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Weekly investment update – It’s getting real

Many market observers have been predicting a sharp rise in US real yields for at least a year (ourselves included). In 2021 it never quite came. That was mostly because resurgences in Covid infections regularly triggered rallys in US Treasury bonds. Will.



Many market observers have been predicting a sharp rise in US real yields for at least a year (ourselves included). In 2021 it never quite came. That was mostly because resurgences in Covid infections regularly triggered rallys in US Treasury bonds. Will 2022 be different?

This time, it may be for real. While we certainly do not belittle the risk to the economy from future coronavirus variants, the pattern with Delta and Omicron has been that the economic impact of each new strain has been progressively less negative.

At the same time, the messaging from the US Federal Reserve on its monetary policy stance has clearly changed.

Whereas last year, the central bank’s view was that higher inflation would prove transitory, it has become increasingly clear that at least some of the price pressures will persist. While Covid may be fading (slowly) as a macroeconomic factor, the supply chain disruptions will likely linger.

More importantly, the US labour market appears to be near full employment, meaning that recent wage gains could last for longer. To the degree that the Fed is behind the curve in terms of tackling inflation, we believe the risk now is that the bank raises its policy rate by even more than the market already expects (see Exhibit 1).

US labour market – In better shape than it may look

Many observers viewed the latest US non-farm payrolls report as disappointing. Only 199 000 jobs were created in December versus a consensus forecast of 400 000. The disappointment, though, was more due to overly optimistic expectations than to a surprising slowdown in job growth.

It is important to recognise the surge in Covid infections that occurred during the month (and which has worsened since). Whenever this has happened in the last two years, job growth has slowed sharply. This has been the case particularly in those industries sensitive to lockdown restrictions or people’s nervousness when the risk of infection appeared to be high. As the Omicron wave passes, we expect job growth to rebound.

Other indicators suggest the labour market is in fact quite strong. Even though the participation rate edged up in December (although not among older workers), the unemployment rate fell, meaning a greater share of those people re-entering the labour force found jobs.

This offsets some of the pessimism around the high ‘quits’ rate that reflects the number of people who are voluntarily leaving their job. This ‘great resignation’ is a positive sign of the dynamism of the US labour market.

We all appreciate that society and the economy will in many ways be fundamentally different in the future due to the pandemic. That means the labour market needs to be reconfigured. So many people quitting (and presumably taking new, better jobs), suggests this reconfiguration is happening quickly (and in contrast to Europe, where stability comes at the cost of economic dynamism).

Market segments benefiting from higher rates

Strong consumer and business demand combined with lingering supply chain constraints and a smaller labour force are the factors driving inflation to higher, sustained levels.

At a minimum, US monetary does not need to be accommodative (as it is today). A return to even a neutral policy rate would still imply real yields that are perhaps 75-100bp higher than they are today.

The potential impact of such an increase is already evident in the market moves over the course of the real yield sell-off this month. Indices with a positive sensitivity to higher rates such as those for value stocks and commodities have outperformed, while assets at risk from higher rates such as technology stocks have lagged sharply (see Exhibit 2).

Any views expressed here are those of the author as of the date of publication, are based on available information, and are subject to change without notice. Individual portfolio management teams may hold different views and may take different investment decisions for different clients. This document does not constitute investment advice.

The value of investments and the income they generate may go down as well as up and it is possible that investors will not recover their initial outlay. Past performance is no guarantee for future returns.

Investing in emerging markets, or specialised or restricted sectors is likely to be subject to a higher-than-average volatility due to a high degree of concentration, greater uncertainty because less information is available, there is less liquidity or due to greater sensitivity to changes in market conditions (social, political and economic conditions). Some emerging markets offer less security than the majority of international developed markets. For this reason, services for portfolio transactions, liquidation and conservation on behalf of funds invested in emerging markets may carry greater risk.

Writen by Daniel Morris. The post Weekly investment update – It’s getting real appeared first on Investors' Corner - The official blog of BNP Paribas Asset Management, the sustainable investor for a changing world.

