Background: Gastric cancer is one of the deadliest malignant diseases, and the non-invasive screening and diagnostics options for it are limited. In this article, we present a multi-modular device for breath analysis coupled with a machine learning approach for the detection of cancer-specific breath from the shapes of sensor response curves (taxonomies of clusters). Methods: We analyzed the breaths of 54 gastric cancer patients and 85 control group participants. The analysis was carried out using a breath analyzer with gold nanoparticle and metal oxide sensors. The response of the sensors was analyzed on the basis of the curve shapes and other features commonly used for comparison. These features were then used to train machine learning models using Naïve Bayes classifiers, Support Vector Machines and Random Forests. Results: The accuracy of the trained models reached 77.8% . The use of the proposed shape-based features improved the accuracy in most cases, especially the overall accuracy and sensitivity. Conclusions: The results show that this point-of-care breath analyzer and data analysis approach constitute a promising combination for the detection of gastric cancer-specific breath. The cluster taxonomy-based sensor reaction curve representation improved the results, and could be used in other similar applications. Gastric cancer ranks fourth among the most common cancers worldwide. It shows a lack of specific symptoms in the early stages, and is commonly characterized by late diagnosis, poor prognosis, and likely relapse . The earBreathomics is a branch of metabolomics that helps us to diagnose diseases by analyzing volatile metabolites produced by changes in the metabolic processes caused by the disease ,3. So faSensor-based exhaled breath analysis has shown significant promise for early gastric cancer detection in scientific and clinical practice because of its high accuracy, low cost, non-invasiveness, and ease of operation ,9,10,11.Various sensor-based electronic noses have been used in recent years to complement GC-MS for the detection and differentiation of various types of cancers, including gastric cancer ,15,16,17In this work, we report the diagnostic performance of a modular point-of-care breath analyzer with gold nanoparticle (GNP) and two different types of metal oxide (MOX) semiconductor sensors for the detection and identification of gastric cancer in an online mode that requires no additional breath collection procedures or laboratory settings. The proposed device was built on the basis of previous studies in laboratory settings , measureThe study was approved by the Ethics Committee of the Riga East University Hospital Support Foundation . All of the participants provided signed consent before entering the study.-known active malignant diseases other than gastric cancer,-ongoing neoadjuvant chemotherapy,-a history of stomach surgery (except vagotomy and ulcer suturing),-inflammatory bowel disease (Crohn’s disease and ulcerative colitis),-end-stage renal insufficiency,-diabetes mellitus type I,-active bronchial asthma, and-a history of small bowel resections.Patients who had reached at least 18 years of age and were able to undergo a breath exam were included, and signed a consent form. The following exclusion criteria were applied:Patients with morphologically confirmed gastric adenocarcinoma were included in the study prior to their gastric surgery. A control group (patients without gastric cancer) was included prior to upper endoscopy. Histological reports were assessed after the examination, and the final grouping was performed after that.-fast for at least 12 h;-refrain from drinking coffee, tea and soft drinks for at least 12 h;-refrain from smoking for at least two hours;-avoid alcohol for at least 24 h;-do not clean your teeth within two hours before the procedure ;-avoid chewing gum and using any mouth fresheners for at least 12 h;-refrain from using cosmetics/fragrances on the day of the test prior to the procedure;-avoid excessive physical activity  for at least two hours prior to the test.After their inclusion in the study, the subjects were given specific instructions to follow before the measurements were made in order to minimize the effects of any strong and potentially interfering VOCs. They were informed by telephone or in writing at least 24 h prior to the planned procedure in order to ensure compliance with the requirements. The instructions included the following restrictions:The study participants were invited to Latvia Oncology Centre, Riga East University Hospital , where a separate room was designated solely for breath sample measurements in order to reduce the risk of contamination with other VOCs. The control and cancer group subjects were included randomly during the same time period (starting November 2019). A lifestyle and medical history questionnaire, including potential confounding factors, was completed prior to the breath sampling. After the questionnaire, the participants exhaled into a table-top device that analyzed their breath immediately after the exhalation.The point-of-care breath analyzer used in this study was a modular breath analyzer prototype developed by JLM Innovation GmbH , volatile sulfur compounds, CO2, and breath VOC mixtures.The GNP sensors were functionalized with eight organic ligands to enhance their affinity for gastric cancer VOCs. These ligands were selected from a reservoir of 40, following a previous study carried out on more than 1000 clinical breath samples . The orgIn order to take a measurement, the device was connected to a computer and the measurement process was controlled from a computer-based graphical interface. Before the measurement began, medical personnel entered metadata, and a sample of the room air was taken and measured automatically to provide data from the sensor response to any potential background VOCs. Then, the patient was notified to prepare for exhalation by a message on the computer screen and flashing lights on the device. The patient exhaled into the device, and the last part of the breath was pumped into the sensor chamber and analyzed. The metadata and the sensor responses to the room air and the exhaled air were saved in a single JSON file on the computer.The raw breath measurement data were first preprocessed to exclude any faulty measurements, equalize the lengths of the measurements, and clean the data, as described in Before the sensor response curves were processed, measurements that lacked some of the sensor readings owing to technical problems were excluded from the analysis (ten gastric cancer patient and two control group measurements). Then, the measurements (one time series per sensor) were preprocessed by equalizing the measurement lengths (some sensors had extra time points at the beginning or end of the measurement because of the measurement specifics) such that they could be analyzed using methods that require equal-length time series. The readings were normalized against the final values of the baseline measurements  when the sensor response had stabilized in order to remove the effects of the environmental air and the VOCs present in it. All of the measurements were also examined in order to identify outliers by projecting the observations to the principal components, calculating orthogonal and score distances, and evaluating them as proposed by Rodionova and Pomerantsev . The medThis resulted in one preprocessed time series of the sensor reactions to the breath for each sensor type. The GNP, analogue MOX, and digital MOX sensor responses (and common features) are shown in The preprocessing was carried out in order to prepare the sensor response curves in the classification model training. They are usually analyzed using the features of the curve, including the steady-state response and transient responses such as the minimum, mean or maximum values, or the area under the curve ,29. HoweCurve shape taxonomies holding the curves that can potentially be used for classification were built by repeatedly clustering the measurement curves into k similar groups and identifying the characteristic shape of each cluster. The number of the groups could not be defined precisely before the analysis; as such, we used hierarchical clustering and cuts in the resulting dendrograms to generate 2–10 clusters, all of which were used to create a hierarchical taxonomy  of the gThe clustering was performed by the hierarchical agglomerative method, using dynamic time warping (DTWARP) and Euclidean distances, each being tested by two different linkage methods: complete and Ward linkage.d is calculated by comparing the distances of every two points  of the two time-series (TS1 and TS2):Euclidean distance was chosen because it is the most common distance metric. The distance DTWARP  was usedThe linkages chosen for this study have previously shown their strength in the creation of clusters with more similar sizes, seldom creating small clusters with a few data points that are further away from other points on the attribute axes but are not considered outliers. Ward’s distance minimizes the variance of data points in the clusters, while the complete distance considers the maximum distance between two clusters.The results of the clustering  representing the naturally-occurring groups of sensor responses were then used as input data in classifier induction to discriminate between the control and cancer classes. The membership of each breath to a cluster (at each level in the taxonomy) was used as a separate attribute. Different approaches can be used to determine the optimum number of clusters and the necessary cut in the taxonomy. Some classification algorithms have built-in feature selection procedures . If the method of choice lacks a built-in option, the cut can be determined on the basis of expert choice, distances among clusters and other dissimilarity evaluation metrics, or feature selection algorithms. In our study, we chose the feature selection approach to ensure the best cluster sets for classification. In order to find the best cuts, we applied the following feature selection algorithms: Information Gain, ReliefF, and symmetrical uncertainty. Information Gain  is a meThe