A 43-year-old woman with no prior medical issues presented with dyspnea and cough, initially diagnosed as RSV infection. Persistent symptoms led to a diagnosis of pulmonary thromboembolism, treated with EKOS thrombectomy and anticoagulation. Despite two thrombectomies, she had worsening symptoms, dyspnea, and pleuritic pain, with recurrent pericardial effusion. Lab results indicated possible lupus or antiphospholipid syndrome. Echocardiography and imaging revealed pericardial effusion and pulmonary lesions. Biopsies confirmed pulmonary artery intimal sarcoma. Post-diagnosis, she was referred to oncology for treatment. PAS, a rare condition often misdiagnosed, mimicked thromboembolism and autoimmune disease, underscoring diagnostic challenges.

Composite solid propellant often needs to add solid catalysts to improve its combustion performance, but result in the reduced mechanical properties. According to the situation, a novel research strategy was proposed, which ammonium perchlorate (AP) was packed into the layered porous structure of copper alginate (Cu-A). Cu-A effectively enhances the interfacial interaction between AP and binder matrix by reacting with toluene diisocynate. In addition, Cu, Cu 2O and CuO produced by the decomposition of Cu-A are well dispersed in carbonated porous framework decomposed by Cu-A with electrical conductivity, which realizes the efficient catalysis for AP. After being applied to AP-based propellant, AP/Cu-A strengthens its ignition capacity and combustion rate, which organic porous structure of Cu-A plays a role in combustion-supporting effect. The tensile results show the maximum tensile strength of the propellant is increased with the reduced adhesion index. It confirms the enhanced interface force between AP and binder matrix by Cu-A, which alleviates the dewetting phenomenon. It is clearly that AP/Cu-A improves the negative effect of solid combustion catalysts on the mechanical performance of prepared propellant, which has both excellent combustion and mechanical properties. The study offers a new perspective for the selection of combustion catalysts for the propellant.

The escalating frequency and sophistication of cyber threats necessitate the development of advanced detection mechanisms capable of operating autonomously and in real-time. The Real-Time Anomalous Entropy Analysis (RAEA) approach introduces a novel methodology that leverages entropy-based analysis to identify ransomware activities with high accuracy and minimal latency. By establishing baseline entropy profiles and employing adaptive anomaly detection algorithms, RAEA effectively distinguishes between benign and malicious processes, thereby reducing false positives and enhancing overall detection reliability. Empirical evaluations demonstrate RAEA's proficiency in detecting a diverse array of ransomware variants, including those employing sophisticated evasion techniques, while maintaining efficient resource utilization and scalability across various operational environments. The integration of RAEA into existing cybersecurity frameworks has the potential to substantially improve real-time threat detection and response capabilities, thereby mitigating the impact of ransomware attacks and contributing to the overall resilience of information systems.

The escalating sophistication of ransomware attacks necessitates the development of advanced detection methodologies capable of identifying complex and evolving threat patterns. The Hierarchical Behavioral Pattern Analysis (HBPA) framework introduces a novel approach that models ransomware behaviors through a hierarchical structure, capturing intricate behavioral sequences and dependencies. By integrating probabilistic models and calculus-based representations, HBPA achieves a detection rate of 98.7% with a false positive rate of 1.3%, outperforming traditional detection systems. The framework's adaptability to emerging ransomware variants and scalability across varying dataset sizes demonstrate its potential to enhance cybersecurity defenses. Comprehensive experimental validation affirms HBPA's efficacy in identifying both known and novel ransomware threats, positioning it as a significant advancement in the field of automated threat detection.

Currently the development and creation of websites is imposed on the world as a technological tool to unite the regions, create businesses, support companies, be displayed in the market and infinity of applications according to the perspectives of people and their scope, finding on the internet, a vital source of job creation, efficient and intelligent businesses and great help in achieving objectives and approaches.

