Treatments of Chlamydia Trachomatis and Neisseria Gonorrhoeae

Chlamydia Trachomatis and Neisseria Gonorrhoeae rank as the two most commonly reported sexually transmitted diseases (STDs) in the United States. Under limited budget, publicly funded clinics are not able to screen and treat the two diseases for all patients. They have to make a decision as to which group of population shall go through the procedure for screening and treating the two diseases. Therefore, we propose a cubic integer programming model on maximizing the number of units of cured diseases. At the same time, a two-step algorithm is established to solve the cubic integer program. We further develop a web-server, which immediately make recommendation on identifying population groups, screening assays and treatment regimens. Running on the empirical data provided by the Centers for Disease Control and Prevention, our program gives more accurate optimal results comparing to MS Excel solver within a very short time

Study of nonlinear Alfvén waves in an electron–positron plasma with a three‐dimensional electromagnetic particle code

Results from three‐dimensional (3‐D) electromagnetic particle simulations of Alfvén waves generated by an electron beam in a nonrelativistic electron–positron plasma are presented. The results show that electrostatic modes are excited due to the beam instability. The bunches of the particles (electrons and positrons) caused by electrostatic waves are directly involved in the generation of Alfvén waves. The Alfvén waves propagate along the beam as damped solitons accelerating the background particles. The simulation results are in good agreement with theoretical analysis.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/69730/2/PHPAEN-1-1-103-1.pd

Can ChatGPT-like Generative Models Guarantee Factual Accuracy? On the Mistakes of New Generation Search Engines

Although large conversational AI models such as OpenAI's ChatGPT have demonstrated great potential, we question whether such models can guarantee factual accuracy. Recently, technology companies such as Microsoft and Google have announced new services which aim to combine search engines with conversational AI. However, we have found numerous mistakes in the public demonstrations that suggest we should not easily trust the factual claims of the AI models. Rather than criticizing specific models or companies, we hope to call on researchers and developers to improve AI models' transparency and factual correctness

Decomposition in pasture soil receiving excreta from ruminants fed alfalfa forage diet supplemented with increasing proportions of Sericea Lespedeza legume

Healthy soil is fundamental to a productive pasture system as it will decompose labile organic matter and promote retention of carbon to build a stable, resistant pool of organic matter. An easy, standardized approach to measure decomposition and litter stabilization that is gaining popularity in both citizen science and research studies is the use of the Tea Bag Index. The Tea Bag Index is a relatively new method evaluating the loss of organic material in two different kinds of commercial tea bags (green tea and Rooibos tea) after burial in the soil for 90 days. The objective of this experiment was to use the Tea Bag Index to determine if decomposition rate and litter stabilization were affected by inputs of excreta from ruminants fed alfalfa forage diets modified with 0%, 9%, 18%, or 27% of the tannin-containing legume sericea lespedeza, urea, or an untreated negative control in soil plots (n = 4). There was no difference in decomposition rate or litter stabilization among any treatments measured in the 8 cm of surface soil during the first spring growing season after treatment application of excreta or urea to the soil. Results of this experiment indicated that animal amendments simulating urine and manure patches did not result in detectable changes in organic matter decomposition during the first spring season after application to silt loam pasture soil growing tall fescue grass in the mid-South

In silico case studies of compliant robots: AMARSI deliverable 3.3

In the deliverable 3.2 we presented how the morphological computing ap- proach can significantly facilitate the control strategy in several scenarios, e.g. quadruped locomotion, bipedal locomotion and reaching. In particular, the Kitty experimental platform is an example of the use of morphological computation to allow quadruped locomotion. In this deliverable we continue with the simulation studies on the application of the different morphological computation strategies to control a robotic system

In situ vaccination using unique TLR9 ligand K3-SPG induces long-lasting systemic immune response and synergizes with systemic and local immunotherapy

Although checkpoint inhibitors (CPIs) have changed the paradigm of cancer therapy, low response rates and serious systemic adverse events remain challenging. In situ vaccine (ISV), intratumoral injection of immunomodulators that stimulate innate immunity at the tumor site, allows for the development of vaccines in patients themselves. K3-SPG, a second-generation nanoparticulate Toll-like receptor 9 (TLR9) ligand consisting of K-type CpG oligodeoxynucleotide (ODN) wrapped with SPG (schizophyllan), integrates the best of conventional CpG ODNs, making it an ideal cancer immunotherapy adjuvant. Focusing on clinical feasibility for pancreaticobiliary and gastrointestinal cancers, we investigated the antitumor activity of K3-SPG-ISV in preclinical models of pancreatic ductal adenocarcinoma (PDAC) and colorectal cancer (CRC). K3-SPG-ISV suppressed tumor growth more potently than K3-ISV or K3-SPG intravenous injections, prolonged survival, and enhanced the antitumor effect of CPIs. Notably, in PDAC model, K3-SPG-ISV alone induced systemic antitumor effect and immunological memory. ISV combination of K3-SPG and agonistic CD40 antibody further enhanced the antitumor effect. Our results imply that K3-SPG-based ISV can be applied as monotherapy or combined with CPIs to improve their response rate or, conversely, with CPI-free local immunotherapy to avoid CPI-related adverse events. In either strategy, the potency of K3-SPG-based ISV would provide the rationale for its clinical application to puncturable pancreaticobiliary and gastrointestinal malignancies

