Quick Recognition Research Articles

ANTICIPATING BIOSECURITY HAZARDS: DISTRIBUTION AND RECOGNITION OF BIOLOGICAL THREATS

Understanding the potential biological weapons that can be created, the technologies involved, and the entities capable of producing them is crucial for biosecurity. Equally important is comprehending the various targets a biological weapon might be aimed at, the deployment methods, and available detection strategies to counteract illegal biological threats. This chapter delves into these aspects, starting with methods of deployment. The Center for Disease Control and Prevention (CDC) categorizes disease transmission into direct and indirect modes. Direct transmission involves contact with an infected person or environmental reservoir, including droplet spread, proven highly effective in densely populated areas. Indirect transmission occurs through contaminated objects or vectors. Vehicle-based transmission involves contaminating inanimate objects like food, water, and biological products. Vector-based transmission involves organisms carrying and transmitting pathogens. Airborne transmission, receiving significant attention for its potential in biological attacks, involves pathogens suspended in the air, posing significant risks, especially in crowded areas. Biological weapons could target humans, agriculture, technology, and the environment. Attacks on humans, often the focus of biosecurity efforts, pose catastrophic outcomes. State-sponsored programs often focus on human pathogens like Bacillus anthracis and Yersinia pestis. Agroterrorism, targeting agriculture and livestock, can lead to economic losses and social instability. Attacks on technology, utilizing synthetic biology, have shown the potential to manipulate DNA to store and deploy malicious code, highlighting vulnerabilities in sequencing systems. While attacks on the environment remain theoretical, advancements in gene drives raise concerns about manipulating ecosystems. Effective biosecurity involves pre- and post-deployment screening techniques. Pre-deployment screening, particularly gene synthesis, focuses on preventing the acquisition of DNA fragments for weapon production. Current measures involve background checks and screening against pathogen lists, yet gaps exist, including inadequate international policies and the potential to hide malicious sequences within longer ones. Post-deployment detection relies on traditional molecular diagnostic methods, but synthetic pathogens may evade detection, necessitating ongoing advancements in detection techniques. Real-time sensors and biosensors offer promising avenues for quick and accurate pathogen recognition, although challenges remain in detecting novel pathogens against a natural background. In conclusion, understanding the range of biological weapons, their potential targets, deployment methods, and detection strategies is essential for effective biosecurity. Continued research and advancements in detection technologies are crucial to staying ahead of evolving biological threats, ensuring a safer and more secure future.

Real-time sign language detection: Empowering the disabled community

Interaction and communication for normal human beings are easier than for a person with disabilities like speaking and hearing who may face communication problems with other people. Sign Language helps reduce this communication gap between a normal and disabled person. The prior solutions proposed using several deep learning techniques, such as Convolutional Neural Networks, Support Vector Machines, and K-Nearest Neighbors, have either demonstrated low accuracy or have not been implemented as real-time working systems. This system addresses both issues effectively. This work extends the difficulties faced while classifying the characters in Indian Sign Language(ISL). It can identify a total of 23 hand poses of the ISL. The system uses a pre-trained VGG16 Convolution Neural Network(CNN) with an attention mechanism. The model's training uses the Adam optimizer and cross-entropy loss function. The results demonstrate the effectiveness of transfer learning for ISL classification, achieving an accuracy of 97.5 % with VGG16 and 99.8 % with VGG16 plus attention mechanism.•Enabling quick and accurate sign language recognition with the help of trained model VGG16 with an attention mechanism.•The system does not require any external gloves or sensors, which helps to eliminate the need for physical sensors while simplifying the process with reduced costs.•Real-time processing makes the system more helpful for people with speaking and hearing disabilities, making it easier for them to communicate with other humans.

Artery of Percheron infarct – An ophthalmologist’s perspective

Thalamus is an important relay station in diencephalon which receives its blood supply mostly from the posterior cerebral artery. The Artery of Percheron (AOP) arises from the posterior cerebral artery and bifurcates to supply paramedian thalami. AOP infarction, though rare, presents with a triad of clinical features including altered mental status, memory impairment, and supranuclear vertical gaze palsy. Our patient had concurrent manifestation of vertical gaze palsy (both upward and downward) and third cranial nerve palsy in the left eye, which is an interesting clinical amalgamation. Our case report elucidates the quick recognition of the abnormalities in ocular motility through a simple, yet effective nine gaze ocular motility evaluation and thereby enhancing clinical recognition of this entity among the practicing general Ophthalmologists.