selected features were then used in classification  was run through the feature selection, classification model training and testing cycle 1000 times. Each run was evaluated using metrics such as overall accuracy , sensitivity (the percentage of the correctly identified cancer group), specificity (the percentage of the correctly identified control group) and the area under the ROC curve (AU-ROC). The accumulated data were used to calculate the mean values and the 95% confidence intervals (95% CI) of the results. The statistical significance of the differences was assessed using ANOVA with Bonferroni post-hoc analysis.The different sensor types gave very different results when analyzed separately. Gold nanoparticle sensors showed high overall accuracies, up to 77.61%, but their sensitivity did not surpass 55% (their specificity was 89%). The overall accuracy of analogue MOX sensors reached 69.94%, but their sensitivity was low (46%), and their specificity reached 83%. The overall accuracy of the digital MOX sensors was up to 67.88%, with rather low sensitivity (47%) and specificity (79%).When all of the sensor modules were used for the classification, the results showed that the overall accuracy of the most accurate classification algorithm (Naïve Bayes) was over 70% (at best 77.81%), with sensitivity ranging from 46.9% to 66.54%, and specificity ranging from 83.64% to 85.27% depending on the features selected  and sensitivity (~10 percentage points) and AU-ROC, while the specificity remained similar to the specificities obtained using the more common features.The overall accuracy  and their characteristic shapes  is given in The second-best results were acquired using Random Forests as classifiers . These would make breath analysis more accessible, and the detection process quicker ,44,45,46Although many approaches and sensor materials have been designed and used to detect gastric cancer through exhaled breath, most of the previous solutions have been either technically complicated and relatively expensive, or insufficiently functional. Clinical diagnosis based on the detection of VOCs faces several challenges related to aspects of the sensor technology, mostly on the following major fronts: complexities of metabolism and VOC kinetics in a multianalyte system, the inter/intra-person variability of VOC profiles in such a complex environment, the standardization of sensor calibration owing to inherent sensor-to-sensor variability, and sensor drift and cross-sensitivities to environmental variables such as temperature. Such limitations of breath sensor technology are addressed in the current study: the proposed device includes not only GNP but also metal oxide sensors, which are selective for a number of VOCs and have been tested for their reproducibility and robustness.In this article, we evaluated the diagnostic performance of a novel multi-sensor-based breath analyzer in gastric cancer patients. The device could differentiate between the breath prints of patients with gastric cancer and healthy controls with an overall accuracy of 77.8% . The specificities are higher so that these models can provide a better fit for screening applications, which should minimize the number of healthy patients having to undergo unnecessary invasive diagnostic procedures. Furthermore, the classification experiments with sensors of a single type showed worse results , indicating that additional modules with different sensors supplement the information and improve the differentiation.The proposed novel machine learning approach, which applies taxonomies of sensor response curve shapes instead of single-point or estimate features, improves the accuracy of the classifiers obtained. However, it is important to choose the correct cuts in the cluster taxonomies . In some cases, the gain is lower than that in others; this is not a fit-for-all solution. Furthermore, the best methods for building taxonomies and classification can be different for different datasets and goals; therefore, it is necessary to apply several methods and compare their results in the actual dataset. The best results in this case were achieved using Naïve Bayes classifiers. Although RF provides good specificity, which is crucial for screening, its sensitivities are unacceptably low.The overall results were better than those achieved by Schuermans et al.  using thThe proposed device allows the breath to be analyzed in any location, thus providing more flexibility than laboratory-based approaches. It uses a combination of GNP and metal oxide sensors, and provides good and stable accuracy. Paired with the suggested data analysis methods, it can provide a quick and accurate technique for the detection of gastric cancer-specific breath, and potentially for other applications, e.g., the detection of other cancers, the monitoring of the course of diseases, and population-based screening.


Kumbh Mela is a significant religious gathering of millions of Hindu devotees in India. It is celebrated on certain auspicious days in the Hindu calendar and attracts millions of pilgrims across the country. Despite the religious intention of millions of Hindu devotees, it raised public health concerns as it became a massive superspreading event for COVID-19. Being the second most populous country, India became the second most affected country during the COVID-19 pandemic. In addition to the arrival of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) variants and the presence of the double mutated variant, which was first identified in India, the Kumbh Mela probably aggravated the country’s COVID-19 situation which resulted in an uncontrollable second wave. Several cases of COVID-19 across India had been contact-traced to returnees from the event who acted as a nidus to help spread the infection. As a consequence, India’s healthcare system was severely challenged as a result of the overwhelming hospitalizations and increasing fatalities resulting in an acute manpower shortage in healthcare along with the depletion of drugs and medical supplies despite being one of the largest pharmaceutical hubs globally. Leaders and governments around the world should learn from India’s experience and thereby take preventive measures to manage potential superspreading events to curb the spike of COVID-19 cases.The  Kumbh Mela or the “festival of pitcher,” a very significant religious gathering of millions of Hindu devotees and sages at the country’s riverbanks on April 1 to April 30, 2021, despite the threat of exponential surge of coronavirus disease 2019 (COVID-19) cases in the country.,,India, a predominantly Hindu country that occupies the greater part of South Asia, recently celebrated the ,Kumbh Mela still attracted millions of Hindu pilgrims across the country because of its religious significance and cultural traditions.This grand religious gathering is usually celebrated on certain auspicious days in the Hindu calendar, which typically commences in January. However, because of the pandemic, the Indian government opted to postpone the event to April and shorten the festival duration to only 30 days from the usual, which is 100 days or more.Kumbh Mela. Since the fight ensued for 12 divine days, this is tantamount into 12 worldly years. Thus, four Kumbh Mela events are celebrated at four different places within a cycle of 12 years in India. These places are namely the Prayagraj in the state of Uttar Pradesh and Haridwar in the state of Uttarakhand both of which are situated on the banks of the river Ganges, along with Ujjain in Madhya Pradesh located on the bank of the river Shipra and at Nashik in Maharashtra positioned on the banks of the river Godavari.According to the Hindu scriptures, the gods and demons engaged in a 12-day fight for a pitcher containing the elixir of life. During the struggle, however, few drops of the elixir spilled on four places of the earth and these places became the venues for the Kumbh Mela raised major public health concerns as it likely became a massive superspreading event for the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). The ritual practice of Snan, wherein the devout immerses oneself in the river water is undertaken, to attain moksha  as the water is believed to be transformed into a sacred elixir. Historically, the ritual was known to pose high concern to health as contamination of water can lead to public health outbreaks such as cholera, dysentery, typhoid, and other water-borne diseases.Kumbh Mela was still celebrated for the whole month of April 2021 with an extremely large number of devotees amid the ongoing pandemic and continued spike of COVID-19 infections in the country. Preventive measures, such as practicing hand hygiene, wearing facemasks, and maintaining physical distance, were also grossly neglected by a majority of attendees.,Kumbh Mela as a superspreader of SARS-CoV-2 was gravely apparent with the growing evidence of spread of infection linked back to the religious gathering.Despite the religious intention of millions of Hindu devotees, the Kumbh Mela were not exceeding the 100,000-mark. However, a few days after its commencement, the daily new cases continuously escalated and eventually exceeded the 100,000-mark. In fact, even during the last day of the superspreading event, the daily new cases unfortunately reached the 400,000-mark for the first time, indicating the second wave’s peak. It can be gleaned that the highest peak, with 414,433 daily new cases, occurred a few days after the Kumbh Mela.The 1-month event was seen as a major culprit for the exponential surge of COVID-19 cases in the country. As depicted in ,,In addition to the recent massive superspreading event, the ongoing second wave of the COVID-19 pandemic was further exacerbated by the presence of the Delta variant, a double mutant variant of SARS-CoV-2, which was first identified in India and recently classified by the WHO as a variant of concern (VOC) because of its high transmissibility and global rapid spread.,,As a result of the second wave of COVID-19, India’s healthcare system was greatly challenged because of the overwhelming hospitalizations and increasing fatalities throughout the country, resulting in a manpower shortage in healthcare