California State University, Los Angeles (Cal State LA) is a public university in Los Angeles, California. It is part of the 23-campus California State University (CSU) system. Cal State LA offers 129 bachelor's degrees, 112 master's degrees, and four doctoral degrees: a Doctor of Philosophy (Ph.D) in special education in collaboration with the University of California, Los Angeles (UCLA), a Doctor of Education (Ed.D) in Educational Leadership, a Doctor of Nursing Practice (DNP) and a Doctor of Audiology (AuD). It also offers 22 teaching credentials.In Fall 2018, Cal State LA received the 5th-most applications of any CSU campus for incoming freshmen, and had the 5th-lowest admit rate. Cal State LA has a student body of 26,361 as of Fall 2019, which includes 22,626 undergraduates, primarily from the greater Los Angeles area, and 3,735 graduate students. While Cal State LA previously operated on the quarter system, the university transitioned to the semester system starting in Fall 2016, with each academic year now consisting of two 15-week semesters. Cal State LA is organized into nine colleges that house a total of four schools and approximately 50 academic departments, divisions and interdisciplinary programs, offering a variety of majors. The Early Entrance Program (EEP) in the Honors College for gifted students as young as 12 is the only one of its kind in the United States in promoting a direct transitional scheme from middle and high school to college without intermediary remedial education. Cal State LA is a Hispanic-serving institution. The 175-acre (71 ha) hilltop campus core is home to the nation's first Charter College of Education, the Pat Brown Institute for Public Affairs, the Hertzberg-Davis Forensic Science Center, the Hydrogen Research and Fueling Facility and the Luckman Fine Arts Complex. It is also home to two high schools: the Marc and Eva Stern Math and Science School and the Los Angeles County High School for the Arts (LACHSA), the only arts high school in Los Angeles that allows students from any district within Los Angeles County to attend.
Econometricians would like to project the image of agricultural experimenters who divide a farm into a set of smaller plots of land and who select randomly the level of fertilizer to be used on each plot. If some plots are assigned a certain amount of fertilizer while others are assigned none, then the difference between the mean yield of the fertilized plots and the mean yield of the unfertilized plots is a measure of the effect of fertilizer on agricultural yields. The econometrician's humble job is only to determine if that difference is large enough to suggest a real effect of fertilizer, or is so small that it is more likely due to random variation. This image of the applied econometrician's art is grossly misleading. I would like to suggest a more accurate one. The applied econometrician is like a farmer who notices that the yield is somewhat higher under trees where birds roost, and he uses this as evidence that bird droppings increase yields. However, when he presents this finding at the annual meeting of the American Ecological Association, another farmer in the audience objects that he used the same data but came up with the conclusion that moderate amounts of shade increase yields. A bright chap in the back of the room then observes that these two hypotheses are indistinguishable, given the available data. He mentions the phrase identification problem, which, though no one knows quite what he means, is said with such authority that it is totally convincing. The meeting reconvenes in the halls and in the bars, with heated discussion
Chronic obstructive pulmonary disease (COPD) is a lung disease that makes it hard to breathe and gets worse over time. People with COPD have damaged airways with swelling and increased mucus production. Ensifentrine is a novel agent that inhibits phosphodiesterase (PDE) 3 and PDE4, two enzymes that affect airway muscles, inflammation, and mucus removal from the lungs. This summary of research provides an overview of a previously published article on the results from the phase 3 ENHANCE trials, which studied the effect of ensifentrine in people with moderate to severe COPD who may have already been taking standard maintenance medications. Ensifentrine improved breathing, symptoms, and quality of life. It also reduced the rate and risk of flare-ups (called exacerbations) and was well tolerated. These results support the use of ensifentrine as an effective and well-tolerated treatment for people with COPD.
Claims of detections of gases in exoplanet atmospheres often rely on comparisons between models including and excluding specific chemical species. However, the space of molecular combinations available for model construction is vast and highly degenerate. Only a limited subset of these combinations is typically explored for any given detection. As a result, apparent detections of trace gases risk being artefacts of incomplete modelling rather than robust identification of atmospheric constituents, especially in the low-signal-to-noise regime. Here, using the sub-Neptune K2-18 b as a case study, we show that recent biosignature claims vanish when the model space is expanded, with numerous alternatives providing equally good or better fits. We demonstrate that the significance of a claimed detection relies on the choice of models being compared, and that model preference does not in itself imply the presence of a specific gas. We recommend treating model comparisons instead as relative adequacy tests, which should be supported by theoretical predictions and complementary metrics of statistical significance to attribute a signal to a particular gas.
Abstract DEPTOR is a 48 kDa protein that binds to mTOR and inhibits this kinase in TORC1 and TORC2 complexes. Overexpression of DEPTOR specifically occurs in a model of multiple myeloma. Its silencing in multiple myeloma cells is sufficient to induce cytotoxicity, suggesting that DEPTOR is a potential therapeutic target. mTORC1 paralysis protects multiple myeloma cells against DEPTOR silencing, implicating mTORC1 in the critical role of DEPTOR in multiple myeloma cell viability. Building on this foundation, we interrogated a small-molecule library for compounds that prevent DEPTOR binding to mTOR in a yeast-two-hybrid assay. One compound was identified that also prevented DEPTOR–mTOR binding in human myeloma cells, with subsequent activation of mTORC1 and mTORC2. In a surface plasmon resonance (SPR) assay, the compound bound to recombinant DEPTOR but not to mTOR. The drug also prevented binding of recombinant DEPTOR to mTOR in the SPR assay. Remarkably, although activating TORC1 and TORC2, the compound induced apoptosis and cell-cycle arrest in multiple myeloma cell lines and prevented outgrowth of human multiple myeloma cells in immunodeficient mice. In vitro cytotoxicity against multiple myeloma cell lines was directly correlated with DEPTOR protein expression and was mediated, in part, by the activation of TORC1 and induction of p21 expression. Additional cytotoxicity was seen against primary multiple myeloma cells, whereas normal hematopoietic colony formation was unaffected. These results further support DEPTOR as a viable therapeutic target in multiple myeloma and suggest an effective strategy of preventing binding of DEPTOR to mTOR. Cancer Res; 76(19); 5822–31. ©2016 AACR.
We propose a novel fine-tuning method to achieve multi-operator learning through training a distributed neural operator with diverse function data and then zero-shot fine-tuning the neural network using physics-informed losses for downstream tasks. Operator learning effectively approximates solution operators for PDEs and various PDE-related problems, yet it often struggles to generalize to new tasks. To address this, we investigate fine-tuning a pretrained model, while carefully selecting an initialization that enables rapid adaptation to new tasks with minimal data. Our approach combines distributed learning to integrate data from various operators in pre-training, while physics-informed methods enable zero-shot fine-tuning, minimizing the reliance on downstream data. We investigate standard fine-tuning and Low-Rank Adaptation fine-tuning, applying both to train complex nonlinear target operators that are difficult to learn only using random initialization. Through comprehensive numerical examples, we demonstrate the advantages of our approach, showcasing significant improvements in accuracy. Our findings provide a robust framework for advancing multi-operator learning and highlight the potential of transfer learning techniques in this domain.