Case report on confusion: an unusual presentation of lung cancer

Introduction: Cancer of the lung can be insidious and manifest clinicallyin relatively unexpected forms. The incidence of hyponatremia in lungcancer is 18.9% [1].Case: An 87-year-old woman presented with confusion. She smoked10–15 cigarettes a day for 70 years. Blood tests done; Na: 118. I made animpression of severe symptomatic hyponatremia secondary to suspectedSiADH from possible small cell lung cancer.I requested CXR, urine Na, paired osmolality, fluid restricted and discussedwith the medical registrar who suggested a transfer to Generalhospital. CXR showed a round mass in the right upper lobe.At the GH, Na was replaced slowly and the patient was planned forCT scans. After 2 days, she developed gradual SOB (? PE). CT scans weredelayed because she could not lie flat and got SOB. ENT and anaesthesiareviewed her and suggested supported airway CT under GADay 5 – Distended neck veins? SVCO. Anaesthetists were no longercomfortable with GA. Unfortunately, steroids did not make her morecomfortable. DNAR in place, family informed of poor prognosis andrequested fast track discharge with palliative support. Died at home after18 days.Conclusion: While it is quite unusual for a respiratory condition to presentwith confusion, quick recognition, open mindedness, and inter disciplinarycooperation are essential in day-to-day practice.

New Insights into Geometric Morphometry Applied to Fish Scales for Species Identification.

The possibility of quick and cheap recognition of a fish species from a single dermal scale would be interesting in a wide range of contexts. The methods of geometric morphometry appear to be quite promising, although wide studies comparing different approaches are lacking. We aimed to apply two methods of geometric morphometry, landmark-based and outline-based, on a dataset of scales from five different teleost species: Danio rerio, Dicentrarchus labrax, Mullus surmuletus, Sardina pilchardus, and Sparus aurata. For the landmark-based method the R library "geomorph" was used. Some issues about landmark selection and positioning were addressed and, for the first time on fish scales, an approach with both landmarks and semilandmarks was set up. For the outline-based method the R library "Momocs" was used. Despite the relatively low number of scales analyzed (from 11 to 81 for each species), both methods achieved quite good clustering of all the species. In particular, the landmark-based method used here gave generally higher R2 values in testing species clustering than the outline-based method, but it failed to distinguish between a few couples of species; on the other hand, the outline-based method seemed to catch the differences among all the couples except one. Larger datasets have the potential to achieve better results with outline-based geometric morphometry. This latter method, being free from the problem of recognizing and positioning landmarks, is also the most suitable for being automatized in future applications.

Body Bag Cooling with Two Different Water Temperatures for the Treatment of Hyperthermia.

INTRODUCTION: Exertional heatstroke (EHS) is a life-threatening condition that requires quick recognition and cooling for survival. Experts recommend using cooling modalities that reduce rectal temperature (TREC) faster than 0.16°C/min though rates above 0.08°C/min are considered "acceptable." Hyperthermic individuals treated in body bags filled with ice water (∼3°C) have excellent cooling rates (0.28 ± 0.09°C/min). However, clinicians may not have access to large amounts of ice or ice water when treating EHS victims. The purpose of this study was to determine if using a body bag filled with water near the upper limits of expert recommendations for EHS treatment would produce acceptable (>0.08°C/min) or "ideal (>0.16°C/min)" TREC cooling rates or different nadir values.METHODS: A total of 12 individuals (9 men, 3 women; age: 21 ± 2 yr; mass: 74.6 ± 10.2 kg; height: 179.5 ± 9.6 cm) exercised in the heat until TREC was 39.5°C. They lay supine while 211.4 ± 19.5 L of 10°C (Ten) or 15°C (Fifteen) water was poured into a body bag. Subjects cooled until TREC was 38°C. They exited the body bag and rested in the heat for 10 min.RESULTS: Subjects exercised in similar conditions and for similar durations (Ten = 46.3 ± 8.6 min, Fifteen = 46.2 ± 7.8 min). TREC cooling rates were faster in Ten than Fifteen (Ten = 0.18 ± 0.07°C/min, Fifteen = 0.14 ± 0.09°C/min). TREC nadir was slightly higher in Fifteen (37.3 ± 0.2°C) than Ten (37.1 ± 0.3°C).DISCUSSION: Body bag cooling rates met expert definitions of acceptable (Fifteen) and ideal (Ten) for EHS treatment. This information is valuable for clinicians who do not have access to or the resources for ice water cooling to treat EHS.Miller KC, Amaria NY. Body bag cooling with two different water temperatures for the treatment of hyperthermia. Aerosp Med Hum Perform. 2024; 95(4):194-199.

