Social Media as a Disguise and an Aid: Disabled Women in the Cyber Workforce in China

Existing literature shows that people living with physical impairment are systematically disadvantaged in the workforce and their voices are often silenced. With a perspective of intersectionality, this article looks into how disabled women suffer from multiple forms of discrimination and how social media may emerge as a tool of empowerment for them in both the workforce and their everyday lives. Drawing on five cases of Chinese disabled women in the cyber workforce, the study finds that the booming Internet economy enables more disabled women to financially support themselves. Social media appears as a cover for these women to disguise their disability identity and get more job opportunities. It serves as an aid in many cases to allow these women to increase social participation, to project their voice, and to form alliances. The risks and challenges that disabled women often encounter in the cyber workforce are also discussed

Nanococktail Based on Supramolecular Glyco-Assembly for Eradicating Tumors In Vivo

The development of robust phototherapeutic strategies for eradicating tumors remains a significant challenge in the transfer of cancer phototherapy to clinical practice. Here, a phototherapeutic nanococktail atovaquone/17-dimethylaminoethylamino-17-demethoxygeldanamycin/glyco-BODIPY (ADB) was developed to enhance photodynamic therapy (PDT) and photothermal therapy (PTT) via alleviation of hypoxia and thermal resistance that was constructed using supramolecular self-assembly of glyco-BODIPY (BODIPY-SS-LAC, BSL-1), hypoxia reliever atovaquone (ATO), and heat shock protein inhibitor 17-dimethylaminoethylamino-17-demethoxygeldanamycin (17-DMAG). Benefiting from a glyco-targeting and glutathione (GSH) responsive units BSL-1, ADB can be rapidly taken up by hepatoma cells, furthermore the loaded ATO and 17-DMAG can be released in original form into the cytoplasm. Using in vitro and in vivo results, it was confirmed that ADB enhanced the synergetic PDT and PTT upon irradiation using 685 nm near-infrared light (NIR) under a hypoxic tumor microenvironment where ATO can reduce O2 consumption and 17-DMAG can down-regulate HSP90. Moreover, ADB exhibited good biosafety, and tumor eradication in vivo. Hence, this as-developed phototherapeutic nanococktail overcomes the substantial obstacles encountered by phototherapy in tumor treatment and offers a promising approach for the eradication of tumors. </p

A Novel Sample Selection Strategy for Imbalanced Data of Biomedical Event Extraction with Joint Scoring Mechanism

Biomedical event extraction is an important and difficult task in bioinformatics. With the rapid growth of biomedical literature, the extraction of complex events from unstructured text has attracted more attention. However, the annotated biomedical corpus is highly imbalanced, which affects the performance of the classification algorithms. In this study, a sample selection algorithm based on sequential pattern is proposed to filter negative samples in the training phase. Considering the joint information between the trigger and argument of multiargument events, we extract triplets of multiargument events directly using a support vector machine classifier. A joint scoring mechanism, which is based on sentence similarity and importance of trigger in the training data, is used to correct the predicted results. Experimental results indicate that the proposed method can extract events efficiently

Biomechanical and histological changes associated with riboflavin ultraviolet-A-induced CXL with different irradiances in young human corneal stroma.

Keratoconus (KC) is a degenerative condition affecting the cornea, characterized by progressive thinning and bulging, which can ultimately result in serious visual impairment. The onset and progression of KC are closely tied to the gradual weakening of the cornea's biomechanical properties. KC progression can be prevented with corneal cross-linking (CXL), but this treatment has shortcomings, and evaluating its tissue stiffening effect is important for determining its efficacy. In this field, the shortage of human corneas has made it necessary for most previous studies to rely on animal corneas, which have different microstructure and may be affected differently from human corneas. In this research, we have used the lenticules obtained through small incision lenticule extraction (SMILE) surgeries as a source of human tissue to assess CXL. And to further improve the results' reliability, we used inflation testing, personalized finite element modeling, numerical optimization and histology microstructure analysis. These methods enabled determining the biomechanical and histological effects of CXL protocols involving different irradiation intensities of 3, 9, 18, and 30 mW/cm2, all delivering the same total energy dose of 5.4 J/cm2. The results showed that the CXL effect did not vary significantly with protocols using 3-18 mW/cm2 irradiance, but there was a significant efficacy drop with 30 mW/cm2 irradiance. This study validated the updated algorithm and provided guidance for corneal lenticule reuse and the effects of different CXL protocols on the biomechanical properties of the human corneal stroma

