Kurukshetra district, Haryana, India. Twelve parameters, namely pH, TDS, TH, sodium (Na+), magnesium (Mg2+), calcium (Ca2+), potassium (K+), chloride (Cl−), sulfate (SO42−), bicarbonate (HCO3−), nitrate (NO3−), and fluoride (F−) were evaluated and the Water Quality Index (WQI) was calculated. Based on the WQI, 31.57% and 68.43% of samples fall in excellent and good drinking water quality. The sodium adsorption ratio (SAR: 5.75–33.8), magnesium hazard ratio (MHR: 0.21–0.75), percent sodium (%Na: 31.34–72.84), residual sodium carbonate (RSC: 139–770), potential salinity (PS: 18.5–90), and Kelley Ratio (KR: 0.9–3.13) were calculated. The SAR, %Na, MHR, and KR indicated that most of the groundwater is suitable for irrigation. In addition, the inverse distance weighted (IDW) interpolation method was also employed to determine the spatial distribution of groundwater quality in the form of maps using ArcGIS software. The Pearson correlation matrix has been calculated to analyze the relationship between the water quality parameters. Gibbs plots have shown that evaporation and rock weathering are primary processes responsible for affecting the hydrochemistry of groundwater. Piper plots suggested that maximum groundwater samples are (HCO3−–Na+), (Na+), and (HCO3−) types.

The aftermath of the lockdown caused by the current pandemic generates many challenges and opportunities for the professionals as well as for organizations. Several organizations forced the people to work on-site whereas many of the organizations have been allowing work from home. However, both ways of working are challenging and cause psychological distress. The present work analyses the psychological distress among professionals residing in India during the COVID-19 pandemic. The work considers both the scenarios of working professionals: professionals working from home and professionals working onsite. The work introduces a novel hybrid machine learning approach called GBETRR. GBETRR combines two approaches, namely gradient-boosting classifier and extra-trees regressor repressor. The present work also uses a hybrid parameter optimization algorithm. Multiple performance metrics are used to evaluate the performance evaluation. Results revealed that the professionals with work from home are more stressed as compared to the professionals working onsite.

Pressing global challenges, such as climate change, the COVID-19 pandemic, or antibiotic resistance, require coordinated international responses guided by evidence-informed decisions. For this purpose, it is critical that scientists engage in providing insights during the decision-making process. However, the mechanisms for the engagement of scientists in policy-making are complex and vary internationally, which often poses significant challenges to their involvement. Herein, we address some of the mechanisms and barriers for scientists to engage in policy-making with a global perspective by early-career scientists. We highlight the importance of scientific academies, societies, universities, and early-career networks as stakeholders and how they can adapt their structures to actively contribute to shaping global policies, with representative examples from chemistry-related disciplines. We showcase the importance of raising awareness, providing resources and training, and leading discussions about connecting emerging scientists with global decision-makers to address societal challenges through policies. published

This work investigates the effectiveness of different pseudonymization techniques, ranging from rule-based substitutions to using pre-trained Large Language Models (LLMs), on a variety of datasets and models used for two widely used NLP tasks: text classification and summarization. Our work provides crucial insights into the gaps between original and anonymized data (focusing on the pseudonymization technique) and model quality and fosters future research into higher-quality anonymization techniques to better balance the trade-offs between data protection and utility preservation. We make our code, pseudonymized datasets, and downstream models publicly available, 10 pages. Accepted for TrustNLP workshop at ACL2023

