Count Estimation Research Articles

Gene count estimation with pytximport enables reproducible analysis of bulk RNA sequencing data in Python.

Transcript quantification tools efficiently map bulk RNA sequencing reads to reference transcriptomes. However, their output consists of transcript count estimates that are subject to multiple biases and cannot be readily used with existing differential gene expression analysis tools in Python.Here we present pytximport, a Python implementation of the tximport R package that supports a variety of input formats, different modes of bias correction, inferential replicates, gene-level summarization of transcript counts, transcript-level exports, transcript-to-gene mapping generation and optional filtering of transcripts by biotype. pytximport is part of the scverse ecosystem of open-source Python software packages for omics analyses and includes both a Python as well as a command-line interface.With pytximport, we propose a bulk RNA sequencing analysis workflow based on Bioconda and scverse ecosystem packages, ensuring reproducible analyses through Snakemake rules. We apply this pipeline to a publicly available RNA-sequencing dataset, demonstrating how pytximport enables the creation of Python-centric workflows capable of providing insights into transcriptomic alterations. pytximport is licensed under the GNU General Public License version 3. The source code is available at https://github.com/complextissue/pytximport and via Zenodo with DOI: 10.5281/zenodo.13907917. A related Snakemake workflow is available through GitHub at https://github.com/complextissue/snakemake-bulk-rna-seq-workflow and Zenodo with DOI: 10.5281/zenodo.12713811. Documentation and a vignette for new users are available at: https://pytximport.readthedocs.io. Supplementary Material is available at Bioinformatics online.

Delineations for Police Patrolling on Street Network Segments with p-Median Location Models

Police patrolling intends to enhance traffic safety by mitigating the risks associated with vehicle crashes and accidents. From a view of operations, patrolling requires an effective distribution of resources and often involves area delineations for this distribution purpose. Given constraints such as budget and human resources for traffic safety, delineating geographic areas optimally for police patrol areas is an important agenda item. This paper considers two p-median location models using segments on a street network as observational units on which traffic issues such as vehicle crashes occur. It also uses two weight sets to construct an enhanced delineation of police patrol areas in the City of Plano, Texas. The first model for the standard p-median formulation gives attention to the cumulative number of motor vehicle crashes from 2011 to 2021 on the major transportation networks in Plano. The second model, an extension of this first p-median one, uses balancing constraints to achieve balanced spatial coverage across patrol areas. These two models are also solved with network kernel density count estimates (NKDCE) instead of crash counts. These smoothed densities on a network enable consideration of uncertainty affiliated with this aggregation. The analysis results of this paper suggest that the p-median models provide effective specifications, including their capability to define patrol areas that encompass the entire study region while minimizing distance costs. The inclusion of balancing constraints ensures a more equitable distribution of workloads among patrol areas, improving overall efficiency. Additionally, the model with NKDCE results in an improved workload balance among delineated areas for police patrolling activities, thus supporting more informed spatial decision-making processes for public safety.

Conservation in action: Cost-effective UAVs and real-time detection of the globally threatened swamp deer (Rucervus duvaucelii)

Uncrewed aerial vehicles (UAVs) have proven to be successful tools for ecological monitoring, providing excellent visual resolution and the ability to cover large areas with spatial accuracy. Artificial Intelligence has further improved the capabilities of UAVs vision through object detection. While deep learning has shown significant success in pattern recognition, it still faces challenges in real-world scenarios. In this study, we focused on enhancing the potential of UAVs for wildlife detection and monitoring, specifically focusing on the globally threatened swamp deer (Rucervus duvaucelii). To improve the accuracy of animal recognition with UAVs, we used single-stage detectors YOLO (You Only Look Once) V3, V5, V7, V8, Object detection V3 and DETR (DEtection TRansformer). We trained our model using 48,957 augmented images derived from a dataset of 8210 original true dataset. The result shows the superior performance of Object Detection 3.0 and YOLO V8, achieving a precision score of more than 92 % and a F1 score of more than 85 %, compared to DETR, YOLO V7, V5, and V3. Overall, our study provides an efficient, cost-effective, and accurate detection framework for ecological monitoring that can be non-invasively and least distractively used in various demographic studies of cervids in different regions and habitat types. We have also developed a UAV-based real-time object detection framework that seamlessly integrates with front-end technologies, enabling live detection results regardless of connectivity. This framework operates on a local server, synchronizing with consumer-grade UAVs at a rate of 32 frames per second (fps) with 320 pixel resolution using a frame sampling technique, notably without requiring a dedicated Graphical Processing Unit (GPU). This deliberate choice of not using GPU underscores the commitment to cost-effectiveness and aligns with the research's purpose, prioritizing accessibility and affordability for broader scientific exploration. A least distractive ecological sampling technique was optimized and a maximum of 77 deer were detected and counted in real time within Haiderpur wetland in the Hastinapur Wildlife Sanctuary. This methodology can be replicated and fine-tuned to study other threatened species of conservation priority. This study exemplifies how combining UAVs with deep learning can facilitate species monitoring and population count estimation and be adopted by forest managers to support conservation decisions.

