Nesting ecology of mourning doves in Knox and Loudon Counties, Tennessee

The nesting ecology of mourning doves (Zenaida macroura) was investigated on a rural area in Loudon County and an urban area in Knox County, Tennessee, from 1 February to 31 October 1979 and 1980. Data were collected on nesting chronology, proportion of annual recruitment occurring after 1 September, nesting success, annual production, and nest site selection. On the rural area, the earliest nests were initiated on 6 April 1979 and 1 April 1980, while on the urban area, the earliest nests were initiated on 26 February 1979 and 20 February 1980. On the rural area, a major peak in nest initiations occurred in April and a minor peak in June during 1979, while during 1980, a small peak occurred in April, followed by a larger peak in August. On the urban area, peak periods of nest initiation occurred in March, May, and August during 1979, while during 1980, peak periods of nest initiation occurred in March and June. The latest nests on the rural area were begun on 3 August 1979 and 2 September 1980. The latest nests on the urban area were started on 8 September 1979 and 31 August 1980. On the rural area, the last young fledged on 27 August 1979 and 30 September 1980. On the urban area, the last young fledged on 6 October 1979 and 26 September 1980. The proportion of annual recruitment occurring after 1 September varied widely between years on the same area. On the rural area, no young were produced after 1 September 1979, while 7 (35.0%) young were produced after 1 September 1980. On the urban area, 2 (6.0%) and 10 (10.2%) young were produced after 1 September 1979 and 1 September 1980, respectively. On the rural area, 38.9% of the nests in 1979 and 61.1% of the nests in 1980 were successful in fledging at least 1 young. On the urban area, nest success was 85.7% in 1979 and 70.1% in 1980. Production on the rural area was 0.7 young per nest attempt in 1979 and 1.1 young per nest attempt in 1980. On the urban area, production was 1.6 young per nest attempt in 1979 and 1.3 young per nest attempt in 1980. On the rural area, doves made 3.0 nest attempts per pair in 1979 and 3.6 nest attempts per pair in 1980, while on the urban area, doves initiated 3.5 nests per pair in 1979 and 4.8 nests per pair in 1980. On the rural area, 2.2 young per pair were produced in 1979 and 4.0 young per pair in 1980, while on the urban area, there was a production of 5.5 young per pair in 1979 and 6.1 young per pair in 1980. On the rural area, most (72.2%) nests were located in wooded fencerows, while on the urban area, most (94.9%) nests were in ornamental landscape plantings

Thrombin-mediated proteoglycan synthesis utilizes both protein-tyrosine kinase and serine/threonine kinase receptor transactivation in vascular smooth muscle cells

Background: GPCR transactivation of PTKRs and TGF-αRs mediates proteoglycan synthesis in human VSMC. Results: Transactivation of TGF-αRs is integrin-dependent, and inhibition of both transactivation pathways blocks proteoglycan synthesis. Conclusion: GPCR utilize transactivation pathways and not classical signaling in proteoglycan synthesis. Significance: GPCR transactivation of receptor kinase pathways may be broader and more significant than previously recognized

Language in a Bottle: Language Model Guided Concept Bottlenecks for Interpretable Image Classification

Concept Bottleneck Models (CBM) are inherently interpretable models that factor model decisions into human-readable concepts. They allow people to easily understand why a model is failing, a critical feature for high-stakes applications. CBMs require manually specified concepts and often under-perform their black box counterparts, preventing their broad adoption. We address these shortcomings and are first to show how to construct high-performance CBMs without manual specification of similar accuracy to black box models. Our approach, Language Guided Bottlenecks (LaBo), leverages a language model, GPT-3, to define a large space of possible bottlenecks. Given a problem domain, LaBo uses GPT-3 to produce factual sentences about categories to form candidate concepts. LaBo efficiently searches possible bottlenecks through a novel submodular utility that promotes the selection of discriminative and diverse information. Ultimately, GPT-3's sentential concepts can be aligned to images using CLIP, to form a bottleneck layer. Experiments demonstrate that LaBo is a highly effective prior for concepts important to visual recognition. In the evaluation with 11 diverse datasets, LaBo bottlenecks excel at few-shot classification: they are 11.7% more accurate than black box linear probes at 1 shot and comparable with more data. Overall, LaBo demonstrates that inherently interpretable models can be widely applied at similar, or better, performance than black box approaches.Comment: 18 pages, 12 figures, 16 table

Interpretable-by-Design Text Classification with Iteratively Generated Concept Bottleneck

