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More than 2 million people live with multiple sclerosis worldwide and the prevalence has been increasing over time. Patients living with multiple sclerosis often explore diet and lifestyle interventions as a means of managing their symptoms and reducing reliance on medication; yet, these approaches are rarely discussed with their physicians. Currently, there is a lack of evidence on when to stop disease-modifying therapies (DMT), and recent research showed no statistically significant difference in the time between relapses when comparing participants who stopped DMT to those who did not, especially over the age of 45. This case report presents 2 patients with multiple sclerosis who made an informed decision to stop their DMT medications and have been managing their condition with a whole food plant-based diet and a healthy lifestyle approach. Over the period of 5 to 6 years since stopping the medications, each patient only had 1 multiple sclerosis flare-up to date. In the report, the focus is on the impact of diet on multiple sclerosis. It adds to currently available literature and encourages further research in the field of managing multiple sclerosis with lifestyle interventions.

After more than a decade since the introduction of Statistical Engineering by Roger Hoerl and Ronald Snee, a group of leading applied statisticians from academia, industry, and government were invited to discuss their perspectives on progress made, the current status of this important movement, and what future Statistical Engineering holds on the path forward in a series of two panel discussion papers. In this first article, the invited panelists focus their discussion on the past and present of Statistical Engineering. They discuss notable advances and current obstacles to progress. They also consider the unique value added by Statistical Engineering, and the possible addition of decision making to the body of knowledge. The format of the article consists of the posed questions from the moderators, a summary of key ideas from all the panelists, and then the individual detailed answers. The goal of this series of articles is to inspire statisticians to consider their possible role to advance the adoption of Statistical Engineering to solve important problems.

A significant proportion of cancers could be prevented by adopting healthy lifestyle behaviours. In addition, healthy lifestyle factors can have a positive impact on cancer outcomes and survival. Yet, most physicians, including oncologists, do not dedicate a significant amount of time addressing these factors with their patients, who instead look to mainstream media and other non-medical sources for information. This has led to an increase in the number of ‘influencers’ in the wellness space who can accumulate a large and captive audience. At times, this has caused friction amongst healthcare professionals who feel that ‘influencers’ may overstate the potential benefits. The reality is that most people, physicians and the public alike, fail to recognise the immense power that lifestyle interventions hold. Rather than shy away from addressing these issues, we should be empowering our patients to take back control over their health. Here, we provide a personal perspective on why addressing lifestyle factors within cancer care is so important and that we can indeed work together with ‘influencers’ to amplify the message.

A year ago, I wrote a JPT Guest Editorial on stripper wells in the US. It was the result of data analysis that I personally found shocking and the result of frustration I was having with clients looking for acquisitions at that time. The piece was written right before oil prices started an incredible climb and when natural gas prices had been under $3.00/Mcf for an extended period. On the acquisition and divestiture front, although corporate consolidation was ongoing, deal counts were at a decade low, and it was extremely hard to transact. With oil prices at $45/bbl and the so-called “shale bust” of the past few years, many investors would not even consider an unconventional asset. Given this backdrop, I had clients on the hunt for conventional assets with stable base cash flow and limited risk. One client was a very serious buyer; however, some decision makers were adamant about certain asset characteristics, namely conventional, onshore US, certain states, operated working interest, proved producing (PDP), and no stripper wells. Despite this seeming like a reasonable set of criteria, asset after asset kept getting screened out. Finally, I had a call with the CFO and in frustration told him, “They’re all stripper wells.” Something about saying that out loud stuck in my head and got me thinking. I started wondering “how many stripper wells are there?” I began to think about the well count across the US, well IPs (initial production or starting flow rates), and the approximate rates at which I generally saw most wells go uneconomic in financial modeling. Given about 1 million active wells in the US, it seemed like there ought to be a decent percentage of active, producing, conventional wells over the 20–30 BOPD mark. I started pulling well data and found that for many US regions, 95% or more of the active vertical wells were producing 15 BOPD or less. The article generated a lot of interest as it brought to light something many people think is a problem in the back of their mind but had not seen quantified in a simple way. The follow-up question I got over and over was “What about horizontal wells?” The US Energy Information Administration (EIA) estimates that about 80% of US oil and gas wells are producing less than 15 BOEPD. For horizontal wells, EIA estimates about half are 50 BOEPD or less (the use of 6:1 BOE tends to improve the overall daily rate in an amount that is disproportionate to the value and economic impact of adding the gas stream). This rate includes both conventional and unconventional wells. It is natural to assume that the older conventional wells bring down the average and that more recent production from the hottest US shale plays is much higher.