and depletion of drugs and medical supplies.Kumbh Mela celebration. Many devotees traveled in crowded trains and other public transportation vehicles. There were also reported accounts of groups of mask-less pilgrims on the riverbanks while singing the glories of the Ganges. As stated by eminent epidemiologists, pilgrims presented an ideal setting for the virus to rapidly spread.Although the first wave of COVID-19 outbreak had been mitigated to an extent through movement restrictions and containment measures, the lockdown was subsequently lifted because of societal and economic pressures following the increased number of new cases leading to a devastating second wave. Despite the government’s efforts in reminding the people of continued observance of protective measures, such as hand hygiene, wearing face masks, observing social distancing, and imposing curfews, many citizens became complacent and exhibited irresponsible behaviors before and during the Kumbh Mela celebration, which became a cause for global concern. Critics attributed the reluctance of the Indian Prime Minister to cancel the mass gathering to the possible backlash from Hindu religious leaders since priests, seers, and ascetics play a significant role in gathering Hindu votes during elections. Uttarakhand’s former chief minister stated that he had initially planned to limit the celebration into a symbolic event as public health experts initially warned of new COVID-19 variants and the continued threat of the disease. However, this plan did not materialize as he was replaced by a new chief minister days before the festival who remarked that the blessings of Ma Ganga or the river goddess would protect them from the virus.Despite the grim state of the country before the onset of the event, there had been no significant talks or measures as to halt or limit the ,,,As the second wave continues to ravage across India, several countries around the world have lent their help and support by sending out vaccines, drugs, oxygen concentrators, ventilators, and other necessary resources despite the shortage and increasing demand of medicines and hospital supplies globally.Kumbh Mela was possibly “the biggest superspreading event” in the pandemic history.Kumbh Mela will most definitely still pose an important public health risk if the pandemic remains. Thus, prevention and mitigation of superspreading events will require quick recognition and understanding of the event that caused or may likely cause a wide transmission of the disease. After recognizing and understanding the dynamics of transmission, governments could then implement better control measures that would best mitigate the risk of widespread transmissions. Implementing public health interventions to prevent and mitigate superspreading events are equally crucial in the containment and mitigation phases.–Public health experts stated that Firstly, all participants must undergo mandatory reverse transcription polymerase chain reaction (RT-PCR) testing, the gold standard for diagnosing COVID-19, to ensure that all are not carriers of SARS-CoV-2. Secondly, the authorities must also limit the number of participants who will be attending the event, which would also help monitor the movement of people. Thirdly, the government, along with the other groups or organizations, should work closely to promote public health messages such as implementing protective measures throughout the juncture. Fourthly, leaders must also invest to ensure that adequate number of screening sites and handwashing stations are present throughout the area of gathering. Fifthly, vulnerable individuals including the elderly and the sick should be encouraged to not participate in person and in cases posing serious risk to the public, strict prohibition can be enforced. Lastly, emergency infrastructures for quarantining the infected must also be planned in preparation for untoward scenarios of overwhelming spread leading to increase in number of cases.Umrah and Hajj pilgrimages in Saudi Arabia and the postponement of Olympic Games in Japan.–Umrah pilgrimage, which was known to gather thousands of pilgrims daily, 2 days after detecting its first COVID-19 case in the country.,Umrah pilgrimage resumed and the annual Hajj pilgrimage commenced. The Saudi government only allowed pilgrims who were aged 20 to 65, healthy, not pregnant, nonreactive to COVID-19 through RT-PCR testing, and quarantined for 2 weeks before the pilgrimage. During their pilgrimage, the participants were assigned in safe “bubbles” with designated tracks and preventive measures. After their pilgrimage, participants underwent another RT-PCR test and quarantined for another 2 weeks before returning home.Some countries have successfully controlled superspreading events during the COVID-19 pandemic. Best examples were the temporary suspension of Furthermore, the Olympic Games in Japan, which was supposed to be held in 2020, was also postponed to 2021.Kumbh Mela as the likely culprit for the surge of COVID-19 cases in India’s second wave presented how complacency in observing COVID-19 preventive protocols and underestimating viral transmission have gone awry. Since this religious gathering depicts how societal and cultural underpinnings play a critical role in people’s behaviors, early engagement of communities and understanding people’s knowledge, attitudes, and practices are of great importance in designing emergency health responses specifically in the preventive and mitigation phases.The abovementioned recommendations and strategies can be emulated by other countries to prevent potential superspreading events such as what happened in India. Unfortunately, the case of Kumbh Mela to celebrate life have ironically faced the grim reality of having to face death themselves or among those around them. The COVID-19 situation in India is surely beyond heartbreaking. Societal beliefs and practices of people may at times be detrimental to themselves and others if observed without attention to public health concerns. As the global community suffers from the pandemic crisis, this article provides a perspective on how societies can help mitigate the further spread of the virus through observing COVID-19 preventive practices. Hence, everyone must remember to continue to remain vigilant with leaders and governments around the world hopefully learning from this ordeal, to prevent potential superspreading events from occurring in the future.It has been quite unfortunate that a large majority of people who attended


Hand dexterity is the ability to execute the skilful movements using the hand and fingers. It is commonly impaired poststroke resulting in a profound deterioration in the quality of life for patients with stroke. Transcranial direct current stimulation (tDCS) is a form of non-invasive brain stimulation, which has gained a popularity as an adjunct therapy in recovering motor dysfunction poststroke. Promising results have been gained from applying tDCS in combination with motor rehabilitation, however, the outcome of tDCS on the upper limb motor function poststroke has been varied. Different results are potentially related to the discrepancy of the area of brain stimulation. Therefore, we aim to enhance the application of tDCS to improve its effectiveness in recovering hand dexterity through testing our hypothesis that stimulating the primary motor cortex could improve fine dexterity more than gross dexterity.This protocol has been reported according to Preferred Reporting Items for Systematic Review and Meta-Analyses Protocols guidelines. CENTRAL, MEDLINE, EMBASE, SCOPUS, Web of Science and CINAHL databases will be searched with no restriction in language and publication date. The selected studies will be randomised controlled trial investigating the effect of tDCS alone or in combination with motor rehabilitation in improving hand dexterity of patients with stroke with upper limb hemiparesis. The outcomes of interest are fine and gross hand dexterity measures. Two independent reviewers will assess the eligibility of the study, extract data and appraise the methodological quality. The data will be pooled in a meta-analysis if applicable or interpreted narratively. Grading of Recommendations, Assessment, Development and Evaluation approach will be used to assess the overall quality of evidence for the fine and gross dexterity measures.Ethical approval is not required for this study. The dissemination plan is to publish the results in a peer-review journal and presenting results in a conference.CRD42021262186. This study will include the three stages of recovery of patients with stroke.The search strategy designed to access both published and non-published studies.The search strategy is not limited to publication date or language.This study will be limited to the randomised controlled trials.Stroke is a common cause of disability in the UK.8et al conducted a prospective cohort study,2However, patients with stroke with upper limb hemiparesis demonstrate dexterity dysfunction. They perform movements in a slow, uncoordinated manner because their hand movements lack accuracy and proper finger sequencing.Stroke rehabilitation involves assessing and improving hand dexterity in patients with stroke in order to improve upper limb function.17 18A recent development has been the use of transcranial direct current stimulation (tDCS), a form of non-invasive brain stimulation. tDCS device includes a portable electrical current generator and two electrodes: cathode and anode, and works by delivering a low intensity constant electrical current to the brain that alters the cortical excitability.12 19 23 24Studies exploring the effect of stroke on the brain using functional MRI have observed decreased neuronal activity of the ipsilesional side few days poststroke.27 28Researchers have investigated factors to enhance the application of tDCS to increase its effectiveness, for example, the area of brain stimulation and the orientation of the electrodes.29tDCS has shown potential effectiveness in improving upper limb function poststroke when conducted both aloneet alet al found that the fine movements, like picking up paper clips, are more improved than gross movement