Prevalence and risk factors analysis of postpartum depression at early stage using hybrid deep learning model

Postpartum Depression Disorder (PPDD) is a prevalent mental health condition and results in severe depression and suicide attempts in the social community. Prompt actions are crucial in tackling PPDD, which requires a quick recognition and accurate analysis of the probability factors associated with this condition. This concern requires attention. The primary aim of our research is to investigate the feasibility of anticipating an individual's mental state by categorizing individuals with depression from those without depression using a dataset consisting of text along with audio recordings from patients diagnosed with PPDD. This research proposes a hybrid PPDD framework that combines Improved Bi-directional Long Short-Term Memory (IBi-LSTM) with Transfer Learning (TL) based on two Convolutional Neural Network (CNN) architectures, respectively CNN-text and CNN audio. In the proposed model, the CNN section efficiently utilizes TL to obtain crucial knowledge from text and audio characteristics, whereas the improved Bi-LSTM module combines written material and sound data to obtain intricate chronological interpersonal relationships. The proposed model incorporates an attention technique to augment the effectiveness of the Bi-LSTM scheme. An experimental analysis is conducted on the PPDD online textual and speech audio dataset collected from UCI. It includes textual features such as age, women's health tracks, medical histories, demographic information, daily life metrics, psychological evaluations, and ‘speech records’ of PPDD patients. Data pre-processing is applied to maintain the data integrity and achieve reliable model performance. The proposed model demonstrates a great performance in better precision, recall, accuracy, and F1-score over existing deep learning models, including VGG-16, Base-CNN, and CNN-LSTM. These metrics indicate the model's ability to differentiate among women at risk of PPDD vs. non-PPDD. In addition, the feature importance analysis demonstrates that specific risk factors substantially impact the prediction of PPDD. The findings of this research establish a basis for improved precision and promptness in assessing the risk of PPDD, which may ultimately result in earlier implementation of interventions and the establishment of support networks for women who are susceptible to PPDD.

Abstract WP46: Using a Nursing Focused Approach to Decrease Thrombolytic Administration Times

Care of a stroke patient is time dependent as well as the benefits of thrombolytic administration. An interdisciplinary subgroup was created to evaluate the Emergency Department process for code strokes and administration of thrombolytics. This groups initiative was to improve door to needle time for eligible stroke patients. An opportunity was identified to improve thrombolytic administration times by identifying and creating a timer keeper role as well as having a nursing code stroke checklist. A form was developed for nursing staff which included stroke metrics as well as time goals for each metric. Face to face education was completed with all Emergency Department Nurses in April 2023 on code stroke metrics, timekeeper role, and use of nursing code stroke checklist. Outcome of this project was a significant decrease in thrombolytic administration times in the Emergency Department. Data was collected on all alteplase cases 12 months prior to implementation and four months post implementation. Results showed immediate improvement with a 42% reduction in door to thrombolytic times after implementation of the new timekeeper role and checklist. This improvement included achieving the hospitals first door to thrombolytic time under 30 minutes. Use of the nursing code stroke checklist and use of timekeeper has increased awareness to minutes passed since arrival, resulting in reduced door to thrombolytic times for stroke patients. Education continues with new staff, and staff continue to strive for even quicker times. Improvement in treatment times was possible due to staff understanding the importance of quick recognition and treatment for our stroke patients as well as having a designated timekeeper during the code stroke process.

