Category: 7. Science

Specialty

Please chooseI’m not a medical professional.Allergy and ImmunologyAnatomyAnesthesiologyBiostatisticsCardiac/Thoracic/Vascular SurgeryCardiologyCritical CareDentistryDermatologyDiabetes and EndocrinologyEmergency MedicineEpidemiology and Public HealthFamily MedicineForensic MedicineGastroenterologyGeneral PracticeGeneticsGeriatricsHealth PolicyHematologyHIV/AIDSHospital-based MedicineI’m not a medical professional.Infectious DiseaseIntegrative/Complementary MedicineInternal MedicineInternal Medicine-PediatricsMedical Education and SimulationMedical PhysicsMedical StudentNephrologyNeurological SurgeryNeurologyNuclear MedicineNutritionObstetrics and GynecologyOccupational HealthOncologyOphthalmologyOptometryOral MedicineOrthopaedicsOsteopathic MedicineOtolaryngologyPain ManagementPalliative CarePathologyPediatricsPediatric SurgeryPharmacologyPhysical Medicine and RehabilitationPlastic SurgeryPodiatryPreventive MedicinePsychiatryPsychologyPulmonologyRadiation OncologyRadiologyRheumatologySubstance Use and AddictionSurgeryTherapeuticsTraumaUrologyMiscellaneous

Sixty-six million years ago, North America saw long, dark winters. Fossils now suggest tiny primates were already there, coping with ice and snow.

A new analysis turns the usual tropical origin story on its head. The research combines hundreds of fossils with climate models to track primate ancestors through time.

“Our findings flip that narrative entirely,” said Jorge Avaria-Llautureo, an evolutionary biologist at the University of Reading. His team traced the primate family tree back to chilly northern forests.

Primates adapted to freezing climates

The team worked with the Köppen-Geiger climate system, a scheme that sorts environments by average heat and rain. Their maps place the earliest true primates in a zone with hot summers and sub-freezing winters.

That habitat fits today’s upper Midwest more than a steamy jungle. It means our lineage started where annual temperature swings could reach 70 degrees Fahrenheit.

These ancestors likely looked like nimble squirrel-sized creatures. They foraged at night, avoiding daytime chill.

Statistical models show a 70% chance that the first crown primates lived in what is now North America, with 30% pointing to Western Europe – territories then sitting near 45° N before plate motion. Their later travels reflect how moving land and changing skies steered evolution.

Fossils reveal primate movement

Fossils alone cannot solve the puzzle because they capture animals only where sediments preserve bones. The new study adds computer methods that simulate how species move across shifting landmasses.

By feeding 902 living and extinct species into BayesTraits software, the group estimated branch-by-branch journeys. They then matched positions with climate layers produced by the Hadley climate model.

The results indicate that early primates stayed in cold or temperate zones for at least 18 million years. Tropical forests entered the picture much later.

Warmer global temperature spikes such as the Paleocene-Eocene Thermal Maximum did not speed their spread. Instead, local temperature swings mattered most.

Early primates made long journeys

When ancestral lineages ventured into new climate categories, they tended to roam farther. Major moves often involved crossing climate boundaries rather than continents.

Median treks reached roughly 349 miles, compared with 85 miles for moves within familiar conditions. The bigger jumps exposed populations to novel habitats and pressures.

Such leaps helped generate new species by separating kin groups long enough for them to drift apart genetically. The pattern echoes broad ecological models that link dispersal to diversification.

Independent work on North American mammals predicts roughly 9 % of species will fail to outrun current warming trends, underlining the cost of slow feet. Swift past wanderers hint at which modern lineages could thrive.

How primates survived cold

How could small primates endure months of scarce food and sub-freezing nights? One answer is hibernation, a state in which body temperature and metabolism plunge.

Today’s dwarf lemurs in Madagascar sleep underground for up to seven months each year, a strategy first documented in 2004.

Later fieldwork showed that other dwarf lemur species also hibernate when mountain air turns icy.

These living examples show that primate physiology can slow to bear-like torpor, supporting the idea that ancient relatives did something similar.

Fast climate shifts caused changes

The Reading group found that the rate, not the direction, of local change predicted primate success. Rapid swings in heat or rain pushed species to travel or perish.

That insight matters today because many forests are shifting faster than ever measured. Flexible species may cope, while specialists could hit a wall.

The study also separates global averages from local reality. A place can warm overall yet still see harsher cold snaps or erratic storms that jar wildlife.

Conservation planners often model future ranges with coarse climate grids. The new work argues for finer, neighborhood-scale maps.

Old origin ideas were wrong

Textbooks have long tied primate origins to lush equatorial canopies. That view leaned on early fossil finds labeled “paratropical” without rigorous climate checks.

By applying a single classification standard, the team showed that many supposed rain-forest sites were actually cool mixed forests.

The conclusion challenges popular origin theories like “visual predation,” which assume dense, warm vegetation shaped grasping hands and forward eyes. Those traits may have first evolved among conifers instead.

The researchers also found that rosid plants, common in temperate woods, diversified around the same time, offering fruit and sap to budding primates.

Primates adapted to shifting climates

Early primates were tougher and more mobile than usually portrayed. They met freezing dawns, marched hundreds of miles, and only later settled in the tropics where most descendants remain.

Their story suggests resilience has always underpinned primate history, yet it came with extinctions for lineages that failed to keep pace.

Modern humans, one branch of that hardy clan, now drive the climate engine. Understanding our icy roots may remind us how quickly fortune can flip.

The study is published in Proceedings of the National Academy of Sciences.

—–

Like what you read? Subscribe to our newsletter for engaging articles, exclusive content, and the latest updates. 

Check us out on EarthSnap, a free app brought to you by Eric Ralls and Earth.com.

—–

On Aug. 12, the gap between Venus and Jupiter will be 0.9 degrees.

getty

Those who rise early — very early — on Monday, Aug. 12, will see one of the skywatching highlights of 2025 as Jupiter and Venus pass each other very closely in the eastern sky.

It’s a night sky sight that’s already begun, with the two planets getting visibly closer with each passing day. Best of all, you don’t need any optical aid to enjoy this celestial sight, just your naked eyes (and an alarm clock).

When Is The Conjunction Of Venus And Jupiter?

It’s happening right now, but for the best view of the two planets at their closest, look to the east-northeast horizon about an hour before sunrise on Aug. 12. As you do, Venus and Jupiter will be virtually alongside each other.

This is not a “proximate” conjunction, according to When The Curves Line Up. That status is reserved for very close conjunctions, when two planets appear to be less than half a degree from each other. On Aug. 12, Venus and Jupiter will appear to have a separation of 0.9 degrees. That’s close — the width of a little finger held at arm’s length.

Tuesday, August 12: Conjunction Of Venus And Jupiter

Stellarium

Why You Need To See The Conjunction Of Venus And Jupiter

The real reason that the conjunction of Venus and Jupiter is unmissable this year is that the two planets are so bright. During the conjunction, Venus will shine at -3.9 magnitude and Jupiter at -1.9 magnitude. Venus will be about six times brighter than Jupiter, but both will dominate the pre-dawn night sky.

If you have a pair of binoculars, point them at Jupiter and you’ll see some bright dots in a line around it — its four Galilean moons, Ganymede, Europa, Callisto and Io.

This Venus-Jupiter conjunction — which happens every year — is worth seeing in 2025 because the next one, on June 6, 2026, will see the planets significantly farther apart.

A crescent moon hangs in the sky above Venus (on the left) and Jupiter in the evening sky on 3 December, 2008. (Photo by Jamie Cooper/SSPL/Getty Images)

SSPL via Getty Images

Is The Conjunction Of Venus And Jupiter Real?

