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Significance Bacteria have evolved appendages called flagella that are spun by an ingenious rotary motor that harnesses electrochemical energy to power rotation. To uncover and understand nature's blueprint of this nanoscale engine, an integrative structural biology approach is required. We used a combination of mutagenesis, cryogenic electron tomography, and crystallography to reveal the architecture of a circle of rings scaffold that likely serves to organize and stabilize individual power-generating units of the flagellar motor in their active form. The knowledge about the structure–function relationships within the bacterial flagella motor is a source of inspiration for nanotechnology and can be one of the first steps toward making artificial motors on the same scale or controlling motility for medical applications., The flagellar motor stator is an ion channel nanomachine that assembles as a ring of the MotA5MotB2 units at the flagellar base. The role of accessory proteins required for stator assembly and activation remains largely enigmatic. Here, we show that one such assembly factor, the conserved protein FliL, forms an integral part of the Helicobacter pylori flagellar motor in a position that colocalizes with the stator. Cryogenic electron tomography reconstructions of the intact motor in whole wild-type cells and cells lacking FliL revealed that the periplasmic domain of FliL (FliL-C) forms 18 circumferentially positioned rings integrated with the 18 MotAB units. FliL-C formed partial rings in the crystal, and the crystal structure–based full ring model was consistent with the shape of the rings observed in situ. Our data suggest that each FliL ring is coaxially sandwiched between the MotA ring and the dimeric periplasmic MotB moiety of the stator unit and that the central hole of the FliL ring has density that is consistent with the plug/linker region of MotB in its extended, active conformation. Significant structural similarities were found between FliL-C and stomatin/prohibitin/flotillin/HflK/C domains of scaffolding proteins, suggesting that FliL acts as a scaffold. The binding energy released upon association of FliL with the stator units could be used to power the release of the plug helices. The finding that isolated FliL-C forms stable partial rings provides an insight into the putative mechanism by which the FliL rings assemble around the stator units.

A low-cost, scalable and reproducible approach for the mass production of screen-printed electrode (SPE) platforms that have varying percentage mass incorporations of 2D hexagonal boron nitride (2D-hBN) (2D-hBN/SPEs) is demonstrated herein. These novel 2D-hBN/SPEs are explored as a potential metal-free electrocatalysts towards oxygen reduction reactions (ORRs) within acidic media where their performance is evaluated. A 5% mass incorporation of 2D-hBN into the SPEs resulted in the most beneficial ORR catalysis, reducing the ORR onset potential by ca. 200 mV in comparison to bare/unmodified SPEs. Furthermore, an increase in the achievable current of 83% is also exhibited upon the utilisation of a 2D-hBN/SPE in comparison to its unmodified equivalent. The screen-printed fabrication approach replaces the less-reproducible and time-consuming drop-casting technique of 2D-hBN and provides an alternative approach for the large-scale manufacture of novel electrode platforms that can be utilised in a variety of applications.

Although the respiratory tract is the primary site of SARS-CoV-2 infection and the ensuing immunopathology, respiratory immune responses are understudied and urgently needed to understand mechanisms underlying COVID-19 disease pathogenesis. We collected paired longitudinal blood and respiratory tract samples (endotracheal aspirate, sputum or pleural fluid) from hospitalized COVID-19 patients and non-COVID-19 controls. Cellular, humoral and cytokine responses were analysed and correlated with clinical data. SARS-CoV-2-specific IgM, IgG and IgA antibodies were detected using ELISA and multiplex assay in both the respiratory tract and blood of COVID-19 patients, although a higher receptor binding domain (RBD)-specific IgM and IgG seroconversion level was found in respiratory specimens. SARS-CoV-2 neutralization activity in respiratory samples was detected only when high levels of RBD-specific antibodies were present. Strikingly, cytokine/chemokine levels and profiles greatly differed between respiratory samples and plasma, indicating that inflammation needs to be assessed in respiratory specimens for the accurate assessment of SARS-CoV-2 immunopathology. Diverse immune cell subsets were detected in respiratory samples, albeit dominated by neutrophils. Importantly, we also showed that dexamethasone and/or remdesivir treatment did not affect humoral responses in blood of COVID-19 patients. Overall, our study unveils stark differences in innate and adaptive immune responses between respiratory samples and blood and provides important insights into effect of drug therapy on immune responses in COVID-19 patients.