SSVEP-Enhanced Threat Detection and Its Impact on Image Segmentation

Selective attention, essential in discerning visual stimuli, enables the identification of threats such as snakes—a prime evolutionary influence on the human visual system. This phenomenon is encapsulated in snake detection theory (SDT), which posits that our ancestors' need to recognize these predators led to specialized perceptual abilities. This investigation utilizes steady-state visual evoked potentials (SSVEP) alongside the random image structure evolution technique, which systematically increases visual clarity through the interpolation of random noise, to probe the neural mechanisms underpinning selective attention, with a focus on serpentine forms. These findings underscore snakes' unique perceptual impact due to their curved forms and scaled textures, crucial for quick recognition—advancing image semantic segmentation and recognition tech.. This is particularly relevant for security and wildlife management, showcasing the evolutionary progression and cognitive prowess of the human visual apparatus.

DNA barcoding and morphological identification of Astragalus in Iran: Advantages to molecular approach

Astragalus is a crucial forage plant in the rangelands of northeastern Iran. However, identifying different species of Astragalus can be challenging due to their overlapping morphological features. In this study, we aimed to determine the relationships between 12 Astragalus species using both morphological and molecular traits. We also evaluated the effectiveness of DNA barcoding as a tool for identifying rangeland species of Astragalus in Northeast Iran. We examined five DNA barcodes, including three cpDNA regions (trnH-psbA, rpl32-trnL(UAG), and matK) and two nuclear sequences (ITS and ETS). We found that the plastid markers (rpl32-trnL(UAG, matK) were the most effective in differentiating between species. We also found that morphological data, as represented by the Neighbor-Net network and UPGMA dendrogram, had the potential to separate Astragalus species. We identified matK as the best and most accurate marker for barcoding, as trnH-psbA had some defects due to sequence size and alignment issues. Our study highlights the importance of using barcoding for quick and accurate recognition of plant species and shows the plastid markers were the most effective in differentiating between species.

Fake Currency Detection

Due to the great technological developments in the field of color printing in the past few years, it is becoming increasingly recognized that counterfeiting is a serious problem. It used to be possible and very simple for anyone to quickly prepare and print counterfeit currency notes using a computer and a laser printer at homes or places of employment. In the past, only printing houses had these facilities. The most crucial issue is now how to accurately distinguish fake currency from real currency using automatic machines. Almost all nations struggle greatly with the issue of counterfeit currency notes. But since counterfeiting has become such a pressing issue in India, it is thought to be the most serious issue there. Therefore, it is necessary to create a module that will aid in the quick and efficient recognition and detection of paper currency notes. An approach for the identification and verification of Indian currency is described in this proposed system. The authenticity of the currency will be verified using image processing methods, regardless of whether it is genuine.

Choking in children: causes, prevention and intervention strategies.

Choking episodes in children are a significant public health problem that can lead to serious consequences if not addressed quickly and effectively. Early diagnosis, appropriate treatment and prevention are key to ensuring the safety of children. This review article aims to comprehensively examine the causes, symptoms, diagnosis, and intervention methods of choking in the pediatric population. The review methods included the analysis of scientif i c publications located in databases such as PubMed and scientif i c journals, including meta-analyses, randomized trials and systematic reviews regarding the scope of the problem of choking in children, excluding case reports. Choking is most often caused by the aspiration of foreign objects such as small toys, food (e.g. grapes, nuts, pieces of meat) and other small objects that children often put into their mouths out of curiosity. Children aged 1 to 4 are particularly vulnerable as they have a natural tendency to explore their surroundings using their mouths. Symptoms of choking may include sudden difficulty breathing, intense coughing, wheezing, cyanosis and loss of consciousness. Quick recognition of symptoms is crucial to prevent serious consequences, such as cerebral hypoxia or cardiac arrest. In diagnostics, it is also important to take a thorough history and use imaging tests, such as X-ray or bronchoscopy, to locate and remove the foreign body. This article seeks to better understand the factors contributing to choking in children and provide the latest evidence-based recommendations for prevention and intervention.