Supplementary Data from 89Zr-panitumumab Combined With 18F-FDG PET Improves Detection and Staging of Head and Neck Squamous Cell Carcinoma

Supplementary Figure from 89Zr-panitumumab Combined With 18F-FDG PET Improves Detection and Staging of Head and Neck Squamous Cell Carcinoma

Purpose:Determine the safety and specificity of a tumor-targeted radiotracer (89Zr-pan) in combination with 18F-FDG PET/CT to improve diagnostic accuracy in head and neck squamous cell carcinoma (HNSCC).Experimental Design:Adult patients with biopsy-proven HNSCC scheduled for standard-of-care surgery were enrolled in a clinical trial and underwent systemic administration of 89Zirconium-panitumumab and panitumumab-IRDye800 followed by preoperative 89Zr-pan PET/CT and intraoperative fluorescence imaging. The sensitivity, specificity, and AUC were evaluated.Results:A total of fourteen patients were enrolled and completed the study. Four patients (28.5%) had areas of high 18F-FDG uptake outside the head and neck region with maximum standardized uptake values (SUVmax) greater than 2.0 that were not detected on 89Zr-pan PET/CT. These four patients with incidental findings underwent further workup and had no evidence of cancer on biopsy or clinical follow-up. Forty-eight lesions (primary tumor, LNs, incidental findings) with SUVmax ranging 2.0–23.6 were visualized on 18F-FDG PET/CT; 34 lesions on 89Zr-pan PET/CT with SUVmax ranging 0.9–10.5. The combined ability of 18F-FDG PET/CT and 89Zr-pan PET/CT to detect HNSCC in the whole body was improved with higher specificity of 96.3% [confidence interval (CI), 89.2%–100%] compared to 18F-FDG PET/CT alone with specificity of 74.1% (CI, 74.1%–90.6%). One possibly related grade 1 adverse event of prolonged QTc (460 ms) was reported but resolved in follow-up.Conclusions:89Zr-pan PET/CT imaging is safe and may be valuable in discriminating incidental findings identified on 18F-FDG PET/CT from true positive lesions and in localizing metastatic LNs.

Supplementary Table from 89Zr-panitumumab Combined With 18F-FDG PET Improves Detection and Staging of Head and Neck Squamous Cell Carcinoma

In the second of two panel discussion articles focused on the evolution of statistical engineering (SE) as introduced by Roger Hoerl and Ronald Snee, a group of leading applied statisticians from academia, industry, and government present their perspectives on what the future might hold for this important movement. The invited panelists discuss the challenges and opportunities presented by the emergence of data science and the abundance of large amounts of data. They also consider the possible paths forward for SE, and the roles for statisticians in academia, industry, and government. The final question addresses what additional skills would be helpful to increase the effectiveness of the practice and advance SE. As with the first article, the format of the article follows the order of a posed question, a summary of key ideas, and then the detailed individual panelist answers. The article seeks to inspire statisticians to consider their possible role to leverage the potential of SE to solve important problems.

Machine learning models are increasingly used in practice. However, many machine learning methods are sensitive to test or operational data that is dissimilar to training data. Out-of-distribution (OOD) data is known to increase the probability of error and research into metrics that identify what dissimilarities in data affect model performance is on-going. Recently, combinatorial coverage metrics have been explored in the literature as an alternative to distribution-based metrics. Results show that coverage metrics can correlate with classification error. However, other results show that the utility of coverage metrics is highly dataset-dependent. In this paper, we show that this dataset-dependence can be alleviated with metric learning, a machine learning technique for learning latent spaces where data from different classes is further apart. In a study of 6 open-source datasets, we find that metric learning increased the difference between set-difference coverage metrics (SDCCMs) calculated on correctly and incorrectly classified data, thereby demonstrating that metric learning improves the ability of SDCCMs to anticipate classification error. Paired t-tests validate the statistical significance of our findings. Overall, we conclude that metric learning improves the ability of coverage metrics to anticipate classifier error and identify when OOD data is likely to degrade model performance., Accepted 2022 IEEE International Conference on Software Testing International Workshop on Combinatorial Testing (IEEE ICST IWCT)

