Subclass-balancing Contrastive Learning for Long-tailed Recognition

Long-tailed recognition with imbalanced class distribution naturally emerges in practical machine learning applications. Existing methods such as data reweighing, resampling, and supervised contrastive learning enforce the class balance with a price of introducing imbalance between instances of head class and tail class, which may ignore the underlying rich semantic substructures of the former and exaggerate the biases in the latter. We overcome these drawbacks by a novel ``subclass-balancing contrastive learning (SBCL)'' approach that clusters each head class into multiple subclasses of similar sizes as the tail classes and enforce representations to capture the two-layer class hierarchy between the original classes and their subclasses. Since the clustering is conducted in the representation space and updated during the course of training, the subclass labels preserve the semantic substructures of head classes. Meanwhile, it does not overemphasize tail class samples, so each individual instance contribute to the representation learning equally. Hence, our method achieves both the instance- and subclass-balance, while the original class labels are also learned through contrastive learning among subclasses from different classes. We evaluate SBCL over a list of long-tailed benchmark datasets and it achieves the state-of-the-art performance. In addition, we present extensive analyses and ablation studies of SBCL to verify its advantages

NLPBench: Evaluating Large Language Models on Solving NLP Problems

Recent developments in large language models (LLMs) have shown promise in enhancing the capabilities of natural language processing (NLP). Despite these successes, there remains a dearth of research dedicated to the NLP problem-solving abilities of LLMs. To fill the gap in this area, we present a unique benchmarking dataset, NLPBench, comprising 378 college-level NLP questions spanning various NLP topics sourced from Yale University's prior final exams. NLPBench includes questions with context, in which multiple sub-questions share the same public information, and diverse question types, including multiple choice, short answer, and math. Our evaluation, centered on LLMs such as GPT-3.5/4, PaLM-2, and LLAMA-2, incorporates advanced prompting strategies like the chain-of-thought (CoT) and tree-of-thought (ToT). Our study reveals that the effectiveness of the advanced prompting strategies can be inconsistent, occasionally damaging LLM performance, especially in smaller models like the LLAMA-2 (13b). Furthermore, our manual assessment illuminated specific shortcomings in LLMs' scientific problem-solving skills, with weaknesses in logical decomposition and reasoning notably affecting results

The early posterior cortex pixel value ratio: a novel reliable indicator for distraction osteogenesis

AimsWe aimed to explore the associations of the early PVR in four cortices with Healing Index (HI), Lengthening Index (LI), and External Fixator Index (EFI) in the bone union and non-union groups.MethodsA total of 52 patients, including 39 bone union and 13 bone non-union subjects, were recruited in this study. The general characteristics and PVR in four cortices in each group were explored. Afterward, the early PVR in four cortices, including medial, lateral, anterior, and posterior sides, were compared. Finally, the associations of the early PVR in four cortices with HI, LI, and EFI were also investigated.ResultsThe general characteristics of these patients were consistent, except for HI (31.54 ± 12.24 vs. 45.08 ± 27.10, P = 0.018) and EFI (57.63 ± 18.15 vs. 71.29 ± 24.60, P = 0.046). The growth of regenerated callus was asymmetrical in the bone union group (the posterior PVR seems to grow faster), whereas no statistical difference was obtained in the bone non-union group. Furthermore, the posterior PVR in the bone union group was significantly higher than that in the bone non-union group (the first month: 0.96 ± 0.17 vs. 0.86 ± 0.06, p = 0.047; the second month: 0.98 ± 0.14 vs. 0.89 ± 0.09, p = 0.041; the third month: 1.00 ± 0.12 vs. 0.92 ± 0.09, p = 0.039). Most importantly, the posterior PVR was inversely associated with HI, LI, and EFI (the first month: r = −0.343, p = 0.041; r = −0.346, p = 0.042; r = −0.352, p = 0.041; the second month: r = −0.459, p = 0.004; r = −0.277, p = 0.101; r = −0.511, p = 0.002; the third month: r = −0.479, p = 0.003; r = −0.398, p = 0.018; r = −0.551, p = 0.001) in the bone union group, respectively. However, this finding was lost in the bone non-union group.ConclusionThe early posterior cortex PVR seems to grow faster than the medial, lateral, and anterior sides in the bone union group, which represents an asymmetrical development pattern. Moreover, the posterior cortex PVR was negatively associated with HI, LI, and EFI, respectively. The posterior cortex PVR may be a novel and reliable detection index in the process of DO

An Interpretable Computer-Aided Diagnosis Method for Periodontitis From Panoramic Radiographs

Periodontitis is a prevalent and irreversible chronic inflammatory disease both in developed and developing countries, and affects about 20–50% of the global population. The tool for automatically diagnosing periodontitis is highly demanded to screen at-risk people for periodontitis and its early detection could prevent the onset of tooth loss, especially in local communities and health care settings with limited dental professionals. In the medical field, doctors need to understand and trust the decisions made by computational models and developing interpretable models is crucial for disease diagnosis. Based on these considerations, we propose an interpretable method called Deetal-Perio to predict the severity degree of periodontitis in dental panoramic radiographs. In our method, alveolar bone loss (ABL), the clinical hallmark for periodontitis diagnosis, could be interpreted as the key feature. To calculate ABL, we also propose a method for teeth numbering and segmentation. First, Deetal-Perio segments and indexes the individual tooth via Mask R-CNN combined with a novel calibration method. Next, Deetal-Perio segments the contour of the alveolar bone and calculates a ratio for individual tooth to represent ABL. Finally, Deetal-Perio predicts the severity degree of periodontitis given the ratios of all the teeth. The Macro F1-score and accuracy of the periodontitis prediction task in our method reach 0.894 and 0.896, respectively, on Suzhou data set, and 0.820 and 0.824, respectively on Zhongshan data set. The entire architecture could not only outperform state-of-the-art methods and show robustness on two data sets in both periodontitis prediction, and teeth numbering and segmentation tasks, but also be interpretable for doctors to understand the reason why Deetal-Perio works so well