It’s not. All conjunctions are merely a line-of-sight illusion. Venus orbits the sun in 224 days, a little faster than Earth’s 365, while Jupiter takes 12 years to complete one orbit. On Aug. 12, Venus will be 115 million miles (186 million kilometers) from Earth and Jupiter 552 million miles (889 million kilometers), so around 438 million miles (703 million kilometers) apart. Jupiter will be about 4.8 times farther from Earth than Venus during the conjunction.

They appear to be close because all planets in the solar system orbit the sun along the same plane, a line through the sky from east to west that’s the same as the path of the sun through the daytime sky. This is called the ecliptic, and it’s where planets are found — and often appear to catch up and overtake each other from our point of view on a fast-moving planet.

For exact timings, use a sunrise and sunset calculator for where you are, Stellarium Web for a sky chart and Night Sky Tonight: Visible Planets at Your Location for positions and rise/set times for planets.

Diet altered the gut microbial composition in mice

The study design are shown in Fig. 1. Prior to the transition from the basic maintenance (normal) diet, the dominant genera in the three groups of mice (normal, high-fat, and high-Fiber) were Alistipes, Mucispirillum, Lactobacillus, and Bacteroides, which together constituted 80% of the gut microbiota (Fig. 2A and Supplementary Data 1). After switching from the normal diet to either a high-fat or high-fiber diet, both groups exhibited significant changes in alpha diversity, as evaluated by the Shannon index, and in beta diversity, as determined by principal coordinate analysis (PCoA) (Fig. 3A and Supplementary Fig. 1A). In the high-fat group, the relative abundance of Alistipes decreased from 28.71% to 4.85% and Bacteroides from 7.81% to 2.88%. Conversely, Lactococcus, Enterococcus, Anaerotruncus, and Escherichia increased from 0% to 20.55%, 0% to 0.04%, 4.88% to 5.58%, and 0.07% to 0.25%, respectively (Fig. 2B and Supplementary Data 2). In the high-fiber group, Alistipes decreased from 22.74% to 0.8%, while Parabacteroides and Bacteroides increased from 3.07% to 40.37% and from 7.12% to 14.03%, respectively (Fig. 2B and Supplementary Data 3). Similar trends were observed when comparing the high-fat group to the normal diet group, and the high-fiber group to the normal diet group. Lactococcus, Enterococcus, Anaerotruncus, and Escherichia were identified as biomarkers in the high-fat group, while Parabacteroides and Bacteroides were identified as biomarkers in the high-fiber group (Supplementary Fig. 2). These findings underscored the ability of high-fat and high-fiber diets to alter the taxonomic composition of the gut microbiota.

High-fat diet increased the abundances of the resistome, virulome, and mobilome in mice

Prior to dietary transition, the gut resistome in mice mainly comprised genes encoding resistance to tetracycline, vancomycin, macrolide−lincosamide−streptogramin (MLS), bacitracin, and multidrug classes (Fig. 2C, Supplementary Data 4–6). Twenty-one days after switching to a high-fat diet, the total abundance of the resistome increased significantly from 0.14 to 0.25 (ARG/16S rRNA gene ratio; p < 0.001, Supplementary Data 5). In contrast, the high-fiber diet led to a decrease in resistome abundance from 0.14 to 0.09 (p < 0.05) (Fig. 2D and Supplementary Data 6). Distinct patterns in alpha and beta diversity were observed across the groups, indicating significant differences in resistome composition after the dietary change (Fig. 3B and Supplementary Fig. 1B). Notably, the relative abundance of vancomycin resistance genes (vanD, vanG, vanR, and vanS) in the high-fat group increased significantly from 0.019 to 0.071 ARG/16S rRNA gene ratio (p < 0.01, Fig. 2D, Supplementary Fig. 3A, and Supplementary Data 5). Conversely, the high-fiber diet resulted in significant decreases across most ARG categories, including resistance to bacitracin (bacA and bcrA), chloramphenicol (cat), MLS (lsa, vatB, and vatC), and vancomycin (vanD, vanG, vanR, and vanS) (Fig. 2D, Supplementary Fig. 3B, and Supplementary Data 6). These findings suggested that the high-fat diet promoted an increase in resistome abundance, whereas the high-fiber diet reduced it.

Similarly, the virulome—comprising 13 main categories of virulence genes—was significantly affected by diet. In the high-fat group (Supplementary Data 7–9), the virulome abundance increased from 0.56 to 0.91 VG/16S rRNA gene ratio (p < 0.001, Supplementary Data 8), whereas in the high-fiber group, it decreased from 0.58 to 0.50 (p < 0.05) (Fig. 2E, F, Supplementary Fig. 1C, and Supplementary Data 9). Alpha diversity increased significantly in the high-fat group but not in the high-fiber group (Fig. 3C). PCoA revealed distinct changes in beta diversity in the virulome induced by the high-fiber diet (Fig. 3C and Supplementary Fig. 3B). Functional category analysis showed increased abundances of genes associated with adherence, effector delivery system, motility, and immune modulation following the change to a high-fat diet (p < 0.01), along with the emergence of corresponding virulence systems (Fig. 2F, Supplementary Fig. 3C, and Supplementary Data 8). In contrast, the high-fiber diet was associated with decreases in genes related to adherence, biofilm, and stress survival, along with their corresponding virulence systems (Fig. 2F, Supplementary Fig. 3D, and Supplementary Data 9). Overall, these results suggested that the high-fat diet largely altered the virulome by increasing its abundance, while the high-fiber diet exerted a modest reductive effect.

The mobilome, which encompasses all MGEs including plasmids, transposons, and integrons in the microbiome, also showed large changes in response to diet (Fig. 2G, Supplementary Data 10–12). Following the switch from the normal diet, the total relative abundance of the mobilome increased 8-fold (from 0.20 to 1.66 ratio of MGE/16S rRNA gene ratio, Supplementary Data 11) on the high-fat diet, while it decreased from 0.22 to 0.13 on the high-fiber diet (Fig. 2H and Supplementary Data 12). Specifically, the high-fiber diet did not affect plasmid abundance, whereas the high-fat diet increased transposon abundance from 0.09 to 1.26 (MGE/16S rRNA ratio; p < 0.001, Fig. 2H). Further analyses revealed increases in the abundances of intl1, int2, Tn916-orf6, Xis-Tn916, and IS91 in the high-fat group, with corresponding decreases in the high-fiber group (Supplementary Fig. 3E, F, Supplementary Data 11 and 12). Although alpha diversity of the mobilome did not show significant differences after the diet change (Fig. 3D), PCoA of beta diversity indicated distinct gene clustering in the high-fat group compared to the high-fiber and normal groups (Fig. 3D and Supplementary Fig. 1D). Collectively, these findings highlighted the profound impact of a high-fat diet on the mobilome.

Changes in ARGs and VGs were closely related to changes in host bacteria in mice

We used assembled contigs to evaluate the host bacteria carrying specific ARGs and VGs. Prior to the diet change, Bacteroides and Alistipes were identified as hosts for both fosmidomycin resistance gene rosA and tetracycline resistance gene tet37. Anaerotruncus hosted vancomycin resistance genes, while Lactobacillus was the primary host for the multidrug resistance gene mdtG (Fig. 4A and Supplementary Data 13). Most host bacteria–ARG relationships remained unchanged following high-fat diet feeding, however, Lactobacillus, Lactococcus, and Parabacteroides emerged as new hosts for certain ARGs (Supplementary Fig. 4A and Supplementary Data 14). Notably, vanG and vanY were absent in Anaerotruncus after high-fiber diet feeding (Supplementary Fig. 4B and Supplementary Data 15), suggesting a potential elimination of these host bacteria under the high-fiber diet (Fig. 2A). Changes in both the relationship and abundance of host bacteria and ARGs were observed in the high-fat and high-fiber groups (Fig. 4B, C). Regarding VGs, Alistipes was associated with capsular polysaccharide, type III secretion system effectors, and type VI secretion system-related VGs, while Anaerotruncus was linked with capsule-related VGs, and Bacteroides was associated with capsular polysaccharide and capsule-related VGs prior to the diet change (Fig. 4D and Supplementary Data 16). Similar to ARGs, both increased and decreased abundances of VGs and their related host bacteria were observed following high-fat and high-fiber diet feeding, respectively (Fig. 4E, F, Supplementary Data 17 and 18). Additionally, the presence or absences of VGs in their corresponding host bacteria were also noted in both experimental diet groups (Supplementary Fig. 4C, D).

