The rising issue of ocean acidification could render some of the ocean’s oldest apex predators as ‘all shark and no bite’, after a new study finds that more acidic oceans could leave many of the species with more brittle and weaker teeth.
Even the sharks’ famous ability to replace their teeth – with new ones always growing as they are using up the current set – might not be enough to stave off the pressures of the warming planet and an ocean environment left vulnerable to increasing levels of acidification.
These findings are the result of recently published research from the German institute, Heinrich Heine University in Dusseldorf in which shark teeth were examined under different ocean acidification scenarios. It found that shark teeth – “despite being composed of highly mineralised phosphates” – are still vulnerable to corrosion under future ocean acidification scenarios.
“They are highly developed weapons built for cutting flesh, not resisting ocean acid,” said the paper’s first author, Maximilian Baum, a biologist at Heinrich Heine University Dusseldorf in Frontiers in Marine Science. “Our results show just how vulnerable even nature’s sharpest weapons can be.”
Ocean acidification is the process by which the ocean’s pH values keeps decreasing, resulting in more acidic water. It is mostly driven by the release of human-generated carbon oxide. According to the article, the current average pH of the world’s ocean is 8.1. It is expected to drop to 7.3 by the year 2300, making it tens times more acidic than it currently is.
Damage to coral reefs, loss of habitats, and a threat to the survival of shell-building marine creatures are among the impacts already being felt across the ocean due to ocean acidification. Until only recently, it was deemed not to have crossed its ‘planetary boundary’, but a major study led by the UK’s Plymouth Marine Laboratory and the US’ National Oceanic and Atmospheric Administration released in June this year cited that the boundary had in fact been crossed five years ago.
As a result of the ocean acidification experienced so far, selected tropical and sub-tropical coral reefs have lost 43% of their suitable habitats, sea butterflies in polar regions have lost up to 61% of their habitat, and coastal shellfish species have lost 13% of their global coastline habitats in which they can sustain their essential biological processes.
Now, it would appear that sharks could stand to lose their teeth.
Pei, F. et al. Monitoring the vegetation activity in China using vegetation health indices. Agric. For. Meteorol.248, 215–227 (2018).
ADS
Google Scholar
Horel, Á., Zsigmond, T., Molnár, S., Zagyva, I. & Bakacsi, Z. Long-term soil water content dynamics under different land uses in a small agricultural catchment. J. Hydrology Hydromechanics. 70, 284–294. https://doi.org/10.2478/johh-2022-0015 (2022).
Article
CAS
Google Scholar
Li, Y., Ye, W., Wang, M. & Yan, X. Climate change and drought: a risk assessment of crop-yield impacts. Climate Res.39, 31–46 (2009).
ADS
CAS
Google Scholar
Palazzi, F., Biddoccu, M., Borgogno Mondino, E. C. & Cavallo, E. Use of remotely sensed data for the evaluation of inter-row cover intensity in vineyards. Remote Sens.15, 41 (2022).
ADS
Google Scholar
Zsigmond, T., Braun, P., Mészáros, J., Waltner, I. & Horel, Á. Investigating plant response to soil characteristics and slope positions in a small catchment. Land11, 774. https://doi.org/10.3390/land11060774 (2022).
Article
Google Scholar
Boiarskii, B. & Hasegawa, H. Comparison of NDVI and NDRE indices to detect differences in vegetation and chlorophyll content. J. Mech. Contin Math. Sci.4, 20–29 (2019).
Google Scholar
Horel, Á., Cseresnyés, I., Zagyva, I. & Zsigmond, T. Soil moisture content and plant health monitoring under different inter-row cropping vineyard. Plant. Soil. 1–16. https://doi.org/10.1007/s11104-025-07612-2 (2025).
Zhong, S., Sun, Z. & Di, L. Characteristics of vegetation response to drought in the CONUS based on long-term remote sensing and meteorological data. Ecol. Ind.127, 107767 (2021).
Google Scholar
Kohzuma, K., Tamaki, M. & Hikosaka, K. Corrected photochemical reflectance index (PRI) is an effective tool for detecting environmental stresses in agricultural crops under light conditions. J. Plant. Res.134, 683–694. https://doi.org/10.1007/s10265-021-01316-1 (2021).
Article
CAS
PubMed
Google Scholar
Yudina, L. et al. A light-induced decrease in the photochemical reflectance index (PRI) can be used to estimate the energy-dependent component of non-photochemical quenching under heat stress and soil drought in pea, wheat, and pumpkin. Photosynth. Res.146, 175–187 (2020).
CAS
PubMed
Google Scholar
D’Odorico, P. et al. Drone-based physiological index reveals long-term acclimation and drought stress responses in trees. Plant. Cell. Environ.44, 3552–3570. https://doi.org/10.1111/pce.14177 (2021).
Article
CAS
PubMed
PubMed Central
Google Scholar
Horel, Á. & Zsigmond, T. Plant growth and soil water content changes under different inter-row soil management methods in a sloping vineyard. Plants12, 1549. https://doi.org/10.3390/plants12071549 (2023).
Article
CAS
PubMed
PubMed Central
Google Scholar
Xue, J. & Su, B. Significant remote sensing vegetation indices: A review of developments and applications. J. Sens.2017, 1353691 (2017).
Google Scholar
Fensholt, R., Sandholt, I. & Rasmussen, M. S. Evaluation of MODIS LAI, fAPAR and the relation between fAPAR and NDVI in a semi-arid environment using in situ measurements. Remote Sens. Environ.91, 490–507. https://doi.org/10.1016/j.rse.2004.04.009 (2004).