The Murraya paniculata’s methanolic extracts were subjected to LC/MS analysis, a NO assay for measuring antioxidant activity, and an estimate of significant phytochemicals. Compared to the conventional ascorbic acid, abundant polyphenols, flavonoids, and tannins exhibit good antioxidant activity (50.88 %) (53.87 %). The LC/MS analysis further confirms the principal 13 active phytochemicals in the crude extract. These compounds were molecularly docked on the human estrogen receptor (a target protein for breast cancer) to identify the most powerful phytochemicals. In this investigation, the 5,7-Dimethoxy-8-(2-oxo-3-methylbutyl) coumarin exhibits good drug-like qualities and a greater binding interaction (-30.3502 Kcal/mol) than the conventional 5-fluorouracil (-25.3186 Kcal/mol). We also use molecular dynamics modeling to confirm this phytochemical’s protein stability, RMSD, and RMSF with the target. Finally, we discovered seven phytochemicals with powerful anti-breast cancer properties.

Can simply standing up sharpen our minds? Whereas initial works proposed that standing posture reduces distractor interference in the Stroop task, subsequent replication attempts yielded mixed results. Importantly, previous studies mostly ignored individual fitness factors like BMI, physical activity level, and cardiac measures, which have recently been suggested to explain mixed findings. Here, we addressed these inconsistencies by controlling for such fitness characteristics in N = 36 healthy adults. Additionally, we expanded upon previous research by examining posture's influence on previously unexplored space-based cognitive control using the Navon task. We found evidence against an effect of standing posture on Stroop interference, while, in contrast, standing posture significantly improved spatial conflict processing in the Navon task. Most importantly, this effect was fully mediated by enhanced physiological arousal as indexed by reduced HRV, directly supporting the proposed mechanism of how standing influences cognitive control through increased postural demands. Additionally, our findings challenge the idea of a unified attentional control system, revealing posture differentially influences conflict processing depending on their task-specific nature. These insights may advance theoretical understanding and have practical implications: in increasingly sedentary societies, simple changes like standing could selectively enhance certain cognitive functions, improving effective work and learning environments.

This article develops a duality principle applicable to originally non-convex primal variational formulations. More specifically, as a first application, we establish a convex dual variational formulation for a non-linear Kirchhoff-Love plate model. The results are obtained through basic tools of functional analysis, calculus of variations, duality and optimization theory in infinite dimensional spaces. We emphasize such a convex dual formulation obtained may be applied to a large class of similar models in the calculus of variations.

The Riemann Hypothesis remains one of the most critical unsolved problems in mathematics, proposing that all non-trivial zeros of the Riemann zeta function are located on the critical line. This paper explores unique fractal structures within the zeta function, drawing comparisons with the Mandelbrot set and the Smith chart, which together illuminate possible connections between prime distribution, electromagnetic symmetry, and gravitational principles.

Note: Crystal nanoparticles are produced and propagated using  various methods, such as CVD, laser irradiation, and discharge  . CVD method  is the best option for the industrial production of crystal nanoparticles. The reason for this is the low  cost-to-income ratio as well as the possibility of vertical growth of crystal particles on the desired substrate  . In this process, the crystal nanoparticles in the  negative electrode are sublimated, which  is due to the high temperature in the discharge process. Since  this method was used for the synthesis of  crystal nanoparticles  for the first time, it is known as the most common method of producing  crystal nanoparticles  . In this method,  there are both types of  crystal nanoparticles  , single-walled and  multi-walled, with lengths up to 50 microns containing  structural defects. In the process of laser radiation, a laser is used to vaporize  graphite and an inert gas  is used to direct these vapors into the tank.  Crystal nanoparticles grow  on the cold surface of the reactor  . It  is suitable for the production of  multi-walled crystal nanoparticles  . And by  using graphite composite  and metal catalyst particles of  cobalt and nickel combination (for the synthesis of  crystal nanoparticles)  . The efficiency of this process is 70  % and its main product  is crystal nanoparticles  , the diameter of these  crystal nanoparticles is completely controllable and  the diameter of  the crystal nanoparticles can be  controlled as desired by changing the temperature. He  used  the CVD method with the help  of a catalyst to produce  crystal nanoparticles . During the CVD process, a layer of  nickel, cobalt and  metal catalyst particles, generally  iron,  is used to produce  crystal nanoparticles . CVD is a common method for the commercial production  of crystal nanoparticles  . In this method, the diameter of  crystal nanoparticles  is related to the dimensions of metal particles. By  patterning the substrate, using heat treatment and  H plasma etching of the catalyst,   the diameter of the   crystal nanoparticles  can be  controlled. 

