Submission Strategies Research Articles

Insights on feline infectious peritonitis risk factors and sampling strategies from polymerase chain reaction analysis of feline coronavirus in large-scale nationwide submissions.

This nationwide study aimed to investigate risk factors associated with FIP and determine optimal sample submission strategies for its diagnosis. A total of 14,035 clinical samples from cats across the US were analyzed by means of reverse transcriptase quantitative PCR to detect replicating feline coronavirus (FCoV). χ2 and logistic regression analyses were conducted to assess the association between FCoV detection rates and risk factors such as age, gender, breed, and types of submitted samples. Higher FCoV detection rates were observed in younger cats, particularly those aged 0 to 1 year, and in male cats. Purebred cats, notably British Shorthairs [OR: 2.81; P < .001], showed a higher incidence of FCoV infection than other cats. Peritoneal fluid (OR, 7.51; P < .001) exhibited higher FCoV detection rates than other samples, while lower rates were seen in blood samples (OR, 0.08; P < .001) than in other samples. High FCoV detection rates were found in urine, kidney, and lymph node samples. The study identified significant risk factors associated with FIP. Optimal sample submission strategies, particularly emphasizing the use of peritoneal fluid, kidney, and lymph node, were identified to improve FIP detection rates. Urine yielded a relatively high frequency of infection and viral loads compared with most other samples. Understanding the risk factors and optimizing sample selection for FIP diagnosis can aid in the early detection and management of the disease, ultimately improving outcomes for affected cats. These findings contribute valuable insights to FIP epidemiology and underscore the importance of continued research to enhance diagnostic strategies and disease management approaches.

Pre-ANDA strategy and Human Factors activities to de-risk pharmaceutical companies ANDA submission of drug–device combination products: case study of a formative Comparative Use Human Factors study

ABSTRACT Background This article presents a strategy that a Drug Delivery Device Developer (DDDD) has adopted to support Abbreviated New Drug Application (ANDA) submissions of drug–device combination products. As per the related FDA guidance, a threshold analysis should be compiled. If ‘other differences’ between the Reference Listed Drug (RLD) and the generic drug devices are identified, a Comparative Use Human Factors (CUHF) study may be requested. Methods The DDDD performed task analysis and physical comparison to assess the pen injector design differences. Then, a formative CUHF study with 25 participants simulating injections using both RLD and the generic pen injectors was conducted. Results After each participant completed four simulated injections, similar type and rates of use error between the RLD (0.70) and generic (0.68) pen injectors were observed. Conclusion DDDDs can support pharmaceutical companies in the ANDA submission strategy of their drug–device combination product by initiating comparative task analysis and physical comparison of the device as inputs for the threshold analysis. If ‘other differences’ are identified, a formative CUHF study can be performed. As shown in our case study, this approach can be leveraged to support the sample size calculation and non-inferiority margin determination for a CUHF study with the final combination product.

Conflict Prevention in Internet during Spread of Coronavirus Infection (Survey Results)

The article presents the results of a survey of respondents, one of the tasks of which was to propose a response to a conflict-provoking message in such a way as to avoid escalation of the conflict and continue communication with the aggressor. The survey participants were 115 respondents (22% male and 78% female) aged 20 to 35, with incomplete higher or higher education. The respondents were presented with 15 texts, which were real comments on posts in LiveJournal, containing markers of conflict-provoking intention by the sender and published during the COVID-19 pandemic. The study of the respondents’ responses was conducted using methods of linguostylistic, contextual, and statistical analysis. Data analysis identified four speech strategies used by the respondents to prevent conflictual interaction, as well as tactics through which these strategies were implemented: self-defense strategy (distancing tactic, pluralism accentuation tactic, topic change tactic), politeness strategy (gratitude tactic, advice tactic, apology tactic, offer of help tactic), submission strategy (concession tactic, reinforcement of aggressor’s authority tactic), persuasion strategy (reasoning tactic, argumentation tactic).