Attacks classification and data privacy protection in cloud-edge collaborative computing systems

Collaborative computing systems used today are a significant problem, and the variety of attacks and data privacy protection play an important role. Accordingly, this research takes the latest technology, such as federated learning and cloud-edge collaborative computing systems. Among them, multi-national validation with attacks/without attacks architecture is mainly developed, and ‘End-to-end privacy-preserving deep learning for attack classification method is used to classify each episode that occurs and done through End-to-end privacy-preserving deep understanding (E2EPPDL). This is the most essential core component of our research. We justified them with Time, Node Count, Routing count, and Data delivery ratio estimates.

Detection, counting, and maturity assessment of blueberries in canopy images using YOLOv8 and YOLOv9

Blueberry growers currently rely on manual or subjective approaches to harvest decision-making and yield estimation. Canopy image-based, pre-harvest blueberry detection provides a promising means for automated harvest maturity assessment, fruit counting, and yield estimation to optimize crop management and reduce labor costs. Data-driven deep-learning-based object detectors, especially the YOLO (You Only Look Once) series models, have emerged as a powerful tool for a diversity of computer vision tasks in agriculture. However, the detection of such small, densely populated objects as blueberries in unstructured scenes can pose great challenges, especially given the lack of dedicated datasets for model development. This study, therefore, presents the first publicly available dataset of blueberry canopy images with 17,955 ripe and unripe blueberries, which were captured in diverse orchard conditions, and performance evaluation of YOLOv8l (large) and YOLOv9-c (compact) with comparable complexity for blueberry detection and whereby fruit counting and ripe fruit percentage estimation. At the input image resolution of 2560 × 2560 pixels, both models performed similarly in terms of detection accuracy with around 91 % mAP@50, except that YOLOv9-c was far more time-consuming to train. Trained with the input of higher resolution images of 3520 × 3520 pixels, YOLOv8l achieved an overall mAP@50 of nearly 93 %, and an RMSEs (root-mean-square errors) of 10.4 in fruit counting and 3.62 % in estimating the percentage of ripe fruit of each image. Both the blueberry dataset11https://zenodo.org/records/14002517 and software programs22https://github.com/vicdxxx/BlueberryDetectionAndCounting of this study have been or will be made publicly available, which are expected to be useful for promoting greater community efforts for further algorithm development.

Using the Capture–Recapture Technique to Supplement a Point-in-Time Count of Homeless Adults in Kittitas County, Washington

Abstract Capture–recapture (CRC) is a method of population estimation pioneered in biology that allows the surveyor to approximate the proportion of individuals who evaded sampling efforts (i.e., captures) based on repeated samples from the same population. The current national standard for estimating homelessness is a single-day enumeration called the point-in-time (PIT) count. By using a local community action agency’s homeless outreach program and a cold weather shelter as two distinct sampling sources to capture data using CRC methodology, the current study sought to estimate the homeless population in Kittitas County, Washington, and compare CRC with PIT count estimates. Four months of data collection for the two sampling sources yielded an estimate of 115 (95% CI [93, 137]) homeless individuals in the county using CRC methodology. The PIT count conducted in the same year sampled 61 individuals, supporting the idea that cross-sectional enumeration (i.e., PIT counts) underestimate the homeless population and could be supplemented by CRC methods, particularly in rural areas where resources to conduct PIT counts may be limited. Greater accuracy in homelessness enumeration may aid in advocacy efforts, provide baseline data for researching interventions designed to reduce homelessness, and yield statistics to better guide legislative and policy initiatives.