like moving cans. This might be because the distal muscles of the upper limb receive higher input from the PMC  than the proximal muscles.33Given the essential role of the upper limbs in daily life, the recovery of upper limb motor function poststroke is an important issue for stroke rehabilitation community.Moreover, work from our laboratoryWe will conduct this systematic review and meta-analysis to enhance the tDCS application, therefore optimising its therapeutic benefit in upper limb motor recovery poststroke. We will throughly investigate if there are differences in the recovery between fine and gross hand tasks. Specifically, we will investigate our hypothesis that stimulating the PMC using tDCS could improve the fine hand dexterity more than the gross dexterity because the fine hand tasks mainly require fingertip grasping while the gross hand tasks involve a combination of proximal arm movement with hand grasping.6This research aims to answer our research question “what is the difference in the tDCS effect between fine and gross hand dexterity of stroke patients with upper limb hemiparesis?”Specific objectives are:To evaluate the effectiveness of tDCS on fine hand dexterity of stroke patients with upper limb hemiparesis.To evaluate the effectiveness of tDCS on gross hand dexterity of stroke patients with upper limb hemiparesis.To compare the effect of tDCS between fine and gross hand dexterity.To investigate the impact of patient characteristics on the effect of tDCS.This protocol has been reported according to Preferred Reporting Items for Systematic Review and Meta-Analyses (PRISMA) Protocols guidelines.36Randomised controlled double-blinded or single-blinded trials. Using either parallel or crossover design.English and non-English studies.Full-text studies.Published and unpublished studies.Non-randomised clinical trial.Conference abstract.The participants needed to be:Patients with stroke aged 18 years and above, both male and female patients with upper limb hemiparesis.Diagnosed with ischaemic or haemorrhagic stroke, either cortical or subcortical.In the acute (1–7 days poststroke) or subacute (7 days to 6 months poststroke) or chronic phase (6 months or more poststroke) when the intervention is applied.37Suffering from mild or moderate or severe upper limb impairment. The cut-off points of Fugl Meyer Assessment of the upper extremity will be used to classify the impairment into: severe (score 0–28), moderate (score 29–42) and mild (score 43–66).38The participants have received one or multiple sessions of tDCS that conducted alone or with motor rehabilitation. The tDCS either applied before or simultaneously with motor rehabilitation. The tDCS stimulated the primary motor cortex through one of the following montages: anodal, cathodal and bilateral. The motor rehabilitation includes any technique of motor training for upper limb.The participants have received one or multiple sessions of sham tDCS or motor rehabilitation or sham tDCS plus motor rehabilitation.The outcomes of interest are as follows:Validated functional measures for fine hand dexterity; assess the ability to perform a delicate manipulation of very small object by precise movements of the fingertips.Validated functional measures for gross hand dexterity; evaluates the ability to make a manipulation of large object by coordinated arm-hand movement.The study is planned to start on 1 August 2021 and the anticipated completion date of the study is 30 December 2021.We will conduct database and non-database searches with no restriction on publication date.Database searchThe following databases will be searched:CENTRAL .MEDLINE (Ovid).EMBASE (Ovid).SCOPUS.Web of Science.CINAHL .Non-database searchWe will review the reference list of the included studies.We will conduct grey literature search including ProQuest Dissertations and Theses, Open Access Theses and Dissertations and Electronic Thesis Online Service databases.An example of search strategy for searching MEDLINE is available as an 10.1136/bmjopen-2021-056064.supp1Supplementary dataThe search records from the selected databases including the study title, abstract will be exported to a reference management software (EndNote X9). The EndNote X9 will organise the records and recognise the duplicate studies.After removing the duplicate reports using EndNote X9, two independent reviewers will assess the eligibility of the study and discard the irrelevant ones. Initially, the two reviewers will review the studies title and abstract according to the inclusion criteria and exclude the irrelevant studies. Then the full text will be obtained and screened for those studies that potentially meet the inclusion criteria and if more details are required to confirm study exclusion. The two reviewers will decide about the study inclusion and will make discussion if there is disagreement. The third reviewer will make the final decision if the two reviewers cannot reach an agreement. The PRISMA flow diagram will be used to present records of the selection process with clarifying the reason of exclusion.39Two independent reviewers will review the full text of the included studies and extract the required data using a custom data extraction sheet. The third reviewer will be consulted if there is any disagreement between the two reviewers.The corresponding author of the included studies will be contacted if there is missing information.The two reviewers will extract the following data from the included studies:Bibliographic details: study title, authors and year.Study characteristics: design, level of blinding and participants allocation.Participants characteristics: sample size, gender, age mean, handedness, stroke type, stroke location, time since stroke onset and severity of upper limb impairment.The intervention group: tDCS montage, electrode size, intensity, density and duration, the technique of motor rehabilitation if conducted and the number of treatment sessions.The control group: the type of intervention: sham tDCS plus motor rehabilitation or motor rehabilitation only.Outcome measures: the fine dexterity measures and gross dexterity measures.Results: the result of outcome of interest .The primary outcomes of interest are fine dexterity measures and gross dexterity measures.et al,To discover any possibility of risk of bias within the included studies, the two reviewers will critically assess the methodological quality of the included studies using the Physiotherapy Evidence Database (PEDro) scale. The PEDro scale is a valid and reliable tool in critically appraising the RCTs.The fine and gross dexterity measures will be analysed as continuous variables. The analysis will be based on within-subject comparisons for both parallel and crossover design RCTs. We will obtain the sample size, mean and SD for the baseline and postintervention scores of the fine and gross dexterity measures for both the intervention and control groups from each included study. We will contact the corresponding authors if there is missing data.All data analysis will be conducted using the R software. First, we will quantify the Cohen’s d effect size for the difference between baseline and the postintervention score of the fine and gross dexterity measures for the intervention and control groups from each included study. Second, we will calculate pooled effect sizes and CIs of various groups of studies via meta-analyses in R software. We will display separate tables for each meta-analysis containing the corresponding sample sizes and effect sizes of the studies included in the sample.2 test to assess the heterogeneity statistically.2 is a common test being used in meta-analysis. It measures the variability percentage in the effect sizes caused by the variability between studies rather than sampling error.44We will use the I2 <25%).2 >25%),Meta-analysis will be performed using the fixed-effects model if the studies are similar .Fine dexterity meta-analysis (control group).Gross dexterity meta-analysis (intervention group).Gross dexterity meta-analysis (control group).To make comparisons between the outcomes of each meta-analysis, we will inspect the pooled effects sizes and identify whether there are overlapping CIs. If the CIs overlap, we will conclude that there is no difference between the pooled effect sizes. However, if the CIs do not overlap, we will conclude that there is a significant difference between the pooled effect sizes.First, we will assess the treatment effect of the tDCS on fine dexterity through comparing the pooled effect sizes of fine dexterity meta-analysis  and control of fine dexterity meta-analysis . Second, we will evaluate the treatment effect of the tDCS on gross dexterity through comparing the pooled effect sizes of gross dexterity meta-analysis  and control of gross dexterity meta-analysis . Third, we will compare the effect of tDCS between fine and gross dexterity through comparing the pooled effect sizes of fine dexterity meta-analysis  and gross dexterity meta-analysis .Additionally, we will run exploratory subgroup analyses to investigate the impact of patient characteristics on the effect of the tDCS. Specifically, we will split the participants according to severity of upper limb impairment (mild and moderate vs severe), stroke phase (acute and subacute vs chronic) and stroke area  and run separate meta-analyses for each group. Then we will compare the pooled effect sizes.2 >75%),In case of considerable heterogeneity  rather than negative results,47The overall quality of evidence of outcomes of interest of this study (fine and gross dexterity) will be judged by Grading of Recommendations, Assessment, Development and Evaluation (GRADE) approach.48Several systematic reviews have been conducted to assess the effect of tDCS on upper limb motor function.The current study will include crossover design RCTs. A common concern with crossover trials is the carryover effect that occurs when the treatment effect of the first period of the study carries over to the next period, which could affect the participant’s response.Ethical approval is not required for this study. The dissemination plan is to publish the results in a peer-review journal and presenting results in a conference.


