Therapeutic Vaccines for HPV-Associated Oropharyngeal and Cervical Cancer: The Next De-Intensification Strategy?

The rise in human papillomavirus (HPV)-associated oropharyngeal squamous cell carcinoma (OPSCC) has prompted a quest for further understanding of the role of high-risk HPV in tumor initiation and progression. Patients with HPV-positive OPSCC (HPV+ OPSCC) have better prognoses than their HPV-negative counterparts; however, current therapeutic strategies for HPV+ OPSCC are overly aggressive and leave patients with life-long sequalae and poor quality of life. This highlights a need for customized treatment. Several clinical trials of treatment de-intensification to reduce acute and late toxicity without compromising efficacy have been conducted. This article reviews the differences and similarities in the pathogenesis and progression of HPV-related OPSCC compared to cervical cancer, with emphasis on the role of prophylactic and therapeutic vaccines as a potential de-intensification treatment strategy. Overall, the future development of novel and effective therapeutic agents for HPV-associated head and neck tumors promises to meet the challenges posed by this growing epidemic.     

Glioblastoma Extracellular Vesicle-Specific Peptides Inhibit EV-Induced Neuronal Cytotoxicity

WHO Grade 4 IDH-wild type astrocytoma (GBM) is the deadliest brain tumor with a poor prognosis. Meningioma (MMA) is a more common “benign” central nervous system tumor but with significant recurrence rates. There is an urgent need for brain tumor biomarkers for early diagnosis and effective treatment options. Extracellular vesicles (EVs) are tiny membrane-enclosed vesicles that play essential functions in cell-to-cell communications among tumor cells. We aimed to identify epitopes of brain tumor EVs by phage peptide libraries. EVs from GBM plasma, MMA plasma, or brain tumor cell lines were used to screen phage-displayed random peptide libraries to identify high-affinity peptides. We purified EVs from three GBM plasma pools (23 patients), one MMA pool (10 patients), and four brain tumor cell lines. We identified a total of 21 high-affinity phage peptides (12 unique) specific to brain tumor EVs. The peptides shared high sequence homologies among those selected by the same EVs. Dose–response ELISA demonstrated that phage peptides were specific to brain tumor EVs compared to controls. Peptide affinity purification identified unique brain tumor EV subpopulations. Significantly, GBM EV peptides inhibit brain tumor EV-induced complement-dependent cytotoxicity (necrosis) in neurons. We conclude that phage display technology could identify specific peptides to isolate and characterize tumor EVs.     

Synergistic and suppressive effects of dietary phenolic acids and other phytochemicals from cereal extracts on nuclear factor kappa B activity

In this study the combinatorial action of the main phenolic acids (ferulic, caffeic, p-coumaric and sinapic acids) found in extracts of free (ME) and bound phenolic acids (HE) from oat, barley and wheat flour on the modulation of NF-κB activity was investigated. The results show that combination of phenolic acids in low concentrations (<0.1 μg/ml) has a significant synergistic, or enhanced effect, on NF-κB activity, while their combination in high concentrations (0.3–70 μg/ml) helps to suppress the strong effect obtained by individual solutions of ferulic and p-coumaric acids. The modulation of NF-κB activity observed by HE extracts is the effect of combinatorial action of the phenolic acids present therein, while the modulation of NF-κB activity observed with ME extracts is the result of combinatorial action of phenolic acids together with other phenolic compounds present in the extracts. These results increase the knowledge about the health promoting effect from cereal consumption and dietary phenolic acids.     

Modulation of rumen pH by sodium bicarbonate and a blend of different sources of magnesium oxide in lactating dairy cows submitted to a concentrate challenge

With the objective of evaluating the potential effects of sodium bicarbonate or a magnesium-based product on rumen pH and milk performance of dairy cattle exposed to a dietary challenge, 30 lactating Holstein cows (648 ± 67 kg of body weight; 44.4 ± 9.9 kg/d of milk yield; 155 ± 75 d in milk) were blocked by parity (9 primiparous and 21 multiparous) and randomly distributed to 3 treatment groups. One group received a total mixed ration (TMR) that acted as a control (CTR), a second group (SB) received the same TMR but with an additional supplementation of 0.8% of sodium bicarbonate, and a third group (MG) received the same TMR as CTR but an additional supplementation of 0.4% of a magnesium-based product (pHix-Up, Timab, Dinard, France). After 1 wk of exposure to this TMR, all 3 rations were supplemented with 1 kg/d of barley, which was then increased 1 kg/wk until reaching 3 kg/d of barley during wk 4 of the study. Every kilogram of barley replaced 1 kg of forage in the diet. Individual feed intake and behavior were monitored using electronic feed bins. Seven cows per treatment were equipped with an intraruminal bolus that recorded pH every 15 min. As the severity of the barley challenge increased, dry matter intake decreased, but this decrease was more pronounced in SB cows than in MG cows, with an intermediate response for CTR cows. The MG cows produced more milk when challenged with 2 or 3 kg/d of additional barley than when challenged with 1 kg/d, whereas CTR cows produced less milk with the 3 kg/d challenge compared with 1 or 2 kg/d, and the SB cows maintained milk production. Milk fat content decreased with barley challenges, with CTR cows experiencing a more severe decrease than SB cows, which maintained stable butterfat values throughout the study, and MG cows showed a decline in milk fat content only with the 3 kg/d of additional barley. Meal size was also reduced as the severity of barley challenge increased, and this reduction was more modest in MG cows than in SB cows. The number of daily meals consumed by SB and MG cows was more constant than that recorded in CTR cows. Cows on the CTR and SB treatments showed a marked decrease in rumen pH with the 3 kg/d of additional barley, whereas MG cows maintained stable rumen pH during the barley challenges and had greater average rumen pH (5.93 ± 0.04) than CTR cows (5.83 ± 0.04) with the 3 kg/d of additional barley; SB cows showed intermediate values (5.85 ± 0.04). Last, MG cows spent less time (32.3 ± 6.1%) with rumen pH ≤5.8 when exposed to the 3 kg/d of barley challenge than CTR and SB cows (50.7 ± 5.02%). In conclusion, supplementation with MG prevents the decline in dry matter intake and milk production induced by a rumen challenge, whereas supplementation with SB prevents the decay in milk production but does not prevent the decrease in feed intake. These changes were probably due to the ability of the MG treatment to prevent a reduction in rumen pH when challenging cows with 3 kg/d of additional barley in the ration.     