Over the past 25 years a significant (but unknown) number of migrants from Ethiopia have been migrating to South Africa through the “southern route.” This male-dominated migration is becoming more and more irregular and includes multiple transit countries, largely controlled by human smugglers. The size of the Ethiopian immigrant population in South Africa has increased. The profile of individuals on the move has also changed in terms of migrants’ age, ethnicity, place of origin, gender and socioeconomic status. Youth from rural areas have joined the migration trail, and, increasingly, women are migrating for marriage in South Africa. Today, migrants from southern Ethiopia (Hadiya and Kambata) dominate Ethiopian migration to South Africa. The age and socioeconomic status of the migrants have also changed where teenagers, college graduates and civil servants are entering the migration stream in recent years. Equally changing is the nature and operation of the smuggling and settlement processes. Like the broader field of migration studies in which source and destination countries receive the overwhelming focus, the multiple transit countries Ethiopians on the move to South Africa travel through, and the migration journeys themselves, have not received adequate research attention. The effects these journeys have on the settlement processes are also largely ignored. This article, therefore, explores these emerging patterns with a view to understanding the inequalities faced by Ethiopian migrants on their journey to South Africa and the factors behind it. With the intensification of border closures due to multiple factors, including the COVID-19 pandemic, we examine the shift in the nature and trend of smuggling and how it is reflected in inequalities experienced by Ethiopian migrants in South Africa.

Working with clients who are looking to buy upstream oil and gas wells, I almost always hear, “We don’t want stripper wells.” Now that the recent heyday of US unconventional wells is also waning, this often goes along with, “We want conventional production.” With gas prices having been relatively low the past couple of years, it is often, “We want oil-weighted production.” I am used to unicorn hunts and impossible asks, so I thought this was something reasonably doable. After a long year in a terribly gridlocked acquisitions-and-divestitures (A&D) market, I started thinking about it a little more. Most of the conventional packages that were coming to market had low-rate wells with many inactive wells tagging along. We were evaluating a lot of deals but were seeing several fail due to plugging and abandonment (P&A) liability and the inability of low-rate wells to handle the General and Administrative (G&A) structure of clients. One client group was competent and ready to deploy capital, and we had gone on the hunt for off-market deals adjacent to their current production. Our team initially found 19 interesting assets, based on size and proximity, and was pleased to find that nine of the 19 had operators who engaged as seriously interested in selling. Our client turned down all nine assets. Every one of the nine assets was cash flow positive (surprisingly in 2020), in the right region, conventional, and the right size. Every one of the nine was turned down because of the large number of inactive and low-rate wells. In a depressed A&D market with the lowest deal counts in over a decade, having nine assets rejected immediately was frustrating. We decided to screen out assets with low-rate wells and high inactive well counts before making calls and engaging sellers. I downloaded data from all the wells in a Rockies state, screened for current production to get a flowing barrel comp range representing the right deal size, looked for oil-weighted properties, took out unconventional wells, added a rate cutoff around 20 BOED per well, and applied a cutoff where reported inactive wells made up less than 50% of the total well count. Out of about 400 to start with, there were three assets left - only three. At first, I thought that had to be wrong. I knew that US onshore conventional production probably had a large number of low-rate wells, but it seemed reasonable that there would be several assets that had wells doing 20–30 BOED. Thinking about it more, I sent a text to a colleague: “I bet US onshore conventional production is actually dominated by low-rate wells.” I then pulled data for the Texas Gulf Coast because I had another group looking there. The results were shocking: 97% of the wells were 15 BOPD or below. All these clients want conventional assets without stripper wells - but they are all stripper wells.

With the broader adoption of AI-enabled software systems, it is necessary to provide assurance to the layman user that the AI system will behave as intended. This interactive tutorial will provide an overview of AI assurance, introduce a new set of assurance goals for AI systems, discuss the open challenges in AI assurance, and present recommendations to overcome its drawbacks.

The recent increased adoption of AI into cyber-physical systems requires that AI models perform as intended and without security blunders. However, for such critical AI deployments, serious adversaries and data poisoning issues are still persistent. The digital (data-driven) infrastructure of the bioeconomy is progressively being utilized by state and private sector entities; especially through leveraging models and data sets for real-time decision support. Models however, can be easily targeted by adversaries or be prone to unintentional quality issues. Accordingly, our research is dedicated to defining methods that mitigate different types of attacks and their undesired consequences. We are building an AI framework that can detect anomalies and malicious acts (in water supply systems) as well as analyze cause-effect measures in multiple scenarios. The proposed defense strategies include assessing the potential vulnerabilities that can be exploited in the convergence of AI and physical systems. Such outliers can cause extensive civilian harm, produce damaging bio-security incidents, and threaten agricultural and food production. The new framework will be applied to multiple physical systems with the aim of creating meta-learning outcomes for supporting the wide-scale adoption of trustworthy AI.