Hand hygiene (HH) performance on entering intensive care units (ICUs) is commonly accepted but often inadequately performed. We developed a simple, inexpensive module that connects touchless dispensers of alcohol sanitiser (TDAS) to the automatic doors of a paediatric ICU, and assessed the impact of this intervention on HH compliance of hospital staff and visitors. A prospective observational study was conducted over a 3-week period prior to the intervention, followed by a 4-week period post intervention. HH performance was monitored by a research assistant whose office location enabled direct and video-assisted observation of the ICU entrance. A total of 609 entries to the ICU was recorded. Overall HH performance was 46.9% (92/196) before and 98.5% (406/413) after the intervention. Our findings suggest that HH performance on entering an ICU can be improved via a mechanism that makes operation of an automatic door dependent on use of a TDAS system, and thus contribute to infection control. Hand hygiene (HH) is a universally recognised critical infection-control measure as efficient hand antisepsis reduces the incidence of outbreaks of health-care-associated infections (HCAIs), which can often be traced to poor practice of health-care workers (HCWs) . VisitorTechnological solutions have been developed to enhance monitoring of HH compliance, but should be accompanied with educational tools . SimilarOur innovation connected the physical barrier to entering the paediatric ICU (PICU) (automatic electric doors) with readily accessible touchless dispensers of alcohol sanitiser (TDAS) to assess its impact on HH compliance of PICU visitors and staff before and after implementation of the intervention. A secondary aim was to evaluate the baseline compliance with local HH guidelines in different staff sectors.The study was performed at the PICU of Kaplan Medical Centre (KMC), affiliated with the Hebrew University of Jerusalem, Israel. The six-bed PICU has four semi-open single rooms and two rooms for patients who need airborne isolation. The main entrance to the unit is through door A  which caIn order to evaluate the impact of the devices, the connection was not activated in the first stage of the study. Large signs located above the entrance doors instructed visitors to perform HH from dispensers adjacent to the automatic doors before entering the PICU. The PICU secretary, whose office is located in the unit lobby, served as a research assistant for the purpose of the study and monitored HH compliance by staff and visitors using hidden video cameras in the first study stage for 3 weeks. Only the research initiators and members of the institutional review board (IRB) were aware of the study, to prevent the ‘Hawthorne effect’ whereby people improve behaviour only because they are being observed. We included in the study all visitors to the PICU during morning shifts in the study period, and excluded the study authors. Each entry was recorded. In the second stage of the study the connections between the dispensers and the door mechanisms were activated, and HH compliance was monitored for an additional 4 weeks. Hospital staff retained the option to open the doors with a key-card, without activating the TDAS.The primary outcome was defined as the proportion of individuals who performed HH before entering the PICU. ‘HH performance’ was completed if both of the entering individuals' hands were inserted into the TDAS device. The primary objective was to compare rates of HH compliance in different sectors during the first and second study stages for PICU staff, other hospital HCWs patients' parents and other visitors. Data were analysed for statistical significance by using the Fisher exact test.During the 7-week study period, a total 609 entries to the PICU were recorded; 196 were logged in the first 3-week stage prior to the intervention, and 413 post intervention . Overallet al. used video camera monitoring of HH compliance rates of all visitors entering an ICU over a 7-day period and reported rates of 71% for ICU staff, 74% for other staff and 95% for patients’ visitors [Previous studies have reported variable HH compliance rates among ICU staff. Nishimura visitors .Our hospital policy requires all HCWs and visitors to perform HH before entering the ICU. Surprisingly, only half of the patients' parents (12/23) did so prior to the study intervention. Indeed, compliance rates as low as 10% by hospital visitors, despite proper signage and easy access to alcohol dispensers, has been reported. Similarly, as recorded here and by others [The prevalence of HCAI remains high even in developed countries with stringent infection-control measures. Most nosocomial pathogens are transmitted via the hands of health-care personnel and caregivers, from contaminated surfaces or equipment in the hospital environment . The intBarriers to adherence with HH recommendations include structural aspects, knowledge gaps and especially a lack of time in daily routine practice. The latter issue is pivotal  as HH peOur study has some limitations. First, the pre-post study design can be affected by unexpected biases. We made our best effort to keep the study secret. Second, the connection of the door entry mechanism to the TDAS may allow groups of people to enter at the same time without all performing HH. Third, the study wasn't planned to assess the impact of the intervention on the incidence of HAIs, and the load of potentially pathogenic bacteria on the hands of visitors was not evaluated. Nevertheless, this study shows how readily HH compliance can be achieved. The groups (before and after the intervention) differed in size, as we wished to ensure that the effect was sustainable, and data collection was therefore continued for a longer-than-planned period of time. Occasional changes in volume of PICU activity also increased the number of observations between pre- and post-intervention periods. Moreover, data collection was performed only during the day-shifts and not on weekends, because of the logistical issues.In conclusion, our study shows that significant improvement of HH performance on entering the PICU by hospital staff and other visitors was achieved subsequent to implementation of the intervention. The improvement was surprisingly high in hospital staff, since personnel could use key-cards to open the doors. Our findings suggest that HH performance on entering any healthcare facility can be dramatically improved by installing this inexpensive and easy-to use module, and could be extended to out of hospital platforms as part of the global effort to prevent the spread of infectious diseases.


This study presents a novel feature-engineered–natural gradient descent ensemble-boosting (NGBoost) machine-learning framework for detecting fraud in power consumption data. The proposed framework was sequentially executed in three stages: data pre-processing, feature engineering, and model evaluation. It utilized the random forest algorithm-based imputation technique initially to impute the missing data entries in the acquired smart meter dataset. In the second phase, the majority weighted minority oversampling technique (MWMOTE) algorithm was used to avoid an unequal distribution of data samples among different classes. The time-series feature-extraction library and whale optimization algorithm were utilized to extract and select the most relevant features from the kWh reading of consumers. Once the most relevant features were acquired, the model training and testing process was initiated by using the NGBoost algorithm to classify the consumers into two distinct categories . Finally, each input feature’s impact (positive or negative) in predicting the target variable was recognized with the tree SHAP additive-explanations algorithm. The proposed framework achieved an accuracy of 93%, recall of 91%, and precision of 95%, which was greater than all the competing models, and thus validated its efficacy and significance in the studied field of research. The quality of living in modern society is highly associated with the availability of electricity . An uninResearchers have explored several different methods in the literature to overcome the stated issue, among which the hardware and data-driven approaches are the most widely utilized theft-detection strategies . HardwarIt is worth noting that, in comparison to unsupervised machine-learning-based fraud-detection models, supervised machine-learning approaches are the most widely used, owing to their flexibility of training a classifier on multiple classes, ease of execution, and higher interpretability of model outcomes. However, the supervised machine-learning-based electric-theft-detection frameworks encounter few challenges during each of their execution stages; i.e., data preprocessing, feature engineering, and model training and testing. For example, the data acquired from the conventional or smart meters generally contain a lot of missing and inconsistent observations that need to be imputed for achieving realistic outcomes at the end of the classification process. Imbalanced data class distribution and inappropriate selection of the features may also prevent the effective and unbiased evaluation of the theft-detection model. Another important aspect that has been ignored in most of the supervised ML theft-detection frameworks is the probability evaluation of model outcomes. The greater the model outcome probability, the greater the sureness on the produced model prediction, leading to a reduced number of false inspections. Moreover, the contribution of input features on a particular predicted outcome is often overlooked, which consequently impedes the further improvement of the developed model.The broader aim of this research work was the implementation of a novel sequentially executed theft-detection framework to facilitate the power utilities in their campaign against fraudster consumers. The proposed framework initially utilized an ML-based random forest imputer (RFI) to impute the missing entries in the acquired smart meter dataset (SGCC dataset). The RFI is an effective technique to handle different types of missing values by comprehending complex interactions present in the data. To avoid the data class unbalancing, the majority weighted minority oversampling technique (MWMOTE) algorithm was utilized. The MWMOTE algorithm employs the intelligence of the average-linkage, agglomerative-clustering-based technique to generate the required number of minority class samples from the original data to effectively balance the overall data class distribution. In order to precisely portray the underlying characteristics present in consumption data, the proposed approach utilized the intelligence of the time-series feature-extraction library (TSFEL) for extracting the statistical, temporal, and spectral domain-based features from users’ kWh consumption patterns. The whale optimization algorithm (WOA)-based feature-selection method was adopted to avoid overfitting and high data dimensionality by selecting the most significant features that positively contributed towards predicting the target variables, while discarding the less relevant ones. Once the most relevant features were acquired, the model training and testing process was initiated by using the NGBoost algorithm to classify the consumers into two distinct categories . Finally, unlike most of the ensemble and gradient-boosting-based ML models in which the predictions are made as a black box; i.e., the reason for any given prediction was not thoroughly explained, this study utilized the tree Shapley additive explanations (SHAP) algorithm to evaluate the impact of each input feature in predicting the target variable. The proposed model achieves an excellent accuracy, with low false-positive and high detection rates; thus, it saves the cost, labor, effort, and time required for executing onsite inspections.The remainder of the paper is organized into five main sections. As this study explores an application of the supervised ML approach in detecting theft cases from the acquired smart meter dataset, therefore the literature pertaining to the recent advancements in the mentioned field is discussed in detail in this section. As mentioned in the previous section, the supervised machine-learning-based electric-theft-detection frameworks encounter a few major challenges during data preprocessing, feature engineering, and the model training and testing process. It is important to discuss each of the challenges and their available solutions in the literature in detail to highlight the essence of current research work.To achieve realistic outcomes at the end of any classification process, the missing observations in the accumulated dataset need to be logically and intelligently imputed. Since most of the publicly available electric consumption datasets possess a considerable chunk of missing data entries, it therefore is very hard for a classifier to learn and classify such deficient and inconsistent datasets. In order to overcome the stated issue, several data-imputation or data-dropping approaches have been suggested in the literature. These approaches include the look-back and sandwich-based imputation methods , Monte CThe second stage in any supervised ML method is the feature-engineering stage. In this stage, several distinct features are extracted from the acquired dataset using different soft computational techniques. The major challenge during this stage is to deal with the imbalanced data class distribution. In addition, the selection of a suitable technique for choosing the most relevant features from the provided dataset is highly important. The former issue occurred due to the higher number of healthy consumers than fraudster consumers in the acquired dataset, which made the model