One of the possible potential candidates for describing the universe's rapid expansion is modified gravity. In the framework of the modified theory of gravity $f(R,G)$, the present work features the materialization of anisotropic matter, such as compact stars. Specifically, to learn more about the physical behavior of compact stars, the radial, and tangential pressures as well as the energy density of six stars namely $Her X-1$, $SAXJ1808.4-3658$, $4U1820-30$, $PSR J 1614 2230$, $VELA X-1$, and $Cen X-3$ are calculated. Herein, the modified theory of gravity $f(R,G)$ is disintegrated into two parts i.e. the $\tanh$ hyperbolic $f(R)$ model and the three different $f(G)$ model. The study focuses on graphical analysis of compact stars wherein the stability aspects, energy conditions, and anisotropic measurements are mainly addressed. Our calculation revealed that, for the positive value of parameter n of the model $f(G)$, all the six stars behave normally., Comment: Some changes have been made. " To appear in International Journal of Geometric Methods in Modern Physics"

Stress shifts plant growth in nearly all species evident by developmental problems, such as decreased yield of dry matter and leaf area. However, few plants remain least affected or may nearly be remain unaffected. Primarily stress includes metal toxicity, a key source of abiotic stress influencing the health of plants. In general, metal toxification has been one of the major challenges in developmental processes. To combat this, remarkable number of plants have been recognized that have produced noteworthy effects in detoxification. In the current scenario, remediation focused on plants shows a growing potential against organic/inorganic polluted soils which is technically termed as “Phytoremediation.” Phytoremediation requires multiple methods for eliminating toxins from soil, in general through altered expression of genes and enzymes in plants. Few studies have also reported the application of plant-associated microbiota for decontamination polluted soils, sediments, and underground water and thus are currently being expanded as an ecologically sound and budget-friendly technology. Various latest advancements in recognizing plant cell/molecular processes of uptake/tolerance have also been well studied. New innovative molecular technologies have improved the perspective further into metabolic pathways involved in stress tolerance/prevention mechanisms in frequent cultivated crops and hyperaccumulator plants. The purpose of this chapter is to concentrate on the underlying molecular basis associated in detoxification and tolerance to contaminants by plants. In addition, the focus in this chapter is on the ability of “omics” and genetic modification strategies to effectively remediate a contaminated environment has also been described

Machine learning represents a promising frontier in epidemiological research on spine surgery. It consists of a series of algorithms that determines relationships between data. Machine learning maintains numerous advantages over conventional regression techniques, such as a reduced requirement for a priori knowledge on predictors and better ability to manage large datasets. Current studies have made extensive strides in employing machine learning to a greater capacity in spinal cord injury (SCI). Analyses using machine learning algorithms have been done on both traumatic SCI and nontraumatic SCI, the latter of which typically represents degenerative spine disease resulting in spinal cord compression, such as degenerative cervical myelopathy. This article is a literature review of current studies published in traumatic and nontraumatic SCI that employ machine learning for the prediction of a host of outcomes. The studies described utilize machine learning in a variety of capacities, including imaging analysis and prediction in large epidemiological data sets. We discuss the performance of these machine learning-based clinical prognostic models relative to conventional statistical prediction models. Finally, we detail the future steps needed for machine learning to become a more common modality for statistical analysis in SCI.

BackgroundLittle is known about the spatial distribution of idiopathic inflammatory myopathies (IIM) in the United States (U.S.), or their geospatial associations.MethodsWe studied a national myositis patient registry, with cases diagnosed in the contiguous U.S. from 1985–2011 and comprised of dermatomyositis (DM, n = 484), polymyositis (PM, n = 358), and inclusion body myositis (IBM, n = 318) patients. To assess the association of myositis prevalence with distance from roads, we employed log-Gaussian Cox process models, offset with population density.ResultsThe U.S. IIM case distribution demonstrated a higher concentration in the Northest. DM, IBM, and cases with lung disease were more common in the East, whereas PM cases were more common in the Southeast. One area in the West and one area in the South had a significant excess in cases of DM relative to PM and of cases with lung disease relative to those without lung disease, respectively. IIM cases tended to cluster, with between-points interactions more intense in the Northeast and less in the South. There was a trend of a higher prevalence of IIM and its major phenotypes among people living within 50 m of a roadway relative to living beyond 200 m. Demographic characteristics, rural-urban commuting area, and female percentage were significantly associated with the prevalence of IIM and with major phenotypes.ConclusionsUsing a large U.S. database to evaluate the spatial distribution of IIM and its phenotypes, this study suggests clustering in some regions of the U.S. and a possible association of proximity to roadways.

Objective: To analyze various factors contributing to delayed diagnosis of congenital heart disease (CHD) in pediatric population. Study Design: Cross-sectional study. Place and Duration of the Study: Department of Pediatrics and Department of Cardiology, Sheikh Khalifa Bin Zayed Al Nahyan Hospital, Rawalakot from January to December 2020. Material and Methods: A total of 265 children of both genders aged up to 15 years and presenting for the 1st time with the echocardiography confirmed diagnosis of CHD were enrolled. A special proforma was designed and questions were asked from parents/guardians of all study participants. Socio-demographic profile along with improper referral status and inadequate health facilities were noted. SPSS version 26.0 was used for statistical analysis. Qualitative variables like gender, area of residence, literacy status, socio-economic status, types of CHDs, delayed diagnosis (yes/no) and reasons for delayed diagnosis were represented as frequency and percentages. Results: In a total of 265 children included in the study, there were 156 (58.9%) male. Majority of the children, 184 (69.4%) were less than 2 years of age. Body weight below 3rd centile was noted in 164 (61.9%) children. Maternal fetal echocardiography was done in 13 (4.9%). Acyanotic CHD was noted in 180 (67.9%) children while remaining had cyanotic CHD 85 (32.1%). Delayed diagnosis of CHD was noticed in 211 (79.6%) children. Most common factor contributing to delayed diagnosis of CHD was found to be delayed 1st consultation in 85 (40.3%). Delayed or missed diagnosis by the doctor was the 2nd most frequent factor contributing to delayed diagnosis of CHD noted in 52 (24.6%) children. Delayed referrals were observed in 42 (19.9%) children. Conclusion: Delayed diagnosis of CHD was noted among 79.6% cases. Acyanotic CHD was the commonest type of CHD in the present study. Most common factors contributing to delayed diagnosis of CHD were delayed 1st consultation, delayed diagnosis by the doctor and delayed referrals. Keywords: Congenital heart disease, fetal echocardiography, delayed referrals