Increased MGEs were associated with increases in the corresponding ARGs and VGs in mice

Given the important role of HGT in enriching the resistome and virulome, we examined the networks between MGEs and ARGs/VGs. The ARGs were mainly associated with intl1, IS91, ISBf10, Tn916-orf6, tnpA-related transposon, and Xis-Tn916 (Fig. 4G and Supplementary Data 19). Most ARGs and their corresponding MGEs exhibited increased abundances following high-fat diet feeding but decreased after high-fiber diet feeding (Fig. 4H, I, Supplementary Fig. 5A,B, Supplementary Data 11, 12, 20 and 21). Notably, Tn916-orf6 was mainly associated with genes encoding resistance to vancomycin and tetracycline, and its abundance increased significantly after high-fat diet feeding (Fig. 4H, Supplementary Fig. 3C, Supplementary Data 5, 11, and 20). The abundance of tnpA-related transposons dramatically increased in the high-fat group, while the associated ARGs only slightly increased, indicating a potential but unclear association between tnpA and other enriched ARGs (Fig. 4H, Supplementary Data 5, 11, and 20). In the high-fiber group, both MGEs and their associated ARGs generally decreased in abundance, except for class A β-lactamase and its host vector ISBf10, which showed increased abundances (Fig. 4I, Supplementary Data 6, 12, and 21).

We also explored the relationship between MGEs and VGs before and after the dietary change (Fig. 4J, Supplementary Fig. 5C, D, Supplementary Data 22–24). Although the relationship between MGEs and VGs appeared more complex than those between MGEs and ARGs, similar trends were observed following the change to high-fat and high-fiber diet feeding (Fig. 4K, L, Supplementary Data 22–24). Notably, the abundances of capsule-related VGs increased after high-fiber diet feeding, while the abundances of their four associated host vectors (Xis-Tn916, tnpA, Tn916-orf6, and IS91) decreased, with the exception of an increase in ISBf10. These results suggested that ISBf10 may serve as the primary host vector for capsule-related VGs (Fig. 4L, Supplementary Data 9, 12 and 24).

Distinct energy source use by gut microbiota in high-fat and high-fiber groups in mice

We analyzed gene function within the microbiome to further explore the relationship between diet and microbial metabolic activity. Genes related to membrane transport, including phosphotransferase system and ABC transporters involved in carbohydrate uptake, were enriched in response to either the high-fat or high-fiber diet intervention (Supplementary Fig. 6). After high-fat diet feeding, the abundances of genes associated with fatty acid degradation and starch and sucrose metabolism significantly increased, likely due to the high lard and maltodextrin content in the high-fat diet (Supplementary Fig. 6A). In contrast, after high-fiber diet feeding, genes involved in glycan degradation and starch and sucrose metabolism became more abundant, likely reflecting the higher starch and cellulose content in the high-fiber diet (Supplementary Fig. 6B).

High-fat diet and obesity increase human gut resistome and mobilome

We investigated the resistome and mobilome in the human population from the perspectives of dietary habits and BMI (Fig. 1). In terms of resistome, genes encoding MLS, beta-lactam resistance and multidrug resistance were predominant in the human gut (Fig. 5A, E, Supplementary Data 25–30). Notably, the total abundance of the resistome in the high-fat population was significantly higher than in the high-fiber and normal diet populations (Supplementary Fig. 7A). Specifically, the relative abundances of genes conferring resistance to cephalosporin (bla_TME-136, 7.31 × 10⁻⁶ vs 0.00, p < 0.05), MLS (lsa, 2.42 × 10⁻³ vs 9.56 × 10⁻⁴, p < 0.05) and aminoglycoside (aph(3”‘)-III, 7.19 × 10⁻³ vs 1.90 × 10⁻³, p < 0.05) were higher in individuals consuming a high-fat diet compared to those on a normal diet (Supplementary Data 31). A similar trend was observed in the obesity group (Supplementary Fig. 7B), where the relative abundance of genes associated with resistance to cephalosporin (bla_TME-127, 3.25 × 10⁻⁶ vs 0.00, p < 0.05), phenicols-lincosamides-oxazolidinones-pleuromutilins-streptogramin A (cfr, 1.54 × 10⁻⁴ vs 1.80 × 10⁻⁵, p < 0.05), and tigecycline (tet(X), 2.68 × 10⁻³ vs 5.95 × 10⁻⁴, p < 0.05) were higher in individuals with obesity compared to those with a healthy BMI (Supplementary Data 32). Although Shannon diversity of ARGs showed no significant difference among the groups (Fig. 5B, F), the Bray-Curtis distance revealed distinct patterns based on dietary habits (Fig. 5C). Specifically, genes encoding resistance to tetracycline, vancomycin, and aminoglycoside resistance were enriched in the high-fat diet/obesity population (Fig. 5D, H). Regarding the mobilome, the total abundance of MGEs was higher in the high-fat/obesity population compared to the normal diet/healthy BMI population (Supplementary Fig. 8).

We also detected the ESKAPE pathogens (Enterococcus faecium, Staphylococcus aureus, Klebsiella pneumoniae, Acinetobacter baumannii, Pseudomonas aeruginosa, and Enterobacter species) in each group. The results showed that the total abundance of the genera Enterococcus, Staphylococcus, Klebsiella and Pseudomonas (ESKP) was higher in the high-fat and obesity group compared to the normal and healthy group (Supplementary Fig. 9). In terms of ARGs, the ESKP pathogens were the main host of ARGs, and the total abundance of ESKP-carrying ARG contigs in the high-fat population was significantly higher than in the high-fiber and normal diet populations, with an average abundance of 710.25, 89.37 and 133.48 TPM, respectively. Similarly, the total abundance of ESKP-carrying ARG contigs in the obesity population was higher than in the healthy population (1028.72 versus 158.96 TPM) (Fig. 6A, B). Notably, Klebsiella sp., which had the highest abundance among ESKP in different diet populations, mainly carried fosfomycin (fosA) and beta-lactam (bla_SHV and penA) resistance genes (Fig. 6C, D, Supplementary Fig. 9, Supplementary Data 33 and 34). Staphylococcus sp. was associated with tetracycline resistance genes (tetM and tetL), while Enterococcus sp. was associated with tetracycline (tetM, tetL, and tetW), pleuromutilin–lincosamide–streptogramin A (lsa), and florfenicol (fexB) resistance genes. All these ARGs were more abundant in the high-fat diet/obesity population compared to the normal diet/healthy BMI population (Fig. 6C, D, Supplementary Fig. 9, Supplementary Data 33 and 34).

Demographic characteristics

Initially, 478 patients admitted to the ICU were screened, and 254 patients were excluded for not meeting the inclusion criteria or meeting the exclusion ones. A total of 224 patients were included and then followed up, and their stool samples were collected during ICU hospitalization. However, 56 patients did not defecate during ICU stay. Ultimately, 168 patients were enrolled, and 1248 stool samples were collected from them. During ICU stay, 107 patients were negative by CRKP screening, and 61 patients were positive; however, 43 patients were excluded due to incomplete sample collection, and five patients were lost during follow-up. Ultimately, 18 patients and their stool samples (at admission and after CRKP colonization) were used for subsequent analysis. Propensity score matching was conducted to match samples from the CRKP-non-convert group (CRKP-N), resulting in the use of 18 pairs of samples in the final study.

