Tool For Recognition Research Articles

Primary care-based interventions for secondary prevention of opioid dependence in chronic non-cancer pain patients on pharmaceutical opioids: systematic review.

Globally almost one third of adults with chronic non-cancer pain (CNCP) are prescribed opioids. Prevention of opioid dependence among these patients is a public health priority. Synthesise the evidence on the effectiveness of primary care-based interventions for secondary prevention of opioid dependence in CNCP patients on pharmaceutical opioids. Systematic review of randomised controlled trials (RCTs) and comparative non-randomised studies of interventions from high-income countries. We searched five databases for studies on non-tapering secondary prevention interventions such as tools for predicting dependence, screening tools for early recognition of dependence, prescribing/medication monitoring, and specialist support. We examined multiple outcomes, including reduction in opioid dosage. Primary analyses were restricted to RCTs with data synthesised using an effect direction plot. Risk of bias was assessed using the Cochrane risk of bias (RoB2) tool. Of 7,102 identified reports, 18 studies were eligible (8 RCTs). Most used multiple interventions/components. Of the seven RCTs at low risk of bias or 'some concerns', five showed a positive intervention effect on at least one relevant outcome, four of which included a nurse care manager and/or other specialist support. The remaining two RCTs showed no positive effect of automated symptom monitoring and optimised analgesic management by a nurse care manager/physician pain specialist team, or of a mobile opioid management app. We identify a clear need for further adequately powered high quality studies. The conclusions that can be drawn on intervention effectiveness are limited by the sparsity and inconsistency of available data.

Mathematical Analysis of Power Quality Disturbances in Electrical Distribution Systems: Detection, Classification, and Mitigation Strategies

Maintaining a high-quality power source is an important part of modern electricity distribution systems for making sure that many industrial, business, and domestic uses work reliably and efficiently. Power quality disruptions, on the other hand, make this goal much harder to reach. This essay gives a thorough statistical study of power quality problems, focused on how to find them, classify them, and find ways to fix them. The first part of the study talks about how to find power quality problems, which means finding changes in voltage, current, or frequency that don't make sense. Different signal processing methods, like Fourier analysis, wavelet transform, and advanced machine learning algorithms, are tested to see how well they can find and describe different kinds of problems. Second, the paper talks about how to classify power quality problems, which is important for figuring out what causes them and choosing the best ways to fix them. There are different kinds of disturbances, such as voltage sags, swells, interruptions, harmonics, and transients. Each type needs a different way of being analyzed and dealt with. Researchers are looking into whether statistical methods, pattern recognition tools, and artificial intelligence can correctly group events based on their unique features. Lastly, the paper talks about ways to lessen the bad effects of power quality problems on electrical distribution systems and loads that are related to them. These methods include both preventative steps, like making equipment better designed and rearranging networks, and defensive ones, like using active power filters, voltage controls, and energy storage systems. Coordinating spread energy resources with smart grid technologies is also being looked into as a way to make the system more resistant to disruption. In general, the mathematical analysis in this paper gives us useful information about how complicated power quality problems are and gives us a way to find them, sort them into groups, and fix them. Electrical companies, equipment makers, and system workers can come up with better ways to make sure that modern distribution systems have a stable and high-quality power source by using advanced mathematical methods and new technologies.

Normative data of the Italian Famous Face Test

The faces we see in daily life exist on a continuum of familiarity, ranging from personally familiar to famous to unfamiliar faces. Thus, when assessing face recognition abilities, adequate evaluation measures should be employed to discriminate between each of these processes and their relative impairments. We here developed the Italian Famous Face Test (IT-FFT), a novel assessment tool for famous face recognition in typical and clinical populations. Normative data on a large sample (N = 436) of Italian individuals were collected, assessing both familiarity (d′) and recognition accuracy. Furthermore, this study explored whether individuals possess insights into their overall face recognition skills by correlating the Prosopagnosia Index-20 (PI-20) with the IT-FFT; a negative correlation between these measures suggests that people have a moderate insight into their face recognition skills. Overall, our study provides the first online-based Italian test for famous faces (IT-FFT), a test that could be used alongside other standard tests of face recognition because it complements them by evaluating real-world face familiarity, providing a more comprehensive assessment of face recognition abilities. Testing different aspects of face recognition is crucial for understanding both typical and atypical face recognition.