Air pollution is one of the leading risk factors for poor health in Africa, resulting in millions of premature deaths and economic losses. Of particular interest is exposure to fine particulate matter (PM2.5) which is the driver for a majority of deaths across the continent. However, PM monitoring, and by extension, ground-level data on PM2.5 is very limited; this limits our understanding of the widespread societal and health impacts linked to PM pollution. The robustness of low-cost PM sensors and their ability to report in situ data in tropical environments via internet-based platforms as well as relative affordability has created the opportunity to employ low-cost sensors (LCS) for air quality monitoring but calibration methodologies and the usefulness of the high-temporal resolution data for source identification remain a challenge. Increasingly, local governments in African countries are also turning to low-cost sensors to monitor air quality. In this study, two Airnote PM monitors were colocated with reference-grade Teledyne PM mass monitor T640 for ~4 weeks at the University of Ghana, Accra to establish their performance using a simplified data correction methodology - multiple linear regression (MLR) model. A split ratio of 80% and 20% was used to train and test the populated Airnote PM2.5 data respectively based on measurements from Teledyne T640 with temperature and relative humidity values from the Airnote monitor. Sectoral and calendar analysis with wind component data were used to triangulate the sources of PM2.5. We observed a high consistency between the two Airnote monitors. Hourly and 24-hour average PM2.5 values ranged from 25 to 95 μg/m3, and 29 to 54 μg/m3 respectively, and in most cases, were significantly higher than the WHO Air Quality Guideline. MLR using Pearson’s correlation analysis improved the out-of-the-box quality of low-cost Airnote PM2.5 data; the R2 improved from 0.69 to 0.84 and the mean absolute error from 11.75 to 4.20 μg/m3 respectively. Also, the MLR correction model was found to improve the Airnote PM2.5 data quality for higher relative humidity (between 50 and 90%) but not lower. PM2.5 pollution was local and from N, NE and SW winds for the raw, corrected and Teledyne PM mass monitor T640 measurements. Together, these results indicate that with appropriate corrections, low-cost PM sensors can generate the much needed data for air pollution research and mitigation in areas with limited air quality monitoring and data.

The purpose of this study was to examine whether myeloid dendritic cells (mDCs) from patients with multiple sclerosis (MS) and healthy controls (HCs) become similarly tolerogenic when exposed to IL-27 as this may represent a potential mechanism of autoimmune dysregulation. Our study focused on natural mDCs that were isolated from HCs and MS patient peripheral blood mononuclear cells (PBMCs). After a 24-h treatment with IL-27 ± lipopolysaccharide (LPS), the mDCs were either harvested to identify IL-27-regulated gene expression or co-cultured with naive T-cells to measure how the treated DC affected T-cell proliferation and cytokine secretion. mDCs isolated from HCs but not untreated MS patients became functionally tolerogenic after IL-27 treatment. Although IL-27 induced both HC and untreated MS mDCs to produce similar amounts of IL-10, the tolerogenic HC mDCs expressed PD-L2, IDO1, and SOCS1, while the non-tolerogenic untreated MS mDCs expressed IDO1 and IL-6R. Cytokine and RNA analyses identified two signature blocks: the first identified genes associated with mDC tolerizing responses to IL-27, while the second was associated with the presence of MS. In contrast to mDCs from untreated MS patients, mDCs from HCs and IFNb-treated MS patients became tolerogenic in response to IL-27. The genes differentially expressed in the different donor IL-27-treated mDCs may contain targets that regulate mDC tolerogenic responses.

Reactive aluminum–nickel multilayer system shows exothermic energetic materials which act as a heat source for packaging and bonding of microsystems. The main challenge is controlling the self-propagation reaction velocity and temperature generated by thermal management through different thermal conductive substrate materials. The current work investigates the heat distribution of Al/Ni multilayer foils from different thermal conductive substrates which act as heat sink materials during the self-propagating reaction. A two-dimensional numerical model was developed to study thermal conductive heat loss and substrate thermal properties on the self-propagating reaction in Al/Ni multilayer foils. The self-propagating reaction was introduced on the surface of the foils by an electrical spark. Here we investigate the minimum critical thickness of Al/Ni multilayer foils which shows the self-propagation reaction on different substrates and verified from the two-dimensional numerical model. The outcomes of this investigation will facilitate the integration of Al/Ni multilayer foils on different substrates as intrinsic heat sources for different applications of micro/nanodevices. Graphical abstract

Solitary fibrous tumor (SFT) is a spindle cell lesion, classified under mesothelial tumors. Involvement of the nasal cavity, paranasal sinuses, and nasopharynx is rare. We present an extremely rare case of SFT of nasal origin eroding the anterior skull base. Complete local excision is the treatment of choice in the head and neck SFT, and we successfully excised the tumor by endoscopic approach only. The patient followed an uneventful course without any evidence of recurrence on 8-months follow-up.