An easier life to come for mosquito researchers: field-testing across Italy supports VECTRACK system for automatic counting, identification and absolute density estimation of Aedes albopictus and Culex pipiens adults

BackgroundDisease-vector mosquito monitoring is an essential prerequisite to optimize control interventions and evidence-based risk predictions. However, conventional entomological monitoring methods are labor- and time-consuming and do not allow high temporal/spatial resolution. In 2022, a novel system coupling an optical sensor with machine learning technologies (VECTRACK) proved effective in counting and identifying Aedes albopictus and Culex pipiens adult females and males. Here, we carried out the first extensive field evaluation of the VECTRACK system to assess: (i) whether the catching capacity of a commercial BG-Mosquitaire trap (BGM) for adult mosquito equipped with VECTRACK (BGM + VECT) was affected by the sensor; (ii) the accuracy of the VECTRACK algorithm in correctly classifying the target mosquito species genus and sex; (iii) Ae. albopictus capture rate of BGM with or without VECTRACK.MethodsThe same experimental design was implemented in four areas in northern (Bergamo and Padua districts), central (Rome) and southern (Procida Island, Naples) Italy. In each area, three types of traps—one BGM, one BGM + VECT and the combination of four sticky traps (STs)—were rotated each 48 h in three different sites. Each sampling scheme was replicated three times/area. Collected mosquitoes were counted and identified by both the VECTRACK algorithm and operator-mediated morphological examination. The performance of the VECTRACK system was assessed by generalized linear mixed and linear regression models. Aedes albopictus capture rates of BGMs were calculated based on the known capture rate of ST.ResultsA total of 3829 mosquitoes (90.2% Ae. albopictus) were captured in 18 collection-days/trap/site. BGM and BGM + VECT showed a similar performance in collecting target mosquitoes. Results show high correlation between visual and automatic identification methods (Spearman Ae. albopictus: females = 0.97; males = 0.89; P < 0.0001) and low count errors. Moreover, the results allowed quantifying the heterogeneous effectiveness associated with different trap types in collecting Ae. albopictus and predicting estimates of its absolute density.ConclusionsObtained results strongly support the VECTRACK system as a powerful tool for mosquito monitoring and research, and its applicability over a range of ecological conditions, accounting for its high potential for continuous monitoring with minimal human effort.Graphical

Development of a photocell-based system supported by simulations for road vehicle counting and particle emissions estimation

This work presents the design and implementation of an innovative system that utilizes photocells for road vehicle counting and computational simulations to estimate traffic-related particulate (PM10) emissions. The integration of photocell technology has resulted in a robust system with reduced counting errors, ensuring high accuracy in correlating traffic counts with emission values. The system was evaluated under various environmental conditions, demonstrating its effectiveness in providing reliable real-time data on the impact of road traffic on particle emissions. The methodology included the use of the ADMS-Urban model for emission estimation and simulations with Ansys Fluent, enabling the acquisition of dependable real-time data. The results indicate a strong agreement between measured and calculated PM10 concentrations, with the system maintaining precision comparable to direct observations. The observed values of 5.0 µg/m3 and 19.2% for absolute and relative errors, respectively, demonstrate the system's remarkable performance, supported by a robust correlation represented by R2 = 0.88. This strong correlation underscores the reliability of the employed model, suggesting its capability to capture and explain a substantial portion of the variability in PM10 concentrations. Validation campaigns conducted on a street in Portugal confirmed the system's ability to accurately count vehicles and estimate particle emissions. The analysis revealed a significant agreement between measured and calculated average PM10 concentrations, with standard deviation values of 7.0 µg/m3 for measured concentrations and 7.5 µg/m3 for calculated concentrations, suggesting consistency between the datasets. These conclusions confirm that the system is a valuable tool for researchers and policymakers, providing crucial insights for the management and improvement of urban air quality, contributing to the development of urban planning policies and environmental sustainability.

Advancements in monitoring: a comparison of traditional and application-based tools for measuring outdoor recreation.