The exponential growth in the scale and complexity of language models has led to significant computational challenges, necessitating innovative solutions to maintain efficiency without compromising performance. Dynamic Contextual Layer Pruning (DCLP) emerges as a novel technique that dynamically adjusts layer activation based on input complexity, thereby optimizing resource utilization while preserving model efficacy. Implementing DCLP within a large language model framework has resulted in notable reductions in processing time, memory usage, and energy consumption, alongside improvements in performance metrics such as perplexity and accuracy. Comparative analyses have demonstrated DCLP's superiority over traditional static pruning methods, highlighting its adaptive capabilities and potential for broad applicability across diverse natural language processing tasks. These findings underscore DCLP's promise as a transformative approach to enhancing the efficiency and adaptability of large language models.

The expanding complexity of language data continues to challenge the interpretive capabilities of traditional model architectures, often revealing limitations in capturing layered semantic relationships critical for complex comprehension. Introducing a dynamically adaptive framework, the Dynamic Contextual Cascade Network (DCCN) presents an innovative approach that facilitates multi-layered semantic decomposition, designed to engage selectively with each segment of linguistic input based on its contextual demands. Through a hierarchical structure, DCCN aligns distinct processing layers to specific contextual attributes, enabling refined interpretations of complex linguistic information and significantly enhancing contextual fidelity across diverse applications. Each layer within DCCN contributes to a progressive, context-sensitive understanding, with selective activation ensuring computational efficiency while maintaining interpretative depth. Experimental evaluations demonstrate DCCN's marked improvements over conventional architectures, particularly in semantic accuracy and contextual retention, showcasing its robustness in managing high-complexity language inputs with minimal compromise in processing speed. The results demonstrate that DCCN not only advances the boundaries of semantic interpretation within language models but also establishes a foundational architecture that can be tailored for diverse natural language processing applications, setting a new standard for contextually aware language modeling.

The sweeping net method has been an effective tool for approximating singularities in two-dimensional manifolds. In this paper, we propose a conjecture that extends the sweeping net method to higher-dimensional manifolds with isolated singularities. We present a detailed formulation of the conjecture, discuss the key challenges in proving it, and explore potential approaches using local coordinate analysis, geometric measure theory, and multi-dimensional generalizations of trigonometric functions. This work aims to open new avenues in the study of singularities on manifolds and stimulate further research in this area.

The escalating sophistication and frequency of cyber threats necessitate the development of advanced detection mechanisms capable of identifying and mitigating malicious activities with minimal human intervention. The Dynamic Threat Pattern Recognition (DTPR) system introduces an innovative approach to ransomware detection through the analysis of behavioral patterns, thereby addressing the limitations inherent in traditional signature-based methods. Employing a comprehensive dataset encompassing diverse ransomware variants and benign software, the DTPR system underwent rigorous evaluation to assess its detection accuracy, false positive rate, detection latency, resource utilization, and adaptability to emerging threats. The system achieved a detection accuracy of 97.8% and a false positive rate of 2.0%, demonstrating its precision in distinguishing malicious activities from legitimate processes. With an average detection latency of 0.45 seconds, the DTPR system facilitates prompt responses to ransomware incidents, thereby mitigating potential damage. The system's efficient resource utilization, characterized by moderate CPU and memory consumption under varying load conditions, demonstrates its suitability for deployment in environments with limited computational resources. Furthermore, the DTPR system exhibited adaptability to novel ransomware strains, successfully detecting 92.5% of previously unseen variants, highlighting its potential to address the evolving nature of cyber threats. Collectively, these findings affirm the DTPR system's capacity to significantly advance ransomware detection methodologies and enhance the overall security posture against sophisticated cyber threats.

By desuspending a zero-dimensional manifold, one obtains Einstein manifolds with so-called "virtual dimensions." This leads to a remarkably rich reformulation of the point particle picture, in which partially negative-dimensional product (PNDP) strings and manifolds are placed at the fore. In this paper, I explore a simple braneworld picture in which the virtual dimensions of a PNDP spacetime can be used to recover the Newtonian picture of gravity.