Article
ADS
Google Scholar
Gitelson, A. A., Peng, Y. & Huemmrich, K. F. Relationship between fraction of radiation absorbed by photosynthesizing maize and soybean canopies and NDVI from remotely sensed data taken at close range and from MODIS 250 m resolution data. Remote Sens. Environ.147, 108–120 (2014).
ADS
Google Scholar
Rondeaux, G., Steven, M. & Baret, F. Optimization of soil-adjusted vegetation indices. Remote Sens. Environ.55, 95–107 (1996).
ADS
Google Scholar
Horel, Á., Bakacsi, Z., Vass, C. & Zsigmond, T. Inter-row soil management affecting soil moisture in non‐irrigated vineyard ecosystems: A meta‐analysis. Soil Use Manag.40, e13159. https://doi.org/10.1111/sum.13159 (2024).
Article
Google Scholar
Zalai, M., Bujtás, O., Sárospataki, M. & Dorner, Z. Grassy and herbaceous interrow cover crops in European vineyards: A review of their Short-Term effects on water management and regulating ecosystem services. Land14, 1526 (2025).
Google Scholar
Capello, G., Biddoccu, M., Ferraris, S. & Cavallo, E. Effects of tractor passes on hydrological and soil erosion processes in tilled and grassed vineyards. Water11, 2118. https://doi.org/10.3390/w11102118 (2019).
Article
Google Scholar
Guerra, J. G., Cabello, F., Fernández-Quintanilla, C., Peña, J. M. & Dorado, J. How weed management influence plant community composition, taxonomic diversity and crop yield: A long-term study in a mediterranean vineyard. Agric. Ecosyst. Environ.326, 107816 (2022).
CAS
Google Scholar
Callesen, T. O. et al. Understanding carbon sequestration, allocation, and ecosystem storage in a grassed vineyard. Geoderma Reg.34, e00674 (2023).
Google Scholar
Wilson, T. G. et al. Relationships between soil water content, evapotranspiration, and irrigation measurements in a California drip-irrigated Pinot Noir vineyard. Agric. Water Manage.237, 106186 (2020).
Google Scholar
Costa, J., Egipto, R., Sánchez-Virosta, A., Lopes, C. & Chaves, M. Canopy and soil thermal patterns to support water and heat stress management in vineyards. Agric. Water Manage.216, 484–496 (2019).
Google Scholar
Liebhard, G. et al. Effects of vineyard inter-row management on soil physical properties and organic carbon in central European vineyards. Soil Use Manag.40, e13101 (2024).
Google Scholar
Pornaro, C. et al. Selection of inter-row herbaceous covers in a sloping, organic, non-irrigated vineyard. Plos One. 17, e0279759. https://doi.org/10.1371/journal.pone.0279759 (2022).
Article
CAS
PubMed
PubMed Central
Google Scholar
Sun, L. et al. Daily mapping of 30 m LAI and NDVI for grape yield prediction in California vineyards. Remote Sens.9, 317 (2017).
ADS
Google Scholar
Gamon, J. A., Kovalchuck, O., Wong, C. Y. S., Harris, A. & Garrity, S. R. Monitoring seasonal and diurnal changes in photosynthetic pigments with automated PRI and NDVI sensors. Biogeosciences12, 4149–4159. https://doi.org/10.5194/bg-12-4149-2015 (2015).
Article
ADS
Google Scholar
Gamon, J. A. et al. Relationships between NDVI, canopy structure, and photosynthesis in three Californian vegetation types. Ecol. Appl.5, 28–41. https://doi.org/10.2307/1942049 (1995).
Article
Google Scholar
Sozzi, M., Kayad, A., Marinello, F., Taylor, J. & Tisseyre, B. Comparing vineyard imagery acquired from Sentinel-2 and unmanned aerial vehicle (UAV) platform. Oeno One. 54, 189–197 (2020).
Google Scholar
Catania, P., Ferro, M. V., Orlando, S. & Vallone, M. Grapevine and cover crop spectral response to evaluate vineyard spatio-temporal variability. Sci. Hort.339, 113844. https://doi.org/10.1016/j.scienta.2024.113844 (2025).
Article
Google Scholar
van Leeuwen, W. J. D., Orr, B. J., Marsh, S. E. & Herrmann, S. M. Multi-sensor NDVI data continuity: uncertainties and implications for vegetation monitoring applications. Remote Sens. Environ.100, 67–81. https://doi.org/10.1016/j.rse.2005.10.002 (2006).
Article
ADS
Google Scholar
Misra, G., Cawkwell, F. & Wingler, A. Status of phenological research using Sentinel-2 data: A review. Remote Sens.12. https://doi.org/10.3390/rs12172760 (2020).
Huang, L. et al. Combining random forest and XGBoost methods in detecting early and Mid-Term winter wheat Stripe rust using canopy level hyperspectral measurements. Agriculture12, 74 (2022).
CAS
Google Scholar
Li, X., Jia, H. & Wang, L. Remote sensing monitoring of drought in Southwest China using random forest and eXtreme gradient boosting methods. Remote Sens.15, 4840 (2023).
ADS
Google Scholar
Gyawali, A., Adhikari, H., Aalto, M. & Ranta, T. From simple linear regression to machine learning methods: canopy cover modelling of a young forest using planet data. Ecol. Inf.82, 102706. https://doi.org/10.1016/j.ecoinf.2024.102706 (2024).
Article
Google Scholar
Matese, A. & Di Gennaro, S. F. Beyond the traditional NDVI index as a key factor to mainstream the use of UAV in precision viticulture. Sci. Rep.11, 2721. https://doi.org/10.1038/s41598-021-81652-3 (2021).