Clustering data analytics of urban land use for change detection

In this study, the author proposes and details a workflow for the spatial-temporal demarcation of urban areal features in 8 cities of Tamilnadu, India. During the inception phase, functional requirements and non-functional parameters are analyzed and designed, within a suitable pixel area and object-oriented derived paradigm. Land use categories are defined from OpenStreetMap (OSM) related works with the scope of conducting climate change, using multispectral sensors onboard Landsat series. Furthermore, we augment the bands dataset with Spatially Invariant Feature Transform (SIFT), Normalized Difference Vegetation Index (NDVI), Normalized Difference Built-Up Index (NDBI), Leaf Area Index (LAI), and Texture base indices, as a means of spatially integrating auto-covariance to stationarity patterns. In doing so, change detection can be pursuit by scaling up the segmentation of regional/zonal boundaries in a multi-dimensional environment, with the aid of Wide Area Networks (WAN) cluster computers such as the BEOWULF/Google Earth Engine clusters. GeoAnalytical measures are analyzed in the design of local and zonal spatial models (GRID, RASTER, DEM, IMAGE COLLECTION). Finally, multi variate geostatistical works are ensued for precision and recall in predictive data analytics. The author proposes reusing machine learning tools (filtering by attribute-based indexing in PaaS clouds) for pattern recognition and visualization of features and feature collection.

Integrating CdIn2S4 semiconductors with S-vacancy MoS2 nanosheets to fabricate a multi-channel aptasensing chip for photoelectrochemical detection of multiple mycotoxins

BackgroundThe importance of multi-target simultaneous detection lies in its ability to significantly boost detection efficiency, making it invaluable for rapid and cost-effective testing. Photoelectrochemical (PEC) sensors have emerged as promising candidates for detecting harmful substances and biomarkers, attributable to their unparalleled sensitivity, minimal background signal, cost-effectiveness, equipment simplicity, and outstanding repeatability. However, designing an effective multi-target detection strategy remains a challenging task in the PEC sensing field. Consequently, there is a pressing need to address the development of PEC sensors capable of simultaneously detecting multiple targets. ResultsCdIn2S4/V–MoS2 heterojunctions were successfully prepared via a hydrothermal method. These heterojunctions exhibited a high photocurrent intensity, representing a 1.53-fold enhancement compared to CdIn2S4 alone. Next, we designed a multi-channel aptasensing chip using ITO as the substrate. Three working electrodes were created via laser etching and subsequently modified with CdIn2S4/V–MoS2 heterojunctions. Thiolated aptamers were then self-assembled onto the CdIn2S4/V–MoS2 heterojunctions via covalent bonds, serving as recognition tool. By empolying the CdIn2S4/V–MoS2 heterojunctions as the sensing platform and aptamers as recognition tool, we successfully developed a disposable aptasensing chip for the simultaneous PEC detection of three typical mycotoxins (aflatoxin B1 (AFB1), ochratoxin A (OTA), and zearalenone (ZEN)). This aptasensing chip exhibited wide detection range for AFB1 (0.05–50 ng/mL), OTA (0.05–500 ng/mL), and ZEN (0.1–250 ng/mL). Furthermore, it demonstrated ultra-low detection limits of 0.017 ng/mL for AFB1, 0.016 ng/mL for OTA, and 0.033 ng/mL for ZEN. Significance and noveltyThe aptasensing chip stands out for its cost-effectiveness, simplicity of fabrication, and multi-channel capabilities. The versatility and practicality enable it to serve as a powerful platform for designing multi-channel PEC aptasensors. With its ability to detect multiple targets with high sensitivity and specificity, the aptasensing chip holds immense potential for applications across diverse fields, such as environmental monitoring, clinical diagnostics, and food safety monitoring, where multi-target detection is crucial.

