Peptide receptor radionuclide therapy (PRRT) is an established treatment for metastatic neuroendocrine tumors (NETs), yet long-term disease control occurs only in a subset of patients. Predicting progression-free survival (PFS) could support individualized treatment planning. This study evaluates laboratory, imaging, and multimodal deep learning models for PFS prediction in PRRT-treated patients. In this retrospective, single-center study 116 patients with metastatic NETs undergoing 177Lu-DOTATOC were included. Clinical characteristics, laboratory values, and pretherapeutic somatostatin receptor positron emission tomography/computed tomographies (SR-PET/CT) were collected. Seven models were trained to classify low- vs. high-PFS groups, including unimodal (laboratory, SR-PET, or CT) and multimodal fusion approaches. Explainability was evaluated by feature importance analysis and gradient maps. Forty-two patients (36%) had short PFS (< 1 year), 74 patients long PFS (>1 year). Groups were similar in most characteristics, except for higher baseline chromogranin A (p = 0.003), elevated gamma-GT (p = 0.002), and fewer PRRT cycles (p < 0.001) in short-PFS patients. The Random Forest model trained only on laboratory biomarkers reached an AUROC of 0.59 +- 0.02. Unimodal three-dimensional convolutional neural networks using SR-PET or CT performed worse (AUROC 0.42 +- 0.03 and 0.54 +- 0.01, respectively). A multimodal fusion model laboratory values, SR-PET, and CT -augmented with a pretrained CT branch - achieved the best results (AUROC 0.72 +- 0.01, AUPRC 0.80 +- 0.01). Multimodal deep learning combining SR-PET, CT, and laboratory biomarkers outperformed unimodal approaches for PFS prediction after PRRT. Upon external validation, such models may support risk-adapted follow-up strategies.

Identifying logical errors in complex, incomplete or even contradictory and overall heterogeneous data like students' experimentation protocols is challenging. Recognizing the limitations of current evaluation methods, we investigate the potential of Large Language Models (LLMs) for automatically identifying student errors and streamlining teacher assessments. Our aim is to provide a foundation for productive, personalized feedback. Using a dataset of 65 student protocols, an Artificial Intelligence (AI) system based on the GPT-3.5 and GPT-4 series was developed and tested against human raters. Our results indicate varying levels of accuracy in error detection between the AI system and human raters. The AI system can accurately identify many fundamental student errors, for instance, the AI system identifies when a student is focusing the hypothesis not on the dependent variable but solely on an expected observation (acc. = 0.90), when a student modifies the trials in an ongoing investigation (acc. = 1), and whether a student is conducting valid test trials (acc. = 0.82) reliably. The identification of other, usually more complex errors, like whether a student conducts a valid control trial (acc. = .60), poses a greater challenge. This research explores not only the utility of AI in educational settings, but also contributes to the understanding of the capabilities of LLMs in error detection in inquiry-based learning like experimentation.

Armed with a newly minted undergraduate degree in geology, Jasper Baur is in the mining business. Not those mines where we extract metals or minerals; the kind that kill and maim thousands of people every year. As a freshman at upstate New York's Binghamton University in 2016, Baur started working with two geophysics professors, Alex Nikulin and Timothy de Smet, to look into employing instrument-equipped drones to speed the slow, hazardous task of finding land mines. Baur stuck with the research all the way through college; now a grad student in volcanology at Columbia University's Lamont-Doherty Earth Observatory, he is still pursuing it. "It seemed like a really relevant and impactful use of science," he said.

Picture this: Colossal, gas-powered autonomous robots bulldoze across acres of homogeneous farmland under a blackened sky that reeks of pollution. The trees have all been chopped down and there are no animals in sight. Pesticides are sprayed in excess because humans no longer tend to the fields. The machines do their jobs--producing massive amounts of food to feed our growing population--but it's not without ecological cost. Or, envision another future: Smaller robots cultivate mosaic plots of many different crops, working around the trees, streams, and wildlife of the natural landscape.

After a year of significant restructuring aimed at positioning Intel for the future, CEO Brian Krzanich has high expectations for the chip giant's channel - explosive growth. "Our CEO has three goals for us," said Greg Baur, Intel's vice president of sales and marketing for the Americas regional sales group. The sales charge comes more than a year after Krzanich kicked off a massive restructuring initiative in April 2016 aimed at transforming the $60 billion behemoth from a "PC-centric company to a smart, connected company that powers the cloud." As the company kicks off its Intel Solutions Summit partner event this week, it is sharply focused on high-growth markets - namely the Internet of Things, data center and cloud as well as newer emerging technologies and markets with great potential including artificial intelligence and autonomous vehicles. Baur, an Intel channel veteran who is charged with heading up all of the Santa Clara, Calif.-based company's partner activities and sales, sees solution providers as key to winning in those markets.

On Tuesday at the 2017 SAP Sapphire Now conference, a panel of top product experts at SAP and AI researchers discussed a critical issue facing businesses: Avoiding hiring bias. The panel included Brenda Reid, product management at SAP SuccessFactors, Patricia Fletcher, solution management at SAP SuccessFactors, Anka Wittenberg, chief diversity and inclusion officer at SAP, and Yvonne Baur, product management at SAP SuccessFactors, and was moderated by Gabriela Burlacu, principal human capital management (HCM) researcher at SAP SuccessFactors. When it comes to diversifying the workplace, many people say "We get the'why,'" said Reid. "But where's the'how'?" Other panelists emphasized this point, illustrating why it's essential for businesses to embrace diversity initiatives. "We're starting to see people say'How do I do this?'" said Burlacu.