Low Hit Research Articles

Abstract 2568: ARTS-021 is a potent and selective CDK2 inhibitor that demonstrates anti-cancer activity in preclinical cancer models with CCNE1 amplification

Abstract Cyclin E1 amplification is prevalent in cancers with high unmet medical need such as high grade serous ovarian cancer, stomach cancer and esophageal cancer. Cancer cell lines with high expression level of cyclin E1 gene exhibited profound sensitivity to CDK2 gene depletion, suggesting CDK2 selective inhibitors have the potential to treat patients harboring Cyclin E1 alterations. Here we report the development and preclinical characterization of ARTS-021, an orally bioavailable small molecule CDK2 selective inhibitor. In enzymatic assay against a panel of different CDKs, ARTS-021 potently inhibits the activity of CDK2/CyclinE1 complex with 50% inhibitory concentration (IC50) in the sub-nanomolar range, displaying superior selectivity against other CDKs (CDK1,4,6,7,9). Very low hit rate (S(10)=0.022, at 1uM) is observed when ARTS-021 is assessed in whole Kinome profiling, further revealing the selectivity of this compound. ARTS-021 also demonstrates potent CDK2 inhibition and selectivity against other CDK family members (CDK1,4,6,9) in a series of cellular assays. Consistent with genetic CDK2 dependence data, cell lines with CCNE1 amplification are particularly sensitive to ARTS-021. Double nanomolar ARTS-021 inhibits Rb phosphorylation and blocks G1/S transition, leading to cell growth arrest specifically in CCNE1 amplified cell lines. This CDK2 dependent anti-tumor activity is further validated in vivo, where daily ARTS-021 administration leads to tumor stasis in CCNE1 amplified but not wild type xenograft models. Taken together, these data demonstrate ARTS-021 as a promising CDK2 selective inhibitor with strong potential towards the development of effective therapies for CCNE1 altered cancer. Citation Format: jiaqi Liang, Dai Cheng, Yali Guo, xiaobin zhang, Xipan Liu, Jia Fu, Qiang Ding, Wen Xiong, Fang Li, Yaoyu Chen, Qing Sheng. ARTS-021 is a potent and selective CDK2 inhibitor that demonstrates anti-cancer activity in preclinical cancer models with CCNE1 amplification [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 2568.

Dynamic Cache Replacement Strategy of Space Information Network Based on Cache Value

With the development of space network communication technology, satellite cache becomes possible. However, due to the limited space on the satellites, content replacement has become a major factor affecting network performance. Aiming at the limited cache resources of space network nodes and the low cache hit rate of nodes, a space information network dynamic cache replacement strategy (SINDR) based on cache value is proposed. On the basis of considering the popularity of the content and the cost of caching, the strategy also calculates the caching value of the content by quantifying the entropy increase of the node, and assigns a reasonable weight to each factor according to the Analytic Hierarchy Process. The cache value function is reconstructed; finally, the content of the virtual node and the physical satellite node that will serve the area is replaced by combining the characteristics of the space network, so as to obtain the optimal utilization of satellite cache resources. The simulation results show that, compared with the existing cache replacement strategies, the cache replacement strategy proposed in this paper can effectively improve the average cache hit rate and reduce the average user acquisition delay in the space information network environment.

Working Memory Deficit and Attentional Distractibility in Schizophrenia

IntroductionMeta-analyses suggest that patients with schizophrenia show deficit in working memory – both verbal and visual – and are more distractible. Working memory disturbances are even regarded as the central deficit in schizophrenia by some researchers. Theta synchronization (especially over fronto-central areas) is related to cognitive control and executive functioning during working memory encoding and retention.ObjectivesThe main goal of the study was to gain more understanding of the nature of working memory deficit and attentional distractibility in schizophrenia.Methods35 patients with schizophrenia and 39 matched controls were enrolled in our study. Participants performed a modified Sternberg working memory task that contained salient and non-salient distractor items in the retention period. A high-density 128 channel EEG was recorded during the task. Event-related theta (4-7 Hz) synchronization was analyzed during working memory encoding (learning) and retention (distractor filtering) in a later time window (350-550 ms).ResultsPatients with schizophrenia showed weaker working memory performance and increased attentional distractibility compared to the control group: patients had significantly lower hit rates (p < 0.0001) and higher distractor-related commission error rates (p < 0.0001). Theta synchronization was modulated by condition (learning < distractor) in both groups but it was modulated by salience only in controls (salient distractor > non-salient distractor, p[patients] = 0.95, p[controls] < 0.001).ConclusionsOur results suggest that patients with schizophrenia show diminished cognitive control compared to controls in response to salient distractors. Difficulties in cognitive control allocation may contribute to the behavioral results observed in this study.DisclosureNo significant relationships.