The demographic, clinical, and laboratory characteristics of the ICU patients are summarized in Table 1. The CRKP-positive patients showed a longer time in the ICU (p = 0.001) and hospital stay (p = 0.031). We also noted that the CRKP-positive patients had a lower survival ratio (60-day survival: 61.11 vs 77.78%, p = 0.278), but the difference was not significant compared to CRKP-negative patients, which is likely due to the small sample size. Additionally, 18 healthy individuals were also recruited, and their demographic information is presented in Table S1.

The feature of CRKP isolated from stool samples in the CRKP-positive group

All isolates from 18 CRKP-positive patients were initially screened using Simmons’ Citrate Agar Incositol (SCAI) medium agar containing 4 mg/L meropenem and 32 mg/L linezolid and subsequently identified as K. pneumoniae via matrix-assisted laser desorption ionization time-of-flight mass spectrometry (MALDI-TOF MS). To further elucidate their characteristics, whole-genome sequencing was performed. As detailed in Table S2, all isolates were confirmed as K. pneumoniae, with the majority belonging to sequence type ST11 (17/18, 94.4%), while only one isolate was identified as ST15 (1/18, 5.6%). Additionally, 16 isolates were assigned to capsular type KL64 (16/18, 88.8%), with the other remaining two isolates belonging to KL47 (1/18, 5.6%) and KL19 (1/18, 5.6%), respectively. Regarding carbapenemase-encoding genes, bla_KPC-2 was detected in all isolates, whereas one isolate also carries bla_NDM-1. Furthermore, all isolates were resistant to meropenem, with MICs ranging from 128 to >512 mg/L.

The microbiome features of different ICU patient groups

To investigate the characteristics of the gut microbiome in different ICU patient groups, we employed 16S rRNA sequencing to analyze the fecal microbiome. The results showed that the ICU admission (ICU-A) group had a lower alpha diversity, as measured by the observed species, Shannon, Simpson, and Chao1 indices, compared to the healthy control group (HCG) (Fig. 1A–D). However, no significant differences in alpha diversity were observed between CRKP-non-convert admission (CRKP-NA) and CRKP-positive-convert admission (CRKP-PA) at the time of admission to the ICU (Supplementary Fig. S1A–D), indicating that these two groups at admission were comparable.

Moreover, significant differences in alpha diversity were found only in the observed species and Chao1 indices between CRKP-N and CRKP-positive conversion (CRKP-P). Notably, CRKP-P exhibited higher indices in both metrics, contrary to expectations (Fig. 1E–H). Nonetheless, the CRKP-P group still had a lower alpha diversity compared with healthy individuals in terms of Shannon and Simpson index (Fig. 1I–L). In contrast, no significant differences were detected in the Shannon and Simpson indices between those two groups (Fig. 1F, G). Additionally, no significant differences in the alpha diversity were observed between CRKP-NA and CRKP-N (Fig. S1E–H), as well as between CRKP-PA and CRKP-P (Fig. S1I–L). These findings suggest significant fluctuations in the alpha diversity of the gut microbiome in healthy individuals, ICU patients, and ICU patients with CRKP colonization.

To further elucidate the differences in microbiome composition among the different groups, principal coordinates analysis (PCoA) was performed (Fig. 1M–P). Significant differences were observed between the ICU-A and HCG groups (Adonis R²: 0.0437, p = 0.024), indicating distinct microbiome compositions between these groups. Similarly, significant differences were found between the CRKP-PA and CRKP-P groups (Adonis R²: 0.0978, p = 0.003). However, the differences between the CRKP-NA and CRKP-PA groups showed borderline significance with an Adonis R² value of 0.053 and p value of 0.056. In contrast, no significant differences were observed between the CRKP-NA and CRKP-N groups (Adonis R²: 0.0264, p = 0.454), suggesting similar microbiome compositions between these two groups.

CRKP was associated with alterations in the abundance of specific taxonomy in ICU patients

Taxonomic analysis revealed significant differences at the genus level among the HCG, CRKP-NA, CRKP-N, CRKP-PA, and CRKP-P groups. The health control population was predominantly composed of Bacteroides, Faecalibacterium, Blautia, Bifidobacterium, Coprococcus, Roseburia, Prevotella, Ruminococcus, and Eubacterium. However, in patients of the ICU-A group, the top 10 prevalent genera were Enterococcus, Bacteroides, Klebsiella, Parabacteroides, Lactobacillus, Bifidobacterium, Ruminococcus, Alistipes, Eubacterium, and Clostridium. Notably, Klebsiella was listed among the top 10 ranked genera in this group (Fig. 2A). In ICU patients in the CRKP-P group who tested positive for CRKP, the most abundant genera were Enterococcus, Klebsiella, Bacteroides, Parabacteroides, Alistipes, Enterobacter, Ruminococcus, Eubacterium, Lactobacillus, and Clostridium. However, in the CRKP-PA group, the top ten genera were Enterococcus, Bacteroides, Parabacteroides, Bifidobacterium, Lactobacillus, Eubacterium, Alistipes, Ruminococcus, Klebsiella, and Clostridium (Fig. 2B).

To identify marker species between different groups, linear discriminant analysis effect size (Lefse) analysis was conducted. The analysis also revealed that 25 ASVs were significantly between ICU-A and HCG groups, including Enterococcus, Lactobacillus, Clostridium, Dorea, Ruminococcus, Roseburia, Coprococcus, Bifidobacterium, Blautia, and Faecalibacterium (Fig. 2C). However, when comparing CRKP-P to the CRKP-N group, more than 38 ASVs showed differences between groups including Klebsiella, Enterococcus, Alistipes, Escherichia_Shigella, Bacteroides, Akkermansia, Citrobacter, Barnesiella, Bifidobacterium and Lactobacillus, among others (Fig. 2D). When comparing the stool microbiome features in patients before (CRKP-PA) and after (CRKP-P) being detected as CRKP-positive, 14 ASVs were found to differ between the two groups, including Klebsiella, Akkermansia, Leptotrichia, Pseudomonas, Dermabacter, Turicibacter, and Bifidobacterium, among others (Fig. 2E). Not surprisingly, patients who developed CRKP-positive or negative group, more than 57 ASVs showed significant differences in their corresponding samples at the time of admission (CRKP-PA vs. CRKP-NA), which including Bifidobacterium, Alistipes, Collinsella, GEMMIGER, Leuconostoc, Coprococcus, Blautia, Odoribacter, Ruminococcus, Roseburia, Clostridium, Weissellam, Faecalibacterium,Anaerofustis, Eggethella, Dorea, Lactococcus, Citrobacter, and Coprobacillus, among others (Fig. 2F). Importantly, the Sankey chart clearly showed that Klebsiella had a significant increase in CRKP-P compared to its CRKP-PA at admission, while there was little change in Klebsiella abundance in patients who were consistently CRKP-negative (Fig. 2G). While comparing the CRKP-NA and CRKP-N groups, only four ASVs were found to be depleted in the CRKP-N group (Fig. S2).

Importantly, we found several featured genera/species when comparing CRKP-positive and negative samples. In the HCG and ICU-A groups compassion, Lactobacillus had an LDA score of 4.2 and Bifidobacterium had an LDA score of 4.5. Furthermore, in the CRKP-PA and CRKP-P group comparison, one strain of B. longum had a linear discriminant analysis (LDA) score of 4.23 and higher relative abundance in the CRKP-PA group (Fig. 2H). Notably, in the comparison of negative (CRKP-N) and positive (CRKP-P) samples, Klebsiella had the highest LDA score, reaching 5.07, and one strain of K. pneumoniae having an LDA score of 4.02 in CRKP-P. Simultaneously, one strain of L. plantarum in the CRKP-N group had an LDA score of 4.63 and higher relative abundance compared with CRKP-P (Fig. 2I).