Article
ADS
CAS
PubMed
PubMed Central
Google Scholar
European Environment Agency (EEA). CORINE Land Cover 2018 (Vector/Raster 100 m), Europe, 6-Yearly. (2018). https://doi.org/10.2909/71c95a07-e296-44fc-b22b-415f42acfdf0 Accessed 25 Aug 2021.
GIS Geographic Information System (Version 3.40.6) Open Source Geospatial Foundation. (2025).
IUSS Working Group WRB. (ed FAO) (International Union of Soil Sciences (IUSS), 2022).
León-Tavares, J. et al. Correction of directional effects in vegetation NDVI time-series. Remote Sens.13, 1130 (2021).
ADS
Google Scholar
Shamrikova, E., Vanchikova, E., Kyzyurova, E. & Zhangurov, E. Methods for measuring organic carbon content in carbonate-containing soils: a review. Eurasian Soil. Sci.57, 380–394 (2024).
ADS
CAS
Google Scholar
Loggenberg, K., Strever, A., Greyling, B. & Poona, N. Modelling water stress in a Shiraz vineyard using hyperspectral imaging and machine learning. Remote Sens.10, 202 (2018).
ADS
Google Scholar
Taylor, J. A., Bates, T. R., Jakubowski, R. & Jones, H. Machine-Learning methods to identify key predictors of Site-Specific vineyard yield and vine size. Am. J. Enol. Viticult.74, 1-11 (2023).
Chen, T. & Guestrin, C. Proceedings of the 22nd ACM Sigkdd International Conference on Knowledge Discovery and Data Mining. 785–794.
Li, X., Yuan, W. & Dong, W. A machine learning method for predicting vegetation indices in China. Remote Sens.13, 1147 (2021).
ADS
Google Scholar
Ma, N. et al. Assessment of vegetation dynamics in Xinjiang using NDVI data and machine learning models from 2000 to 2023. Sustainability17, 306 (2025).
Google Scholar
Narmilan, A. et al. Predicting canopy chlorophyll content in sugarcane crops using machine learning algorithms and spectral vegetation indices derived from UAV multispectral imagery. Remote Sens.14, 1140 (2022).
ADS
Google Scholar
Fornara, D. A. & Tilman, D. Plant functional composition influences rates of soil carbon and nitrogen accumulation. J. Ecol.96, 314–322. https://doi.org/10.1111/j.1365-2745.2007.01345.x (2008).
Article
CAS
Google Scholar
Van Der Krift, T. A. & Berendse, F. The effect of plant species on soil nitrogen mineralization. J. Ecol.89, 555–561 (2001).
PubMed
Google Scholar
Wang, R. et al. Seasonal variation in the NDVI–species richness relationship in a prairie grassland experiment (Cedar Creek). Remote Sens.8, 128 (2016).
ADS
Google Scholar
Watzig, C. et al. Grassland cut detection based on Sentinel-2 time series to respond to the environmental and technical challenges of the Austrian fodder production for livestock feeding. Remote Sens. Environ.292, 113577 (2023).
Google Scholar
Schwieder, M. et al. Mapping grassland mowing events across Germany based on combined Sentinel-2 and Landsat 8 time series. Remote Sens. Environ.269, 112795 (2022).
Google Scholar
Pfitzner, K., Bartolo, R., Whiteside, T. & Loewensteiner, D. Observations and geoinformation. Int. J. Appl. Earth Obs. Geoinf.112, 102870 (2022).
Google Scholar
Hajdu, E. Viticulture of Hungary. Acta Agrar. Debreceniensis. 150, 175–182. https://doi.org/10.34101/actaagrar/150/1713 (2018).
Article
Google Scholar
Cogato, A. et al. Evaluating the spectral and physiological responses of grapevines (Vitis vinifera L.) to heat and water stresses under different vineyard cooling and irrigation strategies. Agronomy11, 1940 (2021).
Google Scholar
Hillel, D. Fundamentals of Soil Physics (Academic, 2013).
Hubbard, S. S. et al. Estimation of soil classes and their relationship to grapevine Vigor in a Bordeaux vineyard: advancing the practical joint use of electromagnetic induction (EMI) and NDVI datasets for precision viticulture. Precision Agric.22, 1353–1376 (2021).
Google Scholar
Czigány, S. et al. Impact of agricultural land use types on soil moisture retention of loamy soils. Sustainability15, 4925. https://doi.org/10.3390/su15064925 (2023).
Article
Google Scholar
Thapa, S. et al. Use of NDVI for characterizing winter wheat response to water stress in a semi-arid environment. J. Crop Improv.33, 633–648 (2019).
CAS
Google Scholar
Kandylakis, Z., Falagas, A., Karakizi, C. & Karantzalos, K. Water stress Estimation in vineyards from aerial SWIR and multispectral UAV data. Remote Sens.12, 2499 (2020).
ADS
Google Scholar
Zhao, T., Stark, B., Chen, Y., Ray, A. L. & Doll, D. International Conference on Unmanned Aircraft Systems (ICUAS). 520–525 (IEEE, 2015).
Milazzo, F., Brocca, L. & Vanwalleghem, T. NDVI prediction of mediterranean permanent grasslands using soil moisture products. Agronomy14, 1798 (2024).
Google Scholar
Gao, B. C. NDWI—A normalized difference water index for remote sensing of vegetation liquid water from space. Remote Sens. Environ.58, 257–266 (1996).
ADS
Google Scholar
Caturegli, L. et al. Effects of water stress on spectral reflectance of Bermudagrass. Sci. Rep.10, 15055 (2020).
PubMed
PubMed Central
Google Scholar
Abubakar, M. A., Chanzy, A., Flamain, F. & Courault, D. Characterisation of grapevine canopy leaf area and inter-row management using Sentinel-2 time series. OENO One57, 1-13 (2023).