Earth system modeling on modular supercomputing architecture: coupled atmosphere–ocean simulations with ICON 2.6.6-rc

Abstract. The confrontation of complex Earth system model (ESM) codes with novel supercomputing architectures poses challenges to efficient modeling and job submission strategies. The modular setup of these models naturally fits a modular supercomputing architecture (MSA), which tightly integrates heterogeneous hardware resources into a larger and more flexible high-performance computing (HPC) system. While parts of the ESM codes can easily take advantage of the increased parallelism and communication capabilities of modern GPUs, others lag behind due to the long development cycles or are better suited to run on classical CPUs due to their communication and memory usage patterns. To better cope with these imbalances between the development of the model components, we performed benchmark campaigns on the Jülich Wizard for European Leadership Science (JUWELS) modular HPC system. We enabled the weather and climate model Icosahedral Nonhydrostatic (ICON) to run in a coupled atmosphere–ocean setup, where the ocean and the model I/O is running on the CPU Cluster, while the atmosphere is simulated simultaneously on the GPUs of JUWELS Booster (ICON-MSA). Both atmosphere and ocean are running globally with a resolution of 5 km. In our test case, an optimal configuration in terms of model performance (core hours per simulation day) was found for the combination of 84 GPU nodes on the JUWELS Booster module to simulate the atmosphere and 80 CPU nodes on the JUWELS Cluster module, of which 63 nodes were used for the ocean simulation and the remaining 17 nodes were reserved for I/O. With this configuration the waiting times of the coupler were minimized. Compared to a simulation performed on CPUs only, the MSA approach reduces energy consumption by 45 % with comparable runtimes. ICON-MSA is able to scale up to a significant portion of the JUWELS system, making best use of the available computing resources. A maximum throughput of 170 simulation days per day (SDPD) was achieved when running ICON on 335 JUWELS Booster nodes and 268 Cluster nodes.

Do male and female authors employ different journal choice strategies?

AbstractCompared to their male colleagues, female scientists are less likely to secure senior positions and more likely to drop out of academia. The mechanisms behind these patterns have been the subject of debate in recent years, entailing serious policy implications. In this project we investigate one such mechanism, namely the journal submission strategies of male and female authors. In view of the evidence pertaining to higher self-confidence and/or risk acceptance among males, it may be expected that males would generally tend to follow a more ambitious journal choice strategy. To verify this conjecture, we developed a novel method and looked to acquire a new dataset, surveying scholars in three relatively gender-balanced disciplines representing humanities (history), social sciences (economics), and natural sciences (environmental sciences). Focusing on their specific, recently published papers, we ask about the journals to which they had submitted these papers and the journals to which they could potentially look to engage with. In the 1111 complete responses we found evidence that males are not only more self-confident but also more forward-looking in their journal choice.

Learning from pediatric CAR-T development: insights from manufacturing unique patient doses

Chris Brown is the Director of GMP Manufacturing within the Therapeutic Cell Production Core, Seattle Children’s Therapeutics’ GMP cell manufacturing facility. He leads the facility’s manufacturing/process development team and played a key role in initial design, stand-up, and ongoing development of the TCPC facilities, team, and manufacturing methodologies. He has more than 20 years’ experience in the manufacturing of cellular products for Phase 1 and 2 clinical trials, with a focus in translating cutting-edge research into first-in-human cellular therapeutics. He joined Seattle Children’s Research Institute from the Fred Hutchinson Cancer Research Center in 2010, where he led the manufacturing team within the Cellular Production Facility cleanroom. He holds a BA in Biology from Carleton College in Northfield, Minnesota.Sean Werner&#160;is the Chief Technology Officer – Cell Processing at BioLife Solutions, a leading provider of bioproduction tools and services to the cell and gene therapy and broader biopharma markets. BioLife acquired Sexton Biotechnologies in 2021 where Sean was President of the company known for providing processing and handling solutions for the CGT industry. Sean received his PhD from Purdue University in Biology followed by post-doctoral positions at the Indiana University School of Medicine and Eli Lilly. Sean has previous experience filling various roles in the global regulatory and general management functions supporting medical devices, autologous cell therapy, and single use disposable development programs. In his 15 years working in the life science industry, he has guided pre-clinical and clinical testing and submission strategies leading to global commercialization of multiple medical devices and bioprocessing tools.