Parameter Identification of Permanent Magnet Synchronous Motor with Dynamic Forgetting Factor Based on H∞ Filtering Algorithm

To address system parameter changes during permanent magnet synchronous motor (PMSM) operation, an H∞ filtering algorithm with a dynamic forgetting factor is proposed for online identification of motor resistance and inductance. First, a standard linear discrete PMSM parameter identification model is established; then, the discrete H∞ filtering algorithm is derived using game theory reducing state and measurement noise influence. A cost function is defined, solving extremes values of different terms. A dynamic forgetting factor is introduced to the weighted combination of initial and current measurement noise covariance matrices, eliminating identification issues from different initial values. On this basis, a dynamic forgetting factor is added to weigh the combination of the initial measurement noise covariance matrix and the current measurement noise covariance matrix, which eliminates the influence of the discrimination error caused by the different initial values. Finally, the identification model is built in MATLAB/Simulink for simulation analysis to verify the feasibility of the proposed algorithm. The simulation results show the proposed H∞ filtering algorithm rapidly and accurately identifies resistance and inductance values with significantly improved robustness. The forgetting factor enables quick stable recognition even with poor initial values, enhancing PMSM control performance.

A Novel Framework for Detection of Harmful Snakes Using YOLO Algorithm

The spotlight of our discussion here is on developing a model for detecting snakes present in the agricultural fields using cameras and machine learning algorithms. The idea is to construct a machine learning model to identify the snakes, which will be trained on a vast set of images of snakes. These images will be carefully selected to include the snake patterns, skin color of different species of snakes (extraction of the unique features). This is done to train the model properly to identify different types of snakes that the farmer encounters in the fields. Most famers are unaware and have a need for a way to detect these snakes so that they can either avoid them or get rid of them. Hence, this system can be implemented to prevent thousands of deaths and can help famers alleviate the repercussions of snake bites. Mistakes in recognizing potentially harmful animal species based only on visual cues are major contributors to the high death toll from venomous animal attacks. Since they spend so much time in the fields, where rice and wheat are cultivated, farmers are at a higher risk of being bitten by a snake than the general population. Because of their ignorance, illiterate farmers are more inclined to believe in superstitions, which can lead to their untimely deaths from snakebites despite medical intervention. Animals that would normally pose no threat to humans are responsible for the deaths of thousands of people every year because of environmental factors. However, because it is hard for humans to recognize these dangers, a new design paradigm has been developed to make it simpler. Researchers in the field of animal biology can use it to search for endangered species. Predators may enter gardens and green areas like tea and coffee plantations. There is not yet a plan in place to implement automated sorting for discovering distinctions. By applying the suggested framework to photographs of potentially dangerous animals, several factors useful for studying animal organization may be easily identified. For the classifier to run in real-time, it is suggested that YOLO be utilized for double processing within the framework. Furthermore, the YOLO technique enables quick animal recognition in a similar fashion. First, the YOLO method will be used to determine whether a snake is present in the proposed work. Here, 87 percent precision is also reached in the detection of snakes.

A lightweight efficient semantic segmentation with encoder-decoder for arc interference in robotic arc welding

Recognizing laser stripes under conditions of arc interference is a persistent challenge faced by linear structured light sensors due to their similarity with noise in morphological characteristics. In this paper, a symmetrical encoder-decoder semantic segmentation model named LSRNet (lightweight stripe recognition network) is proposed that enables real-time detection of laser stripes in environments with strong arc interference. It analyzes the difference between arc and laser stripe from the semantic point of view. Firstly, to facilitate quick and accurate recognition of laser stripes, we designed a Depthwise-separable Factorized Module that is both efficient and scalable with a larger receptive field. Moreover, due to the difficulty in distinguishing laser stripes from arcs in a local context, a Multi-scale Depthwise-separable Factorized Module is designed to capture more context information. Secondly, the Unified Attention Fusion Module is used to enhance the feature representation of the laser stripe. Additionally, The combination of DiceLoss(dice coefficient loss) and CELoss(cross-entropy loss) is introduced to optimize the loss, which addresses the pixel imbalance challenge that the pixels of the laser stripe are much less than the background pixels. Finally, based on the images collected in the industrial field, our ArcDataset is created for model training. Experimental results show that the proposed method achieves the highest intersection over union of 88.0% in laser stripe segmentation and a recognition speed of 101.3 FPS(frames per second).