Discovery of new PKN2 inhibitory chemotypes via QSAR-guided selection of docking-based pharmacophores.

Serine/threonine-protein kinase N2 (PKN2) plays an important role in cell cycle progression, cell migration, cell adhesion and transcription activation signaling processes. In cancer, however, it plays important roles in tumor cell migration, invasion and apoptosis. PKN2 inhibitors have been shown to be promising in treating cancer. This prompted us to model this interesting target using our QSAR-guided selection of docking-based pharmacophores approach where numerous pharmacophores are extracted from docked ligand poses and allowed to compete within the context of QSAR. The optimal pharmacophore was sterically-refined, validated by receiver operating characteristic (ROC) curve analysis and used as virtual search query to screen the National Cancer Institute (NCI) database for new promising anti-PKN2 leads of novel chemotypes. Three low micromolar hits were identified with IC50 values ranging between 9.9 and 18.6µM. Pharmacological assays showed promising cytotoxic properties for active hits in MTT and wound healing assays against MCF-7 and PANC-1 cancer cells.

Efficient Identification of Anti-SARS-CoV-2 Compounds Using Chemical Structure- and Biological Activity-Based Modeling.

Identification of anti-SARS-CoV-2 compounds through traditional high-throughput screening (HTS) assays is limited by high costs and low hit rates. To address these challenges, we developed machine learning models to identify compounds acting via inhibition of the entry of SARS-CoV-2 into human host cells or the SARS-CoV-2 3-chymotrypsin-like (3CL) protease. The optimal classification models achieved good performance with area under the receiver operating characteristic curve (AUC-ROC) values of >0.78. Experimental validation showed that the best performing models increased the assay hit rate by 2.1-fold for viral entry inhibitors and 10.4-fold for 3CL protease inhibitors compared to those of the original drug repurposing screens. Twenty-two compounds showed potent (<5 μM) antiviral activities in a SARS-CoV-2 live virus assay. In conclusion, machine learning models can be developed and used as a complementary approach to HTS to expand compound screening capacities and improve the speed and efficiency of anti-SARS-CoV-2 drug discovery.

Evidence for a Dual-Pathway, 2-Hit Genetic Model for Focal Cortical Dysplasia and Epilepsy.

Background and ObjectivesThe 2-hit model of genetic disease is well established in cancer, yet has only recently been reported to cause brain malformations associated with epilepsy. Pathogenic germline and somatic variants in genes in the mechanistic target of rapamycin (mTOR) pathway have been implicated in several malformations of cortical development. We investigated the 2-hit model by performing genetic analysis and searching for germline and somatic variants in genes in the mTOR and related pathways.MethodsWe searched for germline and somatic pathogenic variants in 2 brothers with drug-resistant focal epilepsy and surgically resected focal cortical dysplasia (FCD) type IIA. Exome sequencing was performed on blood- and brain-derived DNA to identify pathogenic variants, which were validated by droplet digital PCR. In vitro functional assays of a somatic variant were performed.ResultsExome analysis revealed a novel, maternally inherited, germline pathogenic truncation variant (c.48delG; p.Ser17Alafs*70) in NPRL3 in both brothers. NPRL3 is a known FCD gene that encodes a negative regulator of the mTOR pathway. Somatic variant calling in brain-derived DNA from both brothers revealed a low allele fraction somatic variant (c.338C>T; p.Ala113Val) in the WNT2 gene in 1 brother, confirmed by droplet digital PCR. In vitro functional studies suggested a loss of WNT2 function as a consequence of this variant. A second somatic variant has not yet been found in the other brother.DiscussionWe identify a pathogenic germline mTOR pathway variant (NPRL3) and a somatic variant (WNT2) in the intersecting WNT signaling pathway, potentially implicating the WNT2 gene in FCD and supporting a dual-pathway 2-hit model. If confirmed in other cases, this would extend the 2-hit model to pathogenic variants in different genes in critical, intersecting pathways in a malformation of cortical development. Detection of low allele fraction somatic second hits is challenging but promises to unravel the molecular architecture of FCDs.

