GPT4Table: Can Large Language Models Understand Structured Table Data? A Benchmark and Empirical Study

Large language models (LLMs) are becoming attractive as few-shot reasoners to solve Natural Language (NL)-related tasks. However, there is still much to learn about how well LLMs understand structured data, such as tables. While it is true that tables can be used as inputs to LLMs with serialization, there is a lack of comprehensive studies examining whether LLMs can truly comprehend such data. In this paper, we try to understand this by designing a benchmark to evaluate the structural understanding capabilities (SUC) of LLMs. The benchmark we create includes seven tasks, each with its own unique challenges, \eg, cell lookup, row retrieval, and size detection. We conduct a series of evaluations on GPT-3.5 and GPT-4. We find that the performance varied depending on several input choices, including table input format, content order, role prompting, and partition marks. Drawing from the insights gained through the benchmark evaluations, we propose \textit{self-augmentation} for effective structural prompting, such as critical value / range identification using LLMs' internal knowledge. When combined with carefully chosen input choices, these structural prompting methods lead to promising improvements in LLM performance on a variety of tabular tasks, \eg, TabFact($\uparrow2.31\%$), HybridQA($\uparrow2.13\%$), SQA($\uparrow2.72\%$), Feverous($\uparrow0.84\%$), and ToTTo($\uparrow5.68\%$). We believe that our benchmark and proposed prompting methods can serve as a simple yet generic selection for future research.Comment: This paper has been accepted as a full paper at WSDM 202

Overexpression of SIRT1 in Mouse Forebrain Impairs Lipid/Glucose Metabolism and Motor Function

SIRT1 plays crucial roles in glucose and lipid metabolism, and has various functions in different tissues including brain. The brain-specific SIRT1 knockout mice display defects in somatotropic signaling, memory and synaptic plasticity. And the female mice without SIRT1 in POMC neuron are more sensitive to diet-induced obesity. Here we created transgenic mice overexpressing SIRT1 in striatum and hippocampus under the control of CaMKIIα promoter. These mice, especially females, exhibited increased fat accumulation accompanied by significant upregulation of adipogenic genes in white adipose tissue. Glucose tolerance of the mice was also impaired with decreased Glut4 mRNA levels in muscle. Moreover, the SIRT1 overexpressing mice showed decreased energy expenditure, and concomitantly mitochondria-related genes were decreased in muscle. In addition, these mice showed unusual spontaneous physical activity pattern, decreased activity in open field and rotarod performance. Further studies demonstrated that SIRT1 deacetylated IRS-2, and upregulated phosphorylation level of IRS-2 and ERK1/2 in striatum. Meanwhile, the neurotransmitter signaling in striatum and the expression of endocrine hormones in hypothalamus and serum T3, T4 levels were altered. Taken together, our findings demonstrate that SIRT1 in forebrain regulates lipid/glucose metabolism and motor function

Epitope mapping of PfCP-2.9, an asexual blood-stage vaccine candidate of Plasmodium falciparum

<p>Abstract</p> <p>Background</p> <p>Apical membrane antigen 1 (AMA-1) and merozoite surface protein 1 (MSP1) of <it>Plasmodium falciparum </it>are two leading blood-stage malaria vaccine candidates. A <it>P. falciparum </it>chimeric protein 2.9 (PfCP-2.9) has been constructed as a vaccine candidate, by fusing AMA-1 domain III (AMA-1 (III)) with a C-terminal 19 kDa fragment of MSP1 (MSP1-19) via a 28-mer peptide hinge. PfCP-2.9 was highly immunogenic in animal studies, and antibodies elicited by the PfCP-2.9 highly inhibited parasite growth <it>in vitro</it>. This study focused on locating the distribution of epitopes on PfCP-2.9.</p> <p>Methods</p> <p>A panel of anti-PfCP-2.9 monoclonal antibodies (mAbs) were produced and their properties were examined by Western blot as well as <it>in vitro </it>growth inhibition assay (GIA). In addition, a series of PfCP-2.9 mutants containing single amino acid substitution were produced in <it>Pichia pastoris</it>. Interaction of the mAbs with the PfCP-2.9 mutants was measured by both Western blot and enzyme-linked immunosorbent assay (ELISA).</p> <p>Results</p> <p>Twelve mAbs recognizing PfCP-2.9 chimeric protein were produced. Of them, eight mAbs recognized conformational epitopes and six mAbs showed various levels of inhibitory activities on parasite growth <it>in vitro</it>. In addition, seventeen PfCP-2.9 mutants with single amino acid substitution were produced in <it>Pichia pastoris </it>for interaction with mAbs. Reduced binding of an inhibitory mAb (mAb7G), was observed in three mutants including M62 (Phe⁴⁹¹→Ala), M82 (Glu⁵¹¹→Gln) and M84 (Arg⁵¹³→Lys), suggesting that these amino acid substitutions are critical to the epitope corresponding to mAb7G. The binding of two non-inhibitory mAbs (mAbG11.12 and mAbW9.10) was also reduced in the mutants of either M62 or M82. The substitution of Leu³¹to Arg resulted in completely abolishing the binding of mAb1E1 (a blocking antibody) to M176 mutant, suggesting that the Leu residue at this position plays a crucial role in the formation of the epitope. In addition, the Asn¹⁵residue may also play an important role in the global folding of PfCP-2.9, as its substitution by Arg lead to reduced binding of most mAbs and abolishing the binding of mAb6G and mAbP5-W12.</p> <p>Conclusions</p> <p>This study provided valuable information on epitopes of PfCP-2.9 vaccine candidate through generation of a panel of mAbs and a series of PfCP-2.9 mutants. The information may prove to be useful for designing more effective malaria vaccines against blood-stage parasites.</p