Crusiol, L. et al. Strategies for monitoring within-field soybean yield using Sentinel-2 Vis-NIR-SWIR spectral bands and machine learning regression methods. Precision Agric.23, 1093–1123 (2022).
Google Scholar
Lyu, H. et al. Using remote and proximal sensing data and vine Vigor parameters for non-destructive and rapid prediction of grape quality. Remote Sens.15, 5412 (2023).
ADS
Google Scholar
Thanh, D. K., Ngoc, D. L., Dieu, H. D. & Tran, V. A. Comparison of random forest and extreme gradient boosting algorithms in land cover classification in Van Yen district, Yen Bai province, Vietnam. J. Hydro-Environ Res.23, 50–59 (2025).
Sony released the Xperia 1 VII, its latest flagship smartphone, in early June, and immediately started receiving a lot of negative feedback from buyers who were experiencing random reboots, shutdowns, and even an inability for the device to power on.
After pausing sales of the phone globally and investigating for a bit, the company announced a replacement program in July, whereby all buyers would receive a new, fixed unit. At the core of the problems was a faulty circuit board, and new units started being produced with it fixed. A few days ago we heard that the Xperia 1 VII would be back on sale in Europe starting this week, and now the company has announced the same thing for Japan as well, while outlining the steps it’s taking to prevent something like this from ever happening again.
The Xperia 1 VII will go back on sale in Japan today. In an official statement (see the Source linked below) in Japanese, the company has profusely apologized for the inconvenience it’s caused with the aforementioned issue, as well as how long it took for some customers to get their replacement units.
Sony Xperia 1 VII 5G
Sony promises to strengthen the quality control system it uses for Xperia products, with a new management system behind it which will ensure “the verification and evaluation of risks affecting quality in the manufacturing process”. This new system is already in operation for the Xperia 1 VII replacement units and the ones currently on sale. Sony promises to expand it to all future Xperia devices.
If there still are customers who purchased an Xperia 1 VII before July 4, they are eligible to get a free replacement. Sony says it takes the harsh criticism it’s received regarding this matter very seriously and strives to do its best to deliver products that can earn its customers’ trust again.
If you’re interested in the Xperia 1 VII, check out our video review embedded above, and if you want to go in-depth, don’t miss our comprehensive written review.
United States President Donald Trump’s 50 percent tariff on Indian goods, which is expected to impact trade worth billions of dollars and risk thousands of jobs in the world’s most populous nation, took effect on Wednesday.
The US first slapped a 25 percent tariff on India on July 30 and a week later imposed an additional 25 percent, citing New Delhi’s purchase of Russian oil.
The new 50 percent rate, one of the US’s highest tariffs, will now apply to a range of goods from gems and jewellery, garments, footwear and furniture to industrial chemicals.
The crushing tariff rate will put India at a disadvantage in export competitiveness against China, and will undermine the economic ambitions of Prime Minister Narendra Modi to transform the country into a major manufacturing hub. Until recently, the US was India’s largest trading partner with annual bilateral trade worth $212bn.
So which industries will be hit the hardest and how will it affect US-India relations?
Which sectors will be worst hit?
The Global Trade Research Initiative (GTRI), a New Delhi-based think tank, told The Financial Times newspaper that Indian exports to the US could fall from $86.5bn this year to about $50bn in 2026 as a result of today’s announcement.
The GTRI said that textiles, gems, jewellery, shrimp and carpets would be worst affected, with the sectors bracing for a 70 percent collapse in exports, “endangering hundreds of thousands of jobs”.
“There will be a huge impact,” MK Venu, founding editor of The Wire news site, told Al Jazeera.
“While India is not a big trading partner for the US, for India, the US is the largest trading partner,” he said, adding that exports would be affected in the areas of textiles, garments, gems and jewellery, fisheries, leather items and crafts.
These are “very, very labour-intensive” and small companies, which cannot survive the hit, Venu said about the sectors to be affected by the tariffs. “They will lose businesses to Vietnam, Bangladesh and Pakistan, and other East Asian economies.”
Will any industries be exempt?
The Indian pharmaceutical industry has been exempted from immediate tariff increases due to the significance of generic drugs in providing affordable healthcare in the US. Roughly half of the US’s generic medication imports come from India.
In 2024, Indian pharmaceutical exports to the United States amounted to approximately $8.7bn.
Meanwhile, semiconductors and consumer electronics will also be covered by separate, sector-specific US tariffs. Finally, aluminium and steel products, together with passenger vehicles, will also be subject to tariffs separate from the blanket 50 percent rate.
What is the Indian government doing to mitigate the impact?
Prime Minister Modi has pledged to protect farmers, cut taxes and push for self-reliance in the wake of tariff hikes.
India “should become self-reliant – not out of desperation, but out of pride … Economic selfishness is on the rise globally and we mustn’t sit and cry about our difficulties,” Modi said in his Independence Day speech at New Delhi’s Red Fort.
Faisal Ahmed, professor of geopolitics at Fore School of Management in New Delhi, says increasing the domestic productive capacity of India is not new. “It was a policy choice taken by Modi during the COVID-19 pandemic. Trump’s tariffs look set to accelerate that process,” Ahmed told Al Jazeera.
On top of the $12bn income tax giveaway announced earlier this year, the Indian prime minister also said that businesses could expect a “massive tax bonanza” soon. It’s also understood that Delhi is planning to lower and simplify the goods and services tax.
This, along with a boost to the salaries of nearly five million state employees and 6.8 million pensioners (which will kick in next year), could help India’s economy retain some growth momentum.