Machine Learning Quantitative Analysis of FDG PET Images of Medial Temporal Lobe Epilepsy Patients.

18F-FDG PET is widely used in epilepsy surgery. We established a robust quantitative algorithm for the lateralization of epileptogenic foci and examined the value of machine learning of 18F-FDG PET data in medial temporal lobe epilepsy (MTLE) patients. We retrospectively reviewed patients who underwent surgery for MTLE. Three clinicians identified the side of MTLE epileptogenesis by visual inspection. The surgical side was set as the epileptogenic side. Two parcellation paradigms and corresponding atlases (Automated Anatomical Labeling and FreeSurfer aparc + aseg) were used to extract the normalized PET uptake of the regions of interest (ROIs). The lateralization index of the MTLE-associated regions in either hemisphere was calculated. The lateralization indices of each ROI were subjected for machine learning to establish the model for classifying the side of MTLE epileptogenesis. Ninety-three patients were enrolled for training and validation, and another 11 patients were used for testing. The hit rate of lateralization by visual analysis was 75.3%. Among the 23 patients whose MTLE side of epileptogenesis was incorrectly determined or for whom no conclusion was reached by visual analysis, the Automated Anatomical Labeling and aparc + aseg parcellated the associated ROIs on the correctly lateralized MTLE side in 100.0% and 82.6%. In the testing set, lateralization accuracy was 100% in the 2 paradigms. Visual analysis of 18F-FDG PET to lateralize MTLE epileptogenesis showed a lower hit rate compared with machine-assisted interpretation. While reviewing 18F-FDG PET images of MTLE patients, considering the regions associated with MTLE resulted in better performance than limiting analysis to hippocampal regions.

Ground validation and error decomposition for six state-of-the-art satellite precipitation products over mainland China

Evaluating the quality of satellite precipitation products (SPPs) is crucially important for SPPs' applications and improvements. This study aims to comprehensively evaluate the accuracy of six widely used SPPs (GSMaP, IMERG, TMPA, CMORPH, PERSIANN, and CHIRPS) from 2000 to 2017 over mainland China using 2372 rain gauges. The performance of SPPs at different spatio-temporal scales and altitudes are explored. The probability density function (PDF) at different precipitation intensities is also considered. In addition, special attention has been paid to analyze the error components of SPPs from different sources. Results demonstrate that: (1) GSMaP outperforms other SPPs with good statistical metrics, followed by IMERG. PERSIANN and CHIRPS have more space for improvement. All SPPs perform well in summer while worse in winter. Meanwhile, the accuracy of all SPPs varies region by region, which is better in southeast coastal basins than in northwest inland basins. (2) The overall performance in low-altitude regions is superior to high-altitude regions. The KGE (or RMSE) shows a significant negative (or positive) correlation with altitude, whereas POD, FAR, and CSI exhibit a slight dependence on altitude. (3) TMPA can well reproduce the PDF of different precipitation intensities. All SPPs' PDF is more consistent with gauges for moderate (5–10 mm/d) and heavy precipitation (10–50 mm/d), while deviates from gauges for tiny/light (0.1–5 mm/d) and violate precipitation (>50 mm/d). (4) The error components of each SPP could be larger than total errors, and the false bias is the major error source. GSMaP achieves a better performance with low hit and missed bias, whereas CHIRPS behaves poorly with large hit, missed and false bias. On the whole, the study demonstrates the error characteristics and accuracy performance of six mainstream SPPs over mainland China, which is expected to provide valuable information for hydrometeorology applications.

