In a message posted on the Telegram social network, the Ministry of Defense of Belarus presented a video saying that its military pilots of Su-25s have completed additional training under the supervision of instructors from the Russian Federation in order to be able to use armaments of a “special designation”.

The video does not say precisely what kind of weapons they had in mind, but defense experts unanimously agree that “special designation” explicitly means nuclear weapon.

This course of action was expected since the meeting of Belarusian and Russian presidents in August 2022. Then, Belarusian President Aleksander Lukashenko disclosed that Russia has upgraded a certain number of jets from his country’s military fleet to enable them to carry tactical nuclear weapons.

The only serious technical issue is that the Su-25 attack aircraft is just a subsonic vehicle with velocities that are sub-optimal for the use of nuclear weapons.

Theoretically, Su-25 could deliver free-falling bombs from their maximum flight altitude, but with a substantial risk that the aircraft together with the pilot would be destroyed during the attack.

Previously, both Russian and Belarusian media assumed that this attack capability was reserved for the Su-24 bomber plane which was specially designed for this kind of tasks. But after the latest announcement, it became obvious that these will be Su-25s, not Su-24s.

The Su-25 was developed in the former USSR and even then it was out of the question that this aircraft could become a nuclear-capable carrier.

Su-24. These planes are much faster than Su-25. Image credit: Armed Forces of Russia

The method for using nuclear munitions requires that the plane flies at a relatively low altitude at its maximum possible speed, drops the bomb, and withdraws in the opposite direction as fast as possible, to avoid being hit by a nuclear explosion.

For this purpose, the Soviet Union built Yak-28 bomber in the 1950s which had a maximum speed of 1830 km/h. Later it was replaced by Su-24 which could reach 1700 km/h, and even at low altitudes, it could fly at velocities up to 1400 km/h. Currently, the Russian Federation has Su-34 reaching 1400-1900 km/h depending on the flight altitude.

Meanwhile, the top speed of the Su-25 is does not exceed 1000 km/h.

PH-40 tactical nuclear bomb. It could fit on Su-25, but the plane still would not be able to lfy fast enough to escape its destruction.

From the structural perspective, Su-25 could take off the ground with a nuclear bomb onboard, such as 30-kiloton PH-40. But would it be able to escape safely after dropping it?

Defense Express notes that Su-25 which appeared in the Belarusian video was not covered in special light-colored “anti-atomic” coating which should protect the aircraft from light radiation during a nuclear explosion.

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The Russian T-90A main battle tank that was spotted in Louisiana sparked lots of heated debates and discussions about the reason why and also how this machine appeared in the United States.

The images circulating on social networks were taken quite freshly. But the tank itself was apparently captured by Ukraine last year, writes The Drive.

The fact of the tank’s capture by the Ukrainian Armed Forces was confirmed by the open source intelligence (OSINT) trackers. The trailer carrying this vehicle was photographed at a truck stop on the southernmost cross-country highway Interstate 10.

The T-90A is considered one of the most modern Russian combat machines among those used during their invasion of Ukraine. As with all tanks, it is also exceptionally heavy – according to the publication, the truck that was hauling this huge load experienced problems with its transmission.

Russian tank spotted in the USA, Louisiana. Namely the T-90A, which the Ukrainian military captured last year in the Kharkiv region. Now it looks like Russian tank was handed over to the Americans for research. https://t.co/YXbBOLYiyL pic.twitter.com/Nplhao1LEj

— Special Kherson Cat 🐈🇺🇦 (@bayraktar_1love) April 13, 2023

The tank itself does not contain its full equipment and add-on systems – for example, its machine guns and fire control systems have been removed. However, some explosive reactive armor containers have been left on the gun turret.

The exact reason why T-90A appears in the United States remains unknown. The experts, however, say that the U.S. Military may have brought this tank to the United States to thoroughly analyze its features in order to gain insights into its capabilities and potential vulnerabilities.

We could expect that later the results of this analysis could be handed over to Ukrainian defenders in order to improve their defensive capabilities against this machine and similar military equipment.

Oryx, an open source intelligence community, reports that the Ukrainian Army has captured at least 549 Russian tanks to this date, not counting those units that were destroyed in combat.

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Model offers actionable insights for physicians, could augment clinical decisions in resource-limited areas.

A new artificial intelligence model designed by researchers at Harvard Medical School and National Cheng Kung University in Taiwan could bring much-needed clarity to doctors delivering prognoses and deciding on treatments for patients with colorectal cancer, the second deadliest cancer worldwide.

Solely by looking at images of tumor samples — microscopic depictions of cancer cells — the new tool accurately predicts how aggressive a colorectal tumor is, how likely the patient is to survive with and without disease recurrence, and what the optimal therapy might be for them.

Having a tool that answers such questions could help clinicians and patients navigate this wily disease, which often behaves differently even among people with similar disease profiles who receive the same treatment — and could ultimately spare some of the 1 million lives that colorectal cancer claims every year.

A report on the team’s work is published in Nature Communications.

The researchers say that the tool is meant to enhance, not replace, human expertise.