This essay focuses on queer Indigenous life in the early twentieth century through an analysis of the archive of Nabor Feliz, an Indigenous sculptor who toured with major circuses. In the essay, the author employs queer critique and Indigenous theory to contend that the circus offered Feliz opportunities for Indigenous expressions of sexuality that might not have been otherwise afforded them in this period. The essay is based on an examination of the photographs, letters, and newspaper articles in the archive, which is housed at the Southwest Museum. To the author's knowledge, this is the first article-length piece on the complexities of queer Indigenous life in the first half of the twentieth century and the first study of Feliz's life and work.

OBJECTIVE: In this article, the authors systematically evaluated the efficacy and safety of tranexamic acid (TXA) in surgeries for spinal trauma. METHODS: Potentially relevant academic articles were identified from the Cochrane Library, MEDLINE, PubMed, and Google Scholar. Secondary sources were identified from the references of the included literature. RevMan software was used to analyze the pooled data. RESULTS: A total of 7 randomized controlled trials (RCTs) and 2 non-RCTs were included in the review. There were significant differences in total blood loss (standard mean difference [SMD] = −2.54 [95% CI, −3.72, −1.37], P = 0.0001), intraoperative blood loss (SMD = −0.96 [95% CI, −1.28, −0.64], P < 0.00001), postoperative blood loss (SMD = −1.42 [95% CI, −1.72, −1.11], P < 0.00001), and length of hospital stay (SMD = −3.73 [95% CI, −4.41, −3.06], P = 0.00001). No significant differences were found regarding transfusion requirement, operative duration, deep vein thrombosis, and pulmonary embolism between the 2 groups. CONCLUSIONS: The present meta-analysis indicates that the use of TXA in spinal surgery decreases blood loss and duration of hospital stay while not increasing the risk of side effects such as deep vein thrombosis and pulmonary embolism. CLINICAL RELEVANCE: The study aims to provide clinicians who operate on spine trauma with information on the use of tranexamic acid to decrease blood loss and related complications. LEVEL OF EVIDENCE: 1.

A deterministic decision support system is developed for the assessment of various Indian cities based on the air quality parameters in this research. The present study shapes the assessment of cities as a multi-criteria decision making (MCDM) problem due to the involvement of many indicators. To solve the present assessment problem, an MCDM method, namely, Distance based approach (DBA) that mainly works on the Euclidean distance calculation for each city from the optimal point and ranks the cities on the basis of their calculated distances. The city scoring minimum distance value is ranked at top position and the city with the maximum distance value on the last position.

In order to mitigate the challenges in microsurgical skill acquisition and training, especially in the COVID-19 era, we devised a novel microsurgical telementoring protocol for imparting microsurgical skill training in a socially distanced setting. We objectively analyzed its feasibility among neurosurgical trainees.In a controlled experimental design, 8 residents at different stages of their tenure participated in a lazy glass microsurgical simulator-based telementoring exercise. Microsuturing with 4-0 silk, 10-0 nylon on silastic sheets, and eggshell peeling tasks were performed by the residents prior to and after a telementoring session by a panel of 4 neurosurgical experts. Impact of telementoring was assessed in terms of surgical accuracy, efficiency, and dexterity by providing objective (Performance score [PS]), subjective (Neurosurgery Education and Training School [NETS] score), and cumulative scores (CS). Subgroup analysis was performed to assess the impact at different stages of residency.PS, NETS score, and CS were significantly improved by telementoring sessions for 10-0 nylon micro-suturing (P0.001), and egg-hell peeling tasks (P0.01). PS and CS improved significantly (P = 0.01) after telementoring sessions for 4-0 silk microsuturing. Both pre- and post-training CS were similar across the 2 subgroups PGY 1-4 and PGY 5-6 (P0.05).Telementoring is a viable alternative for neurosurgical resident training in the COVID-19 era, where reduction in elective surgeries and social distancing norms preclude conventional teaching. Lazy glass microsurgical simulator-based structured telementoring protocol is a cost-effective tool to augment surgical proficiency and finesse, irrespective of stage of residency.

Rapid increase of Industrialization and Urbanization significantly draws the interest of researchers towards the prediction of air quality. Efficient modelling of air quality parameters using deep learning methods can facilitate the imminent implications of air pollution. However, existing methods weakens at consideration of long-term dependencies for multiple parameters. The present study aims prediction of air quality of New Delhi based on concentration of multiple parameters namely PM2.5, PM10, CO, O3, NO2 and SO2. The study uses long short-term memory (LSTM) approach due to its efficiency over other deep learning methods and referred it as A-LSTM prediction model. It supports multiple layers to add more linearity to the desired output. Performance of A-LSTM is evaluated for prediction of year 2019 data. Mean absolute error, root mean squared error, precision, recall and F1-score metrics are considered for comparison with other three prediction models namely support vector regressor (SVR), SVR with LSTM and I-LSTM.