Towards Dependency-Aware Cache Management for Data Analytics Applications

Memory caches are being used aggressively in today's data analytics systems such as Spark, Tez, and Piccolo. The significant performance impact of caches and their limited sizes call for efficient cache management in data analytics clusters. However, prevalent data analytics systems employ rather simple cache management policies-notably Least Recently Used (LRU) and Least Frequently Used (LFU)-that are oblivious to the application semantics of data dependency, expressed as directed acyclic graphs (DAGs). Without this knowledge, cache management can, at best, be performed by guessing the future data access patterns based on history, which frequently results in inefficient, erroneous caching with a low hit rate and a long response time. Worse still, the lack of data dependency knowledge makes it impossible to retain the all-or-nothing cache property of cluster applications, in that a compute task cannot be sped up unless all the dependent data has been kept in the main memory. In this paper, we propose a novel cache replacement policy, named Least Reference Count (LRC), which exploits the application's data dependency information to optimize the cache management. LRC keeps track of the reference count of each data block, defined as the number of dependent child blocks that have not been computed yet, and always evicts the block with the smallest reference count. Furthermore, we incorporate the all-or-nothing requirement into LRC by coordinately managing the reference counts of all the input data blocks for the same computation. We demonstrate the efficacy of LRC through both empirical analysis and cluster deployments against popular benchmarking workloads. Our Spark implementation shows that, the proposed policies well address the all-or-nothing requirement and significantly improve the cache performance. Compared with LRU and a recently proposed caching policy called MEMTUNE, LRC improves the caching performance of typical workloads in production clusters by 22% and 284%, respectively.

Impact of X-ray induced radiation damage on FD-MAPS of the ARCADIA project

Recent advancements in Monolithic Active Pixel Sensors (MAPS) demonstrated the ability to operate in high radiation environments of up to multiple kGy’s, which increased their appeal as sensors for high-energy physics detectors. The most recent example in such application is the new ALICE inner tracking system, entirely instrumented with CMOS MAPS, that covers an area of about 10 m2. However, the full potential of such devices has not yet been fully exploited, especially in respect of the size of the active area, power consumption, and timing capabilities.The ARCADIA project is developing Fully Depleted (FD) MAPS with an innovative sensor design, that uses a proprietary processing of the backside to improve the charge collection efficiency and timing over a wide range of operational and environmental conditions. The innovative sensor design targets very low power consumption, of the order of 20 mW cm−2 at 100 MHz cm−2 hit flux, to enable air-cooled operations of the sensors. Another key design parameter is the ability to further reduce the power regime of the sensor, down to 5 mW cm−2 or better, for low hit rates like e.g. expected in space experiments. In this contribution, we present a comparison between the detector characteristics predicted with Technology Computer Aided Design (TCAD) simulations and the ones measured experimentally. The comparison focuses on the current-voltage (IV) and capacitance-voltage (CV) characteristics, as well as noise estimated from in-pixel capacitances of passive/active pixel matrices. In view of the targeted applications of this technology, an emphasis is set on the modeling of X-ray induced radiation damage at the Si-SiO2 interface and the impact on the in-pixel sensor capacitance. The so-called new Perugia model has been used in the simulations to predict the sensor performance after total ionizing doses of up to 10 Mrad.

6Graph: A graph-theoretic approach to address pattern mining for Internet-wide IPv6 scanning

IPv6 target generation is critical in fast IPv6 scanning for Internet-wide surveys and cybersecurity analysis. However, existing techniques generally suffer from low hit rates because the targets are generated from inappropriate address patterns. To address the problem, we propose 6Graph, a graph-theoretic method for IPv6 address pattern mining. It first divides the IPv6 address space into different regions according to the structural information of a set of known addresses. Then, 6Graph maps the addresses of each region into undirected graphs and conducts the density-based graph cutting for address clustering to mine IPv6 address patterns and detect the misclassified addresses iteratively. Besides, we exploit the random IPv6 target generation based on Hamming distance without additional and complicated target selection. Experiments on 11 large-scale candidate datasets show that the address patterns of 6Graph have a higher seed density than the existing methods. Further results over real-world networks indicate that 6Graph can achieve 12.6%–35.8% hit rates on the candidate datasets, which is an 8.8%–275.0% improvement over the state-of-the-art methods in Internet-wide scanning.