Barriers and facilitators to effective electronic health record-based sepsis screening in the pediatric intensive care unit.

The Pediatric Surviving Sepsis Campaign supports the implementation of automated tools for early sepsis recognition. In 2019 the C.S. Mott Children's Hospital Pediatric Intensive Care Unit deployed an electronic medical record (EMR)-based screening for early recognition and treatment of sepsis. We analyzed all automated primary sepsis alerts, secondary screens, and bedside huddles from November 2019 to January 2020 (Cohort 1) and from November 2020 to January 2021 (Cohort 2) to identify barriers and facilitators for the use of this tool. We distributed surveys to frontline providers to gather feedback on end-user experience. In Cohort 1, 895 primary alerts were triggered, yielding 503 completed secondary screens and 40 bedside huddles. In Cohort 2, 925 primary alerts were triggered, yielding 532 completed secondary screens and 12 bedside huddles. Surveys assessing end-user experience identified the following facilitators: (1) 73% of nurses endorsed the bedside huddle as value added; (2) 74% of medical providers agreed the bedside huddle increased the likelihood of interventions. The greatest barriers to successful implementation included the (1) overall large number of primary alerts from the automated tool and (2) rate of false alerts, many due to routine respiratory therapy interventions. Our data suggests that the successful implementation of EMR-based sepsis screening tools requires countermeasures focusing on 3 key drivers for change: education, technology, and patient safety. While both medical providers and bedside nurses found merit in our EMR-based sepsis early recognition system, continued refinement is necessary to avoid sepsis alert fatigue.

A rare case of chronic headache

Abstract Cerebral venous thrombosis (CVT) is a relatively uncommon and rare condition worldwide, affecting approximately 1 in 100,000 individuals annually and comprising about 0.5% of all strokes. It is more prevalent in women of childbearing age due to hypercoagulable states. We present a case report of a 19 years old female exhibiting symptoms, including headache, occasionally associated vomiting, blurring of vision and one generalised seizure. On general examination and neurological, there were no significant abnormality found, Fundoscopy revealed blurred disc margins and the presence of papilledema. An emergency non-contrast computed tomography (NCCT) scan of the brain showed normal findings. Subsequently, a magnetic resonance imaging (MRI) scan of the brain indicated partial loss of flow void in the posterior part of the superior sagittal sinus and straight sinus, along with the formation of collateral channels, suggestive of possible subacute dural sinus thrombosis. Additionally, a contrast-enhanced MRI (CEMRI) scan with MR venogram showed filling defects in the posterior part of the superior sagittal sinus and straight sinus, with non-visualization of the left transverse sinus, indicative of subacute to chronic dural venous thrombosis, Beta 2 glycoprotein IgM and Cardiolipin IgM were positive and titres were high. This case with history of headache for 4 years underscores the need for early diagnosis, which could have alleviated suffering and further prevent grave consequences, with an insight to such problems and accessibility to high end imaging diagnosis tools for early recognition and management.

Predicting Future Disorders via Temporal Knowledge Graphs and Medical Ontologies.