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U.S. COVID-19 cases increased threefold over past month – Walensky

The United States has seen a threefold increase in daily COVID-19 cases over the past month and a steady increase over the past five weeks, U.S. Centers…



U.S. COVID-19 cases increased threefold over past month – Walensky

WASHINGTON, May 18 (Reuters) – The United States has seen a threefold increase in daily COVID-19 cases over the past month and a steady increase over the past five weeks, U.S. Centers for Disease Control and Prevention Director Rochelle Walenksy said on Wednesday.

The seven-day average of daily cases was up 26% from the previous week to 94,000 cases per day, Walensky said at a White House briefing. The seven-day average for hospitalizations was up 19% to about 3,000 per day and the average for deaths was 275 per day, she said.

People queue to be tested for COVID-19 in Times Square, as the Omicron coronavirus variant continues to spread in Manhattan, New York City, U.S., December 20, 2021. REUTERS/Andrew Kelly/File Photo

“We of course must remember that each person lost to COVID-19 is a tragedy and that nearly 300 deaths a day is still far too many,” said Walensky.

Reporting by Susan Heavey and Ahmed Aboulenein

Our Standards: The Thomson Reuters Trust Principles.


Reuters source:


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In stop-COVID19 trial, Brensocatib did not improve condition of patients with severe COVID-19

Session:  D17, Top Knowledge in COVID Date and Time:  9:25 a.m. PT, Wednesday, May 18,2022 Location:  Room 2018/2020 (West Building, Level 2), Moscone…



Session:  D17, Top Knowledge in COVID
Date and Time:  9:25 a.m. PT, Wednesday, May 18,2022
Location:  Room 2018/2020 (West Building, Level 2), Moscone Center

Credit: ATS

Session:  D17, Top Knowledge in COVID
Date and Time:  9:25 a.m. PT, Wednesday, May 18,2022
Location:  Room 2018/2020 (West Building, Level 2), Moscone Center


ATS 2022, San Francisco, CA – Brensocatib did not improve the clinical status of patients hospitalized with severe SARS-CoV-2 infection in the double-blind randomized, placebo-controlled STOP-COVID19 multicenter clinical trial, according to research published at the ATS 2022 international conference.

The study, which began in June of 2020, took place at 14 UK hospitals, where participants were randomized to receive 25 mg daily of brensocatib or placebo for 28 days.  One-hundred ninety patients received brensocatib, while 214 received placebo.

All patients in the study had confirmed SARS-CoV-2 infection and at least one risk factor for severe COVID-19, such as requiring supplemental oxygen, Individuals on mechanical ventilation were excluded from the study.  All participants received standard of care treatment.

“Treatments currently available to treat COVID-19, such as dexamethasone and anti-IL-6 antibodies, reduce inflammation, but their effect is not primarily on neutrophils or neutrophilic inflammation,” said presenting author Holly Keir, PhD, postdoctoral researcher, University of Dundee School of Medicine, Dundee, United Kingdom. “We performed the STOP-COVID trial to test the hypothesis that directly targeting neutrophilic inflammation by inhibiting dipeptidyl peptidase-1 (DPP1) would provide additional benefits to patients with severe COVID-19 on top of standard of care.”

Severe COVID-19 infection is primarily caused by an excessive and damaging immune response to the virus.  A number of different immune cells are involved in this response, including neutrophils. Neutrophils release enzymes and other substances that cause severe lung damage. Studies have consistently shown that high levels of neutrophilic inflammation are associated with worse outcomes in COVID-19. 

Brensocatib is an investigational oral inhibitor of DPP1, an enzyme responsible for the activation of neutrophil serine proteases.

In STOP-COVID19, time to clinical improvement and time to discharge were not different between groups. Mortality was 10.7 percent and 15.3 percent in the placebo and brensocatib treated groups, respectively. Oxygen and new ventilation use were also numerically greater in the brensocatib treated patients. Prespecified subgroup analyses based on age, sex, baseline severity, co-medications and duration of symptoms supported the primary results. Adverse events were reported in 46.3 percent of placebo treated patients and 44.8 percent of brensocatib treated patients.