This article explores online articulations of Sindhi Hindu identity in 2010. The early 2000s mark a key moment of generational shift in the post-Partition Sindhi Hindu diaspora during an era of engagement with websites and virtual discussion forums more broadly. Tracking three diasporic websites, this article helps us to understand what virtual spaces have offered those who are distal from the imagined homelands of their longing. My analysis of the websites and a discussion forum reveal how Sindhi Hindu identity is negotiated around dominant narratives of Hinduness and Indian national identity, but in ambivalent ways that elude religious nationalist binary taxonomies in post-Partition South Asia. In its focus on websites and virtual discussion forums, this article thus brings together questions of deterritorialization and the digital to extend insights about the multiplicity, pluralism, and interconnectedness of the Sindhi diaspora more broadly.

A hybrid approach, namely “entropy-combinative distance-Based assessment (CODAS-E),” is proposed and presented to select and rank software reliability growth models based on multiple performance indexes, which is hitherto not applied in the open literature for the purpose. In the proposed hybrid approach, i.e., CODAS-E, the Shannon entropy approach is used to obtain the performance indexes’ priority weights and the CODAS method is used for optimum selection and ranking. The methodology is illustrated through two previously published different failure datasets. The ranking results depict that “Zhang-Teng-Pham” is the least suited model for software reliability estimation, whereas “Musa Okumoto” and “Yamada Imperfect debugging2” are best suitable for dataset-1 and dataset-2, respectively. The CODAS-E method is validated comparing with existing multicriteria decision-making methods; namely, technique for order preference by similarity to ideal solution and analytic hierarchy process. The significant contributions of the present research article include implementation of efficient, user-friendly, and time effective CODAS-E methodology to find the optimal model and the best overall ranking of employed models for any given dataset, and importance to the taxonomy of NHPP SRGMs rather than adding any new model. The presented model selection strategy will undoubtedly lead to high-quality software development.

Fuzzy Based CPU scheduler becomes an emerging component of an operating system. It can handle the imprecise nature of parameters used in scheduler. This paper introduces an adaptive fuzzy based Highest Response Ratio Next CPU scheduler which is an extension of conventional CPU scheduler. Proposed scheduler works in 2 layers. At the first layer, a Fuzzy Inference System is defined which handles the uncertainties of parameters and at the second layer, an adaptive scheduling algorithm is used to schedule each task. Proposed scheduler intelligently generates the response ratio for each ready to run task which makes the system adaptive at run time. The work is compared with the conventional highest response ratio next scheduling and the existing fuzzy highest ratio next scheduling algorithms. Results validate the better performance of proposed scheduler. The proposed scheduler also provides comparable results with respect to shortest job first scheduling and shortest remaining task first scheduling algorithms.

In the present research work, an attempt has been made to fabricate graphene (Gr) and silicon carbide (SiC) reinforced aluminum (Al-5086) metal matrix composite (MMC) using friction stir processing (FSP) technique. The effect of FSP process parameters, such as rotational speed, traverse speed, and tool geometry, on the microstructure and mechanical properties, has been studied. The experiments were planned according to the L27 orthogonal array, based on Taguchi’s methodology. Microstructure, elemental, and phases composition of the developed composite has been examined using filed-emission scanning electron microscopy (FE-SEM), energy dispersive spectroscopy (EDS), and X-ray diffraction (XRD) technique, respectively. Mechanical properties, such as hardness and tensile strength, were determined using nano-indentation technique and tensile testing, respectively. The maximum micro-hardness and tensile strength of the FSP-processed composite have been found about 1.75GPa and 78 MPa, respectively.