Despite the vast potential for insights and value present in Electronic Health Records (EHRs), it is challenging to fully leverage all the available information, particularly that contained in the free-text data written by clinicians describing the health status of patients. The utilization of Named Entity Recognition and Linking tools allows not only for the structuring of information contained within free-text data, but also for the integration with medical ontologies, which may prove highly beneficial for the analysis of patient medical histories with the aim of forecasting future medical outcomes, such as the diagnosis of a new disorder. In this paper, we propose MedTKG, a Temporal Knowledge Graph (TKG) framework that incorporates both the dynamic information of patient clinical histories and the static information of medical ontologies. The TKG is used to model a medical history as a series of snapshots at different points in time, effectively capturing the dynamic nature of the patient's health status, while a static graph is used to model the hierarchies of concepts extracted from domain ontologies. The proposed method aims to predict future disorders by identifying missing objects in the quadruple 〈s, r, ?, t 〉, where s and r denote the patient and the disorder relation type, respectively, and t is the timestamp of the query. The method is evaluated on clinical notes extracted from MIMIC-III and demonstrates the effectiveness of the TKG framework in predicting future disorders and of medical ontologies in improving its performance.

Computer vision-based post-earthquake inspections for building safety assessment

Assessing the safety of earthquake-affected buildings is a critical structural health monitoring task that facilitates the timely response to present dangers and reduces potential threats to life and property. However, post-earthquake time constraints and harsh environmental conditions mean that images and videos taken on-site are frequently affected by poor resolution and blurriness, which may negatively affect the accuracy and usefulness of artificial intelligence image recognition tools. In this study, a HybridGAN model was developed that incorporates ESRGAN for resolution improvement and DeblurGANv2 for blurriness improvement. Additionally, a transfer learning U-Net (TF-Unet) was integrated to detect building components (i.e., columns and structural walls), classify building damage types, and identify building damage levels. Based on recognition results from three case studies and the relevant Taiwan codes, an automated system for building safety evaluation was proposed. The model was trained to directly classify and recognize the level of building component damage. The mean Intersection over Union (mIoU) results for the column and structural wall using the testing dataset were 81.326 % and 57.009 %, respectively. The pre-trained model was used to predict three case studies to test the capability of TF-Unet to handle real-word datasets.

Exploring the Correlation between Geoheritage and Geodiversity through Comprehensive Mapping: A Study within the Sesia Val Grande UNESCO Global Geopark (NW Italy)

While geoheritage and geodiversity have been well defined in the literature, the multiplicity of definitions given to these two concepts makes it difficult to establish an unambiguous relationship between them. Basing on semantic-ontological studies, this study aims to reduce this ambiguity within the concepts by investigating the relationship between the geodiversity richness and the presence of geoheritage, and discussing whether the areas with the greatest geodiversity are the ones with the most relevant geoheritage, thus questioning the potential use of the geodiversity index map. Upon a strong theoretical framework, a quantitative geodiversity index map was created for the Alagna Valsesia municipality, within the Sesia Val Grande UNESCO Global Geopark (Italian Western Alps). Then, 25 geosites were identified and mapped in the same area. Notably, the exploration into the correlation between geodiversity and geoheritage on the field shows that in our study area there is no spatial correlation between the geodiversity class and the number of geosites, proving that some geosites may occur in areas of low geodiversity and the greatest geodiversity are not the ones with the most relevant geoheritage. Moreover, all the non-parametric regression models tested are not significant, indicating that there is no predictable relationship between geodiversity and geoheritage in Alagna Valsesia (NW Alps). For that reason, our work highlights that although the quantitative geodiversity map can have an important role for geoconservation and within biodiversity studies, it could not be a strong tool for geosites recognition and tourism promotion, while for this purpose should be better use a qualitative geodiversity map. Finally, the potential use of the geodiversity map depends on the purpose of the study and the approach used to produce it. For a comprehensive geoconservation and geoheritage promotion strategy, the two approaches (qualitative and quantitative) may be complementary.