An Indian commerce ministry official told Reuters earlier this week that exporters hit by tariffs would receive financial assistance and other giveaways to diversify into markets like Latin America and the Middle East.
Venu, who is also a former editor of the Financial Express newspaper, says that assurances have come from the central bank and the prime minister, but there is no real policy.
“Who will fund the subsidy? Will it be taxpayers or some of the big companies that benefitted from the Russian oil exports? So, there is no clarity on the details of how the subsidies would be provided. Even if subsidies are provided, it won’t be enough to cushion such a huge hit,” Venu told Al Jazeera from New Delhi.
He said that the government did not prepare for what was coming. “India should have had a policy, it should have done its homework because we knew that Trump was not going to relent, he was going to punish India for buying Russian oil.”
Ahmed from the Fore School of Management said that the tariffs “shouldn’t have a significant impact on India’s GDP… probably around 1 percent”.
Teresa John, lead economist at Nirmal Bank, echoed Ahmed: “We estimate a [negative] impact of about $36bn, or 0.9 percent of GDP,” she told Reuters.
Earlier this year, the International Monetary Fund forecast that India’s economy would grow by 6.4 percent in 2026. That could change.
What reason has Trump given for tariffs?
Talks to defuse a trade war broke down after five rounds of negotiations, following Trump’s calls for India to halt its imports of Russian oil and gas.
Despite the persistent threat of higher US tariffs, India has continued to buy Russian crude this year – albeit at falling levels.
New Delhi has also been hit because of the geopolitical rivalry between Russia and the West. Top Trump officials, including US Treasury Secretary Scott Bessent, have accused India of funding Russia’s war against Ukraine. He pointed out that India’s Russian oil imports went from 1 percent before the Ukraine war to 37 percent. He accused India of “profiteering”.
India’s foreign ministry said that New Delhi would “take all necessary steps to protect its national interests” and pointed out that Russian oil imports were driven by market forces and the energy needs of the country’s 1.4 billion people.
New Delhi has also accused Washington of selectively targeting India for purchasing Russian oil, when both the European Union and China – with whom Trump has brokered trade deals – continue to import energy from Russia.
Trump, who has unleashed a tariff war that has shaken the global economy, has been highlighting the high tariffs imposed by India.
“India has been, to us, just about the highest-tariffed nation anywhere in the world. It’s very hard to sell to India because they have trade barriers and very strong tariffs,” Trump said during Prime Minister Modi’s visit to the US in February.
New Delhi pledged to remove levies on certain industrial goods from the US and to increase defence and fuel purchases – to assuage Trump’s grievances over trade imbalances. But it refused to open its vast farming and dairy sector to cheap US imports.
“Modi will stand like a wall against any policy that threatens their interests. India will never compromise when it comes to protecting the interests of our farmers,” the Indian prime minister said on August 15.
For context, the simple average tariff rate that India imposed on agricultural imports was 39 percent at the end of 2024. By contrast, the simple average tariff rate that the US charged on its agricultural imports was 4 percent. Trump took umbrage with that.
Last year, bilateral trade between India and the US stood at approximately $212bn, with a trade gap of about $46bn in India’s favour.
Trump’s tough stance has pushed India to mend ties with rival China – the world’s second-largest economy and one of New Delhi’s biggest trading partners with a bilateral trade of around $136bn. India is also preparing to roll out the red carpet to Russian President Vladimir Putin as New Delhi moves to strengthen its traditional ties with Moscow.
“Most strategic experts in India have already said that the trust between India and the US is at an all-time low. So there is an assessment that India will rebalance towards Russia, towards China and towards BRICS,” Venu, the veteran Indian journalist, said.
The 499Ps return to the spotlight in the FIA World Endurance Championship with the 6 Hours of COTA, the sixth round of the 2025 season, which kicks off on Sunday 7 September at 1.00 p. m., local time. Ferrari arrives in Texas defending its lead in both the World Manufacturers’ and Drivers’ Championships standings.
Fifty-six days after the previous round contested in Brazil, the Ferrari – AF Corse works team heads to the Circuit of The Americas with a clear goal for both crews: the number 50 of Antonio Fuoco, Miguel Molina, and Nicklas Nielsen, and the number 51 of Alessandro Pier Guidi, James Calado, and Antonio Giovinazzi, who both finished outside the top ten at São Paulo, need to score crucial points in the world title races.
On the American circuit, which is hosting the Lone Star Le Mans for the second consecutive year and is renowned for its dramatic elevation changes and 5.513-kilometre layout combining high-speed sections with corners of varying radius and speed, the spotlight will also fall on the number 83 499P, eighth in Brazil. Entered by the AF Corse privateer team, it will be driven by Maranello-based manufacturer official driver Yifei Ye alongside Robert Kubica and Phil Hanson.
The situation. As indicated, Ferrari tops the World Manufacturers’ Championship standings on 175 points, 55 clear of second place, ahead of a race that awards 25 points to the winner. In the World Drivers’ Championship, Pier Guidi, Calado, and Giovinazzi lead on 105 points, with two wins and two pole positions so far. Ye, Kubica, and Hanson follow them in second place on 93 points, while Fuoco, Molina, and Nielsen sit fourth on 57, with one win and one Hyperpole.
AF Corse also leads the FIA World Cup for Hypercar Teams, 64 points clear of their closest rivals on the eve of a race – the third-to-last of the season – which is crucial for the independent teams’ title.
The programme. The Lone Star Le Mans starts on Friday, 5 September, with the first two 90-minute free practice sessions at 11.30 a. m. and 4.00 p. m.; on Saturday, 6 September, after the third free practice session (11.00 a. m.-12.00 a. m.), qualifying for the Hypercar class takes place from (at 3.40 p. m.), followed by the Hyperpole (at 4.00 p. m.). The 6 Hours of COTA, as mentioned, starts on Sunday, 7 September at 1.00 p. m. (local times).