The TOP-SCOPE Survey of Planck Galactic Cold Clumps : Survey Overview and Results of an Exemplar Source, PGCC G26.53+0.17

The low dust temperatures (<14 K) of Planck Galactic cold clumps (PGCCs) make them ideal targets to probe the initial conditions and very early phase of star formation. "TOP-SCOPE" is a joint survey program targeting similar to 2000 PGCCs in J = 1-0 transitions of CO isotopologues and similar to 1000 PGCCs in 850 mu m continuum emission. The objective of the "TOP-SCOPE" survey and the joint surveys (SMT 10 m, KVN 21 m, and NRO 45 m) is to statistically study the initial conditions occurring during star formation and the evolution of molecular clouds, across a wide range of environments. The observations, data analysis, and example science cases for these surveys are introduced with an exemplar source, PGCC G26.53+0.17 (G26), which is a filamentary infrared dark cloud (IRDC). The total mass, length, and mean line mass (M/L) of the G26 filament are similar to 6200 M-circle dot, similar to 12 pc, and similar to 500 M-circle dot pc(-1), respectively. Ten massive clumps, including eight starless ones, are found along the filament. The most massive clump as a whole may still be in global collapse, while its denser part seems to be undergoing expansion owing to outflow feedback. The fragmentation in the G26 filament from cloud scale to clump scale is in agreement with gravitational fragmentation of an isothermal, nonmagnetized, and turbulent supported cylinder. A bimodal behavior in dust emissivity spectral index (beta) distribution is found in G26, suggesting grain growth along the filament. The G26 filament may be formed owing to large-scale compression flows evidenced by the temperature and velocity gradients across its natal cloud.Peer reviewe

Evaluation of individual and ensemble probabilistic forecasts of COVID-19 mortality in the United States

Short-term probabilistic forecasts of the trajectory of the COVID-19 pandemic in the United States have served as a visible and important communication channel between the scientific modeling community and both the general public and decision-makers. Forecasting models provide specific, quantitative, and evaluable predictions that inform short-term decisions such as healthcare staffing needs, school closures, and allocation of medical supplies. Starting in April 2020, the US COVID-19 Forecast Hub (https://covid19forecasthub.org/) collected, disseminated, and synthesized tens of millions of specific predictions from more than 90 different academic, industry, and independent research groups. A multimodel ensemble forecast that combined predictions from dozens of groups every week provided the most consistently accurate probabilistic forecasts of incident deaths due to COVID-19 at the state and national level from April 2020 through October 2021. The performance of 27 individual models that submitted complete forecasts of COVID-19 deaths consistently throughout this year showed high variability in forecast skill across time, geospatial units, and forecast horizons. Two-thirds of the models evaluated showed better accuracy than a naïve baseline model. Forecast accuracy degraded as models made predictions further into the future, with probabilistic error at a 20-wk horizon three to five times larger than when predicting at a 1-wk horizon. This project underscores the role that collaboration and active coordination between governmental public-health agencies, academic modeling teams, and industry partners can play in developing modern modeling capabilities to support local, state, and federal response to outbreaks

The United States COVID-19 Forecast Hub dataset

Academic researchers, government agencies, industry groups, and individuals have produced forecasts at an unprecedented scale during the COVID-19 pandemic. To leverage these forecasts, the United States Centers for Disease Control and Prevention (CDC) partnered with an academic research lab at the University of Massachusetts Amherst to create the US COVID-19 Forecast Hub. Launched in April 2020, the Forecast Hub is a dataset with point and probabilistic forecasts of incident cases, incident hospitalizations, incident deaths, and cumulative deaths due to COVID-19 at county, state, and national, levels in the United States. Included forecasts represent a variety of modeling approaches, data sources, and assumptions regarding the spread of COVID-19. The goal of this dataset is to establish a standardized and comparable set of short-term forecasts from modeling teams. These data can be used to develop ensemble models, communicate forecasts to the public, create visualizations, compare models, and inform policies regarding COVID-19 mitigation. These open-source data are available via download from GitHub, through an online API, and through R packages

