The phosphatase PAC1 acts as a T cell suppressor and attenuates host antitumor immunity.

Cancer cells subvert immune surveillance through inhibition of T cell effector function. Elucidation of the mechanism of T cell dysfunction is therefore central to cancer immunotherapy. Here, we report that dual specificity phosphatase 2 (DUSP2; also known as phosphatase of activated cells 1, PAC1) acts as an immune checkpoint in T cell antitumor immunity. PAC1 is selectively upregulated in exhausted tumor-infiltrating lymphocytes and is associated with poor prognosis of patients with cancer. PAC1hi effector T cells lose their proliferative and effector capacities and convert into exhausted T cells. Deletion of PAC1 enhances immune responses and reduces cancer susceptibility in mice. Through activation of EGR1, excessive reactive oxygen species in the tumor microenvironment induce expression of PAC1, which recruits the Mi-2ß nucleosome-remodeling and histone-deacetylase complex, eventually leading to chromatin remodeling of effector T cells. Our study demonstrates that PAC1 is an epigenetic immune regulator and highlights the importance of targeting PAC1 in cancer immunotherapy.

Improved optimization for the neural-network quantum states and tests on the chromium dimer.

The advent of Neural-network Quantum States (NQS) has significantly advanced wave function ansatz research, sparking a resurgence in orbital space variational Monte Carlo (VMC) exploration. This work introduces three algorithmic enhancements to reduce computational demands of VMC optimization using NQS: an adaptive learning rate algorithm, constrained optimization, and block optimization. We evaluate the refined algorithm on complex multireference bond stretches of H2O and N2 within the cc-pVDZ basis set and calculate the ground-state energy of the strongly correlated chromium dimer (Cr2) in the Ahlrichs SV basis set. Our results achieve superior accuracy compared to coupled cluster theory at a relatively modest CPU cost. This work demonstrates how to enhance optimization efficiency and robustness using these strategies, opening a new path to optimize large-scale restricted Boltzmann machine-based NQS more effectively and marking a substantial advancement in NQS's practical quantum chemistry applications.

Exploring Changes in Land Surface Temperature Possibly Associated with Earthquake: Case of the April 2015 Nepal Mw 7.9 Earthquake.

Satellite thermal infrared remote sensing has received worldwide attention in the exploration for earthquake precursors; however, this method faces great controversy. Obtaining repeatable phenomena related to earthquakes is helpful to reduce this controversy. In this paper, a total of 15 or 17 years of Moderate-resolution Imaging Spectroradiometer (MODIS)/Aqua and MODIS/Terra satellite remote sensing land surface temperature (LST) products is selected to analyze the temperature changes before and after the Mw 7.9 earthquake in Nepal on 25 April 2015 and to explore possible thermal information associated with this earthquake. Major findings are given as follows: (1) from the time course, the temperature slowly cooled before the earthquake, reached a minimum at the time of the earthquake, and returned to normal after the earthquake. Since these changes were initiated before the earthquake, they may even have been precursors to the Nepal earthquake. (2) From the space distribution, the cooling areas correspond to the seismogenic structure during the earthquake. These cooling areas are distributed along the Himalayas and are approximately 1300 km long. The widths of the East and West sides are slightly different, with an average temperature decrease of 5.6 °C. For these cooling areas, the Western section is approximately 90 km wide and 500 km long; the East side is approximately 190 km wide and 800 km long. The Western side of the cooling strips appeared before the earthquake. In short, these kinds of spatial and temporal changes are tectonically related to the earthquake and may have been caused by the tectonic activity associated with the Nepal earthquake. This process began before the earthquake and therefore might even be potentially premonitory information associated with the Nepal earthquake.

The Time Course of Injury Risk After Return-to-Play in Professional Football (Soccer).