“Our model performs tasks that human pathologists cannot do based on image viewing alone,” said study co-senior author Kun-Hsing Yu, assistant professor of biomedical informatics in the Blavatnik Institute at HMS. Yu led an international team of pathologists, oncologists, biomedical informaticians, and computer scientists.

“What we anticipate is not a replacement of human pathology expertise, but augmentation of what human pathologists can do,” Yu added. “We fully expect that this approach will augment the current clinical practice of cancer management.”

The researchers caution that any individual patient’s prognosis depends on multiple factors and that no model can perfectly predict any given patient’s survival. However, they add, the new model could be useful in guiding clinicians to follow up more closely, consider more aggressive treatments, or recommend clinical trials testing experimental therapies if their patients have worse predicted prognoses based on the tool’s assessment.

The tool could be particularly useful in resource-limited areas both in this country and around the world where advanced pathology and tumor genetic sequencing may not be readily available, the researchers noted.

The new tool goes beyond many current AI tools, which primarily perform tasks that replicate or optimize human expertise. The new tool, by comparison, detects and interprets visual patterns on microscopy images that are indiscernible to the human eye.

The tool, called MOMA (for Multi-omics Multi-cohort Assessment) is freely available to researchers and clinicians.

Extensive training and testing

The model was trained on information obtained from nearly 2,000 patients with colorectal cancer from diverse national patient cohorts that together include more than 450,000 participants — the Health Professionals Follow-up Study, the Nurses’ Health Study, the Cancer Genome Atlas Program, and the NIH’s PLCO (Prostate, Lung, Colorectal and Ovarian) Cancer Screening Trial.

During the training phase, the researchers fed the model information about the patients’ age, sex, cancer stage, and outcomes. They also gave it information about the tumors’ genomic, epigenetic, protein, and metabolic profiles.

Then the researchers showed the model pathology images of tumor samples and asked it to look for visual markers related to tumor types, genetic mutations, epigenetic alterations, disease progression, and patient survival.

The researchers then tested how the model might perform in “the real world” by feeding it a set of images it had not seen before of tumor samples from different patients. They compared its performance with the actual patient outcomes and other available clinical information.

The model accurately predicted the patients’ overall survival following diagnosis, as well as how many of those years would be cancer-free.

The tool also accurately predicted how an individual patient might respond to different therapies, based on whether the patient’s tumor harbored specific genetic mutations that rendered the cancer more or less prone to progression or spread.

In both of those areas the tool outperformed human pathologists as well as current AI models.

The researchers said the model will undergo periodic upgrading as science evolves and new data emerge.

“It is critical that with any AI model, we continuously monitor its behavior and performance because we may see shifts in the distributions of disease burden or new environmental toxins that contribute to cancer development,” Yu said. “It’s important to augment the model with new and more data as they come along so that its performance never lags behind.”

Discerning telltale patterns

The new model takes advantage of recent advances in tumor imaging techniques that offer unprecedented levels of detail, which nonetheless remain indiscernible to human evaluators. Based on these details, the model successfully identified indicators of how aggressive a tumor was and how likely it was to behave in response to a particular treatment.

Based on an image alone, the model also pinpointed characteristics associated with the presence or absence of specific genetic mutations — something that typically requires genomic sequencing of the tumor. Sequencing can be time-consuming and costly, particularly for hospitals where such services are not routinely available.

It is precisely in such situations that the model could provide timely decision support for treatment choice in resource-limited settings or in situations where there is no tumor tissue available for genetic sequencing, the researchers said.

The researchers said that before deploying the model for use in clinics and hospitals, it should be tested in a prospective, randomized trial that assesses the tool’s performance in actual patients over time after initial diagnosis. Such a study would provide the gold-standard demonstration of the model’s capabilities, Yu said, by directly comparing the tool’s real-life performance using images alone with that of human clinicians who use knowledge and test results that the model does not have access to.

Another strength of the model, the researchers said, is its transparent reasoning. If a clinician using the model asks why it made a given prediction, the tool would be able to explain its reasoning and the variables it used.

This feature is important for increasing clinicians’ confidence in the AI models they use, Yu said.

Gauging disease progression, optimal treatment

The model accurately pinpointed image characteristics related to differences in survival.

For example, it identified three image features that portended worse outcomes:

• Greater cell density within a tumor.
• The presence of connective supportive tissue around tumor cells, known as stroma.
• Interactions of tumor cells with smooth muscle cells.

The model also identified patterns within the tumor stroma that indicated which patients were more likely to live longer without cancer recurrence.

The tool also accurately predicted which patients would benefit from a class of cancer treatments known as immune checkpoint inhibitors. While these therapies work in many patients with colon cancer, some experience no measurable benefit and have serious side effects. The model could thus help clinicians tailor treatment and spare patients who wouldn’t benefit, Yu said.

The model also successfully detected epigenetic changes associated with colorectal cancer. These changes — which occur when molecules known as methyl groups attach to DNA and alter how that DNA behaves — are known to silence genes that suppress tumors, causing the cancers to grow rapidly. The model’s ability to identify these changes marks another way it can inform treatment choice and prognosis.