Diffuse optical tomography (DOT), based on functional near-infrared spectroscopy, is a portable, low-cost, noninvasive functional neuroimaging technology for studying the human brain in normal and diseased conditions. The goal of the present study was to evaluate the performance of a cap-based brain-wide DOT (BW-DOT) framework in mapping brain-wide networked activities. We first analyzed point-spread-function (PSF)-based metrics on a realistic head geometry. Our simulation results indicated that these metrics of the optode cap varied across the brain and were of lower quality in brain areas deep or away from the optodes. We further reconstructed brain-wide resting-state networks using experimental data from healthy participants, which resembled the template networks established in the fMRI literature. The preliminary results of the present study highlight the importance of evaluating PSF-based metrics on realistic head geometries for DOT and suggest that BW-DOT technology is a promising functional neuroimaging tool for studying brain-wide neural activities and large-scale neural networks, which was not available by patch-based DOT. A full-scope evaluation and validation in more realistic head models and more participants are needed in the future to establish the findings of the present study further.Clinical relevance- Via simulations and experimental evaluation, this work establishes a novel framework to image large-scale brain networks, which benefits the patient population, such as bedridden patients, infants, etc., who otherwise cannot undergo conventional brain monitoring modalities like fMRI and PET.

S55 Figure 1Transmission Electron Micrographs of ciliated epithelial cells 3months- 1 year post-COVID. Red circles represent microtubular subunits assembling during ciliogenesis, the white arrow shows short regenerating cilia. Yellow boxes surround intracytoplasmic cilia seen in 90% post COVID cases and suggestive of a defect in ciliogenesis[Figure omitted. See PDF]ConclusionEpithelial disruption and defects in epithelial regeneration persist for up to 1 year post infection with SARS-CoV-2, irrespective of initial illness severity. Persisting inflammation, or a failure of repair, are possible pathological processes for further exploration.

Aim To determine if there is a difference in radiological, biochemical, or clinical severity between patients infected with Alpha-variant SARS-CoV-2 compared with those infected with pre-existing strains, and to determine if the computed tomography (CT) severity score (CTSS) for COVID-19 pneumonitis correlates with clinical severity and can prognosticate outcomes. Materials And Methods Blinded CTSS scoring was applied to 137 hospital patients who had undergone both CT pulmonary angiography (CTPA) and whole-genome sequencing of SARS-CoV-2 within 14 days of CTPA between 1/12/20–5/1/21. Results There was no evidence of a difference in imaging severity on CTPA, viral load, clinical parameters of severity, or outcomes between Alpha and preceding variants. CTSS on CTPA strongly correlates with clinical and biochemical severity at the time of CTPA, and with patient outcomes. Classifying CTSS into a binary value of “high” and “low”, with a cut-off score of 14, patients with a high score have a significantly increased risk of deterioration, as defined by subsequent admission to critical care or death (multivariate hazard ratio [HR] 2.76, p