The researchers also conducted a sub-study at two study sites to directly measure inflammation in patients receiving DPP1 inhibition or placebo.  They observed a strong anti-inflammatory effect of DPP1 inhibition on neutrophil protease enzymes.  Active blood neutrophil elastase levels were reduced by day eight in the treatment group and remained significantly lower up to day 29.

“Although we did not find a beneficial effect of treatment in this population, these results are important for future efforts to target neutrophilic inflammation in the lungs. STOP-COVID19 is the largest completed trial of DPP1 inhibition in humans and we have performed extensive characterization of how DPP1 inhibition affects the immune system’s response,” noted Dr. Keir. “Using state-of-the-art proteomics (the study of the structures, functions, and interactions of proteins) we have already seen important changes in neutrophils with DPP1 inhibition that will help us to better understand the potential role of this treatment in other diseases.”

One of these diseases is bronchiectasis, where a phase 2 trial published in 2020 showed that brensocatib reduced the risk of exacerbations.

The STOP-COVID19 study was an investigator-initiated study sponsored by the University of Dundee and funded by Insmed Incorporated.



You may be interested in other newsworthy research on pulmonary infections, such as “A Nine-Gene Blood-Based Signature Meets the World Health Organization Target Product Profiles for Diagnosis of Active Tuberculosis and Predicting Progression from Latent to Active Disease.”


Grant Hill

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Next-generation weather models cross the divide to real-world impact

Each winter, spring, and summer, extreme weather forecasters and researchers meet to test the latest, most promising severe weather forecast tools and…



Each winter, spring, and summer, extreme weather forecasters and researchers meet to test the latest, most promising severe weather forecast tools and innovations to see how they perform in real-world settings.

Credit: NOAA

Each winter, spring, and summer, extreme weather forecasters and researchers meet to test the latest, most promising severe weather forecast tools and innovations to see how they perform in real-world settings.

These testbed experiments, orchestrated by the National Oceanic and Atmospheric Administration (NOAA), forecast winter storms, severe thunderstorms, and flash flooding, respectively.

The Hydrometeorology Testbed recently held the 12th annual Winter Weather Experiment (WWE). An immersive, collaborative, “research-to-operations” experience, it brought together members of the forecasting, research, and academic communities to evaluate and discuss winter weather forecast challenges.

“We generate operational forecasts using new inputs or new models testing how well they work,” said Keith Brewster, senior research scientist and operations director of the Center for Analysis and Prediction of Storms (CAPS) at the University of Oklahoma. “If we promise a forecast where thunderstorms occur, can we expect a forecaster to use it?”

Humming along in the background of the experiment are supercomputers at the Texas Advanced Computing Center (TACC) — among the fastest available to academic researchers in the world.

The CAPS team began using TACC systems for the Hazardous Weather Testbed Spring Experiment in 2011 to better predict severe thunderstorms. They computed at that time on the original Stampede supercomputer at TACC — the 6th fastest in the world in its prime. From 2017-2021, they used Stamepde2 (12th fastest) through the Extreme Science and Engineering Discovery Environment (XSEDE). Since 2021, they have used Frontera, the fastest university supercomputer in the world and currently the 13th fastest overall.

“What TACC and XSEDE offer us is the ability to do these real-time, or near-real-time experiments,” Brewster said.

The CAPS team submits their forecasting simulations by 10:00pm, after weather observations and other input data comes in for the 00 UTC cycle. The simulations run overnight and are ready by 8:00am the next morning, predicting weather events out to three-and-a-half days.

“On Stampede, we worked with TACC to have a special queue set up where we have a dedicated number of cores allocated to us,” Brewster said. This type of ‘urgent computing’ has become a hallmark of TACC, enabling the center to forecast hurricane storm surge, monitor space junk in low Earth orbit, and power COVID-19 models. “More recently, on Frontera, the capacity is such that we can run in the regular queue, using a VIP priority, making our use more efficient and less disruptive to other research users.”

This year’s Winter Weather Experiment had three key science goals: to subjectively gauge the utility of convection-allowing model (CAM) forecasts to improve two-to-three day snowfall forecasts; objectively score the snowfall forecasts using community standard verification systems; and determine the optimal combination of physics to use in next-generation models.