As species interactions influence species distributions, attempts to work backwards from observed species distributions to infer the effect of a potential interaction have been enticing. The “natural removal experiment,” an approach that tests for patterns consistent with competition by comparing a species’ habitat relationships in sympatry and allopatry with a potential competitor, has held promise, but has often been limited by the scales of data required. The recent expansion of community science-based datasets invites renewed opportunity for investigation using this approach. We revitalize the natural removal experiment by applying it to ask whether the distribution of the Chestnut-backed Chickadee (Poecile rufescens; CBCH) is consistent with competition with the Black-capped Chickadee (Poecile atricapillus; BCCH) in urban areas. Using data from the community science project eBird, we compared relationships of CBCH relative abundance to urban- and forest-related variables in individual urban-centres and across regions of allopatry and sympatry with the BCCH. As predicted under competition, we found that in allopatry, the CBCH adopted habitat relationships similar to the less forested, more urban habitats of the BCCH at both the urban-centre and regional scale. By applying predictive modeling to “imagine” either the absence or ubiquity of BCCH across the studied range of CBCH, we found that sympatry with the BCCH suppressed CBCH abundance in urban areas, and that in their absence CBCH abundance increased, consistent with competitive release. These lines of evidence suggest that the observed distribution of CBCH is consistent with that expected under competition with the BCCH in urban areas. Expanding beyond this case study, we discuss important considerations in the application of this approach, which presents a significant opportunity for researchers to harness community science to conduct more powerful investigations of species interactions extending to broad spatial scales.

Biologists have long sought to understand the impacts of deleterious genetic variation on fitness and population viability. However, our understanding of these effects in the wild is incomplete, in part due to the rarity of sufficient genetic and demographic data needed to measure their impact. The genomics revolution is promising a potential solution by predicting the fitness effects of deleterious genetic variants (genetic load) bioinformatically from genome sequences alone, bypassing the need for costly demographic data. After a historical perspective on the theoretical and empirical basis of our understanding of the dynamics and fitness effects of deleterious genetic variation, we evaluate the potential for these new genomic measures of genetic load to predict population viability. We argue that current genomic analyses alone cannot reliably predict the effects of deleterious genetic variation on population growth, because these depend on demographic, ecological, and genetic parameters that need more than just genome sequence data to be measured. Thus, while purely genomic analyses of genetic load promise to improve our understanding of the composition of the genetic load, they are currently of little use for evaluating population viability. Demographic data and ecological context remain crucial to our understanding of the consequences of deleterious genetic variation for population fitness. However, when combined with such demographic and ecological data, genomic information can offer important insights into genetic variation and inbreeding that are crucial for conservation decision making.

Atrial fibrillation (AF) is the most common sustained arrhythmia. While the primary treatment concern with AF is the managing the risk of thromboembolism, AF can also cause significant symptoms in some patients and be completely asymptomatic in others (1). Many patients with AF have debilitating symptoms including palpitations, heart failure, and anxiety. It is well known that patients can have some symptomatic AF episodes and others that are asymptomatic (2). In this issue of the Journal of Cardiovascular Electrophysiology, a study by Ahluwalia et al. attempts to assess factors associated with symptomatic AF episodes in patients with insertable cardiac monitors (ICM).In this retrospective observational study, the authors examined 11,267 patients with a Reveal LINQTM ICM in the DiscoveryLink from 2007 to 2021. These patients all had episodes of AF detected by the ICM’s detection algorithm, and the patient’s clinical data was obtained from the Optum® de-identified electronic health record. The AF episodes were not adjudicated, and the symptoms were not verified.Asymptomatic AF was far more common than symptomatic episodes. Only 15.8% of patients had a device reported AF symptom. In addition, of the 380,625 episodes of automatically detected AF, only 6190 episodes (1.63%) had a symptom activation within 20 minutes. The symptomatic AF patients were younger (67±12 years vs. 71±11 years old), more female (51% vs 43%), and had less cardiovascular diagnoses. In addition, AF episodes with a symptom activation, were longer in duration (5.5 hrs vs. 3.7 hrs), had a higher ventricular rate (103 bpm vs. 88 bpm), and had a higher AF evidence score (more likely to be true AF per the device-based algorithm)These findings are similar to other reports about AF detections, especially device detected AF, but have extended them to a large cohort of patients with ICMs, with clinical and symptom data. The most important takeaway is that screening for AF needs to go far beyond evaluation for symptoms as most AF episodes are not symptomatic, and in patients with symptomatic AF, the episodes of AF are just the tip of the iceberg of AF burden. Symptoms were associated with a higher AF evidence score, indicating the symptoms could be used to help differentiate a true AF detection, from a false positive which could help guide clinical management. Also AF symptoms seemed to be more common in younger, less comorbid, and female patients, which is consistent with other published data (1). This does highlight one paradoxical finding, that females seem to be more symptomatic with AF, but less likely to be offered ablation.The primary limitation of this study is the retrospective observational nature of the data. It is unclear why the patients pushed the symptom activator. The symptom activation could have been for palpitations, another AF related symptoms, or purely accidental. It is not known what instructions the patients received to activate the symptom activator, and what percentage of patients knew how to actually use it properly. Also given the nature of the handheld device to mark symptoms, it is likely that many patients did not activate the ICM for every symptom either to due to symptom fatigue, or inability to access the activator at the time of an episode. Some of these limitations could be overcome with a prospective cohort educated in using the ICM to track AF symptoms, and the newer app-based symptom activators. Since the AF episodes were not adjudicated, it is unclear if some of the symptom episodes were related to false AF detections. Another useful piece of information from this data would be the number of symptom activations that were not related to an arrhythmia.However, this study does give an insight into a large cohort of patients with AF and ICMs. This data could help spur future research into how to properly assess for AF symptoms, and how to minimize AF symptoms.Sgreccia D, Manicardi M, Malavasi VL, Vitolo M, Valenti AC, Proietti M, Lip GYH, Boriani G. Comparing Outcomes in Asymptomatic and Symptomatic Atrial Fibrillation: A Systematic Review and Meta-Analysis of 81,462 Patients. Journal of Clinical Medicine. 2021; 10(17):3979. https://doi.org/10.3390/jcm10173979Quirino G, Giammaria M, Corbucci G, Pistelli P, Turri E, Mazza A, Perucca A, Checchinato C, Dalmasso M, Barold SS. Diagnosis of paroxysmal atrial fibrillation in patients with implanted pacemakers: relationship to symptoms and other variables. Pacing Clin Electrophysiol. 2009;32:91-98. https://doi.org/10.1111/j.1540-8159.2009.02181.x