CRKP colonization alters the function of the gut microbiota in ICU patients

To further investigate the changes in gut microbiome function after CRKP colonization, we performed a PICRUSt2 functional prediction analysis. Bray–Curtis principal coordinates analysis (PCoA) method was applied to analyze the abundance of KEGG pathways predicted by Phylogenetic Investigation of Communities by Reconstruction of Unobserved States (PICRUSt2) at levels L1, L2, L3, and MetaCyc pathways. Our results showed that the predicted functions between the HCG and ICU-A groups exhibited significant differences. Notably, almost all pathways with significant changes displayed opposing abundance trends between the two groups, with pathways upregulated in HCG being downregulated in ICU-A, and conversely (Fig. S3). Meanwhile, the CRKP-P group showed significant differences from the other groups in all pathways (Fig. 3A–E and Fig. S4). Similarly, we found that the CRKP-P group had significantly different pathways in all predicted functions, which were opposite or significantly different from those of the other three groups. At the KEGG L2 level, pathways such as Cancer: overview, Cancer: specific types, cellular community-prokaryotes, neurodegenerative disease, and Xenobiotics biodegradation and metabolism were significantly higher in the CRKP-P group, while pathways such as Cell growth and death, Endocrine and metabolic disease, endocrine system, replication and repair, and translation were significantly lower (Fig. 3E). Similar results were observed at the KEGG L2 level and MetaCyc (Fig. S4). However, these drastic changes were not observed in CRKP-N and CRKP-NA (Fig. 3D, E). These findings indicate that ICU patients who were colonized with CRKP exhibited significant changes in their gut microbiome function.

CRKP colonization induces metabolic profile shifts in ICU patients

We were interested in determining whether changes in the gut microbiome would lead to shifts in gut metabolic products. Therefore, we conducted a non-targeted metabolomics analysis. The metabolome profiles of the HCG, ICU-A, CRKP-NA, CRKP-N, CRKP-PA, and CRKP-P groups were distinct, as revealed by OPLS-DA (Fig. 4). KEGG pathway enrichment analysis revealed that there were significant differences between HCG group and ICU-A group in the main enrichment top 25 pathways, including metabolism for amino acids (e.g, tyrosine, β-alanine, and tryptophan), nucleotides (e.g., purine and pyrimidine), vitamin (e.g., vitamin B6 and thiamine), carbohydrates (e.g., glycolysis/gluconeogenesis and galactose), lipids (e.g., arachidonic acid and ether lipid), and xenobiotic, as well as five other related metabolic pathways (Fig. 4A–C).

Further comparison of the differences between CRKP-P and CRKP-N revealed correspondingly enriched pathways. The metabolic pathways related to amino acid metabolism (e.g., 2-aminoacrylic acid, D-aspartic acid, D-serine, L-homoserine, and L-lysine), carbohydrate metabolism (1-deoxy-1-(N6-lysino)-D-fructose), lipid metabolism (e.g., But-2enoic acid, N,N-dimethylsphingosine, and D-tocotrienol), and vitamin metabolism (riboflavin and pyridoxal), nucleotide metabolism, xenobiotic metabolism (4-aminophenol, 1-hydroxy-6-methoxypyrene, and picrotoxinin), and nitrogen metabolism (1-aminocyclopropanecarboxylic acid) remained the most significantly changed pathways (Fig. 4D–F).

Additionally, in the comparison of the differences between CRKP-PA and CRKP-P revealed enriched pathways such as primary bile acid biosynthesis (tauroursodeoxycholic acid), nicotinate and nicotinamide metabolism (2-hydroxyadenine), pyruvate metabolism (palmitic acid and (R)-3-hydroxy-tetradecanoic acid), biotin metabolism (alanylglycine and palmitic acid), ether lipid metabolism (LysoPC(16:0) and 1-(5Z,8Z,11Z,14Z-eicosatetraenoyl)-sn-glycero-3-phosphate), and tyrosine metabolism (4-(2-furanylmethylene)-3,4-dihydro-2h-pyrrole) (Fig. 4G–I). However, fewer significantly different metabolic pathways were observed in the comparison between CRKP-NA and CRKP-N groups, including amino acid metabolism, primary bile acid biosynthesis, and nitrogen metabolism (Fig. 4J–L).

In vitro experiments verified the inhibitory effect of the altered microbes on CRKP

Our investigation of the fecal microbiome and non-targeted metabolomics revealed significant alterations in the microecological structure and function, as well as the composition of metabolites in CRKP-positive ICU patients. We selected L. plantarum 21790 and B. longum 6188, which exhibited notable differences in samples from the ICU patients, to verify their capacity to inhibit CRKP in vitro.

First, we demonstrated that two clinically isolated strains of K. pneumoniae 020003 and K. pneumoniae 020120 could grow in the Reinforced Clostridium Medium (RCM) but could only survive and not proliferate in the Man, Rogosa and Sharpe broth (MRS). Subsequently, we investigated the effects of B. longum 6188 and L. plantarum 21790 on K. pneumoniae proliferation by adding 10–30% of their overnight culture supernatants to co-culture with K. pneumoniae. We observed a concentration-dependent inhibition of K. pneumoniae growth, with the 30% L. plantarum 21790 culture supernatants being the most effective, reducing K. pneumoniae to undetectable levels within 6 h of co-culture. Meanwhile, the 30% B. longum 6188 culture supernatant exhibited inhibitory activity against K. pneumoniae, although its effect was not as pronounced as that of L. plantarum 21790. The inhibitory effect of other concentrations of B. longum 6188 culture supernatants decreased over time and was completely lost within 24 h (Fig. 5A–E).

Ex vivo experiments verified the inhibitory effect of the probiotics on CRKP

To further confirm the inhibitory effects of B. longum 6188 and L. plantarum 21790 on K. pneumoniae proliferation, we simulated a CRKP-positive fecal environment by adding K. pneumoniae to fecal samples from healthy individuals and then co-culturing these samples with B. longum 6188 and L. plantarum 21790 (Fig. 5F). Our results validated that the supplement of B. longum 6188 and L. plantarum 21790 provided resistance to K. pneumoniae colonization, significantly reducing the abundance of K. pneumoniae in the culture system, consistent with our previous observations. Similar results were observed when both B. longum 6188 and L. plantarum 21790 were added to the culture system (Fig. 5G).

In vivo experiments verified the inhibitory effect of the altered microbes on CRKP

To validate our findings from clinical samples and in vitro studies, we established a CRKP-positive mouse model by administering mice with carbapenem antibiotics and subsequently gavaging mice with K. pneumoniae 020120 (Fig. 5H). We then investigated the effects of orally administered B. longum 6188 and L. plantarum 21790 on CRKP suppression in the mouse model. In the B. longum 6188 and L. plantarum 21790 treatment groups, no significant inhibitory effects on K. pneumoniae 020120 were observed on days 12 and 14 (Fig. 5I, J). However, on days 18 and 25, the fecal CRKP loads were significantly reduced, and B. longum 6188 exhibited a better inhibition effect than L. plantarum 21790.

Lactobacillus and Bifidobacterium played a crucial role in restoring the normal gut microbiome, thereby enhancing the host’s resistance to CRKP colonization and accelerating the clearance of CRKP from the gastrointestinal tract. To further investigate the impact of gut microbiota dysbiosis on CRKP colonization, we used MEM to induce dysbiosis in mice, followed by CRKP administration. Notably, CRKP was slowly cleared from the mice after colonization. However, upon resupply of MEM, CRKP abundance rapidly increased. In contrast, when CRKP was co-administered with MEM, the abundance of CRKP in the stool continued to increase until MEM was removed, at which point CRKP abundance began to decrease gradually. Interestingly, in mice that did not receive CRKP gavage, CRKP abundance in the feces increased in a time-dependent manner with MEM treatment (Fig. 6A, B). To further confirm the importance of a healthy gut microbiome in providing resistance to CRKP colonization, we performed fecal microbiota transplantation (FMT) in mice after CRKP gavage. As expected, FMT significantly accelerated the clearance of CRKP compared to mice that did not receive FMT from healthy mice (Fig. 6C, D), and this effect was even more significant after the MEM was stopped. These results suggest that a healthy gut microbiome provides important resistance to CRKP colonization, and that restoration of the gut microbiome through supplementation with specific bacteria or probiotics is one potential method.