Background The impact of COVID-19 on physical and mental health and employment after hospitalisation with acute disease is not well understood. The aim of this study was to determine the effects of COVID-19-related hospitalisation on health and employment, to identify factors associated with recovery, and to describe recovery phenotypes. Methods The Post-hospitalisation COVID-19 study (PHOSP-COVID) is a multicentre, long-term follow-up study of adults (aged ≥18 years) discharged from hospital in the UK with a clinical diagnosis of COVID-19, involving an assessment between 2 and 7 months after discharge, including detailed recording of symptoms, and physiological and biochemical testing. Multivariable logistic regression was done for the primary outcome of patient-perceived recovery, with age, sex, ethnicity, body-mass index, comorbidities, and severity of acute illness as covariates. A post-hoc cluster analysis of outcomes for breathlessness, fatigue, mental health, cognitive impairment, and physical performance was done using the clustering large applications k-medoids approach. The study is registered on the ISRCTN Registry (ISRCTN10980107). Findings We report findings for 1077 patients discharged from hospital between March 5 and Nov 30, 2020, who underwent assessment at a median of 5·9 months (IQR 4·9–6·5) after discharge. Participants had a mean age of 58 years (SD 13); 384 (36%) were female, 710 (69%) were of white ethnicity, 288 (27%) had received mechanical ventilation, and 540 (50%) had at least two comorbidities. At follow-up, only 239 (29%) of 830 participants felt fully recovered, 158 (20%) of 806 had a new disability (assessed by the Washington Group Short Set on Functioning), and 124 (19%) of 641 experienced a health-related change in occupation. Factors associated with not recovering were female sex, middle age (40–59 years), two or more comorbidities, and more severe acute illness. The magnitude of the persistent health burden was substantial but only weakly associated with the severity of acute illness. Four clusters were identified with different severities of mental and physical health impairment (n=767): very severe (131 patients, 17%), severe (159, 21%), moderate along with cognitive impairment (127, 17%), and mild (350, 46%). Of the outcomes used in the cluster analysis, all were closely related except for cognitive impairment. Three (3%) of 113 patients in the very severe cluster, nine (7%) of 129 in the severe cluster, 36 (36%) of 99 in the moderate cluster, and 114 (43%) of 267 in the mild cluster reported feeling fully recovered. Persistently elevated serum C-reactive protein was positively associated with cluster severity. Interpretation We identified factors related to not recovering after hospital admission with COVID-19 at 6 months after discharge (eg, female sex, middle age, two or more comorbidities, and more acute severe illness), and four different recovery phenotypes. The severity of physical and mental health impairments were closely related, whereas cognitive health impairments were independent. In clinical care, a proactive approach is needed across the acute severity spectrum, with interdisciplinary working, wide access to COVID-19 holistic clinical services, and the potential to stratify care. Funding UK Research and Innovation and National Institute for Health Research.

The risk of unknowingly disseminating leiomyosarcoma by morcellation in women undergoing laparoscopic hysterectomy has massively impacted gynaecological practice. Here, we present the results of an in vitro assessment of a novel protection system developed to mitigate this hazard.The Tissue Containment System for Manual Morcellation (Guardenia™, Advanced Surgical Concepts, Wicklow, Ireland) is an evolved wound protection/specimen extraction guarded bag system compatible with any 12mm trocar. Device use was assessed by device-naïve gynaecological and general surgeon volunteers (providing expert and inexpert morcellation cohorts, respectively) on a bench model consisting of biological tissue in a custom-built moulded rig with camera control after the operators were instructed in its use.Twenty surgeons (10 gynaecologists/10 general surgeons, median duration of practice experience: 8 years, median annual number of laparoscopic operative procedures: 150 and 80, respectively) completed the user assessment. All subjects understood and correctly performed each step; i.e., (i) placement of the bag through the trocar, (ii) specimen bagging, (iii) incision extension (range 25-60 mm) after tethering the bag through the port, (iv) insertion of the device guard through the mouth of the bag after trocar removal, and (v) sufficient tissue morcellation within the bag to enable complete specimen removal (mean specimen weight 390g, range 201-1800g). There was 100% bag integrity by water-leak testing following use, despite scalpel contact with the guard in 14/20 cases (70%).Among first-time clinical users, this novel device enabled complete containment of morcellation debris and removal of a laparoscopic specimen, which would support further submission for regulatory approval.

Oral squamous cell carcinoma (OSCC) is the most common malignant epithelial neoplasm and anatomical subtype of head and neck squamous cell carcinoma (HNSCC) with an average 5-year survival rate of less than 50%. To improve the survival rate of OSCC, the discovery of novel anti-cancer drugs is urgently needed. In the present study, we performed metanalysis of 5 gene expression datasets (GSE23558, GSE25099, GSE30784, GSE37991 and TCGA-OSCC) that resulted in 1851 statistically significant DEGs in OSCC. The DEGs were involved in key biological pathways that drive the progression of OSCC. A comprehensive protein-protein interaction (PPI) network was constructed from the DEGs and the top protein clusters (modules) were extracted in Cytoscape. The DEGs from the top modules were searched for antineoplastic agents using L1000CDS2 server. The search resulted in a total of 37 perturbing agents from which 12 well-characterized antineoplastic agents were selected. The selected 12 antineoplastic agents namely Teniposide, Palbociclib, Etoposide, Fedratinib, Tivozanib, Afatinib, Vemurafenib, Mitoxantrone, Idamycin, Canertinib, Dovitinib and Selumetinib. These drugs showed interactions with the over expressed hub genes that regulate cellular proliferation and growth in OSCC progression. These identified antineoplastic agents are candidates for their potential role in treating OSCC.