Outdoor recreation has experienced a boom in recent years and continues to grow. While outdoor recreation provides wide-ranging benefits to human well-being, there are growing concerns about the sustainability of recreation with the increased pressures placed on ecological systems and visitor experiences. These concerns emphasize the need for managers to access accurate and timely recreation data at scales that match the growing extent of the recreation footprint. Here, we compare spatial and temporal patterns of winter and summer recreation using traditional (trail cameras, infrared counters, aerial surveys, participatory mapping) and application-based tools (Strava Metro, Strava Global Heatmap, Wikiloc) across the Columbia and Canadian Rocky Mountains of western Canada. We demonstrate how recreation use can be estimated using traditional and application-based tools, although their accuracy and utility varies across space, season and activity type. We found that trail cameras and infrared counters captured similar broad-scale patterns in count estimates of pedestrians and all recreation activities. Aerial surveys captured areas with low recreation intensity and participatory mapping captured coarser information on the intensity and extent of recreation across large spatial and temporal scales. Application-based data provided detailed spatiotemporal information on recreation use, but datasets were biased towards specific activities. Strava Metro data was more suited for capturing broad-scale spatial patterns in biking than pedestrian recreation. Application-based data should be supplemented with data from traditional tools to identify biases in data and fill in data gaps. We provide a comparison of each tool for measuring recreation use, highlight each tools' strengths and limitations and applications to address real-world monitoring and management scenarios. Our research contributes towards a better understanding of which tool, or combinations of tools, to use that can expand the rigor and scope of recreation research. These findings support decision-making to mitigate pressures on wildlife and their habitats while allowing for high-quality recreation experiences.

Development of 16N monitoring system for real-time detection of small leakage in reactor coolant systems

This paper introduces a16N monitoring system designed for the real-time detection of minor coolant leaks in nuclear reactors. Conventional coolant leak detection methods in nuclear power plants rely on observing changes in sump tank water levels, as guided by Nuclear Regulatory Commission standards. However, these methods are slow and often inaccurate, particularly for detecting small leakages of less than 0.5 gallons per minute (gpm). Such delays pose risks of corrosion from boric acid and potential safety hazards due to mechanical or thermal fatigue at critical junctions. To address these challenges, we have developed a Reactor Coolant System (RCS) leakage detection system that can swiftly identify small coolant leaks. This system employs beta-ray emissions from 16N, a nuclide predominantly produced during reactor operation and accounting for 90.4% of the total radioactivity in the APR1400 RCS coolant. High-energy beta-rays emitted by 16N are ideal for monitoring due to their significant energy levels, which distinguish them from other nuclides. This system is specifically designed for installation within the annulus zone of the reactor containment, where it captures air near reactor pipes to measure 16N beta-rays. Given the short half-life of 16N (7.13 s), direct measurement is challenging; hence, we use an indirect method to determine calibration factors between dose rate with count rate and estimate radioactivity concentrations based on simulation data and experimental measurements with a surrogate 90Sr/Y source. This system can enhance the safety protocols of nuclear plants by enabling early detection of leaks. Our findings demonstrate that this system can detect leaks as small as 0.01 gpm within seconds, significantly improving response times compared to conventional methods.

Proposal of a novel cardiovascular risk prediction score in lupus nephritis.

Patients with systemic lupus erythematosus are prone to develop cardiovascular disease (CVD), and have increased morbidity and mortality. We conducted a retrospective analysis on lupus nephritis patients to assess the occurrence and predictors of major adverse cardiovascular events (MACE). Data were collected from patients who underwent kidney biopsy between 2005 and 2020. Statistical analysis was performed to unveil correlations. 91 patients were analyzed in this period, with a mean age of 37.3 ± 12.3 years and 86% being female. The mean follow-up time was 62 ± 48 months. 15.38% of the patients underwent at least one MACE. Two patients deceased of CVD. Increased age (35.81 ± 11.14 vs 45.5 ± 15.11 years, p=0.012) entailed a higher occurrence of MACEs. Neutrophil count (5.15 ± 2.83 vs 7.3 ± 2.99 Giga/L, p=0.001) was higher, whereas diastolic blood pressure (DBP) was lower (89.51 ± 10.96 vs 78.43 ± 6.9 mmHg, p<0.001) at the time of the biopsy in patients with MACE. Age, neutrophil count, and DBP proved to be independent predictors of MACEs. We propose a new model (CANDE - Cardiovascular risk based on Age, Neutrophil count, and Diastolic blood pressure Estimation score) calculated from these variables, which predicts the probability of MACE occurrence. This study underscores the importance of actively screening for cardiovascular risks in this vulnerable patient population. Age, neutrophil count, and diastolic blood pressure have been established as independent risk factors for MACE in lupus nephritis. The CANDE score derived from these parameters may serve as a prompt, cost-effective, and easily accessible estimation tool for assessing the likelihood of major adverse cardiovascular risk. These findings emphasize the necessity for comprehensive management strategies addressing both immune dysregulation and cardiovascular risk factors in systemic lupus erythematosus to mitigate adverse outcomes.