Award rate inequities in biomedical research

PurposeThe analysis of existing institutional research proposal databases can provide novel insights into science funding parity. The purpose of this study was to analyze the relationship between race/ethnicity and extramural research proposal and award rates across a medical school faculty and to determine whether there was evidence that researchers changed their submission strategies because of differential inequities across submission categories.MethodThe authors performed an analysis of 14,263 biomedical research proposals with proposed start dates between 2010–2022 from the University of Michigan Medical School, measuring the proposal submission and award rates for each racial/ethnic group across 4 possible submission categories (R01 & Equivalent programs, other federal, industry, and non-profit).ResultsResearchers from each self-identified racial/ethnic group (Asian, Black/African American, Hispanic/Latino) pursued a different proposal submission strategy than the majority group (White). The authors found that Black/African American researchers experienced negative award rate differentials across all submission categories, which resulted in the lowest R01 & Equivalent and Other Federal submission rates of any racial/ethnic group and the highest submission rate to non-profit sources. The authors did not find support for the hypothesis that researchers changed submission strategies in response to award rate inequalities across submission categories.ConclusionsBiomedical researchers from different racial/ethnic groups follow markedly different proposal submission strategies within the University of Michigan Medical School. There is also a clear relationship between race/ethnicity and rates of proposal award. Black/African American and Asian researchers appear disadvantaged across all submission categories relative to White researchers. This study can be easily replicated by other academic research institutions, revealing opportunities for positive intervention.

Order submission, information asymmetry, and tick size

Our study investigates traders' order submission strategies with varying market conditions under information asymmetry. It examines how a reduction in tick size affects informed traders' order choices in response to changes in market conditions and subsequently influences the information content of the limit order book on the Tokyo Stock Exchange. We quantify the permanent price impact of orders with different levels of aggressiveness to measure the information content of different kinds of orders before and after a reduction in tick size. The results show that market orders and limit orders placed within or at the best bid and offer (BBO) are more informative than limit orders placed behind the BBO. Moreover, when the quoted spread is wider (narrower) and volatility is low (high), the information content of limit orders placed within or at the BBO is larger (smaller) than market orders, providing evidence that informed traders' order choice on market orders or limit orders is a trade-off between transaction costs and adverse selection costs. We further observe that the information content increases in market orders but decreases in limit orders placed within or at the BBO when the quoted spread becomes narrower and volatility changes from low to high after tick size reduction. This suggests that informed traders tend to shift from limit orders to market orders as transaction costs decrease and adverse selection costs increase, transactions become more informative, which reduces the information content of the limit order book.

Hidden liquidity, market quality, and order submission strategies

Using exogenous changes in hidden liquidity from the Tick Size Pilot Program, we show that hidden liquidity on lit venues has significant effects on various measures of market quality and order submission strategies after controlling for the impact of dark pools and stock attributes. We find that spreads, depths, trading volume, and trade size increase with hidden liquidity. The extent of informed trading and the price impact of a trade also increase with hidden liquidity on lit venues. Traders use more inside-the-quote limit orders, while algorithmic traders engage in more pinging activities and raise message traffic with more hidden liquidity.

Who will Win the Data Science Competition? Insights from KDD Cup 2019 and Beyond

Data science competitions are becoming increasingly popular for enterprises collecting advanced innovative solutions and allowing contestants to sharpen their data science skills. Most existing studies about data science competitions have a focus on improving task-specific data science techniques, such as algorithm design and parameter tuning. However, little effort has been made to understand the data science competition itself. To this end, in this article, we shed light on the team’s competition performance, and investigate the team’s evolving performance in the crowd-sourcing competitive innovation context. Specifically, we first acquire and construct multi-sourced datasets of various data science competitions, including the KDD Cup 2019 machine learning competition and beyond. Then, we conduct an empirical analysis to identify and quantify a rich set of features that are significantly correlated with teams’ future performances. By leveraging team’s rank as a proxy, we observe “the stronger, the stronger” rule; that is, top-ranked teams tend to keep their advantages and dominate weaker teams for the rest of the competition. Our results also confirm that teams with diversified backgrounds tend to achieve better performances. After that, we formulate the team’s future rank prediction problem and propose the Multi-Task Representation Learning (MTRL) framework to model both static features and dynamic features. Extensive experimental results on four real-world data science competitions demonstrate the team’s future performance can be well predicted by using MTRL. Finally, we envision our study will not only help competition organizers to understand the competition in a better way, but also provide strategic implications to contestants, such as guiding the team formation and designing the submission strategy.