Background During the COVID-19 pandemic, disruption of surgical hands-on training has hampered the skills acquisition by budding neurosurgeons. Online and virtual classrooms have not been able to substitute the hands-on experience and learning via direct interaction with senior colleagues. To overcome these challenges, we organized a hybrid workshop where simulation-based learning modules, and direct and virtual interaction with surgeons during live surgeries or didactic lectures were utilized to help delegates in understanding the nuances of neurosurgery. Methods A 3-day hybrid workshop was held in March 2021, which was attended by 133 delegates. A structured questionnaire was utilized to record their feedback. Results An overwhelming majority of the respondents (94.1%, n = 64) found hybrid conferences to be better than an online conference. Most of the respondents (88.3%, n = 60) rated the utility of direct face-to-face interaction to be more satisfying as compared with online interaction with faculty during a webinar. Again, many the respondents (86.8%, n = 59) believed that similar hybrid events will be the new normal given the current situation of COVID-19 pandemic. A large majority (88.2%, n = 60) of the respondents reported that they will prefer a hybrid event over an online conference. Conclusions In this era of the COVID-19 pandemic, “hybrid” microneurosurgery workshops offer unique opportunities to enhance surgical skills acquisition by hands-on simulation-based learning and observing live surgical demonstrations, apart from 2-way interactions with experts under one roof. This may be a stepping stone for what lies ahead in the future of neurosurgical training.

Severe acute respiratory syndrome-coronavirus-2 (SARS-CoV-2) viral RNA associates with different RNA-binding host proteins at each stage of its life cycle, although the potential role of human antigen R (HuR) is unclear. Here, we found that HuR bound the 5′-untranslated region (5′-UTR) of SARS-CoV-2, and knockdown and knockout studies revealed the importance of such binding in viral translation. We identified 5′-UTR mutations in SARS-CoV-2 variants of concern that altered the HuR-binding affinity. Interestingly, HuR promoted non-structural protein translation through the genomic 5′-UTR and suppressed the structural protein translation from the sub-genomic 5′-UTR, which required polypyrimidine tract-binding protein binding to the 5′-UTR. HuR knockout increased the sensitivity to remdesivir treatment by decreasing its half-maximal inhibitory concentration by 10-fold. An antisense oligonucleotide (whose binding site overlapped the HuR-binding site) reduced viral RNA production and viral titers in wild-type cells but not HuR-knockout cells, further suggesting that HuR binds the SARS-CoV-2 5′ UTR and promotes replication. Our results indicate that HuR supports SARS-CoV-2 life cycle by promoting differential translational reprogramming of genomic and sub-genomic RNAs, implying that HuR can potentially be targeted for therapeutic interventions.

The "hierarchical equations of motion" (HEOM) method is a powerful exact numerical approach to solve the dynamics and find the steady-state of a quantum system coupled to a non-Markovian and non-perturbative environment. Originally developed in the context of physical chemistry, it has also been extended and applied to problems in solid-state physics, optics, single-molecule electronics, and biological physics. Here we present a numerical library in Python, integrated with the powerful QuTiP platform, which implements the HEOM for both bosonic and fermionic environments. We demonstrate its utility with a series of examples. For the bosonic case, we include demonstrations of fitting arbitrary spectral densities, and an example of the dynamics of energy transfer in the Fenna-Matthews-Olson photosynthetic complex, showing how a suitable non-Markovian environment can protect against pure dephasing. We also demonstrate how the HEOM can be used to benchmark different strategies for dynamical decoupling of a spin from its environment, and show that the Uhrig pulse-spacing scheme is less optimal than equally spaced pulses when the environment's spectral density is very broad. For the fermionic case, we present an integrable single-impurity example, used as a benchmark of the code, and a more complex example of an impurity strongly coupled to a single vibronic mode, with applications to single-molecule electronics., 20 pages, 15 figures. Updated to describe package inclusion in QuTiP v4.7 (www.qutip.org), new examples (optimal pulse spacing in dynamical decoupling, quantum heat transport), and inclusion of additional contributors in author list. Further updates to expand examples, clarify content and summarize existing packages

Intelligent writing assistants powered by large language models (LLMs) are more popular today than ever before, but their further widespread adoption is precluded by sub-optimal performance. In this position paper, we argue that a major reason for this sub-optimal performance and adoption is a singular focus on the information content of language while ignoring its social aspects. We analyze the different dimensions of these social factors in the context of writing assistants and propose their incorporation into building smarter, more effective, and truly personalized writing assistants that would enrich the user experience and contribute to increased user adoption., Comment: 2 pages, Accepted to In2Writing Workshop (CHI 2023)