Method for Constructing Neural Network Means for Recognizing Scenes of Political Extremism in Graphic Materials of Online Social Networks

Countering the spread of calls for political extremism through graphic content on online social networks is becoming an increasingly pressing problem that requires the development of new technological solutions, since traditional approaches to countering are based on the results of recognizing destructive content only in text messages. Since in modern conditions neural network tools for analyzing graphic information are considered the most effective, it is assumed that it is advisable to use such tools for analyzing images and video materials in online social networks, taking into account the need to adapt them to the expected conditions of use, which are determined by the wide variability in the size of graphic content, the presence of typical interference, limited computing resources of recognition tools. Using this thesis, a method has been proposed that makes it possible to implement the construction of neural network recognition tools adapted to the specified conditions. For recognition, the author's neural network model was used, which, due to the reasonable determination of the architectural parameters of the low-resource convolutional neural network of the MobileNetV2 type and the recurrent neural network of the LSTM type, which makes up its structure, ensures high accuracy of recognition of scenes of political extremism both in static images and in video materials under limited computing conditions resources. A mechanism was used to adapt the input field of the neural network model to the variability of the size of graphic resources, which provides for scaling within acceptable limits of the input graphic resource and, if necessary, filling the input field with zeros. Levelling out typical noise is ensured by using advanced solutions in the method for correcting brightness, contrast and eliminating blur of local areas in images of online social networks. Neural network tools developed on the basis of the proposed method for recognizing scenes of political extremism in graphic materials of online social networks demonstrate recognition accuracy at the level of the most well-known neural network models, while ensuring a reduction in resource intensity by more than 10 times. This allows the use of less powerful equipment, increases the speed of content analysis, and also opens up prospects for the development of easily scalable recognition tools, which ultimately ensures an increase in security and a reduction in the spread of extremist content on online social networks. It is advisable to correlate the paths for further research with the introduction of the Attention mechanism into the neural network model used in the method, which will make it possible to increase the efficiency of neural network analysis of video materials.

Assessing pronunciation using dictation tools

Abstract Language institutions need efficient and reliable placement tests to ensure students are placed in appropriate classes. This can be achieved by automating the scoring of pronunciation tests via the use of speech recognition, as its reliability has been shown to be comparable to that of human raters. However, this technology can be costly as it requires development and maintenance, placing it beyond the means of many institutions. This study investigates the feasibility of assessing English second language pronunciation in placement tests through the use of a free automatic speech recognition tool, Google Voice Typing (GVT). We compared human-rated and GVT-rated scores of 56 pronunciation placement tests. Our results indicate a strong correlation between scores for the final rating and for each criterion on the rubric used by human raters. We conclude that leveraging this free speech technology could increase the test usefulness of language placement tests.

Enhancing accessibility and discoverability of digital archive images through automated image recognition tool

This research paper presents a comprehensive evaluation of the effectiveness of Imagga and Google cloud vision application programming interface (API) as image recognition tools for generating metadata in digital archive images. The assessment encompasses a diverse range of archive images, including those without text, images with text, and both color and black-and-white images. Through the use of evaluation metrics such as cosine similarity, word overlap similarity, recall, precision, and F1 score, the performance of these tools is quantitatively measured. The findings highlight the strong individual performance of both Imagga and Google cloud vision API, with the combined metadata outputs achieving significantly higher scores across all metrics. This emphasizes the potential benefits of employing a combined approach, leveraging the strengths of multiple tools to enhance the reliability and robustness of the metadata extraction process. The findings contribute to the advancement of metadata management in digital archives and underscore the importance of utilizing multiple tools for improved performance in image metadata generation.

Clustering data analytics of urban land use for change detection

In this study, the author proposes and details a workflow for the spatial-temporal demarcation of urban areal features in 8 cities of Tamilnadu, India. During the inception phase, functional requirements and non-functional parameters are analyzed and designed, within a suitable pixel area and object-oriented derived paradigm. Land use categories are defined from OpenStreetMap (OSM) related works with the scope of conducting climate change, using multispectral sensors onboard Landsat series. Furthermore, we augment the bands dataset with Spatially Invariant Feature Transform (SIFT), Normalized Difference Vegetation Index (NDVI), Normalized Difference Built-Up Index (NDBI), Leaf Area Index (LAI), and Texture base indices, as a means of spatially integrating auto-covariance to stationarity patterns. In doing so, change detection can be pursuit by scaling up the segmentation of regional/zonal boundaries in a multi-dimensional environment, with the aid of Wide Area Networks (WAN) cluster computers such as the BEOWULF/Google Earth Engine clusters. GeoAnalytical measures are analyzed in the design of local and zonal spatial models (GRID, RASTER, DEM, IMAGE COLLECTION). Finally, multi variate geostatistical works are ensued for precision and recall in predictive data analytics. The author proposes reusing machine learning tools (filtering by attribute-based indexing in PaaS clouds) for pattern recognition and visualization of features and feature collection.