Managing the risk of a COVID-19 outbreak from border arrivals

In an attempt to maintain the elimination of COVID-19 in New Zealand, all international arrivals are required to spend 14 days in government-managed quarantine and to return a negative test result before being released. We model the testing, isolation and transmission of COVID-19 within quarantine facilities to estimate the risk of community outbreaks being seeded at the border. We use a simple branching process model for COVID-19 transmission that includes a time-dependent probability of a false-negative test result. We show that the combination of 14-day quarantine with two tests is highly effective in preventing an infectious case entering the community, provided there is no transmission within quarantine facilities. Shorter quarantine periods, or reliance on testing only with no quarantine, substantially increases the risk of an infectious case being released. We calculate the fraction of cases detected in the second week of their two-week stay and show that this may be a useful indicator of the likelihood of transmission occurring within quarantine facilities. Frontline staff working at the border risk exposure to infected individuals and this has the potential to lead to a community outbreak. We use the model to test surveillance strategies and evaluate the likely size of the outbreak at the time it is first detected. We conclude with some recommendations for managing the risk of potential future outbreaks originating from the border.     

Plasmon-induced trap filling at grain boundaries in perovskite solar cells

The deep-level traps induced by charged defects at the grain boundaries (GBs) of polycrystalline organic–inorganic halide perovskite (OIHP) films serve as major...     

Platform‐independent profiling in a virtual execution environment

Virtual execution environments, such as the Java virtual machine, promote platform‐independent software development. However, when it comes to analyzing algorithm complexity and performance bottlenecks, available tools focus on platform‐specific metrics, such as the CPU time consumption on a particular system. Other drawbacks of many prevailing profiling tools are high overhead, significant measurement perturbation, as well as reduced portability of profiling tools, which are often implemented in platform‐dependent native code. This article presents a novel profiling approach, which is entirely based on program transformation techniques, in order to build a profiling data structure that provides calling‐context‐sensitive program execution statistics. We explore the use of platform‐independent profiling metrics in order to make the instrumentation entirely portable and to generate reproducible profiles. We implemented these ideas within a Java‐based profiling tool called JP. A significant novelty is that this tool achieves complete bytecode coverage by statically instrumenting the core runtime libraries and dynamically instrumenting the rest of the code. JP provides a small and flexible API to write customized profiling agents in pure Java, which are periodically activated to process the collected profiling information. Performance measurements point out that, despite the presence of dynamic instrumentation, JP causes significantly less overhead than a prevailing tool for the profiling of Java code. Copyright © 2008 John Wiley & Sons, Ltd.     

Pointwise and pairwise clothing annotation: combining features from social media

In this paper, we present effective algorithms to automatically annotate clothes from social media data, such as Facebook and Instagram. Clothing annotation can be informally stated as recognizing, as accurately as possible, the garment items appearing in the query photo. This task brings huge opportunities for recommender and e-commerce systems, such as capturing new fashion trends based on which clothes have been used more recently. It also poses interesting challenges for existing vision and recognition algorithms, such as distinguishing between similar but different types of clothes or identifying a pattern of a cloth even if it has different colors and shapes. We formulate the annotation task as a multi-label and multi-modal classification problem: (i) both image and textual content (i.e., tags about the image) are available for learning classifiers, (ii) the classifiers must recognize a set of labels (i.e., a set of garment items), and (iii) the decision on which labels to assign to the query photo comes from a set of instances that is used to build a function, which separates labels that should be assigned to the query photo, from those that should not be assigned. Using this configuration, we propose two approaches: (i) the pointwise one, called MMCA, which receives a single image as input, and (ii) a multi-instance classification, called M3CA, also known as pairwise approach, which uses pair of images to create the classifiers. We conducted a systematic evaluation of the proposed algorithms using everyday photos collected from two major fashion-related social media, namely pose.com and chictopia.com. Our results show that the proposed approaches provide improvements when compared to popular first choice multi-label, multi-modal, multi-instance algorithms that range from 20 % to 30 % in terms of accuracy.     

An adaptive generation method for electrophoretic display driving waveform design

Electrophoretic display (EPD) technology is attractive when used for reading devices such as e‐paper because of its paper‐like appearance. EPD driving is more complex than other display technologies such as liquid crystal display or organic light emission diode because the driving result of a pixel strongly depends on the initial display state. The particle size and distribution in the EPD film may vary, even if the initial state is the same. Therefore, the display devices vary between different manufacturing batches. Furthermore, different display modes such as videos, pictures or documents need different driving waveforms to achieve an optimal result. EPD manufacturers need to build a customized driving waveform for every manufacturing batch. This is very inconvenient if new applications on EPD are to be developed. And the workload is huge. In this work, an adaptive method is described for automatically creating EPD driving waveforms to fit different conditions. The central idea of this method is generally adjusting the driving time and the voltage state after getting the feedback from a measurement model. In this method, a new driving waveform is used to reduce refresh time and visual flicker. The experimental results show that the proposed approach can automatically and adaptively generate an EPD driving waveform with reasonable quality.