Scientists have recently discovered a monster black hole which is estimated to be 36 billion times the size of the sun, making it one of the heaviest ever found in the universe.

The recently identified massive black hole, known as the “dormant black hole” is located approximately 5 billion light years away within the Cosmic Horseshoe Galaxy.

The discovery of this ultramassive blackhole was based upon two parameters. The first one relies on the measurement of how gravity bends light, predicted by Einstein’s general theory of relativity and the second one tracks the motion of stars.

Collectively, these two techniques give insights into the unprecedented weight of the black hole.

The findings published in the Monthly Notices of the Royal Astronomical Society called the object potentially the bigger and more extreme one as our Milky Way’s central black hole only holds the mass of about 4.15 million suns.

Study co-author Thomas Collett said: “This is amongst the top 10 most massive black holes ever discovered and quite possibly the most massive.”

“We think the size of both is intimately linked, because when galaxies grow they can funnel matter down onto the central blackhole. Some of this matter grows the black hole, but lots if it shines away in an incredibly bright source called a quasar,” Collett issued a statement.

According to Collett , this ultramassive black hole could be formed from the integration of two galaxies, demonstrating the end state of galaxy and black hole formation. 

James A. Lovell Jr., the famed astronaut and commander of NASA’s Apollo 13 mission, has died at 97. Lovell passed away on August 8, 2025, in Lake Forest, Illinois, his family confirmed in a statement to NASA. Though Lovell never walked on the Moon, his leadership during Apollo 13’s near-disastrous oxygen tank explosion turned the mission into one of space exploration’s greatest rescue stories. His calm, quick thinking under intense pressure saved the lives of his crew and inspired millions worldwide. Lovell’s heroism was immortalized in the 1995 film Apollo 13, where Tom Hanks portrayed him. Beyond his space legacy, Lovell remains a symbol of resilience and courage, admired by generations of astronauts and space enthusiasts alike.

Jim Lovell’s early life and path to NASA

Born March 25, 1928, in Cleveland, Ohio, James Arthur Lovell Jr. lost his father at a young age and was raised in Milwaukee by his mother. Fascinated by rocketry as a teenager, he even built a homemade gunpowder rocket—a passion that eventually shaped his career.After attending the University of Wisconsin–Madison for two years, Lovell entered the US Naval Academy, graduating in 1952. He went on to become a Navy test pilot before being selected in 1962 as part of NASA’s second group of astronauts, destined for the Gemini and Apollo programs.

Jim Lovell record-breaking space career before Apollo 13

Before Apollo 13, Lovell already held the record for most hours in space among astronauts of the Mercury, Gemini, and Apollo eras—logging over 715 hours.

By the time Apollo 13 launched, Lovell was one of NASA’s most experienced astronauts.

Jim Lovell, Apollo 13 mission journey

Apollo 13 spacecraft damaged: The oxygen tank blast that changed NASA history

Apollo 13 lifted off on April 11, 1970 with Lovell as commander, Fred W. Haise Jr. as lunar module pilot, and John L. “Jack” Swigert Jr. as command module pilot. The plan was for Lovell and Haise to land in the Fra Mauro highlands while Swigert orbited above.But 56 hours into the mission, roughly 200,000 miles from Earth, disaster struck. An oxygen tank in the service module exploded after a damaged wire ignited during a routine stir. The blast crippled the spacecraft—knocking out power, oxygen, and water supplies. It was then that the phrase—misquoted in popular culture as “Houston, we have a problem”—entered the American lexicon. In reality, Swigert first radioed, “Houston, we’ve had a problem,” with Lovell repeating it shortly afterward.

Apollo 13 crisis management: How the lunar module saved the crew

With the main command module crippled, the astronauts and NASA engineers devised an audacious survival plan—use the lunar module (LM) as a lifeboat. Designed for only two astronauts for two days, the LM now had to sustain three astronauts for four days.To conserve resources, they:

An immediate return to Earth was too risky, so Apollo 13 looped around the Moon for a slingshot trajectory home. Lovell manually guided crucial rocket burns using Earth’s position through the spacecraft window as a navigation point.

Apollo 13’s safe return: From ocean rescue to Presidential honour

On April 17, 1970, after one of the most tense survival stories in modern history, Apollo 13 splashed down safely in the Pacific Ocean, 610 miles southeast of American Samoa. Three orange-and-white parachutes signaled the end of the crisis. President Richard Nixon awarded Lovell, Haise, and Swigert the Presidential Medal of Freedom, calling the mission “a successful failure”—failed in its lunar landing but victorious in its safe return.Lovell co-authored the 1994 book Lost Moon: The Perilous Voyage of Apollo 13 with Jeffrey Kluger, which became the basis for Ron Howard’s hit film Apollo 13. In the movie, Tom Hanks played Lovell, immortalizing his calm leadership for a new generation.Lovell even made a cameo as the captain of the USS Iwo Jima, the recovery ship that retrieved the Apollo 13 crew.

James Lovell’s life After NASA: Leadership, family, and lasting honours

Lovell retired from NASA and the Navy in 1973, going on to lead the Bay-Houston Towing Company and hold senior roles in telecommunications. He also ran Lovell Communications, a Chicago-based consulting firm. His family operated a Lake Forest restaurant decorated with space memorabilia until it closed in 2015. Lovell is survived by his four children—James III (Jay), Jeffrey, Barbara, and Susan—11 grandchildren, and nine great-grandchildren. His wife of more than 60 years, Marilyn Lovell, died in 2023.In addition to the Presidential Medal of Freedom, Lovell was awarded the Congressional Space Medal of Honor by President Bill Clinton in 1995. In later years, Lovell often reflected that while missing the Moon was a disappointment, Apollo 13’s rescue was a greater triumph.“It was a triumph in a different direction—getting people back from a certain catastrophe,” he said.Also Read | NASA Hubble Space Telescope captures image of interstellar comet 3I/ATLAS speeding at 130,000 mph

Conceptual framework

We speculate that there is an opportunity to estimate PMI using the relative abundance of radioactive decay products generated from naturally occurring (^{222}textrm{Rn}) and that is absorbed by living systems. More specifically, the inhalation of radon gas and its decay products is functionally universal on Earth, as (^{222}textrm{Rn}) is generated by uranium isotope-bearing minerals within the lithosphere and migrates via free-phase gas and water to the surface, where it enters the atmosphere of including indoor and outdoor air environments occupied by people^20,21. Human populations are exposed to some amount of radon and radon decay products daily, which is typically measured in an indirect manner by evaluating the amount of alpha particle emissions per second (Bq) per cubic metre of air ((Bq/m^3))²². (^{222}textrm{Rn}) levels in outside air are typically measured in the 1-60 (Bq/m^3) range^23,24,25, although can be (>100) (Bq/m^3) in exceptional cases²⁶. Indoor air or subterranean air radon amounts can be anything from 10-100,000 (Bq/m^3), depending on building and region²⁰. (^{222}textrm{Rn}) has a relatively short half-life of 3.8 days before undergoing a series of radioactive decays to form a series of very short lived ((t_{1/2}) = microseconds to minutes) isotopes of Po, Pb and Bi before becoming long-lived (^{210}textrm{Pb}) ((t_{1/2}) = 22.3 years), then (^{210}textrm{Bi}) ((t_{1/2}) = 5 days), (^{210}textrm{Po}) ((t_{1/2}) = 138 days), and finally stable (^{206}textrm{Pb}) (Figure 1).