Eckhaus Instability in Laser Cavities with Harmonically Swept Filters

This work was supported in part by National Key R&D Program of China (2019YFB1803904), in part by Science, Technology and Innovation Commission of Shenzhen Municipality (SGDX2019081623060558), in part by Research Grants Council, University Grants Committee of Hong Kong SAR (PolyU152241/18E), and in part by Guangdong Basic and Applied Basic Research Foundation (2021A1515012544) (Corresponding author: Dongmei Huang).Peer reviewedPostprin

Ginsenoside Rh2 inhibiting HCT116 colon cancer cell proliferation through blocking PDZ-binding kinase/T-LAK cell-originated protein kinase

AbstractBackgroundGinsenoside Rh2 (GRh2) is the main bioactive component in American ginseng, a commonly used herb, and its antitumor activity had been studied in previous studies. PDZ-binding kinase/T-LAK cell-originated protein kinase (PBK/TOPK), a serine/threonine protein kinase, is highly expressed in HCT116 colorectal cancer cells.MethodsWe examined the effect of GRh2 on HCT116 cells ex vivo. Next, we performed in vitro binding assay and in vitro kinase assay to search for the target of GRh2. Furthermore, we elucidated the underlying molecular mechanisms for the antitumor effect of GRh2 ex vivo and in vivo.ResultsThe results of our in vitro studies indicated that GRh2 can directly bind with PBK/TOPK and GRh2 also can directly inhibit PBK/TOPK activity. Ex vivo studies showed that GRh2 significantly induced cell death in HCT116 colorectal cancer cells. Further mechanistic study demonstrated that these compounds inhibited the phosphorylation levels of the extracellular regulated protein kinases 1/2 (ERK1/2) and (H3) in HCT116 colorectal cancer cells. In vivo studies showed GRh2 inhibited the growth of xenograft tumors of HCT116 cells and inhibited the phosphorylation levels of the extracellular regulated protein kinases 1/2 and histone H3.ConclusionThe results indicate that GRh2 exerts promising antitumor effect that is specific to human HCT116 colorectal cancer cells through inhibiting the activity of PBK/TOPK

Inferring Tabular Analysis Metadata by Infusing Distribution and Knowledge Information

Many data analysis tasks heavily rely on a deep understanding of tables (multi-dimensional data). Across the tasks, there exist comonly used metadata attributes of table fields / columns. In this paper, we identify four such analysis metadata: Measure/dimension dichotomy, common field roles, semantic field type, and default aggregation function. While those metadata face challenges of insufficient supervision signals, utilizing existing knowledge and understanding distribution. To inference these metadata for a raw table, we propose our multi-tasking Metadata model which fuses field distribution and knowledge graph information into pre-trained tabular models. For model training and evaluation, we collect a large corpus (~582k tables from private spreadsheet and public tabular datasets) of analysis metadata by using diverse smart supervisions from downstream tasks. Our best model has accuracy = 98%, hit rate at top-1 > 67%, accuracy > 80%, and accuracy = 88% for the four analysis metadata inference tasks, respectively. It outperforms a series of baselines that are based on rules, traditional machine learning methods, and pre-trained tabular models. Analysis metadata models are deployed in a popular data analysis product, helping downstream intelligent features such as insights mining, chart / pivot table recommendation, and natural language QA...Comment: 13pages, 7 figures, 9 table

Visualising Personal Data Flows: Insights from a Case Study of Booking.com

Commercial organisations are holding and processing an ever-increasing amount of personal data. Policies and laws are continually changing to require these companies to be more transparent regarding collection, storage, processing and sharing of this data. This paper reports our work of taking Booking.com as a case study to visualise personal data flows extracted from their privacy policy. By showcasing how the company shares its consumers’ personal data, we raise questions and extend discussions on the challenges and limitations of using privacy policies to inform online users about the true scale and the landscape of personal data flows. This case study can inform us about future research on more data flow-oriented privacy policy analysis and on the construction of a more comprehensive ontology on personal data flows in complicated business ecosystems