Chotosan (Diaoteng San)-induced improvement of cognitive deficits in senescence-accelerated mouse (SAMP8) involves the amelioration of angiogenic/neurotrophic factors and neuroplasticity systems in the brain

<p>Abstract</p> <p>Background</p> <p>Chotosan (CTS, <it>Diaoteng San</it>), a Kampo medicine (<it>ie </it>Chinese medicine) formula, is reportedly effective in the treatment of patients with cerebral ischemic insults. This study aims to evaluate the therapeutic potential of CTS in cognitive deficits and investigates the effects and molecular mechanism(s) of CTS on learning and memory deficits and emotional abnormality in an animal aging model, namely 20-week-old senescence-accelerated prone mice (SAMP8), with and without a transient ischemic insult (T2VO).</p> <p>Methods</p> <p>Age-matched senescence-resistant inbred strain mice (SAMR1) were used as control. SAMP8 received T2VO (T2VO-SAMP8) or sham operation (sham-SAMP8) at day 0. These SAMP8 groups were administered CTS (750 mg/kg, p.o.) or water daily for three weeks from day 3.</p> <p>Results</p> <p>Compared with the control group, both sham-SAMP8 and T2VO-SAMP8 groups exhibited cognitive deficits in the object discrimination and water maze tests and emotional abnormality in the elevated plus maze test. T2VO significantly exacerbated spatial cognitive deficits of SAMP8 elucidated by the water maze test. CTS administration ameliorated the cognitive deficits and emotional abnormality of sham- and T2VO-SAMP8 groups. Western blotting and immunohistochemical studies revealed a marked decrease in the levels of phosphorylated forms of neuroplasticity-related proteins, N-methyl-D-aspartate receptor 1 (NMDAR1), Ca²⁺/calmodulin-dependent protein kinase II (CaMKII), cyclic AMP responsive element binding protein (CREB) and brain-derived neurotrophic factor (BDNF) in the frontal cortices of sham-SAMP8 and T2VO-SAMP8. Moreover, these animal groups showed significantly reduced levels of vasculogenesis/angiogenesis factors, vascular endothelial growth factor (VEGF), VEGF receptor type 2 (VEGFR2), platelet-derived growth factor-A (PDGF-A) and PDGF receptor α (PDGFRα). CTS treatment reversed the expression levels of these factors down-regulated in the brains of sham- and T2VO-SAMP8.</p> <p>Conclusion</p> <p>Recovery of impaired neuroplasticity system and VEGF/PDGF systems may play a role in the ameliorative effects of CTS on cognitive dysfunction caused by aging and ischemic insult.</p

Chain-of-Knowledge: Grounding Large Language Models via Dynamic Knowledge Adapting over Heterogeneous Sources

We present chain-of-knowledge (CoK), a novel framework that augments large language models (LLMs) by dynamically incorporating grounding information from heterogeneous sources. It results in more factual rationales and reduced hallucination in generation. Specifically, CoK consists of three stages: reasoning preparation, dynamic knowledge adapting, and answer consolidation. Given a knowledge-intensive question, CoK first prepares several preliminary rationales and answers while identifying the relevant knowledge domains. If there is no majority consensus among the answers from samples, CoK corrects the rationales step by step by adapting knowledge from the identified domains. These corrected rationales can plausibly serve as a better foundation for the final answer consolidation. Unlike prior studies that primarily use unstructured data, CoK also leverages structured knowledge sources such as Wikidata and tables that provide more reliable factual information. To access both unstructured and structured knowledge sources in the dynamic knowledge adapting stage, we propose an adaptive query generator that allows the generation of queries for various types of query languages, including SPARQL, SQL, and natural sentences. Moreover, to minimize error propagation between rationales, CoK corrects the rationales progressively using preceding corrected rationales to generate and correct subsequent rationales. Extensive experiments show that CoK consistently improves the performance of LLMs on knowledge-intensive tasks across different domains.Comment: Accepted by ICLR 202