Although exposure to some amount of gaseous (^{222}textrm{Rn}) and solid radon decay products is functionally universal, the specific amount of these radioisotopes inhaled by a person occurs at variable amounts across a lifetime depending on where they live, in what buildings they occupy, and behavioural factors such as occupation, tobacco use, diet and more^{27,28,29,30,31}. It is important to understand that human exposure to radon and its decay products is a function of how much gaseous (^{222}textrm{Rn}) is constantly entering a given environment from geogenic sources, and also those radon-decay products that have equilibrated within the same environment via (^{222}textrm{Rn}) decay and attachment to particulates such as smoke or dust²⁰. Both gaseous radon and the ‘attached fraction’ of radon decay products are inhaled and will be absorbed into living tissue via the lungs^32,33,34. To a lesser extent, radon and its decay products can be ingested via drinking groundwater, eating certain foods such as (^{210}textrm{Pb})-rich lichen-eating wild game (e.g. caribou, elk, deer, moose), or tobacco smoking^34,35,36. As all these routes of exposure demand that a person is alive, the deposition of (^{222}textrm{Rn}) and radon decay products within tissue is innately associated with life, whilst the cessation of breathing, drinking, and/or eating that occurs at death marks the end of incoming (^{222}textrm{Rn}) and radon decay products.

Careful measurement of the relative abundance of these decay products is now possible using recent advances in technology and so, for the first time, it is theoretically possible to use a radioisotopic decay of (^{222}textrm{Rn}) to evaluate PMI. Indeed, previous work by Ziad and colleagues³⁷ explored the use of radon decay product ratio measurements to estimate time of death. Assumptions made during that investigation included constant radon exposure during life and an initial equilibrium value of (^{210}textrm{Pb}):(^{210}textrm{Po}) that complicates time of death estimation³⁷; as these assumptions were undefined, they added uncertainties. We note that while the theoretical idea to measure (^{222}textrm{Rn}) decay products to estimate PMI is straightforward, its practical application is not. The accumulation of radon decay products in tissue is in the order of femtogram per gram (fg/g). Consequently, hypersensitive instruments are needed to measure isotope abundance (i.e. number of atoms) of radon decay products in biological tissues. If a low abundance measuring procedure is in place, as has been achieved for uranium or plutonium isotopes³⁸, the next problem is to identify tissues that are helpful to measure within the context of a corpse, based how radon decay products are stored in the body. Current evidence suggests that, in addition to the lungs, keratinizing tissue (nails), adipose tissue (fat), and bone are all tissues where radon decay products have been empirically found to accumulate^33,34. To develop and evaluate a theoretical model, we divided our work into two consecutive phases: the forward model and the actual Radon Time of Death Clock calculation. Our rationale was to use the radioactive decay equations under the assumption of a steady source of radon to generate data that could then be used to test the accuracy of the Radon Time of Death Clock. A critical component of the Radon Time of Death Clock developed here is the use of (1) relative numbers of isotopes and (2) two pairs of isotope abundance ratios. These input data enable a determination of the elapsed time since death to very high accuracy and is a key feature of this approach.

The forward model

For the forward model, we conceptualized a person who inhaled air containing an average of 100 (Bq/m^3) radon from birth to the time at which they died (time of death = (t_d)). The radon exposure assumption reflects the reality of radon in many nations including, for example, Canada where recent outcomes of national surveying found that the geometric mean, weighted level of radon in a residential building is 84.7 (Bq/m^3), with 42(%) of people experiencing levels of radon 100 (Bq/m^3) or more. For the forward model, we also assumed inhalation of radon and all radon decay products ceased entirely at the time of death, and that the numbers of (^{210}textrm{Pb}), (^{210}textrm{Bi}), and (^{210}textrm{Po}) atoms each began to decrease according to radioactive decay laws and their respective radioisotopic decay probabilities. At specific times after the discovery of the theoretical corpse by investigating authorities, we assumed the measurements of radon decay products in one or more tissues from the body could be performed (time of measurement = (t_m)), including the numbers of remaining atoms and isotope number ratios.

We suggest that two ratios that involve three radon decay isotopes ((^{210}textrm{Pb}), (^{210}textrm{Bi}), and (^{210}textrm{Po})) are of particular interest, as these isotopes have relatively longer half-lives; these ratios are: (r_1 = frac{^{210}textrm{Pb}}{^{210}textrm{Po}}) and (r_2 = frac{^{210}textrm{Pb}}{^{210}textrm{Bi}}). Starting with zero atoms for all the species of interest, and having a set of decay rates, we can obtain the ratios of the number of atoms at any time before or after death. The decay equations guarantee existing a unique set of ratios for any specific set ({t_d, t_m}),

In this scenario, the value A (Bq) represents the constant activity level of radon in air, and ((lambda _0, N_{0})), ((lambda _1, N_{1})), ((lambda _2, N_{2})), ((lambda _3, N_{3})) denote decay constants and number of atoms for (^{222}textrm{Rn}), (^{210}textrm{Pb}), (^{210}textrm{Bi}) and (^{210}textrm{Po}), respectively. However, after death, the first equation follows:

In this scenario, the supply of radon to the body ceases, the (^{222}textrm{Rn}) atoms undergo decay and, since the person is no longer inhaling air, there is no source to replenish the (^{222}textrm{Rn}) atoms. Below are the solutions to the above equations before death:

A Monte Carlo simulation was done where the age of the individual and the time that elapsed between death ((t_d)) and the measurement ((t_m)) were varied. Specifically, (t_{d}) varied between 20 and 40 years and (t_m) varied up to 20 days. The simulation was run 3,500 times to create multiple sets of (frac{^{210}textrm{Pb}}{^{210}textrm{Bi}}) and (frac{^{210}textrm{Pb}}{^{210}textrm{Po}}) isotope number ratios, with each pair having a known (t_{d}) and (t_{m}).

The radon time of death clock

We next used the data generated from Monte Carlo simulations as input quantities to the Radon Time of Death Clock, to test its theoretical accuracy, meaning how well outcomes predicted a known time of death. The calculation of the elapsed time for measuring the numbers of (^{210}textrm{Pb}), (^{210}textrm{Bi}), and (^{210}textrm{Po}) atoms ((t_{m})) was based only on the relative isotope number ratios. Thus, the input quantities to the Radon Time of Death Clock were the (r_1) and (r_2) ratios. Here, we assumed the individual was exposed to a constant level of radon over their lifetime. The model was implemented in Python 3.0 and employs the “brute force” method that involved calculating the function’s value at each point on a multidimensional grid to determine the function’s global minimum, and the Nelder-Mead minimization algorithm to calculate the elapsed times between death and measurement of isotopic composition.

The model solved the radioactive decay equations simultaneously for the two pairs of number ratios such that, although the radioactive decay probabilities for each radionuclide are different, the elapsed time for radioactive decays must be the same. Very importantly – as it would be impractical in the field – knowledge of the absolute radon exposure of the person at the time of death was not needed, as this quantity was cancelled out in the calculation since equations employed the relative number of isotopes (i.e. (^{210}textrm{Pb}) to (^{210}textrm{Bi}) and (^{210}textrm{Pb}) to (^{210}textrm{Po})). The use of two sets of the relative numbers of isotopes enabled the calculation of a unique answer. In the case of using a single isotope number ratio in the calculation (i.e. only (^{210}textrm{Pb}) to (^{210}textrm{Bi})), the result from the model was not unique. However, if both relative isotope number ratios were input to the model, then the result was constrained to a much narrower range of possible values for the time elapsed between death and measurement, where a solution to the equations is found.