Safety of cardiopulmonary exercise testing in a large contemporary cohort of adults with congenital heart disease: implications on levels of test supervision

Abstract Background Exercise intolerance is common in adults with congenital heart disease (ACHD). Cardiopulmonary exercise testing (CPET) is used routinely as an objective assessment of the exercise capacity, the severity and mechanisms of exercise intolerance, and the effect of intervention. Little is known on the safety of CPET in ACHD, resulting in the American Heart Association recommending physician supervision of all patients with intracardiac shunts. Purpose To assess the safety of CPET in ACHD. Methods We retrospectively examined 9309 CPETs performed on ACHD patients in a tertiary referral Centre over 20 years to document major or minor events. All tests were performed in-hospital, with an emergency alarm system on site, and supervised routinely by 2 clinical exercise physiologists. Major events were classified as death within 48 hours and cardiac arrest, syncope, sustained/symptomatic ventricular tachycardia (VT) and sustained/symptomatic supraventricular tachycardia (SVT) during the test. Minor events were classified as non-sustained/asymptomatic VT, non-sustained/asymptomatic SVT, bradyarrhythmia, chest pain and significant ST segment ECG changes, new onset of left or right bundle branch block). Results During the study period, 9309 CPETs were performed on 4941 ACHD patients (age 33.0±14.7 years, 52.3% male), of varying complexity by Bethesda classification (18.3% simple, 51.2% moderate, 27.4% great) Figure 1. A major event occurred in 56 (0.6%) tests and a minor event in 870 (9.3%) tests. There were no deaths within 48 hours, nor cardiac arrests during the test. There were 6 (0.1%) cases of sustained or symptomatic VT, 4 (<0.1%) syncopal episodes and 46 (0.5%) instances of sustained or symptomatic SVT. A total of 31 (0.3%) episodes of bradyarrhythmia, 377 (4%) significant ECG changes and 267 (2.8%) episodes of chest pain were recorded. Moreover, there were 107 (1.2%) cases of non-sustained or asymptomatic VT and 88 (1%) cases of non-sustained or asymptomatic SVT. Patient fatigue (3701, 40%), shortness of breath (2246, 24.1%) and leg discomfort (1286, 13.8%) were common reasons for stopping the test, whereas only 95 (1%) tests were stopped due to chest pain. Conclusions Our data on a large ACHD population undergoing CPET demonstrate a potential risk for arrhythmic complications during exercise and recovery, mostly due to sustained or symptomatic SVT, while the risk of all the other major cardiovascular events is low. Our data support the use of CPET in routine ACHD practice, with quick recognition and management of potential adverse events in a controlled environment by appropriately trained cardiac physiologists.

A smartphone-based centrifugal mHealth platform implementing hollow daisy-shaped quick response chip for hematocrit measurement

Due to the ever-increasing challenge of emerging and reemerging infections on global health, the development of POCT tools has been propelled. However, conventional point-of-care testing methods suffer from several limitations, including cumbersome operation, long detection times, and low accuracy, which hamper their widespread application. Compared to traditional disease diagnostic equipment, mobile health platforms offer several advantages, including portability, ease of operation, and automated analysis of detection results through recognition algorithms. Consequently, they hold great promise for the future. Here, we developed a smartphone-based centrifugal mHealth platform implementing daisy-shaped quick response chip for hematocrit measurement. The centrifugal microfluidic chip is combined with a smartphone through a back-clip-on mobile phone adapter whose control circuit is designed with low power consumption to enable the platform to operate without requiring a high-power source that is inconvenient to carry, thereby achieving the goal of portability. Concurrently, we designed a quick response chip featuring a unique hollow daisy structure that is in line with the properties of hematocrit detection. The distinctive configuration of the chip enables adequate centrifugal force to be supplied for hematocrit detection. Additionally, our customized quick response code recognition algorithm is able to recognize this chip, facilitating non-experts in performing hematocrit intelligent recognition with their smartphones.