The team was primarily interested in predicting the amount of snowfall accumulation, but they also tested their ability to determine the differences between snow, sleet, and freezing rain in forecasts, and predict other facets of winter weather, like wind speed.

“Giving forecasters the opportunity to use these experimental models in real situations allows forecasters and researchers to determine the strengths, operational challenges, and forecaster usability early on in the development stage,” said James Correia Jr., coordinator for the Hydrometeorology Testbed. “This allows us, together, in NOAA testbeds, to make improvements in our forecasting process, models, and the way we approach and solve research and operational challenges.”

Recent testbed programs have also included the important task of evaluating NOAA’s next-generation weather model, the FV3 model. This model has shown success in global-scale forecasting, and the agency plans to also utilize it operationally for much higher resolution regional modeling as represented in the high-impact testbeds. The new multi-scale forecast system is known as the Unified Forecasting System (UFS).

“In addition to real-time testing, CAPS has been using TACC supercomputers to re-run cases to identify the root-cause of issues that were identified during prior testbeds,” Brewster said. “This leads to tuning and other enhancements to the original codes.”

The Winter Weather Experiment ran for 27 case days on Frontera in near-real-time over the course of the winter, including objective verification and machine learning training — a forward-looking aspect of the research. Brewster presented the results as a webinar organized by NOAA in March 2022.

Following the experiment, researchers typically do more detailed studies on specific facets of the forecasts, with funding from NOAA’s Weather Program Office as part of the Testbed competition in collaboration with NOAA’s Weather Prediction Center and Storm Prediction Center – both divisions of the National Weather Service.

Testing Ensemble Consensus Methods

Most weather watchers are familiar with the idea of ensemble models — the swarms of tracks that represent the results from various simulations, which are averaged and interpreted by weather forecasters.

Using Frontera, Brewster’s team generates real-time ensemble forecasts.

“In decision theory, it has been shown that when you get a consensus of experts, you get better advice than from a single person,” Brewster said. “Thanks to TACC, we can generate 13 models — 13 ‘experts’ predicting what the weather is going to be. From there, we’re working on how to develop ensemble consensus products that best help improve forecasts.”

Sometimes useability by a human operator trumps pure prediction skills. Communicating the consensus decision from an ensemble of forecasts is such an example.

“We researchers are in there, observing and participating, for one week — as if we were in the weather office, creating forecasts, so people like myself can see the issues,” Brewster explained. “We try to be realistic: can someone really look at ten to 15 models? Or does it create more uncertainty?”

One approach the CAPS team has been exploring for ensemble consensus methods is the local probability match mean (LPM) method. The LPM method divides an area into patches, calculates the atmospheric dynamics over that patch, and distributes the results locally. (Nathan Snook and the CAPS team described the method, and compared various ways of computing this mean, in a 2020 paper in Geophysical Research Letters.)

An assessment of the accuracy by NOAA showed the local probability match mean (LPM) performed slightly worse than probability match (PM) mean in objective precipitation scoring.

“But this is where the testbed activities come in,” Brewster said. “When a human looks at a forecast, they’re not looking at raw numbers at a site. They’re looking at the shape — consensus reflectivity — and in this respect, LPM was deemed to be better. That was a win for our team.”

The LPM has since been implemented in NOAA’s operational High Resolution Ensemble Forecast system. This is the goal of the NOAA Testbed program: taking research ideas and getting them through testing and evaluation in quasi-operational settings to actual operational deployment.

“That’s what we call technology transfer,” Brewster said. “There’s a tech divide where researchers like our team work on models, produce papers, and it can be hard to get new models or concepts into the operations. Tech transfer happened because it was proven, not just running on TACC and to other researchers, but to other forecasters. That gets us over the divide from journal articles to impacting real-world forecasts.”

The 2022 NOAA Hazardous Weather Testbed Spring Forecasting Experiment is being held from May 3 – June 2 and is again leveraging supercomputers at TACC. For more information about the upcoming experiment, contact: Keli Pirtle, Public Affairs Specialist, NOAA Communications,

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