Evaluation of artificial intelligence-assisted morphological analysis for platelet count estimation.

This study aims to assess the performance of the platelet count estimation using artificial intelligence technology on the MC-80 digital morphology analyzer. Digital morphology analyzer uses two different computational principles for platelet count estimation: based on PLT/RBC ratio (PLT-M1) and estimate factor (PLT-M2). 977 samples with various platelet counts (low, median, and high) were collected. Out of these, 271 samples were immunoassayed using CD61 and CD41 antibodies. The platelet counts obtained from the hematology analyzer (PLT-I and PLT-O), digital morphology analyzer (PLT-M1 and PLT-M2), and flow cytometry (PLT-IRM) were compared. There was no significant deviation observed before and after verification for both PLT-M1 and PLT-M2 across the analysis range (average bias: -0.845/-0.682, 95% limit of agreement (LOA): -28.675-26.985/-29.420-28.056). When platelet alarms appeared, PLT-M1/PLT-M2 showed the strongest correlation with PLT-IRM than PLT-I with PLT-IRM (r: 0.9814/0.9796 > 0.9601). The correlation between PLT-M1/PLT-M2 and PLT-IRM was strong for samples with interference, such as large platelets or RBC fragments, but relatively weak in small RBCs. The deviation between PLT-M1 and PLT-M2 is related to the number of RBCs. Compared with PLT-I, PLT-M1/PLT-M2 showed higher accuracy for platelet transfusion decisions, especially for samples with low-value PLT. The novel platelet count estimation on the MC-80 digital morphology analyzer provides high accuracy, especially the reviewed result, which can effectively confirm suspicious platelet count.

Apple Watch 6 vs. Galaxy Watch 4: A Validity Study of Step-Count Estimation in Daily Activities.

The purpose of this study was to examine the validity of two wearable smartwatches (the Apple Watch 6 (AW) and the Galaxy Watch 4 (GW)) and smartphone applications (Apple Health for iPhone mobiles and Samsung Health for Android mobiles) for estimating step counts in daily life. A total of 104 healthy adults (36 AW, 25 GW, and 43 smartphone application users) were engaged in daily activities for 24 h while wearing an ActivPAL accelerometer on the thigh and a smartwatch on the wrist. The validities of the smartwatch and smartphone estimates of step counts were evaluated relative to criterion values obtained from an ActivPAL accelerometer. The strongest relationship between the ActivPAL accelerometer and the devices was found for the AW (r = 0.99, p < 0.001), followed by the GW (r = 0.82, p < 0.001), and the smartphone applications (r = 0.93, p < 0.001). For overall group comparisons, the MAPE (Mean Absolute Percentage Error) values (computed as the average absolute value of the group-level errors) were 6.4%, 10.5%, and 29.6% for the AW, GW, and smartphone applications, respectively. The results of the present study indicate that the AW and GW showed strong validity in measuring steps, while the smartphone applications did not provide reliable step counts in free-living conditions.

A Pilot Randomized Control Trial of the Striving Towards EmPowerment and Medication Adherence (STEP-AD) Intervention for Black Women Living with HIV