highly biased towards the majority-class sample, creating a low representation of the minority-class samples. Since the under-represented class in any theft detection framework needs to be identified, a balanced data class distribution is essentially required for the effective and unbiased evaluation of any theft-detection model. Several researchers have attempted to tackle this challenge using different soft computational methods. Glauner et al.  addresseAnother challenge that emerges during the feature-engineering stage is the choice of an appropriate method for extracting and selecting the most relevant features from the acquired dataset. The raw data obtained from the smart meters generally lacked statistical significance, and contained high data dimensionality with redundant and irrelevant features. If such a dataset is directly provided to the classifier, its performance in classifying the healthy and theft patterns will be highly affected. Therefore, a number of highly relevant features are extracted from the acquired raw dataset to enhance the classifier’s performance. In the literature, several statistical and deep-learning-based techniques have been explored for extracting the supplementary information from the given data ,21. HoweOn completion of the feature-engineering procedure, the next challenge in developing an efficient theft-detection framework was to shortlist the fraudster consumers by using a suitable ML classifier. Numerous ML classifiers were utilized in the literature for developing an effective theft-detection framework. Jindal et al.  and MariConcluding the detailed literature review, the mapping of the identified problems and their proposed novel solutions are presented in a tabular form in The overall framework was classified into three major stages: data preprocessing, feature engineering, and model evaluation, as shown in In data preprocessing, the original data is reshaped into an appropriate representation useful for the effective learning of ML models. In the current research work, the consumers’ kWh consumption data (January 2014 to October 2016), acquired from the State Grid Corporation of China (SGCC) , containOne of the key characteristics of theft consumers that most likely distinguished them from the healthy ones was their unsymmetrical energy consumption pattern. It can be observed in the mentioned figures that the considered fraudster samples contained comparatively more nonperiodicity and zero consumption values than the healthy consumers, thus validating the genuineness of the considered dataset. Once the labeled dataset was explored, the next task for developing an efficient supervised ML classification framework was to accurately impute the missing entries in the acquired data. n is the number of consumers and It can be observed in This stage was further divided into two substages; i.e., data class balancing and feature engineering. This section discusses each stage in detail.Data class balancing is the process of balancing the minority and majority class samples with each other to improve the generalization ability of the model and avoid overfitting issues. In this study, the acquired prelabeled data contained only 6.7% of the theft consumers, which was comparatively lower than the healthy consumers. To increase the minority class samples, the oversampling technique based on the MWMOTE algorithm was utilized. The working mechanism of the MWMOTE algorithm is based on three stages.In the first stage, the identification process of highly important minority class samples was carried out. In the second stage, samples were categorized in terms of their Euclidean distance from the nearest majority class sample. Finally, the average-linkage agglomerative clustering-based technique was employed to produce new samples from highly important categories of minority class samples formed in the second stage. The minority class samples; i.e., synthetic theft cases generated at the end of this process, were merged with the original dataset to balance the overall data class distribution. The data class distribution before and after using the MWMOTE algorithm was visualized using the t-distributed stochastic neighbor embedding (t-SNE) approach, as shown in In this subsection, the proposed feature engineering procedure is discussed in detail. Feature engineering is the process of selecting the most relevant features and discarding the redundant and less significant ones from the acquired dataset. This procedure aims to boost the learning ability of the ML model while dealing with complex data patterns. The SGCC dataset utilized in this study lacked statistical characteristics. For achieving an efficient performance by any theft-detection model, its input features must reflect sufficient underlying abnormalities in customer consumption data. Therefore, in this study, statistical, temporal, and several spectral domain-based features (more than 50) were extracted from each consumer’s consumption data using the TSFEL technique. The TSFEL technique facilitated rapid data exploration and automatic feature extraction from the given kWh consumption data. All the extracted features in the current study are depicted in The newly added supplementary information obtained after employing the TSFEL technique enhanced the learning ability of the model in classifying the complex data patterns effectively. Nevertheless, increasing the number of features increased the data dimensions, training time, and computational resources. To overcome this challenge, a feature selection approach was adopted at the later stage to select a small subset of the extracted features. The resulting shortlisted features were generally packed with high-quality and the most relevant information for data class prediction. These feature selection techniques have been found to be effective in avoiding overfitting of the model, lowering the computational and storage requirements, mitigating problems caused due to high data dimensions, and achieving improved readability and interpretability of the model.In this study, the most essential features from the available kWh consumption data were selected by using the WOA-based feature selection (FS) technique. The WOA-FS technique worked iteratively to select the essential features from the given dataset. The WOA is a stochastic population-based metaheuristic optimization algorithm whose working mechanism is based on mimicking humpback whales’ prey-hunting behavior. It consists of two stages: exploration (random hunting for prey) and exploitation (encircling and attacking prey). Like other population-based metaheuristic algorithms, it iteratively generates random solutions within the bounded search space until the optimum solution is achieved. A simplified working mechanism of the proposed WOA-FS technique is illustrated in nfn represents Conn: consumer number and fn: feature number). Before initiating the feature selection process through WOA, the number of iterations, number of search agents, convergence criteria, search space boundaries, decision variables, and the learning classifier had to be decided. During the first iteration, the random subsets of features were selected from consumers’ consumption data and their extracted features to train and test the learning classifier. Afterward, the performance of the classifier with each feature subset was evaluated using the fitness function (FF) provided in Equation (1):e represents the classification error rate of the classifier (NGBoost in the current case), C represents the cardinality of the chosen feature subset, and N denotes the total input features. The The proposed WOA-FS optimally selected those minimum number of highly important features that attained maximum classification accuracy for any given volume of consumption data and extracted features  and max(D) are the minimum and maximum values of D, respectively.It is worth mentioning here that the minimization of the classification error with the limited number of essential features was taken as the current optimization objective. The WOA accomplished the mentioned task by evaluating the FF’s magnitude for each iteration search agent. The search agent with a minimum value of FF was taken as the best solution candidate. Similarly, for each subsequent iteration, the process was repeated until the predecided termination criterion was met. Finally, the selection of the feature subset was made by choosing the fittest solution obtained at the end of the optimization process. The final feature set obtained at the end of the feature selection process contained several data points that were spread across a broad spectrum. Such features with higher magnitude could induce bias during the model training. Therefore, all the computed features had to be standardized on a common uniform scale. The current study employed the well-known min–max approach for data standardization to address this challenge, using the following expression:The theoretical background of the performance metrics and proposed NTL detection classifier are discussed in detail in the following sections.The efficacy of the supervised machine learning models depended on their ability to predict the unlabeled data. Numerous metrics exist in the literature for evaluating the performance of an ML model, such as those utilized in . Since iNGBoost is a supervised natural gradient descent (NGD)-based boosting algorithm. It can be utilized for both probabilistic regression and classification tasks. In this study, the NGBoost-algorithm-based classification approach was explored for model training and evaluation purposes. A conceptual representation of the NGBoost model is shown in M is the base learner, T is the prediction target, and https://stanfordmlgroup.github.io/projects/ngboost (accessed on 18 October 2021).In In this section, the performance of the proposed classifier against the performance metrics mentioned in score, and MCC. The precision metric estimated the ability of the classifier to accurately classify the theft cases. The proposed theft-detection model achieved a high average precision value of 0.95. Another important performance evaluation metric is F1score. It is the harmonic mean between precision and recall. The F1score for the current case was calculated as 0.92, which was significantly higher for any supervised ML classifier. Likewise, the MCC metric yielded a high score only if the prediction had achieved good scores in all four confusion matrix categories. The proposed classifier attained an average MCC value of 0.88.It can be observed in Another important metric for evaluating the theft-detection models is the AUC ,46. The In addition, the cumulative gain curve obtained using the proposed model is shown in x-axis in the curve represents the percentage-wise proportion of consumers while the y-axis signifies the gain for all the model responses in a given data class. The class-0 and class-1 represent “Healthy” and “Theft” consumers, respectively. It can be observed in The Considering the outcomes of all the performance evaluation metrics, we concluded that the proposed theft-detection model correctly classified the majority of the Theft and Healthy cases in the provided dataset, and thus proved its essence and significance.The confusion matrix (CM) is a popular metric for assessing the performance of supervised classification models. It is applicable to both binary and multiclass classifications issues. It is essentially a square matrix, with the rows representing the actual class of the instance and the columns representing their predicted class, as illustrated in In this section, the proposed theft-detection model’s model outcomes are explained by employing the tree SHAP algorithm developed by Lundberg et al. . The aimIt can be observed in In this section, the efficacy of the proposed theft detection framework is assessed and compared with the latest gradient boosting decision trees and the most frequently used classical ML models under identical datasets. To accomplish the mentioned objective, the performance assessment of all the studied ML models was made based on five of the most widely utilized performance metrics (detailed in We concluded from the results depicted in In this study, a novel feature-engineered electric-theft-detection framework was developed using the intelligence of the NGBoost algorithm. For ease of understanding and presentation, the proposed framework was divided into three major stages: data preprocessing; feature engineering; and model training, testing, and interpretation. To effectively classify the consumers into healthy and fraudster categories, each stage was executed sequentially. Initially, the data preprocessing stage was commenced, in which the problem of missing entries in the acquired smart meter dataset was addressed by the random-forest-based imputation technique. Afterward, the feature engineering stage was initiated, in which the unbalancing of the target variable was tackled by the minority class oversampling method, called the MWMOTE algorithm. Later, the combination of TSFEL and the whale optimization algorithm was utilized to extract and select the most important features from the balanced dataset acquired at the end of the previous stage. In the final stage, the NGBoost algorithm was used for model training and testing purposes. To assess and validate the performance of the proposed theft-detection framework, its performance was compared with that of the latest gradient descent ML machines, ensemble boosting and bagging classifiers, and the classic ML models. The proposed framework achieved superior performance compared to the other models based on a few well-known performance assessment metrics such as accuracy, precision, recall, Kappa, and MCC values; thus, it validated its efficacy and essence.