Due to their unique longevity and capacity to secrete high levels of protein, plasma B cells have the potential to be used as a cell therapy for protein replacement. Here, we show that ex vivo engineered human plasma cells exhibit single-cell RNA profiles, scanning electron micrograph ultrastructural features, and in vivo homing capacity of long-lived plasma cells. After transferring human plasma cells to immunodeficient mice in the presence of the human cytokines BAFF and IL-6, we observe increases in retention of plasma cells in the bone marrow, with engraftment exceeding a year. The most profound in vivo effects of human IL-6 are observed within 20 days of transfer and could be explained by decreased apoptosis in newly differentiated plasma cells. Collectively, these results show that ex vivo engineered and differentiated human plasma cells have the potential for long-lived in vivo protein secretion, which can be modeled in small animals.

We have built a Terahertz Dispersive Fourier Transform Spectrometer \cite{Birch1987} to study frequency properties of superconducting films used for fabrication of THz detectors. The signal reflected from the tested film is measured in time domain, which allows to separate it from the other reflections. The complex conductivity of the film depends on frequency and determines the reflection coefficient. By comparing the film reflection in the superconducting state (temperature is below $T_c$) with the reflection of the normal state, we characterise the film quality at terahertz frequencies. The method was applied to 70 and 200nm thick Nb films on a silicon wafer and to 360nm thick NbTiN films on silicon and quartz wafers. The strong-coupling coefficient, $\alpha$, was found to be 3.52 for Nb, and 3.71-4.02 for the NbTiN films. The experimental results were fitted using extended Mattis-Bardeen theory \cite{Noguchi2012} and show a good agreement., Comment: The following article has been accepted by Applied Physics Letters. After it is published, it will be found at https://aip.scitation.org/journal/apl

X-ray diffraction is a powerful measurement technique for determining material properties, and it is now possible to perform these experiments at pressures exceeding 1 TPa [Rygg et al., Rev. Sci. Instrum. 91, 043902 (2020)] at the National Ignition Facility (NIF). The x-ray source for these experiments is the quasi-monochromatic He α emission from metal foils heated to multi-keV temperatures. A critical aspect for understanding the sample's thermodynamic state is the amount of heating caused by absorption of the probe x-rays. In this work, we characterize the performance of the Ge foil x-ray source over more than 60 NIF x-ray diffraction experiments. We use this information to constrain the level of diffraction sample heating from the x-ray source and discuss the impact on the thermodynamic state.

An investigation of twenty two-shock campaign indirectly driven capsules on the National Ignition Facility was conducted using the xRAGE computer code. The two-shock platform was developed to look at the sensitivity of fuel–ablator mix with shock timing, asymmetry, surface roughness, and convergence on roughly ignition size scale capsules. This platform used CH/CD (plastic/deuterated plastic) shell capsules that were about 685- μm outer radius and filled with D2 or hydrogen-tritium (HT) gas. The experimental radius and velocity vs time, neutron yield, burn averaged ion temperature (Tion), burn width, and self-emission image size were compared to one-dimensional (1D) and two-dimensional (2D) simulations. Our 2D simulations suggest that the mixing of glass from the fill tube was the dominant source of impurity in the gas region of the capsule during burn, along with fuel–ablator mix. The mass of glass mixed in is about 5–10 ng. Our 2D simulations capture most of the yield trends from different degradation mechanisms, and they match the observed burn width and Tion measurements. Our 2D models match all the available data to within 2.5 times the normalized experimental error for 19 of 20 capsules.

Previously, hydrodynamic simulations [Clark et al., Phys. Plasmas 23, 072707 (2016)] suggested that precisely tailoring the capsule ablator thickness (shimming) could counterbalance cylindrical Hohlraum Legendre P4 drive asymmetries at the capsule in laser indirect drive implosions. As a result, the stagnated deuterium–tritium (DT) fuel areal density P4 asymmetry is reduced, potentially resulting in a nuclear yield increase. Inflight radiographs of various level of shimmed capsules with plastic (CH) ablators showed that shims can indeed control the in-flight capsule shell P4 asymmetry, with a linear sensitivity to shim amplitude that is close to analytic estimates and simulations. Furthermore, the stagnated DT fuel areal density P4 asymmetry inferred from downscattered neutron imaging was reduced when the capsule shim was applied, in agreement with simulations matching the inflight shell asymmetry. A nuclear yield improvement via shim was not observed, as predicted, likely due to implosion instabilities and as built capsule shim deviations from an ideal P4 shape.