An overhaul of NHS dental contracts on the Isle of Man could slash waiting lists by more than 93%, Manx Care has claimed.
Set to cost up to £700,000 per year, the health care provider believes new contracts negotiated with providers could reduce the number of people waiting for appointments from 6,112 to 417 by April 2026.
As part of the update, patient repeat appointment times will be “stretched” from six-monthly check-ups to up to two years for some patients, to create extra capacity.
Manx Care confirmed it was also in the process of securing extra services in the north of the island after a Ramsey practice handed back its contract in February.
Chief executive Teresa Cope said the funding would come from the provider’s existing annual budget of £361m in this financial year, and the funds would need to be built into future budgets.
She said NHS dental care contracts had “fallen out of step” with those offered in England and Manx providers would now see payments for “a unit of dental activity” increase from between £28 and £34, to a standard rate of £36.
The agreements – which contain targets for the number of procedures to be carried out over a given period – are awarded to a number of independent practices across the Isle of Man.
Five out of seven providers have agreed to sign up to the new contract, with practices in Douglas, Onchan, Ramsey, Peel and Port Erin.
Ms Cope said the new structure was “fair and equitable” would “incentivise” dentists to take on more NHS dental work.
While she understood concerns from patients who would be used to more regular check ups, she said any changes to recall times would be based on clinical need.
As part of the changes, Manx Care said a tender process would also begin in the coming weeks to “replace and expand” on NHS services previously offered by Grove Mount Dental Practice in Ramsey until March.
When in place, NHS patients from the practice who have been waiting to be reassigned will be transferred, and extra capacity for a further 900 patients created.
The body also confirmed a dentist from the Manx Care-run Hillside Dental Practice has been seeing patients for one morning per week in a bid to reduce waiting times.
Herman Van Rooyen, who owns dental practices in Douglas, said the changes marked “the beginning, not the end” of the island’s “improvement journey”.
“The roll-out will take time, but it creates the foundation we need to address fundamental systemic issues,” he said.
Health minister Claire Christian said the changes would ensure that “more people can access timely, high-quality dental care, no matter where they live on the island”.
A strict framework of punishments has been prepared for domestic cricketers as well in case of violations of the Anti-Corruption Code. On the directions of the ICC, Pakistan will also adopt the Global Anti-Corruption Code, though disputes will be settled in accordance with local laws.
According to details, the International Cricket Council is striving to protect the game from corrupt elements and has been taking stringent measures. Some time ago, all member boards were directed to adopt the Global Anti-Corruption Code. In case of disputes, local laws will apply. Recently, during the PCB Governing Board meeting, participants were informed by a legal official that the ICC has instructed the adoption of the Global Anti-Corruption Code, for which procedural changes were necessary. The members granted approval.
Sources say that strict laws will also be applied in domestic cricket, with severe punishments for corruption. Along with bans, fines will also be imposed. However, players will retain the right to appeal. If any player fails to inform the national Anti-Corruption Unit about suspicious contact, he will also be considered guilty. Accepting gifts from suspicious individuals will not be allowed.
Punishments will be determined keeping in view the nature of the offense, intent, previous record, and cooperation during investigations. First-time offenders who cooperate may receive lighter punishments, but repeat offenders could face lifetime bans.
Under the Anti-Corruption Code:
Match-fixing or spot-fixing carries a 5-year to lifetime ban.
Betting on cricket may result in a 1-to-5-year ban depending on the case.
Sharing team’s internal information could also lead to a 1-to-5-year ban.
Failure to report corrupt approaches will bring a 2-to-5-year ban.
Lying during investigations, destroying evidence, or refusing cooperation can result in a 2-to-5-year ban.
Players failing to participate in Anti-Corruption Education Programs will remain suspended until they complete them.
PCB sources say that the board has already been implementing a zero-tolerance policy against corruption. As in the past, those who tarnish the image of the game will be dealt with firmly. Whether domestic or international cricket, the ICC’s Anti-Corruption Code will be fully enforced, and education programs for players will also continue.
RIGA (Latvia) – It was a beautiful Sunday in Spain back on September 9, 2007, when an eight-year-old Neemias Queta watched as his compatriots picked up a memorable 94-85 win over Israel in the Second Round of FIBA EuroBasket 2007.
Little did he know that Portugal would wait another 18 years until he was big enough to carry the nation to their next win. Portugal recorded their first EuroBasket win since 2007 with a 62-50 triumph over Czechia to open up Group A proceedings in Latvia.
Turning Point
Portugal were one step ahead of Czechia from their 7-0 start to the game, but could not break away for good until midway through the third quarter. That’s when they had two capital threes, Rafael Lisboa knocking them down on consecutive possessions to open up a 40-30 lead.
With Czechia struggling to break 50 points in this game, Portugal could afford a stress-less finish to the game.
TCL Player of the Game
When you’re an NBA champ and a clear leader of your national team, you might feel pressure on such a big stage. Not Neemias Queta, though.
The Boston Celtics center was all over the place, getting 15 points, 6 rebounds and 3 blocks in the first half alone, and plenty of players would be happy with that stat line. Not Neemias Queta, though.
He controlled the paint after the break, finishing with 23 points, 18 rebounds, 4 blocks, 2 steals and an efficiency rating of 39.
Queta is the first player to register at least 20 points and 15 rebounds in their EuroBasket debut since FIBA began tracking rebounding numbers.