Deep neural networks excel in text classification tasks, yet their application in high-stakes domains is hindered by their lack of interpretability. To address this, we propose Text Bottleneck Models (TBMs), an intrinsically interpretable text classification framework that offers both global and local explanations. Rather than directly predicting the output label, TBMs predict categorical values for a sparse set of salient concepts and use a linear layer over those concept values to produce the final prediction. These concepts can be automatically discovered and measured by a Large Language Model (LLM), without the need for human curation. On 12 diverse datasets, using GPT-4 for both concept generation and measurement, we show that TBMs can rival the performance of established black-box baselines such as GPT-4 fewshot and finetuned DeBERTa, while falling short against finetuned GPT-3.5. Overall, our findings suggest that TBMs are a promising new framework that enhances interpretability, with minimal performance tradeoffs, particularly for general-domain text

Permissivity of Dipeptidyl Peptidase 4 Orthologs to Middle East Respiratory Syndrome Coronavirus Is Governed by Glycosylation and Other Complex Determinants

ABSTRACT Middle East respiratory syndrome coronavirus (MERS-CoV) utilizes dipeptidyl peptidase 4 (DPP4) as an entry receptor. While bat, camel, and human DPP4 support MERS-CoV infection, several DPP4 orthologs, including mouse, ferret, hamster, and guinea pig DPP4, do not. Previous work revealed that glycosylation of mouse DPP4 plays a role in blocking MERS-CoV infection. Here, we tested whether glycosylation also acts as a determinant of permissivity for ferret, hamster, and guinea pig DPP4. We found that, while glycosylation plays an important role in these orthologs, additional sequence and structural determinants impact their ability to act as functional receptors for MERS-CoV. These results provide insight into DPP4 species-specific differences impacting MERS-CoV host range and better inform our understanding of virus-receptor interactions associated with disease emergence and host susceptibility. IMPORTANCE MERS-CoV is a recently emerged zoonotic virus that is still circulating in the human population with an ∼35% mortality rate. With no available vaccines or therapeutics, the study of MERS-CoV pathogenesis is crucial for its control and prevention. However, in vivo studies are limited because MERS-CoV cannot infect wild-type mice due to incompatibilities between the virus spike and the mouse host cell receptor, mouse DPP4 (mDPP4). Specifically, mDPP4 has a nonconserved glycosylation site that acts as a barrier to MERS-CoV infection. Thus, one mouse model strategy has been to modify the mouse genome to remove this glycosylation site. Here, we investigated whether glycosylation acts as a barrier to infection for other nonpermissive small-animal species, namely, ferret, guinea pig, and hamster. Understanding the virus-receptor interactions for these DPP4 orthologs will help in the development of additional animal models while also revealing species-specific differences impacting MERS-CoV host range

Extracellular ATP released by osteoblasts is a key local inhibitor of bone mineralisation

Previous studies have shown that exogenous ATP (>1µM) prevents bone formation in vitro by blocking mineralisation of the collagenous matrix. This effect is thought to be mediated via both P2 receptor-dependent pathways and a receptor-independent mechanism (hydrolysis of ATP to produce the mineralisation inhibitor pyrophosphate, PPi). Osteoblasts are also known to release ATP constitutively. To determine whether this endogenous ATP might exert significant biological effects, bone-forming primary rat osteoblasts were cultured with 0.5-2.5U/ml apyrase (which sequentially hydrolyses ATP to ADP to AMP + 2Pi). Addition of 0.5U/ml apyrase to osteoblast culture medium degraded extracellular ATP to <1% of control levels within 2 minutes; continuous exposure to apyrase maintained this inhibition for up to 14 days. Apyrase treatment for the first 72 hours of culture caused small decreases (≤25%) in osteoblast number, suggesting a role for endogenous ATP in stimulating cell proliferation. Continuous apyrase treatment for 14 days (≥0.5U/ml) increased mineralisation of bone nodules by up to 3-fold. Increases in bone mineralisation were also seen when osteoblasts were cultured with the ATP release inhibitors, NEM and brefeldin A, as well as with P2X1 and P2X7 receptor antagonists. Apyrase decreased alkaline phosphatase (TNAP) activity by up to 60%, whilst increasing the activity of the PPi-generating ecto-nucleotide pyrophosphatase/phosphodiesterases (NPPs) up to 2.7-fold. Both collagen production and adipocyte formation were unaffected. These data suggest that nucleotides released by osteoblasts in bone could act locally, via multiple mechanisms, to limit mineralisation