Background and aim Since individuals in the early stages of liver cirrhosis are typically asymptomatic, the prevalence of liver cirrhosis may be underestimated. Liver cirrhosis has a significant morbidity and mortality rate, with 1.03 million deaths worldwide each year. For end-stage liver disease, liver transplantation is a potential therapeutic option. The goal of our research was to examine the current trend in liver transplants using data from a national database. Methods Using the International Classification of Diseases (ICD)-9 codes, we identified individuals who had a liver transplant during the index hospital admission in the Nationwide Inpatient Sample from 2007 to 2011. This national sample of patients is from the United States. We looked at the yearly trend in liver transplants and related outcomes, such as duration of hospitalization (DOH), hospital expenses, and mortality in the hospital. In order to find determinants of mortality, we used a multivariate analysis. Results There were 25,331 patients hospitalized (weighted for national estimate). Between 2007 and 2011, the number of transplants grew by 1.2%. The majority of transplant recipients were Caucasian (57%), with an average age of 54 years, had a private healthcare plan (53%), and had average earnings in the upper quartile by zip code (26%). Patients with a higher Charlson Comorbidity Index (79% had a score of four) were more likely to be admitted to a southern hospital (33%), an academic hospital (>99%), and a large capacity hospital (90%). Seventy percent of liver transplant recipients received cadaver donors. Hepatitis C was the most prevalent reason for transplant (30%), followed by hepatocellular carcinoma (HCC) (29%) and alcoholic liver disease (25%). In 2011, compared to 2007, there was an upward rise in fatality (from 3.8% to 5.1%), average hospital expenditures (from $335,504 to $498,369), and DOH (from 17.4 to 22.7 days). The cost of hospitalization was two billion dollars per year. The independent variables related to an increased mortality on multivariate analysis were African American race (OR: 2.0, 95%, CI: 1.2-3.2; p=0.005) and large capacity hospitals (OR: 2.5, 95% CI: 1.6-4.1; p=0.0002). Predictors linked to lower mortality included private healthcare coverage (vs. Medicare: OR: 0.7, 95%, CI: 0.51-0.97; p=0.03), academic hospital (OR: 0.6, 95% CI: 0.4-0.8; p=0.005), cadaver donor (OR: 0.6, 95% CI: 0.5-0.8; p=0.002), HCC (OR: 0.6, 95% CI: 0.4-0.9; p=0.01), and non-alcoholic steatohepatitis (NASH) cirrhosis (OR: 0.4, 95% CI: 0.2-0.9; p=0.02). Conclusion Our study found an increasing trend in worse outcomes (increased mortality, average hospital costs, and average DOH) after a liver transplant. Patients of the African American race and large capacity hospitals were associated with a higher risk of death, whereas private healthcare plans, academic hospitals, cadaver donors, HCC, and NASH cirrhosis were associated with a lower risk.

The present study was carried out to determine incidence of overweight and obesity in Pakistani servicemen with reference to their area of duty, feeding habits and also to identify risk factors. Accordingly, 2,501 servicemen selected from all over Pakistan using multiple stage stratified sampling protocol. Nutrition assessment performed using body mass index (BMI), waist to hip ratio (WHR) and dietary assessment using food frequency questionnaire. Collected data was analyzed using the SPSS version 25. Regression was used to find risk factors of obesity and WHR. Results indicated that about 1/4th of servicemen were smokers. Approximately, 1/5th of them were overweight and about one quarter were eating fruits and vegetables for

SummaryHead and neck cancer is the sixth leading cause of cancer across the globe and is significantly more prevalent in South Asian countries, including Pakistan. Prediction of pathological stages of cancer can play a pivotal role in early diagnosis and personalized medicine. This project ventures into the prediction of different stages of head and neck squamous cell carcinoma (HNSCC) using prioritized DNA methylation patterns. DNA methylation profiles for each HNSCC stage (stage-I-IV) were used to extensively analyze 485,577 methylation CpG sites and prioritize them on the basis of the highest predictive power using a wrapper-based feature selection method, along with different classification models. We identified 68 high-power methylation sites which predicted the pathological stage of HNSCC samples with 90.62 % accuracy using a Random Forest classifier. We set out to construct a protein-protein interaction network for the proteins encoded by the 67 genes associated with these sites to study its network topology and also undertook enrichment analysis of nodes in their immediate neighborhood for GO and KEGG Pathway annotations which revealed their role in cancer-related pathways, cell differentiation, signal transduction, metabolic and biosynthetic processes. With information on the predictive power of each of the 67 genes in each HNSCC stage, we unveil a dynamic stage-course network for HNSCC. We also intend to further study these genes in light of functional datasets from CRISPR, RNAi, drug screens for their putative role in HNSCC initiation and progression.

With clinical datasets continuing to grow in size and complexity, there has been an increased interest in using powerful computational tools such as machine learning (ML) for the analysis of complicated associations to provide an important insight to the clinician. Recent neurosurgical literature has exemplified this interest, as there has been a significant rise in the breadth and depth of ML applications to a host of areas in neurosurgery. These include neuro-oncology, stroke, epilepsy, neurotrauma, and spine surgery. Within these areas, ML models have been developed to aid in clinical settings encompassing the entire operative timeline, including preoperative neurosurgical diagnostics, intraoperative management, and postoperative prediction of outcomes. The recent adoption of ML in neurosurgery has occurred due to ML’s numerous advantages over conventional statistical tools. These include ML’s ability to handle large datasets, decipher important predictors without a priori specification, and determine complex nonlinear relationships within data. However, the promise behind ML is not without limitations, as ML algorithms are heavily reliant on high-quality data and carry important medico-legal ramifications if used without regulation. This chapter will discuss the current applications of ML to neurosurgery across multiple areas, potential future directions that ML could take, and the important limitations of these computational tools. Via this discussion, we hope to provide the reader with an insight into the potentially significant impact that ML can have on neurosurgery.