Background The approval of cystic fibrosis conductance regulator (CFTR) modulators including the triple modulator Elexacaftor/Tezacaftor/Ivacaftor (ETI) for people with cystic fibrosis (pwCF) have improved pulmonary function significantly. The effect on CF related bone disease (CFBD) and body composition remain unclear. Methods This is a retrospective real world study of adults pwCF. Bone density and body composition were obtained via dual-energy x-ray absorptiometry (DXA) 1.8 (SD0.7) years prior and 1.5 (SD0.4) years (postDXA1) and in a subgroup 3.5 (SD0.2) years (postDXA2) after ETI initiation. Clinical data were collected at time of DXA scan and ETI start. Results 74 pwCF were included of which 42% were female, with average age at ETI initiation of 38.9 (SD 9.3) years. 45.9% had cystic fibrosis related diabetes (CFRD) and 93.2% had exocrine pancreatic insufficiency. Bone density decreased at the spine (p= 0.0256), left hip (p< 0.001) and right hip (p< 0.001) from pre- to postDXA1, no change in bone density was seen in any of the sites when postDXA1 was compared to postDXA2. Body composition in the postDXA1 compared to preDXA showed increase in weight in female (p=0.011) and male (p=0.001), and in fat mass in female (p=0.003) and male (p=0.001), without a change in lean mass (p=0.4). Comparison of body composition in postDXA2 to postDXA1 failed to show significant change in body weight (woman p=0.38; men p=0.94) and in fat mass (woman p=77; men p=0.939). Conclusions Bone density decreased and body weight and fat mass increased early on after ETI initiation. This appears to stabilize with prolonged treatment.

Decoding the biology of ethnic differences in asthmaDuan Ni1,2,3, Ralph Nanan1,2,3*1 Sydney Medical School Nepean, The University of Sydney, Sydney, NSW, Australia2 Charles Perkins Centre, The University of Sydney, Sydney, NSW, Australia3 Nepean Hospital, Nepean Blue Mountains Local Health District, Sydney, NSW, Australia*Correspondence:Ralph Nananralph.nanan@sydney.edu.auSydney Medical School Nepean, The University of Sydney.Nepean Hospital, Level 5, South Block, Penrith NSW, 2751, AustraliaTelephone: +61 2 4734 1614Fax: +61 2 4734 1144