Predicting multi-label emojis, emotions, and sentiments in code-mixed texts using an emojifying sentiments framework

In the era of social media, the use of emojis and code-mixed language has become essential in online communication. However, selecting the appropriate emoji that matches a particular sentiment or emotion in the code-mixed text can be difficult. This paper presents a novel task of predicting multiple emojis in English-Hindi code-mixed sentences and proposes a new dataset called SENTIMOJI, which extends the SemEval 2020 Task 9 SentiMix dataset. Our approach is based on exploiting the relationship between emotion, sentiment, and emojis to build an end-to-end framework. We replace the self-attention sublayers in the transformer encoder with simple linear transformations and use the RMS-layer norm instead of the normal layer norm. Moreover, we employ Gated Linear Unit and Fully Connected layers to predict emojis and identify the emotion and sentiment of a tweet. Our experimental results on the SENTIMOJI dataset demonstrate that the proposed multi-task framework outperforms the single-task framework. We also show that emojis are strongly linked to sentiment and emotion and that identifying sentiment and emotion can aid in accurately predicting the most suitable emoji. Our work contributes to the field of natural language processing and can help in the development of more effective tools for sentiment analysis and emotion recognition in code-mixed languages. The codes and data will be available at https://www.iitp.ac.in/~ai-nlp-ml/resources.html#SENTIMOJI to facilitate research.

Quantifying the Microstructure of Dried Deposits Using Height-Height Correlation Function.

Colloidal self-assembly has garnered significant attention in recent research, owing to applications in medical and engineering domains. Understanding the arrangement of particles in self-assembled systems is crucial for comprehending the underlying physics and synthesizing complex nano- and microscale structures. In this study, we introduce a novel methodology for analyzing the spatial distribution of particles in colloidal assemblies, focusing specifically on quantifying the microstructure of deposits formed by the evaporation of colloidal particle-laden drops. Utilizing a height-height correlation-function-based approach, we quantify variations in the height profile of deposits in radial and azimuthal directions. This approach enables the classification of the patterns into typical examples encountered in an evaporation-driven assembly. The method is demonstrated to be robust for quantifying synthetic and experimentally obtained deposit patterns, exhibiting excellent agreement in the estimated parameters. The mapping developed between pattern morphology and the quantitative measures introduced in this work may be used in a variety of applications including disease diagnosis as well as in developing pattern recognition tools.

An STS analysis of a digital humanities collaboration: trading zones, boundary objects, and interactional expertise in the DECRYPT project

A widely shared recognition over the past decade is that the methodology and the basic concepts of science and technology studies (STS) can be used to analyze collaborations in the cross-disciplinary field of digital humanities (DH). The concepts of trading zones (Galison, 2010), boundary objects (Star and Griesemer, 1989), and interactional expertise (Collins and Evans, 2007) are particularly fruitful for describing projects in which researchers from massively different epistemic cultures (Knorr Cetina, 1999) are trying to develop a common language. The literature, however, primarily concentrates on examples where only two parties, historians and IT experts, work together. More exciting perspectives open up for analysis when more than two, more nuanced and different epistemic cultures seek a common language and common research goals. In the DECRYPT project funded by the Swedish Research Council, computational linguists, historians, computer scientists and AI experts, cryptologists, computer vision specialists, historical linguists, archivists, and philologists collaborate with strikingly different methodologies, publication patterns, and approaches. They develop and use common resources (including a database and a large collection of European historical texts) and tools (among others a code-breaking software, a hand-written text recognition tool for transcription), researching partly overlapping topics (handwritten historical ciphers and keys) to reach common goals. In this article, we aim to show how the STS concepts are illuminating when describing the mechanisms of the DECRYPT collaboration and shed some light on the best practices and challenges of a truly cross-disciplinary DH project.