Throughout the brain, parallel processing streams compose the building blocks of complex neural functions. One of the most salient models for studying the functional specialization of parallel visual streams in the primate brain is the lateral geniculate nucleus (LGN) of the dorsal thalamus, through which the parvocellular and magnocellular channels, On-center and Off-center channels, and ipsilateral and contralateral eye channels are maintained and provide the foundation for cortical processing. We examined three aspects of neural processing in these streams: (1) the relationship between extraclassical surround suppression, a widespread visual computation thought to represent a canonical neural computation, and the parallel channels of the LGN; (2) the magnitude of binocular interaction in the parallel streams; and (3) the magnitude of suppression elicited by perceptual competition  in each stream. Our results show that surround suppression is almost exclusive to Off channel cells; further, we found evidence for two different components of monocular surround suppression—an early-stage suppression exhibited by all magnocellular cells, and a late-stage suppression exhibited only by Off cells in both the parvocellular and magnocellular pathways. This finding indicates that stream-specific circuits contribute to surround suppression in the primate LGN and suggests a distinct role for suppression in the Off channel to the cortex. We also examined the responses of LGN neurons in alert macaque monkeys to determine whether neurons that supply the cortex with visual information are influenced by stimulation of both eyes. Our results demonstrate that LGN neurons are not influenced by stimulation of the non-dominant eye. This was the case when dichoptic stimuli were presented to classical receptive fields of neurons, extraclassical receptive fields of neurons, and when stimuli were appropriate to produce the perception of binocular rivalry. Throughout the visual system, information is carried in parallel processing streams that contribute to functionally specific computations. The lateral geniculate nucleus (LGN) of the thalamus is remarkable in that the feedforward inputs of many parallel streams originating in the retinas  are kept anatomically segregated at a cellular level within the structure, making functional characterization of individual streams more approachable for the physiologist than in later stages of the visual system. Opportunities for stream mixing exist through local interneurons, thalamic reticular nucleus (TRN), and cortical feedback (among others), which opens the interesting possibility that circuitry subserving inter-stream interactions, if present, could be differentiated based on the time course of the interactions. The LGN thus provides an excellent model for understanding the processing characteristics inherent to the individual streams, and for understanding the extent and mechanism of stream mixing in the earliest stages of visual processing.For many neurons in the LGN, stimuli that extend beyond the classical receptive field of the neuron have a suppressive effect on the cells’ spiking activity  described in this study were: (1) to determine the characteristics of surround suppression in the parallel streams of the LGN, (2) to determine the extent to which non-dominant eye stimulation influences the spiking activity of LGN cells, and (3) to determine the magnitude of the effect of perceptual suppression on the sensitivity of LGN cells. In our investigation of the stream-specificity of monocular surround suppression in the LGN, we found that surround suppression is carried predominantly by Off-channel cells, with magnocellular and parvocellular Off cells showing distinct temporal dynamics, indicating a functional specialization for these cells. Based on previous studies, we had the advantage of knowing that any binocular effects, if present, were likely to be small. We, therefore, designed our experiments examining interocular interactions with a focus on maximal sensitivity and statistical power. Our results show that binocular signal interactions are not evident in either the parvocellular or magnocellular layers of the primate LGN. These results demonstrate that in the earliest stages of visual processing, monocular visual streams within the magnocellular and parvocellular pathways are kept strictly independent within these layers.Macaca mulatta) were used in this study. All procedures were approved by the Institutional Animal Care and Use Committee at the University of California, Davis, and conformed to NIH guidelines. Surgical procedures have been described previously  were made from the LGN in the alert animals using platinum-in-glass electrodes . Voltage recordings were amplified and recorded by a PC equipped with a Power 1401 data acquisition system and Spike2 software package (Cambridge Electronic Design).Two female macaque monkeys . The stereoscope was aligned and vergence and accommodation were matched by replacing the mirrors with beamsplitters and aligning a central fixation point on each monitor to an LED located an equal distance behind the beamsplitters. Once the stereoscope was aligned, the beamsplitters were replaced with mirrors. For all experiments, identical high-contrast annular checkerboards surrounded the fixation points in order to stabilize binocular fusion.Stimuli were presented on two gamma-calibrated Sony Trinitron GCM-F520 monitors running at 100 Hz with a resolution of 944 × 708 pixels. The viewing distance was 61 cm. The stimuli were generated on two synchronized VSG2/5 visual stimulus generators . The mean luminance of each monitor was 38 candelas/mBoth the lack of histological verification of recording sites and complicated layering in the caudal/parafoveal region of the LGN introduce a certain level of subjectivity in reporting the magnocellular/parvocellular classification of recorded neurons. We, therefore, partitioned cell classes based on spiking response patterns using two clustering algorithms. For characterization, we used each cell’s averaged peri-stimulus time-histogram (PSTH) based on responses to optimally-sized static gratings . We thenOur first experiment characterized the spiking responses elicited by LGN cells to brief presentations of static sine-wave gratings. We used a range of stimuli designed to measure monocular surround suppression, binocular interactions between congruent stimuli, and interocular transfer of surround suppression. For every cell, the location of the receptive field center was mapped by hand, after which an area-summation tuning curve was generated using drifting gratings in order to determine the cell’s preferred stimulus size. The preferred phase was determined from presentations of static gratings centered over a cell’s receptive field. Spatial frequency was selected based on cell class and eccentricity and confirmed with spatial frequency tuning curves for a subset of cells. This was important to minimize suppressive influences from the classical surround on our measurements of extraclassical suppression  consisted of monocular or binocular optimally-sized gratings, monocular or binocular large (4–8 degree) gratings, and an optimal grating presented to the dominant eye simultaneously with a small or large surround annulus presented to the non-dominant eye. The inner diameter of the annuli was equal to the outer diameter of the grating presented to the dominant eye. The phase of the gratings was always matched in the two eyes. Spikes were binned at 1 ms resolution and averaged over all trials within conditions. Temporal comparisons between conditions were made using the difference in the cumulative spike counts elicited by the stimuli. Reported measurements of response latency and suppression latency are the x-intercepts of linear fits to the baseline-subtracted initial stimulus response and the cumulative suppression curves, respectively.Animals fixated on a binocularly-presented central fixation point; after 250 ms, one of six pseudo-randomized stimulus combinations was presented for 250 ms. The stimulus set . The stimulus (oriented 90 degrees) in the dominant eye was held fixed over the cell’s receptive field location, and the horizontal position of the stimulus in the non-dominant eye  was varied pseudorandomly across trials for several repeats of nine positional steps to include crossed phase match, zero disparity, and uncrossed phase match. The significance of modulation was assessed with an ANOVA, and we quantified disparity tuning using a disparity discrimination index  of the temporal kernels.where n = 14), a high-contrast grating drifting orthogonally to the probe was used instead of an annulus; there was no detectable difference in these results, and the data from the two suppressor conditions were aggregated.Monocular and dichoptic stimulus presentations were randomly interleaved. During dichoptic trials, a thin annulus  of a drifting grating  was presented in the corresponding location of the non-dominant eye. This configuration drives robust binocular rivalry between the probe and the annulus , and wasd’ = Z (hits) − Z  , we measured the temporal properties of perceptual suppression elicited by the reverse-correlation stimulus set and the total proportion of probe visibility during all trials . Observepost-hoc analysis of eye position during trials in order to identify and eliminate trials or recordings in which differences between the eye positions across different stimulus conditions may have affected our results. For each trial, we measured the average Mahalanobis distance  and putative magnocellular (transient) cells based on recorded responses to 250 ms presentations of optimally-sized static gratings . Among the magnocellular cells, we observed statistically