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Front Cell Dev Biol. 2021 Apr 15;9:630355. doi: 10.3389/fcell.2021.630355. eCollection 2021.

ABSTRACT

Bone-related malignancies, such as osteosarcoma, Ewings sarcoma, multiple myeloma, and cancer bone metastases have similar histological context, but they are distinct in origin and biological behavior. We hypothesize that a distinct immune infiltrative microenvironment exists in these four most common malignant bone-associated tumors and can be used for tumor diagnosis and patient prognosis. After sample cleaning, data integration, and batch effect removal, we used 22 publicly available datasets to draw out the tumor immune microenvironment using the ssGSEA algorithm. The diagnostic model was developed using the random forest. Further statistical analysis of the immune microenvironment and clinical data of patients with osteosarcoma and Ewings sarcoma was carried out. The results suggested significant differences in the microenvironment of bone-related tumors, and the diagnostic accuracy of the model was higher than 97%. Also, high infiltration of multiple immune cells in Ewings sarcoma was suggestive of poor patient prognosis. Meanwhile, increased infiltration of macrophages and B cells suggested a better prognosis for patients with osteosarcoma, and effector memory CD8 T cells and type 2 T helper cells correlated with patients chemotherapy responsiveness and tumor metastasis. Our study revealed that the random forest diagnostic model based on immune infiltration can accurately perform the differential diagnosis of bone-related malignancies. The immune microenvironment of osteosarcoma and Ewings sarcoma has an important impact on patient prognosis. Suppressing the highly inflammatory environment of Ewings sarcoma and promoting macrophage and B cell infiltration may have good potential to be a novel adjuvant treatment option for osteosarcoma and Ewings sarcoma.

PMID:33937231 | PMC:PMC8082117 | DOI:10.3389/fcell.2021.630355

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Application of Immune Infiltration Signature and Machine Learning Model in the Differential Diagnosis and Prognosis of Bone-Related Malignancies -...

Artificial Intelligence is one of the greatest achievements in the tech world and while machine learning was only limited to reading still frames up until now and is quite efficient in it, the next step being taken in the advancement of machine learning and artificial intelligence is identifying individual objects within video in order to open up new considerations in brand placement, visual effects, accessibility features and more.

The first and successful step taken towards making AI identify individual objects within video was done by Google. Google had been working towards accomplishing this feature for some time now and after a lot of efforts it has now introduced new advances in its YouTube option which includes being able to tag products in that are present in video clips and provide direct links to shop for those products.

This simply means that companies now can tag their products in YouTube videos no matter at what timing it is being displayed, it can tag its product at that specific time. Along with this it will also provide direct shopping options, facilitating broader ecommerce opportunities in the app.

After the successful introduction of this feature in YouTube, Facebook is taking the next step and introducing a similar feature on its platform and the company claims that their feature will be much better at singling out individual objects within video frames.

Facebook explained that they have collaborated with researchers at Inria with whom they have developed a new method called DINO. This method will be used to train Vision Transformers (ViT) with no supervision. The company has claimed that besides setting a new state of the art among self-supervised methods, this approach leads to a remarkable result that is unique to this combination of AI techniques. Facebook further said that their model can discover and segment objects in an image or a video with absolutely no supervision and without being given a segmentation-targeted objective and all this will make this process effectively automated.

Hence that is why the company claims that their feature is the best of the best.

Facebook is still working towards this feature and once it is launched we cannot wait to see if it out does YouTubes similar feature or not. However, we know that both YouTube and Facebook have always delivered their best and therefore we are sure that they will deliver the best this time as well.

Read next:According to the exec, over 60 percent Instagram users are connected to Facebook Messenger

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YouTube Introduces Machine Learning Individual Object Recognition on Videos and Facebook Follows Next - Digital Information World

The AI & Machine Learning Operationalization Software Market report is a compilation of first-hand information, qualitative and quantitative assessment by industry analysts, inputs from industry experts and industry participants across the value chain. The report provides in-depth analysis of parent market trends, macro-economic indicators and governing factors along with market attractiveness as per segments. The report also maps the qualitative impact of various market factors on market segments and geographies.

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Top LeadingCompaniesof Global AI & Machine Learning Operationalization Software Market areAlgorithmia, Spell, Valohai Ltd, 5Analytics, Cognitivescale, Datatron Technologies, Acusense Technologies, Determined AI, DreamQuark, Logical Clocks, IBM, Imandra, Iterative, Databricks, ParallelM, MLPerf, Neptune Labs, Numericcal, Peltarion, Weights & Biases, WidgetBrainand others.

Regional Outlook of AI & Machine Learning Operationalization Software Market report includes the following geographic areas such as: North America, Europe, China, Japan, Southeast Asia, India and ROW.

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Web-Based

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Large Enterprises

SMEs

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While the Biden administrations take on immigration initially seemed to be the opposite of his predecessors, its a bit more nuanced. Our Practical Law attorney-editors discuss what that means for attorneys and employers.

Navigating intricate changes may make an already-complex practice area even more complicated. From humanitarian issues likes refugees to family unification to employment at all skills levels, immigration law has presented challenges to the U.S. practically and politically from our very roots to the present day. Many presidential administrations have attempted fixes to our immigration system, with limited success.

A compromise on immigration has been out of reach for decades. One reason for the limitations is the breadth of issues encompassed by immigration and the complex, seemingly unrelated interests represented by those issues. Any chance to achieve the long-sought goal for comprehensive immigration reform revolves around building enough unity in important common interests to have all parties meet somewhere in the middle. Essentially, comprehensive reform likely requires stronger enforcement in balance to stronger immigration benefits.