Purpose: Determine the safety and specificity of a tumor-targeted radiotracer (89Zr-pan) in combination with 18F-FDG PET/CT to improve diagnostic accuracy in head and neck squamous cell carcinoma (HNSCC). Experimental Design: Adult patients with biopsy-proven HNSCC scheduled for standard-of-care surgery were enrolled in a clinical trial and underwent systemic administration of 89Zirconium-panitumumab and panitumumab-IRDye800 followed by preoperative 89Zr-pan PET/CT and intraoperative fluorescence imaging. The sensitivity, specificity, and AUC were evaluated. Results: A total of fourteen patients were enrolled and completed the study. Four patients (28.5%) had areas of high 18F-FDG uptake outside the head and neck region with maximum standardized uptake values (SUVmax) greater than 2.0 that were not detected on 89Zr-pan PET/CT. These four patients with incidental findings underwent further workup and had no evidence of cancer on biopsy or clinical follow-up. Forty-eight lesions (primary tumor, LNs, incidental findings) with SUVmax ranging 2.0–23.6 were visualized on 18F-FDG PET/CT; 34 lesions on 89Zr-pan PET/CT with SUVmax ranging 0.9–10.5. The combined ability of 18F-FDG PET/CT and 89Zr-pan PET/CT to detect HNSCC in the whole body was improved with higher specificity of 96.3% [confidence interval (CI), 89.2%–100%] compared to 18F-FDG PET/CT alone with specificity of 74.1% (CI, 74.1%–90.6%). One possibly related grade 1 adverse event of prolonged QTc (460 ms) was reported but resolved in follow-up. Conclusions: 89Zr-pan PET/CT imaging is safe and may be valuable in discriminating incidental findings identified on 18F-FDG PET/CT from true positive lesions and in localizing metastatic LNs.

Sinonasal respiratory epithelium is a highly regulated barrier that employs mucociliary clearance (MCC) as the airways first line of defense. The biological properties of the airway surface liquid (ASL), combined with coordinated ciliary beating, are critical components of the mucociliary apparatus. The ASL volume and viscosity is modulated, in part, by the cystic fibrosis transmembrane conductance regulator (CFTR). The CFTR is an anion transporter of chloride (Cl−) and bicarbonate (HCO3−) that is located on the apical surface of respiratory epithelium and exocrine glandular epithelium. Improved understanding of how dysfunction or deficiency of CFTR influences the disease process in both genetically defined cystic fibrosis (CF) and acquired conditions has provided further insight into potential avenues of treatment. This review discusses the latest data regarding acquired CFTR deficiency and use of CFTR specific treatment strategies for CRS and other chronic airway diseases. Keywords: CFTR, Cystic fibrosis, CFTR deficiency, CFTR dysfunction, Chronic sinusitis, Chronic rhinosinusitis, Chronic obstructive pulmonary disease, Tobacco, Hypoxia, Resveratrol, l-ascorbate

How should we understand the interconnections between environmental change, migration, and conflict in Africa? Should the rise of Islamic terrorism and Boko Haram in northeast Nigeria be directly linked to the drying of Lake Chad? Should cattle raiding in Kenya be seen as a result of drought across East Africa? Does the constrained migration of the pastoral Tuareg in the Sahel causally connect to desertification and their rebellion against governmental forces? Despite the compelling and often persuasive case for directly connecting environmental change to migration and conflict, there is a growing agreement in both the environment-migration and climate-conflict spheres that intervening variables determine if and how environmental change causes population movements and political violence. This article presents a case for migration as an intermediary and bidirectional causal variable. The article argues that close attention needs to be paid to local-level manifestations of conflict and (mal)adaptive forms of migration to understand the potential propensity of environmental change to lead to conflict in Africa.

‘Afrophobia’, as an explanation for attacks on African outsiders in an African country, is a term that has become increasingly utilised in the South African press. This chapter questions the Afrophobia hypothesis, which rests on the idea that attacks on ‘foreigners’ in South Africa are a form of black self-hate. Empirical research conducted on South African attitudes to ‘foreigners’ following violent attacks on immigrants and others seriously undermine the Afrophobia hypothesis. The author finds that ‘xenophobia’ provides a much stronger conceptual framework around which to explain violence directed at perceived or actual outsiders. Bias within the media itself, as well as a lack of critical reporting and the underreporting of smaller-scale incidents, means the media has a propensity to uncritically adopt explanations such as Afrophobia.

Meat and dairy farming are a leading cause of climate change and ecological collapse. The most comprehensive analyses of the global farming system concluded that shifting to a plant-based diet would have the most impact on planetary health compared to any other single driver of climate change.1 International consensus supports a global transition to a plant-based diet as an urgent imperative. The Eat-Lancet Commission on Food, Planet, Health2 evaluated the best available science and developed recommendations for a global healthy reference diet, termed the ‘Planetary Health Diet’ (https://eatforum.org/learn-and-discover/the-planetary-health-diet). This diet consists predominantly of whole plant foods, with minimal animal-derived and processed foods. Meat, eggs, and dairy are considered optional and if consumed, provide