Request, Coalesce, Serve, and Forget: Miss-Optimized Memory Systems for Bandwidth-Bound Cache-Unfriendly Applications on FPGAs

Applications such as large-scale sparse linear algebra and graph analytics are challenging to accelerate on FPGAs due to the short irregular memory accesses, resulting in low cache hit rates. Nonblocking caches reduce the bandwidth required by misses by requesting each cache line only once, even when there are multiple misses corresponding to it. However, such reuse mechanism is traditionally implemented using an associative lookup. This limits the number of misses that are considered for reuse to a few tens, at most. In this article, we present an efficient pipeline that can process and store thousands of outstanding misses in cuckoo hash tables in on-chip SRAM with minimal stalls. This brings the same bandwidth advantage as a larger cache for a fraction of the area budget, because outstanding misses do not need a data array, which can significantly speed up irregular memory-bound latency-insensitive applications. In addition, we extend nonblocking caches to generate variable-length bursts to memory, which increases the bandwidth delivered by DRAMs and their controllers. The resulting miss-optimized memory system provides up to 25% speedup with 24× area reduction on 15 large sparse matrix-vector multiplication benchmarks evaluated on an embedded and a datacenter FPGA system.

Adaptive Multi-Grained Buffer Management for Database Systems

The traditional page-grained buffer manager in database systems has a low hit ratio when only a few tuples within a page are frequently accessed. To handle this issue, this paper proposes a new buffering scheme called the AMG-Buffer (Adaptive Multi-Grained Buffer). AMG-Buffer proposes to use two page buffers and a tuple buffer to organize the whole buffer. In this way, the AMG-Buffer can hold more hot tuples than a single page-grained buffer. Further, we notice that the tuple buffer may cause additional read I/Os when writing dirty tuples into disks. Thus, we introduce a new metric named clustering rate to quantify the hot-tuple rate in a page. The use of the tuple buffer is determined by the clustering rate, allowing the AMG-Buffer to adapt to different workloads. We conduct experiments on various workloads to compare the AMG-Buffer with several existing schemes, including LRU, LIRS, CFLRU, CFDC, and MG-Buffer. The results show that AMG-Buffer can significantly improve the hit ratio and reduce I/Os compared to its competitors. Moreover, the AMG-Buffer achieves the best performance on a dynamic workload as well as on a large data set, suggesting its adaptivity and scalability to changing workloads.

Distractor filtering and its electrophysiological correlates in schizophrenia

ObjectivePatients with schizophrenia are characterized by compromised working memory (WM) performance and increased distractibility. Theta synchronization (especially over the frontal midline areas) is related to cognitive control and executive processes during WM encoding and retention. Alpha event-related desynchronization (ERD) is associated with information processing and attention. MethodsParticipants (35 patients and 39 matched controls) performed a modified Sternberg WM task, containing salient and non-salient distractor items in the retention period. A high-density 128 channel EEG was recorded during the task. Theta (4–7 Hz) and fast alpha (10–13 Hz) event-related spectral perturbation (ERSP) were analyzed during the retention and encoding period. ResultsPatients with schizophrenia showed worse WM performance and increased attentional distractibility in terms of lower hit rates and increased distractor-related commission errors compared to healthy controls. Theta synchronization was modulated by condition (learning vs. distractor) in both groups but it was modulated by salience only in controls. Furthermore, salience of distractors modulated less the fast alpha ERD in patients. ConclusionsOur results suggest that patients with schizophrenia process salient and non-salient distracting information less efficiently and show weaker cognitive control compared to controls. SignificanceThese differences may partly account for diminished WM performance and increased distractibility in schizophrenia.

Disruption in Surface-Based Functional Connectivity in the Right Posterior Hippocampal CA3 Subfield: A Probable Neural Basis of Visuospatial Working Memory Impairment in Patients With Right Temporal Lobe Epilepsy.