To hear the latest on immigration and other current topics of interest, attend our webcast.

The Biden administrations first 100 days of action on immigration is clearest seen in comparison to the Trump administration that preceded it. A major component of the Trump administrations immigration goals was to limit immigration. Most of President Bidens early actions on immigration have been to rescind or repudiate the policies introduced and embedded by President Trumps administration throughout the immigration system. That includes rescinding President Trumps key executive actions on immigration and the administrations rule redefining the entry and admission bar for public charges.

The Department of Homeland Security and its sub-agencies responsible for immigration (principally U.S. Citizenship and Immigration Services (USCIS), Immigration and Customs Enforcement (ICE), and US Customs and Border Protection (CBP)) have also rescinded more restrictionist policies and reverted to or introduced policies that are more benefits-oriented.

Ultimately, the more pro-immigration policies are likely to benefit employers that sponsor foreign workers. However, there are warning signs that the Biden administration may retain or introduce rules or policies that are more restrictionist or enforcement-minded than expected. For example, the administration retained the high premium processing fee introduced in 2020 (more than double what it was in 2018) and delayed, but did not withdraw, rules to redefine H-1B selection by wage level and a DOL rule increasing prevailing wages for immigration matters. Employers and their counsel must remain engaged advocates for immigration.

To learn more about evolving immigration policies and other matters impacting lawyers and employers, watch our webcast, A conversation with Practical Law attorney-editors on some of the impact of Bidens first 100 days.

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Immigration & the first 100 days of Biden's administration | Legal Blog - Westlaw Insider - Westlaw Insider

The consequences of hearing loss are many, including social isolation1-3 and depression.10-13 The failure of hearing aids to either prevent or improve depression may stem from a complex relationship between hearing loss, depression, and the social dynamism of hearing loss that we have yet to fully understand. To this end, there are several opportunities to explore the relationship between hearing loss and depression. Moreover, there may be utility in identifying depression in patients with hearing loss at the point of care considering the established link between hearing loss and depression.

Shutterstock/Photographee.eu, technology. Hearing loss, mental health.

Using machine learning to predict depression scores (adapted from Crowson et. al., 2020). Technology. Hearing loss, mental health.

The primary objective of our work was to use a predictive approach using machine learning and audiometric data to determine if these data accurately predict patient-reported depression. We hypothesized that an advanced machine learning model may be useful for identifying depression in patients with audiometric data. We also sought to determine if the addition of other clinical and demographic variables combined with the audiometric data would produce further gains in model accuracy.

In our study, we developed a supervised machine learning model using National Health and Nutrition Examination Survey (NHANES) data, composed of subjective and objective audiometric variables and several other health determinant predictors, to predict scores on a validated depression scalethe Patient Health Questionnaire (PHQ-9). The NHANES is a cross-sectional public health survey designed to assess the health and nutrition status of individuals in the United States. NHANES data has been used in numerous research investigations to incorporate the interplay between determinants of health and specific medical conditions.

In a sample of participants from a survey cycle of the NHANES database, our supervised machine learning approach accurately predicted depression scale scores using audiometric and health determinant predictors. The model's most influential audiometric predictors of higher scores on the depression scale were functional dimensions and not objective audiometric testing variables. Among the most influential predictors, half were related to the social dynamics of hearing loss. The remaining predictors associated with depression in hearing loss were related to noise exposure, tinnitus, and objective audiometric testing. When expanding to include predictors ranging from demographics to other medical and health status content, a social context of hearing loss ranked in the top five most influential.

A strong association between social isolation and hearing loss has been demonstrated in adults.1-3 The observation that hearing loss leads to social isolation is intuitive given that hearing loss leads to impaired efficiency of communication. The connection between social isolation and depression is a natural extension as humans are social creatures.

If hearing care professionals treat hearing loss with conventional hearing aid devices, would it be reasonable to expect social isolation and associated depression to improve? Unfortunately, the relationship appears more complex. Prior work has shown that hearing aids do not result in consistent improvement in social isolation11,14 or depression.10-13 Perhaps the disconnect might be explained by noting that hearing amplification exclusively does not address hearing performance in real-life social situations. Basic sound amplification can and does help individuals with hearing loss. More advanced hearing aids incorporate signal processing technologies to better isolate the relevant sounds in noisy environments. Perhaps future research involving hearing aids with enhanced signal processing technology may lead to further insights into the utility of hearing aids to directly augment the social dynamics of hearing loss.

In summary, we found the NHANES dataset is useful for training machine learning models to accurately predict depression scale scores from audiometric data. As many of the variables collected in the NHANES data are the same clinical data we extract from our patients in real-life clinical encounters, such a predictive model could be useful in predicting depression scale scores at the point of care. We found the most influential audiometric predictors of higher scores on the depression scale were functional dimensions of hearing loss and not simply objective audiometric data like thresholds and word recognition scores. Among these influential functional dimensions, our model indicated the specific effect of hearing loss on social relations was particularly powerful. This is an interesting finding, as prior investigations into the effect of hearing aids on depression and social isolation have failed to show a consistent benefit. Simply giving a patient a hearing aid is not a guarantee that social isolation or depression will improve. Thus, our model output puts forth a new hypothesis that simply amplifying sound alone, in general, fails to address or improve the social dynamism of hearing loss. For today's hearing care clinicians, we suggest that recognizing that social aspects of hearing loss may carry more influence on the development or maintenance of depression than previously thought. Moreover, we may need to reimagine our aural rehabilitation strategies to include specific interventions to optimize social dynamics.

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Predicting Depression from Hearing Loss Using Artificial... : The Hearing Journal - LWW Journals