A Machine Learning Based Computational Method for Prediction of Functional SNPs in Rice Genome

<p>Single nucleotide polymorphisms (SNPs) are the most prevalent and stable class of genetic diversity that exist in most organisms. Functional SNPs are the most commonly used genetic markers for diversity study and molecular breeding in plants, and their quick recognition is in urgent demand. In this work, a computational approach to identify functional SNPs in rice genome based on machine learning is presented. To characterize and prioritize variants, two different categories of features, the nucleotide-sequence based features and the allele-specific based features, are extracted. In particular, the weighted Euclidean distance is employed to measure the changes of the transcription factors (TFs) binding affinities caused by SNPs. To deal with the classification problem on unbalanced data, the support vector machine (SVM) together with an oversampling method is employed. We use mRMR to find the optimal feature set, and the result shows that our method can achieve accuracy with sensitivity of ~74.2% and specificity of ~72.3% after 10-fold cross-validation. Furthermore, the sources of data to build the proposed prediction model are mainly sequence context of SNP and TF profiles in JASPAR database, which are all easy to be acquired. So, the prediction method can be easily applied to other plant species.</p> <p> </p>

Турнікет-синдром у дітей (огляд літератури та власне спостереження)

Tourniquet syndrome is a complex of pathological changes that occur in response to strangulation circular compression of part/parts of the body or organ, characterized by neurovascular disorders that can lead to the development of necrosis and gangrene. Strangulation of soft tissues can occur with various fibrous materials - wool, cotton, various cords, jewelry, Christmas tree decorations. In children, the most common cause of strangulation is hair and threads, in adults - jewelry. Purpose - to draw the attention of the medical community to a rare pathology, the tourniquet syndrome in children, which, despite its rather characteristic picture, is not always immediately recognized, which can lead to serious consequences. The leading etiological factor in the development of tourniquet syndrome can be postpartum telogen outflow in mothers, which is manifested by a large amount of hair prone to loss. The authors recognize the toes as the most frequent localization of the strangulation hair bundle in tourniquet syndrome, the II and III toes of the feet and hands are more often caught in the loop. Also, it is not uncommon for several fingers to be entwined. With tourniquet syndrome, a complication in the form of necrosis of the fingers, which requires their amputation, is about 7% of all cases of finger and foot pathology. Quick recognition and removal of the tourniquet for genitaltourniquet syndromeprevents devascularization of the clitoris and penis and their possible self-amputation. Conclusions. Hair bundle syndrome, or tourniquet syndrome, most often occurs in young children and can have serious consequences. To prevent this phenomenon, children’s clothes should be checked for the presence of threads and loops on the inside, the presence of piles of hair and threads in the “blind” parts of the clothes, turn the clothes inside out before washing, which helps prevent the accumulation of future bundles. It is also recommended that mother and child sleep separately. In case of injuries of the upper limbs, it is recommended to know jewelry from the fingers. Alertness about tourniquet syndrome will help avoid serious complications in children. It should also be remembered about the intentional cruel treatment of children with the imposition of potential bounties. No conflict of interests was declared by the authors.

American Sign Language Recognition and Generation : A CNN-based Approach

Although not a global language, sign language is an essential tool for the deaf community. Communication between these communities and hearing population is severely hampered by this, as human-based interpretation can be both costly and time-consuming. In this paper, we present a real-time American Sign Language (ASL) generation and recognition system that makes use of Convolutional Neural Networks and deep learning (CNNs). Despite differences in lighting, skin tones, and backdrops, our technology is capable of correctly identifying and generating ASL signs. We trained our model on a large dataset of ASL signs in order to obtain a high level of accuracy. Our findings show that, with accuracy rates of 98.53% and 98.84%, respectively, our system achieves high accuracy rates in both training and validation. Our approach uses the advantages of CNNs to accomplish quick and precise recognition of individual letters and words, making it particularly effective for sign fingerspelling recognition. We believe that our technology has the ability to transform communication between the hearing community and the deaf and hard-of-hearing communities by providing a dependable and cost-effective way of sign language interpretation. Our method could help people who use sign language communicate more easily and live better in a range of environments, including schools, hospitals, and public places.