BACKGROUND: Injury risk in professional football (soccer) is increased in the weeks following return-to-play (RTP). However, the time course of injury risk after RTP (the hazard curve) as well as its influencing factors are largely unknown. This knowledge gap, which is arguably due to the volatility of instantaneous risk when calculated for short time intervals, impedes on informed RTP decision making and post-RTP player management. OBJECTIVES: This study aimed to characterize the hazard curve for non-contact time-loss injuries after RTP in male professional football and to investigate the influence of the severity of the index injury and playing position. METHODS: Media-based injury records from the first German football league were collected over four seasons as previously published. Time-to-event analysis was employed for non-contact time-loss injury after RTP. The Kaplan-Meier survival function was used to calculate the cumulative hazard function, from which the continuous hazard function was retrieved by derivation. RESULTS: There were 1623 observed and 1520 censored events from 646 players analyzed. The overall shape of the hazard curve was compatible with an exponential decline of injury risk, from an approximately two-fold level shortly after RTP towards baseline, with a half-time of about 4 weeks. Interestingly, the peak of the hazard curve was slightly delayed for moderate and more clearly for severe index injuries. CONCLUSIONS: The time course of injury risk after RTP (the hazard curve) can be characterized based on the Kaplan-Meier model. The shape of the hazard curve and its influencing factors are of practical as well as methodological relevance and warrant further investigation.

CD36+ pro-inflammatory macrophages interact with ZCCHC12+ tumor cells in papillary thyroid cancer promoting tumor progression and recurrence.

Local recurrence and distal metastasis negatively impact the survival and quality of life in patients with papillary thyroid cancer (PTC). Therefore, identifying potential biomarkers and therapeutic targets for PTC is clinically crucial. In this study, we performed a multi-omics analysis that identified a subset of CD36+ pro-inflammatory macrophages within the tumor microenvironment of PTC. The recruitment of CD36+ macrophages to pre-malignant regions strongly correlated with unfavorable outcomes in PTC and the presence of tumor-infiltrating CD36+ macrophages was determined to be a risk factor for recurrence. The CD36+ macrophages exhibited interactions with metabolically active ZCCHC12+ tumor cells. By secreting SPP1, the CD36+ macrophages activated the PI3K-AKT signaling pathway, thereby promoting proliferation of the cancer cells. Dysregulation of iodine metabolism was closely related to the acquisition of the pro-inflammatory phenotype in macrophages. Iodine supplementation inhibited the activation of pro-inflammatory signaling and impeded the development of CD36+ macrophages by enhancing DUSP2 expression. Overall, our findings shed light on the intricate crosstalk between CD36+ macrophages and ZCCHC12+ tumor cells, providing valuable insights for the treatment and prognosis of PTC.

Why Humble Farmers May in Fact Grow Bigger Potatoes: A Call for Street-Smart Decision-Making in Sport.

BACKGROUND: The main task of applied sport science is to inform decision-making in sports practice, that is, enabling practitioners to compare the expectable outcomes of different options (e.g. training programs). MAIN BODY: The "evidence" provided may range from group averages to multivariable prediction models. By contrast, many decisions are still largely based on the subjective, experience-based judgement of athletes and coaches. While for the research scientist this may seem "unscientific" and even "irrational", it is important to realize the different perspectives: science values novelty, universal validity, methodological rigor, and contributions towards long-term advancement. Practitioners are judged by the performance outcomes of contemporary, specific athletes. This makes out-of-sample predictive accuracy and robustness decisive requirements for useful decision support. At this point, researchers must concede that under the framework conditions of sport (small samples, multifactorial outcomes etc.) near certainty is unattainable, even with cutting-edge methods that might theoretically enable near-perfect accuracy. Rather, the sport ecosystem favors simpler rules, learning by experience, human judgement, and integration across different sources of knowledge. In other words, the focus of practitioners on experience and human judgement, complemented-but not superseded-by scientific evidence is probably street-smart after all. A major downside of this human-driven approach is the lack of science-grade evaluation and transparency. However, methods are available to merge the assets of data- and human-driven strategies and mitigate biases. SHORT CONCLUSION: This work presents the challenges of learning, forecasting and decision-making in sport as well as specific opportunities for turning the prevailing "evidence vs. eminence" contrast into a synergy.