A multipronged approach unravels unprecedented protein-protein interactions in the human 2-oxoglutarate dehydrogenase multienzyme complex

The human 2-oxoglutaric acid dehydrogenase complex (hOGDHc) plays a pivotal role in the tricarboxylic acid (TCA) cycle, and its diminished activity is associated with neurodegenerative diseases. The hOGDHc comprises three components, hE1o, hE2o, and hE3, and we recently reported functionally active E1o and E2o components, enabling studies on their assembly. No atomic-resolution structure for the hE2o component is currently available, so here we first studied the interactions in the binary subcomplexes (hE1o-hE2o, hE1o-hE3, and hE2o-hE3) to gain insight into the strength of their interactions and to identify the interaction loci in them. We carried out multiple physico-chemical studies, including fluorescence, hydrogen-deuterium exchange MS (HDX-MS), and chemical cross-linking MS (CL-MS). Our fluorescence studies suggested a strong interaction for the hE1o-hE2o subcomplex, but a much weaker interaction in the hE1o-hE3 subcomplex, and failed to identify any interaction in the hE2o-hE3 subcomplex. The HDX-MS studies gave evidence for interactions in the hE1o-hE2o and hE1o-hE3 subcomplexes comprising full-length components, identifying: (i) the N-terminal region of hE1o, in particular the two peptides 18YVEEM22 and 27ENPKSVHKSWDIF39 as constituting the binding region responsible for the assembly of the hE1o with both the hE2o and hE3 components into hOGDHc, an hE1 region absent in available X-ray structures; and (ii) a novel hE2o region comprising residues from both a linker region and from the catalytic domain as being a critical region interacting with hE1o. The CL-MS identified the loci in the hE1o and hE2o components interacting with each other

HIF1A is a critical downstream mediator for hemophagocytic lymphohistiocytosis

Hemophagocytic lymphohistiocytosis (HLH) is a life-threatening syndrome characterized by overwhelming immune activation. A steroid and chemotherapy-based regimen remains as the first-line of therapy but it has substantial morbidity. Thus, novel, less toxic therapy for HLH is urgently needed. Although differences exist between familial HLH (FHL) and secondary HLH (sHLH), they have many common features. Using bioinformatic analysis with FHL and systemic juvenile idiopathic arthritis, which is associated with sHLH, we identified a common hypoxia-inducible factor 1A (HIF1A) signature. Furthermore, HIF1A protein levels were found to be elevated in the lymphocytic choriomeningitis virus infected Prf1−/− mouse FHL model and the CpG oligodeoxynucleotide-treated mouse sHLH model. To determine the role of HIF1A in HLH, a transgenic mouse with an inducible expression of HIF1A/ARNT proteins in hematopoietic cells was generated, which caused lethal HLH-like phenotypes: severe anemia, thrombocytopenia, splenomegaly, and multi-organ failure upon HIF1A induction. Mechanistically, these mice show type 1 polarized macrophages and dysregulated natural killler cells. The HLH-like phenotypes in this mouse model are independent of both adaptive immunity and interferon-γ, suggesting that HIF1A is downstream of immune activation in HLH. In conclusion, our data reveal that HIF1A signaling is a critical mediator for HLH and could be a novel therapeutic target for this syndrome

Human 2-Oxoglutarate Dehydrogenase Complex E1 Component Forms a Thiamin-derived Radical by Aerobic Oxidation of the Enamine Intermediate.

Herein are reported unique properties of the human 2-oxoglutarate dehydrogenase multienzyme complex (OGDHc), a rate-limiting enzyme in the Krebs (citric acid) cycle. (a) Functionally competent 2-oxoglutarate dehydrogenase (E1o-h) and dihydrolipoyl succinyltransferase components have been expressed according to kinetic and spectroscopic evidence. (b) A stable free radical, consistent with the C2-(C2alpha-hydroxy)-gamma-carboxypropylidene thiamin diphosphate (ThDP) cation radical was detected by electron spin resonance upon reaction of the E1o-h with 2-oxoglutarate (OG) by itself or when assembled from individual components into OGDHc. (c) An unusual stability of the E1o-h-bound C2-(2alpha-hydroxy)-gamma-carboxypropylidene thiamin diphosphate (the "ThDP-enamine"/C2alpha-carbanion, the first postdecarboxylation intermediate) was observed, probably stabilized by the 5-carboxyl group of OG, not reported before. (d) The reaction of OG with the E1o-h gave rise to superoxide anion and hydrogen peroxide (reactive oxygen species (ROS)). (e) The relatively stable enzyme-bound enamine is the likely substrate for oxidation by O2, leading to the superoxide anion radical (in d) and the radical (in b). (f) The specific activity assessed for ROS formation compared with the NADH (overall complex) activity, as well as the fraction of radical intermediate occupying active centers of E1o-h are consistent with each other and indicate that radical/ROS formation is an "off-pathway" side reaction comprising less than 1% of the "on-pathway" reactivity. However, the nearly ubiquitous presence of OGDHc in human tissues, including the brain, makes these findings of considerable importance in human metabolism and perhaps disease