To say that we’re looking forward to seeing him match up with Nikola Jokic, Alperen Sengun and Kristaps Porzingis in the upcoming Group A games would be an understatement.
Stats Don’t Lie
Czechia looks surprised by the defensive intensity that coach Mario Gomes got from his players. Portugal forced 19 Czech turnovers, paving the way towards an 11-3 advantage in fast break points and 21-10 in points off turnovers.
Bottom Line
Both teams will enjoy a day off on Thursday, before the back-to-back program of Friday and Saturday.
Portugal will take on Serbia and Türkiye, respectively, while Czechia face Türkiye and Estonia. They got 10 points from Vit Krejci in this one, but will need more offense than this if they plan to pick up a win or two in Riga.
They Said
“It’s a big win for Portugal, we surprised everyone a bit, but now we have to keep working because now people are going to be looking at us differently. So let’s stay humble, it’s only one win. We know what we’re gonna do, let’s try to keep improving, do better things for the next game.” – Miguel Queiroz, Portugal
“We missed some shots early and had 3 fast break points, against a team with Queta down there, it’s really hard to play 5v5 when he’s in the paint. It’s a learning curve.” – Vit Krejci, Czechia
“I know he had like 23, but we still didn’t give him the ball enough. I think if we were to give him the ball like six more possessions, he’d have 37 points or something. He’s a big time player, EuroBasket is the perfect place for him to show his talent and sometimes it doesn’t get recognized in the NBA, but he’s playing great, that’s our guy, we wanna go through him.” – Travante Williams, Portugal, talking about Queta
For more quotes, tune in to the official post-game press conference!
Among the 1,572 participants included in this survey, 852 were females (54.2%) and 720 were males (45.8%), indicating a slightly higher proportion of females. Most participants were in the younger age group (30 years and below), totaling 1,266 individuals (80.5%). Most participants held a bachelor’s or higher degree, accounting for 1,303 individuals (82.9%). In terms of occupational distribution, 643 participants (40.9%) were engaged in mental or physical labor, 591 (37.6%) were students, and 392 (21.5%) were unemployed or having other occupations, students and mental laborers constituted the majority part of participants in this study. A total of 267 participants (17.0%) reported a history of previous illnesses, with the highest prevalence being GI diseases, affecting 98 individuals (6.2%). Additionally, 299 participants (19.0%) were smokers, and 96 participants (6.1%) reported regular alcohol consumption. Refer to Table 1 for specific details.
Table 1 Characteristics associated with OCD risk level
Comparative analysis between variables and the risk factors of OCD
In this survey, a total of 478 individuals (30.4%) had OCI-R composite scores > 27, indicating a high risk of OCD. Among demographic characteristics, males had a higher proportion in the high-risk group compared to females (χ² = 8.230, P < 0.005), suggesting that gender may play a role in the development of OCD. Regarding education background, respondents with a bachelor’s degree were more prevalent in the high-risk group (χ² = 11.415, P < 0.005), indicating a possible association between educational attainment and OCD risk. Furthermore, individuals with a previous medical history exhibited a significantly higher risk of OCD compared to those without (χ² = 76.275, P < 0.001). No statistically significant differences were observed with respect to age and occupation. In terms of lifestyle factors, smokers were more prevalent in the high-risk group (χ² = 48.954, P < 0.001), indicating that smoking may increase the risk of OCD. Similarly, respondents who consumed alcohol regularly had a higher risk of OCD (χ² = 17.335, P < 0.001). Sleep quality was significantly poorer in the high-risk group (χ² = 74.856, P < 0.001). Significant differences were also found in GI symptoms, frequency of GI symptoms, abnormal bowel movements, and frequency of abnormal bowel movements (all P < 0.05), while no significant differences were observed in early awakening sleep disorder and bowel movement frequency. Regarding dietary habits, significant differences among OCD risk groups were found in regular diet, daily meal times, eating speed, picky eating, overeating, food allergy, and preferred food temperature (all P < 0.05). Significant differences were also observed in the consumption frequency of fresh fruits, fresh vegetables, pickled vegetables, meat, haslet, processed meat, fried foods, leftover foods, and beverages (all P < 0.05). However, no significant differences were noted in the consumption frequency of whole grains, poultry, seafood, nuts, tubers, eggs, dairy products, and legumes. Refer to Table 1 for detailed information.
Assessment of multicollinearity among independent variables
Multicollinearity among independent variables was then assessed. A Tolerance value < 0.100 and a VIF > 10.000 were used as indicators of multicollinearity. The results showed that all variables had Tolerance values > 0.100 and VIF values < 10.000, suggesting that multicollinearity did not significantly affect the model estimation. Refer to Supplemental Table 1 for detailed information.
Variable selection via Lasso regression
Lasso regression was applied to screen the initially included variables. Figure 1A and B depict the relationship between the coefficients of the 63 variables and the Lambda values. The vertical dashed line in both figures represents the optimal Lambda value (0.00947), selected via 10-fold cross-validation. In Fig. 1B, variables positively and negatively associated with the risk of OCD are shown in red and blue, respectively, while variables with coefficients shrunk to zero are shown in gray. These gray variables were considered non-informative and excluded from subsequent analyses. The results identified 26 variables positively associated with the risk of OCD, including: frequency of abnormal defecation, burning sensation, overeating, dreaminess, light sleep, sour regurgitation, alcohol drinking, fruits, diarrhea, picky eating, other-colored stools, vegetables, restless sleep, insomnia, cardiopulmonary diseases, frequency of GI symptoms, abnormal defecation, GI symptoms, animal meat, sleep disorders, smoking, medical history, coronary heart diseases, red or black stools, nausea and vomiting, and GI diseases. An additional 10 variables were found to be negatively associated with OCD risk: age, daily meal times, haslet, defecation frequency, regular diet, fried food, pickled vegetables, beverages, stomachache, and diabetes. Variables with zero coefficients were excluded from further statistical analysis. Refer to Fig. 1C for detailed information.