Black women living with HIV (BWLWH) face adversities associated with lower HIV medication adherence, viral non-suppression, and mental health symptoms (e.g., post-traumatic stress disorder) such as trauma/violence, racism, HIV-related discrimination/stigma, and gender-related stressors. We developed the first intervention based in cognitive behavioral therapy and culturally congruent coping for BWLWH to increase medication adherence and decrease PTSD symptoms by enhancing resilience, self-care, engagement in care, and coping for trauma, racism, HIV-related discrimination/stigma, and gender-related stressors. A pilot randomized control trial was conducted with BWLWH and histories of trauma who were at risk for their HIV viral load remaining or becoming detectable (i.e., below 80% medication adherence, detectable viral load in the past year, and/or missed HIV-related appointments). 119 BWLWH were assessed at baseline and 70 met inclusion criteria, completed one session of Life-Steps adherence counseling, and were randomized to either nine sessions of STEP-AD (Striving Towards EmPowerment and Medication Adherence) or ETAU (enhanced treatment as usual consisting of biweekly check-ins). Women completed a post intervention follow up assessment (3 months post baseline) and 3-month post intervention follow-up (6 months post baseline). Via STATA the difference-in-difference methodology with mixed models compared STEP-AD to ETAU on changes in outcomes over time. BWLWH in STEP-AD compared to E-TAU had significantly higher ART adherence (estimate = 9.36 p = 0.045) and lower likelihood of being clinically diagnosed with PTSD (OR = .07, estimate = − 2.66, p = 0.03) as well as borderline significance on higher CD4 count (estimate = 161.26, p = 0.05). Our findings suggest preliminary efficacy of STEP-AD in improving ART adherence, mental health, and immune function.

A Novel cascaded deep architecture with weak-supervision for video crowd counting and density estimation

A Novel cascaded deep architecture with weak-supervision for video crowd counting and density estimation

Prevalence of Human Papillomavirus Infection and its Association with the Risk of Cervical Cancer among Hiv-Positive Women in Plateau State, North-Central Nigeria

Study’s Novelty/Excerpt This study investigates the prevalence and risk factors of HPV among HIV-infected women in Plateau State, Nigeria, highlighting a significant correlation between low CD4+ counts, high viral loads, and increased HPV infection rates. By utilizing comprehensive diagnostic methods including ELISA for HPV detection and cytology for cervical abnormalities, the research offers robust data linking immunosuppression and HPV-related cervical pathology in a high-risk population. The findings emphasize the urgent need for targeted interventions to improve sexual health behaviors and further research on how low immunity accelerates cervical cancer progression in both HIV-positive women and the broader population. Full Abstract Human papillomavirus (HPV) is one of the most common sexually transmitted infections (STI) associated with cervical, uterine, and anogenital cancers. Persistent infection with HPV is associated with abnormal cervical cells, which can develop into cervical cancer if left untreated. Human papillomaviruses are the first viruses to have been acknowledged to prompt carcinogenesis, and they are linked with cancers of the uterine cervix, anogenital tumours, and head and neck malignancies. A hospital-based study of HIV-infected women across the three senatorial zones of Plateau State, Nigeria, was conducted between November 2018 to November 2020. Ethical approval for the study was first obtained from the ethical committee of Plateau State Specialist Hospital Jos, and informed consent to participate in the research was also obtained from each participant. HIV status confirmation was first done through standard rapid test procedures, followed by cytology testing via the Pap smear procedure to detect any precancerous or malignant changes in the cervix. Subsequent detection of HPV utilized the ELISA procedure, while CD4+ cell count and viral load estimations were done using flow cytometry and nucleic acid amplification techniques, respectively. Questionnaires were administered to obtain information on cervical cancer risk factors and clinical presentations. The overall prevalence of HPV was 28% among HIV-infected women. More HPV infection (31.9%) occurred in women with low CD4+ count (0-200 cells/mm3), and also highest (50.0%) among women with the highest HIV viral load (&gt;100 copies/mL). The possible risk factors identified in this study include multiple sexual partnering, low condom usage, and coinfection with other STIs, among others. In conclusion, this study identified a high HPV prevalence, low CD4+ counts, and coinfection with other STIs among high-risk populations (HIV-infected women). We, therefore, recommend improved sexual behaviours and further research on the impact of low immunity on the rate of progression of cervical abnormality to cervical cancer, not just in HIV-positive women but in the general population.

Significance of neutrophil to lymphocyte ratio evaluation as a prognostic indicator in dengue patients