Hidden Liquidity, Market Quality, and Order Submission Strategies

Hidden Liquidity, Market Quality, and Order Submission Strategies

Cell and gene manufacturing: a case study approach to overcoming challenges – understanding the value of an expert supplier

Watch the video or read the poster to learn: What are the biggest hurdles for cell and gene therapies?What are the challenges with current technologies and manufacturing methods?What are the biggest payoffs of solving these problems?Sean Werner President, Sexton Biotechnologies. A manufacturer of processing and handling tools and reagents for the development and manufacture of cell and gene therapies. Sean received his PhD from Purdue University in Biology followed by post-doctoral positions at the Indiana University School of Medicine and Eli Lilly. Prior to his current role he spent 11 years with Cook Medical and Cook Regentec filling various roles in the global regulatory and general management functions supporting medical devices, autologous cell therapy, and single use disposable development programs. In his 15 years working in the life science industry, he has guided pre-clinical and clinical testing and submission strategies leading to global commercialization of multiple medical devices and bioprocessing tools.

Comparison of postal and non-postal post-vasectomy semen sample submission strategies on compliance and failures: an 11-year analysis of the audit database of the Association of Surgeons in Primary Care of the UK

BackgroundVasectomy occlusive success is defined by the recommendation of ‘clearance’ to stop other contraception, and is elicited by post-vasectomy semen analysis (PVSA). We evaluated how the choice of either a...

Trusted audit with untrusted auditors: A decentralized data integrity Crowdauditing approach based on blockchain

Edge computing emerges as an alternative to cloud computing in the scenarios where the end devices require lower latency and faster access speeds. Edge nodes are deployed at the proximity of the end devices to reduce response time. On the other hand, the edge nodes are usually owned by small organizations that have limited operations and maintenance capabilities. Data on the edge may be easily damaged, due to external attacks or internal hardware failures. Therefore, it is essential to verify data integrity in edge computing. However, edge environment requires a different trust model compared with other computing and storage paradigm. Besides, compared with cloud storage, edge storage is decentralized and storage service participants may pose greater internal and external threats. This paper proposes a blockchain-based intelligent crowdsourcing audit approach (Crowdauditing) to achieve on-chain and off-chain credibility of audit results. The model relies on an untrusted auditor committee from the crowd to audit data integrity and uses smart contracts as the core of the intelligent system to ensure the reliability of result submission, the accuracy of the result judgment, and reasonable punishments and rewards. Specifically, an unbiased selection algorithm is proposed to achieve fairness during the auditor committee construction. An innovative two-stage submission strategy is proposed to ensure that the auditor committee can reach a consensus on the off-chain audit results. An incentive mechanism is carefully designed to force auditors providing audit services honestly to maximize their own rewards. Moreover, we modeled that as a game of n players, which proves the reliability of the result. Finally, we implement a prototype of Crowdauditing based on smart contracts. The extensive experimental results demonstrate the effectiveness of Crowdauditing.

SmartStore: A blockchain and clustering based intelligent edge storage system with fairness and resilience

With the development of edge computing, edge storage solutions are attracting widespread attention. When facing the requirements of lower latency and faster access speed from end devices, edge storage solutions are considered to be an alternative to the cloud. However, edges are usually owned by small organizations which have limited operations and maintenance capabilities. This makes these edge devices can be easily disabled by external attacks or internal hardware failures. Besides, the heterogeneity of the edge devices will also make it difficult to price the edge resources uniformly. To tackle these problems, we propose SmartStore: an auction mechanism based on blockchain to allocate edge resources. Considering centralized solutions have access bottlenecks and trust issues, we built SmartStore on the smart contract. With Bayesian game theory, SmartStore can analyze how data owners (DO) and edges price the resources can maximize their benefits. From an economic perspective, both DO and edges can make full use of edge heterogeneous resources with SmartStore. Besides, a two-stage submission strategy is proposed to complete the sealed auction. Furthermore, considering the reliability of edge storage, we propose a cluster-based block distribution algorithm for SmartStore's intelligent edge recommendation process. SmartStore ensures the reliability of edge storage while maximizing the benefits and resource utilization of both parties. Finally, we conduct specific experiments on the proposed auction smart contract through “Ethereum” and the experimental results of implementation show the effectiveness and efficiency of our SmartStore.