Constant lifetime radon exposure

In the hypothetical scenario of constant radon exposure during a lifetime, we define a two-dimensional vector field, (vec {r} = vec {S}(t_d, t_m)), where (vec {r} = (r_1, r_2)). Vector field (vec {S}) can be evaluated based on decay equations for any valid set ({t_d, t_m}), either analytically or numerically. We consider evaluating vector field (vec {S}) at any given time set to be ‘solving the direct problem’ – i.e., solving the direct problem simulates a reality in which a person lived for a specific time with a constant radon exposure, died at (t_d), and the (r_1) and (r_2) ratios are measured at (t_d + t_m). The components of (vec {S}(t_d, t_m)) give us the ratio values in the ideal case of no uncertainty in the measurement results.

Assuming that there is a unique set of times, ({t_{d,T}, t_{m,T}}), which leads to a specific set of measured ratios, ({r_{1,T}, r_{2,T}}) (i.e. (vec {r}_T = (r_{1,T}, r_{2,T}) = vec {S}(t_{d, T}, t_{m, T}))) that we define ’solving the inverse problem’ as follows. The subscript T denotes the true value, which is presumed to be measured. Due to the absence of uncertainty, this true value should correspond to the value obtained from the forward model. When defining the scalar field,

we suggest that there should be one single global minimum for (E_{vec {r}_T}) which happens at ((t_{d, T}, t_{m, T})). Hence, for a given set of valid ratios, (vec {r}_T), one can find the set of times, ({t_{d, T}, t_{m, T}}), that leads to the measured ratios, by minimizing the scalar field (E_{vec {r}_T}).

To test the accuracy of the model, we next calculated the elapsed time since death for the isotope pairs produced by the Monte Carlo simulations. The results are shown in Figure 2a, where the difference between actual and calculated elapsed measurement times (optimization error) are plotted as a function of the time that has elapsed since death. Outcomes demonstrate that the Radon Time of Death Clock could provide reliable results within just a two-week window after actual the death of the individual, and were remarkably precise with the average absolute error of 114 milliseconds. The consistency of the results was supported by a standard deviation of approximately 141 milliseconds. The error distribution arising from a scenario involving constant radon exposure and a single time measurement of two isotope pairs is shown in Figure 2b.

Accounting for variation in radon exposure across a lifetime

In reality, people are exposed to (^{222}textrm{Rn}) and solid radon decay products at a variable rate and at different levels across their lifespan^20,21,28,39. Empirical evidence for ever changing radon exposure across a lifetime exists and has been demonstrated, with shifts in exposure caused by life events such as entering the workforce, changing profession, starting or stopping tobacco smoking, moving house, entering retirement, obtaining a radon reduction, and/or more systemic events such as implementation of pandemic emergency lockdown responses telecommuting, or nation wide building code changes^{27,28,29,30,31}. So, to make the model more realistic, we next modeled a scenario where we assumed that the rate of radon exposure undergoes multiple changes during individual’s lifespan (albeit, for simplicity, each period of exposure was constant between jumps).

Similar to the case of constant exposure, we define the vector field (vec {r} = vec {S}_{f(t)}(t_d, t_m)), where f(t) is a piece-wise constant function of time that gives the overall rate of radon exposure activity over time. Note that the rate of radon exposure activity is defined only within the time between conception and death and that, after death, all relevant radionuclides will decay with no further input from parental (^{222}textrm{Rn}) and/or solid radon decay products. Therefore, all the discontinuities of f(t) should satisfy (0, and (f(t)=0) for (t>t_d). Furthermore, for a constant function f(t) within (0, we have, (vec {S}_{f(t)} = vec {S}). Mathematically, we define,

Here, N is the number of jumps in the rate of radon exposure activity the person experiences, while (t_n in T_{jumps}) is the time of (n^{th}) jumps, and (a_j in jumps) is the constant value describing the rate of radon exposure activity between two successive jumps.

In an ideal scenario, solving the inverse problem would determine the set of parameters ({t_d, N, jumps, T_{jumps}, t_m}), in which N is the number of jumps, jumps, is the set of activity values between the jumps, and (T_{jumps}) is the set of times of jumps before (t_d), such that these lead to a set of ratios that match with the measured values in reality (or the outcome of (vec {S}_{f(t)}(t_d, t_m))). However, from a practical perspective, solving such a problem would far too computationally expensive to be helpful (i.e., impractical using regular computers, as the number of unknown parameters is large). It can be claimed that if there is any piece-wise constant function (tilde{f}(t) ne f(t)) and (tilde{t}_dne t_d) such that,

Then will have the equation below for any (t_m):

The implication of this is that the post-mortem evolution of these radioisotopic ratios is predicted to follow a similar pattern regardless of the lifespan or lifetime radon exposure of the person, meaning this readout of PMI is agnostic to inter-individual differences, and so is likely helpful to forensic investigators who need to ascertain PMI. To move forward with evaluation our model, we proceeded under the assumption that it is feasible to replicate the post-mortem characteristics of (r_1) and (r_2) from a scenario with the death occurring at time (t_d) and multiple fluctuations in the rate of radon exposure activity over the lifespan. We used a less complex scenario where death happens at a different time (t_d^prime) ((t_d ne t_d^prime)) and involves a single change in rate of radon exposure activity. Although this approach is an approximation and will not exactly mirror the reality of lifetime radon exposure, it is a reasonable estimation for a wide range of cases. To formulate the aforementioned approximation, we initially define a step-function g(t) as follows,

Since we deal with the ratios, the absolute values of (a_1) and (a_2) do not affect the results directly, rather, the ratio (a = a_2/a_1) is what is important for us. So, we redefine the function g(t) to reduce the number of parameters,

Now, with having (vec {r}_T = vec {S}_{f(t)}(t_{d, T}, t_{m, T})), we define the scalar field,

Similar to the case of constant activity, by minimizing the scalar field (E_{vec {r}_T}), we can obtain the set of parameters that result in the ratios of interest, and those parameters include (t_m).

Measurement of radioisotope ratios at multiple times postmortem

The theoretical approach we have described so far yields generally satisfactory outcomes. However, in certain instances, we observed that it can lead to very large uncertainty on (t_m) values. The discrepancy arises because the parameters ({t_d, t_m, t_1, a}) that result in (vec {r}_T) are not unique, and an example where the method fails is depicted in Figure 3.

In all cases, the resulting ratios match those obtained from the forward model simulation. However, there is a notable discrepancy in the estimation of (t_m). To address this issue, one approach we suggest is to incorporate radioisotope ratios with their rates of change (time derivative) into the analysis. As an approximation of the time derivative, a second measurement of ratios can be introduced into the scenario. The first measurement takes place at (t_{1,T} = t_{d,T} + t_{m,T}), and the second measurement at the time (t_{2,T} = t_{d,T} + t_{m,T} + t_text {step}). The outcomes are denoted as (vec {r}_{1,T} = (r_{1,1,T}, r_{2,1,T})) for the first measurement and (vec {r}_{2,T} = (r_{1,2,T}, r_{2,2,T})) for the second one.

Then we define the scalar field that should be minimized as,

The error distribution for a model with varying radon exposure and two measurements of two isotope pairs that are separated in time is in Figure 4. Since no measurement of isotopic composition is free from uncertainty, a preliminary study was carried out to explore the sensitivity of the Radon Time of Death Clock to measurement errors. An initial run introduced random error to the the (r_1) and (r_2) isotope number ratios and the model calculations were repeated using a relative standard deviation of 0.01 percent in the ratios. Under these conditions, the uncertainty in the elapsed time calculation increased to approximately 1 day. To explore the theoretical limit of the model’s temporal resolution, calculations were repeated assuming a relative uncertainty of 0.01(%) in the isotope ratios. While this level of precision is not currently achievable using standard detection techniques, it serves as an upper-bound scenario to evaluate model behavior under idealized conditions, such as might become accessible through future advances in measurement technology.