PTIR1 acts as an isoform of DDX58 and promotes tumor immune resistance through activation of UCHL5.

Cancer evades host immune surveillance by virtue of poor immunogenicity. Here, we report an immune suppressor, designated as PTIR1, that acts as a promotor of tumor immune resistance. PTIR1 is selectively induced in human cancers via alternative splicing of DDX58 (RIG-I), and its induction is closely related to poor outcome in patients with cancer. Through blocking the recruitment of leukocytes, PTIR1 facilitates cancer immune escape and tumor-intrinsic resistance to immunotherapeutic treatments. Unlike RIG-I, PTIR1 is capable of binding to the C terminus of UCHL5 and activates its ubiquitinating function, which in turn inhibits immunoproteasome activity and limits neoantigen processing and presentation, consequently blocking T cell recognition and attack against cancer. Moreover, we find that the adenosine deaminase ADAR1 induces A-to-I RNA editing on DDX58 transcript, thus triggering PTIR1 production. Collectively, our data uncover the immunosuppressive role of PTIR1 in tumorigenesis and propose that ADAR1-PTIR1-UCHL5 signaling is a potential cancer immunotherapeutic target.

A Nonstochastic Optimization Algorithm for Neural-Network Quantum States.

Neural-network quantum states (NQS) employ artificial neural networks to encode many-body wave functions in a second quantization through variational Monte Carlo (VMC). They have recently been applied to accurately describe electronic wave functions of molecules and have shown the challenges in efficiency compared with traditional quantum chemistry methods. Here, we introduce a general nonstochastic optimization algorithm for NQS in chemical systems, which deterministically generates a selected set of important configurations simultaneously with energy evaluation of NQS. This method bypasses the need for Markov-chain Monte Carlo within the VMC framework, thereby accelerating the entire optimization process. Furthermore, this newly developed nonstochastic optimization algorithm for NQS offers comparable or superior accuracy compared to its stochastic counterpart and ensures more stable convergence. The application of this model to test molecules exhibiting strong electron correlations provides further insight into the performance of NQS in chemical systems and opens avenues for future enhancements.

Deubiquitinase OTUD3 regulates metabolism homeostasis in response to nutritional stresses.

The ovarian-tumor-domain-containing deubiquitinases (OTUDs) block ubiquitin-dependent protein degradation and are involved in diverse signaling pathways. We discovered a rare OTUD3 c.863G>A mutation in a family with an early age of onset of diabetes. This mutation reduces the stability and catalytic activity of OTUD3. We next constructed an experiment with Otud3-/- mice and found that they developed worse obesity, dyslipidemia, and insulin resistance than wild-type mice when challenged with a high-fat diet (HFD). We further found that glucose and fatty acids stimulate CREB-binding-protein-dependent OTUD3 acetylation, promoting its nuclear translocation, where OTUD3 regulates various genes involved in glucose and lipid metabolism and oxidative phosphorylation by stabilizing peroxisome-proliferator-activated receptor delta (PPARδ). Moreover, targeting PPARδ using a specific agonist can partially rescue the phenotype of HFD-fed Otud3-/- mice. We propose that OTUD3 is an important regulator of energy metabolism and that the OTUD3 c.863G>A is associated with obesity and a higher risk of diabetes.

FAM69C, a kinase critical for synaptic function and memory, is defective in neurodegenerative dementia.

Synapse loss and memory decline are the primary features of neurodegenerative dementia. However, the molecular underpinnings that drive memory loss remain largely unknown. Here, we report that FAM69C is a kinase critically involved in neurodegenerative dementia. Biochemical analyses uncover that FAM69C is a serine/threonine kinase. We generate the Fam69c knockout mice and show by single-cell RNA sequencing that FAM69C deficiency drives cell-type-specific transcriptional changes relevant to synapse dysfunction. Electrophysiological, morphological, and behavioral experiments demonstrate impairments in synaptic plasticity, dendritic spine density, and memory in Fam69c knockout mice, as well as stress-induced neuronal death. Phosphoproteomic characterizations reveal that FAM69C substrates are involved in synaptic structure and function. Finally, reduced levels of FAM69C are found in postmortem brains of Alzheimer's disease patients. Our study demonstrates that FAM69C is a protective regulator of memory and suggests FAM69C as a potential therapeutic target for memory loss in neurodegenerative dementia.