Dengue is the most important arthropod borne re-emerging tropical viral infection in human beings resulting in more illness and deaths. Platelet count and hematocrit estimations are the most common parameters to assess the severity of dengue. However, due to the heterogeneous nature of the clinical presentation of dengue patients, it’s difficult to identify sensitive clinical or laboratory predictor to assess dengue severity. Neutrophil - Lymphocyte ratio (NLR) has been proved as a prognostic marker in various diseases and may emerge as a potential predictor for the dengue severity and its prognosis. This study aims to evaluate the NLR in dengue patients and to assess its role in relation with patients clinical and serological profile as well as platelet counts. A retrospective observational study was conducted in which 100 cases (≥12 years of age) of dengue which were diagnosed at our laboratory were studied and their clinical and laboratory data were analysed. Test of significance was done by Chi Square test with consideration of P value ≤ 0.05 as statistically significant. Male predominance was observed with dengue infection. NS1Ag (+) was the most common dengue serological profile. NLR ≤2 significantly correlates with thrombocytopenia. A significant relation was found between NLR and severity of the dengue patients. In conclusion, NLR ≤2 correlates with low platelet count which corresponds to more severity of the dengue disease. NLR &gt;2 indicates more platelet count and less severity of the disease. Hence NLR evaluation can be used as a prognostic indicator in dengue patients.

Using machine learning to count Antarctic shag (Leucocarbo bransfieldensis) nests on images captured by remotely piloted aircraft systems

Using 51 orthomosaics of 11 breeding locations of the Antarctic shag (Leucocarbo bransfieldensis), we propose a method for automating counting of shag nests. This is achieved by training an object detection model based on the “You Only Look Once” (YOLO) architecture and identifying nests on sections of the orthomosaic, which are later combined with predictions for the entire orthomosaic. Our results show that the current use of Remotely Piloted Aircraft Systems (RPAS) to collect images of areas with shag colonies, combined with machine learning algorithms, can provide reliable and fast estimates of shag nest counts (F1 score > 0.95). By using data from only two shag colonies for training, we show that models can be obtained that generalise well to images of both spatially and temporally distinct colonies. The proposed practical application opens the possibility of using aerial imagery to perform large-scale surveys of Antarctic islands in search of undiscovered shag colonies. We discuss the conditions for optimal performance of the model as well as its limitations. The code, data and trained model allowing for full reproducibility of the results are available at https://github.com/Appsilon/Antarctic-nests.

How much is enough? The role of R&amp;D investment in the innovation process

AbstractWhile both production function (PF) and Absorptive Capacity (AC) explanations have played an important role in explaining the relationship between a firm's Research and Development (R&amp;D) and its innovation, each has developed independently of the contributions of the other. The purpose of this study is to theoretically and empirically develop a concept of AC that incorporates the role of diminishing returns and external spillovers (i.e., strategic alliances) into a biotechnology firm's R&amp;D‐innovation process. In using count estimations, this study finds that a firm's R&amp;D‐innovation process is subject to a nonlinear ‐U‐shaped‐ learning process and that this process is moderated by its strategic partnerships. The contribution of this study is that it challenges the linearity assumptions and findings of AC research and that it offers a greater openness to PF explanations of the R&amp;D‐innovation process.

Evaluation of Urinary Free Light Chains as a Marker of Severity of HIV Disease and Its Correlation with CD4 Count: A Pilot Study.

Human immunodeficiency virus (HIV) infection weakens immunity. Monitoring the immune status of the patient has become an important aspect of evaluating the progression of the disease and informing follow-up after treatment. Estimation of CD4 counts is quite costly and requires expertise in flow cytometry. In certain pathologies, free light chains (FLCs) are secreted in serum and urine and the magnitude can be used to monitor the severity, progression, and therapeutic monitoring of the disease. Urine as a specimen proves cost-effective and presents reduced risks during sample collection. The stability of light chains in urine at room temperature over extended periods simplifies the management of sample transportation as well. Hence, a pilot cross-sectional study was planned to evaluate the levels of urinary immunoglobulins in patients with HIV. The study was conducted at PGIMER, Dr. Ram Manohar Lohia Hospital (presently ABVIMS), New Delhi. Sixty-nine consecutive ART-naive HIV patients aged between 18 and 40 years and 69 age- and sex-matched healthy controls were included in the study. Urinary FLC kappa (κ) and lambda (λ) were measured using an immunoglobulin ELISA kit. Baseline urinary κ light chain levels were significantly higher in cases when compared with controls (p < .001) and were found to be increased with increasing WHO immunological classes (p < .001) and inversely related to CD4 cell count. However, no significant difference in mean urinary λ immunoglobulin light chain between cases and controls was found and no correlation with CD4 cell count or with stages of WHO immunological classification of HIV disease was observed. It is suggested that urinary free κ chain measurements combined with serum light chain measurements may be a useful marker in the follow-up and monitoring of response to therapies in patients with HIV where testing by flow cytometry is not available.