Robust estimation of bacterial cell count from optical density

Optical density (OD) is widely used to estimate the density of cells in liquid culture, but cannot be compared between instruments without a standardized calibration protocol and is challenging to relate to actual cell count. We address this with an interlaboratory study comparing three simple, low-cost, and highly accessible OD calibration protocols across 244 laboratories, applied to eight strains of constitutive GFP-expressing E. coli. Based on our results, we recommend calibrating OD to estimated cell count using serial dilution of silica microspheres, which produces highly precise calibration (95.5% of residuals &lt;1.2-fold), is easily assessed for quality control, also assesses instrument effective linear range, and can be combined with fluorescence calibration to obtain units of Molecules of Equivalent Fluorescein (MEFL) per cell, allowing direct comparison and data fusion with flow cytometry measurements: in our study, fluorescence per cell measurements showed only a 1.07-fold mean difference between plate reader and flow cytometry data

Impact of opioid-free analgesia on pain severity and patient satisfaction after discharge from surgery: multispecialty, prospective cohort study in 25 countries

Background: Balancing opioid stewardship and the need for adequate analgesia following discharge after surgery is challenging. This study aimed to compare the outcomes for patients discharged with opioid versus opioid-free analgesia after common surgical procedures.Methods: This international, multicentre, prospective cohort study collected data from patients undergoing common acute and elective general surgical, urological, gynaecological, and orthopaedic procedures. The primary outcomes were patient-reported time in severe pain measured on a numerical analogue scale from 0 to 100% and patient-reported satisfaction with pain relief during the first week following discharge. Data were collected by in-hospital chart review and patient telephone interview 1 week after discharge.Results: The study recruited 4273 patients from 144 centres in 25 countries; 1311 patients (30.7%) were prescribed opioid analgesia at discharge. Patients reported being in severe pain for 10 (i.q.r. 1-30)% of the first week after discharge and rated satisfaction with analgesia as 90 (i.q.r. 80-100) of 100. After adjustment for confounders, opioid analgesia on discharge was independently associated with increased pain severity (risk ratio 1.52, 95% c.i. 1.31 to 1.76; P &lt; 0.001) and re-presentation to healthcare providers owing to side-effects of medication (OR 2.38, 95% c.i. 1.36 to 4.17; P = 0.004), but not with satisfaction with analgesia (beta coefficient 0.92, 95% c.i. -1.52 to 3.36; P = 0.468) compared with opioid-free analgesia. Although opioid prescribing varied greatly between high-income and low- and middle-income countries, patient-reported outcomes did not.Conclusion: Opioid analgesia prescription on surgical discharge is associated with a higher risk of re-presentation owing to side-effects of medication and increased patient-reported pain, but not with changes in patient-reported satisfaction. Opioid-free discharge analgesia should be adopted routinely

A blood atlas of COVID-19 defines hallmarks of disease severity and specificity.

Treatment of severe COVID-19 is currently limited by clinical heterogeneity and incomplete description of specific immune biomarkers. We present here a comprehensive multi-omic blood atlas for patients with varying COVID-19 severity in an integrated comparison with influenza and sepsis patients versus healthy volunteers. We identify immune signatures and correlates of host response. Hallmarks of disease severity involved cells, their inflammatory mediators and networks, including progenitor cells and specific myeloid and lymphocyte subsets, features of the immune repertoire, acute phase response, metabolism, and coagulation. Persisting immune activation involving AP-1/p38MAPK was a specific feature of COVID-19. The plasma proteome enabled sub-phenotyping into patient clusters, predictive of severity and outcome. Systems-based integrative analyses including tensor and matrix decomposition of all modalities revealed feature groupings linked with severity and specificity compared to influenza and sepsis. Our approach and blood atlas will support future drug development, clinical trial design, and personalized medicine approaches for COVID-19