Visuospatial working memory (VSWM) impairment is common in patients with right temporal lobe epilepsy (rTLE). The posterior hippocampus is critical for spatial memory, but the contributions of the different subfields to VSWM deficits remain unclear. Forty-six rTLE patients and 42 healthy controls (HCs) were recruited. Resting-state fMRI (rsfMRI) and structural MRI scans were administered, followed by a VSWM_Nback test. The right posterior hippocampus was automatically segmented, and the surface-based functional connectivity (SBFC) of the subiculum (Sub), CA1, CA3, dentate gyrus (DG), hippocampal tail, and right entorhinal cortex (EC) were compared between groups. Correlation analysis was performed between the altered SBFC and VSWM_Nback scores for rTLE patients. The results showed that rTLE patients underperformed in the VSWM_Nback test, with longer mean reaction time of accurate response (ACCmeanRT) in 0back and 2back condition, lower hit rate (HR) and higher false alarm rate (FAR) in 2back condition. Compared with HCs, the rCA3 in the rTLE group exhibited decreased SBFC with inferior parietal cortex (IPC), temporal lateral cortex (TLC), and posterior visual cortex (PVC) in the right hemisphere as well as the bilateral dorsolateral prefrontal cortex (DLPFC). The SBFC of the rEC and right anterior cingulate cortex (rACC) increased in the rTLE group. Within the rTLE group, the decreased SBFC of the rCA3-rIPC and rCA3-rLTC were correlated with worse VSWM performance. Therefore, the decreased SBFC of the rCA3-rIPC and rCA3-rLTC might be the critical aberrant FC pattern reflecting VSWM impairment in rTLE patients. The mechanism might involve functional disruption between the core subsystem and the medial temporal subsystem of the default mode network (DMN).

Evaluation of satellite-based precipitation products from GPM IMERG and GSMaP over the three-river headwaters region, China

AbstractThe three-river headwaters region (TRHR) is the birthplace of the Yangtze River, the Yellow River and the Lantsang River in China. Based on the grid surface precipitation data released by China Meteorological Administration (CMA), this paper evaluated the accuracy and error components of four near-real-time satellite precipitation products (GSMaP-NRT, GSMaP-MVK, IMERG-Early and IMERG-Late) in the era of a GPM (Global Precipitation Measurement) in TRHR. The conclusions are as follows: (1) The precipitation in TRHR is concentrated in the east and south, and the precipitation in the west is very low. IMERG (Early and Late) has a good spatial distribution of precipitation, while GSMaP has an obvious spatial smoothing of precipitation distribution, and does not better highlight the local precipitation characteristics. (2) The inversion accuracy of the satellite products is the best in the source region of the Lantsang River, followed by the source region of the Yellow River. The satellite products all show the lower correlation coefficient and serious underestimation of precipitation in the west of the TRHR. In addition, the closer to the west of the TRHR, the lower hit rate and the higher false alarm rate of the satellite products, especially the NRT and MVK products. In the eastern margin of the Yellow River headwater region and the Lantsang River headwater Region, RMSE and overestimated precipitation were higher in NRT and MVK, and FAR was higher in spite of higher POD and CSI. (3) The errors of GSMaP in the source region of the Yellow River and the Lantsang River are mainly caused by misreporting precipitation and overestimating the precipitation level, while the errors of GSMaP in the west of the TRHR are mainly caused by missing measurements of precipitation events. The underestimated precipitation of IMERG mainly comes from the missed measurement of precipitation and the underestimate of precipitation level, and there is no large false precipitation. (4) In addition, we found that the satellite products in the lake distribution area of the TRHR have serious missed precipitation errors, indicating that the GPM satellite products have the poor detection ability of precipitation near plateau lakes. On the whole, the precipitation inversion accuracy of IMERG (Early and Late) products is higher, which can better detect the occurrence of precipitation events, but the estimation of precipitation level is still not accurate. The precision of precipitation of satellite products near inland lakes on the plateau is poor, so the algorithm improvement of new products needs to be further solved in the future.

Implementation of Multi-tenancy Adaptive Cache Admission Model

Cyber performance is an evolving issue for enterprises and individuals. However, Internet applications with many concurrent accessing are obsessed with the tradeoff between the scale of users and the efficiency for each user. Compared with multi-tenancy catch technology, it is almost impossible to meet the requirements of enormous accessing when improving the hardware performance. Different from the classical multi-tenancy cache system focused on the size of cache partition, we propose a multi-tenancy cache model with adaptive admission strategy. The model evaluates the hit efficiency of cache objects in cache space, and expels cache objects with low hit rate through an effective algorithm. The experimental results show that the proposed method improve the response efficiency, especially in the high concurrent accessing scene for Internet applications.