GeSn p-i-n photodetector for all telecommunication bands detection.

Using a 820 nm-thick high-quality Ge0.97Sn0.03 alloy film grown on Si(001) by molecular beam epitaxy, GeSn p-i-n photodectectors have been fabricated. The detectors have relatively high responsivities, such as 0.52 A/W, 0.23 A/W, and 0.12 A/W at 1310 nm, 1540 nm, and 1640 nm, respectively, under a 1 V reverse bias. With a broad detection spectrum (800-1800 nm) covering the whole telecommunication windows and compatibility with conventional complementary metal-oxide-semiconductors (CMOS) technology, the GeSn devices are attractive for applications in both optical communications and optical interconnects.

PTENα functions as an immune suppressor and promotes immune resistance in PTEN-mutant cancer.

PTEN is frequently mutated in human cancers and PTEN mutants promote tumor progression and metastasis. PTEN mutations have been implicated in immune regulation, however, the underlying mechanism is largely unknown. Here, we report that PTENα, the isoform of PTEN, remains active in cancer bearing stop-gained PTEN mutations. Through counteraction of CD8+ T cell-mediated cytotoxicity, PTENα leads to T cell dysfunction and accelerates immune-resistant cancer progression. Clinical analysis further uncovers that PTENα-active mutations suppress host immune responses and result in poor prognosis in cancer as relative to PTENα-inactive mutations. Furthermore, germline deletion of Ptenα in mice increases cell susceptibility to immune attack through augmenting stress granule formation and limiting synthesis of peroxidases, leading to massive oxidative cell death and severe inflammatory damage. We propose that PTENα protects tumor from T cell killing and thus PTENα is a potential target in antitumor immunotherapy.

Metabolic detection and systems analyses of pancreatic ductal adenocarcinoma through machine learning, lipidomics, and multi-omics.

Pancreatic ductal adenocarcinoma (PDAC) is one of the most lethal cancers, characterized by rapid progression, metastasis, and difficulty in diagnosis. However, there are no effective liquid-based testing methods available for PDAC detection. Here we introduce a minimally invasive approach that uses machine learning (ML) and lipidomics to detect PDAC. Through greedy algorithm and mass spectrum feature selection, we optimized 17 characteristic metabolites as detection features and developed a liquid chromatography-mass spectrometry-based targeted assay. In this study, 1033 patients with PDAC at various stages were examined. This approach has achieved 86.74% accuracy with an area under curve (AUC) of 0.9351 in the large external validation cohort and 85.00% accuracy with 0.9389 AUC in the prospective clinical cohort. Accordingly, single-cell sequencing, proteomics, and mass spectrometry imaging were applied and revealed notable alterations of selected lipids in PDAC tissues. We propose that the ML-aided lipidomics approach be used for early detection of PDAC.

Identification of a Rare PSEN1 Mutation (Thr119Ile) in Late-Onset Alzheimer's Disease With Early Presentation of Behavioral Disturbance.

Alzheimer's disease (AD) is the most common form of neurodegenerative dementia. In this study, whole genome sequencing identifies one rare and likely pathogenic mutation in the presenilin 1 (PSEN1) gene (c.356C > T, p.T119I) associated with a frontal variant of AD. Affected individuals in the kindred developed late-onset cognitive decline accompanied with early presentation of psychiatric symptoms. Positive amyloid PiB PET tracing suggested presence of pathophysiological biomarker for AD. Whole genome sequencing analysis evaluated rare coding mutations in susceptible genes for various types of dementia and supported the role of PSEN1 as a causal gene. Identification of this T119I variant in PSEN1 might broaden the spectrum of genetic basis and clinical diversity of familial AD.