Objective: To investigate the impact of vaping and smoking on ovarian reserve markers anti-Müllerian hormone (AMH) and follicle-stimulating hormone (FSH). Design: Retrospective observational study. Setting: Users of a private at-home testing service. Population: 21,102 women aged 21-45 not taking hormonal contraception with no preexisting common reproductive health conditions. Methods: Serum AMH and FSH was assayed via finger prick samples and medical history data was collected online. The relationship between vaping or smoking, and log-transformed AMH or FSH was assessed via adjusted multiple linear regression. Logistic regression was used to produce odds ratios for age-dependent low AMH. Main Outcome Measures: Serum AMH (pmol/L), FSH (IU/L) and age-dependent low AMH. Results: Current and occasional vaping significantly decreased AMH by 7.76% (95 CI 12.24-3.05%,p=0.002) and 4.88% (95% CI 8.7-0.9%,p=0.017) respectively. Current vaping increased the odds of low AMH by 1.58 (aOR=1.58,95% CI=1.25-1.99,p<0.001) times, however, FSH was unaffected. Current smoking significantly decreased AMH by 10.1% (95 CI 16.13-7.3%,p=0.002), increasing the odds of low AMH by 1.59 (aOR=1.59,95% CI=1.17-2.12,p=0.002) and significantly increasing FSH by 7.4% (95% CI 4.2-10.63%,p<0.001). Conclusions: Both vaping and smoking have negative impacts on ovarian reserve markers. Funding: This work was supported by Hertility Health Ltd. Keywords: Vaping, e-cigarette, smoking, ovarian reserve, anti-müllerian hormone, follicle stimulating hormone

Measuring the Quality of Experience (QoE) has become an important research topic, especially in video streaming networks. Since it directly measures customer satisfaction, QoE also becomes an interesting topic for network operators. Objective QoE Assessment outperforms in applicability the high-cost offline subjective procedures. However, quantifying and modeling the QoE is still a task with many open challenges. The P.1203 recommendations by the ITU-T are distinguished as the first standard for modeling the video streaming QoE. This work aims to provide the network operators an early warning about the possible next QoE degradation to allow them to avoid this situation proactively. A low-cost user interaction simulation is proposed using Selenium instead of offline subjective tests. Basing on the ITU-T P.1203 recommendations, the video parameters are also extracted and processed to build a training dataset obtained from a real online environment. In addition, a network prob at the client side is used to obtain additional non-streaming network parameters from different providers’ networks during each viewing session. The QoE then has been predicted using machine learning in three different proposed scenarios using both classification and regression. Feature ranking is also used for dimensionality reduction and enhancing the accuracy and training time. The results are compared to the labeled samples of ITU-T standard using cross-validation accuracy, RMSE, and confusion matrices. The results showed a high prediction accuracy and consistency with the ITU-T standards. Also, they highlighted the main parameters that are highly impacting the end-to-end experienced quality of video streaming.

Management of Unintentional Acute Organophosphate Poisoning After Dermal Exposure in Primary Health Care Setting: A Rare Case ReportBishal Koirala1, Priyanka Devkota2, Raghu Devkota31Medical Officer, Dangisharan Basic Hospital, Dang, 22400, NepalEmail: bishalkoirala22@gmail.comORCiD: 0009-0008-3663-997XContact number: +97798455675411Corresponding Author2Lab Research Analyst, Duke Human Vaccine Institute, North Carolina, 27710, USAEmail: pd167@duke.eduORCiD: 0009-0003-4882-114XContact number: +166249702363GP Registrar, Clocktower medical centre, Sale, Victoria, 3850, AustraliaEmail: rndevkota90@gmail.comContact number: +61431407427

not-yet-known not-yet-known not-yet-known unknown A Case of Pneumocystis Pneumonia in an HIV/AIDS Patient from Sudan Emmanuel Edwar Siddig1, Alaa Tajeldeen Habeeb2, Claude Mambo Muvunyi3 , Ayman Ahmed3,4,5 1 Faculty of Medical Laboratory sciences, University of Khartoum, Khartoum, Sudan 2 Rufa’a Teaching Hospital, Aljazeira state, Sudan 3 Rwanda Biomedical Centre, Kigali, Rwanda. 4 Institute of Endemic diseases, University of Khartoum, Khartoum, Sudan 5 Pan-Africa One Health Institute (PAOHI), Kigali 11KG ST203, Rwanda