Heavy precipitation forecasts over Switzerland – An evaluation of bias-corrected ECMWF predictions

Heavy precipitation episodes are particularly hazardous in mid-sized alpine catchments where runoff tends to increase rapidly after strong rainfall, leaving limited time for warning. A high-quality, unbiased forecast of heavy precipitation with long enough lead times (2–5 days) and an adequate spatial resolution is thus crucial for decision makers. However, at present, weather forecast models are sometimes still too coarsely resolved for such catchments or limited to short lead times. Here we present a computationally cheap post-processing approach for operational applications to bias-correct deterministic heavy precipitation forecasts at medium-range lead times. We assessed forecast performance of uncorrected ECMWF Integrated Forecasting System (IFS) high-resolution (HRES) and ensemble median run (ENS) forecasts with lead times of 2 and 5 days between 2010 and 2019 and evaluated the improvement after interpolation and bias correction. The study is based on observations from 787 meteorological stations, with a focus on areal precipitation in three medium-sized Swiss catchments (Emme, Simme, and Vispa). We found a significantly higher performance of the HRES forecast compared to ENS, especially for a lead time of 5 days. The post-processing approach that we present in this study removes large biases, and lowers false alarm rates. Nonetheless, improvements may be limited for single heavy precipitation events where the model considerably underestimates daily precipitation. Also, a lower false alarm rate can be at the cost of lower hit rates. Quantile mapping is clearly limited for short term prediction in the cases where the error does not substantially exceed natural variability. Despite these limitations, we conclude that bias-corrected forecasts can help improving forecast credibility, which is a key element for decision makers to take actions.

I, robot: depression plays different roles in human\u2013human and human\u2013robot interactions

Socially engaging robots have been increasingly applied to alleviate depressive symptoms and to improve the quality of social life among different populations. Seeing that depression negatively influences social reward processing in everyday interaction, we investigate this influence during simulated interactions with humans or robots. In this study, 35 participants with mild depression and 35 controls (all from nonclinical populations) finished the social incentive delay task with event-related potential recording, in which they received performance feedback from other persons or from a robot. Compared to the controls, the mild depressive symptom (MDS) group represented abnormalities of social reward processing in the human feedback condition: first, the MDS group showed a lower hit rate and a smaller contingent-negative variation (correlated with each other) during reward anticipation; second, depression level modulated both the early phase (indexed by the feedback-related negativity (FRN)) and the late phase (indexed by the P3) of reward consumption. In contrast, the effect of depression was evident only on FRN amplitude in the robot feedback condition. We suggest that compared to human–human interaction, the rewarding properties of human–robot interaction are less likely to be affected by depression. These findings have implications for the utilization of robot-assisted intervention in clinical practice.

Transcriptome-Wide Off-Target Effects of Steric-Blocking Oligonucleotides.

Steric-blocking oligonucleotides (SBOs) are short, single-stranded nucleic acids designed to modulate gene expression by binding to RNA transcripts and blocking access from cellular machinery such as splicing factors. SBOs have the potential to bind to near-complementary sites in the transcriptome, causing off-target effects. In this study, we used RNA-seq to evaluate the off-target differential splicing events of 81 SBOs and differential expression events of 46 SBOs. Our results suggest that differential splicing events are predominantly hybridization driven, whereas differential expression events are more common and driven by other mechanisms (including spurious experimental variation). We further evaluated the performance of in silico screens for off-target splicing events, and found an edit distance cutoff of three to result in a sensitivity of 14% and false discovery rate (FDR) of 99%. A machine learning model incorporating splicing predictions substantially improved the ability to prioritize low edit distance hits, increasing sensitivity from 4% to 26% at a fixed FDR of 90%. Despite these large improvements in performance, this approach does not detect the majority of events at an FDR <99%. Our results suggest that in silico methods are currently of limited use for predicting the off-target effects of SBOs, and experimental screening by RNA-seq should be the preferred approach.