CHIMERA is the first non-volatile deep neural network (DNN) chip for edge AI training and inference using foundry on-chip resistive RAM (RRAM) macros and no off-chip memory. CHIMERA achieves 0.92 TOPS peak performance and 2.2 TOPS/W. We scale inference to 6x larger DNNs by connecting 6 CHIMERAs with just 4% execution time and 5% energy costs, enabled by communication-sparse DNN mappings that exploit RRAM non-volatility through quick chip wakeup/shutdown (33 µs). We demonstrate the first incremental edge AI training which overcomes RRAM write energy, speed, and endurance challenges. Our training achieves the same accuracy as traditional algorithms with up to 283x fewer RRAM weight update steps and 340x better energy-delay product. We thus demonstrate 10 years of 20 samples/minute incremental edge AI training on CHIMERA.

Public Private Partnership Model (PPP) by Ministry of Road Transport and Highways (MORTH) renewal was undertaken by introduction of the hybrid annuity model (HAM) wherein government decided to share the risk of financing by contributing 40% towards the project. With the launch of this scheme more than fifty percent projects got green signal in 201617 under the HAM scheme. This work compares these two models by undertaking a questionnaire survey from the key stakeholders involved in such projects and identifies challenges & risk faced and issues pertaining to delays and success of the proposed model. The outcome from the work highlights that funding has been a major challenge faced by PPP projects and thus innovation into the model is necessary. There also seems to be a lack of transparency in the entire process which makes it riskier for the private investor. It is also concluded from the work that financial risk is predominant whether it is a PPP or HAM model and thus a search for a new sustainable model that is sustainable is essential.

Indonesia contains large areas of peatland that have been drained and cleared of natural vegetation, making them susceptible to burning. Peat fires emit considerable amounts of carbon dioxide, particulate matter (PM) and other trace gases, contributing to climate change and causing regional air pollution. However, emissions from peat fires are uncertain, due to uncertainties in emission factors and fuel consumption. We used the Weather Research and Forecasting model with chemistry and measurements of PM concentrations to constrain PM emissions from Indonesian fires during 2015, one of the largest fire seasons in recent decades. We estimate primary PM2.5 (particles with diameters less than 2.5 µm) emissions from fires across Sumatra and Borneo during September–October 2015 were 7.33 Tg, a factor 3.5 greater than those in the Fire Inventory from NCAR (FINNv1.5), which does not include peat burning. We estimate similar dry fuel consumption and CO2 emissions to those in the Global Fire Emissions Database (GFED4s, including small fires) but PM2.5 emissions that are a factor of 1.8 greater, due to updated PM2.5 emission factors for Indonesian peat. Fires were responsible for an additional 3.12 Tg of secondary organic aerosol formation. Through comparing simulated and measured PM concentrations, our work provides independent support of these updated emission factors. We estimate peat burning contributed 71 % of total primary PM2.5 emissions from fires in Indonesia during September–October 2015. We show that using satellite-retrieved soil moisture to modify the assumed depth of peat burn improves the simulation of PM, increasing the correlation between simulated and observed PM from 0.48 to 0.56. Overall, our work suggests that peat fires in Indonesia produce substantially greater PM emissions than estimated in current emission inventories, with implications for the predicted air quality impacts of peat burning.

The Late Paleocene–Middle Eocene phosphate nodules of Solan District of Himachal Pradesh belong to green facies of Subathu Formation of Sirmur group. Field observations indicate sporadic distribution of phosphatic nodules in the yellow brown to rusty brown, buff-colored bleach shales. Phosphatic nodules are dull earthy to dark grayish in color and break with sharp and conchoidal surfaces. Studies of phosphatic nodules using petrological microscope, X-ray diffractometry, and scanning electron microscopy (SEM) attached with energy dispersive X-ray (EDX) reveal that cryptocrystalline apatite (fluorapatite) is the dominant phosphate mineral. Silt sized silica is the dominant gangue, whereas pyrite and muscovite are present as minor gangue. The phosphate minerals occur as oolites, pellets, and cryptocrystalline apatite in the groundmass. Silica veins form a network replacing the original phosphatic matrix. Brown to brownish black color may be attributed by the organic matter in the phosphatic nodules. Completely phosphatized radiolaria and partially pyritized planktonic foraminifera are observed. There is corroboration of organic matter in the form of microbial filaments in the groundmass of apatite. The findings in the present investigation indicate that the different forms of the minerals, their texture, and distribution in the phosphatic nodules might be due to environmental vicissitudes in oxidizing to reducing conditions followed by replacement processes. Presence of microbial filaments may be due to the active role of microorganisms during the formation of these phosphatic nodules.