Spoofing and pinging in foreign exchange markets

This paper investigates the susceptibility of foreign exchange (FX) spot markets to limit order submission strategies that are either intended to create a false impression of the state of the market (‘spoof orders’) or to extract hidden information from the market (‘ping orders’). Using a complete limit order book dataset from Electronic Broking Services (EBS), our findings suggest that spoofing is more likely to succeed in liquid markets, or on primary electronic trading platforms. Pinging, by contrast, might be more prevalent in illiquid markets, or on secondary electronic trading platforms.

Companies' Health Technology Assessment Strategies and Practices in Australia, Canada, England, France, Germany, Italy and Spain: An Industry Metrics Study.

Background: Health technology assessment (HTA) has increased in importance in supporting payer decision making by assessing the relative effectiveness and cost effectiveness of new medicines. Thus, pharmaceutical companies need to address the HTA requirements early during development to improve reimbursement outcomes. Currently, there is a lack of research to assess the impact of HTA on development and jurisdictional outcome from companies’ perspectives. This study aimed to assess companies’ HTA strategy and characterise HTA practice in seven jurisdictions. Methods: A multi-year, annual study collected information for individual products, focusing on development activities regarding inclusion of HTA requirements and selection of global comparators. The generation of local contextual information, submission strategies and predictability of HTA outcomes was examined jurisdictionally in Australia, Canada, England, France, Germany, Italy and Spain. The study questionnaire was built into a secure online data collection platform and data were provided annually by participating companies. Results: Data for 169 compounds were provided by nine international companies between 2014 and 2018. HTA requirements were implemented in evidence generation plan for 63% of products during development. Global comparators were accepted by HTA bodies for more than half of studied products; Spain showed the highest acceptance rate (85%). Companies took advantages of parallel process in Australia and Canada to shorten product rollout time. Australia demonstrated general consistency in HTA review time, and England had the longest variation (interquartile range, 216 days). Requirements for additional information after submission occurred at all HTA bodies. Germany and Italy showed the highest percentage of products being reimbursed as per regulatory label (80 and 68%, respectively). Canada was the most predictable jurisdiction, with the highest proportion of review outcome (90%) that met companies’ expectations. Conclusion: Companies are addressing HTA requirements during development for many products; however, they are challenged by varying requirements and practices and product success ultimately depends on how HTA organisations and payers assess added value in the context of the national healthcare systems. This ongoing study created a baseline to help capture fact-based changes for company HTA strategies and HTA body practices.

Splitting and Shuffling: Institutional Trading Motives and Order Submissions Across Brokers

This paper studies order submission strategies by institutional investors when trading on private information. By merging institutional daily transactions with original/confidential 13F filings, I separate informed trades from uninformed ones. Informed large orders tend to be split across more brokers and over more days. While same brokers tend to work uninformed large orders over multiple days, the brokers who facilitated early parts of broken-up informed orders rarely receive the remaining parts of the same orders on later days. Institutional investors also provide camouflage for their informed orders by mixing an informed order with other uninformed orders simultaneously sent to the same broker. As a result, a higher degree of shuffling a portfolio of orders is associated with a larger share of informed trading volume. The splitting and shuffling strategies designed to conceal informed trades from brokers and other market participants tend to lower institutional trading costs, especially on informed orders.

Price discovery, order submission, and tick size during preopen period

Using limit order book data, this study investigates the effects of minimum tick size reduction on price discovery and order submission strategies during the preopen call auction period in the Tokyo Stock Exchange. Studying the largest stocks' changes before and after the tick size reduction, our findings suggest that price discovery becomes more efficient when a smaller tick size is employed. A reduction in tick size induces a decrease in market depth and spread and enhances the speed of price discovery by encouraging more aggressive order to be placed and providing investors a better learning and communicating environment to incorporate information into order decisions. Our results demonstrate that, although orders are not matched and no transaction occurs during the preopen period, the order placement at these moments is neither necessarily noisy nor completely manipulative; factors that impact the investor's order choice during the normal trading period also work in this period.

8 Reasons Your Submission Strategy Sucks (And What You Can Do About It)

8 Reasons Your Submission Strategy Sucks (And What You Can Do About It)