Fig. 1
Lasso regression feature selection (A–B) Relationship between Lasso regression coefficients and lambda values for OCD risk factors (C) Lasso regression coefficients of risk factors at the optimal lambda value
Logistic analysis of risk factors for OCD
The 36 variables selected through Lasso regression were included in a logistic regression model for further analysis. Model calibration was first assessed using the Hosmer-Lemeshow goodness-of-fit test, which yielded χ² = 17.335 and P = 0.745, indicating a good fit between the observed and predicted values. This result indicates a good model fit, with no significant difference between the predicted and observed values. Subsequently, the model’s discriminative performance was evaluated using the AUC, which was 0.759 (0.689–0.788). As the AUC exceeded 0.75, the model demonstrated acceptable discriminative ability. Refer to Table 2 for detailed information.
Table 2 Logistic analysis
Under the same conditions, compared to individuals without a previous medical history, those with a previous medical history had an average 70.9% increase in the likelihood of developing OCD (OR = 1.709, 95% CI: 1.064–2.738). In contrast, individuals with a history of diabetes had an average 77.4% decrease in the likelihood of developing OCD (OR = 0.226, 95% CI: 0.096–0.512) compared to those without. Likewise, compared to individuals without a history of sleep disorders, those with a history of sleep disorders had an average 46.0% increase in the likelihood of developing OCD (OR = 1.460, 95% CI: 1.005–2.137). Similarly, compared to people without a history of insomnia, those with insomnia had an average 60.7% increase in the likelihood of developing OCD (OR = 1.607, 95% CI: 1.015–2.137). In similar situations, compared to individuals without GI symptoms, those with GI symptoms had an average 56.2% increase in the likelihood of developing OCD (OR = 1.562, 95% CI: 1.057–2.311). Additionally, participants reporting nausea and vomiting symptoms exhibited a 75.1% increase in OCD likelihood compared to those without such symptoms (OR = 1.751, 95% CI: 1.162–2.643). In a similar vein, compared to individuals without red or black stools, those with red or black stools had an average 50.3% higher chance of developing OCD than those without these symptoms (OR = 1.503, 95% CI: 1.142–1.978). What’s more, picky eaters had a 41.4% higher likelihood of developing OCD compared to non-picky eaters (OR = 1.414, 95% CI: 1.037–1.925). With respect to meat consumption, those who occasionally or rarely ate meat were 81.8–102.9% more likely to develop OCD than individuals who frequently consumed meat (OR = 1.818, 95% CI: 1.401–2.360; OR = 2.029, 95% CI: 1.238–3.327). Conversely, individuals who occasionally or rarely drank beverages were 29.1–41.0% less likely to develop OCD than those who frequently consumed beverages (OR = 0.709, 95% CI: 0.511–0.983; OR = 0.590, 95% CI: 0.403–0.865).
The associations between risk factors and OCD
Based on the results of Lasso regression, 36 variables with statistically significant associations were used as input features for three machine learning models: SVM model, RF model, and BP Neural Network model. The outcome variable was the risk of OCD. The dataset was randomly split into training and testing sets at a 7:3 ratio, resulting in 1,100 participants in the training set and 472 participants in the testing set. To begin with, the performance of the three machine learning models was compared, with the ROC curves presented in Fig. 2. Based on the AUC and several other stability metrics (see Table 3 for details), the RF model demonstrated the best overall performance among the three. Furthermore, 10-fold cross-validation was conducted to evaluate the generalizability and stability of the models. After comparing the results of three models, the RF model exhibited the best generalizability and stability with AUC ranged from 75% (95% CI: 72%−78%) to 71% (95% CI: 69%−74%). Refer to Supplemental Fig. 1A-C for more details.
Fig. 2
ROC curves of the three machine learning models
Table 3 Three machine learning models’ performance evaluation
In term of feature importance, the SVM model identified the top 20 most influential factors associated with the risk of OCD, ranked from highest to lowest in importance, were: smoking, medical history, overeating, defecation time, frequency of abnormal defecation, daily meal times, abnormal defecation, restless sleep, age, burning sensation, sleep disorders, dreaminess, regular diet, diabetes, frequency of GI symptoms, light sleep, pickled vegetables, diarrhea, fruits, other color’s stools (Fig. 3A, Supplemental Table 2).
Fig. 3
The normalized importance of predictor variables (A–C) the normalized importance of predictor variables for Support Vector Machine model (A), Random Forest model (B), and BP Neural Network model (C)
Similarly, in the RF model, the top 20 most influential factors associated with the risk of OCD, ranked from highest to lowest in importance, were: abnormal defecation, coronary heart diseases, cardiopulmonary diseases, burning sensation, sour regurgitation, diabetes, restless sleep, stomachache, age, other color’s stools, diarrhea, GI diseases, red or black stools, insomnia, overeating, nausea and vomiting, defecation times, haslet, smoking, and medical history (Fig. 3B, Supplemental Table 3).
In addition, in the BP Neural Network model, the top 20 most influential factors associated with the risk of OCD, ranked from highest to lowest in importance, were: animal meat, beverages, particular about food, overeating, frequency of abnormal defecation, regular diet, restless sleep, fruits, other color’s stools, sour regurgitation, nausea and vomiting, insomnia, daily meal times, red or black stools, vegetables, sleep disorders, diarrhea, medical history, fried food, and pickled vegetables (Fig. 3C, Supplemental Table 4).