Modern Approach in Pattern Recognition Using Circular Fermatean Fuzzy Similarity Measure for Decision Making with Practical Applications

The circular Fermatean fuzzy (CFF) set is an advancement of the Fermatean fuzzy (FF) set and the interval-valued Fermatean fuzzy (IVFF) set which deals with uncertainty. The CFF set is represented as a circle of radius ranging from 0 to 2 with the center at the degree of association (DA) and degree of nonassociation (DNA). If multiple people are involved in making decisions, the CFF set, as an alternative to the FF and IVFF sets, can deal with ambiguity more effectively by encircling the decision values within a circle rather than taking an average. Using algorithms, a pattern can be observed computationally or visually. Machine learning algorithm utilizes pattern recognition as an instrument for identifying patterns and also similarity measure (SM) is a beneficial pattern recognition tool used to classify items, discover variations, and make future predictions for decision making. In this work, we introduce the CFF cosine and Dice similarity measures (CFFDMs and CFFSMs), and their properties are studied. Unlike traditional approaches of decision making, which emphasize a single number, the proposed CFFSMs observe the pattern over the circular region to help in dealing with uncertainty more effectively. We introduce an innovative decision-making method in the FF setting. Available bank loans and applicants’ eligibility levels are represented as CFF set using their FF criteria and are taken as loan patterns and customer eligibility patterns. The loan is allocated to the applicant by measuring the CFFCSM and CFFDSM between the two patterns. Also, laptops are suggested to the customers by measuring the similarity between specification pattern and requirement pattern. The correctness and consistency of the proposed models are ensured by comparison analysis and graphical simulations of the input and similarity CFFNs.

Introducing Bed Word: a new automated speech recognition tool for sociolinguistic interview transcription

Abstract We present Bed Word, a tool leveraging industrial automatic speech recognition (ASR) to transcribe sociophonetic data. While we find lower accuracy for minoritized English varieties, the resulting vowel measurements are overall very close to those derived from human-corrected gold data, so fully automated transcription may be suitable for some research purposes. For purposes requiring greater accuracy, we present a pipeline for human post-editing of automatically generated drafts, which we show is far faster than transcribing from scratch. Thus, we offer two ways to leverage ASR in sociolinguistic research: full automation and human post-editing. Augmenting the DARLA tool developed by Reddy and Stanford (2015b. Toward completely automated vowel extraction: Introducing DARLA. Linguistics Vanguard 1(1). 15–28), we hope that this resource can help speed up transcription for sociophonetic research.

Polyfluoroalkyl Tag Decoration Enables Significantly Enhanced Tumor Penetration Ability of a PTK7 Targeting Aptamer.

Aptamers are widely used molecular recognition tools in targeted therapy, but their ability to effectively penetrate deep into solid tumors remains a significant challenge, leading to suboptimal treatment efficacy. Here, we developed a polyfluoroalkyl (PFA) decoration strategy to enhance aptamer recognition, cell internalization, and solid tumor penetration. Our results indicate that PFA with around 11 fluorine atoms significantly improves aptamer internalization both in vitro and in vivo settings. However, we also observed that the use of PFA tags containing 19 and 23 fluorine atoms on aptamers resulted in nonspecific cell anchoring in control cell lines, affecting the specificity of aptamers. Overall, we found that using a chemical modification strategy could enhance the deep tumor penetration ability of aptamers and validate their effectiveness in vivo. This approach has significant practical applications in targeted drug delivery for cancer treatment.