"Osteogenesis imperfecta (OI) is one of the genetic disorder which was characterized by bone fragility. Previous studies reported that there are several mechanical properties has been used to investigate the strength of OI bone. However, little data is yet available to describe bone material properties in individuals with this disorder for type III alone. Therefore, the aim of this study is to investigate the mechanical properties in individual with OI bone type III at interstitial and osteon. Bone tissue reduced modulus and hardness were measured by nanoindentation in three specimens with total 40 indents. These properties were compared between osteogenesis imperfecta types III for interstitial and osteonal microstructural regions. Individual with osteogenesis imperfecta type III had higher hardness and reduced modulus at interstitial rather than osteonal bone regions. Overall, the mean and standard deviation of hardness is 0.6 ± 0.11 GPa, while 0.44± 0.06 GPa for osteon. The current study presents the dataset describing the bone material properties in individual with Osteogenesis Imperfecta Type III. Results indicate that intrinsic bone tissues properties were affected by phenotype. Knowledge on the mechanical properties of severity osteogenesis imperfecta may help to assist the model and prevent the fracture risk for those having this disorder."

Based on the analysis of the Russian broadband access market, the main drivers of growth in the cost of telecommunications companies in this business sector; its main trends – the preservation of high dividend yield of companies in the Russian broadband market, stable and moderate growth of the customer base, a large debt burden, the presence of significant investment projects have been determined. The influence of capital costs and charges on capitalization of telecommunications companies in the broadband sector of the national market and companies with the greatest growth prospects have been identified.

Isoprene (C5H8) plays an important role in the formation of surface ozone (O3) and the secondary organic aerosol (SOA) which contributed to the climate change. This study aims to determine hourly distribution of tropospheric isoprene over the Western Coast of Antarctic Peninsula (WCAP) during the Malaysian Antarctic Scientific Expedition Cruise 2016 (MASEC′16). In-situ measurements of isoprene were taken using a custom-built gas chromatography with photoionization detector, known as iDirac. Biological parameters such as chlorophyll a (chl-a) and particulate organic carbon (POC) were compared to the in-situ isoprene measurements. Significant positive correlation was observed between isoprene and POC concentrations (r2 = 0.67, p

A total of 44 bis-aryl-monocyclic polyamines, monoaryl-monocyclic polyamines and their transition metal complexes were prepared, chemically characterized, and screened in vitro against the Leishmania donovani promastigotes, axenic amastigotes and intracellular amastigotes in THP1 cells. The IC(50) and/or IC(90) values showed that 10 compounds were similarly active at about 2-fold less potent than known drug pentamidine against promastigotes. The most potent compound had an IC(50) of 2.82 μM (compared to 2.93 μM for pentamidine). Nine compounds were 1.1–13.6-fold more potent than pentamidine against axenic amastigotes, the most potent one being about 2-fold less potent than amphotericin B. Fourteen compounds were about 2–10 fold more potent than pentamidine, the most potent one is about 2-fold less potent than amphotericin B against intracellular amastigotes in THP1 cells. The 2 most promising compounds (FeL7Cl(2) and MnL7Cl(2)), with strong activity against both promastigotes and amastigotes and no observable toxicity against the THP1 cells are the Fe(2+)- and Mn(2+)- complexes of a dibenzyl cyclen derivative. Only 2 of the 44 compounds showed observable cytotoxicity against THP1 cells. Tetraazamacrocyclic monocyclic polyamines represent a new class of antileishmanial lead structures that warrant follow up studies.

In tumors, extravascular fibrin forms provisional scaffolds for endothelial cell (EC) growth and motility during angiogenesis. We report that fibrin-mediated angiogenesis was inhibited and tumor growth delayed following postnatal deletion of Tgfbr2 in the endothelium of Cdh5-Cre(ERT2) Tgfbr2(fl/fl) mice (Tgfbr2(iECKO) mice). ECs from Tgfbr2(iECKO) mice failed to upregulate the fibrinolysis inhibitor plasminogen activator inhibitor 1 (Serpine1, also known as PAI-1), due in part to uncoupled TGF-β–mediated suppression of miR-30c. Bypassing TGF-β signaling with vascular tropic nanoparticles that deliver miR-30c antagomiRs promoted PAI-1–dependent tumor growth and increased fibrin abundance, whereas miR-30c mimics inhibited tumor growth and promoted vascular-directed fibrinolysis in vivo. Using single-cell RNA-Seq and a NanoString miRNA array, we also found that subtypes of ECs in tumors showed spectrums of Serpine1 and miR-30c expression levels, suggesting functional diversity in ECs at the level of individual cells; indeed, fresh EC isolates from lung and mammary tumor models had differential abilities to degrade fibrin and launch new vessel sprouts, a finding that was linked to their inverse expression patterns of miR-30c and Serpine1 (i.e., miR-30c(hi) Serpine1(lo) ECs were poorly angiogenic and miR-30c(lo) Serpine1(hi) ECs were highly angiogenic). Thus, by balancing Serpine1 expression in ECs downstream of TGF-β, miR-30c functions as a tumor suppressor in the tumor microenvironment through its ability to promote fibrin degradation and inhibit blood vessel formation.