significant surround suppression in Off cells  and On cells . Magnocellular Off cells exhibited far greater surround suppression than magnocellular On cells . Magnocellular Off cells did not show stronger surround suppression than parvocellular Off cells , but there was a significant difference in the strength of magnocellular On-cell suppression and parvocellular On-cell suppression . These differences in suppression were not due to sampling biases in receptive field eccentricities . The traces in ricities . For celricities .We next considered whether there is evidence of binocular modulation in any of the cell groups. The rows of We observed drastically different temporal characteristics in surround suppression exhibited by parvocellular and magnocellular cells. The mean time-course of suppression in Off cells and On cells in these groups is plotted in A comparison of the timing of firing-rate responses and the time-course of surround suppression is illustrated in We used a reverse-correlation procedure to estimate the temporal kernels of LGN cells during monocular viewing and during binocular rivalry in order to determine changes in sensitivity brought about by perceptual suppression. We chose this experimental strategy because we felt it would maximize the amount of data generated in short periods of single-unit isolation and that it would be the most sensitive procedure for revealing small effects. An illustration of the experimental procedure is shown in As in the experiment with static gratings, we first partitioned the sample of recorded cells based on exhibited response patterns. The use of binocular rivalry suppression in this experiment is indirect; that is, we do not explicitly compare responses during epochs when a probe is reported to be suppressed or dominant, but rather measure the sensitivity of cells when a probe is presented alone (known to be dominant) or during periods of binocular rivalry when the probe is assumed to be perceptually suppressed for a significant proportion of the trial. To understand quantitatively what proportion of the time the probe is perceptually suppressed, we used three human observers to characterize the rivalry dynamics of the dichoptic stimulus we used in this experiment. Observers viewed the stimuli  through a mirror stereoscope and were instructed to hold down a button when the probe was visible. The total proportion of time through the entire sequence that the probes were reported visible is plotted in t-test, p = 0.24) or the magnocellular cells ; we did not observe a difference in rivalry suppression between On cells and Off cells . These data are plotted and histogrammed as percent suppression (percent reduction of monocular kernel magnitude) in the bottom row of Representative examples of kernels recovered with this method are shown in The goals of this study were threefold: (1) to determine the characteristics of surround suppression in the parallel streams  of the LGN; (2) to determine the extent to which non-dominant eye stimulation influences the spiking activity of LGN cells, and (3) to determine the magnitude of the effect of perceptual suppression on the sensitivity of LGN cells. Our data allowed us to characterize the timing and strength of both monocular surround suppression and non-dominant eye suppression, potentially enabling us to evaluate the possibility that they would be affected through the same circuits. We found evidence for two different components of monocular surround suppression—an early-stage suppression exhibited by all magnocellular cells, likely inherited from the retina  Administration, University of California, Davis. The patients/participants provided their written informed consent to participate in this study. The animal study was reviewed and approved by Institutional Animal Care and Use Committee (IACUC) University of California, Davis.BL and WU conceived the project, wrote, and edited the manuscript. BL collected and analyzed data. All authors contributed to the article and approved the submitted version.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


Glycans have highly complex and dynamic structures. Small variations in their composition and types of linkage may lead to significant changes in their biochemical and biophysical properties and are determinant for their specific recognition by proteins. Microbial recognition of glycans represents major biological processes including adhesion, biofilm formation, host invasion, and signalling. In some diseases, alterations in the ability of microbial pathogens to bind glycans can lead to dysbiosis and result in inflammation. On the other hand, microbial glycans are recognized as key mediators of the host immune response, although the presentation of host-like glycans on microbial surfaces can be used to evade this detection. To advance our understanding of these complex interactions at the atomic level, structural-based studies and in the development of new techniques are key.Frontiers of Molecular Biosciences, we have eight articles taking us to the frontiers of knowledge on the roles of glycans in mediating host-microbe interactions during infection and in stimulating a host immune response, from a structural perspective. These emphasise the importance of glycans during host-microbial invasion/colonization, host responses to invading pathogens, microbial subversion of host responses and the development of new vaccines.In this Research Topic of Siukstaite et al.). Glycolipids are common cell surface structures and Gb3 is a host target receptor for a range of microbial pathogens. As well as delivering an overview, the authors illustrate in detail the structural basis for Gb3-binding by three bacterial lectins: Shiga toxin B-subunit (StxB) binding to Gb3 and inducing clathrin-dependent endocytosis and toxin uptake; Gb3 recognition by the Pseudomonas aeruginosa lectin LecA and subsequent bacterial membrane engulfment; and host recognition by the Streptococcus suis SadP and uropathogenic Escherichia coli PapG adhesins. The review also covers structural features of Gb3-binding lectins from fungi, plants and animal sources and discuss the evolution for recognition of this glycan epitope.Siukstaite, Imberty and Romer review the structural diversity in lectins that bind the glycosphingolipid globotriaosylceramide (Gb3) and their different binding mechanisms . They focus on three Helicobacter pylori adhesins: the blood-group antigen binding adhesin (BabA), the sialic acid-binding adhesin (SabA), the LacdiNAc-binding adhesin (LabA), and discuss their roles in H. pylori colonization of human gastric mucosa. In addition, they describe the knowledge so far on the non-H. pylori Heliobacter (NHPH) species which lack the above-mentioned H. pylori adhesins. The adhesion molecules used by NHPH and the full host adhesion mechanisms remain to be elucidated.Staying focussed on bacterial systems, Matos and Reis describe the role of host glycans during colonization of the human gastric mucosa by Miller et al.). Using a structure-based rationale of the recognition by lectins and antibodies they categorize “glycoepitopes” on the virus surface glycans in three classes: minimal glycoepitopes, glycan-protein glycoepitopes and topological glycoepitopes. They further discuss the potential value of topological glycoepitopes in the development of novel antiviral targets, using as example the recognition of HIV envelope glycoprotein by the neutralizing 2G12 antibody.The next group of articles in this Topic address the role of glycans in host-viral interactions. Sasisekharan and colleagues provide a structural perspective of the interactions between host or viral glycoproteins with their complementary viral or host glycan binding proteins . Here the authors review the recent advances in the structural basis for host glycan recognition by VP8*, the distal region of the viral outer capsid spike protein and the link with host tropism. The final article of this group  is highly topical and reviews the glycosylation of spike protein and other proteins in SARS-CoV-2, highlighting their role in mediating host interactions and helping to evade host immune responses. They also discuss the potential role of glycans for increasing the protective immune response of spike protein-based vaccines.The next article by Duan and colleagues concentrates on Rotavirus, a highly infectious viral pathogen that leads to acute gastroenteritis in children  in which polysaccharides from traditional Chinese medicines  and their role in modulating human immune responses are presented and the potential of these polysaccharides as vaccine adjuvants are discussed.Further into the development of vaccines, the inclusion and optimization of vaccine adjuvants are essential to produce a stronger immune response to the antigen. This is tackled by Liu and Zhao in their review . Lectin co-operation, oligomerization and multiple site binding is discussed and how this allows specificity in the detection microbial glycans.Mammalian lectin receptors have a major role in recognizing glycans presented on the surface of invading pathogens and this is reviewed by Yamaguchi and colleague , Paracoccin (PCN) from the human fungal pathogen Paracoccidioides brasiliensis is overviewed. This protein is an archetypal example of a glycan binding protein with multiple functions. The authors review its role as a chitinase but also its interactions with host Toll-like receptors and subsequent stimulation of protective immunity.In the final article of this Topic (We hope that with this Research Topic the reader will find a useful selection of reviews that brings together the importance of glycans in supporting host-pathogen interactions and stimulates new ideas for using glycans to target and develop new therapies.